Transaction Fraud Detection (ML for Classification).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "37cdce29",
   "metadata": {},
   "source": [
    "<h1> Transaction Fraud Detection (Machine Learning for Classification) </h1>\n",
    "<br> \n",
    "<br> \n",
    "\n",
    "**This project utilizes machine learning algorithms for classification, particularly for transaction fraud detection. The dataset presented here consists of thousands of transaction records, in fact more than 6 million transactions, and key information about each transaction, including the sender's and recipient's account balances before and after the transaction, the amount of money transferred, and whether the transaction was in fact fraudulent or not. The aim of this project is to build a machine learning classification model that can accurately detect transaction fraud. Further, it focuses on both prediction efficiency and model interpretability, and tries to leverage both. This project was originally completed as per required for the final project of my course, 'Supervised Machine Learning: Classification', offered online by IBM. Overall, it displays a wide variety of data analysis tasks, classification algorithms, and model interpretation techniques.**\n",
    "\n",
    "<br>\n",
    "\n",
    "**The dataset being used here was taken from Kaggle.com, a popular website for finding and publishing datasets. You can quickly access it by clicking [here](https://www.kaggle.com/datasets/rupakroy/online-payments-fraud-detection-dataset). It's a huge dataset that presents, as mentioned, thousands and thousands of monetary transactions and whether or not they were deemed fraud by the relevant authority, which will be utilized as material for developing and training the classification models.** <br> \n",
    "<br> \n",
    "\n",
    "**You can view each coloumn and its description in the table below:** <br><br>  \n",
    "\n",
    "| **Variable**      | **Description**                                                                                         |\n",
    "| :-----------------| :------------------------------------------------------------------------------------------------------ |\n",
    "| **step**          | Represents a unit of time where 1 step = 1 hour                                                         |\n",
    "| **type**          | Type of online transaction (Transfer, Payment, Debit, Cash-in, Cash-out)                                |\n",
    "| **amount**        | The amount of money in a transaction                                                                    |\n",
    "| **nameOrig**      | Name of the sender                                                                                      |\n",
    "| **oldbalanceOrg** | The sender's balance before the transaction                                                             |\n",
    "| **newbalanceOrig**| The sender's balance after the transaction                                                              |\n",
    "| **nameDest**      | Name of the recipient                                                                                   |\n",
    "| **oldbalanceDest**| The recipient's balance before the transaction                                                          |\n",
    "| **newbalanceDest**| The recipient's balance after the transaction                                                           |\n",
    "| **isFraud**       | Specifies if a transaction is fraud (1) or not fraud (0)                                                |\n",
    "| **isFlaggedFraud**     | Indicates if a transaction was flagged as fraud (1) or not (0)                                     |\n",
    "\n",
    "<br>\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec47ac1b",
   "metadata": {},
   "source": [
    "**As stated, the task here is to develop a classification model that can accurately detect transaction fraud. The data is first prepared, statistically analyzed, and preprocessed, before being used to train and test different classifiers. Note however, given that fraudulent cases, as we will see, make up less than 1% of all transactions, thus resulting in an extremely skewed or imbalanced dataset, different techniques are applied to deal with this problem first before developing the classifiers. Particularly, oversampling techniques were used to try and generate more data points for the minority class (fraud), by which way to balance the classes in the dataset to ensure that the classifiers are not biased during learning and can indeed predict transaction fraud reliably. Along the way, different classifiers are trained, tested and their parameters tuned to try to identify the best one for the data. Also, a subset of data was reserved for testing or out-of-sample evaluation to estimate how each classifier is likely to perform in the real world with novel datasets, unseen during the model training. Finally, after selecting the best classifier, model interpretation techniques are applied, such as permutation feature importance and partial dependence plots, in order to better understand how the final classifier was making its predictions, or, that is, based on what factors it was classifying one transaction as fraudulent and another as not. The most impactful features are identified and analyzed in more detail using a partial dependency plot, which illustrates through visualization the nature and direction of the relationship between that given feature and the likelihood of fraud as discerned by the model.** <br>\n",
    "\n",
    "<br>\n",
    "\n",
    "**Overall, the project is broken down into five parts: <br>\n",
    "&emsp; 1) Reading and Inspecting the Data <br>\n",
    "&emsp; 2) Exploratory Data Analysis <br>\n",
    "&emsp; 3) Data Preprocessing <br>\n",
    "&emsp; 4) Model Development, Tuning, and Evaluation <br>\n",
    "&emsp; 5) Model Interpretation** <br>\n",
    "\n",
    "<br>\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "008fee3b",
   "metadata": {},
   "source": [
    "**Installing and Importing Python Modules**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c39f745",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#If you're using the executable notebook version, please run this cell first \n",
    "#to install the necessary Python libraries for the task \n",
    "!pip install numpy \n",
    "!pip install pandas \n",
    "!pip install matplotlib \n",
    "!pip install seaborn \n",
    "!pip install scikit-learn\n",
    "!pip install imbalanced-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6742309e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the modules for use \n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE, BorderlineSMOTE, ADASYN\n",
    "from sklearn.inspection import permutation_importance, PartialDependenceDisplay\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedShuffleSplit\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, fbeta_score, precision_recall_fscore_support, roc_curve, roc_auc_score, confusion_matrix\n",
    "from sklearn.ensemble import RandomForestClassifier, StackingClassifier\n",
    "from sklearn.kernel_approximation import Nystroem\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f96c34e5",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2dd51b2",
   "metadata": {},
   "source": [
    "**Defining functions for model evaluation and interpretation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b702e4a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining functions to compute and report error scores\n",
    "def error_scores(ytest, ypred, classes):\n",
    "    error_metrics = {\n",
    "        'Accuracy': accuracy_score(ytest, ypred),\n",
    "        'Precision': precision_score(ytest, ypred, average=None),\n",
    "        'Recall': recall_score(ytest, ypred, average=None),\n",
    "        'F5': fbeta_score(ytest, ypred, beta=5, average=None) }\n",
    "\n",
    "    return pd.DataFrame(error_metrics, index=classes).apply(lambda x:round(x,2)).T\n",
    "\n",
    "def error_scores_dict(ytest, ypred, strategy):\n",
    "    #create empty dict for storing results \n",
    "    error_dict = {}\n",
    "    \n",
    "    #specify type of result \n",
    "    error_dict['Strategy'] = strategy\n",
    "    \n",
    "    #Get accuracy score\n",
    "    error_dict['Accuracy'] = round(accuracy_score(ytest, ypred),2)\n",
    "    \n",
    "    #Get Precision, recall, F-beta scores\n",
    "    precision, recall, f_beta, _ = precision_recall_fscore_support(ytest, ypred, beta=5, average='binary')\n",
    "    #store results\n",
    "    error_dict['Precision'], error_dict['Recall'], error_dict['F5'] = round(recall,2), round(precision,2), round(f_beta,2) \n",
    "    \n",
    "    return error_dict\n",
    "\n",
    "\n",
    "#Defining a function to plot out confusion matrix\n",
    "def plot_cm(ytest, ypred, classes):\n",
    "    cm = confusion_matrix(ytest, ypred)\n",
    "    fig,ax = plt.subplots()\n",
    "    ax = sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', annot_kws={\"size\": 20, \"weight\": \"bold\"})\n",
    "    labels = classes\n",
    "    ax.set_xticklabels(labels)\n",
    "    ax.set_yticklabels(labels)\n",
    "    ax.set_xlabel('Prediction', fontsize=15)\n",
    "    ax.set_ylabel('Actual', fontsize=15)\n",
    "    plt.show()\n",
    "\n",
    "    \n",
    "#Defining a function to plot the ROC curve\n",
    "def plot_ROC_curve(model, xtest, ytest):\n",
    "    #Get model's predicted probabilities\n",
    "    y_prob = model.predict_proba(xtest)\n",
    "    y_pred = model.predict(xtest)\n",
    "    #Get false positive and true positive rates\n",
    "    false_pos_rate, true_pos_rate, thresholds = roc_curve(ytest, y_prob[:,1])\n",
    "    #Get best auc score \n",
    "    auc_best = roc_auc_score(ytest, y_pred, average=None)\n",
    "\n",
    "    #Plot the ROC curve\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(false_pos_rate, true_pos_rate, linewidth=2.5)\n",
    "    plt.fill_between(false_pos_rate, true_pos_rate, alpha=0.1)\n",
    "    \n",
    "    #Plot the diagonal chance line \n",
    "    ax.plot([0, 1], [0, 1], ls='--', color='black', lw=.3) \n",
    "    plt.annotate('AUC=0.5', xy=(0.5, 0.5), xytext=(0.6, 0.3), \n",
    "                 arrowprops=dict(facecolor='black', headwidth=8, width=2.5, shrink=0.05))\n",
    "    \n",
    "    #Plot the best auc score\n",
    "    ax.plot(auc_best, marker='o', color='r')\n",
    "    plt.annotate(f'AUC={round(auc_best,2)}', xy=(0,auc_best), xytext=(0.1,0.8), \n",
    "                 arrowprops=dict(facecolor='gray', headwidth=7, width=2, shrink=0.15))\n",
    "    \n",
    "    #Set title and labels \n",
    "    ax.set(title='ROC curve',\n",
    "           xlabel='False Positive Rate',\n",
    "           ylabel='True Positive Rate')\n",
    "    \n",
    "    #add grid \n",
    "    ax.grid(True)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    \n",
    "#Defining a function to plot ROC curve for multiple models \n",
    "def plot_ROC_curve_multiple(xtest, xtest_svc, ytest, estimators):\n",
    "    #Get ROC curve for each model\n",
    "    LR_fpr, LR_tpr, thresold = roc_curve(ytest, estimators[0].predict(xtest))\n",
    "    KNN_fpr, KNN_tpr, threshold = roc_curve(ytest, estimators[1].predict(xtest))\n",
    "    SVC_fpr, SVC_tpr, threshold = roc_curve(ytest, estimators[2].predict(xtest_svc))\n",
    "    RF_fpr, RF_tpr, threshold = roc_curve(ytest, estimators[3].predict(xtest))\n",
    "    \n",
    "    #Obtain figure and set title and labels \n",
    "    plt.figure(figsize=(13,7))\n",
    "    plt.title('ROC Curve per classifier', fontsize=16)\n",
    "    plt.xlabel('False Positive Rate', fontsize=14)\n",
    "    plt.ylabel('True Positive Rate', fontsize=14)\n",
    "    \n",
    "    #Plot the ROC curve for each model \n",
    "    plt.plot(LR_fpr, LR_tpr, label='Logistic Regression classifier score: {:.2f}'.format(roc_auc_score(ytest, estimators[0].predict(xtest))))\n",
    "    plt.plot(KNN_fpr, KNN_tpr, label='KNN classifier score: {:.2f}'.format(roc_auc_score(ytest, estimators[1].predict(xtest))))\n",
    "    plt.plot(SVC_fpr, SVC_tpr, label='Kernel SVM classifier score: {:.2f}'.format(roc_auc_score(ytest, estimators[2].predict(xtest_svc))))\n",
    "    plt.plot(RF_fpr, RF_tpr, label='Random Forest classifier score: {:.2f}'.format(roc_auc_score(ytest, estimators[3].predict(xtest))))    \n",
    "\n",
