retail_fraud_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shrey\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import os\n",
    "from pprint import pprint\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Shrey'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Change directory to where dataset is\n",
    "os.chdir(\"X:\\\\Datasets\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "transaction_data=pd.read_excel('Fraud_Analytics_Dataset.xlsx',0)\n",
    "customer_data=pd.read_excel('Fraud_Analytics_Dataset.xlsx',1)\n",
    "sales_data=pd.read_excel('Fraud_Analytics_Dataset.xlsx',2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "   Transaction_Id First Time Customers.  Bigger than Average Orders.   \\\n",
       "0          235360                     No                           No   \n",
       "1          234773                     No                           No   \n",
       "2          235242                     No                           No   \n",
       "3          235350                     No                          Yes   \n",
       "4          234914                     No                           No   \n",
       "\n",
       "  Fast Shipping  Unusual Location.  Large quantity of Same Product .   \\\n",
       "0             No                 No                                No   \n",
       "1             No                 No                                No   \n",
       "2             No                 No                                No   \n",
       "3            Yes                 No                                No   \n",
       "4             No                 No                                No   \n",
       "\n",
       "   Multiple Shipping Addresses.  \\\n",
       "0                            No   \n",
       "1                            No   \n",
       "2                            No   \n",
       "3                           Yes   \n",
       "4                            No   \n",
       "\n",
       "  Shipping and Billing Address are not the same.  \\\n",
       "0                                             No   \n",
       "1                                             No   \n",
       "2                                             No   \n",
       "3                                            Yes   \n",
       "4                                             No   \n",
       "\n",
       "  Several Cards used from the Same Ip Address.  \\\n",
       "0                                           No   \n",
       "1                                           No   \n",
       "2                                           No   \n",
       "3                                          Yes   \n",
       "4                                           No   \n",
       "\n",
       "  Payment Information typed with Capital letters.   \\\n",
       "0                                               No   \n",
       "1                                               No   \n",
       "2                                               No   \n",
       "3                                              Yes   \n",
       "4                                               No   \n",
       "\n",
       "  Many Transactions in a short time period.   \n",
       "0                                         No  \n",
       "1                                         No  \n",
       "2                                         No  \n",
       "3                                        Yes  \n",
       "4                                         No  "
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       "   CUSTOMER_ID LOYALTY_ID  MEMBERSHIP_TYPE__ID MEMBERSHIP_TYPE  \\\n",
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       "4                SLV            Y               Individual            1003.0   \n",
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       "0              ENT       High Value        ...               April   \n",
       "1             INDV        Low Value        ...            December   \n",
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       "3              ENT        Low Value        ...            December   \n",
       "4             INDV     Medium Value        ...             January   \n",
       "\n",
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       "0        2004             2       Bellevue                  US   \n",
       "1        1984             4        Jackson                  US   \n",
       "2        1999             1    Chattanooga                  US   \n",
       "3        1997             4      Worcester                  US   \n",
       "4        1992             1    Los Angeles                  US   \n",
       "\n",
       "   EVER_BANKRUPT_FLG  NO_TIMES_DELINQUENT_in_365_days BLACKLISTED_FLG  \\\n",
       "0                  N                                1               N   \n",
       "1                  N                                1               N   \n",
       "2                  N                                1               N   \n",
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       "4                  N                                1               N   \n",
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       "3                1                 \\r  \n",
       "4                1                 \\r  \n",
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       "      <td>88200</td>\n",
       "      <td>76565868</td>\n",
       "      <td>921361924</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>2016-03-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>Mark</td>\n",
       "      <td>S102\\r</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>235296</td>\n",
       "      <td>87952</td>\n",
       "      <td>87952</td>\n",
       "      <td>76565700</td>\n",
       "      <td>958327875</td>\n",
       "      <td>LID876647</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>19</td>\n",
       "      <td>901.0</td>\n",
       "      <td>Gold</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>2016-03-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\\r</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Transaction_Id  Invoice_Id  Order_Id  Customer_Id  Mdm_Customer_Id  \\\n",
       "0          235459       88203     88203     76565680        967122832   \n",
       "1          234676       88442     88442     76566015        978889718   \n",
