Parte 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicciones de fraude a partir de una Regresión Logística"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importacion de paquetes necesarios para trabajar.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.feature_extraction import FeatureHasher\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import (\n",
    "    LabelEncoder,\n",
    "    OrdinalEncoder,\n",
    "    MinMaxScaler,\n",
    "    Normalizer,\n",
    "    OneHotEncoder,\n",
    "    PowerTransformer,\n",
    "    RobustScaler,\n",
    "    StandardScaler,\n",
    ")\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, accuracy_score, roc_auc_score, ConfusionMatrixDisplay, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0.1\n"
     ]
    }
   ],
   "source": [
    "import sklearn \n",
    "print(sklearn.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>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": [
    "transaction_train = pd.read_csv('train_transaction.csv')\n",
    "transaction_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
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       "        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>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_train = pd.read_csv('train_identity.csv')\n",
    "identity_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join_train_values = transaction_train.merge(identity_train,on='TransactionID',how='left')\n",
    "join_train_values.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eliminacion de los dataframes viejos para liberar RAM\n",
    "del transaction_train\n",
    "del identity_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Me fijo cuales son las features con mas correlacion\n",
    "data = abs(join_train_values.corr()['isFraud']).nlargest(11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>isFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>V257</th>\n",
       "      <td>0.383060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V246</th>\n",
       "      <td>0.366878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V244</th>\n",
       "      <td>0.364129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V242</th>\n",
       "      <td>0.360590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V201</th>\n",
       "      <td>0.328005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V200</th>\n",
       "      <td>0.318783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V189</th>\n",
       "      <td>0.308219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V188</th>\n",
       "      <td>0.303582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V258</th>\n",
       "      <td>0.297151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V45</th>\n",
       "      <td>0.281832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       isFraud\n",
       "V257  0.383060\n",
       "V246  0.366878\n",
       "V244  0.364129\n",
       "V242  0.360590\n",
       "V201  0.328005\n",
       "V200  0.318783\n",
       "V189  0.308219\n",
       "V188  0.303582\n",
       "V258  0.297151\n",
       "V45   0.281832"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =data.drop(axis=0,index='isFraud')\n",
    "df = pd.DataFrame(df)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 3840x1920 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={\"figure.dpi\":200,'figure.figsize':(19.2,9.6)})\n",
    "ax = sns.barplot(x=df.index, y=\"isFraud\", data=df)\n",
    "ax.set_title(\"Features mas imp\",fontsize=15)\n",
    "plt.legend(labels=[\"No es fraude\",\"Fraude\"], title = \"Casos\")\n",
    "ax.set_xlabel(\"Codigo\",fontsize=14)\n",
    "ax.set_ylabel(\"Cantidad\",fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para la imputacion de nulos para aquellos _features_ categoricos, se usará la palabra \"deconcido\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in join_train_values.columns:\n",
    "    if join_train_values[column].dtype == 'object':\n",
    "        join_train_values[column].fillna('desconocido',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>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>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>0</td>\n",
       "      <td>86400</td>\n",
       "      <td>68.50</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>credit</td>\n",
       "      <td>...</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>NaN</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>86401</td>\n",
       "      <td>29.00</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>credit</td>\n",
       "      <td>...</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>NaN</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>86469</td>\n",
       "      <td>59.00</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>debit</td>\n",
       "      <td>...</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>NaN</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>86499</td>\n",
       "      <td>50.00</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>debit</td>\n",
       "      <td>...</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>NaN</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>86506</td>\n",
       "      <td>50.00</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>credit</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>...</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>590535</th>\n",
       "      <td>0</td>\n",
       "      <td>15811047</td>\n",
       "      <td>49.00</td>\n",
       "      <td>W</td>\n",
       "      <td>6550</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>226.0</td>\n",