    "    #plot the diagonal chance line (line of no discrimination)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.annotate('AUC=0.5', xy=(0.5, 0.5), xytext=(0.6, 0.3), fontsize=13, arrowprops=dict(facecolor='black', headwidth=8, width=2.5, shrink=0.07))\n",
    "    plt.annotate('Line of no discrimination', xy=(0.4,0.45), xytext=(0.4,0.45), fontsize=12, rotation=25)\n",
    "    \n",
    "    #set axes and grid \n",
    "    plt.axis([-0.01, 1, 0, 1])\n",
    "    plt.grid(True)\n",
    "    \n",
    "    #add legend \n",
    "    plt.legend()\n",
    "    \n",
    "    #display plot \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "#Defining a function to visualize feature importances via box plot (for permutation feature importance)\n",
    "def visualize_feature_importances(importance_array, cols):\n",
    "    # Sort the array based on mean value\n",
    "    sorted_idx = importance_array.importances_mean.argsort()      #sorts array from lowest to highest and returns their indices\n",
    "\n",
    "    # Visualize the feature importances using boxplot\n",
    "    fig, ax = plt.subplots()     \n",
    "    fig.tight_layout()\n",
    "\n",
    "    #create box plot \n",
    "    ax.boxplot(importance_array.importances[sorted_idx].T,\n",
    "               labels=cols[sorted_idx], \n",
    "               vert=False)    #horizontal box plot\n",
    "    \n",
    "    #assign title \n",
    "    ax.set_title(\"Permutation Importances (training set)\")\n",
    "    #display figure \n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97b66294",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91a22587",
   "metadata": {},
   "source": [
    "**Defining a random state for reproducible results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8f77195a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#specify random seed\n",
    "rs = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "659fb5bd",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93a3dda6",
   "metadata": {},
   "source": [
    "## Part One: Reading and Inspecting the Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26209112",
   "metadata": {},
   "source": [
    "**Loading and reading the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e59397de",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Access the data file\n",
    "df = pd.read_csv(\"Online Transaction Fraud.csv\")\n",
    "\n",
    "#drop unnecessary columns \n",
    "df = df.drop(['isFlaggedFraud', 'nameOrig', 'nameDest'], axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a12f24bb",
   "metadata": {},
   "source": [
    "**Inspecting the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1f7ac223",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of coloumns: 8\n",
      "Number of rows: 6362620\n"
     ]
    }
   ],
   "source": [
    "#inspect data shape\n",
    "shape = df.shape\n",
    "print('Number of coloumns:', shape[1])\n",
    "print('Number of rows:', shape[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7f989e59",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>9839.64</td>\n",
       "      <td>170136.00</td>\n",
       "      <td>160296.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>1864.28</td>\n",
       "      <td>21249.00</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>181.00</td>\n",
       "      <td>181.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>181.00</td>\n",
       "      <td>181.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21182.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>41554.00</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>7817.71</td>\n",
       "      <td>53860.00</td>\n",
       "      <td>46042.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>7107.77</td>\n",
       "      <td>183195.00</td>\n",
       "      <td>176087.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>7861.64</td>\n",
       "      <td>176087.23</td>\n",
       "      <td>168225.59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>4024.36</td>\n",
       "      <td>2671.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>DEBIT</td>\n",
       "      <td>5337.77</td>\n",
       "      <td>41720.00</td>\n",
       "      <td>36382.23</td>\n",
       "      <td>41898.0</td>\n",
       "      <td>40348.79</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   step      type    amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
       "0     1   PAYMENT   9839.64      170136.00       160296.36             0.0   \n",
       "1     1   PAYMENT   1864.28       21249.00        19384.72             0.0   \n",
       "2     1  TRANSFER    181.00         181.00            0.00             0.0   \n",
       "3     1  CASH_OUT    181.00         181.00            0.00         21182.0   \n",
       "4     1   PAYMENT  11668.14       41554.00        29885.86             0.0   \n",
       "5     1   PAYMENT   7817.71       53860.00        46042.29             0.0   \n",
       "6     1   PAYMENT   7107.77      183195.00       176087.23             0.0   \n",
       "7     1   PAYMENT   7861.64      176087.23       168225.59             0.0   \n",
       "8     1   PAYMENT   4024.36        2671.00            0.00             0.0   \n",
       "9     1     DEBIT   5337.77       41720.00        36382.23         41898.0   \n",
       "\n",
       "   newbalanceDest  isFraud  \n",
       "0            0.00        0  \n",
       "1            0.00        0  \n",
       "2            0.00        1  \n",
       "3            0.00        1  \n",
       "4            0.00        0  \n",
       "5            0.00        0  \n",
       "6            0.00        0  \n",
       "7            0.00        0  \n",
       "8            0.00        0  \n",
       "9        40348.79        0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#preview first 10 entries \n",
    "df.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7ccb253",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8de61e38",
   "metadata": {},
   "source": [
    "As we can see, we have 8 coloumns in total and over 6 million rows, each row corresponding to one online transaction. Further, we have our coloumns specifying the characteristics of a given transaction, and whether that transaction was fraud or not. Note however, given that the dataset is particularly gigantic, and given that some of the classification models that will be employed are computationally costly, it will be very challenging for me to analyze and process the dataset in its entirety, especially with my current computational resources. Thus, due to limited computational resources, I will extract only a subset of data to work with (100,000 entries). <br>\n",
    "To ensure that this doesn't impact the analysis and modeling in any significant way, I will take careful measures when extracting the data. Particularly, I will use stratified shuffle splitting to extract the subset of data, which shuffles the data and returns data subsets that have the same class distribution as the original set. I will set the size for the data subset to be 100,000 rows, which is still a large enough amount of data to analyze and use for modeling. To make sure that the smaller subset resembles the same distribution as the original set, I will visualize the distribution before and after the data split using a histogram.    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f25e16c",
   "metadata": {},
   "source": [
    "Data distribution before data splitting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1c1aae94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a histogram for each column separately \n",
    "fig,axes = plt.subplots(ncols=8, figsize=(20,8))\n",
    "for col,ax in zip(df.columns, axes):\n",
    "    ax.hist(df[col])\n",
    "    ax.set_title(col)\n",
    "    if col=='type':\n",
    "        ax.set_xticklabels(ax.get_xticklabels(), rotation=90)        \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ef03904",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "138b9f7a",
   "metadata": {},
   "source": [
    "**Data Sampling** <br> \n",
    "Using stratified shuffle splitting to extract a smaller sample of 100,000 entries only whilst simultaneously maintaining the same class distribution as the original \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "de6bd8d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of coloumns: 8\n",
      "Number of rows: 100000\n"
     ]
    }
   ],
   "source": [
    "#Identify the target coloumn \n",
    "target = 'isFraud'\n",
    "#stratified sampling and obtaining a new, smaller dataframe \n",
    "sample_inx, _ = next(StratifiedShuffleSplit(n_splits=1, train_size=100000, random_state=rs).split(df[df.columns.drop(target)], df[target]))\n",
    "x_data, y_data = df.loc[sample_inx, df.columns[:-1]], df.loc[sample_inx, df.columns[-1:]]\n",
    "df = pd.concat([x_data, y_data], axis=1).reset_index(drop=True)\n",
    "\n",
    "#report data shape again\n",
    "shape = df.shape\n",
    "print('Number of coloumns:', shape[1])\n",
    "print('Number of rows:', shape[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "635e16f1",
   "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>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>163</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>148753.50</td>\n",
       "      <td>24482.00</td>\n",
       "      <td>173235.50</td>\n",
       "      <td>315720.90</td>\n",
       "      <td>166967.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>10180.55</td>\n",
       "      <td>8680.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>129</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>14806.33</td>\n",
       "      <td>25871.46</td>\n",
       "      <td>11065.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>181</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>38336.09</td>\n",
       "      <td>10445.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>163</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>188096.77</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>391329.68</td>\n",
       "      <td>579426.45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>228</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>231247.60</td>\n",
       "      <td>49900.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>231247.60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>283</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>28778.92</td>\n",
       "      <td>14316.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>106610.16</td>\n",
       "      <td>135389.09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>139</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>153207.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>618991.42</td>\n",
       "      <td>772198.41</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>311</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>13299.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>43</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>65588.77</td>\n",
       "      <td>331946.19</td>\n",
       "      <td>397534.96</td>\n",
       "      <td>1014731.07</td>\n",
       "      <td>949142.30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step      type     amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
       "0   163   CASH_IN  148753.50       24482.00       173235.50       315720.90   \n",
       "1    15   PAYMENT   10180.55        8680.01            0.00            0.00   \n",
       "2   129   PAYMENT   14806.33       25871.46        11065.14            0.00   \n",
       "3   181   PAYMENT   38336.09       10445.00            0.00            0.00   \n",
       "4   163  CASH_OUT  188096.77           0.00            0.00       391329.68   \n",
       "5   228  CASH_OUT  231247.60       49900.00            0.00            0.00   \n",
       "6   283  CASH_OUT   28778.92       14316.00            0.00       106610.16   \n",
       "7   139  CASH_OUT  153207.00           0.00            0.00       618991.42   \n",
       "8   311   PAYMENT   13299.61           0.00            0.00            0.00   \n",
       "9    43   CASH_IN   65588.77      331946.19       397534.96      1014731.07   \n",
       "\n",
       "   newbalanceDest  isFraud  \n",
       "0       166967.40        0  \n",
       "1            0.00        0  \n",
       "2            0.00        0  \n",
       "3            0.00        0  \n",
       "4       579426.45        0  \n",
       "5       231247.60        0  \n",
       "6       135389.09        0  \n",
       "7       772198.41        0  \n",
       "8            0.00        0  \n",
