logistic_regression_fraud.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/ieee-fraud-detection/test_identity.csv\n",
      "/kaggle/input/ieee-fraud-detection/sample_submission.csv\n",
      "/kaggle/input/ieee-fraud-detection/train_identity.csv\n",
      "/kaggle/input/ieee-fraud-detection/train_transaction.csv\n",
      "/kaggle/input/ieee-fraud-detection/test_transaction.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['test_identity.csv',\n",
       " 'sample_submission.csv',\n",
       " 'train_identity.csv',\n",
       " 'train_transaction.csv',\n",
       " 'test_transaction.csv']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('../input/ieee-fraud-detection')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_identity_df = pd.read_csv(\"../input/ieee-fraud-detection/train_identity.csv\")\n",
    "train_transaction_df = pd.read_csv(\"../input/ieee-fraud-detection/train_transaction.csv\")\n",
    "test_transaction_df = pd.read_csv(\"../input/ieee-fraud-detection/test_transaction.csv\")\n",
    "test_identity_df = pd.read_csv(\"../input/ieee-fraud-detection/test_identity.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions of the Train Identity set: (144233, 41)\n",
      "Dimensions of the Train Transaction set: (590540, 394)\n",
      "Dimensions of the Test Transaction set: (506691, 393)\n",
      "Dimensions of the Test Identity set: (141907, 41)\n"
     ]
    }
   ],
   "source": [
    "# shape for the dimensions of the dataset\n",
    "print('Dimensions of the Train Identity set:', train_identity_df.shape)\n",
    "print('Dimensions of the Train Transaction set:', train_transaction_df.shape)\n",
    "print('Dimensions of the Test Transaction set:', test_transaction_df.shape)\n",
    "print('Dimensions of the Test Identity set:', test_identity_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
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       "      <td>T</td>\n",
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       "      <td>chrome 62.0</td>\n",
       "      <td>24.0</td>\n",
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       "      <td>T</td>\n",
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       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>desktop</td>\n",
       "      <td>MacOS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "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",
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       "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_identity_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <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",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <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",
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      ],
      "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_transaction_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "144233\n",
      "141907\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(train_transaction_df['TransactionID'].isin(train_identity_df['TransactionID'].unique())))\n",
    "print(np.sum(test_transaction_df['TransactionID'].isin(test_identity_df['TransactionID'].unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.merge(train_transaction_df, train_identity_df, how=\"left\", on=\"TransactionID\")\n",
    "test_df = pd.merge(test_transaction_df, test_identity_df, how=\"left\", on=\"TransactionID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Dataset shape:  (590540, 434)\n",
      "Test Dataset shape  (506691, 433)\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Dataset shape: \", train_df.shape)\n",
    "print(\"Test Dataset shape \", test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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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>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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reduce memory by moving all numbers to the lowest possible numpy data type\n",
    "def reduce_mem_usage(df, verbose=True):\n",
    "    numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "    start_mem = df.memory_usage().sum() / 1024**2\n",
    "    for col in df.columns:\n",
    "        col_type = df[col].dtypes\n",
    "        if col_type in numerics:\n",
    "            c_min = df[col].min()\n",
    "            c_max = df[col].max()\n",
    "            if str(col_type)[:3] == 'int':\n",
    "                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
    "                    df[col] = df[col].astype(np.int8)\n",
    "                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
    "                    df[col] = df[col].astype(np.int16)\n",
    "                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
    "                    df[col] = df[col].astype(np.int32)\n",
    "                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
    "                    df[col] = df[col].astype(np.int64)\n",
    "            else:\n",
    "                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
    "                    df[col] = df[col].astype(np.float16)\n",
    "                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
    "                    df[col] = df[col].astype(np.float32)\n",
    "                else:\n",
    "                    df[col] = df[col].astype(np.float64)\n",
    "    end_mem = df.memory_usage().sum() / 1024**2\n",
