fraud_detection_imbalance_classes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dd655665",
   "metadata": {},
   "source": [
    "### Fraud Detection models (Weighted Logistic Regression)\n",
    "In this notebook we train a **Logistic Regression**  model to detect fraud in data, taking the **imbalance** of the classes into consideration:\n",
    "\n",
    "The accuracy the **Logistic Regression** model using the test data is **80%**!! This is without any hyperparameter tuning.\n",
    "\n",
    "The class weights can be determined using one of the following ways:\n",
    "\n",
    "* **Input from domain expertise** \n",
    "* **Hyperparameter Tuning** using methods such as **grid search**\n",
    "* **Best practice** based on generalapproach in the inductry\n",
    "\n",
    "**The CAP curve analysis shows that that if 50% of the data is analysed without the model only 50% of the fraudulent cases would be discovered. But using the model, we would be able to identify more than 90% of the fraudulent cases in the same amount of data!!** \n",
    "\n",
    "#### Using the model: \n",
    "* the amount of **backlog** (incoming and unfinished work) would be **reduced significantly**, \n",
    "* the **amount of staff** needed to do the job would be **reduced drastically**, \n",
    "* the **processing time** would be **shortened significantly** and \n",
    "* **more cases of fraudulent transactions** would be tracked down in a given amount of data processed - **`more than 40% increase in effeciency!`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5ebf98cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from scipy.stats import zscore\n",
    "from statsmodels.formula.api import ols\n",
    "from sklearn import metrics, linear_model, preprocessing\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectKBest, f_regression, chi2\n",
    "from sklearn.feature_selection import VarianceThreshold, SelectFromModel, f_classif\n",
    "from sklearn.metrics import roc_curve, auc, confusion_matrix\n",
    "from classification_evaluation import cap_curve\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91795788",
   "metadata": {},
   "source": [
    "#### Read the data `FraudDetection.xlsx` into a DataFrame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b67b4898",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16281, 15)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transaction_ID</th>\n",
       "      <th>Card Tenure_months</th>\n",
       "      <th>WebsiteRegion</th>\n",
       "      <th>Trans_value</th>\n",
       "      <th>Seller_Category</th>\n",
       "      <th>Items_transaction</th>\n",
       "      <th>Shipping_Address</th>\n",
       "      <th>Purchase_Category</th>\n",
       "      <th>Othercard_owner</th>\n",
       "      <th>Seller_way</th>\n",
       "      <th>LastTransaction</th>\n",
       "      <th>Lastflagedvalue</th>\n",
       "      <th>LastMonthsTrans_Freq</th>\n",
       "      <th>Countryissuedcard</th>\n",
       "      <th>Fraud_Detected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>US</td>\n",
       "      <td>256.41</td>\n",
       "      <td>Store + online</td>\n",
       "      <td>10</td>\n",
       "      <td>US</td>\n",
       "      <td>online services</td>\n",
       "      <td>Dependent</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>US</td>\n",
       "      <td>409.54</td>\n",
       "      <td>Online only</td>\n",
       "      <td>8</td>\n",
       "      <td>International</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>EU</td>\n",
       "      <td>293.09</td>\n",
       "      <td>Online only</td>\n",
       "      <td>5</td>\n",
       "      <td>International</td>\n",
       "      <td>Electornics</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>US</td>\n",
       "      <td>444.07</td>\n",
       "      <td>Online only</td>\n",
       "      <td>7</td>\n",
       "      <td>International</td>\n",
       "      <td>online services</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Yes</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>EU</td>\n",
       "      <td>183.11</td>\n",
       "      <td>Online only</td>\n",
       "      <td>7</td>\n",
       "      <td>US</td>\n",
       "      <td>Food</td>\n",
       "      <td>Dependent</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Transaction_ID  Card Tenure_months WebsiteRegion  Trans_value  \\\n",
       "0               1                  25            US       256.41   \n",
       "1               2                  38            US       409.54   \n",
       "2               3                  28            EU       293.09   \n",
       "3               4                  44            US       444.07   \n",
       "4               5                  18            EU       183.11   \n",
       "\n",
       "  Seller_Category  Items_transaction Shipping_Address Purchase_Category  \\\n",
       "0  Store + online                 10               US   online services   \n",
       "1     Online only                  8    International       Accessories   \n",
       "2     Online only                  5    International       Electornics   \n",
       "3     Online only                  7    International   online services   \n",
       "4     Online only                  7               US              Food   \n",
       "\n",
       "  Othercard_owner Seller_way  LastTransaction  Lastflagedvalue  \\\n",
       "0       Dependent        Yes                0                0   \n",
       "1         Husband        Yes                0                0   \n",
       "2         Husband        Yes                0                0   \n",
       "3         Husband        Yes             7688                0   \n",
       "4       Dependent         No                0                0   \n",
       "\n",
       "   LastMonthsTrans_Freq Countryissuedcard Fraud_Detected  \n",
       "0                    40     United-States             No  \n",
       "1                    50     United-States             No  \n",
       "2                    40     United-States            Yes  \n",
       "3                    40     United-States            Yes  \n",
       "4                    30     United-States             No  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('./data/FraudDetection.xlsx', na_values = 'NA')\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08fc345e",
   "metadata": {},
   "source": [
    "#### Any missing values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fe31ce8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Othercard_owner    6482\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "z = df.isnull().sum()\n",
    "print(z[z > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cba160e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Othercard_owner', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fd0c721d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], dtype: int64)\n"
     ]
    }
   ],
   "source": [
    "z = df.isnull().sum()\n",
    "print(z[z > 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada2637f",
   "metadata": {},
   "source": [
    "#### Change column names `Transaction_ID` to `ID` and `Fraud_Detected` to `target`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7890ff43",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Card Tenure_months</th>\n",
       "      <th>WebsiteRegion</th>\n",
       "      <th>Trans_value</th>\n",
       "      <th>Seller_Category</th>\n",
       "      <th>Items_transaction</th>\n",
       "      <th>Shipping_Address</th>\n",
       "      <th>Purchase_Category</th>\n",
       "      <th>Seller_way</th>\n",
       "      <th>LastTransaction</th>\n",
       "      <th>Lastflagedvalue</th>\n",
       "      <th>LastMonthsTrans_Freq</th>\n",
       "      <th>Countryissuedcard</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>US</td>\n",
       "      <td>256.41</td>\n",
       "      <td>Store + online</td>\n",
       "      <td>10</td>\n",
       "      <td>US</td>\n",
       "      <td>online services</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>US</td>\n",
       "      <td>409.54</td>\n",
       "      <td>Online only</td>\n",
       "      <td>8</td>\n",
       "      <td>International</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>EU</td>\n",
       "      <td>293.09</td>\n",
