Milestone02-Salinee_Kingbaisomboon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capstone - Initial Models\n",
    "## Author - Salinee Kingbaisomboon\n",
    "### UW NetID: 1950831"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Milestone 2 continues your work with the diaper manufacturing problem, using the same datasets:\n",
    "1. A dataset file SECOM containing 1567 examples, each with 591 features, presented in a 1567 x 591 matrix\n",
    "2. A labels file listing the classifications and date time stamp for each example\n",
    "\n",
    "## Instructions\n",
    "1. Split prepared data from Milestone 1 into training and testing\n",
    "2. Build a decision tree model that detects faulty products\n",
    "3. Build an ensemble model that detects faulty products\n",
    "4. Build an SVM model\n",
    "5. Evaluate all three models\n",
    "6. Solicit specific feedback on your code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "from sklearn import preprocessing, tree, svm, metrics\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.feature_selection import RFE # Recursive Feature Elimination (for backward model selection)\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "from IPython.display import Image\n",
    "from subprocess import check_call\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\") # To suppress warning\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "pd.options.display.max_rows = None\n",
    "pd.options.display.max_columns = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Declare Functions used in this milestone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to replace missing numeric values\n",
    "def replace_missing_value(x, col):\n",
    "    # We determine the locations of NaN\n",
    "    HasNan = np.isnan(x.loc[:, col]) \n",
    "    # If there are NaN\n",
    "    if sum(HasNan) > 0:\n",
    "        # Calculate the Median for current column without Nan\n",
    "        Median = np.nanmedian(x.loc[:, col])\n",
    "        # Replace the missing value with Median\n",
    "        x.loc[HasNan, col] = Median\n",
    "    return x.loc[:, col]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read, merge and perform data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data and label\n",
    "dataFilename = 'https://archive.ics.uci.edu/ml/machine-learning-databases/secom/secom.data'\n",
    "data_df = pd.read_csv(dataFilename, header=None, delim_whitespace=True)\n",
    "\n",
    "labelFilename = 'https://archive.ics.uci.edu/ml/machine-learning-databases/secom/secom_labels.data'\n",
    "label_df = pd.read_csv(labelFilename, header=None, delim_whitespace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set pseudo names for input data\n",
    "for col in data_df.columns:\n",
    "    data_df.rename(columns = {col:'feature_'+ str(col)}, inplace = True)\n",
    "    \n",
    "# Set pseudo name for label data\n",
    "label_df.rename(columns = {0:'HouseLineTestingResult'}, inplace = True)\n",
    "label_df.rename(columns = {1:'TimeStamp'}, inplace = True)\n",
    "\n",
    "# Since we didn't do the time series analysis, we don't need the timestamp column\n",
    "# Drop the unused columns\n",
    "label_df.drop(['TimeStamp'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean input data\n",
    "for col in data_df.columns:\n",
    "    # Replace missing values with Median for current column without Nan\n",
    "    data_df.loc[:, col] = replace_missing_value(data_df, col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge input and label data into one\n",
    "df = pd.concat([data_df,label_df],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize data frame\n",
    "cols = df.columns\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "np_scaled = min_max_scaler.fit_transform(df)\n",
    "df_normalized = pd.DataFrame(np_scaled, columns = cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <th>feature_365</th>\n",
       "      <th>feature_366</th>\n",
       "      <th>feature_367</th>\n",
       "      <th>feature_368</th>\n",
       "      <th>feature_369</th>\n",
       "      <th>feature_370</th>\n",
       "      <th>feature_371</th>\n",
       "      <th>feature_372</th>\n",
       "      <th>feature_373</th>\n",
       "      <th>feature_374</th>\n",
       "      <th>feature_375</th>\n",
       "      <th>feature_376</th>\n",
       "      <th>feature_377</th>\n",
       "      <th>feature_378</th>\n",
       "      <th>feature_379</th>\n",
       "      <th>feature_380</th>\n",
       "      <th>feature_381</th>\n",
       "      <th>feature_382</th>\n",
       "      <th>feature_383</th>\n",
       "      <th>feature_384</th>\n",
       "      <th>feature_385</th>\n",
       "      <th>feature_386</th>\n",
       "      <th>feature_387</th>\n",
       "      <th>feature_388</th>\n",
       "      <th>feature_389</th>\n",
       "      <th>feature_390</th>\n",
       "      <th>feature_391</th>\n",
       "      <th>feature_392</th>\n",
       "      <th>feature_393</th>\n",
       "      <th>feature_394</th>\n",
       "      <th>feature_395</th>\n",
       "      <th>feature_396</th>\n",
       "      <th>feature_397</th>\n",
       "      <th>feature_398</th>\n",
       "      <th>feature_399</th>\n",
       "      <th>feature_400</th>\n",
       "      <th>feature_401</th>\n",
       "      <th>feature_402</th>\n",
       "      <th>feature_403</th>\n",
       "      <th>feature_404</th>\n",
       "      <th>feature_405</th>\n",
       "      <th>feature_406</th>\n",
       "      <th>feature_407</th>\n",
       "      <th>feature_408</th>\n",
       "      <th>feature_409</th>\n",
       "      <th>feature_410</th>\n",
       "      <th>feature_411</th>\n",
       "      <th>feature_412</th>\n",
       "      <th>feature_413</th>\n",
       "      <th>feature_414</th>\n",
       "      <th>feature_415</th>\n",
       "      <th>feature_416</th>\n",
       "      <th>feature_417</th>\n",
       "      <th>feature_418</th>\n",
       "      <th>feature_419</th>\n",
       "      <th>feature_420</th>\n",
       "      <th>feature_421</th>\n",
       "      <th>feature_422</th>\n",
       "      <th>feature_423</th>\n",
       "      <th>feature_424</th>\n",
       "      <th>feature_425</th>\n",
       "      <th>feature_426</th>\n",
       "      <th>feature_427</th>\n",
       "      <th>feature_428</th>\n",
       "      <th>feature_429</th>\n",
       "      <th>feature_430</th>\n",
       "      <th>feature_431</th>\n",
       "      <th>feature_432</th>\n",
       "      <th>feature_433</th>\n",
       "      <th>feature_434</th>\n",
       "      <th>feature_435</th>\n",
       "      <th>feature_436</th>\n",
       "      <th>feature_437</th>\n",
       "      <th>feature_438</th>\n",
       "      <th>feature_439</th>\n",
       "      <th>feature_440</th>\n",
       "      <th>feature_441</th>\n",
       "      <th>feature_442</th>\n",
       "      <th>feature_443</th>\n",
       "      <th>feature_444</th>\n",
       "      <th>feature_445</th>\n",
       "      <th>feature_446</th>\n",
       "      <th>feature_447</th>\n",
       "      <th>feature_448</th>\n",
       "      <th>feature_449</th>\n",
       "      <th>feature_450</th>\n",
       "      <th>feature_451</th>\n",
       "      <th>feature_452</th>\n",
       "      <th>feature_453</th>\n",
       "      <th>feature_454</th>\n",
       "      <th>feature_455</th>\n",
       "      <th>feature_456</th>\n",
       "      <th>feature_457</th>\n",
       "      <th>feature_458</th>\n",
       "      <th>feature_459</th>\n",
       "      <th>feature_460</th>\n",
       "      <th>feature_461</th>\n",
       "      <th>feature_462</th>\n",
       "      <th>feature_463</th>\n",
       "      <th>feature_464</th>\n",
       "      <th>feature_465</th>\n",
       "      <th>feature_466</th>\n",
       "      <th>feature_467</th>\n",
       "      <th>feature_468</th>\n",
       "      <th>feature_469</th>\n",
       "      <th>feature_470</th>\n",
       "      <th>feature_471</th>\n",
       "      <th>feature_472</th>\n",
       "      <th>feature_473</th>\n",
       "      <th>feature_474</th>\n",
       "      <th>feature_475</th>\n",
       "      <th>feature_476</th>\n",
       "      <th>feature_477</th>\n",
       "      <th>feature_478</th>\n",
       "      <th>feature_479</th>\n",
       "      <th>feature_480</th>\n",
       "      <th>feature_481</th>\n",
       "      <th>feature_482</th>\n",
       "      <th>feature_483</th>\n",
       "      <th>feature_484</th>\n",
       "      <th>feature_485</th>\n",
       "      <th>feature_486</th>\n",
       "      <th>feature_487</th>\n",
       "      <th>feature_488</th>\n",
       "      <th>feature_489</th>\n",
       "      <th>feature_490</th>\n",
       "      <th>feature_491</th>\n",
       "      <th>feature_492</th>\n",
       "      <th>feature_493</th>\n",
       "      <th>feature_494</th>\n",
       "      <th>feature_495</th>\n",
       "      <th>feature_496</th>\n",
       "      <th>feature_497</th>\n",
       "      <th>feature_498</th>\n",
       "      <th>feature_499</th>\n",
       "      <th>feature_500</th>\n",
       "      <th>feature_501</th>\n",
       "      <th>feature_502</th>\n",
       "      <th>feature_503</th>\n",
       "      <th>feature_504</th>\n",
       "      <th>feature_505</th>\n",
       "      <th>feature_506</th>\n",
       "      <th>feature_507</th>\n",
       "      <th>feature_508</th>\n",
       "      <th>feature_509</th>\n",
       "      <th>feature_510</th>\n",
       "      <th>feature_511</th>\n",
       "      <th>feature_512</th>\n",
       "      <th>feature_513</th>\n",
       "      <th>feature_514</th>\n",
       "      <th>feature_515</th>\n",
       "      <th>feature_516</th>\n",
       "      <th>feature_517</th>\n",
       "      <th>feature_518</th>\n",
       "      <th>feature_519</th>\n",
       "      <th>feature_520</th>\n",
       "      <th>feature_521</th>\n",
       "      <th>feature_522</th>\n",
       "      <th>feature_523</th>\n",
       "      <th>feature_524</th>\n",
       "      <th>feature_525</th>\n",
       "      <th>feature_526</th>\n",
       "      <th>feature_527</th>\n",
       "      <th>feature_528</th>\n",
       "      <th>feature_529</th>\n",
       "      <th>feature_530</th>\n",
       "      <th>feature_531</th>\n",
       "      <th>feature_532</th>\n",
       "      <th>feature_533</th>\n",
       "      <th>feature_534</th>\n",
       "      <th>feature_535</th>\n",
       "      <th>feature_536</th>\n",
       "      <th>feature_537</th>\n",
       "      <th>feature_538</th>\n",
       "      <th>feature_539</th>\n",
       "      <th>feature_540</th>\n",
       "      <th>feature_541</th>\n",
       "      <th>feature_542</th>\n",
       "      <th>feature_543</th>\n",
       "      <th>feature_544</th>\n",
       "      <th>feature_545</th>\n",
       "      <th>feature_546</th>\n",
       "      <th>feature_547</th>\n",
       "      <th>feature_548</th>\n",
       "      <th>feature_549</th>\n",
       "      <th>feature_550</th>\n",
       "      <th>feature_551</th>\n",
       "      <th>feature_552</th>\n",
       "      <th>feature_553</th>\n",
       "      <th>feature_554</th>\n",
       "      <th>feature_555</th>\n",
       "      <th>feature_556</th>\n",
       "      <th>feature_557</th>\n",
       "      <th>feature_558</th>\n",
       "      <th>feature_559</th>\n",
       "      <th>feature_560</th>\n",
       "      <th>feature_561</th>\n",
       "      <th>feature_562</th>\n",
       "      <th>feature_563</th>\n",
       "      <th>feature_564</th>\n",
       "      <th>feature_565</th>\n",
       "      <th>feature_566</th>\n",
       "      <th>feature_567</th>\n",
       "      <th>feature_568</th>\n",
       "      <th>feature_569</th>\n",
       "      <th>feature_570</th>\n",
       "      <th>feature_571</th>\n",
       "      <th>feature_572</th>\n",
       "      <th>feature_573</th>\n",
       "      <th>feature_574</th>\n",
       "      <th>feature_575</th>\n",
       "      <th>feature_576</th>\n",
       "      <th>feature_577</th>\n",
       "      <th>feature_578</th>\n",
       "      <th>feature_579</th>\n",
       "      <th>feature_580</th>\n",
       "      <th>feature_581</th>\n",
       "      <th>feature_582</th>\n",
       "      <th>feature_583</th>\n",
       "      <th>feature_584</th>\n",
       "      <th>feature_585</th>\n",
       "      <th>feature_586</th>\n",
       "      <th>feature_587</th>\n",
       "      <th>feature_588</th>\n",
       "      <th>feature_589</th>\n",
       "      <th>HouseLineTestingResult</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.469231</td>\n",
       "      <td>0.589292</td>\n",
       "      <td>0.499096</td>\n",
       "      <td>0.379841</td>\n",
       "      <td>0.000609</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.328562</td>\n",
       "      <td>0.965785</td>\n",
       "      <td>0.665019</td>\n",
       "      <td>0.542479</td>\n",
       "      <td>0.358362</td>\n",
       "      <td>0.880692</td>\n",
       "      <td>0.226185</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.329909</td>\n",
       "      <td>0.165668</td>\n",
       "      <td>0.056644</td>\n",
       "      <td>0.958559</td>\n",
       "      <td>0.500188</td>\n",
       "      <td>0.848739</td>\n",
       "      <td>0.814395</td>\n",
       "      <td>0.242124</td>\n",
       "      <td>0.797675</td>\n",
       "      <td>0.481224</td>\n",
       "      <td>0.538057</td>\n",
       "      <td>0.647599</td>\n",
       "      <td>0.863698</td>\n",
       "      <td>0.398104</td>\n",
       "      <td>0.261259</td>\n",
       "      <td>0.476550</td>\n",
       "      <td>0.514343</td>\n",
       "      <td>0.529986</td>\n",
       "      <td>0.009554</td>\n",
       "      <td>0.121293</td>\n",
       "      <td>0.078721</td>\n",
       "      <td>0.019008</td>\n",
       "      <td>0.921279</td>\n",
       "      <td>0.458921</td>\n",
       "      <td>0.603022</td>\n",
       "      <td>0.267583</td>\n",
       "      <td>0.706799</td>\n",
       "      <td>0.120954</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.288419</td>\n",
       "      <td>0.453178</td>\n",
       "      <td>0.319010</td>\n",
       "      <td>0.261238</td>\n",
       "      <td>0.798994</td>\n",
       "      <td>0.411986</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.280399</td>\n",
       "      <td>0.815716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.837429</td>\n",
       "      <td>0.842447</td>\n",
       "      <td>0.244444</td>\n",
       "      <td>0.902087</td>\n",
       "      <td>0.591398</td>\n",
       "      <td>0.774060</td>\n",
       "      <td>0.138291</td>\n",
       "      <td>0.533284</td>\n",
       "      <td>0.500043</td>\n",
       "      <td>0.132027</td>\n",
       "      <td>0.077698</td>\n",
       "      <td>0.359149</td>\n",
       "      <td>0.221741</td>\n",
       "      <td>0.443966</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.762021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.187416</td>\n",
       "      <td>0.288633</td>\n",
       "      <td>0.901588</td>\n",
       "      <td>0.042461</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.349287</td>\n",
       "      <td>0.640619</td>\n",
       "      <td>0.499369</td>\n",
       "      <td>0.531470</td>\n",
       "      <td>0.306768</td>\n",
       "      <td>0.537934</td>\n",
       "      <td>0.265006</td>\n",
       "      <td>0.584689</td>\n",
       "      <td>0.439410</td>\n",
       "      <td>0.441088</td>\n",
       "      <td>0.629771</td>\n",
       "      <td>0.469649</td>\n",
       "      <td>0.887923</td>\n",
       "      <td>0.251477</td>\n",
       "      <td>0.053474</td>\n",
       "      <td>0.380564</td>\n",
       "      <td>0.041128</td>\n",
       "      <td>0.174447</td>\n",
       "      <td>0.559211</td>\n",
       "      <td>0.677419</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.301208</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.722961</td>\n",
       "      <td>0.355945</td>\n",
       "      <td>0.603774</td>\n",
       "      <td>0.634146</td>\n",
       "      <td>0.804489</td>\n",
       "      <td>0.539474</td>\n",
       "      <td>0.642105</td>\n",
       "      <td>0.825980</td>\n",
       "      <td>0.436066</td>\n",
       "      <td>0.273557</td>\n",
       "      <td>0.899621</td>\n",
       "      <td>0.983952</td>\n",
       "      <td>0.709486</td>\n",
       "      <td>0.759737</td>\n",
       "      <td>0.947492</td>\n",
       "      <td>0.753458</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.537668</td>\n",
       "      <td>0.984375</td>\n",
       "      <td>0.021720</td>\n",
       "      <td>0.749743</td>\n",
       "      <td>0.983757</td>\n",
       "      <td>0.522891</td>\n",
       "      <td>0.688525</td>\n",
       "      <td>0.185512</td>\n",
       "      <td>0.827957</td>\n",
       "      <td>0.746269</td>\n",
       "      <td>0.257061</td>\n",
       "      <td>0.613568</td>\n",
       "      <td>0.109093</td>\n",
       "      <td>0.818485</td>\n",
       "      <td>0.598316</td>\n",
       "      <td>0.649733</td>\n",
       "      <td>0.094488</td>\n",
       "      <td>0.375533</td>\n",
       "      <td>0.500089</td>\n",
       "      <td>0.190863</td>\n",
       "      <td>0.069444</td>\n",
       "      <td>0.289565</td>\n",
       "      <td>0.177496</td>\n",
       "      <td>0.216394</td>\n",
       "      <td>0.198042</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.049582</td>\n",
       "      <td>0.280992</td>\n",
       "      <td>0.096812</td>\n",
       "      <td>0.277366</td>\n",
       "      <td>0.058171</td>\n",
       "      <td>0.029261</td>\n",
       "      <td>0.014613</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.139841</td>\n",
       "      <td>0.015460</td>\n",
       "      <td>0.000345</td>\n",
       "      <td>0.055674</td>\n",
       "      <td>0.055944</td>\n",
       "      <td>0.021480</td>\n",
       "      <td>0.018032</td>\n",
       "      <td>0.085751</td>\n",
       "      <td>0.346161</td>\n",
       "      <td>0.130535</td>\n",
       "      <td>0.231894</td>\n",
       "      <td>0.028095</td>\n",
       "      <td>0.009981</td>\n",
       "      <td>0.094044</td>\n",
       "      <td>0.026417</td>\n",
       "      <td>0.028911</td>\n",
       "      <td>0.059113</td>\n",
       "      <td>0.03750</td>\n",
       "      <td>0.052023</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.499474</td>\n",
       "      <td>0.21645</td>\n",
       "      <td>0.451751</td>\n",
       "      <td>0.548731</td>\n",
       "      <td>0.451990</td>\n",
       "      <td>0.687359</td>\n",
       "      <td>0.882481</td>\n",
       "      <td>0.062396</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.068966</td>\n",
       "      <td>0.179819</td>\n",
       "      <td>0.172214</td>\n",
       "      <td>0.015819</td>\n",
       "      <td>0.014212</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.016473</td>\n",
       "      <td>0.317196</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044557</td>\n",
       "      <td>0.009205</td>\n",
       "      <td>0.044654</td>\n",
       "      <td>0.020751</td>\n",
       "      <td>0.020266</td>\n",
       "      <td>0.036098</td>\n",
       "      <td>0.042520</td>\n",
       "      <td>0.008235</td>\n",
       "      <td>0.017259</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.019953</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013866</td>\n",
       "      <td>0.550662</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.093498</td>\n",
       "      <td>0.180031</td>\n",
       "      <td>0.107155</td>\n",
       "      <td>0.039844</td>\n",
       "      <td>0.068849</td>\n",
       "      <td>0.136795</td>\n",
       "      <td>0.074698</td>\n",
       "      <td>0.158440</td>\n",
       "      <td>0.314760</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0.177596</td>\n",
       "      <td>0.240855</td>\n",
       "      <td>0.020993</td>\n",
       "      <td>0.082317</td>\n",
       "      <td>0.015559</td>\n",
       "      <td>0.516125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.461122</td>\n",
       "      <td>0.229985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.060606</td>\n",
       "      <td>0.117117</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000706</td>\n",
       "      <td>0.008972</td>\n",
       "      <td>0.010889</td>\n",
       "      <td>0.054945</td>\n",
       "      <td>0.016285</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.181065</td>\n",
       "      <td>0.000808</td>\n",
       "      <td>0.000812</td>\n",
       "      <td>0.021479</td>\n",
       "      <td>0.042857</td>\n",
       "      <td>0.375657</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0.393982</td>\n",
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       "      <td>0.656639</td>\n",
       "      <td>0.412315</td>\n",
       "      <td>0.228669</td>\n",
       "      <td>0.932908</td>\n",
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       "      <td>0.457039</td>\n",
       "      <td>0.165391</td>\n",
       "      <td>0.048683</td>\n",
       "      <td>0.963740</td>\n",
       "      <td>0.476296</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0.457065</td>\n",
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       "      <td>0.542759</td>\n",
       "      <td>0.448168</td>\n",
       "      <td>0.411832</td>\n",
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       "      <td>0.169405</td>\n",
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       "      <td>0.506806</td>\n",
       "      <td>0.855614</td>\n",
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       "      <td>0.184224</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.533299</td>\n",
       "      <td>0.244275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000577</td>\n",
       "      <td>0.007667</td>\n",
       "      <td>0.010116</td>\n",
       "      <td>0.200200</td>\n",
       "      <td>0.023787</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.087568</td>\n",
       "      <td>0.000443</td>\n",
       "      <td>0.007073</td>\n",
       "      <td>0.135580</td>\n",
       "      <td>0.059080</td>\n",
       "      <td>0.349148</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.227312</td>\n",
       "      <td>0.678268</td>\n",
       "      <td>0.267896</td>\n",
       "      <td>0.328244</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>0.161698</td>\n",
       "      <td>0.098976</td>\n",
       "      <td>0.783838</td>\n",
       "      <td>0.285129</td>\n",
       "      <td>0.019741</td>\n",
       "      <td>0.080035</td>\n",
       "      <td>0.018618</td>\n",
       "      <td>0.024819</td>\n",
       "      <td>0.080819</td>\n",
       "      <td>0.018547</td>\n",
       "      <td>0.061075</td>\n",
       "      <td>0.071960</td>\n",
       "      <td>0.017995</td>\n",
       "      <td>0.983876</td>\n",
       "      <td>0.300757</td>\n",
       "      <td>0.246729</td>\n",
       "      <td>0.186578</td>\n",
       "      <td>0.358488</td>\n",
       "      <td>0.601972</td>\n",
       "      <td>0.004113</td>\n",
       "      <td>0.899070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.869764</td>\n",
       "      <td>0.003337</td>\n",
       "      <td>0.804376</td>\n",
       "      <td>0.799922</td>\n",
       "      <td>0.596317</td>\n",
       "      <td>0.017006</td>\n",
       "      <td>0.057042</td>\n",
       "      <td>0.018066</td>\n",
       "      <td>0.077769</td>\n",
       "      <td>0.015949</td>\n",
       "      <td>0.050771</td>\n",
       "      <td>0.629073</td>\n",
       "      <td>0.589309</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.112383</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.020612</td>\n",
       "      <td>0.021401</td>\n",
       "      <td>0.020152</td>\n",
       "      <td>0.629073</td>\n",
       "      <td>0.589309</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.112383</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.400385</td>\n",
       "      <td>0.466998</td>\n",
       "      <td>0.543479</td>\n",
       "      <td>0.244894</td>\n",
       "      <td>0.000574</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469123</td>\n",
       "      <td>0.946345</td>\n",
       "      <td>0.638590</td>\n",
       "      <td>0.319564</td>\n",
       "      <td>0.359499</td>\n",
       "      <td>0.933515</td>\n",
       "      <td>0.219610</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.425265</td>\n",
       "      <td>0.180762</td>\n",
       "      <td>0.053078</td>\n",
       "      <td>0.960286</td>\n",
       "      <td>0.495005</td>\n",
       "      <td>0.835631</td>\n",
       "      <td>0.808915</td>\n",
       "      <td>0.235237</td>\n",
       "      <td>0.724308</td>\n",
       "      <td>0.443066</td>\n",
       "      <td>0.454757</td>\n",
       "      <td>0.950029</td>\n",
       "      <td>0.976130</td>\n",
       "      <td>0.955045</td>\n",
       "      <td>0.190989</td>\n",
       "      <td>0.695296</td>\n",
       "      <td>0.689243</td>\n",
       "      <td>0.479595</td>\n",
       "      <td>0.077053</td>\n",
       "      <td>0.068333</td>\n",
       "      <td>0.067953</td>\n",
       "      <td>0.014225</td>\n",
       "      <td>0.932047</td>\n",
       "      <td>0.090745</td>\n",
       "      <td>0.530100</td>\n",
       "      <td>0.245947</td>\n",
       "      <td>0.891127</td>\n",
       "      <td>0.035697</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.586889</td>\n",
       "      <td>0.293581</td>\n",
       "      <td>0.340259</td>\n",
       "      <td>0.378747</td>\n",
       "      <td>0.794558</td>\n",
       "      <td>0.447084</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.496776</td>\n",
       "      <td>0.685566</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.737240</td>\n",
       "      <td>0.756256</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.616372</td>\n",
       "      <td>0.458781</td>\n",
       "      <td>0.585405</td>\n",
       "      <td>0.270716</td>\n",
       "      <td>0.747554</td>\n",
       "      <td>0.322790</td>\n",
       "      <td>0.168684</td>\n",
       "      <td>0.063833</td>\n",
       "      <td>0.215625</td>\n",
       "      <td>0.168451</td>\n",
       "      <td>0.564325</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>0.750309</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.309017</td>\n",
       "      <td>0.224819</td>\n",
       "      <td>0.935748</td>\n",
       "      <td>0.037773</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.146849</td>\n",
       "      <td>0.541973</td>\n",
       "      <td>0.271771</td>\n",
       "      <td>0.649816</td>\n",
       "      <td>0.786844</td>\n",
       "      <td>0.147160</td>\n",
       "      <td>0.644955</td>\n",
       "      <td>0.674706</td>\n",
       "      <td>0.558284</td>\n",
       "      <td>0.386707</td>\n",
       "      <td>0.629771</td>\n",
       "      <td>0.504473</td>\n",
       "      <td>0.985819</td>\n",
       "      <td>0.260880</td>\n",
       "      <td>0.062068</td>\n",
       "      <td>0.241362</td>\n",
       "      <td>0.592886</td>\n",
       "      <td>0.302211</td>\n",
       "      <td>0.493421</td>\n",
       "      <td>0.612903</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.381341</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.674539</td>\n",
       "      <td>0.359907</td>\n",
       "      <td>0.528302</td>\n",
       "      <td>0.585366</td>\n",
       "      <td>0.558569</td>\n",
       "      <td>0.507519</td>\n",
       "      <td>0.642105</td>\n",
       "      <td>0.696078</td>\n",
       "      <td>0.504918</td>\n",
       "      <td>0.470442</td>\n",
       "      <td>0.307630</td>\n",
       "      <td>0.983952</td>\n",
       "      <td>0.709486</td>\n",
       "      <td>0.759737</td>\n",
       "      <td>0.951279</td>\n",
       "      <td>0.838080</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.433794</td>\n",
       "      <td>0.982422</td>\n",
       "      <td>0.022619</td>\n",
       "      <td>0.764161</td>\n",
       "      <td>0.953390</td>\n",
       "      <td>0.570761</td>\n",
       "      <td>0.147541</td>\n",
       "      <td>0.279992</td>\n",
       "      <td>0.473118</td>\n",
       "      <td>0.238806</td>\n",
       "      <td>0.489502</td>\n",
       "      <td>0.084797</td>\n",
       "      <td>0.234602</td>\n",
       "      <td>0.800770</td>\n",
       "      <td>0.651403</td>\n",
       "      <td>0.649947</td>\n",
       "      <td>0.098425</td>\n",
       "      <td>0.672475</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.251494</td>\n",
       "      <td>0.073718</td>\n",
       "      <td>0.315364</td>\n",
       "      <td>0.331220</td>\n",
       "      <td>0.229509</td>\n",
       "      <td>0.091734</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.037088</td>\n",
       "      <td>0.247934</td>\n",
       "      <td>0.137629</td>\n",
       "      <td>0.078064</td>\n",
       "      <td>0.171403</td>\n",
       "      <td>0.015092</td>\n",
       "      <td>0.015973</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.368715</td>\n",
       "      <td>0.024733</td>\n",
       "      <td>0.001243</td>\n",
       "      <td>0.048449</td>\n",
       "      <td>0.056383</td>\n",
       "      <td>0.016706</td>\n",
       "      <td>0.026843</td>\n",
       "      <td>0.085751</td>\n",
       "      <td>0.346161</td>\n",
       "      <td>0.110255</td>\n",
       "      <td>0.085132</td>\n",
       "      <td>0.090478</td>\n",
       "      <td>0.081473</td>\n",
       "      <td>0.071055</td>\n",
       "      <td>0.059439</td>\n",
       "      <td>0.030432</td>\n",
       "      <td>0.044335</td>\n",
       "      <td>0.03750</td>\n",
       "      <td>0.076590</td>\n",
       "      <td>0.177758</td>\n",
       "      <td>0.749269</td>\n",
       "      <td>0.05504</td>\n",
       "      <td>0.317131</td>\n",
       "      <td>0.303522</td>\n",
       "      <td>0.317131</td>\n",
       "      <td>0.556166</td>\n",
       "      <td>0.467084</td>\n",
       "      <td>0.189398</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.173669</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>0.221879</td>\n",
       "      <td>0.258313</td>\n",
       "      <td>0.046552</td>\n",
       "      <td>0.014099</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.039707</td>\n",
       "      <td>0.248131</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012400</td>\n",
       "      <td>0.010574</td>\n",
       "      <td>0.059019</td>\n",
       "      <td>0.118263</td>\n",
       "      <td>0.020292</td>\n",
       "      <td>0.052696</td>\n",
       "      <td>0.046787</td>\n",
       "      <td>0.023202</td>\n",
       "      <td>0.033681</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.022848</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.039591</td>\n",
       "      <td>0.596389</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.191744</td>\n",
       "      <td>0.137598</td>\n",
       "      <td>0.196252</td>\n",
       "      <td>0.024766</td>\n",
       "      <td>0.069388</td>\n",
       "      <td>0.206231</td>\n",
       "      <td>0.199927</td>\n",
       "      <td>0.127982</td>\n",
       "      <td>0.580084</td>\n",
       "      <td>0.102564</td>\n",
       "      <td>0.177596</td>\n",
       "      <td>0.267933</td>\n",
       "      <td>0.001211</td>\n",
       "      <td>0.068999</td>\n",
       "      <td>0.033642</td>\n",
       "      <td>0.189833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.060852</td>\n",
       "      <td>0.037361</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.233766</td>\n",
       "      <td>0.117117</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000706</td>\n",
       "      <td>0.008972</td>\n",
       "      <td>0.010889</td>\n",
       "      <td>0.038869</td>\n",
       "      <td>0.021027</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.066520</td>\n",
       "      <td>0.000707</td>\n",
       "      <td>0.000959</td>\n",
       "      <td>0.070540</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.368520</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.228261</td>\n",
       "      <td>0.740340</td>\n",
       "      <td>0.214005</td>\n",
       "      <td>0.128333</td>\n",
       "      <td>0.224515</td>\n",
       "      <td>0.325138</td>\n",
       "      <td>0.237197</td>\n",
       "      <td>0.076404</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.051124</td>\n",
       "      <td>0.205882</td>\n",
       "      <td>0.174451</td>\n",
       "      <td>0.061584</td>\n",
       "      <td>0.195076</td>\n",
       "      <td>0.015324</td>\n",
       "      <td>0.015972</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.379937</td>\n",
       "      <td>0.016116</td>\n",
       "      <td>0.001153</td>\n",
       "      <td>0.050204</td>\n",
       "      <td>0.059948</td>\n",
       "      <td>0.011419</td>\n",
       "      <td>0.030100</td>\n",
       "      <td>0.087342</td>\n",
       "      <td>0.295574</td>\n",
       "      <td>0.105625</td>\n",
       "      <td>0.078314</td>\n",
       "      <td>0.101073</td>\n",
       "      <td>0.074373</td>\n",
       "      <td>0.054141</td>\n",
       "      <td>0.043154</td>\n",
       "      <td>0.025658</td>\n",
       "      <td>0.059341</td>\n",
       "      <td>0.038230</td>\n",
       "      <td>0.082791</td>\n",
       "      <td>0.214442</td>\n",
       "      <td>0.645991</td>\n",
       "      <td>0.042495</td>\n",
       "      <td>0.291107</td>\n",
       "      <td>0.338779</td>\n",
       "      <td>0.291107</td>\n",
       "      <td>0.629897</td>\n",
       "      <td>0.442995</td>\n",
       "      <td>0.198089</td>\n",
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       "      <td>0.257655</td>\n",
       "      <td>0.176495</td>\n",
       "      <td>0.214990</td>\n",
       "      <td>0.254268</td>\n",
       "      <td>0.042066</td>\n",
       "      <td>0.014292</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.037218</td>\n",
       "      <td>0.272055</td>\n",
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       "      <td>0.001770</td>\n",
       "      <td>0.006850</td>\n",
       "      <td>0.043215</td>\n",
       "      <td>0.095441</td>\n",
       "      <td>0.014425</td>\n",
       "      <td>0.019971</td>\n",
       "      <td>0.044517</td>\n",
       "      <td>0.015594</td>\n",
       "      <td>0.022959</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.025210</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.032710</td>\n",
       "      <td>0.471081</td>\n",
       "      <td>0.025988</td>\n",
       "      <td>0.020710</td>\n",
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       "      <td>0.144969</td>\n",
       "      <td>0.141460</td>\n",
       "      <td>0.174003</td>\n",
       "      <td>0.023833</td>\n",
       "      <td>0.122360</td>\n",
       "      <td>0.262425</td>\n",
       "      <td>0.178681</td>\n",
       "      <td>0.162123</td>\n",
       "      <td>0.587569</td>\n",
       "      <td>0.061224</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.216749</td>\n",
       "      <td>0.000734</td>\n",
       "      <td>0.065216</td>\n",
       "      <td>0.038694</td>\n",
       "      <td>0.246101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.082621</td>\n",
       "      <td>0.077957</td>\n",
       "      <td>0.078431</td>\n",
       "      <td>0.101796</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.256410</td>\n",
       "      <td>0.136986</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000638</td>\n",
       "      <td>0.009223</td>\n",
       "      <td>0.010308</td>\n",
       "      <td>0.018331</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.076237</td>\n",
       "      <td>0.00064</td>\n",
       "      <td>0.001005</td>\n",
       "      <td>0.075149</td>\n",
       "      <td>0.080357</td>\n",
       "      <td>0.379336</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.205752</td>\n",
       "      <td>0.895015</td>\n",
       "      <td>0.265404</td>\n",
       "      <td>0.069722</td>\n",
       "      <td>0.326774</td>\n",
       "      <td>0.325398</td>\n",
       "      <td>0.230829</td>\n",
       "      <td>0.139176</td>\n",
       "      <td>0.011690</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.032533</td>\n",
       "      <td>0.254314</td>\n",
       "      <td>0.149307</td>\n",
       "      <td>0.317954</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013757</td>\n",
       "      <td>0.021478</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.178066</td>\n",
       "      <td>0.030406</td>\n",
       "      <td>0.013348</td>\n",
       "      <td>0.047951</td>\n",
       "      <td>0.049464</td>\n",
       "      <td>0.014325</td>\n",
       "      <td>0.022634</td>\n",
       "      <td>0.039272</td>\n",
       "      <td>0.033513</td>\n",
       "      <td>0.076472</td>\n",
       "      <td>0.182039</td>\n",
       "      <td>0.012940</td>\n",
       "      <td>0.013475</td>\n",
       "      <td>0.003418</td>\n",
       "      <td>0.049635</td>\n",
       "      <td>0.039964</td>\n",
       "      <td>0.042575</td>\n",
       "      <td>0.188672</td>\n",
       "      <td>0.814559</td>\n",
       "      <td>0.062034</td>\n",
       "      <td>0.347155</td>\n",
       "      <td>0.345258</td>\n",
       "      <td>0.275466</td>\n",
       "      <td>0.578484</td>\n",
       "      <td>0.467799</td>\n",
       "      <td>0.204319</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.158941</td>\n",
       "      <td>0.180870</td>\n",
       "      <td>0.221457</td>\n",
       "      <td>0.250769</td>\n",
       "      <td>0.046542</td>\n",
       "      <td>0.015670</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.039851</td>\n",
       "      <td>0.139495</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.012245</td>\n",
       "      <td>0.024286</td>\n",
       "      <td>0.052237</td>\n",
       "      <td>0.133787</td>\n",
       "      <td>0.032089</td>\n",
       "      <td>0.183893</td>\n",
       "      <td>0.105580</td>\n",
       "      <td>0.116718</td>\n",
       "      <td>0.029767</td>\n",
       "      <td>0.128898</td>\n",
       "      <td>0.016615</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.045744</td>\n",
       "      <td>0.659675</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.225149</td>\n",
       "      <td>0.101557</td>\n",
       "      <td>0.306714</td>\n",
       "      <td>0.089090</td>\n",
       "      <td>0.104719</td>\n",
       "      <td>0.072065</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.338798</td>\n",
       "      <td>0.458802</td>\n",
       "      <td>0.099165</td>\n",
       "      <td>0.193162</td>\n",
       "      <td>0.274080</td>\n",
       "      <td>0.000899</td>\n",
       "      <td>0.065222</td>\n",
       "      <td>0.113768</td>\n",
       "      <td>0.209962</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.712188</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.251524</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000577</td>\n",
       "      <td>0.007667</td>\n",
       "      <td>0.010116</td>\n",
       "      <td>0.022349</td>\n",
       "      <td>0.019112</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.078260</td>\n",
       "      <td>0.000646</td>\n",
       "      <td>0.005099</td>\n",
       "      <td>0.066436</td>\n",
       "      <td>0.070943</td>\n",
       "      <td>0.351567</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.227783</td>\n",
       "      <td>0.758946</td>\n",
       "      <td>0.199941</td>\n",
       "      <td>0.328244</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>0.161698</td>\n",
       "      <td>0.203893</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.110708</td>\n",
       "      <td>0.261473</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.245416</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.524168</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.434376</td>\n",
       "      <td>0.656406</td>\n",
       "      <td>0.591822</td>\n",
       "      <td>0.591411</td>\n",
       "      <td>0.375329</td>\n",
       "      <td>0.348325</td>\n",
       "      <td>0.200886</td>\n",
       "      <td>0.210858</td>\n",
       "      <td>0.231504</td>\n",
       "      <td>0.203801</td>\n",
       "      <td>0.190569</td>\n",
       "      <td>0.267141</td>\n",
       "      <td>0.783557</td>\n",
       "      <td>0.594043</td>\n",
       "      <td>0.012838</td>\n",
       "      <td>0.050282</td>\n",
       "      <td>0.010993</td>\n",
       "      <td>0.055956</td>\n",
       "      <td>0.012198</td>\n",
       "      <td>0.043312</td>\n",
       "      <td>0.309942</td>\n",
       "      <td>0.152542</td>\n",
       "      <td>0.123188</td>\n",
       "      <td>0.100153</td>\n",
       "      <td>0.662500</td>\n",
       "      <td>0.009137</td>\n",
       "      <td>0.007782</td>\n",
       "      <td>0.008734</td>\n",
       "      <td>0.309942</td>\n",
       "      <td>0.152542</td>\n",
       "      <td>0.123188</td>\n",
       "      <td>0.100153</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.471367</td>\n",
       "      <td>0.500400</td>\n",
       "      <td>0.678327</td>\n",
       "      <td>0.357067</td>\n",
       "      <td>0.000765</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.387631</td>\n",
       "      <td>0.960342</td>\n",
       "      <td>0.670606</td>\n",
       "      <td>0.392050</td>\n",
       "      <td>0.315131</td>\n",
       "      <td>0.915301</td>\n",
       "      <td>0.220658</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.480818</td>\n",
       "      <td>0.177310</td>\n",
       "      <td>0.059646</td>\n",
       "      <td>0.972126</td>\n",
       "      <td>0.483114</td>\n",
       "      <td>0.834120</td>\n",
       "      <td>0.763975</td>\n",
       "      <td>0.234118</td>\n",
       "      <td>0.720752</td>\n",
       "      <td>0.485779</td>\n",
       "      <td>0.516127</td>\n",
       "      <td>0.931950</td>\n",
       "      <td>0.969992</td>\n",
       "      <td>0.949862</td>\n",
       "      <td>0.185584</td>\n",
       "      <td>0.875018</td>\n",
       "      <td>0.938247</td>\n",
