v1.2-ModelingandEvaluation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CapitalOne Data Challenge - Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**I removed duplicates in a previous step (data processing) and imported the processed data by using pandas package.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load package\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import accuracy_score,precision_score, recall_score, f1_score,confusion_matrix # Timestamp Data Import# Timestamp Data Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_transactions=pd.read_csv('~/Desktop/clean_data.csv') # import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(730174, 16)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_transactions.shape # dimension of processed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerId</th>\n",
       "      <th>merchantName</th>\n",
       "      <th>transactionAmount</th>\n",
       "      <th>transactionType</th>\n",
       "      <th>transactionKey</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>creditLimit</th>\n",
       "      <th>availableMoney</th>\n",
       "      <th>merchantCountryCode</th>\n",
       "      <th>posEntryMode</th>\n",
       "      <th>posConditionCode</th>\n",
       "      <th>merchantCategoryCode</th>\n",
       "      <th>currentBalance</th>\n",
       "      <th>cardPresent</th>\n",
       "      <th>expirationDateKeyInMatch</th>\n",
       "      <th>isFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100088067</td>\n",
       "      <td>1st BBQ</td>\n",
       "      <td>200.12</td>\n",
       "      <td>PURCHASE</td>\n",
       "      <td>T541917</td>\n",
       "      <td>2016-04-08 19:56:36</td>\n",
       "      <td>50000</td>\n",
       "      <td>49953.81</td>\n",
       "      <td>US</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>food</td>\n",
       "      <td>46.19</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100088067</td>\n",
       "      <td>1st Pub</td>\n",
       "      <td>95.11</td>\n",
       "      <td>PURCHASE</td>\n",
       "      <td>T541962</td>\n",
       "      <td>2016-11-09 07:26:06</td>\n",
       "      <td>50000</td>\n",
       "      <td>49929.31</td>\n",
       "      <td>US</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>food</td>\n",
       "      <td>70.69</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100088067</td>\n",
       "      <td>1st Sandwitch Bar #758805</td>\n",
       "      <td>5.26</td>\n",
       "      <td>PURCHASE</td>\n",
       "      <td>T541920</td>\n",
       "      <td>2016-05-06 02:54:01</td>\n",
       "      <td>50000</td>\n",
       "      <td>49996.04</td>\n",
       "      <td>US</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>food</td>\n",
       "      <td>3.96</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerId               merchantName  transactionAmount transactionType  \\\n",
       "0   100088067                    1st BBQ             200.12        PURCHASE   \n",
       "1   100088067                    1st Pub              95.11        PURCHASE   \n",
       "2   100088067  1st Sandwitch Bar #758805               5.26        PURCHASE   \n",
       "\n",
       "  transactionKey            Timestamp  creditLimit  availableMoney  \\\n",
       "0        T541917  2016-04-08 19:56:36        50000        49953.81   \n",
       "1        T541962  2016-11-09 07:26:06        50000        49929.31   \n",
       "2        T541920  2016-05-06 02:54:01        50000        49996.04   \n",
       "\n",
       "  merchantCountryCode  posEntryMode  posConditionCode merchantCategoryCode  \\\n",
       "0                  US           9.0               1.0                 food   \n",
       "1                  US           2.0               1.0                 food   \n",
       "2                  US           2.0               1.0                 food   \n",
       "\n",
       "   currentBalance  cardPresent  expirationDateKeyInMatch  isFraud  \n",
       "0           46.19         True                     False    False  \n",
       "1           70.69         True                     False    False  \n",
       "2            3.96         True                     False    False  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_transactions.head(3) # quickly check the dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**I explored the distribution of transaction amounts across fraud vs non-fraud cases. It shows that median of fraudulent transactions is slightly higher than median of non-fraud transactions. When creating a categorical variable (high vs low), I defined threshold as $250.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1d54d590>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,5))\n",
    "plt.title('Distribution of Transaction Amounts (Fraud vs Non-Fraud)',weight='bold')\n",
    "sns.set()\n",
