Final_Project_Complete.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lance Bozeman\n",
    "# Mohamed Tounkara\n",
    "# Screekala Santhakumari"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "Xcow1_eKINhn"
   },
   "outputs": [],
   "source": [
    "# libraries \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "#Visualizers\n",
    "from yellowbrick.classifier import ClassificationReport\n",
    "from yellowbrick.classifier import ClassPredictionError\n",
    "from yellowbrick.classifier import ConfusionMatrix\n",
    "from yellowbrick.classifier import ROCAUC\n",
    "from yellowbrick.classifier import PrecisionRecallCurve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#Metrics\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "from sklearn.metrics import hamming_loss\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.metrics import zero_one_loss\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "\n",
    "#Classifiers\n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import svm\n",
    "from sklearn.tree import export_graphviz\n",
    "import graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "LW6fvY2RI1hR"
   },
   "outputs": [],
   "source": [
    "from time import time\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from pandas import set_option\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "import seaborn as sns; sns.set() \n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import metrics\n",
    "import re\n",
    "from numpy import random\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "QGazzqoeI740",
    "outputId": "798b993e-d61e-483c-f416-8d6879c9f1c2"
   },
   "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>A2M</th>\n",
       "      <th>AAAS</th>\n",
       "      <th>AAK1</th>\n",
       "      <th>AAMP</th>\n",
       "      <th>AARS</th>\n",
       "      <th>AARSD1</th>\n",
       "      <th>AASDH</th>\n",
       "      <th>AASDHPPT</th>\n",
       "      <th>AASS</th>\n",
       "      <th>AATF</th>\n",
       "      <th>...</th>\n",
       "      <th>ZSWIM6</th>\n",
       "      <th>ZSWIM7</th>\n",
       "      <th>ZUFSP</th>\n",
       "      <th>ZW10</th>\n",
       "      <th>ZWILCH</th>\n",
       "      <th>ZXDC</th>\n",
       "      <th>ZYG11B</th>\n",
       "      <th>ZYX</th>\n",
       "      <th>ZZZ3</th>\n",
       "      <th>Classes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-3.80147</td>\n",
       "      <td>-3.889900</td>\n",
       "      <td>-3.985616</td>\n",
       "      <td>2.651558</td>\n",
       "      <td>2.170748</td>\n",
       "      <td>-2.550822</td>\n",
       "      <td>4.807330</td>\n",
       "      <td>3.961170</td>\n",
       "      <td>-0.192665</td>\n",
       "      <td>3.614482</td>\n",
       "      <td>...</td>\n",
       "      <td>2.909466</td>\n",
       "      <td>-3.118284</td>\n",
       "      <td>-1.538324</td>\n",
       "      <td>-1.550699</td>\n",
       "      <td>-1.558581</td>\n",
       "      <td>-1.920271</td>\n",
       "      <td>3.007439</td>\n",
       "      <td>-2.509017</td>\n",
       "      <td>-2.149696</td>\n",
       "      <td>Class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-3.80147</td>\n",
       "      <td>-3.889900</td>\n",
       "      <td>-3.158708</td>\n",
       "      <td>2.358992</td>\n",
       "      <td>-6.041792</td>\n",
       "      <td>-0.056092</td>\n",
       "      <td>3.606735</td>\n",
       "      <td>-2.632250</td>\n",
       "      <td>2.249388</td>\n",
       "      <td>6.857517</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.821098</td>\n",
       "      <td>-3.118284</td>\n",
       "      <td>-1.538324</td>\n",
       "      <td>-1.550699</td>\n",
       "      <td>-1.558581</td>\n",
       "      <td>-3.068620</td>\n",
       "      <td>2.539560</td>\n",
       "      <td>2.164481</td>\n",
       "      <td>-2.149696</td>\n",
       "      <td>Class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-3.80147</td>\n",
       "      <td>-3.889900</td>\n",
       "      <td>1.733125</td>\n",
       "      <td>-5.820241</td>\n",
       "      <td>-6.041792</td>\n",
       "      <td>-0.576957</td>\n",
       "      <td>-2.473517</td>\n",
       "      <td>-4.354127</td>\n",
       "      <td>0.063178</td>\n",
       "      <td>-2.570976</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.821098</td>\n",
       "      <td>5.521892</td>\n",
       "      <td>-1.538324</td>\n",
       "      <td>-1.550699</td>\n",
       "      <td>-1.558581</td>\n",
       "      <td>0.174665</td>\n",
       "      <td>-0.165409</td>\n",
       "      <td>0.734268</td>\n",
       "      <td>-2.149696</td>\n",
       "      <td>Class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.80147</td>\n",
       "      <td>-3.889900</td>\n",
       "      <td>-1.665669</td>\n",
       "      <td>3.514271</td>\n",
       "      <td>-6.041792</td>\n",
       "      <td>-3.699171</td>\n",
       "      <td>4.509461</td>\n",
       "      <td>-4.354127</td>\n",
       "      <td>2.985972</td>\n",
       "      <td>-2.570976</td>\n",
       "      <td>...</td>\n",
       "      <td>4.952176</td>\n",
       "      <td>-0.854351</td>\n",
       "      <td>-1.538324</td>\n",
       "      <td>-1.550699</td>\n",
       "      <td>-1.558581</td>\n",
       "      <td>-3.068620</td>\n",
       "      <td>-1.884744</td>\n",
       "      <td>-2.509017</td>\n",
       "      <td>-2.149696</td>\n",
       "      <td>Class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-3.80147</td>\n",
       "      <td>3.742495</td>\n",
       "      <td>-2.166992</td>\n",
       "      <td>-5.820241</td>\n",
       "      <td>2.094729</td>\n",
       "      <td>4.021873</td>\n",
       "      <td>5.535007</td>\n",
       "      <td>4.019633</td>\n",
       "      <td>2.560370</td>\n",
       "      <td>-2.570976</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.821098</td>\n",
       "      <td>4.328808</td>\n",
       "      <td>-1.538324</td>\n",
       "      <td>7.021985</td>\n",
       "      <td>-1.558581</td>\n",
       "      <td>4.590946</td>\n",
       "      <td>-0.128456</td>\n",
       "      <td>-2.509017</td>\n",
       "      <td>-2.149696</td>\n",
       "      <td>Class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.98770</td>\n",
       "      <td>-3.889900</td>\n",
       "      <td>4.691156</td>\n",
       "      <td>-4.006471</td>\n",
       "      <td>-3.449348</td>\n",
       "      <td>4.309767</td>\n",
       "      <td>4.002960</td>\n",
       "      <td>5.123457</td>\n",
       "      <td>0.341512</td>\n",
       "      <td>-2.570976</td>\n",
       "      <td>...</td>\n",
       "      <td>4.176419</td>\n",
       "      <td>-2.370500</td>\n",
       "      <td>-1.538324</td>\n",
       "      <td>-1.550699</td>\n",
       "      <td>-1.558581</td>\n",
       "      <td>-3.068620</td>\n",
       "      <td>-3.421540</td>\n",
       "      <td>-2.509017</td>\n",
       "      <td>-2.149696</td>\n",
       "      <td>Class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-3.80147</td>\n",
       "      <td>-3.889900</td>\n",
       "      <td>2.656469</td>\n",
       "      <td>2.207608</td>\n",
       "      <td>-6.041792</td>\n",
       "      <td>4.235937</td>\n",
       "      <td>0.716257</td>\n",
       "      <td>-1.164354</td>\n",
       "      <td>2.630053</td>\n",
       "      <td>-2.570976</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.821098</td>\n",
       "      <td>0.071490</td>\n",
       "      <td>-1.538324</td>\n",
       "      <td>5.651368</td>\n",
       "      <td>-1.558581</td>\n",
       "      <td>3.617386</td>\n",
       "      <td>1.384471</td>\n",
       "      <td>-2.509017</td>\n",
       "      <td>-2.149696</td>\n",
       "      <td>Class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-3.80147</td>\n",
       "      <td>-3.889900</td>\n",
       "      <td>-1.914759</td>\n",
       "      <td>2.417820</td>\n",
       "      <td>3.162904</td>\n",
       "      <td>-3.699171</td>\n",
       "      <td>-2.473517</td>\n",
       "      <td>-4.354127</td>\n",
       "      <td>1.970206</td>\n",
       "      <td>-2.570976</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.821098</td>\n",
       "      <td>3.615788</td>\n",
       "      <td>-1.538324</td>\n",
       "      <td>-1.550699</td>\n",
       "      <td>-1.558581</td>\n",
       "      <td>1.829214</td>\n",
       "      <td>5.237998</td>\n",
       "      <td>-2.509017</td>\n",
       "      <td>6.779879</td>\n",
       "      <td>Class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-3.80147</td>\n",
       "      <td>-3.889900</td>\n",
       "      <td>0.483560</td>\n",
       "      <td>-5.820241</td>\n",
       "      <td>-6.041792</td>\n",
       "      <td>-3.699171</td>\n",
       "      <td>-2.473517</td>\n",
       "      <td>4.889074</td>\n",
       "      <td>-3.847544</td>\n",
       "      <td>-2.570976</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.821098</td>\n",
       "      <td>-3.118284</td>\n",
       "      <td>-1.538324</td>\n",
       "      <td>-1.550699</td>\n",
       "      <td>-1.558581</td>\n",
       "      <td>-3.068620</td>\n",
       "      <td>-0.775154</td>\n",
       "      <td>1.953289</td>\n",
       "      <td>-2.149696</td>\n",
       "      <td>Class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-3.80147</td>\n",
       "      <td>4.316243</td>\n",
       "      <td>1.828663</td>\n",
       "      <td>2.140173</td>\n",
       "      <td>-6.041792</td>\n",
       "      <td>3.721094</td>\n",
       "      <td>3.479903</td>\n",
       "      <td>-4.354127</td>\n",
       "      <td>-4.860593</td>\n",
       "      <td>-2.570976</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.821098</td>\n",
       "      <td>-0.860025</td>\n",
       "      <td>0.841462</td>\n",
       "      <td>-1.550699</td>\n",
       "      <td>-1.558581</td>\n",
       "      <td>5.157919</td>\n",
       "      <td>4.710859</td>\n",
       "      <td>5.078266</td>\n",
       "      <td>4.977025</td>\n",
       "      <td>Class1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 5949 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       A2M      AAAS      AAK1      AAMP      AARS    AARSD1     AASDH  \\\n",
       "0 -3.80147 -3.889900 -3.985616  2.651558  2.170748 -2.550822  4.807330   \n",
       "1 -3.80147 -3.889900 -3.158708  2.358992 -6.041792 -0.056092  3.606735   \n",
       "2 -3.80147 -3.889900  1.733125 -5.820241 -6.041792 -0.576957 -2.473517   \n",
       "3 -3.80147 -3.889900 -1.665669  3.514271 -6.041792 -3.699171  4.509461   \n",
       "4 -3.80147  3.742495 -2.166992 -5.820241  2.094729  4.021873  5.535007   \n",
       "5 -1.98770 -3.889900  4.691156 -4.006471 -3.449348  4.309767  4.002960   \n",
       "6 -3.80147 -3.889900  2.656469  2.207608 -6.041792  4.235937  0.716257   \n",
       "7 -3.80147 -3.889900 -1.914759  2.417820  3.162904 -3.699171 -2.473517   \n",
       "8 -3.80147 -3.889900  0.483560 -5.820241 -6.041792 -3.699171 -2.473517   \n",
       "9 -3.80147  4.316243  1.828663  2.140173 -6.041792  3.721094  3.479903   \n",
       "\n",
       "   AASDHPPT      AASS      AATF  ...    ZSWIM6    ZSWIM7     ZUFSP      ZW10  \\\n",
       "0  3.961170 -0.192665  3.614482  ...  2.909466 -3.118284 -1.538324 -1.550699   \n",
       "1 -2.632250  2.249388  6.857517  ... -1.821098 -3.118284 -1.538324 -1.550699   \n",
       "2 -4.354127  0.063178 -2.570976  ... -1.821098  5.521892 -1.538324 -1.550699   \n",
       "3 -4.354127  2.985972 -2.570976  ...  4.952176 -0.854351 -1.538324 -1.550699   \n",
       "4  4.019633  2.560370 -2.570976  ... -1.821098  4.328808 -1.538324  7.021985   \n",
       "5  5.123457  0.341512 -2.570976  ...  4.176419 -2.370500 -1.538324 -1.550699   \n",
       "6 -1.164354  2.630053 -2.570976  ... -1.821098  0.071490 -1.538324  5.651368   \n",
       "7 -4.354127  1.970206 -2.570976  ... -1.821098  3.615788 -1.538324 -1.550699   \n",
       "8  4.889074 -3.847544 -2.570976  ... -1.821098 -3.118284 -1.538324 -1.550699   \n",
       "9 -4.354127 -4.860593 -2.570976  ... -1.821098 -0.860025  0.841462 -1.550699   \n",
       "\n",
       "     ZWILCH      ZXDC    ZYG11B       ZYX      ZZZ3  Classes  \n",
       "0 -1.558581 -1.920271  3.007439 -2.509017 -2.149696   Class1  \n",
       "1 -1.558581 -3.068620  2.539560  2.164481 -2.149696   Class1  \n",
       "2 -1.558581  0.174665 -0.165409  0.734268 -2.149696   Class1  \n",
       "3 -1.558581 -3.068620 -1.884744 -2.509017 -2.149696   Class1  \n",
       "4 -1.558581  4.590946 -0.128456 -2.509017 -2.149696   Class1  \n",
       "5 -1.558581 -3.068620 -3.421540 -2.509017 -2.149696   Class1  \n",
       "6 -1.558581  3.617386  1.384471 -2.509017 -2.149696   Class1  \n",
       "7 -1.558581  1.829214  5.237998 -2.509017  6.779879   Class1  \n",
       "8 -1.558581 -3.068620 -0.775154  1.953289 -2.149696   Class1  \n",
       "9 -1.558581  5.157919  4.710859  5.078266  4.977025   Class1  \n",
       "\n",
       "[10 rows x 5949 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Data_Glioblastoma5Patients_SC.csv')\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kzWAweYpJRcq",
    "outputId": "beb97706-35f5-426c-ea2f-c30bc4065500"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class1    118\n",
      "Class2     94\n",
      "Class3     75\n",
      "Class4     73\n",
      "Class5     70\n",
      "Name: Classes, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Identifying classes\n",
    "\n",
    "print(df['Classes'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fSxwV-VvJX5J",
    "outputId": "6a393b36-02a2-422d-a7c6-47942631490e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                A2M          AAAS          AAK1          AAMP          AARS  \\\n",
      "count  4.300000e+02  4.300000e+02  4.300000e+02  4.300000e+02  4.300000e+02   \n",
      "mean  -5.116023e-11  9.069777e-11  1.627909e-11 -7.674580e-11 -8.139630e-11   \n",
      "std    4.907659e+00  3.641663e+00  2.474651e+00  3.563029e+00  3.909578e+00   \n",
      "min   -3.801470e+00 -3.889900e+00 -3.985616e+00 -5.820241e+00 -6.041792e+00   \n",
      "25%   -3.801470e+00 -3.889900e+00 -1.871573e+00 -3.807913e+00 -4.560471e+00   \n",
      "50%   -3.801470e+00  4.099148e-01 -3.150282e-01  1.746972e+00  1.776033e+00   \n",
      "75%    4.943281e+00  3.711243e+00  2.002047e+00  2.686033e+00  3.116038e+00   \n",
      "max    1.170195e+01  5.840230e+00  5.389576e+00  4.520579e+00  6.366184e+00   \n",
      "\n",
      "             AARSD1         AASDH      AASDHPPT          AASS          AATF  \\\n",
      "count  4.300000e+02  4.300000e+02  4.300000e+02  4.300000e+02  4.300000e+02   \n",
      "mean  -1.302323e-10 -2.255812e-10 -2.325458e-12  5.814004e-11 -1.511620e-10   \n",
      "std    3.317166e+00  2.883981e+00  3.759008e+00  3.236368e+00  3.461157e+00   \n",
      "min   -3.699171e+00 -2.473517e+00 -4.354127e+00 -6.493931e+00 -2.570976e+00   \n",
      "25%   -3.699171e+00 -2.473517e+00 -4.354127e+00 -1.967954e+00 -2.570976e+00   \n",
      "50%    1.097698e-01 -2.473517e+00  1.255356e+00  1.192040e+00 -2.570976e+00   \n",
      "75%    3.238343e+00  2.622538e+00  3.553135e+00  2.261182e+00  3.911515e+00   \n",
      "max    6.012985e+00  6.798128e+00  5.729251e+00  4.324112e+00  6.992394e+00   \n",
      "\n",
      "       ...       ZSCAN30        ZSWIM6        ZSWIM7         ZUFSP  \\\n",
      "count  ...  4.300000e+02  4.300000e+02  4.300000e+02  4.300000e+02   \n",
      "mean   ...  2.209304e-10 -1.906982e-10 -3.953487e-11 -4.418642e-11   \n",
      "std    ...  3.227682e+00  2.730348e+00  2.946168e+00  2.702606e+00   \n",
      "min    ... -2.593571e+00 -1.821098e+00 -3.118284e+00 -1.538324e+00   \n",
      "25%    ... -2.593571e+00 -1.821098e+00 -3.118284e+00 -1.538324e+00   \n",
      "50%    ... -2.593571e+00 -1.821098e+00 -8.136609e-01 -1.538324e+00   \n",
      "75%    ...  3.196094e+00  2.038805e+00  2.848862e+00  8.320662e-01   \n",
      "max    ...  7.499714e+00  7.931653e+00  6.105923e+00  8.186486e+00   \n",
      "\n",
      "               ZW10        ZWILCH          ZXDC        ZYG11B           ZYX  \\\n",
      "count  4.300000e+02  4.300000e+02  4.300000e+02  4.300000e+02  4.300000e+02   \n",
      "mean  -1.651165e-10  1.906968e-10 -1.348836e-10  3.255803e-11 -2.023251e-10   \n",
      "std    2.836726e+00  2.788284e+00  2.859633e+00  2.811249e+00  3.001418e+00   \n",
      "min   -1.550699e+00 -1.558581e+00 -3.068620e+00 -3.421540e+00 -2.509017e+00   \n",
      "25%   -1.550699e+00 -1.558581e+00 -3.068620e+00 -2.423300e+00 -2.509017e+00   \n",
      "50%   -1.550699e+00 -1.558581e+00 -8.339072e-01 -4.421617e-01 -2.509017e+00   \n",
      "75%   -1.687454e-01  3.335339e-01  2.459752e+00  2.514619e+00  2.939355e+00   \n",
      "max    8.073628e+00  7.998381e+00  5.950488e+00  6.703674e+00  6.918941e+00   \n",
      "\n",
      "               ZZZ3  \n",
      "count  4.300000e+02  \n",
