Parte 3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import (\n",
    "    classification_report, \n",
    "    accuracy_score, \n",
    "    roc_auc_score, \n",
    "    ConfusionMatrixDisplay,\n",
    "    confusion_matrix\n",
    ")\n",
    "\n",
    "from sklearn.preprocessing import (\n",
    "    LabelEncoder,\n",
    "    OrdinalEncoder,\n",
    "    MinMaxScaler,\n",
    "    Normalizer,\n",
    "    OneHotEncoder,\n",
    "    PowerTransformer,\n",
    "    RobustScaler,\n",
    "    StandardScaler,\n",
    ")\n",
    "\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TransactionID</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>TransactionDT</th>\n",
       "      <th>TransactionAmt</th>\n",
       "      <th>ProductCD</th>\n",
       "      <th>card1</th>\n",
       "      <th>card2</th>\n",
       "      <th>card3</th>\n",
       "      <th>card4</th>\n",
       "      <th>card5</th>\n",
       "      <th>...</th>\n",
       "      <th>V330</th>\n",
       "      <th>V331</th>\n",
       "      <th>V332</th>\n",
       "      <th>V333</th>\n",
       "      <th>V334</th>\n",
       "      <th>V335</th>\n",
       "      <th>V336</th>\n",
       "      <th>V337</th>\n",
       "      <th>V338</th>\n",
       "      <th>V339</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2987000</td>\n",
       "      <td>0</td>\n",
       "      <td>86400</td>\n",
       "      <td>68.5</td>\n",
       "      <td>W</td>\n",
       "      <td>13926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>discover</td>\n",
       "      <td>142.0</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2987001</td>\n",
       "      <td>0</td>\n",
       "      <td>86401</td>\n",
       "      <td>29.0</td>\n",
       "      <td>W</td>\n",
       "      <td>2755</td>\n",
       "      <td>404.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>102.0</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2987002</td>\n",
       "      <td>0</td>\n",
       "      <td>86469</td>\n",
       "      <td>59.0</td>\n",
       "      <td>W</td>\n",
       "      <td>4663</td>\n",
       "      <td>490.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>166.0</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2987003</td>\n",
       "      <td>0</td>\n",
       "      <td>86499</td>\n",
       "      <td>50.0</td>\n",
       "      <td>W</td>\n",
       "      <td>18132</td>\n",
       "      <td>567.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>117.0</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2987004</td>\n",
       "      <td>0</td>\n",
       "      <td>86506</td>\n",
       "      <td>50.0</td>\n",
       "      <td>H</td>\n",
       "      <td>4497</td>\n",
       "      <td>514.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>102.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 394 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TransactionID  isFraud  TransactionDT  TransactionAmt ProductCD  card1  \\\n",
       "0        2987000        0          86400            68.5         W  13926   \n",
       "1        2987001        0          86401            29.0         W   2755   \n",
       "2        2987002        0          86469            59.0         W   4663   \n",
       "3        2987003        0          86499            50.0         W  18132   \n",
       "4        2987004        0          86506            50.0         H   4497   \n",
       "\n",
       "   card2  card3       card4  card5  ... V330  V331  V332  V333  V334 V335  \\\n",
       "0    NaN  150.0    discover  142.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
       "1  404.0  150.0  mastercard  102.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
       "2  490.0  150.0        visa  166.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
       "3  567.0  150.0  mastercard  117.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
       "4  514.0  150.0  mastercard  102.0  ...  0.0   0.0   0.0   0.0   0.0  0.0   \n",
       "\n",
       "  V336  V337  V338  V339  \n",
       "0  NaN   NaN   NaN   NaN  \n",
       "1  NaN   NaN   NaN   NaN  \n",
       "2  NaN   NaN   NaN   NaN  \n",
       "3  NaN   NaN   NaN   NaN  \n",
       "4  0.0   0.0   0.0   0.0  \n",
       "\n",
       "[5 rows x 394 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions = pd.read_csv('train_transaction.csv')\n",
    "transactions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TransactionID</th>\n",
       "      <th>id_01</th>\n",
       "      <th>id_02</th>\n",
       "      <th>id_03</th>\n",
       "      <th>id_04</th>\n",
       "      <th>id_05</th>\n",
       "      <th>id_06</th>\n",
       "      <th>id_07</th>\n",
       "      <th>id_08</th>\n",
       "      <th>id_09</th>\n",
       "      <th>...</th>\n",
       "      <th>id_31</th>\n",
       "      <th>id_32</th>\n",
       "      <th>id_33</th>\n",
       "      <th>id_34</th>\n",
       "      <th>id_35</th>\n",
       "      <th>id_36</th>\n",
       "      <th>id_37</th>\n",
       "      <th>id_38</th>\n",
       "      <th>DeviceType</th>\n",
       "      <th>DeviceInfo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2987004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70787.0</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>samsung browser 6.2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2220x1080</td>\n",
       "      <td>match_status:2</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>mobile</td>\n",
       "      <td>SAMSUNG SM-G892A Build/NRD90M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2987008</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>98945.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>mobile safari 11.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1334x750</td>\n",
       "      <td>match_status:1</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>mobile</td>\n",
       "      <td>iOS Device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2987010</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>191631.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>desktop</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2987011</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>221832.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>desktop</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2987016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7460.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 62.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1280x800</td>\n",
       "      <td>match_status:2</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>desktop</td>\n",
       "      <td>MacOS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TransactionID  id_01     id_02  id_03  id_04  id_05  id_06  id_07  id_08  \\\n",
       "0        2987004    0.0   70787.0    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1        2987008   -5.0   98945.0    NaN    NaN    0.0   -5.0    NaN    NaN   \n",
       "2        2987010   -5.0  191631.0    0.0    0.0    0.0    0.0    NaN    NaN   \n",
       "3        2987011   -5.0  221832.0    NaN    NaN    0.0   -6.0    NaN    NaN   \n",
       "4        2987016    0.0    7460.0    0.0    0.0    1.0    0.0    NaN    NaN   \n",
       "\n",
       "   id_09  ...                id_31  id_32      id_33           id_34  id_35  \\\n",
       "0    NaN  ...  samsung browser 6.2   32.0  2220x1080  match_status:2      T   \n",
       "1    NaN  ...   mobile safari 11.0   32.0   1334x750  match_status:1      T   \n",
       "2    0.0  ...          chrome 62.0    NaN        NaN             NaN      F   \n",
       "3    NaN  ...          chrome 62.0    NaN        NaN             NaN      F   \n",
       "4    0.0  ...          chrome 62.0   24.0   1280x800  match_status:2      T   \n",
       "\n",
       "  id_36 id_37  id_38  DeviceType                     DeviceInfo  \n",
       "0     F     T      T      mobile  SAMSUNG SM-G892A Build/NRD90M  \n",
       "1     F     F      T      mobile                     iOS Device  \n",
       "2     F     T      T     desktop                        Windows  \n",
       "3     F     T      T     desktop                            NaN  \n",
       "4     F     T      T     desktop                          MacOS  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "identity = pd.read_csv('train_identity.csv')\n",
    "identity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "joined_data = transactions.merge(identity,on='TransactionID',how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TransactionID</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>TransactionDT</th>\n",
       "      <th>TransactionAmt</th>\n",
       "      <th>ProductCD</th>\n",
       "      <th>card1</th>\n",
       "      <th>card2</th>\n",
       "      <th>card3</th>\n",
       "      <th>card4</th>\n",
       "      <th>card5</th>\n",
       "      <th>...</th>\n",
       "      <th>id_31</th>\n",
       "      <th>id_32</th>\n",
       "      <th>id_33</th>\n",
       "      <th>id_34</th>\n",
       "      <th>id_35</th>\n",
       "      <th>id_36</th>\n",
       "      <th>id_37</th>\n",
       "      <th>id_38</th>\n",
       "      <th>DeviceType</th>\n",
       "      <th>DeviceInfo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2987000</td>\n",
       "      <td>0</td>\n",
       "      <td>86400</td>\n",
       "      <td>68.5</td>\n",
       "      <td>W</td>\n",
       "      <td>13926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>discover</td>\n",
       "      <td>142.0</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2987001</td>\n",
       "      <td>0</td>\n",
       "      <td>86401</td>\n",
       "      <td>29.0</td>\n",
       "      <td>W</td>\n",
       "      <td>2755</td>\n",
       "      <td>404.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>102.0</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2987002</td>\n",
       "      <td>0</td>\n",
       "      <td>86469</td>\n",
       "      <td>59.0</td>\n",
       "      <td>W</td>\n",
       "      <td>4663</td>\n",
       "      <td>490.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>166.0</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2987003</td>\n",
       "      <td>0</td>\n",
       "      <td>86499</td>\n",
       "      <td>50.0</td>\n",
       "      <td>W</td>\n",
       "      <td>18132</td>\n",
       "      <td>567.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>117.0</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2987004</td>\n",
       "      <td>0</td>\n",
       "      <td>86506</td>\n",
       "      <td>50.0</td>\n",
       "      <td>H</td>\n",
       "      <td>4497</td>\n",
       "      <td>514.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>102.0</td>\n",
       "      <td>...</td>\n",
       "      <td>samsung browser 6.2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2220x1080</td>\n",
       "      <td>match_status:2</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>mobile</td>\n",
       "      <td>SAMSUNG SM-G892A Build/NRD90M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 434 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TransactionID  isFraud  TransactionDT  TransactionAmt ProductCD  card1  \\\n",
       "0        2987000        0          86400            68.5         W  13926   \n",
       "1        2987001        0          86401            29.0         W   2755   \n",
       "2        2987002        0          86469            59.0         W   4663   \n",
       "3        2987003        0          86499            50.0         W  18132   \n",
       "4        2987004        0          86506            50.0         H   4497   \n",
       "\n",
       "   card2  card3       card4  card5  ...                id_31  id_32  \\\n",
       "0    NaN  150.0    discover  142.0  ...                  NaN    NaN   \n",
       "1  404.0  150.0  mastercard  102.0  ...                  NaN    NaN   \n",
       "2  490.0  150.0        visa  166.0  ...                  NaN    NaN   \n",
       "3  567.0  150.0  mastercard  117.0  ...                  NaN    NaN   \n",
       "4  514.0  150.0  mastercard  102.0  ...  samsung browser 6.2   32.0   \n",
       "\n",
       "       id_33           id_34  id_35 id_36 id_37  id_38  DeviceType  \\\n",
       "0        NaN             NaN    NaN   NaN   NaN    NaN         NaN   \n",
       "1        NaN             NaN    NaN   NaN   NaN    NaN         NaN   \n",
       "2        NaN             NaN    NaN   NaN   NaN    NaN         NaN   \n",
       "3        NaN             NaN    NaN   NaN   NaN    NaN         NaN   \n",
       "4  2220x1080  match_status:2      T     F     T      T      mobile   \n",
       "\n",
       "                      DeviceInfo  \n",
       "0                            NaN  \n",
       "1                            NaN  \n",
       "2                            NaN  \n",
       "3                            NaN  \n",
       "4  SAMSUNG SM-G892A Build/NRD90M  \n",
       "\n",
       "[5 rows x 434 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "del transactions\n",
    "del identity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Para esta tercera parte, separaré mi train-validation en 80-20\n",
    "train = joined_data.loc[joined_data.index <= (len(joined_data)*0.8)]\n",
    "validation = joined_data.loc[joined_data.index >= (len(joined_data)*0.8)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TransactionID</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>TransactionDT</th>\n",
       "      <th>TransactionAmt</th>\n",
       "      <th>ProductCD</th>\n",
       "      <th>card1</th>\n",
       "      <th>card2</th>\n",
       "      <th>card3</th>\n",
       "      <th>card4</th>\n",
       "      <th>card5</th>\n",
       "      <th>...</th>\n",
       "      <th>id_31</th>\n",
       "      <th>id_32</th>\n",
       "      <th>id_33</th>\n",
       "      <th>id_34</th>\n",
       "      <th>id_35</th>\n",
       "      <th>id_36</th>\n",
       "      <th>id_37</th>\n",
       "      <th>id_38</th>\n",
       "      <th>DeviceType</th>\n",
       "      <th>DeviceInfo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2987000</td>\n",
       "      <td>0</td>\n",
       "      <td>86400</td>\n",
       "      <td>68.5</td>\n",
       "      <td>W</td>\n",
       "      <td>13926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>discover</td>\n",
       "      <td>142.0</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2987001</td>\n",
       "      <td>0</td>\n",
       "      <td>86401</td>\n",
       "      <td>29.0</td>\n",
       "      <td>W</td>\n",
       "      <td>2755</td>\n",
       "      <td>404.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>102.0</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2987002</td>\n",
       "      <td>0</td>\n",
       "      <td>86469</td>\n",
       "      <td>59.0</td>\n",
       "      <td>W</td>\n",
       "      <td>4663</td>\n",
       "      <td>490.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>166.0</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2987003</td>\n",
       "      <td>0</td>\n",
       "      <td>86499</td>\n",
       "      <td>50.0</td>\n",
       "      <td>W</td>\n",
       "      <td>18132</td>\n",
       "      <td>567.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>117.0</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2987004</td>\n",
       "      <td>0</td>\n",
       "      <td>86506</td>\n",
       "      <td>50.0</td>\n",
       "      <td>H</td>\n",
       "      <td>4497</td>\n",
       "      <td>514.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>102.0</td>\n",
       "      <td>...</td>\n",
       "      <td>samsung browser 6.2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2220x1080</td>\n",
       "      <td>match_status:2</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>mobile</td>\n",
       "      <td>SAMSUNG SM-G892A Build/NRD90M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 434 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TransactionID  isFraud  TransactionDT  TransactionAmt ProductCD  card1  \\\n",
       "0        2987000        0          86400            68.5         W  13926   \n",
       "1        2987001        0          86401            29.0         W   2755   \n",
       "2        2987002        0          86469            59.0         W   4663   \n",
       "3        2987003        0          86499            50.0         W  18132   \n",
       "4        2987004        0          86506            50.0         H   4497   \n",
       "\n",
       "   card2  card3       card4  card5  ...                id_31  id_32  \\\n",
       "0    NaN  150.0    discover  142.0  ...                  NaN    NaN   \n",
       "1  404.0  150.0  mastercard  102.0  ...                  NaN    NaN   \n",
       "2  490.0  150.0        visa  166.0  ...                  NaN    NaN   \n",
       "3  567.0  150.0  mastercard  117.0  ...                  NaN    NaN   \n",
       "4  514.0  150.0  mastercard  102.0  ...  samsung browser 6.2   32.0   \n",
       "\n",
       "       id_33           id_34  id_35 id_36 id_37  id_38  DeviceType  \\\n",
       "0        NaN             NaN    NaN   NaN   NaN    NaN         NaN   \n",
       "1        NaN             NaN    NaN   NaN   NaN    NaN         NaN   \n",
       "2        NaN             NaN    NaN   NaN   NaN    NaN         NaN   \n",
       "3        NaN             NaN    NaN   NaN   NaN    NaN         NaN   \n",
       "4  2220x1080  match_status:2      T     F     T      T      mobile   \n",
       "\n",
       "                      DeviceInfo  \n",
       "0                            NaN  \n",
       "1                            NaN  \n",
       "2                            NaN  \n",
       "3                            NaN  \n",
       "4  SAMSUNG SM-G892A Build/NRD90M  \n",
       "\n",
       "[5 rows x 434 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Comienzo la busqueda de los mejor features para mi modelo de ML, para eso, primeramente saco los obligatorios a usar, \n",
    "# estos son id_31, id_33, DeviceType, DeviceInfo.\n",
    "# Para los features categoricos, usare la ganancia de informacion para dejarlos o sacarlos\n",
    "# Para los features numericos, en base a su correlacion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_categoricos = []\n",
    "feature_numeric = []\n",
    "obligatorios = ['id_31', 'id_33', 'DeviceType', 'DeviceInfo']\n",
    "\n",
    "for c in train.columns:\n",
    "    if train[c].dtype == object and c not in obligatorios:\n",
    "        features_categoricos.append(c)\n",
    "    elif train[c].dtype != object and c not in obligatorios:\n",
