group_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ef2abafc",
   "metadata": {},
   "source": [
    "# Mid Term Project - Group 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fd883ab",
   "metadata": {},
   "source": [
    "We have two data sets train_transaction.csv and train_identity.csv . \n",
    "\n",
    "Using these training data sets our aim is to use machine learning algorithms and create a model and predict whether the transaction is fraudulant or not."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d52f881",
   "metadata": {},
   "source": [
    "In one of the data sets (train_transaction) there is column (isFraud) which has two unique values i.e., 0 and 1 \n",
    "\n",
    "'0' represents a legit transaction \n",
    "'1' represents a fradulant transaction "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44be2ae1",
   "metadata": {},
   "source": [
    "## Objective and Performance Metric \n",
    "\n",
    "Objective: Maximize the area under ROC between predicted and given values.\n",
    "\n",
    "Performance Metric = Area under ROC curve (AUC)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61db5fd7",
   "metadata": {},
   "source": [
    "Before we jump into creating model, first we need to perform exploratory data analysis which includes cleaning, preprocessing of the data sets given to us so it can be an ideal data set to create a model on."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42caacd6",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "63786a29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all the dependencies/ libraries we need \n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import gc\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f2553cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading identity and transaction file of training data sets\n",
    "\n",
    "train_identity = pd.read_csv('train_identity.csv')\n",
    "train_transaction = pd.read_csv('train_transaction.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4bf39416",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TransactionID</th>\n",
       "      <th>id_01</th>\n",
       "      <th>id_02</th>\n",
       "      <th>id_03</th>\n",
       "      <th>id_04</th>\n",
       "      <th>id_05</th>\n",
       "      <th>id_06</th>\n",
       "      <th>id_07</th>\n",
       "      <th>id_08</th>\n",
       "      <th>id_09</th>\n",
       "      <th>...</th>\n",
       "      <th>id_31</th>\n",
       "      <th>id_32</th>\n",
       "      <th>id_33</th>\n",
       "      <th>id_34</th>\n",
       "      <th>id_35</th>\n",
       "      <th>id_36</th>\n",
       "      <th>id_37</th>\n",
       "      <th>id_38</th>\n",
       "      <th>DeviceType</th>\n",
       "      <th>DeviceInfo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2987004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70787.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>samsung browser 6.2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2220x1080</td>\n",
       "      <td>match_status:2</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>mobile</td>\n",
       "      <td>SAMSUNG SM-G892A Build/NRD90M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2987008</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>98945.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>mobile safari 11.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1334x750</td>\n",
       "      <td>match_status:1</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>mobile</td>\n",
       "      <td>iOS Device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2987010</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>191631.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>desktop</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2987011</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>221832.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>desktop</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2987016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7460.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 62.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1280x800</td>\n",
       "      <td>match_status:2</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>desktop</td>\n",
       "      <td>MacOS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TransactionID  id_01     id_02  id_03  id_04  id_05  id_06  id_07  id_08  \\\n",
       "0        2987004    0.0   70787.0    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1        2987008   -5.0   98945.0    NaN    NaN    0.0   -5.0    NaN    NaN   \n",
       "2        2987010   -5.0  191631.0    0.0    0.0    0.0    0.0    NaN    NaN   \n",
       "3        2987011   -5.0  221832.0    NaN    NaN    0.0   -6.0    NaN    NaN   \n",
       "4        2987016    0.0    7460.0    0.0    0.0    1.0    0.0    NaN    NaN   \n",
       "\n",
       "   id_09  ...                id_31  id_32      id_33           id_34  id_35  \\\n",
       "0    NaN  ...  samsung browser 6.2   32.0  2220x1080  match_status:2      T   \n",
       "1    NaN  ...   mobile safari 11.0   32.0   1334x750  match_status:1      T   \n",
       "2    0.0  ...          chrome 62.0    NaN        NaN             NaN      F   \n",
       "3    NaN  ...          chrome 62.0    NaN        NaN             NaN      F   \n",
       "4    0.0  ...          chrome 62.0   24.0   1280x800  match_status:2      T   \n",
       "\n",
       "  id_36 id_37  id_38  DeviceType                     DeviceInfo  \n",
       "0     F     T      T      mobile  SAMSUNG SM-G892A Build/NRD90M  \n",
       "1     F     F      T      mobile                     iOS Device  \n",
       "2     F     T      T     desktop                        Windows  \n",
       "3     F     T      T     desktop                            NaN  \n",
       "4     F     T      T     desktop                          MacOS  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's see the head data of both data sets\n",
    "train_identity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e555b0a9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TransactionID</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>TransactionDT</th>\n",
       "      <th>TransactionAmt</th>\n",
       "      <th>ProductCD</th>\n",
       "      <th>card1</th>\n",
       "      <th>card2</th>\n",
       "      <th>card3</th>\n",
       "      <th>card4</th>\n",
       "      <th>card5</th>\n",
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       "      <th>V337</th>\n",
       "      <th>V338</th>\n",
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       "  </thead>\n",
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       "      <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",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2987001</td>\n",
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       "      <td>86401</td>\n",
       "      <td>29.0</td>\n",
       "      <td>W</td>\n",
       "      <td>2755</td>\n",
       "      <td>404.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>102.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2987002</td>\n",
       "      <td>0</td>\n",
       "      <td>86469</td>\n",
       "      <td>59.0</td>\n",
       "      <td>W</td>\n",
       "      <td>4663</td>\n",
       "      <td>490.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>visa</td>\n",
       "      <td>166.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2987003</td>\n",
       "      <td>0</td>\n",
       "      <td>86499</td>\n",
       "      <td>50.0</td>\n",
       "      <td>W</td>\n",
       "      <td>18132</td>\n",
       "      <td>567.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>117.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2987004</td>\n",
       "      <td>0</td>\n",
       "      <td>86506</td>\n",
       "      <td>50.0</td>\n",
       "      <td>H</td>\n",
       "      <td>4497</td>\n",
       "      <td>514.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>mastercard</td>\n",
       "      <td>102.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 394 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TransactionID  isFraud  TransactionDT  TransactionAmt ProductCD  card1  \\\n",
       "0        2987000        0          86400            68.5         W  13926   \n",
       "1        2987001        0          86401            29.0         W   2755   \n",
       "2        2987002        0          86469            59.0         W   4663   \n",
       "3        2987003        0          86499            50.0         W  18132   \n",
       "4        2987004        0          86506            50.0         H   4497   \n",
       "\n",
       "   card2  card3       card4  card5  ... V330  V331  V332  V333  V334 V335  \\\n",
       "0    NaN  150.0    discover  142.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
       "1  404.0  150.0  mastercard  102.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
       "2  490.0  150.0        visa  166.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
       "3  567.0  150.0  mastercard  117.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
       "4  514.0  150.0  mastercard  102.0  ...  0.0   0.0   0.0   0.0   0.0  0.0   \n",
       "\n",
       "  V336  V337  V338  V339  \n",
       "0  NaN   NaN   NaN   NaN  \n",
       "1  NaN   NaN   NaN   NaN  \n",
       "2  NaN   NaN   NaN   NaN  \n",
       "3  NaN   NaN   NaN   NaN  \n",
       "4  0.0   0.0   0.0   0.0  \n",
       "\n",
       "[5 rows x 394 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_transaction.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "256f2692",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of identity:  (144233, 41)\n",
      "shape of transaction:  (590540, 394)\n"
     ]
    }
   ],
   "source": [
    "print('shape of identity: ', train_identity.shape)\n",
    "print('shape of transaction: ', train_transaction.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ce35082",
   "metadata": {},
   "source": [
    "The Identity data set has 41 features and 144233 data rows\n",
    "\n",
    "The transaction data set has 394 features and 590540 data rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4433ad3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merging both training data sets based on transaction ID given in the project description \n",
    "# we use merge function using attributes 'how' and 'on'\n",
    "# 'how' attribute represents how we join the data sets in this case we used left as some of the transaction may not  \n",
    "#  --contain transaction ID in identity column\n",
    "# 'on' attribute represents the primary key column i.e., transactionID \n",
    "\n",
    "train = pd.merge(train_transaction,train_identity, on='TransactionID', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b0d90ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the above cell in my laptop took 10 mins to run, so I am using memory reduction function \n",
    "\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: \n",
    "        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": 8,
   "id": "053ac19d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mem. usage decreased to 650.48 Mb (66.8% reduction)\n",
      "training data set shape:  (590540, 434)\n"
     ]
    }
   ],
   "source": [
    "#reducing the memory using the reduction function\n",
    "train = reduce_mem_usage(train)\n",
    "print('training data set shape: ', train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a316d24e",
   "metadata": {},
   "source": [
    "- 67% memory is reduced using this function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ffa8c227",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now lets dive into data analysis on the each column/ features given in training data set\n",
    "# first on given target column i.e., isFraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4d629fe6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91733\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96.5 % is the legit data\n"
     ]
    }
   ],
   "source": [
    "sns.countplot(train['isFraud'])\n",
    "plt.show()\n",
    "print(np.round(train[train['isFraud']==0].shape[0]/train.shape[0]*100,2), '% is the legit data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "11d5108d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As we can see this is highly imbalanced data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d68eb4f",
   "metadata": {},
   "source": [
    "**Analysis on TransactionDT**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "73b35b39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5.905400e+05\n",
       "mean     7.372311e+06\n",
       "std      4.617224e+06\n",
       "min      8.640000e+04\n",
       "25%      3.027058e+06\n",
       "50%      7.306528e+06\n",
       "75%      1.124662e+07\n",
       "max      1.581113e+07\n",
       "Name: TransactionDT, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe() function best describes the column by calculating it's numerical attributes \n",
    "#like count, mean, std etc \n",
    "\n",
    "# we'll see how TransactionDT gets described\n",
    "train['TransactionDT'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cd9f710e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.066300e+04\n",
       "mean     7.690033e+06\n",
       "std      4.416551e+06\n",
       "min      8.976000e+04\n",
       "25%      3.965858e+06\n",
       "50%      7.575230e+06\n",
       "75%      1.121743e+07\n",
       "max      1.581088e+07\n",
       "Name: TransactionDT, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we are taking account of when fradulant transaction happened and describing them\n",
    "train[train['isFraud']==1]['TransactionDT'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e36c84c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5.698770e+05\n",
       "mean     7.360791e+06\n",
       "std      4.623930e+06\n",
       "min      8.640000e+04\n",
       "25%      3.010651e+06\n",
       "50%      7.271678e+06\n",
       "75%      1.125334e+07\n",
