4_ProcesamientoCluster.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# Feauture Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Imports & Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "# Import the required packages\n",
    "import os\n",
    "import sys\n",
    "in_colab = 'google.colab' in sys.modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "if in_colab:\n",
    "    from google.colab import drive\n",
    "    drive.mount('/content/drive')\n",
    "    BASE_DIR = \"/content/drive/My Drive/Colab Notebooks/Data/\"\n",
    "else:\n",
    "    BASE_DIR = \"./Data/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas_profiling as pp\n",
    "import statsmodels.api as sm\n",
    "import pickle\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import QuantileTransformer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from xgboost.sklearn import XGBRegressor\n",
    "\n",
    "from IPython.display import display\n",
    "from time import time\n",
    "pd.options.display.float_format = '{:.2f}'.format\n",
    "%matplotlib inline \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "# Cargar endpoint\n",
    "infile = open(BASE_DIR+'21_ETL_Tabular.pkl','rb')\n",
    "tabular_ft = pickle.load(infile)\n",
    "infile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "filename = BASE_DIR+'221_Graph_Metrics.pkl'\n",
    "infile = open(filename, 'rb')\n",
    "gmetrics_ft = pickle.load(infile)\n",
    "infile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "filename = BASE_DIR+'222_ETRD_ROUTES_ELEC.pkl'\n",
    "infile = open(filename, 'rb')\n",
    "path_ft = pickle.load(infile)\n",
    "infile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "filename = BASE_DIR+'1_OUTFT_UNTRD_FIC.pkl'\n",
    "infile = open(filename, 'rb')\n",
    "DEPVAR = pickle.load(infile)\n",
    "infile.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Preparar Datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "def prepare_data(X, categoricas):\n",
    "    \n",
    "    X = pd.get_dummies(X.dropna(), columns=categoricas, dummy_na= True, drop_first= True)\n",
    "#    X = pd.get_dummies(X, columns = categoricas)\n",
    "\n",
    "    FIC = X.FIC\n",
    "    bin = np.array(FIC.values)\n",
    "    FIC_bin = pd.cut(bin, bins=4, labels=False)\n",
    " \n",
    "    X = X.drop(['FIC'], axis=1)\n",
    "    X = X.fillna(0)\n",
    "\n",
    "    return X, FIC, FIC_bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "def backwardElimination(x, y, sl):\n",
    "    numVars = len(x[0])\n",
    "    for i in range(0, numVars):\n",
    "        regressor_OLS = sm.OLS(y, x).fit()\n",
    "        maxVar = max(regressor_OLS.pvalues)\n",
    "        if maxVar > sl:\n",
    "            for j in range(0, numVars - i):\n",
    "                if (regressor_OLS.pvalues[j] == maxVar):\n",
    "                    x = np.delete(x, j, 1)\n",
    "    regressor_OLS.summary()\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "def backwardEliminationRQ(x, y, SL):\n",
    "    numVars = len(x[0])\n",
    "    temp = np.zeros((x.shape)).astype(int)\n",
    "    for i in range(0, numVars):\n",
    "        regressor_OLS = sm.OLS(y, x).fit()\n",
    "        maxVar = max(regressor_OLS.pvalues)\n",
    "        adjR_before = regressor_OLS.rsquared_adj.astype(float)\n",
    "        if maxVar > SL:\n",
    "            for j in range(0, numVars - i):\n",
    "                if (regressor_OLS.pvalues[j].astype(float) == maxVar):\n",
    "                    temp[:,j] = x[:, j]\n",
    "                    x = np.delete(x, j, 1)\n",
    "                    tmp_regressor = sm.OLS(y, x).fit()\n",
    "                    adjR_after = tmp_regressor.rsquared_adj.astype(float)\n",
    "                    if (adjR_before >= adjR_after):\n",
    "                        x_rollback = np.hstack((x, temp[:,[0,j]]))\n",
    "                        x_rollback = np.delete(x_rollback, j, 1)\n",
    "                        print (regressor_OLS.summary())\n",
    "                        return x_rollback\n",
    "                    else:\n",
    "                        continue\n",
    "    regressor_OLS.summary()\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "tabular_cols = tabular_ft.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "gmetrics_cols = gmetrics_ft.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "path_ft.drop(['ROUTE_SUB','SID_ROUTE_SUB'], axis=1, inplace=True)\n",
    "path_cols = path_ft.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "data = pd.merge(tabular_ft, gmetrics_ft, on = 'COD_ID')\n",
    "data = pd.merge(data, path_ft, on = 'COD_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "data = pd.merge(data, DEPVAR.reset_index(), left_on='COD_ID', right_on='UNI_TR_D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "data.drop(['PAC_1','UNI_TR_D_x','UNI_TR_D_y','UNTRD_PCON_x','UNTRD_PCON_y','UNI_TR_S',\n",
    "           'CTMT_x','CTMT_y','SUB_x','SUB_y','CONJ','DESCR','CTMT_PCON'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3540, 70)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.set_index('COD_ID', drop=True, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "      <td>...</td>\n",
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       "      <td>168.00</td>\n",
       "      <td>95.00</td>\n",
       "      <td>168.00</td>\n",
       "      <td>95.00</td>\n",
       "      <td>7258.76</td>\n",
       "      <td>12744.86</td>\n",
       "      <td>14667.00</td>\n",
       "      <td>-29.66</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3540 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         FAS_CON_P FAS_CON_S FAS_CON_T  TEN_LIN_SE CAP_ELO CAP_CHA CONF POSTO  \\\n",
       "COD_ID                                                                          \n",
       "26879192       ABC       ABC         0        0.38      2H      12   RA    PT   \n",
       "26880293       ABC       ABC         0        0.38      5H      12   RA    PT   \n",
       "26880293       ABC       ABC         0        0.38      5H      12   RA    PT   \n",
       "26880295       ABC       ABC         0        0.38      3H      12   RA    PT   \n",
       "26880295       ABC       ABC         0        0.38      3H      12   RA    PT   \n",
       "...            ...       ...       ...         ...     ...     ...  ...   ...   \n",
       "26878633       ABC      ABCN         0        0.38      1H      12   RA    PT   \n",
       "26878634       ABC       ABC         0        0.22     05H      12   RA    PT   \n",
       "26878635         B        BN      NULL        0.23      1H      12   RA    PT   \n",
       "26878636       ABC      ABCN         0        0.38      2H      12   RA    PT   \n",
       "26878637       ABC      ABCN         0        0.38     05H      12   RA    PT   \n",
       "\n",
       "          POT_NOM  PER_FER  ...  FCONV_R I_MAX_MED_R I_MAX_MIN_R I_NOM_MED_R  \\\n",
       "COD_ID                      ...                                                \n",
       "26879192    45.00     0.47  ...     0.47      365.00      130.00      365.00   \n",
       "26880293   112.50     0.38  ...     0.46      438.00       97.00      438.00   \n",
       "26880293   112.50     0.38  ...     0.46      438.00       97.00      438.00   \n",
       "26880295    75.00     0.43  ...     0.46      438.00       97.00      438.00   \n",
       "26880295    75.00     0.43  ...     0.46      438.00       97.00      438.00   \n",
       "...           ...      ...  ...      ...         ...         ...         ...   \n",
       "26878633    30.00     0.54  ...     0.44      168.00       95.00      168.00   \n",
       "26878634    15.00     0.62  ...     0.46      168.00       95.00      168.00   \n",
       "26878635    10.00     0.54  ...     0.45      168.00       95.00      168.00   \n",
       "26878636    45.00     0.47  ...     0.43      168.00       95.00      168.00   \n",
       "26878637    15.00     0.62  ...     0.43      168.00       95.00      168.00   \n",
       "\n",
       "         I_NOM_MIN_R REAC_SUB RESI_SUB Z_MOD_SUB  Z_ANG_SUB   FIC  \n",
       "COD_ID                                                             \n",
       "26879192      130.00  2014.67  3303.26   3869.16     -31.38  1.00  \n",
       "26880293       97.00  1400.15  4011.34   4248.68     -19.24  0.00  \n",
       "26880293       97.00  1400.15  4011.34   4248.68     -19.24  0.00  \n",
       "26880295       97.00  1448.22  4062.81   4313.21     -19.62  0.00  \n",
       "26880295       97.00  1448.22  4062.81   4313.21     -19.62  0.00  \n",
       "...              ...      ...      ...       ...        ...   ...  \n",
       "26878633       95.00  6754.61 11607.73  13429.97     -30.20 16.00  \n",
       "26878634       95.00  6680.84 11380.55  13196.61     -30.41  8.00  \n",
       "26878635       95.00  7114.83 12292.00  14202.61     -30.06  8.00  \n",
       "26878636       95.00  7182.67 12571.93  14479.10     -29.74 13.00  \n",
       "26878637       95.00  7258.76 12744.86  14667.00     -29.66 18.00  \n",
       "\n",
       "[3540 rows x 69 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['FAS_CON_P', 'FAS_CON_S', 'FAS_CON_T', 'TEN_LIN_SE', 'CAP_ELO',\n",
       "       'CAP_CHA', 'CONF', 'POSTO', 'POT_NOM', 'PER_FER', 'PER_TOT',\n",