       "2          235246       87976     87976     76566552        914799361   \n",
       "3          235283       88200     88200     76565868        921361924   \n",
       "4          235296       87952     87952     76565700        958327875   \n",
       "\n",
       "  Loyalty_Id  Gender  Age  Membership_Id Membership_Type  ...   \\\n",
       "0        NaN    MALE   38            NaN             NaN  ...    \n",
       "1        NaN    MALE   36            NaN             NaN  ...    \n",
       "2  LID876566  FEMALE   38          903.0          Bronze  ...    \n",
       "3        NaN  FEMALE   34            NaN             NaN  ...    \n",
       "4  LID876647  FEMALE   19          901.0            Gold  ...    \n",
       "\n",
       "  Cancel_Reason_Code  Cancel_Description  Return_Flag  Return_Dt Return_Amt  \\\n",
       "0                  0                 NaN            Y 2016-03-31       5622   \n",
       "1                  0                 NaN            N 2016-03-31          0   \n",
       "2                  0                 NaN            N 2016-03-31          0   \n",
       "3                  0                 NaN            N 2016-03-31          0   \n",
       "4                  0                 NaN            N 2016-03-31          0   \n",
       "\n",
       "   Order_Returnpaid_Gross_Amt  Lastmodified_Dt  Sales_Rep_Name  Sales_Rep_Id  \\\n",
       "0                        4441       2016-10-14            Dave        S105\\r   \n",
       "1                           0       2016-05-24             NaN            \\r   \n",
       "2                           0       2016-08-04             NaN            \\r   \n",
       "3                           0       2016-10-11            Mark        S102\\r   \n",
       "4                           0       2016-07-11             NaN            \\r   \n",
       "\n",
       "  Fraud  \n",
       "0    No  \n",
       "1   Yes  \n",
       "2    No  \n",
       "3    No  \n",
       "4    No  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(transaction_data.head())\n",
    "display(customer_data.head())\n",
    "display(sales_data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Joining all the 3 sheets\n",
    "'''\n",
    "#caller.join(other.set_index('key'), on='key')\n",
    "f1=sales_data.join(transaction_data.set_index('Transaction_Id'),on='Transaction_Id')\n",
    "original_data = f1.join(customer_data.set_index('CUSTOMER_ID'),on='Customer_Id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transaction_Id</th>\n",
       "      <th>Invoice_Id</th>\n",
       "      <th>Order_Id</th>\n",
       "      <th>Customer_Id</th>\n",
       "      <th>Mdm_Customer_Id</th>\n",
       "      <th>Loyalty_Id</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Membership_Id</th>\n",
       "      <th>Membership_Type</th>\n",
       "      <th>...</th>\n",
       "      <th>BIRTH_MONTH</th>\n",
       "      <th>BIRTH_YEAR</th>\n",
       "      <th>BIRTH_QUARTER</th>\n",
       "      <th>PLACE_OF_BIRTH</th>\n",
       "      <th>COUNTRY_OF_BIRTH_SC</th>\n",
       "      <th>EVER_BANKRUPT_FLG</th>\n",
       "      <th>NO_TIMES_DELINQUENT_in_365_days</th>\n",
       "      <th>BLACKLISTED_FLG</th>\n",
       "      <th>NO_OF_DEPENDANTS</th>\n",
       "      <th>Record_created_By</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>235459</td>\n",
       "      <td>88203</td>\n",
       "      <td>88203</td>\n",
       "      <td>76565680</td>\n",
       "      <td>967122832</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MALE</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>December</td>\n",
       "      <td>1986</td>\n",
       "      <td>4</td>\n",
       "      <td>Norfolk</td>\n",
       "      <td>US</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>\\r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>234676</td>\n",
       "      <td>88442</td>\n",
       "      <td>88442</td>\n",
       "      <td>76566015</td>\n",
       "      <td>978889718</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MALE</td>\n",
       "      <td>36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>October</td>\n",
       "      <td>1999</td>\n",
       "      <td>4</td>\n",
       "      <td>Chula Vista</td>\n",
       "      <td>US</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>\\r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>235246</td>\n",
       "      <td>87976</td>\n",
       "      <td>87976</td>\n",
       "      <td>76566552</td>\n",
       "      <td>914799361</td>\n",
       "      <td>LID876566</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>38</td>\n",
       "      <td>903.0</td>\n",
       "      <td>Bronze</td>\n",
       "      <td>...</td>\n",
       "      <td>March</td>\n",
       "      <td>1990</td>\n",
       "      <td>1</td>\n",
       "      <td>Oakland</td>\n",
       "      <td>US</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>\\r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>235283</td>\n",
       "      <td>88200</td>\n",
       "      <td>88200</td>\n",
       "      <td>76565868</td>\n",
       "      <td>921361924</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>\\r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>235296</td>\n",
       "      <td>87952</td>\n",
       "      <td>87952</td>\n",
       "      <td>76565700</td>\n",
       "      <td>958327875</td>\n",
       "      <td>LID876647</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>19</td>\n",
       "      <td>901.0</td>\n",
       "      <td>Gold</td>\n",
       "      <td>...</td>\n",
       "      <td>March</td>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>Jackson</td>\n",
       "      <td>US</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>\\r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>234714</td>\n",
       "      <td>88472</td>\n",
       "      <td>88472</td>\n",
       "      <td>76565786</td>\n",
       "      <td>965982087</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MALE</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>December</td>\n",
       "      <td>1977</td>\n",
       "      <td>4</td>\n",
       "      <td>Tampa</td>\n",
       "      <td>US</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>\\r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>234841</td>\n",
