thedatajango/Fraud_Detection_Complete.ipynb

## Fraud_Detection_Complete.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.gridspec as gridspec\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.base import BaseEstimator,TransformerMixin\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split,cross_val_predict,cross_val_score, GridSearchCV,RandomizedSearchCV\n",
    "from sklearn.preprocessing import StandardScaler,MinMaxScaler\n",
    "from sklearn.metrics import confusion_matrix,classification_report,f1_score,recall_score,precision_score,accuracy_score,precision_recall_curve,roc_curve,roc_auc_score\n",
    "\n",
    "from sklearn.linear_model import SGDClassifier, LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, VotingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.combine import SMOTEENN\n",
    "from collections import Counter\n",
    "\n",
    "from scipy.stats import norm, multivariate_normal\n",
    "\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import random\n",
    "random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def data_preparation(data):\n",
    "    features = data.iloc[:,0:-1]\n",
    "    label = data.iloc[:,-1]\n",
    "    x_train,x_test,y_train,y_test = train_test_split(features,label,test_size=0.2,random_state=0)\n",
    "\n",
    "    #Standarad scaler is not applied since all the features are outcomes of PCA and are already normalized.\n",
    "    #sc = StandardScaler()\n",
    "    #x_train = sc.fit_transform(x_train)\n",
    "    #x_test = sc.transform(x_test)\n",
    "    \n",
    "    print(\"Length of training data\",len(x_train))\n",
    "    print(\"Length of test data\",len(x_test))\n",
    "    return x_train,x_test,y_train,y_test\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def build_model_train_test(model,x_train,x_test,y_train,y_test):\n",
    "    model.fit(x_train,y_train)\n",
    "\n",
    "    y_pred = model.predict(x_train)\n",
    "    \n",
    "    print(\"\\n----------Accuracy Scores on Train data------------------------------------\")\n",
    "    #print(\"Clasification Report\")\n",
    "    #print(classification_report(y_train,y_pred))\n",
    "\n",
    "    print(\"F1 Score: \", f1_score(y_train,y_pred))\n",
    "    print(\"Precision Score: \", precision_score(y_train,y_pred))\n",
    "    print(\"Recall Score: \", recall_score(y_train,y_pred))\n",
    "\n",
    "    print(\"\\n----------Accuracy Scores on Cross validation data------------------------------------\")\n",
    "    y_pred_cv = cross_val_predict(model,x_train,y_train,cv=5)\n",
    "    print(\"F1 Score: \", f1_score(y_train,y_pred_cv))\n",
    "    print(\"Precision Score: \", precision_score(y_train,y_pred_cv))\n",
    "    print(\"Recall Score: \", recall_score(y_train,y_pred_cv))\n",
    "\n",
    "\n",
    "    print(\"\\n----------Accuracy Scores on Test data------------------------------------\")\n",
    "    y_pred_test = model.predict(x_test)\n",
    "    \n",
    "    #print(\"Clasification Report\")\n",
    "    #print(classification_report(y_test,y_pred_test))\n",
    "\n",
    "    print(\"F1 Score: \", f1_score(y_test,y_pred_test))\n",
    "    print(\"Precision Score: \", precision_score(y_test,y_pred_test))\n",
    "    print(\"Recall Score: \", recall_score(y_test,y_pred_test))\n",
    "\n",
    "    #Confusion Matrix\n",
    "    plt.figure(figsize=(18,6))\n",
    "    gs = gridspec.GridSpec(1,2)\n",
    "\n",
    "    ax1 = plt.subplot(gs[0])\n",
    "    cnf_matrix = confusion_matrix(y_train,y_pred)\n",
    "    row_sum = cnf_matrix.sum(axis=1,keepdims=True)\n",
    "    cnf_matrix_norm =cnf_matrix / row_sum\n",
    "    sns.heatmap(cnf_matrix_norm,cmap='YlGnBu',annot=True)\n",
    "    plt.title(\"Normalized Confusion Matrix - Train Data\")\n",
    "\n",
    "#     ax2 = plt.subplot(gs[1])\n",
    "#     cnf_matrix = confusion_matrix(y_train,y_pred_cv)\n",
    "#     sns.heatmap(cnf_matrix,cmap='YlGnBu',annot=True,fmt='d')\n",
    "#     plt.title(\"Confusion Matrix - CV Data\")\n",
    "\n",
    "    ax3 = plt.subplot(gs[1])\n",
    "    cnf_matrix = confusion_matrix(y_test,y_pred_test)\n",
    "    row_sum = cnf_matrix.sum(axis=1,keepdims=True)\n",
    "    cnf_matrix_norm =cnf_matrix / row_sum\n",
    "    sns.heatmap(cnf_matrix_norm,cmap='YlGnBu',annot=True)\n",
    "    plt.title(\"Normalized Confusion Matrix - Test Data\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def build_model_train(model,x_train,y_train):\n",
    "    model.fit(x_train,y_train)\n",
    "    y_pred = model.predict(x_train)\n",
    "    \n",
    "    #print(\"Clasification Report\")\n",