       "       'TIP_TRAFO', 'ARE_LOC', 'CLAS_SUB', 'CNAE', 'TIP_CC', 'TEN_FORN',\n",
       "       'GRU_TAR', 'CAR_INST', 'UCBT_ENE_MED', 'UCBT_ENE_STD', 'UCBT_ENE_MAX',\n",
       "       'NCON', 'NCON_URB', 'CTMT_ENE_MED', 'CTMT_ENE_STD', 'CTMT_ENE_MAX',\n",
       "       'PERD_A3a', 'PERD_A4', 'PERD_B', 'PERD_MED', 'PERD_A3a_B', 'PERD_A4_B',\n",
       "       'PERD_B_A3a', 'PERD_B_A4', 'CTMT_PNTMT_MED', 'CTMT_PNTMT_STD',\n",
       "       'CTMT_PNTMT_MAX', 'CTMT_PNTBT_MED', 'CTMT_PNTBT_STD', 'CTMT_PNTBT_MAX',\n",
       "       'POT_NOM_TRS', 'POT_F01', 'POT_F02', 'PER_FER_TRS', 'PER_TOT_TRS',\n",
       "       'TIP_TRAFO_TRS', 'UNTRS_ENES_MED', 'UNTRS_ENES_STD', 'UNTRS_ENES_MAX',\n",
       "       'UNTRS_ENET_MED', 'UNTRS_ENET_STD', 'UNTRS_ENET_MAX', 'DEGREE',\n",
       "       'NEIG_DEGREE', 'BET_CEN', 'CLO_CEN', 'PAGE_RANK', 'DISTANCE_SUB',\n",
       "       'FCONV_R', 'I_MAX_MED_R', 'I_MAX_MIN_R', 'I_NOM_MED_R', 'I_NOM_MIN_R',\n",
       "       'REAC_SUB', 'RESI_SUB', 'Z_MOD_SUB', 'Z_ANG_SUB', 'FIC'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### Evaluación de correlacion y sesgo mediante el perfilador de pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "#pp.ProfileReport(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "## Reconfiguración del dataset de acuerdo al analisis del perfilador\n",
    "\n",
    "# Se eliminan variables con alta correlacion y con alta cardinalidad\n",
    "\n",
    "data.drop(['UCBT_ENE_MAX', 'NCON', 'CTMT_ENE_MAX', 'PERD_A3a', 'PERD_A3a_B', 'PERD_B_A3a', 'PERD_B_A4',\n",
    "           'CTMT_PNTMT_MAX', 'POT_F01', 'POT_F02', 'PER_TOT_TRS', 'TIP_TRAFO_TRS', 'UNTRS_ENES_MED', \n",
    "           'UNTRS_ENES_STD', 'UNTRS_ENES_MAX', 'UNTRS_ENET_MED', 'UNTRS_ENET_STD', 'UNTRS_ENET_MAX', \n",
    "           'RESI_SUB', 'REAC_SUB', 'I_MAX_MED_R', 'I_MAX_MIN_R', 'Z_MOD_SUB'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['TEN_LIN_SE', 'POT_NOM', 'PER_FER', 'PER_TOT', 'CAR_INST',\n",
       "       'UCBT_ENE_MED', 'UCBT_ENE_STD', 'NCON_URB', 'CTMT_ENE_MED',\n",
       "       'CTMT_ENE_STD', 'PERD_A4', 'PERD_B', 'PERD_MED', 'PERD_A4_B',\n",
       "       'CTMT_PNTMT_MED', 'CTMT_PNTMT_STD', 'CTMT_PNTBT_MED', 'CTMT_PNTBT_STD',\n",
       "       'CTMT_PNTBT_MAX', 'POT_NOM_TRS', 'PER_FER_TRS', 'DEGREE', 'NEIG_DEGREE',\n",
       "       'BET_CEN', 'CLO_CEN', 'PAGE_RANK', 'DISTANCE_SUB', 'FCONV_R',\n",
       "       'I_NOM_MED_R', 'I_NOM_MIN_R', 'Z_ANG_SUB', 'FIC'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continuas = data.describe(exclude='O').columns\n",
    "continuas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "Collapsed": "false"
   },
   "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>TEN_LIN_SE</th>\n",
       "      <th>POT_NOM</th>\n",
       "      <th>PER_FER</th>\n",
       "      <th>PER_TOT</th>\n",
       "      <th>CAR_INST</th>\n",
       "      <th>UCBT_ENE_MED</th>\n",
       "      <th>UCBT_ENE_STD</th>\n",
       "      <th>NCON_URB</th>\n",
       "      <th>CTMT_ENE_MED</th>\n",
       "      <th>CTMT_ENE_STD</th>\n",
       "      <th>...</th>\n",
       "      <th>NEIG_DEGREE</th>\n",
       "      <th>BET_CEN</th>\n",
       "      <th>CLO_CEN</th>\n",
       "      <th>PAGE_RANK</th>\n",
       "      <th>DISTANCE_SUB</th>\n",
       "      <th>FCONV_R</th>\n",
       "      <th>I_NOM_MED_R</th>\n",
       "      <th>I_NOM_MIN_R</th>\n",
       "      <th>Z_ANG_SUB</th>\n",
       "      <th>FIC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3540.00</td>\n",
       "      <td>3540.00</td>\n",
       "      <td>3540.00</td>\n",
       "      <td>3540.00</td>\n",
       "      <td>3540.00</td>\n",
       "      <td>3540.00</td>\n",
       "      <td>3540.00</td>\n",
       "      <td>3540.00</td>\n",
       "      <td>3540.00</td>\n",
       "      <td>3540.00</td>\n",
       "      <td>...</td>\n",
       "      <td>3540.00</td>\n",
       "      <td>3540.00</td>\n",
       "      <td>3540.00</td>\n",
       "      <td>3540.00</td>\n",
       "      <td>3540.00</td>\n",
       "      <td>3538.00</td>\n",
       "      <td>3538.00</td>\n",
       "      <td>3538.00</td>\n",
       "      <td>3540.00</td>\n",
       "      <td>3540.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.34</td>\n",
       "      <td>50.37</td>\n",
       "      <td>0.49</td>\n",
       "      <td>354.06</td>\n",
       "      <td>242.22</td>\n",
       "      <td>5580.52</td>\n",
       "      <td>62.13</td>\n",
       "      <td>2.61</td>\n",
       "      <td>992189.85</td>\n",
       "      <td>57921.00</td>\n",
       "      <td>...</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11459.53</td>\n",
       "      <td>0.47</td>\n",
       "      <td>364.27</td>\n",
       "      <td>85.78</td>\n",
       "      <td>-31.51</td>\n",
       "      <td>7.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.07</td>\n",
       "      <td>50.72</td>\n",
       "      <td>0.11</td>\n",
       "      <td>350.13</td>\n",
       "      <td>315.36</td>\n",
       "      <td>19925.54</td>\n",
       "      <td>269.36</td>\n",
       "      <td>6.66</td>\n",
       "      <td>515926.90</td>\n",
       "      <td>32526.83</td>\n",
       "      <td>...</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9423.00</td>\n",
       "      <td>0.09</td>\n",
       "      <td>94.76</td>\n",
       "      <td>53.63</td>\n",
       "      <td>11.29</td>\n",
       "      <td>6.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.22</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>148659.28</td>\n",
       "      <td>12429.24</td>\n",
       "      <td>...</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>97.00</td>\n",
       "      <td>54.00</td>\n",
       "      <td>-62.69</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.23</td>\n",
       "      <td>15.00</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.89</td>\n",
       "      <td>18.50</td>\n",
       "      <td>283.75</td>\n",
       "      <td>19.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>612463.25</td>\n",
       "      <td>39736.28</td>\n",
       "      <td>...</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3955.11</td>\n",
       "      <td>0.41</td>\n",
       "      <td>330.00</td>\n",
       "      <td>54.00</td>\n",
       "      <td>-39.89</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.38</td>\n",
       "      <td>45.00</td>\n",
       "      <td>0.47</td>\n",
       "      <td>245.00</td>\n",
       "      <td>75.55</td>\n",
       "      <td>1634.00</td>\n",
       "      <td>24.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>884878.76</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>...</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8985.54</td>\n",
       "      <td>0.45</td>\n",
       "      <td>365.00</td>\n",
       "      <td>95.00</td>\n",
       "      <td>-31.13</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.38</td>\n",
       "      <td>75.00</td>\n",
       "      <td>0.54</td>\n",
       "      <td>695.00</td>\n",
       "      <td>387.92</td>\n",
       "      <td>8342.88</td>\n",
       "      <td>36.93</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1239099.83</td>\n",
       "      <td>66985.50</td>\n",
       "      <td>...</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16985.15</td>\n",
       "      <td>0.50</td>\n",
       "      <td>438.00</td>\n",
       "      <td>97.00</td>\n",
       "      <td>-22.22</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.44</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>0.76</td>\n",
       "      <td>945.00</td>\n",
       "      <td>2400.73</td>\n",
       "      <td>1112697.50</td>\n",
       "      <td>10184.95</td>\n",
       "      <td>101.00</td>\n",
       "      <td>2276963.08</td>\n",
       "      <td>141887.74</td>\n",
       "      <td>...</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.02</td>\n",
       "      <td>46826.17</td>\n",
       "      <td>0.90</td>\n",
       "      <td>505.00</td>\n",
       "      <td>495.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>40.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       TEN_LIN_SE  POT_NOM  PER_FER  PER_TOT  CAR_INST  UCBT_ENE_MED  \\\n",
       "count     3540.00  3540.00  3540.00  3540.00   3540.00       3540.00   \n",
       "mean         0.34    50.37     0.49   354.06    242.22       5580.52   \n",
       "std          0.07    50.72     0.11   350.13    315.36      19925.54   \n",
       "min          0.22     5.00     0.17     0.14      0.00          0.00   \n",
       "25%          0.23    15.00     0.43     1.89     18.50        283.75   \n",
       "50%          0.38    45.00     0.47   245.00     75.55       1634.00   \n",
       "75%          0.38    75.00     0.54   695.00    387.92       8342.88   \n",
       "max          0.44  1500.00     0.76   945.00   2400.73    1112697.50   \n",
       "\n",
       "       UCBT_ENE_STD  NCON_URB  CTMT_ENE_MED  CTMT_ENE_STD  ...  NEIG_DEGREE  \\\n",
       "count       3540.00   3540.00       3540.00       3540.00  ...      3540.00   \n",
       "mean          62.13      2.61     992189.85      57921.00  ...         2.20   \n",
       "std          269.36      6.66     515926.90      32526.83  ...         0.41   \n",