       "      <td>88736</td>\n",
       "      <td>88736</td>\n",
       "      <td>76566563</td>\n",
       "      <td>924634299</td>\n",
       "      <td>LID876577</td>\n",
       "      <td>MALE</td>\n",
       "      <td>36</td>\n",
       "      <td>903.0</td>\n",
       "      <td>Bronze</td>\n",
       "      <td>...</td>\n",
       "      <td>January</td>\n",
       "      <td>1984</td>\n",
       "      <td>1</td>\n",
       "      <td>Jersey City</td>\n",
       "      <td>US</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>\\r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>235101</td>\n",
       "      <td>87910</td>\n",
       "      <td>87910</td>\n",
       "      <td>76566223</td>\n",
       "      <td>954136284</td>\n",
       "      <td>LID876603</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>35</td>\n",
       "      <td>904.0</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>...</td>\n",
       "      <td>December</td>\n",
       "      <td>1980</td>\n",
       "      <td>4</td>\n",
       "      <td>Irvine</td>\n",
       "      <td>US</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>\\r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>235115</td>\n",
       "      <td>88084</td>\n",
       "      <td>88084</td>\n",
       "      <td>76565784</td>\n",
       "      <td>975198853</td>\n",
       "      <td>LID876577</td>\n",
       "      <td>MALE</td>\n",
       "      <td>36</td>\n",
       "      <td>903.0</td>\n",
       "      <td>Bronze</td>\n",
       "      <td>...</td>\n",
       "      <td>September</td>\n",
       "      <td>1983</td>\n",
       "      <td>3</td>\n",
       "      <td>Hartford</td>\n",
       "      <td>US</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>3</td>\n",
       "      <td>\\r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>235276</td>\n",
       "      <td>88688</td>\n",
       "      <td>88688</td>\n",
       "      <td>76566561</td>\n",
       "      <td>962329895</td>\n",
       "      <td>LID876631</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>38</td>\n",
       "      <td>902.0</td>\n",
       "      <td>Silver</td>\n",
       "      <td>...</td>\n",
       "      <td>June</td>\n",
       "      <td>1995</td>\n",
       "      <td>2</td>\n",
       "      <td>Jacksonville</td>\n",
       "      <td>US</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>\\r</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 157 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Transaction_Id  Invoice_Id  Order_Id  Customer_Id  Mdm_Customer_Id  \\\n",
       "0          235459       88203     88203     76565680        967122832   \n",
       "1          234676       88442     88442     76566015        978889718   \n",
       "2          235246       87976     87976     76566552        914799361   \n",
       "3          235283       88200     88200     76565868        921361924   \n",
       "4          235296       87952     87952     76565700        958327875   \n",
       "5          234714       88472     88472     76565786        965982087   \n",
       "6          234841       88736     88736     76566563        924634299   \n",
       "7          235101       87910     87910     76566223        954136284   \n",
       "8          235115       88084     88084     76565784        975198853   \n",
       "9          235276       88688     88688     76566561        962329895   \n",
       "\n",
       "  Loyalty_Id  Gender  Age  Membership_Id Membership_Type        ...         \\\n",
       "0        NaN    MALE   38            NaN             NaN        ...          \n",
       "1        NaN    MALE   36            NaN             NaN        ...          \n",
       "2  LID876566  FEMALE   38          903.0          Bronze        ...          \n",
       "3        NaN  FEMALE   34            NaN             NaN        ...          \n",
       "4  LID876647  FEMALE   19          901.0            Gold        ...          \n",
       "5        NaN    MALE   22            NaN             NaN        ...          \n",
       "6  LID876577    MALE   36          903.0          Bronze        ...          \n",
       "7  LID876603  FEMALE   35          904.0        Platinum        ...          \n",
       "8  LID876577    MALE   36          903.0          Bronze        ...          \n",
       "9  LID876631  FEMALE   38          902.0          Silver        ...          \n",
       "\n",
       "  BIRTH_MONTH  BIRTH_YEAR  BIRTH_QUARTER  PLACE_OF_BIRTH COUNTRY_OF_BIRTH_SC  \\\n",
       "0    December        1986              4         Norfolk                  US   \n",
       "1     October        1999              4     Chula Vista                  US   \n",
       "2       March        1990              1         Oakland                  US   \n",
       "3         NaN           0              0             NaN                 NaN   \n",
       "4       March        1985              1         Jackson                  US   \n",
       "5    December        1977              4           Tampa                  US   \n",
       "6     January        1984              1     Jersey City                  US   \n",
       "7    December        1980              4          Irvine                  US   \n",
       "8   September        1983              3        Hartford                  US   \n",
       "9        June        1995              2    Jacksonville                  US   \n",
       "\n",
       "   EVER_BANKRUPT_FLG  NO_TIMES_DELINQUENT_in_365_days  BLACKLISTED_FLG  \\\n",
       "0                  N                                1                N   \n",
       "1                  N                                0                N   \n",
       "2                  N                                1                N   \n",
       "3                NaN                                0              NaN   \n",
       "4                  N                                1                N   \n",
       "5                  N                                0                N   \n",
       "6                  N                                1                N   \n",
       "7                  N                                1                N   \n",
       "8                  N                                0                N   \n",
       "9                  N                                0                N   \n",