    "    if verbose: print('Mem. usage decreased to {:5.2f} Mb({:.1f}% reduction)'.format(end_mem, 100 * (start_mem - end_mem) /start_mem))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mem. usage decreased to 650.48 Mb(66.8% reduction)\n",
      "Mem. usage decreased to 565.37 Mb(66.3% reduction)\n"
     ]
    }
   ],
   "source": [
    "train_df = reduce_mem_usage(train_df)\n",
    "test_df = reduce_mem_usage(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'card4' has 5 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'card6' has 5 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'P_emaildomain' has 60 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'M1' has 3 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'M2' has 3 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'M3' has 3 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'M4' has 4 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'M5' has 3 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'M6' has 3 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'M7' has 3 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'M8' has 3 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'M9' has 3 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'id_30' has 76 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'id_31' has 131 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'id_33' has 261 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'id_34' has 5 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n",
      "Feature 'DeviceInfo' has 1787 categories\n",
      "********************************\n",
      "Test set:\n",
      "Feature 'ProductCD' has 5 categories\n",
      "Feature 'card4' has 5 categories\n",
      "Feature 'card6' has 4 categories\n",
      "Feature 'P_emaildomain' has 61 categories\n",
      "Feature 'R_emaildomain' has 61 categories\n",
      "Feature 'M1' has 3 categories\n",
      "Feature 'M2' has 3 categories\n",
      "Feature 'M3' has 3 categories\n",
      "Feature 'M4' has 4 categories\n",
      "Feature 'M5' has 3 categories\n",
      "Feature 'M6' has 3 categories\n",
      "Feature 'M7' has 3 categories\n",
      "Feature 'M8' has 3 categories\n",
      "Feature 'M9' has 3 categories\n",
      "Feature 'id_12' has 3 categories\n",
      "Feature 'id_15' has 4 categories\n",
      "Feature 'id_16' has 3 categories\n",
      "Feature 'id_23' has 4 categories\n",
      "Feature 'id_27' has 3 categories\n",
      "Feature 'id_28' has 3 categories\n",
      "Feature 'id_29' has 3 categories\n",
      "Feature 'id_30' has 87 categories\n",
      "Feature 'id_31' has 136 categories\n",
      "Feature 'id_33' has 391 categories\n",
      "Feature 'id_34' has 3 categories\n",
      "Feature 'id_35' has 3 categories\n",
      "Feature 'id_36' has 3 categories\n",
      "Feature 'id_37' has 3 categories\n",
      "Feature 'id_38' has 3 categories\n",
      "Feature 'DeviceType' has 3 categories\n",
      "Feature 'DeviceInfo' has 2227 categories\n"
     ]
    }
   ],
   "source": [
    "# features\n",
    "print('Training set:')\n",
    "for col_name in train_df.columns:\n",
    "    if train_df[col_name].dtypes == 'object':\n",
    "        unique_cat = len(train_df[col_name].unique())\n",
    "        print(\"Feature '{col_name}' has {unique_cat} categories\".format(col_name=col_name, unique_cat=unique_cat))\n",
    "        \n",
    "        print(\"********************************\")\n",
    "        \n",
    "        print('Test set:')\n",
    "        for col_name in test_df.columns:\n",
    "            if test_df[col_name].dtypes == 'object':\n",
    "                unique_cat = len(test_df[col_name].unique())\n",
    "                print(\"Feature '{col_name}' has {unique_cat} categories\".format(col_name=col_name, unique_cat=unique_cat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TransactionID</th>\n",
       "      <td>590540.0</td>\n",
       "      <td>3.282270e+06</td>\n",
       "      <td>1.704744e+05</td>\n",
       "      <td>2.987000e+06</td>\n",
       "      <td>3.134635e+06</td>\n",
       "      <td>3282269.50</td>\n",
       "      <td>3429904.25</td>\n",
       "      <td>3577539.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isFraud</th>\n",
       "      <td>590540.0</td>\n",
       "      <td>3.499001e-02</td>\n",
       "      <td>1.837546e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TransactionDT</th>\n",
       "      <td>590540.0</td>\n",
       "      <td>7.372311e+06</td>\n",
       "      <td>4.617224e+06</td>\n",
       "      <td>8.640000e+04</td>\n",
       "      <td>3.027058e+06</td>\n",
       "      <td>7306527.50</td>\n",
       "      <td>11246620.00</td>\n",
       "      <td>15811131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TransactionAmt</th>\n",
       "      <td>590540.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.509766e-01</td>\n",
       "      <td>4.331250e+01</td>\n",
       "      <td>68.75</td>\n",
       "      <td>125.00</td>\n",
       "      <td>31936.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>card1</th>\n",
       "      <td>590540.0</td>\n",
       "      <td>9.898735e+03</td>\n",
       "      <td>4.901170e+03</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>6.019000e+03</td>\n",
       "      <td>9678.00</td>\n",
       "      <td>14184.00</td>\n",
       "      <td>18396.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>card2</th>\n",