       "      <td>0.439918</td>\n",
       "      <td>0.140226</td>\n",
       "      <td>0.073974</td>\n",
       "      <td>0.041585</td>\n",
       "      <td>0.015487</td>\n",
       "      <td>0.958415</td>\n",
       "      <td>0.406311</td>\n",
       "      <td>0.383630</td>\n",
       "      <td>0.448568</td>\n",
       "      <td>0.885105</td>\n",
       "      <td>0.060199</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.306446</td>\n",
       "      <td>0.594777</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.529771</td>\n",
       "      <td>0.328147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.885496</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.867675</td>\n",
       "      <td>0.891566</td>\n",
       "      <td>0.474074</td>\n",
       "      <td>0.871589</td>\n",
       "      <td>0.465950</td>\n",
       "      <td>0.671606</td>\n",
       "      <td>0.084682</td>\n",
       "      <td>0.617807</td>\n",
       "      <td>0.143601</td>\n",
       "      <td>0.304040</td>\n",
       "      <td>0.108989</td>\n",
       "      <td>0.607576</td>\n",
       "      <td>0.350179</td>\n",
       "      <td>0.819041</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.738046</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.454369</td>\n",
       "      <td>0.204606</td>\n",
       "      <td>0.901588</td>\n",
       "      <td>0.042461</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.153092</td>\n",
       "      <td>0.791103</td>\n",
       "      <td>0.369794</td>\n",
       "      <td>0.507700</td>\n",
       "      <td>0.380772</td>\n",
       "      <td>0.479985</td>\n",
       "      <td>0.548531</td>\n",
       "      <td>0.583567</td>\n",
       "      <td>0.529592</td>\n",
       "      <td>0.507553</td>\n",
       "      <td>0.629771</td>\n",
       "      <td>0.672524</td>\n",
       "      <td>0.984904</td>\n",
       "      <td>0.420575</td>\n",
       "      <td>0.052593</td>\n",
       "      <td>0.482834</td>\n",
       "      <td>0.558288</td>\n",
       "      <td>0.120393</td>\n",
       "      <td>0.559211</td>\n",
       "      <td>0.612903</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.425907</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.586788</td>\n",
       "      <td>0.361887</td>\n",
       "      <td>0.641509</td>\n",
       "      <td>0.512195</td>\n",
       "      <td>0.725996</td>\n",
       "      <td>0.533835</td>\n",
       "      <td>0.636842</td>\n",
       "      <td>0.879902</td>\n",
       "      <td>0.436066</td>\n",
       "      <td>0.439099</td>\n",
       "      <td>0.191558</td>\n",
       "      <td>0.983952</td>\n",
       "      <td>0.709486</td>\n",
       "      <td>0.759737</td>\n",
       "      <td>0.947492</td>\n",
       "      <td>0.724166</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.663772</td>\n",
       "      <td>0.984375</td>\n",
       "      <td>0.021547</td>\n",
       "      <td>0.658084</td>\n",
       "      <td>0.859463</td>\n",
       "      <td>0.513000</td>\n",
       "      <td>0.475410</td>\n",
       "      <td>0.283059</td>\n",
       "      <td>0.602151</td>\n",
       "      <td>0.417910</td>\n",
       "      <td>0.294407</td>\n",
       "      <td>0.416111</td>\n",
       "      <td>0.252299</td>\n",
       "      <td>0.846983</td>\n",
       "      <td>0.514879</td>\n",
       "      <td>0.764354</td>\n",
       "      <td>0.291339</td>\n",
       "      <td>0.641536</td>\n",
       "      <td>0.312662</td>\n",
       "      <td>0.155349</td>\n",
       "      <td>0.065171</td>\n",
       "      <td>0.412399</td>\n",
       "      <td>0.329319</td>\n",
       "      <td>0.237705</td>\n",
       "      <td>0.167338</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018692</td>\n",
       "      <td>0.338843</td>\n",
       "      <td>0.052285</td>\n",
       "      <td>0.103358</td>\n",
       "      <td>0.175844</td>\n",
       "      <td>0.020226</td>\n",
       "      <td>0.017281</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.365474</td>\n",
       "      <td>0.019617</td>\n",
       "      <td>0.000313</td>\n",
       "      <td>0.038249</td>\n",
       "      <td>0.066530</td>\n",
       "      <td>0.023071</td>\n",
       "      <td>0.019493</td>\n",
       "      <td>0.085751</td>\n",
       "      <td>0.346161</td>\n",
       "      <td>0.089719</td>\n",
       "      <td>0.067866</td>\n",
       "      <td>0.046043</td>\n",
       "      <td>0.039136</td>\n",
       "      <td>0.153605</td>\n",
       "      <td>0.022014</td>\n",
       "      <td>0.034388</td>\n",
       "      <td>0.152709</td>\n",
       "      <td>0.03125</td>\n",
       "      <td>0.098266</td>\n",
       "      <td>0.412944</td>\n",
       "      <td>0.325225</td>\n",
       "      <td>0.11173</td>\n",
       "      <td>0.420777</td>\n",
       "      <td>0.539230</td>\n",
       "      <td>0.420777</td>\n",
       "      <td>0.516470</td>\n",
       "      <td>0.155876</td>\n",
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       "      <td>0.054310</td>\n",
       "      <td>0.148177</td>\n",
       "      <td>0.481371</td>\n",
       "      <td>0.027887</td>\n",
       "      <td>0.007670</td>\n",
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       "      <td>0.009560</td>\n",
       "      <td>0.026842</td>\n",
       "      <td>0.043215</td>\n",
       "      <td>0.020498</td>\n",
       "      <td>0.084313</td>\n",
       "      <td>0.043626</td>\n",
       "      <td>0.017217</td>\n",
       "      <td>0.052990</td>\n",
       "      <td>0.000035</td>\n",
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       "      <td>0.034741</td>\n",
       "      <td>0.520678</td>\n",
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       "      <td>0.123013</td>\n",
       "      <td>0.151014</td>\n",
       "      <td>0.118910</td>\n",
       "      <td>0.081406</td>\n",
       "      <td>0.081380</td>\n",
       "      <td>0.293175</td>\n",
       "      <td>0.302819</td>\n",
       "      <td>0.140435</td>\n",
       "      <td>0.399073</td>\n",
       "      <td>0.147436</td>\n",
       "      <td>0.177596</td>\n",
       "      <td>0.285986</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.021241</td>\n",
       "      <td>0.131553</td>\n",
       "      <td>0.360554</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111562</td>\n",
       "      <td>0.097040</td>\n",
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       "      <td>0.233766</td>\n",
       "      <td>0.076577</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000706</td>\n",
       "      <td>0.008972</td>\n",
       "      <td>0.010889</td>\n",
       "      <td>0.054945</td>\n",
       "      <td>0.021645</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.116690</td>\n",
       "      <td>0.000303</td>\n",
       "      <td>0.000710</td>\n",
       "      <td>0.018766</td>\n",
       "      <td>0.255844</td>\n",
       "      <td>0.348360</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.210145</td>\n",
       "      <td>0.396007</td>\n",
       "      <td>0.261501</td>\n",
       "      <td>0.129935</td>\n",
       "      <td>0.372699</td>\n",
       "      <td>0.400649</td>\n",
       "      <td>0.252994</td>\n",
       "      <td>0.134665</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.016811</td>\n",
       "      <td>0.382353</td>\n",
       "      <td>0.084412</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.178030</td>\n",
       "      <td>0.020867</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.433239</td>\n",
       "      <td>0.014182</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>0.037992</td>\n",
       "      <td>0.066834</td>\n",
       "      <td>0.019618</td>\n",
       "      <td>0.020794</td>\n",
       "      <td>0.087342</td>\n",
       "      <td>0.295574</td>\n",
       "      <td>0.081125</td>\n",
       "      <td>0.063827</td>\n",
       "      <td>0.051388</td>\n",
       "      <td>0.033925</td>\n",
       "      <td>0.143427</td>\n",
       "      <td>0.017552</td>\n",
       "      <td>0.026265</td>\n",
       "      <td>0.192557</td>\n",
       "      <td>0.031330</td>\n",
       "      <td>0.098605</td>\n",
       "      <td>0.438575</td>\n",
       "      <td>0.274937</td>\n",
       "      <td>0.124774</td>\n",
       "      <td>0.515035</td>\n",
       "      <td>0.550731</td>\n",
       "      <td>0.515035</td>\n",
       "      <td>0.480411</td>\n",
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       "      <td>0.129335</td>\n",
       "      <td>0.511249</td>\n",
       "      <td>0.025879</td>\n",
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       "      <td>0.064236</td>\n",
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       "      <td>0.021564</td>\n",
       "      <td>0.044056</td>\n",
       "      <td>0.013757</td>\n",
       "      <td>0.021763</td>\n",
       "      <td>0.041687</td>\n",
       "      <td>0.013180</td>\n",
       "      <td>0.042679</td>\n",
       "      <td>0.000023</td>\n",
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       "      <td>0.523812</td>\n",
       "      <td>0.014878</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.158766</td>\n",
       "      <td>0.103986</td>\n",
       "      <td>0.081388</td>\n",
       "      <td>0.145666</td>\n",
       "      <td>0.343936</td>\n",
       "      <td>0.334356</td>\n",
       "      <td>0.154489</td>\n",
       "      <td>0.477924</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.258621</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.009223</td>\n",
       "      <td>0.010308</td>\n",
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       "      <td>0.148777</td>\n",
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       "      <td>0.025298</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.227876</td>\n",
       "      <td>0.402014</td>\n",
       "      <td>0.258377</td>\n",
       "      <td>0.063612</td>\n",
       "      <td>0.419404</td>\n",
       "      <td>0.320067</td>\n",
       "      <td>0.232980</td>\n",
       "      <td>0.174124</td>\n",
       "      <td>0.022761</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018136</td>\n",
       "      <td>0.342704</td>\n",
       "      <td>0.055977</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.867173</td>\n",
       "      <td>0.018581</td>\n",
       "      <td>0.023231</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.155050</td>\n",
       "      <td>0.024216</td>\n",
       "      <td>0.003146</td>\n",
       "      <td>0.037808</td>\n",
       "      <td>0.058576</td>\n",
       "      <td>0.019786</td>\n",
       "      <td>0.016637</td>\n",
       "      <td>0.031911</td>\n",
       "      <td>0.026848</td>\n",
       "      <td>0.044064</td>\n",
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       "      <td>0.028516</td>\n",
       "      <td>0.005022</td>\n",
       "      <td>0.003883</td>\n",
       "      <td>0.162293</td>\n",
       "      <td>0.029770</td>\n",
       "      <td>0.036276</td>\n",
       "      <td>0.442722</td>\n",
       "      <td>0.341290</td>\n",
       "      <td>0.123632</td>\n",
       "      <td>0.450012</td>\n",
       "      <td>0.565307</td>\n",
       "      <td>0.381202</td>\n",
       "      <td>0.520663</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.430047</td>\n",
       "      <td>0.036868</td>\n",
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       "      <td>0.060569</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.034206</td>\n",
       "      <td>0.044904</td>\n",
       "      <td>0.024435</td>\n",
       "      <td>0.051751</td>\n",
       "      <td>0.023942</td>\n",
       "      <td>0.163012</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.046049</td>\n",
       "      <td>0.044048</td>\n",
       "      <td>0.155278</td>\n",
       "      <td>0.015655</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.038534</td>\n",
       "      <td>0.616301</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.171549</td>\n",
       "      <td>0.279827</td>\n",
       "      <td>0.463317</td>\n",
       "      <td>0.241628</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.589887</td>\n",
       "      <td>0.750039</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.320499</td>\n",
       "      <td>0.137547</td>\n",
       "      <td>0.193162</td>\n",
       "      <td>0.284524</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.018977</td>\n",
       "      <td>0.478876</td>\n",
       "      <td>0.356150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.29314</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.327954</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000577</td>\n",
       "      <td>0.007667</td>\n",
       "      <td>0.010116</td>\n",
       "      <td>0.031641</td>\n",
       "      <td>0.020095</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.118629</td>\n",
       "      <td>0.000322</td>\n",
       "      <td>0.003801</td>\n",
       "      <td>0.018540</td>\n",
       "      <td>0.254892</td>\n",
       "      <td>0.342445</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.210571</td>\n",
       "      <td>0.399498</td>\n",
       "      <td>0.275713</td>\n",
       "      <td>0.328244</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>0.161698</td>\n",
       "      <td>0.288382</td>\n",
       "      <td>0.554255</td>\n",
       "      <td>0.640120</td>\n",
       "      <td>0.141604</td>\n",
       "      <td>0.107669</td>\n",
       "      <td>0.047437</td>\n",
       "      <td>0.151880</td>\n",
       "      <td>0.128819</td>\n",
       "      <td>0.044709</td>\n",
       "      <td>0.221421</td>\n",
       "      <td>0.100875</td>\n",
       "      <td>0.043172</td>\n",
       "      <td>0.138504</td>\n",
       "      <td>0.145148</td>\n",
       "      <td>0.066355</td>\n",
       "      <td>0.157791</td>\n",
       "      <td>0.318094</td>\n",
       "      <td>0.348224</td>\n",
       "      <td>0.132553</td>\n",
       "      <td>0.145696</td>\n",
       "      <td>0.127795</td>\n",
       "      <td>0.139212</td>\n",
       "      <td>0.131638</td>\n",
       "      <td>0.168456</td>\n",
       "      <td>0.788872</td>\n",
       "      <td>0.595294</td>\n",
       "      <td>0.011729</td>\n",
       "      <td>0.073099</td>\n",
       "      <td>0.012653</td>\n",
       "      <td>0.090288</td>\n",
       "      <td>0.011097</td>\n",
       "      <td>0.069148</td>\n",
       "      <td>0.311612</td>\n",
       "      <td>0.151239</td>\n",
       "      <td>0.134058</td>\n",
       "      <td>0.098045</td>\n",
       "      <td>0.068750</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.309942</td>\n",
       "      <td>0.152542</td>\n",
       "      <td>0.123188</td>\n",
       "      <td>0.100153</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   feature_0  feature_1  feature_2  feature_3  feature_4  feature_5  \\\n",
       "0   0.469231   0.589292   0.499096   0.379841   0.000609        0.0   \n",
       "1   0.575003   0.445535   0.666763   0.393982   0.000133        0.0   \n",
       "2   0.308868   0.583388   0.493903   0.457065   0.000744        0.0   \n",
       "3   0.400385   0.466998   0.543479   0.244894   0.000574        0.0   \n",
       "4   0.471367   0.500400   0.678327   0.357067   0.000765        0.0   \n",
       "\n",
       "   feature_6  feature_7  feature_8  feature_9  feature_10  feature_11  \\\n",
       "0   0.328562   0.965785   0.665019   0.542479    0.358362    0.880692   \n",
       "1   0.428942   0.969673   0.656639   0.412315    0.228669    0.932908   \n",
       "2   0.283455   0.965008   0.542759   0.448168    0.411832    0.929265   \n",
       "3   0.469123   0.946345   0.638590   0.319564    0.359499    0.933515   \n",
       "4   0.387631   0.960342   0.670606   0.392050    0.315131    0.915301   \n",
       "\n",
       "   feature_12  feature_13  feature_14  feature_15  feature_16  feature_17  \\\n",
       "0    0.226185         0.0    0.329909    0.165668    0.056644    0.958559   \n",
       "1    0.205145         0.0    0.457039    0.165391    0.048683    0.963740   \n",
       "2    0.221497         0.0    0.420091    0.169405    0.049237    0.957079   \n",
       "3    0.219610         0.0    0.425265    0.180762    0.053078    0.960286   \n",
       "4    0.220658         0.0    0.480818    0.177310    0.059646    0.972126   \n",
       "\n",
       "   feature_18  feature_19  feature_20  feature_21  feature_22  feature_23  \\\n",
       "0    0.500188    0.848739    0.814395    0.242124    0.797675    0.481224   \n",
       "1    0.476296    0.830040    0.740957    0.238978    0.712274    0.525355   \n",
       "2    0.506806    0.855614    0.849836    0.238104    0.738940    0.480961   \n",
       "3    0.495005    0.835631    0.808915    0.235237    0.724308    0.443066   \n",
       "4    0.483114    0.834120    0.763975    0.234118    0.720752    0.485779   \n",
       "\n",
       "   feature_24  feature_25  feature_26  feature_27  feature_28  feature_29  \\\n",
       "0    0.538057    0.647599    0.863698    0.398104    0.261259    0.476550   \n",
       "1    0.455345    0.938169    0.981245    0.964903    0.487686    0.562509   \n",
       "2    0.445790    0.948944    0.988650    0.989554    0.418016    0.585923   \n",
       "3    0.454757    0.950029    0.976130    0.955045    0.190989    0.695296   \n",
       "4    0.516127    0.931950    0.969992    0.949862    0.185584    0.875018   \n",
       "\n",
       "   feature_30  feature_31  feature_32  feature_33  feature_34  feature_35  \\\n",
       "0    0.514343    0.529986    0.009554    0.121293    0.078721    0.019008   \n",
       "1    0.701594    0.492686    0.076817    0.139530    0.083009    0.019793   \n",
       "2    0.554980    0.559058    0.070202    0.067069    0.032024    0.014189   \n",
       "3    0.689243    0.479595    0.077053    0.068333    0.067953    0.014225   \n",
       "4    0.938247    0.439918    0.140226    0.073974    0.041585    0.015487   \n",
       "\n",
       "   feature_36  feature_37  feature_38  feature_39  feature_40  feature_41  \\\n",
       "0    0.921279    0.458921    0.603022    0.267583    0.706799    0.120954   \n",
       "1    0.916991    0.000000    0.757277    0.295521    0.907068    0.075058   \n",
       "2    0.967976    0.302570    0.000000    0.318310    0.152753    0.145166   \n",
       "3    0.932047    0.090745    0.530100    0.245947    0.891127    0.035697   \n",
       "4    0.958415    0.406311    0.383630    0.448568    0.885105    0.060199   \n",
       "\n",
       "   feature_42  feature_43  feature_44  feature_45  feature_46  feature_47  \\\n",
       "0         0.0    0.288419    0.453178    0.319010    0.261238    0.798994   \n",
       "1         0.0    0.274732    0.360793    0.360565    0.278107    0.780757   \n",
       "2         0.0    0.625997    0.260730    0.340259    0.388916    0.791108   \n",
       "3         0.0    0.586889    0.293581    0.340259    0.378747    0.794558   \n",
       "4         0.0    0.306446    0.594777    1.000000    1.000000    0.529771   \n",
       "\n",
       "   feature_48  feature_49  feature_50  feature_51  feature_52  feature_53  \\\n",
       "0    0.411986         0.0    0.280399    0.815716         0.0    0.837429   \n",
       "1    0.535243         0.0    0.395517    0.755483         0.0    0.835539   \n",
       "2    0.408149         0.0    0.495000    0.666551         0.0    0.737240   \n",
       "3    0.447084         0.0    0.496776    0.685566         0.0    0.737240   \n",
       "4    0.328147         0.0    1.000000    0.885496         0.0    0.867675   \n",
       "\n",
       "   feature_54  feature_55  feature_56  feature_57  feature_58  feature_59  \\\n",
       "0    0.842447    0.244444    0.902087    0.591398    0.774060    0.138291   \n",
       "1    0.843373    0.385185    0.913323    0.573477    0.736297    0.151144   \n",
       "2    0.756256    1.000000    0.616372    0.458781    0.585405    0.267903   \n",
       "3    0.756256    1.000000    0.616372    0.458781    0.585405    0.270716   \n",
       "4    0.891566    0.474074    0.871589    0.465950    0.671606    0.084682   \n",
       "\n",
       "   feature_60  feature_61  feature_62  feature_63  feature_64  feature_65  \\\n",
       "0    0.533284    0.500043    0.132027    0.077698    0.359149    0.221741   \n",
       "1    0.555275    0.364963    0.157978    0.049586    0.299589    0.204983   \n",
       "2    0.810180    0.304415    0.165988    0.051723    0.228662    0.175564   \n",
       "3    0.747554    0.322790    0.168684    0.063833    0.215625    0.168451   \n",
       "4    0.617807    0.143601    0.304040    0.108989    0.607576    0.350179   \n",
       "\n",
       "   feature_66  feature_67  feature_68  feature_69  feature_70  feature_71  \\\n",
       "0    0.443966    0.000070    0.762021         0.0    0.187416    0.288633   \n",
       "1    0.468744    0.000103    0.833427         0.0    0.273964    0.280663   \n",
       "2    0.565086    0.000063    0.727359         0.0    0.313769    0.291806   \n",
       "3    0.564325    0.000077    0.750309         0.0    0.309017    0.224819   \n",
       "4    0.819041    0.000070    0.738046         0.0    0.454369    0.204606   \n",
       "\n",
       "   feature_72  feature_73  feature_74  feature_75  feature_76  feature_77  \\\n",
       "0    0.901588    0.042461         0.0    0.349287    0.640619    0.499369   \n",
       "1    0.901588    0.042461         0.0    0.300238    0.643714    0.441733   \n",
       "2    0.852204    0.123622         0.0    0.288644    0.594197    0.418174   \n",
       "3    0.935748    0.037773         0.0    0.146849    0.541973    0.271771   \n",
       "4    0.901588    0.042461         0.0    0.153092    0.791103    0.369794   \n",
       "\n",
       "   feature_78  feature_79  feature_80  feature_81  feature_82  feature_83  \\\n",
       "0    0.531470    0.306768    0.537934    0.265006    0.584689    0.439410   \n",
       "1    0.509207    0.132827    0.502097    0.386335    0.601795    0.309224   \n",
       "2    0.618514    0.325111    0.115517    0.510856    0.677510    0.403956   \n",
       "3    0.649816    0.786844    0.147160    0.644955    0.674706    0.558284   \n",
       "4    0.507700    0.380772    0.479985    0.548531    0.583567    0.529592   \n",
       "\n",
       "   feature_84  feature_85  feature_86  feature_87  feature_88  feature_89  \\\n",
       "0    0.441088    0.629771    0.469649    0.887923    0.251477    0.053474   \n",
       "1    0.555891    0.629771    0.424601    0.981244    0.636734    0.055898   \n",
       "2    0.567976    0.629771    0.673163    0.974840    0.122208    0.028206   \n",
       "3    0.386707    0.629771    0.504473    0.985819    0.260880    0.062068   \n",
       "4    0.507553    0.629771    0.672524    0.984904    0.420575    0.052593   \n",
       "\n",
       "   feature_90  feature_91  feature_92  feature_93  feature_94  feature_95  \\\n",
       "0    0.380564    0.041128    0.174447    0.559211    0.677419    0.666667   \n",
       "1    0.301473    0.698208    0.272727    0.562500    0.483871    0.555556   \n",
       "2    0.573214    0.572878    0.324324    0.519737    0.645161    0.611111   \n",
       "3    0.241362    0.592886    0.302211    0.493421    0.612903    0.611111   \n",
       "4    0.482834    0.558288    0.120393    0.559211    0.612903    0.444444   \n",
       "\n",
       "   feature_96  feature_97  feature_98  feature_99  feature_100  feature_101  \\\n",
       "0    0.301208         0.0    0.722961    0.355945     0.603774     0.634146   \n",
       "1    0.517771         0.0    0.553006    0.362524     0.490566     0.487805   \n",
       "2    0.337377         0.0    0.654084    0.459857     0.679245     0.560976   \n",
       "3    0.381341         0.0    0.674539    0.359907     0.528302     0.585366   \n",
       "4    0.425907         0.0    0.586788    0.361887     0.641509     0.512195   \n",
       "\n",
       "   feature_102  feature_103  feature_104  feature_105  feature_106  \\\n",
       "0     0.804489     0.539474     0.642105     0.825980     0.436066   \n",
       "1     0.552208     0.533835     0.636842     0.725490     0.436066   \n",
       "2     0.658545     0.569549     0.542105     0.674020     0.478689   \n",
       "3     0.558569     0.507519     0.642105     0.696078     0.504918   \n",
       "4     0.725996     0.533835     0.636842     0.879902     0.436066   \n",
       "\n",
       "   feature_107  feature_108  feature_109  feature_110  feature_111  \\\n",
       "0     0.273557     0.899621     0.983952     0.709486     0.759737   \n",
       "1     0.594922     0.345103     0.983952     0.709486     0.759737   \n",
       "2     0.488494     0.368777     0.983952     0.709486     0.759737   \n",
       "3     0.470442     0.307630     0.983952     0.709486     0.759737   \n",
       "4     0.439099     0.191558     0.983952     0.709486     0.759737   \n",
       "\n",
       "   feature_112  feature_113  feature_114  feature_115  feature_116  \\\n",
       "0     0.947492     0.753458          0.0     0.537668     0.984375   \n",
       "1     0.947492     0.724980          0.0     0.492045     0.978516   \n",
       "2     0.958854     0.567128          0.0     0.458737     0.975586   \n",
       "3     0.951279     0.838080          0.0     0.433794     0.982422   \n",
       "4     0.947492     0.724166          0.0     0.663772     0.984375   \n",
       "\n",
       "   feature_117  feature_118  feature_119  feature_120  feature_121  \\\n",
       "0     0.021720     0.749743     0.983757     0.522891     0.688525   \n",
       "1     0.022636     0.704428     0.932203     0.576019     0.688525   \n",
       "2     0.021913     0.763131     0.961158     0.553733     0.721311   \n",
       "3     0.022619     0.764161     0.953390     0.570761     0.147541   \n",
       "4     0.021547     0.658084     0.859463     0.513000     0.475410   \n",
       "\n",
       "   feature_122  feature_123  feature_124  feature_125  feature_126  \\\n",
       "0     0.185512     0.827957     0.746269     0.257061     0.613568   \n",
       "1     0.166731     0.795699     0.671642     0.259244     0.261054   \n",
       "2     0.232081     0.827957     0.776119     0.263657     0.456693   \n",
       "3     0.279992     0.473118     0.238806     0.489502     0.084797   \n",
       "4     0.283059     0.602151     0.417910     0.294407     0.416111   \n",
       "\n",
       "   feature_127  feature_128  feature_129  feature_130  feature_131  \\\n",
       "0     0.109093     0.818485     0.598316     0.649733     0.094488   \n",
       "1     0.113750     0.840051     0.590733     0.837353     0.192913   \n",
       "2     0.188148     0.840051     0.575565     0.852721     0.165354   \n",
       "3     0.234602     0.800770     0.651403     0.649947     0.098425   \n",
       "4     0.252299     0.846983     0.514879     0.764354     0.291339   \n",
       "\n",
       "   feature_132  feature_133  feature_134  feature_135  feature_136  \\\n",
       "0     0.375533     0.500089     0.190863     0.069444     0.289565   \n",
       "1     0.363087     0.435511     0.148084     0.042735     0.170196   \n",
       "2     0.242176     0.443796     0.282551     0.033120     0.347709   \n",
       "3     0.672475     0.000000     0.251494     0.073718     0.315364   \n",
       "4     0.641536     0.312662     0.155349     0.065171     0.412399   \n",
       "\n",
       "   feature_137  feature_138  feature_139  feature_140  feature_141  \\\n",
       "0     0.177496     0.216394     0.198042     0.000036          0.0   \n",
       "1     0.195880     0.298361     0.124114     0.000020          0.0   \n",
       "2     0.245008     0.207378     0.172835     0.000029          0.0   \n",
       "3     0.331220     0.229509     0.091734     0.000016          0.0   \n",
       "4     0.329319     0.237705     0.167338     0.000034          0.0   \n",
       "\n",
       "   feature_142  feature_143  feature_144  feature_145  feature_146  \\\n",
       "0     0.049582     0.280992     0.096812     0.277366     0.058171   \n",
       "1     0.038957     0.404959     0.174060     0.168338     0.142096   \n",
       "2     0.064830     0.314050     0.072525     0.117314     0.174067   \n",
       "3     0.037088     0.247934     0.137629     0.078064     0.171403   \n",
       "4     0.018692     0.338843     0.052285     0.103358     0.175844   \n",
       "\n",
       "   feature_147  feature_148  feature_149  feature_150  feature_151  \\\n",
       "0     0.029261     0.014613          0.0     0.139841     0.015460   \n",
       "1     0.016530     0.014714          0.0     0.235832     0.020685   \n",
       "2     0.018275     0.009231          0.0     0.165960     0.012016   \n",
       "3     0.015092     0.015973          0.0     0.368715     0.024733   \n",
       "4     0.020226     0.017281          0.0     0.365474     0.019617   \n",
       "\n",
       "   feature_152  feature_153  feature_154  feature_155  feature_156  \\\n",
       "0     0.000345     0.055674     0.055944     0.021480     0.018032   \n",
       "1     0.000446     0.045899     0.055695     0.016706     0.014882   \n",
       "2     0.000364     0.059924     0.036581     0.026253     0.017622   \n",
       "3     0.001243     0.048449     0.056383     0.016706     0.026843   \n",
       "4     0.000313     0.038249     0.066530     0.023071     0.019493   \n",
       "\n",
       "   feature_157  feature_158  feature_159  feature_160  feature_161  \\\n",
       "0     0.085751     0.346161     0.130535     0.231894     0.028095   \n",
       "1     0.085751     0.346161     0.072905     0.014149     0.007828   \n",
       "2     0.085751     0.346161     0.072135     0.188969     0.020004   \n",
       "3     0.085751     0.346161     0.110255     0.085132     0.090478   \n",
       "4     0.085751     0.346161     0.089719     0.067866     0.046043   \n",
       "\n",
       "   feature_162  feature_163  feature_164  feature_165  feature_166  \\\n",
       "0     0.009981     0.094044     0.026417     0.028911     0.059113   \n",
       "1     0.088877     0.117032     0.063291     0.037736     0.068966   \n",
       "2     0.056955     0.195402     0.064392     0.020694     0.064039   \n",
       "3     0.081473     0.071055     0.059439     0.030432     0.044335   \n",
       "4     0.039136     0.153605     0.022014     0.034388     0.152709   \n",
       "\n",
       "   feature_167  feature_168  feature_169  feature_170  feature_171  \\\n",
       "0      0.03750     0.052023     0.000000     0.499474      0.21645   \n",
       "1      0.05000     0.066474     0.469462     0.879312      0.37683   \n",
       "2      0.06875     0.130058     0.248861     0.917437      0.05772   \n",
       "3      0.03750     0.076590     0.177758     0.749269      0.05504   \n",
       "4      0.03125     0.098266     0.412944     0.325225      0.11173   \n",
       "\n",
       "   feature_172  feature_173  feature_174  feature_175  feature_176  \\\n",
       "0     0.451751     0.548731     0.451990     0.687359     0.882481   \n",
       "1     0.673815     0.662408     0.673815     0.783361     0.429815   \n",
       "2     0.441744     0.526618     0.441744     0.712416     0.536181   \n",
       "3     0.317131     0.303522     0.317131     0.556166     0.467084   \n",
       "4     0.420777     0.539230     0.420777     0.516470     0.155876   \n",
       "\n",
       "   feature_177  feature_178  feature_179  feature_180  feature_181  \\\n",
       "0     0.062396          0.0          0.0     0.294118     0.068966   \n",
       "1     0.153506          0.0          0.0     0.218742     0.099425   \n",
       "2     0.154059          0.0          0.0     0.213394     0.187356   \n",
       "3     0.189398          0.0          0.0     0.173669     0.172414   \n",
       "4     0.483158          0.0          0.0     0.148714     0.054310   \n",
       "\n",
       "   feature_182  feature_183  feature_184  feature_185  feature_186  \\\n",
       "0     0.179819     0.172214     0.015819     0.014212          0.0   \n",
       "1     0.134478     0.166988     0.018602     0.018724          0.0   \n",
       "2     0.195640     0.263465     0.051960     0.027334          0.0   \n",
       "3     0.221879     0.258313     0.046552     0.014099          0.0   \n",
       "4     0.148177     0.481371     0.027887     0.007670          0.0   \n",
       "\n",
       "   feature_187  feature_188  feature_189  feature_190  feature_191  \\\n",
       "0     0.016473     0.317196          0.0          0.0          0.0   \n",
       "1     0.009380     0.412343          0.0          0.0          0.0   \n",
       "2     0.038613     0.288834          0.0          0.0          0.0   \n",
       "3     0.039707     0.248131          0.0          0.0          0.0   \n",
       "4     0.064035     0.353834          0.0          0.0          0.0   \n",
       "\n",
       "   feature_192  feature_193  feature_194  feature_195  feature_196  \\\n",
       "0          0.0          0.0          0.0     0.044557     0.009205   \n",
       "1          0.0          0.0          0.0     0.029844     0.005046   \n",
       "2          0.0          0.0          0.0     0.012400     0.008469   \n",
       "3          0.0          0.0          0.0     0.012400     0.010574   \n",
       "4          0.0          0.0          0.0     0.035729     0.009560   \n",
       "\n",
       "   feature_197  feature_198  feature_199  feature_200  feature_201  \\\n",
       "0     0.044654     0.020751     0.020266     0.036098     0.042520   \n",
       "1     0.042561     0.022766     0.022925     0.024490     0.029558   \n",
       "2     0.043218     0.086532     0.017917     0.068836     0.040228   \n",
       "3     0.059019     0.118263     0.020292     0.052696     0.046787   \n",
       "4     0.026842     0.043215     0.020498     0.084313     0.043626   \n",
       "\n",
       "   feature_202  feature_203  feature_204  feature_205  feature_206  \\\n",
       "0     0.008235     0.017259     0.000025     0.019953          0.0   \n",
       "1     0.007459     0.014920     0.000032     0.020960          0.0   \n",
       "2     0.022544     0.030238     0.000007     0.034618          0.0   \n",
       "3     0.023202     0.033681     0.000030     0.022848          0.0   \n",
       "4     0.017217     0.052990     0.000035     0.021369          0.0   \n",
       "\n",
       "   feature_207  feature_208  feature_209  feature_210  feature_211  \\\n",
       "0     0.013866     0.550662          0.0     0.093498     0.180031   \n",
       "1     0.008840     0.771849          0.0     0.050980     0.145398   \n",
       "2     0.028040     0.444691          0.0     0.004128     0.147270   \n",
       "3     0.039591     0.596389          0.0     0.191744     0.137598   \n",
       "4     0.034741     0.520678          0.0     0.123013     0.151014   \n",
       "\n",
       "   feature_212  feature_213  feature_214  feature_215  feature_216  \\\n",
       "0     0.107155     0.039844     0.068849     0.136795     0.074698   \n",
       "1     0.269165     0.120391     0.037052     0.275371     0.233248   \n",
       "2     0.106474     0.028828     0.028025     0.231751     0.092274   \n",
       "3     0.196252     0.024766     0.069388     0.206231     0.199927   \n",
       "4     0.118910     0.081406     0.081380     0.293175     0.302819   \n",
       "\n",
       "   feature_217  feature_218  feature_219  feature_220  feature_221  \\\n",
       "0     0.158440     0.314760     0.019231     0.177596     0.240855   \n",
       "1     0.100225     0.376415     0.275641     0.177596     0.266508   \n",
       "2     0.095874     0.179285     0.044872     0.177596     0.362470   \n",
       "3     0.127982     0.580084     0.102564     0.177596     0.267933   \n",
       "4     0.140435     0.399073     0.147436     0.177596     0.285986   \n",
       "\n",
       "   feature_222  feature_223  feature_224  feature_225  feature_226  \\\n",
       "0     0.020993     0.082317     0.015559     0.516125          0.0   \n",
       "1     0.001110     0.055375     0.010163     0.529993          0.0   \n",
       "2     0.015038     0.086508     0.013387     0.195426          0.0   \n",
       "3     0.001211     0.068999     0.033642     0.189833          0.0   \n",
       "4     0.000101     0.021241     0.131553     0.360554          0.0   \n",
       "\n",
       "   feature_227  feature_228  feature_229  feature_230  feature_231  \\\n",
       "0     0.461122     0.229985          0.0          0.0          0.0   \n",
       "1     0.095335     0.079573          0.0          0.0          0.0   \n",
       "2     0.087897     0.047550          0.0          0.0          0.0   \n",
       "3     0.060852     0.037361          0.0          0.0          0.0   \n",
       "4     0.111562     0.097040          0.0          0.0          0.0   \n",
       "\n",
       "   feature_232  feature_233  feature_234  feature_235  feature_236  \\\n",
       "0          0.0          0.0          0.0          0.0          0.0   \n",
       "1          0.0          0.0          0.0          0.0          0.0   \n",
       "2          0.0          0.0          0.0          0.0          0.0   \n",
       "3          0.0          0.0          0.0          0.0          0.0   \n",
       "4          0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "   feature_237  feature_238  feature_239  feature_240  feature_241  \\\n",
       "0          0.0     0.060606     0.117117          0.0          0.0   \n",
       "1          0.0     0.220779     0.099099          0.0          0.0   \n",
       "2          0.0     0.212121     0.117117          0.0          0.0   \n",
       "3          0.0     0.233766     0.117117          0.0          0.0   \n",
       "4          0.0     0.233766     0.076577          0.0          0.0   \n",
       "\n",
       "   feature_242  feature_243  feature_244  feature_245  feature_246  \\\n",
       "0          0.0          0.0     0.000706     0.008972     0.010889   \n",
       "1          0.0          0.0     0.000706     0.008972     0.010889   \n",
       "2          0.0          0.0     0.000706     0.008972     0.010889   \n",
       "3          0.0          0.0     0.000706     0.008972     0.010889   \n",
       "4          0.0          0.0     0.000706     0.008972     0.010889   \n",
       "\n",
       "   feature_247  feature_248  feature_249  feature_250  feature_251  \\\n",
       "0     0.054945     0.016285          0.0     0.181065     0.000808   \n",
       "1     0.054945     0.012781          0.0     0.156566     0.000404   \n",
       "2     0.351852     0.025046          0.0     0.075902     0.000404   \n",
       "3     0.038869     0.021027          0.0     0.066520     0.000707   \n",
       "4     0.054945     0.021645          0.0     0.116690     0.000303   \n",
       "\n",
       "   feature_252  feature_253  feature_254  feature_255  feature_256  \\\n",
       "0     0.000812     0.021479     0.042857     0.375657          0.0   \n",
       "1     0.001229     0.021026     0.089610     0.560982          0.0   \n",
       "2     0.001325     0.144472     0.059740     0.362760          0.0   \n",
       "3     0.000959     0.070540     0.071429     0.368520          0.0   \n",
       "4     0.000710     0.018766     0.255844     0.348360          0.0   \n",
       "\n",
       "   feature_257  feature_258  feature_259  feature_260  feature_261  \\\n",
       "0          0.0          0.0          0.0          0.0          0.0   \n",
       "1          0.0          0.0          0.0          0.0          0.0   \n",
       "2          0.0          0.0          0.0          0.0          0.0   \n",
       "3          0.0          0.0          0.0          0.0          0.0   \n",
       "4          0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "   feature_262  feature_263  feature_264  feature_265  feature_266  \\\n",
       "0          0.0          0.0          0.0          0.0          0.0   \n",
       "1          0.0          0.0          0.0          0.0          0.0   \n",
       "2          0.0          0.0          0.0          0.0          0.0   \n",
       "3          0.0          0.0          0.0          0.0          0.0   \n",
       "4          0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "   feature_267  feature_268  feature_269  feature_270  feature_271  \\\n",
       "0     0.198551     1.000000     0.363058     0.107949     0.192718   \n",
       "1     0.154348     0.680295     0.263354     0.094508     0.152027   \n",
       "2     0.209420     0.676785     0.295056     0.055980     0.253441   \n",
       "3     0.228261     0.740340     0.214005     0.128333     0.224515   \n",
       "4     0.210145     0.396007     0.261501     0.129935     0.372699   \n",
       "\n",
       "   feature_272  feature_273  feature_274  feature_275  feature_276  \\\n",
       "0     0.138595     0.209072     0.167202     0.000036          0.0   \n",
       "1     0.133478     0.250543     0.126757     0.000020          0.0   \n",
       "2     0.222988     0.203891     0.147742     0.000030          0.0   \n",
       "3     0.325138     0.237197     0.076404     0.000014          0.0   \n",
       "4     0.400649     0.252994     0.134665     0.000029          0.0   \n",
       "\n",
       "   feature_277  feature_278  feature_279  feature_280  feature_281  \\\n",
       "0     0.064120     0.294118     0.119302     0.181818     0.096591   \n",
       "1     0.040633     0.352941     0.187394     0.143695     0.160985   \n",
       "2     0.062194     0.352941     0.079910     0.115836     0.166667   \n",
       "3     0.051124     0.205882     0.174451     0.061584     0.195076   \n",
       "4     0.016811     0.382353     0.084412     0.063050     0.178030   \n",
       "\n",
       "   feature_282  feature_283  feature_284  feature_285  feature_286  \\\n",
       "0     0.030323     0.014753          0.0     0.164027     0.013831   \n",
       "1     0.017281     0.014619          0.0     0.255980     0.015611   \n",
       "2     0.019237     0.010183          0.0     0.166883     0.008829   \n",
       "3     0.015324     0.015972          0.0     0.379937     0.016116   \n",
       "4     0.020867     0.016870          0.0     0.433239     0.014182   \n",
       "\n",