    "    #print(classification_report(y_train,y_pred))\n",
    "\n",
    "    print(\"\\n----------Accuracy Scores on Train data------------------------------------\")\n",
    "    print(\"F1 Score: \", f1_score(y_train,y_pred))\n",
    "    print(\"Precision Score: \", precision_score(y_train,y_pred))\n",
    "    print(\"Recall Score: \", recall_score(y_train,y_pred))\n",
    "\n",
    "    print(\"\\n----------Accuracy Scores on Cross validation data------------------------------------\")\n",
    "    y_pred_cv = cross_val_predict(model,x_train,y_train,cv=5)\n",
    "    print(\"F1 Score: \", f1_score(y_train,y_pred_cv))\n",
    "    print(\"Precision Score: \", precision_score(y_train,y_pred_cv))\n",
    "    print(\"Recall Score: \", recall_score(y_train,y_pred_cv))\n",
    "    \n",
    "    cnf_matrix = confusion_matrix(y_train,y_pred)\n",
    "    row_sum = cnf_matrix.sum(axis=1,keepdims=True)\n",
    "    cnf_matrix_norm =cnf_matrix / row_sum \n",
    "    sns.heatmap(cnf_matrix_norm,cmap='YlGnBu',annot=True)\n",
    "    plt.title(\"Normalized Confusion Matrix - Train data\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def build_model_test(model,x_test,y_test):\n",
    "\n",
    "    y_pred_test = model.predict(x_test)\n",
    "    #print(\"Clasification Report - Test Data\")\n",
    "    #print(classification_report(y_test,y_pred_test))\n",
    "\n",
    "    print(\"F1 Score: \", f1_score(y_test,y_pred_test))\n",
    "    print(\"Precision Score: \", precision_score(y_test,y_pred_test))\n",
    "    print(\"Recall Score: \", recall_score(y_test,y_pred_test))\n",
    "\n",
    "    cnf_matrix = confusion_matrix(y_test,y_pred_test)\n",
    "    row_sum = cnf_matrix.sum(axis=1,keepdims=True)\n",
    "    cnf_matrix_norm =cnf_matrix / row_sum \n",
    "    sns.heatmap(cnf_matrix_norm,cmap='YlGnBu',annot=True)\n",
    "    plt.title(\"Normalized Confusion Matrix - Test data\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def SelectThresholdByCV(probs,y):\n",
    "    best_threshold = 0\n",
    "    best_f1 = 0\n",
    "    f = 0\n",
    "    precision =0\n",
    "    recall=0\n",
    "    best_recall = 0\n",
    "    best_precision = 0\n",
    "    precisions=[]\n",
    "    recalls=[]\n",
    "    \n",
    "    thresholds = np.arange(0.0,1.0,0.001)\n",
    "    for threshold in thresholds:\n",
    "        predictions = (probs > threshold)\n",
    "        f = f1_score(y, predictions)\n",
    "        precision = precision_score(y, predictions)\n",
    "        recall = recall_score(y, predictions)\n",
    "        #print(\"Threshold {0},Precision {1},Recall {2}\".format(threshold,precision,recall))\n",
    "        \n",
    "        if f > best_f1:\n",
    "            best_f1 = f\n",
    "            best_precision = precision\n",
    "            best_recall = recall\n",
    "            best_threshold = threshold\n",
    "\n",
    "        precisions.append(precision)\n",
    "        recalls.append(recall)\n",
    "\n",
    "    #Precision-Recall Trade-off\n",
    "    plt.plot(thresholds,precisions,label='Precision')\n",
    "    plt.plot(thresholds,recalls,label='Recall')\n",
    "    plt.xlabel(\"Threshold\")\n",
    "    plt.title('Precision Recall Trade Off')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "    print ('Best F1 Score %f' %best_f1)\n",
    "    print ('Best Precision Score %f' %best_precision)\n",
    "    print ('Best Recall Score %f' %best_recall)\n",
    "    print ('Best Epsilon Score', best_threshold)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def SelectThresholdByCV_Anomaly(probs,y):\n",
    "    best_epsilon = 0\n",
    "    best_f1 = 0\n",
    "    f = 0\n",
    "    precision =0\n",
    "    recall=0\n",
    "    best_recall = 0\n",
    "    best_precision = 0\n",
    "    \n",
    "    epsilons = sorted(np.unique(probs))\n",
    "    #print(epsilons)\n",
    "    \n",
    "    precisions=[]\n",
    "    recalls=[]\n",
    "    for epsilon in epsilons:\n",
    "        predictions = (probs < epsilon)\n",
    "        f = f1_score(y, predictions)\n",
    "        precision = precision_score(y, predictions)\n",
    "        recall = recall_score(y, predictions)\n",
    "        #print(\"Theshold {0},Precision {1},Recall {2}\".format(epsilon,precision,recall))\n",
    "          \n",
    "        if f > best_f1:\n",
    "            best_f1 = f\n",
    "            best_precision = precision\n",
    "            best_recall = recall\n",
    "            best_epsilon = epsilon\n",
    "        \n",
    "        precisions.append(precision)\n",
    "        recalls.append(recall)\n",
    "\n",