       "      <td>581607.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>2.140000e+02</td>\n",
       "      <td>361.00</td>\n",
       "      <td>512.00</td>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>card3</th>\n",
       "      <td>588975.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>1.500000e+02</td>\n",
       "      <td>150.00</td>\n",
       "      <td>150.00</td>\n",
       "      <td>231.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>card5</th>\n",
       "      <td>586281.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>1.660000e+02</td>\n",
       "      <td>226.00</td>\n",
       "      <td>226.00</td>\n",
       "      <td>237.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>addr1</th>\n",
       "      <td>524834.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>2.040000e+02</td>\n",
       "      <td>299.00</td>\n",
       "      <td>330.00</td>\n",
       "      <td>540.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>addr2</th>\n",
       "      <td>524834.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>8.700000e+01</td>\n",
       "      <td>87.00</td>\n",
       "      <td>87.00</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dist1</th>\n",
       "      <td>238269.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>10288.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dist2</th>\n",
       "      <td>37627.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>206.00</td>\n",
       "      <td>11624.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C1</th>\n",
       "      <td>590540.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4684.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C2</th>\n",
       "      <td>590540.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5692.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C3</th>\n",
       "      <td>590540.0</td>\n",
       "      <td>5.649567e-03</td>\n",
       "      <td>1.506348e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C4</th>\n",
       "      <td>590540.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C5</th>\n",
       "      <td>590540.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>349.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C6</th>\n",
       "      <td>590540.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C7</th>\n",
       "      <td>590540.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C8</th>\n",
       "      <td>590540.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3332.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C9</th>\n",
       "      <td>590540.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C10</th>\n",
       "      <td>590540.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C11</th>\n",
       "      <td>590540.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C12</th>\n",
       "      <td>590540.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C13</th>\n",
       "      <td>590540.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>2918.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C14</th>\n",
       "      <td>590540.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1429.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D1</th>\n",
       "      <td>589271.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>122.00</td>\n",
       "      <td>640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D2</th>\n",
       "      <td>309743.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.600000e+01</td>\n",
       "      <td>97.00</td>\n",
       "      <td>276.00</td>\n",
       "      <td>640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D3</th>\n",
       "      <td>327662.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>819.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D4</th>\n",
       "      <td>421618.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.220000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>26.00</td>\n",
       "      <td>253.00</td>\n",
       "      <td>869.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V333</th>\n",
       "      <td>82351.0</td>\n",
       "      <td>1.014620e+03</td>\n",
       "      <td>7.955782e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>160000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V334</th>\n",
       "      <td>82351.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>55136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V335</th>\n",
       "      <td>82351.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>55136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V336</th>\n",
       "      <td>82351.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>55136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V337</th>\n",
       "      <td>82351.0</td>\n",
       "      <td>5.535242e+01</td>\n",
       "      <td>6.683759e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>104060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V338</th>\n",
       "      <td>82351.0</td>\n",
       "      <td>1.511605e+02</td>\n",
       "      <td>1.095058e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>104060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V339</th>\n",
       "      <td>82351.0</td>\n",
       "      <td>1.007009e+02</td>\n",
       "      <td>8.149532e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>104060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_01</th>\n",