       "min            0.00      0.00     148659.28      12429.24  ...         1.33   \n",
       "25%           19.31      0.00     612463.25      39736.28  ...         2.00   \n",
       "50%           24.78      0.00     884878.76      49923.49  ...         2.00   \n",
       "75%           36.93      2.00    1239099.83      66985.50  ...         2.50   \n",
       "max        10184.95    101.00    2276963.08     141887.74  ...         5.00   \n",
       "\n",
       "       BET_CEN  CLO_CEN  PAGE_RANK  DISTANCE_SUB  FCONV_R  I_NOM_MED_R  \\\n",
       "count  3540.00  3540.00    3540.00       3540.00  3538.00      3538.00   \n",
       "mean      0.08     0.01       0.00      11459.53     0.47       364.27   \n",
       "std       0.13     0.01       0.00       9423.00     0.09        94.76   \n",
       "min       0.00     0.00       0.00          0.00     0.13        97.00   \n",
       "25%       0.00     0.00       0.00       3955.11     0.41       330.00   \n",
       "50%       0.01     0.01       0.00       8985.54     0.45       365.00   \n",
       "75%       0.09     0.01       0.00      16985.15     0.50       438.00   \n",
       "max       0.51     0.09       0.02      46826.17     0.90       505.00   \n",
       "\n",
       "       I_NOM_MIN_R  Z_ANG_SUB     FIC  \n",
       "count      3538.00    3540.00 3540.00  \n",
       "mean         85.78     -31.51    7.95  \n",
       "std          53.63      11.29    6.74  \n",
       "min          54.00     -62.69    0.00  \n",
       "25%          54.00     -39.89    2.00  \n",
       "50%          95.00     -31.13    6.00  \n",
       "75%          97.00     -22.22   13.00  \n",
       "max         495.00       0.00   40.50  \n",
       "\n",
       "[8 rows x 32 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[continuas].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "categoricas = data.describe(include ='O').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['FAS_CON_P', 'FAS_CON_S', 'FAS_CON_T', 'CAP_ELO', 'CAP_CHA', 'CONF',\n",
       "       'POSTO', 'TIP_TRAFO', 'ARE_LOC', 'CLAS_SUB', 'CNAE', 'TIP_CC',\n",
       "       'TEN_FORN', 'GRU_TAR'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categoricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "Collapsed": "false"
   },
   "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>FAS_CON_P</th>\n",
       "      <th>FAS_CON_S</th>\n",
       "      <th>FAS_CON_T</th>\n",
       "      <th>CAP_ELO</th>\n",
       "      <th>CAP_CHA</th>\n",
       "      <th>CONF</th>\n",
       "      <th>POSTO</th>\n",
       "      <th>TIP_TRAFO</th>\n",
       "      <th>ARE_LOC</th>\n",
       "      <th>CLAS_SUB</th>\n",
       "      <th>CNAE</th>\n",
       "      <th>TIP_CC</th>\n",
       "      <th>TEN_FORN</th>\n",
       "      <th>GRU_TAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3540</td>\n",
       "      <td>3540</td>\n",
       "      <td>3540</td>\n",
       "      <td>3540</td>\n",
       "      <td>3540</td>\n",
       "      <td>3540</td>\n",
       "      <td>3540</td>\n",
       "      <td>3540</td>\n",
       "      <td>3540</td>\n",
       "      <td>3540</td>\n",
       "      <td>3540</td>\n",
       "      <td>3540</td>\n",
       "      <td>3540</td>\n",
       "      <td>3540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>70</td>\n",
       "      <td>47</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>ABC</td>\n",
       "      <td>ABC</td>\n",
       "      <td>0</td>\n",
       "      <td>2H</td>\n",
       "      <td>12</td>\n",
       "      <td>RA</td>\n",
       "      <td>PT</td>\n",
       "      <td>T</td>\n",
       "      <td>UB</td>\n",
       "      <td>RE1</td>\n",
       "      <td>99999</td>\n",
       "      <td>RES-Tipo2</td>\n",
       "      <td>15</td>\n",
       "      <td>B1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>2519</td>\n",
       "      <td>2019</td>\n",
       "      <td>2620</td>\n",
       "      <td>767</td>\n",
       "      <td>3402</td>\n",
       "      <td>3535</td>\n",
       "      <td>3533</td>\n",
       "      <td>2519</td>\n",
       "      <td>1975</td>\n",
       "      <td>3066</td>\n",
       "      <td>3014</td>\n",
       "      <td>571</td>\n",
       "      <td>2478</td>\n",
       "      <td>3049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       FAS_CON_P FAS_CON_S FAS_CON_T CAP_ELO CAP_CHA  CONF POSTO TIP_TRAFO  \\\n",
       "count       3540      3540      3540    3540    3540  3540  3540      3540   \n",
       "unique         7        11         4      11       2     2     3         4   \n",
       "top          ABC       ABC         0      2H      12    RA    PT         T   \n",
       "freq        2519      2019      2620     767    3402  3535  3533      2519   \n",
       "\n",
       "       ARE_LOC CLAS_SUB   CNAE     TIP_CC TEN_FORN GRU_TAR  \n",
       "count     3540     3540   3540       3540     3540    3540  \n",
       "unique       2       16     70         47        5       4  \n",
       "top         UB      RE1  99999  RES-Tipo2       15      B1  \n",
       "freq      1975     3066   3014        571     2478    3049  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[categoricas].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### Explore Output Var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c57dd64518>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.FIC, bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### Transformar target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "qt = QuantileTransformer(output_distribution='normal', n_quantiles=1000)\n",
    "normal_FIC = qt.fit_transform(data.FIC.to_numpy().reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c57db55eb8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(normal_FIC, kde=False, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c57dbe67f0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.log1p(data.FIC), kde=False, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c57dc76c88>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=data['FIC'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### Proceso de Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "data_dfo = data[np.abs(data.FIC-data.FIC.mean())<=(1.5*data.FIC.std())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3306, 46)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dfo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "Collapsed": "false"
   },
   "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>FAS_CON_P</th>\n",
       "      <th>FAS_CON_S</th>\n",
       "      <th>FAS_CON_T</th>\n",
       "      <th>TEN_LIN_SE</th>\n",
       "      <th>CAP_ELO</th>\n",
       "      <th>CAP_CHA</th>\n",
       "      <th>CONF</th>\n",
       "      <th>POSTO</th>\n",
       "      <th>POT_NOM</th>\n",
       "      <th>PER_FER</th>\n",
       "      <th>...</th>\n",
       "      <th>NEIG_DEGREE</th>\n",
       "      <th>BET_CEN</th>\n",
       "      <th>CLO_CEN</th>\n",
       "      <th>PAGE_RANK</th>\n",
       "      <th>DISTANCE_SUB</th>\n",
       "      <th>FCONV_R</th>\n",
       "      <th>I_NOM_MED_R</th>\n",
       "      <th>I_NOM_MIN_R</th>\n",
       "      <th>Z_ANG_SUB</th>\n",
       "      <th>FIC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COD_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26877802</th>\n",
       "      <td>ABC</td>\n",
       "      <td>ABC</td>\n",
       "      <td>0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2H</td>\n",
       "      <td>12</td>\n",
       "      <td>RA</td>\n",
       "      <td>PT</td>\n",
       "      <td>45.00</td>\n",
       "      <td>0.47</td>\n",
       "      <td>...</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1762.25</td>\n",
       "      <td>0.42</td>\n",
       "      <td>230.00</td>\n",
       "      <td>97.00</td>\n",
       "      <td>-32.08</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52605794</th>\n",
       "      <td>ABC</td>\n",
       "      <td>ABC</td>\n",
       "      <td>0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2H</td>\n",
       "      <td>12</td>\n",
       "      <td>RA</td>\n",
       "      <td>PT</td>\n",
       "      <td>45.00</td>\n",
       "      <td>0.47</td>\n",
       "      <td>...</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1723.68</td>\n",
       "      <td>0.36</td>\n",
       "      <td>495.00</td>\n",
       "      <td>130.00</td>\n",
       "      <td>-44.17</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26880567</th>\n",
       "      <td>ABC</td>\n",
       "      <td>ABC</td>\n",
       "      <td>0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>3H</td>\n",
       "      <td>12</td>\n",
       "      <td>RA</td>\n",
       "      <td>PT</td>\n",
       "      <td>75.00</td>\n",
       "      <td>0.43</td>\n",
       "      <td>...</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8043.04</td>\n",
       "      <td>0.39</td>\n",
       "      <td>330.00</td>\n",
       "      <td>54.00</td>\n",
       "      <td>-34.97</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877638</th>\n",
       "      <td>ABC</td>\n",
       "      <td>ABC</td>\n",
       "      <td>0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>3H</td>\n",
       "      <td>12</td>\n",