       "9       949142.30        0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#preview first 10 entries\n",
    "df.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "976f6714",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fae62f10",
   "metadata": {},
   "source": [
    "As seen, now we have a smaller dataset with only 100,000 online transactions. Now let's look at the data distribution once again to make sure it resembles the original dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aadbc451",
   "metadata": {},
   "source": [
    "Data distribution after data splitting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "415162c2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a histogram for each column separately \n",
    "fig,axes = plt.subplots(ncols=8, figsize=(20,8))\n",
    "for col,ax in zip(df.columns, axes):\n",
    "    ax.hist(df[col])\n",
    "    ax.set_title(col)\n",
    "    if col=='type':\n",
    "        ax.set_xticklabels(ax.get_xticklabels(), rotation=90)     \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3f386ea",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e2c6216",
   "metadata": {},
   "source": [
    "Indeed, as illustrated by the histograms above, the data sampling was sucessful. Now we have a smaller dataset that has much the same distribution and characteristics as the original, with the only difference between the two graphs being the frequency counts given the differences in size. That way we are quite sure than no important data is lost. Next I will move to exploratory data analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f675b9f9",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4b4dab3",
   "metadata": {},
   "source": [
    "## Part Two: Exploratory Data Analysis\n",
    "<br>\n",
    "\n",
    "**In this section, I will explore the data in more detail to make sure there are no missing or null entries, check the data type of each variable, and, importantly, get a better understanding of the important variables in the dataset and how they relate to our main question of what makes a transaction fraudulent.**\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc81576",
   "metadata": {},
   "source": [
    "**Descriptive Statistics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "34c27ce7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>step</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>242.98</td>\n",
       "      <td>141.78</td>\n",
       "      <td>1.00</td>\n",
       "      <td>156.00</td>\n",
       "      <td>238.00</td>\n",
       "      <td>334.00</td>\n",
       "      <td>7.400000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>179709.72</td>\n",
       "      <td>576598.21</td>\n",
       "      <td>0.28</td>\n",
       "      <td>13335.11</td>\n",
       "      <td>74516.04</td>\n",
       "      <td>209161.04</td>\n",
       "      <td>5.139181e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>820600.45</td>\n",
       "      <td>2851747.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14210.50</td>\n",
       "      <td>107276.50</td>\n",
       "      <td>3.893942e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>841607.63</td>\n",
       "      <td>2887882.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>145449.68</td>\n",
       "      <td>3.894623e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>1096373.43</td>\n",
       "      <td>3428704.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>129868.85</td>\n",
       "      <td>943428.41</td>\n",
       "      <td>3.023942e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>newbalanceDest</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>1221523.22</td>\n",
       "      <td>3692353.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>211519.66</td>\n",
       "      <td>1115671.53</td>\n",
       "      <td>3.025609e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isFraud</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count        mean         std   min       25%        50%  \\\n",
       "step            100000.0      242.98      141.78  1.00    156.00     238.00   \n",
       "amount          100000.0   179709.72   576598.21  0.28  13335.11   74516.04   \n",
       "oldbalanceOrg   100000.0   820600.45  2851747.36  0.00      0.00   14210.50   \n",
       "newbalanceOrig  100000.0   841607.63  2887882.97  0.00      0.00       0.00   \n",
       "oldbalanceDest  100000.0  1096373.43  3428704.69  0.00      0.00  129868.85   \n",
       "newbalanceDest  100000.0  1221523.22  3692353.29  0.00      0.00  211519.66   \n",
       "isFraud         100000.0        0.00        0.04  0.00      0.00       0.00   \n",
       "\n",
       "                       75%           max  \n",
       "step                334.00  7.400000e+02  \n",
       "amount           209161.04  5.139181e+07  \n",
       "oldbalanceOrg    107276.50  3.893942e+07  \n",
       "newbalanceOrig   145449.68  3.894623e+07  \n",
       "oldbalanceDest   943428.41  3.023942e+08  \n",
       "newbalanceDest  1115671.53  3.025609e+08  \n",
       "isFraud               0.00  1.000000e+00  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get statistical summary of data \n",
    "df.describe().apply(lambda x:round(x,2)).T\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5b31b57",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbe9bc8f",
   "metadata": {},
   "source": [
    "**Checking the data type**\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2da9839a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 8 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   step            100000 non-null  int64  \n",
      " 1   type            100000 non-null  object \n",
      " 2   amount          100000 non-null  float64\n",
      " 3   oldbalanceOrg   100000 non-null  float64\n",
      " 4   newbalanceOrig  100000 non-null  float64\n",
      " 5   oldbalanceDest  100000 non-null  float64\n",
      " 6   newbalanceDest  100000 non-null  float64\n",
      " 7   isFraud         100000 non-null  int64  \n",
      "dtypes: float64(5), int64(2), object(1)\n",
      "memory usage: 6.1+ MB\n"
     ]
    }
   ],
   "source": [
    "#check data types and values count\n",
    "df.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c64777a",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "99a21e67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of missing entries per coloumn:\n",
      "step              0\n",
      "type              0\n",
      "amount            0\n",
      "oldbalanceOrg     0\n",
      "newbalanceOrig    0\n",
      "oldbalanceDest    0\n",
      "newbalanceDest    0\n",
      "isFraud           0\n",
      "dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Making sure there are no missing or null entries \n",
    "print(f'Number of missing entries per coloumn:\\n{df.isnull().sum()}')\n",
    "print()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88b9d479",
   "metadata": {},
   "source": [
    "It seems that there are no missing entries in the data and all the data have the correct format."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cca38ee",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfe52810",
   "metadata": {},
   "source": [
    "**Identifying variables that need preprocessing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fe4f1f2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step (int64): 468\n",
      "type (object): 5\n",
      "amount (float64): 99573\n",
      "oldbalanceOrg (float64): 54156\n",
      "newbalanceOrig (float64): 43123\n",
      "oldbalanceDest (float64): 57379\n",
      "newbalanceDest (float64): 61581\n",
      "isFraud (int64): 2\n"
     ]
    }
   ],
   "source": [
    "#Get the total number of unique values for each variable (& data type)\n",
    "for col in df.columns:\n",
    "    print(f'{col} ({df[col].dtype}): {len(df[col].unique())}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "703f4af7",
   "metadata": {},
   "source": [
    "We can see all of our variables are continuous variables, except for the two variables: 'type', which specifies the type of transaction, and the target variable, 'isFraud'. Now I take a closer look at some of the important variables in the data and how they relate to the target variable.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8bf293a",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf5c820c",
   "metadata": {},
   "source": [
    "**Bivariate Analysis**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b7cad9",
   "metadata": {},
   "source": [
    "First, I will look at the relationship between the mode or type of money transaction and fraud to see which transaction type is most common with fraud cases, followed by examining the relationship between fraud and transaction amounts, and lastly, the relationship between fraud and the victim's account balance before and after the transaction as well as the relationship between fraud and the perpetrator's account balance before and after the transaction."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6cbfcc8",
   "metadata": {},
   "source": [
    "**Relationship between type of transaction and fraud**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3bd2eb7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check frequency of each type of transaction again\n",
    "df['type'].value_counts().plot(kind='bar', edgecolor='k')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d359ae5",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1066b3a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Show fraud count by type of transaction (looking at a maximum count of 1000 only for comparison)\n",
    "fraud_by_type = pd.crosstab(index=df['type'],columns=df['isFraud'])\n",
    "fraud_by_type.plot(kind='bar', ylim=[0,1000])\n",
    "plt.gcf().axes[0].yaxis.get_major_formatter().set_scientific(False)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12a59469",
   "metadata": {},
   "source": [
    "We can see that fraud transactions are almost exclusively transfer or cash-out transactions, with slightly more case of transfer transactions, but none of the other transaction types."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2cb00fa",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1fb258d",
   "metadata": {},
   "source": [
    "**Relationship between amount of money transferred and fraud**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b42ef4d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Get dataframe with only fraud cases\n",
    "df_fraud = df[df['isFraud']==1]\n",
    "\n",
    "#Sort dataframe by amount \n",
    "fraud_by_amount = df_fraud.sort_values(by=['amount'], ascending=False)\n",
    "\n",
    "#Create a histogram to show distribution of transaction amounts for fraud cases \n",
    "fraud_by_amount['amount'].plot(kind='hist', figsize=(10,5), edgecolor='black')\n",
    "plt.xlabel('Transaction Amount (currency unspecified)')\n",
    "plt.gcf().axes[0].xaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22829214",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69d990a5",
   "metadata": {},
   "source": [
    "We can gather from the histogram above that most fraud transactions are in the range of 0–1,000,000, making up approximately 85 cases, followed by the ranges from 1,000,000–2,000,000 and from 2,000,000 to 3,000,000, making up approximately 10 cases each, and then less and less for the ranges onwards. Although, strinkingly, there are cases of fraud that span up to the 10 million range! Unfortunately, information about the kind or value of the currency in this dataset was not disclosed by the publisher. Next I will look at the relationship between fraud and the sender's account balance before vs. after the transaction.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fdd32b5",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87f847b6",
   "metadata": {},
   "source": [
    "**Relationship between fraud and sender's balance before and after transaction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "938ce9b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Sort data by balance before transaction \n",
    "fraud_by_SenderBalanceBefore = df_fraud.sort_values(by=['oldbalanceOrg'], ascending=False)\n",
    "fraud_by_SenderBalanceBefore = fraud_by_SenderBalanceBefore['oldbalanceOrg']\n",
    "\n",
    "#Sort data by balance after transaction  \n",
    "fraud_by_SenderBalancAfter = df_fraud.sort_values(by=['newbalanceOrig'], ascending=False)\n",
    "fraud_by_SenderBalanceAfter = fraud_by_SenderBalancAfter['newbalanceOrig']\n",