       "      <td>debit</td>\n",
       "      <td>...</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>NaN</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590536</th>\n",
       "      <td>0</td>\n",
       "      <td>15811049</td>\n",
       "      <td>39.50</td>\n",
       "      <td>W</td>\n",
       "      <td>10444</td>\n",
       "      <td>225.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>224.0</td>\n",
       "      <td>debit</td>\n",
       "      <td>...</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>NaN</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
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       "    <tr>\n",
       "      <th>590537</th>\n",
       "      <td>0</td>\n",
       "      <td>15811079</td>\n",
       "      <td>30.95</td>\n",
       "      <td>W</td>\n",
       "      <td>12037</td>\n",
       "      <td>595.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>224.0</td>\n",
       "      <td>debit</td>\n",
       "      <td>...</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>NaN</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590538</th>\n",
       "      <td>0</td>\n",
       "      <td>15811088</td>\n",
       "      <td>117.00</td>\n",
       "      <td>W</td>\n",
       "      <td>7826</td>\n",
       "      <td>481.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>224.0</td>\n",
       "      <td>debit</td>\n",
       "      <td>...</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>NaN</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590539</th>\n",
       "      <td>0</td>\n",
       "      <td>15811131</td>\n",
       "      <td>279.95</td>\n",
       "      <td>W</td>\n",
       "      <td>15066</td>\n",
       "      <td>170.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>102.0</td>\n",
       "      <td>credit</td>\n",
       "      <td>...</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>NaN</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>590540 rows × 433 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        isFraud  TransactionDT  TransactionAmt ProductCD  card1  card2  card3  \\\n",
       "0             0          86400           68.50         W  13926    NaN  150.0   \n",
       "1             0          86401           29.00         W   2755  404.0  150.0   \n",
       "2             0          86469           59.00         W   4663  490.0  150.0   \n",
       "3             0          86499           50.00         W  18132  567.0  150.0   \n",
       "4             0          86506           50.00         H   4497  514.0  150.0   \n",
       "...         ...            ...             ...       ...    ...    ...    ...   \n",
       "590535        0       15811047           49.00         W   6550    NaN  150.0   \n",
       "590536        0       15811049           39.50         W  10444  225.0  150.0   \n",
       "590537        0       15811079           30.95         W  12037  595.0  150.0   \n",
       "590538        0       15811088          117.00         W   7826  481.0  150.0   \n",
       "590539        0       15811131          279.95         W  15066  170.0  150.0   \n",
       "\n",
       "             card4  card5   card6  ...                id_31  id_32  \\\n",
       "0         discover  142.0  credit  ...          desconocido    NaN   \n",
       "1       mastercard  102.0  credit  ...          desconocido    NaN   \n",
       "2             visa  166.0   debit  ...          desconocido    NaN   \n",
       "3       mastercard  117.0   debit  ...          desconocido    NaN   \n",
       "4       mastercard  102.0  credit  ...  samsung browser 6.2   32.0   \n",
       "...            ...    ...     ...  ...                  ...    ...   \n",
       "590535        visa  226.0   debit  ...          desconocido    NaN   \n",
       "590536  mastercard  224.0   debit  ...          desconocido    NaN   \n",
       "590537  mastercard  224.0   debit  ...          desconocido    NaN   \n",
       "590538  mastercard  224.0   debit  ...          desconocido    NaN   \n",
       "590539  mastercard  102.0  credit  ...          desconocido    NaN   \n",
       "\n",
       "              id_33           id_34        id_35        id_36        id_37  \\\n",
       "0       desconocido     desconocido  desconocido  desconocido  desconocido   \n",
       "1       desconocido     desconocido  desconocido  desconocido  desconocido   \n",
       "2       desconocido     desconocido  desconocido  desconocido  desconocido   \n",
       "3       desconocido     desconocido  desconocido  desconocido  desconocido   \n",
       "4         2220x1080  match_status:2            T            F            T   \n",
       "...             ...             ...          ...          ...          ...   \n",
       "590535  desconocido     desconocido  desconocido  desconocido  desconocido   \n",
       "590536  desconocido     desconocido  desconocido  desconocido  desconocido   \n",
       "590537  desconocido     desconocido  desconocido  desconocido  desconocido   \n",
       "590538  desconocido     desconocido  desconocido  desconocido  desconocido   \n",
       "590539  desconocido     desconocido  desconocido  desconocido  desconocido   \n",
       "\n",
       "              id_38   DeviceType                     DeviceInfo  \n",
       "0       desconocido  desconocido                    desconocido  \n",
       "1       desconocido  desconocido                    desconocido  \n",
       "2       desconocido  desconocido                    desconocido  \n",