       "      <td>RA</td>\n",
       "      <td>PT</td>\n",
       "      <td>75.00</td>\n",
       "      <td>0.43</td>\n",
       "      <td>...</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3187.16</td>\n",
       "      <td>0.43</td>\n",
       "      <td>365.00</td>\n",
       "      <td>54.00</td>\n",
       "      <td>-26.22</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876816</th>\n",
       "      <td>ABC</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2H</td>\n",
       "      <td>12</td>\n",
       "      <td>RA</td>\n",
       "      <td>PT</td>\n",
       "      <td>45.00</td>\n",
       "      <td>0.47</td>\n",
       "      <td>...</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13267.09</td>\n",
       "      <td>0.42</td>\n",
       "      <td>438.00</td>\n",
       "      <td>54.00</td>\n",
       "      <td>-22.72</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         FAS_CON_P FAS_CON_S FAS_CON_T  TEN_LIN_SE CAP_ELO CAP_CHA CONF POSTO  \\\n",
       "COD_ID                                                                          \n",
       "26877802       ABC       ABC         0        0.38      2H      12   RA    PT   \n",
       "52605794       ABC       ABC         0        0.38      2H      12   RA    PT   \n",
       "26880567       ABC       ABC         0        0.38      3H      12   RA    PT   \n",
       "26877638       ABC       ABC         0        0.38      3H      12   RA    PT   \n",
       "26876816       ABC         A         0        0.38      2H      12   RA    PT   \n",
       "\n",
       "          POT_NOM  PER_FER  ...  NEIG_DEGREE BET_CEN CLO_CEN PAGE_RANK  \\\n",
       "COD_ID                      ...                                          \n",
       "26877802    45.00     0.47  ...         2.00    0.00    0.03      0.00   \n",
       "52605794    45.00     0.47  ...         2.00    0.00    0.00      0.00   \n",
       "26880567    75.00     0.43  ...         2.00    0.00    0.01      0.00   \n",
       "26877638    75.00     0.43  ...         2.00    0.04    0.02      0.00   \n",
       "26876816    45.00     0.47  ...         2.00    0.01    0.00      0.00   \n",
       "\n",
       "         DISTANCE_SUB FCONV_R I_NOM_MED_R I_NOM_MIN_R  Z_ANG_SUB  FIC  \n",
       "COD_ID                                                                 \n",
       "26877802      1762.25    0.42      230.00       97.00     -32.08 4.00  \n",
       "52605794      1723.68    0.36      495.00      130.00     -44.17 2.00  \n",
       "26880567      8043.04    0.39      330.00       54.00     -34.97 3.00  \n",
       "26877638      3187.16    0.43      365.00       54.00     -26.22 1.00  \n",
       "26876816     13267.09    0.42      438.00       54.00     -22.72 8.00  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dfo.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c57ddf2630>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=data_dfo['FIC'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Split Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "#### Vemos la frecuencia de ocurrencia de los valores de FIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "data_X, data_FIC, FIC_bin = prepare_data(data_dfo, categoricas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3305, 218)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3305,)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_FIC.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "Collapsed": "false"
   },
   "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>FIC</th>\n",
       "      <th>frecuencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.00</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.00</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.00</td>\n",
       "      <td>286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.00</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13.00</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.00</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12.00</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.00</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16.00</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.00</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8.00</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.00</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.00</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9.00</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7.00</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10.00</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17.00</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18.00</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.50</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10.50</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12.50</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>14.50</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>15.50</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>9.50</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7.50</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>17.50</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>13.50</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>8.50</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>16.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>6.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3.50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1.50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2.50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>11.50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     FIC  frecuencia\n",
       "0   1.00         356\n",
       "1   0.00         328\n",
       "2   2.00         322\n",
       "3   3.00         286\n",
       "4   4.00         208\n",
       "5  13.00         178\n",
       "6   5.00         168\n",
       "7  12.00         161\n",
       "8  14.00         159\n",
       "9  16.00         133\n",
       "10 11.00         128\n",
       "11  8.00         120\n",
       "12 15.00         119\n",
       "13  6.00         115\n",
       "14  9.00         110\n",
       "15  7.00         105\n",
       "16 10.00          94\n",
       "17 17.00          71\n",
       "18 18.00          65\n",
       "19  5.50           9\n",
       "20 10.50           8\n",
       "21 12.50           8\n",
       "22 14.50           7\n",
       "23 15.50           6\n",
       "24  9.50           6\n",
       "25  7.50           5\n",
       "26 17.50           4\n",
       "27 13.50           4\n",
       "28  8.50           4\n",
       "29 16.50           3\n",
       "30  6.50           3\n",
       "31  4.50           3\n",
       "32  3.50           2\n",
       "33  1.50           2\n",
       "34  2.50           2\n",
       "35 11.50           2\n",
       "36  0.50           1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.value_counts(np.array(data_FIC)).to_frame().reset_index()\n",
    "df.rename(columns={\"index\":\"FIC\",0:\"frecuencia\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluamos la distribucion de la variable dependiente\n",
    "hist, bin_edges = np.histogram(data_FIC, 25)\n",
    "plt.hist(data_FIC, bins=bin_edges)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "Collapsed": "false"
   },
   "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>FIC</th>\n",
       "      <th>frecuencia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FIC  frecuencia\n",
       "0    0        1510\n",
       "1    1         639\n",
       "2    2         589\n",
       "3    3         567"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vemos la frecuencia de ocurrencia de los valores de FIC discretizado con 4 bins\n",
    "df = pd.value_counts(np.array(FIC_bin)).to_frame().reset_index()\n",
    "df.rename(columns={\"index\":\"FIC\",0:\"frecuencia\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluamos la distribucion de la variable dependiente discretizada\n",
    "hist, bin_edges = np.histogram(FIC_bin, 7)\n",
    "plt.hist(FIC_bin, bins=bin_edges)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Split and get baselines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from boruta import BorutaPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "#data_labeled = pd.get_dummies(data.dropna(), columns=categorical, dummy_na= True, drop_first= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "#X_train, X_test, y_train, y_test = train_test_split(data_labeled.drop('FIC',axis=1), data_labeled.FIC, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data_X, data_FIC, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, eval_metric='mae', gamma=1,\n",
       "             importance_type='gain', learning_rate=0.03, max_delta_step=0,\n",
       "             max_depth=3, min_child_weight=1, missing=None, n_estimators=1000,\n",
       "             n_jobs=-1, nthread=None, objective='reg:squarederror',\n",
       "             random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "             seed=None, silent=None, subsample=0.8, verbosity=1)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xb_reg = XGBRegressor(learning_rate=0.03,\n",
    "                         n_estimators=1000,\n",