    "\n",
    "#Create histogram for sender's balance before vs. after transaction \n",
    "fig,ax = plt.subplots(1,2, figsize=(12,6), sharex=True)\n",
    "ax[0].hist(fraud_by_SenderBalanceBefore, edgecolor='black')\n",
    "ax[1].hist(fraud_by_SenderBalanceAfter, edgecolor='black')\n",
    "fig.suptitle('Sender\\'s balance before vs. after fraud transaction', fontsize=16)\n",
    "ax[0].set_title('Balance before fraud transaction')\n",
    "ax[1].set_title('Balance after fraud transaction')\n",
    "plt.gcf().axes[0].xaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))\n",
    "plt.gcf().axes[1].xaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "653c10b6",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "303c07c8",
   "metadata": {},
   "source": [
    "As illustrated, on the left, we see the distribution of money in the fraud victims' accounts before the fraudulent transaction, with varying amounts spreading out across multiple ranges, from 0 to 10 million. Notably, however, in the great majority of cases, the victim's account before the transaction has a low balance, falling in the lowermost range of 0-1,000,000. On the right, we see the distribution of money in the victims' accounts after the transaction; and the results are striking: it seems that all fraud cases in the dataset (at least in this partition of the set) involve completely draining out the victim's account, leaving them penniless. Interestingly, this maps perfectly onto the 'amount' histogram above, in which we saw the transaction amounts in fraud cases exhibiting an almost identical distribution! Now let's examine the perpetrator's account balance before and after a fraud transaction as well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df2b7ff8",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94f799bc",
   "metadata": {},
   "source": [
    "**Relationship between fraud and recipient's balance before and after transaction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c66f1b07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Sort data by recipient's balance before transaction \n",
    "fraud_by_RecipientBalanceBefore = df_fraud.sort_values(by=['oldbalanceDest'], ascending=False)\n",
    "fraud_by_RecipientBalanceBefore = fraud_by_RecipientBalanceBefore['oldbalanceDest']\n",
    "\n",
    "#Sort data by recipient's balance after transaction  \n",
    "fraud_by_RecipientBalancAfter = df_fraud.sort_values(by=['newbalanceDest'], ascending=False)\n",
    "fraud_by_RecipientBalanceAfter = fraud_by_RecipientBalancAfter['newbalanceDest']\n",
    "\n",
    "#Create histogram for recipient's balance before vs. after transaction \n",
    "fig,ax = plt.subplots(1,2, figsize=(12,6), sharex=True)\n",
    "ax[0].hist(fraud_by_RecipientBalanceBefore, color='orange', edgecolor='black')\n",
    "ax[1].hist(fraud_by_RecipientBalanceAfter, color='orange', edgecolor='black')\n",
    "fig.suptitle('Recipient\\'s balance before vs. after fraud transaction', fontsize=16)\n",
    "ax[0].set_title('Balance before fraudulent transaction')\n",
    "ax[1].set_title('Balance after fraudulent transaction')\n",
    "plt.gcf().axes[0].xaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))\n",
    "plt.gcf().axes[1].xaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e588c7d",
   "metadata": {},
   "source": [
    "As shown, the fraud perpetrator's account balance before the transaction is very likely to be comparatively very low in balance, with their balance falling in the lowermost range of 0 to 1,000,000, and very rarely in the ranges onwards. On the right, their account balance after the transaction is also most likely to fall in the lowermost range (of 0-1,000,000), however we see higher balances post-transaction more frequently, which evidently indicates the success of their fraud for larger amounts. Curiously, I would speculate that the high frequency of low account balance post-transaction is likely evidence that most fraud transactions involve comparatively low amount anyway, as demonstrated by the transaction amount histogram from earlier. At any rate, now that we have a better insight into our data, I will proceed  to perform some data preprocessing and preparation before building the classification models that can detect fraud."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48fe48b0",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd18ce62",
   "metadata": {},
   "source": [
    "## Part Three: Data Preprocessing \n",
    "<br>\n",
    "\n",
    "**In this section, I will make the necessary preperations and preprocessing for the data to make sure it is ready for analysis and model development. This will involve: performing one-hot encoding on the categorical variable, 'type' to make it viable for numerical analysis; data selection and splitting to obtain a training set for modeling and a testing set for model evaluation; feature scaling to normalize the scales of the numerical variables, such that all the variables would have equivalent scales, facilitating their processing and comparison between them; and finally, performing oversampling to balance the classes in the dataset before building the models.**\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04d7919c",
   "metadata": {},
   "source": [
    "**Dealing with Categorical Variables: One-Hot Encoding** <br>\n",
    "\n",
    "First off, I will perform one-hot encoding on the categorical variable, 'type', to convert its values to numeric type data to make it viable for numerical analysis and modeling. Briefly, one-hot encoding involves creating new, binary categories for each of the unique values in a given categorical variable, assigning 1 to flag its presence or 0 for its absence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3aebb483",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape: (100000, 11) \n",
      "\n",
      "\n"
     ]
    },
    {
     "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>step</th>\n",
       "      <th>amount</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>type_CASH_OUT</th>\n",
       "      <th>type_DEBIT</th>\n",
       "      <th>type_PAYMENT</th>\n",
       "      <th>type_TRANSFER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>163</td>\n",
       "      <td>148753.50</td>\n",
       "      <td>24482.00</td>\n",
       "      <td>173235.50</td>\n",
       "      <td>315720.90</td>\n",
       "      <td>166967.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>10180.55</td>\n",
       "      <td>8680.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>129</td>\n",
       "      <td>14806.33</td>\n",
       "      <td>25871.46</td>\n",
       "      <td>11065.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>181</td>\n",
       "      <td>38336.09</td>\n",
       "      <td>10445.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>163</td>\n",
       "      <td>188096.77</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>391329.68</td>\n",
       "      <td>579426.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step     amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
       "0   163  148753.50       24482.00       173235.50       315720.90   \n",
       "1    15   10180.55        8680.01            0.00            0.00   \n",
       "2   129   14806.33       25871.46        11065.14            0.00   \n",
       "3   181   38336.09       10445.00            0.00            0.00   \n",
       "4   163  188096.77           0.00            0.00       391329.68   \n",
       "\n",
       "   newbalanceDest  isFraud  type_CASH_OUT  type_DEBIT  type_PAYMENT  \\\n",
       "0       166967.40        0              0           0             0   \n",
       "1            0.00        0              0           0             1   \n",
       "2            0.00        0              0           0             1   \n",
       "3            0.00        0              0           0             1   \n",
       "4       579426.45        0              1           0             0   \n",
       "\n",
       "   type_TRANSFER  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#one-hot encoding the 'type' coloumn\n",
    "df = pd.get_dummies(df, columns=['type'], drop_first=True, dtype='int')\n",
    "\n",
    "#examine shape again \n",
    "print('Data shape:', df.shape, '\\n\\n') \n",
    "#preview the data again \n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2407d7e",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66173fae",
   "metadata": {},
   "source": [
    "**Data Selection**<br>\n",
    "Identifying the predictors and target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3ec9f1e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Label the classes for later analysis \n",
    "classes = ['Not Fraud', 'Fraud']\n",
    "\n",
    "#Identify predictors and target variable\n",
    "features = df.columns.drop(target) \n",
    "x_data = df[features]\n",
    "y_data = df[target]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07c4ed2a",
   "metadata": {},
   "source": [
    "**Examine Class Distribution**<br>\n",
    "\n",
    "We can check the distribution of classes for the target variable, 'isFraud', to see the proportion of the majority class to that of the minority class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fe026017",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage of normal vs. fraudulent transactions:\n",
      "\n",
      "0    99.871%\n",
      "1     0.129%\n",
      "Name: isFraud, dtype: object \n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='isFraud', ylabel='count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Get percentage of the non-fraud vs. fraud cases in the dataset\n",
    "print('The percentage of normal vs. fraudulent transactions:\\n')\n",
    "print(y_data.value_counts(normalize=True).apply(lambda x: str(x*100)+'%'),'\\n\\n')\n",
    "#Visualizing the class distribution in the data using count plot\n",
    "sns.countplot(x=y_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e294989",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbea5bf7",
   "metadata": {},
   "source": [
    "As illustrated, the two classes are extremely unbalanced, with fraud cases making up only about 0.13%! As such, I will now perform stratified data splitting, which ensures that the same proportion of each class in the target is represented in the training and testing sets followed by oversampling in order to ensure that the two classes in the dataset are balanced and of the equivalent proportions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56f02186",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99526972",
   "metadata": {},
   "source": [
    "**Data Splitting** <br>\n",
    "\n",
    "I will now split the data into a training and testing sets for model training and evaluation. Particularly, I will perform stratified data splitting which preserves the class distribution when splitting, such that both the training and testing sets would have the same class distribution as the original. As such, I will preserve 80% of the data for training and the remaining 20% for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7ca36ea6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training samples: 80000\n",
      "Number of testing samples: 20000\n"
     ]
    }
   ],
   "source": [
    "#Performing stratified data splitting (80% training/20% testing)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, train_size=0.8, stratify=y_data, random_state=rs)\n",
    "\n",
    "#check the sizes of the training and testing sets\n",
    "print('Number of training samples:', x_train.shape[0])    \n",
    "print('Number of testing samples:', x_test.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a87db7c9",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47a2d50f",
   "metadata": {},
   "source": [
    "**Feature Scaling** <br>\n",
    "\n",
    "Now, given that the features in the dataset have differing scales, with values falling into vastly different ranges (e.g., amount of money vs. transaction time in hours), I will perform feature scaling to ensure that the diversity of scales doesn't affect analysis and the development of certain models like K-nearest neighbors classifiers. Feature scaling would also speed up processing for other models. To scale the features, I will employ feature normalization which redistributes the values on a scale from 0 to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d47737b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#First, identify numerical variables only for feature normalization \n",