       "max      1.581113e+07\n",
       "Name: TransactionDT, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train['isFraud']==0]['TransactionDT'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "20ff48c4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x23635433be0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution of TransactionDT column \n",
    "sns.displot(train['TransactionDT'], kde=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d552d35",
   "metadata": {},
   "source": [
    "**Analysis on TransactionAmt**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c56f3eca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    569877.000000\n",
       "mean        134.511665\n",
       "std         239.395078\n",
       "min           0.251000\n",
       "25%          43.970000\n",
       "50%          68.500000\n",
       "75%         120.000000\n",
       "max       31937.391000\n",
       "Name: TransactionAmt, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we know that 0 in isFraud is legit transaction, lets take those transaction into one data frame\n",
    "legit_transaction = train_transaction.loc[train_transaction['isFraud']==0]\n",
    "legit_transaction['TransactionAmt'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0f4f5f35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    20663.000000\n",
       "mean       149.244779\n",
       "std        232.212163\n",
       "min          0.292000\n",
       "25%         35.044000\n",
       "50%         75.000000\n",
       "75%        161.000000\n",
       "max       5191.000000\n",
       "Name: TransactionAmt, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# similarily for fraudulant transactions we'll create a seperate data frame\n",
    "fraud_transaction = train_transaction.loc[train_transaction['isFraud']==1]\n",
    "fraud_transaction['TransactionAmt'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6a189646",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the average Transaction amount for legit transaction is:  134.51166457896034\n"
     ]
    }
   ],
   "source": [
    "print('the average Transaction amount for legit transaction is: ', np.mean(legit_transaction['TransactionAmt']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5ceeb1b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the average transaction amount for fraud transaction is:  149.2447785897534\n"
     ]
    }
   ],
   "source": [
    "print('the average transaction amount for fraud transaction is: ', np.mean(fraud_transaction['TransactionAmt']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "43395f11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.98 percentile values:  3777.160400000168\n",
      "99.99 percentile values:  4224.0\n"
     ]
    }
   ],
   "source": [
    "# we are calculating percentiles just to observe if these values are outliers or not \n",
    "# outliers are the values which vastly affect our data set\n",
    "print('99.98 percentile values: ',np.percentile(train['TransactionAmt'],99.98))\n",
    "print('99.99 percentile values: ',np.percentile(train['TransactionAmt'],99.99))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4d2f711",
   "metadata": {},
   "source": [
    "- 99.98 percentile and 99.99 percentile has very large difference, lets check what are these values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "598108b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TransactionID</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>TransactionDT</th>\n",
       "      <th>TransactionAmt</th>\n",
       "      <th>ProductCD</th>\n",
       "      <th>card1</th>\n",
       "      <th>card2</th>\n",
       "      <th>card3</th>\n",
       "      <th>card4</th>\n",
       "      <th>card5</th>\n",
       "      <th>...</th>\n",
       "      <th>id_31</th>\n",
       "      <th>id_32</th>\n",
       "      <th>id_33</th>\n",
       "      <th>id_34</th>\n",
       "      <th>id_35</th>\n",
       "      <th>id_36</th>\n",
       "      <th>id_37</th>\n",
       "      <th>id_38</th>\n",
       "      <th>DeviceType</th>\n",
       "      <th>DeviceInfo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>274336</th>\n",
       "      <td>3261336</td>\n",
       "      <td>0</td>\n",
       "      <td>6652360</td>\n",
       "      <td>31936.0</td>\n",
       "      <td>W</td>\n",
       "      <td>16075</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <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>274339</th>\n",
       "      <td>3261339</td>\n",
       "      <td>0</td>\n",
       "      <td>6652391</td>\n",
       "      <td>31936.0</td>\n",
       "      <td>W</td>\n",
       "      <td>16075</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <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>2 rows × 434 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TransactionID  isFraud  TransactionDT  TransactionAmt ProductCD  \\\n",
       "274336        3261336        0        6652360         31936.0         W   \n",
       "274339        3261339        0        6652391         31936.0         W   \n",
       "\n",
       "        card1  card2  card3       card4  card5  ... id_31  id_32  id_33  \\\n",
       "274336  16075  514.0  150.0  mastercard  102.0  ...   NaN    NaN    NaN   \n",
       "274339  16075  514.0  150.0  mastercard  102.0  ...   NaN    NaN    NaN   \n",
       "\n",
       "        id_34  id_35 id_36 id_37  id_38  DeviceType  DeviceInfo  \n",
       "274336    NaN    NaN   NaN   NaN    NaN         NaN         NaN  \n",
       "274339    NaN    NaN   NaN   NaN    NaN         NaN         NaN  \n",
       "\n",
       "[2 rows x 434 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train['TransactionAmt']>10000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "426a55da",
   "metadata": {},
   "source": [
    "- Only 2 values which have higher than 10,000 and these 2 values seems like duplicate.\n",
    "- Lets remove these 2 values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "38d1b0bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train[train['TransactionAmt']<10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "74a964be",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since some values are high in the dataset lets take log value of this amt and create a new feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "43f331f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['LogTransactionAmt'] = np.log(train['TransactionAmt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "c3728e82",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91733\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\91733\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2369147e730>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train[train['isFraud']==0]['LogTransactionAmt'])\n",
    "sns.distplot(train[train['isFraud']==1]['LogTransactionAmt'])\n",
    "plt.legend(['legit','fraud'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0533e541",
   "metadata": {},
   "source": [
    "- from the figure we can observe that overlap values of fraud and legit is higher after 5 and before 3\n",
    "- And logTransactionAmt from 3 to 5 have higher chance it is legit transaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4b5f1c23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='TransactionDT', ylabel='TransactionAmt'>"
      ]
     },
     "execution_count": 26,
     "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": [
    "# lets see on what dates the fraudulant transaction took place \n",
    "sns.scatterplot(x=\"TransactionDT\",y=\"TransactionAmt\",hue=\"isFraud\",data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1685a3e",
   "metadata": {},
   "source": [
    "**Analysis on ProductCD column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f07137ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ProductCD', ylabel='percentage'>"
      ]
     },
     "execution_count": 27,
     "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": [
    "# we are trying to find on which products most fraudulant transactions happened \n",
    "# for this we use groupby attribute, value_counts tells us the number of unique values are there\n",
    "# then we'll rename it so as to not get confused and finally we'll sort the values alphabetically\n",
    "\n",
    "train_ProductCD = train.groupby(['isFraud'])['ProductCD'].value_counts(normalize=True).rename('percentage').mul(100).reset_index().sort_values('ProductCD')\n",
    "sns.barplot(x=\"ProductCD\", y=\"percentage\", hue=\"isFraud\", data=train_ProductCD)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c94ac497",
   "metadata": {},
   "source": [
    "- we can observe that if ProductCD is 'C' there is 40% chance that it is fraud\n",
    "- similarily ProductCDs 'H', 'R', 'S' rate of doing fraud is also high"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "868e254b",
   "metadata": {},
   "source": [
    "**Let's Analyse card1 column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "58648b7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    590538.000000\n",
       "mean       9898.713741\n",
       "std        4901.165273\n",
       "min        1000.000000\n",
       "25%        6019.000000\n",
       "50%        9678.000000\n",
       "75%       14182.750000\n",
       "max       18396.000000\n",
       "Name: card1, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describing card1 column\n",
    "train['card1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3bc667ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91733\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\91733\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23633ef5880>"
      ]
     },
     "execution_count": 29,
     "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": [
    "# lets plot fraud transactions done on card1\n",
    "\n",
    "sns.distplot(train[train['isFraud']==0]['card1'])\n",
    "sns.distplot(train[train['isFraud']==1]['card1'])\n",
    "plt.legend(['legit','fraud'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e5deb6e",
   "metadata": {},
   "source": [
    "- looks like density of fraudulant transactions on card1 is 0.00016, which is good"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6cf98df",
   "metadata": {},
   "source": [
    "**Similarily I'll do the analysis on card2 - card 5**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "020553fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    581607.000000\n",
       "mean        362.555488\n",
       "std         157.793246\n",
       "min         100.000000\n",
       "25%         214.000000\n",
       "50%         361.000000\n",
       "75%         512.000000\n",
       "max         600.000000\n",
       "Name: card2, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_transaction['card2'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "02f08970",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91733\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\91733\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23634068040>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X56GRwPseqpMQQnjc5hyjezE5yggcydFBtDg0BZUNg1ktn9DTwfE7gPlANYDW+iAQ56lKCSGEp2VkV+DvZ2JERCAASdHGLhI5ZfWDWS2f0NPA0aS1trX+4FwEKFNzhRA+KyOngikJEfiZjdtgiL8fMSFWssvqBrlm3q+ng+NfK6V+BQQqpRYBtwMfeq5aQgxd/Tn4K1yz2R3sOlLFT04bdcLx5Khg9h6tRmuNUrIgsDM9DRwPAD8GdgK3AB8DL3iqUkII0S86mUadW23G7ohmXP0WRtQWAsY06hGRgWzOraC4polhYQEDWVOf0qPAobV2KKXeB97XWpd4tkpCCOFZWbVmAEaFttBYdfx4XKg/AJnFtRI4utDlGIcy/I9SqhTYB+xXSpUopX4zMNUTQoj+l1VjfGdODW054Xhr4DhYVDPgdfIl3Q2O34Mxm2qm1jpaax0FzAbmK6V+5unKCSGEJ2TVmInxbyHMcuIcnxB/PwIsJg4W1w5SzXxDd11V1wGLtNalrQe01lnO/cY/Bx7zZOWEEEOEcyyibeqWY8xR7Q6c3eePy6oxM6pdawNAKUVcaACZEji61F2Lw9I2aLRyjnNYXJwvhBBe73CtH6NdBA4wuqskcHStu8Bh6+VrACilzldK7VdKZSqlHnDxulJKPel8fYdSarobZe9TSmmlVEx39RBCiFZVNkVZk4nUELvL1+NC/Smrs1Fe1+0tbsjqLnBMUUpVu3jUAKd2VVApZQaeARYDE4GrlFIT2522GEhzPpYDz/WkrFIqEVgEdJzwLoQQXThUc3xGlStxztlU0uroXJeBQ2tt1lqHuXiEaq2766qaBWRqrbOcq87foOM+5cuAl7VhPRChlIrvQdnHgP9CVq8LIdx0uNb1jKpWsc6ZVYdKJHB0xp09x901Eshr83M+xoys7s4Z2VVZpdRS4IjWerus7BRCuGPD4XLWF0QDUFhcSmlpx++e4YEWrH4msku7Tj0ylFf4ezJwuLqrt/8tdXaOy+NKqSDg18C53X64Ussxur9IShoav0whRPdKbBYiLc1YTK47LExKkRIdRFY3gWMo62mSw97IBxLb/JwAFPTwnM6OjwZSge1KqWzn8S1KqQ7b2mqtV2it07XW6bGxsjGLEMJQYrMQa23u8pzUmGAOS+DolCcDxyYgTSmVqpSyAlcCK9udsxK4zjm7ag5QpbUu7Kys1nqn1jpOa52itU7BCDDTtdZHPXgdQnRpR7kfuQ3Wwa6G6KGSJgux/l0HjpSYYHLL6mlxyDCqKx4LHFprO3An8BmwF3hTa71bKXWrUupW52kfA1lAJvA3jKy7nZb1VF2F6K3qZsU130Twq70pZFSGDHZ1RDccGspsFuK6aXGMignG1uKQTZ064ckxDrTWH2MEh7bHnm/zXGNsEtWjsi7OSel7LYXovZczA6lpNpEQ0MRjWSN4bvKhwa6ST7I74J2cAFbnDuPahGKsnYw/9FV5sx8tKGK6CRwpzk2dskrrSHTuECiO82RXlRAntTq74oWDQZwd38QtyYXYtYnd1XKT6Y2bvgvn/s1hfFYSyct5nttctKTJWEXQXVdVaqwROLqbWTVUSeDwIQ22Fj7aUUh9k+sVr2JgbSixUGkzceOYekYHNxJoamFnjQQOd+0uqGJtkT93T6hj6bAyviiNZJOHuv1KbM7A0U2LIzbEnxB/Pxkg74RHu6pEP2izEc3zu4N5Ym8wZjQ/G32EmRFtFiiZoyD9xkGo4NC1tcyCWWmmRzezsx4mhdazqzq4H1LwDS1vbMzDatLclFbPnvwyvq8I4/PiyBP/vvtJa+CIsXb95UspRUpMkASOTkiLw0e0aHgrO4AZ0TZirM18Whw52FUa8raWWxgfbifI+fXrlLB6imxWyXHkhnqbnfe3HuHChCYirBqzgtOjK9lZE0RJU/9/r21uqONu60pOyX+dsTmvk1i0Cn+bi4y8QGpMiASOTkjg8BHfFFkpaDBzU1oDc6Oq2VMTRI1dfn2DpUXDtnI/pkUd7/I4NdS4yWRJqooeW59VRk2TnR8mH5+9dHp0NRrF12Xh/fdBLXY48CkPN/yWe01vENBUjtVeQ3zpOqYcfIbhpetAnzggnxoTTH5FPTa7o//qcZKQO4+PeCs7gEirg3Pim5gZUYsDxZYqmf45WDKrzdTaTUyPPt7lMTLARoCphcLqxkGsmW/ZlF2Bn0kxI/p4AI7zb+aU0Dq+LQ/rnw9pqoH1z8CBT1mrp3Gv/4PsSLudXaOXs3XsTykPG09y0Zek7/nDCcEjNSYIh4bc8vr+qcdJRMY4fERGqYXTh9vwN8PooEaiLc1srAjl9Ojqwa7aMUMpd8+WMqOvfHqbG55SRvAolsDRYxnZ5ZwyMpxAv6ITjs+KqOHFvOEcaezjwsrqAtj0N2iqxTHtOm5dfx5LIsuBEgCaLWFkJlyKrehzxua+QWVoGplJlwNGVxXA4dI6xsTJl7S2pMXhA0oaFUWNZk6JMG5SSsHU8Dr21ga1b12LAbKr0o9wi4Pk4BMzrCYENlFc0zRItfItTfYWtudXMTOl43hdunNgvE+LKg98Bt89AY4WmHcXRZHptGjVcUaVUuQOW8SR2NNI3/N/RFYZa41TnWs5DpdK12N70uLwAbsrjW+3p0Qe7xZJDWpkVWkEpTY/Yv09PD0346WOW3p22M7T2PbzUNJlnq2LlzhU48eYMDvtEzQnBNj4usxOg811ym5x3K4jVdjsDtJToqDdAu1oq51RQQ1sqgwFatx7Y61hw1/hs19C2AhI/wkERpBfauzD4XIqrjKxbsr/seSbi5m1+3/hnPMID7IQFWzlcKl0VbUnLQ4fsLvCiO8TI44HiORAozskpyFgUOo01B3qZM/qxECjtVEk3VXd2pRdAUB6susZgukRtWTWBVDc6MZtqsUOH98Hn94P4y6AuXdBYAQAR+qN9+ls8V+zJZyt435OdNUu2PJPoDXZobQ42pPA4QN2VfqREmInzHK8XyopsAmFJrvefxBrNjRVNytKGs0u96weGWBMxZXuqu7tPFJFYlQg0SGu/4bTw2vRKFYV9HCcoyIH/nkRbHoB5t0Fl78CfsffO7+uixaHU/aICymKSofV/wu2OlKiJUuuKxI4fMCuSguTIk7sjgowa4b726TFMQiyjm092rGLMMbajNVsoqhGWhzdOVhUw7hhnc+cSgpsItZq44uCrr8cmVqa4JtH4bl5cHQnXPJXOPd/wXTi7S2/3ky4n73rPFhKsX3s3VBfChkvMio2mKLqJuokW8MJJHB4uSqbIq/OzCkRHf9wk4OayJEWx4DLqjG6DkeFdGxxmJSx9ajMrOqaze4gq6SOscM6H/xWyuiu+rbYSp293WCS1oTXZDL5wFNc/NW5sOpBSF0It30LU650+X75deZuc1QBlEZOg1FnwHdPkBZp3CJl//ETSeDwcpnOb7djwzoGjpTAJopsVupb5Nc4kLJqzPgpTbKLwAFG4CitldXjXckuq8Pu0IwbHtrleekRtdgcim+KrMagd2UeU/Y/wYXfLOXCby9h0qG/URZ+KtzwEVz1OkSmdPpe+fWmbnNUHXP6A1BXwvRyI0H3/qNuDtCf5GRWlZfLqTV+RSku+tOTg4xvtbkN0uoYSIdqzCQFt2DpJF5HB1vZnldJY3MLARbzwFbOR7TeiMcO6zpwTAipJ8bSRG3mt3DwC6gtZoIyUxyVzv7ka8kfdhaNAbFcndL1eiGHhoJ6M5Njexg4kufCyHSid79EoOV37JPAcQIJHF4uu9aMCU1icMfAMcI5EFvY10VSwi1ZNX4uZ1S1igq2ooH8inrGxHV9YxyqDhTVYDYpRjnTl3cmsv4wH1v/SlxNETo8CTX5Ct6LvYMmq3u52oobTdgc3e/DcYLZt6Le/QlXRB7kQFGMW593spM+Di93uNZMQrADq4vfVKy1GTOao849BoTntWjjdzLaxcB4q+hgI5DnlMn8/84cKKohJToIf79OWmRaM7xsPeOzXyXY7OB62/1sGH8/JM11O2gA5NYanzOsB2Mcx0xcBiHDuNLxsbQ42pEWh5fLrjWTHOL6JmVWMMzf5mxxyGDsQDhSZ3xz7bLF4Zxemj2YgaNNOv5uDUI6/gNFtYzvYnxjZMlaEkq+pjx0PP6zb+L7j0cypqCBOT3tamont641cLgx9uRnhRk3MO7rR7A2HqGstqnTqcNDjbQ4vJjWmuwaM6mdDMICDA9oprBJuqoGyiHnjCpXazhaBVvN+PuZyC2T+f+uNDa3kFNW1+n4Rlx5BgklX1MSMYWDiZcRHGBlfpyNz4744+hlip3cOqPLt8eD462mXo1C8wPzNzJA3oYEDi9WVmejxm4ipYvAEe9vo6jJ2uv/oYR7DnWxhqOVUoroYCs5klXVpcziWhza9cB4cH0+yYWfUhGSRtaIi2jN6bI0sZH8ejPrS3rXLZtbZyY+yIGfu3e8yBRsifO41LyWvYXek1B0sEng8GKt+x131eKID7DR5DBR1CC/yoGQVetHhNVBlH/XkToq2CpjHJ04WGx8cx83vN0ajuYGxuS/S7MllEMJl4A6/je9OKGJUIuDN7MDe/WZObXmDgkpe8oy40ekmIqoObC2V+VPRnK38WKtqQ66anEMd/bZHq6VaZ8D4VB11wPjraJD/MmvqMfeIpsAtbf/aC0WsyI5ut2Mqn0f4d9cRWbCD2gxn5gRIcAMFyc18km+f68SSObWmUnq4v+jrqiJy2gwBZFW8EGvyp+MZHDci+WU1WNCk9DFN6X4gOOBY95AVWwIy6o1c8bw7gdYo4KtNLdoCqsaSYwKGoCa+Y4DRTWMjg3BYm7zvbUyF3K+oyhqJrVBiS7LXZHSyCuHgtieX8mcUdE9/rzaZkVZk4mkbloco3PfOv5Du+zPuaHTOKPyO46uXsHwMMugTCjwJtLi8GJ5FfXEBzk6XWgGEGWxY1EOsmvkO4CntSY3dJVqpD2Zktu5A0U1pLUd39Aadr0N/qHkx53RablTIu1MjGgmI9v1HuGdaZ1R1V3g6IopcRbBqomiwzt6/R4nE7nbeLH8igaXC//aMimj1XG41uz1UzB9XWtyw9Eu0r+0F+UMHNlldSxIk8Vjreqa7ORXNHDlzDatij0fGC2OKVfRYu86aecVKY38dpuFgsoGRkT0bLwjt8745pUc0kJ9Ze/qnZyYxOGd8UQUrQdm9O5NTiLS4vBieeX13QYOgOH+zTLGMQC6Sm7YXligBaufSfarbuegM1ngsRZHi91IYR4yHBJmdlv+4qRG/EyKjJyetzpaF//1pcVhNSvW+88j2XYQ6st6/T4nCwkcXqqxuYXimiYSgrr/Y4/3t5Fba8buoXHYrBoz9+9JYfn2MTyfPdxjn+PtDnWT3LAtk1IkRQUdmxknDAecayHGtQaOHW9A2UEYf8EJs6g6E27VTBoRxra8Spp7OPEgs8aPaH8H4da+zVlvGGa0NOpytvTpfU4G0lXlpY5UGntpJgZ3/z9HfICNZq0oqDeRFNK/d/V6m53bvg+nzAanhNWzpiyCymY/ZqfWuz8n3sdltU1uWJYJWV9B6UHQdib6D6ckcjolEVOPnZ8SHSQtjnYOFNUQYDEZEwYcLcY+GsMnw7BTe/we6SlRbM+vYndBFVMTu08/cqDaz2V2aXfNTg5jU85YxuZvMcZlhjAJHF4qz3nD6VlXVevMKj+SQvo3nfcTqw5yoNrMr9LymBxWz6SSel7IHc7z++HOCUPrpphV48eYEBvs/RAOrQJrCCSkg9mK+cguRhV8SFT1XvLiz8VmjSApKpjvMsvQWqPab07uIa9tyAU4tkf87NSOe8MPpv1FNYyJC8FsUrDzPSg/BJe/DPU973o6p/4T/mMdxa59+/mhzjMOml1fp9ZwsNrMpcl9T8kzMdzOn81zmdn0T2PDKCL6/J6+SgKHl8qraG1x9KCrqs2U3NP7sQ42u4O3MvI5f2QTk8OMILEotpK9NYE8vieMM4bbOCXS+3ZGa715tnX17K7TbnenRUN2reIxv7/CoU2QNA8mXQJmYyXzzoAFxFVsJvnoZ5y98Sa+nP0PUmJCaGhuoaSmibgw2akRjBbH/DExxh3928cgZhyMv+jYHt89YVJwRkwV/y6I5WijheEBnacROVJvos5uYmx43/9OlQLHsKk0F74K2/4NUbf0+T191RDrbPAd+RX1WM0m4gK673oK92shxM9Bdj8PkH+5t4jyOhuXp5z4be2mpCKi/R38bFMYjb0fb/QpR+pMPGB6lYn1m2D8Eph8+bGgAYBSxh4RSVcSVnuY0zffRXK48fsY1GSHXqSqvpmi6iYj1UjWGijaBfN/2mGL1544I7oKhWZNWXiX5x2oNr4bjw3rnz/UBYlWvnZMxr79LdBDdLAPaXF4rfzyBkZGBmLqpofDaqsismY/DwVsxL8YyA6GqDEQOvxYnp/eejMjj/jwABYOt5GRffx4iJ+DR9JruP7bCP64M4TfTj35t9Wszc7gRr/PODr8TIaPPrvT86pDRrN+8u+Zv/1+/Pc/CZxBTlkds7ysy2gwHChuMzD+/TMQHAenXtqr94qy2pkWXsfXZeFcPqK08888Fjj6p2U8N66ZB/3mck7js0zd9xdqglNOPMFVl9lJOPVdAoeXyquoJyGy83nqfvY6Ugo/Iap6DwoYgQUNsMvZbA+OhZQFkDALLO7n96lubOabg6XcfNoozKpj18/pw23cMKaelzKDGBdu58rUkzite2kmaTn/ZoNjPGmTLuo2IOeMuICYym2M276Cs80R5JSNGaCKercDRUbgmGApgMwv4cxfg1/v05SfFVPJnw8lsK0qpNOsCQeqzAwLaOnzjKpWJgVxyROoO+xPcPnujoFjiJDA4aXyKxqYNCKcDYc7DhqG1OczNvffmB2NFMTMpyRyGqubJ/P0viD2LT6AtXQf5G2A3e/Bvo+MAdzkBRA2osefvy6zjBaH5sxxsdDJuOV/T67lcI2ZX28JpdJmYorFvRW9PsHRAu/dQjN+/D9u4/PAnt2Ato77OeMadvBwyQv8oeg0YJxn6+kDDhytIdhqZtjul8DsD+k39en9poXXEu5n56uycG6nwfVnVvv1y/hGW5eM0nx+KJ3zarZQ5DgPbRp6t1EZ4/BCdU12yutsJEZ1bCkE1x9hXM6/aDH7s2vUzeQPO5smaxSpoS04MJHbEgvJ82DBz2DBvRA/BfI2wtpH4LsnjIBi675r6ZuDJQRbzUxL6ny6o58JnplTzbkjmnh4Zwj37k5l9b5ijlQ0oE+W6Yob/gpHMvib9VpCQ7vuT2/LYfaHpU8SrSs4LX+FByvoO/YdrWFGnAO14w2YcgUE921FvZ+CBVHVbK4KoaKpYyuwtlmxt8qPUyP6N3CMCHKQFTabIN1AWE1mv763r/BoqFRKnQ88AZiBF7TWD7d7XTlfvwCoB27QWm/pqqxS6k/ARYANOATcqLWu9OR1DLS8CudU3MggKDl+3NJczbjc17Gbg9ibch02S9ix11oz6B6uNTOmdSAwIgmmXgMTLzaCR853sP11QMGWVyB2HITEGTNc7I3QXA+NVVBfzh1HC/m5xYH12VAw+ZGoY6gMTaMmKOmErpoQi+bZOdV8mN/Es7stfLm3iC/3FhETYuW0tFiunJmIqbuBGm9VmQerH0KnnceL+xZwYaKbU50T0tkW9wOWFb2LPX8rfgnTPFNPH6C1Zk9hNY8OX2X8rc25vV/e9/ToKj4qjmJlXgDXjzmx1bGp1EKLVsyL698p6gCnpqVSsjUMS+leCB/f7+/v7TwWOJRSZuAZYBGQD2xSSq3UWu9pc9piIM35mA08B8zupuwXwC+11nal1B+BXwL3e+o6BkN+uXMqblQQTa0HtYMx+e9i0s3sSb7hhKABkOrckc7lzCprMIw+E0adDlX5ULzXuPkX74Xsb43nliDwC4CAcOqt0ay3h3BqUgxRESbIz2B49XpGlK2j3j+OI7ELQc8/FkCUgqWJTQyzF7It7mL2H61hU3Y57209QlF1I89dM4PwIB/cF/3zX4PWlCz8PVU7DzC+FwOsedPvI/GTLwhdeQ9+t64G09BMDXOksoGmxgYWVLwPo8+GuAn98r7JQU2kBDbyxuEArhvdcMLw0/clFqwmzYxo97ebbd9F3H49zJkjWnhry2wubVxDRcviDmngT3aebHHMAjK11lkASqk3gGVA28CxDHhZG/0a65VSEUqpeCCls7Ja68/blF8P9G5ahhdrbXEkRAZyyHksvvR7wupzyRx5MY3+HZv4EVZNpNVxLJ+SS8pktEIikrqc6fHBxlx+uX8nqy4+HWJDIOMltmQWElmzj/jSdaTlvw22PTDlKvA/cRe30AAL6SlRzEiOJCO7go92FnLFiu/5109m+9Z+zVlfGcn3zvw1exsiAHrVV548cgQPNf+IJ4ufgYwXYdbNXZ6fX1HPyu1HyC2vZ/7omC67Cn3JnoJqlpi+J7CpFOb2T2uj1aLYCv6WG09GmYWZMceDxPclVqZGNRPogbucxQRHwyZjrf0Ca/kBGmIn9/+HeDFPjnGMBPLa/JzvPNaTc3pSFuAm4BNXH66UWq6UylBKZZSUlLg6xWvllTcQaDEfS81ttVUysmQt5aHjKIvo/A80NaSlX9ZyZGRXEB1sZVTM8Y12Wsz+lEZMYefoW8gZdq6RauObvxjdOS4opZiZGsXfb0gnu6yOm1/OoLHZRxZ9tDTDJ/dDZArMu/t4fqVetDjGxIWw0jGPvIhZsOohqC3u9NzG5hZufGkTGdkV2Oyatzbn88Weot5eBWB8c27/GAx7Cqr4id8nOGLGGS2OfrQgqppwi4N/ZB4fE6yyKXZV+Hmkm6rV6Lhwsh3DCCzf0/3JJxlPBg5XHdvtR0w7O6fbskqpXwN24F+uPlxrvUJrna61To+Nje1Bdb1HfkU9iVGBx9JUJB/9DICc4ed3WS4l1N4/gSOnnBnJka7TZCgTR2PmGAu3lILvnzKCSCdOS4vlscunsiW3kl+9t9M3Bs03roCSfXDe/4ElgH1Ha4gLaCGym+1iXQnx9yM+PJBXo+4yxpA+/3+dnvvIp/s5WFzLj+Ym89Oz05iWGMFX+4spqHQ9Y8iXtGStZaIpB9PcO/q8vqi9ALPmitQGPj3if2xP+LdzAtAoFg7zXOAYHmjne7+ZjG4+iNlW47HP8UaeDBz5QNutvBKAgh6e02VZpdT1wBLgGu0TdyL35FU0kBBp7BoXUpdLVM1+CmIXYLN2PasnNaSFwgYzDX2YRFJc00hOWT0zU7pZsBaeAPPvgcAo40Zb1vnsksWnxnPPOWm8u+UI72w50vvKDYTaYvjqYRhzDoxbDBjrD8b1YeXxmLgQvq+Ogvl3G9lgs7/rcE5uWT3/WHeYa+ckkRYXitmkWDJ5BEH+fry/7QgOh2//mc8reo0ac4Sx4t4DfjK2gRA/zX2bwqi0KZ7eG8xpcTamR3s2JU5j9ARMSmMvOeDRz/E2ngwcm4A0pVSqUsoKXAmsbHfOSuA6ZZgDVGmtC7sq65xtdT+wVGt90uVy0FqTX15PYmQgaE1i8SpsfiEcjZ7TbdnUkC4GyHtoc3YFAOkpPehbDwiHuXdCUBRseoHAxs67Ye46K405o6L4f+/v4lCJF680//J/oLkBzv8jKEWLQ3OwuIZxfVgLkBYXyoGiGuzz74XwJPjo50Z3WBsvrTuMSSnuPDPt2LFAq5nFk4aTX9HAF3v71mU1mGqytzHXsYXdiVf1ajFqT8QFOHhoeg1byy3M+SiGCpuJ+0/1/N/ZmJggdjlSia8eWjsDeixwaK3twJ3AZ8Be4E2t9W6l1K1KqVudp30MZAGZwN+A27sq6yzzNBAKfKGU2qaUet5T1zAYqhvs1DTZjbTTmasIq8/jSOxpOEzdz0pqnVmVVdv70cCMnAr8/UxMGtHDNQv+ITDrFjBbGZfzGoGNrm9wZpPi8SumEWAxcddrW2mye+F4R94m2PYvmHsHxBirvbNKamlsdjChD2sBpiZF0NjsYF9ZCyz+I5TshfXPHXu9urGZNzflcdGUEQwPP3F2zpTECKKCrTy9OtM3uvlcqFvzF2p1AKqbiQF9dVFCE3+cUc0VKQ38fnr1gCTgtJo0GdZ0kh15WBs7T31ysvHoAkCt9cda67Fa69Fa6987jz2vtX7e+Vxrre9wvn6q1jqjq7LO42O01ola66nOx60dP9l3HZtRFREAax+hyRJOScT0HpUdHWpHoTlQ1YfAkV3O1MQIrO5sthEUBbOW4+do5IyM27A0u+7vHR4ewJ8uncKewmr+9On+XtfRI1rs8PHPITQeFv7i2OFteZUATIl0f0pnq+lJEQBsya0wNiwauxjW/AHKjDlz72zOp87Wwo8XpHYoazYpzhgby84jVXx1wLcmeQBQkUNczke84TiLU0Yne/SjlIIrUht5cFot14wauBQ4dZETaNEKv9K9A/aZg01WjnuZ1o1/xtl2Qd4GCqPnons49z/AbCwE3F/du66qepudXQXVPeumai88gQOJlxFee5j5236B0q5bFOdMHMaP5iTz9+8Os3GQZvi4tHEFFG6H8//PaEU5bc+vJNTfj1GhvW8hjYwIJC7Uny05RjcgSx4zcjS9dyu6xc4bG/OYkhDOKSNdt/KmJkUwMiKQp1YddK/VofXgbzj0/dM4UHwYdDEfbCvgtQ25xx4ni3FRJr53TCK+Zsfg//ceIBI4vEx2mbHVaOLu5yEohpJI91Ybjwu3s7+XLY5teZW0ODTp3Q2Md6I6ZDSbJv6KEaXfMXXfo52e98Di8SRGBnHfW9upa/KC/Tyq8mHN7yHtXGOVfRs78qs4NSG82yzFXVFKMS0pgi25lcaBsHi44M+Qv5GjHz7I/qIarpzV+X4hfiYTt54+ii25lXx/qIv9rptqGZX/Hgu23MvU/Y+Rvuf3TNvzf8TtWkHp3rWUl3U+BuURdaXoLa/wgWMBgdGebW0MplA/BxssM4lxlEFlzmBXZ0AMvexcXi67tI45IUX4Za2Cs/4bR5F7sX1cWAufHfGnwY7bC58ysitQCqb3YdHZoaTLiKjNZEL2y1SFjOE1LnF53nmThvPCN1nc9I9NLJvqaolO3zdf6rFP7jeSGV7w5xOmijY2t7C3sJqfnDaqzx8xPSmSz3YXUVrbREyIv5FOPGsN8dueZLE1gIumnNdl+cvSE3lqdSZPrc5k3ph2C0Abq2DdU7D+OebYaqkNiGeTnsB2+3Ci/GyM9zvCWfZ1BB79ipzK8RD7QwgZgCnq658DeyPPNl9IenSQ5z9vEFWGjaOpwoLO3UxAZMpgV8fjpMXhZbLL6vmJ5TMj/Uf6j90uPz7cjkZxsNr97wQZORWMGxZKeGDf0oNsGf8LCqPnMnP374gt3+LynNSYYOaPiWHD4fLBnWW17yPY9x844wGIPPFb8d7CappbNFMSIvr8Ma3df98edA6gKkXxwt+zW6fwuN9ThBRv7bJ8gMXM8oWj+D6rjIxsZxdfcyOsexqemApr/wRpi/h01stcGbiCa+vuIS/ubFImzsI2/hI2jv05f1WXEtmQS8vaPxnJLj2pthjWP0d23Dkc0iNJjjq5A8eYcM2Xjmmowq3Gl5CTnAQOL1NZWsjpjath8hXGoLObWqeN7nMzcLQ4NFtyKno3vtGONvnx7bQ/UReUwGlb7iG43vXajUUThxEVbGXltgLsjkHYTa2uFN671RgQtxqpVdo+tn5nLLycUv5Znz9qWmIkSVFBvLbxeN/+y5uKucn2C0yhw+DVH0LO912+x9Wzk4gOtvLsqn2w9VV4Ot3IpxU/BZZ/hb70Jf6eG8euwhquSyjikviyY11sAVYLo8eewmWO37NTjzaSXe75oF92sWs7bnFs7OKbvxitDXUlo2ODiQiydlluMFe194cxQQ185JiHv70WSk/+NR0SOLxIvc3OOfWfYtE2mHNbr94jOaSFALN2e5xjb2E1tU327hf+9VCzJZyvZzyFSdtZuOUu/Ox1Hc6xmE0smRxPSW0T32V20XfvCVrDB3dCcx1Mu9Zl8sHviq2khNiJD+r7zdVkUlw9O4mNh8s5UFRDcU0jr6zPYerE8Vhu/A8ER8M/l8B3T2JyuJ7BFWRq4S+jt/BgznXwwR1GWvLrPoDr3ocR03j8y4Nsyi7n9LGxXDisokP5UD8HS5Oa+WHDr9gRdqaxfevWVzusKemz8sOQ8SK2U6/kvbwgzpk4rH/f3wv5maA4KI0aguDI5sGujsdJ4PAiuSVV/MjvC0ri5vY6e6hZGdtk7q10L3C0DrrOGRXdq891pSY4hW+n/Znw2izmbX/A5bfb8cPDmBAfxup9RVTWey49RAebXoADn8D4pRDWcYyl2QHrSyws6MdcR5fNSMBqNvHr93by439kYLM7+NmisRCRCDevgbTz4Iv/x5K1FzH5wFOMKP6auLKNpB5ZCR/+FB6dyBkH/kC9NZI7uZ9DF38Io87A3uLgL5/v54lVB5mRFMmiLm7U6RG1nDXCzjXlP6Yh7SIo2AJvXg/2pk7LuO3TB8Bs5esRN2N3aBZNOPkDB8D4sGY+tM/GcXRH//739EIyOO5FGne8z3hVTva05fRl6HJKVDPv5gTQoo1A0hPrs8oYFRPMsLD+TQ99NGYeW8b/gvS9DzPlwFNsH/fTDucsmRzP41/W8NHOQq6ZPQCzb4r2wOf/DWMWQerCY4fbdpXsqw2kzm5iQT/mOooO8ec3F03kT5/tp7qxmb/9KJ3xw53p8QMj4Mp/QeYq6j7+AxMPvYCJNoHWEgwxaXDqZQQGjOfbNdF8+cTXXJDQyN5KP/ZWWbg0uYEfRu/D5Drv5DH3TqxlcUE0zzQv5b5JVtj9Drx+JVzxL7D2bSxiRPHXcOBTWPQQH2VDVLCVaUmRHCjy4mwB/eSUsDreKljI1S1roGArJHWf7cFXSeDwIvF7XyTLMZyYqRf26X2mRzXzyqEg9lf5MbEHK55bHJqNh8tZMqXnW8u640Dy1YTXZjIp6wWqQkaTPXLJCa9HBlk5c1wcn+8pIrO4ljFxIZ28k3taHBqlwNQ2qV5jNbx9o5EO/uJnjcFxF3ZUB6PQzI3t326ca+ckc8m0kRRVNzIqtt11KgVp57BqzlgszdWE1R7G7Gii0T+GJWfMN7qVgCQ0H59Tzu93hLD2qJXYAAdPz67iwoQmNmZ3X4cJES1cmNDIS5mB3LR4IVFjzoKVdxnjLFf/GwLCun8TF6y2Kmbt+h3Ejqdy8o/57NNvuHjaCMy+upGXm5IDmzhkSaPQbyTxOd+d1IFDuqq8RX4Gw6p38Zb5AsIC+7ZvRevGNVvKeva9YE9BNTVNduaM6p/xjQ6UImPiryiKmsnsnb9heOm6DqfMHxNDZJCFj3YW0NLHhH6NzS18uKOA//1oDw/9Zw+vrs+htLbJmO3y7s1GNt8f/t3Y/bAT26uCGR3cSLi1/xd0Bfv7dQwa7TRbwiiLnEJx9CyqQ0aB+cSZbiOCHDwzp5qMi8r4ZFEFSxKb3Eo6e8/EOurtir/uDzLGeH74AuRvhFcuhvpeDFJrzczdDxFgK4dL/sprmwtpaG7h+nkp7r+XjzIpmBvbzGstZ0NVHlSePIsc25PA4S3WP0edCmJ37JLuz+1GYrCDGH8HW8p7Nq3220xjiujcfhzfaE+bLHwz7TGqQ1JZuPmnxJZnnPC6xWxi8SnxFFU3sSm797NrSmqaeHnVFjYeKiU9rIKFkWVkHq1k6Z8/peqFi41ulEmXQHmWMXvKhaNNFjLrA5kdcfKmyk4La2FZUhMvHwqipKYJTvkhXP4KHN0J/7wIat1LbzIu+xWSj37GjrTbaR42mZfX5TB/TPTxrrghYl5cM/9oWIjDZIWcjl+QThbSVeUNqgvQe97nHX0eifGdfwvuKaVgWnQzW8p6FjhW7S1i0ogw4vphfGN07ltdvp414iImHv4nZ226hS9nvUhZ5JRjr00aEUZqTDBf7i3q1dqJxuYWbvrHJgqbrPzXmHymhhszuc6KqaQmK4PwgrXYUs7CmrKgy/f5tiwMhWZ+VDXgHTe+1zbkMjq36+1M3XX3hDo+zPPnua8O8ZuLJhp5tK7+N7x+Nby0GK5fCWHdd18mFn7O9H1/JnfYIvaM+jFfrcnkaHUjf7x0aO2KBzAvzkYN0WSFzWRMwSaYuHSwq+QR0uLwBhv/BtrBiqZFjB0W2v35PTAjupnsWj+KG7v+FZfVNrElt4JzBmjmi90vmL0pP6LZL5gzM24lrmzTsdeUUlx4ajwNthZW73M/jfiDH+5h55Eq7k4tOBY00JrTaj9juXqPt+ync2/NNV2mE9Iavi0PZ2JoPdFWL0iH4kGjQlv4QXIjr27I4WiVMyng6LPgR+9CzVH4+3mQ281CwU1/Z/62X1AaMZnvp/yBvIpGnlqdySXTRnL6WN/aQK0/jAppYVhAC+9yNrTYIP/knJorgWOwNdVCxouUJSwiX8eRNqx/BoZPH2ZMB1xd2HHhVVtr9pfg0AxY4ABotoSyN/k6GvxjOXPTLSQXHB+gHhERyIzkSL7PKnNrRfnbm/N5fWMut50xmvQIZzntIPnoZySUrIXE2ZSMu4r/HAnk7ZzOW1a7a4IobLJyWlR1r6/Pl9w9oQ6HQ/PMmjYbcSXPM1obAC+eZwyct93lUWtjZtprV8JH91IYexqrZ64gs6KFl9YdZnhYAP+zdNLAXoiXUArmxDbzZkUaOjwJcr87KRMfSuAYbFtfgcZK1g2/BqDfWhzjw1tIDG7h8yNdD7R/uaeIYWH+nDJyYLtkbNZwPp/7CqURk5m//QEmHnrh2P9giyYOw2I28YePepamek9BNb9+bydzR0Xz80VjATC3NDI2702Gl2+kMHo2TL6CW8Y3MjvGxm+3hrjc7EpreLMghkhLs7Ob6uSXGOzg8pmJvLEpl/yKNvuijZwOt6+D2bfA9n8bq9QfPxWemAJ/Gg3PzYVDq2HCUg5HL2TDpg28+M0hokwNvDkvj/Ddrw7eRQ2yuXE2SpvMFA9bYLTcDq8d7Cr1Owkcg6nFDt8/C0lzWdeYSlSw1UiA1w+UgnNHNPFtsZXaZtfTbcrrbKzeV8ziU+Jd7y/uYc2WcNbMXEF2/GKmHniC07beg9VWRWiAhTPHxbFqXzEfbm+/2/CJqhubuf1fmwkPtPDkVdPwM5sIbjjCKVl/I7wmk+zh55M7/DxQJswKHptVjcUEP90QRnO79Yjbq4PZXxfEJcPLsJpOvm+JnbnrrDEopXhqVbvtf/1DjY2nfrbL2H99xHQw+0NYApxyKZz132yLPJcH9o3i3aMxzI+q5qHxOYzsh5X2vqx1CveXap6Ryub7pwe5Rv1PBscH0573oSoXFv+RA6trSOun9Qutzh3RxN8PBvHVUStLEjuuZH17cx62FsfAZaF1wWG2sm7KHykPn8SU/Y9z4bcXkzHhAeaPXsTR6kZ+9d5OpidHMjKi45ajzS0OLnvue3LL67n5tFGs2ZHFpEMrmJT1Eja/UPamXkdt0InXNiLIwf/NqOH29eHcnxHKI+k1+Jkgr87Es9nxxPvbOCumaqAuf1C0zwl1qKWEq2cl8cr6HG5ckNJxJlRIHMy93Xg4Z6I1O+Avu4NZsT+ISIudB8bkMS28Y1oZT9TX2yUFtzAisIV1ZUFck3KakaGgeG+vs0F4Iwkcg0Vr+O4JiE5Djz2Pg69/ycXTXKcX7630mGZGBrXwUmYgFyacOM/f4dC8vjGPmSmR/dY95q62M7CazUHsSb2RUQUfctq2+5gSlMi5acs4u3A21z/zOW/89PwTWmM2u4P73trO/qIaLj81nPOq32bCthcJtJVTHDGN3OGLaDG7Hsu4IKGJeyfW8uieEHLqzMyNNVbaN2v4xZh8LN20NnztRtYTPz07jQ+3F3D/Ozt597Z5XS7aK2tS3LE+nPUlVq5MbeC88DyCzEO7ldGWUkZ31epCf1qmzcec9ZWR9v7iZwe7av1GAsdgOfw1HN0BFz3B4bIGaprs/T7OYFZw67h6/t/WUNaXWJgbd3wV9Afbj3C4tI57zknrUK797mztp4F6Sn1gPLtG/YS4ii2MLFlL2Pan2RTyFi/Xzebhpw/zg/POZMqYZPYXVPDSp+uwFu3gzZgDTMv6HktLA0VRM1k77mkiqvd1+1l3T6wnLtDBs/uCeXqflZnRNm5LKmBkwADmy/IikcFWfrt0Ene/vpWnVh/knnPGujxvd6Ufy9eFU9Jo4tGZ1fwguZENhzsJGm3WyQzU35C3WDjMxjs5geyqi2DKjOuNmZMLfwFRHbcH9kUSOAbLN49CcBxMvpJtO40FeFMT+57SvL3LUhp4cm8Qf9wVwhunVxBghvImxUOr9zItKYKLJnsmzUivKRPFUemURExlln8OgbnrWd74H1TjSvjgtwBMdz6wQmNTBDkjLiAz4VLKI04B6FHgALgytZErUhqpblaEWzUbDg/cPtXe6KLJ8Xy1v5jHvzzIyIhALktPPOH1ldsL+K81kURaHbx9RgWTo07u6cp9sWCYDYVmbZGVKcvuMYLoN3+BZSfHeIcEjsGQ/Z3R4jj392AJYGtuJcFWc59yNLXvPmldHBZght9MqeWuDeHc9n04V6Q28MSeYKobmvm/H5yKyUvzCGmTHyTMhISZqOZ6bDGT2Ld7C7XVlYQF+TNm1GgCEqbwXlYAWvVuj3UwuhU8kVakx1ysXh+sb+dKKR7+wWSOVjXyi7d3sPFwORdPG0ltk51/b8pj9b5ixgU3cO/oIzRUtbDh5B4K6pNof80pkXbWHrVyV1g8zLgeMl6E035+UrQ6JHAMNK2N/a1DhsFMY4e/bXmVTEmM8FgyuIsSm6i01fCbrSGsOepPtL+DFdfN8J10EJYgrGlnMDntjA4v6cMnbz6gwWD1M/HSjTN5/MuD/PXrQ7y1OR+A0AA/7j9/PJOL3sdP5mL2yMJhNp7fH0R1YzNhC+6FLa/A6ofg0hcHu2p9JoFjoGWugpzvYPEjYAk8tq/18oV939e6Kz8a3cD5IxvJqvFjbJidyPFDY4+EVq4GtPuasuNk5e9n5v7zx3PLwlHsyK8i2N+PSSPCCLCY2dB1RhnRxpnDbTyzL5iv9pewdMoImHcXrH0E5twBCTMGu3p9IoFjILXY4bNfQdQomHEjALuOVGF3aKYmRnj847MKjc2aDtTCIecA+GBOxfUFJ+MMqp6KCLKy0IfThgz27256dDNxAS18srPQCBzz74bNL8Fnv4QbPwWT7zbdJHAMpM0vQel+uPI18DNSgazPMm7m05P7d2B8sP+nEa61n7F2de+HZ4SXMyk4f2QTb+4vpt5mJ8g/FBb9Dt6/Dba+DDNuGOwq9poEjoFSXQCrfgepp8O4C44