       "\n",
       "   NO_OF_DEPENDANTS Record_created_By  \n",
       "0                 1                \\r  \n",
       "1                 1                \\r  \n",
       "2                 1                \\r  \n",
       "3                 0                \\r  \n",
       "4                 1                \\r  \n",
       "5                 1                \\r  \n",
       "6                 1                \\r  \n",
       "7                 2                \\r  \n",
       "8                 3                \\r  \n",
       "9                 1                \\r  \n",
       "\n",
       "[10 rows x 157 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Preprocessing - > Wrangling + Cleaning\n",
    "\n",
    "# Correcting column names for ease of usage\n",
    "original_data.columns = original_data.columns.str.replace(' ','')\n",
    "original_data.columns = original_data.columns.str.replace('.','')\n",
    "        \n",
    "original_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Please don't touch this piece of code, otherwise the destruction caused would be equivalent to World War 3\n",
    "master_data = original_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 947 entries, 0 to 946\n",
      "Columns: 157 entries, Transaction_Id to Record_created_By\n",
      "dtypes: datetime64[ns](4), float64(13), int64(39), object(101)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "master_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Through a basic Excel Sheet analysis, we have are seeing a lot of redundant variables as well as duplicate ones. \n",
    "\n",
    "1. Columns representing unique values such as Transaction ID's or Customer ID's can be removed directly.\n",
    "\n",
    "2. Multiple variables such as Loyalty ID, Membership related Info, etc. are representing the same information, leading to **Multicollinearity in the data**. Such variables can be romoved or clubbed together to create a single informative column.\n",
    "\n",
    "3. Some variables, which are not relevant within the aim of this project, such as Country, can be removed, as they contains only a single value. If and when this data is extrapolated for other countries, it may prove to be useful.\n",
    "\n",
    "4. Fields with large number of missing values, as we can't assume the values in such cases for better chances of accuracy.\n",
    "\n",
    "We will not be using such columns, and a single list of columns being used for model building will be informed.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial features list for feature selection at model building stages\n",
    "feature_list = ['FastShipping','credit_risk_score','age_range','Interest_2','INFLUENCE',\n",
    " 'Interests_1', 'INCOME_RANGE',\n",
    " 'LOYALITY_FLG', 'NO_TIMES_DELINQUENT_in_365_days',\n",
    " 'City_Sc', 'credit_risk_rating',\n",
    " 'CUSTOMER_STATUS_SC',\n",
    " 'fbck_user_FLG', 'NO_OF_DEPENDANTS',\n",
    " 'No_Of_Items', 'Shipment_Flg',\n",
    " 'Order_Item_Paidprice_Gross_Amt_',\n",
    " 'REGION', 'fbck_following_CNT',\n",
    " 'Order_Item_Listprice_Gross_Amt_','twt_follower_CNT','BiggerthanAverageOrders',\n",
    " 'No_Of_Items_After_Return',\n",
    " 'Return_Flag','MultipleShippingAddresses',\n",
    " 'SeveralCardsusedfromtheSameIpAddress',\n",
    " 'UnusualLocation','ShippingandBillingAddressarenotthesame',\n",
    " 'Order_Returnpaid_Gross_Amt',\n",
    " 'Return_Amt','PaymentInformationtypedwithCapitalletters',\n",
    " 'Customer_Type','Lastmodified_Dt',\n",
    " 'FirstTimeCustomers','Discount_Percentage',\n",
    " 'customer_type_sc','Quantity_Returned',\n",
    " 'Discount_Amt','Cancel_Date',\n",
    " 'ManyTransactionsinashorttimeperiod',\n",
    " 'Mode_Of_Payment','Return_Dt']\n",
    "target = 'Fraud'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0\n",
      "1    1\n",
      "2    0\n",
      "3    0\n",
      "4    0\n",
      "dtype: int8\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 947 entries, 0 to 946\n",
      "Data columns (total 42 columns):\n",
      "FastShipping                                 947 non-null object\n",
      "credit_risk_score                            947 non-null int64\n",
      "age_range                                    921 non-null object\n",
      "Interest_2                                   459 non-null object\n",
      "INFLUENCE                                    921 non-null object\n",
      "Interests_1                                  460 non-null object\n",
      "INCOME_RANGE                                 921 non-null object\n",
      "LOYALITY_FLG                                 921 non-null object\n",
      "NO_TIMES_DELINQUENT_in_365_days              947 non-null int64\n",
      "City_Sc                                      947 non-null object\n",
      "credit_risk_rating                           921 non-null object\n",
      "CUSTOMER_STATUS_SC                           921 non-null object\n",
      "fbck_user_FLG                                921 non-null object\n",
      "NO_OF_DEPENDANTS                             947 non-null int64\n",
      "No_Of_Items                                  947 non-null int64\n",
      "Shipment_Flg                                 947 non-null object\n",
      "Order_Item_Paidprice_Gross_Amt_              947 non-null int64\n",
      "REGION                                       921 non-null object\n",
      "fbck_following_CNT                           947 non-null int64\n",
      "Order_Item_Listprice_Gross_Amt_              947 non-null int64\n",
      "twt_follower_CNT                             947 non-null int64\n",
      "BiggerthanAverageOrders                      947 non-null object\n",
      "No_Of_Items_After_Return                     947 non-null int64\n",
      "Return_Flag                                  947 non-null object\n",
      "MultipleShippingAddresses                    947 non-null object\n",
      "SeveralCardsusedfromtheSameIpAddress         947 non-null object\n",
      "UnusualLocation                              947 non-null object\n",
      "ShippingandBillingAddressarenotthesame       947 non-null object\n",
      "Order_Returnpaid_Gross_Amt                   947 non-null int64\n",
      "Return_Amt                                   947 non-null int64\n",
      "PaymentInformationtypedwithCapitalletters    947 non-null object\n",