    "        feature_numeric.append(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Para la seleccion de features categóricos utilizaré mutual_info_classif, donde obtendré una lista de los\n",
    "# features que mas informacion aportan a mi set de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selector\n",
    "---\n",
    "Para algunos features categoricos usaré el feature selector de sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4147: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().fillna(\n"
     ]
    }
   ],
   "source": [
    "# Separo en un nuevo dataframe para los features categoricos.\n",
    "X_fc_train = train[features_categoricos]\n",
    "X_fc_train.fillna('desconocido',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import mutual_info_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-13-f1cfe8a27be5>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))\n"
     ]
    }
   ],
   "source": [
    "# Primero para utilizar los features selectors, debo encodiar las columnas.\n",
    "# Estas las haré usando OrdinalEncoder\n",
    "categorical_ec_dic = {}\n",
    "for c in X_fc_train.columns:\n",
    "    ordinal_encoder = OrdinalEncoder(handle_unknown='use_encoded_value',unknown_value=-1)\n",
    "    categorical_ec_dic[c] = ordinal_encoder\n",
    "    X_fc_train[c] = ordinal_encoder.fit_transform(X_fc_train[c].astype(str).values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4., 2., 1., ..., 2., 2., 2.],\n",
       "       [4., 3., 1., ..., 2., 2., 2.],\n",
       "       [4., 4., 2., ..., 2., 2., 2.],\n",
       "       ...,\n",
       "       [4., 3., 2., ..., 2., 2., 2.],\n",
       "       [4., 4., 1., ..., 2., 2., 2.],\n",
       "       [0., 4., 2., ..., 0., 1., 0.]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Entreno el Feature Selector.\n",
    "Y_fc_train = train.isFraud\n",
    "fs = SelectKBest(score_func=mutual_info_classif, k='all')\n",
    "fs.fit_transform(X_fc_train,Y_fc_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ProductCD: 0.086447\n",
      "Feature card4: 0.059572\n",
      "Feature card6: 0.078911\n",
      "Feature P_emaildomain: 0.026984\n",
      "Feature R_emaildomain: 0.066458\n",
      "Feature M1: 0.056885\n",
      "Feature M2: 0.056837\n",
      "Feature M3: 0.055220\n",
      "Feature M4: 0.049474\n",
      "Feature M5: 0.059543\n",
      "Feature M6: 0.036230\n",
      "Feature M7: 0.067618\n",
      "Feature M8: 0.064685\n",
      "Feature M9: 0.064034\n",
      "Feature id_12: 0.082189\n",
      "Feature id_15: 0.080061\n",
      "Feature id_16: 0.088284\n",
      "Feature id_23: 0.098035\n",
      "Feature id_27: 0.108087\n",
      "Feature id_28: 0.087139\n",
      "Feature id_29: 0.087876\n",
      "Feature id_30: 0.075971\n",
      "Feature id_34: 0.006114\n",
      "Feature id_35: 0.088334\n",
      "Feature id_36: 0.091120\n",
      "Feature id_37: 0.083670\n",
      "Feature id_38: 0.086289\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(fs.scores_)):\n",
    "    print('Feature %s: %f' % (features_categoricos[i], fs.scores_[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(features_categoricos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ya tengo la lista de los indices para los features mas importantes numericos,\n",
    "# procedo a quedarme con esos de mi lista de train\n",
    "features_categoricos_mas_importantes = []\n",
    "scores = pd.Series(fs.scores_)\n",
    "for i in range(len(features_categoricos)):\n",
    "    if i in scores.nlargest(20).index:\n",
    "        features_categoricos_mas_importantes.append(features_categoricos[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Limpio la memoria ya que no uso mas estos dataframes\n",
    "del scores\n",
    "del X_fc_train\n",
    "del Y_fc_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "#minf = train[most_important_numeric_features] # Most important numeric features\n",
    "minf = train[feature_numeric] # Most important numeric features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_categorical_feature = features_categoricos_mas_importantes + obligatorios\n",
    "#final_categorical_feature = features_categoricos + obligatorios\n",
    "micf = train[final_categorical_feature] # Most important category features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472430</th>\n",
       "      <td>3459430</td>\n",
       "      <td>0</td>\n",
       "      <td>12192743</td>\n",
       "      <td>15.000</td>\n",
       "      <td>7919</td>\n",
       "      <td>194.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>87.0</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472431</th>\n",
       "      <td>3459431</td>\n",
       "      <td>0</td>\n",
       "      <td>12192842</td>\n",
       "      <td>49.000</td>\n",
       "      <td>13749</td>\n",
       "      <td>321.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>87.0</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472432</th>\n",
       "      <td>3459432</td>\n",
       "      <td>1</td>\n",
       "      <td>12192900</td>\n",
       "      <td>33.261</td>\n",
       "      <td>9300</td>\n",
       "      <td>103.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>225.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>417.0</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>472433 rows × 403 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TransactionID  isFraud  TransactionDT  TransactionAmt  card1  card2  \\\n",
       "0             2987000        0          86400          68.500  13926    NaN   \n",
       "1             2987001        0          86401          29.000   2755  404.0   \n",
       "2             2987002        0          86469          59.000   4663  490.0   \n",
       "3             2987003        0          86499          50.000  18132  567.0   \n",
       "4             2987004        0          86506          50.000   4497  514.0   \n",
       "...               ...      ...            ...             ...    ...    ...   \n",
       "472428        3459428        0       12192736          49.000  17188  321.0   \n",
       "472429        3459429        0       12192742          40.000  16659  170.0   \n",
       "472430        3459430        0       12192743          15.000   7919  194.0   \n",
       "472431        3459431        0       12192842          49.000  13749  321.0   \n",
       "472432        3459432        1       12192900          33.261   9300  103.0   \n",
       "\n",
       "        card3  card5  addr1  addr2  ...  id_17  id_18  id_19  id_20  id_21  \\\n",
       "0       150.0  142.0  315.0   87.0  ...    NaN    NaN    NaN    NaN    NaN   \n",
       "1       150.0  102.0  325.0   87.0  ...    NaN    NaN    NaN    NaN    NaN   \n",
       "2       150.0  166.0  330.0   87.0  ...    NaN    NaN    NaN    NaN    NaN   \n",
       "3       150.0  117.0  476.0   87.0  ...    NaN    NaN    NaN    NaN    NaN   \n",
       "4       150.0  102.0  420.0   87.0  ...  166.0    NaN  542.0  144.0    NaN   \n",
       "...       ...    ...    ...    ...  ...    ...    ...    ...    ...    ...   \n",
       "472428  150.0  226.0  220.0   87.0  ...    NaN    NaN    NaN    NaN    NaN   \n",
       "472429  150.0  226.0  272.0   87.0  ...  166.0    NaN  312.0  597.0    NaN   \n",
       "472430  150.0  166.0  220.0   87.0  ...    NaN    NaN    NaN    NaN    NaN   \n",
       "472431  150.0  226.0  476.0   87.0  ...    NaN    NaN    NaN    NaN    NaN   \n",
       "472432  185.0  138.0    NaN    NaN  ...  225.0   15.0  427.0  417.0    NaN   \n",
       "\n",
       "        id_22  id_24  id_25  id_26  id_32  \n",
       "0         NaN    NaN    NaN    NaN    NaN  \n",
       "1         NaN    NaN    NaN    NaN    NaN  \n",
       "2         NaN    NaN    NaN    NaN    NaN  \n",
       "3         NaN    NaN    NaN    NaN    NaN  \n",
       "4         NaN    NaN    NaN    NaN   32.0  \n",
       "...       ...    ...    ...    ...    ...  \n",
       "472428    NaN    NaN    NaN    NaN    NaN  \n",
       "472429    NaN    NaN    NaN    NaN   24.0  \n",
       "472430    NaN    NaN    NaN    NaN    NaN  \n",
       "472431    NaN    NaN    NaN    NaN    NaN  \n",
       "472432    NaN    NaN    NaN    NaN    NaN  \n",
       "\n",
       "[472433 rows x 403 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "del minf['isFraud']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ya tengo los features numericos y categoricos que voy a utilizar, comienzo la etapa de encoding.\n",
    "# Para id_31, DeviceInfo,R_emaildomain,usaré el CountVectorizer\n",
    "# Para ProductCD, card4, card6,DeviceType id_23, es decir, aquellas que posee poca cardinalidad usaré One-Hot-Encoding.\n",
    "# Para el resto de las categoricas usaré Mean-Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(472433, 24)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ProductCD             0\n",
       "card4                 0\n",
       "card6                 0\n",
       "R_emaildomain         0\n",
       "M5                    0\n",
       "M7                    0\n",
       "M8                    0\n",
       "M9                    0\n",
       "id_12                 0\n",
       "id_15                 0\n",
       "id_16                 0\n",
       "id_23                 0\n",
       "id_27                 0\n",
       "id_28                 0\n",
       "id_29                 0\n",
       "id_30                 0\n",
       "id_35                 0\n",
       "id_36                 0\n",
       "id_37                 0\n",
       "id_38                 0\n",
       "id_31                 0\n",
       "id_33            410620\n",
       "DeviceType            0\n",
       "DeviceInfo            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(micf.shape)\n",
    "micf.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:6245: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    }
   ],
   "source": [
    "# Las columnas que tienen poca caardinalidad y usare con hot-one-encoding, reemplazo los nans por \"desconocido\"\n",
    "micf['id_23'].fillna('IP_Desconocido',inplace=True)\n",
    "micf['card4'].fillna('card4_desconocido',inplace=True)\n",
    "micf['card6'].fillna('card6_desconocido',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "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>ProductCD</th>\n",
       "      <th>card4</th>\n",
       "      <th>card6</th>\n",
       "      <th>R_emaildomain</th>\n",
       "      <th>M5</th>\n",
       "      <th>M7</th>\n",
       "      <th>M8</th>\n",
       "      <th>M9</th>\n",
       "      <th>id_12</th>\n",
       "      <th>id_15</th>\n",
       "      <th>...</th>\n",
       "      <th>id_29</th>\n",
       "      <th>id_30</th>\n",
       "      <th>id_35</th>\n",
       "      <th>id_36</th>\n",
       "      <th>id_37</th>\n",
       "      <th>id_38</th>\n",
       "      <th>id_31</th>\n",
       "      <th>id_33</th>\n",
       "      <th>DeviceType</th>\n",
       "      <th>DeviceInfo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>W</td>\n",
       "      <td>discover</td>\n",
       "      <td>credit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>id_29_Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>W</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>credit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>id_29_Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>W</td>\n",
       "      <td>visa</td>\n",
       "      <td>debit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>id_29_Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>W</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>debit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>id_29_Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>credit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>New</td>\n",
       "      <td>...</td>\n",
       "      <td>NotFound</td>\n",
       "      <td>Android 7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>samsung browser 6.2</td>\n",
       "      <td>2220x1080</td>\n",
       "      <td>mobile</td>\n",
       "      <td>SAMSUNG SM-G892A Build/NRD90M</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>472428</th>\n",
       "      <td>W</td>\n",
       "      <td>visa</td>\n",
       "      <td>debit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>id_29_Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472429</th>\n",
       "      <td>H</td>\n",
       "      <td>visa</td>\n",
       "      <td>credit</td>\n",
       "      <td>gmail.com</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Found</td>\n",
       "      <td>...</td>\n",
       "      <td>Found</td>\n",
       "      <td>Mac OS X 10_13_3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>chrome 65.0</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>desktop</td>\n",
       "      <td>MacOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472430</th>\n",
       "      <td>W</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>debit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>id_29_Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472431</th>\n",
       "      <td>W</td>\n",
       "      <td>visa</td>\n",
       "      <td>credit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>id_29_Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472432</th>\n",
       "      <td>C</td>\n",
       "      <td>visa</td>\n",
       "      <td>debit</td>\n",
       "      <td>gmail.com</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>New</td>\n",
       "      <td>...</td>\n",
       "      <td>NotFound</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>chrome 65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>desktop</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>472433 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ProductCD       card4   card6 R_emaildomain  M5  M7  M8  M9  id_12  \\\n",
       "0              W    discover  credit           NaN   1   0   0   0      0   \n",
       "1              W  mastercard  credit           NaN   1   0   0   0      0   \n",
       "2              W        visa   debit           NaN   1   1   1   1      0   \n",
       "3              W  mastercard   debit           NaN   1   0   0   0      0   \n",
       "4              H  mastercard  credit           NaN   0   0   0   0      1   \n",
       "...          ...         ...     ...           ...  ..  ..  ..  ..    ...   \n",
       "472428         W        visa   debit           NaN   1   0   0   0      0   \n",
       "472429         H        visa  credit     gmail.com   0   0   0   0      1   \n",
       "472430         W  mastercard   debit           NaN   1   0   0   0      0   \n",
       "472431         W        visa  credit           NaN   1   0   0   0      0   \n",
       "472432         C        visa   debit     gmail.com   0   0   0   0      1   \n",
       "\n",
       "          id_15  ...          id_29             id_30  id_35 id_36 id_37  \\\n",
       "0       Unknown  ...  id_29_Unknown               NaN      0     0     0   \n",
       "1       Unknown  ...  id_29_Unknown               NaN      0     0     0   \n",
       "2       Unknown  ...  id_29_Unknown               NaN      0     0     0   \n",
       "3       Unknown  ...  id_29_Unknown               NaN      0     0     0   \n",
       "4           New  ...       NotFound       Android 7.0      1     1     1   \n",
       "...         ...  ...            ...               ...    ...   ...   ...   \n",
       "472428  Unknown  ...  id_29_Unknown               NaN      0     0     0   \n",
       "472429    Found  ...          Found  Mac OS X 10_13_3      1     1     1   \n",
       "472430  Unknown  ...  id_29_Unknown               NaN      0     0     0   \n",
       "472431  Unknown  ...  id_29_Unknown               NaN      0     0     0   \n",
       "472432      New  ...       NotFound               NaN      1     1     1   \n",
       "\n",
       "       id_38                id_31      id_33  DeviceType  \\\n",
       "0          0                  NaN        NaN         NaN   \n",
       "1          0                  NaN        NaN         NaN   \n",
       "2          0                  NaN        NaN         NaN   \n",
       "3          0                  NaN        NaN         NaN   \n",
       "4          1  samsung browser 6.2  2220x1080      mobile   \n",
       "...      ...                  ...        ...         ...   \n",
       "472428     0                  NaN        NaN         NaN   \n",
       "472429     1          chrome 65.0   1440x900     desktop   \n",
       "472430     0                  NaN        NaN         NaN   \n",
       "472431     0                  NaN        NaN         NaN   \n",
       "472432     1          chrome 65.0        NaN     desktop   \n",
       "\n",
       "                           DeviceInfo  \n",
       "0                                 NaN  \n",
       "1                                 NaN  \n",
       "2                                 NaN  \n",
       "3                                 NaN  \n",
       "4       SAMSUNG SM-G892A Build/NRD90M  \n",
       "...                               ...  \n",
       "472428                            NaN  \n",
       "472429                          MacOS  \n",
       "472430                            NaN  \n",
       "472431                            NaN  \n",
       "472432                            NaN  \n",
       "\n",
       "[472433 rows x 24 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "micf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Para estas columnas voy a usar mean-encoding\n",
    "micf['id_15'].fillna('Unknown',inplace=True)\n",
    "micf['id_29'].fillna('id_29_Unknown',inplace=True)\n",
    "micf['id_28'].fillna('id_28_Unknown',inplace=True)\n",
    "micf['id_16'].fillna('id_16_Unknown',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-96-9487c01df59b>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  micf['id_27'] = micf['id_27'].isnull().map({True:0,False:1})\n",
      "<ipython-input-96-9487c01df59b>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  micf['id_36'] = micf['id_36'].isnull().map({True:0,False:1})\n",
      "<ipython-input-96-9487c01df59b>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  micf['id_38'] = micf['id_38'].isnull().map({True:0,False:1})\n",
      "<ipython-input-96-9487c01df59b>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  micf['id_37'] = micf['id_37'].isnull().map({True:0,False:1})\n",
      "<ipython-input-96-9487c01df59b>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  micf['id_12'] = micf['id_12'].isnull().map({True:0,False:1})\n",
      "<ipython-input-96-9487c01df59b>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  micf['M9'] = micf['M9'].isnull().map({True:0,False:1})\n",
      "<ipython-input-96-9487c01df59b>:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  micf['M8'] = micf['M8'].isnull().map({True:0,False:1})\n",