      "mean  -1.604656e-10  \n",
      "std    3.052755e+00  \n",
      "min   -2.149696e+00  \n",
      "25%   -2.149696e+00  \n",
      "50%   -2.149696e+00  \n",
      "75%    2.228821e+00  \n",
      "max    8.064823e+00  \n",
      "\n",
      "[8 rows x 5948 columns]\n",
      "Classes\n",
      "Class1    118\n",
      "Class2     94\n",
      "Class3     75\n",
      "Class4     73\n",
      "Class5     70\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Partitioning data\n",
    "\n",
    "Classes = df['Classes'].values\n",
    "\n",
    "Features = df.drop(['Classes'], axis=1)\n",
    "\n",
    "print(Features.describe())\n",
    "\n",
    "print(df.groupby('Classes').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# When creating a manual Machine learning algorithm one of the first things that has to be done\n",
    "# is the partitioning of the data. Code must be written in order to identify and organize feature\n",
    "# data from the output data. Descriptions and sample sizes must all be recognizable in order to \n",
    "# identify missing values or potential imbalance problems within the data. By not indentifying these\n",
    "# anomalies could result in less than accurate Machine learning. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 535
    },
    "id": "5xVOQuQ6JnPH",
    "outputId": "d39735ff-40de-4f71-84b9-83b2c955758a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2304426f280>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eeugxDBw4GEVFRQAuf+I+JKQlzp49U+l+v/zyMyZPfhAAoFQqccstY/Dvvzvwzz9/Y+TI0VAqqx81adWqNYqLLR+Ku0QURZw4cQwhIaFXrfVKVxshqU5oaCvEx59HixaB1j8mkwmLFs1HZmb6dT/HTz99i3HjJuLNN2fittvuRNeuEbhwIQVAw6xYEBYWjpSUJBQWXp5hT0iIr3SM/Nevv67EP//8jZ49++Dpp5/D99+vQo8evbBx40Z4e3vD09MLqakXKvUqOvosvvhimfU11bX/18umw67JZEJWVpb105ldunRBREQEpk2bhhMnTmD//v2YPn06Jk6ciCZNmshc7fUTBAE92vgBAA5HZ8pcDRER2arNmzfCZDLhvvseRMuWYZX+PPDAI1AoFPjjjzW47bb/4YsvPsXu3Ttx4UIKPvtsKc6fj0Pfvv0wYsQouLm5Y9astxEXF4uoqLOYN+99tGoVBn//JujYsRPi4mKxZcsmpKZewLffflkpVFbH3z8A5eVl2L59G9LT03Hw4H7MmPEGAMBgsPza/J577sfp0yfx5ZfLkZychH37duO7775Ev34DrI8zevQ4nDlzCpGRBzF69LhqnwuwLCfWunU43nnnLRw9ehgJCfGYP/8jnDsXhzvuuKfW/dTpnJGWlorMzNotX3rXXfchJiYKH388BwkJ8Th16gRmznwDKSlJCAys/jM3Op1lxjkq6iwqKsqr3cffvwlOnjyO6OgoXLiQglWrfsKaNZZVKKob36xvw4aNhIeHJ955523Exsbg1KmTePfdtwHUHEjz8/OwYMFH2L17J9LT03DgwD7ExkajW7duEAQB9977IH77bRVWr16FCxdSsHPnDsyb9yG0Wifr5690Oh1SUpKrveBHfbLp35enpaVh6NCh+OCDDzBp0iQIgoAlS5bgnXfewYMPPgitVotbbrkFr7/+utyl3rCIcD9s2JeIE+dzoDeYoFHL+8EJIiKyPRs3rkePHr0QFBRS5bbmzVtgwICB2LJlE3777U8olUrMnfsBiouLEBYWjrlzF1rvN3/+EixevABPPvkwnJyc0Ldvf0yZ8jwAYOTI0YiNjcaCBXNhMpkwZMgw3HnnPTh58niNdQ0ePBTR0fdjyZIFKCkpRtOmzTB27ATs3v0vzp49g4kTgdat2+D99+fhq6+W46efvoOPjy/uuOPuSiMBgYFBaN++I8xmM4KDq77GS5RKJebPX4qlSz/BG2+8DINBj7Zt22Phwk/RsWOnWvdz4sTbMHv2DDz44N3488+t19y/Y8dOmD9/Cb788lM88sh9cHbWoXv3nnjmmeetH/z6r4iInmjfviOeeuoRvP32rGr3mTbtFXz00WxMmfI4NBo1wsLC8dZb72DGjDcQFXUGXbp0q/Vruh4ajQYff7wYCxZ8hCeeeBju7u64//6HERMTBZWq+qj48MOTYTAYsGDBXOTm5sDb2weTJt2BJ554AoWF5bj77vug1Wrx228rsXjxAnh7+2D8+Fvx6KNPWB/j9tvvwtKln+D8+XP47rufJXt9gthQK/raGZPJjNzc6xuYvh6iKOKVT/cip7ACz07qhG7hfg323HWlUing5eWCvLwSu5zRs1Xsq3TYW+mwt9JorH0VRRF33jkRDzzwMMaNmyjJczTW3l5NWloqkpOT0KtXH+u27OwsTJw4CkuXflHrsN3QvfX2dqnVB9RseoyhMREEARHhlg/ZRUZnXWNvIiIix2E0GvHPP39j/vyPUFZWimHDRspdUqNSUVGBl19+DitW/IDU1AuIiYnCnDnvoUWLIHToUPsz5bbKpscYGpvubfywNTIZx+OyYTSZobKzRdqJiIiuh0qlwiefzAMATJ8+yzrnSg0jJCQUM2fOxvfff42vvloOrdYJPXr0wiefLKtxjMGe2P8rcCBhzT3g7qJBYYkeUYl56NjSR+6SiIiIGsTatZvkLqFRGzx4GAYPHiZ3GZLgqUMbolAIiLh4UYnDMRxlICIiIrpRDLs2pnsby9zu0Zgs6wLRRERERHR9GHZtTJsgT7g4qVBYakBsSr7c5RARERHZNYZdG6NSKtA17OIoA1dlICIiIrohDLs2KOLS1dRishrkMoFEREREjoph1wZ1CPGGVq1EXlEFEtKL5C6HiIiIyG4x7NogjVqJzq0sy45FRmfKXA0REdmikpJiDB3aD+PGjYDRaJS7HJv08cdzMHz4zRg5ciByc3Oq3J6eno5t2zZb//v228fhq68+q9cajEYjVq36qV4f88SJYzh+/Fi9PuaVpKhZTgy7Nqr7pVGGaI4yEBFRVdu2bYGXlzdKSoqxc+d2ucuxOXFxsfj991/xzDPP4dtvf4a3d9W162fPnoEDB/ZJWsfWrX9h8eIF9fqYTz/9GC5cSK7Xx7ySFDXLiWHXRnVq6QOVUoHMvDJcyCqRuxwiIrIxGzb8gT59bkJERA+sW7dG7nJsTlFRIQCgV68+aNq0WbX7NMTJJHs8YWWPNV8Nw66N0mlV6BjqDYAXmCAiosoSEuJx5swp9OzZG4MGDcGRI5FISkq03m40GvHll8tx221jMXRoPzz66P04dGi/9faUlGS89toLGDlyIEaPHooZM95AXl4uAGD27JmYMuXxSs935ba0tFT0798DP/zwDcaPH4k77piAkpJinD8fh1deeR633DIYgwb1wR13TMDPP/9Y6XEOHNiHJ554GEOH9sOtt47G558vg8lkwi+//Izhw29GeXm5dV+z2Yxbbx2N1at/qbYHhYUF+PjjOZg0aQyGDOmHp556BEeORAIANm5cj2effQIAcOedEzB79swq93/qqck4duwINm36E7ffPs66PScnG2+88TKGDeuPMWOGYvHi+TCZTNbbT548jmeemYwhQ/ph0qQx+PjjOSgpKa62xo0b1+P9998BAPTv38Na3549u/DII/dhyJB++N//JuKLLz6FXq+33m/fvj149NH7MXRoP4wdOxyzZ89EYWGh9XEA4P3336n2dQHAmTOn8PTTj2H48AG45ZbBePPNl5Genm69PSsrEzNmvI5bbhmE0aOH4tVXpyE5OemqNdszhl0bdnmUgXO7RET1TRRFiIYK+f7cwNmzDRv+gE7njD59bsLNNw+GSqXCunWrrbd/8sk8rF27GlOmPI/vv1+FXr364NVXX0BSUgKKiorwzDOTodfrsXDhcnzyyVKkpqbg7bdfq1MNmzb9iYULP8WsWR9AqVRh2rRn4O7ugeXLv8YPP/yCwYOHYunSTxAbGw0AOHXqBF5++Tl06dIVX3/9E1599S2sW7ca3377JUaMGAWj0VBpHCMy8iAKCvIxfPgtVZ7bZDJh2rQpOHHiKN5++1189dUPaNkyDC+8MAVnz57G0KHDMXv2RwCAL774Ds8991KVx/jww3no2LEzhgwZji+++L5Sb7t2jcD336/CU09NxapVK7Bp058ALKMRzz//NHr37ovvvvsZM2bMRnT0WUybNqXaf8+hQ4dj6tQXAQDr1v2FTp26YP/+vZg+/TWMH38rfvhhFV588TVs374Vs2ZNBwDk5+fjzTdfxpgx4/HTT7/h/ffn4tixo1i2bKH1cQBg6tQXq31dJpMJr7wyDV27RuC771Zi4cJPkZGRgQ8+eBcAUFZWZv1BYPHiz7FkyWfw8PDE448/hKyszGprtncquQugmnUJ84VSISAlqwQZuaVo4u0sd0lERA5BFEWU/jEb5ow42WpQNmkN3fg3IAhCne5nNBqxefNG9O9/M7RaJ2i1TujVqy82bdqAxx9/BiaTERs2rMPzz7+MwYOHAQCeeOIZAEBJSQmOHDmM0tISzJz5Ptzd3QEAr776NrZt21zp7OK13HrrHQgNbQkAyMvLwx133I1Jk+6Es7Ple9Wjjz6BFSu+x7lzcWjdug1+/XUl2rfviKeffg4AEBwcgpdffgN5eXnw9PREv34DsHnzRowcORqAJUz363eztcYrHTy4H9HRZ/H99yvRsmUYAOCll17H2bOnsWLFD5g160O4uVnu5+npBVdX1yqP4eHhAZVKBa1WCy8vL+v2gQOH4M477wYANGvWHL/+uhJRUWcxduwE/Pzz9+jVqw8eeOARAEBgYBBmzpyNO++cgKNHDyMiokel59BqnazP7eNjWUP/+++/xvjxkzBx4m0AgObNW+Dll9/A1KlPIi0tFcXFRdDr9WjSJAABAU0RENAUc+ZcPrt86XFcXV2rfV0lJSUoKMiHr68fAgKaolmz5njnnfeRl5cHAPj7780oLi7C22/PgkpliYGvvfY2jh49jD/++B2PPvpElZrtHcOuDXPVqdE2yBOnE/JwJCYLo/oEy10SEZHDEFC3kGkr9u/fg9zcHAwdOsK6bdiwkdi7dxd27NiGkJBQGAwGdOjQqdL9LgXeTZv+RGBgUKUQGRbWGmFhretUR4sWgda/e3l5YdKkO7B161+IjY1GSkoy4uJiAVjGEQDg/Pk49OrVp9JjDBo01Pr3MWPG47XXXkR2djacnXX4998deO+9j6p97vPn4+Dq6moNugAgCAK6dInAwYM39oGzwMCgSv/t5uYGvb4CABAdHY2UlCQMHz6gyv0SExOqhN3qxMRE4ezZ0/jzz7XWbZfOCickxKNv334YNmwkXn11Gnx8fNGzZ2/cdNMA3HzzoFrV7+7ujnvueQALFnyEL79cju7de6Jv334YMmS49TUUFhZi1KjBle6n1+uRmJhQq+ewNwy7Ni6ijT9OJ+QhMpphl4iovgiCAN34NwBj7c9k1juVps5ndQFgw4b1AIA333y5ym3r1q3GCy9cfRzh0tm8urhyZvUSrVZr/XtOTjaeeOJheHl5oV+/m9GzZx+0a9cekyaNqfXz9urVF97ePti69S94eHjAzc29Sji+pKYREFE0X9fru5JCUXXC89LziaIZI0aMsp7ZvZKnp1eVbdUxm0Xcc88DGDVqbJXbLp1JnTlzNh55ZDL279+LQ4cOYNast9G5c1csXPhprZ7jqaeexa233oH9+3cjMvIgFiz4CCtWfI+vv/4JomhGUFAwPvxwfpX76XS6Wj2+veHMro2LaO0LAUB8WiFyC8uvuT8REdWOIAgQ1Fr5/lxH0M3Ly8W+fbsxevQ4fPPNT5X+jBkzHidPnoAgCFCpVIiKOl3pvo8//hBWrfoJISEtkZychOLiyx+qio6Owtixw5GZmQGVSo3S0sqrAF368FJNtm79C4WFhfj006/x0EOPYeDAwSgqslwU6VJQDAlpibNnz1S63y+//IzJkx8EACiVStxyyxj8++8O/PPP3xg5cjSUSmW1z9eqVWsUF1s+FHeJKIo4ceIYQkJCr1rrler6bxAa2grx8efRokWg9Y/JZMKiRfORmZle7X3++xwtW7ZCUlJipcfIzMzA0qULUVpagtOnT2HRoo8RFBSCO++8B3PnLsTrr0/H4cOHrB8ivJqkpATMm/cBvLy8MHHi7XjvvY/w8ceLkZAQj7i4GISGtkJ6ehpcXd2szx8Q0BTLly/GsWNHr6svto5h18Z5uGoR1sIDAFdlICJq7DZv3giTyYT77nsQLVuGVfrzwAOPQKFQ4I8/1uC22/6HL774FLt378SFCyn47LOlOH8+Dn379sOIEaPg5uaOWbPeRlxcLKKizmLevPfRqlUY/P2boGPHToiLi8WWLZuQmnoB3377ZaVQWR1//wCUl5dh+/ZtSE9Px8GD+zFjxhsAAIPBcvb8nnvux+nTJ/Hll8uRnJyEfft247vvvkS/fpdHAkaPHoczZ04hMvIgRo8eV+1zAZblxFq3Dsc777yFo0cPIyEhHvPnf4Rz5+Jwxx331LqfOp0z0tJSkZmZUav977rrPsTEROHjj+cgISEep06dwMyZbyAlJQmBgdX/9vXS2dKoqLOoqCjHvfc+gH/++RvffPMFkpISERl5EO+//w5KSorh4+MLFxcXrFnzK5YtW4SUlGScPx+Hv//eghYtguDh4WmtOyEhHgUF+VWez8PDE9u2bcbcue8jISEeSUmJ2LTpT7i5uSM4OAQjR46Gu7sH3nrrFZw+fQqJiQl4770Z2L9/L1q1Cqu2ZnvHMQY70D3cD7EpBTgSnYXhPQKvfQciInJIGzeuR48evRAUFFLltubNW2DAgIHYsmUTfvvtTyiVSsyd+wGKi4sQFhaOuXMXWu83f/4SLF68AE8++TCcnJzQt29/TJnyPABg5MjRiI2NxoIFc2EymTBkyDDceec9OHnyeI11DR48FNHR92PJkgUoKSlG06bNMHbsBOze/S/Onj2DiROB1q3b4P335+Grr5bjp5++g4+PL+644+5KIwGBgUFo374jzGYzgoOrvsZLlEol5s9fiqVLP8Ebb7wMg0GPtm3bY+HCT9GxY6ca7/dfEyfehtmzZ+DBB+/Gn39uveb+HTt2wvz5S/Dll5/ikUfug7OzDt2798QzzzwPtVpd7X0iInqiffuOeOqpR/D227MwZMgwvPMO8MMPX+P777+Gu7s7+vW7GU89NRUAEBISitmz5+Kbb77A77//CoVCgYiInvj440XWEYu77roXK1Z8j8TEeMyZU/niDx4enpg3bxGWL1+CJ554CCaTCR06dMYnnyyDi4vlg2dLlnyOpUs/wYsvToHJZEabNm2xYMFS61nx6mq2Z4LoaCsH1xOTyYzcXNu4mEN2QRle+XQfBAFYMKU/3F00stajUing5eWCvLwSGI1mWWtxJOyrdNhb6bC30misfRVFEXfeOREPPPAwxo2bKMlzNNbeNoSG7q23twuUymsPKXCMwQ74eugQHOAGUQSOxnKUgYiIHIvRaMQ///yN+fM/QllZKYYNGyl3SeRAGHbtRA/rBSYYdomIyLGoVCp88sk87Nr1D6ZPn+WwqwKQPDizayciwv2weud5nE3MQ2m5Ac5O1c8GERER2aO1azfJXQI5KJ7ZtRNNfVzQ3NcFJrOI43E5cpdDREREZBcYdu1IRLhllCEyOlPmSoiIiIjsA8OuHel+cW73VHwuKvRVr2ZDRERERJUx7NqRQH9X+Hk6wWA04+R5jjIQERERXQvDrh0RBAHdw/0B8GpqRERERLXBsGtnLo0yHI/LhoGLYRMRERFdFcOunQlt5g5PVw3K9SacSciVuxwiIiIim8awa2cUV44y8AITRERERFfFsGuHIi6OMhyNzYLJzFEGIiIiopow7Nqh8EAPuOrUKCk3IiYpX+5yiIiIiGwWw64dUioU6NbaFwAQyVUZiIiIiGrEsGunLq3KcCQmC2ZRlLkaIiIiItvEsGun2gV7Q6dVoqBYj/MXCuUuh4iIiMgmMezaKbVKgS6tLKMMh2MyZa6GiIiIyDYx7NqxS6MMh6OzIHKUgYiIiKgKhl071jHUBxqVAtkF5UjKKJa7HCIiIiKbw7Brx7QaJTq19AHAUQYiIiKi6jDs2rmIK0YZiIiIiKgyhl0716WVL5QKAWk5pUjNLpG7HCIiIiKbwrBr55ydVGgf4g0AOMwLTBARERFVwrDrAKwXmOAoAxEREVElDLsOoGtrXwgCkJhRhKz8MrnLISIiIrIZDLsOwN1ZgzaBngAslw8mIiIiIguGXQfRvY0/AK7KQERERHQlhl0HERFumduNu1CA/OIKmashIiIisg0Muw7Cy02LVs3cAXCUgYiIiOgShl0HwgtMEBEREVXGsOtAul8cZYhOykdxmUHmaoiIiIjkx7DrQPy9nBHo7wqzKOJoLM/uEhERETHsOphLZ3d5gQkiIiIihl2Hc+lqaqcTclFWYZS5GiIiIiJ5Mew6mGa+Lmji7QyjScSJczlyl0NEREQkK4ZdByMIAnpYV2XIlLkaIiIiInkx7DqgSxeYOHE+B3qDSeZqiIiIiOTDsOuAQgLc4OOuhd5gxun4XLnLISIiIpINw64DEgQBEeH+AIBIrspAREREjRjDroO6tCrD8bhsGE1mmashIiIikgfDroMKa+4BdxcNSiuMiErMk7scIiIiIlkw7DoohUJARGtfAMDhGI4yEBERUePEsOvAurexzO0ejcmC2SzKXA0RERFRw2PYdWBtgjzh4qRCYakBsSn5cpdDRERE1OAYdh2YSqlA17CLowxclYGIiIgaIYZdBxdx6WpqMVkQRY4yEBERUePCsOvgOoZ6Q6tWIq+oAgnpRXKXQ0RERNSgGHYdnFqlROdWPgCAyOhMmashIiIialgMu43ApQtMHI7mKAMRERE1Lgy7jUCnlj5QKRXIzCvDhawSucshIiIiajAMu42ATqtCx1BvALzABBERETUuDLuNxOVRBs7tEhERUePBsNtIdAnzhVIhICWrBBm5pXKXQ0RERNQgGHYbCVedGm2DPAFwlIGIiIgaD4bdRiSijT8AXk2NiIiIGg/Zw67ZbMaiRYswYMAAdO3aFZMnT0ZycnKN+yckJODxxx9Hjx49cPPNN2PRokUwGo0NWLH9imjtCwFAfFohcgvL5S6HiIiISHKyh91ly5ZhxYoVmDVrFlauXAmz2YzHHnsMer2+yr4FBQW49957UVZWhu+++w7z58/Hpk2bMH36dBkqtz8erlqEtfAAwFEGIiIiahxkDbt6vR5ff/01pk6dikGDBqFt27ZYsGAB0tPTsWXLlir7//777ygtLcXChQvRoUMH9OjRA++99x5Wr16NlJQUGV6B/el+cZThCEcZiIiIqBGQNexGRUWhpKQEffv2tW5zd3dH+/btcejQoSr7JyYmomXLlvD29rZua9++PQAgMjJS+oIdQES4LwAgJiUfhSVVz54TERERORJZw256ejoAoGnTppW2+/v7W2/77/bMzEyYTCbrtgsXLgAAcnJyJKzUcfh66BAc4AZRBI7G8uwuEREROTaVnE9eVlYGANBoNJW2a7VaFBQUVNl/1KhRWLZsGT744AO88MILKC0txXvvvQeVSgWDwVDv9alUso80S6JXO38kphfhSEw2hvYIrPP9lUpFpa9UP9hX6bC30mFvpcG+Soe9lY6t9lbWsOvk5ATAMrt76e8AUFFRAZ1OV2X/kJAQLFy4ENOnT8dPP/0EZ2dnPPvss4iLi4Obm1u91qZQCPDycqnXx7QVQ3oF49cd53AmIRdqJw1cderrehx396r/RnTj2FfpsLfSYW+lwb5Kh72Vjq31Vtawe2l8ITMzE0FBQdbtmZmZaNOmTbX3GTJkCIYMGYLMzEx4enrCaDTiww8/RGBg3c9QXo3ZLKKw0DGvNOaiVqC5nwsuZJXgn4OJ6Ne56bXvdAWlUgF3dx0KC8tgMpklqrLxYV+lw95Kh72VBvsqHfZWOg3dW3d3Xa3OIssadtu2bQtXV1ccOHDAGnYLCwtx5swZ3HfffVX2j4yMxMKFC/HNN9/A39+yqsDGjRuh0+kQERFR7/UZjY77P0FEaz9cyCrBwbMZ6N2+yXU9hslkdugeyYV9lQ57Kx32Vhrsq3TYW+nYWm9lHarQaDS47777MG/ePPz999+IiorCtGnTEBAQgBEjRsBkMiErKwvl5ZYLILRs2RLR0dGYM2cOkpOTsW3bNrz33nt44okn4OrqKudLsTvd2/gBAE7F56JCb7rG3kRERET2SfYJ4qlTp+L222/HW2+9hbvvvhtKpRJfffUV1Go10tLS0L9/f2zcuBEA4O3tjeXLl+P48eMYO3YsPvzwQ0yZMgVPPvmkzK/C/gT6u8LP0wkGoxknz3MlCyIiInJMgiiKotxF2CKTyYzc3BK5y5DULzvi8NeBJPRu3wRPjO9Q6/upVAp4ebkgL6/Epn5NYe/YV+mwt9Jhb6XBvkqHvZVOQ/fW29ulVjO7sp/ZJfl0D7eMMhyPy4aB/8MTERGRA2LYbcRCm7nD01WDcr0JZxJy5S6HiIiIqN4x7DZiCkFA93DLqhaHo3k1NSIiInI8DLuNXMTFVRmOxmbBZOYoAxERETkWht1GLjzQA646NUrKjYhOype7HCIiIqJ6xbDbyCkVCnRr7QsAOBzDUQYiIiJyLAy7hO5tLHO7R2KyYOZKdERERORAGHYJ7YK9oNMqUVCsx/kLhXKXQ0RERFRvGHYJapUCXcIujTJkylwNERERUf1h2CUAly8wcTg6C7yoHhERETkKhl0CAHQM9YFGpUB2QTmSMorlLoeIiIioXjDsEgBAq1GiU0sfABxlICIiIsfBsEtWly4wwaupERERkaNg2CWrLq18oVQISMspRWp2idzlEBEREd0whl2ycnZSoX2INwBeYIKIiIgcA8MuVdL94ijDEY4yEBERkQNg2KVKurb2hSAAiRlFyMovk7scIiIiohvCsEuVuDtr0CbQE4Dl8sFERERE9oxhl6ro3sYfAFdlICIiIvvHsEtVRFy8mlrchQLkF1fIXA0RERHR9WPYpSq83LRo1cwdAEcZiIiIyL4x7FK1eIEJIiIicgQMu1St7hdHGaKT8lFcZpC5GiIiIqLrw7BL1fL3ckagvyvMooijsTy7S0RERPaJYZdqxAtMEBERkb1j2KUaXRplOJ2Qi7IKo8zVEBEREdUdwy7VqJmvCwK8nWE0iThxLkfucoiIiIjqjGGXaiQIgnWU4XB0pszVEBEREdUdwy5d1aULTJw4nwO9wSRzNURERER1w7BLVxUS4AYfdy30BjNOxefKXQ4RERFRnTDs0lUJgoCIcH8AvMAEERER2R+GXbqmS3O7x+OyYTSZZa6GiIiIqPYYdumawpp7wN1Fg9IKI6IS8+Quh4iIiKjWGHbpmhQKwfpBtcMxHGUgIiIi+8GwS7Vy6QITR2OyYDaLMldDREREVDsMu1QrbYI84eKkQmGpATHJ+XKXQ0RERFQrDLtUKyqlAl3DfAEAkVG8wAQRERHZB4ZdqrWIi6syREZlQhQ5ykBERES2j2GXaq1jqDe0aiVyiyoQy1EGIiIisgMMu1RrapUSnVv5AABW74jl2V0iIiKyeQy7VCcjegZCqRCw90Qa1u2Ol7scIiIioqti2KU6adXcAw+OagsAWLPzPD+sRkRERDaNYZfqbFC35hg/oCUA4MsNZ5CYXiRzRURERETVY9il6/LIuA7o1NIHeoMZi1afQH5xhdwlEREREVXBsEvXRalU4JlJndDUxxl5RRVYsuYkDEaT3GURERERVcKwS9fN2UmFqbd1houTCudTC/HNpiiu0EBEREQ2hWGXbkgTb2c8PbEjFIKA/aczsHF/otwlEREREVkx7NINaxfijXuHtwYArN55HkdjsmSuiIiIiMiCYZfqxeCIFhgS0RwA8Pn6M0jK4AoNREREJD+GXao3dw9rjfYhXqgwmLB49QkUlujlLomIiIgaOYZdqjdKhQJPTeyIJl465BRWYMnvJ2EwmuUui4iIiBoxhl2qVy5Oaky9vTN0WhXiUgrw/V9coYGIiIjkw7BL9a6pjwuemtgBCkHAnlPp2HwwWe6SiIiIqJFi2CVJdAz1wV1DwwAAv+6Iw7G4bJkrIiIiosaIYZckM7R7Cwzq2gwigM/+OI2UrGK5SyIiIqJGhmGXJCMIAu4ZHo62QZ6o0Juw6LcTKCzlCg1ERETUcBh2SVIqpQJP39oJ/p46ZBeUY9nvp2A0cYUGIiIiahgMuyQ5V92lFRqUiEnOxw+bo7lCAxERETUIhl1qEM18XfDE+I4QBGDXiTRsjUyRuyQiIiJqBBh2qcF0buWD/w22rNCwanssTpzLkbkiIiIicnQMu9SghvcMRP/OTSGKwGd/nEJqdoncJREREZEDY9ilBiUIAh4Y2QbhLTxQVmFZoaG4zCB3WUREROSgGHapwamUCjw9qRN8PZyQmV+GZb+f5AoNREREJAmGXZKFu7MGU2/vDK1GiaikfKzYGsMVGoiIiKje1TnsVlRUSFEHNUIt/FzxxLgOEAD8cywV249ckLskIiIicjB1Drv9+vXDjBkzcOLECSnqoUama2tf3D6oFQDg522xOB2fK3NFRERE5EjqHHYfeeQR7N+/H//73/8wevRofPnll8jKypKiNmokbukdhJs6BsAsili29hTScrhCAxEREdWPOofdp59+Gps3b8ZPP/2E7t2747PPPsPgwYPx+OOPY/PmzTAY+Ml6qhtBEPDgLW3Rqrk7yiqMWLT6JErKeRwRERHRjbvuD6hFRERg1qxZ2LNnDxYuXIiysjI8//zz6N+/P+bMmYMLFzh/SbWnVikwZVJn+LhrkZFbik/XnoLJzBUaiIiI6Mbc0GoMaWlp+Prrr7Fo0SIcOnQIISEhmDRpEv7991+MHj0aGzdurK86qRHwcNHg2ds6Q6NW4ExCHlZui5O7JCIiIrJzdQ67xcXFWL16Ne6//34MHToUn3/+OTp06IAVK1Zg06ZNePXVV7Fhwwb06dMH77//vhQ1kwMLauKGyWM7AAD+PpKCHUf5GwIiIiK6fqq63qFfv36oqKhA165d8e6772L06NFwdnausl+nTp1w5syZeimSGpfubfww6eaWWPPvefy0JQYBXjq0C/GWuywiIiKyQ3U+s3vvvfdi48aNWLlyJW6//fZqgy4APPzww/jnn39utD5qpMb0DUaf9k2sKzRk5JXKXRIRERHZoTqH3VdeeQV5eXlYunSpdduZM2fw3HPP4dSpU9ZtLi4uUCqV9VMlNTqCIOChUW0R2tQdJeVGLPrtBEq5QgMRERHVUZ3D7s6dO/Hggw9i9+7d1m2CICAhIQH33HMPIiMj67VAarw0aiWeva0TvNy0SMspxfJ1p7lCAxEREdVJncPu4sWLMWbMGKxYscK6rV27dli3bh1GjRqF+fPn12uB1Lh5umox9bbO0KgUOBWfi1+2n5O7JCIiIrIjdQ67586dw8SJEyEIQpXbJk6ciKioqHopjOiS4AA3PDa2PQBga2Qy/j2eKnNFREREZC/qHHbd3NwQHx9f7W3Jyck1fmCtJmazGYsWLcKAAQPQtWtXTJ48GcnJyTXun5OTgxdffBF9+vRB7969MW3aNGRkZNTpOcn+9Gjrj4n9QwEAP2yORnRSnswVERERkT2oc9gdPnw4Fi5ciB07dlTavmvXLixcuBDDhw+v0+MtW7YMK1aswKxZs7By5UqYzWY89thj0Ov11e7//PPPIzU1Fd988w2++eYbpKam4plnnqnryyA7NK5fCHq29YfJLGLp76eQmV8md0lERERk4+ocdqdNm4bg4GA89dRT6NSpEwYMGIBOnTrh8ccfR2BgIF588cVaP5Zer8fXX3+NqVOnYtCgQWjbti0WLFiA9PR0bNmypcr+hYWFOHjwICZPnox27dqhffv2ePzxx3Hy5Enk5+fX9aWQnREEAY+MaYeQADcUlxmw+LcTKKswyl0WERER2bA6X1TC1dUVK1euxM6dO3H48GEUFBTAzc0NPXr0wKBBg6BQ1D4/R0VFoaSkBH379rVuc3d3R/v27XHo0CGMHTu20v5OTk5wcXHB2rVr0atXLwDAunXrEBoaCnd397q+FLJDWrUSz97WGe9+dwgXskvw2R+nMfW2zlAoqs6QExEREdU57AKAQqHA4MGDMXjw4Cq3iaJY7YfXqpOeng4AaNq0aaXt/v7+1tuupNFo8OGHH2L69Ono0aMHBEGAv78/fvzxxzqF7NpSqer/MR2BUqmo9LWh+XnpMO3OLpj9/WGcOJeD1f+ex93DWstSS32Su6+OjL2VDnsrDfZVOuytdGy1t9cVdjdu3IiDBw9Cr9dDFEUAlpBbWlqKY8eO4d9//63V45SVWWYuNRpNpe1arRYFBQVV9hdFEWfPnkW3bt3w2GOPwWQyYcGCBXj66afx888/w9XV9XpeTrUUCgFeXi719niOyN1dJ9tzd/dywfN3mTH3x8PYtD8R4cHeGNYrSLZ66pOcfXV07K102FtpsK/SYW+lY2u9rXPYXbJkCZYsWQI3NzcYjUao1WqoVCrk5uZCoVDgjjvuqPVjOTk5AbDM7l76OwBUVFRAp6vaqE2bNuHHH3/Ejh07rMF2+fLlGDx4MH777Tc89NBDdX05NTKbRRQW8hK11VEqFXB316GwsAwmk3wXeegU4oXx/UPxx+54LP3tGNyclAgP9JStnhtlK311ROytdNhbabCv0mFvpdPQvXV319XqLHKdw+7vv/+OiRMn4oMPPsCiRYuQmpqKOXPm4NSpU3j88cfRunXtf518aXwhMzMTQUGXz8plZmaiTZs2VfaPjIxEaGhopTO4Hh4eCA0NRWJiYl1fyjUZjfyf4GpMJrPsPRrfLwQXsopxODoLC389jrcf7AFfD9v6ibKubKGvjoq9lQ57Kw32VTrsrXRsrbd1HqrIyMjAuHHjIAgC2rVrh6NHjwIAOnbsiCeffBK//vprrR+rbdu2cHV1xYEDB6zbCgsLcebMGfTs2bPK/gEBAUhMTERFRYV1W2lpKVJSUhASElLXl0IOQCEIeGxMewT5u6Ko1IBFv51EuZ4rNBAREZFFncOus7Oz9QNowcHBSElJQXl5OQDLZYNTUlJq/VgajQb33Xcf5s2bh7///htRUVGYNm0aAgICMGLECJhMJmRlZVkff+LEiQAsa+1GRUUhKioKL7zwArRaLSZNmlTXl0IOQqtRYurtneHuokFKVjG+WH8G5ouz5ERERNS41TnsdurUCWvXrgUAhIaGQqlUYt++fQAslxL+74fNrmXq1Km4/fbb8dZbb+Huu++GUqnEV199BbVajbS0NPTv3x8bN24EYFmlYcWKFRBFEQ8++CAefvhhqNVqrFixAm5ubnV9KeRAvN2d8OykTlApFTgam43f/z0vd0lERERkAwRRrNspsEOHDuHhhx9G//79sXz5crz55pvYunUrevfujd27d2PYsGGYO3euVPU2GJPJjNzcErnLsEkqlQJeXi7IyyuxqZkcANh3Kh1f/HkGADB5bHv07Rggc0W1Z8t9tXfsrXTYW2mwr9Jhb6XT0L319naR5gNqPXv2xG+//Ybo6GgAwPTp06FQKHDkyBHccssteO211+peLVE96dsxABeyS7BxfyK+2RQFfy8dWjX3kLssIiIikkmdw+6yZcswcuRITJgwAYBlTdxZs2bVe2FE12vSwJZIyynB0dhsLF5zEtMf7AFvd6dr35GIiIgcTp1ndj/77LM6fQiNqKEpBAGPjW2PFn4uKCzR4/0fD2PX8VQYuZ4iERFRo1PnsBsWFob4+HgpaiGqNzqtClNv7wwfdy1yCyvwzaYovPXFAew5mQaTmaGXiIiosajzGMPgwYMxf/587Nq1C23atIGzs3Ol2wVBwDPPPFNvBRJdL18PHd6b3Af/HL2AjfsTkZlfhq82nMWfexMwvn8oerdrAoVCkLtMIiIiklCdV2No27bt1R9QEHD27NkbKsoWcDWGmtnjJ1kr9CZsP5KCTQeSUFxmAAA09XHGhP6h6NHWHwpB/tBrj321F+ytdNhbabCv0mFvpeMwqzFERUVdV0FEctJqlBjVJxiDujXH9iMp+OtAEtJySrF83Wk035OACf1DEdHGzyZCLxEREdWfOoddInum06owpm8IhkS0wNbIZGw+mIwL2SVYtvYUWvi5YuKAUHRr7Wu9SiARERHZtzqH3ddff/2a+3zwwQfXVQxRQ9FpVRjfLxTDurfAlkPJ2BqZjJSsYixZcxJBTVwxsX9LdAnzYeglIiKyc3UOuwcOHKiyrbS0FPn5+fD09ESnTp3qpTCihuDspMbEAS0xrEcgthxKwtbIFCRlFGPR6hMICXDDxAEt0amlN0MvERGRnapz2N2+fXu128+dO4cpU6Zg4sSJN1oTUYNz1akx6eZWGN4jEJsPJmPb4WQkpBfhk1+Po1Uzd0wYEIoOIQy9RERE9qbO6+zWpFWrVnj22WexZMmS+npIogbn5qzB7YNa4aMnb8ItvYKgUSlwLrUQ81cdxwc/HcHZhFzUcQETIiIiklG9hV0AcHV1xYULF+rzIYlk4e6iwZ1DwjDnyb4Y3iMQapUCcSkFmLvyGD5acRTRSXlyl0hERES1UOcxhtTU1CrbTCYTMjIysGjRIrRq1apeCiOyBR6uWtw9rDVu6R2EjfsTsfPYBUQn52POiqNoF+yFiQNC0bqFp9xlEhERUQ3qHHaHDBlS7dyiKIpwcnLiGAM5JC83Le4dHo5RvYOwYV8i/j2eirOJeTibmIcOod6Y2D8UrZp7yF0mERER/Uedw+77779fJewKggBXV1f07t0bbm5u9VYcka3xdnfC/SPbYFQfS+jdfSINp+NzcTo+F51a+mDigFCENnWXu0wiIiK6qM5hd9KkSTCbzYiJibFeOjgrKwtnzpyBTqer9wKJbJGvhw4P3tIWo/sEY/3eBOw9mY6T53Nw8nwOuob5YkL/UAQH8Ac/IiIiudX5A2oZGRmYMGECpkyZYt125swZPPHEE7jvvvuQn59fn/UR2TQ/Tx0eGd0Osx/vjX4dAyAIwLG4bLzz7SEsWXMSyZnFcpdIRETUqNU57H700UfQ6/WYN2+eddvAgQOxZs0a5Ofn4+OPP67XAonsQRMvZzw6tj1mT+6DPh2aQABwJCYLM74+iGW/n0RKFkMvERGRHOocdvfu3YuXXnoJXbt2rbS9ffv2eO6557Bjx476qo3I7gR4O+PxcR0w67He6NXOHwKAyOgszPjqIJavO4XU7BK5SyQiImpU6jyzq9froVQqq71Np9OhpITfzIma+brgyQkdMfamYvyxOx6R0Vk4eDYTh85moneHJhjfLxQB3s5yl0lEROTw6nxmt0uXLvjmm29gMBgqbTcajfj+++/RuXPneiuOyN618HPF07d2wsyHe6Jba1+IAPafzsCbX+zHV3+eQWZeqdwlEhERObQ6n9mdOnUq7r//fgwdOhQ333wzfHx8kJubiz179iAnJwc//PCDFHUS2bWgJm549rbOSEwvwrrd8TgWl409p9Kx73QGbuoUgHE3haCpr4vcZRIRETkcQRRFsa53OnPmDJYvX44jR44gPz8fbm5u6NGjB55++mm0a9dOijobnMlkRm4uRzKqo1Ip4OXlgry8EhiNZrnLsUvxaYVYuyseJ8/nAACUCgE3d2mG+8a0hxoi+1rPeMxKh72VBvsqHfZWOg3dW29vFyiV1x5SuK6wC1guEXxpdresrAxGo9GhLijBsFszvlHUn3MXCrB2dzxOx+cCADRqJV6+uxtaNeOFKeoTj1npsLfSYF+lw95Kx1bDbp1ndg0GA2bMmIE777zTuu3o0aPo27cv5syZA7OZBw5RbbVq7oEX/9cVr90bgdYtPKA3mLBkzQnkFVXIXRoREZFDqHPYXbx4Mf744w+MGTPGuq19+/Z46aWX8Msvv+DLL7+s1wKJGoPwQE+8ck8EggPcUFCsx6drT8Fo4g+OREREN6rOYXf9+vV49dVX8cgjj1i3eXp64qGHHsK0adPw22+/1WuBRI2FVqPEGw/3grNWhbgLBVj5d6zcJREREdm9OofdvLw8BAYGVntby5YtkZ6efsNFETVWzXxd8eTEjgCA7UcuYM/JNJkrIiIism91DrstW7bE5s2bq71t+/btCA4OvuGiiBqzrq19MaF/KADg+83RSEwvkrkiIiIi+1XndXYfeOABvPbaa8jPz8ewYcOs6+zu2LEDmzZtwgcffCBFnUSNyrh+IUhIK8TxczlYsuYkZjzcE646tdxlERER2Z06h92JEyeipKQEy5Ytw5YtW6zbvby8MH36dEyYMKFeCyRqjBSCgMnj2uPdbyORmV+Gz9adwrQ7u0KhEOQujYiIyK7UeYwBAO69917s3r0bGzduxIoVK/Dnn39i7dq1yMnJwZAhQ+q7RqJGydlJjSmTOkGjVuB0Qh5+33Ve7pKIiIjsznWFXQAQBAEtW7ZESUkJFixYgKFDh2LJkiXWC00Q0Y1r4e+Kh0dZrkq4YV8iDkdnylwRERGRfanzGAMA5Obm4rfffsMvv/yCCxcuwNXVFbfeeismTJiAHj161HeNRI1a7/ZNEJ9WiC2HkvHlhrNo5uuCpj4ucpdFRERkF+oUdvfv349Vq1Zh27ZtMJlM6N69Oy5cuIClS5eiV69eUtVI1OjdMbgVkjKKEJWUjyVrTuKtB3pAp72un1WJiIgalVqNMXz77bcYNWoUHnroIZw5cwZPP/00tm/fjqVLl0IURQgCPzRDJCWlQoEnJ3SEl5sWaTml+GrDWYiiKHdZRERENq9WYffDDz+ERqPB999/j82bN+Opp55CQEAAQy5RA3J30eDpWztCpRRwJCYLG/cnyl0SERGRzatV2B0zZgwSExPxxBNP4Omnn8bWrVthNBqlro2I/qNVMw/cOzwcALDm3/M4FZ8jc0VERES2rVZDfx9//DGKi4uxfv16rFmzBs8++yy8vLwwbNgwCILAM7xEDWhg1+aITyvEv8fT8Nm605jxUE/4eurkLouIiMgm1XrpMVdXV9x999349ddfsX79ekyYMAHbt2+HKIp44403sHDhQsTFxUlZKxFddO/wcIQ2dUNJuRFLfj8JvcEkd0lEREQ26brW2W3dujVee+017Ny5E4sXL0bLli3xxRdfYNy4cRg/fnx910hE/6FWKfHMrZ3g5qxGUkYxvt8czQ+sERERVeO6LyoBACqVCsOHD8fy5cvxzz//4IUXXuAsL1ED8XZ3wpMTOkIQgL2n0rHj6AW5SyIiIrI5NxR2r+Tr64vJkydj48aN9fWQRHQN7YK9cMegMADAz9tiEZdSIHNFREREtqXewi4RyWNkr0D0bOsPk1nE0rUnkV9cIXdJRERENoNhl8jOCYKAh0e3RXNfFxQU67Fs7SkYTWa5yyIiIrIJDLtEDsBJo8KUSZ2g0yoRl1KAVdu5MgoRERHAsEvkMJp4O2Py2A4AgL8Pp2DvqTSZKyIiIpIfwy6RA+na2hfj+4UAAL77KxqJ6UXyFkRERCQzhl0iBzO+fyg6t/KBwWjG0t9PorjMIHdJREREsmHYJXIwCkHA5HHt4efphOyCcnz+x2mYzbzgBBERNU4Mu0QOyMVJjSmTOkOjUuBUfC7W7j4vd0lERESyYNglclCB/q54aFRbAMCfexNxJCZL5oqIiIgaHsMukQPr0yEAw3sEAgC+/PMM0nJKZK6IiIioYTHsEjm4Owa3QnigJ8r1JixZcxJlFUa5SyIiImowDLtEDk6lVOCpiR3h6apBWk4pvt54FqLID6wREVHjwLBL1Ah4uGjwzK2doFQIOBydhb8OJMldEhERUYNg2CVqJFo198C9w8MBAL/tPIfTCbkyV0RERCQ9hl2iRmRg12bo37kpRBH4bN1pZBeUyV0SERGRpBh2iRoRQRBw/4hwhAS4objMgKVrTkFvMMldFhERkWQYdokaGbVKiWdu7QRXnRqJGUX4YUs0P7BGREQOi2GXqBHy8XDCkxM6QBCAPSfT8c+xVLlLIiIikgTDLlEj1T7EG7cPagUAWLE1BnEXCmSuiIiIqP4x7BI1Yrf0CkKPNn4wmUUs+/0kCoor5C6JiIioXjHsEjVigiDg4dHt0MzXBfnFeny69hSMJrPcZREREdUbhl2iRk6nVeGZWztCp1UiJqUAv+yIk7skIiKiesOwS0Ro6uOCx8a0BwBsi0zBvtPpMldERERUPxh2iQgA0C3cD2NvCgEAfLcpCkkZRfIWREREVA8YdonIamL/UHRs6Q290Ywla06iuMwgd0lEREQ3hGGXiKwUCgGPj+sAXw8nZBeU4/P1p2E284ITRERkvxh2iagSV50aUyZ1gkalwKnzuVi3O17ukoiIiK4bwy4RVRHUxA0P3tIWALB+bwKOxmbJXBEREdH1Ydglomr17RiAYd1bAAC+/PMM0nNLZa6IiIio7hh2iahGdw4JQ3gLD5RVmLBkzUmU641yl0RERFQnDLtEVCOVUoGnJnaEh6sGqdkl+HpjFESRH1gjIiL7wbBLRFfl4arFM7d2glIhIDIqE5sPJstdEhERUa3JHnbNZjMWLVqEAQMGoGvXrpg8eTKSk6v/Zrp48WK0adOm2j+vv/56A1dO1HiENffAPcNaAwB+/ScOZxNyZa6IiIiodmQPu8uWLcOKFSswa9YsrFy5EmazGY899hj0en2VfR955BHs3r270p9HH30Uzs7OeOihhxq+eKJGZFC35ujXKQCiCHy67jRyCsrlLomIiOiaZA27er0eX3/9NaZOnYpBgwahbdu2WLBgAdLT07Fly5Yq+7u4uMDPz8/6JysrC99//z2mT5+ONm3ayPAKiBoPQRBw/4g2CG7ihuIyA5b+fhIGo0nusoiIiK5K1rAbFRWFkpIS9O3b17rN3d0d7du3x6FDh655/3fffRc9evTArbfeKmWZRHSRRq3EM5M6wlWnRkJ6EWZ8fQj/HL2ACgNDLxER2SZZw256ejoAoGnTppW2+/v7W2+ryY4dO3D06FG8+uqrktVHRFX5eujw1IQO0GlVSM8txfebo/Hysr1Y8+855BdXyF0eERFRJSo5n7ysrAwAoNFoKm3XarUoKCi46n2/+eYbDB48GO3atZOsPpVK9pFmm6RUKip9pfphT33tFOaLT6b2x85jqdhyMAnZBeX4c28iNu1PQp8OAbildxCCA9zkLtPKnnprb9hbabCv0mFvpWOrvZU17Do5OQGwzO5e+jsAVFRUQKfT1Xi/1NRUHDhwAJ9//rlktSkUAry8XCR7fEfg7l7zvxFdP3vpqxeAu2/xwJ0j2mL/qTSs23kOZxNysedkGvacTEPnMF9MGNgKPdo2gUIhyF0uAPvprT1ib6XBvkqHvZWOrfVW1rB7aXwhMzMTQUFB1u2ZmZlX/cDZtm3b4O3tjX79+klWm9ksorCQl0etjlKpgLu7DoWFZTCZzHKX4zDsua/tAz3Q/r4InLtQgM0HknDwbCZOxGXjRFw2ArydMbJ3EPp3agqtRilLffbcW1vH3kqDfZUOeyudhu6tu7uuVmeRZQ27bdu2haurKw4cOGANu4WFhThz5gzuu+++Gu8XGRmJXr16QaWStnyjkf8TXI3JZGaPJGDPfQ1u4obHx3fAbQNb4e/DKdh5PBXpuaX4blMUftsRh0HdmmNIRAt4uWllqc+ee2vr2FtpsK/SYW+lY2u9lTXsajQa3HfffZg3bx68vb3RvHlzzJ07FwEBARgxYgRMJhNyc3Ph5uZWaczhzJkzuO2222SsnIiuxsfDCXcOCcO4fiHYfTIN2yKTkZVfjg37EvHXgST0auePET1ta66XiIgck6xhFwCmTp0Ko9GIt956C+Xl5ejZsye++uorqNVqpKSkYOjQofjggw8wadIk632ysrLg6ekpX9FEVCs6rQrDewRiaEQLHI3NxtZDSYhJKcC+0xnYdzoDbYM8MbxnILqE+UIh2MZcLxERORZBFEVR7iJskclkRm5uidxl2CSVSgEvLxfk5ZXY1K8p7F1j6Wt8WiG2HkrGoahMmMyWt58mXjoM6xEo2VxvY+mtHNhbabCv0mFvpdPQvfX2drH9mV0ianxCm7rj8fEdcPugVvj7SAp2Hk1FRl4Zftoag7W7zuPmrs0wNKIFvN2drv1gRERE18CwS0Sy8HZ3wh2DwjDuphDsOZmOrZHJyMwrw6b9SdhyMBk92/ljRM9AhAS4y10qERHZMYZdIpKVk0aFod1bYHC35jgel40th5IRnZyP/aczsP90BsIDPTGiZyC6hvnazHq9RERkPxh2icgmKBQCuoX7oVu4HxLTi7DlkGW93pjkfMQk58PfU4dhPVqgf+emcNLwrYuIiGqH3zGIyOYEB7hh8rgOuH1QGLYfScE/Ry8gM78MK7bFYu2ueAzs2gxDu3Oul4iIro1h1waYi7JhvHBa7jJqzaTWwNSlLwDbuvY1OR4vNy1uG9gKY/uGYO+pNGw5lIyMvDJsOpCEzVfM9YY25VwvERFVj2HXBpRv/wymjFi5y6iT5D0/Qdt9ApTtBkNQ8DAiaWk1SgyOaIGB3ZrjxLkcbDmYhKikfBw4k4EDZzLQuoUHRvQMQrfWnOslIqLKmFJsgLrzSAgxrnKXUWvmgnSY89NQtucnCKf+hlOfu6AM6gKBFwUgiSkEAV3DfNE1zBdJGUXYcigZB85kIDalALEpJ+Hn6WRdr1en5dsbERHxohI14kUlaqZUiFAl7kfOPysglhVZtjXvAG3fu6D0DpS5OvvFhc6vT15RBXYcTcGOIxdQUm4EYLly28AulrleHw8n9lZC7K002FfpsLfSsdWLSjDs1oBht2aXDubc9CyURv4B/cktgNkICALUbQZC03MSFDrOUNYV34BvTIXBhH2n0rHlUDLSc0sBWM4E92jrh1F9gtGjYzP2VgI8bqXBvkqHvZUOw66dYdit2X8PZnNhJioO/AJjfKRlB7UTNN3GQdNxOASVRt5i7QjfgOuHWRRx6nwONh9MxtnEPOv2Di19MO6mYIS38JSvOAfE41Ya7Kt02FvpMOzaGYbdmtV0MBvTolGx72eYsxMAAIKbH7S974AqtCfneWuBb8D1LymjCFsjLXO9RpPlra5NoCcmDghFmyAvmatzDDxupcG+Soe9lQ7Drp1h2K3Z1Q5mUTTDGLsPFQd/hViaDwBQBoRD2/duKP1CZajWfvANWDpFZQZsPZyCv/YlWENvu2AvTBwQitY803tDeNxKg32VDnsrHYZdO8OwW7PaHMyioQL64xuhP74JMOkt92vdD9pet0PhwjNq1VGKBrg7iSgyOfENuJ5dOmbPJeZg3a54/Hs8FSaz5a2vQ6g3JvYPRavmHjJXaZ8YHKTBvkqHvZUOw66dYditWV0OZnNxLioO/gpj3L6Ld9ZA02U0NF1GQVBpG6Ba2yaKIkzpMTBE74bx/EHAqIfzzQ9C2XaQ3KU5lP8es9kFZdiwLxG7T6RZQ2/nVj6Y0D+UF6ioIwYHabCv0mFvpcOwa2cYdmt2PQezKfM8yvetgDkjDgAguHhB2/N2qFr3hSA0viuxmYtzYIjZA0PMboiFmVVu1/a+E5ouo2WozDHVdMxm5Zdh/d4E7D2ZDvPFt8KuYb6Y0D8UwQFucpVrVxgcpMG+Soe9lQ7Drp1h2K3Z9R7MoijCeP4QKg6sglicAwBQ+IVC2/ceqAJaS1WuzRCNehgTDsMQvRumC2cAXPxfT+0EdcuecGp/M5QZZ5G/dw0AWFa06DGJH+6rB9c6ZjPySvHnngTsPZ2OS++IEeF+mNA/FIH+9nPBFzkwOEiDfZUOeysdhl07w7Bbsxs9mEWjHvpTW6A/+idgKLc8Zste0Pa+Awo3v/ouV1aiKMKceQ6G6N0wnDsAGMqstymbtoW6zQCoQntAUGutfU37eyXK9v8KAFB3HA5t37sb5dnv+lTbYzY9txR/7InHgdMZl34UQY+2/pjQLwTN/Rh6q8PgIA32VTrsrXQYdu0Mw27N6utgNpcWQB+5BoaofwGIgFIFTaeR0HQdC0Gjq7+CZWAuyYMhdi+MMbthzk+zbhdcfaAO7w91eH8o3CsH+yv7Wnp8Kyr2/GDZHj4ATjc/DEHBwHu96nrMpmaX4I898Th0NhMiAAFAz3b+mNA/FE19XCSv154wOEiDfZUOeysdhl07w7Bbs/o+mE05SajY9zNMqWcBAILOHZoek6Buc7NdBTzRZIAx8RgM0btgSjkJ6+/DlRqoWvaAOrw/lM3a1niW9r99NcTsQfnOLwFRhKplTzgNfgKCUtWAr8hxXO8xm5JVjD92xyMyOgsAIAhAn/ZNML5fKJp4O0tVrl1hcJAG+yod9lY6DLt2hmG3ZlIczKIowpR4DOUHVkIsyAAAKLwDoe17N1TN29fLc0hBFEWYcxJhiN4FQ9x+oOLyMaNoEgZ1mwFQt+xVqzPV1fXVEB+J8r8/BcwmKAM7Qzf8Ga5icR1u9JhNyijCut3xOBqbDcASem/qEIBx/ULg79W4Qy+DgzTYV+mwt9Jh2LUzDLs1k/JgFk1GGM78jYrD6wB9qeX5grtB2/t/UHgG1Otz3QhzWSGMsftgiNkNc26ydbvg4gV1636WMYU61lvjlemST6Jsy2LApIeyaRvoRj5v92MeDa2+jtmE9EKs2xWP4+cufsBSENCvUwDG3RQCX8/G+W/C4CAN9lU67K10GHbtDMNuzRriYBbLi1FxeC0MZ7YDohkQlFB3GApt9wkQtPLMTIpmI0xJJ2GI2QVj4nFANFluUKqgCo6Aus0AKJt3uO7Ri6v11Zgeg7JNCwBDGRR+oXAe9SIEJ35gqrbq+5g9n1qIdbvjcfK8JfQqFQL6d26KsX1D4OPhdMOPb08YHKTBvkqHvZUOw66dYditWUMezKb8VFTsXwVT0nHLBq0LtN0nQt1+MARFw8yvmnJTYIjeBWPcPohlhdbtCr9Qy4fNwvrUSwC/Vl9N2Qko2zAPYkUxFF7NoRvzMhTOnjf8vI2BVMds3IUCrNt1HqcT8gBYQu/NXZthTJ9geLs3jtDL4CAN9lU67K10GHbtDMNuzeR4ozCmnELFvpUw56UAABSeTaHt8z8oA7tIsg6tWF4Mw7n9METvhjk7wbpd0LlD1fomy4fNvFvU63PWpq+mvAso2zAXYmk+BHd/OI952eGWa5OC1MdsTHI+1u2Ox9lES+hVKRUY2LUZxvQNhqerY89YMzhIg32VDnsrHYZdO8OwWzO53ihEswmGqJ3QR/4OsbwIAKBs3gHavnfXS/AUzWaYUk7BELMbxoQjgNlouUFQQhXcFeo2/aEM7CTZGeXa9tVcmInSDXMhFmVBcPG2BF7PppLU5Cga6piNSszD2t3xiEnOBwCoVQoM7tYco/oEw8NFI9nzyonBQRrsq3TYW+kw7NoZht2ayf1GIepLUXFkPQyntloCqSBA3XYQND1uhULnXufHM+enwRCzG4aYPRBL863bFT6BUIcPgCqsz3U9bl3Vpa/mkjyUbZgLc34qBCc36Ea/BKVvsOQ12quGPGZFUcTZxDys3RWPuAsFAACNSoEhES1wS58guDs7VuiV+/3AUbGv0mFvpcOwa2cYdmtmK28U5sJMVBz4Bcb4SMsGtQ7aiHFQdxwOQam+6n1FfRkM5w5YVlPIiLt8g9YF6rC+lg+bNXB4rGtfzWWFKNv4Mcw5iYBGB+dRL0LZJKwBKrU/chyzoijidEIu1u6Kx/lUy6y3Vq3E0O4tcEvvILjqrn6M2gtbeT9wNOyrdNhb6TDs2hmG3ZrZ2huFMS0aFftWwJydCAAQ3Pyg7X2n5TK8V8zziqIZptQoy4fN4g8DJr3lBkGAMrAz1OH9oQrues2gLJXr6auoL0XZpgUwZcQCKi10I5+z6XWJ5SLnMSuKIk6ez8HaXfFISLeM32g1Sgzv0QIjetp/6LW19wNHwb5Kh72VDsOunWHYrZktvlGIohnG2L2oOPibdRRB2bQNtH3uhqB1hiFmDwwxuyEW51jvo/BsBnWb/lC1vskmVjW43r6KhgqUbV0MU8opQKGCbtgzUIV0k7BS+2MLx6woijgel4O1u84jKbMYAKDTKjG8RyBG9AyEs5N9hl5b6K0jYl+lw95Kh2HXzjDs1syW3yhEQwX0xzdCf3zT5TO3V9LooG7VG+o2A6DwaynJSg7X60b6KpoMKP97OYwJhwFBAafBk6EO6ytRpfbHlo5ZURRxJCYb63bHIyXLEnqdtSqM6BWI4T0CodPa1yWhbam3joR9lQ57Kx2GXTvDsFsze3ijMBfnoOLgbzDG7QMgQNm8PdRtBkAVEgFBZZsfELrRvopmE8p3fgVj7F4AArQDHoSm3aB6r9Me2eIxaxZFHInOwtrd8UjNtrzXuDipcEvvIAyJaGE3odcWe+sI2FfpsLfSYdi1Mwy7NbOnNwpzcQ4gKKBw8ZK7lGuqj76KohkVe360XHkOgLb3/6DpMqo+y7RLtnzMms0iDkVlYt3ueKTnWi6R7apTY+KAUAzs2gzK67wiX0Ox5d7aM/ZVOuytdGw17NrHqQOi66Rw9ZG7hAYlCApo+90PQaOD/tgGVBxYBdFQBk33W21qZIMuUygE9G7fBD3b+uPA2Qz8sTseGXll+HFLDLYfuYC7hoahY2jjOo6JiOoTwy6RgxEEAdpedwBqHfSHfoP+yB8Q9WXQ9r0bgmDbZwkbM4VCQN8OAejVzh87j6Vi7S7LeMP8VcfRuZUP/jckDE19bvyy1EREjQ3DLpGD0nYbC0HjZBlrOLUVMJRDO+BhCDb+a/HGTqmwXICid/smWL8nAX8fTsGJczk4HZ+LwRHNMb5fqN0vV0ZE1JD4XY/IgWk6DIPToMcAQYAhehfKt38K0WSUuyyqBRcnNe4a2hqzHuuNrmG+MJlFbItMweuf7cPfh1NgNHHWkIioNhh2iRycOrw/nIY+DSiUMJ4/hLItiyAaq1mWjWxSgLczpt7eGS/+ryua+7qgpNyIn7bGYMbXB3HyfM61H4CIqJFj2CVqBNQte0I38nlAqYEp+QTKNn0MUV8md1lUBx1CvTHzkZ64f2QbuOrUSMspxYJfjmPBL8etS5cREVFVDLtEjYQqsBN0o18E1DqY0qJRuuEjiOXFcpdFdaBUKDC4W3N8+EQfjOwVCKVCwMnzOZj+1UH8tDUGxWUGuUskIrI5DLtEjYiqaRs4j30VgtYV5qx4lK7/EOaLl1cm++HspMb/hrTGe4/1RrfWvjCLIv4+bJnn3RqZzHleIqIrMOwSNTJKvxDoxr8OwdkT5rwUlP7xAcxF2XKXRdehibcznr2tM166qyta+FnmeX/eFovpXx3EiXPZ4DWDiIgYdokaJaVXcziPfwOCmy/EwgyU/vE+zPnpcpdF16l9iDdmPtwLD9zSBm7OaqTnluKTX09gwS/HcSGLoypE1Lgx7BI1Ugp3fziPfxMKz6YQS3JRuv59mHKS5C6LrpNCIWBQ1+b44PG+uKV3EJQKAaficzHj60P4cUs0ikq5AgcRNU4Mu0SNmMLFC7pxr0PhEwyxrBCl6z+EKSNO7rLoBjg7qXDn4DC8N7k3IsL9YBZFbD9yAa9/th9bDnGel4gaH4ZdokZOoXOH89hXoGzSGtCXonTDXBgvnJG7LLpBTbycMWVSJ7x8dzcE+ruitMKIlX/H4u2vDuJYHOd5iajxYNglIghaF+hGvwRl8w6AsQJlf82HMfGo3GVRPWgX7IUZD/XEQ6Pawt1ZjYzcUiz67QTmrzqGFM7zElEjwLBLRAAAQa2F7pbnoQqJAExGlG1ZAkPcfrnLonqgUAi4uUszfPBEX4zqEwSVUsDphDzM+PogftgcjULO8xKRA2PYJSIrQamG07BnoArrC4gmlG//DPqz/8hdFtUTnVaFOwaF4b3JfdC9jR9EEdhx1DLPu/lgEud5icghMewSUSWCQgmnwZOhbj8EgIiKXd9Cf+IvucuieuTvqcMzt3bCq/d0Q5C/K8oqjFi1PQ5vfXkAR2OzOM9LRA6FYZeIqhAEBbT97oemy2gAQMX+laiI/J0hyMG0CfLC9Id64uFRbeHuokFmXhkWrz6JeSuPISWT87xE5BhUchdARLZJEARoe98JaJyhP/Qb9EfWQTSUQ9vnLgiCIHd5VE8UCgEDujRDj7b+2Lg/EZsPJuNsYh5mfHMQA7s0w8QBLeHuopG7zDozmszIKSxHdn45sgvKkF1QjuyCcigVAoL8XRHYxA1BTVzh4qSWu1QikhjDLhFdlbbbWAhqJ1Ts/RGGk5sBQxm0/R+CoOAvhhyJTqvCbQNb4eYuzfDrP+cQGZWJf46l4sDZDIy7KRRDu7eAWmU7/+Ymsxm5hRWWEJt/OcxeCrb5RRWo6fcQe6/4u6+HEwL9XRHcxA1BFwOwl5uWP9ARORCGXSK6Jk3HYRA0Tijf+RUMUf9C1JfDafDjEJR8C3E0fp46PD2xI2KS8/HztlgkZhThlx1x+OfoBdw5JAzdWvs2SBA0m0XkF1vCbFZ+GXIKypFVcPFrfjnyiipgvsZYjUalgI+HE/w8dfDxcIKvhxMMBjOSMouRlFF0RUAux9HYbOv9XHVqBDVxRZC/JfwGNXFDgLczFAoGYCJ7xO9URFQr6vD+gEqL8u3LYTx/EGXGCjjd/DAUzp5yl0YSCA/0xNsP9cDek+lYvfMcMvPLsGTNSbQN8sRdQ1sjqInbDT2+WRRRWKK3jhlkFZQjp6AMWfnlyCkoR05hOUzmq4dZlVKAj4cOvh5O8PNwuhhodfD1tHx1d1ZfNZiXlBuQnGEJvokZxUjOLEJqdimKyww4k5CHMwl51n01KgVa+FuCb9DFry38XKBRK2+oD0QkPUHkJ06qZTKZkZtbIncZNkmlUsDLywV5eSUwGrlUUX2xl74ak0+gbMsSwGRZm1XQeUDhGwSlTxAUPsFQ+gRB8PCHINjOr7ztpbe2qqzCiE0HEvHXAcvlhgUAA7o0w6SbW8Lbw6na3oqiiKJSQ6XRgv+OHFxrqTOlQoC3u9YSYD2c4Ot58evFUOvhqoGins8yG4wmpGSVIDmzGIkZRUjKKEJyZjH0hqq1CgLQ1MfFehY4uIllFthVd+NzwDxmpcPeSqehe+vt7QKl8trfaxh2a8CwWzO+UUjDnvpqTI9BxZ4fYM5NAap7C1FpofAJtARgX0sAVng1h6CS54NO9tRbW5adX4bfdp7DwbOZAAAnjRLj+4eiZaAXEi/kIyO3tNLsbHUB8UqCAHi7XQyvF8/GXhlmvdy0NjE6YDaLyMgrRVJGMZIyiyxfM4pQVGqodn8fdy2CmrhVmgX2dq/bHDCPWemwt9Jh2LUzDLs14xuFNOyxr6KhAua8FJiyE2HOSYIpJwnmnBTrWd9KBAUUns0sIdj3irPATq6S12mPvbVlMcn5WPl3LBLSi666nwDA001rDbA+Hjr4XQqznpYwq6rFNypbJIoi8ov1SLp49vfSHHBWfnm1+7s4qf4TgF0R4OMMZQ0f9OQxKx32VjoMu3aGYbdmfKOQhqP0VTSbYC7IsITfiyHYnJMEsbz6YCS4eEPpGwyFTxAUPkFQ+gZBcK3fD0E5Sm9tiVkUse9UOv4+nAKtVgUvVw183C1zs34Xz9B6uzvZ1AoODaG03IjkK87+JmUWIzW7pNr5Y7VKgRZ+LhdXgbDMArfwd4VWreQxKyH2VjoMu3aGYbdmfKOQhiP3VRRFiKX5MOckwpSdZD0LLBZmVn8Hje7yDPDFs8AKr6YQFNf3mVpH7q3c2NtrMxjNSM0uuXgWuBiJmZY54Aq9qcq+ggAEeDsjOMANHVr5ISLMGzoNP0ten3jMSsdWwy7/DyIiyQmCAMHFCwoXL6iCulq3i/pSmHKSL54FtoRgc14KoC+DKS0aprRoWKciFSoovJpfDL8XzwL7BEHQ6OR4SUS1plYpEBzghuCAyytYmEURWXllSLz4AbjEi0G4sESPtJxSpOWUYv/pDPykUWJYj0CM7BXIC2AQXSeGXSKSjaBxhqppG6BpG+s20WSEOT+10hiEKScJ0JfBnJMIc05i5cdw9794FjjIehZYcPbkRQHIpikEAU28ndHE2xm92jWxbi8orkBiRjFSsopxJCYb51ML8OfeBPx9OBkjegZheI9AODvxWzdRXXCMoQYcY6gZfwUkDfa1ZqIoQizKvvgBuMujEGJJbrX7C05uV8wAB0PjHwyf0FbILyhnb+sZj1tpqFQKeHg44+8DCViz8xxSsizfj5y1KozsFYhhPQKh0zL0Xg8es9Kx1TEGht0aMOzWjG8U0mBf685cXgRzTnKlAGzOT612OTSVpz+0ve6EENydZ33rEY9baVzZV73BhMPRWVi3Ox6p2ZbvSy5OKtzSOwhDu7eAE2d664THrHQYdu0Mw27N+EYhDfa1fohGPcy5KRfPAidZv8JoWQ5NGRAObZ+7oPRvKXOljoHHrTSq66vZLOJQVCbW7Y5Hem4pAMuljUf1CcKQbi2g1fBqbrXBY1Y6DLt2hmG3ZnyjkAb7Kh2laIAQtQ15+363hl5V65ug7Xk7FK7eMldn33jcSuNqfTWbRRw4k4F1e+KRmVcGAHB3VmN0n2AM6taclzC+Bh6z0mHYtTMMuzXjG4U02FfpXOptTnIySvb9CmPsHssNSg00XUZB02U0BLVW3iLtFI9badSmryazGftPZ+CPPfHWi1l4uGgwum8wBnVtBrWKobc6PGalw7BrZxh2a8Y3Cmmwr9L5b29NmedRse9nmDJiAQCCixe0PW+HqnVfCELjugjCjeJxK4269NVoMmPvqXSs35OAnEJL6PVy02JM32AM6Nys0V3Y41p4zEqHYdfOMOzWjG8U0mBfpVNdb0VRhDH+ECoO/AKxKBsAoPALhbbv3VAFhMtZrl3hcSuN6+mr0WTG7hNp+HNfAnILKwAA3u5ajO0bgv6dm9rtpZnrG49Z6TDs2hmG3ZrxjUIa7Kt0rtZb0aiH/tRW6I+uBwyWs2Kqlj2h7XUnFO5+cpRrV3jcSuNG+mowmrHrRCr+3JuA/GLLjLqPuxPG9QvBTR0DGn3o5TErHYZdO8OwWzO+UUiDfZVObXprLi2APvJ3GKJ3WpYuU6ig6TQCmm7jeJW2q+BxK4366KvBaMI/x1KxcV8iCkosodfP0wnj+4WiT4cmUCoaZ+jlMSsdhl07w7BbM75RSIN9lU5demvKSUbF/pUwXTgNABB07tD0mAR1m5shNNJwcDU8bqVRn33VG0z45+gFbNyfiMJSywW4m3jpML5fKHq3bwKFonGtO81jVjoMu3aGYbdmfKOQBvsqnbr2VhRFmJKOo3z/SogF6QAAhXcLaPvcDVWLDlKXa1d43EpDir5W6E3YfjQFm/YnobjMEnqb+jhjfL9Q9Gzr32hCL49Z6TDs2hmG3ZrxjUIa7Kt0rre3oskIw5ntqDiyDqiwvB8og7rAqc9dUHg2lapcu8LjVhpS9rVcb8Tfh1Pw14EklJQbAQDNfF0woX8ourfxg8LBrzDIY1Y6DLt2hmG3ZnyjkAb7Kp0b7a1YXoyKI+tgOL0dEE2AoIS6wxBoIyZAcHKVoGL7weNWGg3R17IKI7ZFJmPzwWSUVlhCbws/F0zo3xIR4b4Oe1ltHrPSYdi1Mwy7NeMbhTTYV+nUV2/N+Wko378SpqTjlg1aF2i7T4S6/WAIClU9VWtfeNxKoyH7WlpuwNbIFGw5lISyChMAIKiJKyb0D0XXMMcLvTxmpcOwa2cYdmvGNwppsK/Sqe/eGlNOoWLfSpjzUgAACo8AaPvcBWVQF4cLBtfC41YacvS1pNyAzQeTsTUyGRV6S+gNCXDDxAGh6NTSx2GObR6z0rHVsCv7R4vNZjMWLVqEAQMGoGvXrpg8eTKSk5Nr3N9gMODjjz+27n/ffffh7NmzDVgxETV2qhYd4XzbO9D2fxCCkxvMBeko2/wJyjbOgym35vcvIlvm4qTGpJtbYu5TN2FM32Bo1UokpBfhk19PYPYPh3EqPgc8P0b2SPawu2zZMqxYsQKzZs3CypUrYTab8dhjj0Gv11e7/8yZM7FmzRq8//77WL16Nby9vTF58mQUFRU1cOVE1JgJCiU07QfD5a450HQZDShUMF04jdLV01H+77cwlxXKXSLRdXHVqXHbwFaY81Rf3NI7CBqVAudTCzF/1XF88NMRnEnIZegluyLrGINer0efPn3w0ksv4Z577gEAFBYWYsCAAZg9ezbGjh1baf/k5GQMHz4cy5cvx6BBg6z7T5w4EbNnz0bfvn3rrTaOMdSMvwKSBvsqnYborbkwExUHfoExPtKyQe0ETbfx0HQaDkGpluQ55WIuzYc5Jwmm7CSgKANuQeEwtoiAWeUid2kOw5beDwpK9Ni0PxE7jl6A4WIt4YGeuHVAKNoEecla2/Wwpd46GlsdY5D1ExVRUVEoKSmpFFLd3d3Rvn17HDp0qErY3bNnD9zc3HDzzTdX2n/79u0NVjMRUXUU7v7QDZ8CY1o0Kvb9DHN2AvQHf4Hh7A5oe98JVWgPu5t5FEUzxIJMmHKSYM5JtHzNToT4n7PWOVG7AMW3UAV3g7pNfyhbdIKgUMpUNdU3DxcN7hraGrf0DsLGfYn451gqYpLzMWfFUbQL9sKE/qEID/SUu0yiGskadtPTLYu1N21aeb1Kf39/621Xio+PR2BgILZs2YLPP/8cGRkZaN++PV577TW0atWq3utTqWSf8rBJl36Kqs1PU1R77Kt0GrK3qsB20LaYCX30XpQd+BViURbKty2Fqmkb6PrdDZV/S8lruB6iUQ9TbgpM2YkwZSfBmH3xzK2xopq9BSg8A6D0C4bKowlMKSehTz8PY3wkjPGREJw9oAnvB23bAVB6N2/w1+IIbPH9wNdThwdGtcXYfiFYvycB/xy9gLOJeTibmIeOod7o2a4J/Dyd4Oepg4+HE1Q2VPuVbLG3jsJWeyvrGMO6devwyiuv4OzZs1BccRnOV155BZmZmfj2228r7f/mm2/ir7/+QvPmzfHKK6/A3d0dn376KY4cOYKNGzfCx8en3moTRdHuzsIQkW0x68uRv28tCvavg2i0fA7BtdMgeA+6Byr3+nu/qitTaRH0mQmoSI+HPiMeFRnxMGRfAMSqv3YUVBpo/IOhaRIKbZMQaAJCofELgkLjVGm/iowEFJ/YgaJT/8JcevnMr7ZZa7h1GQKX9v2gdOKYgyPJzCvFr3/HYuuBRJjMlaOEIAA+7k7w93ZGE29n+Hs7I+Di1ybeLvD1cLK5QESOS9awu3nzZkydOhXHjx+Hk9PlN87nnnsOer0en376aaX9Z8yYgZUrV2Ljxo3WM7nl5eUYOHAgJk+ejMcee6zeajOZzCgsLKu3x3MkSqUC7u46FBaWwWTivFN9YV+lI3dvzcW5KNv/C/Qxey0bVBo4dRsDp66jIai1kj2vKIowF2Vbz9aashNhzE6EWJxb