      "Customer_Type                                921 non-null object\n",
      "Lastmodified_Dt                              947 non-null object\n",
      "FirstTimeCustomers                           947 non-null object\n",
      "Discount_Percentage                          947 non-null int64\n",
      "customer_type_sc                             921 non-null object\n",
      "Quantity_Returned                            947 non-null int64\n",
      "Discount_Amt                                 947 non-null float64\n",
      "Cancel_Date                                  947 non-null datetime64[ns]\n",
      "ManyTransactionsinashorttimeperiod           947 non-null object\n",
      "Mode_Of_Payment                              947 non-null object\n",
      "Return_Dt                                    947 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](2), float64(1), int64(13), object(26)\n",
      "memory usage: 310.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# Segregating Target Data and Feature Set\n",
    "target = 'Fraud'\n",
    "label_data = master_data[target].astype('category').cat.codes  # Converting Fraud to binary categorical variables [0,1]\n",
    "master_data.drop('Fraud', axis = 1)\n",
    "\n",
    "master_data = master_data[feature_list]\n",
    "master_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['FastShipping', 'age_range', 'Interest_2', 'INFLUENCE', 'Interests_1', 'INCOME_RANGE', 'LOYALITY_FLG', 'City_Sc', 'credit_risk_rating', 'CUSTOMER_STATUS_SC', 'fbck_user_FLG', 'Shipment_Flg', 'REGION', 'BiggerthanAverageOrders', 'Return_Flag', 'MultipleShippingAddresses', 'SeveralCardsusedfromtheSameIpAddress', 'UnusualLocation', 'ShippingandBillingAddressarenotthesame', 'PaymentInformationtypedwithCapitalletters', 'Customer_Type', 'Lastmodified_Dt', 'FirstTimeCustomers', 'customer_type_sc', 'Cancel_Date', 'ManyTransactionsinashorttimeperiod', 'Mode_Of_Payment', 'Return_Dt']\n"
     ]
    }
   ],
   "source": [
    "# Finding columns with datatypes which classifiers will not recognise such as object and datetime\n",
    "obj_type_variables = [column for column in master_data.columns if master_data[column].dtype in ['object', 'datetime64[ns]']]\n",
    "print(obj_type_variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shrey\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# Making object type data into categorical variables, for creating categorical codes\n",
    "for column in obj_type_variables:\n",
    "    master_data[column] = master_data[column].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 947 entries, 0 to 946\n",
      "Data columns (total 42 columns):\n",
      "FastShipping                                 947 non-null category\n",
      "credit_risk_score                            947 non-null int64\n",
      "age_range                                    921 non-null category\n",
      "Interest_2                                   459 non-null category\n",
      "INFLUENCE                                    921 non-null category\n",
      "Interests_1                                  460 non-null category\n",
      "INCOME_RANGE                                 921 non-null category\n",
      "LOYALITY_FLG                                 921 non-null category\n",
      "NO_TIMES_DELINQUENT_in_365_days              947 non-null int64\n",
      "City_Sc                                      947 non-null category\n",
      "credit_risk_rating                           921 non-null category\n",
      "CUSTOMER_STATUS_SC                           921 non-null category\n",
      "fbck_user_FLG                                921 non-null category\n",
      "NO_OF_DEPENDANTS                             947 non-null int64\n",
      "No_Of_Items                                  947 non-null int64\n",
      "Shipment_Flg                                 947 non-null category\n",
      "Order_Item_Paidprice_Gross_Amt_              947 non-null int64\n",
      "REGION                                       921 non-null category\n",
      "fbck_following_CNT                           947 non-null int64\n",
      "Order_Item_Listprice_Gross_Amt_              947 non-null int64\n",
      "twt_follower_CNT                             947 non-null int64\n",
      "BiggerthanAverageOrders                      947 non-null category\n",
      "No_Of_Items_After_Return                     947 non-null int64\n",
      "Return_Flag                                  947 non-null category\n",
      "MultipleShippingAddresses                    947 non-null category\n",
      "SeveralCardsusedfromtheSameIpAddress         947 non-null category\n",
      "UnusualLocation                              947 non-null category\n",
      "ShippingandBillingAddressarenotthesame       947 non-null category\n",
      "Order_Returnpaid_Gross_Amt                   947 non-null int64\n",
      "Return_Amt                                   947 non-null int64\n",
      "PaymentInformationtypedwithCapitalletters    947 non-null category\n",
      "Customer_Type                                921 non-null category\n",
      "Lastmodified_Dt                              947 non-null category\n",
      "FirstTimeCustomers                           947 non-null category\n",
      "Discount_Percentage                          947 non-null int64\n",
      "customer_type_sc                             921 non-null category\n",
      "Quantity_Returned                            947 non-null int64\n",
      "Discount_Amt                                 947 non-null float64\n",
      "Cancel_Date                                  947 non-null category\n",
      "ManyTransactionsinashorttimeperiod           947 non-null category\n",
      "Mode_Of_Payment                              947 non-null category\n",
      "Return_Dt                                    947 non-null category\n",
      "dtypes: category(28), float64(1), int64(13)\n",
      "memory usage: 153.6 KB\n"
     ]
    }
   ],
   "source": [
    "master_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shrey\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3140: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[k1] = value[k2]\n"
     ]
    }
   ],
   "source": [
    "# Converting categorical into corresponding numerical values\n",