      "<ipython-input-96-9487c01df59b>:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  micf['M7'] = micf['M7'].isnull().map({True:0,False:1})\n",
      "<ipython-input-96-9487c01df59b>:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  micf['M5'] = micf['M5'].isnull().map({True:0,False:1})\n",
      "<ipython-input-96-9487c01df59b>:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  micf['id_35'] = micf['id_35'].isnull().map({True:0,False:1})\n"
     ]
    }
   ],
   "source": [
    "# Para los features de True-False, dado la cantidad de NaNs, pongo 1 si hay valor y 0 sino\n",
    "micf['id_27'] = micf['id_27'].isnull().map({True:0,False:1})\n",
    "micf['id_36'] = micf['id_36'].isnull().map({True:0,False:1})\n",
    "micf['id_38'] = micf['id_38'].isnull().map({True:0,False:1})\n",
    "micf['id_37'] = micf['id_37'].isnull().map({True:0,False:1})\n",
    "micf['id_12'] = micf['id_12'].isnull().map({True:0,False:1})\n",
    "micf['M9'] = micf['M9'].isnull().map({True:0,False:1})\n",
    "micf['M8'] = micf['M8'].isnull().map({True:0,False:1})\n",
    "micf['M7'] = micf['M7'].isnull().map({True:0,False:1})\n",
    "micf['M5'] = micf['M5'].isnull().map({True:0,False:1})\n",
    "micf['id_35'] = micf['id_35'].isnull().map({True:0,False:1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-32-268b9c815d5a>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  micf['M1'] = micf['M1'].isnull().map({True:0,False:1})\n",
      "<ipython-input-32-268b9c815d5a>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  micf['M2'] = micf['M2'].isnull().map({True:0,False:1})\n",
      "<ipython-input-32-268b9c815d5a>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  micf['M3'] = micf['M3'].isnull().map({True:0,False:1})\n"
     ]
    }
   ],
   "source": [
    "#micf['M1'] = micf['M1'].isnull().map({True:0,False:1})\n",
    "#micf['M2'] = micf['M2'].isnull().map({True:0,False:1})\n",
    "#micf['M3'] = micf['M3'].isnull().map({True:0,False:1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-35-359e3c4be80b>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  micf['M6'] = micf['M6'].isnull().map({True:0,False:1})\n"
     ]
    }
   ],
   "source": [
    "#micf['M6'] = micf['M6'].isnull().map({True:0,False:1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-38-729ea7ef1fa2>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  micf['id_34'] = micf['id_34'].isnull().map({True:0,False:1})\n"
     ]
    }
   ],
   "source": [
    "#micf['id_34'] = micf['id_34'].isnull().map({True:0,False:1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Las columnas que tendran one hot\n",
    "count_vectorized_columns = ['R_emaildomain','id_30','id_31','DeviceInfo']\n",
    "\n",
    "# Primero le cambio los NaNs por desconocido_columna\n",
    "for c in micf.columns:\n",
    "    if c in count_vectorized_columns:\n",
    "        micf[c].fillna('Desconocido_'+c,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ProductCD</th>\n",
       "      <th>card4</th>\n",
       "      <th>card6</th>\n",
       "      <th>R_emaildomain</th>\n",
       "      <th>M5</th>\n",
       "      <th>M7</th>\n",
       "      <th>M8</th>\n",
       "      <th>M9</th>\n",
       "      <th>id_12</th>\n",
       "      <th>id_15</th>\n",
       "      <th>...</th>\n",
       "      <th>id_29</th>\n",
       "      <th>id_30</th>\n",
       "      <th>id_35</th>\n",
       "      <th>id_36</th>\n",
       "      <th>id_37</th>\n",
       "      <th>id_38</th>\n",
       "      <th>id_31</th>\n",
       "      <th>id_33</th>\n",
       "      <th>DeviceType</th>\n",
       "      <th>DeviceInfo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>W</td>\n",
       "      <td>discover</td>\n",
       "      <td>credit</td>\n",
       "      <td>Desconocido_R_emaildomain</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>id_29_Unknown</td>\n",
       "      <td>Desconocido_id_30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Desconocido_id_31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Desconocido_DeviceInfo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>W</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>credit</td>\n",
       "      <td>Desconocido_R_emaildomain</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>id_29_Unknown</td>\n",
       "      <td>Desconocido_id_30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Desconocido_id_31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Desconocido_DeviceInfo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>W</td>\n",
       "      <td>visa</td>\n",
       "      <td>debit</td>\n",
       "      <td>Desconocido_R_emaildomain</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>id_29_Unknown</td>\n",
       "      <td>Desconocido_id_30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Desconocido_id_31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Desconocido_DeviceInfo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>W</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>debit</td>\n",
       "      <td>Desconocido_R_emaildomain</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>id_29_Unknown</td>\n",
       "      <td>Desconocido_id_30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Desconocido_id_31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Desconocido_DeviceInfo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>credit</td>\n",
       "      <td>Desconocido_R_emaildomain</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>New</td>\n",
       "      <td>...</td>\n",
       "      <td>NotFound</td>\n",
       "      <td>Android 7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>samsung browser 6.2</td>\n",
       "      <td>2220x1080</td>\n",
       "      <td>mobile</td>\n",
       "      <td>SAMSUNG SM-G892A Build/NRD90M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  ProductCD       card4   card6              R_emaildomain  M5  M7  M8  M9  \\\n",
       "0         W    discover  credit  Desconocido_R_emaildomain   1   0   0   0   \n",
       "1         W  mastercard  credit  Desconocido_R_emaildomain   1   0   0   0   \n",
       "2         W        visa   debit  Desconocido_R_emaildomain   1   1   1   1   \n",
       "3         W  mastercard   debit  Desconocido_R_emaildomain   1   0   0   0   \n",
       "4         H  mastercard  credit  Desconocido_R_emaildomain   0   0   0   0   \n",
       "\n",
       "   id_12    id_15  ...          id_29              id_30  id_35 id_36 id_37  \\\n",
       "0      0  Unknown  ...  id_29_Unknown  Desconocido_id_30      0     0     0   \n",
       "1      0  Unknown  ...  id_29_Unknown  Desconocido_id_30      0     0     0   \n",
       "2      0  Unknown  ...  id_29_Unknown  Desconocido_id_30      0     0     0   \n",
       "3      0  Unknown  ...  id_29_Unknown  Desconocido_id_30      0     0     0   \n",
       "4      1      New  ...       NotFound        Android 7.0      1     1     1   \n",
       "\n",
       "  id_38                id_31      id_33  DeviceType  \\\n",
       "0     0    Desconocido_id_31        NaN         NaN   \n",
       "1     0    Desconocido_id_31        NaN         NaN   \n",
       "2     0    Desconocido_id_31        NaN         NaN   \n",
       "3     0    Desconocido_id_31        NaN         NaN   \n",
       "4     1  samsung browser 6.2  2220x1080      mobile   \n",
       "\n",
       "                      DeviceInfo  \n",
       "0         Desconocido_DeviceInfo  \n",
       "1         Desconocido_DeviceInfo  \n",
       "2         Desconocido_DeviceInfo  \n",
       "3         Desconocido_DeviceInfo  \n",
       "4  SAMSUNG SM-G892A Build/NRD90M  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "micf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-99-468046d689f2>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  micf['DeviceType'] = micf['DeviceType'].fillna('desconocido')\n"
     ]
    }
   ],
   "source": [
    "micf['DeviceType'] = micf['DeviceType'].fillna('desconocido')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_vectorized_dic = {}\n",
    "copy_mcf = micf.copy()\n",
    "for c in copy_mcf.columns:\n",
    "    if c in count_vectorized_columns:\n",
    "        cv = CountVectorizer(max_features=20)\n",
    "        encoded =  cv.fit_transform(copy_mcf[c])\n",
    "        count_vectorized_dic[c] = cv\n",
    "        encoded_dt = pd.DataFrame(encoded.toarray(),columns= cv.get_feature_names_out())\n",
    "        copy_mcf = pd.concat([copy_mcf,encoded_dt],axis=1)\n",
    "        del copy_mcf[c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ProductCD</th>\n",
       "      <th>card4</th>\n",
       "      <th>card6</th>\n",
       "      <th>M5</th>\n",
       "      <th>M7</th>\n",
       "      <th>M8</th>\n",
       "      <th>M9</th>\n",
       "      <th>id_12</th>\n",
       "      <th>id_15</th>\n",
       "      <th>id_16</th>\n",
       "      <th>...</th>\n",
       "      <th>mmb29k</th>\n",
       "      <th>mmb29m</th>\n",
       "      <th>moto</th>\n",
       "      <th>mra58k</th>\n",
       "      <th>nrd90m</th>\n",
       "      <th>rv</th>\n",
       "      <th>samsung</th>\n",
       "      <th>sm</th>\n",
       "      <th>trident</th>\n",
       "      <th>windows</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>W</td>\n",
       "      <td>discover</td>\n",
       "      <td>credit</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>id_16_Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>W</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>credit</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>id_16_Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>W</td>\n",
       "      <td>visa</td>\n",
       "      <td>debit</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>id_16_Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>W</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>debit</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>id_16_Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>credit</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>New</td>\n",
       "      <td>NotFound</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  ProductCD       card4   card6  M5  M7  M8  M9  id_12    id_15  \\\n",
       "0         W    discover  credit   1   0   0   0      0  Unknown   \n",
       "1         W  mastercard  credit   1   0   0   0      0  Unknown   \n",
       "2         W        visa   debit   1   1   1   1      0  Unknown   \n",
       "3         W  mastercard   debit   1   0   0   0      0  Unknown   \n",
       "4         H  mastercard  credit   0   0   0   0      1      New   \n",
       "\n",
       "           id_16  ... mmb29k  mmb29m moto mra58k  nrd90m  rv  samsung  sm  \\\n",
       "0  id_16_Unknown  ...      0       0    0      0       0   0        0   0   \n",
       "1  id_16_Unknown  ...      0       0    0      0       0   0        0   0   \n",
       "2  id_16_Unknown  ...      0       0    0      0       0   0        0   0   \n",
       "3  id_16_Unknown  ...      0       0    0      0       0   0        0   0   \n",
       "4       NotFound  ...      0       0    0      0       1   0        1   1   \n",
       "\n",
       "  trident windows  \n",
       "0       0       0  \n",
       "1       0       0  \n",
       "2       0       0  \n",
       "3       0       0  \n",
       "4       0       0  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy_mcf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#copy_mcf['M4'] = copy_mcf['M4'].fillna('desconocido_M4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy_mcf['id_33'] = copy_mcf['id_33'].fillna('id_33_desconocido')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Comienzo a hacer los hot encodings.\n",
    "hot_encodings = ['ProductCD', 'card4','card6','DeviceType','id_23']\n",
    "hot_encodings_dic = {}\n",
    "for c in copy_mcf.columns:\n",
    "    if c in hot_encodings:\n",
    "        hot_encoder = OneHotEncoder(handle_unknown='ignore')\n",
    "        encoded =  hot_encoder.fit_transform(copy_mcf[[c]].astype(str)).todense().astype(int)\n",
    "        hot_encodings_dic[c] = hot_encoder\n",
    "        column_encoded = pd.DataFrame(encoded).add_prefix('ec_'+c)\n",
    "        copy_mcf = pd.concat([copy_mcf,column_encoded],axis=1)\n",
    "        del copy_mcf[c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>M5</th>\n",
       "      <th>M7</th>\n",
       "      <th>M8</th>\n",
       "      <th>M9</th>\n",
       "      <th>id_12</th>\n",
       "      <th>id_15</th>\n",
       "      <th>id_16</th>\n",
       "      <th>id_27</th>\n",
       "      <th>id_28</th>\n",
       "      <th>id_29</th>\n",
       "      <th>...</th>\n",
       "      <th>ec_card62</th>\n",
       "      <th>ec_card63</th>\n",
       "      <th>ec_card64</th>\n",
       "      <th>ec_id_230</th>\n",
       "      <th>ec_id_231</th>\n",
       "      <th>ec_id_232</th>\n",
       "      <th>ec_id_233</th>\n",
       "      <th>ec_DeviceType0</th>\n",
       "      <th>ec_DeviceType1</th>\n",
       "      <th>ec_DeviceType2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>id_16_Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>id_28_Unknown</td>\n",
       "      <td>id_29_Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>id_16_Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>id_28_Unknown</td>\n",
       "      <td>id_29_Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>id_16_Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>id_28_Unknown</td>\n",
       "      <td>id_29_Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>id_16_Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>id_28_Unknown</td>\n",
       "      <td>id_29_Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>New</td>\n",
       "      <td>NotFound</td>\n",
       "      <td>0</td>\n",
       "      <td>New</td>\n",
       "      <td>NotFound</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 117 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   M5  M7  M8  M9  id_12    id_15          id_16  id_27          id_28  \\\n",
       "0   1   0   0   0      0  Unknown  id_16_Unknown      0  id_28_Unknown   \n",
       "1   1   0   0   0      0  Unknown  id_16_Unknown      0  id_28_Unknown   \n",
       "2   1   1   1   1      0  Unknown  id_16_Unknown      0  id_28_Unknown   \n",
       "3   1   0   0   0      0  Unknown  id_16_Unknown      0  id_28_Unknown   \n",
       "4   0   0   0   0      1      New       NotFound      0            New   \n",
       "\n",
       "           id_29  ...  ec_card62  ec_card63  ec_card64  ec_id_230 ec_id_231  \\\n",
       "0  id_29_Unknown  ...          1          0          0          1         0   \n",
       "1  id_29_Unknown  ...          1          0          0          1         0   \n",
       "2  id_29_Unknown  ...          0          1          0          1         0   \n",
       "3  id_29_Unknown  ...          0          1          0          1         0   \n",
       "4       NotFound  ...          1          0          0          1         0   \n",
       "\n",
       "   ec_id_232  ec_id_233  ec_DeviceType0  ec_DeviceType1  ec_DeviceType2  \n",
       "0          0          0               1               0               0  \n",
       "1          0          0               1               0               0  \n",
       "2          0          0               1               0               0  \n",
       "3          0          0               1               0               0  \n",
       "4          0          0               0               0               1  \n",
       "\n",
       "[5 rows x 117 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy_mcf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id_33_desconocido', '1920x1080', '1366x768', '1334x750', '2208x1242',\n",
       "       '1440x900', '2048x1536', '1600x900', '1280x800', '2560x1600'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_id_33_appearances = copy_mcf['id_33'].value_counts().index[:10]\n",
    "top_id_33_appearances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy_mcf['isFraud'] = train.isFraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy_mcf['id_33'] = copy_mcf['id_33'].map({'1920x1080':'1920x1080', '1366x768':'1366x768', '1334x750':'1334x750', '2208x1242':'2208x1242', '1440x900':'1440x900','2048x1536':'2048x1536', '1600x900':'1600x900', '1280x800':'1280x800', '2560x1600':'2560x1600', 'id_33_desconocido':'id_33_desconocido'})\n",
    "copy_mcf['id_33'] = copy_mcf['id_33'].fillna('otros')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1280x800': 0.039851222104144525,\n",
       " '1334x750': 0.05944305605141021,\n",
       " '1366x768': 0.06537711800869696,\n",
       " '1440x900': 0.024261603375527425,\n",
       " '1600x900': 0.02065848934796643,\n",
       " '1920x1080': 0.02946058091286307,\n",
       " '2048x1536': 0.017363344051446947,\n",
       " '2208x1242': 0.09457513202112337,\n",
       " '2560x1600': 0.022552255225522552,\n",
       " 'id_33_desconocido': 0.03357849106229604,\n",
       " 'otros': 0.052102695167286245}"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_encoded_id_33 = copy_mcf.groupby(['id_33'])['isFraud'].mean().to_dict()\n",
    "mean_encoded_id_33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>M5</th>\n",
       "      <th>M7</th>\n",
       "      <th>M8</th>\n",
       "      <th>M9</th>\n",
       "      <th>id_12</th>\n",
       "      <th>id_15</th>\n",
       "      <th>id_16</th>\n",
       "      <th>id_27</th>\n",
       "      <th>id_28</th>\n",
       "      <th>id_29</th>\n",
       "      <th>...</th>\n",
       "      <th>ec_card63</th>\n",
       "      <th>ec_card64</th>\n",
       "      <th>ec_id_230</th>\n",
       "      <th>ec_id_231</th>\n",
       "      <th>ec_id_232</th>\n",
       "      <th>ec_id_233</th>\n",
       "      <th>ec_DeviceType0</th>\n",
       "      <th>ec_DeviceType1</th>\n",
       "      <th>ec_DeviceType2</th>\n",
       "      <th>isFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>id_16_Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>id_28_Unknown</td>\n",
       "      <td>id_29_Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>id_16_Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>id_28_Unknown</td>\n",
       "      <td>id_29_Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>id_16_Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>id_28_Unknown</td>\n",
       "      <td>id_29_Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>id_16_Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>id_28_Unknown</td>\n",
       "      <td>id_29_Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>New</td>\n",
       "      <td>NotFound</td>\n",
       "      <td>0</td>\n",
       "      <td>New</td>\n",