       "      <td>Online only</td>\n",
       "      <td>5</td>\n",
       "      <td>International</td>\n",
       "      <td>Electornics</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>US</td>\n",
       "      <td>444.07</td>\n",
       "      <td>Online only</td>\n",
       "      <td>7</td>\n",
       "      <td>International</td>\n",
       "      <td>online services</td>\n",
       "      <td>Yes</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>EU</td>\n",
       "      <td>183.11</td>\n",
       "      <td>Online only</td>\n",
       "      <td>7</td>\n",
       "      <td>US</td>\n",
       "      <td>Food</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Card Tenure_months WebsiteRegion  Trans_value Seller_Category  \\\n",
       "0   1                  25            US       256.41  Store + online   \n",
       "1   2                  38            US       409.54     Online only   \n",
       "2   3                  28            EU       293.09     Online only   \n",
       "3   4                  44            US       444.07     Online only   \n",
       "4   5                  18            EU       183.11     Online only   \n",
       "\n",
       "   Items_transaction Shipping_Address Purchase_Category Seller_way  \\\n",
       "0                 10               US   online services        Yes   \n",
       "1                  8    International       Accessories        Yes   \n",
       "2                  5    International       Electornics        Yes   \n",
       "3                  7    International   online services        Yes   \n",
       "4                  7               US              Food         No   \n",
       "\n",
       "   LastTransaction  Lastflagedvalue  LastMonthsTrans_Freq Countryissuedcard  \\\n",
       "0                0                0                    40     United-States   \n",
       "1                0                0                    50     United-States   \n",
       "2                0                0                    40     United-States   \n",
       "3             7688                0                    40     United-States   \n",
       "4                0                0                    30     United-States   \n",
       "\n",
       "  target  \n",
       "0     No  \n",
       "1     No  \n",
       "2    Yes  \n",
       "3    Yes  \n",
       "4     No  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = df.copy()\n",
    "df_new.rename(columns = {'Transaction_ID': 'ID', 'Fraud_Detected': 'target'}, inplace = True)\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "668edc08",
   "metadata": {},
   "source": [
    "##### Convert the 'target' into binary `Yes/No = 1/0`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ec80e85a",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Card Tenure_months</th>\n",
       "      <th>WebsiteRegion</th>\n",
       "      <th>Trans_value</th>\n",
       "      <th>Seller_Category</th>\n",
       "      <th>Items_transaction</th>\n",
       "      <th>Shipping_Address</th>\n",
       "      <th>Purchase_Category</th>\n",
       "      <th>Seller_way</th>\n",
       "      <th>LastTransaction</th>\n",
       "      <th>Lastflagedvalue</th>\n",
       "      <th>LastMonthsTrans_Freq</th>\n",
       "      <th>Countryissuedcard</th>\n",
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       "      <th>Y</th>\n",
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       "      <td>25</td>\n",
       "      <td>US</td>\n",
       "      <td>256.41</td>\n",
       "      <td>Store + online</td>\n",
       "      <td>10</td>\n",
       "      <td>US</td>\n",
       "      <td>online services</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>US</td>\n",
       "      <td>409.54</td>\n",
       "      <td>Online only</td>\n",
       "      <td>8</td>\n",
       "      <td>International</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>No</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>EU</td>\n",
       "      <td>293.09</td>\n",
       "      <td>Online only</td>\n",
       "      <td>5</td>\n",
       "      <td>International</td>\n",
       "      <td>Electornics</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>US</td>\n",
       "      <td>444.07</td>\n",
       "      <td>Online only</td>\n",
       "      <td>7</td>\n",
       "      <td>International</td>\n",
       "      <td>online services</td>\n",
       "      <td>Yes</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>EU</td>\n",
       "      <td>183.11</td>\n",
       "      <td>Online only</td>\n",
       "      <td>7</td>\n",
       "      <td>US</td>\n",
       "      <td>Food</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Card Tenure_months WebsiteRegion  Trans_value Seller_Category  \\\n",
       "0   1                  25            US       256.41  Store + online   \n",
       "1   2                  38            US       409.54     Online only   \n",
       "2   3                  28            EU       293.09     Online only   \n",
       "3   4                  44            US       444.07     Online only   \n",
       "4   5                  18            EU       183.11     Online only   \n",
       "\n",
       "   Items_transaction Shipping_Address Purchase_Category Seller_way  \\\n",
       "0                 10               US   online services        Yes   \n",
       "1                  8    International       Accessories        Yes   \n",
       "2                  5    International       Electornics        Yes   \n",
       "3                  7    International   online services        Yes   \n",
       "4                  7               US              Food         No   \n",
       "\n",
       "   LastTransaction  Lastflagedvalue  LastMonthsTrans_Freq Countryissuedcard  \\\n",
       "0                0                0                    40     United-States   \n",
       "1                0                0                    50     United-States   \n",
       "2                0                0                    40     United-States   \n",
       "3             7688                0                    40     United-States   \n",
       "4                0                0                    30     United-States   \n",
       "\n",
       "  target  Y  \n",
       "0     No  0  \n",
       "1     No  0  \n",
       "2    Yes  1  \n",
       "3    Yes  1  \n",
       "4     No  0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['Y'] = (df_new.target == 'Yes') + 0                 \n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5e81bfe",
   "metadata": {},
   "source": [
    "#### Data types of variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4298899d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID                        int64\n",
      "Card Tenure_months        int64\n",
      "WebsiteRegion            object\n",
      "Trans_value             float64\n",
      "Seller_Category          object\n",
      "Items_transaction         int64\n",
      "Shipping_Address         object\n",
      "Purchase_Category        object\n",
      "Seller_way               object\n",
      "LastTransaction           int64\n",
      "Lastflagedvalue           int64\n",
      "LastMonthsTrans_Freq      int64\n",
      "Countryissuedcard        object\n",
      "target                   object\n",
      "Y                         int32\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df_new.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a96148e",
   "metadata": {},
   "source": [
    "#### `char` variables in data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "98720eba",
   "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>WebsiteRegion</th>\n",
       "      <th>Seller_Category</th>\n",
       "      <th>Shipping_Address</th>\n",
       "      <th>Purchase_Category</th>\n",
       "      <th>Seller_way</th>\n",
       "      <th>Countryissuedcard</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>Store + online</td>\n",
       "      <td>US</td>\n",
       "      <td>online services</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United-States</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US</td>\n",
       "      <td>Online only</td>\n",
       "      <td>International</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United-States</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EU</td>\n",