    "sns.boxplot(x='isFraud', y='transactionAmount', data=credit_transactions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**I applied under-sampling technique by applying stratified clustering approach to reduce dimensionality. In the dataset, there are 2040 customers who faced fraudulent cases. I firstly took all fraud observations from 2040 customers. Then, I took a sample (ratio=0.08) from non-fraud observations of the same customers. Lastly, I took a sample from customer who did not encounter any fraud cases in year 2016 to add noise.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_minor=credit_transactions[credit_transactions.isFraud==True] # take only fraud observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11710"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_minor) # There are 11710 fraud cases in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2040"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_minor.customerId.nunique() # 2040 unique customer have encountered fraudulent cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_minor=df_minor.reset_index(drop=True) # reset index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cust_list=df_minor.customerId.unique() # take a uniuq list of customers\n",
    "df_new_fraud=credit_transactions[credit_transactions.customerId.isin(cust_list)] # take non-fraud cases of 2040 customers\n",
    "df_new_fraud=df_new_fraud[df_new_fraud.isFraud==False] # remove fraud cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_major_1=df_new_fraud.sample(frac=0.08,random_state=42) # take an sample from the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_major_1=df_major_1.reset_index(drop=True) # reset index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new_2=credit_transactions[~credit_transactions.transactionKey.isin(df_major_1.transactionKey)] # take transactions which \n",
    "df_new_2=df_new_2[df_new_2.isFraud==False] # take non-fraud cases\n",
    "df_major_2=df_new_2.sample(frac=0.025,random_state=42) # take an sample from the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_major_2=df_major_2.reset_index(drop=True) # reset index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.concat([df_major_1,df_major_2,df_minor],axis=0) # merge dataframes vertically\n",
    "df['ID'] = df.customerId.map(str) + \"-\" + df.merchantName"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    64085\n",
       "True     11710\n",
       "Name: isFraud, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isFraud.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1545"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(11710/(11710+64085),4) # ratio of fraud cases to population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75795"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "11710+64085"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standardization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**I standardized transaction amounts for each customer by using their mean and stdev of transaction amounts in year 2016.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply z-score standardization\n",
    "def z_score(df):\n",
    "    # copy the dataframe\n",
    "    df_std = df.copy()\n",
    "    key=df_std['transactionKey']\n",
    "    df_std=df_std.drop(['transactionKey'],axis=1)\n",
    "    # apply the z-score method\n",
    "    if len(df_std)>=2:\n",
    "        for column in df_std.columns:\n",
    "            df_std[column] = (df_std[column] - df_std[column].mean()) / df_std[column].std()\n",
    "        df_std=pd.concat([df_std,key],axis=1)\n",
    "    else:\n",
    "        df_std=pd.concat([df_std,key],axis=1)\n",
    "        df_std.transactionAmount=0\n",
    "    return df_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standardize for each customer\n",
    "df_standardized={}\n",
    "cst=[]\n",
    "key=[]\n",
    "for i in df.customerId.unique():\n",
    "    sample=df[df.customerId==i][['transactionAmount','transactionKey']]\n",
    "    process = z_score(sample)\n",
    "    df_standardized[i]=process\n",
    "    for k in range(len(sample)):\n",
    "        cst.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "nrow=len(df_standardized.keys())\n",
    "df_d=pd.DataFrame()\n",
    "for i in range(nrow):\n",
    "    df_inter=df_standardized[[*df_standardized.keys()][i]]\n",
    "    df_d=df_d.append(df_inter)\n",
    "df_d=df_d.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer=pd.DataFrame(cst)\n",
    "customer.columns=['customerId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "combine=pd.concat([customer,df_d],axis=1)\n",
    "combine.drop(['customerId'],axis=1,inplace=True)\n",
    "combine.columns=['Amount_Std','transactionKey']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>customerId</th>\n",
       "      <th>merchantName</th>\n",
       "      <th>transactionAmount</th>\n",
       "      <th>transactionType</th>\n",
       "      <th>transactionKey</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>creditLimit</th>\n",
       "      <th>availableMoney</th>\n",
       "      <th>merchantCountryCode</th>\n",
       "      <th>posEntryMode</th>\n",
       "      <th>posConditionCode</th>\n",
       "      <th>merchantCategoryCode</th>\n",
       "      <th>currentBalance</th>\n",
       "      <th>cardPresent</th>\n",
       "      <th>expirationDateKeyInMatch</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>ID</th>\n",