       "      <td>NotFound</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 118 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   M5  M7  M8  M9  id_12    id_15          id_16  id_27          id_28  \\\n",
       "0   1   0   0   0      0  Unknown  id_16_Unknown      0  id_28_Unknown   \n",
       "1   1   0   0   0      0  Unknown  id_16_Unknown      0  id_28_Unknown   \n",
       "2   1   1   1   1      0  Unknown  id_16_Unknown      0  id_28_Unknown   \n",
       "3   1   0   0   0      0  Unknown  id_16_Unknown      0  id_28_Unknown   \n",
       "4   0   0   0   0      1      New       NotFound      0            New   \n",
       "\n",
       "           id_29  ...  ec_card63  ec_card64  ec_id_230  ec_id_231  ec_id_232  \\\n",
       "0  id_29_Unknown  ...          0          0          1          0          0   \n",
       "1  id_29_Unknown  ...          0          0          1          0          0   \n",
       "2  id_29_Unknown  ...          1          0          1          0          0   \n",
       "3  id_29_Unknown  ...          1          0          1          0          0   \n",
       "4       NotFound  ...          0          0          1          0          0   \n",
       "\n",
       "   ec_id_233  ec_DeviceType0  ec_DeviceType1  ec_DeviceType2  isFraud  \n",
       "0          0               1               0               0        0  \n",
       "1          0               1               0               0        0  \n",
       "2          0               1               0               0        0  \n",
       "3          0               1               0               0        0  \n",
       "4          0               0               0               1        0  \n",
       "\n",
       "[5 rows x 118 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cambio la columna a mean-encoding\n",
    "copy_mcf['id_33'] = copy_mcf.groupby(['id_33'])['isFraud'].transform('mean')\n",
    "copy_mcf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ahora me quedan hacer los means encodingas para cada columna restantes\n",
    "ordinal = ['id_15','id_16','id_28','id_29']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unknown    364264\n",
      "Found       55413\n",
      "New         52756\n",
      "Name: id_15, dtype: int64\n",
      "id_16_Unknown    364264\n",
      "Found             54090\n",
      "NotFound          54079\n",
      "Name: id_16, dtype: int64\n",
      "id_28_Unknown    354636\n",
      "Found             62843\n",
      "New               54954\n",
      "Name: id_28, dtype: int64\n",
      "id_29_Unknown    354636\n",
      "Found             61610\n",
      "NotFound          56187\n",
      "Name: id_29, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for i in ordinal:\n",
    "    print(copy_mcf[i].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hago un ordinal enconding para aquellas columnas que no esten en labels_hot_encoding\n",
    "ordinal_dic = {}\n",
    "for c in copy_mcf.columns:\n",
    "    if c in ordinal:\n",
    "        ordinal_encoder = OrdinalEncoder(handle_unknown='use_encoded_value',unknown_value=-1)\n",
    "        ordinal_dic[c] = ordinal_encoder\n",
    "        copy_mcf[c] = ordinal_encoder.fit_transform(copy_mcf[c].astype(str).values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "      <th></th>\n",
       "      <th>M5</th>\n",
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       "      <th>...</th>\n",
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       "      <th>ec_card64</th>\n",
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       "      <th>ec_id_232</th>\n",
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       "      <th>ec_DeviceType0</th>\n",
       "      <th>ec_DeviceType1</th>\n",
       "      <th>ec_DeviceType2</th>\n",
       "      <th>isFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>472432</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>472433 rows × 118 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        M5  M7  M8  M9  id_12  id_15  id_16  id_27  id_28  id_29  ...  \\\n",
       "0        1   0   0   0      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "1        1   0   0   0      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "2        1   1   1   1      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "3        1   0   0   0      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "4        0   0   0   0      1    1.0    1.0      0    1.0    1.0  ...   \n",
       "...     ..  ..  ..  ..    ...    ...    ...    ...    ...    ...  ...   \n",
       "472428   1   0   0   0      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "472429   0   0   0   0      1    0.0    0.0      0    0.0    0.0  ...   \n",
       "472430   1   0   0   0      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "472431   1   0   0   0      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "472432   0   0   0   0      1    1.0    1.0      0    1.0    1.0  ...   \n",
       "\n",
       "        ec_card63  ec_card64  ec_id_230  ec_id_231  ec_id_232  ec_id_233  \\\n",
       "0               0          0          1          0          0          0   \n",
       "1               0          0          1          0          0          0   \n",
       "2               1          0          1          0          0          0   \n",
       "3               1          0          1          0          0          0   \n",
       "4               0          0          1          0          0          0   \n",
       "...           ...        ...        ...        ...        ...        ...   \n",
       "472428          1          0          1          0          0          0   \n",
       "472429          0          0          1          0          0          0   \n",
       "472430          1          0          1          0          0          0   \n",
       "472431          0          0          1          0          0          0   \n",
       "472432          1          0          1          0          0          0   \n",
       "\n",
       "        ec_DeviceType0  ec_DeviceType1  ec_DeviceType2  isFraud  \n",
       "0                    1               0               0        0  \n",
       "1                    1               0               0        0  \n",
       "2                    1               0               0        0  \n",
       "3                    1               0               0        0  \n",
       "4                    0               0               1        0  \n",
       "...                ...             ...             ...      ...  \n",
       "472428               1               0               0        0  \n",
       "472429               0               1               0        0  \n",
       "472430               1               0               0        0  \n",
       "472431               1               0               0        0  \n",
       "472432               0               1               0        1  \n",
       "\n",
       "[472433 rows x 118 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy_mcf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "del minf['TransactionID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:6245: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    }
   ],
   "source": [
    "for c in minf.columns:\n",
    "    minf[c].fillna(minf[c].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
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       "        TransactionDT  TransactionAmt  card1       card2  card3  card5  \\\n",
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       "472431       12192842          49.000  13749  321.000000  150.0  226.0   \n",
       "472432       12192900          33.261   9300  103.000000  185.0  138.0   \n",
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       "2       14.18649  351.508264  401.538336  371.406287  16.008425  12.763924   \n",
       "3       14.18649  351.508264  401.538336  371.406287  16.008425  12.763924   \n",
       "4       14.18649  542.000000  144.000000  371.406287  16.008425  12.763924   \n",
       "...          ...         ...         ...         ...        ...        ...   \n",
       "472428  14.18649  351.508264  401.538336  371.406287  16.008425  12.763924   \n",
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       "472432  15.00000  427.000000  417.000000  371.406287  16.008425  12.763924   \n",
       "\n",
       "             id_25       id_26      id_32  \n",
       "0       330.097504  148.262011  26.596037  \n",
       "1       330.097504  148.262011  26.596037  \n",
       "2       330.097504  148.262011  26.596037  \n",
       "3       330.097504  148.262011  26.596037  \n",
       "4       330.097504  148.262011  32.000000  \n",
       "...            ...         ...        ...  \n",
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       "472432  330.097504  148.262011  26.596037  \n",
       "\n",
       "[472433 rows x 401 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = pd.concat([copy_mcf,minf],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
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       "      <th>M9</th>\n",
       "      <th>id_12</th>\n",
       "      <th>id_15</th>\n",
       "      <th>id_16</th>\n",
       "      <th>id_27</th>\n",
       "      <th>id_28</th>\n",
       "      <th>id_29</th>\n",
       "      <th>...</th>\n",
       "      <th>id_17</th>\n",
       "      <th>id_18</th>\n",
       "      <th>id_19</th>\n",
       "      <th>id_20</th>\n",
       "      <th>id_21</th>\n",
       "      <th>id_22</th>\n",
       "      <th>id_24</th>\n",
       "      <th>id_25</th>\n",
       "      <th>id_26</th>\n",
       "      <th>id_32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>188.693151</td>\n",
       "      <td>14.18649</td>\n",
       "      <td>351.508264</td>\n",
       "      <td>401.538336</td>\n",
       "      <td>371.406287</td>\n",
       "      <td>16.008425</td>\n",
       "      <td>12.763924</td>\n",
       "      <td>330.097504</td>\n",
       "      <td>148.262011</td>\n",
       "      <td>26.596037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>188.693151</td>\n",
       "      <td>14.18649</td>\n",
       "      <td>351.508264</td>\n",
       "      <td>401.538336</td>\n",
       "      <td>371.406287</td>\n",
       "      <td>16.008425</td>\n",
       "      <td>12.763924</td>\n",
       "      <td>330.097504</td>\n",
       "      <td>148.262011</td>\n",
       "      <td>26.596037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>188.693151</td>\n",
       "      <td>14.18649</td>\n",
       "      <td>351.508264</td>\n",
       "      <td>401.538336</td>\n",
       "      <td>371.406287</td>\n",
       "      <td>16.008425</td>\n",
       "      <td>12.763924</td>\n",
       "      <td>330.097504</td>\n",
       "      <td>148.262011</td>\n",
       "      <td>26.596037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>188.693151</td>\n",
       "      <td>14.18649</td>\n",
       "      <td>351.508264</td>\n",
       "      <td>401.538336</td>\n",
       "      <td>371.406287</td>\n",
       "      <td>16.008425</td>\n",
       "      <td>12.763924</td>\n",
       "      <td>330.097504</td>\n",
       "      <td>148.262011</td>\n",
       "      <td>26.596037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>14.18649</td>\n",
       "      <td>542.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>371.406287</td>\n",
       "      <td>16.008425</td>\n",
       "      <td>12.763924</td>\n",
       "      <td>330.097504</td>\n",
       "      <td>148.262011</td>\n",
       "      <td>32.000000</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>188.693151</td>\n",
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       "      <td>351.508264</td>\n",
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       "      <td>12.763924</td>\n",
       "      <td>330.097504</td>\n",
       "      <td>148.262011</td>\n",
       "      <td>26.596037</td>\n",
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       "    <tr>\n",
       "      <th>472429</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>166.000000</td>\n",
       "      <td>14.18649</td>\n",
       "      <td>312.000000</td>\n",
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       "      <td>148.262011</td>\n",
       "      <td>24.000000</td>\n",
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       "    <tr>\n",
       "      <th>472430</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>188.693151</td>\n",
       "      <td>14.18649</td>\n",
       "      <td>351.508264</td>\n",
       "      <td>401.538336</td>\n",
       "      <td>371.406287</td>\n",
       "      <td>16.008425</td>\n",
       "      <td>12.763924</td>\n",
       "      <td>330.097504</td>\n",
       "      <td>148.262011</td>\n",
       "      <td>26.596037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472431</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>188.693151</td>\n",
       "      <td>14.18649</td>\n",
       "      <td>351.508264</td>\n",
       "      <td>401.538336</td>\n",
       "      <td>371.406287</td>\n",
       "      <td>16.008425</td>\n",
       "      <td>12.763924</td>\n",
       "      <td>330.097504</td>\n",
       "      <td>148.262011</td>\n",
       "      <td>26.596037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472432</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>15.00000</td>\n",
       "      <td>427.000000</td>\n",
       "      <td>417.000000</td>\n",
       "      <td>371.406287</td>\n",
       "      <td>16.008425</td>\n",
       "      <td>12.763924</td>\n",
       "      <td>330.097504</td>\n",
       "      <td>148.262011</td>\n",
       "      <td>26.596037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>472433 rows × 519 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        M5  M7  M8  M9  id_12  id_15  id_16  id_27  id_28  id_29  ...  \\\n",
       "0        1   0   0   0      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "1        1   0   0   0      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "2        1   1   1   1      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "3        1   0   0   0      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "4        0   0   0   0      1    1.0    1.0      0    1.0    1.0  ...   \n",
       "...     ..  ..  ..  ..    ...    ...    ...    ...    ...    ...  ...   \n",
       "472428   1   0   0   0      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "472429   0   0   0   0      1    0.0    0.0      0    0.0    0.0  ...   \n",
       "472430   1   0   0   0      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "472431   1   0   0   0      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "472432   0   0   0   0      1    1.0    1.0      0    1.0    1.0  ...   \n",
       "\n",
       "             id_17     id_18       id_19       id_20       id_21      id_22  \\\n",
       "0       188.693151  14.18649  351.508264  401.538336  371.406287  16.008425   \n",
       "1       188.693151  14.18649  351.508264  401.538336  371.406287  16.008425   \n",
       "2       188.693151  14.18649  351.508264  401.538336  371.406287  16.008425   \n",
       "3       188.693151  14.18649  351.508264  401.538336  371.406287  16.008425   \n",
       "4       166.000000  14.18649  542.000000  144.000000  371.406287  16.008425   \n",
       "...            ...       ...         ...         ...         ...        ...   \n",
       "472428  188.693151  14.18649  351.508264  401.538336  371.406287  16.008425   \n",
       "472429  166.000000  14.18649  312.000000  597.000000  371.406287  16.008425   \n",
       "472430  188.693151  14.18649  351.508264  401.538336  371.406287  16.008425   \n",
       "472431  188.693151  14.18649  351.508264  401.538336  371.406287  16.008425   \n",
       "472432  225.000000  15.00000  427.000000  417.000000  371.406287  16.008425   \n",
       "\n",
       "            id_24       id_25       id_26      id_32  \n",
       "0       12.763924  330.097504  148.262011  26.596037  \n",
       "1       12.763924  330.097504  148.262011  26.596037  \n",
       "2       12.763924  330.097504  148.262011  26.596037  \n",
       "3       12.763924  330.097504  148.262011  26.596037  \n",
       "4       12.763924  330.097504  148.262011  32.000000  \n",
       "...           ...         ...         ...        ...  \n",
       "472428  12.763924  330.097504  148.262011  26.596037  \n",
       "472429  12.763924  330.097504  148.262011  24.000000  \n",
       "472430  12.763924  330.097504  148.262011  26.596037  \n",
       "472431  12.763924  330.097504  148.262011  26.596037  \n",
       "472432  12.763924  330.097504  148.262011  26.596037  \n",
       "\n",
       "[472433 rows x 519 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Proceso de mappeo de validation.\n",
    "validation.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "del validation['index']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_categorical = validation[final_categorical_feature]\n",
    "#validation_numerical = validation[most_important_numeric_features]\n",
    "validation_numerical = validation[feature_numeric]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>DeviceType</th>\n",
       "      <th>DeviceInfo</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
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       "      <td>F</td>\n",
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       "      <td>mobile</td>\n",
       "      <td>F3311</td>\n",
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       "    <tr>\n",
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       "      <td>Windows</td>\n",
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       "      <th>3</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NotFound</td>\n",
       "      <td>New</td>\n",
       "      <td>...</td>\n",
       "      <td>NotFound</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>firefox 59.0</td>\n",
       "      <td>3200x1800</td>\n",
       "      <td>desktop</td>\n",
       "      <td>Windows</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>118103</th>\n",
       "      <td>W</td>\n",
       "      <td>visa</td>\n",
       "      <td>debit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118104</th>\n",
       "      <td>W</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>debit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118105</th>\n",
       "      <td>W</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>debit</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118106</th>\n",
       "      <td>W</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>debit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118107</th>\n",