    "                         max_depth=3,\n",
    "                         subsample=0.8,\n",
    "                         colsample_bytree=1,\n",
    "                         gamma=1,\n",
    "                         objective='reg:squarederror',\n",
    "                         n_jobs=-1,\n",
    "                         eval_metric='mae')\n",
    "\n",
    "# Entreno sobre el logarimo de la variable de salidad\n",
    "xb_reg.fit(X_train, np.log1p(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "desc_importance = np.sort(xb_reg.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "ft_qty = (desc_importance[::-1] > 0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DISTANCE_SUB', 'CTMT_PNTMT_STD', 'CLO_CEN', 'TIP_CC_RES-Tipo3',\n",
       "       'POT_NOM', 'PERD_A4_B', 'I_NOM_MED_R', 'CTMT_ENE_STD', 'CTMT_PNTMT_MED',\n",
       "       'UCBT_ENE_MED', 'I_NOM_MIN_R', 'CTMT_PNTBT_MAX', 'PAGE_RANK',\n",
       "       'TIP_TRAFO_M', 'FAS_CON_S_ABCN', 'CTMT_ENE_MED', 'GRU_TAR_B3',\n",
       "       'TIP_CC_RES-Tipo10', 'PERD_A4', 'TIP_CC_RES-Tipo5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices = np.argsort(xb_reg.feature_importances_)[::-1]\n",
    "#top_features = data_X.columns[indices][:ft_qty]\n",
    "top_features = data_X.columns[indices[0:20]]\n",
    "top_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "Collapsed": "false"
   },
   "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>DISTANCE_SUB</th>\n",
       "      <th>CTMT_PNTMT_STD</th>\n",
       "      <th>CLO_CEN</th>\n",
       "      <th>TIP_CC_RES-Tipo3</th>\n",
       "      <th>POT_NOM</th>\n",
       "      <th>PERD_A4_B</th>\n",
       "      <th>I_NOM_MED_R</th>\n",
       "      <th>CTMT_ENE_STD</th>\n",
       "      <th>CTMT_PNTMT_MED</th>\n",
       "      <th>UCBT_ENE_MED</th>\n",
       "      <th>I_NOM_MIN_R</th>\n",
       "      <th>CTMT_PNTBT_MAX</th>\n",
       "      <th>PAGE_RANK</th>\n",
       "      <th>TIP_TRAFO_M</th>\n",
       "      <th>FAS_CON_S_ABCN</th>\n",
       "      <th>CTMT_ENE_MED</th>\n",
       "      <th>GRU_TAR_B3</th>\n",
       "      <th>TIP_CC_RES-Tipo10</th>\n",
       "      <th>PERD_A4</th>\n",
       "      <th>TIP_CC_RES-Tipo5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COD_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26876488</th>\n",
       "      <td>19671.69</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1</td>\n",
       "      <td>25.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>157.00</td>\n",
       "      <td>54.00</td>\n",
       "      <td>158336.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876462</th>\n",
       "      <td>19119.05</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>192.00</td>\n",
       "      <td>54.00</td>\n",
       "      <td>158336.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877055</th>\n",
       "      <td>30452.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>230.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>67.50</td>\n",
       "      <td>54.00</td>\n",
       "      <td>110460.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26876583</th>\n",
       "      <td>26896.22</td>\n",
       "      <td>236.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>506374.22</td>\n",
       "      <td>438.00</td>\n",
       "      <td>49923.49</td>\n",
       "      <td>909.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>54.00</td>\n",
       "      <td>158336.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>844268.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>180278.34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26877196</th>\n",
       "      <td>35719.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>475052.12</td>\n",
       "      <td>171.00</td>\n",
       "      <td>21457.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>122.50</td>\n",
       "      <td>54.00</td>\n",
       "      <td>110460.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>459459.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100388.53</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          DISTANCE_SUB  CTMT_PNTMT_STD  CLO_CEN  TIP_CC_RES-Tipo3  POT_NOM  \\\n",
       "COD_ID                                                                       \n",
       "26876488      19671.69          236.63     0.01                 1    25.00   \n",
       "26876462      19119.05          236.63     0.01                 0    10.00   \n",
       "26877055      30452.04            0.00     0.00                 0     5.00   \n",
       "26876583      26896.22          236.63     0.00                 0     5.00   \n",
       "26877196      35719.12            0.00     0.00                 0    10.00   \n",
       "\n",
       "          PERD_A4_B  I_NOM_MED_R  CTMT_ENE_STD  CTMT_PNTMT_MED  UCBT_ENE_MED  \\\n",
       "COD_ID                                                                         \n",
       "26876488  506374.22       438.00      49923.49          909.23        157.00   \n",
       "26876462  506374.22       438.00      49923.49          909.23        192.00   \n",
       "26877055  475052.12       230.00      21457.60            0.00         67.50   \n",
       "26876583  506374.22       438.00      49923.49          909.23          0.00   \n",
       "26877196  475052.12       171.00      21457.60            0.00        122.50   \n",
       "\n",
       "          I_NOM_MIN_R  CTMT_PNTBT_MAX  PAGE_RANK  TIP_TRAFO_M  FAS_CON_S_ABCN  \\\n",
       "COD_ID                                                                          \n",
       "26876488        54.00       158336.61       0.00            1               0   \n",
       "26876462        54.00       158336.61       0.00            0               0   \n",
       "26877055        54.00       110460.45       0.00            1               0   \n",
       "26876583        54.00       158336.61       0.00            1               0   \n",
       "26877196        54.00       110460.45       0.00            1               0   \n",
       "\n",
       "          CTMT_ENE_MED  GRU_TAR_B3  TIP_CC_RES-Tipo10   PERD_A4  \\\n",
       "COD_ID                                                            \n",
       "26876488     844268.40           0                  0 180278.34   \n",
       "26876462     844268.40           0                  0 180278.34   \n",
       "26877055     459459.02           0                  0 100388.53   \n",
       "26876583     844268.40           0                  0 180278.34   \n",
       "26877196     459459.02           0                  0 100388.53   \n",
       "\n",
       "          TIP_CC_RES-Tipo5  \n",
       "COD_ID                      \n",
       "26876488                 0  \n",
       "26876462                 0  \n",
       "26877055                 0  \n",
       "26876583                 0  \n",
       "26877196                 0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_top = data_X[top_features]\n",
    "data_top.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### Se evalúa con Regresion Lineal las top features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3305, 20)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_top.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3305,)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_FIC.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data_top, data_FIC, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=-1, normalize=False)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = LinearRegression(n_jobs=-1)\n",
    "regressor.fit(data_top, data_FIC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "#### Regresión por Cuadrados Mínimos Ordinarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "# Consideramos un nivel de significancia del 0.05 para evaluarlo contra el P-value \n",
    "# para saber que features vamos descartando"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "X = np.append(arr = np.ones((data_top.shape[0], 1)).astype(int), values = data_top, axis = 1)\n",
    "SL = 0.05\n",