    "    #Precision-Recall Trade-off\n",
    "    plt.plot(epsilons,precisions,label='Precision')\n",
    "    plt.plot(epsilons,recalls,label='Recall')\n",
    "    plt.xlabel(\"Epsilon\")\n",
    "    plt.title('Precision Recall Trade Off')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "    print ('Best F1 Score %f' %best_f1)\n",
    "    print ('Best Precision Score %f' %best_precision)\n",
    "    print ('Best Recall Score %f' %best_recall)\n",
    "    print ('Best Epsilon Score', best_epsilon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Print_Accuracy_Scores(y,y_pred):\n",
    "    print(\"F1 Score: \", f1_score(y,y_pred))\n",
    "    print(\"Precision Score: \", precision_score(y,y_pred))\n",
    "    print(\"Recall Score: \", recall_score(y,y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Loading Dataset\n",
    "cc_dataset = pd.read_csv(\"D:/HRao/TechDocs/DataScience/MLPython_DataJango/Notes/data/creditcard.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...         V21       V22       V23       V24  \\\n",
       "0  0.098698  0.363787  ...   -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425  ...   -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  ...    0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024  ...   -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  ...   -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28  Amount  Class  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>3.919560e-15</td>\n",
       "      <td>5.688174e-16</td>\n",
       "      <td>-8.769071e-15</td>\n",
       "      <td>2.782312e-15</td>\n",
       "      <td>-1.552563e-15</td>\n",
       "      <td>2.010663e-15</td>\n",
       "      <td>-1.694249e-15</td>\n",
       "      <td>-1.927028e-16</td>\n",
       "      <td>-3.137024e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.537294e-16</td>\n",
       "      <td>7.959909e-16</td>\n",
       "      <td>5.367590e-16</td>\n",
       "      <td>4.458112e-15</td>\n",
       "      <td>1.453003e-15</td>\n",
       "      <td>1.699104e-15</td>\n",
       "      <td>-3.660161e-16</td>\n",
       "      <td>-1.206049e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  3.919560e-15  5.688174e-16 -8.769071e-15  2.782312e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean  -1.552563e-15  2.010663e-15 -1.694249e-15 -1.927028e-16 -3.137024e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "           ...                 V21           V22           V23           V24  \\\n",
       "count      ...        2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean       ...        1.537294e-16  7.959909e-16  5.367590e-16  4.458112e-15   \n",
       "std        ...        7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min        ...       -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%        ...       -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%        ...       -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%        ...        1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max        ...        2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   1.453003e-15  1.699104e-15 -3.660161e-16 -1.206049e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      False\n",
       "V1        False\n",
       "V2        False\n",
       "V3        False\n",
       "V4        False\n",
       "V5        False\n",
       "V6        False\n",
       "V7        False\n",
       "V8        False\n",
       "V9        False\n",
       "V10       False\n",
       "V11       False\n",
       "V12       False\n",
       "V13       False\n",
       "V14       False\n",
       "V15       False\n",
       "V16       False\n",
       "V17       False\n",
       "V18       False\n",
       "V19       False\n",
       "V20       False\n",
       "V21       False\n",
       "V22       False\n",
       "V23       False\n",
       "V24       False\n",
       "V25       False\n",
       "V26       False\n",
       "V27       False\n",
       "V28       False\n",
       "Amount    False\n",
       "Class     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code for checking if any feature has null values\n",
    "cc_dataset.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Counts for each class in the dataset. As you can see, we have only 492 (0.17%) fraud cases out of 284807 records. Remaining 284315 (99.8%) of the records belong to genuine cases.\n",
    "cc_dataset['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x147e5828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Data Visualization for checking the distribution for Genuine cases & Fraud cases for each feature\n",