    "master_data[obj_type_variables] = master_data[obj_type_variables].apply(lambda column: column.cat.codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 947 entries, 0 to 946\n",
      "Data columns (total 42 columns):\n",
      "FastShipping                                 947 non-null int8\n",
      "credit_risk_score                            947 non-null int64\n",
      "age_range                                    947 non-null int8\n",
      "Interest_2                                   947 non-null int8\n",
      "INFLUENCE                                    947 non-null int8\n",
      "Interests_1                                  947 non-null int8\n",
      "INCOME_RANGE                                 947 non-null int8\n",
      "LOYALITY_FLG                                 947 non-null int8\n",
      "NO_TIMES_DELINQUENT_in_365_days              947 non-null int64\n",
      "City_Sc                                      947 non-null int8\n",
      "credit_risk_rating                           947 non-null int8\n",
      "CUSTOMER_STATUS_SC                           947 non-null int8\n",
      "fbck_user_FLG                                947 non-null int8\n",
      "NO_OF_DEPENDANTS                             947 non-null int64\n",
      "No_Of_Items                                  947 non-null int64\n",
      "Shipment_Flg                                 947 non-null int8\n",
      "Order_Item_Paidprice_Gross_Amt_              947 non-null int64\n",
      "REGION                                       947 non-null int8\n",
      "fbck_following_CNT                           947 non-null int64\n",
      "Order_Item_Listprice_Gross_Amt_              947 non-null int64\n",
      "twt_follower_CNT                             947 non-null int64\n",
      "BiggerthanAverageOrders                      947 non-null int8\n",
      "No_Of_Items_After_Return                     947 non-null int64\n",
      "Return_Flag                                  947 non-null int8\n",
      "MultipleShippingAddresses                    947 non-null int8\n",
      "SeveralCardsusedfromtheSameIpAddress         947 non-null int8\n",
      "UnusualLocation                              947 non-null int8\n",
      "ShippingandBillingAddressarenotthesame       947 non-null int8\n",
      "Order_Returnpaid_Gross_Amt                   947 non-null int64\n",
      "Return_Amt                                   947 non-null int64\n",
      "PaymentInformationtypedwithCapitalletters    947 non-null int8\n",
      "Customer_Type                                947 non-null int8\n",
      "Lastmodified_Dt                              947 non-null int16\n",
      "FirstTimeCustomers                           947 non-null int8\n",
      "Discount_Percentage                          947 non-null int64\n",
      "customer_type_sc                             947 non-null int8\n",
      "Quantity_Returned                            947 non-null int64\n",
      "Discount_Amt                                 947 non-null float64\n",
      "Cancel_Date                                  947 non-null int8\n",
      "ManyTransactionsinashorttimeperiod           947 non-null int8\n",
      "Mode_Of_Payment                              947 non-null int8\n",
      "Return_Dt                                    947 non-null int8\n",
      "dtypes: float64(1), int16(1), int64(13), int8(27)\n",
      "memory usage: 130.5 KB\n"
     ]
    }
   ],
   "source": [
    "master_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "Series([], dtype: float64)\n"
     ]
    }
   ],
   "source": [
    "#list of columns with null values \n",
    "list_of_clm_null_value= master_data.columns[master_data.isna().any()].tolist()\n",
    "print(list_of_clm_null_value)\n",
    "\n",
    "null_columns=master_data.columns[master_data.isnull().any()]\n",
    "count = master_data[null_columns].isnull().sum()\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# replacing the NaN value with -1 after EDA\n",
    "for i in list_of_clm_null_value:\n",
    "    master_data[i].fillna(-1, inplace = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shrey\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 947 entries, 0 to 946\n",
      "Data columns (total 42 columns):\n",
      "FastShipping                                 947 non-null category\n",
      "credit_risk_score                            947 non-null int64\n",
      "age_range                                    947 non-null category\n",
      "Interest_2                                   947 non-null category\n",
      "INFLUENCE                                    947 non-null category\n",
      "Interests_1                                  947 non-null category\n",
      "INCOME_RANGE                                 947 non-null category\n",
      "LOYALITY_FLG                                 947 non-null category\n",
      "NO_TIMES_DELINQUENT_in_365_days              947 non-null int64\n",
      "City_Sc                                      947 non-null category\n",
      "credit_risk_rating                           947 non-null category\n",
      "CUSTOMER_STATUS_SC                           947 non-null category\n",
      "fbck_user_FLG                                947 non-null category\n",
      "NO_OF_DEPENDANTS                             947 non-null int64\n",
      "No_Of_Items                                  947 non-null int64\n",
      "Shipment_Flg                                 947 non-null category\n",
      "Order_Item_Paidprice_Gross_Amt_              947 non-null int64\n",
      "REGION                                       947 non-null category\n",
      "fbck_following_CNT                           947 non-null int64\n",
      "Order_Item_Listprice_Gross_Amt_              947 non-null int64\n",
      "twt_follower_CNT                             947 non-null int64\n",
      "BiggerthanAverageOrders                      947 non-null category\n",
      "No_Of_Items_After_Return                     947 non-null int64\n",
      "Return_Flag                                  947 non-null category\n",
      "MultipleShippingAddresses                    947 non-null category\n",
      "SeveralCardsusedfromtheSameIpAddress         947 non-null category\n",
      "UnusualLocation                              947 non-null category\n",
      "ShippingandBillingAddressarenotthesame       947 non-null category\n",
      "Order_Returnpaid_Gross_Amt                   947 non-null int64\n",