       "   feature_287  feature_288  feature_289  feature_290  feature_291  \\\n",
       "0     0.000282     0.058345     0.058331     0.015096     0.018758   \n",
       "1     0.000393     0.050204     0.057298     0.013337     0.014687   \n",
       "2     0.000331     0.063772     0.040739     0.017562     0.018758   \n",
       "3     0.001153     0.050204     0.059948     0.011419     0.030100   \n",
       "4     0.000267     0.037992     0.066834     0.019618     0.020794   \n",
       "\n",
       "   feature_292  feature_293  feature_294  feature_295  feature_296  \\\n",
       "0     0.087342     0.295574     0.106294     0.198576     0.031887   \n",
       "1     0.087342     0.295574     0.059482     0.013255     0.008947   \n",
       "2     0.087342     0.295574     0.063922     0.164338     0.020891   \n",
       "3     0.087342     0.295574     0.105625     0.078314     0.101073   \n",
       "4     0.087342     0.295574     0.081125     0.063827     0.051388   \n",
       "\n",
       "   feature_297  feature_298  feature_299  feature_300  feature_301  \\\n",
       "0     0.008549     0.070859     0.019589     0.025492     0.055231   \n",
       "1     0.082598     0.095365     0.054403     0.032610     0.093520   \n",
       "2     0.048728     0.151976     0.056536     0.017988     0.071542   \n",
       "3     0.074373     0.054141     0.043154     0.025658     0.059341   \n",
       "4     0.033925     0.143427     0.017552     0.026265     0.192557   \n",
       "\n",
       "   feature_302  feature_303  feature_304  feature_305  feature_306  \\\n",
       "0     0.037953     0.042791     0.011254     0.462699     0.180832   \n",
       "1     0.046519     0.085116     0.526415     0.738474     0.307866   \n",
       "2     0.057762     0.143256     0.282276     0.747415     0.052893   \n",
       "3     0.038230     0.082791     0.214442     0.645991     0.042495   \n",
       "4     0.031330     0.098605     0.438575     0.274937     0.124774   \n",
       "\n",
       "   feature_307  feature_308  feature_309  feature_310  feature_311  \\\n",
       "0     0.507358     0.647463     0.507997     0.707874     0.757555   \n",
       "1     0.715291     0.738177     0.715291     0.739064     0.362637   \n",
       "2     0.472809     0.582545     0.472809     0.710536     0.581731   \n",
       "3     0.291107     0.338779     0.291107     0.629897     0.442995   \n",
       "4     0.515035     0.550731     0.515035     0.480411     0.174451   \n",
       "\n",
       "   feature_312  feature_313  feature_314  feature_315  feature_316  \\\n",
       "0     0.052696          0.0          0.0          0.0     0.337770   \n",
       "1     0.135154          0.0          0.0          0.0     0.277853   \n",
       "2     0.141570          0.0          0.0          0.0     0.284887   \n",
       "3     0.198089          0.0          0.0          0.0     0.257655   \n",
       "4     0.457338          0.0          0.0          0.0     0.204451   \n",
       "\n",
       "   feature_317  feature_318  feature_319  feature_320  feature_321  \\\n",
       "0     0.092093     0.190226     0.202104     0.018104     0.012908   \n",
       "1     0.116296     0.134761     0.195077     0.019169     0.015191   \n",
       "2     0.246026     0.192459     0.245936     0.049201     0.024912   \n",
       "3     0.176495     0.214990     0.254268     0.042066     0.014292   \n",
       "4     0.049533     0.129335     0.511249     0.025879     0.006513   \n",
       "\n",
       "   feature_322  feature_323  feature_324  feature_325  feature_326  \\\n",
       "0          0.0     0.017558     0.317877          0.0          0.0   \n",
       "1          0.0     0.012071     0.430930          0.0          0.0   \n",
       "2          0.0     0.033111     0.277554          0.0          0.0   \n",
       "3          0.0     0.037218     0.272055          0.0          0.0   \n",
       "4          0.0     0.064236     0.420716          0.0          0.0   \n",
       "\n",
       "   feature_327  feature_328  feature_329  feature_330  feature_331  \\\n",
       "0          0.0          0.0          0.0          0.0     0.049185   \n",
       "1          0.0          0.0          0.0          0.0     0.051607   \n",
       "2          0.0          0.0          0.0          0.0     0.001770   \n",
       "3          0.0          0.0          0.0          0.0     0.001770   \n",
       "4          0.0          0.0          0.0          0.0     0.055706   \n",
       "\n",
       "   feature_332  feature_333  feature_334  feature_335  feature_336  \\\n",
       "0     0.006089     0.042794     0.018645     0.013385     0.015504   \n",
       "1     0.003517     0.035214     0.017611     0.014952     0.012054   \n",
       "2     0.005797     0.035449     0.073723     0.013786     0.017984   \n",
       "3     0.006850     0.043215     0.095441     0.014425     0.019971   \n",
       "4     0.006219     0.021564     0.044056     0.013757     0.021763   \n",
       "\n",
       "   feature_337  feature_338  feature_339  feature_340  feature_341  \\\n",
       "0     0.041103     0.006250     0.016080     0.000015     0.023483   \n",
       "1     0.029579     0.005727     0.014392     0.000017     0.019084   \n",
       "2     0.039797     0.015173     0.020623     0.000004     0.032864   \n",
       "3     0.044517     0.015594     0.022959     0.000016     0.025210   \n",
       "4     0.041687     0.013180     0.042679     0.000023     0.025941   \n",
       "\n",
       "   feature_342  feature_343  feature_344  feature_345  feature_346  \\\n",
       "0          0.0     0.011224     0.520854     0.014878     0.010864   \n",
       "1          0.0     0.007948     0.692392     0.014878     0.010864   \n",
       "2          0.0     0.028642     0.397078     0.046219     0.077279   \n",
       "3          0.0     0.032710     0.471081     0.025988     0.020710   \n",
       "4          0.0     0.033017     0.523812     0.014878     0.010864   \n",
       "\n",
       "   feature_347  feature_348  feature_349  feature_350  feature_351  \\\n",
       "0          0.0     0.053410     0.198646     0.099133     0.035344   \n",
       "1          0.0     0.018598     0.165538     0.256153     0.114948   \n",
       "2          0.0     0.000954     0.148984     0.103293     0.024481   \n",
       "3          0.0     0.144969     0.141460     0.174003     0.023833   \n",
       "4          0.0     0.068193     0.158766     0.103986     0.081388   \n",
       "\n",
       "   feature_352  feature_353  feature_354  feature_355  feature_356  \\\n",
       "0     0.109250     0.140490     0.079755     0.164667     0.362985   \n",
       "1     0.063365     0.322730     0.275307     0.118866     0.353454   \n",
       "2     0.043700     0.266402     0.087423     0.125409     0.162512   \n",
       "3     0.122360     0.262425     0.178681     0.162123     0.587569   \n",
       "4     0.145666     0.343936     0.334356     0.154489     0.477924   \n",
       "\n",
       "   feature_357  feature_358  feature_359  feature_360  feature_361  \\\n",
       "0     0.020408     0.233333     0.188424     0.015652     0.082922   \n",
       "1     0.285714     0.233333     0.211823     0.000978     0.066408   \n",
       "2     0.040816     0.233333     0.278325     0.011005     0.088241   \n",
       "3     0.061224     0.233333     0.216749     0.000734     0.065216   \n",
       "4     0.142857     0.233333     0.258621     0.000000     0.019220   \n",
       "\n",
       "   feature_362  feature_363  feature_364  feature_365  feature_366  \\\n",
       "0     0.018622     0.493740          0.0     0.435897     0.344086   \n",
       "1     0.009915     0.609365          0.0     0.102564     0.104839   \n",
       "2     0.015478     0.220204          0.0     0.105413     0.061828   \n",
       "3     0.038694     0.246101          0.0     0.082621     0.077957   \n",
       "4     0.131802     0.428938          0.0     0.131054     0.153226   \n",
       "\n",
       "   feature_367  feature_368  feature_369  feature_370  feature_371  \\\n",
       "0     0.347339     0.341317          0.0          0.0          0.0   \n",
       "1     0.226891     0.098802          0.0          0.0          0.0   \n",
       "2     0.084034     0.110778          0.0          0.0          0.0   \n",
       "3     0.078431     0.101796          0.0          0.0          0.0   \n",
       "4     0.145658     0.080838          0.0          0.0          0.0   \n",
       "\n",
       "   feature_372  feature_373  feature_374  feature_375  feature_376  \\\n",
       "0          0.0          0.0          0.0          0.0     0.076923   \n",
       "1          0.0          0.0          0.0          0.0     0.230769   \n",
       "2          0.0          0.0          0.0          0.0     0.217949   \n",
       "3          0.0          0.0          0.0          0.0     0.256410   \n",
       "4          0.0          0.0          0.0          0.0     0.269231   \n",
       "\n",
       "   feature_377  feature_378  feature_379  feature_380  feature_381  \\\n",
       "0     0.123288          0.0          0.0          0.0          0.0   \n",
       "1     0.123288          0.0          0.0          0.0          0.0   \n",
       "2     0.150685          0.0          0.0          0.0          0.0   \n",
       "3     0.136986          0.0          0.0          0.0          0.0   \n",
       "4     0.109589          0.0          0.0          0.0          0.0   \n",
       "\n",
       "   feature_382  feature_383  feature_384  feature_385  feature_386  \\\n",
       "0     0.000638     0.009223     0.010308     0.032803     0.015359   \n",
       "1     0.000638     0.009223     0.010308     0.032803     0.013399   \n",
       "2     0.000638     0.009223     0.010308     0.106609     0.030065   \n",
       "3     0.000638     0.009223     0.010308     0.018331     0.019608   \n",
       "4     0.000638     0.009223     0.010308     0.032803     0.026471   \n",
       "\n",
       "   feature_387  feature_388  feature_389  feature_390  feature_391  \\\n",
       "0          0.0     0.161401      0.00064     0.000860     0.037946   \n",
       "1          0.0     0.171639      0.00032     0.001069     0.027530   \n",
       "2          0.0     0.065466      0.00064     0.001156     0.118304   \n",
       "3          0.0     0.076237      0.00064     0.001005     0.075149   \n",
       "4          0.0     0.148777      0.00032     0.000677     0.025298   \n",
       "\n",
       "   feature_392  feature_393  feature_394  feature_395  feature_396  \\\n",
       "0     0.053571     0.412896          0.0          0.0          0.0   \n",
       "1     0.098214     0.595400          0.0          0.0          0.0   \n",
       "2     0.066964     0.389517          0.0          0.0          0.0   \n",
       "3     0.080357     0.379336          0.0          0.0          0.0   \n",
       "4     0.321429     0.382353          0.0          0.0          0.0   \n",
       "\n",
       "   feature_397  feature_398  feature_399  feature_400  feature_401  \\\n",
       "0          0.0          0.0          0.0          0.0          0.0   \n",
       "1          0.0          0.0          0.0          0.0          0.0   \n",
       "2          0.0          0.0          0.0          0.0          0.0   \n",
       "3          0.0          0.0          0.0          0.0          0.0   \n",
       "4          0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "   feature_402  feature_403  feature_404  feature_405  feature_406  \\\n",
       "0          0.0          0.0          0.0     0.196903     0.961486   \n",
       "1          0.0          0.0          0.0     0.128319     0.673011   \n",
       "2          0.0          0.0          0.0     0.283186     0.744295   \n",
       "3          0.0          0.0          0.0     0.205752     0.895015   \n",
       "4          0.0          0.0          0.0     0.227876     0.402014   \n",
       "\n",
       "   feature_407  feature_408  feature_409  feature_410  feature_411  \\\n",
       "0     0.351426     0.069301     0.293749     0.168188     0.219048   \n",
       "1     0.210572     0.038697     0.164781     0.195182     0.296683   \n",
       "2     0.261631     0.032243     0.370075     0.232190     0.209703   \n",
       "3     0.265404     0.069722     0.326774     0.325398     0.230829   \n",
       "4     0.258377     0.063612     0.419404     0.320067     0.232980   \n",
       "\n",
       "   feature_412  feature_413  feature_414  feature_415  feature_416  \\\n",
       "0     0.193718     0.027914          0.0     0.046925     0.282559   \n",
       "1     0.117047     0.026484          0.0     0.034992     0.405573   \n",
       "2     0.140498     0.019624          0.0     0.062341     0.315882   \n",
       "3     0.139176     0.011690          0.0     0.032533     0.254314   \n",
       "4     0.174124     0.022761          0.0     0.018136     0.342704   \n",
       "\n",
       "   feature_417  feature_418  feature_419  feature_420  feature_421  \\\n",
       "0     0.103878     0.525456     0.000000     0.027221     0.019579   \n",
       "1     0.187268     0.000000     0.369459     0.015114     0.019926   \n",
       "2     0.083233     0.000000     0.000000     0.016687     0.012373   \n",
       "3     0.149307     0.317954     0.000000     0.013757     0.021478   \n",
       "4     0.055977     0.000000     0.867173     0.018581     0.023231   \n",
       "\n",
       "   feature_422  feature_423  feature_424  feature_425  feature_426  \\\n",
       "0          0.0     0.076432     0.019399     0.003620     0.055398   \n",
       "1          0.0     0.096890     0.025965     0.005186     0.045580   \n",
       "2          0.0     0.068851     0.014991     0.004240     0.059430   \n",
       "3          0.0     0.178066     0.030406     0.013348     0.047951   \n",
       "4          0.0     0.155050     0.024216     0.003146     0.037808   \n",
       "\n",
       "   feature_427  feature_428  feature_429  feature_430  feature_431  \\\n",
       "0     0.049008     0.018321     0.015200     0.046918     0.082890   \n",
       "1     0.049107     0.014358     0.012785     0.026096     0.005664   \n",
       "2     0.031933     0.022341     0.014735     0.025791     0.072916   \n",
       "3     0.049464     0.014325     0.022634     0.039272     0.033513   \n",
       "4     0.058576     0.019786     0.016637     0.031911     0.026848   \n",
       "\n",
       "   feature_432  feature_433  feature_434  feature_435  feature_436  \\\n",
       "0     0.026513     0.049211     0.025126     0.006768     0.007790   \n",
       "1     0.008537     0.200640     0.021584     0.014273     0.004195   \n",
       "2     0.018862     0.110043     0.035626     0.014412     0.002243   \n",
       "3     0.076472     0.182039     0.012940     0.013475     0.003418   \n",
       "4     0.044064     0.000000     0.028516     0.005022     0.003883   \n",
       "\n",
       "   feature_437  feature_438  feature_439  feature_440  feature_441  \\\n",
       "0     0.062885     0.052260     0.043740     0.006564     0.559564   \n",
       "1     0.066153     0.056985     0.036447     0.479342     0.956043   \n",
       "2     0.063351     0.070455     0.088241     0.243030     1.000000   \n",
       "3     0.049635     0.039964     0.042575     0.188672     0.814559   \n",
       "4     0.162293     0.029770     0.036276     0.442722     0.341290   \n",
       "\n",
       "   feature_442  feature_443  feature_444  feature_445  feature_446  \\\n",
       "0     0.218521     0.474430     0.572678     0.420197     0.689345   \n",
       "1     0.369227     0.685787     0.678428     0.656668     0.816699   \n",
       "2     0.065352     0.471550     0.554005     0.401825     0.724393   \n",
       "3     0.062034     0.347155     0.345258     0.275466     0.578484   \n",
       "4     0.123632     0.450012     0.565307     0.381202     0.520663   \n",
       "\n",
       "   feature_447  feature_448  feature_449  feature_450  feature_451  \\\n",
       "0     0.879925     0.067085          0.0          0.0          0.0   \n",
       "1     0.424393     0.163845          0.0          0.0          0.0   \n",
       "2     0.553904     0.163636          0.0          0.0          0.0   \n",
       "3     0.467799     0.204319          0.0          0.0          0.0   \n",
       "4     0.158528     0.501080          0.0          0.0          0.0   \n",
       "\n",
       "   feature_452  feature_453  feature_454  feature_455  feature_456  \\\n",
       "0     0.289094     0.070448     0.182459     0.170278     0.013521   \n",
       "1     0.215474     0.103315     0.132981     0.164608     0.016913   \n",
       "2     0.195336     0.197642     0.195491     0.255385     0.052516   \n",
       "3     0.158941     0.180870     0.221457     0.250769     0.046542   \n",
       "4     0.145241     0.053757     0.096233     0.430047     0.036868   \n",
       "\n",
       "   feature_457  feature_458  feature_459  feature_460  feature_461  \\\n",
       "0     0.016029          0.0     0.017129     0.155453          0.0   \n",
       "1     0.020299          0.0     0.009460     0.229751          0.0   \n",
       "2     0.031262          0.0     0.038755     0.172564          0.0   \n",
       "3     0.015670          0.0     0.039851     0.139495          0.0   \n",
       "4     0.008849          0.0     0.060569     0.162191          0.0   \n",
       "\n",
       "   feature_462  feature_463  feature_464  feature_465  feature_466  \\\n",
       "0          0.0          0.0          0.0          0.0          0.0   \n",
       "1          0.0          0.0          0.0          0.0          0.0   \n",
       "2          0.0          0.0          0.0          0.0          0.0   \n",
       "3          0.0          0.0          0.0          0.0          0.0   \n",
       "4          0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "   feature_467  feature_468  feature_469  feature_470  feature_471  \\\n",
       "0     0.041839     0.311676     0.041580     0.022640     0.035065   \n",
       "1     0.028196     0.463345     0.039419     0.025871     0.037186   \n",
       "2     0.012245     0.021367     0.037459     0.098493     0.028514   \n",
       "3     0.012245     0.024286     0.052237     0.133787     0.032089   \n",
       "4     0.034206     0.044904     0.024435     0.051751     0.023942   \n",
       "\n",
       "   feature_472  feature_473  feature_474  feature_475  feature_476  \\\n",
       "0     0.108668     0.068986     0.032924     0.015873     0.116373   \n",
       "1     0.128937     0.054287     0.031877     0.013608     0.112280   \n",
       "2     0.283684     0.087665     0.109625     0.026701     0.048719   \n",
       "3     0.183893     0.105580     0.116718     0.029767     0.128898   \n",
       "4     0.163012     0.047619     0.046049     0.044048     0.155278   \n",
       "\n",
       "   feature_477  feature_478  feature_479  feature_480  feature_481  \\\n",
       "0     0.014323          0.0     0.016713     0.501563          0.0   \n",
       "1     0.014288          0.0     0.010319     0.723415          0.0   \n",
       "2     0.025803          0.0     0.032332     0.399204          0.0   \n",
       "3     0.016615          0.0     0.045744     0.659675          0.0   \n",
       "4     0.015655          0.0     0.038534     0.616301          0.0   \n",
       "\n",
       "   feature_482  feature_483  feature_484  feature_485  feature_486  \\\n",
       "0     0.613667     0.294585     0.496259     0.179251     0.843543   \n",
       "1     0.000000     0.249401     0.000000     0.361212     0.130702   \n",
       "2     0.434522     0.153381     0.000000     0.191536     0.747295   \n",
       "3     0.225149     0.101557     0.306714     0.089090     0.104719   \n",
       "4     0.171549     0.279827     0.463317     0.241628     0.000000   \n",
       "\n",
       "   feature_487  feature_488  feature_489  feature_490  feature_491  \\\n",
       "0     0.000000     0.053242     0.000000     0.267087     0.016538   \n",
       "1     0.824298     0.194921     0.000000     0.344205     0.255969   \n",
       "2     0.074391     0.192235     0.251683     0.158261     0.038669   \n",
       "3     0.072065     0.000000     0.338798     0.458802     0.099165   \n",
       "4     0.589887     0.750039     0.000000     0.320499     0.137547   \n",
       "\n",
       "   feature_492  feature_493  feature_494  feature_495  feature_496  \\\n",
       "0     0.193162     0.247859     0.016777     0.077927     0.057286   \n",
       "1     0.193162     0.276554     0.000862     0.046041     0.036612   \n",
       "2     0.193162     0.360679     0.011773     0.085471     0.065966   \n",
       "3     0.193162     0.274080     0.000899     0.065222     0.113768   \n",
       "4     0.193162     0.284524     0.000024     0.018977     0.478876   \n",
       "\n",
       "   feature_497  feature_498  feature_499  feature_500  feature_501  \\\n",
       "0     0.532868          0.0      0.00000     0.000000          0.0   \n",
       "1     0.566033          0.0      0.00000     0.000000          0.0   \n",
       "2     0.184224          0.0      0.00000     0.000000          0.0   \n",
       "3     0.209962          0.0      0.00000     0.712188          0.0   \n",
       "4     0.356150          0.0      0.29314     0.000000          0.0   \n",
       "\n",
       "   feature_502  feature_503  feature_504  feature_505  feature_506  \\\n",
       "0          0.0          0.0          0.0          0.0          0.0   \n",
       "1          0.0          0.0          0.0          0.0          0.0   \n",
       "2          0.0          0.0          0.0          0.0          0.0   \n",
       "3          0.0          0.0          0.0          0.0          0.0   \n",
       "4          0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "   feature_507  feature_508  feature_509  feature_510  feature_511  \\\n",
       "0          0.0          0.0          0.0     0.143240     0.000000   \n",
       "1          0.0          0.0          0.0     0.313269     0.000000   \n",
       "2          0.0          0.0          0.0     0.533299     0.244275   \n",
       "3          0.0          0.0          0.0     0.251524     0.000000   \n",
       "4          0.0          0.0          0.0     0.327954     0.000000   \n",
       "\n",
       "   feature_512  feature_513  feature_514  feature_515  feature_516  \\\n",
       "0          0.0          0.0          0.0          0.0     0.000577   \n",
       "1          0.0          0.0          0.0          0.0     0.000577   \n",
       "2          0.0          0.0          0.0          0.0     0.000577   \n",
       "3          0.0          0.0          0.0          0.0     0.000577   \n",
       "4          0.0          0.0          0.0          0.0     0.000577   \n",
       "\n",
       "   feature_517  feature_518  feature_519  feature_520  feature_521  \\\n",
       "0     0.007667     0.010116     0.031641     0.015026          0.0   \n",
       "1     0.007667     0.010116     0.031641     0.011821          0.0   \n",
       "2     0.007667     0.010116     0.200200     0.023787          0.0   \n",
       "3     0.007667     0.010116     0.022349     0.019112          0.0   \n",
       "4     0.007667     0.010116     0.031641     0.020095          0.0   \n",
       "\n",
       "   feature_522  feature_523  feature_524  feature_525  feature_526  \\\n",
       "0     0.197330     0.000751     0.004341     0.020390     0.041814   \n",
       "1     0.175280     0.000373     0.006535     0.020267     0.088719   \n",
       "2     0.087568     0.000443     0.007073     0.135580     0.059080   \n",
       "3     0.078260     0.000646     0.005099     0.066436     0.070943   \n",
       "4     0.118629     0.000322     0.003801     0.018540     0.254892   \n",
       "\n",
       "   feature_527  feature_528  feature_529  feature_530  feature_531  \\\n",
       "0     0.368353          0.0          0.0          0.0          0.0   \n",
       "1     0.543008          0.0          0.0          0.0          0.0   \n",
       "2     0.349148          0.0          0.0          0.0          0.0   \n",
       "3     0.351567          0.0          0.0          0.0          0.0   \n",
       "4     0.342445          0.0          0.0          0.0          0.0   \n",
       "\n",
       "   feature_532  feature_533  feature_534  feature_535  feature_536  \\\n",
       "0          0.0          0.0          0.0          0.0          0.0   \n",
       "1          0.0          0.0          0.0          0.0          0.0   \n",
       "2          0.0          0.0          0.0          0.0          0.0   \n",
       "3          0.0          0.0          0.0          0.0          0.0   \n",
       "4          0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "   feature_537  feature_538  feature_539  feature_540  feature_541  \\\n",
       "0          0.0          0.0     0.210414     1.000000     0.365785   \n",
       "1          0.0          0.0     0.163981     0.682074     0.280117   \n",
       "2          0.0          0.0     0.227312     0.678268     0.267896   \n",
       "3          0.0          0.0     0.227783     0.758946     0.199941   \n",
       "4          0.0          0.0     0.210571     0.399498     0.275713   \n",
       "\n",
       "   feature_542  feature_543  feature_544  feature_545  feature_546  \\\n",
       "0     0.328244     0.142857     0.322581     0.161698     0.174636   \n",
       "1     0.328244     0.142857     0.322581     0.161698     0.256975   \n",
       "2     0.328244     0.142857     0.322581     0.161698     0.098976   \n",
       "3     0.328244     0.142857     0.322581     0.161698     0.203893   \n",
       "4     0.328244     0.142857     0.322581     0.161698     0.288382   \n",
       "\n",
       "   feature_547  feature_548  feature_549  feature_550  feature_551  \\\n",
       "0     0.465385     0.371708     0.053952     0.054312     0.016832   \n",
       "1     0.736007     0.284025     0.096097     0.078761     0.030859   \n",
       "2     0.783838     0.285129     0.019741     0.080035     0.018618   \n",
       "3     0.000000     0.110708     0.261473     1.000000     1.000000   \n",
       "4     0.554255     0.640120     0.141604     0.107669     0.047437   \n",
       "\n",
       "   feature_552  feature_553  feature_554  feature_555  feature_556  \\\n",
       "0     0.060752     0.054464     0.016315     0.117357     0.052390   \n",
       "1     0.097870     0.080038     0.025645     0.161878     0.071466   \n",
       "2     0.024819     0.080819     0.018547     0.061075     0.071960   \n",
       "3     0.245416     1.000000     1.000000     0.524168     1.000000   \n",
       "4     0.151880     0.128819     0.044709     0.221421     0.100875   \n",
       "\n",
       "   feature_557  feature_558  feature_559  feature_560  feature_561  \\\n",
       "0     0.015983     0.229765     0.367205     0.201402     0.374432   \n",
       "1     0.029880     0.115285     0.104204     0.046262     0.115808   \n",
       "2     0.017995     0.983876     0.300757     0.246729     0.186578   \n",
       "3     1.000000     0.434376     0.656406     0.591822     0.591411   \n",
       "4     0.043172     0.138504     0.145148     0.066355     0.157791   \n",
       "\n",
       "   feature_562  feature_563  feature_564  feature_565  feature_566  \\\n",
       "0     0.318094     0.348224     0.132553     0.145696     0.127795   \n",
       "1     0.318094     0.348224     0.132553     0.145696     0.127795   \n",
       "2     0.358488     0.601972     0.004113     0.899070     0.000000   \n",
       "3     0.375329     0.348325     0.200886     0.210858     0.231504   \n",
       "4     0.318094     0.348224     0.132553     0.145696     0.127795   \n",
       "\n",
       "   feature_567  feature_568  feature_569  feature_570  feature_571  \\\n",
       "0     0.139212     0.131638     0.168456     0.795609     0.642926   \n",
       "1     0.139212     0.131638     0.168456     0.799892     0.826067   \n",
       "2     0.869764     0.003337     0.804376     0.799922     0.596317   \n",
       "3     0.203801     0.190569     0.267141     0.783557     0.594043   \n",
       "4     0.139212     0.131638     0.168456     0.788872     0.595294   \n",
       "\n",
       "   feature_572  feature_573  feature_574  feature_575  feature_576  \\\n",
       "0     0.011995     0.116901     0.011971     0.150986     0.011285   \n",
       "1     0.005277     0.093239     0.005750     0.102807     0.004934   \n",
       "2     0.017006     0.057042     0.018066     0.077769     0.015949   \n",
       "3     0.012838     0.050282     0.010993     0.055956     0.012198   \n",
       "4     0.011729     0.073099     0.012653     0.090288     0.011097   \n",
       "\n",
       "   feature_577  feature_578  feature_579  feature_580  feature_581  \\\n",
       "0     0.112244     0.311612     0.151239     0.134058     0.098045   \n",
       "1     0.068396     0.221387     0.220339     0.181159     0.282386   \n",
       "2     0.050771     0.629073     0.589309     0.500000     0.112383   \n",
       "3     0.043312     0.309942     0.152542     0.123188     0.100153   \n",
       "4     0.069148     0.311612     0.151239     0.134058     0.098045   \n",
       "\n",
       "   feature_582  feature_583  feature_584  feature_585  feature_586  \\\n",
       "0     0.709375     0.012325     0.017510     0.011880     0.312448   \n",
       "1     0.753125     0.034637     0.036965     0.033099     0.221387   \n",
       "2     0.562500     0.020612     0.021401     0.020152     0.629073   \n",
       "3     0.662500     0.009137     0.007782     0.008734     0.309942   \n",
       "4     0.068750     1.000000     1.000000     1.000000     0.309942   \n",
       "\n",
       "   feature_587  feature_588  feature_589  HouseLineTestingResult  \n",
       "0     0.151239     0.130435     0.097518                     0.0  \n",
       "1     0.220339     0.181159     0.282386                     0.0  \n",
       "2     0.589309     0.500000     0.112383                     1.0  \n",
       "3     0.152542     0.123188     0.100153                     0.0  \n",
       "4     0.152542     0.123188     0.100153                     0.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View first 5 rows\n",
    "df_normalized.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1567, 591)\n",
      "feature_0                 float64\n",
      "feature_1                 float64\n",
      "feature_2                 float64\n",
      "feature_3                 float64\n",
      "feature_4                 float64\n",
      "feature_5                 float64\n",
      "feature_6                 float64\n",
      "feature_7                 float64\n",
      "feature_8                 float64\n",
      "feature_9                 float64\n",
      "feature_10                float64\n",
      "feature_11                float64\n",
      "feature_12                float64\n",
      "feature_13                float64\n",
      "feature_14                float64\n",
      "feature_15                float64\n",
      "feature_16                float64\n",
      "feature_17                float64\n",
      "feature_18                float64\n",
      "feature_19                float64\n",
      "feature_20                float64\n",
      "feature_21                float64\n",
      "feature_22                float64\n",
      "feature_23                float64\n",
      "feature_24                float64\n",
      "feature_25                float64\n",
      "feature_26                float64\n",
      "feature_27                float64\n",
      "feature_28                float64\n",
      "feature_29                float64\n",
      "feature_30                float64\n",
      "feature_31                float64\n",
      "feature_32                float64\n",
      "feature_33                float64\n",
      "feature_34                float64\n",
      "feature_35                float64\n",
      "feature_36                float64\n",
      "feature_37                float64\n",
      "feature_38                float64\n",
      "feature_39                float64\n",
      "feature_40                float64\n",
      "feature_41                float64\n",
      "feature_42                float64\n",
      "feature_43                float64\n",
      "feature_44                float64\n",
      "feature_45                float64\n",
      "feature_46                float64\n",
      "feature_47                float64\n",
      "feature_48                float64\n",
      "feature_49                float64\n",
      "feature_50                float64\n",
      "feature_51                float64\n",
      "feature_52                float64\n",
      "feature_53                float64\n",
      "feature_54                float64\n",
      "feature_55                float64\n",
      "feature_56                float64\n",
      "feature_57                float64\n",
      "feature_58                float64\n",
      "feature_59                float64\n",
      "feature_60                float64\n",
      "feature_61                float64\n",
      "feature_62                float64\n",
      "feature_63                float64\n",
      "feature_64                float64\n",
      "feature_65                float64\n",
      "feature_66                float64\n",
      "feature_67                float64\n",
      "feature_68                float64\n",
      "feature_69                float64\n",
      "feature_70                float64\n",
      "feature_71                float64\n",
      "feature_72                float64\n",
      "feature_73                float64\n",
      "feature_74                float64\n",
      "feature_75                float64\n",
      "feature_76                float64\n",
      "feature_77                float64\n",
      "feature_78                float64\n",
      "feature_79                float64\n",
      "feature_80                float64\n",
      "feature_81                float64\n",
      "feature_82                float64\n",
      "feature_83                float64\n",
      "feature_84                float64\n",
      "feature_85                float64\n",
      "feature_86                float64\n",
      "feature_87                float64\n",
      "feature_88                float64\n",
      "feature_89                float64\n",
      "feature_90                float64\n",
      "feature_91                float64\n",
      "feature_92                float64\n",
      "feature_93                float64\n",
      "feature_94                float64\n",
      "feature_95                float64\n",
      "feature_96                float64\n",
      "feature_97                float64\n",
      "feature_98                float64\n",
      "feature_99                float64\n",
      "feature_100               float64\n",
      "feature_101               float64\n",
      "feature_102               float64\n",
      "feature_103               float64\n",
      "feature_104               float64\n",
      "feature_105               float64\n",
      "feature_106               float64\n",
      "feature_107               float64\n",
      "feature_108               float64\n",
      "feature_109               float64\n",
      "feature_110               float64\n",
      "feature_111               float64\n",
      "feature_112               float64\n",
      "feature_113               float64\n",
      "feature_114               float64\n",
      "feature_115               float64\n",
      "feature_116               float64\n",
      "feature_117               float64\n",
      "feature_118               float64\n",
      "feature_119               float64\n",
      "feature_120               float64\n",
      "feature_121               float64\n",
      "feature_122               float64\n",
      "feature_123               float64\n",
      "feature_124               float64\n",
      "feature_125               float64\n",
      "feature_126               float64\n",
      "feature_127               float64\n",
      "feature_128               float64\n",
      "feature_129               float64\n",
      "feature_130               float64\n",
      "feature_131               float64\n",
      "feature_132               float64\n",
      "feature_133               float64\n",
      "feature_134               float64\n",
      "feature_135               float64\n",
      "feature_136               float64\n",
      "feature_137               float64\n",
      "feature_138               float64\n",
      "feature_139               float64\n",
      "feature_140               float64\n",
      "feature_141               float64\n",
      "feature_142               float64\n",
      "feature_143               float64\n",
      "feature_144               float64\n",
      "feature_145               float64\n",
      "feature_146               float64\n",
      "feature_147               float64\n",
      "feature_148               float64\n",
      "feature_149               float64\n",
      "feature_150               float64\n",
      "feature_151               float64\n",
      "feature_152               float64\n",
      "feature_153               float64\n",
      "feature_154               float64\n",
      "feature_155               float64\n",
      "feature_156               float64\n",
      "feature_157               float64\n",
      "feature_158               float64\n",
      "feature_159               float64\n",
      "feature_160               float64\n",
      "feature_161               float64\n",
      "feature_162               float64\n",
      "feature_163               float64\n",
      "feature_164               float64\n",
      "feature_165               float64\n",
      "feature_166               float64\n",
      "feature_167               float64\n",
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      "feature_274               float64\n",
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      "feature_279               float64\n",
      "feature_280               float64\n",
      "feature_281               float64\n",
      "feature_282               float64\n",
      "feature_283               float64\n",
      "feature_284               float64\n",
      "feature_285               float64\n",
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      "feature_287               float64\n",
      "feature_288               float64\n",
      "feature_289               float64\n",
      "feature_290               float64\n",
      "feature_291               float64\n",
      "feature_292               float64\n",
      "feature_293               float64\n",
      "feature_294               float64\n",
      "feature_295               float64\n",
      "feature_296               float64\n",
      "feature_297               float64\n",
      "feature_298               float64\n",
      "feature_299               float64\n",
      "feature_300               float64\n",
      "feature_301               float64\n",
      "feature_302               float64\n",
      "feature_303               float64\n",
      "feature_304               float64\n",
      "feature_305               float64\n",
      "feature_306               float64\n",
      "feature_307               float64\n",
      "feature_308               float64\n",
      "feature_309               float64\n",
      "feature_310               float64\n",
      "feature_311               float64\n",
      "feature_312               float64\n",
      "feature_313               float64\n",
      "feature_314               float64\n",
      "feature_315               float64\n",
      "feature_316               float64\n",
      "feature_317               float64\n",
      "feature_318               float64\n",
      "feature_319               float64\n",
      "feature_320               float64\n",
      "feature_321               float64\n",
      "feature_322               float64\n",
      "feature_323               float64\n",
      "feature_324               float64\n",
      "feature_325               float64\n",
      "feature_326               float64\n",
      "feature_327               float64\n",
      "feature_328               float64\n",
      "feature_329               float64\n",
      "feature_330               float64\n",
      "feature_331               float64\n",
      "feature_332               float64\n",
      "feature_333               float64\n",
      "feature_334               float64\n",
      "feature_335               float64\n",
      "feature_336               float64\n",
      "feature_337               float64\n",
      "feature_338               float64\n",
      "feature_339               float64\n",
      "feature_340               float64\n",
      "feature_341               float64\n",
      "feature_342               float64\n",
      "feature_343               float64\n",
      "feature_344               float64\n",
      "feature_345               float64\n",
      "feature_346               float64\n",
      "feature_347               float64\n",
      "feature_348               float64\n",
      "feature_349               float64\n",
      "feature_350               float64\n",
      "feature_351               float64\n",
      "feature_352               float64\n",
      "feature_353               float64\n",
      "feature_354               float64\n",
      "feature_355               float64\n",
      "feature_356               float64\n",
      "feature_357               float64\n",
      "feature_358               float64\n",
      "feature_359               float64\n",
      "feature_360               float64\n",
      "feature_361               float64\n",
      "feature_362               float64\n",
      "feature_363               float64\n",
      "feature_364               float64\n",
      "feature_365               float64\n",
      "feature_366               float64\n",
      "feature_367               float64\n",
      "feature_368               float64\n",
      "feature_369               float64\n",
      "feature_370               float64\n",
      "feature_371               float64\n",
      "feature_372               float64\n",
      "feature_373               float64\n",
      "feature_374               float64\n",
      "feature_375               float64\n",
      "feature_376               float64\n",