    "v_features = cc_dataset.columns\n",
    "plt.figure(figsize=(12,31*4))\n",
    "gs = gridspec.GridSpec(31,1)\n",
    "\n",
    "for i, col in enumerate(v_features):\n",
    "    ax = plt.subplot(gs[i])\n",
    "    sns.distplot(cc_dataset[col][cc_dataset['Class']==0],color='g',label='Genuine Class')\n",
    "    sns.distplot(cc_dataset[col][cc_dataset['Class']==1],color='r',label='Fraud Class')\n",
    "    ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature selection: \n",
    "    1) We can see Normal Distribution of anomalous transactions (class = 1) is matching with Normal Distribution of genuine transactions (class = 0) for V28','V27','V26','V25','V24','V23','V22','V20','V15','V13','V8' features. It is better to delete these features as they may not be useful in finding anomalous records.\n",
    "    2) Time is also not useful variable since it contains the seconds elapsed between the transaction for that record and the first transaction in the dataset. So the data is in increasing order always.\n",
    "\n",
    "However we'll delete these features only after building a dumb model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
       "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
       "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Splitting the input features and target label into different arrays\n",
    "X = cc_dataset.iloc[:,0:-1]\n",
    "Y = cc_dataset.iloc[:,-1]\n",
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Train Test split - By default train_test_split does STRATIFIED split based on label (y-value).\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Feature Scaling - Standardizing the scales for all x variables\n",
    "#PN: We should apply fit_transform() method on train set & only transform() method on test set\n",
    "sc = StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Approach 1 - Adhoc Approach \n",
    "\n",
    "In this approach, we applied mutiple models on the data, did error analysis & tried to improve model performance.\n",
    "\n",
    "Step1 - SGDClassifier model\n",
    "\n",
    "Step2 - Ensemble Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step1 - SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight='balanced',\n",
       "       epsilon=0.1, eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n",
       "       n_jobs=1, penalty='l2', power_t=0.5, random_state=0, shuffle=True,\n",
       "       tol=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Build a basic SGDClassifier model\n",
    "sgd_cls = SGDClassifier(class_weight='balanced', random_state=0)\n",
    "sgd_cls.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Predict target variable (Y-label)\n",
    "y_pred = sgd_cls.predict(x_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the data set is imbalanced, we should go with f1-score for comparing models & shouldn’t use accuracy_score() & roc_auc_score() for the below reasons.\n",
    "\n",
    "    1) accuracy_score looks into how many records are predicted correctly. So in the imbalance data set, even if we predict all records as 0 (genuine class) the accuracy score will still be more than 99%.\n",
    "    2) roc_auc_score is the plot between TRUE POSITIVE RATE (recall/sensitivity) and FALSE POSITIVE RATE (1 - true negative rate). Since it focuses on both positives and negatives equally, it works well for the balanced datasets and doesn’t work well for imbalanced datasets.\n",
    "    3) F1-Score is a harmonic mean of precision and recall (harmonic mean gives more weight to low value). As a result, the classifier will give high F1-Score only if both Precision and Recall are high. F1-Score focuses more on true positive cases than true negative cases. So we should use F1-Score to compare multiple models as the positive case is rare.\n",
    "\n",
    "For Fraud detection data, recall score is more important than precison score, since the classifier should classify all the fraud cases correctly (High Recall). However it is OK to incorrectly predict some genuine classes as fraud classes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img height=\"600\" width=\"750\" src=\"precision_recall.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.95      0.97    227454\n",
      "          1       0.03      0.93      0.06       391\n",
      "\n",
      "avg / total       1.00      0.95      0.97    227845\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Classification_report provides main classification metrics(precision, recall & f1-score) for each class\n",