dXrWvmCmJEf22YlwMHf355aB9QAPfao166xelxSON1PVf7S/hglPjYcpVsO01+OI3MHYxhPpml7EEjoGgNfznXmhphiWP0boSr7S2iW15lfyskznz4kSubm5CeLNZsc3EhPjz7pZ8I3AoZdwDnl9gtDyuedsnu6wkcAyEra8aaQcWPQTRo48d/mp/CVrDWeP7vgeHu1pXbbu6F4/ueKgDb/2GJ8RgcvX/xRUz5/LcV4fIr6gnITLI2Dv+vN/DRz+H9c/CvDsHoaZ9I4HD047ugo/vM7qo5t5xwkutmWknjfCRabFiSHHVwuvJl4qTUV++KF09O5nnvjrE6xtz+cV5442D6T+GQ2uMLqthk2D0mf1U04EhgcOTao7CG1dDQISxp7Pp+Ejo0apGvtxbxA3zUgYlM60vOOF/1sN/8bmb1lDtWpPW6IlGRgRy9oRhvL4xj9vOGEOIv5/RZXXJ88ZmWW9eDzd9CsMmDnZVe0wCh6fUl8OrP4S6UrjhQyPDaBuvrs+hRWuun5cyOPUTHtPd9rknkLUkQ8KdZ45h2TPf8devD/Hzc8cZB/1D4eo34O/nGvu8X7/SaH34AN8blfEFVfnGns2lB+CKV2DkiYt96prsvLYxl3MmDCMxKmiQKim8wYbD5R0e4uQzJTGCpVNG8Ldvsk7cMCsiCW74CMxW456RuWrwKukGCRz97eAXsOIMY/rtte/AmLM7nPLHT/dRUW/jtjN8rfPl5CI3bDGQ/uv8cZiV4o7XttJkbzn+QvRouOkTY4Osf10Kq/8X7B33z/Em0lXVXypyjDw0O9+CuElGPpq48R1OW7OvmJe/z+HG+SlMT+r/bLhCuMutrjXRawmRQfzl8inc+uoW7n1zO49ePgV/P+e4Z2QK/PhzYyLN2j/B7vfhrF/DhGVeOV3XozVSSp2vlNqvlMpUSj3g4nWllHrS+foOpdT07soqpaKUUl8opQ46/x28u6+tDvZ+CP/+ETw5zXh+2n1w8+oOQUNrzbtb8rnllc2MHx7Kf53XMagIIU5u558Szy8Xj+ejHYVctWI9ewra7G3vH2IMmF/zNigTvHUDPDkVvvojFO021oN5CaU9VBmllBk4ACwC8oFNwFVa6z1tzrkAuAu4AJgNPKG1nt1VWaXUI0C51vphZ0CJ1Frf31Vd0tPTdUZGhvsXYauHhnJorIKGSqgrhvLDUJ5lbAVZuB0czRAYBdN/BLNugXBjFz+tNWV1NrJL69iWV8mH2wvYnl/FzJRIXrhuJuFBlh5VYcNbf3G/3kIIrzH7sp93OPafHQX8+r1dVDc2Myc1mjPHxzIhPoxhYQFEBVsJ9zfht28lavNLkP2NUSggHIZPNh5RqUaG7ZBhEBgBfgFgCQJLoPFQJuiH2ZpKqc1a6/T2xz3ZVTULyNRaZzkr8AawDNjT5pxlwMvaiF7rlVIRSql4IKWLssuAM5zl/wl8BXQZOHrts1/C5n90PB4cZyzimXuHMf86eT6YTwwEdodm9h9W0eIwAnNaXAh//OGp/HB6An5m72t6CiEGzpLJIzgtLZZ/fJfNyu1H+MPHrjYfC8Bivp3Lx9/D7ycVwpHNxq6hGX8Hew82HVMmMPnBVW+4HGvtC08GjpFAXpuf8zFaFd2dM7KbssO01oUAWutCpZTLZddKqeXAcuePtUqp/b25CNeqgUzgE+B3PSqRA3zZuw+LAUp7V9QrSP0Hl6/XH3z+Gu7rU/3/4Hz02m/O6UvpZFcHPRk4XLWT2veLdXZOT8p2SWu9AljhThlvpJTKcNVU9BVS/8Hl6/UH378GX6+/K57sM8kH2m5anAAU9PCcrsoWObuzcP5b3I91FkII0Q1PBo5NQJpSKlUpZQWuBFa2O2clcJ1zdtUcoMrZDdVV2ZXA9c7n1wMfePAahBBCtOOxriqttV0pdSfwGWAGXtRa71ZK3ep8/XngY4wZVZlAPXBjV2Wdb/0w8KZS6sdALnCZp67BS/h6d5vUf3D5ev3B96/B1+vfgcem4wohhDg5ybxQIYQQbpHAIYQQwi0SOAaRUupFpVSxUmpXm2OdplRRSv3SmYJlv1LqvMGp9XFKqUSl1Bql1F6l1G6l1E+dx33iGpRSAUqpjUqp7c76P+g87hP1b6WUMiultiql/uP82dfqn62U2qmU2qaUynAe85lrcC5cflsptc/5/8JcX6p/r2it5TFID2AhMB3Y1ebYI8ADzucPAH90Pp8IbAf8gVTgEGAe5PrHA9Odz0Mx0sRM9JVrwFgvFOJ8bgE2AHN8pf5truNe4DXgP772N+SsVzYQ0+6Yz1wDRgaLnzifW4EIX6p/bx7S4hhEWuu1QPt83ssw/hBx/ntxm+NvaK2btNaHMWaizRqIenZGa12otd7ifF4D7MVY9e8T16ANtc4fLc6HxkfqD6CUSgAuBF5oc9hn6t8Fn7gGpVQYxhfAvwNorW1a60p8pP69JYHD+5yQUgVoTanSWXoWr6CUSgGmYXxr95lrcHbzbMNYSPqF1tqn6g88DvwX4GhzzJfqD0aw/lwptdmZKgh85xpGASXAS87uwheUUsH4Tv17RQKH7+hzGhZPUUqFAO8A92itq7s61cWxQb0GrXWL1noqRnaCWUqpU7o43avqr5RaAhRrrTf3tIiLY97wNzRfaz0dWAzcoZRa2MW53nYNfhjdzc9pracBdRhdU53xtvr3igQO79NZSpWepHAZcEopC0bQ+JfW+l3nYZ+6BgBn98JXwPn4Tv3nA0uVUtnAG8BZSqlX8Z36A6C1LnD+Wwy8h9F14yvXkA/kO1uqAG9jBBJfqX+vSODwPp2lVFkJXKmU8ldKpQJpwMZBqN8xSimF0be7V2v9aJuXfOIalFKxSqkI5/NA4BxgHz5Sf631L7XWCVrrFIy0PKu11tfiI/UHUEoFK6VCW58D5wK78JFr0FofBfKUUuOch87G2P7BJ+rfa4M9Oj+UH8DrQCHQjPFN5MdANLAKOOj8N6rN+b/GmIWxH1jsBfVfgNHM3gFscz4u8JVrACYDW5313wX8xnncJ+rf7lrO4PisKp+pP8YYwXbnYzfwax+8hqlAhvPv6H0g0pfq35uHpBwRQgjhFumqEkII4RYJHEIIIdwigUMIIYRbJHAIIYRwiwQOIYQQbpHAIYQXcmaMjeksA7EQg8ljW8cKIXpGKeWntbZ38rId+LnWeotzodxmpdQXWus9A1hFIU4ggUOIfqSUug64j+MLI98E/hsj3XYZcI3Wukgp9T/ACCAFKFVK3YWxIDQWYyWxgmMJ8lqT5dUopVozEEvgEINGAocQ/UQpNQljVfB8rXWpUioKI4DM0VprpdRPMDLZ/txZZAawQGvdoJR6EvhWa/07pdSFwHIX75/C8QzEQgwaCRxC9J+zgLe11qUAWutypdSpwL+die6swOE256/UWjc4ny8EfuAs95FSqqLtG7uRgVgIj5PBcSH6j6JjiuyngKe11qcCtwABbV6ra3euy/w/nWQgFmLQSOAQov+sAi5XSkWDsW82EA4ccb5+fWcFgbXANc5yizES5XWVgViIQSNdVUL0E631bqXU74GvlVItGJl3/wd4Syl1BFiPsc+0Kw8CryultgBfA7nO4/OBHwE7nTsVAvxKa/2xZ65CiO5JdlwhhBBuka4qIYQQbpHAIYQQwi0SOIQQQrhFAocQQgi3SOAQQgjhFgkcQggh3CKBQwghhFv+P1kRhniVDUvIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting fraud transactions done on card2\n",
    "sns.distplot(train[train['isFraud']==0]['card2'])\n",
    "sns.distplot(train[train['isFraud']==1]['card2'])\n",
    "plt.legend(['legit','fraud'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "385646c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    588975.000000\n",
       "mean        153.194925\n",
       "std          11.336444\n",
       "min         100.000000\n",
       "25%         150.000000\n",
       "50%         150.000000\n",
       "75%         150.000000\n",
       "max         231.000000\n",
       "Name: card3, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#card 3 \n",
    "train_transaction['card3'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1a06e257",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91733\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\91733\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23634076fa0>"
      ]
     },
     "execution_count": 33,
     "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": [
    "#plotting fraudulant transaction on card3\n",
    "\n",
    "sns.distplot(train[train['isFraud']==0]['card3'])\n",
    "sns.distplot(train[train['isFraud']==1]['card3'])\n",
    "plt.legend(['legit','fraud'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "875541b7",
   "metadata": {},
   "source": [
    "- It is interesting to note that majority of the fraudulant transactions are of 2 values only in card 3.\n",
    "\n",
    "**Card 4**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f911848a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='card4', ylabel='percentage'>"
      ]
     },
     "execution_count": 34,
     "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": [
    "# card 4 contains the names of the payment network previous card(s) used in the transaction.\n",
    "# so I will create a data frame just like I did for ProductCD by using groupby, value_counts attributes \n",
    "# mul here is multiplication as we are taking percentage of number of unique values are there to total number of transaction\n",
    "\n",
    "train_card4 = (train[~train['card4'].isnull()].groupby(['isFraud'])['card4']\n",
    "                     .value_counts(normalize=True)\n",
    "                     .rename('percentage')\n",
    "                     .mul(100)\n",
    "                     .reset_index()\n",
    "                     .sort_values('card4'))\n",
    "sns.barplot(x=\"card4\", y=\"percentage\", hue=\"isFraud\", data=train_card4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60d6cf27",
   "metadata": {},
   "source": [
    "- WOW, just look at percentage of fraudulant transactions in visa and mastercard, 60% and 30% !!\n",
    "- although the percentage of fraud transaction in visa and mastercard may be high but discover has more fraudulant transactions than their legit transactions, if you have discover payment network on your card change it xD \n",
    "\n",
    "**Card 5**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "948c9040",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    586281.000000\n",
       "mean        199.278897\n",
       "std          41.244453\n",
       "min         100.000000\n",
       "25%         166.000000\n",
       "50%         226.000000\n",
       "75%         226.000000\n",
       "max         237.000000\n",
       "Name: card5, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_transaction['card5'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "06fb63de",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91733\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\91733\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x236340f4880>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting fraudulant transaction on card5\n",
    "\n",
    "sns.distplot(train[train['isFraud']==0]['card5'])\n",
    "sns.distplot(train[train['isFraud']==1]['card5'])\n",
    "plt.legend(['legit','fraud'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0ca7439",
   "metadata": {},
   "source": [
    "- at some intervals there is high chance for fraud for example at 225. \n",
    "- similarily legit transaction happen most at 160\n",
    "\n",
    "**Card 6**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "35acd7ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='card6', ylabel='percentage'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAaTUlEQVR4nO3de5RV5Z3m8e8TwJQMGkUKBilNYcJ4Q6zEQhM1NGocL4nBGeOtM5EIge54CZmJydCdHqP2TJaJs7LiaNoeMklEoyRKx1bpSGJoEY1GLWgEBG3SXksJFHiLCor4mz/2W1jiqapdl12nqvbzWavW2ff927tOPWfXe/Z5jyICMzMrjw9UuwAzM+tbDn4zs5Jx8JuZlYyD38ysZBz8ZmYlM7TaBeQxatSoqK+vr3YZZmYDyvLlyzdHRO2u0wdE8NfX19PU1FTtMszMBhRJz1Sa7qYeM7OScfCbmZWMg9/MrGQGRBu/mVlf2759O83NzWzbtq3apXSqpqaGuro6hg0blmt5B7+ZWQXNzc3sscce1NfXI6na5bQrItiyZQvNzc2MHz8+1zpu6jEzq2Dbtm3ss88+/Tr0ASSxzz77dOk/Ewe/mVk7+nvot+pqnQ5+M7OScfCbmeV09NFHdzi/vr6eww47jIaGBhoaGnjggQd6vYapU6f2+AOtfnPXrGBHfOOGapfA8qvOq3YJg0KeIL/nnnsYNWpUxXk7duxgyJAhvV1Wl/mK38wspxEjRgCwYcMGpkyZQkNDAxMnTuS+++7rcJ1LL72Uo446igcffJArrriCyZMnM3HiRGbPnk3rtyC2vZLfvHkzrf2Tbd26lXPOOYdJkyZx9tlns3Xr1h4fh4PfzKyLbr75Zk466SRWrlzJo48+SkNDw855xx13HA0NDRx11FEAvP7660ycOJGHHnqIY489losuuohHHnmENWvWsHXrVhYtWtThvq677jqGDx/OqlWr+Na3vsXy5ct7XL+beszMumjy5MnMmDGD7du3c/rpp78n+Hdt6hkyZAhnnHHGe+Z/73vf44033uDFF1/k0EMP5bTTTmt3X8uWLeOrX/0qAJMmTWLSpEk9rt9X/GZmXTRlyhSWLVvGuHHj+OIXv8gNN7T/Pk5NTc3Odv1t27ZxwQUXsHDhQlavXs2sWbN23n8/dOhQ3nnnnZ3LtdXbt5U6+M3MuuiZZ55h9OjRzJo1i5kzZ7JixYpc67UG+qhRo3jttddYuHDhznn19fU7m3HaTp8yZQo33XQTAGvWrGHVqlU9rt9NPWZmXbR06VKuuuoqhg0bxogRIzq84m9rr732YtasWRx22GHU19czefLknfMuueQSzjrrLG688UaOP/74ndO/8pWvcP755zNp0iQaGho48sgje1y/Wt9R7s8aGxvDX8RiA5Vv5xyY1q1bx8EHH1ztMnKrVK+k5RHRuOuybuoxMysZB7+ZWck4+M3MSsbBb2ZWMoUFv6QDJa1s8/OqpK9JGinpbknr0+PeRdVgZmbvV1jwR8QTEdEQEQ3AEcAbwG3AXGBJREwAlqRxMzPrI311H/8JwL9FxDOSpgFT0/T5wFLgv/dRHWZm3dbbt+bmuc128eLFzJkzhx07dvDlL3+ZuXN7fq3cV2385wAL0vCYiNgAkB5HV1pB0mxJTZKaWlpa+qhMM7P+Y8eOHVx44YXcddddrF27lgULFrB27doeb7fw4Je0G/A54NaurBcR8yKiMSIaa2triynOzKwfe/jhh/noRz/KAQccwG677cY555zD7bff3uPt9sUV/ynAiojYmMY3ShoLkB439UENZmYDzvPPP89+++23c7yuro7nn3++x9vti+A/l3ebeQDuAKan4elAz1++zMwGoUpd6vRGT52FBr+k4cCJwC/bTL4SOFHS+jTvyiJrMDMbqOrq6njuued2jjc3N7Pvvvv2eLuF3tUTEW8A++wybQvZXT5mZtaByZMns379ep566inGjRvHz3/+c26++eYeb9fdMpuZ5dTXvZwOHTqUa6+9lpNOOokdO3YwY8YMDj300J5vtxdqMzOzgpx66qmceuqpvbpN99VjZlYyDn4zs5Jx8JuZlYyD38ysZBz8ZmYl4+A3MysZ385pZpbTs1cc1qvb2//S1Z0uM2PGDBYtWsTo0aNZs2ZNr+zXV/xmZv3Yl770JRYvXtyr23Twm5n1Y1OmTGHkyJG9uk0Hv5lZyTj4zcxKxsFvZlYyDn4zs5Lx7ZxmZjnluf2yt5177rksXbqUzZs3U1dXx+WXX87MmTN7tE0Hv5lZP7ZgwYLOF+oiN/WYmZVM0d+5u5ekhZIel7RO0icljZR0t6T16XHvImswM7P3KvqK/2pgcUQcBBwOrAPmAksiYgKwJI2bmfU7EVHtEnLpap2FBb+kPYEpwI8BIuKtiHgZmAbMT4vNB04vqgYzs+6qqalhy5Yt/T78I4ItW7ZQU1OTe50i39w9AGgBfirpcGA5MAcYExEbACJig6TRlVaWNBuYDbD//vsXWKaZ2fvV1dXR3NxMS0tLtUvpVE1NDXV1dbmXLzL4hwIfBy6OiIckXU0XmnUiYh4wD6CxsbF/v+Sa2aAzbNgwxo8fX+0yClFkG38z0BwRD6XxhWQvBBsljQVIj5sKrMHMzHZRWPBHxB+B5yQdmCadAKwF7gCmp2nTgduLqsHMzN6v6A9wXQzcJGk34EngfLIXm1skzQSeBc4suAYzM2uj0OCPiJVAY4VZJxS5XzMza58/uWtmVjIOfjOzknHwm5mVjIPfzKxkHPxmZiXj4DczKxkHv5lZyTj4zcxKxsFvZlYyDn4zs5Jx8JuZlYyD38ysZBz8ZmYl4+A3MysZB7+ZWck4+M3MSsbBb2ZWMkV/9aKZ9QPPXnFYtUsAYP9LV1e7BMNX/GZmpVPoFb+kp4E/ATuAtyOiUdJI4BdAPfA0cFZEvFRkHWZm9q6+uOI/LiIaIqL1S9fnAksiYgKwJI2bmVkfqUZTzzRgfhqeD5xehRrMzEqr6OAP4DeSlkuanaaNiYgNAOlxdKUVJc2W1CSpqaWlpeAyzczKo+i7eo6JiBckjQbulvR43hUjYh4wD6CxsTGKKtDMrGxyX/FL2l3SgV3ZeES8kB43AbcBRwIbJY1N2xwLbOrKNs3MrGdyBb+k04CVwOI03iDpjk7W+XeS9mgdBv4jsAa4A5ieFpsO3N6tys3MrFvyNvVcRna1vhQgIlZKqu9knTHAbZJa93NzRCyW9Ahwi6SZwLPAmV0v28zMuitv8L8dEa+kEM8lIp4EDq8wfQtwQu4NWWn1h0+b+pOmNhjlDf41kv4cGCJpAvBV4IHiyjIzs6LkfXP3YuBQ4E1gAfAq8LWCajIzswLluuKPiDeAb6UfMzMbwHIFv6Q7yT6M1dYrQBPwfyNiW28XZmZmxcjb1PMk8Brwo/TzKrAR+A9p3MzMBoi8b+5+LCKmtBm/U9KyiJgi6bEiCjMzs2LkveKvlbR/60gaHpVG3+r1qszMrDB5r/i/Dtwv6d8AAeOBC9Incud3uKaZmfUree/q+VW6f/8gsuB/vM0buj8oqDYzMytAV3rnnAAcCNQAkyQRETcUU5aZmRUl7+2c3wamAocAvwJOAe4HHPxmZgNM3jd3P0/Wv84fI+J8sj54PlhYVWZmVpi8wb81It4B3pa0J1kf+gcUV5aZmRUlbxt/k6S9yD6stZzsw1wPF1WUmZkVJ+9dPRekwb+XtBjYMyJWFVeWmZkVJe83cC1pHY6IpyNiVdtpZmY2cHR4xS+pBhgOjJK0N9k9/AB7AvsWXJuZmRWgs6aevyDrd39fsrb91uB/FfhhcWWZmVlROgz+iLgauFrSxRFxTXd2IGkIWffNz0fEZyWNBH4B1ANPA2dFxEvd2baZmXVdrjb+iLhG0tGS/lzSea0/OfcxB1jXZnwusCQiJgBL0riZmfWRvG/u3gj8b+BYYHL6acyxXh3wGeD/tZk8jXc7dpsPnJ6/XDMz66m89/E3AodExK7fwtWZHwDfBPZoM21MRGwAiIgNkkZXWlHSbGA2wP77719pETMz64a8n9xdA/z7rmxY0meBTRGxvMtVARExLyIaI6Kxtra2O5swM7MK8l7xjwLWSnoYeLN1YkR8roN1jgE+J+lUsh4995T0M2CjpLHpan8sWfcPZmbWR/IG/2Vd3XBE/BXwVwCSpgKXRMR/kXQVMB24Mj3e3tVtm5lZ9+XtsuFeSR8GJkTEbyUNB4Z0c59XArdImgk8C5zZze2YmVk35O2PfxbZG60jgY8A44C/J+uquVMRsRRYmoa35F3PzMx6X943dy8ka7N/FSAi1gMV78YxM7P+LW/wvxkRb7WOSBoKdPXWTjMz6wfyBv+9kv4a2F3SicCtwJ3FlWVmZkXJG/xzgRZgNVnHbb8C/qaooszMrDh5b+fcHfhJRPwIdna8tjvwRlGFmZlZMfJe8S8hC/pWuwO/7f1yzMysaHmDvyYiXmsdScPDiynJzMyKlDf4X5f08dYRSUcAW4spyczMipS3jX8OcKukF9L4WODsYkoyM7MidRr86Y3cTwEHAQeSff3i4xGxveDazMysAJ029UTEDmBaRGyPiDURsdqhb2Y2cOVt6vmdpGvJviv39daJEbGikKrMzKwweYP/6PR4RZtpARzfu+WYmVnR8nbLfFzRhZiZWd/I+2XrYyT9WNJdafyQ1J++mZkNMHnv478e+DWwbxr/V+BrBdRjZmYFyxv8oyLiFuAdgIh4G9hRWFVmZlaYrnxydx9SH/ySPgG8UlhVZmZWmLx39fw34A7gAEm/A2qBz3e0gqQaYBnwwbSfhRHxbUkjyW4LrQeeBs6KiJe6Vb2ZmXVZ3iv+tcBtwCPARuBHZO38HXkTOD4iDgcagJPTfwpzgSURMYGs18+53ajbzMy6KW/w30DWZcN3gGuACcCNHa0QmdYePYelnwCmAfPT9PnA6V0r2czMeiJvU8+B6cq91T2SHu1spdTPz3Lgo8API+IhSWMiYgNARGyQ5C9tNzPrQ3mv+P8lNdMAIOko4HedrRQROyKiAagDjpQ0MW9hkmZLapLU1NLSknc1MzPrRN7gPwp4QNLTkp4GHgT+TNJqSas6WzkiXgaWAicDGyWNBUiPm9pZZ15ENEZEY21tbc4yzcysM3mbek7u6oYl1QLbI+JlSbsDnwa+S3Z30HTgyvR4e1e3bWZm3Ze3r55nurHtscD81M7/AeCWiFgk6UHgltTlw7PAmd3YtpmZdVPeK/4ui4hVwMcqTN8CnFDUfs3MrGN52/jNzGyQcPCbmZWMg9/MrGQc/GZmJePgNzMrGQe/mVnJOPjNzErGwW9mVjIOfjOzknHwm5mVTGFdNtjAdcQ3bqh2CQDctke1KzAbnHzFb2ZWMg5+M7OScfCbmZWMg9/MrGQc/GZmJePgNzMrGQe/mVnJOPjNzEqmsOCXtJ+keyStk/SYpDlp+khJd0tanx73LqoGMzN7vyKv+N8Gvh4RBwOfAC6UdAgwF1gSEROAJWnczMz6SGHBHxEbImJFGv4TsA4YB0wD5qfF5gOnF1WDmZm9X5+08UuqBz4GPASMiYgNkL04AKPbWWe2pCZJTS0tLX1RpplZKRQe/JJGAP8AfC0iXs27XkTMi4jGiGisra0trkAzs5IpNPglDSML/Zsi4pdp8kZJY9P8scCmImswM7P3KvKuHgE/BtZFxPfbzLoDmJ6GpwO3F1WDmZm9X5H98R8DfBFYLWllmvbXwJXALZJmAs8CZxZYg5mZ7aKw4I+I+wG1M/uEovZrZmYd8yd3zcxKxsFvZlYyDn4zs5Jx8JuZlYyD38ysZBz8ZmYl4+A3MysZB7+ZWck4+M3MSsbBb2ZWMg5+M7OScfCbmZWMg9/MrGQc/GZmJePgNzMrGQe/mVnJOPjNzErGwW9mVjJFftn6TyRtkrSmzbSRku6WtD497l3U/s3MrLIir/ivB07eZdpcYElETACWpHEzM+tDhQV/RCwDXtxl8jRgfhqeD5xe1P7NzKyyvm7jHxMRGwDS4+j2FpQ0W1KTpKaWlpY+K9DMbLDrt2/uRsS8iGiMiMba2tpql2NmNmj0dfBvlDQWID1u6uP9m5mVXl8H/x3A9DQ8Hbi9j/dvZlZ6Rd7OuQB4EDhQUrOkmcCVwImS1gMnpnEzM+tDQ4vacESc286sE4rap5mZda7fvrlrZmbFcPCbmZWMg9/MrGQc/GZmJePgNzMrGQe/mVnJOPjNzErGwW9mVjIOfjOzknHwm5mVjIPfzKxkHPxmZiXj4DczKxkHv5lZyTj4zcxKxsFvZlYyDn4zs5Jx8JuZlYyD38ysZKoS/JJOlvSEpD9ImluNGszMyqrPg1/SEOCHwCnAIcC5kg7p6zrMzMqqGlf8RwJ/iIgnI+It4OfAtCrUYWZWSkOrsM9xwHNtxpuBo3ZdSNJsYHYafU3SE31QW0+NAjZXu4jB4sP94Xx+W1XdfW/pF+cSBs35pL+cz859uNLEagR/pd98vG9CxDxgXvHl9B5JTRHRWO06Bgufz97jc9m7Bvr5rEZTTzOwX5vxOuCFKtRhZlZK1Qj+R4AJksZL2g04B7ijCnWYmZVSnzf1RMTbki4Cfg0MAX4SEY/1dR0FGVBNUwOAz2fv8bnsXQP6fCrifc3rZmY2iPmTu2ZmJePgNzMrmVIFv6TrJX2+2nX0BklTJS2qdh19TdJr6XFfSQvTcIOkU6tbWXVJukzSJR3Mr/jcl9Qo6f+k4amSji6yzt7U2TGnZQb0cUuql7QmDfdazdW4j39AkiSy90TeqdL+h0TEjmrsu2iShkbE211ZJyJeAFr/oBuARuBXvVzaoBcRTUBTGp0KvAY8ULWC+khvHnd3nr/dfM73Ws2D9opf0nmSVkl6VNKNbWZNkfSApCdbrwQkjZC0RNIKSaslTUvT6yWtk/R3wApgP0n/Q9Ljku6WtKD1ikPSRyQtlrRc0n2SDqpQ0whJP037WCXpjDT9OklNkh6TdHmb5Z+WdKmk+4EzU+d2j6fx/1zYySvArr+PdCX2fUn3AN9t7/yl234flPSIpL9ts716SWvSLcFXAGdLWinp7CodYp+T9K3U2eFvgQPTtI6eh59O0/5V0mfT8lMlLZJUD/wl8F/TefxUnx9QDpWOOU0v5LgljZT0j+m5+3tJk9L0yyTNk/Qb4IYKdX4z/Z0/KunKNG2ppO9IuheYI+kISfemmn8taWxa7oi03oPAhW222Xu/q4gYdD/AocATwKg0PjI9Xg/cSvaCdwhZn0GQ/eezZxoeBfyB7BPG9cA7wCfSvEZgJbA7sAewHrgkzVsCTEjDRwH/XKGu7wI/aDO+9y71DQGWApPS+NPAN9NwDVlXFxNSbbcAi6p9rrv7+0i/i0XAkI7OH9lnPM5LwxcCr6XhemBNGv4ScG21j7OPz+kRwGpgOLBnes5e0sF5vB5YnJ77E8g+SFlDduW4KC1zWevzuT/+tHfMnTx/enTcwDXAt9Pw8cDKNussB3avsM4pZFfiw1uf7+lxKfB3aXhYWqY2jZ9Ndms7wCrgz9LwVW2e5732uxqsTT3HAwsjYjNARLzYZt4/RtZcs1bSmDRNwHckTSEL+nFA67xnIuL3afhY4PaI2Aog6c70OAI4GrhV2tkjxQcr1PVpsg+skep6KQ2epaxvoqHAWLIXpVVp3i/S40HAUxGxPu3zZ7zbl1F/977fRzpPt0bEjk7O3zHAGWn4RrIXT4NPAbdFxBsAku4gC7SOnoe3pOf+eklPkj2nBpJKx5zn768nx30s6fkXEf8saR9JH0rz7mjNgl18Gvhpa5275E/r3/OBwETg7lTzEGBD2vZeEXFvWu5GsheSXjVYg19U6P8neXOX5QC+ANQCR0TEdklPk/0RAbxeYfldfQB4OSIaulqXpPFkV2qTI+IlSde32feu+x+oH7po7/fRemydnb+BetxF2/W8dPU8DsTzWqnmIo+7o77FXq8wr3Wd9vbxeptlHouIT75nRWmvLtbXLYO1jX8J2VX0PpC103Wy/IeATSn0j6OdHu2A+4HTJNWkq4zPAETEq8BTks5M+5Okwyus/xvgotYRSXuT/cv6OvBK+g+kvVf3x4Hxkj6Sxs/t5Jj6kw5/H52cv9/x7n9JX2hn+38ia3ork2XAf5K0u6Q9gNOAN+j4eXimpA+k59ABZM1vbfX381jpmPP8/fXkuJeRnneSpgKb0/468htghqThab1K+fMEUCvpk2mZYZIOjYiXybLg2LRcIc/5QRn8kXUB8b+AeyU9Cny/k1VuAholNZGd6Mfb2e4jZG3OjwK/JHuH/ZU0+wvAzLS/x6j8HQP/E9hb2ZuSjwLHRcSjwL+kdX5CFnSV9r2NrGnnn5S9uftMJ8fUb+T8fbR3/uYAF0p6hOwFupJ7gENUojd3I2IFWbPBSuAfgPvSrI6eh08A9wJ3AX+ZnlNt3UkWrP3yzd0OjhmKO+7LyLJhFXAlMD1HnYvJcqJJ0kqy/+h3XeYtsrvSvptqXknWXAVwPvDD9OZupaakzmrulLts6CJJIyLitfRqvgyYnZ6QZmYDwmBt4y/SPGVfFVkDzHfom9lA4yt+M7OSGZRt/GZm1j4Hv5lZyTj4zcxKxsFvVgBl/SyNSsN7SVqorJ+lda33bptVi+/qMeshdd7T4tXA4oj4vLJO5Yb3UWlmFTn4zdqQdB7ZB26CrL+kW4C/AXYDtgBfiIiNki4D9iXrLG6zpIuBBWRdfzxM+qi/pD2BKWQdybV+cOetPjsgswp8O6dZIulQsk9kHxMRm9NH7YOsH5iQ9GXg4Ij4egr+04BjI2Krsi/I2BwRV0j6DFnPo7VAHdkXc68FDifr0XFORLTXz4tZ4dzGb/auSr261gG/lrQa+AZZF9Ot2vbOOAX4WVrvn4DWnleHAh8HrouIj5H1yzS36AMx64iD3+xdlXpVvIasr//DgL+g/Z5TqbAuZP2/N0fEQ2l8IdkLgVnVOPjN3lWpF9EPAc+n+R110NW2F8dTgL0BIuKPwHOSWr8t6gSyZh+zqvGbu2ZJRDwmqbUX0R1kvaZeRvYFH88DvwfGt7P65cACSSvIeoJ8ts28i4Gb0h09T5L1vmhWNX5z18ysZNzUY2ZWMg5+M7OScfCbmZWMg9/MrGQc/GZmJePgNzMrGQe/mVnJ/H8F8H4mIqF73QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# card 6 tells us whether the card used in transaction is for credit or debit \n",
    "# since we have unique values we use the same process we did on card 4 \n",
    "\n",
    "train_card6 = (train[~train['card6'].isnull()].groupby(['isFraud'])['card6']\n",
    "                     .value_counts(normalize=True)\n",
    "                     .rename('percentage')\n",
    "                     .mul(100)\n",
    "                     .reset_index()\n",
    "                     .sort_values('card6'))\n",
    "sns.barplot(x=\"card6\", y=\"percentage\", hue=\"isFraud\", data=train_card6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b3a85f4",
   "metadata": {},
   "source": [
    "- credit card holders have high chance of having fraud transaction than debit card holders\n",
    "- charge card and debit or credit cards have 0% affect on data set so lets replace them into one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0285d9a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def replaceToOther(value):\n",
    "    if value==np.nan:\n",
    "        return value\n",
    "    if value=='debit or credit' or value=='charge card':\n",
    "        return 'debit'\n",
    "    else:\n",
    "        return value\n",
    "train['card6'] = train['card6'].apply(replaceToOther)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3868a32",
   "metadata": {},
   "source": [
    "**Analysis of address columns**\n",
    "\n",
    "- address 1 is the purchaser zip code\n",
    "- address 2 is the billing country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f5348fa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    524834.000000\n",
       "mean        290.733794\n",
       "std         101.741072\n",
       "min         100.000000\n",
       "25%         204.000000\n",
       "50%         299.000000\n",
       "75%         330.000000\n",
       "max         540.000000\n",
       "Name: addr1, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_transaction['addr1'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "aed986bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91733\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\91733\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23634725ee0>"
      ]
     },
     "execution_count": 40,
     "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": [
    "# plotting fraudulant transactions based on address column in data set\n",
    "sns.distplot(train[(train['isFraud']==0) & (~train['addr1'].isnull())]['addr1'])\n",
    "sns.distplot(train[(train['isFraud']==1) & (~train['addr1'].isnull())]['addr1'])\n",
    "plt.legend(['Legit','Fraud'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9b355a31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    524834.000000\n",
       "mean         86.800630\n",
       "std           2.690623\n",
       "min          10.000000\n",
       "25%          87.000000\n",
       "50%          87.000000\n",
       "75%          87.000000\n",
       "max         102.000000\n",
       "Name: addr2, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Similarily for address 2 \n",
    "train_transaction['addr2'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "96313e53",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91733\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\91733\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2363433b100>"
      ]
     },
     "execution_count": 42,
     "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": [
    "# plotting fraudulant transactions based on address column in data set\n",
    "sns.distplot(train[(train['isFraud']==0) & (~train['addr2'].isnull())]['addr2'])\n",
    "sns.distplot(train[(train['isFraud']==1) & (~train['addr2'].isnull())]['addr2'])\n",
    "plt.legend(['Legit','Fraud'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbc72e4d",
   "metadata": {},
   "source": [
    "- Most of the data is from 80-100 which means that most of the data is from a particular country. \n",
    "- Now lets check whether this is true or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e9db1bb3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87.0    520479\n",
       "60.0      3084\n",
       "96.0       638\n",
       "32.0        91\n",
       "65.0        82\n",
       "         ...  \n",
       "50.0         1\n",
       "15.0         1\n",
       "25.0         1\n",
       "82.0         1\n",
       "22.0         1\n",
       "Name: addr2, Length: 74, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['addr2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b1567032",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country with code 87 has  88.14 % of total data\n"
     ]
    }
   ],
   "source": [
    "print('country with code 87 has ',np.round(520479/train.shape[0]*100,2), '% of total data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94f583b9",
   "metadata": {},
   "source": [
    "- As you can see more than half a million values are from a country with code 87, which is interesting."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c557bb8",
   "metadata": {},
   "source": [
    "**Analysis for P_emaildomain and R_emaildomain**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e17ba6ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gmail.com        228355\n",
       "yahoo.com        100932\n",
       "hotmail.com       45250\n",
       "anonymous.com     36998\n",
       "aol.com           28289\n",
       "comcast.net        7888\n",
       "icloud.com         6267\n",
       "outlook.com        5096\n",
       "msn.com            4092\n",
       "att.net            4033\n",
       "Name: P_emaildomain, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['P_emaildomain'].value_counts()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "893272e0",
   "metadata": {},
   "source": [
    "- As we can see we have mails from different domains, we can find out which domain has suffered from most fraudulant transactions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "960ff0b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# return domain function here splits P_emaildomain column into two arrays, one before dot and one after dot\n",
    "# before .(dot) we have the domain of the email. After domain we have the extension of the email\n",
    "# We use domain to plot fraud transactions\n",
    "\n",
    "def returndomain(email):\n",
    "    return email.split(\".\")[0]\n",
    "train['edomain'] = train[~train['P_emaildomain'].isnull()]['P_emaildomain'].apply(returndomain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "043dd572",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_email has the new features values sorted and has percentage of total data\n",
    "train_email = (train.groupby(['isFraud'])['edomain']\n",
    "                     .value_counts(normalize=True)\n",
    "                     .rename('percentage')\n",
    "                     .mul(100)\n",
    "                     .reset_index()\n",
    "                     .sort_values('edomain'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "042688fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "        34, 35, 36, 37, 38, 39, 40, 41, 42, 43]),\n",
       " [Text(0, 0, 'aim'),\n",
       "  Text(1, 0, 'anonymous'),\n",
       "  Text(2, 0, 'aol'),\n",
       "  Text(3, 0, 'att'),\n",
       "  Text(4, 0, 'bellsouth'),\n",
       "  Text(5, 0, 'cableone'),\n",
       "  Text(6, 0, 'centurylink'),\n",
       "  Text(7, 0, 'cfl'),\n",
       "  Text(8, 0, 'charter'),\n",
       "  Text(9, 0, 'comcast'),\n",
       "  Text(10, 0, 'cox'),\n",
       "  Text(11, 0, 'earthlink'),\n",
       "  Text(12, 0, 'embarqmail'),\n",
       "  Text(13, 0, 'frontier'),\n",
       "  Text(14, 0, 'frontiernet'),\n",
       "  Text(15, 0, 'gmail'),\n",
       "  Text(16, 0, 'gmx'),\n",
       "  Text(17, 0, 'hotmail'),\n",
       "  Text(18, 0, 'icloud'),\n",
       "  Text(19, 0, 'juno'),\n",
       "  Text(20, 0, 'live'),\n",
       "  Text(21, 0, 'mac'),\n",
       "  Text(22, 0, 'mail'),\n",
       "  Text(23, 0, 'me'),\n",
       "  Text(24, 0, 'msn'),\n",
       "  Text(25, 0, 'netzero'),\n",
       "  Text(26, 0, 'optonline'),\n",
       "  Text(27, 0, 'outlook'),\n",
       "  Text(28, 0, 'prodigy'),\n",
       "  Text(29, 0, 'protonmail'),\n",
       "  Text(30, 0, 'ptd'),\n",
       "  Text(31, 0, 'q'),\n",
       "  Text(32, 0, 'roadrunner'),\n",
       "  Text(33, 0, 'rocketmail'),\n",
       "  Text(34, 0, 'sbcglobal'),\n",
       "  Text(35, 0, 'sc'),\n",
       "  Text(36, 0, 'servicios-ta'),\n",
       "  Text(37, 0, 'suddenlink'),\n",
       "  Text(38, 0, 'twc'),\n",
       "  Text(39, 0, 'verizon'),\n",
       "  Text(40, 0, 'web'),\n",
       "  Text(41, 0, 'windstream'),\n",
       "  Text(42, 0, 'yahoo'),\n",
       "  Text(43, 0, 'ymail')])"
      ]
     },
     "execution_count": 48,
     "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": [
    "# xticks function : Get or set the current tick locations and labels of the x-axis\n",
    "sns.barplot(x=\"edomain\", y=\"percentage\", hue=\"isFraud\", data=train_email)\n",
    "plt.xticks(rotation = 90)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dad790d",
   "metadata": {},
   "source": [
    "- from the function returndomain, we have two features domain name and its extension \n",
    "- also from the figure most of the data has email from gmail domain which is fair to say also has more fraud transactions. \n",
    "- hotmail is the only domain which has more fraud transactions than legit transactions. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5c6a5a0",
   "metadata": {},
   "source": [
    "**Analysis for C1 - C14 columns**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1312955e",
   "metadata": {},
   "source": [
    "We could use the same describe function and then plot it's distribution with respect to isFraud column for every feature from C1 to C14 but instead I am creating a correlation matrix which probably best to find relation between them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "df325acd",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_between_c = train[['C1','C2','C3','C4','C5','C6','C7','C8','C9','C10','C11','C12','C13','C14','isFraud']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6aefc861",
   "metadata": {},
   "outputs": [],
   "source": [
    "# corr() functions calculates the correlations between columns in the data frame\n",
    "# I am using a data frame which has 'C' features and 'isFraud' column\n",
    "\n",
    "cr = correlation_between_c.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "070f0158",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figsize is used to give size of the figure and from seaborn library we are using heatmap to visualize relationship\n",
    "# annot : If True, write the data value in each cell. Generally used if data is array like instead of data\n",
    "\n",
    "plt.figure(1,figsize=(18,18))\n",
    "sns.heatmap(cr,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db44a5bd",
   "metadata": {},
   "source": [
    "- from the heatmap, every 'C' feature has no correlation to isFraud column\n",
    "- also some features are highly correlated like C1 and C4, C1 and C6, C6 and C11\n",