       "3       desconocido  desconocido                    desconocido  \n",
       "4                 T       mobile  SAMSUNG SM-G892A Build/NRD90M  \n",
       "...             ...          ...                            ...  \n",
       "590535  desconocido  desconocido                    desconocido  \n",
       "590536  desconocido  desconocido                    desconocido  \n",
       "590537  desconocido  desconocido                    desconocido  \n",
       "590538  desconocido  desconocido                    desconocido  \n",
       "590539  desconocido  desconocido                    desconocido  \n",
       "\n",
       "[590540 rows x 433 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del join_train_values['TransactionID']\n",
    "join_train_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para la parte de validation y train, separaré en 70-30 mi dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separo mi dataset en train y validation\n",
    "train = join_train_values.loc[join_train_values.index <= (len(join_train_values)*0.7)]\n",
    "validation = join_train_values.loc[join_train_values.index >= (len(join_train_values)*0.7)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>card6</th>\n",
       "      <th>...</th>\n",
       "      <th>id_31</th>\n",
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       "      <th>id_33</th>\n",
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       "      <th>id_38</th>\n",
       "      <th>DeviceType</th>\n",
       "      <th>DeviceInfo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>...</td>\n",
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       "      <th>1</th>\n",
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       "      <td>2755</td>\n",
       "      <td>404.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>102.0</td>\n",
       "      <td>credit</td>\n",
       "      <td>...</td>\n",
       "      <td>desconocido</td>\n",
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       "      <td>490.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>166.0</td>\n",
       "      <td>debit</td>\n",
       "      <td>...</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>NaN</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
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       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>debit</td>\n",
       "      <td>...</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>credit</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 × 433 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   isFraud  TransactionDT  TransactionAmt ProductCD  card1  card2  card3  \\\n",
       "0        0          86400            68.5         W  13926    NaN  150.0   \n",
       "1        0          86401            29.0         W   2755  404.0  150.0   \n",
       "2        0          86469            59.0         W   4663  490.0  150.0   \n",
       "3        0          86499            50.0         W  18132  567.0  150.0   \n",
       "4        0          86506            50.0         H   4497  514.0  150.0   \n",
       "\n",
       "        card4  card5   card6  ...                id_31  id_32        id_33  \\\n",
       "0    discover  142.0  credit  ...          desconocido    NaN  desconocido   \n",
       "1  mastercard  102.0  credit  ...          desconocido    NaN  desconocido   \n",
       "2        visa  166.0   debit  ...          desconocido    NaN  desconocido   \n",
       "3  mastercard  117.0   debit  ...          desconocido    NaN  desconocido   \n",
       "4  mastercard  102.0  credit  ...  samsung browser 6.2   32.0    2220x1080   \n",
       "\n",
       "            id_34        id_35        id_36        id_37        id_38  \\\n",
       "0     desconocido  desconocido  desconocido  desconocido  desconocido   \n",
       "1     desconocido  desconocido  desconocido  desconocido  desconocido   \n",
       "2     desconocido  desconocido  desconocido  desconocido  desconocido   \n",
       "3     desconocido  desconocido  desconocido  desconocido  desconocido   \n",
       "4  match_status:2            T            F            T            T   \n",
       "\n",
       "    DeviceType                     DeviceInfo  \n",
       "0  desconocido                    desconocido  \n",
       "1  desconocido                    desconocido  \n",
       "2  desconocido                    desconocido  \n",
       "3  desconocido                    desconocido  \n",
       "4       mobile  SAMSUNG SM-G892A Build/NRD90M  \n",
       "\n",
       "[5 rows x 433 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>debit</td>\n",
       "      <td>...</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>NaN</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413379</th>\n",
       "      <td>1</td>\n",
       "      <td>10438017</td>\n",
       "      <td>1265.5</td>\n",
       "      <td>W</td>\n",
       "      <td>18227</td>\n",
       "      <td>583.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>226.0</td>\n",
       "      <td>credit</td>\n",
       "      <td>...</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>NaN</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413380</th>\n",
       "      <td>0</td>\n",
       "      <td>10438043</td>\n",
       "      <td>50.0</td>\n",
       "      <td>H</td>\n",
       "      <td>11919</td>\n",
       "      <td>170.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>224.0</td>\n",
       "      <td>debit</td>\n",
       "      <td>...</td>\n",
       "      <td>mobile safari generic</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2436x1125</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>413381</th>\n",