    "X_opt = X[:, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>FIC</td>       <th>  R-squared:         </th> <td>   0.653</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.651</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   386.9</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 30 Oct 2019</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:32:28</td>     <th>  Log-Likelihood:    </th> <td> -8599.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  3305</td>      <th>  AIC:               </th> <td>1.723e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  3288</td>      <th>  BIC:               </th> <td>1.734e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    16</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    4.6153</td> <td>    0.381</td> <td>   12.121</td> <td> 0.000</td> <td>    3.869</td> <td>    5.362</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.0003</td> <td> 9.58e-06</td> <td>   31.175</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.0001</td> <td>  1.4e-05</td> <td>    9.791</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>  -87.0829</td> <td>   15.971</td> <td>   -5.453</td> <td> 0.000</td> <td> -118.396</td> <td>  -55.769</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>   -0.0074</td> <td>    0.001</td> <td>   -5.539</td> <td> 0.000</td> <td>   -0.010</td> <td>   -0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>    <td> 1.656e-05</td> <td> 9.79e-07</td> <td>   16.913</td> <td> 0.000</td> <td> 1.46e-05</td> <td> 1.85e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>    <td>   -0.0039</td> <td>    0.001</td> <td>   -5.837</td> <td> 0.000</td> <td>   -0.005</td> <td>   -0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th>    <td>-8.727e-06</td> <td> 3.36e-06</td> <td>   -2.594</td> <td> 0.010</td> <td>-1.53e-05</td> <td>-2.13e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x8</th>    <td> 6.031e-05</td> <td> 3.22e-06</td> <td>   18.702</td> <td> 0.000</td> <td>  5.4e-05</td> <td> 6.66e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x9</th>    <td>   -0.0025</td> <td>    0.001</td> <td>   -2.074</td> <td> 0.038</td> <td>   -0.005</td> <td>   -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x10</th>   <td>-5.871e-06</td> <td> 1.21e-06</td> <td>   -4.851</td> <td> 0.000</td> <td>-8.24e-06</td> <td> -3.5e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x11</th>   <td>  233.9157</td> <td>   70.556</td> <td>    3.315</td> <td> 0.001</td> <td>   95.578</td> <td>  372.253</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x12</th>   <td>    1.0985</td> <td>    0.185</td> <td>    5.950</td> <td> 0.000</td> <td>    0.737</td> <td>    1.461</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x13</th>   <td>-4.062e-06</td> <td> 4.38e-07</td> <td>   -9.265</td> <td> 0.000</td> <td>-4.92e-06</td> <td> -3.2e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x14</th>   <td>   -1.2409</td> <td>    0.242</td> <td>   -5.131</td> <td> 0.000</td> <td>   -1.715</td> <td>   -0.767</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x15</th>   <td>   -0.6179</td> <td>    0.259</td> <td>   -2.381</td> <td> 0.017</td> <td>   -1.127</td> <td>   -0.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x16</th>   <td>-4.316e-06</td> <td> 8.96e-07</td> <td>   -4.818</td> <td> 0.000</td> <td>-6.07e-06</td> <td>-2.56e-06</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>156.037</td> <th>  Durbin-Watson:     </th> <td>   1.273</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 246.391</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.407</td>  <th>  Prob(JB):          </th> <td>3.14e-54</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.062</td>  <th>  Cond. No.          </th> <td>1.55e+09</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.55e+09. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    FIC   R-squared:                       0.653\n",
       "Model:                            OLS   Adj. R-squared:                  0.651\n",
       "Method:                 Least Squares   F-statistic:                     386.9\n",
       "Date:                Wed, 30 Oct 2019   Prob (F-statistic):               0.00\n",
       "Time:                        08:32:28   Log-Likelihood:                -8599.6\n",
       "No. Observations:                3305   AIC:                         1.723e+04\n",
       "Df Residuals:                    3288   BIC:                         1.734e+04\n",
       "Df Model:                          16                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          4.6153      0.381     12.121      0.000       3.869       5.362\n",
       "x1             0.0003   9.58e-06     31.175      0.000       0.000       0.000\n",
       "x2             0.0001    1.4e-05      9.791      0.000       0.000       0.000\n",
       "x3           -87.0829     15.971     -5.453      0.000    -118.396     -55.769\n",
       "x4            -0.0074      0.001     -5.539      0.000      -0.010      -0.005\n",
       "x5          1.656e-05   9.79e-07     16.913      0.000    1.46e-05    1.85e-05\n",
       "x6            -0.0039      0.001     -5.837      0.000      -0.005      -0.003\n",
       "x7         -8.727e-06   3.36e-06     -2.594      0.010   -1.53e-05   -2.13e-06\n",
       "x8          6.031e-05   3.22e-06     18.702      0.000     5.4e-05    6.66e-05\n",
       "x9            -0.0025      0.001     -2.074      0.038      -0.005      -0.000\n",
       "x10        -5.871e-06   1.21e-06     -4.851      0.000   -8.24e-06    -3.5e-06\n",
       "x11          233.9157     70.556      3.315      0.001      95.578     372.253\n",
       "x12            1.0985      0.185      5.950      0.000       0.737       1.461\n",
       "x13        -4.062e-06   4.38e-07     -9.265      0.000   -4.92e-06    -3.2e-06\n",
       "x14           -1.2409      0.242     -5.131      0.000      -1.715      -0.767\n",
       "x15           -0.6179      0.259     -2.381      0.017      -1.127      -0.109\n",
       "x16        -4.316e-06   8.96e-07     -4.818      0.000   -6.07e-06   -2.56e-06\n",
       "==============================================================================\n",
       "Omnibus:                      156.037   Durbin-Watson:                   1.273\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              246.391\n",
       "Skew:                           0.407   Prob(JB):                     3.14e-54\n",
       "Kurtosis:                       4.062   Cond. No.                     1.55e+09\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.55e+09. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_Modeled = backwardElimination(X_opt, data_FIC, SL)\n",
    "regressor_OLS = sm.OLS(endog = data_FIC, exog = X_Modeled).fit()\n",
    "regressor_OLS.summary()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "###########################################################################################################\n",
    "# Se puede ver que ninguna de las variables esta por encima del P-value con este modelo\n",
    "###########################################################################################################"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "###########################################################################################################\n",
    "# Se hace Backward Elimination con P-values y Adjusted R Squared:\n",
    "###########################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    FIC   R-squared:                       0.654\n",
      "Model:                            OLS   Adj. R-squared:                  0.652\n",
      "Method:                 Least Squares   F-statistic:                     344.6\n",
      "Date:                Wed, 30 Oct 2019   Prob (F-statistic):               0.00\n",
      "Time:                        08:32:28   Log-Likelihood:                -8596.8\n",
      "No. Observations:                3305   AIC:                         1.723e+04\n",
      "Df Residuals:                    3286   BIC:                         1.735e+04\n",
      "Df Model:                          18                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          4.6111      0.381     12.117      0.000       3.865       5.357\n",
      "x1             0.0003   1.01e-05     30.011      0.000       0.000       0.000\n",
      "x2             0.0001   1.41e-05      9.628      0.000       0.000       0.000\n",
      "x3           -86.4857     15.972     -5.415      0.000    -117.802     -55.169\n",
      "x4            -0.0074      0.001     -5.383      0.000      -0.010      -0.005\n",
      "x5          1.667e-05   9.83e-07     16.960      0.000    1.47e-05    1.86e-05\n",
      "x6            -0.0039      0.001     -5.782      0.000      -0.005      -0.003\n",
      "x7         -8.483e-06   3.37e-06     -2.516      0.012   -1.51e-05   -1.87e-06\n",
      "x8          6.046e-05   3.23e-06     18.731      0.000    5.41e-05    6.68e-05\n",
      "x9         -5.558e-06   2.88e-06     -1.931      0.054   -1.12e-05    8.57e-08\n",
      "x10           -0.0024      0.001     -2.037      0.042      -0.005   -9.11e-05\n",
      "x11         -6.04e-06   1.22e-06     -4.965      0.000   -8.43e-06   -3.65e-06\n",
      "x12          234.0178     70.545      3.317      0.001      95.701     372.335\n",
      "x13           -0.2544      0.173     -1.471      0.141      -0.593       0.085\n",
      "x14            1.0201      0.189      5.407      0.000       0.650       1.390\n",
      "x15        -4.025e-06   4.38e-07     -9.180      0.000   -4.88e-06   -3.17e-06\n",
      "x16           -1.2305      0.242     -5.076      0.000      -1.706      -0.755\n",
      "x17           -0.6082      0.260     -2.340      0.019      -1.118      -0.098\n",
      "x18        -4.484e-06   9.01e-07     -4.980      0.000   -6.25e-06   -2.72e-06\n",
      "==============================================================================\n",
      "Omnibus:                      153.073   Durbin-Watson:                   1.271\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              239.550\n",
      "Skew:                           0.403   Prob(JB):                     9.60e-53\n",