7f6CkyuUvsFQ+gZD5RsEpW8wFJ4BVx1J+G9vRZMRhsTj0Ef9C0Pi8csBWqmGumV3aNveDFXz9hAUDDpXI/cxWxdZ+WXYFpmC1OxiZOeXIyu/DPprzGsqBAHe7lr4eerg62E5G+x78aywr4cOXm5ayS5fbE+9tTcN3Vt3d53tz+xeGl/IzMxEUFCQdXtmZibatGlTZf+AgACoVKpKIwtOTk4IDAxESkpKvdfHwfWrM5nM7JEE2FfpyNZbJ09oBz0OVfthKN+3AuaMOJQf+h0VZ/6BtuftULXuC0G4sfAnmo0w56VZZmuzL8/YQl/9D+2Cmx+UPkFQ+AZbvwrOnpV+oyUCMJkBmK/ds8u9VUAR1A1OQd2gKS2AMW4fDNG7Yc5LgSF2Pwyx+yG4eEMd3g/q8P5QeDS5odft6Ozh/cDLVYs7Bl3+viyKIopKDcgqKENOgSX85hSUI7ugHFkF5cgpKIPRJCL74rbqKBUCfNyd4OvpBF8PJ/h46ODn4QRfD0sodnfR3PBlje2ht/bK1nora9ht27YtXF1dceDAAWvYLSwsxJkzZ3DfffdV2b9nz54wGo04efIkOnXqBMByZjc5ORljxoxp0NqJiOpK6d8SzuPfhPH8IVQcWAWxOAfl/3wBxelt0Pa9B6qA1rV6HFFfdvFDY0nWVRHMeRcAs7HqzgolFF7NofC5eKbWJwhKn0AIGud6fnXVPLWzBzSdb4G600iYsxNgiN4FQ9x+iCW50B9dD/3R9VAGhEPdZoDlA3waneQ1kfQEQYC7iwbuLhq0auZR5XazKKKgWG8JwgVlltCbf/FrQRlyCytgMovIzC9DZn71P6yplAr4eliCsO/Fs8O+l8KwhxPcnNUcRSQr2a+gtmDBAqxcuRLvv/8+mjdvjrlz5yIlJQV//vknFAoFcnNz4ebmZh1zePjhh5GRkYF3330Xnp6eWLRoESIjI/Hnn3/C29u73uri0mM147It0mBfpWOLvRWNeuhPbYH+6J+AwXJ2S9WyF7S974DCzc+yjyhCLM2/4mxtEkw5SRALM6t/ULUOSt+gi4HW8lXh1RyCUrrzGnXtrWjUw5h4DIaYXTClnAIufQtSaaFq2QPq8AFQNg2/4TPd9s4Wj9mGYjaLyCuqQPalIHwxBGfnW/6eW1SOayUXjVphDb5XhmBfTycE+LigRVMP5OeXNrreSs1Wlx6TPeyaTCbMnz8fa9asQXl5OXr27Inp06ejRYsWSElJwdChQ/HBBx9g0qRJAIDi4mLMmzcPf/31F8rLyxEREYE33ngDYWFh9VwXw25NGvObsJTYV+nYcm/NpQXQR66BIepfACKgVEHVqjfEEstatmJ59RfMEVy8ofAJvOJsbRAEN78GP5t1I701l+TBELsHhujdEAsur8AjuPlBHd4f6vB+ULj51nfJNk0UzZYfZvKS4YRyGPzaQXRpXD24FqPJbAnD+f8Jwxf/nl9UgWsFG293LbqE+aJbmC/aBnvZ7MoR9oZh184w7NbMloODPWNfpWMPvTXlJKFi388wpf7n8ueCAIVn04uB1hJsFT6BUOjc5Sn0P+qjt6IowpwRB0PMLhjOHbSe6QYEKJu3gzq8P1Sh3SGopPswnxxEox7mvFSYchJhzk6yfM1JrrLcm7Jpm8ujHmqnGh6NLjEYzcgtvDQjXHVuuKCk8hVadVoVurTyQUS4Hzq29IaTRtYJT7vGsGtnGHZrZg/BwR6xr9Kxl96KoghT0nEY06Kg8AiwjCJ4t4Cg0shdWo3qu7eisQLG+MMwRO+qHPzVOqhb9YK6zQAo/FvZ3TymWF4MU24yzNmXLs6RBHN+arXLvUGphtInEGqdDuWJZ4BL5ynVTlCF9rRcuCMg3O56YCtMoojU3HL8czgJh6OzUHhF+FWrFOgQ4o2IcD90be0LV51jXf1Qagy7doZht2b2EhzsDfsqHfZWOlL21lyUDUPMHhhidkMsyrJuV3gEQNWmP9St+0HhYluXqxVFEWJxjvVqc9Y56+KcavcXtK5Q+AZbRlIuroqh8AiAWqOGl5cLcpKSUBa12zLqUZhx+X7u/pdHPVzlW7PZHl15zOr1JpxLLcCRmCwciclCVv7l1SEUgoDwQA9EhPshItwP3u48q34tDLt2hmG3ZgwO0mBfpcPeSqcheiuKZpjSomGI2Q3j+UPAxQt0QBCgbNHJMuYQ3LXBz4CLZiPM+WkXRxAuf4AQFdV/77i83NulDxAGQ3DxqvYM7X/7KooiTBmxMEbvguH8of+MerS3jDmERNj0bwFsRU3HrCiKSMkqsQbf5MziSvcLCXCzBt9mvrxASnUYdu0Mw27NGBykwb5Kh72VTkP3VtSXwXj+EAwxu2FKj7l8g9YF6lZ9oG7THwrfkHr/Fb+oL4MpN6XS2VpzXgpgqma5N0EJhXczKHyCratiKH0CIWhrH5Cu1lfRUAFjfKRl1CMt6vINGh3UrXpbRj38WnLMoQa1PWYz88tw9GLwjUspqPSht6Y+ztbgGxLgxl5fxLBrZxh2a8bgIA32VTrsrXTk7K25IAOGmN0wxOyBWHL5qnAKrxZQt+kPVeubruuDfObS/Cs+MHZxDKEgE6juM/5qp8oX5/AJgsKrGQTljc161rav5sLMy6MeV4xKKDybQRXeH+rwm6Bw9ryhWhzN9RyzBSV6HI21BN+zCXmVLo/s5aZFRGs/RIT7IjzIE8pGfHVAhl07w7BbMwYHabCv0mFvpWMLvRXNZphSz8AQvRvGhMjLZ1sFJVRBnaFq0x+qoC4QFKoq9xMLM6zztZdGEcSywmqfR3DxqrSGsdI3GIKbryRrAtd5/WLRDFNqFAzRu2CMPwyYLo16KKAMvGLU4wZDuCO40WO2tNyIE+ezcSQmGyfP5aDCYLLe5uKkQtcwX0S08UOHEG9o1DVfatsRMezaGYbdmtnCNzdHxL5Kh72Vjq31VqwogeHcAcslirPOW7cLTm6WM72eTS+PIVSzzJdlZwEKj4vLvV28SIfCJ6hBl3u7kb6K+jIYzh+EIXoXzBlx1u2C1hWqsD5QtxkApW9wfZdsN+rzmDUYTTidkIcjMVk4FpuN4jKD9TatWomOLS0rO3Rp5QNnJ8f/QYNh184w7NbM1r65OQr2VTrsrXRsubemvAuWs72xeyGWFVS/k1IDhU8L6wfGlL6XlnuTd03f+uqrOT/NMuYQuwdiSZ51u8In0HK2N6yvzazZ3FCkOmZNZjNiky+u7BCbhdzCyz9IKRUC2gZ7ISLcD91a+8LT1bHWjL6EYdfOMOzWzJa/udkz9lU67K107KG3otkEU8pJGGL2QtSXQuF9+cpzCo8ACDY4Y1nv6xebzTBdOHVx1OMIYL446qFQQhXU1bJ2b2CnKqMejqhhVhARkZhRdHFlh2ykZl/OEwKAVs0vLWnmC38vZ0lqkAPDrp1h2K2ZPXxzs0fsq3TYW+mwt9KQsq9iebFl1CNmN8xZ8dbtgs4dqtY3QR0+AErv5vX6nLZEjmM2PbcUR2KycDg6C/FplWfCW/i5WFd2CPR3teuVHRh27QzDbs34zU0a7Kt02FvpsLfSaKi+mnJTLOsXx+6t9ME8hV+o5aIVYX3qtGSaPZD7mM0rqrCu5RudlA/zFTHM18PJGnzDmntAobCv4Muwa2cYdmsm9xuFo2JfpcPeSoe9lUaDr19sNsKUdBKGmF0wJh4HxIsrDChVUAVHWD7U1ryDTY581JUtHbPFZQYcj8vGkZgsnI7Phf6Ketyd1eja2g9dw3zh7a6Fs5MKzlo1nLRKKGz07C/Drp1h2K2ZLb1ROBL2VTrsrXTYW2nIun5xWSGMcfssK1rkJlu3Cy5eULfuB3V4fyg8Axq0pvpkq8dshd6EU/G5OBKTheNx2SitqOaCJbDM/Oq0Kkv4dVLBWauCs5P64tcrt1nC8X+3adVKyUYlbDXsOv4kOhEREdWaQucOTaeRUHccAXNOEgzRu2CI2wexJA/6Y39Cf+xPKJu0hqpNf6hb9oKg0cldskPQapTo3sYP3dv4wWgyIzop3zLqkJyPkjIDSiuMMBjNEAGUVhgtYbiGRUauRqkQLodla0C+IixXCs3qSttcnFRQq+xv7WCGXSIiIqpCEAQofYOh9A2Gts//YEw8ZrlMc/IJmDJiYcqIRcWen6Bq2QPq8P5QNm3rEGMOtkClVKBDqDc6hHpX2m4wmlBaYUJpuQGl5ZbAe/nrf7aVG/5zuxEmswiTWURxmaHSmsB1re1SAHZxUkF3xdllN50aI24KhYeTbQVihl0iIiK6KkGphrplT6hb9oS5NB+GmL0wxuyCOT8Nxti9MMbuvWLN4uDLF+SwgTWLHYlapYSHSgkPF02d7yuKIvQG8+VgbA3FxkrbSsqNKKsUpC+HaFEEjCYzCkv0KCzRV/s8iZnFeOmurjf4SusXwy4RERHVmsLZE9quo6HpMgrmrPOWMYdzBwB9GcyZ52HOvHzlOlu4Gh1ZCIIArUYJrUYJL7e6/wBiFkVU6E01nkkuKTdAbzRjSE/buzofwy4RERHVmSAIUPq3gtK/FbT9HoBYmAFTduIVl2NOglhWCHN+Ksz5qTCe23/5vi5elgB8MfwqfYMhuPlCEDgGYasUgmXWV6dVwaeGfa78gJotYdglIiKiGyIoFBA8m0Lh2RQI6wPA8mtzsawA5uwkmHIuh2CxIANiSR5MJXkwJR2//CBqHZQ+gdbwq/AJgsKrOQQlowrdGB5BREREVO8EQYDg7AlFkCdUQZ2t20V9GUy5KTBfeRY4NwUwlMGUHgNTegysH51SKKHwanbxLPDFWWCfQIe70AVJi2GXiIiIGoyg0UEV0BoIaG3dJpqNMOenXTwLbBmBMGUnAvpSmHOSYc5JhhF7Lj+Gm98VIxCWr4KLt11fapekw7BLREREshIUKii9A6H0DoQa/QBcHIMozrGE3yvOAovFORCLsmAsygISDl9+DK0rFBeDryUIB0PhGQBBYVvLYFHDY9glIiIimyMIAgQ3XyjcfIGQCOt2sbwYptzkSrPA5rxUiBXFMF04A9OFM5fHIJRqKLxbVPognNI/CADHIBoThl0iIiKyG4KTK1TN2gHN2lm3iUY9zHmplvCbfXEMIjcZMJTDnBUPc1a8dd9SCChy8YBZFAFRjlfgwAQBQp9xQNvhcldSCcMuERER2TVBpYHSLwRKvxDrNlE0QyzMvDgGccVyaKX5MJXky1aroyu/EAMtwy4RERGRtARBAcEjAAqPAKBlL+t2hb4Irio9CgvLYDLx1G59UqmU8GkZhvyCcrlLqYRhl4iIiBoNhbMHtF4uUGlKAKNZ7nIcilKlsMkPBPJSJURERETksBh2iYiIiMhhMewSERERkcNi2CUiIiIih8WwS0REREQOi2GXiIiIiBwWwy4REREROSyGXSIiIiJyWAy7REREROSwGHaJiIiIyGEx7BIRERGRw2LYJSIiIiKHxbBLRERERA6LYZeIiIiIHBbDLhERERE5LEEURVHuImyRKIowm9mamiiVCphMZrnLcDjsq3TYW+mwt9JgX6XD3kqnIXurUAgQBOGa+zHsEhEREZHD4hgDERERETkshl0iIiIiclgMu0RERETksBh2iYiIiMhhMewSERERkcNi2CUiIiIih8WwS0REREQOi2GXiIiIiBwWwy4REREROSyGXSIiIiJyWAy7REREROSwGHaJiIiIyGEx7BIRERGRw2LYJSIiIiKHxbBLRERERA6LYZeIiIiIHBbDLhERERE5LIZdIiIiInJYDLtUa/n5+Zg+fTpuvvlmRERE4O6770ZkZKTcZTmU+Ph4dOvWDWvWrJG7FIexdu1ajB49Gp06dcKYMWOwadMmuUtyCEajEQsXLsTgwYPRrVs33HvvvTh27JjcZdm9zz77DPfff3+lbWfPnsV9992Hrl27YsiQIfj+++9lqs5+VdfX7du347bbbkO3bt0wZMgQzJkzB+Xl5TJVaL+q6+2V3nrrLQwZMqQBK6qKYZdq7YUXXsDRo0cxf/58rF69Gu3atcOjjz6K8+fPy12aQzAYDHjppZdQWloqdykOY926dXjzzTdx7733YsOGDRg7dqz1OKYb8+mnn+LXX3/FrFmzsHbtWoSGhuKxxx5DZmam3KXZrZ9++gmffPJJpW15eXl4+OGHERQUhNWrV+OZZ57BvHnzsHr1anmKtEPV9TUyMhJTpkzB8OHD8fvvv2PGjBnYuHEj3nnnHXmKtFPV9fZK27Ztw6+//tpwBdWAYZdqJTExEXv27MHMmTPRo0cPhIaG4u2334a/vz/Wr18vd3kOYfHixXB1dZW7DIchiiIWLlyIBx54APfeey+CgoLw1FNP4aabbsLBgwflLs/ubdu2DWPHjkX//v0RHByM1157DUVFRTy7ex0yMjLw5JNPYt68eQgJCal02y+//AK1Wo13330XrVq1wm233YaHHnoIn3/+uTzF2pGr9XXlypXo3bs3nnzySYSEhGDgwIGYNm0a1q9fD71eL0/BduRqvb0kMzMTb7/9Nnr16tWwxVWDYZdqxcvLC59//jk6depk3SYIAgRBQGFhoYyVOYZDhw5h1apV+PDDD+UuxWHEx8fjwoULGDduXKXtX331FZ544gmZqnIcPj4+2LFjB1JSUmAymbBq1SpoNBq0bdtW7tLszunTp6FWq/HHH3+gS5culW6LjIxEr169oFKprNv69OmDhIQEZGdnN3SpduVqfX3kkUfw6quvVtqmUChgMBhQXFzckGXapav1FrCcbHjttdcwYcIEmwi7qmvvQgS4u7tj4MCBlbZt3rwZiYmJeOONN2SqyjEUFhbilVdewVtvvYWmTZvKXY7DiI+PBwCUlpbi0UcfxZkzZ9CiRQs89dRTss+POYI333wTzz33HIYOHQqlUgmFQoHFixcjKChI7tLszpAhQ2o8JtPT0xEeHl5pm7+/PwAgLS0Nvr6+ktdnr67W1/bt21f6b4PBgG+//RYdO3aEt7d3Q5Rn167WWwD49ttvkZWVheXLl+Ozzz5rwMqqxzO7dF2OHDmC119/HSNGjMCgQYPkLseuzZw5E926datyBpJuzKWzM6+++irGjh2Lr7/+Gv369cPTTz+Nffv2yVyd/YuLi4ObmxuWLl2KVatWYdKkSXjppZdw9uxZuUtzKOXl5dBoNJW2abVaAEBFRYUcJTkco9GIV155BbGxsZgxY4bc5di9qKgoLFmyBHPnzq1y7MqFZ3apzrZt24aXXnoJERERmDdvntzl2LW1a9ciMjKSc88SUKvVAIBHH30Ut956KwCgXbt2OHPmDL755hv07dtXzvLsWlpaGl588UV8++236NGjBwCgU6dOiIuLw+LFi7Fs2TKZK3QcTk5OVWZIL4VcZ2dnOUpyKMXFxXj++edx8OBBLFmyBJ07d5a7JLtWUVGBl156CU899ZRNjTTxzC7VyY8//ohnn30WgwcPxvLly61nGOj6rF69Gjk5ORg0aBC6deuGbt26AQBmzJiBxx57TObq7FuTJk0AoMqvgMPCwpCSkiJHSQ7j+PHjMBgMlWb4AaBLly5ITEyUqSrHFBAQUGWFi0v/fekYp+uTmZlpXTLvq6++qjKqR3V3/PhxxMbGYsmSJdbvaZ999hlSU1PRrVs32ZYr5ZldqrUVK1Zg1qxZuP/++/Hmm29CEAS5S7J78+bNq7Ku44gRIzB16lSMHz9epqocQ4cOHeDi4oLjx49bzz4CQExMDOdKb1BAQAAAIDo6utKZsJiYmBo/mU3Xp2fPnli5ciVMJhOUSiXw//buPqbK8g3g+JcDKioIeBR0mYqoDJwH8eUQksoczUJpqTWZDEFBEVCXkMAxUdQIZYIvvA0FxaGppYQVFpIzM8SXFc1C29KYL5PcSeVFcQpHfn80zjgdIFEMOb/rs/HHcz/3c9/X9TC269znfh6As2fP4ujoiFKp7OLouq+amhqCgoK4f/8++/fvx9nZuatDMgkqlYrjx48btOXn53P8+HHy8/O77AOaFLviqVRWVvLxxx/zxhtvEBYWZvAUsKWlJdbW1l0YXffV1h++UqmUVZvnZGlpSWhoKBkZGTg4OKBSqSgqKqK0tJS8vLyuDq9bU6lUTJgwgdjYWNatW8egQYMoLCykrKyMAwcOdHV4JmXu3Lnk5OTw4YcfEhoaysWLF8nLy5P3wT6npKQkbty4QU5ODv3790er1erP9e/fX//BQnSMpaUlw4YNM2izsbHBwsLCqP2/JMWueCrFxcU0NDRQUlJCSUmJwbnZs2fLK7PESykiIoLevXuzdetWbt++jZOTE2lpaXh4eHR1aN2aQqEgKyuLbdu2odFoqKmpYfTo0eTl5bX6GiLx7JRKJTk5OSQmJjJ79mwGDhxITEyMfh+66DidTsexY8doaGggKCjI6PyJEycYMmRIF0QmXhSzpqampq4OQgghhBBCiBdBHlATQgghhBAmS4pdIYQQQghhsqTYFUIIIYQQJkuKXSGEEEIIYbKk2BVCCCGEECZLil0hhBBCCGGypNgVQgghhBAmS4pdIUS3ExgYiKurK7/88kur56dPn05cXNx/EktcXBzTp0//T+bqiMbGRuLi4nB3d2f8+PGcPXvWqM+5c+dwdnYmIiKi1TEKCgpwdnbm5s2bTz3vzZs3cXZ2pqCgoNOv+S9/r0II0yHFrhCiW9LpdGg0Gh4/ftzVobyUTp8+zeeff05wcDDZ2dmMHTu2zb4nTpzgiy++6JR57e3tOXToEN7e3p0ynhBCPC8pdoUQ3ZK1tTW///47GRkZXR3KS6m6uhqAOXPmMGnSJPr27dtm3379+pGYmMhff/313PP27NmTcePG0b9//+ceSwghOoMUu0KIbsnFxYV33nmHnJwcfv3113b7Ojs7k5aWZtCWlpaGs7Oz/jguLo6QkBAOHTqEj48PKpUKf39/KisrOXnyJH5+fri5ufHee+9x+fJlozmaVzNVKhVBQUFcunTJ4PytW7eIiopCrVbj5uZm1Kf5q/w9e/bw5ptv4ubmxpEjR1rNR6fTsX//fvz8/FCpVHh7e7NlyxYePXqkz6X5634fHx8CAwPbvT8rV66kvr6ehISEdvt1JI+WWxLKy8sJCAhg3LhxeHt7s3fvXoKDg422JGi1WlasWIG7uztqtZr4+HgePHhg0KehoYGPPvqISZMmMXHiRGJjY7l7965Bn9LSUubPn8+ECRPw8PAgOjqaqqoq/fmCggJcXV357LPP8PLyQq1Wc+XKFa5fv87SpUvx8PDAzc2NefPmcerUqX+9J0KIl5sUu0KIbmv16tXY2dl12naG8vJy9u3bR1xcHElJSVy9epUlS5aQlJREWFgYqampVFVV8cEHHxhc9+eff5Kens77779PamoqNTU1BAYGcuvWLQDu3r2Lv78/FRUVxMfHk5KSwpMnTwgICODq1asGY6WlpbF48WKSk5Px8vJqNc61a9eSlJSEj48PWVlZBAQEsG/fPiIiImhqaiIiIoLw8HAA0tPTWbduXbt5Ozk5sXz5ckpKSvjqq6/a7NeRPJpdvXqV4OBgAFJTU1m+fDk7d+7kxx9/NOq7fft2Bg8eTGZmJkFBQXz66aekp6cb9Pn666+pqKhg06ZNxMbG8t1337F48WJ0Oh0AhYWFLFq0iMGDB5OamopGo6G8vJx58+Zx584d/Tg6nY7du3eTmJiIRqPB0dGRsLAwHj58SHJyMpmZmdja2hIeHs61a9favX9CiJebRVcHIIQQz8rGxoYNGzYQHh5ORkYGK1eufK7xHjx4wLZt23BycgLg/PnzHDx4kLy8PDw9PQG4du0amzdvpra2ln79+gF/F04ZGRmoVCoA3Nzc8PHxIT8/n9jYWPbu3Ut1dTUHDhzglVdeAWDq1Kn4+vqyfft2duzYoY/hrbfeYu7cuW3GeOXKFQ4fPkx0dDRLliwBwMvLC3t7e2JiYvj++++ZNm0aQ4cOBf5eAR8yZMi/5h4SEkJJSQkbN27ktddeY8CAAUZ9OpJHs+zsbKytrcnJyaF3794AjBgxAn9/f6O+M2bMQKPRAODp6UlpaanRg3V2dnbk5ubSp08f/XFkZKQ+7y1btvD666+TkpKiv2b8+PH4+vqSm5tLTEyMvn3p0qX6vcVarZY//viDiIgIpk2bBoBKpSI9PV32hQvRzcnKrhCiW5s+fTpvv/02OTk5VFRUPNdYNjY2+kIX0Bd8bm5u+jZbW1sAamtr9W2vvvqqvtAFGDhwIOPGjePChQsAlJWV4eLigoODA42NjTQ2NqJQKJg6dSpnzpwxiMHFxaXdGM+fPw/AzJkzDdpnzpyJubk5586de9p0DZibm5OUlER9fT3r169vtU9H8mh29uxZpk6dqi90Adzd3fXFcksTJ040OB4yZIjBfQaYNm2avtCFv3//FhYWXLhwgcrKSrRaLbNmzTK4ZujQobi7u+vvXbOW93rAgAGMHDmS+Ph4YmNj+fLLL3ny5AkajYZRo0a1mpsQonuQlV0hRLe3Zs0aysrK0Gg0be5zfRpWVlattrcsrlrT2iqoUqnU7xOtrq7m2rVrjBkzptXrHz58+NRz1dTUAH8X1C1ZWFhgZ2dHXV1du9e3Z+TIkSxbtozU1FSKioqMznckj2Z3795FqVQatbd2z1oWxAAKhYKmpiaDtn/mrVAosLOzo7a2Vv9QXmtjDxgwwGgfdct7bWZmxu7du8nKyqKkpITCwkJ69OiBj48P69evx8bGxmhMIUT3IMWuEKLbs7GxISEhgcjISDIzM1vt07yns1l9fX2nzd9cgLak1Wr1bySwtrZGrVYbfIXeUs+ePZ96ruaiS6vVGqyONjQ0cO/ePezs7DoSupHQ0FCOHz/Oxo0bCQkJMTj3LHkMGjSo1bc83LlzhxEjRnQ4vuaCtplOp+PevXsolUr9qntr82m12n+9Nw4ODiQkJLBu3Tp+++03vvnmG3bt2oWdnd2/7nsWQry8ZBuDEMIk+Pj4MGvWLHbu3Gn0dL6VlRW3b982aPvpp586be7KykquX7+uP66qqqK8vBwPDw8A1Go1lZWVODo6MnbsWP3P0aNHOXz4MObm5k89l1qtBjBaeS0qKkKn0zFhwoTnysXc3JxNmzZx//59srOzjebuaB6TJk3i9OnT+jdFAFy6dKlD/6iipdLSUhobG/XHxcXFNDY24uHhgaOjIwMHDjR6yO7GjRv8/PPPjB8/vs1xy8vLmTx5MhcvXsTMzAwXFxdWrlzJ6NGj9Q8aCiG6Jyl2hRAmIz4+HltbW6Ov0729vSkqKuLgwYOUlZWxatWqTn3CvlevXoSHh/Ptt99SXFxMSEgItra2BAUFARAcHMyTJ08IDg7m2LFjlJWVER8fT35+Po6Ojh2aa+TIkcyePZsdO3awfft2zpw5Q25uLuvXr8fDw4MpU6Y8dz6jRo0iMjLSaEvEs+SxdOlS6urqCA0N5eTJkxw9epRly5ahUCgwMzPrcGxarZbly5dz5swZPvnkE9auXYuXlxeenp4oFAqioqL44YcfiI6O5tSpUxQWFrJw4UJsbGxYuHBhm+O6urpiaWlJTEwMRUVFnDt3jq1bt3L58mVmzJjR4TiFEC8P2cYghDAZtra2JCQksGzZMoN2jUZDY2MjmzdvxsLCAl9fX6Kjo1mzZk2nzOvq6sqMGTNISEigrq4OT09PVq9erd/G4ODgwMGDB0lJSSEhIYFHjx4xfPhwEhMTeffddzs8X2JiIsOGDePIkSPs2rULe3t7FixYQEREBApF56xhLF68mJKSEoOH/p4lj2HDhpGbm0tycjIrVqxAqVQSFhZGVlZWu//ooi3z58+nrq6OyMhIevbsiZ+fH6tWrdIXznPmzKFv375kZ2cTGRmJlZUVU6ZMISoqymi/b0u9evVi9+7dpKSkkJiYSG1tLcOHD2fDhg3MmTOnw3EKIV4eZk3/3P0vhBBCdJKysjJ69Ohh8KaF2tpaJk+eTExMDAsWLOjC6IQQ/w9kZVcIIcQLU1FRwY4dO4iKimLMmDFUV1ezZ88erK2tjV4RJoQQL4IUu0IIIV6YRYsW8fjxYw4cOEBVVRV9+vRBrVaTlJSk3+YhhBAvkmxjEEIIIYQQJkvexiCEEEIIIUyWFLtCCCGEEMJkSbErhBBCCCFMlhS7QgghhBDCZEmxK4QQQgghTJYUu0IIIYQQwmRJsSuEEEIIIUyWFLtCCCGEEMJkSbErhBBCCCFM1v8AQ0/Zk1anQV0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Knn fit\n",
    "X_train, X_test, y_train, y_test = train_test_split(Features, Classes, random_state=42)\n",
    "\n",
    "training_acc = []\n",
    "test_acc = []\n",
    "\n",
    "neighbors_setting = range(1,15)\n",
    "\n",
    "for n_neighbors in neighbors_setting:\n",
    "    knn = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "    knn.fit(X_train,y_train)\n",
    "    training_acc.append(knn.score(X_train, y_train))\n",
    "    test_acc.append(knn.score(X_test, y_test))\n",
    " \n",
    "plt.plot(neighbors_setting,training_acc, label='Accuracy of the training set')\n",
    "plt.plot(neighbors_setting,test_acc, label='Accuracy of the test set')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Number of Neighbors')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the training set for 6NN: 0.788820\n",
      "Accuracy of the test set for 6NN: 0.657407\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy of the training set for 6NN: {:3f}\".format(training_acc[2]))\n",
    "print(\"Accuracy of the test set for 6NN: {:3f}\".format(test_acc[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A Knn fit algorithm was first applied to the data in order to find the the nearest neighbors for the dataset.\n",
    "# A graph had to be created in order to identify where the training set and the test set were the closest. Despite \n",
    "# a use of a loop being used, accuracy still had to be manually tested to find the most accurate number of neighbors.\n",
    "# In this case, the most accurate number of neighbors for the training accuracy and the test accuracy was 2. The accuracy\n",
    "# of the training set was found to be 78% and the accuracy of the test set was found to be 65%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "id": "Ju3EYGLTJzF2",
    "outputId": "1ac061fc-827b-492e-a231-26c021f1fd44"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(random_state=42)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision tree algorithm\n",
    "\n",
    "tree = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "x0FA91brKBni",
    "outputId": "4c62df0b-8217-4ae9-c7bd-47f7b651f026"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision tree has 35 nodes with maximum depth 6.\n",
      "Model Accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "print(f'Decision tree has {tree.tree_.node_count} nodes with maximum depth {tree.tree_.max_depth}.') \n",
    "print(f'Model Accuracy: {tree.score(X_train, y_train)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In order to improve the accruacy of the Machine Learning a second algorithm was used. Our team decided on the use of a\n",
    "# Decision Tree Classifier. The Decision Tree was found to have 35 nodes with a maximum depth of 6.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;max_depth&#x27;: [2, 3, 4, 5, 6, 7],\n",
       "                         &#x27;max_leaf_nodes&#x27;: [5, 9, 15, 21, 27],\n",
       "                         &#x27;min_samples_leaf&#x27;: [2, 20, 50, 70]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;max_depth&#x27;: [2, 3, 4, 5, 6, 7],\n",
       "                         &#x27;max_leaf_nodes&#x27;: [5, 9, 15, 21, 27],\n",
       "                         &#x27;min_samples_leaf&#x27;: [2, 20, 50, 70]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
       "             param_grid={'max_depth': [2, 3, 4, 5, 6, 7],\n",
       "                         'max_leaf_nodes': [5, 9, 15, 21, 27],\n",
       "                         'min_samples_leaf': [2, 20, 50, 70]})"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = { 'max_depth': [2, 3, 4, 5, 6, 7], 'min_samples_leaf': [2, 20, 50,70], \n",
    "               'max_leaf_nodes': [5, 9, 15, 21, 27]}\n",
    "gs_dt = GridSearchCV(DecisionTreeClassifier(), param_grid, cv=5, n_jobs=-1)\n",
    "gs_dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best model is: \n",
      "DecisionTreeClassifier(max_depth=4, max_leaf_nodes=5, min_samples_leaf=2)\n"
     ]
    }
   ],
   "source": [
    "print(f'The best model is: \\n{gs_dt.best_estimator_}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision tree has 9 nodes with maximum depth 4.\n",
      "Model Accuracy: 0.906832298136646\n"
     ]
    }
   ],
   "source": [
    "tree_opt = gs_dt.best_estimator_\n",
    "\n",
    "print(f'Decision tree has {tree_opt.tree_.node_count} nodes with maximum depth {tree_opt.tree_.max_depth}.') \n",
    "\n",
    "print(f'Model Accuracy: {tree_opt.score(X_train, y_train)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Further optimization using GridSearch CV results in a Decision Tree with 9 nodes and a maximum depth of 4. Model Accuracy\n",
    "# is at 90%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CxQj-MIGKHF7",
    "outputId": "6804a77a-95e7-4398-a68e-548a3c34b1a4"
   },
   "outputs": [],