    "print(classification_report(y_train,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score:  0.05901532442125856\n",
      "Precision Score:  0.030479077208049172\n",
      "Recall Score:  0.9258312020460358\n"
     ]
    }
   ],
   "source": [
    "Print_Accuracy_Scores(y_train,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the basic SGDClassifier model we got a very good score for Recall but Precision is very low. So we need to approach GridSearchCV to tune and find best hyper-parameter values.\n",
    "\n",
    "Note: We have commented GridSearchCV code as it is taking long time to execute. So we took only couple of hyper-parameters & build the best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "----------Accuracy Scores on Train data------------------------------------\n",
      "F1 Score:  0.8120104438642297\n",
      "Precision Score:  0.8293333333333334\n",
      "Recall Score:  0.7953964194373402\n",
      "\n",
      "----------Accuracy Scores on Cross validation data------------------------------------\n",
      "F1 Score:  0.8010139416983523\n",
      "Precision Score:  0.7939698492462312\n",
      "Recall Score:  0.8081841432225064\n",
      "\n",
      "----------Accuracy Scores on Test data------------------------------------\n",
      "F1 Score:  0.8177339901477833\n",
      "Precision Score:  0.8137254901960784\n",
      "Recall Score:  0.8217821782178217\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14616710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Build the best model out of GridSearchCV\n",
    "best_sgd_clf = SGDClassifier(alpha=5, class_weight='balanced',random_state=0)\n",
    "build_model_train_test(best_sgd_clf,x_train,x_test,y_train,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "By looking at the performance measures on Test data, So we can conclude that SGDClassifier has provided good results on both train and test data sets with F1, Precision and Recall scores around 80%\n",
    "\n",
    "Note: For fraud detection, recall score is more important than precision score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I've used class_weight='balanced' parameter for all the models since the data is imbalanced. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as \"n_samples / (n_classes * np.bincount(y))\". So the model applies more weights to rare classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confusion Matrix - Normalization can be interesting in case of class imbalance to have a more visual interpretation of which class is being misclassified. You can see from the above normalized confusion matrix that 20% of the Fraud records are misclassified in this model. So there is still lot of scope for improvement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V11       0.049538\n",
      "V4        0.046046\n",
      "V2        0.015425\n",
      "V21       0.013185\n",
      "V27       0.008045\n",
      "Amount    0.005440\n",
      "V28       0.005412\n",
      "V19       0.004297\n",
      "V20       0.003498\n",
      "V25       0.000611\n",
      "V26       0.000114\n",
      "V15       0.000028\n",
      "V22      -0.000997\n",
      "Time     -0.003113\n",
      "V5       -0.004090\n",
      "V24      -0.004184\n",
      "V13      -0.005872\n",
      "V23      -0.006081\n",
      "V8       -0.009319\n",
      "V18      -0.011246\n",
      "V1       -0.014094\n",
      "V6       -0.017773\n",
      "V9       -0.021456\n",
      "V7       -0.031597\n",
      "V16      -0.038148\n",
      "V3       -0.047460\n",
      "V17      -0.054354\n",
      "V10      -0.057447\n",
      "V12      -0.070584\n",
      "V14      -0.107986\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "featimp = pd.Series(best_sgd_clf.coef_.ravel(),index=X.columns).sort_values(ascending=False)\n",
    "print(featimp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that coefficients for SGDClassifier model are almost close to zero for the features: V27,Amount,V28,V19,V20,V25,V26,V15,V22,Time,V5,V24,V13,V8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Decision scores or Confidence scores - Is a measure of distance of that sample to decision boundary hyperplane\n",
    "y_pred_decision = best_sgd_clf.decision_function(x_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Precision recall curve - Computes precision-recall pairs for different probability thresholds\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_train,y_pred_decision)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Precision Recall Curve')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c68afd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(recalls,precisions)\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision Recall Curve')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cab6278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Precision-Recall Trade-off\n",