       "      <th>Amount_Std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>432609023</td>\n",
       "      <td>South Steakhouse #879581</td>\n",
       "      <td>202.09</td>\n",
       "      <td>PURCHASE</td>\n",
       "      <td>T287662</td>\n",
       "      <td>2016-03-11 00:07:16</td>\n",
       "      <td>5000</td>\n",
       "      <td>2734.79</td>\n",
       "      <td>US</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>food</td>\n",
       "      <td>2265.21</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>432609023-South Steakhouse #879581</td>\n",
       "      <td>0.185984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>245632880</td>\n",
       "      <td>amazon.com</td>\n",
       "      <td>93.57</td>\n",
       "      <td>PURCHASE</td>\n",
       "      <td>T346070</td>\n",
       "      <td>2016-10-13 23:15:27</td>\n",
       "      <td>500</td>\n",
       "      <td>486.48</td>\n",
       "      <td>US</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>online_retail</td>\n",
       "      <td>13.52</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>245632880-amazon.com</td>\n",
       "      <td>-0.236448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>852890707</td>\n",
       "      <td>Subway #257172</td>\n",
       "      <td>314.99</td>\n",
       "      <td>PURCHASE</td>\n",
       "      <td>T665204</td>\n",
       "      <td>2016-09-10 06:39:59</td>\n",
       "      <td>15000</td>\n",
       "      <td>13352.56</td>\n",
       "      <td>US</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>fastfood</td>\n",
       "      <td>1647.44</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>852890707-Subway #257172</td>\n",
       "      <td>0.705380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerId              merchantName  transactionAmount transactionType  \\\n",
       "0   432609023  South Steakhouse #879581             202.09        PURCHASE   \n",
       "1   245632880                amazon.com              93.57        PURCHASE   \n",
       "2   852890707            Subway #257172             314.99        PURCHASE   \n",
       "\n",
       "  transactionKey            Timestamp  creditLimit  availableMoney  \\\n",
       "0        T287662  2016-03-11 00:07:16         5000         2734.79   \n",
       "1        T346070  2016-10-13 23:15:27          500          486.48   \n",
       "2        T665204  2016-09-10 06:39:59        15000        13352.56   \n",
       "\n",
       "  merchantCountryCode  posEntryMode  posConditionCode merchantCategoryCode  \\\n",
       "0                  US           5.0               1.0                 food   \n",
       "1                  US           5.0               1.0        online_retail   \n",
       "2                  US           2.0               1.0             fastfood   \n",
       "\n",
       "   currentBalance  cardPresent  expirationDateKeyInMatch  isFraud  \\\n",
       "0         2265.21         True                     False    False   \n",
       "1           13.52        False                     False    False   \n",
       "2         1647.44         True                     False    False   \n",
       "\n",
       "                                   ID  Amount_Std  \n",
       "0  432609023-South Steakhouse #879581    0.185984  \n",
       "1                245632880-amazon.com   -0.236448  \n",
       "2            852890707-Subway #257172    0.705380  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_std=df.merge(combine,how='inner',on='transactionKey')\n",
    "data_std.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature:MerchantCategoryCode**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>merchantCategoryCode</th>\n",
       "      <th>Fraud_Count</th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>personal care</td>\n",
       "      <td>78</td>\n",
       "      <td>1516</td>\n",
       "      <td>0.051451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>health</td>\n",
       "      <td>85</td>\n",
       "      <td>1623</td>\n",
       "      <td>0.052372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>hotels</td>\n",
       "      <td>240</td>\n",
       "      <td>3145</td>\n",
       "      <td>0.076312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>subscriptions</td>\n",
       "      <td>204</td>\n",
       "      <td>2104</td>\n",
       "      <td>0.096958</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   merchantCategoryCode  Fraud_Count  Total   Percent\n",
       "10        personal care           78   1516  0.051451\n",
       "6                health           85   1623  0.052372\n",
       "7                hotels          240   3145  0.076312\n",
       "12        subscriptions          204   2104  0.096958"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_groupby=pd.DataFrame(data_std.groupby(['merchantCategoryCode','isFraud']).count()['Timestamp']).reset_index()\n",
    "r=pos_groupby[pos_groupby.isFraud==True][['merchantCategoryCode','Timestamp']]\n",
    "r.columns=['merchantCategoryCode','Fraud_Count']\n",