      "Kurtosis:                       4.043   Cond. No.                     1.55e+09\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.55e+09. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>FIC</td>       <th>  R-squared:         </th> <td>   0.651</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.649</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   360.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 30 Oct 2019</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>08:32:28</td>     <th>  Log-Likelihood:    </th> <td> -8611.4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  3305</td>      <th>  AIC:               </th> <td>1.726e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  3287</td>      <th>  BIC:               </th> <td>1.737e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    17</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    4.7639</td> <td>    0.381</td> <td>   12.499</td> <td> 0.000</td> <td>    4.017</td> <td>    5.511</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.0003</td> <td> 1.01e-05</td> <td>   30.198</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.0001</td> <td> 1.41e-05</td> <td>    9.623</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>  -85.1174</td> <td>   16.039</td> <td>   -5.307</td> <td> 0.000</td> <td> -116.564</td> <td>  -53.671</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>   -0.0073</td> <td>    0.001</td> <td>   -5.258</td> <td> 0.000</td> <td>   -0.010</td> <td>   -0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>    <td> 1.681e-05</td> <td> 9.87e-07</td> <td>   17.035</td> <td> 0.000</td> <td> 1.49e-05</td> <td> 1.87e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>    <td>   -0.0040</td> <td>    0.001</td> <td>   -5.958</td> <td> 0.000</td> <td>   -0.005</td> <td>   -0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th>    <td>-8.014e-06</td> <td> 3.38e-06</td> <td>   -2.368</td> <td> 0.018</td> <td>-1.47e-05</td> <td>-1.38e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x8</th>    <td> 6.127e-05</td> <td> 3.24e-06</td> <td>   18.922</td> <td> 0.000</td> <td> 5.49e-05</td> <td> 6.76e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x9</th>    <td>-6.746e-06</td> <td> 2.88e-06</td> <td>   -2.340</td> <td> 0.019</td> <td>-1.24e-05</td> <td>-1.09e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x10</th>   <td>   -0.0021</td> <td>    0.001</td> <td>   -1.785</td> <td> 0.074</td> <td>   -0.004</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x11</th>   <td>-6.359e-06</td> <td> 1.22e-06</td> <td>   -5.211</td> <td> 0.000</td> <td>-8.75e-06</td> <td>-3.97e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x12</th>   <td>  230.4752</td> <td>   70.845</td> <td>    3.253</td> <td> 0.001</td> <td>   91.571</td> <td>  369.379</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x13</th>   <td>-4.179e-06</td> <td> 4.39e-07</td> <td>   -9.511</td> <td> 0.000</td> <td>-5.04e-06</td> <td>-3.32e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x14</th>   <td>   -0.7926</td> <td>    0.229</td> <td>   -3.454</td> <td> 0.001</td> <td>   -1.243</td> <td>   -0.343</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x15</th>   <td>   -0.5716</td> <td>    0.261</td> <td>   -2.190</td> <td> 0.029</td> <td>   -1.083</td> <td>   -0.060</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x16</th>   <td>-4.203e-06</td> <td> 9.03e-07</td> <td>   -4.655</td> <td> 0.000</td> <td>-5.97e-06</td> <td>-2.43e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x17</th>   <td>         0</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>        0</td> <td>        0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x18</th>   <td>   -0.4379</td> <td>    0.170</td> <td>   -2.572</td> <td> 0.010</td> <td>   -0.772</td> <td>   -0.104</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>163.699</td> <th>  Durbin-Watson:     </th> <td>   1.263</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 259.561</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.421</td>  <th>  Prob(JB):          </th> <td>4.33e-57</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.084</td>  <th>  Cond. No.          </th> <td>1.86e+28</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 1.45e-41. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    FIC   R-squared:                       0.651\n",
       "Model:                            OLS   Adj. R-squared:                  0.649\n",
       "Method:                 Least Squares   F-statistic:                     360.0\n",
       "Date:                Wed, 30 Oct 2019   Prob (F-statistic):               0.00\n",
       "Time:                        08:32:28   Log-Likelihood:                -8611.4\n",
       "No. Observations:                3305   AIC:                         1.726e+04\n",
       "Df Residuals:                    3287   BIC:                         1.737e+04\n",
       "Df Model:                          17                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          4.7639      0.381     12.499      0.000       4.017       5.511\n",
       "x1             0.0003   1.01e-05     30.198      0.000       0.000       0.000\n",
       "x2             0.0001   1.41e-05      9.623      0.000       0.000       0.000\n",
       "x3           -85.1174     16.039     -5.307      0.000    -116.564     -53.671\n",
       "x4            -0.0073      0.001     -5.258      0.000      -0.010      -0.005\n",
       "x5          1.681e-05   9.87e-07     17.035      0.000    1.49e-05    1.87e-05\n",
       "x6            -0.0040      0.001     -5.958      0.000      -0.005      -0.003\n",
       "x7         -8.014e-06   3.38e-06     -2.368      0.018   -1.47e-05   -1.38e-06\n",
       "x8          6.127e-05   3.24e-06     18.922      0.000    5.49e-05    6.76e-05\n",
       "x9         -6.746e-06   2.88e-06     -2.340      0.019   -1.24e-05   -1.09e-06\n",
       "x10           -0.0021      0.001     -1.785      0.074      -0.004       0.000\n",
       "x11        -6.359e-06   1.22e-06     -5.211      0.000   -8.75e-06   -3.97e-06\n",
       "x12          230.4752     70.845      3.253      0.001      91.571     369.379\n",
       "x13        -4.179e-06   4.39e-07     -9.511      0.000   -5.04e-06   -3.32e-06\n",
       "x14           -0.7926      0.229     -3.454      0.001      -1.243      -0.343\n",
       "x15           -0.5716      0.261     -2.190      0.029      -1.083      -0.060\n",
       "x16        -4.203e-06   9.03e-07     -4.655      0.000   -5.97e-06   -2.43e-06\n",
       "x17                 0          0        nan        nan           0           0\n",
       "x18           -0.4379      0.170     -2.572      0.010      -0.772      -0.104\n",
       "==============================================================================\n",
       "Omnibus:                      163.699   Durbin-Watson:                   1.263\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              259.561\n",
       "Skew:                           0.421   Prob(JB):                     4.33e-57\n",
       "Kurtosis:                       4.084   Cond. No.                     1.86e+28\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 1.45e-41. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_Modeled = backwardEliminationRQ(X_opt, data_FIC, SL)\n",
    "regressor_OLS = sm.OLS(endog = data_FIC, exog = X_Modeled).fit()\n",
    "regressor_OLS.summary()\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "###########################################################################################################\n",
    "# Tampoco se ven variables a ser descartadas con este modelo\n",
    "###########################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "### Evaluación de correlacion y sesgo de dataset procesado mediante el perfilador de pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "#data_top.index\n",
    "data_dfo.reset_index(inplace=True)\n",
    "data_top.reset_index(inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_FIC  = data_dfo.FIC\n",
    "data_top_full = pd.concat((data_top, df_FIC), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
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       "      <th>COD_ID</th>\n",
       "      <th>DISTANCE_SUB</th>\n",
       "      <th>CTMT_PNTMT_STD</th>\n",
       "      <th>CLO_CEN</th>\n",
       "      <th>TIP_CC_RES-Tipo3</th>\n",
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       "      <td>8194.44</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>112.50</td>\n",
       "      <td>408741.02</td>\n",
       "      <td>438.00</td>\n",
       "      <td>67863.54</td>\n",
       "      <td>22266.87</td>\n",
       "      <td>...</td>\n",
       "      <td>119688.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1648865.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>350609.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26880293</td>\n",
       "      <td>3774.91</td>\n",
       "      <td>8194.44</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>112.50</td>\n",
       "      <td>408741.02</td>\n",
       "      <td>438.00</td>\n",
       "      <td>67863.54</td>\n",
       "      <td>22266.87</td>\n",
       "      <td>...</td>\n",
       "      <td>119688.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1648865.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>350609.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26880295</td>\n",
       "      <td>3897.62</td>\n",
       "      <td>8194.44</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>408741.02</td>\n",
       "      <td>438.00</td>\n",