       "      <td>0</td>\n",
       "      <td>10438052</td>\n",
       "      <td>161.0</td>\n",
       "      <td>W</td>\n",
       "      <td>2377</td>\n",
       "      <td>203.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>166.0</td>\n",
       "      <td>debit</td>\n",
       "      <td>...</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>NaN</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413382</th>\n",
       "      <td>0</td>\n",
       "      <td>10438065</td>\n",
       "      <td>88.0</td>\n",
       "      <td>W</td>\n",
       "      <td>7919</td>\n",
       "      <td>194.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>166.0</td>\n",
       "      <td>debit</td>\n",
       "      <td>...</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>NaN</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "      <td>desconocido</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 433 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        isFraud  TransactionDT  TransactionAmt ProductCD  card1  card2  card3  \\\n",
       "413378        0       10438003            49.0         W   7239  452.0  150.0   \n",
       "413379        1       10438017          1265.5         W  18227  583.0  150.0   \n",
       "413380        0       10438043            50.0         H  11919  170.0  150.0   \n",
       "413381        0       10438052           161.0         W   2377  203.0  150.0   \n",
       "413382        0       10438065            88.0         W   7919  194.0  150.0   \n",
       "\n",
       "             card4  card5   card6  ...                  id_31  id_32  \\\n",
       "413378  mastercard  117.0   debit  ...            desconocido    NaN   \n",
       "413379        visa  226.0  credit  ...            desconocido    NaN   \n",
       "413380  mastercard  224.0   debit  ...  mobile safari generic   32.0   \n",
       "413381        visa  166.0   debit  ...            desconocido    NaN   \n",
       "413382  mastercard  166.0   debit  ...            desconocido    NaN   \n",
       "\n",
       "              id_33           id_34        id_35        id_36        id_37  \\\n",
       "413378  desconocido     desconocido  desconocido  desconocido  desconocido   \n",
       "413379  desconocido     desconocido  desconocido  desconocido  desconocido   \n",
       "413380    2436x1125  match_status:1            T            F            F   \n",
       "413381  desconocido     desconocido  desconocido  desconocido  desconocido   \n",
       "413382  desconocido     desconocido  desconocido  desconocido  desconocido   \n",
       "\n",
       "              id_38   DeviceType   DeviceInfo  \n",
       "413378  desconocido  desconocido  desconocido  \n",
       "413379  desconocido  desconocido  desconocido  \n",
       "413380            T       mobile   iOS Device  \n",
       "413381  desconocido  desconocido  desconocido  \n",
       "413382  desconocido  desconocido  desconocido  \n",
       "\n",
       "[5 rows x 433 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Aquellas columnas que me parecen importantes, les realizo un hot-encoding y aquellas que no, un label enconding\n",
    "# Estas columnas que parecerian ser relevantes son ProductCD,card4,card6,P_emaildomain,R_emaildomain,\n",
    "#id_23, id_30,id_34,DeviceType"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n",
      "<ipython-input-37-e22b18326ee1>: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",
      "  train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))\n"
     ]
    }
   ],
   "source": [
    "# Hago un label enconding para aquellas columnas que no esten en labels_hot_encoding\n",
    "ordinal_dic = {}\n",
    "for column in train.columns:\n",
    "    if train[column].dtypes == object:\n",
    "        ordinal_encoder = OrdinalEncoder(handle_unknown='use_encoded_value',unknown_value=-1)\n",
    "        ordinal_dic[column] = ordinal_encoder\n",
    "        train[column] = ordinal_encoder.fit_transform(train[column].astype(str).values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "        isFraud  TransactionDT  TransactionAmt  ProductCD  card1  card2  \\\n",
       "0             0          86400            68.5        4.0  13926    NaN   \n",
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       "\n",
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       "413377  150.0    4.0  226.0    1.0  ...   59.0    NaN  183.0    0.0    2.0   \n",
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       "\n",
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       "413378    2.0    2.0    2.0         0.0      1489.0  \n",
       "\n",
       "[413379 rows x 433 columns]"
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     "execution_count": 38,
     "metadata": {},
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   ],
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  {
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>86499</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18132</td>\n",
       "      <td>567.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>59.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>183.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1489.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 433 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   isFraud  TransactionDT  TransactionAmt  ProductCD  card1  card2  card3  \\\n",
       "0        0          86400            68.5        4.0  13926    NaN  150.0   \n",
       "1        0          86401            29.0        4.0   2755  404.0  150.0   \n",