      "feature_377               float64\n",
      "feature_378               float64\n",
      "feature_379               float64\n",
      "feature_380               float64\n",
      "feature_381               float64\n",
      "feature_382               float64\n",
      "feature_383               float64\n",
      "feature_384               float64\n",
      "feature_385               float64\n",
      "feature_386               float64\n",
      "feature_387               float64\n",
      "feature_388               float64\n",
      "feature_389               float64\n",
      "feature_390               float64\n",
      "feature_391               float64\n",
      "feature_392               float64\n",
      "feature_393               float64\n",
      "feature_394               float64\n",
      "feature_395               float64\n",
      "feature_396               float64\n",
      "feature_397               float64\n",
      "feature_398               float64\n",
      "feature_399               float64\n",
      "feature_400               float64\n",
      "feature_401               float64\n",
      "feature_402               float64\n",
      "feature_403               float64\n",
      "feature_404               float64\n",
      "feature_405               float64\n",
      "feature_406               float64\n",
      "feature_407               float64\n",
      "feature_408               float64\n",
      "feature_409               float64\n",
      "feature_410               float64\n",
      "feature_411               float64\n",
      "feature_412               float64\n",
      "feature_413               float64\n",
      "feature_414               float64\n",
      "feature_415               float64\n",
      "feature_416               float64\n",
      "feature_417               float64\n",
      "feature_418               float64\n",
      "feature_419               float64\n",
      "feature_420               float64\n",
      "feature_421               float64\n",
      "feature_422               float64\n",
      "feature_423               float64\n",
      "feature_424               float64\n",
      "feature_425               float64\n",
      "feature_426               float64\n",
      "feature_427               float64\n",
      "feature_428               float64\n",
      "feature_429               float64\n",
      "feature_430               float64\n",
      "feature_431               float64\n",
      "feature_432               float64\n",
      "feature_433               float64\n",
      "feature_434               float64\n",
      "feature_435               float64\n",
      "feature_436               float64\n",
      "feature_437               float64\n",
      "feature_438               float64\n",
      "feature_439               float64\n",
      "feature_440               float64\n",
      "feature_441               float64\n",
      "feature_442               float64\n",
      "feature_443               float64\n",
      "feature_444               float64\n",
      "feature_445               float64\n",
      "feature_446               float64\n",
      "feature_447               float64\n",
      "feature_448               float64\n",
      "feature_449               float64\n",
      "feature_450               float64\n",
      "feature_451               float64\n",
      "feature_452               float64\n",
      "feature_453               float64\n",
      "feature_454               float64\n",
      "feature_455               float64\n",
      "feature_456               float64\n",
      "feature_457               float64\n",
      "feature_458               float64\n",
      "feature_459               float64\n",
      "feature_460               float64\n",
      "feature_461               float64\n",
      "feature_462               float64\n",
      "feature_463               float64\n",
      "feature_464               float64\n",
      "feature_465               float64\n",
      "feature_466               float64\n",
      "feature_467               float64\n",
      "feature_468               float64\n",
      "feature_469               float64\n",
      "feature_470               float64\n",
      "feature_471               float64\n",
      "feature_472               float64\n",
      "feature_473               float64\n",
      "feature_474               float64\n",
      "feature_475               float64\n",
      "feature_476               float64\n",
      "feature_477               float64\n",
      "feature_478               float64\n",
      "feature_479               float64\n",
      "feature_480               float64\n",
      "feature_481               float64\n",
      "feature_482               float64\n",
      "feature_483               float64\n",
      "feature_484               float64\n",
      "feature_485               float64\n",
      "feature_486               float64\n",
      "feature_487               float64\n",
      "feature_488               float64\n",
      "feature_489               float64\n",
      "feature_490               float64\n",
      "feature_491               float64\n",
      "feature_492               float64\n",
      "feature_493               float64\n",
      "feature_494               float64\n",
      "feature_495               float64\n",
      "feature_496               float64\n",
      "feature_497               float64\n",
      "feature_498               float64\n",
      "feature_499               float64\n",
      "feature_500               float64\n",
      "feature_501               float64\n",
      "feature_502               float64\n",
      "feature_503               float64\n",
      "feature_504               float64\n",
      "feature_505               float64\n",
      "feature_506               float64\n",
      "feature_507               float64\n",
      "feature_508               float64\n",
      "feature_509               float64\n",
      "feature_510               float64\n",
      "feature_511               float64\n",
      "feature_512               float64\n",
      "feature_513               float64\n",
      "feature_514               float64\n",
      "feature_515               float64\n",
      "feature_516               float64\n",
      "feature_517               float64\n",
      "feature_518               float64\n",
      "feature_519               float64\n",
      "feature_520               float64\n",
      "feature_521               float64\n",
      "feature_522               float64\n",
      "feature_523               float64\n",
      "feature_524               float64\n",
      "feature_525               float64\n",
      "feature_526               float64\n",
      "feature_527               float64\n",
      "feature_528               float64\n",
      "feature_529               float64\n",
      "feature_530               float64\n",
      "feature_531               float64\n",
      "feature_532               float64\n",
      "feature_533               float64\n",
      "feature_534               float64\n",
      "feature_535               float64\n",
      "feature_536               float64\n",
      "feature_537               float64\n",
      "feature_538               float64\n",
      "feature_539               float64\n",
      "feature_540               float64\n",
      "feature_541               float64\n",
      "feature_542               float64\n",
      "feature_543               float64\n",
      "feature_544               float64\n",
      "feature_545               float64\n",
      "feature_546               float64\n",
      "feature_547               float64\n",
      "feature_548               float64\n",
      "feature_549               float64\n",
      "feature_550               float64\n",
      "feature_551               float64\n",
      "feature_552               float64\n",
      "feature_553               float64\n",
      "feature_554               float64\n",
      "feature_555               float64\n",
      "feature_556               float64\n",
      "feature_557               float64\n",
      "feature_558               float64\n",
      "feature_559               float64\n",
      "feature_560               float64\n",
      "feature_561               float64\n",
      "feature_562               float64\n",
      "feature_563               float64\n",
      "feature_564               float64\n",
      "feature_565               float64\n",
      "feature_566               float64\n",
      "feature_567               float64\n",
      "feature_568               float64\n",
      "feature_569               float64\n",
      "feature_570               float64\n",
      "feature_571               float64\n",
      "feature_572               float64\n",
      "feature_573               float64\n",
      "feature_574               float64\n",
      "feature_575               float64\n",
      "feature_576               float64\n",
      "feature_577               float64\n",
      "feature_578               float64\n",
      "feature_579               float64\n",
      "feature_580               float64\n",
      "feature_581               float64\n",
      "feature_582               float64\n",
      "feature_583               float64\n",
      "feature_584               float64\n",
      "feature_585               float64\n",
      "feature_586               float64\n",
      "feature_587               float64\n",
      "feature_588               float64\n",
      "feature_589               float64\n",
      "HouseLineTestingResult    float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df_normalized.shape) # Print DataFrame's size\n",
    "print(df_normalized.dtypes) # Print DataFrame's data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_0</th>\n",
       "      <th>feature_1</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>feature_3</th>\n",
       "      <th>feature_4</th>\n",
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       "      <th>feature_18</th>\n",
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       "      <th>feature_20</th>\n",
       "      <th>feature_21</th>\n",
       "      <th>feature_22</th>\n",
       "      <th>feature_23</th>\n",
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       "      <th>feature_31</th>\n",
       "      <th>feature_32</th>\n",
       "      <th>feature_33</th>\n",
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       "      <th>feature_35</th>\n",
       "      <th>feature_36</th>\n",
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       "      <th>feature_40</th>\n",
       "      <th>feature_41</th>\n",
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       "      <th>feature_51</th>\n",
       "      <th>feature_52</th>\n",
       "      <th>feature_53</th>\n",
       "      <th>feature_54</th>\n",
       "      <th>feature_55</th>\n",
       "      <th>feature_56</th>\n",
       "      <th>feature_57</th>\n",
       "      <th>feature_58</th>\n",
       "      <th>feature_59</th>\n",
       "      <th>feature_60</th>\n",
       "      <th>feature_61</th>\n",
       "      <th>feature_62</th>\n",
       "      <th>feature_63</th>\n",
       "      <th>feature_64</th>\n",
       "      <th>feature_65</th>\n",
       "      <th>feature_66</th>\n",
       "      <th>feature_67</th>\n",
       "      <th>feature_68</th>\n",
       "      <th>feature_69</th>\n",
       "      <th>feature_70</th>\n",
       "      <th>feature_71</th>\n",
       "      <th>feature_72</th>\n",
       "      <th>feature_73</th>\n",
       "      <th>feature_74</th>\n",
       "      <th>feature_75</th>\n",
       "      <th>feature_76</th>\n",
       "      <th>feature_77</th>\n",
       "      <th>feature_78</th>\n",
       "      <th>feature_79</th>\n",
       "      <th>feature_80</th>\n",
       "      <th>feature_81</th>\n",
       "      <th>feature_82</th>\n",
       "      <th>feature_83</th>\n",
       "      <th>feature_84</th>\n",
       "      <th>feature_85</th>\n",
       "      <th>feature_86</th>\n",
       "      <th>feature_87</th>\n",
       "      <th>feature_88</th>\n",
       "      <th>feature_89</th>\n",
       "      <th>feature_90</th>\n",
       "      <th>feature_91</th>\n",
       "      <th>feature_92</th>\n",
       "      <th>feature_93</th>\n",
       "      <th>feature_94</th>\n",
       "      <th>feature_95</th>\n",
       "      <th>feature_96</th>\n",
       "      <th>feature_97</th>\n",
       "      <th>feature_98</th>\n",
       "      <th>feature_99</th>\n",
       "      <th>feature_100</th>\n",
       "      <th>feature_101</th>\n",
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       "      <th>feature_103</th>\n",
       "      <th>feature_104</th>\n",
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       "      <th>feature_110</th>\n",
       "      <th>feature_111</th>\n",
       "      <th>feature_112</th>\n",
       "      <th>feature_113</th>\n",
       "      <th>feature_114</th>\n",
       "      <th>feature_115</th>\n",
       "      <th>feature_116</th>\n",
       "      <th>feature_117</th>\n",
       "      <th>feature_118</th>\n",
       "      <th>feature_119</th>\n",
       "      <th>feature_120</th>\n",
       "      <th>feature_121</th>\n",
       "      <th>feature_122</th>\n",
       "      <th>feature_123</th>\n",
       "      <th>feature_124</th>\n",
       "      <th>feature_125</th>\n",
       "      <th>feature_126</th>\n",
       "      <th>feature_127</th>\n",
       "      <th>feature_128</th>\n",
       "      <th>feature_129</th>\n",
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       "      <th>feature_135</th>\n",
       "      <th>feature_136</th>\n",
       "      <th>feature_137</th>\n",
       "      <th>feature_138</th>\n",
       "      <th>feature_139</th>\n",
       "      <th>feature_140</th>\n",
       "      <th>feature_141</th>\n",
       "      <th>feature_142</th>\n",
       "      <th>feature_143</th>\n",
       "      <th>feature_144</th>\n",
       "      <th>feature_145</th>\n",
       "      <th>feature_146</th>\n",
       "      <th>feature_147</th>\n",
       "      <th>feature_148</th>\n",
       "      <th>feature_149</th>\n",
       "      <th>feature_150</th>\n",
       "      <th>feature_151</th>\n",
       "      <th>feature_152</th>\n",
       "      <th>feature_153</th>\n",
       "      <th>feature_154</th>\n",
       "      <th>feature_155</th>\n",
       "      <th>feature_156</th>\n",
       "      <th>feature_157</th>\n",
       "      <th>feature_158</th>\n",
       "      <th>feature_159</th>\n",
       "      <th>feature_160</th>\n",
       "      <th>feature_161</th>\n",
       "      <th>feature_162</th>\n",
       "      <th>feature_163</th>\n",
       "      <th>feature_164</th>\n",
       "      <th>feature_165</th>\n",
       "      <th>feature_166</th>\n",
       "      <th>feature_167</th>\n",
       "      <th>feature_168</th>\n",
       "      <th>feature_169</th>\n",
       "      <th>feature_170</th>\n",
       "      <th>feature_171</th>\n",
       "      <th>feature_172</th>\n",
       "      <th>feature_173</th>\n",
       "      <th>feature_174</th>\n",
       "      <th>feature_175</th>\n",
       "      <th>feature_176</th>\n",
       "      <th>feature_177</th>\n",
       "      <th>feature_178</th>\n",
       "      <th>feature_179</th>\n",
       "      <th>feature_180</th>\n",
       "      <th>feature_181</th>\n",
       "      <th>feature_182</th>\n",
       "      <th>feature_183</th>\n",
       "      <th>feature_184</th>\n",
       "      <th>feature_185</th>\n",
       "      <th>feature_186</th>\n",
       "      <th>feature_187</th>\n",
       "      <th>feature_188</th>\n",
       "      <th>feature_189</th>\n",
       "      <th>feature_190</th>\n",
       "      <th>feature_191</th>\n",
       "      <th>feature_192</th>\n",
       "      <th>feature_193</th>\n",
       "      <th>feature_194</th>\n",
       "      <th>feature_195</th>\n",
       "      <th>feature_196</th>\n",
       "      <th>feature_197</th>\n",
       "      <th>feature_198</th>\n",
       "      <th>feature_199</th>\n",
       "      <th>feature_200</th>\n",
       "      <th>feature_201</th>\n",
       "      <th>feature_202</th>\n",
       "      <th>feature_203</th>\n",
       "      <th>feature_204</th>\n",
       "      <th>feature_205</th>\n",
       "      <th>feature_206</th>\n",
       "      <th>feature_207</th>\n",
       "      <th>feature_208</th>\n",
       "      <th>feature_209</th>\n",
       "      <th>feature_210</th>\n",
       "      <th>feature_211</th>\n",
       "      <th>feature_212</th>\n",
       "      <th>feature_213</th>\n",
       "      <th>feature_214</th>\n",
       "      <th>feature_215</th>\n",
       "      <th>feature_216</th>\n",
       "      <th>feature_217</th>\n",
       "      <th>feature_218</th>\n",
       "      <th>feature_219</th>\n",
       "      <th>feature_220</th>\n",
       "      <th>feature_221</th>\n",
       "      <th>feature_222</th>\n",
       "      <th>feature_223</th>\n",
       "      <th>feature_224</th>\n",
       "      <th>feature_225</th>\n",
       "      <th>feature_226</th>\n",
       "      <th>feature_227</th>\n",
       "      <th>feature_228</th>\n",
       "      <th>feature_229</th>\n",
       "      <th>feature_230</th>\n",
       "      <th>feature_231</th>\n",
       "      <th>feature_232</th>\n",
       "      <th>feature_233</th>\n",
       "      <th>feature_234</th>\n",
       "      <th>feature_235</th>\n",
       "      <th>feature_236</th>\n",
       "      <th>feature_237</th>\n",
       "      <th>feature_238</th>\n",
       "      <th>feature_239</th>\n",
       "      <th>feature_240</th>\n",
       "      <th>feature_241</th>\n",
       "      <th>feature_242</th>\n",
       "      <th>feature_243</th>\n",
       "      <th>feature_244</th>\n",
       "      <th>feature_245</th>\n",
       "      <th>feature_246</th>\n",
       "      <th>feature_247</th>\n",
       "      <th>feature_248</th>\n",
       "      <th>feature_249</th>\n",
       "      <th>feature_250</th>\n",
       "      <th>feature_251</th>\n",
       "      <th>feature_252</th>\n",
       "      <th>feature_253</th>\n",
       "      <th>feature_254</th>\n",
       "      <th>feature_255</th>\n",
       "      <th>feature_256</th>\n",
       "      <th>feature_257</th>\n",
       "      <th>feature_258</th>\n",
       "      <th>feature_259</th>\n",
       "      <th>feature_260</th>\n",
       "      <th>feature_261</th>\n",
       "      <th>feature_262</th>\n",
       "      <th>feature_263</th>\n",
       "      <th>feature_264</th>\n",
       "      <th>feature_265</th>\n",
       "      <th>feature_266</th>\n",
       "      <th>feature_267</th>\n",
       "      <th>feature_268</th>\n",
       "      <th>feature_269</th>\n",
       "      <th>feature_270</th>\n",
       "      <th>feature_271</th>\n",
       "      <th>feature_272</th>\n",
       "      <th>feature_273</th>\n",
       "      <th>feature_274</th>\n",
       "      <th>feature_275</th>\n",
       "      <th>feature_276</th>\n",
       "      <th>feature_277</th>\n",
       "      <th>feature_278</th>\n",
       "      <th>feature_279</th>\n",
       "      <th>feature_280</th>\n",
       "      <th>feature_281</th>\n",
       "      <th>feature_282</th>\n",
       "      <th>feature_283</th>\n",
       "      <th>feature_284</th>\n",
       "      <th>feature_285</th>\n",
       "      <th>feature_286</th>\n",
       "      <th>feature_287</th>\n",
       "      <th>feature_288</th>\n",
       "      <th>feature_289</th>\n",
       "      <th>feature_290</th>\n",
       "      <th>feature_291</th>\n",
       "      <th>feature_292</th>\n",
       "      <th>feature_293</th>\n",
       "      <th>feature_294</th>\n",
       "      <th>feature_295</th>\n",
       "      <th>feature_296</th>\n",
       "      <th>feature_297</th>\n",
       "      <th>feature_298</th>\n",
       "      <th>feature_299</th>\n",
       "      <th>feature_300</th>\n",
       "      <th>feature_301</th>\n",
       "      <th>feature_302</th>\n",
       "      <th>feature_303</th>\n",
       "      <th>feature_304</th>\n",
       "      <th>feature_305</th>\n",
       "      <th>feature_306</th>\n",
       "      <th>feature_307</th>\n",
       "      <th>feature_308</th>\n",
       "      <th>feature_309</th>\n",
       "      <th>feature_310</th>\n",
       "      <th>feature_311</th>\n",
       "      <th>feature_312</th>\n",
       "      <th>feature_313</th>\n",
       "      <th>feature_314</th>\n",
       "      <th>feature_315</th>\n",
       "      <th>feature_316</th>\n",
       "      <th>feature_317</th>\n",
       "      <th>feature_318</th>\n",
       "      <th>feature_319</th>\n",
       "      <th>feature_320</th>\n",
       "      <th>feature_321</th>\n",
       "      <th>feature_322</th>\n",
       "      <th>feature_323</th>\n",
       "      <th>feature_324</th>\n",
       "      <th>feature_325</th>\n",
       "      <th>feature_326</th>\n",
       "      <th>feature_327</th>\n",
       "      <th>feature_328</th>\n",
       "      <th>feature_329</th>\n",
       "      <th>feature_330</th>\n",
       "      <th>feature_331</th>\n",
       "      <th>feature_332</th>\n",
       "      <th>feature_333</th>\n",
       "      <th>feature_334</th>\n",
       "      <th>feature_335</th>\n",
       "      <th>feature_336</th>\n",
       "      <th>feature_337</th>\n",
       "      <th>feature_338</th>\n",
       "      <th>feature_339</th>\n",
       "      <th>feature_340</th>\n",
       "      <th>feature_341</th>\n",
       "      <th>feature_342</th>\n",
       "      <th>feature_343</th>\n",
       "      <th>feature_344</th>\n",
       "      <th>feature_345</th>\n",
       "      <th>feature_346</th>\n",
       "      <th>feature_347</th>\n",
       "      <th>feature_348</th>\n",
       "      <th>feature_349</th>\n",
       "      <th>feature_350</th>\n",
       "      <th>feature_351</th>\n",
       "      <th>feature_352</th>\n",
       "      <th>feature_353</th>\n",
       "      <th>feature_354</th>\n",
       "      <th>feature_355</th>\n",
       "      <th>feature_356</th>\n",
       "      <th>feature_357</th>\n",
       "      <th>feature_358</th>\n",
       "      <th>feature_359</th>\n",
       "      <th>feature_360</th>\n",
       "      <th>feature_361</th>\n",
       "      <th>feature_362</th>\n",
       "      <th>feature_363</th>\n",
       "      <th>feature_364</th>\n",
       "      <th>feature_365</th>\n",
       "      <th>feature_366</th>\n",
       "      <th>feature_367</th>\n",
       "      <th>feature_368</th>\n",
       "      <th>feature_369</th>\n",
       "      <th>feature_370</th>\n",
       "      <th>feature_371</th>\n",
       "      <th>feature_372</th>\n",
       "      <th>feature_373</th>\n",
       "      <th>feature_374</th>\n",
       "      <th>feature_375</th>\n",
       "      <th>feature_376</th>\n",
       "      <th>feature_377</th>\n",
       "      <th>feature_378</th>\n",
       "      <th>feature_379</th>\n",
       "      <th>feature_380</th>\n",
       "      <th>feature_381</th>\n",
       "      <th>feature_382</th>\n",
       "      <th>feature_383</th>\n",
       "      <th>feature_384</th>\n",
       "      <th>feature_385</th>\n",
       "      <th>feature_386</th>\n",
       "      <th>feature_387</th>\n",
       "      <th>feature_388</th>\n",
       "      <th>feature_389</th>\n",
       "      <th>feature_390</th>\n",
       "      <th>feature_391</th>\n",
       "      <th>feature_392</th>\n",
       "      <th>feature_393</th>\n",
       "      <th>feature_394</th>\n",
       "      <th>feature_395</th>\n",
       "      <th>feature_396</th>\n",
       "      <th>feature_397</th>\n",
       "      <th>feature_398</th>\n",
       "      <th>feature_399</th>\n",
       "      <th>feature_400</th>\n",
       "      <th>feature_401</th>\n",
       "      <th>feature_402</th>\n",
       "      <th>feature_403</th>\n",
       "      <th>feature_404</th>\n",
       "      <th>feature_405</th>\n",
       "      <th>feature_406</th>\n",
       "      <th>feature_407</th>\n",
       "      <th>feature_408</th>\n",
       "      <th>feature_409</th>\n",
       "      <th>feature_410</th>\n",
       "      <th>feature_411</th>\n",
       "      <th>feature_412</th>\n",
       "      <th>feature_413</th>\n",
       "      <th>feature_414</th>\n",
       "      <th>feature_415</th>\n",
       "      <th>feature_416</th>\n",
       "      <th>feature_417</th>\n",
       "      <th>feature_418</th>\n",
       "      <th>feature_419</th>\n",
       "      <th>feature_420</th>\n",
       "      <th>feature_421</th>\n",
       "      <th>feature_422</th>\n",
       "      <th>feature_423</th>\n",
       "      <th>feature_424</th>\n",
       "      <th>feature_425</th>\n",
       "      <th>feature_426</th>\n",
       "      <th>feature_427</th>\n",
       "      <th>feature_428</th>\n",
       "      <th>feature_429</th>\n",
       "      <th>feature_430</th>\n",
       "      <th>feature_431</th>\n",
       "      <th>feature_432</th>\n",
       "      <th>feature_433</th>\n",
       "      <th>feature_434</th>\n",
       "      <th>feature_435</th>\n",
       "      <th>feature_436</th>\n",
       "      <th>feature_437</th>\n",
       "      <th>feature_438</th>\n",
       "      <th>feature_439</th>\n",
       "      <th>feature_440</th>\n",
       "      <th>feature_441</th>\n",
       "      <th>feature_442</th>\n",
       "      <th>feature_443</th>\n",
       "      <th>feature_444</th>\n",
       "      <th>feature_445</th>\n",
       "      <th>feature_446</th>\n",
       "      <th>feature_447</th>\n",
       "      <th>feature_448</th>\n",
       "      <th>feature_449</th>\n",
       "      <th>feature_450</th>\n",
       "      <th>feature_451</th>\n",
       "      <th>feature_452</th>\n",
       "      <th>feature_453</th>\n",
       "      <th>feature_454</th>\n",
       "      <th>feature_455</th>\n",
       "      <th>feature_456</th>\n",
       "      <th>feature_457</th>\n",
       "      <th>feature_458</th>\n",
       "      <th>feature_459</th>\n",
       "      <th>feature_460</th>\n",
       "      <th>feature_461</th>\n",
       "      <th>feature_462</th>\n",
       "      <th>feature_463</th>\n",
       "      <th>feature_464</th>\n",
       "      <th>feature_465</th>\n",
       "      <th>feature_466</th>\n",
       "      <th>feature_467</th>\n",
       "      <th>feature_468</th>\n",
       "      <th>feature_469</th>\n",
       "      <th>feature_470</th>\n",
       "      <th>feature_471</th>\n",
       "      <th>feature_472</th>\n",
       "      <th>feature_473</th>\n",
       "      <th>feature_474</th>\n",
       "      <th>feature_475</th>\n",
       "      <th>feature_476</th>\n",
       "      <th>feature_477</th>\n",
       "      <th>feature_478</th>\n",
       "      <th>feature_479</th>\n",
       "      <th>feature_480</th>\n",
       "      <th>feature_481</th>\n",
       "      <th>feature_482</th>\n",
       "      <th>feature_483</th>\n",
       "      <th>feature_484</th>\n",
       "      <th>feature_485</th>\n",
       "      <th>feature_486</th>\n",
       "      <th>feature_487</th>\n",
       "      <th>feature_488</th>\n",
       "      <th>feature_489</th>\n",
       "      <th>feature_490</th>\n",
       "      <th>feature_491</th>\n",
       "      <th>feature_492</th>\n",
       "      <th>feature_493</th>\n",
       "      <th>feature_494</th>\n",
       "      <th>feature_495</th>\n",
       "      <th>feature_496</th>\n",
       "      <th>feature_497</th>\n",
       "      <th>feature_498</th>\n",
       "      <th>feature_499</th>\n",
       "      <th>feature_500</th>\n",
       "      <th>feature_501</th>\n",
       "      <th>feature_502</th>\n",
       "      <th>feature_503</th>\n",
       "      <th>feature_504</th>\n",
       "      <th>feature_505</th>\n",
       "      <th>feature_506</th>\n",
       "      <th>feature_507</th>\n",
       "      <th>feature_508</th>\n",
       "      <th>feature_509</th>\n",
       "      <th>feature_510</th>\n",
       "      <th>feature_511</th>\n",
       "      <th>feature_512</th>\n",
       "      <th>feature_513</th>\n",
       "      <th>feature_514</th>\n",
       "      <th>feature_515</th>\n",
       "      <th>feature_516</th>\n",
       "      <th>feature_517</th>\n",
       "      <th>feature_518</th>\n",
       "      <th>feature_519</th>\n",
       "      <th>feature_520</th>\n",
       "      <th>feature_521</th>\n",
       "      <th>feature_522</th>\n",
       "      <th>feature_523</th>\n",
       "      <th>feature_524</th>\n",
       "      <th>feature_525</th>\n",
       "      <th>feature_526</th>\n",
       "      <th>feature_527</th>\n",
       "      <th>feature_528</th>\n",
       "      <th>feature_529</th>\n",
       "      <th>feature_530</th>\n",
       "      <th>feature_531</th>\n",
       "      <th>feature_532</th>\n",
       "      <th>feature_533</th>\n",
       "      <th>feature_534</th>\n",
       "      <th>feature_535</th>\n",
       "      <th>feature_536</th>\n",
       "      <th>feature_537</th>\n",
       "      <th>feature_538</th>\n",
       "      <th>feature_539</th>\n",
       "      <th>feature_540</th>\n",
       "      <th>feature_541</th>\n",
       "      <th>feature_542</th>\n",
       "      <th>feature_543</th>\n",
       "      <th>feature_544</th>\n",
       "      <th>feature_545</th>\n",
       "      <th>feature_546</th>\n",
       "      <th>feature_547</th>\n",
       "      <th>feature_548</th>\n",
       "      <th>feature_549</th>\n",
       "      <th>feature_550</th>\n",
       "      <th>feature_551</th>\n",
       "      <th>feature_552</th>\n",
       "      <th>feature_553</th>\n",
       "      <th>feature_554</th>\n",
       "      <th>feature_555</th>\n",
       "      <th>feature_556</th>\n",
       "      <th>feature_557</th>\n",
       "      <th>feature_558</th>\n",
       "      <th>feature_559</th>\n",
       "      <th>feature_560</th>\n",
       "      <th>feature_561</th>\n",
       "      <th>feature_562</th>\n",
       "      <th>feature_563</th>\n",
       "      <th>feature_564</th>\n",
       "      <th>feature_565</th>\n",
       "      <th>feature_566</th>\n",
       "      <th>feature_567</th>\n",
       "      <th>feature_568</th>\n",
       "      <th>feature_569</th>\n",
       "      <th>feature_570</th>\n",
       "      <th>feature_571</th>\n",
       "      <th>feature_572</th>\n",
       "      <th>feature_573</th>\n",
       "      <th>feature_574</th>\n",
       "      <th>feature_575</th>\n",
       "      <th>feature_576</th>\n",
       "      <th>feature_577</th>\n",
       "      <th>feature_578</th>\n",
       "      <th>feature_579</th>\n",
       "      <th>feature_580</th>\n",
       "      <th>feature_581</th>\n",
       "      <th>feature_582</th>\n",
       "      <th>feature_583</th>\n",
       "      <th>feature_584</th>\n",
       "      <th>feature_585</th>\n",
       "      <th>feature_586</th>\n",
       "      <th>feature_587</th>\n",
       "      <th>feature_588</th>\n",
       "      <th>feature_589</th>\n",
       "      <th>HouseLineTestingResult</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
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       "      <td>1567.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "      <td>1567.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.442338</td>\n",
       "      <td>0.490215</td>\n",
       "      <td>0.549443</td>\n",
       "      <td>0.375604</td>\n",
       "      <td>0.003133</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.402906</td>\n",
       "      <td>0.947316</td>\n",
       "      <td>0.584143</td>\n",
       "      <td>0.409652</td>\n",
       "      <td>0.398704</td>\n",
       "      <td>0.937931</td>\n",
       "      <td>0.198578</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.390583</td>\n",
       "      <td>0.162033</td>\n",
       "      <td>0.055264</td>\n",
       "      <td>0.967060</td>\n",
       "      <td>0.449571</td>\n",
       "      <td>0.836579</td>\n",
       "      <td>0.823362</td>\n",
       "      <td>0.214255</td>\n",
       "      <td>0.738279</td>\n",
       "      <td>0.500450</td>\n",
       "      <td>0.501762</td>\n",
       "      <td>0.870658</td>\n",
       "      <td>0.944339</td>\n",
       "      <td>0.866902</td>\n",
       "      <td>0.545897</td>\n",
       "      <td>0.597494</td>\n",
       "      <td>0.597857</td>\n",
       "      <td>0.586221</td>\n",
       "      <td>0.096091</td>\n",
       "      <td>0.086199</td>\n",
       "      <td>0.075251</td>\n",
       "      <td>0.028710</td>\n",
       "      <td>0.924750</td>\n",
       "      <td>0.428382</td>\n",
       "      <td>0.570757</td>\n",
       "      <td>0.321381</td>\n",
       "      <td>0.786788</td>\n",
       "      <td>0.090228</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.370070</td>\n",
       "      <td>0.356934</td>\n",
       "      <td>0.413482</td>\n",
       "      <td>0.376466</td>\n",
       "      <td>0.671633</td>\n",
       "      <td>0.378452</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.411952</td>\n",
       "      <td>0.536811</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.838354</td>\n",
       "      <td>0.840161</td>\n",
       "      <td>0.408641</td>\n",
       "      <td>0.856418</td>\n",
       "      <td>0.620618</td>\n",
       "      <td>0.594900</td>\n",
       "      <td>0.162019</td>\n",
       "      <td>0.619292</td>\n",
       "      <td>0.414018</td>\n",
       "      <td>0.170238</td>\n",
       "      <td>0.066123</td>\n",
       "      <td>0.331255</td>\n",
       "      <td>0.200570</td>\n",
       "      <td>0.537285</td>\n",
       "      <td>0.002233</td>\n",
       "      <td>0.747632</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.265040</td>\n",
       "      <td>0.381900</td>\n",
       "      <td>0.897523</td>\n",
       "      <td>0.046507</td>\n",
       "      <td>0.000638</td>\n",
       "      <td>0.291338</td>\n",
       "      <td>0.606642</td>\n",
       "      <td>0.410243</td>\n",
       "      <td>0.560052</td>\n",
       "      <td>0.380864</td>\n",
       "      <td>0.477773</td>\n",
       "      <td>0.492148</td>\n",
       "      <td>0.614073</td>\n",
       "      <td>0.513912</td>\n",
       "      <td>0.474534</td>\n",
       "      <td>0.621329</td>\n",
       "      <td>0.509174</td>\n",
       "      <td>0.949315</td>\n",
       "      <td>0.377516</td>\n",
       "      <td>0.056889</td>\n",
       "      <td>0.427003</td>\n",
       "      <td>0.499417</td>\n",
       "      <td>0.322034</td>\n",
       "      <td>0.544734</td>\n",
       "      <td>0.635795</td>\n",
       "      <td>0.533220</td>\n",
       "      <td>0.375320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.669977</td>\n",
       "      <td>0.374785</td>\n",
       "      <td>0.562040</td>\n",
       "      <td>0.583607</td>\n",
       "      <td>0.643795</td>\n",
       "      <td>0.434408</td>\n",
       "      <td>0.625547</td>\n",
       "      <td>0.676550</td>\n",
       "      <td>0.453712</td>\n",
       "      <td>0.516605</td>\n",
       "      <td>0.452665</td>\n",
       "      <td>0.982182</td>\n",
       "      <td>0.706429</td>\n",
       "      <td>0.768775</td>\n",
       "      <td>0.941580</td>\n",
       "      <td>0.748773</td>\n",
       "      <td>0.002960</td>\n",
       "      <td>0.534441</td>\n",
       "      <td>0.948534</td>\n",
       "      <td>0.022474</td>\n",
       "      <td>0.731418</td>\n",
       "      <td>0.915799</td>\n",
       "      <td>0.494538</td>\n",
       "      <td>0.551456</td>\n",
       "      <td>0.426843</td>\n",
       "      <td>0.709314</td>\n",
       "      <td>0.544209</td>\n",
       "      <td>0.405296</td>\n",
       "      <td>0.248721</td>\n",
       "      <td>0.387017</td>\n",
       "      <td>0.819563</td>\n",
       "      <td>0.517419</td>\n",
       "      <td>0.694035</td>\n",
       "      <td>0.165670</td>\n",
       "      <td>0.453106</td>\n",
       "      <td>0.581898</td>\n",
       "      <td>0.195810</td>\n",
       "      <td>0.064030</td>\n",
       "      <td>0.393073</td>\n",
       "      <td>0.327958</td>\n",
       "      <td>0.309737</td>\n",
       "      <td>0.234980</td>\n",
       "      <td>0.002582</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.048186</td>\n",
       "      <td>0.344455</td>\n",
       "      <td>0.147735</td>\n",
       "      <td>0.184104</td>\n",
       "      <td>0.143444</td>\n",
       "      <td>0.013459</td>\n",
       "      <td>0.009507</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.260980</td>\n",
       "      <td>0.022489</td>\n",
       "      <td>0.001126</td>\n",
       "      <td>0.035380</td>\n",
       "      <td>0.033913</td>\n",
       "      <td>0.029115</td>\n",
       "      <td>0.021451</td>\n",
       "      <td>0.089472</td>\n",
       "      <td>0.346911</td>\n",
       "      <td>0.113252</td>\n",
       "      <td>0.133141</td>\n",
       "      <td>0.107134</td>\n",
       "      <td>0.130002</td>\n",
       "      <td>0.146477</td>\n",
       "      <td>0.070387</td>\n",
       "      <td>0.076670</td>\n",
       "      <td>0.097963</td>\n",
       "      <td>0.058484</td>\n",
       "      <td>0.132066</td>\n",
       "      <td>0.323125</td>\n",
       "      <td>0.451914</td>\n",
       "      <td>0.229147</td>\n",
       "      <td>0.456076</td>\n",
       "      <td>0.528080</td>\n",
       "      <td>0.456076</td>\n",
       "      <td>0.445435</td>\n",
       "      <td>0.465769</td>\n",
       "      <td>0.191993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.244794</td>\n",
       "      <td>0.130390</td>\n",
       "      <td>0.146829</td>\n",
       "      <td>0.318660</td>\n",
       "      <td>0.035987</td>\n",
       "      <td>0.020398</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.038833</td>\n",
       "      <td>0.197587</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.043508</td>\n",
       "      <td>0.017573</td>\n",
       "      <td>0.044613</td>\n",
       "      <td>0.047028</td>\n",
       "      <td>0.022591</td>\n",
       "      <td>0.073258</td>\n",
       "      <td>0.061918</td>\n",
       "      <td>0.020717</td>\n",
       "      <td>0.045360</td>\n",
       "      <td>0.003206</td>\n",
       "      <td>0.021228</td>\n",
       "      <td>0.000638</td>\n",
       "      <td>0.036072</td>\n",
       "      <td>0.406643</td>\n",
       "      <td>0.000638</td>\n",
       "      <td>0.117287</td>\n",
       "      <td>0.170048</td>\n",
       "      <td>0.075267</td>\n",
       "      <td>0.044203</td>\n",
       "      <td>0.086025</td>\n",
       "      <td>0.223138</td>\n",
       "      <td>0.270233</td>\n",
       "      <td>0.095399</td>\n",
       "      <td>0.352414</td>\n",
       "      <td>0.163594</td>\n",
       "      <td>0.179667</td>\n",
       "      <td>0.193433</td>\n",
       "      <td>0.008600</td>\n",
       "      <td>0.052161</td>\n",
       "      <td>0.034646</td>\n",
       "      <td>0.253418</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.087368</td>\n",
       "      <td>0.051632</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.151131</td>\n",
       "      <td>0.143003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001422</td>\n",
       "      <td>0.010887</td>\n",
       "      <td>0.011595</td>\n",
       "      <td>0.084126</td>\n",
       "      <td>0.022852</td>\n",
       "      <td>0.002574</td>\n",
       "      <td>0.080678</td>\n",
       "      <td>0.004023</td>\n",
       "      <td>0.001521</td>\n",
       "      <td>0.053763</td>\n",
       "      <td>0.159003</td>\n",
       "      <td>0.346792</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.368022</td>\n",
       "      <td>0.385502</td>\n",
       "      <td>0.279712</td>\n",
       "      <td>0.095900</td>\n",
       "      <td>0.291654</td>\n",
       "      <td>0.298238</td>\n",
       "      <td>0.330007</td>\n",
       "      <td>0.206416</td>\n",
       "      <td>0.002582</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.052140</td>\n",
       "      <td>0.328635</td>\n",
       "      <td>0.170804</td>\n",
       "      <td>0.157362</td>\n",
       "      <td>0.155162</td>\n",
       "      <td>0.013594</td>\n",
       "      <td>0.009896</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.258746</td>\n",
       "      <td>0.017113</td>\n",
       "      <td>0.001081</td>\n",
       "      <td>0.036948</td>\n",
       "      <td>0.036915</td>\n",
       "      <td>0.018629</td>\n",
       "      <td>0.023450</td>\n",
       "      <td>0.091229</td>\n",
       "      <td>0.297528</td>\n",
       "      <td>0.102105</td>\n",
       "      <td>0.126093</td>\n",
       "      <td>0.120718</td>\n",
       "      <td>0.126394</td>\n",
       "      <td>0.121182</td>\n",
       "      <td>0.058418</td>\n",
       "      <td>0.065299</td>\n",
       "      <td>0.111106</td>\n",
       "      <td>0.057804</td>\n",
       "      <td>0.137404</td>\n",
       "      <td>0.369750</td>\n",
       "      <td>0.413010</td>\n",
       "      <td>0.204433</td>\n",
       "      <td>0.473585</td>\n",
       "      <td>0.546496</td>\n",
       "      <td>0.473587</td>\n",
       "      <td>0.471094</td>\n",
       "      <td>0.443300</td>\n",
       "      <td>0.187886</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.309353</td>\n",
       "      <td>0.148009</td>\n",
       "      <td>0.147820</td>\n",
       "      <td>0.340380</td>\n",
       "      <td>0.036957</td>\n",
       "      <td>0.021036</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.042104</td>\n",
       "      <td>0.223583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.056671</td>\n",
       "      <td>0.011779</td>\n",
       "      <td>0.038524</td>\n",
       "      <td>0.041372</td>\n",
       "      <td>0.015318</td>\n",
       "      <td>0.021552</td>\n",
       "      <td>0.059802</td>\n",
       "      <td>0.015433</td>\n",
       "      <td>0.036505</td>\n",
       "      <td>0.002895</td>\n",
       "      <td>0.022202</td>\n",
       "      <td>0.000638</td>\n",
       "      <td>0.031863</td>\n",
       "      <td>0.369510</td>\n",
       "      <td>0.019500</td>\n",
       "      <td>0.018059</td>\n",
       "      <td>0.000638</td>\n",
       "      <td>0.068543</td>\n",
       "      <td>0.182341</td>\n",
       "      <td>0.070137</td>\n",
       "      <td>0.041422</td>\n",
       "      <td>0.129169</td>\n",