       "      <td>Online only</td>\n",
       "      <td>International</td>\n",
       "      <td>Electornics</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United-States</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>Online only</td>\n",
       "      <td>International</td>\n",
       "      <td>online services</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United-States</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EU</td>\n",
       "      <td>Online only</td>\n",
       "      <td>US</td>\n",
       "      <td>Food</td>\n",
       "      <td>No</td>\n",
       "      <td>United-States</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  WebsiteRegion Seller_Category Shipping_Address Purchase_Category Seller_way  \\\n",
       "0            US  Store + online               US   online services        Yes   \n",
       "1            US     Online only    International       Accessories        Yes   \n",
       "2            EU     Online only    International       Electornics        Yes   \n",
       "3            US     Online only    International   online services        Yes   \n",
       "4            EU     Online only               US              Food         No   \n",
       "\n",
       "  Countryissuedcard target  \n",
       "0     United-States     No  \n",
       "1     United-States     No  \n",
       "2     United-States    Yes  \n",
       "3     United-States    Yes  \n",
       "4     United-States     No  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_obj = df_new.select_dtypes(include = ['object'])\n",
    "df_obj.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b0a24edf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['WebsiteRegion', 'Seller_Category', 'Shipping_Address', 'Purchase_Category', 'Seller_way', 'Countryissuedcard', 'target']\n"
     ]
    }
   ],
   "source": [
    "char_variables = list(df_obj)\n",
    "print(char_variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b508242a",
   "metadata": {},
   "source": [
    "### Selecting good predictors of the target using Chi-squared statistics\n",
    "* Check relationship between **'WebsiteRegion'** and **target** by first creating a **cross tab** showing frequency in each cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "829fbfa7",
   "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>target</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WebsiteRegion</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EU</th>\n",
       "      <td>1625</td>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eastern Europe</th>\n",
       "      <td>793</td>\n",
       "      <td>369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>9049</td>\n",
       "      <td>2740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unclassified</th>\n",
       "      <td>968</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target            No   Yes\n",
       "WebsiteRegion             \n",
       "EU              1625   384\n",
       "Eastern Europe   793   369\n",
       "US              9049  2740\n",
       "Unclassified     968   353"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.crosstab(df_obj['WebsiteRegion'], df_obj['target'], margins = False)\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7bd3e14",
   "metadata": {},
   "source": [
    "Calculate **Chi-squared statistics, p value, degree of freedom, expected** and confirm if it looks like significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e7195822",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi-square = 73.216\n",
      "pvalue = 0.0\n",
      "deg. of freedom(dof) = 3\n",
      "\n",
      "expected:\n",
      "[[1534.42141146  474.57858854]\n",
      " [ 887.50506726  274.49493274]\n",
      " [9004.12843191 2784.87156809]\n",
      " [1008.94508937  312.05491063]]\n"
     ]
    }
   ],
   "source": [
    "chi_square, p_value, dof, expected = stats.chi2_contingency(table)\n",
    "\n",
    "print(f'chi-square = {round(chi_square, 3)}')\n",
    "print(f'pvalue = {round(p_value, 3)}')\n",
    "print(f'deg. of freedom(dof) = {dof}\\n')\n",
    "print(f'expected:\\n{expected}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5009ad35",
   "metadata": {},
   "source": [
    "A large **chi-square = 73.216** and a very small **pvalue = 0.0 << 0.05** mean that the variable **'WebsiteRegion'**, is **correlated** with **'target'**. This means **'WebsiteRegion'** is a good predictor and can be used in the final model to predict the **'target'**.  \n",
    "\n",
    "#### Chi-squared statistics for all the character variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ec23e33d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi-squares:\n",
      "[73.216, 343.931, 3272.593, 1798.965, 729.93, 13.211]\n",
      "\n",
      "pvalues:\n",
      "[0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n"
     ]
    }
   ],
   "source": [
    "chi_sqs = []\n",
    "pvals = []\n",
    "for var in char_variables[:-1]:\n",
    "    table = pd.crosstab(df_obj[var], df_obj['target'], margins = False)\n",
    "    chi_sq, p_value, dof, expected = stats.chi2_contingency(table)\n",
    "    chi_sqs.append(round(chi_sq, 3))\n",
    "    pvals.append(round(p_value, 3))\n",
    "    \n",
    "print(f'chi-squares:\\n{chi_sqs}\\n')\n",
    "print(f'pvalues:\\n{pvals}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab4755d0",
   "metadata": {},
   "source": [
    "All the **chi-square statistics** are relatively large enough and the all **pvalues are less than 0.05**. This suggests that all the variables are **correlated with the target**. These variables can therefore be included in the final model to predict the target.\n",
    "\n",
    "### Levels for each categorical variable \n",
    "* The number of dummy variables will depend on the number of levels of each categorical variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "efbd1756",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Levels of WebsiteRegion is ['US', 'EU', 'Unclassified', 'Eastern Europe']\n",
      "Number of levels for WebsiteRegion is 4\n",
      "\n",
      "Levels of Seller_Category is ['Store + online', 'Online only']\n",
      "Number of levels for Seller_Category is 2\n",
      "\n",
      "Levels of Shipping_Address is ['US', 'International', 'Canada']\n",
      "Number of levels for Shipping_Address is 3\n",
      "\n",
      "Levels of Purchase_Category is ['online services', 'Accessories', 'Electornics', 'Food', 'Books', 'Paying bills', 'Tickets - flights']\n",
      "Number of levels for Purchase_Category is 7\n",
      "\n",
      "Levels of Seller_way is ['Yes', 'No']\n",
      "Number of levels for Seller_way is 2\n",
      "\n",
      "Levels of Countryissuedcard is ['United-States', 'Other']\n",
      "Number of levels for Countryissuedcard is 2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for var in char_variables[:-1]:\n",
    "            varnames = list(df_obj[var].unique())\n",
    "            print(f'Levels of {var} is {varnames}')\n",
    "            print(f'Number of levels for {var} is {len(varnames)}\\n') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d31c426e",
   "metadata": {},
   "source": [
    "### Value Counts for each level of each CHAR feature in the data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8fdc9f83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of observations for each level of \"WebsiteRegion\": \n",
      "WebsiteRegion\n",
      "US                11789\n",
      "EU                 2009\n",
      "Unclassified       1321\n",
      "Eastern Europe     1162\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Number of observations for each level of \"Seller_Category\": \n",
      "Seller_Category\n",
      "Online only       13479\n",
      "Store + online     2802\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Number of observations for each level of \"Shipping_Address\": \n",
      "Shipping_Address\n",
      "US               7834\n",
      "International    7417\n",
      "Canada           1030\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Number of observations for each level of \"Purchase_Category\": \n",
      "Purchase_Category\n",
      "Accessories          3211\n",
      "Food                 2813\n",
      "online services      2741\n",
      "Paying bills         2372\n",
      "Electornics          2366\n",