       "2        0          86469            59.0        4.0   4663  490.0  150.0   \n",
       "3        0          86499            50.0        4.0  18132  567.0  150.0   \n",
       "\n",
       "   card4  card5  card6  ...  id_31  id_32  id_33  id_34  id_35  id_36  id_37  \\\n",
       "0    2.0  142.0    1.0  ...   59.0    NaN  183.0    0.0    2.0    2.0    2.0   \n",
       "1    3.0  102.0    1.0  ...   59.0    NaN  183.0    0.0    2.0    2.0    2.0   \n",
       "2    4.0  166.0    2.0  ...   59.0    NaN  183.0    0.0    2.0    2.0    2.0   \n",
       "3    3.0  117.0    2.0  ...   59.0    NaN  183.0    0.0    2.0    2.0    2.0   \n",
       "\n",
       "   id_38  DeviceType  DeviceInfo  \n",
       "0    2.0         0.0      1489.0  \n",
       "1    2.0         0.0      1489.0  \n",
       "2    2.0         0.0      1489.0  \n",
       "3    2.0         0.0      1489.0  \n",
       "\n",
       "[4 rows x 433 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": [
    "train.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "model = LogisticRegression(random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = train.isFraud\n",
    "del train['isFraud']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train,label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_label = validation.isFraud\n",
    "del validation['isFraud']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Procesando:  ProductCD\n",
      "Procesando:  card4\n",
      "Procesando:  card6\n",
      "Procesando:  P_emaildomain\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Procesando:  R_emaildomain\n",
      "Procesando:  M1\n",
      "Procesando:  M2\n",
      "Procesando:  M3\n",
      "Procesando:  M4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Procesando:  M5\n",
      "Procesando:  M6\n",
      "Procesando:  M7\n",
      "Procesando:  M8\n",
      "Procesando:  M9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Procesando:  id_12\n",
      "Procesando:  id_15\n",
      "Procesando:  id_16\n",
      "Procesando:  id_23\n",
      "Procesando:  id_27\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Procesando:  id_28\n",
      "Procesando:  id_29\n",
      "Procesando:  id_30\n",
      "Procesando:  id_31\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Procesando:  id_33\n",
      "Procesando:  id_34\n",
      "Procesando:  id_35\n",
      "Procesando:  id_36\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n",
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Procesando:  id_37\n",
      "Procesando:  id_38\n",
      "Procesando:  DeviceType\n",
      "Procesando:  DeviceInfo\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-47-96d084cb467f>: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",
      "  validation[c] = validation_encoded\n"
     ]
    }
   ],
   "source": [
    "for c in validation.columns:\n",
    "    if (c) in ordinal_dic:\n",
    "        print(\"Procesando: \", c)\n",
    "        validation_encoded = ordinal_dic[c].transform(validation[c].astype(str).values.reshape(-1,1))\n",
    "        validation[c] = validation_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(177162, 432)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.99      0.98    171037\n",
      "           1       0.00      0.00      0.00      6125\n",
      "\n",
      "    accuracy                           0.96    177162\n",
      "   macro avg       0.48      0.50      0.49    177162\n",
      "weighted avg       0.93      0.96      0.95    177162\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(validation_label, model.predict(validation)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.664945700136729"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(validation_label, model.predict_proba(validation)[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import uniform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "distributions = dict(C=uniform(loc=0, scale=4),penalty=['l2', 'none'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomizedSearchCV(model, distributions, random_state=0,verbose=100,n_iter=10,n_jobs=1,cv=3,scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV 1/3; 1/10] START C=2.195254015709299, penalty=none..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/3; 1/10] END C=2.195254015709299, penalty=none;, score=0.681 total time=  18.8s\n",
      "[CV 2/3; 1/10] START C=2.195254015709299, penalty=none..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/3; 1/10] END C=2.195254015709299, penalty=none;, score=0.647 total time=  17.2s\n",
      "[CV 3/3; 1/10] START C=2.195254015709299, penalty=none..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 3/3; 1/10] END C=2.195254015709299, penalty=none;, score=0.670 total time=  17.1s\n",
      "[CV 1/3; 2/10] START C=3.3770629943240693, penalty=none.........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/3; 2/10] END C=3.3770629943240693, penalty=none;, score=0.681 total time=  16.1s\n",
      "[CV 2/3; 2/10] START C=3.3770629943240693, penalty=none.........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/3; 2/10] END C=3.3770629943240693, penalty=none;, score=0.647 total time=  17.1s\n",
      "[CV 3/3; 2/10] START C=3.3770629943240693, penalty=none.........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 3/3; 2/10] END C=3.3770629943240693, penalty=none;, score=0.670 total time=  16.4s\n",
      "[CV 1/3; 3/10] START C=2.1795327319875875, penalty=none.........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/3; 3/10] END C=2.1795327319875875, penalty=none;, score=0.681 total time=  16.1s\n",