    "numeric_vars = [col for col in df.columns if len(df[col].unique())!=2] \n",
    "\n",
    "#feature scaling numerical features \n",
    "Scaler = MinMaxScaler()\n",
    "x_train[numeric_vars] = Scaler.fit_transform(x_train[numeric_vars])\n",
    "x_test[numeric_vars] = Scaler.transform(x_test[numeric_vars])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2989fad2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>step</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>newbalanceDest</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type_CASH_OUT</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type_DEBIT</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type_PAYMENT</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type_TRANSFER</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count  mean   std  min  max\n",
       "step            80000.0  0.33  0.19  0.0  1.0\n",
       "amount          80000.0  0.00  0.01  0.0  1.0\n",
       "oldbalanceOrg   80000.0  0.02  0.07  0.0  1.0\n",
       "newbalanceOrig  80000.0  0.02  0.07  0.0  1.0\n",
       "oldbalanceDest  80000.0  0.00  0.01  0.0  1.0\n",
       "newbalanceDest  80000.0  0.01  0.02  0.0  1.0\n",
       "type_CASH_OUT   80000.0  0.35  0.48  0.0  1.0\n",
       "type_DEBIT      80000.0  0.01  0.08  0.0  1.0\n",
       "type_PAYMENT    80000.0  0.34  0.47  0.0  1.0\n",
       "type_TRANSFER   80000.0  0.09  0.28  0.0  1.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now we can look at the distribution of data after rescaling \n",
    "x_train.describe().drop(['25%','50%','75%']).apply(lambda x:round(x,2)).T\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c39447dd",
   "metadata": {},
   "source": [
    "As demonstrated in the above table, now all the features have the same scale, with a minimum value of 0 and a maximum of 1."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70355664",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "515f9a51",
   "metadata": {},
   "source": [
    "**Dealing with Imbalanced Classes: Oversampling** <br>\n",
    "\n",
    "Now as discussed earlier, the classes in the dataset are extremely imbalanced, with the fraud cases that we're interested in predicting making up only about 0.13% of cases. This would pose a considerable obstacle for our current task, particularly as most models are generally biased towards maximizing prediction accuracy, which implies that they will likely learn the 99% of non-fraud cases at the expense of the less than 1% of fraud cases. Whilst this is completely reasonable for most cases, for cases such as the current one, where the target class we're interested in predicting makes up 1% or less, this completely defeats the purpose. As such, to deal with this problem, and ensure that the models to be developed learn as much about the fraud cases as about the non-fraud ones, I will apply oversampling. <br>\n",
    "Oversampling involves generating novel data points for the minority class until it's equivalent in size to the majority class. There are different ways of doing this, such as random oversampling, synthetic oversampling (SMOTE), or adaptive synthetic oversampling (ADASYN), which involves duplicating rows from the minority class, as is the case with random oversampling, or advising intricate ways for generating completely new data points that exhibits more or less the same characteristics as the existing minority data, as with the diversity of synthetic oversampling techniques. Regardless of the technique, the final end is to balance the classes in the dataset by way of increasing the size of the minority class, which would allow better model learning and model prediction. Now, it's hard to tell in advance which technique would work best, and thus I will employ and test out different techniques, four in total, and compare and contrast them to identify the best oversampling technique for the current dataset. To do so, I will build a preliminary model, particularly, a logistic regression classifier, to get a baseline measure of classification validity, and then I will implement a loop that iterates over the four different techniques, balance the classes and train the classifier with each, and measuring the classifier performance for each technique individually. This will give us some idea about the best oversampling technique for the data. For evaluation, I will use the standard battery of error metrics, including precision, recall, and F-beta, however I will adjust the F-beta score to take a beta of 5, to try and prioritize recall for the fraud class and minimize false negatives, making the cost of falsely predicting fraud cases 5 times that of falsely predicting normal cases. Further, given that it's an imbalanced dataset, I will use the F-beta score as the primary guide for model performance (rather than accuracy which is typically maximized for such cases)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "874a336d",
   "metadata": {},
   "source": [
    "**Evaluating classification before oversampling** <br>\n",
    "Developing a simple logistic regression classifier and evaluating its performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cb513467",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>Not Fraud</th>\n",
       "      <th>Fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Not Fraud  Fraud\n",
       "Accuracy         1.0    1.0\n",
       "Precision        1.0    0.0\n",
       "Recall           1.0    0.0\n",
       "F5               1.0    0.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create logistic regression object\n",
    "LR = LogisticRegression(solver='saga', random_state=rs, max_iter=500, n_jobs=10)\n",
    "#fit the model\n",
    "model = LR.fit(x_train, y_train)\n",
    "#generate predictions\n",
    "y_pred = model.predict(x_test)\n",
    "#get error scores \n",
    "error_scores(y_test, y_pred, classes)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a28d11c",
   "metadata": {},
   "source": [
    "Indeed, we can see accuracy is (approximately) 1.0, which is typically the case with datasets such as the current one, as the model learns about the 99% of normal cases perfectly, at the cost of learning about the minority 1%. However, if we looked at the other metrics for the fraud class, precision, recall, and f5 score are all 0! Now I will evaluate different oversampling techniques for balancing the classes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d8f4b7a",
   "metadata": {},
   "source": [
    "**Oversampling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0c496985",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strategy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ROS</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMOTE</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Borderline SMOTE</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADASYN</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Accuracy  Precision  Recall    F5\n",
       "Strategy                                           \n",
       "ROS                   0.85       0.92    0.01  0.17\n",
       "SMOTE                 0.85       0.92    0.01  0.17\n",
       "Borderline SMOTE      0.92       0.92    0.02  0.28\n",
       "ADASYN                0.82       0.96    0.01  0.15"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get samplers \n",
    "samplers = [('ROS', RandomOverSampler(random_state=rs)), ('SMOTE',SMOTE(random_state=rs)), \n",
    "            ('Borderline SMOTE', BorderlineSMOTE(random_state=rs)), ('ADASYN', ADASYN(random_state=rs))]\n",
    "\n",
    "#create empty dictionary to store evaluation results \n",
    "results=[]\n",
    "\n",
    "#loop over and test each oversampling technique on the logistic regression classifier \n",
    "for label,sampler in samplers:\n",
    "    x_over, y_over = sampler.fit_resample(x_train, y_train)\n",
    "    LR = LogisticRegression(solver='saga', random_state=rs, max_iter=500, n_jobs=10)\n",
    "    model = LR.fit(x_over, y_over)\n",
    "    y_pred = model.predict(x_test)\n",
    "    #get error scores and store them \n",
    "    result = error_scores_dict(y_test, y_pred, label)\n",
    "    results.append(result)\n",
    "\n",
    "#Report the results\n",
    "results_table = pd.DataFrame(results).set_index('Strategy')\n",
    "results_table\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3da2a37",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47879163",
   "metadata": {},
   "source": [
    "Ideally, we want strong recall for such user case, however not at the cost of precision. Thus, the F5 score is a better indicator for performance as it leverages both, with a tad more emphasis on recall. Accordingly, as indicated by the F5 scores here, Borderline SMOTE seems to be the most appropriate oversampling method for the data, raising the F-beta score to 0.28 for the fraud class, which is a significant feat considering the first model had an F-beta of 0. Now I will build different classifiers with borderline SMOTE-oversampled training data and simultaneously perform hyperparameter tuning on each in order to obtain the best performance out of each classifier and decide on the best one for our task.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb0eaea9",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2cb560b",
   "metadata": {},
   "source": [
    "**Borderline SMOTE** <br>\n",
    "\n",
    "Now that we've determined that borderline SMOTE is the best oversampling method for our data, I will use it to perform oversampling on the training data one final time and use the new sets to train the upcoming classifiers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "567b688a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage of normal vs. fraudulent transactions:\n",
      "\n",
      "0    50.0%\n",
      "1    50.0%\n",
      "Name: isFraud, dtype: object \n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='isFraud', ylabel='count'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Obtain new training data balanced with borderline SMOTE \n",
    "x_over, y_over = BorderlineSMOTE(random_state=rs).fit_resample(x_train, y_train)\n",
    "\n",
    "#preview class distribution after oversampling  \n",
    "print('The percentage of normal vs. fraudulent transactions:\\n')\n",
    "print(y_over.value_counts(normalize=True).apply(lambda x: str(x*100)+'%'),'\\n\\n')\n",
    "sns.countplot(x=y_over)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e71ef3a4",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef0aff92",
   "metadata": {},
   "source": [
    "As illustrated, both classes have the same size now and are perfectly balanced. Now I will move to model development, tuning, and selection."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45367ea1",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09943d0c",
   "metadata": {},
   "source": [
    "## Part Four: Model Development, Tuning, and Evaluation \n",
    "<br>\n",
    "\n",
    "**In this section, I will train and test different classification models to predict transaction fraud, which will include: logistic regression classifier, k-nearest neighbors (KNN) classifier, kernel support vector machines (SVM) classifier, and finally, a random forest classifier. To obtain the best performance out of each model, I will perform hyperparameter tuning via grid search with 4-fold cross validation. I will compare and contrast the performances of each using the same error metrics as above, and if necessary, I will develop a stacking ensemble model that leverages the strengths of the individual models by training a meta-model on their predictions to generate the final set of class predictions. The final model should ideally maximize recall as well as precision.**\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03adfa02",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53366bd2",
   "metadata": {},
   "source": [
    "### Model One: Logistic Regression classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "980ba4c4",
   "metadata": {},
   "source": [
    "**Model Development and Hyperparameter Tuning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dc90982c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameter values for the logistic regression classifier:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'C': 100, 'penalty': 'l1'}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training and tuning a logistic regression classifier \n",
    "LR = LogisticRegression(solver='saga', max_iter=500, random_state=rs, n_jobs=10)\n",