       "      <td>0.250216</td>\n",
       "      <td>0.291073</td>\n",
       "      <td>0.111466</td>\n",
       "      <td>0.374308</td>\n",
       "      <td>0.142466</td>\n",
       "      <td>0.235397</td>\n",
       "      <td>0.150738</td>\n",
       "      <td>0.006957</td>\n",
       "      <td>0.054462</td>\n",
       "      <td>0.037222</td>\n",
       "      <td>0.268555</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.099651</td>\n",
       "      <td>0.075627</td>\n",
       "      <td>0.105633</td>\n",
       "      <td>0.094916</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.153953</td>\n",
       "      <td>0.160362</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001379</td>\n",
       "      <td>0.011132</td>\n",
       "      <td>0.011150</td>\n",
       "      <td>0.044939</td>\n",
       "      <td>0.024447</td>\n",
       "      <td>0.002592</td>\n",
       "      <td>0.084214</td>\n",
       "      <td>0.003961</td>\n",
       "      <td>0.001400</td>\n",
       "      <td>0.058366</td>\n",
       "      <td>0.180036</td>\n",
       "      <td>0.376280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.398361</td>\n",
       "      <td>0.368650</td>\n",
       "      <td>0.279714</td>\n",
       "      <td>0.062585</td>\n",
       "      <td>0.402357</td>\n",
       "      <td>0.320837</td>\n",
       "      <td>0.314912</td>\n",
       "      <td>0.240633</td>\n",
       "      <td>0.024046</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044157</td>\n",
       "      <td>0.351225</td>\n",
       "      <td>0.163667</td>\n",
       "      <td>0.320455</td>\n",
       "      <td>0.309423</td>\n",
       "      <td>0.012426</td>\n",
       "      <td>0.012676</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.136157</td>\n",
       "      <td>0.027534</td>\n",
       "      <td>0.005936</td>\n",
       "      <td>0.035346</td>\n",
       "      <td>0.030158</td>\n",
       "      <td>0.026270</td>\n",
       "      <td>0.018232</td>\n",
       "      <td>0.046031</td>\n",
       "      <td>0.055877</td>\n",
       "      <td>0.099886</td>\n",
       "      <td>0.206328</td>\n",
       "      <td>0.036820</td>\n",
       "      <td>0.023411</td>\n",
       "      <td>0.018768</td>\n",
       "      <td>0.091900</td>\n",
       "      <td>0.064224</td>\n",
       "      <td>0.087808</td>\n",
       "      <td>0.326339</td>\n",
       "      <td>0.486969</td>\n",
       "      <td>0.247861</td>\n",
       "      <td>0.480358</td>\n",
       "      <td>0.551791</td>\n",
       "      <td>0.423063</td>\n",
       "      <td>0.448127</td>\n",
       "      <td>0.465426</td>\n",
       "      <td>0.203635</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.236662</td>\n",
       "      <td>0.135683</td>\n",
       "      <td>0.139756</td>\n",
       "      <td>0.310025</td>\n",
       "      <td>0.040494</td>\n",
       "      <td>0.023487</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.039485</td>\n",
       "      <td>0.150193</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.042261</td>\n",
       "      <td>0.223871</td>\n",
       "      <td>0.040700</td>\n",
       "      <td>0.052493</td>\n",
       "      <td>0.034376</td>\n",
       "      <td>0.262617</td>\n",
       "      <td>0.109775</td>\n",
       "      <td>0.090545</td>\n",
       "      <td>0.040525</td>\n",
       "      <td>0.073180</td>\n",
       "      <td>0.015849</td>\n",
       "      <td>0.000638</td>\n",
       "      <td>0.042068</td>\n",
       "      <td>0.312845</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.318224</td>\n",
       "      <td>0.207860</td>\n",
       "      <td>0.214792</td>\n",
       "      <td>0.200967</td>\n",
       "      <td>0.301855</td>\n",
       "      <td>0.238522</td>\n",
       "      <td>0.353431</td>\n",
       "      <td>0.273000</td>\n",
       "      <td>0.291430</td>\n",
       "      <td>0.154364</td>\n",
       "      <td>0.194608</td>\n",
       "      <td>0.198036</td>\n",
       "      <td>0.007231</td>\n",
       "      <td>0.047543</td>\n",
       "      <td>0.115429</td>\n",
       "      <td>0.256747</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.262860</td>\n",
       "      <td>0.240858</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.123486</td>\n",
       "      <td>0.275627</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001276</td>\n",
       "      <td>0.009480</td>\n",
       "      <td>0.010802</td>\n",
       "      <td>0.049029</td>\n",
       "      <td>0.021395</td>\n",
       "      <td>0.011610</td>\n",
       "      <td>0.089043</td>\n",
       "      <td>0.003846</td>\n",
       "      <td>0.005360</td>\n",
       "      <td>0.051132</td>\n",
       "      <td>0.158462</td>\n",
       "      <td>0.344173</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.381018</td>\n",
       "      <td>0.382810</td>\n",
       "      <td>0.281506</td>\n",
       "      <td>0.450869</td>\n",
       "      <td>0.178307</td>\n",
       "      <td>0.293328</td>\n",
       "      <td>0.191477</td>\n",
       "      <td>0.162386</td>\n",
       "      <td>0.627129</td>\n",
       "      <td>0.345141</td>\n",
       "      <td>0.083572</td>\n",
       "      <td>0.085941</td>\n",
       "      <td>0.027986</td>\n",
       "      <td>0.090726</td>\n",
       "      <td>0.089983</td>\n",
       "      <td>0.025683</td>\n",
       "      <td>0.175317</td>\n",
       "      <td>0.079690</td>\n",
       "      <td>0.026659</td>\n",
       "      <td>0.166228</td>\n",
       "      <td>0.254818</td>\n",
       "      <td>0.127885</td>\n",
       "      <td>0.266798</td>\n",
       "      <td>0.299667</td>\n",
       "      <td>0.372020</td>\n",
       "      <td>0.166122</td>\n",
       "      <td>0.177969</td>\n",
       "      <td>0.155747</td>\n",
       "      <td>0.172453</td>\n",
       "      <td>0.161846</td>\n",
       "      <td>0.210269</td>\n",
       "      <td>0.783393</td>\n",
       "      <td>0.637547</td>\n",
       "      <td>0.055231</td>\n",
       "      <td>0.130956</td>\n",
       "      <td>0.048069</td>\n",
       "      <td>0.155025</td>\n",
       "      <td>0.054592</td>\n",
       "      <td>0.130554</td>\n",
       "      <td>0.315614</td>\n",
       "      <td>0.161674</td>\n",
       "      <td>0.144003</td>\n",
       "      <td>0.111763</td>\n",
       "      <td>0.696757</td>\n",
       "      <td>0.019798</td>\n",
       "      <td>0.020879</td>\n",
       "      <td>0.019062</td>\n",
       "      <td>0.320450</td>\n",
       "      <td>0.173062</td>\n",
       "      <td>0.155177</td>\n",
       "      <td>0.135158</td>\n",
       "      <td>0.066369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.119849</td>\n",
       "      <td>0.116663</td>\n",
       "      <td>0.115397</td>\n",
       "      <td>0.118394</td>\n",
       "      <td>0.050369</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.131775</td>\n",
       "      <td>0.069484</td>\n",
       "      <td>0.158679</td>\n",
       "      <td>0.117744</td>\n",
       "      <td>0.105758</td>\n",
       "      <td>0.037778</td>\n",
       "      <td>0.036189</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.161524</td>\n",
       "      <td>0.035006</td>\n",
       "      <td>0.024407</td>\n",
       "      <td>0.029726</td>\n",
       "      <td>0.059854</td>\n",
       "      <td>0.069807</td>\n",
       "      <td>0.061151</td>\n",
       "      <td>0.087610</td>\n",
       "      <td>0.080769</td>\n",
       "      <td>0.111685</td>\n",
       "      <td>0.100339</td>\n",
       "      <td>0.128370</td>\n",
       "      <td>0.092255</td>\n",
       "      <td>0.162383</td>\n",
       "      <td>0.186973</td>\n",
       "      <td>0.143592</td>\n",
       "      <td>0.131167</td>\n",
       "      <td>0.195659</td>\n",
       "      <td>0.090358</td>\n",
       "      <td>0.085378</td>\n",
       "      <td>0.118983</td>\n",
       "      <td>0.084152</td>\n",
       "      <td>0.118983</td>\n",
       "      <td>0.100037</td>\n",
       "      <td>0.121161</td>\n",
       "      <td>0.083344</td>\n",
       "      <td>0.282360</td>\n",
       "      <td>0.061717</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.180439</td>\n",
       "      <td>0.110121</td>\n",
       "      <td>0.116305</td>\n",
       "      <td>0.135275</td>\n",
       "      <td>0.186894</td>\n",
       "      <td>0.120760</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.143188</td>\n",
       "      <td>0.279039</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.051871</td>\n",
       "      <td>0.055148</td>\n",
       "      <td>0.190494</td>\n",
       "      <td>0.109142</td>\n",
       "      <td>0.149485</td>\n",
       "      <td>0.135424</td>\n",
       "      <td>0.048251</td>\n",
       "      <td>0.122415</td>\n",
       "      <td>0.118061</td>\n",
       "      <td>0.041877</td>\n",
       "      <td>0.038105</td>\n",
       "      <td>0.116748</td>\n",
       "      <td>0.062456</td>\n",
       "      <td>0.083951</td>\n",
       "      <td>0.042203</td>\n",
       "      <td>0.052372</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.067608</td>\n",
       "      <td>0.145553</td>\n",
       "      <td>0.055120</td>\n",
       "      <td>0.052526</td>\n",
       "      <td>0.025262</td>\n",
       "      <td>0.065757</td>\n",
       "      <td>0.127458</td>\n",
       "      <td>0.130959</td>\n",
       "      <td>0.079518</td>\n",
       "      <td>0.144875</td>\n",
       "      <td>0.186284</td>\n",
       "      <td>0.107863</td>\n",
       "      <td>0.100385</td>\n",
       "      <td>0.163076</td>\n",
       "      <td>0.152022</td>\n",
       "      <td>0.087193</td>\n",
       "      <td>0.119271</td>\n",
       "      <td>0.058773</td>\n",
       "      <td>0.112070</td>\n",
       "      <td>0.037839</td>\n",
       "      <td>0.116385</td>\n",
       "      <td>0.121599</td>\n",
       "      <td>0.079336</td>\n",
       "      <td>0.098945</td>\n",
       "      <td>0.056068</td>\n",
       "      <td>0.057930</td>\n",
       "      <td>0.054932</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.054364</td>\n",
       "      <td>0.044295</td>\n",
       "      <td>0.066954</td>\n",
       "      <td>0.053741</td>\n",
       "      <td>0.075428</td>\n",
       "      <td>0.057568</td>\n",
       "      <td>0.044746</td>\n",
       "      <td>0.078339</td>\n",
       "      <td>0.097779</td>\n",
       "      <td>0.086597</td>\n",
       "      <td>0.117142</td>\n",
       "      <td>0.025907</td>\n",
       "      <td>0.059529</td>\n",
       "      <td>0.053445</td>\n",
       "      <td>0.074051</td>\n",
       "      <td>0.098721</td>\n",
       "      <td>0.040292</td>\n",
       "      <td>0.128642</td>\n",
       "      <td>0.092739</td>\n",
       "      <td>0.025046</td>\n",
       "      <td>0.082796</td>\n",
       "      <td>0.063196</td>\n",
       "      <td>0.051878</td>\n",
       "      <td>0.162840</td>\n",
       "      <td>0.172771</td>\n",
       "      <td>0.116133</td>\n",
       "      <td>0.169882</td>\n",
       "      <td>0.129954</td>\n",
       "      <td>0.153086</td>\n",
       "      <td>0.157201</td>\n",
       "      <td>0.067629</td>\n",
       "      <td>0.195184</td>\n",
       "      <td>0.175668</td>\n",
       "      <td>0.088351</td>\n",
       "      <td>0.188644</td>\n",
       "      <td>0.160840</td>\n",
       "      <td>0.096968</td>\n",
       "      <td>0.061383</td>\n",
       "      <td>0.207189</td>\n",
       "      <td>0.165252</td>\n",
       "      <td>0.100747</td>\n",
       "      <td>0.148090</td>\n",
       "      <td>0.050471</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.034791</td>\n",
       "      <td>0.105643</td>\n",
       "      <td>0.103399</td>\n",
       "      <td>0.115675</td>\n",
       "      <td>0.096942</td>\n",
       "      <td>0.027829</td>\n",
       "      <td>0.025257</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.154376</td>\n",
       "      <td>0.057943</td>\n",
       "      <td>0.025255</td>\n",
       "      <td>0.039680</td>\n",
       "      <td>0.027504</td>\n",
       "      <td>0.089016</td>\n",
       "      <td>0.036144</td>\n",
       "      <td>0.044061</td>\n",
       "      <td>0.053039</td>\n",
       "      <td>0.126102</td>\n",
       "      <td>0.137759</td>\n",
       "      <td>0.111664</td>\n",
       "      <td>0.177654</td>\n",
       "      <td>0.127391</td>\n",
       "      <td>0.133397</td>\n",
       "      <td>0.123882</td>\n",
       "      <td>0.055126</td>\n",
       "      <td>0.039523</td>\n",
       "      <td>0.068799</td>\n",
       "      <td>0.180302</td>\n",
       "      <td>0.184093</td>\n",
       "      <td>0.125264</td>\n",
       "      <td>0.169693</td>\n",
       "      <td>0.156762</td>\n",
       "      <td>0.169703</td>\n",
       "      <td>0.163690</td>\n",
       "      <td>0.203737</td>\n",
       "      <td>0.156170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.084303</td>\n",
       "      <td>0.064463</td>\n",
       "      <td>0.080315</td>\n",
       "      <td>0.100598</td>\n",
       "      <td>0.034441</td>\n",
       "      <td>0.027020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.028528</td>\n",
       "      <td>0.117187</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.082952</td>\n",
       "      <td>0.039777</td>\n",
       "      <td>0.043226</td>\n",
       "      <td>0.054068</td>\n",
       "      <td>0.042378</td>\n",
       "      <td>0.044149</td>\n",
       "      <td>0.040256</td>\n",
       "      <td>0.029743</td>\n",
       "      <td>0.035379</td>\n",
       "      <td>0.056411</td>\n",
       "      <td>0.036253</td>\n",
       "      <td>0.025262</td>\n",
       "      <td>0.038498</td>\n",
       "      <td>0.167754</td>\n",
       "      <td>0.025262</td>\n",
       "      <td>0.086184</td>\n",
       "      <td>0.077431</td>\n",
       "      <td>0.053421</td>\n",
       "      <td>0.041113</td>\n",
       "      <td>0.075491</td>\n",
       "      <td>0.089624</td>\n",
       "      <td>0.093616</td>\n",
       "      <td>0.068933</td>\n",
       "      <td>0.150636</td>\n",
       "      <td>0.105124</td>\n",
       "      <td>0.041506</td>\n",
       "      <td>0.110714</td>\n",
       "      <td>0.056456</td>\n",
       "      <td>0.031761</td>\n",
       "      <td>0.049151</td>\n",
       "      <td>0.124183</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.072679</td>\n",
       "      <td>0.052103</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.073458</td>\n",
       "      <td>0.064887</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.025247</td>\n",
       "      <td>0.025880</td>\n",
       "      <td>0.025189</td>\n",
       "      <td>0.103833</td>\n",
       "      <td>0.050747</td>\n",
       "      <td>0.038112</td>\n",
       "      <td>0.049693</td>\n",
       "      <td>0.037828</td>\n",
       "      <td>0.025245</td>\n",
       "      <td>0.050316</td>\n",
       "      <td>0.118599</td>\n",
       "      <td>0.150681</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.214296</td>\n",
       "      <td>0.210790</td>\n",
       "      <td>0.129857</td>\n",
       "      <td>0.058670</td>\n",
       "      <td>0.145871</td>\n",
       "      <td>0.153663</td>\n",
       "      <td>0.109205</td>\n",
       "      <td>0.129342</td>\n",
       "      <td>0.050470</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.037683</td>\n",
       "      <td>0.099723</td>\n",
       "      <td>0.114106</td>\n",
       "      <td>0.095000</td>\n",
       "      <td>0.104964</td>\n",
       "      <td>0.027861</td>\n",
       "      <td>0.025229</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150118</td>\n",
       "      <td>0.047286</td>\n",
       "      <td>0.025256</td>\n",
       "      <td>0.039801</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
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       "      <td>0.427715</td>\n",
       "      <td>0.473043</td>\n",
       "      <td>0.291756</td>\n",
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       "      <td>0.473249</td>\n",
       "      <td>0.332034</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.918944</td>\n",
       "      <td>0.178285</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.147885</td>\n",
       "      <td>0.051820</td>\n",
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       "      <td>0.411959</td>\n",
       "      <td>0.829783</td>\n",
       "      <td>0.792108</td>\n",
       "      <td>0.170291</td>\n",
       "      <td>0.705128</td>\n",
       "      <td>0.454756</td>\n",
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       "      <td>0.791799</td>\n",
       "      <td>0.928853</td>\n",
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       "      <td>0.431532</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.508765</td>\n",
       "      <td>0.472793</td>\n",
       "      <td>0.058321</td>\n",
       "      <td>0.062050</td>\n",
       "      <td>0.042078</td>\n",
       "      <td>0.011358</td>\n",
       "      <td>0.925193</td>\n",
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       "      <td>0.290760</td>\n",
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       "      <td>0.102429</td>\n",
       "      <td>0.122321</td>\n",
       "      <td>0.149792</td>\n",
       "      <td>0.784051</td>\n",
       "      <td>0.569943</td>\n",
       "      <td>0.008780</td>\n",
       "      <td>0.082418</td>\n",
       "      <td>0.009045</td>\n",
       "      <td>0.098824</td>\n",
       "      <td>0.008298</td>\n",
       "      <td>0.074905</td>\n",
       "      <td>0.311612</td>\n",
       "      <td>0.151239</td>\n",
       "      <td>0.134058</td>\n",
       "      <td>0.098045</td>\n",
       "      <td>0.628125</td>\n",
       "      <td>0.011900</td>\n",
       "      <td>0.013619</td>\n",
       "      <td>0.011304</td>\n",
       "      <td>0.253551</td>\n",
       "      <td>0.096480</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.060177</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.437523</td>\n",
       "      <td>0.495361</td>\n",
       "      <td>0.551465</td>\n",
       "      <td>0.345949</td>\n",
       "      <td>0.000570</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.411304</td>\n",
       "      <td>0.951788</td>\n",
       "      <td>0.581435</td>\n",
       "      <td>0.406080</td>\n",
       "      <td>0.401593</td>\n",
       "      <td>0.942319</td>\n",
       "      <td>0.193901</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.388369</td>\n",
       "      <td>0.160273</td>\n",
       "      <td>0.054698</td>\n",
       "      <td>0.969906</td>\n",
       "      <td>0.441333</td>\n",
       "      <td>0.842506</td>\n",
       "      <td>0.826818</td>\n",
       "      <td>0.227544</td>\n",
       "      <td>0.728615</td>\n",
       "      <td>0.499281</td>\n",
       "      <td>0.509356</td>\n",
       "      <td>0.927828</td>\n",
       "      <td>0.967703</td>\n",
       "      <td>0.948543</td>\n",
       "      <td>0.527330</td>\n",
       "      <td>0.601568</td>\n",
       "      <td>0.607968</td>\n",
       "      <td>0.497769</td>\n",
       "      <td>0.087086</td>\n",
       "      <td>0.074107</td>\n",
       "      <td>0.056520</td>\n",
       "      <td>0.015968</td>\n",
       "      <td>0.943480</td>\n",
       "      <td>0.431843</td>\n",
       "      <td>0.566452</td>\n",
       "      <td>0.308461</td>\n",
       "      <td>0.907541</td>\n",
       "      <td>0.082988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.317473</td>\n",
       "      <td>0.359251</td>\n",
       "      <td>0.408400</td>\n",
       "      <td>0.373990</td>\n",
       "      <td>0.743642</td>\n",
       "      <td>0.379400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397325</td>\n",
       "      <td>0.655376</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.841210</td>\n",
       "      <td>0.844300</td>\n",
       "      <td>0.392593</td>\n",
       "      <td>0.890851</td>\n",
       "      <td>0.623656</td>\n",
       "      <td>0.562141</td>\n",
       "      <td>0.151854</td>\n",
       "      <td>0.591729</td>\n",
       "      <td>0.419830</td>\n",
       "      <td>0.168830</td>\n",
       "      <td>0.062151</td>\n",
       "      <td>0.319068</td>\n",
       "      <td>0.192954</td>\n",
       "      <td>0.535735</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.749601</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.264552</td>\n",
       "      <td>0.373984</td>\n",
       "      <td>0.901588</td>\n",
       "      <td>0.042461</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.293103</td>\n",
       "      <td>0.608511</td>\n",
       "      <td>0.398401</td>\n",
       "      <td>0.561935</td>\n",
       "      <td>0.363061</td>\n",
       "      <td>0.514678</td>\n",
       "      <td>0.501916</td>\n",
       "      <td>0.618340</td>\n",
       "      <td>0.518770</td>\n",
       "      <td>0.471299</td>\n",
       "      <td>0.629771</td>\n",
       "      <td>0.515655</td>\n",
       "      <td>0.972095</td>\n",
       "      <td>0.380518</td>\n",
       "      <td>0.057882</td>\n",
       "      <td>0.426396</td>\n",
       "      <td>0.496040</td>\n",
       "      <td>0.319410</td>\n",
       "      <td>0.555921</td>\n",
       "      <td>0.645161</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.372017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.672282</td>\n",
       "      <td>0.373700</td>\n",
       "      <td>0.566038</td>\n",
       "      <td>0.585366</td>\n",
       "      <td>0.642463</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.626316</td>\n",
       "      <td>0.683824</td>\n",
       "      <td>0.442623</td>\n",
       "      <td>0.518350</td>\n",
       "      <td>0.452110</td>\n",
       "      <td>0.983952</td>\n",
       "      <td>0.709486</td>\n",
       "      <td>0.759737</td>\n",
       "      <td>0.947492</td>\n",
       "      <td>0.756713</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.543581</td>\n",
       "      <td>0.981445</td>\n",
       "      <td>0.022177</td>\n",
       "      <td>0.737384</td>\n",
       "      <td>0.906073</td>\n",
       "      <td>0.495680</td>\n",
       "      <td>0.540984</td>\n",
       "      <td>0.422767</td>\n",
       "      <td>0.709677</td>\n",
       "      <td>0.522388</td>\n",
       "      <td>0.386381</td>\n",
       "      <td>0.239249</td>\n",
       "      <td>0.393294</td>\n",
       "      <td>0.820282</td>\n",
       "      <td>0.583149</td>\n",
       "      <td>0.723266</td>\n",
       "      <td>0.163386</td>\n",
       "      <td>0.431366</td>\n",
       "      <td>0.582068</td>\n",
       "      <td>0.179983</td>\n",
       "      <td>0.054487</td>\n",
       "      <td>0.379284</td>\n",
       "      <td>0.312203</td>\n",
       "      <td>0.295903</td>\n",
       "      <td>0.191768</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044466</td>\n",
       "      <td>0.322314</td>\n",
       "      <td>0.126666</td>\n",
       "      <td>0.162233</td>\n",
       "      <td>0.121226</td>\n",
       "      <td>0.011910</td>\n",
       "      <td>0.008761</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.219913</td>\n",
       "      <td>0.016787</td>\n",
       "      <td>0.000340</td>\n",
       "      <td>0.031874</td>\n",
       "      <td>0.032942</td>\n",
       "      <td>0.014320</td>\n",
       "      <td>0.017165</td>\n",
       "      <td>0.085751</td>\n",
       "      <td>0.346161</td>\n",
       "      <td>0.079964</td>\n",
       "      <td>0.105036</td>\n",
       "      <td>0.068893</td>\n",
       "      <td>0.048385</td>\n",
       "      <td>0.125392</td>\n",
       "      <td>0.048982</td>\n",
       "      <td>0.055995</td>\n",
       "      <td>0.088670</td>\n",
       "      <td>0.056250</td>\n",
       "      <td>0.124277</td>\n",
       "      <td>0.333637</td>\n",
       "      <td>0.453865</td>\n",
       "      <td>0.213564</td>\n",
       "      <td>0.464975</td>\n",
       "      <td>0.530385</td>\n",
       "      <td>0.464975</td>\n",
       "      <td>0.431588</td>\n",
       "      <td>0.460827</td>\n",
       "      <td>0.139150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.236567</td>\n",
       "      <td>0.123851</td>\n",
       "      <td>0.135057</td>\n",
       "      <td>0.326594</td>\n",
       "      <td>0.032170</td>\n",
       "      <td>0.018028</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.038597</td>\n",
       "      <td>0.181390</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.037621</td>\n",
       "      <td>0.012755</td>\n",
       "      <td>0.041658</td>\n",
       "      <td>0.033646</td>\n",
       "      <td>0.014974</td>\n",
       "      <td>0.071407</td>\n",
       "      <td>0.053426</td>\n",
       "      <td>0.017250</td>\n",
       "      <td>0.045408</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.017120</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034631</td>\n",
       "      <td>0.406545</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.098658</td>\n",
       "      <td>0.159126</td>\n",
       "      <td>0.058773</td>\n",
       "      <td>0.040859</td>\n",
       "      <td>0.075586</td>\n",
       "      <td>0.227300</td>\n",
       "      <td>0.274259</td>\n",
       "      <td>0.080720</td>\n",
       "      <td>0.334310</td>\n",
       "      <td>0.147436</td>\n",
       "      <td>0.177596</td>\n",
       "      <td>0.194299</td>\n",
       "      <td>0.002019</td>\n",
       "      <td>0.050197</td>\n",
       "      <td>0.021377</td>\n",
       "      <td>0.232629</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.069642</td>\n",
       "      <td>0.040272</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.135135</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000706</td>\n",
       "      <td>0.008972</td>\n",
       "      <td>0.010889</td>\n",
       "      <td>0.054945</td>\n",
       "      <td>0.018553</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.074710</td>\n",
       "      <td>0.000707</td>\n",
       "      <td>0.000823</td>\n",
       "      <td>0.048383</td>\n",
       "      <td>0.172727</td>\n",
       "      <td>0.348360</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.368116</td>\n",
       "      <td>0.341773</td>\n",
       "      <td>0.271353</td>\n",
       "      <td>0.092506</td>\n",
       "      <td>0.288557</td>\n",
       "      <td>0.287183</td>\n",
       "      <td>0.312970</td>\n",
       "      <td>0.167789</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.048138</td>\n",
       "      <td>0.323529</td>\n",
       "      <td>0.149128</td>\n",
       "      <td>0.140762</td>\n",
       "      <td>0.132576</td>\n",
       "      <td>0.012064</td>\n",
       "      <td>0.009268</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.220645</td>\n",
       "      <td>0.012759</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.032564</td>\n",
       "      <td>0.036416</td>\n",
       "      <td>0.009523</td>\n",
       "      <td>0.019049</td>\n",
       "      <td>0.087342</td>\n",
       "      <td>0.295574</td>\n",
       "      <td>0.070841</td>\n",
       "      <td>0.097626</td>\n",
       "      <td>0.077276</td>\n",
       "      <td>0.044281</td>\n",
       "      <td>0.100304</td>\n",
       "      <td>0.038790</td>\n",
       "      <td>0.045688</td>\n",
       "      <td>0.101907</td>\n",
       "      <td>0.053340</td>\n",
       "      <td>0.130233</td>\n",
       "      <td>0.385746</td>\n",
       "      <td>0.411288</td>\n",
       "      <td>0.187161</td>\n",
       "      <td>0.477287</td>\n",
       "      <td>0.550946</td>\n",
       "      <td>0.477287</td>\n",
       "      <td>0.464055</td>\n",
       "      <td>0.445742</td>\n",
       "      <td>0.135290</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.295248</td>\n",
       "      <td>0.138550</td>\n",
       "      <td>0.128389</td>\n",
       "      <td>0.366074</td>\n",
       "      <td>0.033493</td>\n",
       "      <td>0.018669</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.042851</td>\n",
       "      <td>0.206918</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.058128</td>\n",
       "      <td>0.008749</td>\n",
       "      <td>0.035846</td>\n",
       "      <td>0.029903</td>\n",
       "      <td>0.010025</td>\n",
       "      <td>0.018404</td>\n",
       "      <td>0.052814</td>\n",
       "      <td>0.013020</td>\n",
       "      <td>0.037246</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.018564</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030624</td>\n",
       "      <td>0.369467</td>\n",
       "      <td>0.014878</td>\n",
       "      <td>0.010864</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058655</td>\n",
       "      <td>0.173062</td>\n",
       "      <td>0.055113</td>\n",
       "      <td>0.037776</td>\n",
       "      <td>0.119446</td>\n",
       "      <td>0.261763</td>\n",
       "      <td>0.308282</td>\n",
       "      <td>0.093057</td>\n",
       "      <td>0.356672</td>\n",
       "      <td>0.122449</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.147783</td>\n",
       "      <td>0.001467</td>\n",
       "      <td>0.054014</td>\n",
       "      <td>0.023458</td>\n",
       "      <td>0.246084</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.082621</td>\n",
       "      <td>0.061828</td>\n",
       "      <td>0.089636</td>\n",
       "      <td>0.083832</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.150685</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000638</td>\n",
       "      <td>0.009223</td>\n",
       "      <td>0.010308</td>\n",
       "      <td>0.032803</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076552</td>\n",
       "      <td>0.000640</td>\n",
       "      <td>0.000711</td>\n",
       "      <td>0.052827</td>\n",
       "      <td>0.196429</td>\n",
       "      <td>0.375943</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.391593</td>\n",
       "      <td>0.305022</td>\n",
       "      <td>0.282366</td>\n",
       "      <td>0.052551</td>\n",
       "      <td>0.391546</td>\n",
       "      <td>0.305866</td>\n",
       "      <td>0.302088</td>\n",
       "      <td>0.203902</td>\n",
       "      <td>0.018114</td>\n",
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       "      <td>0.040256</td>\n",
       "      <td>0.333746</td>\n",
       "      <td>0.142133</td>\n",
       "      <td>0.302384</td>\n",
       "      <td>0.272808</td>\n",
       "      <td>0.010842</td>\n",
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       "      <td>0.116864</td>\n",
       "      <td>0.021233</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.031352</td>\n",
       "      <td>0.029120</td>\n",
       "      <td>0.012023</td>\n",
       "      <td>0.014368</td>\n",
       "      <td>0.027764</td>\n",
       "      <td>0.040953</td>\n",
       "      <td>0.058302</td>\n",
       "      <td>0.151761</td>\n",
       "      <td>0.025494</td>\n",
       "      <td>0.011378</td>\n",
       "      <td>0.006911</td>\n",
       "      <td>0.084330</td>\n",
       "      <td>0.057645</td>\n",
       "      <td>0.079729</td>\n",
       "      <td>0.343816</td>\n",
       "      <td>0.492926</td>\n",
       "      <td>0.231840</td>\n",
       "      <td>0.491361</td>\n",
       "      <td>0.559312</td>\n",
       "      <td>0.428071</td>\n",
       "      <td>0.437635</td>\n",
       "      <td>0.460250</td>\n",
       "      <td>0.147196</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.230001</td>\n",
       "      <td>0.128724</td>\n",
       "      <td>0.128218</td>\n",
       "      <td>0.319279</td>\n",
       "      <td>0.036241</td>\n",
       "      <td>0.020598</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.039314</td>\n",
       "      <td>0.128786</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.036617</td>\n",
       "      <td>0.150359</td>\n",
       "      <td>0.038185</td>\n",
       "      <td>0.037410</td>\n",
       "      <td>0.023749</td>\n",
       "      <td>0.263376</td>\n",
       "      <td>0.095408</td>\n",
       "      <td>0.079002</td>\n",
       "      <td>0.040699</td>\n",
       "      <td>0.058110</td>\n",
       "      <td>0.012338</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040496</td>\n",
       "      <td>0.288991</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.293691</td>\n",
       "      <td>0.149895</td>\n",
       "      <td>0.139213</td>\n",
       "      <td>0.113635</td>\n",
       "      <td>0.250054</td>\n",
       "      <td>0.112755</td>\n",
       "      <td>0.349396</td>\n",
       "      <td>0.220811</td>\n",
       "      <td>0.269785</td>\n",
       "      <td>0.138775</td>\n",
       "      <td>0.193162</td>\n",
       "      <td>0.197925</td>\n",
       "      <td>0.001555</td>\n",
       "      <td>0.045656</td>\n",
       "      <td>0.080182</td>\n",
       "      <td>0.233666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.104070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000577</td>\n",
       "      <td>0.007667</td>\n",
       "      <td>0.010116</td>\n",
       "      <td>0.031641</td>\n",
       "      <td>0.017132</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081754</td>\n",
       "      <td>0.000667</td>\n",
       "      <td>0.004367</td>\n",
       "      <td>0.045786</td>\n",
       "      <td>0.171737</td>\n",
       "      <td>0.345401</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.384590</td>\n",
       "      <td>0.337678</td>\n",
       "      <td>0.274771</td>\n",
       "      <td>0.328244</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>0.161698</td>\n",
       "      <td>0.132053</td>\n",
       "      <td>0.619885</td>\n",
       "      <td>0.240183</td>\n",
       "      <td>0.060732</td>\n",
       "      <td>0.081469</td>\n",
       "      <td>0.026269</td>\n",
       "      <td>0.066383</td>\n",
       "      <td>0.085732</td>\n",
       "      <td>0.024498</td>\n",
       "      <td>0.166090</td>\n",
       "      <td>0.075995</td>\n",
       "      <td>0.025222</td>\n",
       "      <td>0.130281</td>\n",
       "      <td>0.220163</td>\n",
       "      <td>0.096963</td>\n",
       "      <td>0.239612</td>\n",
       "      <td>0.318094</td>\n",
       "      <td>0.348224</td>\n",
       "      <td>0.132553</td>\n",
       "      <td>0.145696</td>\n",
       "      <td>0.127795</td>\n",
       "      <td>0.139212</td>\n",
       "      <td>0.131638</td>\n",
       "      <td>0.168456</td>\n",
       "      <td>0.790277</td>\n",
       "      <td>0.647076</td>\n",
       "      <td>0.011330</td>\n",
       "      <td>0.106432</td>\n",
       "      <td>0.011459</td>\n",
       "      <td>0.126138</td>\n",
       "      <td>0.010705</td>\n",
       "      <td>0.099979</td>\n",
       "      <td>0.311612</td>\n",
       "      <td>0.151239</td>\n",
       "      <td>0.134058</td>\n",
       "      <td>0.098045</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.016575</td>\n",
       "      <td>0.018482</td>\n",
       "      <td>0.015903</td>\n",
       "      <td>0.312448</td>\n",
       "      <td>0.151239</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.097518</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.511001</td>\n",
       "      <td>0.552567</td>\n",
       "      <td>0.618191</td>\n",
       "      <td>0.428036</td>\n",
       "      <td>0.000752</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.475348</td>\n",
       "      <td>0.962675</td>\n",
       "      <td>0.700150</td>\n",
       "      <td>0.481684</td>\n",
       "      <td>0.464164</td>\n",
       "      <td>0.959016</td>\n",
       "      <td>0.221373</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.497734</td>\n",
       "      <td>0.174238</td>\n",
       "      <td>0.057503</td>\n",
       "      <td>0.980266</td>\n",
       "      <td>0.495505</td>\n",
       "      <td>0.857767</td>\n",
       "      <td>0.859700</td>\n",
       "      <td>0.250848</td>\n",
       "      <td>0.776923</td>\n",
       "      <td>0.536883</td>\n",
       "      <td>0.559684</td>\n",
       "      <td>0.943231</td>\n",
       "      <td>0.975838</td>\n",
       "      <td>0.957017</td>\n",
       "      <td>0.694895</td>\n",
       "      <td>0.699234</td>\n",
       "      <td>0.688845</td>\n",
       "      <td>0.534429</td>\n",
       "      <td>0.114135</td>\n",
       "      <td>0.092580</td>\n",
       "      <td>0.074807</td>\n",
       "      <td>0.021817</td>\n",
       "      <td>0.957922</td>\n",
       "      <td>0.468447</td>\n",
       "      <td>0.615746</td>\n",
       "      <td>0.333386</td>\n",
       "      <td>0.929859</td>\n",
       "      <td>0.094607</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.521594</td>\n",
       "      <td>0.433260</td>\n",
       "      <td>0.492751</td>\n",
       "      <td>0.462935</td>\n",
       "      <td>0.831526</td>\n",
       "      <td>0.464477</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.509294</td>\n",
       "      <td>0.763762</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.861059</td>\n",
       "      <td>0.868397</td>\n",
       "      <td>0.540741</td>\n",
       "      <td>0.924559</td>\n",
       "      <td>0.720430</td>\n",
       "      <td>0.714946</td>\n",
       "      <td>0.169052</td>\n",
       "      <td>0.711080</td>\n",
       "      <td>0.486036</td>\n",
       "      <td>0.191715</td>\n",
       "      <td>0.078668</td>\n",
       "      <td>0.384376</td>\n",
       "      <td>0.224999</td>\n",
       "      <td>0.594116</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.778542</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.308065</td>\n",
       "      <td>0.433121</td>\n",
       "      <td>0.901588</td>\n",
       "      <td>0.042461</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.331451</td>\n",
       "      <td>0.693617</td>\n",
       "      <td>0.477493</td>\n",
       "      <td>0.603030</td>\n",
       "      <td>0.440228</td>\n",
       "      <td>0.581014</td>\n",
       "      <td>0.549489</td>\n",
       "      <td>0.672182</td>\n",
       "      <td>0.626173</td>\n",
       "      <td>0.570997</td>\n",
       "      <td>0.629771</td>\n",
       "      <td>0.594569</td>\n",
       "      <td>0.982617</td>\n",
       "      <td>0.448810</td>\n",
       "      <td>0.065264</td>\n",
       "      <td>0.495015</td>\n",
       "      <td>0.566000</td>\n",
       "      <td>0.358722</td>\n",
       "      <td>0.595395</td>\n",
       "      <td>0.677419</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.401419</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.696340</td>\n",
       "      <td>0.394780</td>\n",
       "      <td>0.603774</td>\n",
       "      <td>0.609756</td>\n",
       "      <td>0.682789</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>0.647368</td>\n",
       "      <td>0.715686</td>\n",
       "      <td>0.486885</td>\n",
       "      <td>0.566554</td>\n",
       "      <td>0.518466</td>\n",
       "      <td>0.983952</td>\n",
       "      <td>0.709486</td>\n",
       "      <td>0.759737</td>\n",
       "      <td>0.948618</td>\n",
       "      <td>0.804719</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.611702</td>\n",
       "      <td>0.985352</td>\n",
       "      <td>0.024436</td>\n",
       "      <td>0.781668</td>\n",
       "      <td>0.968927</td>\n",
       "      <td>0.521660</td>\n",
       "      <td>0.655738</td>\n",
       "      <td>0.521464</td>\n",
       "      <td>0.784946</td>\n",
       "      <td>0.656716</td>\n",
       "      <td>0.476496</td>\n",
       "      <td>0.322835</td>\n",
       "      <td>0.461404</td>\n",
       "      <td>0.850064</td>\n",
       "      <td>0.613484</td>\n",
       "      <td>0.842263</td>\n",
       "      <td>0.208661</td>\n",
       "      <td>0.594595</td>\n",
       "      <td>0.696034</td>\n",
       "      <td>0.241811</td>\n",
       "      <td>0.073718</td>\n",
       "      <td>0.557566</td>\n",
       "      <td>0.451347</td>\n",
       "      <td>0.353280</td>\n",
       "      <td>0.279442</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.056665</td>\n",
       "      <td>0.404959</td>\n",
       "      <td>0.169084</td>\n",
       "      <td>0.219799</td>\n",
       "      <td>0.172291</td>\n",
       "      <td>0.017454</td>\n",
       "      <td>0.011006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.330418</td>\n",
       "      <td>0.023051</td>\n",
       "      <td>0.000566</td>\n",
       "      <td>0.048024</td>\n",
       "      <td>0.041025</td>\n",
       "      <td>0.024662</td>\n",
       "      <td>0.025382</td>\n",
       "      <td>0.085751</td>\n",
       "      <td>0.346161</td>\n",
       "      <td>0.123668</td>\n",
       "      <td>0.149760</td>\n",
       "      <td>0.132646</td>\n",
       "      <td>0.172927</td>\n",
       "      <td>0.160920</td>\n",
       "      <td>0.063841</td>\n",
       "      <td>0.077450</td>\n",
       "      <td>0.118227</td>\n",
       "      <td>0.075000</td>\n",
       "      <td>0.169798</td>\n",
       "      <td>0.446673</td>\n",
       "      <td>0.584025</td>\n",
       "      <td>0.270872</td>\n",
       "      <td>0.575411</td>\n",
       "      <td>0.623587</td>\n",
       "      <td>0.575411</td>\n",
       "      <td>0.538148</td>\n",
       "      <td>0.599157</td>\n",
       "      <td>0.207620</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.294500</td>\n",
       "      <td>0.171121</td>\n",
       "      <td>0.196122</td>\n",
       "      <td>0.392637</td>\n",
       "      <td>0.043582</td>\n",
       "      <td>0.024476</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.048523</td>\n",
       "      <td>0.275674</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.045397</td>\n",
       "      <td>0.019677</td>\n",
       "      <td>0.050195</td>\n",
       "      <td>0.063917</td>\n",
       "      <td>0.022344</td>\n",
       "      <td>0.086299</td>\n",
       "      <td>0.074962</td>\n",
       "      <td>0.024323</td>\n",
       "      <td>0.052325</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.024013</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040462</td>\n",
       "      <td>0.509572</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.138596</td>\n",
       "      <td>0.193136</td>\n",
       "      <td>0.093697</td>\n",
       "      <td>0.054453</td>\n",