      "[CV 2/3; 3/10] START C=2.1795327319875875, penalty=none.........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/3; 3/10] END C=2.1795327319875875, penalty=none;, score=0.647 total time=  16.5s\n",
      "[CV 3/3; 3/10] START C=2.1795327319875875, penalty=none.........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 3/3; 3/10] END C=2.1795327319875875, penalty=none;, score=0.670 total time=  16.3s\n",
      "[CV 1/3; 4/10] START C=2.4942547871438894, penalty=l2...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/3; 4/10] END C=2.4942547871438894, penalty=l2;, score=0.565 total time=  16.1s\n",
      "[CV 2/3; 4/10] START C=2.4942547871438894, penalty=l2...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/3; 4/10] END C=2.4942547871438894, penalty=l2;, score=0.639 total time=  17.4s\n",
      "[CV 3/3; 4/10] START C=2.4942547871438894, penalty=l2...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 3/3; 4/10] END C=2.4942547871438894, penalty=l2;, score=0.656 total time=  16.9s\n",
      "[CV 1/3; 5/10] START C=1.75034884505077, penalty=l2.............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/3; 5/10] END C=1.75034884505077, penalty=l2;, score=0.672 total time=  16.0s\n",
      "[CV 2/3; 5/10] START C=1.75034884505077, penalty=l2.............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/3; 5/10] END C=1.75034884505077, penalty=l2;, score=0.691 total time=  17.7s\n",
      "[CV 3/3; 5/10] START C=1.75034884505077, penalty=l2.............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 3/3; 5/10] END C=1.75034884505077, penalty=l2;, score=0.661 total time=  17.0s\n",
      "[CV 1/3; 6/10] START C=0.22685190926977272, penalty=l2..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/3; 6/10] END C=0.22685190926977272, penalty=l2;, score=0.676 total time=  15.4s\n",
      "[CV 2/3; 6/10] START C=0.22685190926977272, penalty=l2..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/3; 6/10] END C=0.22685190926977272, penalty=l2;, score=0.644 total time=  19.7s\n",
      "[CV 3/3; 6/10] START C=0.22685190926977272, penalty=l2..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 3/3; 6/10] END C=0.22685190926977272, penalty=l2;, score=0.655 total time=  16.9s\n",
      "[CV 1/3; 7/10] START C=1.5337660753031108, penalty=l2...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/3; 7/10] END C=1.5337660753031108, penalty=l2;, score=0.564 total time=  16.3s\n",
      "[CV 2/3; 7/10] START C=1.5337660753031108, penalty=l2...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/3; 7/10] END C=1.5337660753031108, penalty=l2;, score=0.645 total time=  19.1s\n",
      "[CV 3/3; 7/10] START C=1.5337660753031108, penalty=l2...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 3/3; 7/10] END C=1.5337660753031108, penalty=l2;, score=0.652 total time=  15.5s\n",
      "[CV 1/3; 8/10] START C=3.2486749151019727, penalty=l2...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/3; 8/10] END C=3.2486749151019727, penalty=l2;, score=0.598 total time=  15.8s\n",
      "[CV 2/3; 8/10] START C=3.2486749151019727, penalty=l2...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/3; 8/10] END C=3.2486749151019727, penalty=l2;, score=0.644 total time=  17.4s\n",
      "[CV 3/3; 8/10] START C=3.2486749151019727, penalty=l2...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 3/3; 8/10] END C=3.2486749151019727, penalty=l2;, score=0.658 total time=  14.9s\n",
      "[CV 1/3; 9/10] START C=2.2721782443757292, penalty=none.........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/3; 9/10] END C=2.2721782443757292, penalty=none;, score=0.681 total time=  15.3s\n",
      "[CV 2/3; 9/10] START C=2.2721782443757292, penalty=none.........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/3; 9/10] END C=2.2721782443757292, penalty=none;, score=0.647 total time=  16.7s\n",
      "[CV 3/3; 9/10] START C=2.2721782443757292, penalty=none.........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1483: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  warnings.warn(\n",
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 3/3; 9/10] END C=2.2721782443757292, penalty=none;, score=0.670 total time=  15.8s\n",
      "[CV 1/3; 10/10] START C=3.34431505414951, penalty=l2............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/3; 10/10] END C=3.34431505414951, penalty=l2;, score=0.682 total time=  14.9s\n",
      "[CV 2/3; 10/10] START C=3.34431505414951, penalty=l2............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/3; 10/10] END C=3.34431505414951, penalty=l2;, score=0.645 total time=  17.9s\n",
      "[CV 3/3; 10/10] START C=3.34431505414951, penalty=l2............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 3/3; 10/10] END C=3.34431505414951, penalty=l2;, score=0.656 total time=  16.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alejo\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "search = clf.fit(train,label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.98    171037\n",
      "           1       0.13      0.01      0.02      6125\n",
      "\n",
      "    accuracy                           0.96    177162\n",
      "   macro avg       0.55      0.50      0.50    177162\n",
      "weighted avg       0.94      0.96      0.95    177162\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(validation_label, search.predict(validation)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6770678997371734"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(validation_label, search.predict_proba(validation)[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('model_backup', 'wb') as model_backup_file:\n",