   "source": [
    "y_pred = tree.predict(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      Class1       1.00      0.95      0.97        37\n",
      "      Class2       0.81      0.96      0.88        23\n",
      "      Class3       0.92      0.92      0.92        13\n",
      "      Class4       0.92      0.79      0.85        14\n",
      "      Class5       0.86      0.86      0.86        21\n",
      "\n",
      "    accuracy                           0.91       108\n",
      "   macro avg       0.90      0.89      0.90       108\n",
      "weighted avg       0.91      0.91      0.91       108\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm= confusion_matrix(y_test, y_pred)\n",
    "\n",
    "f, ax=plt.subplots(figsize=(5,5))\n",
    "sns.heatmap(cm,annot=True,linewidths=0.5,linecolor=\"red\",fmt=\".0f\",ax=ax)\n",
    "plt.xlabel(\"Predicted Class (y_pred)\")\n",
    "plt.ylabel(\"Actual Class (y_test)\")\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# After the use of two algorithms we tested the Machine Learning with a classification report and a confusion matrix.\n",
    "# Our report found that the ML had a macro average score of 90%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UyNztkHqKP6W"
   },
   "source": [
    "Auto ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "Yy4_6iHEL8z1",
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   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A2M</th>\n",
       "      <th>AAAS</th>\n",
       "      <th>AAK1</th>\n",
       "      <th>AAMP</th>\n",
       "      <th>AARS</th>\n",
       "      <th>AARSD1</th>\n",
       "      <th>AASDH</th>\n",
       "      <th>AASDHPPT</th>\n",
       "      <th>AASS</th>\n",
       "      <th>AATF</th>\n",
       "      <th>...</th>\n",
       "      <th>ZSWIM6</th>\n",
       "      <th>ZSWIM7</th>\n",
       "      <th>ZUFSP</th>\n",
       "      <th>ZW10</th>\n",
       "      <th>ZWILCH</th>\n",
       "      <th>ZXDC</th>\n",
       "      <th>ZYG11B</th>\n",
       "      <th>ZYX</th>\n",
       "      <th>ZZZ3</th>\n",
       "      <th>Classes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-3.80147</td>\n",
       "      <td>-3.889900</td>\n",
       "      <td>-3.985616</td>\n",
       "      <td>2.651558</td>\n",
       "      <td>2.170748</td>\n",
       "      <td>-2.550822</td>\n",
       "      <td>4.807330</td>\n",
       "      <td>3.961170</td>\n",
       "      <td>-0.192665</td>\n",
       "      <td>3.614482</td>\n",
       "      <td>...</td>\n",
       "      <td>2.909466</td>\n",
       "      <td>-3.118284</td>\n",
       "      <td>-1.538324</td>\n",
       "      <td>-1.550699</td>\n",
       "      <td>-1.558581</td>\n",
       "      <td>-1.920271</td>\n",
       "      <td>3.007439</td>\n",
       "      <td>-2.509017</td>\n",
       "      <td>-2.149696</td>\n",
       "      <td>Class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-3.80147</td>\n",
       "      <td>-3.889900</td>\n",
       "      <td>-3.158708</td>\n",
       "      <td>2.358992</td>\n",
       "      <td>-6.041792</td>\n",
       "      <td>-0.056092</td>\n",
       "      <td>3.606735</td>\n",
       "      <td>-2.632250</td>\n",
       "      <td>2.249388</td>\n",
       "      <td>6.857517</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.821098</td>\n",
       "      <td>-3.118284</td>\n",
       "      <td>-1.538324</td>\n",
       "      <td>-1.550699</td>\n",
       "      <td>-1.558581</td>\n",
       "      <td>-3.068620</td>\n",
       "      <td>2.539560</td>\n",
       "      <td>2.164481</td>\n",
       "      <td>-2.149696</td>\n",
       "      <td>Class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-3.80147</td>\n",
       "      <td>-3.889900</td>\n",
       "      <td>1.733125</td>\n",
       "      <td>-5.820241</td>\n",
       "      <td>-6.041792</td>\n",
       "      <td>-0.576957</td>\n",
       "      <td>-2.473517</td>\n",
       "      <td>-4.354127</td>\n",
       "      <td>0.063178</td>\n",
       "      <td>-2.570976</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.821098</td>\n",
       "      <td>5.521892</td>\n",
       "      <td>-1.538324</td>\n",
       "      <td>-1.550699</td>\n",
       "      <td>-1.558581</td>\n",
       "      <td>0.174665</td>\n",
       "      <td>-0.165409</td>\n",
       "      <td>0.734268</td>\n",
       "      <td>-2.149696</td>\n",
       "      <td>Class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.80147</td>\n",
       "      <td>-3.889900</td>\n",
       "      <td>-1.665669</td>\n",
       "      <td>3.514271</td>\n",
       "      <td>-6.041792</td>\n",
       "      <td>-3.699171</td>\n",
       "      <td>4.509461</td>\n",
       "      <td>-4.354127</td>\n",
       "      <td>2.985972</td>\n",
       "      <td>-2.570976</td>\n",
       "      <td>...</td>\n",
       "      <td>4.952176</td>\n",
       "      <td>-0.854351</td>\n",
       "      <td>-1.538324</td>\n",
       "      <td>-1.550699</td>\n",
       "      <td>-1.558581</td>\n",
       "      <td>-3.068620</td>\n",
       "      <td>-1.884744</td>\n",
       "      <td>-2.509017</td>\n",
       "      <td>-2.149696</td>\n",
       "      <td>Class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-3.80147</td>\n",
       "      <td>3.742495</td>\n",
       "      <td>-2.166992</td>\n",
       "      <td>-5.820241</td>\n",
       "      <td>2.094729</td>\n",
       "      <td>4.021873</td>\n",
       "      <td>5.535007</td>\n",
       "      <td>4.019633</td>\n",
       "      <td>2.560370</td>\n",
       "      <td>-2.570976</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.821098</td>\n",
       "      <td>4.328808</td>\n",
       "      <td>-1.538324</td>\n",
       "      <td>7.021985</td>\n",
       "      <td>-1.558581</td>\n",
       "      <td>4.590946</td>\n",
       "      <td>-0.128456</td>\n",
       "      <td>-2.509017</td>\n",
       "      <td>-2.149696</td>\n",
       "      <td>Class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.98770</td>\n",
       "      <td>-3.889900</td>\n",
       "      <td>4.691156</td>\n",
       "      <td>-4.006471</td>\n",
       "      <td>-3.449348</td>\n",
       "      <td>4.309767</td>\n",
       "      <td>4.002960</td>\n",
       "      <td>5.123457</td>\n",
       "      <td>0.341512</td>\n",
       "      <td>-2.570976</td>\n",
       "      <td>...</td>\n",
       "      <td>4.176419</td>\n",
       "      <td>-2.370500</td>\n",
       "      <td>-1.538324</td>\n",
       "      <td>-1.550699</td>\n",
       "      <td>-1.558581</td>\n",
       "      <td>-3.068620</td>\n",
       "      <td>-3.421540</td>\n",
       "      <td>-2.509017</td>\n",
       "      <td>-2.149696</td>\n",
       "      <td>Class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-3.80147</td>\n",
       "      <td>-3.889900</td>\n",
       "      <td>2.656469</td>\n",
       "      <td>2.207608</td>\n",
       "      <td>-6.041792</td>\n",
       "      <td>4.235937</td>\n",
       "      <td>0.716257</td>\n",
       "      <td>-1.164354</td>\n",
       "      <td>2.630053</td>\n",
       "      <td>-2.570976</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.821098</td>\n",
       "      <td>0.071490</td>\n",
       "      <td>-1.538324</td>\n",
       "      <td>5.651368</td>\n",
       "      <td>-1.558581</td>\n",
       "      <td>3.617386</td>\n",
       "      <td>1.384471</td>\n",
       "      <td>-2.509017</td>\n",
       "      <td>-2.149696</td>\n",
       "      <td>Class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-3.80147</td>\n",
       "      <td>-3.889900</td>\n",
       "      <td>-1.914759</td>\n",
       "      <td>2.417820</td>\n",
       "      <td>3.162904</td>\n",
       "      <td>-3.699171</td>\n",
       "      <td>-2.473517</td>\n",
       "      <td>-4.354127</td>\n",
       "      <td>1.970206</td>\n",
       "      <td>-2.570976</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.821098</td>\n",
       "      <td>3.615788</td>\n",
       "      <td>-1.538324</td>\n",
       "      <td>-1.550699</td>\n",
       "      <td>-1.558581</td>\n",
       "      <td>1.829214</td>\n",
       "      <td>5.237998</td>\n",
       "      <td>-2.509017</td>\n",
       "      <td>6.779879</td>\n",
       "      <td>Class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-3.80147</td>\n",
       "      <td>-3.889900</td>\n",
       "      <td>0.483560</td>\n",
       "      <td>-5.820241</td>\n",
       "      <td>-6.041792</td>\n",
       "      <td>-3.699171</td>\n",
       "      <td>-2.473517</td>\n",
       "      <td>4.889074</td>\n",
       "      <td>-3.847544</td>\n",
       "      <td>-2.570976</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.821098</td>\n",
       "      <td>-3.118284</td>\n",
       "      <td>-1.538324</td>\n",
       "      <td>-1.550699</td>\n",
       "      <td>-1.558581</td>\n",
       "      <td>-3.068620</td>\n",
       "      <td>-0.775154</td>\n",
       "      <td>1.953289</td>\n",
       "      <td>-2.149696</td>\n",
       "      <td>Class1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-3.80147</td>\n",
       "      <td>4.316243</td>\n",
       "      <td>1.828663</td>\n",
       "      <td>2.140173</td>\n",
       "      <td>-6.041792</td>\n",
       "      <td>3.721094</td>\n",
       "      <td>3.479903</td>\n",
       "      <td>-4.354127</td>\n",
       "      <td>-4.860593</td>\n",
       "      <td>-2.570976</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.821098</td>\n",
       "      <td>-0.860025</td>\n",
       "      <td>0.841462</td>\n",
       "      <td>-1.550699</td>\n",
       "      <td>-1.558581</td>\n",
       "      <td>5.157919</td>\n",
       "      <td>4.710859</td>\n",
       "      <td>5.078266</td>\n",
       "      <td>4.977025</td>\n",
       "      <td>Class1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 5949 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       A2M      AAAS      AAK1      AAMP      AARS    AARSD1     AASDH  \\\n",
       "0 -3.80147 -3.889900 -3.985616  2.651558  2.170748 -2.550822  4.807330   \n",
       "1 -3.80147 -3.889900 -3.158708  2.358992 -6.041792 -0.056092  3.606735   \n",
       "2 -3.80147 -3.889900  1.733125 -5.820241 -6.041792 -0.576957 -2.473517   \n",
       "3 -3.80147 -3.889900 -1.665669  3.514271 -6.041792 -3.699171  4.509461   \n",
       "4 -3.80147  3.742495 -2.166992 -5.820241  2.094729  4.021873  5.535007   \n",
       "5 -1.98770 -3.889900  4.691156 -4.006471 -3.449348  4.309767  4.002960   \n",
       "6 -3.80147 -3.889900  2.656469  2.207608 -6.041792  4.235937  0.716257   \n",
       "7 -3.80147 -3.889900 -1.914759  2.417820  3.162904 -3.699171 -2.473517   \n",
       "8 -3.80147 -3.889900  0.483560 -5.820241 -6.041792 -3.699171 -2.473517   \n",
       "9 -3.80147  4.316243  1.828663  2.140173 -6.041792  3.721094  3.479903   \n",
       "\n",
       "   AASDHPPT      AASS      AATF  ...    ZSWIM6    ZSWIM7     ZUFSP      ZW10  \\\n",
       "0  3.961170 -0.192665  3.614482  ...  2.909466 -3.118284 -1.538324 -1.550699   \n",
       "1 -2.632250  2.249388  6.857517  ... -1.821098 -3.118284 -1.538324 -1.550699   \n",
       "2 -4.354127  0.063178 -2.570976  ... -1.821098  5.521892 -1.538324 -1.550699   \n",
       "3 -4.354127  2.985972 -2.570976  ...  4.952176 -0.854351 -1.538324 -1.550699   \n",
       "4  4.019633  2.560370 -2.570976  ... -1.821098  4.328808 -1.538324  7.021985   \n",
       "5  5.123457  0.341512 -2.570976  ...  4.176419 -2.370500 -1.538324 -1.550699   \n",
       "6 -1.164354  2.630053 -2.570976  ... -1.821098  0.071490 -1.538324  5.651368   \n",
       "7 -4.354127  1.970206 -2.570976  ... -1.821098  3.615788 -1.538324 -1.550699   \n",
       "8  4.889074 -3.847544 -2.570976  ... -1.821098 -3.118284 -1.538324 -1.550699   \n",
       "9 -4.354127 -4.860593 -2.570976  ... -1.821098 -0.860025  0.841462 -1.550699   \n",
       "\n",
       "     ZWILCH      ZXDC    ZYG11B       ZYX      ZZZ3  Classes  \n",
       "0 -1.558581 -1.920271  3.007439 -2.509017 -2.149696   Class1  \n",
       "1 -1.558581 -3.068620  2.539560  2.164481 -2.149696   Class1  \n",
       "2 -1.558581  0.174665 -0.165409  0.734268 -2.149696   Class1  \n",
       "3 -1.558581 -3.068620 -1.884744 -2.509017 -2.149696   Class1  \n",
       "4 -1.558581  4.590946 -0.128456 -2.509017 -2.149696   Class1  \n",
       "5 -1.558581 -3.068620 -3.421540 -2.509017 -2.149696   Class1  \n",
       "6 -1.558581  3.617386  1.384471 -2.509017 -2.149696   Class1  \n",
       "7 -1.558581  1.829214  5.237998 -2.509017  6.779879   Class1  \n",
       "8 -1.558581 -3.068620 -0.775154  1.953289 -2.149696   Class1  \n",
       "9 -1.558581  5.157919  4.710859  5.078266  4.977025   Class1  \n",
       "\n",
       "[10 rows x 5949 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Data_Glioblastoma5Patients_SC.csv')\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "q26W_T1PM_wM",
    "outputId": "d06510d4-a971-43d6-e68f-15917705d675"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class1    118\n",
      "Class2     94\n",
      "Class3     75\n",
      "Class4     73\n",
      "Class5     70\n",
      "Name: Classes, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Identifying classes\n",
    "\n",
    "print(df['Classes'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xf7qx8knNuC7",
    "outputId": "daaccdcd-df10-4f50-eea9-8b5e2742e21b"
   },
   "outputs": [],
   "source": [
    "# Importing pycaret classification method\n",
    "\n",
    "from pycaret.classification import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 677
    },
    "id": "6_fkiEWdO1mx",
    "outputId": "7be48057-2685-4528-e5e0-0589be7dba85"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_e5e99_row9_col1 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_e5e99\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e5e99_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_e5e99_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e99_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_e5e99_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
       "      <td id=\"T_e5e99_row0_col1\" class=\"data row0 col1\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e99_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_e5e99_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_e5e99_row1_col1\" class=\"data row1 col1\" >Classes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e99_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_e5e99_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
       "      <td id=\"T_e5e99_row2_col1\" class=\"data row2 col1\" >Multiclass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e99_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_e5e99_row3_col0\" class=\"data row3 col0\" >Target mapping</td>\n",
       "      <td id=\"T_e5e99_row3_col1\" class=\"data row3 col1\" >Class1: 0, Class2: 1, Class3: 2, Class4: 3, Class5: 4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e99_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_e5e99_row4_col0\" class=\"data row4 col0\" >Original data shape</td>\n",
       "      <td id=\"T_e5e99_row4_col1\" class=\"data row4 col1\" >(430, 5949)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e99_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_e5e99_row5_col0\" class=\"data row5 col0\" >Transformed data shape</td>\n",
       "      <td id=\"T_e5e99_row5_col1\" class=\"data row5 col1\" >(430, 5949)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e99_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_e5e99_row6_col0\" class=\"data row6 col0\" >Transformed train set shape</td>\n",
       "      <td id=\"T_e5e99_row6_col1\" class=\"data row6 col1\" >(301, 5949)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e99_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_e5e99_row7_col0\" class=\"data row7 col0\" >Transformed test set shape</td>\n",
       "      <td id=\"T_e5e99_row7_col1\" class=\"data row7 col1\" >(129, 5949)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e99_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_e5e99_row8_col0\" class=\"data row8 col0\" >Numeric features</td>\n",
       "      <td id=\"T_e5e99_row8_col1\" class=\"data row8 col1\" >5948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e99_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_e5e99_row9_col0\" class=\"data row9 col0\" >Preprocess</td>\n",
       "      <td id=\"T_e5e99_row9_col1\" class=\"data row9 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e99_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_e5e99_row10_col0\" class=\"data row10 col0\" >Imputation type</td>\n",
       "      <td id=\"T_e5e99_row10_col1\" class=\"data row10 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e99_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_e5e99_row11_col0\" class=\"data row11 col0\" >Numeric imputation</td>\n",