       "      <td>0.099636</td>\n",
       "      <td>0.273591</td>\n",
       "      <td>0.320762</td>\n",
       "      <td>0.117254</td>\n",
       "      <td>0.433782</td>\n",
       "      <td>0.198718</td>\n",
       "      <td>0.177596</td>\n",
       "      <td>0.268409</td>\n",
       "      <td>0.005248</td>\n",
       "      <td>0.064631</td>\n",
       "      <td>0.034973</td>\n",
       "      <td>0.315688</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.101420</td>\n",
       "      <td>0.062106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.175676</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000706</td>\n",
       "      <td>0.008972</td>\n",
       "      <td>0.010889</td>\n",
       "      <td>0.061254</td>\n",
       "      <td>0.025046</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100799</td>\n",
       "      <td>0.001009</td>\n",
       "      <td>0.001188</td>\n",
       "      <td>0.063984</td>\n",
       "      <td>0.254545</td>\n",
       "      <td>0.443338</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.520290</td>\n",
       "      <td>0.533849</td>\n",
       "      <td>0.347422</td>\n",
       "      <td>0.112916</td>\n",
       "      <td>0.401506</td>\n",
       "      <td>0.410017</td>\n",
       "      <td>0.384792</td>\n",
       "      <td>0.245732</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.061741</td>\n",
       "      <td>0.382353</td>\n",
       "      <td>0.197243</td>\n",
       "      <td>0.196481</td>\n",
       "      <td>0.183712</td>\n",
       "      <td>0.017933</td>\n",
       "      <td>0.011368</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.334670</td>\n",
       "      <td>0.017348</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>0.050204</td>\n",
       "      <td>0.044447</td>\n",
       "      <td>0.017345</td>\n",
       "      <td>0.028792</td>\n",
       "      <td>0.087342</td>\n",
       "      <td>0.295574</td>\n",
       "      <td>0.108933</td>\n",
       "      <td>0.136532</td>\n",
       "      <td>0.150254</td>\n",
       "      <td>0.172144</td>\n",
       "      <td>0.131459</td>\n",
       "      <td>0.050427</td>\n",
       "      <td>0.063648</td>\n",
       "      <td>0.136345</td>\n",
       "      <td>0.072356</td>\n",
       "      <td>0.175814</td>\n",
       "      <td>0.514848</td>\n",
       "      <td>0.531433</td>\n",
       "      <td>0.233725</td>\n",
       "      <td>0.593090</td>\n",
       "      <td>0.630267</td>\n",
       "      <td>0.593090</td>\n",
       "      <td>0.574363</td>\n",
       "      <td>0.570742</td>\n",
       "      <td>0.188055</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.364653</td>\n",
       "      <td>0.194647</td>\n",
       "      <td>0.203668</td>\n",
       "      <td>0.425460</td>\n",
       "      <td>0.044515</td>\n",
       "      <td>0.025738</td>\n",
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       "      <td>0.052974</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         feature_0    feature_1    feature_2    feature_3    feature_4  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.442338     0.490215     0.549443     0.375604     0.003133   \n",
       "std       0.119849     0.116663     0.115397     0.118394     0.050369   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.364413     0.427715     0.473043     0.291756     0.000302   \n",
       "50%       0.437523     0.495361     0.551465     0.345949     0.000570   \n",
       "75%       0.511001     0.552567     0.618191     0.428036     0.000752   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_5    feature_6    feature_7    feature_8    feature_9  \\\n",
       "count     1567.0  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean         0.0     0.402906     0.947316     0.584143     0.409652   \n",
       "std          0.0     0.131775     0.069484     0.158679     0.117744   \n",
       "min          0.0     0.000000     0.000000     0.000000     0.000000   \n",
       "25%          0.0     0.335448     0.941680     0.473249     0.332034   \n",
       "50%          0.0     0.411304     0.951788     0.581435     0.406080   \n",
       "75%          0.0     0.475348     0.962675     0.700150     0.481684   \n",
       "max          0.0     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "        feature_10   feature_11   feature_12  feature_13   feature_14  \\\n",
       "count  1567.000000  1567.000000  1567.000000      1567.0  1567.000000   \n",
       "mean      0.398704     0.937931     0.198578         0.0     0.390583   \n",
       "std       0.105758     0.037778     0.036189         0.0     0.161524   \n",
       "min       0.000000     0.000000     0.000000         0.0     0.000000   \n",
       "25%       0.333333     0.918944     0.178285         0.0     0.280245   \n",
       "50%       0.401593     0.942319     0.193901         0.0     0.388369   \n",
       "75%       0.464164     0.959016     0.221373         0.0     0.497734   \n",
       "max       1.000000     1.000000     1.000000         0.0     1.000000   \n",
       "\n",
       "        feature_15   feature_16   feature_17   feature_18   feature_19  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.162033     0.055264     0.967060     0.449571     0.836579   \n",
       "std       0.035006     0.024407     0.029726     0.059854     0.069807   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.147885     0.051820     0.959176     0.411959     0.829783   \n",
       "50%       0.160273     0.054698     0.969906     0.441333     0.842506   \n",
       "75%       0.174238     0.057503     0.980266     0.495505     0.857767   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "        feature_20   feature_21   feature_22   feature_23   feature_24  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.823362     0.214255     0.738279     0.500450     0.501762   \n",
       "std       0.061151     0.087610     0.080769     0.111685     0.100339   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.792108     0.170291     0.705128     0.454756     0.461082   \n",
       "50%       0.826818     0.227544     0.728615     0.499281     0.509356   \n",
       "75%       0.859700     0.250848     0.776923     0.536883     0.559684   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "        feature_25   feature_26   feature_27   feature_28   feature_29  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.870658     0.944339     0.866902     0.545897     0.597494   \n",
       "std       0.128370     0.092255     0.162383     0.186973     0.143592   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.791799     0.928853     0.687751     0.431532     0.500000   \n",
       "50%       0.927828     0.967703     0.948543     0.527330     0.601568   \n",
       "75%       0.943231     0.975838     0.957017     0.694895     0.699234   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "        feature_30   feature_31   feature_32   feature_33   feature_34  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.597857     0.586221     0.096091     0.086199     0.075251   \n",
       "std       0.131167     0.195659     0.090358     0.085378     0.118983   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.508765     0.472793     0.058321     0.062050     0.042078   \n",
       "50%       0.607968     0.497769     0.087086     0.074107     0.056520   \n",
       "75%       0.688845     0.534429     0.114135     0.092580     0.074807   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "        feature_35   feature_36   feature_37   feature_38   feature_39  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.028710     0.924750     0.428382     0.570757     0.321381   \n",
       "std       0.084152     0.118983     0.100037     0.121161     0.083344   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.011358     0.925193     0.368999     0.500692     0.290760   \n",
       "50%       0.015968     0.943480     0.431843     0.566452     0.308461   \n",
       "75%       0.021817     0.957922     0.468447     0.615746     0.333386   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "        feature_40   feature_41  feature_42   feature_43   feature_44  \\\n",
       "count  1567.000000  1567.000000      1567.0  1567.000000  1567.000000   \n",
       "mean      0.786788     0.090228         0.0     0.370070     0.356934   \n",
       "std       0.282360     0.061717         0.0     0.180439     0.110121   \n",
       "min       0.000000     0.000000         0.0     0.000000     0.000000   \n",
       "25%       0.866802     0.073109         0.0     0.232979     0.290403   \n",
       "50%       0.907541     0.082988         0.0     0.317473     0.359251   \n",
       "75%       0.929859     0.094607         0.0     0.521594     0.433260   \n",
       "max       1.000000     1.000000         0.0     1.000000     1.000000   \n",
       "\n",
       "        feature_45   feature_46   feature_47   feature_48  feature_49  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000      1567.0   \n",
       "mean      0.413482     0.376466     0.671633     0.378452         0.0   \n",
       "std       0.116305     0.135275     0.186894     0.120760         0.0   \n",
       "min       0.000000     0.000000     0.000000     0.000000         0.0   \n",
       "25%       0.324366     0.273855     0.481368     0.297223         0.0   \n",
       "50%       0.408400     0.373990     0.743642     0.379400         0.0   \n",
       "75%       0.492751     0.462935     0.831526     0.464477         0.0   \n",
       "max       1.000000     1.000000     1.000000     1.000000         0.0   \n",
       "\n",
       "        feature_50   feature_51  feature_52   feature_53   feature_54  \\\n",
       "count  1567.000000  1567.000000      1567.0  1567.000000  1567.000000   \n",
       "mean      0.411952     0.536811         0.0     0.838354     0.840161   \n",
       "std       0.143188     0.279039         0.0     0.051871     0.055148   \n",
       "min       0.000000     0.000000         0.0     0.000000     0.000000   \n",
       "25%       0.307145     0.344857         0.0     0.820416     0.819277   \n",
       "50%       0.397325     0.655376         0.0     0.841210     0.844300   \n",
       "75%       0.509294     0.763762         0.0     0.861059     0.868397   \n",
       "max       1.000000     1.000000         0.0     1.000000     1.000000   \n",
       "\n",
       "        feature_55   feature_56   feature_57   feature_58   feature_59  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.408641     0.856418     0.620618     0.594900     0.162019   \n",
       "std       0.190494     0.109142     0.149485     0.135424     0.048251   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.259259     0.802568     0.530466     0.497132     0.137636   \n",
       "50%       0.392593     0.890851     0.623656     0.562141     0.151854   \n",
       "75%       0.540741     0.924559     0.720430     0.714946     0.169052   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "        feature_60   feature_61   feature_62   feature_63   feature_64  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.619292     0.414018     0.170238     0.066123     0.331255   \n",
       "std       0.122415     0.118061     0.041877     0.038105     0.116748   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.526782     0.354140     0.148621     0.046797     0.256913   \n",
       "50%       0.591729     0.419830     0.168830     0.062151     0.319068   \n",
       "75%       0.711080     0.486036     0.191715     0.078668     0.384376   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "        feature_65   feature_66   feature_67   feature_68  feature_69  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000      1567.0   \n",
       "mean      0.200570     0.537285     0.002233     0.747632         0.0   \n",
       "std       0.062456     0.083951     0.042203     0.052372         0.0   \n",
       "min       0.000000     0.000000     0.000000     0.000000         0.0   \n",
       "25%       0.164943     0.480324     0.000066     0.720457         0.0   \n",
       "50%       0.192954     0.535735     0.000078     0.749601         0.0   \n",
       "75%       0.224999     0.594116     0.000090     0.778542         0.0   \n",
       "max       1.000000     1.000000     1.000000     1.000000         0.0   \n",
       "\n",
       "        feature_70   feature_71   feature_72   feature_73   feature_74  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.265040     0.381900     0.897523     0.046507     0.000638   \n",
       "std       0.067608     0.145553     0.055120     0.052526     0.025262   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.220177     0.304783     0.901588     0.042461     0.000000   \n",
       "50%       0.264552     0.373984     0.901588     0.042461     0.000000   \n",
       "75%       0.308065     0.433121     0.901588     0.042461     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "        feature_75   feature_76   feature_77   feature_78   feature_79  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.291338     0.606642     0.410243     0.560052     0.380864   \n",
       "std       0.065757     0.127458     0.130959     0.079518     0.144875   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.254756     0.521663     0.316365     0.503683     0.291588   \n",
       "50%       0.293103     0.608511     0.398401     0.561935     0.363061   \n",
       "75%       0.331451     0.693617     0.477493     0.603030     0.440228   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "        feature_80   feature_81   feature_82   feature_83   feature_84  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.477773     0.492148     0.614073     0.513912     0.474534   \n",
       "std       0.186284     0.107863     0.100385     0.163076     0.152022   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.384102     0.454023     0.548374     0.404035     0.374622   \n",
       "50%       0.514678     0.501916     0.618340     0.518770     0.471299   \n",
       "75%       0.581014     0.549489     0.672182     0.626173     0.570997   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "        feature_85   feature_86   feature_87   feature_88   feature_89  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.621329     0.509174     0.949315     0.377516     0.056889   \n",
       "std       0.087193     0.119271     0.058773     0.112070     0.037839   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.629771     0.429233     0.919030     0.313995     0.042934   \n",
       "50%       0.629771     0.515655     0.972095     0.380518     0.057882   \n",
       "75%       0.629771     0.594569     0.982617     0.448810     0.065264   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "        feature_90   feature_91   feature_92   feature_93   feature_94  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.427003     0.499417     0.322034     0.544734     0.635795   \n",
       "std       0.116385     0.121599     0.079336     0.098945     0.056068   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.352690     0.436779     0.281327     0.509868     0.612903   \n",
       "50%       0.426396     0.496040     0.319410     0.555921     0.645161   \n",
       "75%       0.495015     0.566000     0.358722     0.595395     0.677419   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "        feature_95   feature_96  feature_97   feature_98   feature_99  \\\n",
       "count  1567.000000  1567.000000      1567.0  1567.000000  1567.000000   \n",
       "mean      0.533220     0.375320         0.0     0.669977     0.374785   \n",
       "std       0.057930     0.054932         0.0     0.054364     0.044295   \n",
       "min       0.000000     0.000000         0.0     0.000000     0.000000   \n",
       "25%       0.500000     0.348882         0.0     0.644571     0.352621   \n",
       "50%       0.500000     0.372017         0.0     0.672282     0.373700   \n",
       "75%       0.555556     0.401419         0.0     0.696340     0.394780   \n",
       "max       1.000000     1.000000         0.0     1.000000     1.000000   \n",
       "\n",
       "       feature_100  feature_101  feature_102  feature_103  feature_104  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.562040     0.583607     0.643795     0.434408     0.625547   \n",
       "std       0.066954     0.053741     0.075428     0.057568     0.044746   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.528302     0.560976     0.600096     0.396617     0.605263   \n",
       "50%       0.566038     0.585366     0.642463     0.428571     0.626316   \n",
       "75%       0.603774     0.609756     0.682789     0.464286     0.647368   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_105  feature_106  feature_107  feature_108  feature_109  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.676550     0.453712     0.516605     0.452665     0.982182   \n",
       "std       0.078339     0.097779     0.086597     0.117142     0.025907   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.642157     0.403279     0.470393     0.380141     0.983952   \n",
       "50%       0.683824     0.442623     0.518350     0.452110     0.983952   \n",
       "75%       0.715686     0.486885     0.566554     0.518466     0.983952   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_110  feature_111  feature_112  feature_113  feature_114  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.706429     0.768775     0.941580     0.748773     0.002960   \n",
       "std       0.059529     0.053445     0.074051     0.098721     0.040292   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.709486     0.759737     0.946571     0.693247     0.000000   \n",
       "50%       0.709486     0.759737     0.947492     0.756713     0.000000   \n",
       "75%       0.709486     0.759737     0.948618     0.804719     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_115  feature_116  feature_117  feature_118  feature_119  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.534441     0.948534     0.022474     0.731418     0.915799   \n",
       "std       0.128642     0.092739     0.025046     0.082796     0.063196   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.465163     0.971680     0.019973     0.687951     0.873588   \n",
       "50%       0.543581     0.981445     0.022177     0.737384     0.906073   \n",
       "75%       0.611702     0.985352     0.024436     0.781668     0.968927   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_120  feature_121  feature_122  feature_123  feature_124  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.494538     0.551456     0.426843     0.709314     0.544209   \n",
       "std       0.051878     0.162840     0.172771     0.116133     0.169882   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.467635     0.442623     0.293407     0.645161     0.447761   \n",
       "50%       0.495680     0.540984     0.422767     0.709677     0.522388   \n",
       "75%       0.521660     0.655738     0.521464     0.784946     0.656716   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_125  feature_126  feature_127  feature_128  feature_129  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.405296     0.248721     0.387017     0.819563     0.517419   \n",
       "std       0.129954     0.153086     0.157201     0.067629     0.195184   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.307599     0.141732     0.271044     0.789730     0.461793   \n",
       "50%       0.386381     0.239249     0.393294     0.820282     0.583149   \n",
       "75%       0.476496     0.322835     0.461404     0.850064     0.613484   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_130  feature_131  feature_132  feature_133  feature_134  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.694035     0.165670     0.453106     0.581898     0.195810   \n",
       "std       0.175668     0.088351     0.188644     0.160840     0.096968   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.573746     0.110236     0.306543     0.483296     0.130128   \n",
       "50%       0.723266     0.163386     0.431366     0.582068     0.179983   \n",
       "75%       0.842263     0.208661     0.594595     0.696034     0.241811   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_135  feature_136  feature_137  feature_138  feature_139  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.064030     0.393073     0.327958     0.309737     0.234980   \n",
       "std       0.061383     0.207189     0.165252     0.100747     0.148090   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.036325     0.208125     0.197147     0.255738     0.137710   \n",
       "50%       0.054487     0.379284     0.312203     0.295903     0.191768   \n",
       "75%       0.073718     0.557566     0.451347     0.353280     0.279442   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_140  feature_141  feature_142  feature_143  feature_144  \\\n",
       "count  1567.000000       1567.0  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.002582          0.0     0.048186     0.344455     0.147735   \n",
       "std       0.050471          0.0     0.034791     0.105643     0.103399   \n",
       "min       0.000000          0.0     0.000000     0.000000     0.000000   \n",
       "25%       0.000010          0.0     0.033153     0.272727     0.086861   \n",
       "50%       0.000020          0.0     0.044466     0.322314     0.126666   \n",
       "75%       0.000040          0.0     0.056665     0.404959     0.169084   \n",
       "max       1.000000          0.0     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_145  feature_146  feature_147  feature_148  feature_149  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000       1567.0   \n",
       "mean      0.184104     0.143444     0.013459     0.009507          0.0   \n",
       "std       0.115675     0.096942     0.027829     0.025257          0.0   \n",
       "min       0.000000     0.000000     0.000000     0.000000          0.0   \n",
       "25%       0.116223     0.087256     0.005852     0.006665          0.0   \n",
       "50%       0.162233     0.121226     0.011910     0.008761          0.0   \n",
       "75%       0.219799     0.172291     0.017454     0.011006          0.0   \n",
       "max       1.000000     1.000000     1.000000     1.000000          0.0   \n",
       "\n",
       "       feature_150  feature_151  feature_152  feature_153  feature_154  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.260980     0.022489     0.001126     0.035380     0.033913   \n",
       "std       0.154376     0.057943     0.025255     0.039680     0.027504   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.149111     0.011367     0.000237     0.015725     0.024633   \n",
       "50%       0.219913     0.016787     0.000340     0.031874     0.032942   \n",
       "75%       0.330418     0.023051     0.000566     0.048024     0.041025   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_155  feature_156  feature_157  feature_158  feature_159  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.029115     0.021451     0.089472     0.346911     0.113252   \n",
       "std       0.089016     0.036144     0.044061     0.053039     0.126102   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.007955     0.011481     0.085751     0.346161     0.052817   \n",
       "50%       0.014320     0.017165     0.085751     0.346161     0.079964   \n",
       "75%       0.024662     0.025382     0.085751     0.346161     0.123668   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_160  feature_161  feature_162  feature_163  feature_164  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.133141     0.107134     0.130002     0.146477     0.070387   \n",
       "std       0.137759     0.111664     0.177654     0.127391     0.133397   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.070743     0.034855     0.012232     0.095089     0.037700   \n",
       "50%       0.105036     0.068893     0.048385     0.125392     0.048982   \n",
       "75%       0.149760     0.132646     0.172927     0.160920     0.063841   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_165  feature_166  feature_167  feature_168  feature_169  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.076670     0.097963     0.058484     0.132066     0.323125   \n",
       "std       0.123882     0.055126     0.039523     0.068799     0.180302   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.040170     0.064039     0.037500     0.082370     0.168186   \n",
       "50%       0.055995     0.088670     0.056250     0.124277     0.333637   \n",
       "75%       0.077450     0.118227     0.075000     0.169798     0.446673   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_170  feature_171  feature_172  feature_173  feature_174  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.451914     0.229147     0.456076     0.528080     0.456076   \n",
       "std       0.184093     0.125264     0.169693     0.156762     0.169703   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.324757     0.146155     0.352395     0.430958     0.352156   \n",
       "50%       0.453865     0.213564     0.464975     0.530385     0.464975   \n",
       "75%       0.584025     0.270872     0.575411     0.623587     0.575411   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_175  feature_176  feature_177  feature_178  feature_179  \\\n",
       "count  1567.000000  1567.000000  1567.000000       1567.0       1567.0   \n",
       "mean      0.445435     0.465769     0.191993          0.0          0.0   \n",
       "std       0.163690     0.203737     0.156170          0.0          0.0   \n",
       "min       0.000000     0.000000     0.000000          0.0          0.0   \n",
       "25%       0.324606     0.334059     0.096632          0.0          0.0   \n",
       "50%       0.431588     0.460827     0.139150          0.0          0.0   \n",
       "75%       0.538148     0.599157     0.207620          0.0          0.0   \n",
       "max       1.000000     1.000000     1.000000          0.0          0.0   \n",
       "\n",
       "       feature_180  feature_181  feature_182  feature_183  feature_184  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.244794     0.130390     0.146829     0.318660     0.035987   \n",
       "std       0.084303     0.064463     0.080315     0.100598     0.034441   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.189712     0.081897     0.088076     0.237852     0.022666   \n",
       "50%       0.236567     0.123851     0.135057     0.326594     0.032170   \n",
       "75%       0.294500     0.171121     0.196122     0.392637     0.043582   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_185  feature_186  feature_187  feature_188  feature_189  \\\n",
       "count  1567.000000       1567.0  1567.000000  1567.000000       1567.0   \n",
       "mean      0.020398          0.0     0.038833     0.197587          0.0   \n",
       "std       0.027020          0.0     0.028528     0.117187          0.0   \n",
       "min       0.000000          0.0     0.000000     0.000000          0.0   \n",
       "25%       0.012971          0.0     0.027477     0.097729          0.0   \n",
       "50%       0.018028          0.0     0.038597     0.181390          0.0   \n",
       "75%       0.024476          0.0     0.048523     0.275674          0.0   \n",
       "max       1.000000          0.0     1.000000     1.000000          0.0   \n",
       "\n",
       "       feature_190  feature_191  feature_192  feature_193  feature_194  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0       1567.0   \n",
       "mean           0.0          0.0          0.0          0.0          0.0   \n",
       "std            0.0          0.0          0.0          0.0          0.0   \n",
       "min            0.0          0.0          0.0          0.0          0.0   \n",
       "25%            0.0          0.0          0.0          0.0          0.0   \n",
       "50%            0.0          0.0          0.0          0.0          0.0   \n",
       "75%            0.0          0.0          0.0          0.0          0.0   \n",
       "max            0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_195  feature_196  feature_197  feature_198  feature_199  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.043508     0.017573     0.044613     0.047028     0.022591   \n",
       "std       0.082952     0.039777     0.043226     0.054068     0.042378   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.029319     0.008355     0.032547     0.020751     0.010262   \n",
       "50%       0.037621     0.012755     0.041658     0.033646     0.014974   \n",
       "75%       0.045397     0.019677     0.050195     0.063917     0.022344   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_200  feature_201  feature_202  feature_203  feature_204  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.073258     0.061918     0.020717     0.045360     0.003206   \n",
       "std       0.044149     0.040256     0.029743     0.035379     0.056411   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.055827     0.039714     0.012438     0.034385     0.000007   \n",
       "50%       0.071407     0.053426     0.017250     0.045408     0.000012   \n",
       "75%       0.086299     0.074962     0.024323     0.052325     0.000019   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_205  feature_206  feature_207  feature_208  feature_209  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.021228     0.000638     0.036072     0.406643     0.000638   \n",
       "std       0.036253     0.025262     0.038498     0.167754     0.025262   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.011802     0.000000     0.027235     0.304578     0.000000   \n",
       "50%       0.017120     0.000000     0.034631     0.406545     0.000000   \n",
       "75%       0.024013     0.000000     0.040462     0.509572     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_210  feature_211  feature_212  feature_213  feature_214  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.117287     0.170048     0.075267     0.044203     0.086025   \n",
       "std       0.086184     0.077431     0.053421     0.041113     0.075491   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.070175     0.130265     0.043441     0.025977     0.050862   \n",
       "50%       0.098658     0.159126     0.058773     0.040859     0.075586   \n",
       "75%       0.138596     0.193136     0.093697     0.054453     0.099636   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_215  feature_216  feature_217  feature_218  feature_219  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.223138     0.270233     0.095399     0.352414     0.163594   \n",
       "std       0.089624     0.093616     0.068933     0.150636     0.105124   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.171217     0.220066     0.057314     0.246180     0.102564   \n",
       "50%       0.227300     0.274259     0.080720     0.334310     0.147436   \n",
       "75%       0.273591     0.320762     0.117254     0.433782     0.198718   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_220  feature_221  feature_222  feature_223  feature_224  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.179667     0.193433     0.008600     0.052161     0.034646   \n",
       "std       0.041506     0.110714     0.056456     0.031761     0.049151   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.177596     0.095962     0.001110     0.036210     0.014543   \n",
       "50%       0.177596     0.194299     0.002019     0.050197     0.021377   \n",
       "75%       0.177596     0.268409     0.005248     0.064631     0.034973   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_225  feature_226  feature_227  feature_228  feature_229  \\\n",
       "count  1567.000000       1567.0  1567.000000  1567.000000       1567.0   \n",
       "mean      0.253418          0.0     0.087368     0.051632          0.0   \n",
       "std       0.124183          0.0     0.072679     0.052103          0.0   \n",
       "min       0.000000          0.0     0.000000     0.000000          0.0   \n",
       "25%       0.162476          0.0     0.047667     0.026443          0.0   \n",
       "50%       0.232629          0.0     0.069642     0.040272          0.0   \n",
       "75%       0.315688          0.0     0.101420     0.062106          0.0   \n",
       "max       1.000000          0.0     1.000000     1.000000          0.0   \n",
       "\n",
       "       feature_230  feature_231  feature_232  feature_233  feature_234  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0       1567.0   \n",
       "mean           0.0          0.0          0.0          0.0          0.0   \n",
       "std            0.0          0.0          0.0          0.0          0.0   \n",
       "min            0.0          0.0          0.0          0.0          0.0   \n",
       "25%            0.0          0.0          0.0          0.0          0.0   \n",
       "50%            0.0          0.0          0.0          0.0          0.0   \n",
       "75%            0.0          0.0          0.0          0.0          0.0   \n",
       "max            0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_235  feature_236  feature_237  feature_238  feature_239  \\\n",
       "count       1567.0       1567.0       1567.0  1567.000000  1567.000000   \n",
       "mean           0.0          0.0          0.0     0.151131     0.143003   \n",
       "std            0.0          0.0          0.0     0.073458     0.064887   \n",
       "min            0.0          0.0          0.0     0.000000     0.000000   \n",
       "25%            0.0          0.0          0.0     0.103896     0.099099   \n",
       "50%            0.0          0.0          0.0     0.142857     0.135135   \n",
       "75%            0.0          0.0          0.0     0.190476     0.175676   \n",
       "max            0.0          0.0          0.0     1.000000     1.000000   \n",
       "\n",
       "       feature_240  feature_241  feature_242  feature_243  feature_244  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0  1567.000000   \n",
       "mean           0.0          0.0          0.0          0.0     0.001422   \n",
       "std            0.0          0.0          0.0          0.0     0.025247   \n",
       "min            0.0          0.0          0.0          0.0     0.000000   \n",
       "25%            0.0          0.0          0.0          0.0     0.000706   \n",
       "50%            0.0          0.0          0.0          0.0     0.000706   \n",
       "75%            0.0          0.0          0.0          0.0     0.000706   \n",
       "max            0.0          0.0          0.0          0.0     1.000000   \n",
       "\n",
       "       feature_245  feature_246  feature_247  feature_248  feature_249  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.010887     0.011595     0.084126     0.022852     0.002574   \n",
       "std       0.025880     0.025189     0.103833     0.050747     0.038112   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.008972     0.010889     0.051180     0.012059     0.000000   \n",
       "50%       0.008972     0.010889     0.054945     0.018553     0.000000   \n",
       "75%       0.008972     0.010889     0.061254     0.025046     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_250  feature_251  feature_252  feature_253  feature_254  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.080678     0.004023     0.001521     0.053763     0.159003   \n",
       "std       0.049693     0.037828     0.025245     0.050316     0.118599   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.050170     0.000404     0.000564     0.034366     0.038961   \n",
       "50%       0.074710     0.000707     0.000823     0.048383     0.172727   \n",
       "75%       0.100799     0.001009     0.001188     0.063984     0.254545   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_255  feature_256  feature_257  feature_258  feature_259  \\\n",
       "count  1567.000000       1567.0       1567.0       1567.0       1567.0   \n",
       "mean      0.346792          0.0          0.0          0.0          0.0   \n",
       "std       0.150681          0.0          0.0          0.0          0.0   \n",
       "min       0.000000          0.0          0.0          0.0          0.0   \n",
       "25%       0.226271          0.0          0.0          0.0          0.0   \n",
       "50%       0.348360          0.0          0.0          0.0          0.0   \n",
       "75%       0.443338          0.0          0.0          0.0          0.0   \n",
       "max       1.000000          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_260  feature_261  feature_262  feature_263  feature_264  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0       1567.0   \n",
       "mean           0.0          0.0          0.0          0.0          0.0   \n",
       "std            0.0          0.0          0.0          0.0          0.0   \n",
       "min            0.0          0.0          0.0          0.0          0.0   \n",
       "25%            0.0          0.0          0.0          0.0          0.0   \n",
       "50%            0.0          0.0          0.0          0.0          0.0   \n",
       "75%            0.0          0.0          0.0          0.0          0.0   \n",
       "max            0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_265  feature_266  feature_267  feature_268  feature_269  \\\n",
       "count       1567.0       1567.0  1567.000000  1567.000000  1567.000000   \n",
       "mean           0.0          0.0     0.368022     0.385502     0.279712   \n",
       "std            0.0          0.0     0.214296     0.210790     0.129857   \n",
       "min            0.0          0.0     0.000000     0.000000     0.000000   \n",
       "25%            0.0          0.0     0.175362     0.222401     0.187624   \n",
       "50%            0.0          0.0     0.368116     0.341773     0.271353   \n",
       "75%            0.0          0.0     0.520290     0.533849     0.347422   \n",
       "max            0.0          0.0     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_270  feature_271  feature_272  feature_273  feature_274  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.095900     0.291654     0.298238     0.330007     0.206416   \n",
       "std       0.058670     0.145871     0.153663     0.109205     0.129342   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.065990     0.160672     0.175025     0.275698     0.123257   \n",
       "50%       0.092506     0.288557     0.287183     0.312970     0.167789   \n",
       "75%       0.112916     0.401506     0.410017     0.384792     0.245732   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_275  feature_276  feature_277  feature_278  feature_279  \\\n",
       "count  1567.000000       1567.0  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.002582          0.0     0.052140     0.328635     0.170804   \n",
       "std       0.050470          0.0     0.037683     0.099723     0.114106   \n",
       "min       0.000000          0.0     0.000000     0.000000     0.000000   \n",
       "25%       0.000010          0.0     0.035972     0.264706     0.099043   \n",
       "50%       0.000020          0.0     0.048138     0.323529     0.149128   \n",
       "75%       0.000040          0.0     0.061741     0.382353     0.197243   \n",
       "max       1.000000          0.0     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_280  feature_281  feature_282  feature_283  feature_284  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000       1567.0   \n",
       "mean      0.157362     0.155162     0.013594     0.009896          0.0   \n",
       "std       0.095000     0.104964     0.027861     0.025229          0.0   \n",
       "min       0.000000     0.000000     0.000000     0.000000          0.0   \n",
       "25%       0.101173     0.094697     0.005543     0.007346          0.0   \n",
       "50%       0.140762     0.132576     0.012064     0.009268          0.0   \n",
       "75%       0.196481     0.183712     0.017933     0.011368          0.0   \n",
       "max       1.000000     1.000000     1.000000     1.000000          0.0   \n",
       "\n",
       "       feature_285  feature_286  feature_287  feature_288  feature_289  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.258746     0.017113     0.001081     0.036948     0.036915   \n",
       "std       0.150118     0.047286     0.025256     0.039801     0.027324   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.152769     0.008433     0.000218     0.016282     0.028702   \n",
       "50%       0.220645     0.012759     0.000300     0.032564     0.036416   \n",
       "75%       0.334670     0.017348     0.000503     0.050204     0.044447   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_290  feature_291  feature_292  feature_293  feature_294  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.018629     0.023450     0.091229     0.297528     0.102105   \n",
       "std       0.061694     0.037151     0.044815     0.051491     0.121199   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.005390     0.012651     0.087342     0.295574     0.047057   \n",
       "50%       0.009523     0.019049     0.087342     0.295574     0.070841   \n",
       "75%       0.017345     0.028792     0.087342     0.295574     0.108933   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_295  feature_296  feature_297  feature_298  feature_299  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.126093     0.120718     0.126394     0.121182     0.058418   \n",
       "std       0.141264     0.126864     0.174149     0.121932     0.126788   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.064941     0.038772     0.011407     0.077318     0.029286   \n",
       "50%       0.097626     0.077276     0.044281     0.100304     0.038790   \n",
       "75%       0.136532     0.150254     0.172144     0.131459     0.050427   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_300  feature_301  feature_302  feature_303  feature_304  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.065299     0.111106     0.057804     0.137404     0.369750   \n",
       "std       0.120847     0.061428     0.039141     0.067449     0.202645   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.032528     0.075430     0.036485     0.089302     0.184433   \n",