       "      <td>144233.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.000000e+02</td>\n",
       "      <td>-1.000000e+01</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_02</th>\n",
       "      <td>140872.0</td>\n",
       "      <td>1.747169e+05</td>\n",
       "      <td>1.596500e+05</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.799200e+04</td>\n",
       "      <td>125800.50</td>\n",
       "      <td>228749.00</td>\n",
       "      <td>999595.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_03</th>\n",
       "      <td>66324.0</td>\n",
       "      <td>6.008911e-02</td>\n",
       "      <td>5.981445e-01</td>\n",
       "      <td>-1.300000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_04</th>\n",
       "      <td>66324.0</td>\n",
       "      <td>-5.905151e-02</td>\n",
       "      <td>7.011719e-01</td>\n",
       "      <td>-2.800000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_05</th>\n",
       "      <td>136865.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-7.200000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_06</th>\n",
       "      <td>136865.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.000000e+02</td>\n",
       "      <td>-6.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_07</th>\n",
       "      <td>5155.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.138281e+01</td>\n",
       "      <td>-4.600000e+01</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_08</th>\n",
       "      <td>5155.0</td>\n",
       "      <td>-inf</td>\n",
       "      <td>2.607812e+01</td>\n",
       "      <td>-1.000000e+02</td>\n",
       "      <td>-4.800000e+01</td>\n",
       "      <td>-34.00</td>\n",
       "      <td>-23.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_09</th>\n",
       "      <td>74926.0</td>\n",
       "      <td>9.118652e-02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-3.600000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_10</th>\n",
       "      <td>74926.0</td>\n",
       "      <td>-3.010254e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.000000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_11</th>\n",
       "      <td>140978.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.000000e+01</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_13</th>\n",
       "      <td>127320.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>4.900000e+01</td>\n",
       "      <td>52.00</td>\n",
       "      <td>52.00</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_14</th>\n",
       "      <td>80044.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.600000e+02</td>\n",
       "      <td>-3.600000e+02</td>\n",
       "      <td>-300.00</td>\n",
       "      <td>-300.00</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_17</th>\n",
       "      <td>139369.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>1.660000e+02</td>\n",
       "      <td>166.00</td>\n",
       "      <td>225.00</td>\n",
       "      <td>229.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_18</th>\n",
       "      <td>45113.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.561523e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>15.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_19</th>\n",
       "      <td>139318.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>2.660000e+02</td>\n",
       "      <td>341.00</td>\n",
       "      <td>427.00</td>\n",
       "      <td>671.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_20</th>\n",
       "      <td>139261.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>2.560000e+02</td>\n",
       "      <td>472.00</td>\n",
       "      <td>533.00</td>\n",
       "      <td>661.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_21</th>\n",
       "      <td>5159.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>2.520000e+02</td>\n",
       "      <td>252.00</td>\n",
       "      <td>486.50</td>\n",
       "      <td>854.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_22</th>\n",
       "      <td>5169.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>6.898438e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>14.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_24</th>\n",
       "      <td>4747.0</td>\n",
       "      <td>1.282031e+01</td>\n",
       "      <td>2.373047e+00</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>11.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_25</th>\n",
       "      <td>5132.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>9.743750e+01</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>3.210000e+02</td>\n",
       "      <td>321.00</td>\n",
       "      <td>371.00</td>\n",
       "      <td>548.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_26</th>\n",
       "      <td>5163.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>3.209375e+01</td>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>1.190000e+02</td>\n",
       "      <td>149.00</td>\n",
       "      <td>169.00</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_32</th>\n",
       "      <td>77586.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>24.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>403 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count          mean           std           min  \\\n",