      "Books                2020\n",
      "Tickets - flights     758\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Number of observations for each level of \"Seller_way\": \n",
      "Seller_way\n",
      "Yes    10860\n",
      "No      5421\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Number of observations for each level of \"Countryissuedcard\": \n",
      "Countryissuedcard\n",
      "United-States    14662\n",
      "Other             1619\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for var in char_variables[:-1]:\n",
    "    print(f'Number of observations for each level of \"{var}\": \\n{df_obj[var].value_counts()}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5c69d54",
   "metadata": {},
   "source": [
    "### Feature engineering \n",
    "Create dummy variables for each categorical variable in data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e07c96a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0c492fc",
   "metadata": {},
   "source": [
    "The variable **'WebsiteRegion'** and **'Shipping_Address'** for example have similar categories like **US**. In order to make sure that **no two categorical variables** will have the **same header for different dummies** we will add prefixes as below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "db82646e",
   "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>WebsiteRegion</th>\n",
       "      <th>Seller_Category</th>\n",
       "      <th>Shipping_Address</th>\n",
       "      <th>Purchase_Category</th>\n",
       "      <th>Seller_way</th>\n",
       "      <th>Countryissuedcard</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US_WE1</td>\n",
       "      <td>Store + online_SC2</td>\n",
       "      <td>US_SA3</td>\n",
       "      <td>online services_PC4</td>\n",
       "      <td>Yes_SW5</td>\n",
       "      <td>United-States_CO6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US_WE1</td>\n",
       "      <td>Online only_SC2</td>\n",
       "      <td>International_SA3</td>\n",
       "      <td>Accessories_PC4</td>\n",
       "      <td>Yes_SW5</td>\n",
       "      <td>United-States_CO6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EU_WE1</td>\n",
       "      <td>Online only_SC2</td>\n",
       "      <td>International_SA3</td>\n",
       "      <td>Electornics_PC4</td>\n",
       "      <td>Yes_SW5</td>\n",
       "      <td>United-States_CO6</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US_WE1</td>\n",
       "      <td>Online only_SC2</td>\n",
       "      <td>International_SA3</td>\n",
       "      <td>online services_PC4</td>\n",
       "      <td>Yes_SW5</td>\n",
       "      <td>United-States_CO6</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EU_WE1</td>\n",
       "      <td>Online only_SC2</td>\n",
       "      <td>US_SA3</td>\n",
       "      <td>Food_PC4</td>\n",
       "      <td>No_SW5</td>\n",
       "      <td>United-States_CO6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  WebsiteRegion     Seller_Category   Shipping_Address    Purchase_Category  \\\n",
       "0        US_WE1  Store + online_SC2             US_SA3  online services_PC4   \n",
       "1        US_WE1     Online only_SC2  International_SA3      Accessories_PC4   \n",
       "2        EU_WE1     Online only_SC2  International_SA3      Electornics_PC4   \n",
       "3        US_WE1     Online only_SC2  International_SA3  online services_PC4   \n",
       "4        EU_WE1     Online only_SC2             US_SA3             Food_PC4   \n",
       "\n",
       "  Seller_way  Countryissuedcard target  \n",
       "0    Yes_SW5  United-States_CO6     No  \n",
       "1    Yes_SW5  United-States_CO6     No  \n",
       "2    Yes_SW5  United-States_CO6    Yes  \n",
       "3    Yes_SW5  United-States_CO6    Yes  \n",
       "4     No_SW5  United-States_CO6     No  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_obj['WebsiteRegion'] = df_obj.WebsiteRegion + \"_WE1\"         \n",
    "df_obj['Seller_Category'] = df_obj.Seller_Category + \"_SC2\"     \n",
    "df_obj['Shipping_Address'] = df_obj.Shipping_Address + \"_SA3\"\n",
    "df_obj['Purchase_Category'] = df_obj.Purchase_Category + \"_PC4\"\n",
    "df_obj['Seller_way'] = df_obj.Seller_way +\"_SW5\"\n",
    "df_obj['Countryissuedcard'] = df_obj.Countryissuedcard + \"_CO6\"\n",
    "\n",
    "df_obj.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c16442f7",
   "metadata": {},
   "source": [
    "### Numeric Features in DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "378b84e7",
   "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>ID</th>\n",
       "      <th>Card Tenure_months</th>\n",
       "      <th>Trans_value</th>\n",
       "      <th>Items_transaction</th>\n",
       "      <th>LastTransaction</th>\n",
       "      <th>Lastflagedvalue</th>\n",
       "      <th>LastMonthsTrans_Freq</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>256.41</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>409.54</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>293.09</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>444.07</td>\n",
       "      <td>7</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>183.11</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Card Tenure_months  Trans_value  Items_transaction  LastTransaction  \\\n",
       "0   1                  25       256.41                 10                0   \n",
       "1   2                  38       409.54                  8                0   \n",
       "2   3                  28       293.09                  5                0   \n",
       "3   4                  44       444.07                  7             7688   \n",
       "4   5                  18       183.11                  7                0   \n",
       "\n",
       "   Lastflagedvalue  LastMonthsTrans_Freq  Y  \n",
       "0                0                    40  0  \n",
       "1                0                    50  0  \n",
       "2                0                    40  1  \n",
       "3                0                    40  1  \n",
       "4                0                    30  0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_num = df_new.select_dtypes(include = ['number'])\n",
    "df_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e06ddb34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original variable name: WebsiteRegion\n",
      "Original variable name: Seller_Category\n",
      "Original variable name: Shipping_Address\n",
      "Original variable name: Purchase_Category\n",
      "Original variable name: Seller_way\n",
      "Original variable name: Countryissuedcard\n"
     ]
    }
   ],
   "source": [
    "for var in char_variables[:-1]:\n",
    "    print(f'Original variable name: {var}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd2973b9",
   "metadata": {},
   "source": [
    "### Create Dummies for categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "987fb018",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dummy names:\n",
      "['EU_WE1', 'Eastern Europe_WE1', 'US_WE1', 'Unclassified_WE1', 'Online only_SC2', 'Store + online_SC2', 'Canada_SA3', 'International_SA3', 'US_SA3', 'Accessories_PC4', 'Books_PC4', 'Electornics_PC4', 'Food_PC4', 'Paying bills_PC4', 'Tickets - flights_PC4', 'online services_PC4', 'No_SW5', 'Yes_SW5', 'Other_CO6', 'United-States_CO6']\n",
      "\n",
      "Number of dummies: 20\n",
      "shape: (16281, 20)\n"
     ]
    }
   ],
   "source": [
    "df_obj_dummies = pd.DataFrame([])\n",
    "for var in char_variables[:-1]:\n",
    "        df_temp = pd.get_dummies(df_obj[var])\n",
    "        df_obj_dummies = pd.concat([df_obj_dummies, df_temp], axis = 1)\n",
    "\n",
    "print(f'dummy names:\\n{list(df_obj_dummies)}\\n')\n",
    "print(f'Number of dummies: {len(list(df_obj_dummies))}')\n",
    "print(f'shape: {df_obj_dummies.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a8e94540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EU_WE1</th>\n",
       "      <th>Eastern Europe_WE1</th>\n",
       "      <th>US_WE1</th>\n",
       "      <th>Unclassified_WE1</th>\n",
       "      <th>Online only_SC2</th>\n",
       "      <th>Store + online_SC2</th>\n",
       "      <th>Canada_SA3</th>\n",
       "      <th>International_SA3</th>\n",
       "      <th>US_SA3</th>\n",
       "      <th>Accessories_PC4</th>\n",
       "      <th>Books_PC4</th>\n",