    "t=pd.DataFrame(pos_groupby.groupby(['merchantCategoryCode']).sum()['Timestamp']).reset_index()\n",
    "t.columns=['merchantCategoryCode','Total']\n",
    "r=r.merge(t,on='merchantCategoryCode',how='inner')\n",
    "r['Percent']=r.Fraud_Count/r.Total\n",
    "r=r.sort_values(by=['Percent'],ascending=True)\n",
    "r.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categ_merchant(x):\n",
    "    if x in ['cable/phone','food_delivery','fuel','gym','mobileapps','online_subscriptions']:\n",
    "        return 0\n",
    "    elif x in ['online_gifts','online_retail','rideshare','airline']:\n",
    "        return 2\n",
    "    elif x in ['entertainment','auto','food','furniture']:\n",
    "        return 1\n",
    "    elif x in ['personal care','health','hotels','subscriptions','fastfood']:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies_1=pd.DataFrame(data_std.merchantCategoryCode.apply(lambda x: categ_merchant(x)))\n",
    "dummies_1.columns=['Categ_merchant']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature:expirationDateKeyInMatch**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With backward selection approach, I removed this categorical variable after checking feature importance of tree based models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dummies_2 = pd.get_dummies(data_std.expirationDateKeyInMatch, prefix='KeyInMatch_')\n",
    "#dummies_2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature: cardPresent**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>crdPresent__False</th>\n",
       "      <th>crdPresent__True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   crdPresent__False  crdPresent__True\n",
       "0                  0                 1\n",
       "1                  1                 0\n",
       "2                  0                 1\n",
       "3                  0                 1\n",
       "4                  1                 0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies_3 = pd.get_dummies(data_std.cardPresent, prefix='crdPresent_')\n",
    "dummies_3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature: CreditLimit**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>CredLimit__250</th>\n",
       "      <th>CredLimit__500</th>\n",
       "      <th>CredLimit__1000</th>\n",
       "      <th>CredLimit__2500</th>\n",
       "      <th>CredLimit__5000</th>\n",
       "      <th>CredLimit__7500</th>\n",
       "      <th>CredLimit__10000</th>\n",
       "      <th>CredLimit__15000</th>\n",
       "      <th>CredLimit__20000</th>\n",
       "      <th>CredLimit__50000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CredLimit__250  CredLimit__500  CredLimit__1000  CredLimit__2500  \\\n",
       "0               0               0                0                0   \n",
       "1               0               1                0                0   \n",
       "2               0               0                0                0   \n",
       "3               0               0                0                0   \n",
       "4               0               0                0                0   \n",
       "\n",
       "   CredLimit__5000  CredLimit__7500  CredLimit__10000  CredLimit__15000  \\\n",
       "0                1                0                 0                 0   \n",
       "1                0                0                 0                 0   \n",
       "2                0                0                 0                 1   \n",
       "3                0                0                 0                 1   \n",
       "4                0                0                 1                 0   \n",
       "\n",
       "   CredLimit__20000  CredLimit__50000  \n",
       "0                 0                 0  \n",
       "1                 0                 0  \n",
       "2                 0                 0  \n",
       "3                 0                 0  \n",
       "4                 0                 0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies_4 = pd.get_dummies(data_std.creditLimit, prefix='CredLimit_')\n",
    "dummies_4.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature: posEntryMode**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**I checked \"posEntryMode\" by looking at percentage of fraud cases over population and then created categorical variables. For example, transactions whose posEntry is 9 are more likely to be fraudulent compared to transactions whose posEntry is 2 or 80 or 90 (medium likelihood).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>posEntryMode</th>\n",
       "      <th>Fraud_Count</th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3253</td>\n",
       "      <td>19084</td>\n",
       "      <td>0.170457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2294</td>\n",
       "      <td>28403</td>\n",
       "      <td>0.080766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0</td>\n",
       "      <td>5341</td>\n",
       "      <td>24419</td>\n",
       "      <td>0.218723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80.0</td>\n",
       "      <td>217</td>\n",
       "      <td>1432</td>\n",