    "    pickle.dump(search, model_backup_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>TransactionDT</th>\n",
       "      <th>TransactionAmt</th>\n",
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       "      <td>111.0</td>\n",
       "      <td>150.0</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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       "      <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",
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       "      <td>NaN</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",
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       "    <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",
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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",
       "      <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",
       "      <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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       "      <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",
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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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 393 columns</p>\n",
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      ],
      "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": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction_test = pd.read_csv('test_transaction.csv')\n",
    "transaction_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>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>3663586</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>280290.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <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>MYA-L13 Build/HUAWEIMYA-L13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3663588</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3579.0</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 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",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>mobile</td>\n",
       "      <td>LGLS676 Build/MXB48T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3663597</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>185210.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <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",
       "      <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 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",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-33.0</td>\n",
       "      <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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "identity_test = pd.read_csv('test_identity.csv')\n",
    "identity_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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": 60,
     "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": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "del transaction_test\n",
    "del identity_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in joined_test_data.columns:\n",
    "    if joined_test_data[column].dtype == 'object':\n",
    "        joined_test_data[column].fillna('desconocido',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "joined_test_data.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "transaction_ids = joined_test_data.TransactionID\n",
    "del joined_test_data['TransactionID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id-01\n",
      "id-02\n",
      "id-03\n",
      "id-04\n",
      "id-05\n",
      "id-06\n",
      "id-07\n",
      "id-08\n",
      "id-09\n",
      "id-10\n",
      "id-11\n",
      "id-12\n",
      "id-13\n",
      "id-14\n",
      "id-15\n",
      "id-16\n",
      "id-17\n",
      "id-18\n",
      "id-19\n",
      "id-20\n",
      "id-21\n",
      "id-22\n",
      "id-23\n",
      "id-24\n",
      "id-25\n",
      "id-26\n",
      "id-27\n",
      "id-28\n",
      "id-29\n",
      "id-30\n",
      "id-31\n",
      "id-32\n",
      "id-33\n",
      "id-34\n",
      "id-35\n",
      "id-36\n",
      "id-37\n",
      "id-38\n"
     ]
    }
   ],
   "source": [
    "col = {}\n",
    "for c in joined_test_data.columns:\n",
    "    if c not in join_train_values.columns:\n",
    "        print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "joined_test_data.rename(columns=c,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TransactionDT       int64\n",
       "TransactionAmt    float64\n",
       "ProductCD          object\n",
       "card1               int64\n",
       "card2             float64\n",
       "                   ...   \n",
       "id_36              object\n",
       "id_37              object\n",
       "id_38              object\n",
       "DeviceType         object\n",
       "DeviceInfo         object\n",
       "Length: 432, dtype: object"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_test_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Procesando:  ProductCD\n",
      "Procesando:  card4\n",
      "Procesando:  card6\n",
      "Procesando:  P_emaildomain\n",
      "Procesando:  R_emaildomain\n",
      "Procesando:  M1\n",
      "Procesando:  M2\n",
      "Procesando:  M3\n",
      "Procesando:  M4\n",
      "Procesando:  M5\n",
      "Procesando:  M6\n",
      "Procesando:  M7\n",
      "Procesando:  M8\n",
      "Procesando:  M9\n",
      "Procesando:  id_12\n",
      "Procesando:  id_15\n",
      "Procesando:  id_16\n",
      "Procesando:  id_23\n",
      "Procesando:  id_27\n",
      "Procesando:  id_28\n",
      "Procesando:  id_29\n",