    "Grid_LR = GridSearchCV(LR, scoring='f1', cv=4, n_jobs=10, \n",
    "                            param_grid={\n",
    "                                'penalty': ['l1', 'l2'], \n",
    "                                'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "                            }).fit(x_over, y_over)\n",
    "\n",
    "#Report best parameters after grid search\n",
    "print('Best parameter values for the logistic regression classifier:')\n",
    "Grid_LR.best_params_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2f4016d",
   "metadata": {},
   "source": [
    "**Model evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7a45fca1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression classification results:\n"
     ]
    },
    {
     "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>Not Fraud</th>\n",
       "      <th>Fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F5</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Not Fraud  Fraud\n",
       "Accuracy        0.97   0.97\n",
       "Precision       1.00   0.04\n",
       "Recall          0.97   0.96\n",
       "F5              0.97   0.54"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get best estimator based off grid search \n",
    "LR_classifier = Grid_LR.best_estimator_\n",
    "\n",
    "#Generate predictions\n",
    "y_pred = LR_classifier.predict(x_test)\n",
    "\n",
    "#Compute and report error scores  \n",
    "print('Logistic Regression classification results:')\n",
    "error_scores(y_test, y_pred, classes=classes)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cec896c",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a217bb9",
   "metadata": {},
   "source": [
    "As demonstrated, prediction for the fraud class was improved with regularized logistic regression (with L1 regularization and C=100) across most of the error measures, with a very high recall of 0.96 and an acceptable overall F5 score of 0.54. However, the precision score is still far from ideal. As mentioned, a good model would ideally maximize recall but not at the cost of precision. That is, we want to correctly predict or 'recall' actual cases of fraud but not at the cost of having many non-fraud cases misclassified as fraud. Thus, this low precision score indicates that we have many non-fraud cases classified wrongly as fraud. We can make more sense of these scores by visualizing the results using a confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8b355830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize confusion matrix\n",
    "plot_cm(y_test, y_pred, classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eb641fc",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "187fb1cc",
   "metadata": {},
   "source": [
    "As intuited, recall was very good for the fraud cases, with 25 cases classified correctly and only one fraud case misclassified! However, whilst the great majority of non-fraud cases were classified correctly, a considerable proportion were not, particularly when pitted against the number of correctly classified fraud cases (536 vs. 25). It would be more ideal if we had as many cases classified correctly as possible, for both classes. Let's see if we can improve classification, especially precision, with other models. Next I will develop and tune a KNN classifier. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c63ac3fd",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93c2738e",
   "metadata": {},
   "source": [
    "### Model Two: K-Nearest Neighbors classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42c7d354",
   "metadata": {},
   "source": [
    "**Model Development and Hyperparameter Tuning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5e1c26cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameter values for the KNN classifier:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 1}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training and tuning a KNN classifier \n",
    "KNN = KNeighborsClassifier(weights='distance', n_jobs=10)\n",
    "Grid_KNN = GridSearchCV(KNN, scoring='f1', cv=4, n_jobs=10,\n",
    "                            param_grid={\n",
    "                                'n_neighbors': range(1,11),\n",
    "                            }).fit(x_over, y_over)\n",
    "\n",
    "#Report best parameters after grid search\n",
    "print('Best parameter values for the KNN classifier:')\n",
    "Grid_KNN.best_params_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5ddc0a9",
   "metadata": {},
   "source": [
    "**Model Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f17d8264",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Nearest Neighbors classification results:\n"
     ]
    },
    {
     "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>Not Fraud</th>\n",
       "      <th>Fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Not Fraud  Fraud\n",
       "Accuracy         1.0   1.00\n",
       "Precision        1.0   0.28\n",
       "Recall           1.0   0.65\n",
       "F5               1.0   0.62"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get best estimator based off grid search \n",
    "KNN_classifier = Grid_KNN.best_estimator_\n",
    "\n",
    "#Generate predictions\n",
    "y_pred = KNN_classifier.predict(x_test)\n",
    "\n",
    "#Report error scores \n",
    "print('K-Nearest Neighbors classification results:')\n",
    "error_scores(y_test, y_pred, classes=classes)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab645012",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5114f1c",
   "metadata": {},
   "source": [
    "As illutrated, the KNN classifier was not particularly better than the logistic regression one, however it managed to balance recall and precision better than the earlier model did. Particularly, whilst recall was decreased, falling from 0.96 to 0.65, precision was improved significantly, rosing to 0.28 from 0.04. This means that we likely have less fraud cases predicted correctly, however less non-fraud cases was classified incorrectly! I will again use a confusion matrix to visualize the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "61ac33a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize confusion matrix\n",
    "plot_cm(y_test, y_pred, classes)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97078e55",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "833c3ebe",
   "metadata": {},
   "source": [
    "Indeed, we can see significantly less cases of normal transactions misclassified as fraudulent, going down from 536 instances earlier to only 44 instances now. However, unfortunately, recall for the fraud instances was also decreased significantly, falling from 25 to 17, which is not a trivial number considering that the fraud cases make up only 26 in total! We need a better model. Next I will develop and test out a support vector machines classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5010e2f2",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9365dda",
   "metadata": {},
   "source": [
    "### Model Three: Kernel SVM classifier \n",
    "\n",
    "Now I will develop a kernel SVM classifier, which will try to identify a hyperplane that best separates the classes within the feature space, maximizing the distance between the two classes by maximizing the margin lengths separating the classes on that hyperplane. It should work better than logistic regression if the data is complex. Further, since the dataset is medium and size and the number of features is small, I have decided to use an rbf kernel. Note however, again, given the limited computational sources and given that kernel SVMs are very computationally costly, especially also with a dataset of 100,000 entries, I will perform kernel approximation with Nystroem sampler, rather than processing the entire dataset to define the kernel function. Kernel approximation, as the name implies, would try to approximate the true kernel function from only a small sample of the data. As such, I will use the Nystroem sampler to approximate the rbf kernel from only 500 samples, before building and tuning a SVM classifier with the obtained data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e82cd6d",
   "metadata": {},
   "source": [
    "**Kernel Approximation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ea335450",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create an instance of Nystroem class and set characteristics\n",
    "NystroemSVC = Nystroem(kernel='rbf', n_components=500, random_state=rs, n_jobs=10)\n",
    "#Fit and transform the data\n",
    "x_train_aprx = NystroemSVC.fit_transform(x_over)\n",
    "x_test_aprx = NystroemSVC.transform(x_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24920211",
   "metadata": {},
   "source": [
    "**Model Development and Hyperparameter Tuning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0c143d56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameter values for the kernel SVM classifier:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'C': 10, 'penalty': 'l2'}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training and tuning a kernel SVM classifier \n",
    "linearSVC = LinearSVC(random_state=rs)\n",
    "Grid_SVC = GridSearchCV(linearSVC, scoring='f1', cv=4, n_jobs=10,\n",
    "                            param_grid={\n",
    "                                'penalty': ['l1', 'l2'], \n",
    "                                'C': [0.01, 0.1, 1, 10, 100]\n",
    "                            }).fit(x_train_aprx, y_over)\n",
    "\n",
    "#Report best parameters after grid search\n",
    "print('Best parameter values for the kernel SVM classifier:')\n",
    "Grid_SVC.best_params_\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5647a6dc",
   "metadata": {},
   "source": [
    "**Model Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e8f6df82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kernel SVM classification results:\n"
     ]
    },
    {
     "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>Not Fraud</th>\n",
       "      <th>Fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F5</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Not Fraud  Fraud\n",
       "Accuracy        0.97   0.97\n",
       "Precision       1.00   0.04\n",
       "Recall          0.97   0.88\n",
       "F5              0.97   0.49"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get best estimator based off grid search \n",
    "SVC_classifier = Grid_SVC.best_estimator_\n",
    "\n",
    "#Generate predictions\n",
    "y_pred = SVC_classifier.predict(x_test_aprx)\n",
    "\n",
    "#Report error scores\n",
    "print('Kernel SVM classification results:')\n",
    "error_scores(y_test, y_pred, classes=classes)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f34faba",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a358162",
   "metadata": {},
   "source": [
    "As seen from the results, the rbf kernel SVM classifier obtained a high recall of 0.88 and overall F5 score of 0.49, however precision was pretty low, equaling 0.04. These results in fact closely resemble the results of the logistic regression classifier, which had almost identical scores. This means that once again we probably have many non-fraud instances misclassified as fraudulent. A confusion matrix would come in handy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "205805fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize confusion matrix\n",
    "plot_cm(y_test, y_pred, classes)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03eceaf2",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f78eb6",
   "metadata": {},
   "source": [
    "Indeed, the matrix here closely resembles the one obtained with the logistic regression model, but with 2 less fraud cases being classified correctly. Thus, we can't say that the kernel SVM classifier was any better for our data, nor did it make different or better contributions to help detect fraud better. We can still do better. Next, I will develop one final model, a random forest classifier, and again perform hyperparameter tuning to optimize the number of decision trees, and evaluate the results to see if it performs better than the models developed thus far. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e446e357",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9175494d",
   "metadata": {},
   "source": [
    "### Model Four: Random Forest "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04fee21b",
   "metadata": {},
   "source": [