    "- Don't know what this means until we go to modelling by removing these features and checking accuracy score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "150b2f28",
   "metadata": {},
   "source": [
    "**Analysis for D features i.e., D1 to D15**\n",
    "\n",
    "- Just like for C features we will be using correlation matrix for D features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5f780f82",
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets find the corelation between these D features with 'isFraud' features\n",
    "cor_d = train[['D1','D2','D3','D4','D5','D6','D7','D8','D9','D10','D11','D12','D13','D14','D15','isFraud']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d8b7fb5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "f = cor_d.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "03afc594",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1,figsize=(18,18))\n",
    "sns.heatmap(f,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00156f55",
   "metadata": {},
   "source": [
    "- Highly correlated features are  'D4 and D12' and 'D6 and D12' and 'D5 and D7'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ba48bf8",
   "metadata": {},
   "source": [
    "**Data Analysis for M features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "af68a2e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T    313071\n",
      "F        25\n",
      "Name: M1, dtype: int64\n",
      "T    6342\n",
      "Name: M1, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "T    280308\n",
      "F     32788\n",
      "Name: M2, dtype: int64\n",
      "T    5158\n",
      "F    1184\n",
      "Name: M2, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "T    247436\n",
      "F     65660\n",
      "Name: M3, dtype: int64\n",
      "T    4293\n",
      "F    2049\n",
      "Name: M3, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "M0    189207\n",
      "M2     53056\n",
      "M1     51397\n",
      "Name: M4, dtype: int64\n",
      "M0    7198\n",
      "M2    6809\n",
      "M1    1429\n",
      "Name: M4, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "F    128977\n",
      "T    103512\n",
      "Name: M5, dtype: int64\n",
      "T    4055\n",
      "F    3514\n",
      "Name: M5, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "F    222459\n",
      "T    190029\n",
      "Name: M6, dtype: int64\n",
      "F    5397\n",
      "T    3295\n",
      "Name: M6, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "F    207283\n",
      "T     32173\n",
      "Name: M7, dtype: int64\n",
      "F    4089\n",
      "T     728\n",
      "Name: M7, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "F    151876\n",
      "T     87593\n",
      "Name: M8, dtype: int64\n",
      "F    3373\n",
      "T    1444\n",
      "Name: M8, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "T    201996\n",
      "F     37473\n",
      "Name: M9, dtype: int64\n",
      "T    3658\n",
      "F    1159\n",
      "Name: M9, dtype: int64\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Finding out what type of values and how many unique values are there in each 'M' feature using value_counts()\n",
    "\n",
    "for i in ['M1','M2','M3','M4','M5','M6','M7','M8','M9']:\n",
    "    print(train[(~train[i].isnull())&(train['isFraud']==0)][i].value_counts())\n",
    "    print(train[(~train[i].isnull())&(train['isFraud']==1)][i].value_counts())\n",
    "    print('\\n\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c688b3e1",
   "metadata": {},
   "source": [
    "\n",
    "- Most of the M features are binary distributed i.e., 0 and 1 \n",
    "- Except M4 feature which has 3 unique values, M0, M1, M2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9449174a",
   "metadata": {},
   "source": [
    "**Data Analysis on V features**\n",
    "\n",
    "- In the transaction data set we have 339 V features. We cannot visualize them one by one.\n",
    "- First we'll seperate V features and then see what can we find out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "316b8ad5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# seperating vfeatures from rest of the features. \n",
    "v_features = []\n",
    "for i in train.columns:\n",
    "    if 'V' in i:\n",
    "        v_features.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2b139aa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a data frame of v features.\n",
    "v_features_df = train_transaction[v_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "75822619",
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</th>\n",
       "      <th>V330</th>\n",
       "      <th>V331</th>\n",
       "      <th>V332</th>\n",
       "      <th>V333</th>\n",
       "      <th>V334</th>\n",
       "      <th>V335</th>\n",
       "      <th>V336</th>\n",
       "      <th>V337</th>\n",
       "      <th>V338</th>\n",
       "      <th>V339</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590535</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>590536</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>590537</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>590538</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>590539</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>590540 rows × 339 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1   V2   V3   V4   V5   V6   V7   V8   V9  V10  ...  V330  V331  \\\n",
       "0       1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  0.0  ...   NaN   NaN   \n",
       "1       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   NaN   NaN   \n",
       "2       1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  0.0  ...   NaN   NaN   \n",
       "3       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   NaN   NaN   \n",
       "4       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   0.0   0.0   \n",
       "...     ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   ...   ...   \n",
       "590535  1.0  1.0  1.0  0.0  0.0  1.0  1.0  1.0  1.0  0.0  ...   NaN   NaN   \n",
       "590536  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  0.0  ...   NaN   NaN   \n",
       "590537  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...   NaN   NaN   \n",
       "590538  1.0  1.0  1.0  2.0  2.0  1.0  1.0  1.0  1.0  0.0  ...   NaN   NaN   \n",
       "590539  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...   NaN   NaN   \n",
       "\n",
       "        V332  V333  V334  V335  V336  V337  V338  V339  \n",
       "0        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "1        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "4        0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "...      ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "590535   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "590536   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "590537   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "590538   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "590539   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[590540 rows x 339 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_features_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "85e61200",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "V1      279287\n",
       "V2      279287\n",
       "V3      279287\n",
       "V4      279287\n",
       "V5      279287\n",
       "         ...  \n",
       "V335    508189\n",
       "V336    508189\n",
       "V337    508189\n",
       "V338    508189\n",
       "V339    508189\n",
       "Length: 339, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of NaN values in each V features.\n",
    "v_features_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "29ab30fb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 590540 entries, 0 to 590539\n",
      "Columns: 339 entries, V1 to V339\n",
      "dtypes: float64(339)\n",
      "memory usage: 1.5 GB\n"
     ]
    }
   ],
   "source": [
    "# info() gives us number of rows, columns, data type and memory usage of the data frame. \n",
    "\n",
    "v_features_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "5dc69ddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# deleting it to save memory usage.\n",
    "del v_features_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f68d6f12",
   "metadata": {},
   "source": [
    "- All the V features are float type\n",
    "- Almost every V feature has higher NaN values\n",
    "- We have to do fill or remove NaN values before applying model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17dff598",
   "metadata": {},
   "source": [
    "**Data Analysis on DeviceInfo and DeviceType**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "3e2a2ae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Windows            47722\n",
       "iOS Device         19782\n",
       "MacOS              12573\n",
       "Trident/7.0         7440\n",
       "rv:11.0             1901\n",
       "                   ...  \n",
       "SCH-I435               1\n",
       "Z812                   1\n",
       "LG-H810/H81021z        1\n",
       "Fusion5_u7             1\n",
       "WAS-LX2J               1\n",
       "Name: DeviceInfo, Length: 1786, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of unique values present in Device Info\n",
    "train['DeviceInfo'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc51702a",
   "metadata": {},
   "source": [
    "- There are 1786 devices used in the transaction which is a lot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "80665697",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DeviceType', ylabel='percentage'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zbpc1+M/LtQozMyubrGP1vJR3IWZmVh5Z+/jNzKyPcPCbmRWMg9/MrGAc/GZmBePgNzMrGAe/mVnBOPjNzAomt+CXdJSkRyStlfSspBlp+yBJiyStS6eH5VWDmZntKc8z/p3AP0bECcBHgCslnQjMBBZHxAhgcTpvZmZlklvwR8T6iFiRvn4bWAscCUwEZqerzQYm5VWDmZntqSx9/JJqgFHAMuDwiFgPyR8HYFg720yX1CCpoampqRxlmpkVQu7BL2kg8BPgixHxVtbtImJWRNRHRP3QoUPzK9DMrGByDX5J+5GE/t0R8dO0+XVJw9Plw4ENedZgZmZ/Ks+regT8G7A2Ir5Zsmg+MDl9PRmYl1cNZma2p6zj8XfG6cBlwGpJK9O2rwA3AfdJmgq8DFyUYw1mZtZKbsEfEb8C1M7is/I6rpmZ7Z3v3DUzKxgHv5lZwTj4zcwKxsFvZlYwDn4zs4Jx8JuZFYyD38ysYBz8ZmYF4+A3MysYB7+ZWcE4+M3MCsbBb2ZWMA5+M7OCcfCbmRWMg9/MrGAc/GZmBePgNzMrGAe/mVnBOPjNzArGwW9mVjAOfjOzgnHwm5kVjIPfzKxgHPxmZgXj4DczK5jcgl/SHZI2SFpT0jZI0iJJ69LpYXkd38zM2pbnGf+dwHmt2mYCiyNiBLA4nTczszLKLfgjYinwRqvmicDs9PVsYFJexzczs7aVu4//8IhYD5BOh7W3oqTpkhokNTQ1NZWtQDOzvq7H/rgbEbMioj4i6ocOHVrpcszM+oxyB//rkoYDpNMNZT6+mVnhlTv45wOT09eTgXllPr6ZWeHleTnnHOAJ4DhJjZKmAjcBZ0taB5ydzpuZWRn1z2vHEXFpO4vOyuuYZma2bz32x10zM8uHg9/MrGAc/GZmBePgNzMrGAe/mVnBOPjNzArGwW9mVjAOfjOzgnHwm5kVjIPfzKxgHPxmZgXj4DczKxgHv5lZwTj4zcwKxsFvZlYwDn4zs4Jx8JuZFYyD38ysYBz8ZmYF4+A3MysYB7+ZWcE4+M3MCsbBb2ZWMA5+M7OCcfCbmRVMRYJf0nmSfiPpd5JmVqIGM7OiKnvwS+oHfBf4S+BE4FJJJ5a7DjOzoqrEGf9pwO8i4oWI2AHcC0ysQB1mZoXUvwLHPBJ4pWS+ERjTeiVJ04Hp6exmSb8pQ22F8AEYAmysdB09wtdU6QqshD+bJbrns/mBthorEfxtvZvYoyFiFjAr/3KKR1JDRNRXug6z1vzZLI9KdPU0AkeVzFcDr1WgDjOzQqpE8D8FjJB0tKT9gUuA+RWow8yskMre1RMROyVdBTwE9APuiIhny11HwbkLzXoqfzbLQBF7dK+bmVkf5jt3zcwKxsFvZlYwDv5eSNL1kq7p4DaXS7q1jfZJvnPaeoL2PteSjpA0N309XtKC8lfXtzj4bRLJ0BlmPVJEvBYRn6p0HX2Jg7+XkHRtOrDdL4Dj0rYPSlooabmkRyUdn7ZfJGmNpGckLW1jX5+Q9ISkccAFwDckrUz3Vyfp15JWSXpA0mHpNksk3SLp8XTfp5Xx7VsvIalG0vOSbk8/J3dL+rikxyStk3SapEGS/j39jP1aUm3JLk6R9HC67rSSfa5p41gHSbpD0lOSnpbkoV8yqsSdu9ZBkk4lud9hFMn/sxXAcpJL3/4hItZJGgP8P+BjwHXAuRHxqqRDW+3rr4AvARMi4g+S5gMLIqLlq/Qq4OqI+KWkG4GvAV9MNz8oIsamfzDuAEbm+b6t1zoWuIhkyJWngL8FziA5yfgKyZAtT0fEJEkfA+4C6tJta4GPAAcBT0v6j70c51rg4YiYkn7On5T0i4jY0v1vqW9x8PcOHwUeiIitAGlYVwFjgful3aNgHJBOHwPulHQf8NOS/ZwJ1APnRMRbrQ8i6c+AQyPil2nTbOD+klXmAETEUkmHSDo0Iv7YDe/P+pYXI2I1gKRngcUREZJWAzUk48dcCBARD0sanH72AOZFxDZgm6RHSAZ1XNnOcc4BLij5XaAKeD+wNof31Kc4+HuP1jdcvA/4Y0TU7bFixD+k3wA+AayU1LLOC8AxwIeAhm6owTeBWFveKXm9q2R+F0nm7Gxjm2g1bd3eFgEXRoQHcOwg9/H3DkuBv5J0oKSDgfOBrcCLki4CUOKU9PUHI2JZRFxHMtJhy9hILwF/Ddwl6aS07W3gYICIeBP4g6SPpssuA1rO/gEuTvd/BvBmur5ZRy0FPg3JVTrAxpJvoBMlVUkaDIwn6Spqz0PA1Uq/8koalVfBfY3P+HuBiFgh6cckX3lfAh5NF30auE3SV4H9SJ5t8AzJj7UjSM6IFqdtdem+fiPp0yRdROen2/xA0heATwGTge9JGkDyDeGzJaX8QdLjwCHAlPzesfVx1wM/TH9P2krymWvxJPAfJF02X4+I1yTVtLOfrwO3AKvS8P898Ml8Su5bPGSDZSJpCXBNRHSmi8jMehB39ZiZFYzP+M3MCsZn/GZmBePgNzMrGAe/mVnBOPitz5HUnI499Gw6XtGXJHXqsy6pXtK3O7HdA2kNv5P0Zvp6paSxnanDrDv5x13rcyRtjoiB6ethwD3AYxHxtQrUMp7kMlhfX249hs/4rU+LiA0kg4Vdld7d3E/SN9IRHVdJ+nsAST+WNKFlO0l3SrpQJeO/Sxoo6YeSVqfbXpi2n6NktNMVku6XNLCtWpSMoFpXMv+YpFol49D/qPWolOk6Xy6p9YZc/iNZ4Tj4rc+LiBdIPuvDgKkkw02MBkYD0yQdTXIHc8uQFPsDZwE/b7Wrf023PTkiaoGHJQ0Bvgp8PCI+TDIG0pfaKeV24PL0GB8CDoiIVemyWpKxlf4CuE7Jw0fOAUaQDFRWB5yajoxq1iUessGKomUI03OAWkktD/b4M5JwfRD4tqQDgPOApRGxrWTkU4CPkwyPDUA6rPUnSR5k81i67v7AE+3UcD/wr5K+TDLkxZ0ly9oalfKMtN6n03UGprXu8YwFs45w8FufJ+kYoBnYQPIH4OqIeKiN9ZYA55Kc+c9pa1fsOVqkgEURcem+6oiIrZIWAROBvyEZInv34tarp/v+PxHx/X3t26wj3NVjfZqkocD3gFsjuZLhIeDzkvZLl39I0kHp6veSDEr30XS91v4TuKpk34cBvwZOl3Rs2jYg7cZpz+3At4GnIuKNkva2RqV8CJjS8puBpCPTH6vNusRn/NYXHShpJcmIpTuBHwHfTJfdTvIwkBXpiI5NJM8dhiTY7wLmR8SONvb7v4DvKnkMYDNwQ0T8VNLlwJy0mwiSPv/ftlVYRCyX9Bbww1aL9hiVEnhN0gnAE2k30mbg70i+uZh1mi/nNCsjSUcAS4DjI2JX2nY9sDki/m8FS7MCcVePWZlI+gywDLi2JfTNKsFn/GZmBeMzfjOzgnHwm5kVjIPfzKxgHPxmZgXj4DczK5j/AWwJgBHXcSQPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DeviceType\n",
    "\n",
    "DeviceType = (train.groupby(['isFraud'])['DeviceType']\n",
    "                     .value_counts(normalize=True)\n",
    "                     .rename('percentage')\n",
    "                     .mul(100)\n",
    "                     .reset_index()\n",
    "                     .sort_values('DeviceType'))\n",
    "sns.barplot(x=\"DeviceType\", y=\"percentage\", hue=\"isFraud\", data=DeviceType)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0a53107",
   "metadata": {},
   "source": [
    "- From the plot, mobile transactions are mostly fraudulant.\n",
    "- Both desktop and mobile same percentage of fraudulant transactions. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8afda02",
   "metadata": {},
   "source": [
    "# Feature Engineering and Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35efad76",
   "metadata": {},
   "source": [