       "      <td id=\"T_e5e99_row11_col1\" class=\"data row11 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e99_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_e5e99_row12_col0\" class=\"data row12 col0\" >Categorical imputation</td>\n",
       "      <td id=\"T_e5e99_row12_col1\" class=\"data row12 col1\" >mode</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e99_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_e5e99_row13_col0\" class=\"data row13 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_e5e99_row13_col1\" class=\"data row13 col1\" >StratifiedKFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e99_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_e5e99_row14_col0\" class=\"data row14 col0\" >Fold Number</td>\n",
       "      <td id=\"T_e5e99_row14_col1\" class=\"data row14 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e99_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_e5e99_row15_col0\" class=\"data row15 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_e5e99_row15_col1\" class=\"data row15 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e99_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_e5e99_row16_col0\" class=\"data row16 col0\" >Use GPU</td>\n",
       "      <td id=\"T_e5e99_row16_col1\" class=\"data row16 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e99_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_e5e99_row17_col0\" class=\"data row17 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_e5e99_row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e99_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_e5e99_row18_col0\" class=\"data row18 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_e5e99_row18_col1\" class=\"data row18 col1\" >clf-default-name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e5e99_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_e5e99_row19_col0\" class=\"data row19 col0\" >USI</td>\n",
       "      <td id=\"T_e5e99_row19_col1\" class=\"data row19 col1\" >9d75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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     },
     "metadata": {},
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   ],
   "source": [
    "exp = setup(data = df, target = 'Classes', session_id=1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# With AutoML there is no need for partitioning the data. This process is automatically done after identifying\n",
    "# The target value, in this case \"Classes.\" Target type, data shape, numeric features, etc are all identified \n",
    "# by the ML."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 682,
     "referenced_widgets": [
      "597d9037d1a1487e95ab66e3ec87b73a",
      "96f1421708b44ecebfb0b1986303bbe3",
      "b9e7d53b718f42fcb0df35bad2d110ad",
      "9557bf76c40a491f8135c29f7aec43c2",
      "d1ca417e6579484a80d1842ef28022e8",
      "86ae946db3ce4fc085f84e0efab86404",
      "4d1f8bf90f914f08b7e1a3082db43e91",
      "ef58acae956c41f39cd8608b69d376f0",
      "bc1716aa54c14fa88e35741b0817f9bf",
      "a04e045b7707421bae128a110d2b3da1",
      "2190c04a66dd4fe7b4e567b2324d9f7b"
     ]
    },
    "id": "D9K-0UE8PNXi",
    "outputId": "759e4f51-4ec7-426f-b989-723d8afc41c1",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table id=\"T_d8e0f\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d8e0f_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_d8e0f_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_d8e0f_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_d8e0f_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_d8e0f_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_d8e0f_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_d8e0f_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_d8e0f_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "      <th id=\"T_d8e0f_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e0f_level0_row0\" class=\"row_heading level0 row0\" >ridge</th>\n",
       "      <td id=\"T_d8e0f_row0_col0\" class=\"data row0 col0\" >Ridge Classifier</td>\n",
       "      <td id=\"T_d8e0f_row0_col1\" class=\"data row0 col1\" >1.0000</td>\n",
       "      <td id=\"T_d8e0f_row0_col2\" class=\"data row0 col2\" >0.0000</td>\n",
       "      <td id=\"T_d8e0f_row0_col3\" class=\"data row0 col3\" >1.0000</td>\n",
       "      <td id=\"T_d8e0f_row0_col4\" class=\"data row0 col4\" >1.0000</td>\n",
       "      <td id=\"T_d8e0f_row0_col5\" class=\"data row0 col5\" >1.0000</td>\n",
       "      <td id=\"T_d8e0f_row0_col6\" class=\"data row0 col6\" >1.0000</td>\n",
       "      <td id=\"T_d8e0f_row0_col7\" class=\"data row0 col7\" >1.0000</td>\n",
       "      <td id=\"T_d8e0f_row0_col8\" class=\"data row0 col8\" >0.4070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e0f_level0_row1\" class=\"row_heading level0 row1\" >lr</th>\n",
       "      <td id=\"T_d8e0f_row1_col0\" class=\"data row1 col0\" >Logistic Regression</td>\n",
       "      <td id=\"T_d8e0f_row1_col1\" class=\"data row1 col1\" >0.9900</td>\n",
       "      <td id=\"T_d8e0f_row1_col2\" class=\"data row1 col2\" >1.0000</td>\n",
       "      <td id=\"T_d8e0f_row1_col3\" class=\"data row1 col3\" >0.9900</td>\n",
       "      <td id=\"T_d8e0f_row1_col4\" class=\"data row1 col4\" >0.9915</td>\n",
       "      <td id=\"T_d8e0f_row1_col5\" class=\"data row1 col5\" >0.9898</td>\n",
       "      <td id=\"T_d8e0f_row1_col6\" class=\"data row1 col6\" >0.9873</td>\n",
       "      <td id=\"T_d8e0f_row1_col7\" class=\"data row1 col7\" >0.9877</td>\n",
       "      <td id=\"T_d8e0f_row1_col8\" class=\"data row1 col8\" >0.9980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e0f_level0_row2\" class=\"row_heading level0 row2\" >svm</th>\n",
       "      <td id=\"T_d8e0f_row2_col0\" class=\"data row2 col0\" >SVM - Linear Kernel</td>\n",
       "      <td id=\"T_d8e0f_row2_col1\" class=\"data row2 col1\" >0.9801</td>\n",
       "      <td id=\"T_d8e0f_row2_col2\" class=\"data row2 col2\" >0.0000</td>\n",
       "      <td id=\"T_d8e0f_row2_col3\" class=\"data row2 col3\" >0.9801</td>\n",
       "      <td id=\"T_d8e0f_row2_col4\" class=\"data row2 col4\" >0.9843</td>\n",
       "      <td id=\"T_d8e0f_row2_col5\" class=\"data row2 col5\" >0.9805</td>\n",
       "      <td id=\"T_d8e0f_row2_col6\" class=\"data row2 col6\" >0.9750</td>\n",
       "      <td id=\"T_d8e0f_row2_col7\" class=\"data row2 col7\" >0.9759</td>\n",
       "      <td id=\"T_d8e0f_row2_col8\" class=\"data row2 col8\" >0.4460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e0f_level0_row3\" class=\"row_heading level0 row3\" >lightgbm</th>\n",
       "      <td id=\"T_d8e0f_row3_col0\" class=\"data row3 col0\" >Light Gradient Boosting Machine</td>\n",
       "      <td id=\"T_d8e0f_row3_col1\" class=\"data row3 col1\" >0.9800</td>\n",
       "      <td id=\"T_d8e0f_row3_col2\" class=\"data row3 col2\" >0.9993</td>\n",
       "      <td id=\"T_d8e0f_row3_col3\" class=\"data row3 col3\" >0.9800</td>\n",
       "      <td id=\"T_d8e0f_row3_col4\" class=\"data row3 col4\" >0.9830</td>\n",
       "      <td id=\"T_d8e0f_row3_col5\" class=\"data row3 col5\" >0.9799</td>\n",
       "      <td id=\"T_d8e0f_row3_col6\" class=\"data row3 col6\" >0.9747</td>\n",
       "      <td id=\"T_d8e0f_row3_col7\" class=\"data row3 col7\" >0.9755</td>\n",
       "      <td id=\"T_d8e0f_row3_col8\" class=\"data row3 col8\" >0.8670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e0f_level0_row4\" class=\"row_heading level0 row4\" >xgboost</th>\n",
       "      <td id=\"T_d8e0f_row4_col0\" class=\"data row4 col0\" >Extreme Gradient Boosting</td>\n",
       "      <td id=\"T_d8e0f_row4_col1\" class=\"data row4 col1\" >0.9634</td>\n",
       "      <td id=\"T_d8e0f_row4_col2\" class=\"data row4 col2\" >0.9977</td>\n",
       "      <td id=\"T_d8e0f_row4_col3\" class=\"data row4 col3\" >0.9634</td>\n",
       "      <td id=\"T_d8e0f_row4_col4\" class=\"data row4 col4\" >0.9679</td>\n",
       "      <td id=\"T_d8e0f_row4_col5\" class=\"data row4 col5\" >0.9633</td>\n",
       "      <td id=\"T_d8e0f_row4_col6\" class=\"data row4 col6\" >0.9537</td>\n",
       "      <td id=\"T_d8e0f_row4_col7\" class=\"data row4 col7\" >0.9549</td>\n",
       "      <td id=\"T_d8e0f_row4_col8\" class=\"data row4 col8\" >0.6710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e0f_level0_row5\" class=\"row_heading level0 row5\" >rf</th>\n",
       "      <td id=\"T_d8e0f_row5_col0\" class=\"data row5 col0\" >Random Forest Classifier</td>\n",
       "      <td id=\"T_d8e0f_row5_col1\" class=\"data row5 col1\" >0.9533</td>\n",
       "      <td id=\"T_d8e0f_row5_col2\" class=\"data row5 col2\" >0.9988</td>\n",
       "      <td id=\"T_d8e0f_row5_col3\" class=\"data row5 col3\" >0.9533</td>\n",
       "      <td id=\"T_d8e0f_row5_col4\" class=\"data row5 col4\" >0.9597</td>\n",
       "      <td id=\"T_d8e0f_row5_col5\" class=\"data row5 col5\" >0.9520</td>\n",
       "      <td id=\"T_d8e0f_row5_col6\" class=\"data row5 col6\" >0.9407</td>\n",
       "      <td id=\"T_d8e0f_row5_col7\" class=\"data row5 col7\" >0.9428</td>\n",
       "      <td id=\"T_d8e0f_row5_col8\" class=\"data row5 col8\" >0.5900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e0f_level0_row6\" class=\"row_heading level0 row6\" >et</th>\n",
       "      <td id=\"T_d8e0f_row6_col0\" class=\"data row6 col0\" >Extra Trees Classifier</td>\n",
       "      <td id=\"T_d8e0f_row6_col1\" class=\"data row6 col1\" >0.9501</td>\n",
       "      <td id=\"T_d8e0f_row6_col2\" class=\"data row6 col2\" >0.9989</td>\n",
       "      <td id=\"T_d8e0f_row6_col3\" class=\"data row6 col3\" >0.9501</td>\n",
       "      <td id=\"T_d8e0f_row6_col4\" class=\"data row6 col4\" >0.9588</td>\n",
       "      <td id=\"T_d8e0f_row6_col5\" class=\"data row6 col5\" >0.9491</td>\n",
       "      <td id=\"T_d8e0f_row6_col6\" class=\"data row6 col6\" >0.9365</td>\n",
       "      <td id=\"T_d8e0f_row6_col7\" class=\"data row6 col7\" >0.9393</td>\n",
       "      <td id=\"T_d8e0f_row6_col8\" class=\"data row6 col8\" >0.5440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e0f_level0_row7\" class=\"row_heading level0 row7\" >gbc</th>\n",
       "      <td id=\"T_d8e0f_row7_col0\" class=\"data row7 col0\" >Gradient Boosting Classifier</td>\n",
       "      <td id=\"T_d8e0f_row7_col1\" class=\"data row7 col1\" >0.9368</td>\n",
       "      <td id=\"T_d8e0f_row7_col2\" class=\"data row7 col2\" >0.9947</td>\n",
       "      <td id=\"T_d8e0f_row7_col3\" class=\"data row7 col3\" >0.9368</td>\n",
       "      <td id=\"T_d8e0f_row7_col4\" class=\"data row7 col4\" >0.9455</td>\n",
       "      <td id=\"T_d8e0f_row7_col5\" class=\"data row7 col5\" >0.9349</td>\n",
       "      <td id=\"T_d8e0f_row7_col6\" class=\"data row7 col6\" >0.9198</td>\n",
       "      <td id=\"T_d8e0f_row7_col7\" class=\"data row7 col7\" >0.9225</td>\n",
       "      <td id=\"T_d8e0f_row7_col8\" class=\"data row7 col8\" >0.6700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e0f_level0_row8\" class=\"row_heading level0 row8\" >lda</th>\n",
       "      <td id=\"T_d8e0f_row8_col0\" class=\"data row8 col0\" >Linear Discriminant Analysis</td>\n",
       "      <td id=\"T_d8e0f_row8_col1\" class=\"data row8 col1\" >0.9270</td>\n",
       "      <td id=\"T_d8e0f_row8_col2\" class=\"data row8 col2\" >0.9934</td>\n",
       "      <td id=\"T_d8e0f_row8_col3\" class=\"data row8 col3\" >0.9270</td>\n",
       "      <td id=\"T_d8e0f_row8_col4\" class=\"data row8 col4\" >0.9375</td>\n",
       "      <td id=\"T_d8e0f_row8_col5\" class=\"data row8 col5\" >0.9264</td>\n",
       "      <td id=\"T_d8e0f_row8_col6\" class=\"data row8 col6\" >0.9076</td>\n",
       "      <td id=\"T_d8e0f_row8_col7\" class=\"data row8 col7\" >0.9102</td>\n",
       "      <td id=\"T_d8e0f_row8_col8\" class=\"data row8 col8\" >0.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e0f_level0_row9\" class=\"row_heading level0 row9\" >dt</th>\n",
       "      <td id=\"T_d8e0f_row9_col0\" class=\"data row9 col0\" >Decision Tree Classifier</td>\n",
       "      <td id=\"T_d8e0f_row9_col1\" class=\"data row9 col1\" >0.8671</td>\n",
       "      <td id=\"T_d8e0f_row9_col2\" class=\"data row9 col2\" >0.9183</td>\n",
       "      <td id=\"T_d8e0f_row9_col3\" class=\"data row9 col3\" >0.8671</td>\n",
       "      <td id=\"T_d8e0f_row9_col4\" class=\"data row9 col4\" >0.8899</td>\n",
       "      <td id=\"T_d8e0f_row9_col5\" class=\"data row9 col5\" >0.8630</td>\n",
       "      <td id=\"T_d8e0f_row9_col6\" class=\"data row9 col6\" >0.8316</td>\n",
       "      <td id=\"T_d8e0f_row9_col7\" class=\"data row9 col7\" >0.8375</td>\n",
       "      <td id=\"T_d8e0f_row9_col8\" class=\"data row9 col8\" >0.4630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e0f_level0_row10\" class=\"row_heading level0 row10\" >nb</th>\n",
       "      <td id=\"T_d8e0f_row10_col0\" class=\"data row10 col0\" >Naive Bayes</td>\n",
       "      <td id=\"T_d8e0f_row10_col1\" class=\"data row10 col1\" >0.7705</td>\n",
       "      <td id=\"T_d8e0f_row10_col2\" class=\"data row10 col2\" >0.8603</td>\n",
       "      <td id=\"T_d8e0f_row10_col3\" class=\"data row10 col3\" >0.7705</td>\n",
       "      <td id=\"T_d8e0f_row10_col4\" class=\"data row10 col4\" >0.8347</td>\n",
       "      <td id=\"T_d8e0f_row10_col5\" class=\"data row10 col5\" >0.7674</td>\n",
       "      <td id=\"T_d8e0f_row10_col6\" class=\"data row10 col6\" >0.7113</td>\n",
       "      <td id=\"T_d8e0f_row10_col7\" class=\"data row10 col7\" >0.7266</td>\n",
       "      <td id=\"T_d8e0f_row10_col8\" class=\"data row10 col8\" >0.5390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e0f_level0_row11\" class=\"row_heading level0 row11\" >knn</th>\n",
       "      <td id=\"T_d8e0f_row11_col0\" class=\"data row11 col0\" >K Neighbors Classifier</td>\n",
       "      <td id=\"T_d8e0f_row11_col1\" class=\"data row11 col1\" >0.6713</td>\n",
       "      <td id=\"T_d8e0f_row11_col2\" class=\"data row11 col2\" >0.9297</td>\n",
       "      <td id=\"T_d8e0f_row11_col3\" class=\"data row11 col3\" >0.6713</td>\n",
       "      <td id=\"T_d8e0f_row11_col4\" class=\"data row11 col4\" >0.8512</td>\n",
       "      <td id=\"T_d8e0f_row11_col5\" class=\"data row11 col5\" >0.6825</td>\n",
       "      <td id=\"T_d8e0f_row11_col6\" class=\"data row11 col6\" >0.5888</td>\n",
       "      <td id=\"T_d8e0f_row11_col7\" class=\"data row11 col7\" >0.6385</td>\n",
       "      <td id=\"T_d8e0f_row11_col8\" class=\"data row11 col8\" >0.6860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e0f_level0_row12\" class=\"row_heading level0 row12\" >ada</th>\n",
       "      <td id=\"T_d8e0f_row12_col0\" class=\"data row12 col0\" >Ada Boost Classifier</td>\n",
       "      <td id=\"T_d8e0f_row12_col1\" class=\"data row12 col1\" >0.4851</td>\n",
       "      <td id=\"T_d8e0f_row12_col2\" class=\"data row12 col2\" >0.7663</td>\n",
       "      <td id=\"T_d8e0f_row12_col3\" class=\"data row12 col3\" >0.4851</td>\n",
       "      <td id=\"T_d8e0f_row12_col4\" class=\"data row12 col4\" >0.3320</td>\n",
       "      <td id=\"T_d8e0f_row12_col5\" class=\"data row12 col5\" >0.3687</td>\n",
       "      <td id=\"T_d8e0f_row12_col6\" class=\"data row12 col6\" >0.3293</td>\n",
       "      <td id=\"T_d8e0f_row12_col7\" class=\"data row12 col7\" >0.4422</td>\n",
       "      <td id=\"T_d8e0f_row12_col8\" class=\"data row12 col8\" >0.5290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e0f_level0_row13\" class=\"row_heading level0 row13\" >qda</th>\n",
       "      <td id=\"T_d8e0f_row13_col0\" class=\"data row13 col0\" >Quadratic Discriminant Analysis</td>\n",
       "      <td id=\"T_d8e0f_row13_col1\" class=\"data row13 col1\" >0.2789</td>\n",
       "      <td id=\"T_d8e0f_row13_col2\" class=\"data row13 col2\" >0.5418</td>\n",
       "      <td id=\"T_d8e0f_row13_col3\" class=\"data row13 col3\" >0.2789</td>\n",
       "      <td id=\"T_d8e0f_row13_col4\" class=\"data row13 col4\" >0.2737</td>\n",
       "      <td id=\"T_d8e0f_row13_col5\" class=\"data row13 col5\" >0.2678</td>\n",
       "      <td id=\"T_d8e0f_row13_col6\" class=\"data row13 col6\" >0.0828</td>\n",
       "      <td id=\"T_d8e0f_row13_col7\" class=\"data row13 col7\" >0.0844</td>\n",
       "      <td id=\"T_d8e0f_row13_col8\" class=\"data row13 col8\" >0.6010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e0f_level0_row14\" class=\"row_heading level0 row14\" >dummy</th>\n",
       "      <td id=\"T_d8e0f_row14_col0\" class=\"data row14 col0\" >Dummy Classifier</td>\n",
       "      <td id=\"T_d8e0f_row14_col1\" class=\"data row14 col1\" >0.2757</td>\n",
       "      <td id=\"T_d8e0f_row14_col2\" class=\"data row14 col2\" >0.5000</td>\n",
       "      <td id=\"T_d8e0f_row14_col3\" class=\"data row14 col3\" >0.2757</td>\n",
       "      <td id=\"T_d8e0f_row14_col4\" class=\"data row14 col4\" >0.0762</td>\n",
       "      <td id=\"T_d8e0f_row14_col5\" class=\"data row14 col5\" >0.1194</td>\n",
       "      <td id=\"T_d8e0f_row14_col6\" class=\"data row14 col6\" >0.0000</td>\n",