       "      <td>W</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>credit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</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>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118108 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ProductCD       card4   card6 R_emaildomain   M5   M7   M8   M9  \\\n",
       "0              C        visa   debit     gmail.com  NaN  NaN  NaN  NaN   \n",
       "1              C        visa   debit    hotmail.fr  NaN  NaN  NaN  NaN   \n",
       "2              C        visa   debit   hotmail.com  NaN  NaN  NaN  NaN   \n",
       "3              C        visa   debit   hotmail.com  NaN  NaN  NaN  NaN   \n",
       "4              H        visa   debit           NaN  NaN  NaN  NaN  NaN   \n",
       "...          ...         ...     ...           ...  ...  ...  ...  ...   \n",
       "118103         W        visa   debit           NaN    T    F    F    T   \n",
       "118104         W  mastercard   debit           NaN    F    F    F    F   \n",
       "118105         W  mastercard   debit           NaN  NaN  NaN  NaN  NaN   \n",
       "118106         W  mastercard   debit           NaN    F  NaN  NaN  NaN   \n",
       "118107         W  mastercard  credit           NaN  NaN    F    F    F   \n",
       "\n",
       "           id_12  id_15  ...     id_29       id_30 id_35 id_36 id_37 id_38  \\\n",
       "0       NotFound    New  ...  NotFound         NaN     F     F     T     F   \n",
       "1       NotFound    New  ...  NotFound         NaN     F     F     T     F   \n",
       "2       NotFound  Found  ...     Found         NaN     F     F     T     F   \n",
       "3       NotFound  Found  ...     Found         NaN     F     F     T     F   \n",
       "4       NotFound    New  ...  NotFound  Windows 10     T     F     T     T   \n",
       "...          ...    ...  ...       ...         ...   ...   ...   ...   ...   \n",
       "118103       NaN    NaN  ...       NaN         NaN   NaN   NaN   NaN   NaN   \n",
       "118104       NaN    NaN  ...       NaN         NaN   NaN   NaN   NaN   NaN   \n",
       "118105       NaN    NaN  ...       NaN         NaN   NaN   NaN   NaN   NaN   \n",
       "118106       NaN    NaN  ...       NaN         NaN   NaN   NaN   NaN   NaN   \n",
       "118107       NaN    NaN  ...       NaN         NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "                             id_31      id_33 DeviceType DeviceInfo  \n",
       "0                      chrome 65.0        NaN    desktop        NaN  \n",
       "1       chrome generic for android        NaN     mobile      F3311  \n",
       "2                      chrome 65.0        NaN    desktop    Windows  \n",
       "3                      chrome 65.0        NaN    desktop    Windows  \n",
       "4                     firefox 59.0  3200x1800    desktop    Windows  \n",
       "...                            ...        ...        ...        ...  \n",
       "118103                         NaN        NaN        NaN        NaN  \n",
       "118104                         NaN        NaN        NaN        NaN  \n",
       "118105                         NaN        NaN        NaN        NaN  \n",
       "118106                         NaN        NaN        NaN        NaN  \n",
       "118107                         NaN        NaN        NaN        NaN  \n",
       "\n",
       "[118108 rows x 24 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Primero hago un mappeo de los features categoricos!\n",
    "# Debo resetear el index dado que corte mi data set al 80%\n",
    "validation_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "del validation_numerical['TransactionID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-137-402776476f98>:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_categorical['id_27'] = validation_categorical['id_27'].isnull().map({True:0,False:1})\n",
      "<ipython-input-137-402776476f98>:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_categorical['id_36'] = validation_categorical['id_36'].isnull().map({True:0,False:1})\n",
      "<ipython-input-137-402776476f98>:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_categorical['id_38'] = validation_categorical['id_38'].isnull().map({True:0,False:1})\n",
      "<ipython-input-137-402776476f98>:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_categorical['id_37'] = validation_categorical['id_37'].isnull().map({True:0,False:1})\n",
      "<ipython-input-137-402776476f98>:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_categorical['id_12'] = validation_categorical['id_12'].isnull().map({True:0,False:1})\n",
      "<ipython-input-137-402776476f98>:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_categorical['id_36'] = validation_categorical['id_36'].isnull().map({True:0,False:1})\n",
      "<ipython-input-137-402776476f98>:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_categorical['M9'] = validation_categorical['M9'].isnull().map({True:0,False:1})\n",
      "<ipython-input-137-402776476f98>:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_categorical['M8'] = validation_categorical['M8'].isnull().map({True:0,False:1})\n",
      "<ipython-input-137-402776476f98>:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_categorical['M7'] = validation_categorical['M7'].isnull().map({True:0,False:1})\n",
      "<ipython-input-137-402776476f98>:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_categorical['M5'] = validation_categorical['M5'].isnull().map({True:0,False:1})\n",
      "<ipython-input-137-402776476f98>:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_categorical['id_35'] = validation_categorical['id_35'].isnull().map({True:0,False:1})\n",
      "<ipython-input-137-402776476f98>:37: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_categorical['DeviceType'] = validation_categorical['DeviceType'].fillna('desconocido')\n",
      "<ipython-input-137-402776476f98>:39: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_categorical['id_33'] = validation_categorical['id_33'].fillna('id_33_desconocido')\n"
     ]
    }
   ],
   "source": [
    "validation_categorical['card4'].fillna('card4_desconocido',inplace=True)\n",
    "validation_categorical['card6'].fillna('card6_desconocido',inplace=True)\n",
    "\n",
    "# Para estas columnas voy a usar mean-encoding\n",
    "validation_categorical['id_15'].fillna('Unknown',inplace=True)\n",
    "validation_categorical['id_29'].fillna('id_29_Unknown',inplace=True)\n",
    "validation_categorical['id_28'].fillna('id_28_Unknown',inplace=True)\n",
    "validation_categorical['id_16'].fillna('id_16_Unknown',inplace=True)\n",
    "\n",
    "# Para los features de True-False, dado la cantidad de NaNs, pongo 1 si hay valor y 0 sino\n",
    "validation_categorical['id_27'] = validation_categorical['id_27'].isnull().map({True:0,False:1})\n",
    "validation_categorical['id_36'] = validation_categorical['id_36'].isnull().map({True:0,False:1})\n",
    "validation_categorical['id_38'] = validation_categorical['id_38'].isnull().map({True:0,False:1})\n",
    "validation_categorical['id_37'] = validation_categorical['id_37'].isnull().map({True:0,False:1})\n",
    "validation_categorical['id_12'] = validation_categorical['id_12'].isnull().map({True:0,False:1})\n",
    "validation_categorical['id_36'] = validation_categorical['id_36'].isnull().map({True:0,False:1})\n",
    "validation_categorical['M9'] = validation_categorical['M9'].isnull().map({True:0,False:1})\n",
    "validation_categorical['M8'] = validation_categorical['M8'].isnull().map({True:0,False:1})\n",
    "validation_categorical['M7'] = validation_categorical['M7'].isnull().map({True:0,False:1})\n",
    "validation_categorical['M5'] = validation_categorical['M5'].isnull().map({True:0,False:1})\n",
    "validation_categorical['id_35'] = validation_categorical['id_35'].isnull().map({True:0,False:1})\n",
    "#validation_categorical['M1'] = validation_categorical['M1'].isnull().map({True:0,False:1})\n",
    "#validation_categorical['M2'] = validation_categorical['M2'].isnull().map({True:0,False:1})\n",
    "#validation_categorical['M3'] = validation_categorical['M3'].isnull().map({True:0,False:1})\n",
    "#validation_categorical['M6'] = validation_categorical['M6'].isnull().map({True:0,False:1})\n",
    "#validation_categorical['id_34'] = validation_categorical['id_34'].isnull().map({True:0,False:1})\n",
    "\n",
    "# Ip\n",
    "validation_categorical['id_23'].fillna('IP_Desconocido',inplace=True)\n",
    "\n",
    "# Primero le cambio los NaNs por desconocido_columna\n",
    "for c in validation_categorical.columns:\n",
    "    if c in count_vectorized_columns:\n",
    "        validation_categorical[c].fillna('Desconocido_'+c,inplace=True)\n",
    "        \n",
    "# Algunos features restantes ... \n",
    "validation_categorical['DeviceType'] = validation_categorical['DeviceType'].fillna('desconocido')\n",
    "#validation_categorical['M4'] = validation_categorical['M4'].fillna('desconocido_M4')\n",
    "validation_categorical['id_33'] = validation_categorical['id_33'].fillna('id_33_desconocido')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ProductCD        0\n",
       "card4            0\n",
       "card6            0\n",
       "R_emaildomain    0\n",
       "M5               0\n",
       "M7               0\n",
       "M8               0\n",
       "M9               0\n",
       "id_12            0\n",
       "id_15            0\n",
       "id_16            0\n",
       "id_23            0\n",
       "id_27            0\n",
       "id_28            0\n",
       "id_29            0\n",
       "id_30            0\n",
       "id_35            0\n",
       "id_36            0\n",
       "id_37            0\n",
       "id_38            0\n",
       "id_31            0\n",
       "id_33            0\n",
       "DeviceType       0\n",
       "DeviceInfo       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_categorical.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Procesando:  id_15\n",
      "Procesando:  id_16\n",
      "Procesando:  id_28\n",
      "Procesando:  id_29\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-139-7e1934e07831>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_categorical[c] = ordinal_dic[c].transform(validation_categorical[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-139-7e1934e07831>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_categorical[c] = ordinal_dic[c].transform(validation_categorical[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-139-7e1934e07831>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_categorical[c] = ordinal_dic[c].transform(validation_categorical[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-139-7e1934e07831>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation_categorical[c] = ordinal_dic[c].transform(validation_categorical[c].astype(str).values.reshape(-1,1))\n"
     ]
    }
   ],
   "source": [
    "for c in validation_categorical.columns:\n",
    "    if c in ordinal_dic:\n",
    "        print(\"Procesando: \", c)\n",
    "        validation_categorical[c] = ordinal_dic[c].transform(validation_categorical[c].astype(str).values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Procesando:  ProductCD\n",
      "Procesando:  card4\n",
      "Procesando:  card6\n",
      "Procesando:  id_23\n",
      "Procesando:  DeviceType\n"
     ]
    }
   ],
   "source": [
    "for c in validation_categorical.columns:\n",
    "    if c in hot_encodings_dic:\n",
    "        print(\"Procesando: \", c)\n",
    "        encoded = hot_encodings_dic[c].transform(validation_categorical[[c]].astype(str)).todense().astype(int)\n",
    "        column_encoded = pd.DataFrame(encoded).add_prefix('ec_'+c)\n",
    "        validation_categorical = pd.concat([validation_categorical,column_encoded],axis=1)\n",
    "        del validation_categorical[c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R_emaildomain</th>\n",
       "      <th>M5</th>\n",
       "      <th>M7</th>\n",
       "      <th>M8</th>\n",
       "      <th>M9</th>\n",
       "      <th>id_12</th>\n",
       "      <th>id_15</th>\n",
       "      <th>id_16</th>\n",
       "      <th>id_27</th>\n",
       "      <th>id_28</th>\n",
       "      <th>...</th>\n",
       "      <th>ec_card62</th>\n",
       "      <th>ec_card63</th>\n",
       "      <th>ec_card64</th>\n",
       "      <th>ec_id_230</th>\n",
       "      <th>ec_id_231</th>\n",
       "      <th>ec_id_232</th>\n",
       "      <th>ec_id_233</th>\n",
       "      <th>ec_DeviceType0</th>\n",
       "      <th>ec_DeviceType1</th>\n",
       "      <th>ec_DeviceType2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gmail.com</td>\n",
       "      <td>0</td>\n",
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       "      <td>hotmail.fr</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hotmail.com</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>Desconocido_R_emaildomain</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>Desconocido_R_emaildomain</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118108 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    R_emaildomain  M5  M7  M8  M9  id_12  id_15  id_16  id_27  \\\n",
       "0                       gmail.com   0   0   0   0      1    1.0    1.0      0   \n",
       "1                      hotmail.fr   0   0   0   0      1    1.0    1.0      0   \n",
       "2                     hotmail.com   0   0   0   0      1    0.0    0.0      0   \n",
       "3                     hotmail.com   0   0   0   0      1    0.0    0.0      0   \n",
       "4       Desconocido_R_emaildomain   0   0   0   0      1    1.0    1.0      0   \n",
       "...                           ...  ..  ..  ..  ..    ...    ...    ...    ...   \n",
       "118103  Desconocido_R_emaildomain   1   1   1   1      0    2.0    2.0      0   \n",
       "118104  Desconocido_R_emaildomain   1   1   1   1      0    2.0    2.0      0   \n",
       "118105  Desconocido_R_emaildomain   0   0   0   0      0    2.0    2.0      0   \n",
       "118106  Desconocido_R_emaildomain   1   0   0   0      0    2.0    2.0      0   \n",
       "118107  Desconocido_R_emaildomain   0   1   1   1      0    2.0    2.0      0   \n",
       "\n",
       "        id_28  ...  ec_card62 ec_card63  ec_card64  ec_id_230  ec_id_231  \\\n",
       "0         1.0  ...          0         1          0          1          0   \n",
       "1         1.0  ...          0         1          0          1          0   \n",
       "2         0.0  ...          0         1          0          1          0   \n",
       "3         0.0  ...          0         1          0          1          0   \n",
       "4         1.0  ...          0         1          0          1          0   \n",
       "...       ...  ...        ...       ...        ...        ...        ...   \n",
       "118103    2.0  ...          0         1          0          1          0   \n",
       "118104    2.0  ...          0         1          0          1          0   \n",
       "118105    2.0  ...          0         1          0          1          0   \n",
       "118106    2.0  ...          0         1          0          1          0   \n",
       "118107    2.0  ...          1         0          0          1          0   \n",
       "\n",
       "        ec_id_232 ec_id_233 ec_DeviceType0 ec_DeviceType1  ec_DeviceType2  \n",
       "0               0         0              0              1               0  \n",
       "1               0         0              0              0               1  \n",
       "2               0         0              0              1               0  \n",
       "3               0         0              0              1               0  \n",
       "4               0         0              0              1               0  \n",
       "...           ...       ...            ...            ...             ...  \n",
       "118103          0         0              1              0               0  \n",
       "118104          0         0              1              0               0  \n",
       "118105          0         0              1              0               0  \n",
       "118106          0         0              1              0               0  \n",
       "118107          0         0              1              0               0  \n",
       "\n",
       "[118108 rows x 41 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Procesando:  R_emaildomain\n",
      "Procesando:  id_30\n",
      "Procesando:  id_31\n",
      "Procesando:  DeviceInfo\n"
     ]
    }
   ],
   "source": [
    "for c in validation_categorical.columns:\n",
    "    if c in count_vectorized_dic:\n",
    "        print(\"Procesando: \",c)\n",
    "        encoded = count_vectorized_dic[c].transform(validation_categorical[c])\n",
    "        encoded_dt = pd.DataFrame(encoded.toarray(),columns= count_vectorized_dic[c].get_feature_names_out())\n",
    "        validation_categorical = pd.concat([validation_categorical,encoded_dt],axis=1)\n",
    "        del validation_categorical[c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
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       "<p>118108 rows × 117 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        M5  M7  M8  M9  id_12  id_15  id_16  id_27  id_28  id_29  ...  mmb29k  \\\n",
       "0        0   0   0   0      1    1.0    1.0      0    1.0    1.0  ...       0   \n",
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       "4        0   0   0   0      1    1.0    1.0      0    1.0    1.0  ...       0   \n",
       "...     ..  ..  ..  ..    ...    ...    ...    ...    ...    ...  ...     ...   \n",
       "118103   1   1   1   1      0    2.0    2.0      0    2.0    2.0  ...       0   \n",
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       "118107   0   1   1   1      0    2.0    2.0      0    2.0    2.0  ...       0   \n",
       "\n",
       "        mmb29m  moto  mra58k nrd90m  rv  samsung  sm  trident  windows  \n",
       "0            0     0       0      0   0        0   0        0        0  \n",
       "1            0     0       0      0   0        0   0        0        0  \n",
       "2            0     0       0      0   0        0   0        0        1  \n",
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       "4            0     0       0      0   0        0   0        0        1  \n",
       "...        ...   ...     ...    ...  ..      ...  ..      ...      ...  \n",
       "118103       0     0       0      0   0        0   0        0        0  \n",
       "118104       0     0       0      0   0        0   0        0        0  \n",
       "118105       0     0       0      0   0        0   0        0        0  \n",
       "118106       0     0       0      0   0        0   0        0        0  \n",