       "TransactionID   590540.0  3.282270e+06  1.704744e+05  2.987000e+06   \n",
       "isFraud         590540.0  3.499001e-02  1.837546e-01  0.000000e+00   \n",
       "TransactionDT   590540.0  7.372311e+06  4.617224e+06  8.640000e+04   \n",
       "TransactionAmt  590540.0           NaN           NaN  2.509766e-01   \n",
       "card1           590540.0  9.898735e+03  4.901170e+03  1.000000e+03   \n",
       "...                  ...           ...           ...           ...   \n",
       "id_22             5169.0           inf  6.898438e+00  1.000000e+01   \n",
       "id_24             4747.0  1.282031e+01  2.373047e+00  1.100000e+01   \n",
       "id_25             5132.0           inf  9.743750e+01  1.000000e+02   \n",
       "id_26             5163.0           inf  3.209375e+01  1.000000e+02   \n",
       "id_32            77586.0           NaN  0.000000e+00  0.000000e+00   \n",
       "\n",
       "                         25%         50%          75%         max  \n",
       "TransactionID   3.134635e+06  3282269.50   3429904.25   3577539.0  \n",
       "isFraud         0.000000e+00        0.00         0.00         1.0  \n",
       "TransactionDT   3.027058e+06  7306527.50  11246620.00  15811131.0  \n",
       "TransactionAmt  4.331250e+01       68.75       125.00     31936.0  \n",
       "card1           6.019000e+03     9678.00     14184.00     18396.0  \n",
       "...                      ...         ...          ...         ...  \n",
       "id_22           1.400000e+01       14.00        14.00        44.0  \n",
       "id_24           1.100000e+01       11.00        15.00        26.0  \n",
       "id_25           3.210000e+02      321.00       371.00       548.0  \n",
       "id_26           1.190000e+02      149.00       169.00       216.0  \n",
       "id_32           2.400000e+01       24.00        32.00        32.0  \n",
       "\n",
       "[403 rows x 8 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count Plot for Fraudulent vs legitimate transaction\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    96.500999\n",
       "1     3.499001\n",
       "Name: isFraud, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train_df['isFraud'])\n",
    "print(\"Count Plot for Fraudulent vs legitimate transaction\")\n",
    "train_df['isFraud'].value_counts(normalize=True) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of Fraudulent records in dataset 3.50\n"
     ]
    }
   ],
   "source": [
    "percent_fraud = len(train_df[train_df['isFraud']==1].index) * 100 / train_df.shape[0]\n",
    "print(\"Percentage of Fraudulent records in dataset {:.2f}\".format(percent_fraud))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Count</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8933</td>\n",
       "      <td>1565</td>\n",
       "      <td>1577</td>\n",
       "      <td>4259</td>\n",
       "      <td>...</td>\n",
       "      <td>450258</td>\n",
       "      <td>512954</td>\n",
       "      <td>517251</td>\n",
       "      <td>512735</td>\n",
       "      <td>449555</td>\n",
       "      <td>449555</td>\n",
       "      <td>449555</td>\n",
       "      <td>449555</td>\n",
       "      <td>449730</td>\n",
       "      <td>471874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.51268</td>\n",
       "      <td>0.265012</td>\n",
       "      <td>0.267044</td>\n",
       "      <td>0.721204</td>\n",
       "      <td>...</td>\n",
       "      <td>76.2451</td>\n",
       "      <td>86.8619</td>\n",
       "      <td>87.5895</td>\n",
       "      <td>86.8248</td>\n",
       "      <td>76.1261</td>\n",
       "      <td>76.1261</td>\n",
       "      <td>76.1261</td>\n",
       "      <td>76.1261</td>\n",
       "      <td>76.1557</td>\n",
       "      <td>79.9055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dtypes</th>\n",
       "      <td>int32</td>\n",
       "      <td>int8</td>\n",
       "      <td>int32</td>\n",
       "      <td>float16</td>\n",
       "      <td>object</td>\n",
       "      <td>int16</td>\n",
       "      <td>float16</td>\n",
       "      <td>float16</td>\n",
       "      <td>object</td>\n",
       "      <td>float16</td>\n",
       "      <td>...</td>\n",
       "      <td>object</td>\n",
       "      <td>float16</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 434 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TransactionID isFraud TransactionDT TransactionAmt ProductCD  card1  \\\n",
       "Count               0       0             0              0         0      0   \n",
       "Percent             0       0             0              0         0      0   \n",
       "dtypes          int32    int8         int32        float16    object  int16   \n",
       "\n",
       "           card2     card3     card4     card5  ...    id_31    id_32  \\\n",
       "Count       8933      1565      1577      4259  ...   450258   512954   \n",
       "Percent  1.51268  0.265012  0.267044  0.721204  ...  76.2451  86.8619   \n",
       "dtypes   float16   float16    object   float16  ...   object  float16   \n",
       "\n",
       "           id_33    id_34    id_35    id_36    id_37    id_38 DeviceType  \\\n",
       "Count     517251   512735   449555   449555   449555   449555     449730   \n",
       "Percent  87.5895  86.8248  76.1261  76.1261  76.1261  76.1261    76.1557   \n",
       "dtypes    object   object   object   object   object   object     object   \n",
       "\n",
       "        DeviceInfo  \n",