    "plt.plot(thresholds,precisions[:-1],label='Precision')\n",
    "plt.plot(thresholds,recalls[:-1],label='Recall')\n",
    "\n",
    "plt.xlabel(\"Decision Score\")\n",
    "plt.title('Precision Recall Trade Off')\n",
    "plt.legend()\n",
    "plt.xlim([-10,20])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Precision Recall Trade Off also suggests that decision score of 0 provides better accuracy scores for Precision and Recall. So we can assume this is the best model from SGDClassifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Demonstration purpose, I created ROC curve as well below. However ROC AUC score is not a good measure for imbalance data sets as discussed above.\n",
    "You can notice that the above model has given us the AUC score of 0.98, which is very high."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cabaf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, thesholds = roc_curve(y_train,y_pred_decision)\n",
    "\n",
    "plt.plot(fpr,tpr)\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"ROC Curve\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9815024119882187"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train,y_pred_decision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step2 - Ensemble Models\n",
    "\n",
    "Ensemble models may handle imbalanced datasets better due to their nature of building multiple models randomly and aggregate their predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# #Use GridSearchCV to find the best parameters for RandomForest algorithm\n",
    "# param_grid = {\n",
    "#     'n_estimators':[50,100,200],\n",
    "#     'max_depth':[4,6,8,10],\n",
    "#     'min_samples_split':[5,10,20,50]\n",
    "# }\n",
    "\n",
    "# rnd_clf = RandomForestClassifier(criterion='gini',class_weight='balanced', n_jobs=-1, random_state=0)\n",
    "# grid_search = GridSearchCV(rnd_clf,param_grid,cv=5,scoring='f1_weighted')\n",
    "# grid_search.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "----------Accuracy Scores on Train data------------------------------------\n",
      "F1 Score:  0.9240196078431372\n",
      "Precision Score:  0.8870588235294118\n",
      "Recall Score:  0.9641943734015346\n",
      "\n",
      "----------Accuracy Scores on Cross validation data------------------------------------\n",
      "F1 Score:  0.8165997322623828\n",
      "Precision Score:  0.8567415730337079\n",
      "Recall Score:  0.7800511508951407\n",
      "\n",
      "----------Accuracy Scores on Test data------------------------------------\n",
      "F1 Score:  0.8426395939086294\n",
      "Precision Score:  0.8645833333333334\n",
      "Recall Score:  0.8217821782178217\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3a3828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Best estimator of random forest\n",
    "rnd_clf = RandomForestClassifier(bootstrap=True, class_weight='balanced',\n",
    "            criterion='gini', max_depth=10, max_features='auto',\n",
    "            max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
    "            min_impurity_split=None, min_samples_leaf=1,\n",
    "            min_samples_split=5, min_weight_fraction_leaf=0.0,\n",
    "            n_estimators=50, n_jobs=-1, oob_score=False, random_state=0,\n",
    "            verbose=0, warm_start=False)\n",
    "build_model_train_test(rnd_clf,x_train,x_test,y_train,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By looking at the performance measures, we can say that Random Forest is definitely giving us better accuracy than SGDClassifier. So I'm going to explore more on Random Forest model further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V14       0.260662\n",
      "V10       0.135446\n",
      "V12       0.104045\n",
      "V4        0.075028\n",
      "V11       0.070356\n",
      "V16       0.052525\n",
      "V2        0.039300\n",
      "V17       0.037054\n",
      "V3        0.030586\n",
      "V18       0.021279\n",
      "V21       0.015656\n",
      "V8        0.015445\n",
      "V19       0.014812\n",
      "V7        0.014673\n",
      "V20       0.011620\n",
      "V5        0.010967\n",
      "V27       0.010901\n",
      "Amount    0.010281\n",
      "V15       0.007762\n",
      "V1        0.007631\n",
      "V26       0.007295\n",
      "V13       0.006983\n",