       "      <th>Electornics_PC4</th>\n",
       "      <th>Food_PC4</th>\n",
       "      <th>Paying bills_PC4</th>\n",
       "      <th>Tickets - flights_PC4</th>\n",
       "      <th>online services_PC4</th>\n",
       "      <th>No_SW5</th>\n",
       "      <th>Yes_SW5</th>\n",
       "      <th>Other_CO6</th>\n",
       "      <th>United-States_CO6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   EU_WE1  Eastern Europe_WE1  US_WE1  Unclassified_WE1  Online only_SC2  \\\n",
       "0   False               False    True             False            False   \n",
       "1   False               False    True             False             True   \n",
       "2    True               False   False             False             True   \n",
       "3   False               False    True             False             True   \n",
       "4    True               False   False             False             True   \n",
       "\n",
       "   Store + online_SC2  Canada_SA3  International_SA3  US_SA3  Accessories_PC4  \\\n",
       "0                True       False              False    True            False   \n",
       "1               False       False               True   False             True   \n",
       "2               False       False               True   False            False   \n",
       "3               False       False               True   False            False   \n",
       "4               False       False              False    True            False   \n",
       "\n",
       "   Books_PC4  Electornics_PC4  Food_PC4  Paying bills_PC4  \\\n",
       "0      False            False     False             False   \n",
       "1      False            False     False             False   \n",
       "2      False             True     False             False   \n",
       "3      False            False     False             False   \n",
       "4      False            False      True             False   \n",
       "\n",
       "   Tickets - flights_PC4  online services_PC4  No_SW5  Yes_SW5  Other_CO6  \\\n",
       "0                  False                 True   False     True      False   \n",
       "1                  False                False   False     True      False   \n",
       "2                  False                False   False     True      False   \n",
       "3                  False                 True   False     True      False   \n",
       "4                  False                False    True    False      False   \n",
       "\n",
       "   United-States_CO6  \n",
       "0               True  \n",
       "1               True  \n",
       "2               True  \n",
       "3               True  \n",
       "4               True  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_obj_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a49209c8",
   "metadata": {},
   "source": [
    "### Drop any duplicates (by ID) in data (only keep last one)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a0391bbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16281, 8)\n",
      "(16281, 8)\n"
     ]
    }
   ],
   "source": [
    "print(df_num.shape)\n",
    "df_num = df_num.drop_duplicates('ID', keep = 'last')\n",
    "print(df_num.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e648aab",
   "metadata": {},
   "source": [
    "### Standardization for numerical features\n",
    "Scipy's **`zscore`** would transform the features to have standard normal distribution having **`mean = 0`** and **`standard deviation = 1`**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ec2628ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Card Tenure_months</th>\n",
       "      <th>Trans_value</th>\n",
       "      <th>Items_transaction</th>\n",
       "      <th>LastTransaction</th>\n",
       "      <th>Lastflagedvalue</th>\n",
       "      <th>LastMonthsTrans_Freq</th>\n",
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       "      <td>1.196864</td>\n",
       "      <td>-0.142662</td>\n",
       "      <td>-0.218062</td>\n",
       "      <td>-0.031432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.055417</td>\n",
       "      <td>0.049638</td>\n",
       "      <td>0.417886</td>\n",
       "      <td>-0.142662</td>\n",
       "      <td>-0.218062</td>\n",
       "      <td>0.769918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.777503</td>\n",
       "      <td>-0.789064</td>\n",
       "      <td>-0.750582</td>\n",
       "      <td>-0.142662</td>\n",
       "      <td>-0.218062</td>\n",
       "      <td>-0.031432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.377835</td>\n",
       "      <td>0.298332</td>\n",
       "      <td>0.028397</td>\n",
       "      <td>0.871091</td>\n",
       "      <td>-0.218062</td>\n",
       "      <td>-0.031432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.499590</td>\n",
       "      <td>-1.581168</td>\n",
       "      <td>0.028397</td>\n",
       "      <td>-0.142662</td>\n",
       "      <td>-0.218062</td>\n",
       "      <td>-0.832781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Card Tenure_months  Trans_value  Items_transaction  LastTransaction  \\\n",
       "0           -0.994129    -1.053243           1.196864        -0.142662   \n",
       "1           -0.055417     0.049638           0.417886        -0.142662   \n",
       "2           -0.777503    -0.789064          -0.750582        -0.142662   \n",
       "3            0.377835     0.298332           0.028397         0.871091   \n",
       "4           -1.499590    -1.581168           0.028397        -0.142662   \n",
       "\n",
       "   Lastflagedvalue  LastMonthsTrans_Freq  \n",
       "0        -0.218062             -0.031432  \n",
       "1        -0.218062              0.769918  \n",
       "2        -0.218062             -0.031432  \n",
       "3        -0.218062             -0.031432  \n",
       "4        -0.218062             -0.832781  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = df_num['Y']\n",
    "\n",
    "df_num_scaled = df_num.iloc[:, 1:-1].apply(zscore)\n",
    "df_num_scaled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "257de080",
   "metadata": {},
   "source": [
    "### Create a DataFrame with numeric features (numeric plus dummies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d637a226",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16281, 26)\n"
     ]
    },
    {
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       "      <th>US_WE1</th>\n",
       "      <th>Unclassified_WE1</th>\n",
       "      <th>...</th>\n",
       "      <th>Books_PC4</th>\n",
       "      <th>Electornics_PC4</th>\n",
       "      <th>Food_PC4</th>\n",
       "      <th>Paying bills_PC4</th>\n",
       "      <th>Tickets - flights_PC4</th>\n",
       "      <th>online services_PC4</th>\n",
       "      <th>No_SW5</th>\n",
       "      <th>Yes_SW5</th>\n",
       "      <th>Other_CO6</th>\n",
       "      <th>United-States_CO6</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>-0.994129</td>\n",
       "      <td>-1.053243</td>\n",
       "      <td>1.196864</td>\n",
       "      <td>-0.142662</td>\n",
       "      <td>-0.218062</td>\n",
       "      <td>-0.031432</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.055417</td>\n",
       "      <td>0.049638</td>\n",
       "      <td>0.417886</td>\n",
       "      <td>-0.142662</td>\n",
       "      <td>-0.218062</td>\n",
       "      <td>0.769918</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.777503</td>\n",
       "      <td>-0.789064</td>\n",
       "      <td>-0.750582</td>\n",
       "      <td>-0.142662</td>\n",
       "      <td>-0.218062</td>\n",
       "      <td>-0.031432</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.377835</td>\n",
       "      <td>0.298332</td>\n",
       "      <td>0.028397</td>\n",
       "      <td>0.871091</td>\n",
       "      <td>-0.218062</td>\n",
       "      <td>-0.031432</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.499590</td>\n",
       "      <td>-1.581168</td>\n",
       "      <td>0.028397</td>\n",
       "      <td>-0.142662</td>\n",
       "      <td>-0.218062</td>\n",
       "      <td>-0.832781</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Card Tenure_months  Trans_value  Items_transaction  LastTransaction  \\\n",
       "0           -0.994129    -1.053243           1.196864        -0.142662   \n",