       "      <td>67863.54</td>\n",
       "      <td>22266.87</td>\n",
       "      <td>...</td>\n",
       "      <td>119688.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1648865.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>350609.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26880295</td>\n",
       "      <td>3897.62</td>\n",
       "      <td>8194.44</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>408741.02</td>\n",
       "      <td>438.00</td>\n",
       "      <td>67863.54</td>\n",
       "      <td>22266.87</td>\n",
       "      <td>...</td>\n",
       "      <td>119688.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1648865.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>350609.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3301</th>\n",
       "      <td>26878634</td>\n",
       "      <td>15732.31</td>\n",
       "      <td>4838.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>379666.04</td>\n",
       "      <td>168.00</td>\n",
       "      <td>93329.28</td>\n",
       "      <td>13712.42</td>\n",
       "      <td>...</td>\n",
       "      <td>320696.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1013653.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>198609.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3302</th>\n",
       "      <td>26878635</td>\n",
       "      <td>16620.79</td>\n",
       "      <td>4838.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>379666.04</td>\n",
       "      <td>168.00</td>\n",
       "      <td>93329.28</td>\n",
       "      <td>13712.42</td>\n",
       "      <td>...</td>\n",
       "      <td>320696.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1013653.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>198609.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3303</th>\n",
       "      <td>26878636</td>\n",
       "      <td>16872.15</td>\n",
       "      <td>4838.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>379666.04</td>\n",
       "      <td>168.00</td>\n",
       "      <td>93329.28</td>\n",
       "      <td>13712.42</td>\n",
       "      <td>...</td>\n",
       "      <td>320696.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1013653.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>198609.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3304</th>\n",
       "      <td>26878637</td>\n",
       "      <td>17049.51</td>\n",
       "      <td>4838.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>379666.04</td>\n",
       "      <td>168.00</td>\n",
       "      <td>93329.28</td>\n",
       "      <td>13712.42</td>\n",
       "      <td>...</td>\n",
       "      <td>320696.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1013653.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>198609.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3305</th>\n",
       "      <td>NaN</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3306 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        COD_ID  DISTANCE_SUB  CTMT_PNTMT_STD  CLO_CEN  TIP_CC_RES-Tipo3  \\\n",
       "0     26879192       5104.49         2088.58     0.02              0.00   \n",
       "1     26880293       3774.91         8194.44     0.01              0.00   \n",
       "2     26880293       3774.91         8194.44     0.03              0.00   \n",
       "3     26880295       3897.62         8194.44     0.01              0.00   \n",
       "4     26880295       3897.62         8194.44     0.03              0.00   \n",
       "...        ...           ...             ...      ...               ...   \n",
       "3301  26878634      15732.31         4838.27     0.00              0.00   \n",
       "3302  26878635      16620.79         4838.27     0.00              0.00   \n",
       "3303  26878636      16872.15         4838.27     0.00              0.00   \n",
       "3304  26878637      17049.51         4838.27     0.00              0.00   \n",
       "3305       NaN           nan             nan      nan               nan   \n",
       "\n",
       "      POT_NOM  PERD_A4_B  I_NOM_MED_R  CTMT_ENE_STD  CTMT_PNTMT_MED  ...  \\\n",
       "0       45.00  389840.98       365.00      43960.32        18885.60  ...   \n",
       "1      112.50  408741.02       438.00      67863.54        22266.87  ...   \n",
       "2      112.50  408741.02       438.00      67863.54        22266.87  ...   \n",
       "3       75.00  408741.02       438.00      67863.54        22266.87  ...   \n",
       "4       75.00  408741.02       438.00      67863.54        22266.87  ...   \n",
       "...       ...        ...          ...           ...             ...  ...   \n",
       "3301    15.00  379666.04       168.00      93329.28        13712.42  ...   \n",
       "3302    10.00  379666.04       168.00      93329.28        13712.42  ...   \n",
       "3303    45.00  379666.04       168.00      93329.28        13712.42  ...   \n",
       "3304    15.00  379666.04       168.00      93329.28        13712.42  ...   \n",
       "3305      nan        nan          nan           nan             nan  ...   \n",
       "\n",
       "      CTMT_PNTBT_MAX  PAGE_RANK  TIP_TRAFO_M  FAS_CON_S_ABCN  CTMT_ENE_MED  \\\n",
       "0          143337.13       0.00         0.00            0.00    1421387.50   \n",
       "1          119688.30       0.00         0.00            0.00    1648865.40   \n",
       "2          119688.30       0.00         0.00            0.00    1648865.40   \n",
       "3          119688.30       0.00         0.00            0.00    1648865.40   \n",
       "4          119688.30       0.00         0.00            0.00    1648865.40   \n",
       "...              ...        ...          ...             ...           ...   \n",
       "3301       320696.78       0.00         0.00            0.00    1013653.45   \n",
       "3302       320696.78       0.00         1.00            0.00    1013653.45   \n",
       "3303       320696.78       0.00         0.00            1.00    1013653.45   \n",
       "3304       320696.78       0.00         0.00            1.00    1013653.45   \n",
       "3305             nan        nan          nan             nan           nan   \n",
       "\n",
       "      GRU_TAR_B3  TIP_CC_RES-Tipo10   PERD_A4  TIP_CC_RES-Tipo5   FIC  \n",
       "0           0.00               0.00 255476.21              0.00  1.00  \n",
       "1           0.00               0.00 350609.30              0.00  0.00  \n",
       "2           0.00               0.00 350609.30              0.00  0.00  \n",
       "3           0.00               0.00 350609.30              0.00  0.00  \n",
       "4           0.00               0.00 350609.30              0.00  0.00  \n",
       "...          ...                ...       ...               ...   ...  \n",
       "3301        0.00               0.00 198609.64              0.00 16.00  \n",
       "3302        0.00               0.00 198609.64              0.00  8.00  \n",
       "3303        0.00               0.00 198609.64              0.00  8.00  \n",
       "3304        0.00               0.00 198609.64              0.00 13.00  \n",
       "3305         nan                nan       nan               nan 18.00  \n",
       "\n",
       "[3306 rows x 22 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_top_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "#pp.ProfileReport(data_top_full)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "#### Verificación de consistencia de datos previa a la grabación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3306, 22)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data_top_full\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "COD_ID                object\n",
       "DISTANCE_SUB         float64\n",
       "CTMT_PNTMT_STD       float64\n",
       "CLO_CEN              float64\n",
       "TIP_CC_RES-Tipo3     float64\n",
       "POT_NOM              float64\n",
       "PERD_A4_B            float64\n",
       "I_NOM_MED_R          float64\n",
       "CTMT_ENE_STD         float64\n",
       "CTMT_PNTMT_MED       float64\n",
       "UCBT_ENE_MED         float64\n",
       "I_NOM_MIN_R          float64\n",
       "CTMT_PNTBT_MAX       float64\n",
       "PAGE_RANK            float64\n",
       "TIP_TRAFO_M          float64\n",
       "FAS_CON_S_ABCN       float64\n",
       "CTMT_ENE_MED         float64\n",
       "GRU_TAR_B3           float64\n",
       "TIP_CC_RES-Tipo10    float64\n",
       "PERD_A4              float64\n",
       "TIP_CC_RES-Tipo5     float64\n",
       "FIC                  float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3306, 22)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "if  (df[df.isnull().any(1)]).empty:\n",
    "    print ('no hay nulos')\n",
    "else:\n",
    "    df.drop(df[df.isnull().any(1)].index, inplace = True)\n",
    "    df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3305, 22)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "#### Grabacion de archivo UNTRD para analisis de calidad del servicio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "data_path = BASE_DIR+'UNTRD_ENF.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "df.to_csv(data_path, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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