       "50%       0.045688     0.101907     0.053340     0.130233     0.385746   \n",
       "75%       0.063648     0.136345     0.072356     0.175814     0.514848   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_305  feature_306  feature_307  feature_308  feature_309  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.413010     0.204433     0.473585     0.546496     0.473587   \n",
       "std       0.160294     0.114792     0.175681     0.144379     0.175696   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.301481     0.134268     0.376200     0.457008     0.376200   \n",
       "50%       0.411288     0.187161     0.477287     0.550946     0.477287   \n",
       "75%       0.531433     0.233725     0.593090     0.630267     0.593090   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_310  feature_311  feature_312  feature_313  feature_314  \\\n",
       "count  1567.000000  1567.000000  1567.000000       1567.0       1567.0   \n",
       "mean      0.471094     0.443300     0.187886          0.0          0.0   \n",
       "std       0.165257     0.197713     0.160112          0.0          0.0   \n",
       "min       0.000000     0.000000     0.000000          0.0          0.0   \n",
       "25%       0.371624     0.307692     0.094334          0.0          0.0   \n",
       "50%       0.464055     0.445742     0.135290          0.0          0.0   \n",
       "75%       0.574363     0.570742     0.188055          0.0          0.0   \n",
       "max       1.000000     1.000000     1.000000          0.0          0.0   \n",
       "\n",
       "       feature_315  feature_316  feature_317  feature_318  feature_319  \\\n",
       "count       1567.0  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean           0.0     0.309353     0.148009     0.147820     0.340380   \n",
       "std            0.0     0.098792     0.074218     0.081534     0.118468   \n",
       "min            0.0     0.000000     0.000000     0.000000     0.000000   \n",
       "25%            0.0     0.243893     0.091427     0.089562     0.249640   \n",
       "50%            0.0     0.295248     0.138550     0.128389     0.366074   \n",
       "75%            0.0     0.364653     0.194647     0.203668     0.425460   \n",
       "max            0.0     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_320  feature_321  feature_322  feature_323  feature_324  \\\n",
       "count  1567.000000  1567.000000       1567.0  1567.000000  1567.000000   \n",
       "mean      0.036957     0.021036          0.0     0.042104     0.223583   \n",
       "std       0.033945     0.026946          0.0     0.028784     0.133253   \n",
       "min       0.000000     0.000000          0.0     0.000000     0.000000   \n",
       "25%       0.024281     0.013483          0.0     0.029301     0.107676   \n",
       "50%       0.033493     0.018669          0.0     0.042851     0.206918   \n",
       "75%       0.044515     0.025738          0.0     0.052974     0.316132   \n",
       "max       1.000000     1.000000          0.0     1.000000     1.000000   \n",
       "\n",
       "       feature_325  feature_326  feature_327  feature_328  feature_329  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0       1567.0   \n",
       "mean           0.0          0.0          0.0          0.0          0.0   \n",
       "std            0.0          0.0          0.0          0.0          0.0   \n",
       "min            0.0          0.0          0.0          0.0          0.0   \n",
       "25%            0.0          0.0          0.0          0.0          0.0   \n",
       "50%            0.0          0.0          0.0          0.0          0.0   \n",
       "75%            0.0          0.0          0.0          0.0          0.0   \n",
       "max            0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_330  feature_331  feature_332  feature_333  feature_334  \\\n",
       "count       1567.0  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean           0.0     0.056671     0.011779     0.038524     0.041372   \n",
       "std            0.0     0.059007     0.032306     0.038743     0.050991   \n",
       "min            0.0     0.000000     0.000000     0.000000     0.000000   \n",
       "25%            0.0     0.043223     0.005790     0.029875     0.018172   \n",
       "50%            0.0     0.058128     0.008749     0.035846     0.029903   \n",
       "75%            0.0     0.068188     0.012916     0.042283     0.053763   \n",
       "max            0.0     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_335  feature_336  feature_337  feature_338  feature_339  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.015318     0.021552     0.059802     0.015433     0.036505   \n",
       "std       0.033566     0.035962     0.043593     0.028637     0.031045   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.007251     0.014677     0.039702     0.009674     0.027616   \n",
       "50%       0.010025     0.018404     0.052814     0.013020     0.037246   \n",
       "75%       0.014923     0.022989     0.071465     0.017276     0.042182   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_340  feature_341  feature_342  feature_343  feature_344  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.002895     0.022202     0.000638     0.031863     0.369510   \n",
       "std       0.051793     0.037689     0.025262     0.034573     0.155028   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000004     0.013797     0.000000     0.024189     0.277380   \n",
       "50%       0.000007     0.018564     0.000000     0.030624     0.369467   \n",
       "75%       0.000011     0.024814     0.000000     0.036269     0.467916   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_345  feature_346  feature_347  feature_348  feature_349  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.019500     0.018059     0.000638     0.068543     0.182341   \n",
       "std       0.048402     0.064622     0.025262     0.056147     0.079178   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.014878     0.010864     0.000000     0.037673     0.140707   \n",
       "50%       0.014878     0.010864     0.000000     0.058655     0.173062   \n",
       "75%       0.014878     0.010864     0.000000     0.081068     0.206922   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_350  feature_351  feature_352  feature_353  feature_354  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.070137     0.041422     0.129169     0.250216     0.291073   \n",
       "std       0.049309     0.039519     0.095098     0.101932     0.099466   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.040555     0.023671     0.076475     0.184891     0.240031   \n",
       "50%       0.055113     0.037776     0.119446     0.261763     0.308282   \n",
       "75%       0.087348     0.051232     0.154406     0.314778     0.351994   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_355  feature_356  feature_357  feature_358  feature_359  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.111466     0.374308     0.142466     0.235397     0.150738   \n",
       "std       0.080495     0.157541     0.101852     0.050124     0.087886   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.063977     0.263135     0.081633     0.233333     0.076355   \n",
       "50%       0.093057     0.356672     0.122449     0.233333     0.147783   \n",
       "75%       0.138495     0.469880     0.163265     0.233333     0.214286   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_360  feature_361  feature_362  feature_363  feature_364  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000       1567.0   \n",
       "mean      0.006957     0.054462     0.037222     0.268555          0.0   \n",
       "std       0.048918     0.031795     0.051546     0.135072          0.0   \n",
       "min       0.000000     0.000000     0.000000     0.000000          0.0   \n",
       "25%       0.000734     0.039982     0.016203     0.167210          0.0   \n",
       "50%       0.001467     0.054014     0.023458     0.246084          0.0   \n",
       "75%       0.004157     0.067776     0.037485     0.344267          0.0   \n",
       "max       1.000000     1.000000     1.000000     1.000000          0.0   \n",
       "\n",
       "       feature_365  feature_366  feature_367  feature_368  feature_369  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000       1567.0   \n",
       "mean      0.099651     0.075627     0.105633     0.094916          0.0   \n",
       "std       0.075624     0.064005     0.075466     0.062957          0.0   \n",
       "min       0.000000     0.000000     0.000000     0.000000          0.0   \n",
       "25%       0.059829     0.041667     0.072829     0.065868          0.0   \n",
       "50%       0.082621     0.061828     0.089636     0.083832          0.0   \n",
       "75%       0.116809     0.091398     0.117647     0.107784          0.0   \n",
       "max       1.000000     1.000000     1.000000     1.000000          0.0   \n",
       "\n",
       "       feature_370  feature_371  feature_372  feature_373  feature_374  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0       1567.0   \n",
       "mean           0.0          0.0          0.0          0.0          0.0   \n",
       "std            0.0          0.0          0.0          0.0          0.0   \n",
       "min            0.0          0.0          0.0          0.0          0.0   \n",
       "25%            0.0          0.0          0.0          0.0          0.0   \n",
       "50%            0.0          0.0          0.0          0.0          0.0   \n",
       "75%            0.0          0.0          0.0          0.0          0.0   \n",
       "max            0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_375  feature_376  feature_377  feature_378  feature_379  \\\n",
       "count       1567.0  1567.000000  1567.000000       1567.0       1567.0   \n",
       "mean           0.0     0.153953     0.160362          0.0          0.0   \n",
       "std            0.0     0.068463     0.063870          0.0          0.0   \n",
       "min            0.0     0.000000     0.000000          0.0          0.0   \n",
       "25%            0.0     0.115385     0.123288          0.0          0.0   \n",
       "50%            0.0     0.153846     0.150685          0.0          0.0   \n",
       "75%            0.0     0.192308     0.191781          0.0          0.0   \n",
       "max            0.0     1.000000     1.000000          0.0          0.0   \n",
       "\n",
       "       feature_380  feature_381  feature_382  feature_383  feature_384  \\\n",
       "count       1567.0       1567.0  1567.000000  1567.000000  1567.000000   \n",
       "mean           0.0          0.0     0.001379     0.011132     0.011150   \n",
       "std            0.0          0.0     0.025248     0.025795     0.025182   \n",
       "min            0.0          0.0     0.000000     0.000000     0.000000   \n",
       "25%            0.0          0.0     0.000638     0.009223     0.010308   \n",
       "50%            0.0          0.0     0.000638     0.009223     0.010308   \n",
       "75%            0.0          0.0     0.000638     0.009223     0.010308   \n",
       "max            0.0          0.0     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_385  feature_386  feature_387  feature_388  feature_389  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.044939     0.024447     0.002592     0.084214     0.003961   \n",
       "std       0.052282     0.050615     0.038116     0.050295     0.037799   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.031356     0.013072     0.000000     0.052775     0.000320   \n",
       "50%       0.032803     0.019608     0.000000     0.076552     0.000640   \n",
       "75%       0.034491     0.027778     0.000000     0.106103     0.000960   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_390  feature_391  feature_392  feature_393  feature_394  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000       1567.0   \n",
       "mean      0.001400     0.058366     0.180036     0.376280          0.0   \n",
       "std       0.025247     0.049751     0.131944     0.144828          0.0   \n",
       "min       0.000000     0.000000     0.000000     0.000000          0.0   \n",
       "25%       0.000460     0.039062     0.044643     0.264706          0.0   \n",
       "50%       0.000711     0.052827     0.196429     0.375943          0.0   \n",
       "75%       0.001048     0.069196     0.285714     0.475867          0.0   \n",
       "max       1.000000     1.000000     1.000000     1.000000          0.0   \n",
       "\n",
       "       feature_395  feature_396  feature_397  feature_398  feature_399  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0       1567.0   \n",
       "mean           0.0          0.0          0.0          0.0          0.0   \n",
       "std            0.0          0.0          0.0          0.0          0.0   \n",
       "min            0.0          0.0          0.0          0.0          0.0   \n",
       "25%            0.0          0.0          0.0          0.0          0.0   \n",
       "50%            0.0          0.0          0.0          0.0          0.0   \n",
       "75%            0.0          0.0          0.0          0.0          0.0   \n",
       "max            0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_400  feature_401  feature_402  feature_403  feature_404  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0       1567.0   \n",
       "mean           0.0          0.0          0.0          0.0          0.0   \n",
       "std            0.0          0.0          0.0          0.0          0.0   \n",
       "min            0.0          0.0          0.0          0.0          0.0   \n",
       "25%            0.0          0.0          0.0          0.0          0.0   \n",
       "50%            0.0          0.0          0.0          0.0          0.0   \n",
       "75%            0.0          0.0          0.0          0.0          0.0   \n",
       "max            0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_405  feature_406  feature_407  feature_408  feature_409  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.398361     0.368650     0.279714     0.062585     0.402357   \n",
       "std       0.236745     0.222749     0.125927     0.061919     0.212129   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.174779     0.197685     0.187794     0.033436     0.215629   \n",
       "50%       0.391593     0.305022     0.282366     0.052551     0.391546   \n",
       "75%       0.577434     0.513564     0.343637     0.073349     0.563820   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_410  feature_411  feature_412  feature_413  feature_414  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000       1567.0   \n",
       "mean      0.320837     0.314912     0.240633     0.024046          0.0   \n",
       "std       0.162035     0.104498     0.142940     0.052620          0.0   \n",
       "min       0.000000     0.000000     0.000000     0.000000          0.0   \n",
       "25%       0.193932     0.259683     0.143982     0.008304          0.0   \n",
       "50%       0.305866     0.302088     0.203902     0.018114          0.0   \n",
       "75%       0.437516     0.359253     0.296433     0.028227          0.0   \n",
       "max       1.000000     1.000000     1.000000     1.000000          0.0   \n",
       "\n",
       "       feature_415  feature_416  feature_417  feature_418  feature_419  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.044157     0.351225     0.163667     0.320455     0.309423   \n",
       "std       0.034171     0.106557     0.109857     0.287718     0.325673   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.029436     0.275320     0.097983     0.000000     0.000000   \n",
       "50%       0.040256     0.333746     0.142133     0.302384     0.272808   \n",
       "75%       0.052124     0.413901     0.190182     0.523983     0.583573   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_420  feature_421  feature_422  feature_423  feature_424  \\\n",
       "count  1567.000000  1567.000000       1567.0  1567.000000  1567.000000   \n",
       "mean      0.012426     0.012676          0.0     0.136157     0.027534   \n",
       "std       0.027516     0.025368          0.0     0.081176     0.061536   \n",
       "min       0.000000     0.000000          0.0     0.000000     0.000000   \n",
       "25%       0.005311     0.009043          0.0     0.082225     0.014401   \n",
       "50%       0.010842     0.011827          0.0     0.116864     0.021233   \n",
       "75%       0.015970     0.014912          0.0     0.174012     0.029043   \n",
       "max       1.000000     1.000000          0.0     1.000000     1.000000   \n",
       "\n",
       "       feature_425  feature_426  feature_427  feature_428  feature_429  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.005936     0.035346     0.030158     0.026270     0.018232   \n",
       "std       0.025900     0.040389     0.027001     0.090107     0.034630   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.002564     0.015471     0.021769     0.006943     0.009620   \n",
       "50%       0.003676     0.031352     0.029120     0.012023     0.014368   \n",
       "75%       0.006027     0.047738     0.036463     0.021161     0.021375   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_430  feature_431  feature_432  feature_433  feature_434  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.046031     0.055877     0.099886     0.206328     0.036820   \n",
       "std       0.090095     0.090932     0.126842     0.226607     0.085219   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.017515     0.027673     0.031212     0.010090     0.018878   \n",
       "50%       0.027764     0.040953     0.058302     0.151761     0.025494   \n",
       "75%       0.043505     0.054349     0.120827     0.305843     0.031882   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_435  feature_436  feature_437  feature_438  feature_439  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.023411     0.018768     0.091900     0.064224     0.087808   \n",
       "std       0.085871     0.086340     0.051748     0.040026     0.059586   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.008738     0.004879     0.061538     0.042676     0.052913   \n",
       "50%       0.011378     0.006911     0.084330     0.057645     0.079729   \n",
       "75%       0.014555     0.009553     0.113167     0.078283     0.110036   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_440  feature_441  feature_442  feature_443  feature_444  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.326339     0.486969     0.247861     0.480358     0.551791   \n",
       "std       0.192911     0.198892     0.130899     0.165292     0.152798   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.135199     0.354682     0.161174     0.384243     0.463096   \n",
       "50%       0.343816     0.492926     0.231840     0.491361     0.559312   \n",
       "75%       0.459894     0.622529     0.294013     0.594736     0.643931   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_445  feature_446  feature_447  feature_448  feature_449  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000       1567.0   \n",
       "mean      0.423063     0.448127     0.465426     0.203635          0.0   \n",
       "std       0.176487     0.165029     0.203900     0.164548          0.0   \n",
       "min       0.000000     0.000000     0.000000     0.000000          0.0   \n",
       "25%       0.308961     0.327992     0.333805     0.102961          0.0   \n",
       "50%       0.428071     0.437635     0.460250     0.147196          0.0   \n",
       "75%       0.549681     0.539312     0.599316     0.216789          0.0   \n",
       "max       1.000000     1.000000     1.000000     1.000000          0.0   \n",
       "\n",
       "       feature_450  feature_451  feature_452  feature_453  feature_454  \\\n",
       "count       1567.0       1567.0  1567.000000  1567.000000  1567.000000   \n",
       "mean           0.0          0.0     0.236662     0.135683     0.139756   \n",
       "std            0.0          0.0     0.081271     0.066994     0.076966   \n",
       "min            0.0          0.0     0.000000     0.000000     0.000000   \n",
       "25%            0.0          0.0     0.185244     0.084708     0.087536   \n",
       "50%            0.0          0.0     0.230001     0.128724     0.128218   \n",
       "75%            0.0          0.0     0.286733     0.178496     0.182365   \n",
       "max            0.0          0.0     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_455  feature_456  feature_457  feature_458  feature_459  \\\n",
       "count  1567.000000  1567.000000  1567.000000       1567.0  1567.000000   \n",
       "mean      0.310025     0.040494     0.023487          0.0     0.039485   \n",
       "std       0.098857     0.035461     0.027887          0.0     0.028751   \n",
       "min       0.000000     0.000000     0.000000          0.0     0.000000   \n",
       "25%       0.232364     0.025180     0.014667          0.0     0.027793   \n",
       "50%       0.319279     0.036241     0.020598          0.0     0.039314   \n",
       "75%       0.384326     0.049496     0.028565          0.0     0.049543   \n",
       "max       1.000000     1.000000     1.000000          0.0     1.000000   \n",
       "\n",
       "       feature_460  feature_461  feature_462  feature_463  feature_464  \\\n",
       "count  1567.000000       1567.0       1567.0       1567.0       1567.0   \n",
       "mean      0.150193          0.0          0.0          0.0          0.0   \n",
       "std       0.094259          0.0          0.0          0.0          0.0   \n",
       "min       0.000000          0.0          0.0          0.0          0.0   \n",
       "25%       0.086761          0.0          0.0          0.0          0.0   \n",
       "50%       0.128786          0.0          0.0          0.0          0.0   \n",
       "75%       0.193132          0.0          0.0          0.0          0.0   \n",
       "max       1.000000          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_465  feature_466  feature_467  feature_468  feature_469  \\\n",
       "count       1567.0       1567.0  1567.000000  1567.000000  1567.000000   \n",
       "mean           0.0          0.0     0.042261     0.223871     0.040700   \n",
       "std            0.0          0.0     0.080740     0.230332     0.041831   \n",
       "min            0.0          0.0     0.000000     0.000000     0.000000   \n",
       "25%            0.0          0.0     0.027786     0.038887     0.029888   \n",
       "50%            0.0          0.0     0.036617     0.150359     0.038185   \n",
       "75%            0.0          0.0     0.043532     0.334715     0.045243   \n",
       "max            0.0          0.0     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_470  feature_471  feature_472  feature_473  feature_474  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.052493     0.034376     0.262617     0.109775     0.090545   \n",
       "std       0.057640     0.048302     0.098887     0.062431     0.059623   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.023076     0.016261     0.195570     0.069462     0.055810   \n",
       "50%       0.037410     0.023749     0.263376     0.095408     0.079002   \n",
       "75%       0.071361     0.034745     0.325742     0.132860     0.108706   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_475  feature_476  feature_477  feature_478  feature_479  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.040525     0.073180     0.015849     0.000638     0.042068   \n",
       "std       0.033406     0.054252     0.035280     0.025262     0.042011   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.030709     0.042146     0.008414     0.000000     0.031649   \n",
       "50%       0.040699     0.058110     0.012338     0.000000     0.040496   \n",
       "75%       0.046657     0.086153     0.017396     0.000000     0.047516   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_480  feature_481  feature_482  feature_483  feature_484  \\\n",
       "count  1567.000000       1567.0  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.312845          0.0     0.318224     0.207860     0.214792   \n",
       "std       0.167431          0.0     0.279030     0.193552     0.212363   \n",
       "min       0.000000          0.0     0.000000     0.000000     0.000000   \n",
       "25%       0.207055          0.0     0.000000     0.083287     0.077254   \n",
       "50%       0.288991          0.0     0.293691     0.149895     0.139213   \n",
       "75%       0.394451          0.0     0.512691     0.262846     0.289829   \n",
       "max       1.000000          0.0     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_485  feature_486  feature_487  feature_488  feature_489  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.200967     0.301855     0.238522     0.353431     0.273000   \n",
       "std       0.218589     0.285372     0.263395     0.250728     0.227751   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.051497     0.000000     0.057562     0.145518     0.114493   \n",
       "50%       0.113635     0.250054     0.112755     0.349396     0.220811   \n",
       "75%       0.284999     0.497638     0.392950     0.508759     0.374588   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_490  feature_491  feature_492  feature_493  feature_494  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.291430     0.154364     0.194608     0.198036     0.007231   \n",
       "std       0.139737     0.099861     0.041425     0.113654     0.051868   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.191070     0.097536     0.193162     0.096944     0.000822   \n",
       "50%       0.269785     0.138775     0.193162     0.197925     0.001555   \n",
       "75%       0.369943     0.186196     0.193162     0.276110     0.004146   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_495  feature_496  feature_497  feature_498  feature_499  \\\n",
       "count  1567.000000  1567.000000  1567.000000       1567.0  1567.000000   \n",
       "mean      0.047543     0.115429     0.256747          0.0     0.262860   \n",
       "std       0.030778     0.112913     0.127255          0.0     0.324700   \n",
       "min       0.000000     0.000000     0.000000          0.0     0.000000   \n",
       "25%       0.033068     0.054336     0.164950          0.0     0.000000   \n",
       "50%       0.045656     0.080182     0.233666          0.0     0.000000   \n",
       "75%       0.057828     0.126370     0.323167          0.0     0.536123   \n",
       "max       1.000000     1.000000     1.000000          0.0     1.000000   \n",
       "\n",
       "       feature_500  feature_501  feature_502  feature_503  feature_504  \\\n",
       "count  1567.000000       1567.0       1567.0       1567.0       1567.0   \n",
       "mean      0.240858          0.0          0.0          0.0          0.0   \n",
       "std       0.323159          0.0          0.0          0.0          0.0   \n",
       "min       0.000000          0.0          0.0          0.0          0.0   \n",
       "25%       0.000000          0.0          0.0          0.0          0.0   \n",
       "50%       0.000000          0.0          0.0          0.0          0.0   \n",
       "75%       0.505613          0.0          0.0          0.0          0.0   \n",
       "max       1.000000          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_505  feature_506  feature_507  feature_508  feature_509  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0       1567.0   \n",
       "mean           0.0          0.0          0.0          0.0          0.0   \n",
       "std            0.0          0.0          0.0          0.0          0.0   \n",
       "min            0.0          0.0          0.0          0.0          0.0   \n",
       "25%            0.0          0.0          0.0          0.0          0.0   \n",
       "50%            0.0          0.0          0.0          0.0          0.0   \n",
       "75%            0.0          0.0          0.0          0.0          0.0   \n",
       "max            0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_510  feature_511  feature_512  feature_513  feature_514  \\\n",
       "count  1567.000000  1567.000000       1567.0       1567.0       1567.0   \n",
       "mean      0.123486     0.275627          0.0          0.0          0.0   \n",
       "std       0.083433     0.329602          0.0          0.0          0.0   \n",
       "min       0.000000     0.000000          0.0          0.0          0.0   \n",
       "25%       0.078240     0.000000          0.0          0.0          0.0   \n",
       "50%       0.104070     0.000000          0.0          0.0          0.0   \n",
       "75%       0.142260     0.554011          0.0          0.0          0.0   \n",
       "max       1.000000     1.000000          0.0          0.0          0.0   \n",
       "\n",
       "       feature_515  feature_516  feature_517  feature_518  feature_519  \\\n",
       "count       1567.0  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean           0.0     0.001276     0.009480     0.010802     0.049029   \n",
       "std            0.0     0.025249     0.025724     0.025177     0.065215   \n",
       "min            0.0     0.000000     0.000000     0.000000     0.000000   \n",
       "25%            0.0     0.000577     0.007667     0.010116     0.029455   \n",
       "50%            0.0     0.000577     0.007667     0.010116     0.031641   \n",
       "75%            0.0     0.000577     0.007667     0.010116     0.035091   \n",
       "max            0.0     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_520  feature_521  feature_522  feature_523  feature_524  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.021395     0.011610     0.089043     0.003846     0.005360   \n",
       "std       0.051177     0.103123     0.052508     0.037262     0.025301   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.011129     0.000000     0.055444     0.000425     0.003011   \n",
       "50%       0.017132     0.000000     0.081754     0.000667     0.004367   \n",
       "75%       0.023259     0.000000     0.111807     0.000965     0.006294   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_525  feature_526  feature_527  feature_528  feature_529  \\\n",
       "count  1567.000000  1567.000000  1567.000000       1567.0       1567.0   \n",
       "mean      0.051132     0.158462     0.344173          0.0          0.0   \n",
       "std       0.049561     0.119308     0.153829          0.0          0.0   \n",
       "min       0.000000     0.000000     0.000000          0.0          0.0   \n",
       "25%       0.032829     0.039050     0.221980          0.0          0.0   \n",
       "50%       0.045786     0.171737     0.345401          0.0          0.0   \n",
       "75%       0.060660     0.254089     0.441790          0.0          0.0   \n",
       "max       1.000000     1.000000     1.000000          0.0          0.0   \n",
       "\n",
       "       feature_530  feature_531  feature_532  feature_533  feature_534  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0       1567.0   \n",
       "mean           0.0          0.0          0.0          0.0          0.0   \n",
       "std            0.0          0.0          0.0          0.0          0.0   \n",
       "min            0.0          0.0          0.0          0.0          0.0   \n",
       "25%            0.0          0.0          0.0          0.0          0.0   \n",
       "50%            0.0          0.0          0.0          0.0          0.0   \n",
       "75%            0.0          0.0          0.0          0.0          0.0   \n",
       "max            0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "       feature_535  feature_536  feature_537  feature_538  feature_539  \\\n",
       "count       1567.0       1567.0       1567.0       1567.0  1567.000000   \n",
       "mean           0.0          0.0          0.0          0.0     0.381018   \n",
       "std            0.0          0.0          0.0          0.0     0.218149   \n",
       "min            0.0          0.0          0.0          0.0     0.000000   \n",
       "25%            0.0          0.0          0.0          0.0     0.181245   \n",
       "50%            0.0          0.0          0.0          0.0     0.384590   \n",
       "75%            0.0          0.0          0.0          0.0     0.539424   \n",
       "max            0.0          0.0          0.0          0.0     1.000000   \n",
       "\n",
       "       feature_540  feature_541  feature_542  feature_543  feature_544  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.382810     0.281506     0.450869     0.178307     0.293328   \n",
       "std       0.210531     0.128382     0.208826     0.081098     0.095274   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.223811     0.187890     0.328244     0.142857     0.258065   \n",
       "50%       0.337678     0.274771     0.328244     0.142857     0.322581   \n",
       "75%       0.530579     0.352163     0.618321     0.206349     0.322581   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_545  feature_546  feature_547  feature_548  feature_549  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.191477     0.162386     0.627129     0.345141     0.083572   \n",
       "std       0.079135     0.101440     0.094663     0.248599     0.088173   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.161698     0.104606     0.572668     0.174736     0.034895   \n",
       "50%       0.161698     0.132053     0.619885     0.240183     0.060732   \n",
       "75%       0.215442     0.218465     0.694374     0.466961     0.103803   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_550  feature_551  feature_552  feature_553  feature_554  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.085941     0.027986     0.090726     0.089983     0.025683   \n",
       "std       0.036178     0.031710     0.094076     0.037031     0.031387   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.069364     0.020148     0.036303     0.072992     0.018535   \n",
       "50%       0.081469     0.026269     0.066383     0.085732     0.024498   \n",
       "75%       0.097914     0.030859     0.112262     0.102146     0.027634   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_555  feature_556  feature_557  feature_558  feature_559  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.175317     0.079690     0.026659     0.166228     0.254818   \n",
       "std       0.107711     0.034599     0.031564     0.135174     0.200567   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.098152     0.063785     0.019576     0.102064     0.079747   \n",
       "50%       0.166090     0.075995     0.025222     0.130281     0.220163   \n",
       "75%       0.216936     0.090715     0.030502     0.175589     0.372285   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_560  feature_561  feature_562  feature_563  feature_564  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.127885     0.266798     0.299667     0.372020     0.166122   \n",
       "std       0.120473     0.202605     0.100673     0.111852     0.077260   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.043224     0.090863     0.286684     0.263809     0.126859   \n",
       "50%       0.096963     0.239612     0.318094     0.348224     0.132553   \n",
       "75%       0.166589     0.392814     0.324763     0.436010     0.200569   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_565  feature_566  feature_567  feature_568  feature_569  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.177969     0.155747     0.172453     0.161846     0.210269   \n",
       "std       0.111537     0.070178     0.105890     0.074482     0.115408   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.097930     0.123365     0.102429     0.122321     0.149792   \n",
       "50%       0.145696     0.127795     0.139212     0.131638     0.168456   \n",
       "75%       0.216632     0.183574     0.207673     0.196932     0.242603   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_570  feature_571  feature_572  feature_573  feature_574  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.783393     0.637547     0.055231     0.130956     0.048069   \n",
       "std       0.064264     0.156376     0.191354     0.116656     0.159309   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.784051     0.569943     0.008780     0.082418     0.009045   \n",
       "50%       0.790277     0.647076     0.011330     0.106432     0.011459   \n",
       "75%       0.797468     0.744870     0.014611     0.140939     0.014516   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_575  feature_576  feature_577  feature_578  feature_579  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.155025     0.054592     0.130554     0.315614     0.161674   \n",
       "std       0.128587     0.188518     0.135154     0.061712     0.079946   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.098824     0.008298     0.074905     0.311612     0.151239   \n",
       "50%       0.126138     0.010705     0.099979     0.311612     0.151239   \n",
       "75%       0.169101     0.013797     0.135302     0.311612     0.151239   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_580  feature_581  feature_582  feature_583  feature_584  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.144003     0.111763     0.696757     0.019798     0.020879   \n",
       "std       0.071929     0.076425     0.106351     0.036495     0.036180   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.134058     0.098045     0.628125     0.011900     0.013619   \n",
       "50%       0.134058     0.098045     0.700000     0.016575     0.018482   \n",
       "75%       0.134058     0.098045     0.767187     0.022312     0.023346   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       feature_585  feature_586  feature_587  feature_588  feature_589  \\\n",
       "count  1567.000000  1567.000000  1567.000000  1567.000000  1567.000000   \n",
       "mean      0.019062     0.320450     0.173062     0.155177     0.135158   \n",
       "std       0.036460     0.103210     0.114796     0.103848     0.127308   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.011304     0.253551     0.096480     0.083333     0.060177   \n",
       "50%       0.015903     0.312448     0.151239     0.130435     0.097518   \n",
       "75%       0.021379     0.371763     0.222947     0.195652     0.155634   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "       HouseLineTestingResult  \n",
       "count             1567.000000  \n",
       "mean                 0.066369  \n",
       "std                  0.249005  \n",
       "min                  0.000000  \n",
       "25%                  0.000000  \n",
       "50%                  0.000000  \n",
       "75%                  0.000000  \n",
       "max                  1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View Statistic Summary\n",
    "df_normalized.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handle Class Imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract features and target variable from data frame\n",
    "targetOutcome = pd.DataFrame(df_normalized,columns=['HouseLineTestingResult'])\n",
    "allInputs = pd.DataFrame(df_normalized,columns=df.columns.difference(['HouseLineTestingResult']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original training targets shape Counter({0.0: 1463, 1.0: 104})\n",
      "Resampled dataset shape Counter({0.0: 1463, 1.0: 1463})\n"
     ]
    }
   ],
   "source": [
    "# Modify data by handling class imbalance using SMOTE method\n",
    "print('Original training targets shape {}'.format(Counter(targetOutcome.values.ravel())))\n",
    "sm = SMOTE(random_state=0)\n",
    "allInputs_resample, targetOutcome_resample = sm.fit_sample(allInputs, targetOutcome.values.ravel())\n",
    "print('Resampled dataset shape {}'.format(Counter(targetOutcome_resample)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False  True\n",
      " False False False False False False False False False False False False\n",
      "  True False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False  True False  True False False False\n",
      " False False False  True False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False  True False False False False False False False\n",
      " False False False False False  True False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      "  True False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False  True False  True False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False]\n",
      "[214 510 528 180 347 468 516  55 117 323 518 535 320 483 457 441 522 524\n",
      "  68 558 461  31 302 195  62 421 173 381 174 532 501 562 306 564 517 257\n",
      " 548 352 166 482 344 475 213 472  88 557 447   1 250  70  87 445 304 469\n",
      "  13  15 285 324 552 561   1  60  16 215  28  96 436 129 322 380 337 396\n",
      " 430  67 210  83 366 197 490 335  90 455   5 387   4 119 220  93 237 231\n",
      "  63 363  34 155 111 104 110 264  46 402 258 299 243 235 225 234 238 206\n",
      " 156  75  77  71 161 383 153 100  30  78  98  59 137  25 568 150 211 134\n",
      " 186 405  50 216 452  84 465 493  73 486 437  41 370  40 332 189 280 459\n",
      " 199 228 550 227 263 229 270 245 262 298 236 563 444 560 260 239 251 247\n",
      "   1  11   1 144 138  17 309 147 102   1 202 128 328 233 226 253 240 401\n",
      " 256 232 255 249 244 274 265 164  33 342 404 428 440 406 438 362   1 242\n",
      "  69 511 448 470 456 425  12  14 230 526 538   1 410 513 415 159  99  95\n",
      " 495 130 168 341 537  66 131 549 198 426 451 316 416 399 336 494   7 527\n",
      "   6 317 514 340 520 275 269 248 385 450 368 311  94 105 389 254  58 408\n",
      " 261 278 252 241 268 427 266 145  52  56  76 183  57 157  29 300   2 140\n",
      " 192 149 379 467 121 122 139 132 185 556 496  51 348 512  85 553 422 521\n",
      " 439 123   3 378 187 361 188 272 417 423 373 200 246 120 292 267 271 259\n",
      " 296 281 536 435 291 277 222 279 283  22 223  19 369 136  18 148 315 395\n",
      "   1 462 507 338 273 287 276 294 286 282 175 372 331 297 284 288 289 551\n",
      " 205 349 429 212 367 409  89 392 176 290 375  86 543 433 546 295  49 133\n",
      " 293 314 523 403  61  48 160  27 171 419 167 508 374  64  65 190  82 411\n",