       "1           -0.055417     0.049638           0.417886        -0.142662   \n",
       "2           -0.777503    -0.789064          -0.750582        -0.142662   \n",
       "3            0.377835     0.298332           0.028397         0.871091   \n",
       "4           -1.499590    -1.581168           0.028397        -0.142662   \n",
       "\n",
       "   Lastflagedvalue  LastMonthsTrans_Freq  EU_WE1  Eastern Europe_WE1  US_WE1  \\\n",
       "0        -0.218062             -0.031432   False               False    True   \n",
       "1        -0.218062              0.769918   False               False    True   \n",
       "2        -0.218062             -0.031432    True               False   False   \n",
       "3        -0.218062             -0.031432   False               False    True   \n",
       "4        -0.218062             -0.832781    True               False   False   \n",
       "\n",
       "   Unclassified_WE1  ...  Books_PC4  Electornics_PC4  Food_PC4  \\\n",
       "0             False  ...      False            False     False   \n",
       "1             False  ...      False            False     False   \n",
       "2             False  ...      False             True     False   \n",
       "3             False  ...      False            False     False   \n",
       "4             False  ...      False            False      True   \n",
       "\n",
       "   Paying bills_PC4  Tickets - flights_PC4  online services_PC4  No_SW5  \\\n",
       "0             False                  False                 True   False   \n",
       "1             False                  False                False   False   \n",
       "2             False                  False                False   False   \n",
       "3             False                  False                 True   False   \n",
       "4             False                  False                False    True   \n",
       "\n",
       "   Yes_SW5  Other_CO6  United-States_CO6  \n",
       "0     True      False               True  \n",
       "1     True      False               True  \n",
       "2     True      False               True  \n",
       "3     True      False               True  \n",
       "4    False      False               True  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_df = pd.concat([df_num_scaled , df_obj_dummies], axis = 1)   \n",
    "print(X_train_df.shape)\n",
    "X_train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2cccded0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16281\n"
     ]
    }
   ],
   "source": [
    "print(len(Y))             "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddd3993f",
   "metadata": {},
   "source": [
    "### Determined the AUC of each variable with the target.\n",
    "This would give an clue of which feature is a good predictor of the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9f30767c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The single AUC of Card Tenure_months is 0.6783597431553071\n",
      "\n",
      "The single AUC of Trans_value is 0.677623737036333\n",
      "\n",
      "The single AUC of Items_transaction is 0.28531270563247135\n",
      "\n",
      "The single AUC of LastTransaction is 0.5891427936972726\n",
      "\n",
      "The single AUC of Lastflagedvalue is 0.5330136156793276\n",
      "\n",
      "The single AUC of LastMonthsTrans_Freq is 0.6714938167289458\n",
      "\n",
      "The single AUC of EU_WE1 is 0.4845822300925813\n",
      "\n",
      "The single AUC of Eastern Europe_WE1 is 0.5160861126845557\n",
      "\n",
      "The single AUC of US_WE1 is 0.4923622180110364\n",
      "\n",
      "The single AUC of Unclassified_WE1 is 0.5069694392118266\n",
      "\n",
      "The single AUC of Online only_SC2 is 0.5646654334207144\n",
      "\n",
      "The single AUC of Store + online_SC2 is 0.4353345665792856\n",
      "\n",
      "The single AUC of Canada_SA3 is 0.4715209259757604\n",
      "\n",
      "The single AUC of International_SA3 is 0.7627954913130179\n",
      "\n",
      "The single AUC of US_SA3 is 0.2656835827112216\n",
      "\n",
      "The single AUC of Accessories_PC4 is 0.4668894789567216\n",
      "\n",
      "The single AUC of Books_PC4 is 0.578779073961511\n",
      "\n",
      "The single AUC of Electornics_PC4 is 0.5788243013435856\n",
      "\n",
      "The single AUC of Food_PC4 is 0.4445944810048132\n",
      "\n",
      "The single AUC of Paying bills_PC4 is 0.5116888318476044\n",
      "\n",
      "The single AUC of Tickets - flights_PC4 is 0.49692601214301885\n",
      "\n",
      "The single AUC of online services_PC4 is 0.42229782074274524\n",
      "\n",
      "The single AUC of No_SW5 is 0.38245296760000674\n",
      "\n",
      "The single AUC of Yes_SW5 is 0.6175470323999932\n",
      "\n",
      "The single AUC of Other_CO6 is 0.48988070258636646\n",
      "\n",
      "The single AUC of United-States_CO6 is 0.5101192974136336\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for var in list(X_train_df):\n",
    "    fpr, tpr, thresholds = roc_curve(np.array(Y), np.array(X_train_df[var]))          \n",
    "    print(f'The single AUC of {var} is {auc(fpr, tpr)}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7404bcf0",
   "metadata": {},
   "source": [
    "### Feature selection\n",
    "Based on the **single AUCs** let's select **18** features (**single AUC > 0.5**)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "65214955",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16281, 18)\n"
     ]
    }
   ],
   "source": [
    "selector = SelectKBest(f_classif, k = 18)               \n",
    "\n",
    "X_train_new = selector.fit_transform(X_train_df, Y)    \n",
    "print(X_train_new.shape)                               "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69a9fe7a",
   "metadata": {},
   "source": [
    "#### Check which feature is selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c2622a85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16281, 18)\n"
     ]
    }
   ],
   "source": [
    "num_rows = X_train_df.shape[0]\n",
    "chosen_indices = selector.get_support(indices = True)     # selected features      \n",
    "selected_df = X_train_df.iloc[0:num_rows, chosen_indices]\n",
    "print(selected_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "53ac7383",
   "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>Card Tenure_months</th>\n",
       "      <th>Trans_value</th>\n",
       "      <th>Items_transaction</th>\n",
       "      <th>LastTransaction</th>\n",
       "      <th>Lastflagedvalue</th>\n",
       "      <th>LastMonthsTrans_Freq</th>\n",
       "      <th>Online only_SC2</th>\n",
       "      <th>Store + online_SC2</th>\n",
       "      <th>Canada_SA3</th>\n",
       "      <th>International_SA3</th>\n",
       "      <th>US_SA3</th>\n",
       "      <th>Accessories_PC4</th>\n",
       "      <th>Books_PC4</th>\n",
       "      <th>Electornics_PC4</th>\n",
       "      <th>Food_PC4</th>\n",
       "      <th>online services_PC4</th>\n",
       "      <th>No_SW5</th>\n",
       "      <th>Yes_SW5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.994129</td>\n",
       "      <td>-1.053243</td>\n",
       "      <td>1.196864</td>\n",
       "      <td>-0.142662</td>\n",
       "      <td>-0.218062</td>\n",
       "      <td>-0.031432</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.055417</td>\n",
       "      <td>0.049638</td>\n",
       "      <td>0.417886</td>\n",
       "      <td>-0.142662</td>\n",
       "      <td>-0.218062</td>\n",
       "      <td>0.769918</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.777503</td>\n",
       "      <td>-0.789064</td>\n",
       "      <td>-0.750582</td>\n",
       "      <td>-0.142662</td>\n",
       "      <td>-0.218062</td>\n",
       "      <td>-0.031432</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.377835</td>\n",
       "      <td>0.298332</td>\n",
       "      <td>0.028397</td>\n",
       "      <td>0.871091</td>\n",
       "      <td>-0.218062</td>\n",
       "      <td>-0.031432</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.499590</td>\n",
       "      <td>-1.581168</td>\n",
       "      <td>0.028397</td>\n",
       "      <td>-0.142662</td>\n",
       "      <td>-0.218062</td>\n",
       "      <td>-0.832781</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Card Tenure_months  Trans_value  Items_transaction  LastTransaction  \\\n",