       "118107       0     0       0      0   0        0   0        0        0  \n",
       "\n",
       "[118108 rows x 117 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_categorical['id_33'] = validation_categorical['id_33'].map({'1920x1080':'1920x1080', '1366x768':'1366x768', '1334x750':'1334x750', '2208x1242':'2208x1242', '1440x900':'1440x900','2048x1536':'2048x1536', '1600x900':'1600x900', '1280x800':'1280x800', '2560x1600':'2560x1600', 'id_33_desconocido':'id_33_desconocido'})\n",
    "validation_categorical['id_33'] = validation_categorical['id_33'].fillna('otros')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_categorical['id_33'] = validation_categorical['id_33'].map(mean_encoded_id_33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:6245: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    }
   ],
   "source": [
    "for c in validation_numerical.columns:\n",
    "    validation_numerical[c].fillna(validation_numerical[c].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
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       "        isFraud  TransactionDT  TransactionAmt  card1      card2  card3  \\\n",
       "0             1       12192900          33.261   9300  103.00000  185.0   \n",
       "1             0       12192911          52.811   8809  179.00000  106.0   \n",
       "2             0       12192913         136.956  10819  555.00000  185.0   \n",
       "3             0       12193040         136.956   9633  130.00000  185.0   \n",
       "4             0       12193199          25.000  17188  321.00000  150.0   \n",
       "...         ...            ...             ...    ...        ...    ...   \n",
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       "118107        0       15811131         279.950  15066  170.00000  150.0   \n",
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       "\n",
       "             id_19       id_20       id_21      id_22     id_24       id_25  \\\n",
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       "3       266.000000  507.000000  353.347098  15.975446  12.97479  327.267494   \n",
       "4       410.000000  225.000000  353.347098  15.975446  12.97479  327.267494   \n",
       "...            ...         ...         ...        ...       ...         ...   \n",
       "118103  361.465399  415.961787  353.347098  15.975446  12.97479  327.267494   \n",
       "118104  361.465399  415.961787  353.347098  15.975446  12.97479  327.267494   \n",
       "118105  361.465399  415.961787  353.347098  15.975446  12.97479  327.267494   \n",
       "118106  361.465399  415.961787  353.347098  15.975446  12.97479  327.267494   \n",
       "118107  361.465399  415.961787  353.347098  15.975446  12.97479  327.267494   \n",
       "\n",
       "             id_26     id_32  \n",
       "0       152.919643  26.00488  \n",
       "1       152.919643  26.00488  \n",
       "2       152.919643  26.00488  \n",
       "3       152.919643  26.00488  \n",
       "4       152.919643  24.00000  \n",
       "...            ...       ...  \n",
       "118103  152.919643  26.00488  \n",
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       "118105  152.919643  26.00488  \n",
       "118106  152.919643  26.00488  \n",
       "118107  152.919643  26.00488  \n",
       "\n",
       "[118108 rows x 402 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_validation = validation_numerical.isFraud\n",
    "del validation_numerical['isFraud']\n",
    "X_validation = pd.concat([validation_categorical,validation_numerical],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
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       "2       225.000000  14.484852  266.000000  507.000000  353.347098  15.975446   \n",
       "3       225.000000  14.484852  266.000000  507.000000  353.347098  15.975446   \n",
       "4       166.000000  14.484852  410.000000  225.000000  353.347098  15.975446   \n",
       "...            ...        ...         ...         ...         ...        ...   \n",
       "118103  193.353245  14.484852  361.465399  415.961787  353.347098  15.975446   \n",
       "118104  193.353245  14.484852  361.465399  415.961787  353.347098  15.975446   \n",
       "118105  193.353245  14.484852  361.465399  415.961787  353.347098  15.975446   \n",
       "118106  193.353245  14.484852  361.465399  415.961787  353.347098  15.975446   \n",
       "118107  193.353245  14.484852  361.465399  415.961787  353.347098  15.975446   \n",
       "\n",
       "           id_24       id_25       id_26     id_32  \n",
       "0       12.97479  327.267494  152.919643  26.00488  \n",
       "1       12.97479  327.267494  152.919643  26.00488  \n",
       "2       12.97479  327.267494  152.919643  26.00488  \n",
       "3       12.97479  327.267494  152.919643  26.00488  \n",
       "4       12.97479  327.267494  152.919643  24.00000  \n",
       "...          ...         ...         ...       ...  \n",
       "118103  12.97479  327.267494  152.919643  26.00488  \n",
       "118104  12.97479  327.267494  152.919643  26.00488  \n",
       "118105  12.97479  327.267494  152.919643  26.00488  \n",
       "118106  12.97479  327.267494  152.919643  26.00488  \n",
       "118107  12.97479  327.267494  152.919643  26.00488  \n",
       "\n",
       "[118108 rows x 518 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in X_validation.columns:\n",
    "    if c not in X_train.columns:\n",
    "        print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "del X_train['isFraud']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import uniform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### El primer modelo es un RandomForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest = RandomForestClassifier(random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(random_state=0)"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Primero veo el scoring sin la busqueda de hiperparametros\n",
    "random_forest.fit(X_train.values,train.isFraud.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99    114044\n",
      "           1       0.85      0.25      0.38      4064\n",
      "\n",
      "    accuracy                           0.97    118108\n",
      "   macro avg       0.91      0.62      0.68    118108\n",
      "weighted avg       0.97      0.97      0.97    118108\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_validation.values, random_forest.predict(X_validation.values)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.801535936097119"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(Y_validation.values, random_forest.predict_proba(X_validation.values)[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "describe = {'max_depth': [3,4,5,6,7,8,9,10,11,12,13,14,15] ,'n_estimators': [300]}\n",
    "search = RandomizedSearchCV(random_forest,describe,verbose=2,n_iter=4,n_jobs=1,cv=3,scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n",
      "[CV] END .....................max_depth=10, n_estimators=300; total time= 8.5min\n",
      "[CV] END .....................max_depth=10, n_estimators=300; total time= 8.9min\n",
      "[CV] END .....................max_depth=10, n_estimators=300; total time= 8.5min\n",
      "[CV] END ......................max_depth=7, n_estimators=300; total time= 5.8min\n",
      "[CV] END ......................max_depth=7, n_estimators=300; total time= 6.3min\n",
      "[CV] END ......................max_depth=7, n_estimators=300; total time= 6.3min\n",
      "[CV] END .....................max_depth=12, n_estimators=300; total time= 9.4min\n",
      "[CV] END .....................max_depth=12, n_estimators=300; total time=10.0min\n",
      "[CV] END .....................max_depth=12, n_estimators=300; total time=10.2min\n",
      "[CV] END .....................max_depth=13, n_estimators=300; total time=10.3min\n",
      "[CV] END .....................max_depth=13, n_estimators=300; total time=10.5min\n",
      "[CV] END .....................max_depth=13, n_estimators=300; total time=10.5min\n",
      "Wall time: 1h 50min 37s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(random_state=0),\n",
       "                   n_iter=4, n_jobs=1,\n",
       "                   param_distributions={'max_depth': [3, 4, 5, 6, 7, 8, 9, 10,\n",
       "                                                      11, 12, 13, 14, 15],\n",
       "                                        'n_estimators': [300]},\n",
       "                   scoring='roc_auc', verbose=2)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "search.fit(X_train.values,train.isFraud.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99    114044\n",
      "           1       0.87      0.21      0.34      4064\n",
      "\n",
      "    accuracy                           0.97    118108\n",
      "   macro avg       0.92      0.60      0.66    118108\n",
      "weighted avg       0.97      0.97      0.96    118108\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_validation.values, search.predict(X_validation.values)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7615637480505522"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(Y_validation.values, search.predict_proba(X_validation.values)[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Como segundo modelo utilizaré un XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in c:\\users\\alejo\\anaconda3\\lib\\site-packages (1.5.1)\n",
      "Requirement already satisfied: numpy in c:\\users\\alejo\\anaconda3\\lib\\site-packages (from xgboost) (1.18.5)\n",
      "Requirement already satisfied: scipy in c:\\users\\alejo\\anaconda3\\lib\\site-packages (from xgboost) (1.5.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ahora como segundo modelo uso XGBoost\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = XGBClassifier(random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train.isFraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16:35:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Wall time: 3min 58s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.300000012,\n",
       "              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=100, n_jobs=8,\n",
       "              num_parallel_tree=1, predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "model.fit(X_train.values,y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.98    114044\n",
      "           1       0.82      0.15      0.25      4064\n",
      "\n",
      "    accuracy                           0.97    118108\n",
      "   macro avg       0.89      0.57      0.62    118108\n",
      "weighted avg       0.97      0.97      0.96    118108\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_validation.values, model.predict(X_validation.values)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8628279697078514"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(Y_validation.values, model.predict_proba(X_validation.values)[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBClassifier()\n",
    "describe = {'max_depth': [3,4,5,6,7,8,9,10,11,12,13,14,15] ,'n_estimators': [100]}\n",
    "\n",
    "#distributions = dict('max_depth':20,penalty=['l2', 'none'],'n_estimators': [130, 180, 230])\n",
    "randomized = RandomizedSearchCV(xgb,describe, verbose=2,n_iter=4,n_jobs=1,cv=3,scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:37:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ......................max_depth=3, n_estimators=100; total time= 1.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:38:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ......................max_depth=3, n_estimators=100; total time= 1.4min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:39:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ......................max_depth=3, n_estimators=100; total time= 1.4min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:41:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ......................max_depth=9, n_estimators=100; total time= 3.6min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:44:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ......................max_depth=9, n_estimators=100; total time= 3.8min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:48:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ......................max_depth=9, n_estimators=100; total time= 3.7min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:52:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .....................max_depth=14, n_estimators=100; total time= 6.0min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:58:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .....................max_depth=14, n_estimators=100; total time= 6.3min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:04:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .....................max_depth=14, n_estimators=100; total time= 6.2min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:10:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .....................max_depth=15, n_estimators=100; total time= 6.8min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:17:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .....................max_depth=15, n_estimators=100; total time= 6.8min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:24:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .....................max_depth=15, n_estimators=100; total time= 6.8min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:31:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3,\n",
       "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None,\n",
       "                                           enable_categorical=False, gamma=None,\n",
       "                                           gpu_id=None, importance_type=None,\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate=None,\n",
       "                                           max_delta_step=None, max_depth=None,\n",
       "                                           min_child_weight=None, missing=nan,\n",
       "                                           monotone_constraints=None,\n",
       "                                           n_estimators=100, n_jobs=None,\n",
       "                                           num_parallel_tree=None,\n",
       "                                           predictor=None, random_state=None,\n",
       "                                           reg_alpha=None, reg_lambda=None,\n",
       "                                           scale_pos_weight=None,\n",
       "                                           subsample=None, tree_method=None,\n",
       "                                           validate_parameters=None,\n",
       "                                           verbosity=None),\n",
       "                   n_iter=4, n_jobs=1,\n",
       "                   param_distributions={'max_depth': [3, 4, 5, 6, 7, 8, 9, 10,\n",
       "                                                      11, 12, 13, 14, 15],\n",
       "                                        'n_estimators': [100]},\n",
       "                   scoring='roc_auc', verbose=2)"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randomized.fit(X_train.values,y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99    114044\n",
      "           1       0.84      0.21      0.33      4064\n",
      "\n",
      "    accuracy                           0.97    118108\n",
      "   macro avg       0.90      0.60      0.66    118108\n",
      "weighted avg       0.97      0.97      0.96    118108\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_validation.values, randomized.predict(X_validation.values)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8487559009031463"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(Y_validation.values, randomized.predict_proba(X_validation.values)[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como se puede ver, el mejor modelo encontrado es el **XGBoost** sin busqueda de hiperparametros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>NaN</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3663552</td>\n",
       "      <td>18403310</td>\n",
       "      <td>284.95</td>\n",
       "      <td>W</td>\n",
       "      <td>10989</td>\n",
       "      <td>360.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>166.0</td>\n",
       "      <td>debit</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <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",
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       "      <th>4</th>\n",
       "      <td>3663553</td>\n",
       "      <td>18403317</td>\n",
       "      <td>67.95</td>\n",
       "      <td>W</td>\n",
       "      <td>18018</td>\n",
       "      <td>452.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>117.0</td>\n",
       "      <td>debit</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>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 393 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TransactionID  TransactionDT  TransactionAmt ProductCD  card1  card2  \\\n",
       "0        3663549       18403224           31.95         W  10409  111.0   \n",
       "1        3663550       18403263           49.00         W   4272  111.0   \n",
       "2        3663551       18403310          171.00         W   4476  574.0   \n",
       "3        3663552       18403310          284.95         W  10989  360.0   \n",
       "4        3663553       18403317           67.95         W  18018  452.0   \n",
       "\n",
       "   card3       card4  card5  card6  ...  V330  V331  V332  V333 V334 V335  \\\n",
       "0  150.0        visa  226.0  debit  ...   NaN   NaN   NaN   NaN  NaN  NaN   \n",
       "1  150.0        visa  226.0  debit  ...   NaN   NaN   NaN   NaN  NaN  NaN   \n",
       "2  150.0        visa  226.0  debit  ...   NaN   NaN   NaN   NaN  NaN  NaN   \n",
       "3  150.0        visa  166.0  debit  ...   NaN   NaN   NaN   NaN  NaN  NaN   \n",
       "4  150.0  mastercard  117.0  debit  ...   NaN   NaN   NaN   NaN  NaN  NaN   \n",
       "\n",
       "   V336  V337  V338  V339  \n",
       "0   NaN   NaN   NaN   NaN  \n",
       "1   NaN   NaN   NaN   NaN  \n",
       "2   NaN   NaN   NaN   NaN  \n",
       "3   NaN   NaN   NaN   NaN  \n",
       "4   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[5 rows x 393 columns]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comienzo el test\n",
    "transaction_test = pd.read_csv('test_transaction.csv')\n",
    "transaction_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
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       "    }\n",
       "</style>\n",
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       "      <th>id-36</th>\n",
       "      <th>id-37</th>\n",
       "      <th>id-38</th>\n",
       "      <th>DeviceType</th>\n",