       "      <td id=\"T_d8e0f_row14_col7\" class=\"data row14 col7\" >0.0000</td>\n",
       "      <td id=\"T_d8e0f_row14_col8\" class=\"data row14 col8\" >0.5280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23057d53520>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing:   0%|          | 0/65 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best = compare_models(turbo = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# By using the compare_models command numerous statistical techiques can be applied to the data and compared to each\n",
    "# other to determine which model provides the most accurate result for the ML. In this case the AutoML has indicated that\n",
    "# the use of a Ridge Classifier results in the most accurate learning. According to the AutoMl KNN results in an\n",
    "# accuracy of only 67%, while Decision Tree has an accuracy of 86%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cad2d585cc3f454b9c45fa8976285373",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The evaluate_model command allows for widgets to be applied to the notebook and can be accessed at the click of a mouse\n",
    "# as long as the proper libraries are installed/imported. These widgets can show Feature Importance, Decision Tree, AUC,\n",
    "# Class Report, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_0bdaf_row10_col0, #T_0bdaf_row10_col1, #T_0bdaf_row10_col2, #T_0bdaf_row10_col3, #T_0bdaf_row10_col4, #T_0bdaf_row10_col5, #T_0bdaf_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_0bdaf\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_0bdaf_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_0bdaf_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th id=\"T_0bdaf_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th id=\"T_0bdaf_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th id=\"T_0bdaf_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th id=\"T_0bdaf_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th id=\"T_0bdaf_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_0bdaf_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_0bdaf_row0_col0\" class=\"data row0 col0\" >0.6129</td>\n",
       "      <td id=\"T_0bdaf_row0_col1\" class=\"data row0 col1\" >0.8719</td>\n",
       "      <td id=\"T_0bdaf_row0_col2\" class=\"data row0 col2\" >0.6129</td>\n",
       "      <td id=\"T_0bdaf_row0_col3\" class=\"data row0 col3\" >0.7763</td>\n",
       "      <td id=\"T_0bdaf_row0_col4\" class=\"data row0 col4\" >0.6134</td>\n",
       "      <td id=\"T_0bdaf_row0_col5\" class=\"data row0 col5\" >0.5181</td>\n",
       "      <td id=\"T_0bdaf_row0_col6\" class=\"data row0 col6\" >0.5637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0bdaf_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_0bdaf_row1_col0\" class=\"data row1 col0\" >0.7667</td>\n",
       "      <td id=\"T_0bdaf_row1_col1\" class=\"data row1 col1\" >0.9137</td>\n",
       "      <td id=\"T_0bdaf_row1_col2\" class=\"data row1 col2\" >0.7667</td>\n",
       "      <td id=\"T_0bdaf_row1_col3\" class=\"data row1 col3\" >0.8367</td>\n",
       "      <td id=\"T_0bdaf_row1_col4\" class=\"data row1 col4\" >0.7702</td>\n",
       "      <td id=\"T_0bdaf_row1_col5\" class=\"data row1 col5\" >0.7038</td>\n",
       "      <td id=\"T_0bdaf_row1_col6\" class=\"data row1 col6\" >0.7213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0bdaf_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_0bdaf_row2_col0\" class=\"data row2 col0\" >0.6000</td>\n",
       "      <td id=\"T_0bdaf_row2_col1\" class=\"data row2 col1\" >0.9158</td>\n",
       "      <td id=\"T_0bdaf_row2_col2\" class=\"data row2 col2\" >0.6000</td>\n",
       "      <td id=\"T_0bdaf_row2_col3\" class=\"data row2 col3\" >0.8824</td>\n",
       "      <td id=\"T_0bdaf_row2_col4\" class=\"data row2 col4\" >0.6089</td>\n",
       "      <td id=\"T_0bdaf_row2_col5\" class=\"data row2 col5\" >0.4972</td>\n",
       "      <td id=\"T_0bdaf_row2_col6\" class=\"data row2 col6\" >0.5768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0bdaf_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_0bdaf_row3_col0\" class=\"data row3 col0\" >0.6000</td>\n",
       "      <td id=\"T_0bdaf_row3_col1\" class=\"data row3 col1\" >0.9539</td>\n",
       "      <td id=\"T_0bdaf_row3_col2\" class=\"data row3 col2\" >0.6000</td>\n",
       "      <td id=\"T_0bdaf_row3_col3\" class=\"data row3 col3\" >0.8521</td>\n",
       "      <td id=\"T_0bdaf_row3_col4\" class=\"data row3 col4\" >0.6069</td>\n",
       "      <td id=\"T_0bdaf_row3_col5\" class=\"data row3 col5\" >0.5014</td>\n",
       "      <td id=\"T_0bdaf_row3_col6\" class=\"data row3 col6\" >0.5692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0bdaf_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_0bdaf_row4_col0\" class=\"data row4 col0\" >0.7333</td>\n",
       "      <td id=\"T_0bdaf_row4_col1\" class=\"data row4 col1\" >0.9146</td>\n",
       "      <td id=\"T_0bdaf_row4_col2\" class=\"data row4 col2\" >0.7333</td>\n",
       "      <td id=\"T_0bdaf_row4_col3\" class=\"data row4 col3\" >0.8974</td>\n",
       "      <td id=\"T_0bdaf_row4_col4\" class=\"data row4 col4\" >0.7562</td>\n",
       "      <td id=\"T_0bdaf_row4_col5\" class=\"data row4 col5\" >0.6662</td>\n",
       "      <td id=\"T_0bdaf_row4_col6\" class=\"data row4 col6\" >0.7074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0bdaf_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_0bdaf_row5_col0\" class=\"data row5 col0\" >0.7333</td>\n",
       "      <td id=\"T_0bdaf_row5_col1\" class=\"data row5 col1\" >0.9806</td>\n",
       "      <td id=\"T_0bdaf_row5_col2\" class=\"data row5 col2\" >0.7333</td>\n",
       "      <td id=\"T_0bdaf_row5_col3\" class=\"data row5 col3\" >0.8974</td>\n",
       "      <td id=\"T_0bdaf_row5_col4\" class=\"data row5 col4\" >0.7568</td>\n",
       "      <td id=\"T_0bdaf_row5_col5\" class=\"data row5 col5\" >0.6667</td>\n",
       "      <td id=\"T_0bdaf_row5_col6\" class=\"data row5 col6\" >0.7078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0bdaf_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_0bdaf_row6_col0\" class=\"data row6 col0\" >0.6333</td>\n",
       "      <td id=\"T_0bdaf_row6_col1\" class=\"data row6 col1\" >0.8906</td>\n",
       "      <td id=\"T_0bdaf_row6_col2\" class=\"data row6 col2\" >0.6333</td>\n",
       "      <td id=\"T_0bdaf_row6_col3\" class=\"data row6 col3\" >0.7595</td>\n",
       "      <td id=\"T_0bdaf_row6_col4\" class=\"data row6 col4\" >0.6383</td>\n",
       "      <td id=\"T_0bdaf_row6_col5\" class=\"data row6 col5\" >0.5436</td>\n",
       "      <td id=\"T_0bdaf_row6_col6\" class=\"data row6 col6\" >0.5877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0bdaf_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_0bdaf_row7_col0\" class=\"data row7 col0\" >0.6667</td>\n",
       "      <td id=\"T_0bdaf_row7_col1\" class=\"data row7 col1\" >0.9282</td>\n",
       "      <td id=\"T_0bdaf_row7_col2\" class=\"data row7 col2\" >0.6667</td>\n",
       "      <td id=\"T_0bdaf_row7_col3\" class=\"data row7 col3\" >0.8889</td>\n",
       "      <td id=\"T_0bdaf_row7_col4\" class=\"data row7 col4\" >0.6775</td>\n",
       "      <td id=\"T_0bdaf_row7_col5\" class=\"data row7 col5\" >0.5828</td>\n",
       "      <td id=\"T_0bdaf_row7_col6\" class=\"data row7 col6\" >0.6432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0bdaf_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_0bdaf_row8_col0\" class=\"data row8 col0\" >0.6333</td>\n",
       "      <td id=\"T_0bdaf_row8_col1\" class=\"data row8 col1\" >0.9526</td>\n",
       "      <td id=\"T_0bdaf_row8_col2\" class=\"data row8 col2\" >0.6333</td>\n",
       "      <td id=\"T_0bdaf_row8_col3\" class=\"data row8 col3\" >0.8356</td>\n",
       "      <td id=\"T_0bdaf_row8_col4\" class=\"data row8 col4\" >0.6671</td>\n",
       "      <td id=\"T_0bdaf_row8_col5\" class=\"data row8 col5\" >0.5461</td>\n",
       "      <td id=\"T_0bdaf_row8_col6\" class=\"data row8 col6\" >0.5995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0bdaf_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_0bdaf_row9_col0\" class=\"data row9 col0\" >0.7333</td>\n",
       "      <td id=\"T_0bdaf_row9_col1\" class=\"data row9 col1\" >0.9752</td>\n",
       "      <td id=\"T_0bdaf_row9_col2\" class=\"data row9 col2\" >0.7333</td>\n",
       "      <td id=\"T_0bdaf_row9_col3\" class=\"data row9 col3\" >0.8857</td>\n",
       "      <td id=\"T_0bdaf_row9_col4\" class=\"data row9 col4\" >0.7301</td>\n",
       "      <td id=\"T_0bdaf_row9_col5\" class=\"data row9 col5\" >0.6620</td>\n",
       "      <td id=\"T_0bdaf_row9_col6\" class=\"data row9 col6\" >0.7087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0bdaf_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_0bdaf_row10_col0\" class=\"data row10 col0\" >0.6713</td>\n",
       "      <td id=\"T_0bdaf_row10_col1\" class=\"data row10 col1\" >0.9297</td>\n",
       "      <td id=\"T_0bdaf_row10_col2\" class=\"data row10 col2\" >0.6713</td>\n",
       "      <td id=\"T_0bdaf_row10_col3\" class=\"data row10 col3\" >0.8512</td>\n",
       "      <td id=\"T_0bdaf_row10_col4\" class=\"data row10 col4\" >0.6825</td>\n",
       "      <td id=\"T_0bdaf_row10_col5\" class=\"data row10 col5\" >0.5888</td>\n",
       "      <td id=\"T_0bdaf_row10_col6\" class=\"data row10 col6\" >0.6385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0bdaf_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_0bdaf_row11_col0\" class=\"data row11 col0\" >0.0609</td>\n",
       "      <td id=\"T_0bdaf_row11_col1\" class=\"data row11 col1\" >0.0336</td>\n",
       "      <td id=\"T_0bdaf_row11_col2\" class=\"data row11 col2\" >0.0609</td>\n",
       "      <td id=\"T_0bdaf_row11_col3\" class=\"data row11 col3\" >0.0472</td>\n",
       "      <td id=\"T_0bdaf_row11_col4\" class=\"data row11 col4\" >0.0625</td>\n",
       "      <td id=\"T_0bdaf_row11_col5\" class=\"data row11 col5\" >0.0745</td>\n",
       "      <td id=\"T_0bdaf_row11_col6\" class=\"data row11 col6\" >0.0630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2304aa5a130>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_model = create_model('knn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(knn_model, plot = 'class_report')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_f4ab5_row10_col0, #T_f4ab5_row10_col1, #T_f4ab5_row10_col2, #T_f4ab5_row10_col3, #T_f4ab5_row10_col4, #T_f4ab5_row10_col5, #T_f4ab5_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_f4ab5\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_f4ab5_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_f4ab5_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th id=\"T_f4ab5_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th id=\"T_f4ab5_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th id=\"T_f4ab5_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th id=\"T_f4ab5_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th id=\"T_f4ab5_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f4ab5_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_f4ab5_row0_col0\" class=\"data row0 col0\" >0.8710</td>\n",
       "      <td id=\"T_f4ab5_row0_col1\" class=\"data row0 col1\" >0.9200</td>\n",
       "      <td id=\"T_f4ab5_row0_col2\" class=\"data row0 col2\" >0.8710</td>\n",
       "      <td id=\"T_f4ab5_row0_col3\" class=\"data row0 col3\" >0.9032</td>\n",
       "      <td id=\"T_f4ab5_row0_col4\" class=\"data row0 col4\" >0.8516</td>\n",
       "      <td id=\"T_f4ab5_row0_col5\" class=\"data row0 col5\" >0.8347</td>\n",
       "      <td id=\"T_f4ab5_row0_col6\" class=\"data row0 col6\" >0.8450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4ab5_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_f4ab5_row1_col0\" class=\"data row1 col0\" >0.9667</td>\n",
       "      <td id=\"T_f4ab5_row1_col1\" class=\"data row1 col1\" >0.9800</td>\n",
       "      <td id=\"T_f4ab5_row1_col2\" class=\"data row1 col2\" >0.9667</td>\n",
       "      <td id=\"T_f4ab5_row1_col3\" class=\"data row1 col3\" >0.9722</td>\n",
       "      <td id=\"T_f4ab5_row1_col4\" class=\"data row1 col4\" >0.9663</td>\n",
       "      <td id=\"T_f4ab5_row1_col5\" class=\"data row1 col5\" >0.9576</td>\n",
       "      <td id=\"T_f4ab5_row1_col6\" class=\"data row1 col6\" >0.9590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4ab5_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_f4ab5_row2_col0\" class=\"data row2 col0\" >0.8667</td>\n",
       "      <td id=\"T_f4ab5_row2_col1\" class=\"data row2 col1\" >0.9200</td>\n",
       "      <td id=\"T_f4ab5_row2_col2\" class=\"data row2 col2\" >0.8667</td>\n",
       "      <td id=\"T_f4ab5_row2_col3\" class=\"data row2 col3\" >0.8869</td>\n",
       "      <td id=\"T_f4ab5_row2_col4\" class=\"data row2 col4\" >0.8704</td>\n",
       "      <td id=\"T_f4ab5_row2_col5\" class=\"data row2 col5\" >0.8305</td>\n",
       "      <td id=\"T_f4ab5_row2_col6\" class=\"data row2 col6\" >0.8341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4ab5_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_f4ab5_row3_col0\" class=\"data row3 col0\" >0.9000</td>\n",
       "      <td id=\"T_f4ab5_row3_col1\" class=\"data row3 col1\" >0.9383</td>\n",
       "      <td id=\"T_f4ab5_row3_col2\" class=\"data row3 col2\" >0.9000</td>\n",
       "      <td id=\"T_f4ab5_row3_col3\" class=\"data row3 col3\" >0.9014</td>\n",
       "      <td id=\"T_f4ab5_row3_col4\" class=\"data row3 col4\" >0.8960</td>\n",
       "      <td id=\"T_f4ab5_row3_col5\" class=\"data row3 col5\" >0.8738</td>\n",
       "      <td id=\"T_f4ab5_row3_col6\" class=\"data row3 col6\" >0.8762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4ab5_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_f4ab5_row4_col0\" class=\"data row4 col0\" >0.8667</td>\n",
       "      <td id=\"T_f4ab5_row4_col1\" class=\"data row4 col1\" >0.9183</td>\n",
       "      <td id=\"T_f4ab5_row4_col2\" class=\"data row4 col2\" >0.8667</td>\n",
       "      <td id=\"T_f4ab5_row4_col3\" class=\"data row4 col3\" >0.8778</td>\n",
       "      <td id=\"T_f4ab5_row4_col4\" class=\"data row4 col4\" >0.8694</td>\n",
       "      <td id=\"T_f4ab5_row4_col5\" class=\"data row4 col5\" >0.8315</td>\n",
       "      <td id=\"T_f4ab5_row4_col6\" class=\"data row4 col6\" >0.8326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4ab5_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_f4ab5_row5_col0\" class=\"data row5 col0\" >0.8333</td>\n",
       "      <td id=\"T_f4ab5_row5_col1\" class=\"data row5 col1\" >0.8938</td>\n",
       "      <td id=\"T_f4ab5_row5_col2\" class=\"data row5 col2\" >0.8333</td>\n",
       "      <td id=\"T_f4ab5_row5_col3\" class=\"data row5 col3\" >0.8519</td>\n",
       "      <td id=\"T_f4ab5_row5_col4\" class=\"data row5 col4\" >0.8171</td>\n",
       "      <td id=\"T_f4ab5_row5_col5\" class=\"data row5 col5\" >0.7872</td>\n",
       "      <td id=\"T_f4ab5_row5_col6\" class=\"data row5 col6\" >0.7953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4ab5_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_f4ab5_row6_col0\" class=\"data row6 col0\" >0.7667</td>\n",
       "      <td id=\"T_f4ab5_row6_col1\" class=\"data row6 col1\" >0.8565</td>\n",
       "      <td id=\"T_f4ab5_row6_col2\" class=\"data row6 col2\" >0.7667</td>\n",
       "      <td id=\"T_f4ab5_row6_col3\" class=\"data row6 col3\" >0.8556</td>\n",
       "      <td id=\"T_f4ab5_row6_col4\" class=\"data row6 col4\" >0.7565</td>\n",
       "      <td id=\"T_f4ab5_row6_col5\" class=\"data row6 col5\" >0.7034</td>\n",
       "      <td id=\"T_f4ab5_row6_col6\" class=\"data row6 col6\" >0.7232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4ab5_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_f4ab5_row7_col0\" class=\"data row7 col0\" >0.8667</td>\n",
       "      <td id=\"T_f4ab5_row7_col1\" class=\"data row7 col1\" >0.9200</td>\n",
       "      <td id=\"T_f4ab5_row7_col2\" class=\"data row7 col2\" >0.8667</td>\n",
       "      <td id=\"T_f4ab5_row7_col3\" class=\"data row7 col3\" >0.9042</td>\n",
       "      <td id=\"T_f4ab5_row7_col4\" class=\"data row7 col4\" >0.8715</td>\n",
       "      <td id=\"T_f4ab5_row7_col5\" class=\"data row7 col5\" >0.8331</td>\n",
       "      <td id=\"T_f4ab5_row7_col6\" class=\"data row7 col6\" >0.8413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4ab5_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_f4ab5_row8_col0\" class=\"data row8 col0\" >0.8667</td>\n",
       "      <td id=\"T_f4ab5_row8_col1\" class=\"data row8 col1\" >0.9165</td>\n",
       "      <td id=\"T_f4ab5_row8_col2\" class=\"data row8 col2\" >0.8667</td>\n",
       "      <td id=\"T_f4ab5_row8_col3\" class=\"data row8 col3\" >0.8667</td>\n",
       "      <td id=\"T_f4ab5_row8_col4\" class=\"data row8 col4\" >0.8644</td>\n",
       "      <td id=\"T_f4ab5_row8_col5\" class=\"data row8 col5\" >0.8310</td>\n",