       "      <td>0.151536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90.0</td>\n",
       "      <td>348</td>\n",
       "      <td>1894</td>\n",
       "      <td>0.183738</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   posEntryMode  Fraud_Count  Total   Percent\n",
       "0           2.0         3253  19084  0.170457\n",
       "1           5.0         2294  28403  0.080766\n",
       "2           9.0         5341  24419  0.218723\n",
       "3          80.0          217   1432  0.151536\n",
       "4          90.0          348   1894  0.183738"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_groupby=pd.DataFrame(data_std.groupby(['posEntryMode','isFraud']).count()['Timestamp']).reset_index()\n",
    "r=pos_groupby[pos_groupby.isFraud==True][['posEntryMode','Timestamp']]\n",
    "r.columns=['posEntryMode','Fraud_Count']\n",
    "t=pd.DataFrame(pos_groupby.groupby(['posEntryMode']).sum()['Timestamp']).reset_index()\n",
    "t.columns=['posEntryMode','Total']\n",
    "r=r.merge(t,on='posEntryMode',how='inner')\n",
    "r['Percent']=r.Fraud_Count/r.Total\n",
    "r.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categ_pos(x):\n",
    "    if x==5:\n",
    "        return 0\n",
    "    elif x in [2,80,90]:\n",
    "        return 1\n",
    "    elif x==9:\n",
    "        return 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies_5=pd.DataFrame(data_std.posEntryMode.apply(lambda x: categ_pos(x)))\n",
    "dummies_5.columns=['Categ_posEntry']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature: posConditionCode**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categ_posCon(x):\n",
    "    if x==8:\n",
    "        return 0\n",
    "    elif x==1:\n",
    "        return 1\n",
    "    elif x==99:\n",
    "        return 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>posConditionCode</th>\n",
       "      <th>Fraud_Count</th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>9671</td>\n",
       "      <td>60904</td>\n",
       "      <td>0.158791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>1807</td>\n",
       "      <td>14016</td>\n",
       "      <td>0.128924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99.0</td>\n",
       "      <td>212</td>\n",
       "      <td>820</td>\n",
       "      <td>0.258537</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   posConditionCode  Fraud_Count  Total   Percent\n",
       "0               1.0         9671  60904  0.158791\n",
       "1               8.0         1807  14016  0.128924\n",
       "2              99.0          212    820  0.258537"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_groupby=pd.DataFrame(data_std.groupby(['posConditionCode','isFraud']).count()['Timestamp']).reset_index()\n",
    "r=pos_groupby[pos_groupby.isFraud==True][['posConditionCode','Timestamp']]\n",
    "r.columns=['posConditionCode','Fraud_Count']\n",
    "t=pd.DataFrame(pos_groupby.groupby(['posConditionCode']).sum()['Timestamp']).reset_index()\n",
    "t.columns=['posConditionCode','Total']\n",
    "r=r.merge(t,on='posConditionCode',how='inner')\n",
    "r['Percent']=r.Fraud_Count/r.Total\n",
    "r.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies_6=pd.DataFrame(data_std.posConditionCode.apply(lambda x: categ_posCon(x)))\n",
    "dummies_6.columns=['Categ_posCon']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Acquisition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**I combined all new features into one dataframe before splitting data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_std['Categ_Transac']=data_std.transactionAmount.apply(lambda x: 1 if x>250 else 0) # create categorical variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric=data_std[['Amount_Std','Categ_Transac']] # take a subset from dataset\n",
    "Fraud=data_std[['isFraud']].apply(lambda x: x.astype(int)) # convert boolean variable as 1 or 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_processed=pd.concat([numeric,dummies_1,dummies_3,dummies_4,dummies_5,dummies_6,Fraud],axis=1) # merge all dataframes vertically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_processed=df_processed[ [ col for col in df_processed.columns if col != 'isFraud' ] + ['isFraud'] ] # place dependent variable as last"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Amount_Std</th>\n",
       "      <th>Categ_Transac</th>\n",
       "      <th>Categ_merchant</th>\n",
       "      <th>crdPresent__False</th>\n",
       "      <th>crdPresent__True</th>\n",
       "      <th>CredLimit__250</th>\n",
       "      <th>CredLimit__500</th>\n",
       "      <th>CredLimit__1000</th>\n",
       "      <th>CredLimit__2500</th>\n",
       "      <th>CredLimit__5000</th>\n",
       "      <th>CredLimit__7500</th>\n",
       "      <th>CredLimit__10000</th>\n",
       "      <th>CredLimit__15000</th>\n",
       "      <th>CredLimit__20000</th>\n",
       "      <th>CredLimit__50000</th>\n",
       "      <th>Categ_posEntry</th>\n",
       "      <th>Categ_posCon</th>\n",