    "- In this part, we'll decide which features should we use and what other transformations or functions should we use so as to make our data set ready to use on ML model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "05b84a82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id_12',\n",
       " 'id_15',\n",
       " 'id_16',\n",
       " 'id_23',\n",
       " 'id_27',\n",
       " 'id_28',\n",
       " 'id_29',\n",
       " 'id_30',\n",
       " 'id_31',\n",
       " 'id_33',\n",
       " 'id_34',\n",
       " 'id_35',\n",
       " 'id_36',\n",
       " 'id_37',\n",
       " 'id_38',\n",
       " 'DeviceType',\n",
       " 'DeviceInfo']"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's start with identity data set\n",
    "# finding out what are categorical features\n",
    "# Categorical features are features of data type 'object'\n",
    "\n",
    "categorical = [feature for feature in train_identity.columns if train_identity[feature].dtype=='O']\n",
    "categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "81695f81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id_12: 0.0% missing values\n",
      "id_15: 2.2519% missing values\n",
      "id_16: 10.3257% missing values\n",
      "id_23: 96.4162% missing values\n",
      "id_27: 96.4162% missing values\n",
      "id_28: 2.2568% missing values\n",
      "id_29: 2.2568% missing values\n",
      "id_30: 46.2224% missing values\n",
      "id_31: 2.7393% missing values\n",
      "id_33: 49.1871% missing values\n",
      "id_34: 46.056% missing values\n",
      "id_35: 2.2519% missing values\n",
      "id_36: 2.2519% missing values\n",
      "id_37: 2.2519% missing values\n",
      "id_38: 2.2519% missing values\n",
      "DeviceType: 2.3732% missing values\n",
      "DeviceInfo: 17.7262% missing values\n"
     ]
    }
   ],
   "source": [
    "# now that we've found out what are categorical features, let's calculate %NaN values present in them.\n",
    "\n",
    "for feature in categorical:\n",
    "    print(\"{}: {}% missing values\".format(feature,np.round(train_identity[feature].isnull().mul(100).mean(),4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df3e24ed",
   "metadata": {},
   "source": [
    "- Since categorical values are important in ML model, we'll remove those features whose %NaN values are high.\n",
    "- In this case id_23 and id_27 which have 96% of NaN values in them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f08b4eb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>TransactionID</th>\n",
       "      <th>id_01</th>\n",
       "      <th>id_02</th>\n",
       "      <th>id_03</th>\n",
       "      <th>id_04</th>\n",
       "      <th>id_05</th>\n",
       "      <th>id_06</th>\n",
       "      <th>id_07</th>\n",
       "      <th>id_08</th>\n",
       "      <th>id_09</th>\n",
       "      <th>...</th>\n",
       "      <th>id_31</th>\n",
       "      <th>id_32</th>\n",
       "      <th>id_33</th>\n",
       "      <th>id_34</th>\n",
       "      <th>id_35</th>\n",
       "      <th>id_36</th>\n",
       "      <th>id_37</th>\n",
       "      <th>id_38</th>\n",
       "      <th>DeviceType</th>\n",
       "      <th>DeviceInfo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2987004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70787.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>samsung browser 6.2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2220x1080</td>\n",
       "      <td>match_status:2</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>mobile</td>\n",
       "      <td>SAMSUNG SM-G892A Build/NRD90M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2987008</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>98945.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>mobile safari 11.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1334x750</td>\n",
       "      <td>match_status:1</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>mobile</td>\n",
       "      <td>iOS Device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2987010</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>191631.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>desktop</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2987011</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>221832.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>desktop</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2987016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7460.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 62.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1280x800</td>\n",
       "      <td>match_status:2</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>desktop</td>\n",
       "      <td>MacOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144228</th>\n",
       "      <td>3577521</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>145955.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 66.0 for android</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>mobile</td>\n",
       "      <td>F3111 Build/33.3.A.1.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144229</th>\n",
       "      <td>3577526</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>172059.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 55.0 for android</td>\n",
       "      <td>32.0</td>\n",
       "      <td>855x480</td>\n",
       "      <td>match_status:2</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>mobile</td>\n",
       "      <td>A574BL Build/NMF26F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144230</th>\n",
       "      <td>3577529</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>632381.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 65.0 for android</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Moto E (4) Plus Build/NMA26.42-152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144231</th>\n",
       "      <td>3577531</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>55528.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 66.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2560x1600</td>\n",
       "      <td>match_status:2</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>desktop</td>\n",
       "      <td>MacOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144232</th>\n",
       "      <td>3577534</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>339406.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>chrome 66.0 for android</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>mobile</td>\n",
       "      <td>RNE-L03 Build/HUAWEIRNE-L03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144233 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TransactionID  id_01     id_02  id_03  id_04  id_05  id_06  id_07  \\\n",
       "0             2987004    0.0   70787.0    NaN    NaN    NaN    NaN    NaN   \n",
       "1             2987008   -5.0   98945.0    NaN    NaN    0.0   -5.0    NaN   \n",
       "2             2987010   -5.0  191631.0    0.0    0.0    0.0    0.0    NaN   \n",
       "3             2987011   -5.0  221832.0    NaN    NaN    0.0   -6.0    NaN   \n",
       "4             2987016    0.0    7460.0    0.0    0.0    1.0    0.0    NaN   \n",
       "...               ...    ...       ...    ...    ...    ...    ...    ...   \n",
       "144228        3577521  -15.0  145955.0    0.0    0.0    0.0    0.0    NaN   \n",
       "144229        3577526   -5.0  172059.0    NaN    NaN    1.0   -5.0    NaN   \n",
       "144230        3577529  -20.0  632381.0    NaN    NaN   -1.0  -36.0    NaN   \n",
       "144231        3577531   -5.0   55528.0    0.0    0.0    0.0   -7.0    NaN   \n",
       "144232        3577534  -45.0  339406.0    NaN    NaN  -10.0 -100.0    NaN   \n",
       "\n",
       "        id_08  id_09  ...                    id_31  id_32      id_33  \\\n",
       "0         NaN    NaN  ...      samsung browser 6.2   32.0  2220x1080   \n",
       "1         NaN    NaN  ...       mobile safari 11.0   32.0   1334x750   \n",
       "2         NaN    0.0  ...              chrome 62.0    NaN        NaN   \n",
       "3         NaN    NaN  ...              chrome 62.0    NaN        NaN   \n",
       "4         NaN    0.0  ...              chrome 62.0   24.0   1280x800   \n",
       "...       ...    ...  ...                      ...    ...        ...   \n",
       "144228    NaN    0.0  ...  chrome 66.0 for android    NaN        NaN   \n",
       "144229    NaN    NaN  ...  chrome 55.0 for android   32.0    855x480   \n",
       "144230    NaN    NaN  ...  chrome 65.0 for android    NaN        NaN   \n",
       "144231    NaN    0.0  ...              chrome 66.0   24.0  2560x1600   \n",
       "144232    NaN    NaN  ...  chrome 66.0 for android    NaN        NaN   \n",
       "\n",
       "                 id_34  id_35 id_36 id_37  id_38  DeviceType  \\\n",
       "0       match_status:2      T     F     T      T      mobile   \n",
       "1       match_status:1      T     F     F      T      mobile   \n",
       "2                  NaN      F     F     T      T     desktop   \n",
       "3                  NaN      F     F     T      T     desktop   \n",
       "4       match_status:2      T     F     T      T     desktop   \n",
       "...                ...    ...   ...   ...    ...         ...   \n",
       "144228             NaN      F     F     T      F      mobile   \n",
       "144229  match_status:2      T     F     T      F      mobile   \n",
       "144230             NaN      F     F     T      F      mobile   \n",
       "144231  match_status:2      T     F     T      F     desktop   \n",
       "144232             NaN      F     F     T      F      mobile   \n",
       "\n",
       "                                DeviceInfo  \n",
       "0            SAMSUNG SM-G892A Build/NRD90M  \n",
       "1                               iOS Device  \n",
       "2                                  Windows  \n",
       "3                                      NaN  \n",
       "4                                    MacOS  \n",
       "...                                    ...  \n",
       "144228             F3111 Build/33.3.A.1.97  \n",
       "144229                 A574BL Build/NMF26F  \n",
       "144230  Moto E (4) Plus Build/NMA26.42-152  \n",
       "144231                               MacOS  \n",
       "144232         RNE-L03 Build/HUAWEIRNE-L03  \n",
       "\n",
       "[144233 rows x 39 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_identity.drop(['id_23', 'id_27'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "298a6479",
   "metadata": {},
   "source": [
    "- Generally we deal with NaN values based on the dtype\n",
    "- If dtype is object then we impute NaN values with mode of the data\n",
    "- If dtype is numerical then we impute NaN values with mean of the data\n",
    "- Or Replace them with specific values\n",
    "- Or we can always drop them, but that doesn't work efficiently with model. \n",
    "\n",
    "\n",
    "\n",
    "- At this point we are dealing NaN values by replacing them '-999' \n",
    "- If roc_auc_score is less using this, then we'll try another method to deal with NaN's. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "2ee84ec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace NaN values\n",
    "train_identity=train_identity.fillna(value=-999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a446f7e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace with numerical values\n",
    "train_identity = train_identity.replace(\"Found\", 1)\n",
    "train_identity = train_identity.replace(\"NotFound\", 2)\n",
    "train_identity = train_identity.replace(\"New\", 3)\n",
    "train_identity = train_identity.replace(\"mobile\", 4)\n",
    "train_identity = train_identity.replace(\"desktop\", 5)\n",
    "train_identity = train_identity.replace(\"T\", 6)\n",
    "train_identity = train_identity.replace(\"F\", 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "cd4ead4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "      <td>-999</td>\n",
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       "      <td>3</td>\n",
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       "      <td>855x480</td>\n",
       "      <td>match_status:2</td>\n",
       "      <td>A574BL Build/NMF26F</td>\n",
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       "      <td>3</td>\n",
       "      <td>-999</td>\n",
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       "      <td>chrome 65.0 for android</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>Moto E (4) Plus Build/NMA26.42-152</td>\n",
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       "      <td>chrome 66.0</td>\n",
       "      <td>2560x1600</td>\n",
       "      <td>match_status:2</td>\n",
       "      <td>MacOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144232</th>\n",
       "      <td>3</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>chrome 66.0 for android</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>RNE-L03 Build/HUAWEIRNE-L03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144233 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id_15 id_23             id_30                    id_31      id_33  \\\n",
       "0          3  -999       Android 7.0      samsung browser 6.2  2220x1080   \n",
       "1          3  -999        iOS 11.1.2       mobile safari 11.0   1334x750   \n",
       "2          1  -999              -999              chrome 62.0       -999   \n",
       "3          3  -999              -999              chrome 62.0       -999   \n",
       "4          1  -999  Mac OS X 10_11_6              chrome 62.0   1280x800   \n",
       "...      ...   ...               ...                      ...        ...   \n",
       "144228     1  -999              -999  chrome 66.0 for android       -999   \n",
       "144229     3  -999     Android 7.1.1  chrome 55.0 for android    855x480   \n",
       "144230     3  -999              -999  chrome 65.0 for android       -999   \n",
       "144231     1  -999   Mac OS X 10_9_5              chrome 66.0  2560x1600   \n",
       "144232     3  -999              -999  chrome 66.0 for android       -999   \n",
       "\n",
       "                 id_34                          DeviceInfo  \n",