       "      <th>DeviceInfo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>-45.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 67.0 for android</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
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       "      <td>3579.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>chrome 67.0 for android</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1280x720</td>\n",
       "      <td>match_status:2</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
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       "      <td>T</td>\n",
       "      <td>mobile</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>ie 11.0 for tablet</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>desktop</td>\n",
       "      <td>Trident/7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3663601</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>252944.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 67.0 for android</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>mobile</td>\n",
       "      <td>MYA-L13 Build/HUAWEIMYA-L13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3663602</td>\n",
       "      <td>-95.0</td>\n",
       "      <td>328680.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>7.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 67.0 for android</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>mobile</td>\n",
       "      <td>SM-G9650 Build/R16NW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TransactionID  id-01     id-02  id-03  id-04  id-05  id-06  id-07  id-08  \\\n",
       "0        3663586  -45.0  280290.0    NaN    NaN    0.0    0.0    NaN    NaN   \n",
       "1        3663588    0.0    3579.0    0.0    0.0    0.0    0.0    NaN    NaN   \n",
       "2        3663597   -5.0  185210.0    NaN    NaN    1.0    0.0    NaN    NaN   \n",
       "3        3663601  -45.0  252944.0    0.0    0.0    0.0    0.0    NaN    NaN   \n",
       "4        3663602  -95.0  328680.0    NaN    NaN    7.0  -33.0    NaN    NaN   \n",
       "\n",
       "   id-09  ...                    id-31  id-32     id-33           id-34  \\\n",
       "0    NaN  ...  chrome 67.0 for android    NaN       NaN             NaN   \n",
       "1    0.0  ...  chrome 67.0 for android   24.0  1280x720  match_status:2   \n",
       "2    NaN  ...       ie 11.0 for tablet    NaN       NaN             NaN   \n",
       "3    0.0  ...  chrome 67.0 for android    NaN       NaN             NaN   \n",
       "4    NaN  ...  chrome 67.0 for android    NaN       NaN             NaN   \n",
       "\n",
       "   id-35 id-36 id-37  id-38  DeviceType                   DeviceInfo  \n",
       "0      F     F     T      F      mobile  MYA-L13 Build/HUAWEIMYA-L13  \n",
       "1      T     F     T      T      mobile         LGLS676 Build/MXB48T  \n",
       "2      F     T     T      F     desktop                  Trident/7.0  \n",
       "3      F     F     T      F      mobile  MYA-L13 Build/HUAWEIMYA-L13  \n",
       "4      F     F     T      F      mobile         SM-G9650 Build/R16NW  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "identity_test = pd.read_csv('test_identity.csv')\n",
    "identity_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TransactionID</th>\n",
       "      <th>TransactionDT</th>\n",
       "      <th>TransactionAmt</th>\n",
       "      <th>ProductCD</th>\n",
       "      <th>card1</th>\n",
       "      <th>card2</th>\n",
       "      <th>card3</th>\n",
       "      <th>card4</th>\n",
       "      <th>card5</th>\n",
       "      <th>card6</th>\n",
       "      <th>...</th>\n",
       "      <th>id-31</th>\n",
       "      <th>id-32</th>\n",
       "      <th>id-33</th>\n",
       "      <th>id-34</th>\n",
       "      <th>id-35</th>\n",
       "      <th>id-36</th>\n",
       "      <th>id-37</th>\n",
       "      <th>id-38</th>\n",
       "      <th>DeviceType</th>\n",
       "      <th>DeviceInfo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3663549</td>\n",
       "      <td>18403224</td>\n",
       "      <td>31.95</td>\n",
       "      <td>W</td>\n",
       "      <td>10409</td>\n",
       "      <td>111.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>226.0</td>\n",
       "      <td>debit</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3663550</td>\n",
       "      <td>18403263</td>\n",
       "      <td>49.00</td>\n",
       "      <td>W</td>\n",
       "      <td>4272</td>\n",
       "      <td>111.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>226.0</td>\n",
       "      <td>debit</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3663551</td>\n",
       "      <td>18403310</td>\n",
       "      <td>171.00</td>\n",
       "      <td>W</td>\n",
       "      <td>4476</td>\n",
       "      <td>574.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>226.0</td>\n",
       "      <td>debit</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3663552</td>\n",
       "      <td>18403310</td>\n",
       "      <td>284.95</td>\n",
       "      <td>W</td>\n",
       "      <td>10989</td>\n",
       "      <td>360.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>166.0</td>\n",
       "      <td>debit</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3663553</td>\n",
       "      <td>18403317</td>\n",
       "      <td>67.95</td>\n",
       "      <td>W</td>\n",
       "      <td>18018</td>\n",
       "      <td>452.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>117.0</td>\n",
       "      <td>debit</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>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 433 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TransactionID  TransactionDT  TransactionAmt ProductCD  card1  card2  \\\n",
       "0        3663549       18403224           31.95         W  10409  111.0   \n",
       "1        3663550       18403263           49.00         W   4272  111.0   \n",
       "2        3663551       18403310          171.00         W   4476  574.0   \n",
       "3        3663552       18403310          284.95         W  10989  360.0   \n",
       "4        3663553       18403317           67.95         W  18018  452.0   \n",
       "\n",
       "   card3       card4  card5  card6  ...  id-31  id-32  id-33  id-34 id-35  \\\n",
       "0  150.0        visa  226.0  debit  ...    NaN    NaN    NaN    NaN   NaN   \n",
       "1  150.0        visa  226.0  debit  ...    NaN    NaN    NaN    NaN   NaN   \n",
       "2  150.0        visa  226.0  debit  ...    NaN    NaN    NaN    NaN   NaN   \n",
       "3  150.0        visa  166.0  debit  ...    NaN    NaN    NaN    NaN   NaN   \n",
       "4  150.0  mastercard  117.0  debit  ...    NaN    NaN    NaN    NaN   NaN   \n",
       "\n",
       "  id-36  id-37  id-38  DeviceType  DeviceInfo  \n",
       "0   NaN    NaN    NaN         NaN         NaN  \n",
       "1   NaN    NaN    NaN         NaN         NaN  \n",
       "2   NaN    NaN    NaN         NaN         NaN  \n",
       "3   NaN    NaN    NaN         NaN         NaN  \n",
       "4   NaN    NaN    NaN         NaN         NaN  \n",
       "\n",
       "[5 rows x 433 columns]"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_test_data = transaction_test.merge(identity_test,on='TransactionID',how='left')\n",
    "joined_test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "del transaction_test\n",
    "del identity_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "c  = {\"id-01\":\"id_01\",\n",
    "\"id-02\":\"id_02\",\n",
    "\"id-03\":\"id_03\",\n",
    "\"id-04\":\"id_04\",\n",
    "\"id-05\":\"id_05\",\n",
    "\"id-06\":\"id_06\",\n",
    "\"id-07\":\"id_07\",\n",
    "\"id-08\":\"id_08\",\n",
    "\"id-09\":\"id_09\",\n",
    "\"id-10\":\"id_10\",\n",
    "\"id-11\":\"id_11\",\n",
    "\"id-12\":\"id_12\",\n",
    "\"id-13\":\"id_13\",\n",
    "\"id-14\":\"id_14\",\n",
    "\"id-15\":\"id_15\",\n",
    "\"id-16\":\"id_16\",\n",
    "\"id-17\":\"id_17\",\n",
    "\"id-18\":\"id_18\",\n",
    "\"id-19\":\"id_19\",\n",
    "\"id-20\":\"id_20\",\n",
    "\"id-21\":\"id_21\",\n",
    "\"id-22\":\"id_22\",\n",
    "\"id-23\":\"id_23\",\n",
    "\"id-24\":\"id_24\",\n",
    "\"id-25\":\"id_25\",\n",
    "\"id-26\":\"id_26\",\n",
    "\"id-27\":\"id_27\",\n",
    "\"id-28\":\"id_28\",\n",
    "\"id-29\":\"id_29\",\n",
    "\"id-30\":\"id_30\",\n",
    "\"id-31\":\"id_31\",\n",
    "\"id-32\":\"id_32\",\n",
    "\"id-33\":\"id_33\",\n",
    "\"id-34\":\"id_34\",\n",
    "\"id-35\":\"id_35\",\n",
    "\"id-36\":\"id_36\",\n",
    "\"id-37\":\"id_37\",\n",
    "\"id-38\":\"id_38\",}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "joined_test_data.rename(columns=c,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TransactionID</th>\n",
       "      <th>TransactionDT</th>\n",
       "      <th>TransactionAmt</th>\n",
       "      <th>ProductCD</th>\n",
       "      <th>card1</th>\n",
       "      <th>card2</th>\n",
       "      <th>card3</th>\n",
       "      <th>card4</th>\n",
       "      <th>card5</th>\n",
       "      <th>card6</th>\n",
       "      <th>...</th>\n",
       "      <th>id_31</th>\n",
       "      <th>id_32</th>\n",
       "      <th>id_33</th>\n",
       "      <th>id_34</th>\n",
       "      <th>id_35</th>\n",
       "      <th>id_36</th>\n",
       "      <th>id_37</th>\n",
       "      <th>id_38</th>\n",
       "      <th>DeviceType</th>\n",
       "      <th>DeviceInfo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3663549</td>\n",
       "      <td>18403224</td>\n",
       "      <td>31.950</td>\n",
       "      <td>W</td>\n",
       "      <td>10409</td>\n",
       "      <td>111.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>226.0</td>\n",
       "      <td>debit</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3663550</td>\n",
       "      <td>18403263</td>\n",
       "      <td>49.000</td>\n",
       "      <td>W</td>\n",
       "      <td>4272</td>\n",
       "      <td>111.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>226.0</td>\n",
       "      <td>debit</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3663551</td>\n",
       "      <td>18403310</td>\n",
       "      <td>171.000</td>\n",
       "      <td>W</td>\n",
       "      <td>4476</td>\n",
       "      <td>574.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>226.0</td>\n",
       "      <td>debit</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3663552</td>\n",
       "      <td>18403310</td>\n",
       "      <td>284.950</td>\n",
       "      <td>W</td>\n",
       "      <td>10989</td>\n",
       "      <td>360.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>166.0</td>\n",
       "      <td>debit</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3663553</td>\n",
       "      <td>18403317</td>\n",
       "      <td>67.950</td>\n",
       "      <td>W</td>\n",
       "      <td>18018</td>\n",
       "      <td>452.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>117.0</td>\n",
       "      <td>debit</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>NaN</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>506686</th>\n",
       "      <td>4170235</td>\n",
       "      <td>34214279</td>\n",
       "      <td>94.679</td>\n",
       "      <td>C</td>\n",
       "      <td>13832</td>\n",
       "      <td>375.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>224.0</td>\n",
       "      <td>debit</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506687</th>\n",
       "      <td>4170236</td>\n",
       "      <td>34214287</td>\n",
       "      <td>12.173</td>\n",
       "      <td>C</td>\n",
       "      <td>3154</td>\n",
       "      <td>408.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>224.0</td>\n",
       "      <td>debit</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 43.0 for android</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>mobile</td>\n",
       "      <td>ALE-L23 Build/HuaweiALE-L23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506688</th>\n",
       "      <td>4170237</td>\n",
       "      <td>34214326</td>\n",
       "      <td>49.000</td>\n",
       "      <td>W</td>\n",
       "      <td>16661</td>\n",
       "      <td>490.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>226.0</td>\n",
       "      <td>debit</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506689</th>\n",
       "      <td>4170238</td>\n",
       "      <td>34214337</td>\n",
       "      <td>202.000</td>\n",
       "      <td>W</td>\n",
       "      <td>16621</td>\n",
       "      <td>516.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>224.0</td>\n",
       "      <td>debit</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506690</th>\n",
       "      <td>4170239</td>\n",
       "      <td>34214345</td>\n",
       "      <td>24.346</td>\n",
       "      <td>C</td>\n",
       "      <td>5713</td>\n",
       "      <td>168.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>147.0</td>\n",
       "      <td>credit</td>\n",
       "      <td>...</td>\n",
       "      <td>samsung browser 8.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>mobile</td>\n",
       "      <td>SAMSUNG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506691 rows × 433 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TransactionID  TransactionDT  TransactionAmt ProductCD  card1  card2  \\\n",
       "0             3663549       18403224          31.950         W  10409  111.0   \n",
       "1             3663550       18403263          49.000         W   4272  111.0   \n",
       "2             3663551       18403310         171.000         W   4476  574.0   \n",
       "3             3663552       18403310         284.950         W  10989  360.0   \n",
       "4             3663553       18403317          67.950         W  18018  452.0   \n",
       "...               ...            ...             ...       ...    ...    ...   \n",
       "506686        4170235       34214279          94.679         C  13832  375.0   \n",
       "506687        4170236       34214287          12.173         C   3154  408.0   \n",
       "506688        4170237       34214326          49.000         W  16661  490.0   \n",
       "506689        4170238       34214337         202.000         W  16621  516.0   \n",
       "506690        4170239       34214345          24.346         C   5713  168.0   \n",
       "\n",
       "        card3       card4  card5   card6  ...                    id_31  id_32  \\\n",
       "0       150.0        visa  226.0   debit  ...                      NaN    NaN   \n",
       "1       150.0        visa  226.0   debit  ...                      NaN    NaN   \n",
       "2       150.0        visa  226.0   debit  ...                      NaN    NaN   \n",
       "3       150.0        visa  166.0   debit  ...                      NaN    NaN   \n",
       "4       150.0  mastercard  117.0   debit  ...                      NaN    NaN   \n",
       "...       ...         ...    ...     ...  ...                      ...    ...   \n",
       "506686  185.0  mastercard  224.0   debit  ...                      NaN    NaN   \n",
       "506687  185.0  mastercard  224.0   debit  ...  chrome 43.0 for android    NaN   \n",
       "506688  150.0        visa  226.0   debit  ...                      NaN    NaN   \n",
       "506689  150.0  mastercard  224.0   debit  ...                      NaN    NaN   \n",
       "506690  144.0        visa  147.0  credit  ...      samsung browser 8.2    NaN   \n",
       "\n",
       "        id_33  id_34 id_35 id_36  id_37  id_38  DeviceType  \\\n",
       "0         NaN    NaN   NaN   NaN    NaN    NaN         NaN   \n",
       "1         NaN    NaN   NaN   NaN    NaN    NaN         NaN   \n",
       "2         NaN    NaN   NaN   NaN    NaN    NaN         NaN   \n",
       "3         NaN    NaN   NaN   NaN    NaN    NaN         NaN   \n",
       "4         NaN    NaN   NaN   NaN    NaN    NaN         NaN   \n",
       "...       ...    ...   ...   ...    ...    ...         ...   \n",
       "506686    NaN    NaN   NaN   NaN    NaN    NaN         NaN   \n",
       "506687    NaN    NaN     F     F      T      F      mobile   \n",
       "506688    NaN    NaN   NaN   NaN    NaN    NaN         NaN   \n",
       "506689    NaN    NaN   NaN   NaN    NaN    NaN         NaN   \n",
       "506690    NaN    NaN     F     F      T      F      mobile   \n",
       "\n",
       "                         DeviceInfo  \n",
       "0                               NaN  \n",
       "1                               NaN  \n",
       "2                               NaN  \n",
       "3                               NaN  \n",
       "4                               NaN  \n",
       "...                             ...  \n",
       "506686                          NaN  \n",
       "506687  ALE-L23 Build/HuaweiALE-L23  \n",
       "506688                          NaN  \n",
       "506689                          NaN  \n",
       "506690                      SAMSUNG  \n",
       "\n",
       "[506691 rows x 433 columns]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>...</th>\n",
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       "      <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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506686</th>\n",
       "      <td>C</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>debit</td>\n",
       "      <td>gmail.com</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506687</th>\n",
       "      <td>C</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>debit</td>\n",
       "      <td>hotmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NotFound</td>\n",
       "      <td>New</td>\n",
       "      <td>...</td>\n",
       "      <td>NotFound</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>chrome 43.0 for android</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>ALE-L23 Build/HuaweiALE-L23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506688</th>\n",
       "      <td>W</td>\n",
       "      <td>visa</td>\n",
       "      <td>debit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506689</th>\n",
       "      <td>W</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>debit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506690</th>\n",
       "      <td>C</td>\n",
       "      <td>visa</td>\n",
       "      <td>credit</td>\n",
       "      <td>hotmail.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NotFound</td>\n",
       "      <td>Found</td>\n",
       "      <td>...</td>\n",
       "      <td>Found</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>samsung browser 8.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>SAMSUNG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506691 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ProductCD       card4   card6 R_emaildomain   M5   M7   M8   M9  \\\n",