       "0           -0.994129    -1.053243           1.196864        -0.142662   \n",
       "1           -0.055417     0.049638           0.417886        -0.142662   \n",
       "2           -0.777503    -0.789064          -0.750582        -0.142662   \n",
       "3            0.377835     0.298332           0.028397         0.871091   \n",
       "4           -1.499590    -1.581168           0.028397        -0.142662   \n",
       "\n",
       "   Lastflagedvalue  LastMonthsTrans_Freq  Online only_SC2  Store + online_SC2  \\\n",
       "0        -0.218062             -0.031432            False                True   \n",
       "1        -0.218062              0.769918             True               False   \n",
       "2        -0.218062             -0.031432             True               False   \n",
       "3        -0.218062             -0.031432             True               False   \n",
       "4        -0.218062             -0.832781             True               False   \n",
       "\n",
       "   Canada_SA3  International_SA3  US_SA3  Accessories_PC4  Books_PC4  \\\n",
       "0       False              False    True            False      False   \n",
       "1       False               True   False             True      False   \n",
       "2       False               True   False            False      False   \n",
       "3       False               True   False            False      False   \n",
       "4       False              False    True            False      False   \n",
       "\n",
       "   Electornics_PC4  Food_PC4  online services_PC4  No_SW5  Yes_SW5  \n",
       "0            False     False                 True   False     True  \n",
       "1            False     False                False   False     True  \n",
       "2             True     False                False   False     True  \n",
       "3            False     False                 True   False     True  \n",
       "4            False      True                False    True    False  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "91a2b098",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Card Tenure_months      float64\n",
      "Trans_value             float64\n",
      "Items_transaction       float64\n",
      "LastTransaction         float64\n",
      "Lastflagedvalue         float64\n",
      "LastMonthsTrans_Freq    float64\n",
      "Online only_SC2            bool\n",
      "Store + online_SC2         bool\n",
      "Canada_SA3                 bool\n",
      "International_SA3          bool\n",
      "US_SA3                     bool\n",
      "Accessories_PC4            bool\n",
      "Books_PC4                  bool\n",
      "Electornics_PC4            bool\n",
      "Food_PC4                   bool\n",
      "online services_PC4        bool\n",
      "No_SW5                     bool\n",
      "Yes_SW5                    bool\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(selected_df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b97f3fff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], dtype: int64)\n"
     ]
    }
   ],
   "source": [
    "z = selected_df.isnull().sum()\n",
    "print(z[z > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7262724f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Card Tenure_months', 'Trans_value', 'Items_transaction', 'LastTransaction', 'Lastflagedvalue', 'LastMonthsTrans_Freq', 'Online only_SC2', 'Store + online_SC2', 'Canada_SA3', 'International_SA3', 'US_SA3', 'Accessories_PC4', 'Books_PC4', 'Electornics_PC4', 'Food_PC4', 'online services_PC4', 'No_SW5', 'Yes_SW5']\n"
     ]
    }
   ],
   "source": [
    "num_type = ['bool', 'float64']    \n",
    "df_num_final = selected_df.select_dtypes(include = num_type)\n",
    "\n",
    "predictor_vars = list(df_num_final)\n",
    "print(predictor_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c4d5c9a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The AUC of Card Tenure_months is 0.6783597431553071\n",
      "The AUC of Trans_value is 0.677623737036333\n",
      "The AUC of Items_transaction is 0.7146872943675286\n",
      "The AUC of LastTransaction is 0.5891427936972726\n",
      "The AUC of Lastflagedvalue is 0.5330136156793276\n",
      "The AUC of LastMonthsTrans_Freq is 0.6714938167289458\n",
      "The AUC of Online only_SC2 is 0.5646654334207144\n",
      "The AUC of Store + online_SC2 is 0.5646654334207144\n",
      "The AUC of Canada_SA3 is 0.5284790740242395\n",
      "The AUC of International_SA3 is 0.7627954913130179\n",
      "The AUC of US_SA3 is 0.7343164172887784\n",
      "The AUC of Accessories_PC4 is 0.5331105210432784\n",
      "The AUC of Books_PC4 is 0.578779073961511\n",
      "The AUC of Electornics_PC4 is 0.5788243013435856\n",
      "The AUC of Food_PC4 is 0.5554055189951868\n",
      "The AUC of online services_PC4 is 0.5777021792572548\n",
      "The AUC of No_SW5 is 0.6175470323999932\n",
      "The AUC of Yes_SW5 is 0.6175470323999932\n"
     ]
    }
   ],
   "source": [
    "for var in predictor_vars:\n",
    "    fpr, tpr, thresholds = roc_curve(np.array(Y), np.array(df_num_final[var]))\n",
    "    if auc(fpr, tpr) + 0 > 0.5:\n",
    "        z = auc(fpr, tpr) + 0\n",
    "    else:       \n",
    "        z = 1 - auc(fpr, tpr) + 0\n",
    "    print(f'The AUC of {var} is {z}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccc64e08",
   "metadata": {},
   "source": [
    "The **single AUCs** are all greater than **`0.5`**! This is indication that there feature are good predictors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e57bc8c",
   "metadata": {},
   "source": [
    "### Training the models\n",
    "I will train a `LogisticRegression` and an `AdaBoostClassifier` model and compare their performances.\n",
    "I will use **Cummulative Accuracy Profile - (CAP)** to evaluate the models robustness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f1c59706",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_num_final\n",
    "y = Y\n",
    "X_train, X_test, y_train, y_test = train_test_split(X , y, test_size = 0.3, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8047dd02",
   "metadata": {},
   "source": [
    "### Determine the class weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fb658583",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils.class_weight import compute_class_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7d33e7d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 0.6497890295358649, 1: 2.1690140845070425}\n"
     ]
    }
   ],
   "source": [
    "class_weights = compute_class_weight(class_weight = \"balanced\",\n",
    "                                     classes = np.unique(y_train),\n",
    "                                     y = y_train)\n",
    "class_weights = dict(zip(np.unique(y_train), class_weights))\n",
    "print(class_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e05ac1f1",
   "metadata": {},
   "source": [
    "#### Another approach could be using the entire data to compute percentage representation of each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "463d65c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# total = len(y_train)\n",
    "# one_counts = sum(y_train)\n",
    "# zero_counts = total - one_counts\n",
    "# print(f'Class 1 counts: {one_counts}\\nClass 0 counts: {zero_counts}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7c1cff9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# weight_class_1 = round((zero_counts / total) * 100, 2)\n",
    "# weight_class_0 = round((one_counts / total) * 100, 2)\n",
    "# print(f'Weight Class 1: {weight_class_1}\\nWeight Class 0: {weight_class_0}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2222adb",
   "metadata": {},
   "source": [
    "### LogisticRegression Model\n",
    "#### Cross Validation Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2a516a27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logistic regression score: 0.7979850241287146\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#log_reg = linear_model.LogisticRegression(C = 0.01, penalty = 'l2', class_weight = {0: 0.2, 1: 0.8})\n",
    "log_reg = linear_model.LogisticRegression(C = 0.01, penalty = 'l2', class_weight = class_weights)\n",
    "log_reg.fit(X_train, y_train)\n",
    "scores = cross_val_score(log_reg, X, y)\n",
    "print(f'logistic regression score: {scores.mean()}\\n') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "092451bf",