       "      <th>isFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.185984</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.236448</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.705380</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Amount_Std  Categ_Transac  Categ_merchant  crdPresent__False  \\\n",
       "0    0.185984              0               1                  0   \n",
       "1   -0.236448              0               2                  1   \n",
       "2    0.705380              1               0                  0   \n",
       "\n",
       "   crdPresent__True  CredLimit__250  CredLimit__500  CredLimit__1000  \\\n",
       "0                 1               0               0                0   \n",
       "1                 0               0               1                0   \n",
       "2                 1               0               0                0   \n",
       "\n",
       "   CredLimit__2500  CredLimit__5000  CredLimit__7500  CredLimit__10000  \\\n",
       "0                0                1                0                 0   \n",
       "1                0                0                0                 0   \n",
       "2                0                0                0                 0   \n",
       "\n",
       "   CredLimit__15000  CredLimit__20000  CredLimit__50000  Categ_posEntry  \\\n",
       "0                 0                 0                 0             0.0   \n",
       "1                 0                 0                 0             0.0   \n",
       "2                 1                 0                 0             1.0   \n",
       "\n",
       "   Categ_posCon  isFraud  \n",
       "0           1.0        0  \n",
       "1           1.0        0  \n",
       "2           1.0        0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None) # pandas setting\n",
    "df_processed.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_processed.Categ_posEntry=df_processed.Categ_posEntry.fillna(0) # fill NA values with 0\n",
    "df_processed.Categ_posCon=df_processed.Categ_posCon.fillna(0) # fill NA values with 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train and Test Split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**I splitted the processsed dataset into train and test data at the ratio of 70:30 to avoid overfitting.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_test_data(df):\n",
    "    q=df.shape[1]\n",
    "    X=df.iloc[:,1:(q-1)]\n",
    "    y=df.iloc[:,(q-1)]\n",
    "    X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.30, random_state=42)\n",
    "    return X_train, X_valid, y_train, y_valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the data by using train_test_data function\n",
    "X_train, X_valid, y_train, y_valid = train_test_data(df_processed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8275"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.sum() # fraud cases in train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3435"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_valid.sum() # fraud cases in test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Random Forest with Randomized Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**I started with Random Forest algorithm and also applied Randomized search in order to optimize hyperparameters.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1093,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV # import package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1094,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hyperparameters\n",
    "model_params = {'n_estimators':range(650,850,50),\n",
    "    'max_depth': range(8,14),\n",
    "    'min_samples_split':range(12,20)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=13, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=5,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=700,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 1112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create random forest classifier model\n",
    "rf_model = RandomForestClassifier()\n",
    "\n",
    "# set up random search meta-estimator\n",
    "# this will train 50 models over 4 folds of cross validation (200 models total)\n",
    "clf = RandomizedSearchCV(rf_model, model_params, n_iter=50, cv=4, scoring='f1',random_state=43)\n",
    "\n",
    "# train the random search meta-estimator to find the best model out of 100 candidates\n",
    "model = clf.fit(X_train, y_train)\n",
    "\n",
    "model.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**I ran the random forest model with best estimators defined in the previous step**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
    "                       criterion='gini', max_depth=13, max_features='auto',\n",
    "                       max_leaf_nodes=None, max_samples=None,\n",
    "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "                       min_samples_leaf=1, min_samples_split=5,\n",
    "                       min_weight_fraction_leaf=0.0, n_estimators=700,\n",
    "                       n_jobs=None, oob_score=False, random_state=None,\n",
    "                       verbose=0, warm_start=False)\n",
    "\n",
    "#Train the model using the training sets y_pred=clf.predict(X_test)\n",