       "0       match_status:2       SAMSUNG SM-G892A Build/NRD90M  \n",
       "1       match_status:1                          iOS Device  \n",
       "2                 -999                             Windows  \n",
       "3                 -999                                -999  \n",
       "4       match_status:2                               MacOS  \n",
       "...                ...                                 ...  \n",
       "144228            -999             F3111 Build/33.3.A.1.97  \n",
       "144229  match_status:2                 A574BL Build/NMF26F  \n",
       "144230            -999  Moto E (4) Plus Build/NMA26.42-152  \n",
       "144231  match_status:2                               MacOS  \n",
       "144232            -999         RNE-L03 Build/HUAWEIRNE-L03  \n",
       "\n",
       "[144233 rows x 7 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_identity.select_dtypes(\"object\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b90bef4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_identity[\"id_15\"]=train_identity[\"id_15\"].astype(str)\n",
    "train_identity[\"id_23\"]=train_identity[\"id_23\"].astype(str)\n",
    "train_identity[\"id_30\"]=train_identity[\"id_30\"].astype(str)\n",
    "train_identity[\"id_31\"]=train_identity[\"id_31\"].astype(str)\n",
    "train_identity[\"id_33\"]=train_identity[\"id_33\"].astype(str)\n",
    "train_identity[\"id_34\"]=train_identity[\"id_34\"].astype(str)\n",
    "train_identity[\"DeviceInfo\"]=train_identity[\"DeviceInfo\"].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5c8b4839",
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_15</th>\n",
       "      <th>id_23</th>\n",
       "      <th>id_30</th>\n",
       "      <th>id_31</th>\n",
       "      <th>id_33</th>\n",
       "      <th>id_34</th>\n",
       "      <th>DeviceInfo</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>-999</td>\n",
       "      <td>Android 7.0</td>\n",
       "      <td>samsung browser 6.2</td>\n",
       "      <td>2220x1080</td>\n",
       "      <td>match_status:2</td>\n",
       "      <td>SAMSUNG SM-G892A Build/NRD90M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>-999</td>\n",
       "      <td>iOS 11.1.2</td>\n",
       "      <td>mobile safari 11.0</td>\n",
       "      <td>1334x750</td>\n",
       "      <td>match_status:1</td>\n",
       "      <td>iOS Device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>chrome 62.0</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>chrome 62.0</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>Mac OS X 10_11_6</td>\n",
       "      <td>chrome 62.0</td>\n",
       "      <td>1280x800</td>\n",
       "      <td>match_status:2</td>\n",
       "      <td>MacOS</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",
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       "    <tr>\n",
       "      <th>144228</th>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>chrome 66.0 for android</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>F3111 Build/33.3.A.1.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144229</th>\n",
       "      <td>3</td>\n",
       "      <td>-999</td>\n",
       "      <td>Android 7.1.1</td>\n",
       "      <td>chrome 55.0 for android</td>\n",
       "      <td>855x480</td>\n",
       "      <td>match_status:2</td>\n",
       "      <td>A574BL Build/NMF26F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144230</th>\n",
       "      <td>3</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>chrome 65.0 for android</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>Moto E (4) Plus Build/NMA26.42-152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144231</th>\n",
       "      <td>1</td>\n",
       "      <td>-999</td>\n",
       "      <td>Mac OS X 10_9_5</td>\n",
       "      <td>chrome 66.0</td>\n",
       "      <td>2560x1600</td>\n",
       "      <td>match_status:2</td>\n",
       "      <td>MacOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144232</th>\n",
       "      <td>3</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>chrome 66.0 for android</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>RNE-L03 Build/HUAWEIRNE-L03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144233 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id_15 id_23             id_30                    id_31      id_33  \\\n",
       "0          3  -999       Android 7.0      samsung browser 6.2  2220x1080   \n",
       "1          3  -999        iOS 11.1.2       mobile safari 11.0   1334x750   \n",
       "2          1  -999              -999              chrome 62.0       -999   \n",
       "3          3  -999              -999              chrome 62.0       -999   \n",
       "4          1  -999  Mac OS X 10_11_6              chrome 62.0   1280x800   \n",
       "...      ...   ...               ...                      ...        ...   \n",
       "144228     1  -999              -999  chrome 66.0 for android       -999   \n",
       "144229     3  -999     Android 7.1.1  chrome 55.0 for android    855x480   \n",
       "144230     3  -999              -999  chrome 65.0 for android       -999   \n",
       "144231     1  -999   Mac OS X 10_9_5              chrome 66.0  2560x1600   \n",
       "144232     3  -999              -999  chrome 66.0 for android       -999   \n",
       "\n",
       "                 id_34                          DeviceInfo  \n",
       "0       match_status:2       SAMSUNG SM-G892A Build/NRD90M  \n",
       "1       match_status:1                          iOS Device  \n",
       "2                 -999                             Windows  \n",
       "3                 -999                                -999  \n",
       "4       match_status:2                               MacOS  \n",
       "...                ...                                 ...  \n",
       "144228            -999             F3111 Build/33.3.A.1.97  \n",
       "144229  match_status:2                 A574BL Build/NMF26F  \n",
       "144230            -999  Moto E (4) Plus Build/NMA26.42-152  \n",
       "144231  match_status:2                               MacOS  \n",
       "144232            -999         RNE-L03 Build/HUAWEIRNE-L03  \n",
       "\n",
       "[144233 rows x 7 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_identity.select_dtypes(\"object\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b378dfc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label Encoder\n",
    "\n",
    "train_identity[\"DeviceInfo\"] = LabelEncoder().fit_transform(train_identity[\"DeviceInfo\"])\n",
    "train_identity[\"id_30\"] = LabelEncoder().fit_transform(train_identity[\"id_30\"])\n",
    "train_identity[\"id_31\"] = LabelEncoder().fit_transform(train_identity[\"id_31\"])\n",
    "train_identity[\"id_33\"] = LabelEncoder().fit_transform(train_identity[\"id_33\"])\n",
    "train_identity[\"id_34\"] = LabelEncoder().fit_transform(train_identity[\"id_34\"])\n",
    "train_identity[\"id_15\"] = LabelEncoder().fit_transform(train_identity[\"id_15\"])\n",
    "train_identity[\"id_23\"] = LabelEncoder().fit_transform(train_identity[\"id_23\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "be7b2635",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 41)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_identity=train_identity.iloc[:100000]\n",
    "train_identity.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "f06228f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_transaction = train_transaction.fillna(value=-999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "e9da4351",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ProductCD',\n",
       " 'card4',\n",
       " 'card6',\n",
       " 'P_emaildomain',\n",
       " 'R_emaildomain',\n",
       " 'M1',\n",
       " 'M2',\n",
       " 'M3',\n",
       " 'M4',\n",
       " 'M5',\n",
       " 'M6',\n",
       " 'M7',\n",
       " 'M8',\n",
       " 'M9']"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find categorical features\n",
    "categorical = [feature for feature in train_transaction.columns if train_transaction[feature].dtype=='O']\n",
    "categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "baacc7d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Typecasting\n",
    "\n",
    "for feature in categorical:\n",
    "    train_transaction[feature]=train_transaction[feature].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "2255304b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Label Encoder\n",
    "\n",
    "for feature in categorical:\n",
    "    train_transaction[feature]=LabelEncoder().fit_transform(train_transaction[feature])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "9ab0f7df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 394)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_transaction=train_transaction.iloc[:100000]\n",
    "train_transaction.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "545c83aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "del train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "cd7b7500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TransactionID</th>\n",
       "      <th>id_01</th>\n",
       "      <th>id_02</th>\n",
       "      <th>id_03</th>\n",
       "      <th>id_04</th>\n",
       "      <th>id_05</th>\n",
       "      <th>id_06</th>\n",
       "      <th>id_07</th>\n",
       "      <th>id_08</th>\n",
       "      <th>id_09</th>\n",
       "      <th>...</th>\n",
       "      <th>V330</th>\n",
       "      <th>V331</th>\n",
       "      <th>V332</th>\n",
       "      <th>V333</th>\n",
       "      <th>V334</th>\n",
       "      <th>V335</th>\n",
       "      <th>V336</th>\n",
       "      <th>V337</th>\n",
       "      <th>V338</th>\n",
       "      <th>V339</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2987004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70787.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2987008</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>98945.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2987010</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>191631.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2987011</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>221832.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2987016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7460.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 434 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TransactionID  id_01     id_02  id_03  id_04  id_05  id_06  id_07  id_08  \\\n",
       "0        2987004    0.0   70787.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0   \n",
       "1        2987008   -5.0   98945.0 -999.0 -999.0    0.0   -5.0 -999.0 -999.0   \n",
       "2        2987010   -5.0  191631.0    0.0    0.0    0.0    0.0 -999.0 -999.0   \n",
       "3        2987011   -5.0  221832.0 -999.0 -999.0    0.0   -6.0 -999.0 -999.0   \n",
       "4        2987016    0.0    7460.0    0.0    0.0    1.0    0.0 -999.0 -999.0   \n",
       "\n",
       "   id_09  ...   V330   V331   V332   V333   V334   V335   V336   V337   V338  \\\n",
       "0 -999.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "1 -999.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "2    0.0  ... -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0   \n",
       "3 -999.0  ... -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0   \n",
       "4    0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "    V339  \n",
       "0    0.0  \n",
       "1    0.0  \n",
       "2 -999.0  \n",
       "3 -999.0  \n",
       "4    0.0  \n",
       "\n",
       "[5 rows x 434 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge both dataframes\n",
    "df = pd.merge(train_identity, train_transaction, on = \"TransactionID\", how = \"outer\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "04ee3084",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TransactionID        0\n",
       "id_01            58554\n",
       "id_02            58554\n",
       "id_03            58554\n",
       "id_04            58554\n",
       "                 ...  \n",
       "V335             58554\n",
       "V336             58554\n",
       "V337             58554\n",
       "V338             58554\n",
       "V339             58554\n",
       "Length: 434, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "59f8a472",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "2bfec9c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TransactionID    0\n",
       "id_01            0\n",
       "id_02            0\n",
       "id_03            0\n",
       "id_04            0\n",
       "                ..\n",
       "V335             0\n",
       "V336             0\n",
       "V337             0\n",
       "V338             0\n",
       "V339             0\n",
       "Length: 434, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "724a6ada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41446, 434)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9bd11cd",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "378aa22d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X, Y and split data\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X=df.drop(['isFraud'], axis=1).values\n",
    "y=df['isFraud'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test=train_test_split(X,y,random_state=0,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "9b428b9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(33156, 433) (33156,) (8290, 433) (8290,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "7bcde23b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest classifier\n",
    "\n",
    "model = RandomForestClassifier(criterion = \"entropy\")\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "68d17856",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "2ae285c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score:  0.9423152024493877\n"
     ]
    }
   ],
   "source": [
    "print('AUC score: ',roc_auc_score(y_test,y_pred[:,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80eb365a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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