      "Return_Amt                                   947 non-null int64\n",
      "PaymentInformationtypedwithCapitalletters    947 non-null category\n",
      "Customer_Type                                947 non-null category\n",
      "Lastmodified_Dt                              947 non-null category\n",
      "FirstTimeCustomers                           947 non-null category\n",
      "Discount_Percentage                          947 non-null int64\n",
      "customer_type_sc                             947 non-null category\n",
      "Quantity_Returned                            947 non-null int64\n",
      "Discount_Amt                                 947 non-null float64\n",
      "Cancel_Date                                  947 non-null category\n",
      "ManyTransactionsinashorttimeperiod           947 non-null category\n",
      "Mode_Of_Payment                              947 non-null category\n",
      "Return_Dt                                    947 non-null category\n",
      "dtypes: category(28), float64(1), int64(13)\n",
      "memory usage: 154.7 KB\n"
     ]
    }
   ],
   "source": [
    "# Converting numerical based categories into category dtype\n",
    "for column in obj_type_variables:\n",
    "    master_data[column] = master_data[column].astype('category')\n",
    "master_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Model Building\n",
    "---\n",
    "\n",
    "We will be testing various models for this Binary Classification problem.\n",
    "\n",
    "Our approach would be to first create baseline untuned models on a pre-decided ratio of training and testing datasets, and after that, through various iterations, arrive at the tuned model which gives a balanced result, with a >=85% accuracy.\n",
    "\n",
    "Along with accuracy, our metrics for evaluation of the model would be Precision, Recall and Confusion matrix.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Stratified Shuffle Split by 70:30 ratio + SelectKBest\n",
    "retail_data = master_data\n",
    "skb = SelectKBest(k=8)\n",
    "retail_data = skb.fit_transform(retail_data, label_data)\n",
    "#features_train, features_test, labels_train, labels_test = train_test_split(retail_data, label_data, test_size=0.3, random_state=42)\n",
    "\n",
    "splitter = StratifiedShuffleSplit(n_splits=10, test_size=0.3, random_state=42)\n",
    "for train_index, test_index in splitter.split(retail_data, label_data):\n",
    "        features_train, features_test = retail_data[train_index], retail_data[test_index]\n",
    "        labels_train, labels_test = label_data[train_index], label_data[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42\n",
      "Feature Scores:\n",
      "\n",
      "[('FastShipping', 8.100001301014169),\n",
      " ('BiggerthanAverageOrders', 5.745887795284878),\n",
      " ('No_Of_Items_After_Return', 4.916972422311143),\n",
      " ('credit_risk_score', 4.381886936924487),\n",
      " ('No_Of_Items', 4.190314173770475),\n",
      " ('age_range', 3.804572951937072),\n",
      " ('Interest_2', 3.7669951329272315),\n",
      " ('Return_Flag', 3.4152996363954338),\n",
      " ('Shipment_Flg', 3.3090168010633767),\n",
      " ('MultipleShippingAddresses', 3.125091243167083),\n",
      " ('SeveralCardsusedfromtheSameIpAddress', 2.8029111724079567),\n",
      " ('UnusualLocation', 2.424618839948198),\n",
      " ('ShippingandBillingAddressarenotthesame', 2.1373884646910644),\n",
      " ('PaymentInformationtypedwithCapitalletters', 1.766565168678694),\n",
      " ('INFLUENCE', 1.6322174530054272),\n",
      " ('Interests_1', 1.5553705001377034),\n",
      " ('Customer_Type', 1.3879597390041023),\n",
      " ('INCOME_RANGE', 1.3067636521158699),\n",
      " ('Lastmodified_Dt', 1.2953773334508918),\n",
      " ('Return_Amt', 1.2294380875470237),\n",
      " ('FirstTimeCustomers', 1.1803379328321633),\n",
      " ('Discount_Percentage', 1.1536995086191966),\n",
      " ('Order_Returnpaid_Gross_Amt', 1.1390949013237044),\n",
      " ('LOYALITY_FLG', 1.0568402044799827),\n",
      " ('NO_TIMES_DELINQUENT_in_365_days', 0.9762501150203332),\n",
      " ('customer_type_sc', 0.9736372338703057),\n",
      " ('City_Sc', 0.9168827623280416),\n",
      " ('credit_risk_rating', 0.8893588048692161),\n",
      " ('CUSTOMER_STATUS_SC', 0.8027255866769573),\n",
      " ('Order_Item_Paidprice_Gross_Amt_', 0.6134121368001867),\n",
      " ('fbck_user_FLG', 0.4968062286480921),\n",
      " ('NO_OF_DEPENDANTS', 0.4038807870652474),\n",
      " ('REGION', 0.35123831031447333),\n",
      " ('Order_Item_Listprice_Gross_Amt_', 0.3095922035763557),\n",
      " ('Discount_Amt', 0.29981281441198626),\n",
      " ('Cancel_Date', 0.24863008748140042),\n",
      " ('ManyTransactionsinashorttimeperiod', 0.24489415391343267),\n",
      " ('fbck_following_CNT', 0.2154204595795659),\n",
      " ('Quantity_Returned', 0.1778587020921885),\n",
      " ('Mode_Of_Payment', 0.162205791611923),\n",
      " ('Return_Dt', 0.15734650877979414),\n",
      " ('twt_follower_CNT', 0.10623261456185738)]\n"
     ]
    }
   ],
   "source": [
    "# Getting feature scores from SelectKBest after fitting\n",
    "unsorted_list = zip(feature_list, skb.scores_)\n",
    "\n",
    "sorted_features = sorted(unsorted_list, key=lambda x: x[1], reverse=True)\n",
    "print(len(sorted_features))\n",
    "print(\"Feature Scores:\\n\")\n",
    "pprint(sorted_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Trying untuned, baseline models on 70:30 train-test dataset\n",
    "gnb = GaussianNB()\n",
    "y_pred_gnb = gnb.fit(features_train, labels_train).predict(features_test)\n",
    "\n",
    "DT = DecisionTreeClassifier()\n",
    "y_pred_DT = DT.fit(features_train, labels_train).predict(features_test)\n",
    "\n",
    "ada = AdaBoostClassifier()\n",
    "y_pred_ada = ada.fit(features_train, labels_train).predict(features_test)\n",
    "\n",
    "logr = LogisticRegression()\n",
    "y_pred_logr = logr.fit(features_train, labels_train).predict(features_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GNB: 0.8070175438596491\n",
      "DT: 0.7859649122807018\n",
      "ADA: 0.8526315789473684\n",
      "Logr: 0.8666666666666667\n"
     ]
    }
   ],
   "source": [
    "# Accuracy Metrics\n",
    "print(\"GNB:\", accuracy_score(labels_test, y_pred_gnb))\n",
    "print(\"DT:\", accuracy_score(labels_test, y_pred_DT))\n",