    "clf.fit(X_train,y_train)\n",
    "\n",
    "y_pred=clf.predict(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clf.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.5, 1.5]), <a list of 2 Text yticklabel objects>)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize confusion matrix with seaborn heatmap\n",
    "CM=confusion_matrix(y_valid, y_pred)\n",
    "\n",
    "cm_matrix = pd.DataFrame(data=CM, columns=['Predict Positive:0', 'Predict Negative:1'], index=['Actual Positive:0', 'Actual Negative:1'])\n",
    "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='RdYlGn')\n",
    "plt.yticks(rotation=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8484\n"
     ]
    }
   ],
   "source": [
    "# Model Accuracy, how often is the classifier correct?\n",
    "print(\"Accuracy:\",round(accuracy_score(y_valid, y_pred),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision Score: 0.4882\n"
     ]
    }
   ],
   "source": [
    "# Precision score on the test data\n",
    "print(\"Precision Score:\",round(precision_score(y_valid, y_pred),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-1 Score: 0.1262\n"
     ]
    }
   ],
   "source": [
    "# Precision score on the test data\n",
    "print(\"F-1 Score:\",round(f1_score(y_valid, y_pred),4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature Importance: I checked the feature importances of Random Forest model by using Gini Index. \"Categ_merchant\", \"Categ_posEntry\" and \"Categ_Transac\" played much more significant role than others in classifiying fraudulent cases.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1128,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_feature_importance(importance,names,model_type):\n",
    "\n",
    "    #Create arrays from feature importance and feature names\n",
    "    feature_importance = np.array(importance)\n",
    "    feature_names = np.array(names)\n",
    "\n",
    "    #Create a DataFrame using a Dictionary\n",
    "    data={'feature_names':feature_names,'feature_importance':feature_importance}\n",
    "    fi_df = pd.DataFrame(data)\n",
    "\n",
    "    #Sort the DataFrame in order decreasing feature importance\n",
    "    fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)\n",
    "\n",
    "    #Define size of bar plot\n",
    "    plt.figure(figsize=(18,9))\n",
    "    #Plot Searborn bar chart\n",
    "    sns.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names'])\n",
    "    #Add chart labels\n",
    "    plt.title(model_type + \" \" +'Feature Importance',weight='bold')\n",
    "    plt.xlabel('Feature Importance')\n",
    "    plt.ylabel('Feature Names')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importance(clf.feature_importances_,X_train.columns,'Random Forest')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Gradient Boosting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Secondly, I executed Gradient Boosting model to classify fraud cases.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define Gradient Boosting model\n",
    "clf = GradientBoostingClassifier(n_estimators=800, \n",
    "                                 learning_rate=0.01,\n",
    "                                 max_depth=6, \n",
    "                                 random_state=5).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# take predictions on the test data\n",
    "predictions = clf.predict(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.5, 1.5]), <a list of 2 Text yticklabel objects>)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize confusion matrix with seaborn heatmap\n",
    "CM_grb=confusion_matrix(y_valid, predictions)\n",
    "\n",
    "cm_matr = pd.DataFrame(data=CM_grb, columns=['Predict Positive:0', 'Predict Negative:1'], index=['Actual Positive:0', 'Actual Negative:1'])\n",
    "sns.heatmap(cm_matr, annot=True, fmt='d', cmap='RdYlGn')\n",
    "plt.yticks(rotation=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8487\n"
     ]
    }
   ],
   "source": [
    "# Model Accuracy, how often is the classifier correct?\n",
    "print(\"Accuracy:\",round(accuracy_score(y_valid, predictions),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision Score: 0.495\n"
     ]
    }
   ],
   "source": [
    "# Precision Score on the test data\n",
    "print(\"Precision Score:\",round(precision_score(y_valid, predictions),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-1 Score: 0.1265\n"
     ]
    }
   ],
   "source": [
    "# Precision Score on the test data\n",
    "print(\"F-1 Score:\",round(f1_score(y_valid, predictions),4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Extreme Gradient Boosting with Grid Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**I applied extreme gradient boosting with grid search tecnhique. This helped me tuning hyper-parameters such as max_depth, n_estimators and learning rate.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1066,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator = xgb.XGBClassifier(\n",
    "    objective= 'binary:logistic',\n",
    "    nthread=4,\n",