       "0              W        visa   debit           NaN  NaN    T    T    T   \n",
       "1              W        visa   debit           NaN  NaN  NaN  NaN  NaN   \n",
       "2              W        visa   debit           NaN    F    F    F    F   \n",
       "3              W        visa   debit           NaN  NaN  NaN  NaN  NaN   \n",
       "4              W  mastercard   debit           NaN  NaN    F    T    T   \n",
       "...          ...         ...     ...           ...  ...  ...  ...  ...   \n",
       "506686         C  mastercard   debit     gmail.com  NaN  NaN  NaN  NaN   \n",
       "506687         C  mastercard   debit   hotmail.com  NaN  NaN  NaN  NaN   \n",
       "506688         W        visa   debit           NaN    F    F    T    T   \n",
       "506689         W  mastercard   debit           NaN    F    F    F    F   \n",
       "506690         C        visa  credit   hotmail.com  NaN  NaN  NaN  NaN   \n",
       "\n",
       "           id_12  id_15  ...     id_29 id_30 id_35 id_36 id_37 id_38  \\\n",
       "0            NaN    NaN  ...       NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "1            NaN    NaN  ...       NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2            NaN    NaN  ...       NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "3            NaN    NaN  ...       NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "4            NaN    NaN  ...       NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "...          ...    ...  ...       ...   ...   ...   ...   ...   ...   \n",
       "506686       NaN    NaN  ...       NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "506687  NotFound    New  ...  NotFound   NaN     F     F     T     F   \n",
       "506688       NaN    NaN  ...       NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "506689       NaN    NaN  ...       NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "506690  NotFound  Found  ...     Found   NaN     F     F     T     F   \n",
       "\n",
       "                          id_31 id_33 DeviceType                   DeviceInfo  \n",
       "0                           NaN   NaN        NaN                          NaN  \n",
       "1                           NaN   NaN        NaN                          NaN  \n",
       "2                           NaN   NaN        NaN                          NaN  \n",
       "3                           NaN   NaN        NaN                          NaN  \n",
       "4                           NaN   NaN        NaN                          NaN  \n",
       "...                         ...   ...        ...                          ...  \n",
       "506686                      NaN   NaN        NaN                          NaN  \n",
       "506687  chrome 43.0 for android   NaN     mobile  ALE-L23 Build/HuaweiALE-L23  \n",
       "506688                      NaN   NaN        NaN                          NaN  \n",
       "506689                      NaN   NaN        NaN                          NaN  \n",
       "506690      samsung browser 8.2   NaN     mobile                      SAMSUNG  \n",
       "\n",
       "[506691 rows x 24 columns]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_categorical = joined_test_data[final_categorical_feature]\n",
    "test_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_numeic = joined_test_data[feature_numeric]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:6245: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._update_inplace(new_data)\n",
      "<ipython-input-201-d0020cb969ec>:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_categorical['id_27'] = test_categorical['id_27'].isnull().map({True:0,False:1})\n",
      "<ipython-input-201-d0020cb969ec>:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_categorical['id_36'] = test_categorical['id_36'].isnull().map({True:0,False:1})\n",
      "<ipython-input-201-d0020cb969ec>:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_categorical['id_38'] = test_categorical['id_38'].isnull().map({True:0,False:1})\n",
      "<ipython-input-201-d0020cb969ec>:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_categorical['id_37'] = test_categorical['id_37'].isnull().map({True:0,False:1})\n",
      "<ipython-input-201-d0020cb969ec>:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_categorical['id_12'] = test_categorical['id_12'].isnull().map({True:0,False:1})\n",
      "<ipython-input-201-d0020cb969ec>:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_categorical['id_36'] = test_categorical['id_36'].isnull().map({True:0,False:1})\n",
      "<ipython-input-201-d0020cb969ec>:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_categorical['M9'] = test_categorical['M9'].isnull().map({True:0,False:1})\n",
      "<ipython-input-201-d0020cb969ec>:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_categorical['M8'] = test_categorical['M8'].isnull().map({True:0,False:1})\n",
      "<ipython-input-201-d0020cb969ec>:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_categorical['M7'] = test_categorical['M7'].isnull().map({True:0,False:1})\n",
      "<ipython-input-201-d0020cb969ec>:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_categorical['M5'] = test_categorical['M5'].isnull().map({True:0,False:1})\n",
      "<ipython-input-201-d0020cb969ec>:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_categorical['id_35'] = test_categorical['id_35'].isnull().map({True:0,False:1})\n",
      "<ipython-input-201-d0020cb969ec>:32: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_categorical['DeviceType'] = test_categorical['DeviceType'].fillna('desconocido')\n",
      "<ipython-input-201-d0020cb969ec>:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_categorical['id_33'] = test_categorical['id_33'].fillna('id_33_desconocido')\n"
     ]
    }
   ],
   "source": [
    "test_categorical['card4'].fillna('card4_desconocido',inplace=True)\n",
    "test_categorical['card6'].fillna('card6_desconocido',inplace=True)\n",
    "\n",
    "# Para estas columnas voy a usar mean-encoding\n",
    "test_categorical['id_15'].fillna('Unknown',inplace=True)\n",
    "test_categorical['id_29'].fillna('id_29_Unknown',inplace=True)\n",
    "test_categorical['id_28'].fillna('id_28_Unknown',inplace=True)\n",
    "test_categorical['id_16'].fillna('id_16_Unknown',inplace=True)\n",
    "\n",
    "# Para los features de True-False, dado la cantidad de NaNs, pongo 1 si hay valor y 0 sino\n",
    "test_categorical['id_27'] = test_categorical['id_27'].isnull().map({True:0,False:1})\n",
    "test_categorical['id_36'] = test_categorical['id_36'].isnull().map({True:0,False:1})\n",
    "test_categorical['id_38'] = test_categorical['id_38'].isnull().map({True:0,False:1})\n",
    "test_categorical['id_37'] = test_categorical['id_37'].isnull().map({True:0,False:1})\n",
    "test_categorical['id_12'] = test_categorical['id_12'].isnull().map({True:0,False:1})\n",
    "test_categorical['id_36'] = test_categorical['id_36'].isnull().map({True:0,False:1})\n",
    "test_categorical['M9'] = test_categorical['M9'].isnull().map({True:0,False:1})\n",
    "test_categorical['M8'] = test_categorical['M8'].isnull().map({True:0,False:1})\n",
    "test_categorical['M7'] = test_categorical['M7'].isnull().map({True:0,False:1})\n",
    "test_categorical['M5'] = test_categorical['M5'].isnull().map({True:0,False:1})\n",
    "test_categorical['id_35'] = test_categorical['id_35'].isnull().map({True:0,False:1})\n",
    "\n",
    "# Ip\n",
    "test_categorical['id_23'].fillna('IP_Desconocido',inplace=True)\n",
    "\n",
    "# Primero le cambio los NaNs por desconocido_columna\n",
    "for c in test_categorical.columns:\n",
    "    if c in count_vectorized_columns:\n",
    "        test_categorical[c].fillna('Desconocido_'+c,inplace=True)\n",
    "        \n",
    "# Algunos features restantes ... \n",
    "test_categorical['DeviceType'] = test_categorical['DeviceType'].fillna('desconocido')\n",
    "test_categorical['id_33'] = test_categorical['id_33'].fillna('id_33_desconocido')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Procesando:  id_15\n",
      "Procesando:  id_16\n",
      "Procesando:  id_28\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-204-7495ace28a5b>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_categorical[c] = ordinal_dic[c].transform(test_categorical[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-204-7495ace28a5b>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_categorical[c] = ordinal_dic[c].transform(test_categorical[c].astype(str).values.reshape(-1,1))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Procesando:  id_29\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-204-7495ace28a5b>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_categorical[c] = ordinal_dic[c].transform(test_categorical[c].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-204-7495ace28a5b>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_categorical[c] = ordinal_dic[c].transform(test_categorical[c].astype(str).values.reshape(-1,1))\n"
     ]
    }
   ],
   "source": [
    "for c in test_categorical.columns:\n",
    "    if c in ordinal_dic:\n",
    "        print(\"Procesando: \", c)\n",
    "        test_categorical[c] = ordinal_dic[c].transform(test_categorical[c].astype(str).values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Procesando:  ProductCD\n",
      "Procesando:  card4\n",
      "Procesando:  card6\n",
      "Procesando:  id_23\n",
      "Procesando:  DeviceType\n"
     ]
    }
   ],
   "source": [
    "for c in test_categorical.columns:\n",
    "    if c in hot_encodings_dic:\n",
    "        print(\"Procesando: \", c)\n",
    "        encoded = hot_encodings_dic[c].transform(test_categorical[[c]].astype(str)).todense().astype(int)\n",
    "        column_encoded = pd.DataFrame(encoded).add_prefix('ec_'+c)\n",
    "        test_categorical = pd.concat([test_categorical,column_encoded],axis=1)\n",
    "        del test_categorical[c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Procesando:  R_emaildomain\n",
      "Procesando:  id_30\n",
      "Procesando:  id_31\n",
      "Procesando:  DeviceInfo\n"
     ]
    }
   ],
   "source": [
    "for c in test_categorical.columns:\n",
    "    if c in count_vectorized_dic:\n",
    "        print(\"Procesando: \",c)\n",
    "        encoded = count_vectorized_dic[c].transform(test_categorical[c])\n",
    "        encoded_dt = pd.DataFrame(encoded.toarray(),columns= count_vectorized_dic[c].get_feature_names_out())\n",
    "        test_categorical = pd.concat([test_categorical,encoded_dt],axis=1)\n",
    "        del test_categorical[c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-203-5d23173eb39c>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_categorical['id_33'] = test_categorical['id_33'].map({'1920x1080':'1920x1080', '1366x768':'1366x768', '1334x750':'1334x750', '2208x1242':'2208x1242', '1440x900':'1440x900','2048x1536':'2048x1536', '1600x900':'1600x900', '1280x800':'1280x800', '2560x1600':'2560x1600', 'id_33_desconocido':'id_33_desconocido'})\n",
      "<ipython-input-203-5d23173eb39c>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_categorical['id_33'] = test_categorical['id_33'].fillna('otros')\n"
     ]
    }
   ],
   "source": [
    "test_categorical['id_33'] = test_categorical['id_33'].map({'1920x1080':'1920x1080', '1366x768':'1366x768', '1334x750':'1334x750', '2208x1242':'2208x1242', '1440x900':'1440x900','2048x1536':'2048x1536', '1600x900':'1600x900', '1280x800':'1280x800', '2560x1600':'2560x1600', 'id_33_desconocido':'id_33_desconocido'})\n",
    "test_categorical['id_33'] = test_categorical['id_33'].fillna('otros')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_categorical['id_33'] = test_categorical['id_33'].map(mean_encoded_id_33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:6245: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    }
   ],
   "source": [
    "for c in test_numeic.columns:\n",
    "    test_numeic[c].fillna(test_numeic[c].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = pd.concat([test_categorical,test_numeic],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>191.070341</td>\n",
       "      <td>14.795735</td>\n",
       "      <td>350.122982</td>\n",
       "      <td>408.88623</td>\n",
       "      <td>507.727021</td>\n",
       "      <td>15.336823</td>\n",
       "      <td>13.166667</td>\n",
       "      <td>332.043064</td>\n",
       "      <td>152.752923</td>\n",
       "      <td>26.217939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>191.070341</td>\n",
       "      <td>14.795735</td>\n",
       "      <td>350.122982</td>\n",
       "      <td>408.88623</td>\n",
       "      <td>507.727021</td>\n",
       "      <td>15.336823</td>\n",
       "      <td>13.166667</td>\n",
       "      <td>332.043064</td>\n",
       "      <td>152.752923</td>\n",
       "      <td>26.217939</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>506686</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>191.070341</td>\n",
       "      <td>14.795735</td>\n",
       "      <td>350.122982</td>\n",
       "      <td>408.88623</td>\n",
       "      <td>507.727021</td>\n",
       "      <td>15.336823</td>\n",
       "      <td>13.166667</td>\n",
       "      <td>332.043064</td>\n",
       "      <td>152.752923</td>\n",
       "      <td>26.217939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506687</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>507.00000</td>\n",
       "      <td>507.727021</td>\n",
       "      <td>15.336823</td>\n",
       "      <td>13.166667</td>\n",
       "      <td>332.043064</td>\n",
       "      <td>152.752923</td>\n",
       "      <td>26.217939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506688</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>191.070341</td>\n",
       "      <td>14.795735</td>\n",
       "      <td>350.122982</td>\n",
       "      <td>408.88623</td>\n",
       "      <td>507.727021</td>\n",
       "      <td>15.336823</td>\n",
       "      <td>13.166667</td>\n",
       "      <td>332.043064</td>\n",
       "      <td>152.752923</td>\n",
       "      <td>26.217939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506689</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>191.070341</td>\n",
       "      <td>14.795735</td>\n",
       "      <td>350.122982</td>\n",
       "      <td>408.88623</td>\n",
       "      <td>507.727021</td>\n",
       "      <td>15.336823</td>\n",
       "      <td>13.166667</td>\n",
       "      <td>332.043064</td>\n",
       "      <td>152.752923</td>\n",
       "      <td>26.217939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506690</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>14.795735</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>127.00000</td>\n",
       "      <td>507.727021</td>\n",
       "      <td>15.336823</td>\n",
       "      <td>13.166667</td>\n",
       "      <td>332.043064</td>\n",
       "      <td>152.752923</td>\n",
       "      <td>26.217939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506691 rows × 519 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        M5  M7  M8  M9  id_12  id_15  id_16  id_27  id_28  id_29  ...  \\\n",
       "0        0   1   1   1      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "1        0   0   0   0      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "2        1   1   1   1      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "3        0   0   0   0      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "4        0   1   1   1      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "...     ..  ..  ..  ..    ...    ...    ...    ...    ...    ...  ...   \n",
       "506686   0   0   0   0      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "506687   0   0   0   0      1    1.0    1.0      0    1.0    1.0  ...   \n",
       "506688   1   1   1   1      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "506689   1   1   1   1      0    2.0    2.0      0    2.0    2.0  ...   \n",
       "506690   0   0   0   0      1    0.0    0.0      0    0.0    0.0  ...   \n",
       "\n",
       "             id_17      id_18       id_19      id_20       id_21      id_22  \\\n",
       "0       191.070341  14.795735  350.122982  408.88623  507.727021  15.336823   \n",
       "1       191.070341  14.795735  350.122982  408.88623  507.727021  15.336823   \n",
       "2       191.070341  14.795735  350.122982  408.88623  507.727021  15.336823   \n",
       "3       191.070341  14.795735  350.122982  408.88623  507.727021  15.336823   \n",
       "4       191.070341  14.795735  350.122982  408.88623  507.727021  15.336823   \n",
       "...            ...        ...         ...        ...         ...        ...   \n",
       "506686  191.070341  14.795735  350.122982  408.88623  507.727021  15.336823   \n",
       "506687  225.000000  15.000000  176.000000  507.00000  507.727021  15.336823   \n",
       "506688  191.070341  14.795735  350.122982  408.88623  507.727021  15.336823   \n",
       "506689  191.070341  14.795735  350.122982  408.88623  507.727021  15.336823   \n",
       "506690  225.000000  14.795735  266.000000  127.00000  507.727021  15.336823   \n",
       "\n",
       "            id_24       id_25       id_26      id_32  \n",
       "0       13.166667  332.043064  152.752923  26.217939  \n",
       "1       13.166667  332.043064  152.752923  26.217939  \n",
       "2       13.166667  332.043064  152.752923  26.217939  \n",
       "3       13.166667  332.043064  152.752923  26.217939  \n",
       "4       13.166667  332.043064  152.752923  26.217939  \n",
       "...           ...         ...         ...        ...  \n",
       "506686  13.166667  332.043064  152.752923  26.217939  \n",
       "506687  13.166667  332.043064  152.752923  26.217939  \n",
       "506688  13.166667  332.043064  152.752923  26.217939  \n",
       "506689  13.166667  332.043064  152.752923  26.217939  \n",
       "506690  13.166667  332.043064  152.752923  26.217939  \n",
       "\n",
       "[506691 rows x 519 columns]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "Td_id = X_test['TransactionID']\n",
    "del X_test['TransactionID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = model.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "proba = []\n",
    "for i in res:\n",
    "    proba.append(i[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_serie = pd.Series(proba)\n",
    "submit = pd.concat([Td_id,predict_serie],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "submit.rename(columns={0:'isFraud'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "submit.to_csv('respuesta_kaggle_parte3.csv',index=False)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como resultado se obtuvo\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}