      " 196 371 519  91 339  44 365  45 118 221  92 307 172 312 318 364  35 154\n",
      " 112 106 109 333  47 203 209 350 353 355 357 359 376 146 565  97 555 125\n",
      " 124 326 101 115  74 394 114 135  26 170 487 413 547 471 453 388 412 460\n",
      " 377 529 431 434  72 502  42 384  39 346 329 463 458 473 169 393 478 480\n",
      " 484 488 491 497 499 503 505 449 492 390 530 533 539 541   1  10   1 143\n",
      " 566 142 540 184 103  43 201 127 301 580 578  36 577 579 581 576 575 569\n",
      " 574 572 571 165  37  32 343 334 330 442 308 386 207 446 358 545  20  24\n",
      " 360 485 305 500  21  23 179 178 414 515 177 194 303 193 443  80 310  81\n",
      " 420  38 163 354 162 351 151 476 152 327 391 397 219 504 474 531   8 454\n",
      "   9 345 534 567 477  79 407 158 204 113 356 107 108 400 466 191 573 525\n",
      " 126 382 141 489  53 319 554 432 424 481 544 498 313 506 559 208 224 218\n",
      " 464 217 325 398 479 542 116 321 509 418 181 570 182  54]\n",
      "\u001b[1m\u001b[4mSeledted features from Backward Stepwise Model Selection are: \u001b[0m\n",
      "feature_47\n",
      "feature_60\n",
      "feature_162\n",
      "feature_164\n",
      "feature_171\n",
      "feature_196\n",
      "feature_209\n",
      "feature_324\n",
      "feature_464\n",
      "feature_466\n"
     ]
    }
   ],
   "source": [
    "# Create linear regression model as the estimator\n",
    "estimator = LinearRegression()\n",
    "# From the Filter method, we select 10 features -- this number is arbitary (need to find the optimal number with further analysis)\n",
    "selector = RFE(estimator, 10, step=1)\n",
    "# Learn from this dataset\n",
    "selector = selector.fit(allInputs, targetOutcome)\n",
    "# The mask of selected features (which variables are selected)\n",
    "print(selector.support_)\n",
    "# Selected features are ranked 1. The variable with the highest rank is the one that is removed first\n",
    "print(selector.ranking_)\n",
    "# Print the name of selected features\n",
    "f = selector.get_support(True) # the most important features\n",
    "print('\\033[1m\\033[4mSeledted features from Backward Stepwise Model Selection are: \\033[0m')\n",
    "for f_index in f:\n",
    "    print(df.columns[f_index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new copy of data frame with selected features\n",
    "selected_feature_names = ['feature_47','feature_60','feature_162','feature_164','feature_171','feature_196','feature_209','feature_324','feature_464','feature_466']\n",
    "allInputs_feature_selected = allInputs[selected_feature_names].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_47</th>\n",
       "      <th>feature_60</th>\n",
       "      <th>feature_162</th>\n",
       "      <th>feature_164</th>\n",
       "      <th>feature_171</th>\n",
       "      <th>feature_196</th>\n",
       "      <th>feature_209</th>\n",
       "      <th>feature_324</th>\n",
       "      <th>feature_464</th>\n",
       "      <th>feature_466</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.798994</td>\n",
       "      <td>0.533284</td>\n",
       "      <td>0.009981</td>\n",
       "      <td>0.026417</td>\n",
       "      <td>0.21645</td>\n",
       "      <td>0.009205</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.317877</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.780757</td>\n",
       "      <td>0.555275</td>\n",
       "      <td>0.088877</td>\n",
       "      <td>0.063291</td>\n",
       "      <td>0.37683</td>\n",
       "      <td>0.005046</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.430930</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.791108</td>\n",
       "      <td>0.810180</td>\n",
       "      <td>0.056955</td>\n",
       "      <td>0.064392</td>\n",
       "      <td>0.05772</td>\n",
       "      <td>0.008469</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.277554</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.794558</td>\n",
       "      <td>0.747554</td>\n",
       "      <td>0.081473</td>\n",
       "      <td>0.059439</td>\n",
       "      <td>0.05504</td>\n",
       "      <td>0.010574</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.272055</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.529771</td>\n",
       "      <td>0.617807</td>\n",
       "      <td>0.039136</td>\n",
       "      <td>0.022014</td>\n",
       "      <td>0.11173</td>\n",
       "      <td>0.009560</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.420716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   feature_47  feature_60  feature_162  feature_164  feature_171  feature_196  \\\n",
       "0    0.798994    0.533284     0.009981     0.026417      0.21645     0.009205   \n",
       "1    0.780757    0.555275     0.088877     0.063291      0.37683     0.005046   \n",
       "2    0.791108    0.810180     0.056955     0.064392      0.05772     0.008469   \n",
       "3    0.794558    0.747554     0.081473     0.059439      0.05504     0.010574   \n",
       "4    0.529771    0.617807     0.039136     0.022014      0.11173     0.009560   \n",
       "\n",
       "   feature_209  feature_324  feature_464  feature_466  \n",
       "0          0.0     0.317877          0.0          0.0  \n",
       "1          0.0     0.430930          0.0          0.0  \n",
       "2          0.0     0.277554          0.0          0.0  \n",
       "3          0.0     0.272055          0.0          0.0  \n",
       "4          0.0     0.420716          0.0          0.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View first 5 rows\n",
    "allInputs_feature_selected.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split test(90%) and train (10%) data\n",
    "X, XX, Y, YY = train_test_split(allInputs_feature_selected, targetOutcome, test_size = 0.1, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tuning the Depth of a Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Dicision Tree (Gini Coefficient)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List of values to try for max_depth:\n",
    "max_depth_range = list(range(1, 6))# List to store the average RMSE for each value of max_depth:\n",
    "\n",
    "# List of values to try for max_depth:\n",
    "accuracy_gini = []\n",
    "for depth in max_depth_range:\n",
    "    clf = DecisionTreeClassifier(max_depth = depth, random_state = 0)\n",
    "    clf.fit(X, Y)    \n",
    "    score = clf.score(XX, YY)\n",
    "    accuracy_gini.append(score)\n",
    "    \n",
    "# Plot max_depth and accuracy for Gini model\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_xlabel('max_depth')\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.set_title('Gini Model - max_depth and Accuracy')\n",
    "ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "ax.plot(max_depth_range, accuracy_gini)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Random Forest (Ensemble Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameters\n",
    "nTrees = 100 # trim number of trees\n",
    "min_node_size = 5 # minimum on those size\n",
    "verbose = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_random_forest = []\n",
    "for depth in max_depth_range:\n",
    "    clf = RandomForestClassifier(n_estimators=nTrees, max_depth=depth, random_state=0, verbose=verbose, min_samples_leaf=min_node_size)\n",
    "    clf.fit(X, Y)    \n",
    "    score = clf.score(XX, YY)\n",
    "    accuracy_random_forest.append(score)\n",
    "    \n",
    "# Plot max_depth and accuracy for Rendom Forest model\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_xlabel('max_depth')\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.set_title('Random Forest Model - max_depth and Accuracy')\n",
    "ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "ax.plot(max_depth_range, accuracy_random_forest)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Decision Trees Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Dicision Tree (Gini Coefficient)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use information gain (default)\n",
    "# Based on the tuning the depth of a tree for Gini, the best max_depth is 3\n",
    "model_gini = DecisionTreeClassifier(max_depth = 3).fit(X, Y)\n",
    "y_gini_predict = model_gini.predict(XX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Random Forest (Ensemble Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Based on the tuning the depth of a tree for Random Forest, any number of max_depth yield the same accuracy. I will do 3\n",
    "model_random_forest = RandomForestClassifier(n_estimators=nTrees, max_depth=3, random_state=0, verbose=verbose, min_samples_leaf=min_node_size).fit(X, Y)\n",
    "y_random_forest_predict = model_random_forest.predict(XX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize Decision Trees Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Dicision Tree (Gini Coefficient)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted HouseLineTestingResult-pass</th>\n",
       "      <th>Predicted HouseLineTestingResult-fail</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>True HouseLineTestingResult-pass</th>\n",
       "      <td>152</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True HouseLineTestingResult-fail</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Predicted HouseLineTestingResult-pass  \\\n",
       "True HouseLineTestingResult-pass                                    152   \n",
       "True HouseLineTestingResult-fail                                      4   \n",
       "\n",
       "                                  Predicted HouseLineTestingResult-fail  \n",
       "True HouseLineTestingResult-pass                                      1  \n",
       "True HouseLineTestingResult-fail                                      0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion Metrix\n",
    "pd.DataFrame(\n",
    "    metrics.confusion_matrix(YY, y_gini_predict),\n",
    "    columns=['Predicted HouseLineTestingResult-pass', 'Predicted HouseLineTestingResult-fail'],\n",
    "    index=['True HouseLineTestingResult-pass', 'True HouseLineTestingResult-fail']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 55,
     "metadata": {
      "image/png": {
       "height": 750,
       "width": 750
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gini_dotfile = open(\"milestone_gini_dtree.dot\", 'w')\n",
    "tree.export_graphviz(model_gini, out_file = gini_dotfile, filled=True, rounded=True, feature_names = selected_feature_names)\n",
    "gini_dotfile.close()\n",
    "\n",
    "# Convert the dot file to a png\n",
    "check_call(['dot','-Tpng','milestone_gini_dtree.dot','-o','milestone_gini_dtree.png'])\n",
    "\n",
    "# show the png\n",
    "Image(filename='milestone_gini_dtree.png', width=750, height=750)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Random Forest (Ensemble Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted HouseLineTestingResult-pass</th>\n",
       "      <th>Predicted HouseLineTestingResult-fail</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>True HouseLineTestingResult-pass</th>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True HouseLineTestingResult-fail</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Predicted HouseLineTestingResult-pass  \\\n",
       "True HouseLineTestingResult-pass                                    153   \n",
       "True HouseLineTestingResult-fail                                      4   \n",
       "\n",
       "                                  Predicted HouseLineTestingResult-fail  \n",
       "True HouseLineTestingResult-pass                                      0  \n",
       "True HouseLineTestingResult-fail                                      0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion Metrix\n",
    "pd.DataFrame(\n",
    "    metrics.confusion_matrix(YY, y_random_forest_predict),\n",
    "    columns=['Predicted HouseLineTestingResult-pass', 'Predicted HouseLineTestingResult-fail'],\n",
    "    index=['True HouseLineTestingResult-pass', 'True HouseLineTestingResult-fail']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 57,
     "metadata": {
      "image/png": {
       "height": 750,
       "width": 750
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the final Random Forest Decision Tree\n",
    "last_index = len(model_random_forest.estimators_) - 1\n",
    "\n",
    "random_forest_dotfile = open(\"milestone_random_forest_dtree.dot\", 'w')\n",
    "tree.export_graphviz(model_random_forest.estimators_[last_index], out_file = random_forest_dotfile, filled=True, rounded=True, feature_names = selected_feature_names)\n",
    "random_forest_dotfile.close()\n",
    "\n",
    "# Convert the dot file to a png\n",
    "check_call(['dot','-Tpng','milestone_random_forest_dtree.dot','-o','milestone_random_forest_dtree.png'])\n",
    "\n",
    "# show the png\n",
    "Image(filename='milestone_random_forest_dtree.png', width=750, height=750)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate feature importances\n",
    "random_forest_importances = model_random_forest.feature_importances_\n",
    "\n",
    "# Sort feature importances in descending order\n",
    "random_forest_indices = np.argsort(random_forest_importances)\n",
    "\n",
    "# Rearrange feature names so they match the sorted feature importances\n",
    "random_forest_names = [selected_feature_names[i] for i in random_forest_indices]\n",
    "\n",
    "# Create plot\n",
    "plt.figure()\n",
    "\n",
    "# Create plot title\n",
    "plt.title(\"Random Forest Feature Importance\")\n",
    "\n",
    "# Add bars\n",
    "plt.barh(range(len(random_forest_indices)), random_forest_importances[random_forest_indices], color='b', align='center')\n",
    "\n",
    "# Add feature names as x-axis labels\n",
    "plt.yticks(range(len(random_forest_indices)), [selected_feature_names[i] for i in random_forest_indices])\n",
    "plt.xlabel('Relative Importance')\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create SVC (SVM Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using sklearn.model_selection.SearchGridCV to find the best settings\n",
    "parameters = {'kernel':['linear', 'rbf', 'sigmoid'], 'C':[0.1, 0.9, 100], 'gamma': [0.1, 1, 5]}\n",
    "svc = svm.SVC()\n",
    "gsc = GridSearchCV(svc, parameters)\n",
    "\n",
    "grid_result = gsc.fit(X, Y)\n",
    "best_params = grid_result.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.1, 'gamma': 0.1, 'kernel': 'linear'}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best hyperparameters\n",
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the best Hyperparameters & SVM Model\n",
    "\n",
    "# Based on the GridSearchCV\n",
    "cost = best_params['C']\n",
    "gamma = best_params['gamma']\n",
    "kernel = best_params['kernel']\n",
    "\n",
    "clf = svm.SVC(gamma=gamma, kernel=kernel, C=cost).fit(X, Y)\n",
    "y_predicted = clf.predict(XX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate all three models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Dicision Tree (Gini Coefficient)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gini Accuracy is : 96.81528662420382%\n",
      "Gini Precision is : 0.0%\n",
      "Gini Recall is : 0.0%\n"
     ]
    },
    {
     "data": {
      "image/png": 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IuFV6Vg4jJ67li692knk8DYCHWlfh0XbVMMYo4L+DIi4iIm7jCvgXOzk6fTtHp25j0A2JPNK2qqdH8wuKuIiIuEVaZg4jJqxl/uc7ODpzBzbbkpOaxcL/fYe11tPj+QVdwxARkXyXlpnD8Alr+OqzHRybtQsczmiXKxfDlCm99IEm+UQRFxGRfHU+M5vh49fy1bztHJ+z2xXwChWKsmjRnZQvX9TDE/oPRVxERPLN+cxs7hq/hq/mbOfE3N2Qe9W8SpVYFi68kzJloj07oJ9RxEVEJF+kZmQzbPwaFs7axonP9rjWq1ePY8GCIZQsGeW54fyUIi4iIr9bakY2w95fw4JPtnJy/l7Xeq1aCXz11RASEop4cDr/pYiLiMjvci4jm2Hvr2b17pNkHDznWq9btwRffnkHcXERHpzOvyniIiLym53LyGbouNWs3XsKYwzFOyRRMzEKeyaT+fNvp1ixcE+P6NcUcRER+U3Opmcx9P01rNt7yrX2bNcaDH0xibS0LKKiQj04XeGgN3sREZHrdjY9iyHvrWLZwt2uN255tksNRtxSkaCgAAW8gGgnLiIi1yUlN+CLJm8mZeVhohok8MYb7bnr5gqeHq3Q0U5cRESuWUp6FneMXcXCCd+TsvIwAGfXHcVuPe3hyQon7cRFROSanElzBnzx+E2c/faoa71Ll6oMHlzbg5MVXoq4iIj8qjNpWdwxZiWLxm3k3IbjrvVevaqTnNybkJBAD05XeCniIiJyVWfOZ3H72JUsGrOB1O9PuNb796/JpEk9CQ5WwD1FERcRkTydPp/JoHdXsGTMRs7/cNK1fscdtRk3rjtBQbq1ypP0X19ERK7o9PlMBr6zgiX/23BJwO+6qy7vv6+AewP9HxARkV84lZrJoDGr2LjlOOl7U1zrd9/dgDFjuhEYqHx4A/1fEBGRS5xKzWTw2FX8cCiF4NgwEgdUJSIyhAcfbMxbb3UmIMB4ekTJpefERUTE5WRuwLcccu6+jYE37mtG4z+3o3z5GIxRwL2JW3fixpgOxpitxpgdxpgnr/B4OWPMImPMemPMRmNMJ3fOIyIieTtxLoN+//2ajZuOAM6Av9qnDv0aliUpqagC7oXcFnFjTCAwGugI1AAGGmNqXHba/wHTrLX1gAHAW+6aR0RE8nbiXAb9/7ucr99cz+EPtpJ1PI1/9qlDnwZlPD2aXIU7d+KNgR3W2l3W2kxgKtD9snMsEJ374xjgoBvnERGRKzh+LoO+//maZf/5loyfzuFIy+b8J7toVbG4p0eTX+HO58RLA/svOj4ANLnsnL8AXxhjHgCKAG3cOI+IiFzm2NkM+ryxjJWj15N55Lxr/S/P3qrPAvcB7tyJX+nJE3vZ8UBgvLW2DNAJmGSM+cVMxphRxpi1xpi1x44dc8OoIiKFz7GzGfR5fSkr3vz2koC/9VYnHnqoqQcnk2vlzogfAMpedFyGX14uHw5MA7DWrgDCgLjLv5C19l1rbUNrbcP4+Hg3jSsiUngcPZtOr9eWsOI/35J1LA1w3sg2dmxX7rmnkYenk2vlzoivAaoYYyoYY0Jw3rg2+7Jz9gGtAYwx1XFGXFttERE3OpqSTq9Xl7DyzW/JOpEOQECAYcKEHgwfXt/D08n1cNtz4tbabGPM/cB8IBAYZ63dbIx5AVhrrZ0N/BEYY4x5BOel9qHW2ssvuYuISD45mpJOj1cWs2b0d2SfzgAgINAwZXIvBgy40cPTyfVy65u9WGvnAfMuW3vuoh//ANzkzhlERMTpSEo6A99dyQ+L9rkCHhhk+HBqH3r3vvwVwOIL9I5tIiKFwOEz6Qwcs5Ldx1OJubkUjnNZpG05yUcz+9G1azVPjye/kSIuIuLnDp1JY+C7K9lzwnkHenBgAMkTe1DKEUC9eiU9PJ38Hoq4iIgfO3g6jZ6vLOZwTg7GGIICDP8dVJ8ON5bw9GiSD/QpZiIifurg6TS6/Pkr1r7xLSc/30tQALw1WAH3J4q4iIgf+ul0Gp2f/ZIN72zAkZbNuY3HqX84i3Y1FXB/osvpIiJ+5sCp83T+vy/YNPZ7bGYOAJHRITyuN3HxO4q4iIgf2X/yPJ2ems/m8d9jMx0ARBcNY/HCIbqJzQ8p4iIifmL/yfN0+NNnbJn4AzbLGfCisWEsXTyUWrUSPTyduIMiLiLiB/afPE/7xz7jx0mbsdnON76MjY9g2eKh1Kihz5zwV4q4iIiP23fiPO0fncuPU7ZAjjPgcYlFWL50GFWr6jPB/ZkiLiLiw/aeSKXf6OVs/2i7K+CJpaP4ZukwKlYs5uHpxN30EjMRER+153gqA95dyZHzWST0rUJAeCClykazavldCnghoZ24iIgP+jngh1OcHyUaVSqSvyf3pl3D0pQuHe3h6aSgKOIiIj5m9/FU+ryxlBPZzjvQw4IDeO/ORtxUOc7Dk0lB0+V0EREfsuvYOW4b/hHfvbaOzCPnCQsOYJwCXmgp4iIiPmLnsXPcNuwj9s7ehSM9h6MfbuPZmyrRXAEvtHQ5XUTEB+w4eo7bhszgwPy9rrVqVYvTqUlZD04lnqaIi4h4uR1Hz9Ji0AwOLtjnWqtVvwRfLxpKdHSoBycTT1PERUS82LbDKbQcOJ1Diw+41uo1LsXSBXcSGRniwcnEG+g5cRERL7X1cAq39pt2ScAb3VSGZQsVcHHSTlxExAv9eCiFFr2ncnTFIddasxblWPDZ7YSHB3twMvEm2omLiHiZHw+n0OnReZcE/NY2FVg0/w4FXC6hiIuIeJEth1IYNGYVOeUiiarv/PSxVh0r8eXcwYSG6uKpXEq/IkREvMQPB1MYPHYlp85nYYyhXOeK9Opfm+cebU5QkPZc8kuKuIiIF9i47zRD3lvF6YxsAKJCg5g4vDH1yumDTCRviriIiId9t/ckLTtNIdsYindKIjoimEnDm1C3bFFPjyZeThEXEfGgb3ed4LZOU0jZegqA4OAAPvlkoAIu10QRFxHxkLU7TtCq42TO7jjtWuvSoDR1yyngcm10p4SIiAes3n6c29pPuiTgd93bkIljumGM8eBk4ksUcRGRArbix6O0ajuRc7vOuNbufrgJY//bSQGX66KIi4gUoGWbj9Cm3SRS9551rT3wp+a8/XoHBVyumyIuIlJAlmw6RPt2kzi//5xr7Y/P3sJ/Xm7rwanElyniIiIF4Nt9p+jaexppB1Nda0+90JJ/vtDKc0OJz1PERUTcbN3eUwx5bzVRLUoTEOF8UdCfX27Ni8+28PBk4uv0EjMRETdat/ckd45bw7mMbIKLh1N1WE2GVivBEw809fRo4gcUcRERN1m96wTDxq8hNTMHgOJFQvjg4VupViLKw5OJv9DldBERN5j99R5aNB/Hyd3Ol5HFRYaQPKqpAi75ShEXEclnHy/ZTe8uyaQfOc+R6dsJP5NF8simVE1UwCV/KeIiIvlo+oId9OuaTPaZTOdCtoM/3lKBKgq4uIGeExcRySfJ87dzR5/p5JzLAsAEBfDe5J4M63+jhycTf6WIi4jkg4lzf+SufjPJOe/8PPCA4AAmTO3N7b1qeHgy8WeKuIjI7/T+rC2MGDgTR5rzLvSAkAA+mNGX/l1v8PBk4u8UcRGR32HMzM3cffvHONKdAQ8MDeTDj/vTu2MVD08mhYEiLiLyG81YvIu7B32EI9MBQGBYIDPnDKR7m0oenkwKC92dLiLyGyzbfoynv/yRyLrxAARFBDF73mAFXAqUduIiItdpybZjjJy4lswcS9GWZYgqEsK4F1rR7uYkT48mhYwiLiJyHRZvPcqoSevIzHZeQi9TLILk6QMoVzzCw5NJYaTL6SIi1+jvb6+mW59pZOS+F3rpouFMHdVUAReP0U5cROQaPP+flfzlkS/AYclxWOoMrsHUUU0pG6uAi+co4iIiv+L/XlvO3x//Cqzz2HHkPKN711bAxeN0OV1E5CqeeGnpJQEPjw9n6ZKh1KsW79nBRNBOXEQkT4/+dTGvP7fEdRyREMHXSxVw8R6KuIjIFTzw7AL++7evXcdFShZhxdJh1Kpc3INTiVxKERcRucwfnviCd19Z4TqOLB3JymXDqFkh1oNTifySIi4icpH7n11wScCjykWxeukwbihfzINTiVyZbmwTEcn12aZDzDl+hsDIYACik6JZ9/VwBVy8liIuIgLM3XiI+5PXY2JCSRxQjYQ68axfPpwqZWM8PZpInhRxESn0Pt14kAenrifH4XwdWbUbirNx6V1ULBXt4clErk7PiYtIoWWtpe/wWXyTmUFImUgAKsUXIXlkUxKiwzw8ncivU8RFpFByOCydB0zn8+lbMCEBJParSo26iSSPakpClAIuvkGX00Wk0MnJcdC+91Q+n74FAJvpIGDbaaaOaqaAi0/RTlxECpXsbAdteySzeO4O11qJegms/XwI8VGhHpxM5Pop4iJSaGRl5dCq6wd8PX+Xa61UoxJ8++UQEmPCPTiZyG+jiItIoZCZmcOtHSazatEe11qZZqX49vM7iNdNbOKjFHER8XsZGdnc1HYS65btc62Vu7k06+bdTpyeAxcfpoiLiF/LznbQ9LbxfLfiJ9daUsuyrJ0zmOKReg5cfJvuThcRv/bR+p/YbRyu44pty7NOARc/oZ24iPitqav38eRHm4i5uRQ2x5JQLJxV0/sTq4CLn9BOXET8UnJuwAGMMdw0sDprZg5QwMWvaCcuIn7l5Mk0hj78GRsSgzGBzn3KjaWjmTy8CUUjQjw8nUj+cutO3BjTwRiz1RizwxjzZB7n9DPG/GCM2WyM+cCd84iIfzt+/Dz1m45lzqRNHJ+9C5vjoFbpGKYMb6qAi19y207cGBMIjAbaAgeANcaY2dbaHy46pwrwFHCTtfaUMSbBXfOIiH87cuQcDZu/x4FdpwE4v+00pdMsk0c0ISY82MPTibiHO3fijYEd1tpd1tpMYCrQ/bJzRgKjrbWnAKy1R904j4j4qYMHz1KvyVhXwAHq3l6Dz17tqICLX3NnxEsD+y86PpC7drGqQFVjzHJjzEpjTAc3ziMifmj//jPUbzKGQ3vPOBcM1L+zJkvG9FDAxe+588Y2c4U1e4XvXwVoCZQBlhljbrTWnr74JGPMKGAUQLly5fJ/UhHxSXv2nKZR8/c4fuiccyHA0GjYjXz1365Ehyng4v/cuRM/AJS96LgMcPAK58yy1mZZa3cDW3FG/RLW2nettQ2ttQ3j4+PdNrCI+I6dO0/SoOnYSwLeZEQtFijgUoi4M+JrgCrGmArGmBBgADD7snM+AW4DMMbE4by8vgsRkavYtu0EDZuO5eSRVOdCoKH53XX48t9diFLApRBxW8SttdnA/cB8YAswzVq72RjzgjGmW+5p84ETxpgfgEXA49baE+6aSUT8w8cbD3I2KwcAE2S4+Z56zP9XZwVcCh1j7eVPU3u3hg0b2rVr13p6DBHxkHeX7uTFeT+SdTqDo9O303xITeb+owORoXrvKvFPxph11tqGV3pMv+pFxGf8b8lO/vHZjwAEFw2l+ws3M2FEE4oo4FJI6Ve+iHi91at/Ysqy3cw6dsa11rhCLO8PbaSAS6GmX/0i4tWWL99Hm3aTyMh0kNC3MmHlomlaMZZxQxsREaI/wqRw06eYiYjXWrx4D63aTCL9fDY228HxeXtoXK6YAi6SSxEXEa/01Ve7aNdhMpnp2QAEFAmi3cMNmTCiiQIukksRFxGvM2/edjp2mkJWhvNlZIGRwXR8ogkzn25FeEigh6cT8R7666yIeJVZs36kd5/p5GQ7AAiMCqHzk0348PEWhAUr4CIX005cRLzG9Omb6dV72oWAx4TQ9ZmmCrhIHhRxEfEKU6ZsZMCAmThynG9AFVQ0lO7/15zkR29VwEXyoMvpIuJx1lqmLNiJw5Eb8NgwejzdjEkP3qSAi1yFduIi4lHWWv75xVZ+SAgh5qaSBMeF0evZ5gq4yDXQTlxEPMZay6vzt/LW4p0AxNxUiq531GLs8MaEBingIr9GERcRj5gx4we2BFveW7HHtdameiJv3V5fARe5Roq4iBS4l15axtNPLyS8UgzxPSthAgNoUz2B0YMVcJHroefERaTAWGt5/vnFPP30QgDSdp7hzDeHnDvwwQ0UcJHrpJ24iBQIa+JfZYUAACAASURBVC3PPLOQl1762rUWWi6KHkNr89bg+oQEaU8hcr0UcRFxO2stjz/+Ja+9tsK1FpYUzaBnmvP20EYKuMhvpIiLiFtZa3nooc95883VrrXwSjEMeqo5bw9tSHCgAi7yWyniIuI2Doflnnvm8u6761xr4VWLcvtTzRl9RwMFXOR3UsRFxC1ychyMGDGH8eO/c61F3FCMO55szpu311fARfKBIi4ibnHyZBqz5m93HRepGcsdTzTjzUH1CVLARfKFfieJSL5zOCz/Xr6LiB6VCIoJoUit4tz51E0KuEg+005cRPKVw2F5dtb3TFm1j6DoEErcUZ0eTcvx+oC6CrhIPlPERSRfpKdns27dQT49cobk1ftc671uKs9rfeso4CJuoIiLyO92/nwWPXpMZeGiPRTvWYnwijEA9KxXmn/2rUNggPHwhCL+SX81FpHfJTU1ky5dPuDLL3eRk+3g2Mc7yDqVTi8FXMTtrjvixphAY8xgdwwjIr7l7NkMOnSYzKJFe1xr0U1L0r91JV5VwEXcLs+IG2OijTFPGWP+a4xpZ5weAHYB/QpuRBHxRqdPp9O23SS+/nq/a61oi9KMeLAxr/ZRwEUKwtWeE58EnAJWACOAx4EQoLu19rur/DwR8XMnT6bRrt0k1q075For1qoMw+9uyMu9axOggIsUiKtFvKK1thaAMWYscBwoZ609WyCTiYhXOnYslbZtJ7FhwxHXWmzbcowY1YCXetVSwEUK0NUinvXzD6y1OcaY3Qq4SOF2+PA52rSZyObNx1xrse3LM3JkfV7sqYCLFLSrRbyOMSYF+Pl3ZfhFx9ZaG+326UTEq8ydt/1CwA0U75jEyOH1+HsPBVzEE/KMuLU2sCAHERHvlp3jYEM4FG1ZmtNLfiKuSwVGDqvH37rfqICLeEieETfGhAF3A5WBjcA4a212QQ0mIt4jO8fBI9M2MGfDQWKalCSiclGGda7GC90UcBFPutrrxCcADYFNQCfgtQKZSES8xp49p0k5m8FDH37HnA0HXet3db6Bv2oHLuJxV3tOvMZFd6e/B6wumJFExBv8+ONxWreeSEBsKAHty2GCnH/nv7NZef7SrSbGKOAinnatd6dn6zesSOHx/fdHadNmIkeOpMLBs0Rk5hDfsxJDmyfx5641FHARL3G1iNfNvRsdnHek6+50kUJgw4bDtGkziePHzwNgggOIahDPsJuSeK6LAi7iTa4W8Q3W2noFNomIeNy6dQdp23YSp06lA2BCAkjoW4V7+9fm2S7VFXARL3O1iNsCm0JEPG7lygN06DCZM2cyADChgST2q8K9/Wrxf50VcBFvdLWIJxhjHs3rQWvtv9wwj4h4wNdf76NTpymcPZsJQEBYIAn9q3J/3xt5upMCLuKtrhbxQCCSC+/YJiJ+aPHiPXTp8gGpqc57WQPCg0gcUJUH+tzIkx1vUMBFvNjVIn7IWvtCgU0iIgXO4bA89tgXFwJeJIjE/tV4oE9NnuyggIt4u6u92Yt+94r4uSyHg8q3VycoNpTAyGBKDLyBh/reqICL+Iir7cRbF9gUIlLg0rNyuGfyOlYeTiFxQDVstoOHe9XksXbVFHARH3G1D0A5WZCDiEjBOHToLMXiIvjDpHUs2eb8RLKgqBDuv60yf2xXVQEX8SFX24mLiJ+ZPHkjI0fOoenI2uyOuPBs2oOtKvNIWwVcxNdc7TlxEfEj77+/niFDPiY9PZvFb60nba/zDRkfbF1FARfxUYq4SCHwzjtrueuu2djct3AKjg0jJC6ch9tU4VEFXMRnKeIifu7NN1dx991zXcfBCeEkDqjKY91r8nCbqh6cTER+Lz0nLuLHXnvtGx577EvXcUjJCBL6VeXxrjV4sHUVD04mIvlBO3ERP/Xii8suCXhoqSIk9q/KE90UcBF/oZ24iJ+x1vL880t4/vklrrXQspEk9K7CE91qcN9tlT04nYjkJ0VcxM+sWXPwkoCHlY8ivldlnuxWg3tbKuAi/kSX00X8TM06idTtdwMAYRWiie9dhae711TARfyQduIifuRcRjbD3l/NqQqRxPesRHjFGJ7pVoNRt1by9Ggi4gaKuIiPczgsaWlZ2KAAho5bzdq9pwCIqFqMZzpVZ+StFT08oYi4iyIu4sNychwMHz6bbdtPEN+3KhsOp7ge+7/O1RlxiwIu4s8UcREflZ3t4M47P+GDDzYBEHrgDAl9qhAQHMCzXWow/OYKHp5QRNxNERfxQVlZOQwa9BEzZvzgWgsuGooJNPy5aw2G3aSAixQGiriIj8nIyKZ//xnMmrXVtRZZL57YtuV4vltNhirgIoWGIi7iQ9LTs+ndexrz5m13rUU1SKBY67L8tceNDGmW5LnhRKTAKeIiPuL8+Sy6d5/KV1/tcq1FNylB0Ral+VuPG7lDARcpdBRxER9w7lwmXbsms3jxHtdaTPOSxNxcir/3rMXtTct7bjgR8RhFXMQH/PGP8y8N+C2lKNq8FH/veSODmyjgIoWV3nZVxAc8/vQtRJYoAkDRlmUo2rwUL/aspYCLFHLaiYt4uVOpmTz4ySaK9qlM6K4zRNaK4x+9ajGgcTlPjyYiHqaduIgXys52AM6ADx67ih8OpRBYJJio2nG80ru2Ai4igHbiIl7n8OFztG8/mYcfa8a0kylsOeR8K1Vj4OXetenXsKyHJxQRb6GduIgX+emnFFq0GM/GjUcYPnQW65bsBZwBf0UBF5HLuDXixpgOxpitxpgdxpgnr3JeH2OMNcY0dOc8It5s374ztGgxnm3bTgBgrcVmW4yBf/apQ18FXEQu47aIG2MCgdFAR6AGMNAYU+MK50UBDwKr3DWLiLfbvfsULVqMZ+dO58eIEmCI616JyJqxvNa3Dr0blPHsgCLildy5E28M7LDW7rLWZgJTge5XOO+vwCtAuhtnEfFa27ef4NZbx7Nnz2nnQqAhvmclom4oxr/61aFXfQVcRK7MnREvDey/6PhA7pqLMaYeUNZa+6kb5xDxWlu2HKNFi/EcOJD7OeCBhoRelYmsUpTX+9elZz0FXETy5s67080V1qzrQWMCgNeBob/6hYwZBYwCKFdOL60R//D990dp3XoiR4+mAmCCAojvXZkiFaJ5vX9dutct/StfQUQKO3fuxA8AF9+JUwY4eNFxFHAjsNgYswdoCsy+0s1t1tp3rbUNrbUN4+Pj3TiySME4cyb90oAHB5DQtwqRFWP494B6CriIXBN3RnwNUMUYU8EYEwIMAGb//KC19oy1Ns5am2StTQJWAt2stWvdOJOIV4iJCeNPz9wMgAkJIKFfVYokRfPvAXXpWqeUh6cTEV/htsvp1tpsY8z9wHwgEBhnrd1sjHkBWGutnX31ryDiv46kpDM3M53Y9uUJSQgnokwU/xlQj861S3p6NBHxIcZa++tneZGGDRvatWu1WRffY63FGMPhM+kMHLOS3cedl9KDAgxvDqxHx1oKuIj8kjFmnbX2iu+jondsEykACxbsonXriWzbf5oB7664JOD/HaSAi8hvo/dOF3Gz+fN30KPHh6SnZ9PolveJ6VWJgJDA3IDXp8ONJTw9ooj4KO3ERdzo00+30a3bVNLTswFIPZFGzvlsggIMowcr4CLy+yjiIm7y8cdb6NXrQzIzcwAIjA4hcVA1IoqH8dbg+rSvqYCLyO+jy+kibvDhh98zePBH5OQ4bxwNKhpK4oCqhMeG8fbgBrSpkejhCUXEH2gnLpLPJk3awKBBFwU8NpTEgdWIiA3nf7cr4CKSf7QTF8lH48atZ8SI2fz8ys3g4mEkDKhKREwY/7ujPq1uUMBFJP8o4iL5ZMaMHxg+/MJ7GAXHh5PYvyrhMaG8c0cDbquW4MHpRMQf6XK6SD5p3boCNWs5Qx2SGEHiwGqEx4TyrgIuIm6inbhIPklxOAjvVoEiNofY1mUJiwxhzJCGtKiqD+0REfdQxEXywZ7jqQwcs5Jj2TnEda5AaFAAY4Y05FYFXETcSBEX+Q2stfzlL4upVCmWWztVZsC7Kzmckg5AaFAA793ZiJurxHl4ShHxd4q4yHWy1vLUUwt4+eXlBAQYKvStQnZSNABhwc6A31RZARcR99ONbSLXwVrLo4/O5+WXlwPgcFgOrjuCtZaw4ADGKeAiUoC0Exe5Rg6H5YEH5vHWWxc+Cje8clHiu1ckPCSQcUMb0bySAi4iBUcRF7kGDoflD3+Yw9ix611rEVWLEdetAhFhwYwb2ohmlYp7cEIRKYwUcZFfkZPj4K67ZjNx4gbXWkT1WOK6VCAiNIj3hzWiaUUFXEQKniIuchXZ2Q6GDPmY5OTvXWtFbixO8Y5JFAkL4v2hjWiigIuIhyjiInmw1nL77R/x4YebXWuRteOI7VCeyNAgxt/VmEZJsR6cUEQKO92dLpIHYwxdulTFGOdxZL14V8AnKOAi4gW0Exe5ioZtkijXvRInfzpHsdvKEBUWzIS7GtGgvAIuIp6niIvkYcuhFAaPXQXVihFbrZhrB96gfDFPjyYiAuhyuojLuXOZ3HffXE6eTOOHgykMGrOSk6mZAESFBjFxuAIuIt5FO3ERICUlg06dprB8+X6WLN9PQKdypDgscCHg9cop4CLiXbQTl0Lv1Kk02radxPLl+wHYvOEIBzcdByAqLIhJI5oo4CLilbQTl0LtxInztG07ifXrD7vWirUuS2TN4kSFBTF5eBPqlC3qwQlFRPKmiEuhdfRoKm3aTGTTpqOutdj25YmqG090WBCTRzShdhkFXES8lyIuhdKhQ2dp3XoiW7Ycd60V75hEZO04YsKDmTy8CbXKxHhwQhGRX6eIS6Hz008ptGo1kW3bTjgXDBTvXIHImsWJCQ9myogm3FhaARcR76eIS6Gyf/8ZWracwK5dp5wLBuK6VqRI9ViKRjh34Aq4iPgK3Z0uhUpMTBjx8REAmABDfI9KroBrBy4ivkYRl0IlOjqUl8d0IaJMJHE9KxFRtRjFIoL5YERTapZSwEXEt+hyuhQq6/ae4r4ZG4gbVA1jDLFFQpgyognVS0Z7ejQRkeumnbj4tU2bjjB+/HcArNt7kjvHreZcRrYr4B+MVMBFxHdpJy5+a/36Q7RtO4kTJ9LYfTyVGSlnSc3MAaB4kRA+GNmUaiWiPDyliMhvp524+KXVq3+iVauJnDiRBsDfnl1ESkoGAHGRISSPUsBFxPdpJy5+55tv9tOhw2TOnnV+AllAWCAJ/aoSGBZEXGQoySObUCVRARcR36eIi19ZunQvnTpNITU1C4DA8CAS+lclJDGCuMhQpo5qQuUEBVxE/IMiLn5jwYJddO2aTFpaNgCBRYJI6F+NkPhw4qNCSR7ZlMoJkR6eUkQk/yji4hc+/3wHPXt+SHq6M+BBkcEkDKhKcPFwEqJCSR7VlErxCriI+BdFXHzenDlb6dNnOpm5d54HRYU4Ax4bpoCLiF9TxMXnZWTkkJPjACA4JoT4AdUILhpKYrTzEnpFBVxE/JReYiY+r0+fGjzzShvnznuQM+AlosOYOqqZAi4ifk07cfF5S7cdY+rJM5QcVgMTFEDJmDCSRzYlKa6Ip0cTEXEr7cTF50ybtpljx1IBWLLtGCMmriUj24EJCqBUTBhTRyngIlI4KOLiU0aPXk3//jNo02YSs1buY+TEtWRmO58Pdwa8GeWLK+AiUjgo4uIzXn99Bfff/xkAGzceYcjdc1wBL100nKmjmlGueIQnRxQRKVCKuPiEl1/+mkcf/cJ1HFa6CDEtywA/B7ypAi4ihY4iLl7vr39dwpNPLnAdh5WJJL5vVQLCgihTzBnwsrEKuIgUPro7XbyWtZbnnlvE3/62zLUWXj6KuF6VCQgJdAW8TDEFXEQKJ0VcvJK1liee+IpXX/3GtRZeIZq4npUICA6kbKzzOfDSRcM9OKWIiGcp4uJ1rLU88sh8/v3vVa61iEoxxPWohAkKoFxsBFNHNaWUAi4ihZwiLl4nIyOH9esPu44jqhQlrltFTFAA5YtHkDxSARcRAd3YJl4oLCyITz8dSPU6iRS5oRhx3Z0BTyquHbiIyMW0Exev9PWek6S3Lk3xQIMJMFSIK0LyyKaUiAnz9GgiIl5DO3HxuKysHD77bLvreO7GQ9yfvB5HcAAmwFBRARcRuSLtxMWjMjNzGDhwJh99tIW33+5MmealeGjqd+Q4LIAz4KOakhitgIuIXE4RF4/JyMimb9/pzJmzDYB77plLqTtuILiU8+NDK8YXYerIpiQo4CIiV6SIi0ekpWXRs+eHzJ+/07UW3SiRoJLODy+pFO/cgSdEKeAiInlRxKXApaZm0q3bVBYu3O1ai2lWgphbSmOMoXJCJMkjmxIfFerBKUVEvJ8iLgXq7NkMOnf+gGXL9rnWit5ciujmJTHGUCUhkg8UcBGRa6KIS4E5cyadjh2nsGLFAddasRaliW5aEoCqic6Ax0Uq4CIi10IRlwJx6lQa7dtPZs2ag661YreVIbpxCQCqJUbxwcgmFFfARUSumSIuBeLbbw9d8laqsW3KEtUgEYAbSkQxZYQCLiJyvfRmL1IgWreuyNSpvQkMCqB4h/KXBPyDkU0VcBGR30A7cSkwOeWiKDmyJoHRzmBXLxnNlBFNiC0S4uHJRER8k3bi4hb795/h0KGzruOpq/fxp5kbXQGvUTKaDxRwEZHfRTtxyXd79pymVasJhIUFsXjxUBbsOcFTH21yPV6zlHMHXjRCARcR+T0UcclXO3eepFWriezbdwaApi3eJ6dbEsYYAG4sHc3k4Qq4iEh+0OV0yTdbtx7n1lvHuwIeHBJA6o3FXAGvVTqGKcObKuAiIvnErRE3xnQwxmw1xuwwxjx5hccfNcb8YIzZaIxZYIwp7855xH1++OEYLVqM5+BB5/PgwSGBFOtRifBKRQGoXSaGySOaEBMR7MkxRUT8itsibowJBEYDHYEawEBjTI3LTlsPNLTW1gZmAK+4ax5xn40bj9Cy5XiOHEkFICQsiNhelQivEANAnTIxTBrehJhwBVxEJD+5cyfeGNhhrd1lrc0EpgLdLz7BWrvIWns+93AlUMaN84gbfPvtIW67bQLHjjn/N4aFB1GsdyXCykcDUKdsUSaNUMBFRNzBnREvDey/6PhA7lpehgOfuXEeyWerVh2gVasJnDyZBkB4ZDBFe1cmrEwUAHXLFmXS8MZEhyngIiLu4M67080V1uwVTzTmdqAh0CKPx0cBowDKlSuXX/PJ7/Tqq99w5kwGABFRIUT3qkRoCefngdcvV5QJdzUmSgEXEXEbd+7EDwBlLzouAxy8/CRjTBvgGaCbtTbjSl/IWvuutbahtbZhfHy8W4aV6zdxYk9atkwiMiaUmD6VXQFvUL6YAi4iUgDcGfE1QBVjTAVjTAgwAJh98QnGmHrAOzgDftSNs4gbREQE0/vJpkT3rUJIQgQADRVwEZEC47aIW2uzgfuB+cAWYJq1drMx5gVjTLfc014FIoHpxpjvjDGz8/hy4gV27Tp1yfG7S3fyz0U7CC4eBkCjpGKMv6sxkaF6DyERkYLg1j9trbXzgHmXrT130Y/buPP7S/6ZNetH+vWbwT/+0ZpHHmnG/5bs5B+f/eh6vHFSLO8Pa0QRBVxEpMDoT1z5VdOnb2bQoI/Iznbw6KNfsP7IWZaS5Xq8cYVY3h+qgIuIFDS97apc1QcfbGLAgJlkZzsAiCsVycKUc67Hm1SIZbx24CIiHqE/eSVPEyZ8x7Bhs7C5LwxMLBtNUNckgqKc733etGIs44Y2IiJEv4xERDxBf/rKFY0Zs44//OFTV8BLJsUQ2CWJwCLOu86bVyrOe3c2Ijwk0INTiogUbrqcLr8wevRqRo26EPBSFYsS2PVCwG+qrICLiHgDRVwu8a9/reD++y+8+23ZqrEEdEkiMPfTx26uHMfYIQq4iIg30OV0cdmz5zRPP73AdVy+enEc7csSmHvT2i1V4hgzpCFhwQq4iIg30E5cXJKSivLRR/0JDg6g4o3xONqXIyA34LdWjVfARUS8jCIul+jYsTIjXmxJVtsyBIQ6g92iajzv3tFAARcR8TKKeCFmrXV9jOjPx//8YivzjqcQkPucd8tq8byjgIuIeCVFvJCy1vLww5/TuPEYDh48i7WWV+ZvZfSina5zblPARUS8mm5sK4QcDsu9987lnXfWAdCq1QT6/uUWJn13wHVOqxsSePv2+oQGKeAiIt5KES9kcnIcjBw5h/ff/861FpIQwcR1+zCBzgszbaonMHqwAi4i4u0U8UIkO9vBsGGzmDx5o2ut7m3lOdkwDhNgAGhTPZHRg+sp4CIiPkARLySysnK4/faPmTZts2utfpskjtcr7gp42xqJjB5Un5Ag3SohIuILFPFCIDMzhwEDZvDxxxc+/7thh4ocrV0MY5wBb18zkTcHKuAiIr5EEfdz6enZ9Okzjblzt7vWGnepzOEaMa6Ad6hZgjcH1SM4UAEXEfElirife/XV5ZcEvFmPqvxUNcoV8I43luA/AxVwERFfpD+5/dzjj99E+/aVALi5T7VLAt65VkkFXETEh+lPbz8XFhbERx/1o9uDDdlXMfJCwGuX5I0BdRVwEREfpj/B/Ux6evYlxw6H5cUvtrIhHFfAu9Quyb/7K+AiIr5Of4r7kZMn07j55nG8+OIywBnwZ2d9z+SV+1zndK1Tijf61yVIARcR8Xm6sc1PHDuWStu2k9iw4Qjr1h0iODiAk5WiSV59IeDd65bitb51FHARET+hiPuBw4fP0abNRDZvPgaAMbBw1wm2nDjtOqdH3VK81q8ugblv7CIiIr5PEfdxBw+epVWrCWzdegKAgABDh3vqsTnywm67V73SvNq3jgIuIuJnFHEftn//GVq1msiOHScBCAw0dLivAd+HXzinV/3SvNpHARcR8UeKuI/avfsUrVpNZM8e5yXzoKAAOj7YgI3B1nVOnwZleLl3bQVcRMRPKeI+aMeOk7RqNYH9+1MACA4OoONDjdgQmOM6p2+DMvxDARcR8WuKuI/JznbQseMUV8BDQwPp+HAj1nPh9eH9GpbhH71qE6CAi4j4Nb3WyMcEBQXw9tudCQ0NJDw8iM6PNb4k4AMalVXARUQKCe3EfVCbNhWZObMf41btZV1mpmt9YOOy/L1HLQVcRKSQ0E7cBzgc9pLj7BwH81NSLwn4oCblFHARkUJGEfdyK1ceoF69d9i713kXenaOg0embWD2hoOucwY3Kcffut+ogIuIFDKKuBdbtmwvbdtOYuPGI86Xk+09zUMffseciwJ+R9Py/K2HAi4iUhjpOXEvtXDhbrp2Teb8+SwAUlIyeDL5O1aeTnWdM6RZeZ7vVtP16WQiIlK4KOJe6IsvdtK9+1TXx4omJhah5SMNWXnqQsCHNk/iz11rKOAiIoWYIu5l5s7dRq9e08jMdL5xS6lSUdzyUANWnjznOkcBFxERUMS9yscfb6F//xlkZTkAKFcuhuYP1GPF8QsBH3ZTEs91UcBFREQR9xrTpm1m0KCZ5OQ4X06WVKEoTe+vx4qjZ13nDL+5Av/XuboCLiIigCLuFRYv3sPAgTNdrwevXCWWRvfW5ZvDKa5zRt5Sgac7KeAiInKBXmLmBZo3L0uXLlUBuKF6HI3uuzTgo26tqICLiMgvaCfuBUJCApk2rQ+PPf4lJypH8c3BM67H/tCiIk92uEEBFxGRX9BO3FsEGlLrx10S8HtaVlLARUQkT4q4B7z22jc8++xC13F6Vg53T1rHwh+PutbubVmJP7WvpoCLiEiedDm9gL344jKeecYZ8JCQQB5/8mb+MGkdS7Ydc51z/22V+WO7qgq4iIhclSJeQKy1PP/8Ep5/folr7Ysvd/FjYijLd51wrT3QqjKPtlXARUTk1yniBcBayzPPLOSll752rbW8LYkSfSpfEvAHW1fhkTZVFHAREbkmiribWWt57LEv+Ne/VrrW2rarSPEelVix95Rr7aHWVXikbVVPjCgiIj5KEXcjh8Py0EOf8d//rnGtdexUhZguSZcE/OE2VXi4jQIuIiLXRxF3E4fDcs89n/Luu9+61rr3uIHQdmUvCfijbavyYOsqnhhRRER8nCLuJg899NklAe/TrwYBLUuz6qKAP9au6v+3d+fRUVZpHse/DwkSlsgSJAcax8gmIQGiRG0O2oQgoCxBZYmNraA4o7id446tI4wojbYL4wHbdrQFlwCCg6Ld2mfQVhQFCSZiIBoipCE0SwiEgBBkufNHVYqsUJhUVYr8PufUOW/d975vnnqo8OTe99Zb3JmqAi4iIr+MPiceIOPHJ9CiRVMAfnt9byylU6UC/sCwC1TARUSkTjQSD5DLLz+P99//LQvfXs/evu1YU6GAP3jlBdye0i2E0YmIyJlAI/EAunTAuZQkxVQq4FOv6qkCLiIi9UJFvB6UlR3l5pvfIz9/j6/twOGjTHrta74uONH28FU9uW1g11CEKCIiZyAV8To6ePAIaWkLeO21bFJT51NQUOIp4H/5mjUFJ0bgjwyP51YVcBERqUe6Jl4HBw78zKhRC/j00wIAtm4tZcHb68mMNtZWmEJ/dEQ8t1zeJURRiojImUpF/BcqLT3M8OFvsXLlVl/bI4/9hjWt4JsKBfw/R/Zi8mXnhyJEERE5w6mI/wIlJWVceeWbrF69zdf2X08MIqttBFlbSnxtj43sxc0q4CIiEiAq4qepuPggQ4e+yTffbPe1/eHpK1jVArIrFPDpo3oxaYAKuIiIBI6K+GnYtesnhgx5g3Xrdvranp09jM8ij/Ht1hMF/PHRCdzYPy4EEYqISGOiIu6n4uKDDBo0nw0bigAwg/+eO5zlxw7z7dZ9vn4zRidwgwq4iIgEgT5i5qfWraNISDgHgCZNjD+9PIr/O3aYbwsrFPCrE1XARUQkaDQS91NkZBPeeutaAIaN6M7S0v3kbCv17X/ymkSuv/S8UIUnIiKNkEbip6FpGQZtxwAAEJ9JREFU0wj+/Foa/7uvcgGfeU1vFXAREQk6FfFabNxYzJNPrsA552vb+9PPXP/K16z/14kCPuva3ky49N9CEaKIiDRymk6vQW5uEampr7NjxwF++ukITz6ZSsnBI1z/ymo2bPcUcDNPAU+/WAVcRERCQyPxKr77bicDB85jx44DAMyevYqs9buYUKWAP3VtHxVwEREJKY3EK8jK2s6QIW9QXHwIgJYtm5KxZBy/X/4D3+/YD3gK+NNj+jAu+dxQhioiIqKReLk1a7aRmvq6r4CffXYzFr+bzovfb69UwP84tq8KuIiINAgBLeJmdqWZ/WBm+WY2tYb9zcxskXf/ajOLC2Q8tfnqq61cccUblJSUAdCmTRSL30tn9nfbKhXwZ8f1ZWy/zqEIUUREpJqATaebWQQwFxgCFAJrzGyZc25DhW6Tgb3OuW5mdh3wFJAeqJhqsmLFPxkxIoMDB34GoF275rz9bjqz1hSwcZfnungTg2fH9+WaC1XARRqTI0eOUFhYSFlZWahDkUYgKiqKzp0707RpU7+PCeQ18UuAfOfcJgAzWwiMBioW8dHAdO/2EmCOmZmr+LmuANl36AiP/Wk1Lz3yKT+XHQOgVZtm3PbHQdUK+HPjk7j6wl8FOiQRaWAKCwuJjo4mLi4OMwt1OHIGc85RXFxMYWEh55/v/5dnBXI6/VfA1grPC71tNfZxzh0F9gExAYzJZ9/BI7ydvY0jxz1/L0S0bEr0mG68lberUgF/Pl0FXKSxKisrIyYmRgVcAs7MiImJOe1Zn0AW8Zre9VVH2P70wcz+w8wyzSyzqKioXoIDaNapFR3G9SCyXRSxEy7grPbNffuaGMy+7kJGJ6mAizRmKuASLL/kvRbI6fRCoOIy7s7Av2rpU2hmkUBrYE/VEznnXgZeBkhOTq6XqfboqEhuG9gVgOPX9aZJxIm/Z5pGGFcldqRXp7Pr40eJiIgERCBH4muA7mZ2vpmdBVwHLKvSZxkw0bs9FvgkGNfDAdq2PIupV/Vk6lU9+f3IXr7tqVf15L6hF6iAi0iDEBERQVJSEomJiYwaNYqSkhLfvvXr15OamkqPHj3o3r07M2bMqHSr6A8//JDk5GTi4+Pp2bMn999/fyhewkllZWVxyy23VGobPXo0/fv3r9Q2adIklixZUqmtVatWvu28vDyGDx9Ot27diI+PZ/z48ezcubNOse3Zs4chQ4bQvXt3hgwZwt69e2vsV/5vlJSURFpamq998+bNXHrppXTv3p309HR+/tmzgHrOnDm89tprdYqtXMCKuPca953A34Fc4G3n3Hoze9zMyl/lq0CMmeUD9wLVPoYmItKYNW/enOzsbHJycmjXrh1z584F4NChQ6SlpTF16lTy8vL49ttv+fLLL3nxxRcByMnJ4c477+TNN98kNzeXnJwcunTpUq+xHT16tM7nmDlzJnfddZfveUlJCd988w0lJSVs3rzZr3OUlZUxYsQIpkyZQn5+Prm5uUyZMoW6Xn6dNWsWgwcPZuPGjQwePJhZs2bV2K/83yg7O5tly06MVR966CHuueceNm7cSNu2bXn11VcBuPnmm3nhhRfqFFu5gN6xzTn3N+BvVdoeq7BdBowLZAwiIvUhbupfA3buglkj/OrXv39/1q1bB0BGRgYDBgxg6NChALRo0YI5c+aQkpLCHXfcwdNPP80jjzxCz549AYiMjOT222+vds4DBw5w1113kZmZiZkxbdo0xowZQ6tWrThwwLPId8mSJXzwwQfMmzePSZMm0a5dO7KyskhKSmLp0qVkZ2fTpk0bALp168bKlStp0qQJt912G1u2bAFg9uzZDBgwoNLP3r9/P+vWraNv376+tnfeeYdRo0YRGxvLwoULefjhh0+Zl4yMDPr378+oUaN8bYMGDfIrpyfz3nvv8emnnwIwceJEUlJSeOqpp/w61jnHJ598QkZGhu/46dOnM2XKFFq0aEFcXBxff/01l1xySZ1i1G1XRUTCwLFjx/j444+ZPHky4JlK79evX6U+Xbt25cCBA5SWlpKTk8N99913yvPOmDGD1q1b89133wHUOmVcUV5eHsuXLyciIoLjx4+zdOlSbrrpJlavXk1cXByxsbFMmDCBe+65h8suu4wtW7YwbNgwcnNzK50nMzOTxMTESm0LFixg2rRpxMbGMnbsWL+KeE5OTrVc1GT//v1cfvnlNe7LyMigV69eldp27txJx44dAejYsSO7du2q8diysjKSk5OJjIxk6tSpXH311RQXF9OmTRsiIz1ltnPnzmzbts13THJyMp9//rmKuIjImezQoUMkJSVRUFBAv379GDJkCOAZ6dW2mvl0VjkvX76chQsX+p63bdv2lMeMGzeOiIgIANLT03n88ce56aabWLhwIenp6b7zbthw4rYgpaWl7N+/n+joaF/b9u3bOeecc3zPd+7cSX5+PpdddhlmRmRkJDk5OSQmJtb4mk53NXd0dDTZ2dmndYw/tmzZQqdOndi0aROpqan07t2bs8+uvq6qYrwdOnTg+++/r/PPVhEXEfGDv1Pe9a38euu+ffsYOXIkc+fO5e677yYhIYEVK1ZU6rtp0yZatWpFdHQ0CQkJrF27ttJUdU1q+2OgYlvVzy63bNnSt92/f3/y8/MpKiri3Xff5dFHHwXg+PHjfPXVVzRv3pzaNG/evNK5Fy1axN69e303OyktLWXhwoU88cQTxMTEVJol2LNnD+3btwcgISGBzz777KSvE05/JB4bG8v27dvp2LEj27dvp0OHDjUe26lTJwC6dOlCSkoKWVlZjBkzhpKSEo4ePUpkZCSFhYW+fuDJ6cly4y99AYqISBho3bo1L7zwAs888wxHjhzh+uuv54svvmD58uWAZ8R+99138+CDDwLwwAMPMHPmTPLy8gBPUX3uueeqnXfo0KHMmTPH97y8UMbGxpKbm+ubLq+NmXHNNddw7733Eh8fT0xMTI3nrWkEHB8fT35+vu/5ggUL+OijjygoKKCgoIC1a9f6ZglSUlJYtGiRb4X3vHnzfNe9J0yYwJdffslf/3pi3cJHH33ku0RQrnwkXtOjagEHSEtLY/78+QDMnz+f0aNHV+uzd+9eDh8+DMDu3btZuXIlvXr1wswYNGiQb0V91ePz8vKqXUr4RZxzYfXo16+fExEJhg0bNoQ6BNeyZctKz0eOHOlef/1155xz69atcwMHDnQ9evRwXbt2ddOnT3fHjx/39X3//ffdRRdd5Hr27Oni4+Pd/fffX+38+/fvdzfeeKNLSEhwffr0ce+8845zzrnFixe7Ll26uIEDB7o77rjDTZw40Tnn3MSJE93ixYsrnWPNmjUOcPPmzfO1FRUVufHjx7vevXu7+Ph4d+utt9b4+hITE11paanbvHmz69SpU6X4nXPuwgsvdKtWrXLOOTd9+nSXmJjo+vbt66699lq3a9cuX7/c3Fw3bNgw161bNxcfH+/S09Pdjh07TprbU9m9e7dLTU113bp1c6mpqa64uNj3eidPnuycc27lypUuMTHR9enTxyUmJrpXXnnFd/yPP/7oLr74Yte1a1c3duxYV1ZWVul1FRUVVfuZNb3ngExXS000F5yPZdeb5ORkl5mZGeowRKQRyM3NJT4+PtRhnNGef/55oqOjq31W/EyWlZXFc889xxtvvFFtX03vOTNb65xLrulcmk4XEZGQmTJlCs2aNQt1GEG1e/duZsyYUS/n0sI2EREJmaioKG644YZQhxFU5Z8wqA8aiYuInES4XXKU8PVL3msq4iIitYiKiqK4uFiFXALOeb9PPCoq6rSO03S6iEgtOnfuTGFhYZ3vwS3ij6ioKDp37nxax6iIi4jUomnTpr4bj4g0RJpOFxERCVMq4iIiImFKRVxERCRMhd0d28ysCPhnPZ6yPbC7Hs/XWCmPdacc1p1yWHfKYd3Vdw7Pc86dU9OOsCvi9c3MMmu7nZ34T3msO+Ww7pTDulMO6y6YOdR0uoiISJhSERcREQlTKuLwcqgDOEMoj3WnHNadclh3ymHdBS2Hjf6auIiISLjSSFxERCRMNZoibmZXmtkPZpZvZlNr2N/MzBZ59682s7jgR9mw+ZHDe81sg5mtM7OPzey8UMTZkJ0qhxX6jTUzZ2ZaJVwDf/JoZuO978f1ZpYR7BgbOj9+n//NzP5hZlne3+nhoYizoTKzv5jZLjPLqWW/mdkL3vyuM7OLAhKIc+6MfwARwI9AF+As4FugV5U+twMvebevAxaFOu6G9PAzh4OAFt7tKcrh6efQ2y8aWAGsApJDHXdDe/j5XuwOZAFtvc87hDruhvTwM4cvA1O8272AglDH3ZAewG+Ai4CcWvYPBz4EDPg1sDoQcTSWkfglQL5zbpNz7mdgITC6Sp/RwHzv9hJgsJlZEGNs6E6ZQ+fcP5xzB71PVwGn93U8Zz5/3ocAM4CngbJgBhdG/MnjvwNznXN7AZxzu4IcY0PnTw4dcLZ3uzXwryDG1+A551YAe07SZTTwuvNYBbQxs471HUdjKeK/ArZWeF7obauxj3PuKLAPiAlKdOHBnxxWNBnPX6FywilzaGYXAuc65z4IZmBhxp/3Yg+gh5mtNLNVZnZl0KILD/7kcDrwOzMrBP4G3BWc0M4Yp/t/5i/SWL6KtKYRddVl+f70acz8zo+Z/Q5IBgYGNKLwc9IcmlkT4HlgUrACClP+vBcj8Uypp+CZEfrczBKdcyUBji1c+JPD3wLznHPPmll/4A1vDo8HPrwzQlBqSmMZiRcC51Z43pnqU0O+PmYWiWf66GRTJY2NPznEzK4AHgHSnHOHgxRbuDhVDqOBROBTMyvAcx1tmRa3VePv7/N7zrkjzrnNwA94irp4+JPDycDbAM65r4AoPPcEF//49X9mXTWWIr4G6G5m55vZWXgWri2r0mcZMNG7PRb4xHlXJwjgRw69U8F/xlPAdQ2yupPm0Dm3zznX3jkX55yLw7OuIM05lxmacBssf36f38Wz0BIza49nen1TUKNs2PzJ4RZgMICZxeMp4kVBjTK8LQNu9K5S/zWwzzm3vb5/SKOYTnfOHTWzO4G/41mV+Rfn3HozexzIdM4tA17FM12Uj2cEfl3oIm54/MzhH4FWwGLvmsAtzrm0kAXdwPiZQzkFP/P4d2ComW0AjgEPOOeKQxd1w+JnDu8D/sfM7sEzDTxJA5sTzGwBnss17b3rBqYBTQGccy/hWUcwHMgHDgI3BSQO/ZuIiIiEp8YynS4iInLGUREXEREJUyriIiIiYUpFXEREJEypiIuIiIQpFXERAcDMjplZdoVHnJmlmNk+7zdZ5ZrZNG/fiu3fm9kzoY5fpDFqFJ8TFxG/HHLOJVVs8H4l7+fOuZFm1hLINrPy+7qXtzcHssxsqXNuZXBDFmncNBIXEb84534C1gJdq7QfArIJwJc7iMjJqYiLSLnmFabSl1bdaWYxeO7nvr5Ke1s89yVfEZwwRaScptNFpFy16XSvy80sCzgOzPLenjPF274OuMDbviOIsYoIKuIicmqfO+dG1tZuZj2AL7zXxLODHZxIY6bpdBGpE+dcHvAH4KFQxyLS2KiIi0h9eAn4jZmdH+pARBoTfYuZiIhImNJIXEREJEypiIuIiIQpFXEREZEwpSIuIiISplTERUREwpSKuIiISJhSERcREQlTKuIiIiJh6v8BC2xYnzNCGm8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gini_bothProbabilities = model_gini.predict_proba(XX)\n",
    "gini_probabilities = gini_bothProbabilities[:,1]\n",
    "\n",
    "# pos_label- the label of the positive class\n",
    "print(\"Gini Accuracy is : {}%\".format(metrics.accuracy_score(YY, y_gini_predict)*100))\n",
    "print(\"Gini Precision is : {}%\".format(metrics.precision_score(YY, y_gini_predict)*100))\n",
    "print(\"Gini Recall is : {}%\".format(metrics.recall_score(YY, y_gini_predict)*100))\n",
    "\n",
    "# False Positive Rate, True Posisive Rate, probability thresholds\n",
    "fpr, tpr, th = metrics.roc_curve(YY, gini_probabilities) \n",
    "AUC = metrics.auc(fpr, tpr)\n",
    "\n",
    "# Plot ROC Curve\n",
    "plt.rcParams[\"figure.figsize\"] = [8, 8] # Square\n",
    "plt.figure()\n",
    "plt.title('ROC Curve - Gini')\n",
    "plt.xlabel('FPR')\n",
    "plt.ylabel('TPR')\n",
    "plt.plot(fpr, tpr, LW=3, label='ROC curve (AUC = %0.2f)' % AUC)\n",
    "plt.plot([0, 1], [0, 1], color='navy', LW=3, linestyle='--') # reference line for random classifier\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Random Forest (Ensemble Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy is : 97.45222929936305%\n",
      "Random Forest Precision is : 0.0%\n",
      "Random Forest Recall is : 0.0%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_forest_bothProbabilities = model_random_forest.predict_proba(XX)\n",
    "random_forest_probabilities = random_forest_bothProbabilities[:,1]\n",
    "\n",
    "# pos_label- the label of the positive class\n",
    "print(\"Random Forest Accuracy is : {}%\".format(metrics.accuracy_score(YY, y_random_forest_predict)*100))\n",
    "print(\"Random Forest Precision is : {}%\".format(metrics.precision_score(YY, y_random_forest_predict)*100))\n",
    "print(\"Random Forest Recall is : {}%\".format(metrics.recall_score(YY, y_random_forest_predict)*100))\n",
    "\n",
    "# False Positive Rate, True Posisive Rate, probability thresholds\n",
    "fpr, tpr, th = metrics.roc_curve(YY, random_forest_probabilities) \n",
    "AUC = metrics.auc(fpr, tpr)\n",
    "\n",
    "# Plot ROC Curve\n",
    "plt.rcParams[\"figure.figsize\"] = [8, 8] # Square\n",
    "plt.figure()\n",
    "plt.title('ROC Curve - Random Forest')\n",
    "plt.xlabel('FPR')\n",
    "plt.ylabel('TPR')\n",
    "plt.plot(fpr, tpr, LW=3, label='ROC curve (AUC = %0.2f)' % AUC)\n",
    "plt.plot([0, 1], [0, 1], color='navy', LW=3, linestyle='--') # reference line for random classifier\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. SVC (SVM Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      0.97      0.99       157\n",
      "         1.0       0.00      0.00      0.00         0\n",
      "\n",
      "    accuracy                           0.97       157\n",
      "   macro avg       0.50      0.49      0.49       157\n",
      "weighted avg       1.00      0.97      0.99       157\n",
      "\n",
      "Accuracy of the Best SVM Model on the training data is 0.93 out of 1\n",
      "Accuracy of the Best SVM Model on the test data is 0.97 out of 1\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(clf.predict(XX), YY))\n",
    "print('Accuracy of the Best SVM Model on the training data is {:.2f} out of 1'.format(clf.score(X, Y)))\n",
    "print('Accuracy of the Best SVM Model on the test data is {:.2f} out of 1'.format(clf.score(XX, YY)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**Summary:**\n",
    "1. Both **Decision Tree (Gini Coefficient)** and **Random Forest (Ensemble Model)** yeild not so good **AUC** (0.5 and 0.48 respectively). This indicate that the decision tree models are not the good choice for analysis.\n",
    "2. On the other hand, **SVC (SVM Model)** yeild much better result. (**Accuracy** around 97% on test data).\n",
    "3. The reason while **SVM** yield much better result than **Decision Trees** might due to the fact that there are so many attributes on the original data set and decision trees not working very well on the too complex type of model (tend to overfitted).\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**Solicit Feedback:**  \n",
    "\n",
    "- I decide to do **Normalization** since **SVM** not scale invariant. I'm not sure if **scaled data** will degrade the **decision trees** models.\n",
    "- In this milestone, it just happend that **Decision Tree** perform better than **Random Forest**. Based on the Lesson 6, usually Random Forest perform better than Decision Trees most of the time. I'm not sure if I did something wrong during data preparation or it just end up like that for this data set.\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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