    "**Model Development and Hyperparameter Tuning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0d5003e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameter values for the random forest classifier:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 50}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training and tuning a random forest model \n",
    "RF = RandomForestClassifier(random_state=rs, max_depth=10, n_jobs=10)\n",
    "Grid_RF = GridSearchCV(RF, scoring='f1', cv=4, n_jobs=10,\n",
    "                            param_grid={'n_estimators': [50, 100, 200, 300, 400]}).fit(x_over, y_over)\n",
    "\n",
    "#Report best parameters after grid search\n",
    "print('Best parameter values for the random forest classifier:')\n",
    "Grid_RF.best_params_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08d61da2",
   "metadata": {},
   "source": [
    "**Model Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "40aca8a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest classification results:\n"
     ]
    },
    {
     "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>Not Fraud</th>\n",
       "      <th>Fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Not Fraud  Fraud\n",
       "Accuracy         1.0   1.00\n",
       "Precision        1.0   0.44\n",
       "Recall           1.0   0.92\n",
       "F5               1.0   0.89"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get best estimator based off grid search\n",
    "RF_classifier = Grid_RF.best_estimator_\n",
    "\n",
    "#Generate predictions \n",
    "y_pred = RF_classifier.predict(x_test) \n",
    "\n",
    "#Report error scores\n",
    "print('Random Forest classification results:')\n",
    "error_scores(y_test, y_pred, classes=classes)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cd219a9",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0212fb59",
   "metadata": {},
   "source": [
    "Based on the obtained results, the random forest classifier (with 50 trees) proved superior to all the previous models, especially in improving precision and balancing it against recall. As demonstrated, this classifier successfully managed to maximize not only recall, but also precision, obtaining a precision of 0.44, recall of 0.92, and an overall F5 score of 0.89. This is a huge improvement from before! What this effectively means is that classification is now more accurate for both classes, with most fraud cases being classified correctly (since recall is high) and fewer non-fraud cases being misclassified (since precision is high). Now let's examine the confusion matrix to get a better view of its performance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5dba651e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize confusion matrix\n",
    "plot_cm(y_test, y_pred, classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88fcab0a",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d50e5f5",
   "metadata": {},
   "source": [
    "Consistent with the above speculation, the confusion matrix for the random forest illustrates very good recall for the actual fraud cases, and very fewer cases of non-fraud misclassification, falling down to only 31 misclassification instances (from 536 misclassifications with the first model). Thus, the random forest classifier seems to be the best model for our data so far. Next, I will develop a stacking classifier, which would learn from the individual models developed and leverage their strengths to come up with the final set of predictions. As we have seen, the different models had different points of weakness and different points of strength. For instance, the logistic regression classifier had very good recall but very poor precision; the KNN classifier established some balance, improving precision significantly; and lastly, the random forest classifier improved both precision and recall however in a completely different way, i.e. by employing decision trees. We can get a quick view of how each model performed by visualizing and comparing the ROC curve of each. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b85621fe",
   "metadata": {},
   "source": [
    "**Model Comparison** <br>\n",
    "Plotting the ROC curve for each model and reporting their AUC score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "19c30fc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Get list of estimators \n",
    "estimators_lst = [LR_classifier, KNN_classifier, SVC_classifier, RF_classifier]\n",
    "\n",
    "#Plot ROC curve for each model \n",
    "plot_ROC_curve_multiple(x_test, x_test_aprx, y_test, estimators_lst)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acbbc379",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42ab80f9",
   "metadata": {},
   "source": [
    "As depicted by the above graph, the KNN classifier performed worst, with a comparatively low true positive rate, followed by the kernel SVM classifier, then the logistic regression classifier, and lastly we have the random forest classifier which performed best. Although the logistic regression model had higher true positive rate, it also had higher false positive rate, and thus the random forest classifier seems best as, altough it has slightly lower true positives, it minimizes the false positive rate to a minimum. Thus, we see different strengths and weaknesses associated with the different models, at least comparing the random forest to the logistic regression classifier. As such, employing a stacking classifier can help us learn from the strengths of each of these models to maximize the overall performance and help us in our mission of transaction fraud detection."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76b0beb4",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf40d159",
   "metadata": {},
   "source": [
    "### Stacking Model\n",
    "\n",
    "As mentioned, to get the best performance and obtain the most robust and reliable class predictions, I will employ here a stacking ensemble, which would leverage the strengths of the different models developed thus far by learning from their collective insights, combining their predictions to obtain the most robust set of predictions. Partiuclarly, I will develop a stacking classifier with a logistic regression meta-model, which will be trained on the predictions of the individual model, using them as training inputs to generate the final set of predictions for the data. However, given that the kernel SVM classifier did not make any noticeable contributions, performing much as the logistic regression one, I will not include it in the ensemble."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab4a2991",
   "metadata": {},
   "source": [
    "**Model Development**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2d6251a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#First, making a list of classifiers to incorporate into the ensemble \n",
    "estimators = [('LR', LR_classifier), ('KNN', KNN_classifier), ('RF', RF_classifier)]\n",
    "\n",
    "#Using Stacking Classifier with a logistic regression meta-model\n",
    "#create Stacking classifier object and specify a meta classifier \n",
    "SC = StackingClassifier(estimators=estimators, final_estimator=LogisticRegression(max_iter=500), n_jobs=10)\n",
    "\n",
    "#training the stacking classifier \n",
    "SC_classifier = SC.fit(x_over, y_over)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08dd541f",
   "metadata": {},
   "source": [
    "**Model Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b7b5a526",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stacking classification results:\n"
     ]
    },
    {
     "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>Not Fraud</th>\n",
       "      <th>Fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Not Fraud  Fraud\n",
       "Accuracy         1.0   1.00\n",
       "Precision        1.0   0.77\n",
       "Recall           1.0   0.92\n",
       "F5               1.0   0.92"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generate final class predictions\n",
    "y_pred = SC_classifier.predict(x_test)\n",
    "\n",
    "#Report error scores\n",
    "print('Stacking classification results:')\n",
    "error_scores(y_test, y_pred, classes) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81a6957d",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2a29f7b",
   "metadata": {},
   "source": [
    " As speculated, the stacking model was successful at learning from the different individual models, producing the best results so far. In the above table, we have very good recall, 0.92, as well as the highest precision of all models, peaking at 0.77. We also obtained the best F5 score thus far, peaking to 0.92. This would indicate that we have very good classification for both classes, accurately predicting normal transaction cases as well as fraudulent transaction cases in great numbers. I will visualize the confusion matrix one last time to make better sense of the results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "62fc4680",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing confusion matrix\n",
    "plot_cm(y_test, y_pred, classes)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91df6daf",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bc68590",
   "metadata": {},
   "source": [
    "As demonstrated, the stacking classifier indeed improved classification for both classes in the dataset: the great majority of normal transaction cases were classified correctly, with only 7 cases of misclassification, the same with the fraud class, correctly classifying or detecting the great majority of fraud instances with only 2 instances of misclassification. We can also examine the resulting ROC curve for this stacking classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "61eb96ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize ROC curve \n",
    "plot_ROC_curve(SC_classifier, x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f109bcf3",
   "metadata": {},
   "source": [
    "Evidently, revealed here, as in the confusion matrix above, overall the stacking classifier reduced the false positive rate to a minimum, with only a slight decline in the true positive rate, resulting in quite a large area under the curve. As annotated, the final AUC score for the stacking classifier is 0.96, which is pretty excellent. Accordingly, we can reasonably deem this classifier the best candidate model for our current dataset, and select it as the final model to be deployed for transaction fraud detection."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "713d44bb",
   "metadata": {},
   "source": [
    "In the next and final section, I will engage in model interpretation to try and understand the model better, and understand the basis upon which the model was performing its classification. That is, we need to understand which of the features exactly were most informative and most impactful on the final classification decision. Now given that the final stacking model we obtained is highly complex, if not impenetrable, composed of multiple different models, each with its own characteristics and classification processes, etc., we will not be able to interpret it directly. Indeed, this model would be considered a non-self-interpretable or \"black-box\" model. Thus, in order to be able to interpret it, I will employ different model interpretation techniques to understand the one we obtained better. These will be permutation feature importance and a partial dependence plot. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f57d23b0",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c82eda23",
   "metadata": {},
   "source": [
    "## Part Five: Model Interpretation \n",
    "<br>\n",
    "\n",