    "    seed=123\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1067,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters=  { 'max_depth': range (5, 11,4),\n",
    "    'n_estimators': range(800, 1000, 50),\n",
    "    'learning_rate': [0.1, 0.01, 0.001]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1071,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(\n",
    "    estimator=estimator,\n",
    "    param_grid=parameters,\n",
    "    scoring='f1',\n",
    "    n_jobs = 10,\n",
    "    cv = 4,\n",
    "    verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1074,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 24 candidates, totalling 96 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Using backend LokyBackend with 10 concurrent workers.\n",
      "[Parallel(n_jobs=10)]: Done  30 tasks      | elapsed: 16.2min\n",
      "[Parallel(n_jobs=10)]: Done  96 out of  96 | elapsed: 51.9min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=4, error_score=nan,\n",
       "             estimator=XGBClassifier(base_score=0.5, booster='gbtree',\n",
       "                                     colsample_bylevel=1, colsample_bynode=1,\n",
       "                                     colsample_bytree=1, gamma=0,\n",
       "                                     learning_rate=0.1, max_delta_step=0,\n",
       "                                     max_depth=3, min_child_weight=1,\n",
       "                                     missing=None, n_estimators=100, n_jobs=1,\n",
       "                                     nthread=4, objective='binary:logistic',\n",
       "                                     random_state=0, reg_alpha=0, reg_lambda=1,\n",
       "                                     scale_pos_weight=1, seed=123, silent=None,\n",
       "                                     subsample=1, verbosity=1),\n",
       "             iid='deprecated', n_jobs=10,\n",
       "             param_grid={'learning_rate': [0.1, 0.01, 0.001],\n",
       "                         'max_depth': range(5, 11, 4),\n",
       "                         'n_estimators': range(800, 1000, 50)},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='f1', verbose=True)"
      ]
     },
     "execution_count": 1074,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1075,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
       "              learning_rate=0.001, max_delta_step=0, max_depth=5,\n",
       "              min_child_weight=1, missing=None, n_estimators=800, n_jobs=1,\n",
       "              nthread=4, objective='binary:logistic', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=123,\n",
       "              silent=None, subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 1075,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**After performing Grid Search, I selected best estimators and applied on the dataset.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_cl = xgb.XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
    "              learning_rate=0.001, max_delta_step=0, max_depth=5,\n",
    "              min_child_weight=1, missing=None, n_estimators=800, n_jobs=1,\n",
    "              nthread=4, objective='binary:logistic', random_state=0,\n",
    "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=123,\n",
    "              silent=None, subsample=1, verbosity=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fit the model\n",
    "xgb_cl.fit(X_train,y_train)\n",
    "\n",
    "#Predict the model\n",
    "preds = xgb_cl.predict(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.5, 1.5]), <a list of 2 Text yticklabel objects>)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize confusion matrix with seaborn heatmap\n",
    "CM_xgb=confusion_matrix(y_valid, preds)\n",
    "\n",
    "cm_matr = pd.DataFrame(data=CM_xgb, columns=['Predict Positive:0', 'Predict Negative:1'], index=['Actual Positive:0', 'Actual Negative:1'])\n",
    "sns.heatmap(cm_matr, annot=True, fmt='d', cmap='RdYlGn')\n",
    "plt.yticks(rotation=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8482\n"
     ]
    }
   ],
   "source": [
    "# check precision rate score on validation data\n",
    "print(\"Accuracy:\",round(accuracy_score(y_valid, preds),4)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision Score: 0.4894\n"
     ]
    }
   ],
   "source": [
    "# check precision rate score on validation data\n",
    "print(\"Precision Score:\",round(precision_score(y_valid, preds),4)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-1 Score: 0.1758\n"
     ]
    }
   ],
   "source": [
    "# check precision rate score on validation data\n",
    "print(\"F-1 Score:\",round(f1_score(y_valid, preds),4)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Xgboost Feature Importance')"
      ]
     },
     "execution_count": 1148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "plt.title('Feature Importance - Extreme Gradient Boosting',weight='bold')\n",
    "sns.set()\n",
    "sorted_idx = xgb_cl.feature_importances_.argsort()\n",
    "plt.barh(X_train.columns[sorted_idx], xgb_cl.feature_importances_[sorted_idx])\n",
    "plt.xlabel(\"Xgboost Feature Importance\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}