       "Count       471874  \n",
       "Percent    79.9055  \n",
       "dtypes      object  \n",
       "\n",
       "[3 rows x 434 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def missing_data(df):\n",
    "    count = df.isnull().sum()\n",
    "    percent = (df.isnull().sum()) / (df.isnull().count()) * 100\n",
    "    total = pd.concat([count, percent], axis=1, keys = ['Count', 'Percent'])\n",
    "    types = []\n",
    "    for col in df.columns:\n",
    "        dtypes = str(df[col].dtype)\n",
    "        types.append(dtypes)\n",
    "    total['dtypes'] = types\n",
    "    \n",
    "    return np.transpose(total)\n",
    "missing_data(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['dist1', 'dist2', 'R_emaildomain', 'D5', 'D6', 'D7', 'D8', 'D9',\n",
       "       'D12', 'D13', 'D14', 'M5', 'M7', 'M8', 'M9', 'V138', 'V139',\n",
       "       'V140', 'V141', 'V142', 'V143', 'V144', 'V145', 'V146', 'V147',\n",
       "       'V148', 'V149', 'V150', 'V151', 'V152', 'V153', 'V154', 'V155',\n",
       "       'V156', 'V157', 'V158', 'V159', 'V160', 'V161', 'V162', 'V163',\n",
       "       'V164', 'V165', 'V166', 'V167', 'V168', 'V169', 'V170', 'V171',\n",
       "       'V172', 'V173', 'V174', 'V175', 'V176', 'V177', 'V178', 'V179',\n",
       "       'V180', 'V181', 'V182', 'V183', 'V184', 'V185', 'V186', 'V187',\n",
       "       'V188', 'V189', 'V190', 'V191', 'V192', 'V193', 'V194', 'V195',\n",
       "       'V196', 'V197', 'V198', 'V199', 'V200', 'V201', 'V202', 'V203',\n",
       "       'V204', 'V205', 'V206', 'V207', 'V208', 'V209', 'V210', 'V211',\n",
       "       'V212', 'V213', 'V214', 'V215', 'V216', 'V217', 'V218', 'V219',\n",
       "       'V220', 'V221', 'V222', 'V223', 'V224', 'V225', 'V226', 'V227',\n",
       "       'V228', 'V229', 'V230', 'V231', 'V232', 'V233', 'V234', 'V235',\n",
       "       'V236', 'V237', 'V238', 'V239', 'V240', 'V241', 'V242', 'V243',\n",
       "       'V244', 'V245', 'V246', 'V247', 'V248', 'V249', 'V250', 'V251',\n",
       "       'V252', 'V253', 'V254', 'V255', 'V256', 'V257', 'V258', 'V259',\n",
       "       'V260', 'V261', 'V262', 'V263', 'V264', 'V265', 'V266', 'V267',\n",
       "       'V268', 'V269', 'V270', 'V271', 'V272', 'V273', 'V274', 'V275',\n",
       "       'V276', 'V277', 'V278', 'V322', 'V323', 'V324', 'V325', 'V326',\n",
       "       'V327', 'V328', 'V329', 'V330', 'V331', 'V332', 'V333', 'V334',\n",
       "       'V335', 'V336', 'V337', 'V338', 'V339', 'id_01', 'id_02', 'id_03',\n",
       "       'id_04', 'id_05', 'id_06', 'id_07', 'id_08', 'id_09', 'id_10',\n",
       "       'id_11', 'id_12', 'id_13', 'id_14', 'id_15', 'id_16', 'id_17',\n",
       "       'id_18', 'id_19', 'id_20', 'id_21', 'id_22', 'id_23', 'id_24',\n",
       "       'id_25', 'id_26', 'id_27', 'id_28', 'id_29', 'id_30', 'id_31',\n",
       "       'id_32', 'id_33', 'id_34', 'id_35', 'id_36', 'id_37', 'id_38',\n",
       "       'DeviceType', 'DeviceInfo'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop columns with more than 50% Null\n",
    "null_percent = train_df.isnull().sum() / train_df.shape[0]*100\n",
    "cols_to_drop = np.array(null_percent[null_percent > 50].index)\n",
    "cols_to_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.drop(cols_to_drop, axis=1)\n",
    "test_df = test_df.drop(cols_to_drop, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(590540, 220)\n",
      "(506691, 219)\n"
     ]
    }
   ],
   "source": [
    "print(train_df.shape)\n",
    "print(test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.fillna(-999)\n",
    "test_df = test_df.fillna(-999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_y = train_df['isFraud']\n",
    "train_x = train_df.drop('isFraud', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "for f in train_x.columns:\n",
    "    if train_x[f].dtype == 'object' or test_df[f].dtype == 'object':\n",
    "        lbl = preprocessing.LabelEncoder()\n",
    "        lbl.fit(list(train_x[f].values) + list(test_df[f].values))\n",
    "        train_x[f] = lbl.transform(list(train_x[f].values))\n",
    "        test_df[f] = lbl.transform(list(test_df[f].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# logistic regression\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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",
       "    <tr>\n",
       "      <th>TransactionID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3663549</th>\n",
       "      <td>0.016161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3663550</th>\n",
       "      <td>0.018259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3663551</th>\n",
       "      <td>0.026362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3663552</th>\n",
       "      <td>0.029719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3663553</th>\n",
       "      <td>0.027054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                isFraud\n",
       "TransactionID          \n",
       "3663549        0.016161\n",
       "3663550        0.018259\n",
       "3663551        0.026362\n",
       "3663552        0.029719\n",
       "3663553        0.027054"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv', index_col = 'TransactionID')\n",
    "submission['isFraud'] = logreg.predict_proba(test_df)[:,1]\n",
    "submission.to_csv('logreg_submission.csv')\n",
    "submission.head()"
   ]
  }
 ],
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