    "**In this section, as mentioned, I will apply model interpretation techniques to try and understand the model better. First, I will perform permutation feature importance, a technique which involves shuffling the values of a given feature multiple times and measuring error each time. This process is carried out multiple times with each feature, one feature at a time, whilst holding all the other features in check. If a feature is indeed important or highly impactful for the final model predictions, then shuffling its values should affect the model's performance significantly, usually by increasing its average error. This, as such, gives us an average estimate of how important each feature is for predicting the target class. Thus as a first step, permutation feature importance will give us a good idea about which factors were important in predicting transaction fraud. Second, after identifying the most important features, I will use a partial dependence plot to understand each individual feature better, and understand how or in which way it contributed to predicting transaction fraud. The partial dependence plot will basically give us a visualization of how the values (or cluster of values) of a given feature relate to the target class, fraud. It should give us a rough idea about the nature and direction of the relationship between a given feature and fraud detection. Some features migh have a positive relationship, such that increasing its values would increase the likelihood of fraud, others might have a negative relationship, whereby increasing its values would decrease the likelihood of fraud, and yet others might have more complicated relationships with the target, where some values would be associated with higher likelihoods of fraud whilst others will not, as we will see later on. That said, now I will move on to carrying out the first step of the analysis.**\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02506351",
   "metadata": {},
   "source": [
    "### Permutation Feature Importance \n",
    "<br>\n",
    "Now I will perform permutation feature importance (with 20 repeats) in order to determine which features are most important or impactful for predicting transaction fraud. Further, as the primary means of estimating error, I will use the F-score. After completing the permutation process, I will then get the mean importance score for each feature and visualize them using a box plot to get a visual representation of how each feature contributes to the model's performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6becb8ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances array shape: (10, 20)\n",
      "\n",
      "\n",
      "Number of features: 10\n",
      "Mean feature importance for each feature:\n",
      "[0.075 0.104 0.309 0.059 0.029 0.073 0.142 0.    0.    0.248]\n"
     ]
    }
   ],
   "source": [
    "#Calculate and store feature importances \n",
    "feature_importances = permutation_importance(estimator=SC_classifier, X=x_over, y=y_over, scoring='f1', \n",
    "                                             n_repeats=20, random_state=rs, n_jobs=10)\n",
    "\n",
    "#get the shape of the resulting feature importances array \n",
    "print('Feature importances array shape:', feature_importances.importances.shape)\n",
    "\n",
    "#Get the mean importances for each feature (mean of the n permutation repeats)\n",
    "print('\\n\\nNumber of features:', len(feature_importances.importances_mean))\n",
    "print('Mean feature importance for each feature:')\n",
    "print(np.round(feature_importances.importances_mean,3))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8b803ff",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4b81e2c",
   "metadata": {},
   "source": [
    "**Visualizing Feature Importances**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "04ae1b7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the feature importances using a Box plot \n",
    "visualize_feature_importances(feature_importances, features)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4af25e5e",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb230bd7",
   "metadata": {},
   "source": [
    "As illustrated in the box plot, the most important features for determining transaction fraud are the sender's balance before the transaction ('oldbalanceOrg') and whether the the transaction was a transfer or not ('type_TRANSFER'), followed by whether the transaction was cash-out type ('type_CASH_OUT') and the amount being sent ('amount'), and then to a lesser degree, each of the features, step (time in hours), recipient's and sender's balance after the transaction ('newbalanceDest' and 'newbalanceOrig'), and the recipient's balance before the transaction ('oldbalanceDest'), respectively. The last two features, 'type_PAYMENT' and 'type_DEBIT', had no little to no impact on fraud prediction, evidently indicating that fraud cases mostly involve bank transfers or cash-out type transactions, but seldom involving payments or debit transactions, which is consistent with the preliminary analysis at the beginning, showing that all fraud cases were transfers or cash-out type transactions.<br>\n",
    "That said, note that the box plot (& permutation feature importance) only tells us which features were most important and impactful on our predictions, however it doesn't tell us exactly in which way they did, or the direction of their relationship to the target. As such, next I will use a partial dependence plot to examine in more detail each of the important features identified individually, and understand the nature and direction of the relationship between each of these features and the target, fraud. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772eec90",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0d77857",
   "metadata": {},
   "source": [
    "### Partial Dependence Plot \n",
    "<br> \n",
    "As mentioned earlier, to better understand how each of the important features contributed to fraud prediction, I will use a partial dependency plot. This plot should provide a visual representation of how the values of a given feature relates or contributes to the predicted target, fraud, revealing the exact relationship between that feature and the target as well as the direction of the relationship. Note however, as two of the features, type_PAYMENT and type_DEBIT, proved irrelevant to fraud detection, I will remove them from analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8c9a5ed3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 950x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 950x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 950x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 950x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#List important features in pairs \n",
    "features_lst = [['type_TRANSFER', 'type_CASH_OUT'], ['oldbalanceOrg', 'newbalanceOrig'], \n",
    "                ['oldbalanceDest', 'newbalanceDest'], ['amount', 'step']]\n",
    "\n",
    "#Create and display partial dependency plot for each of the listed features \n",
    "with plt.rc_context({'figure.figsize': (9.5, 5)}):\n",
    "    pdp_plots = [PartialDependenceDisplay.from_estimator(estimator=SC_classifier, X=x_over, features=lst, categorical_features=features_lst[0], n_jobs=-1) for lst in features_lst]\n",
    "    pdp_plots[0].figure_.suptitle('Partial Dependence Plot per Feature', fontsize=15)\n",
    "    for ax in pdp_plots[0].axes_[0]:\n",
    "        ax.set_xticks([0, 1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6437bca3",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7871b9dd",
   "metadata": {},
   "source": [
    "**Interpretation**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc18bc26",
   "metadata": {},
   "source": [
    "&emsp;&ensp; The partial dependence plots for the features are presented in pairs, mostly in terms of relatedness. We have 8 plots in total depicting 8 relationships between the likelihood of fraud and a given feature. As demonstrated by the plots, some of the relationships are pretty intuitive. Starting off with the transaction types, consistent with the exploratory data analysis from earlier, and the previous box plot, most if not all fraudulent transactions are either transfers or cash-out, with comparatively more transfer cases, but rarely otherwise. This is plausibly the case because it is easier to to engage in fraud over distance, as over the internet, with the perpetrator's identity being hidden.\n",
    "<br>\n",
    "\n",
    "&emsp;&ensp; Moving to the sender's account balance before and after a transaction, we can see a positive relationship between the sender's account balance before a transaction and the likelihood of fraud, with the likelihood of fraud increasing as the account balance increases, before shortly stabilizing more or less, which is arguably because those with very low account balances (as seen above) are unlikely to be engaging in online shopping or buying. Nothing strange there. Conversely, we see a negative relationship between the sender's account balance after the transaction and the likelihood of fraud, which is again very consistent with the picture portrayed by the data analysis at the beginning, illustrating that all of the fraud transactions in our dataset involved completely draining the victim's account. This finding is thus being confirmed once again by the partial dependence plot. \n",
    "<br>\n",
    "\n",
    "&emsp;&ensp; Turning to the recipient's account balance before and after a transaction, first we see a seemingly strong negative relationship between the recipient's balance before a transaction and fraud, with accounts having very low balance being associated with the highest likelihood of fraud. We also see a negative relationship between the recipient's account after a transaction and fraud, such that the higher the account balance the lower the likelihood that the transaction was fraudulent. Whilst counterintuitive, this finding makes sense when considering that the great majority of fraud transactions, as highlighted by the data analysis at the start, are typically in the lower ranges, especially the lowermost range of 0-1,000,000, compared to other non-fraudulent transactions, which might include transactions in tens of millions.\n",
    "<br>\n",
    "\n",
    "&emsp;&ensp; Similarly, moving on to the transaction amount plot, we can see again a negative relationship between amount and the likelihood of fraud, with, notably, the lowest transaction amounts being associated with the highest likelihood of fraud and the highest amounts being associated with the lowest likelihood of fraud. Again whilst preplexing at first glance, this is very consistent with the exploratory data analysis from earlier in which most fraud cases involved transactions in the lower range of 0-1,000,000, followed by, although to a much lower extent, the ranges of 1,000,000 to 2,000,000 and 2,000,000 to 3,000,000, with only a minority of transactions involving amounts in the higher ranges. And whilst these amounts are not necesseraily low in an absolute monetary sense, they're comparatively pretty low considering the range of transactions in this dataset, which peaks all the way up to about 51 million. However, again without knowing the exact type of currency here, we can't draw definitive conclusions. Generally, what we can gather from these findings so far, is that victims of fraud are perhaps likely to be lower to lower-medium income individuals. Finally, turning to the last plot, we see a more complicated relationship between the 'step' feature and fraud, in which the likelihood of fraud is increased when the number steps is very low or higher-medium to very high, and is significantly decreased when the number of steps is in the lower-medium to medium range. Now, this feature is described as a representing units of time, where 1 step = 1 hour, however the publisher of the dataset didn't exactly specify what these hours represent (e.g., is this the transaction processing time? etc.), and so there is little I can say about this feature. \n",
    "<br>\n",
    "\n",
    "&emsp;&ensp; Overall, we have gathered some good clues and insights about fraudulent transactions. First, perpetrators of fraud are most likely to have a low or very low account balance before the transaction is made to their account. Second, the account balance of the victim is very likely to be completely drained and emptied out after a fraudulent transaction. Thus, if a transaction is made with all the money in the account, this should raise our suspicions that the transaction might be fraudulent. Third, most victims of fraud are likely to have a lower balance, suggesting that they might be low income or lower middle income individuals. However, in the absence of more information about the type of currency here, no definitive conclusion can be drawn. Finally, most if not all fraud transactions are bank transfers from one account to another, or cash-out transfers, but not payments or debit transactions, arguably because it's easier to commit fraud at a distance, for instance, through suspicious and unsafe internet websites.\n",
    "\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86a45135",
   "metadata": {},
   "source": [
    " "
   ]
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