      "Procesando:  id_30\n",
      "Procesando:  id_31\n",
      "Procesando:  id_33\n",
      "Procesando:  id_34\n",
      "Procesando:  id_35\n",
      "Procesando:  id_36\n",
      "Procesando:  id_37\n",
      "Procesando:  id_38\n",
      "Procesando:  DeviceType\n",
      "Procesando:  DeviceInfo\n"
     ]
    }
   ],
   "source": [
    "for c in joined_test_data.columns:\n",
    "    if (c) in ordinal_dic:\n",
    "        print(\"Procesando: \", c)\n",
    "        test_encoded = ordinal_dic[c].transform(joined_test_data[c].astype(str).values.reshape(-1,1))\n",
    "        joined_test_data[c] = test_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = search.predict_proba(joined_test_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.97177386, 0.02822614],\n",
       "       [0.96425534, 0.03574466],\n",
       "       [0.97429235, 0.02570765],\n",
       "       ...,\n",
       "       [0.96905763, 0.03094237],\n",
       "       [0.96809299, 0.03190701],\n",
       "       [0.61678755, 0.38321245]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "proba = []\n",
    "for i in predictions:\n",
    "    proba.append(i[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
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       " 0.0457840467075809,\n",
       " 0.020895159547430806,\n",
       " 0.016911934078239235,\n",
       " 0.028552570239018606,\n",
       " 0.05084870295212864,\n",
       " 0.0482371472133431,\n",
       " 0.04501529640804365,\n",
       " 0.031097752813226204,\n",
       " 0.03530089321120307,\n",
       " 0.024229778388700385,\n",
       " 0.02602164340085178,\n",
       " 0.04691772577692107,\n",
       " 0.04330124953629622,\n",
       " 0.032652331197946124,\n",
       " 0.03168829161391812,\n",
       " 0.019702739521868018,\n",
       " 0.06772164278635677,\n",
       " 0.017719306103476055,\n",
       " 0.023114566096665134,\n",
       " 0.014980935881643606,\n",
       " 0.043389825023213174,\n",
       " 0.018485065527302055,\n",
       " 0.043840180100175555,\n",
       " 0.027399304649418584,\n",
       " 0.04332708427143173,\n",
       " 0.0619826704859677,\n",
       " 0.037873450290652616,\n",
       " 0.026849392578417907,\n",
       " 0.015127063456241303,\n",
       " 0.025071177424662413,\n",
       " 0.014951038528570786,\n",
       " 0.019296138969849445,\n",
       " 0.033753320219714895,\n",
       " 0.04379029666064441,\n",
       " 0.01596046864816229,\n",
       " 0.025352955505420348,\n",
       " 0.04555980579908707,\n",
       " 0.015654612622146926,\n",
       " 0.0419106375137269,\n",
       " 0.033909117968961774,\n",
       " 0.045770558927318364,\n",
       " 0.04761635563313274,\n",
       " 0.0263310629837973,\n",
       " 0.03334015964725175,\n",
       " 0.027464185031204895,\n",
       " 0.010077166315628551,\n",
       " 0.010220058246104486,\n",
       " 0.010213710585212338,\n",
       " 0.028962830239473252,\n",
       " 0.049985171698792054,\n",
       " 0.010060249629359924,\n",
       " 0.01005400021709664,\n",
       " 0.03931409447166319,\n",
       " 0.13848423656767003,\n",
       " 0.29109125974083727,\n",
       " 0.026073155325394403,\n",
       " 0.029391402501140224,\n",
       " 0.03552907858059779,\n",
       " 0.05337929597862456,\n",
       " 0.02445618704393812,\n",
       " 0.07590758087779595,\n",
       " 0.01430462970969152,\n",
       " 0.015289242836621829,\n",
       " 0.045761932915469365,\n",
       " 0.05368803303527402,\n",
       " 0.029124503598216948,\n",
       " 0.05508589079618519,\n",
       " 0.0471568490358609,\n",
       " 0.07737379771457219,\n",
       " 0.33421299812256355,\n",
       " 0.04133238518321936,\n",
       " 0.05681837121097195,\n",
       " 0.03607214548784985,\n",
       " 0.040160829877452016,\n",
       " 0.020655026452431367,\n",
       " 0.026303441316206665,\n",
       " 0.01417670416044955,\n",
       " 0.0539710176406519,\n",
       " 0.03564605675927199,\n",
       " 0.023752479666095037,\n",
       " 0.014868553699277193,\n",
       " 0.02627248427521731,\n",
       " ...]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_serie = pd.Series(proba)\n",
    "submit = pd.concat([transaction_ids,predict_serie],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "submit.rename(columns={0:'isFraud'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "submit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "submit.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = pd.read_csv('sample_submission.csv')\n",
    "sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "submit.to_csv('respuesta_kaggle.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>isFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>506690</th>\n",
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       "      <td>0.383212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506691 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TransactionID   isFraud\n",
       "0             3663549  0.028226\n",
       "1             3663550  0.035745\n",
       "2             3663551  0.025708\n",
       "3             3663552  0.030557\n",
       "4             3663553  0.022851\n",
       "...               ...       ...\n",
       "506686        4170235  0.038082\n",
       "506687        4170236  0.564323\n",
       "506688        4170237  0.030942\n",
       "506689        4170238  0.031907\n",
       "506690        4170239  0.383212\n",
       "\n",
       "[506691 rows x 2 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit"
   ]
  },
  {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![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
}