   "metadata": {},
   "source": [
    "### The Predictions (LogisticRegression Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ad8a9ded",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 probabilities:\n",
      "[0.67282954 0.17576788 0.39709653 0.35605528 0.53169716 0.95398039\n",
      " 0.51808657 0.21209738 0.98942983 0.77553172]\n"
     ]
    }
   ],
   "source": [
    "y_pred = log_reg.predict_proba(X_test,)[:, 1]                    \n",
    "print(f'First 10 probabilities:\\n{y_pred[0:10]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b74d73be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (logistic Regression) =  80.59%\n"
     ]
    }
   ],
   "source": [
    "acc_lg = metrics.accuracy_score(y_test, np.where(y_pred > 0.5, 1, 0))\n",
    "print(f'Accuracy (logistic Regression) = {acc_lg * 100: .2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6facfea9",
   "metadata": {},
   "source": [
    "#### Helper Function to plot ROC curve "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "35ce0e74",
   "metadata": {},
   "outputs": [],
   "source": [
    "def auc_curve(y_test, y_pred):\n",
    "    plt.plot([0,1], [0,1], 'r--')\n",
    "\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "\n",
    "    label = 'Model AUC:' + ' {0:.2f}'.format(roc_auc)\n",
    "    plt.plot(fpr, tpr, c = 'g', label = label, linewidth = 2)\n",
    "    plt.xlabel('False Positive Rate', fontsize = 14)\n",
    "    plt.ylabel('True Positive Rate', fontsize = 14)\n",
    "    plt.title('Receiver Operating Characteristic', fontsize = 14)\n",
    "    plt.legend(loc = 'lower right', fontsize = 12)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fd5f870",
   "metadata": {},
   "source": [
    "### Receiver Operating Characteristic (ROC) Curve - Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dc5ab044",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC for logistic regression model: 0.899\n"
     ]
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred)             \n",
    "roc_auc = auc(fpr, tpr)               \n",
    "print(f'AUC for logistic regression model: {round(roc_auc, 3)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1b37bcd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "auc_curve(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73c226a2",
   "metadata": {},
   "source": [
    "### Cummulative Accuracy Profile (CAP) Curve - Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "793a738c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage: 94.26%\n",
      "\n",
      "    Interpret this percentage as follows:\n",
      "    \n",
      "    1. Less than 60%: Useless Model\n",
      "    2. 60% - 70%: Poor Model\n",
      "    3. 70% - 80%: Good Model\n",
      "    4. 80% - 90%: Very Good Model\n",
      "    5. More than 90%: Too Good to be True.\n",
      "\n",
      "    In the fifth case, one must always check for overfitting.\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "cap_curve(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "118480d9",
   "metadata": {},
   "source": [
    "### Confusion Matrix - Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0905a0b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, np.where(y_pred > 0.5, 1, 0), labels = [1, 0])\n",
    "cm_df = pd.DataFrame(cm, index = [1, 0], columns = ['Predict 1', 'Predict 0'])\n",
    "\n",
    "plt.figure(figsize = (7, 5))\n",
    "sns.heatmap(cm_df, annot = True, fmt = 'g')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d639a9c",
   "metadata": {},
   "source": [
    "### Most Important Features - Odds Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1886093b",
   "metadata": {},
   "outputs": [],
   "source": [
    "odds_ratio = np.exp(log_reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f329adf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "odds_ratio_df = pd.DataFrame(odds_ratio, columns = list(df_num_final)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e0a4ecb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "odds_ratio_df.columns = ['odds']\n",
    "data = odds_ratio_df.sort_values(by = 'odds', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f6badb54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data = data.T)\n",
    "plt.title('Important Predictors')\n",
    "plt.ylabel('Odds')\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9f69419",
   "metadata": {},
   "source": [
    "#### Note:\n",
    "The **confusion matrix** clearly demonstrates the tradeoff of using **Weighted Logistic Regression**! The model can detect more class 1 compared to **vanila Logistic Regression**, without class imbalance taken into consideration. However, the model also misclassified more of class 0 (none fraudulent transactions) as class 1 (fraudulent)!\n",
    "\n",
    "### Determine Class Weights using Grid Search "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "dac5c531",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e690970b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.linear_model import LogisticRegression'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.9002235319292985 using {'class_weight': {0: 1, 1: 3}}\n",
      "0.9002235319292985 (0.00808460058650191) with: {'class_weight': {0: 1, 1: 3}}\n",
      "0.9001656297971846 (0.0081024044265105) with: {'class_weight': {0: 1, 1: 4}}\n",
      "0.9001380246220133 (0.008089700425484568) with: {'class_weight': {0: 0.2, 1: 0.8}}\n",
      "0.9002092412884574 (0.008074294631836268) with: {'class_weight': {0: 0.25, 1: 0.75}}\n",
      "0.8997951261316213 (0.008180249798318552) with: {'class_weight': {0: 1, 1: 10}}\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "\n",
    "class_weights_cv = [{0:1,1:3}, {0:1,1:4}, {0: 0.2, 1: 0.8}, {0:.25,1:.75}, {0:1,1:10}]\n",
    "param_grid = dict(class_weight = class_weights_cv)\n",
    "cv = RepeatedStratifiedKFold(n_splits = 10, n_repeats = 3, random_state = 1)\n",
    "grid = GridSearchCV(estimator = model, param_grid = param_grid, n_jobs=-1, cv=cv, scoring='roc_auc')\n",
    "grid_result = grid.fit(X, y)\n",
    "\n",
    "print(f'Best: {grid_result.best_score_} using {grid_result.best_params_}')\n",
    "# report all configurations\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(f'{mean} ({stdev}) with: {param}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ae11b35f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logistic regression score: 0.8079354125946082\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class_weights = grid_result.best_params_['class_weight']\n",
    "log_reg = linear_model.LogisticRegression(C = 0.01, penalty = 'l2', class_weight = class_weights)\n",
    "log_reg.fit(X_train, y_train)\n",
    "scores = cross_val_score(log_reg, X, y)\n",
    "print(f'logistic regression score: {scores.mean()}\\n') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5d97552a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (logistic Regression) =  81.56%\n"
     ]
    }
   ],
   "source": [
    "y_pred = log_reg.predict_proba(X_test,)[:, 1] \n",
    "acc_lg = metrics.accuracy_score(y_test, np.where(y_pred > 0.5, 1, 0))\n",
    "print(f'Accuracy (logistic Regression) = {acc_lg * 100: .2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a9e5b970",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "auc_curve(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6e259d3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage: 94.09%\n",
      "\n",
      "    Interpret this percentage as follows:\n",
      "    \n",
      "    1. Less than 60%: Useless Model\n",
      "    2. 60% - 70%: Poor Model\n",
      "    3. 70% - 80%: Good Model\n",
      "    4. 80% - 90%: Very Good Model\n",
      "    5. More than 90%: Too Good to be True.\n",
      "\n",
      "    In the fifth case, one must always check for overfitting.\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "cap_curve(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "df0dd052",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, np.where(y_pred > 0.5, 1, 0), labels = [1, 0])\n",
    "cm_df = pd.DataFrame(cm, index = [1, 0], columns = ['Predict 1', 'Predict 0'])\n",
    "\n",
    "plt.figure(figsize = (7, 5))\n",
    "sns.heatmap(cm_df, annot = True, fmt = 'g')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d1cc3fb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}