       "      <td id=\"T_f4ab5_row8_col6\" class=\"data row8 col6\" >0.8322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4ab5_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_f4ab5_row9_col0\" class=\"data row9 col0\" >0.8667</td>\n",
       "      <td id=\"T_f4ab5_row9_col1\" class=\"data row9 col1\" >0.9192</td>\n",
       "      <td id=\"T_f4ab5_row9_col2\" class=\"data row9 col2\" >0.8667</td>\n",
       "      <td id=\"T_f4ab5_row9_col3\" class=\"data row9 col3\" >0.8790</td>\n",
       "      <td id=\"T_f4ab5_row9_col4\" class=\"data row9 col4\" >0.8666</td>\n",
       "      <td id=\"T_f4ab5_row9_col5\" class=\"data row9 col5\" >0.8329</td>\n",
       "      <td id=\"T_f4ab5_row9_col6\" class=\"data row9 col6\" >0.8364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4ab5_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_f4ab5_row10_col0\" class=\"data row10 col0\" >0.8671</td>\n",
       "      <td id=\"T_f4ab5_row10_col1\" class=\"data row10 col1\" >0.9183</td>\n",
       "      <td id=\"T_f4ab5_row10_col2\" class=\"data row10 col2\" >0.8671</td>\n",
       "      <td id=\"T_f4ab5_row10_col3\" class=\"data row10 col3\" >0.8899</td>\n",
       "      <td id=\"T_f4ab5_row10_col4\" class=\"data row10 col4\" >0.8630</td>\n",
       "      <td id=\"T_f4ab5_row10_col5\" class=\"data row10 col5\" >0.8316</td>\n",
       "      <td id=\"T_f4ab5_row10_col6\" class=\"data row10 col6\" >0.8375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4ab5_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_f4ab5_row11_col0\" class=\"data row11 col0\" >0.0472</td>\n",
       "      <td id=\"T_f4ab5_row11_col1\" class=\"data row11 col1\" >0.0294</td>\n",
       "      <td id=\"T_f4ab5_row11_col2\" class=\"data row11 col2\" >0.0472</td>\n",
       "      <td id=\"T_f4ab5_row11_col3\" class=\"data row11 col3\" >0.0328</td>\n",
       "      <td id=\"T_f4ab5_row11_col4\" class=\"data row11 col4\" >0.0505</td>\n",
       "      <td id=\"T_f4ab5_row11_col5\" class=\"data row11 col5\" >0.0601</td>\n",
       "      <td id=\"T_f4ab5_row11_col6\" class=\"data row11 col6\" >0.0559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23057da6730>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_model = create_model('dt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(dt_model, plot = 'tree')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_557c0_row10_col0, #T_557c0_row10_col1, #T_557c0_row10_col2, #T_557c0_row10_col3, #T_557c0_row10_col4, #T_557c0_row10_col5, #T_557c0_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_557c0\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_557c0_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_557c0_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th id=\"T_557c0_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th id=\"T_557c0_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th id=\"T_557c0_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th id=\"T_557c0_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th id=\"T_557c0_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_557c0_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_557c0_row0_col0\" class=\"data row0 col0\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row0_col1\" class=\"data row0 col1\" >0.0000</td>\n",
       "      <td id=\"T_557c0_row0_col2\" class=\"data row0 col2\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row0_col3\" class=\"data row0 col3\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row0_col4\" class=\"data row0 col4\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row0_col5\" class=\"data row0 col5\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row0_col6\" class=\"data row0 col6\" >1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_557c0_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_557c0_row1_col0\" class=\"data row1 col0\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row1_col1\" class=\"data row1 col1\" >0.0000</td>\n",
       "      <td id=\"T_557c0_row1_col2\" class=\"data row1 col2\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row1_col3\" class=\"data row1 col3\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row1_col4\" class=\"data row1 col4\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row1_col5\" class=\"data row1 col5\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row1_col6\" class=\"data row1 col6\" >1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_557c0_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_557c0_row2_col0\" class=\"data row2 col0\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row2_col1\" class=\"data row2 col1\" >0.0000</td>\n",
       "      <td id=\"T_557c0_row2_col2\" class=\"data row2 col2\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row2_col3\" class=\"data row2 col3\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row2_col4\" class=\"data row2 col4\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row2_col5\" class=\"data row2 col5\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row2_col6\" class=\"data row2 col6\" >1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_557c0_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_557c0_row3_col0\" class=\"data row3 col0\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row3_col1\" class=\"data row3 col1\" >0.0000</td>\n",
       "      <td id=\"T_557c0_row3_col2\" class=\"data row3 col2\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row3_col3\" class=\"data row3 col3\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row3_col4\" class=\"data row3 col4\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row3_col5\" class=\"data row3 col5\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row3_col6\" class=\"data row3 col6\" >1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_557c0_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_557c0_row4_col0\" class=\"data row4 col0\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row4_col1\" class=\"data row4 col1\" >0.0000</td>\n",
       "      <td id=\"T_557c0_row4_col2\" class=\"data row4 col2\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row4_col3\" class=\"data row4 col3\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row4_col4\" class=\"data row4 col4\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row4_col5\" class=\"data row4 col5\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row4_col6\" class=\"data row4 col6\" >1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_557c0_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_557c0_row5_col0\" class=\"data row5 col0\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row5_col1\" class=\"data row5 col1\" >0.0000</td>\n",
       "      <td id=\"T_557c0_row5_col2\" class=\"data row5 col2\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row5_col3\" class=\"data row5 col3\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row5_col4\" class=\"data row5 col4\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row5_col5\" class=\"data row5 col5\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row5_col6\" class=\"data row5 col6\" >1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_557c0_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_557c0_row6_col0\" class=\"data row6 col0\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row6_col1\" class=\"data row6 col1\" >0.0000</td>\n",
       "      <td id=\"T_557c0_row6_col2\" class=\"data row6 col2\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row6_col3\" class=\"data row6 col3\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row6_col4\" class=\"data row6 col4\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row6_col5\" class=\"data row6 col5\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row6_col6\" class=\"data row6 col6\" >1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_557c0_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_557c0_row7_col0\" class=\"data row7 col0\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row7_col1\" class=\"data row7 col1\" >0.0000</td>\n",
       "      <td id=\"T_557c0_row7_col2\" class=\"data row7 col2\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row7_col3\" class=\"data row7 col3\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row7_col4\" class=\"data row7 col4\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row7_col5\" class=\"data row7 col5\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row7_col6\" class=\"data row7 col6\" >1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_557c0_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_557c0_row8_col0\" class=\"data row8 col0\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row8_col1\" class=\"data row8 col1\" >0.0000</td>\n",
       "      <td id=\"T_557c0_row8_col2\" class=\"data row8 col2\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row8_col3\" class=\"data row8 col3\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row8_col4\" class=\"data row8 col4\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row8_col5\" class=\"data row8 col5\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row8_col6\" class=\"data row8 col6\" >1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_557c0_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_557c0_row9_col0\" class=\"data row9 col0\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row9_col1\" class=\"data row9 col1\" >0.0000</td>\n",
       "      <td id=\"T_557c0_row9_col2\" class=\"data row9 col2\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row9_col3\" class=\"data row9 col3\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row9_col4\" class=\"data row9 col4\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row9_col5\" class=\"data row9 col5\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row9_col6\" class=\"data row9 col6\" >1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_557c0_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_557c0_row10_col0\" class=\"data row10 col0\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row10_col1\" class=\"data row10 col1\" >0.0000</td>\n",
       "      <td id=\"T_557c0_row10_col2\" class=\"data row10 col2\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row10_col3\" class=\"data row10 col3\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row10_col4\" class=\"data row10 col4\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row10_col5\" class=\"data row10 col5\" >1.0000</td>\n",
       "      <td id=\"T_557c0_row10_col6\" class=\"data row10 col6\" >1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_557c0_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_557c0_row11_col0\" class=\"data row11 col0\" >0.0000</td>\n",
       "      <td id=\"T_557c0_row11_col1\" class=\"data row11 col1\" >0.0000</td>\n",
       "      <td id=\"T_557c0_row11_col2\" class=\"data row11 col2\" >0.0000</td>\n",
       "      <td id=\"T_557c0_row11_col3\" class=\"data row11 col3\" >0.0000</td>\n",
       "      <td id=\"T_557c0_row11_col4\" class=\"data row11 col4\" >0.0000</td>\n",
       "      <td id=\"T_557c0_row11_col5\" class=\"data row11 col5\" >0.0000</td>\n",
       "      <td id=\"T_557c0_row11_col6\" class=\"data row11 col6\" >0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x230307c9a60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ridge_model = create_model('ridge', alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True,\n",
    "                max_iter=None, positive=False, random_state=1, solver='auto',\n",
    "                tol=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(ridge_model, plot = 'confusion_matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_8dadb\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_8dadb_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_8dadb_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_8dadb_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_8dadb_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_8dadb_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_8dadb_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_8dadb_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_8dadb_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_8dadb_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_8dadb_row0_col0\" class=\"data row0 col0\" >K Neighbors Classifier</td>\n",
       "      <td id=\"T_8dadb_row0_col1\" class=\"data row0 col1\" >0.6434</td>\n",
       "      <td id=\"T_8dadb_row0_col2\" class=\"data row0 col2\" >0.9188</td>\n",
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    "# By using AutoML our team was able to simply select the most accurate model, in this case it was the Ridge Classifier,\n",
    "# and load a prediction model as well as a confusion matrix using relatively simple commands. This model was found to have\n",
    "# an accuracy of 99%."
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    "# ML vs. AutoML\n",
    "\n",
    "# The first major difference between the two is that with normal ML the user is required to the clean, organize,\n",
    "# and partition the data before it can be used. This includes making sure there are no removing null values,\n",
    "# identifiying Features from Output, and then organizing the data. The user then has to create descriptions for the\n",
    "# data and identify sample sizes. Identification of the sample sizes needs to be noted in case any imbalance problems \n",
    "# arise in the machine learning process. With AutoML all of these issues are performed automatically with, in some cases, \n",
    "# simplier command code. SessionID and Target values are identified with a single line of code. Target type, target mapping,\n",
    "# data shape are all identified by AutoML.\n",
    "\n",
    "# The second major difference has to do with what algorithms are used for the ML process. In normal ML the user has to decide\n",
    "# the most efficient algorithms to use that will produce the most accurate ML. This can be a very time consuming process\n",
    "# because the decision of what algorithms to use is highly predicated on the size of the data, potential imbalance problems,\n",
    "# etc. With the ever present possibility of overfitting the data in the background. Algorithms need to be used, tested,\n",
    "# manipulated, and removed based on extensive testing of the data. With AutoML this process is done with numerous different\n",
    "# statisical models all at the same time. Then the user it shown the most accurate model at the end of the analysis. Very\n",
    "# convenient.\n",
    "\n",
    "# The third major difference has to do with the graphing, predictions, and scoring. In normal ML all of these things have\n",
    "# to be coded. While scores are relatively easy to code in normal ML, prediction models abnd graphs have to be manually coded\n",
    "# for every detail the user wants to be shown. Graphs can be very time consuming and take of a lot space in notebooks. With\n",
    "# AutoML retrieving any number of these things is very easy. In some cases they can be produced all at the same time with\n",
    "# the use of widgets or even a single line of code. The use of widgets is incredibly helpful as it produces a clickable \n",
    "# graphic that loads up helpful graphs and information, as long as the proper libraries have been installed and imported.\n",
    "\n",
    "# The last major difference between the two seems to be in the speed at which it can handle large amounts of data. AutoML\n",
    "# seems to be highly limited by computational power. At 10,000 data entries the process of analysing data causes AutoML \n",
    "# to run very slowly. Even 5,000 data entries seems to slow the process down a considerable amount. With normal ML large\n",
    "# amounts of data  with slow the process down, it will also make EDA reports impossible, but there are still ways to prune\n",
    "# and analysis data in a relatively effective manner.\n",
    "\n",
    "# In conclusion, while there are major differences between ML and AutoML both seem to have a place in field of anaylsing\n",
    "# data. ML requires extensive analysis and testing when using different algorithms in order to produce the most accurate\n",
    "# results. However, this testing and analysis allows for a better understanding of the methods that go into classifiying\n",
    "# data. AutoML makes things very easy. It seems to be limited currently by computational power. In the future this will not\n",
    "# be a problem. But there is a problem with letting a machine perform all analysis of data. What gets lost is the\n",
    "# understanding of how the methods of the machine work. If we let the machine perform all of the work the understanding of \n",
    "# how each statistical method used are completely lost on the user. In that regard it becomes more and more difficult for\n",
    "# the user to explain to a layperson the output of the machine.\n",
    "\n",
    "\n"
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