    "print(\"ADA:\", accuracy_score(labels_test, y_pred_ada))\n",
    "print(\"Logr:\", accuracy_score(labels_test, y_pred_logr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GNB:              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.88      0.90      0.89       248\n",
      "          1       0.20      0.16      0.18        37\n",
      "\n",
      "avg / total       0.79      0.81      0.80       285\n",
      "\n",
      "DT:              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.87      0.88      0.88       248\n",
      "          1       0.15      0.14      0.14        37\n",
      "\n",
      "avg / total       0.78      0.79      0.78       285\n",
      "\n",
      "ADA:              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.87      0.98      0.92       248\n",
      "          1       0.00      0.00      0.00        37\n",
      "\n",
      "avg / total       0.76      0.85      0.80       285\n",
      "\n",
      "Logr:              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.87      1.00      0.93       248\n",
      "          1       0.00      0.00      0.00        37\n",
      "\n",
      "avg / total       0.76      0.87      0.81       285\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Precision, Recall, and actual prediction numbers\n",
    "print(\"GNB:\", classification_report(labels_test, y_pred_gnb))\n",
    "print(\"DT:\", classification_report(labels_test, y_pred_DT))\n",
    "print(\"ADA:\", classification_report(labels_test, y_pred_ada))\n",
    "print(\"Logr:\", classification_report(labels_test, y_pred_logr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Tuning the classifiers\n",
    "\n",
    "We will be using a pipeline, with a MinMax Scaler at stage 1 for int/float values, and the classifier at stage 2. This pipeline will be fed to GridSearchCV along with the classifier instance, as well as parameter list for that particular classifier.\n",
    "\n",
    "Through previously mentioned metrics, we will be determining the best tuned classifier here.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tune_NB() :\n",
    "    print(\"------------------ Using Naive Bayes --------------------\")\n",
    "    nb_clf = GaussianNB()\n",
    "    param_grid = {}             # No parameters for tuning\n",
    "\n",
    "    return nb_clf, param_grid\n",
    "\n",
    "# Decision Tree Classifier\n",
    "def tune_DT():\n",
    "    print(\"------------------ Using Decision Trees --------------------\")\n",
    "    clf = DecisionTreeClassifier()\n",
    "    param_grid = {\n",
    "        'clf__criterion': ['entropy', 'gini'],\n",
    "        'clf__splitter': ['best', 'random'],\n",
    "        'clf__random_state': [42],\n",
    "        'clf__min_samples_split': [2, 4, 5, 6, 7, 8]\n",
    "    }\n",
    "\n",
    "    return clf, param_grid\n",
    "\n",
    "# AdaBoost Classifier\n",
    "def tune_ADB():\n",
    "    print(\"------------------ Using AdaBoost Ensemble --------------------\")\n",
    "    clf = AdaBoostClassifier()\n",
    "    param_grid = {\n",
    "        'clf__algorithm' : ['SAMME', 'SAMME.R'],\n",
    "        'clf__learning_rate': [1, 2],\n",
    "        'clf__random_state': [42],\n",
    "        'clf__n_estimators' : [20, 50, 65, 80, 100]\n",
    "    }\n",
    "\n",
    "    return clf, param_grid\n",
    "\n",
    "# Logistic Regression Classifier\n",
    "def tune_LogR():\n",
    "    print(\"------------------ Using Logistic Regression --------------------\")\n",
    "    clf = LogisticRegression()\n",
    "    param_grid = {\n",
    "        'clf__max_iter' : [100, 500, 1000],\n",
    "        'clf__penalty': ['l1', 'l2'],\n",
    "        'clf__tol': [0.0001, 0.0005, 0.001, 0.005],\n",
    "        'clf__C' : [1.0, 1.5, 0.5, 2.0]\n",
    "    }\n",
    "\n",
    "    return clf, param_grid\n",
    "\n",
    "# Random Forest Classifier\n",
    "def tune_RandomF():\n",
    "    print(\"------------------ Using Random Forest --------------------\")\n",
    "    clf = RandomForestClassifier()\n",
    "    param_grid = {\n",
    "        'clf__criterion': ['entropy', 'gini'],\n",
    "        'clf__min_samples_split': [2, 4, 5, 6, 7, 8, 9, 10],\n",
    "        'clf__random_state': [42],\n",
    "        'clf__n_estimators' : [20, 40, 60, 80, 100, 200],\n",
    "        'clf__max_features' : ['auto', 'log2', None] \n",
    "    }\n",
    "\n",
    "    return clf, param_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------ Using Decision Trees --------------------\n",
      "\n",
      "=> Chosen parameters :\n",
      "{'clf__criterion': 'gini', 'clf__min_samples_split': 8, 'clf__random_state': 42, 'clf__splitter': 'random'}\n"
     ]
    }
   ],
   "source": [
    " # Create pipeline\n",
    "clf, params = tune_DT()\n",
    "scale = MinMaxScaler()\n",
    "estimators = [('scale', scale), ('clf', clf)]\n",
    "pipe = Pipeline(estimators)\n",
    "\n",
    "# Create GridSearchCV Instance\n",
    "grid = GridSearchCV(pipe, params)\n",
    "grid.fit(features_train, labels_train)\n",
    "\n",
    "# Final classifier\n",
    "clf = grid.best_estimator_\n",
    "\n",
    "print('\\n=> Chosen parameters :')\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.856140350877193\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.88      0.96      0.92       248\n",
      "          1       0.36      0.14      0.20        37\n",
      "\n",
      "avg / total       0.81      0.86      0.83       285\n",
      "\n",
      "Confusion Matrix:\n",
      " 239 9 32 5\n"
     ]
    }
   ],
   "source": [
    "# Metrics\n",
    "predictions = clf.predict(features_test)\n",
    "print(\"Accuracy Score:\", accuracy_score(labels_test, predictions))\n",
    "print(\"Classification Report:\\n\", classification_report(labels_test, predictions))\n",
    "tn, fp, fn, tp = confusion_matrix(labels_test, predictions).ravel()\n",
    "print(\"Confusion Matrix:\\n\", tn, fp, fn, tp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Hence, after multiple iterations, we are achieving a respectable accuracy of 85.61% using a tuned Decision Tree Classifier. The dataset is split in 70-30 ration, with top 8 features being used for classification from SelectKBest.***\n",
    "\n",
    "***We are receiving an average Precision Score of 0.81, and recall score of 0.86.***"
   ]
  }
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