      "V28       0.006392\n",
      "V25       0.005870\n",
      "V9        0.005737\n",
      "V23       0.005567\n",
      "V6        0.004747\n",
      "Time      0.004264\n",
      "V22       0.004034\n",
      "V24       0.003082\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Check the Feature importance scores from Random Forest model\n",
    "featimp = pd.Series(rnd_clf.feature_importances_,index=X.columns).sort_values(ascending=False)\n",
    "print(featimp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that most of the features we have decided to remove after looking at the distribution earlier are having low importance scores in random forest model & also coefficients close to zero at SGCClassifier. Let us remove these features for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V9', 'V10', 'V11', 'V12',\n",
       "       'V14', 'V16', 'V17', 'V18', 'V19', 'V21', 'Amount', 'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_dataset.drop(labels = ['V28','V27','V26','V25','V24','V23','V22','V20','V15','V13','V8','Time'], axis = 1, inplace=True)\n",
    "cc_dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>V11</th>\n",
       "      <th>V12</th>\n",
       "      <th>V14</th>\n",
       "      <th>V16</th>\n",
       "      <th>V17</th>\n",
       "      <th>V18</th>\n",
       "      <th>V19</th>\n",
       "      <th>V21</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.919560e-15</td>\n",
       "      <td>5.688174e-16</td>\n",
       "      <td>-8.769071e-15</td>\n",
       "      <td>2.782312e-15</td>\n",
       "      <td>-1.552563e-15</td>\n",
       "      <td>2.010663e-15</td>\n",
       "      <td>-1.694249e-15</td>\n",
       "      <td>-3.137024e-15</td>\n",
       "      <td>1.768627e-15</td>\n",
       "      <td>9.170318e-16</td>\n",
       "      <td>-1.810658e-15</td>\n",
       "      <td>1.479045e-15</td>\n",
       "      <td>1.392007e-15</td>\n",
       "      <td>-7.528491e-16</td>\n",
       "      <td>4.328772e-16</td>\n",
       "      <td>9.049732e-16</td>\n",
       "      <td>1.537294e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>1.088850e+00</td>\n",
       "      <td>1.020713e+00</td>\n",
       "      <td>9.992014e-01</td>\n",
       "      <td>9.585956e-01</td>\n",
       "      <td>8.762529e-01</td>\n",
       "      <td>8.493371e-01</td>\n",
       "      <td>8.381762e-01</td>\n",
       "      <td>8.140405e-01</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>-2.458826e+01</td>\n",
       "      <td>-4.797473e+00</td>\n",
       "      <td>-1.868371e+01</td>\n",
       "      <td>-1.921433e+01</td>\n",
       "      <td>-1.412985e+01</td>\n",
       "      <td>-2.516280e+01</td>\n",
       "      <td>-9.498746e+00</td>\n",
       "      <td>-7.213527e+00</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>-5.354257e-01</td>\n",
       "      <td>-7.624942e-01</td>\n",
       "      <td>-4.055715e-01</td>\n",
       "      <td>-4.255740e-01</td>\n",
       "      <td>-4.680368e-01</td>\n",
       "      <td>-4.837483e-01</td>\n",
       "      <td>-4.988498e-01</td>\n",
       "      <td>-4.562989e-01</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>-9.291738e-02</td>\n",
       "      <td>-3.275735e-02</td>\n",
       "      <td>1.400326e-01</td>\n",
       "      <td>5.060132e-02</td>\n",
       "      <td>6.641332e-02</td>\n",
       "      <td>-6.567575e-02</td>\n",
       "      <td>-3.636312e-03</td>\n",
       "      <td>3.734823e-03</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>4.539234e-01</td>\n",
       "      <td>7.395934e-01</td>\n",
       "      <td>6.182380e-01</td>\n",
       "      <td>4.931498e-01</td>\n",
       "      <td>5.232963e-01</td>\n",
       "      <td>3.996750e-01</td>\n",
       "      <td>5.008067e-01</td>\n",
       "      <td>4.589494e-01</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>2.374514e+01</td>\n",
       "      <td>1.201891e+01</td>\n",
       "      <td>7.848392e+00</td>\n",
       "      <td>1.052677e+01</td>\n",
       "      <td>1.731511e+01</td>\n",
       "      <td>9.253526e+00</td>\n",
       "      <td>5.041069e+00</td>\n",
       "      <td>5.591971e+00</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 V1            V2            V3            V4            V5  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   3.919560e-15  5.688174e-16 -8.769071e-15  2.782312e-15 -1.552563e-15   \n",
       "std    1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00  1.380247e+00   \n",
       "min   -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00 -1.137433e+02   \n",
       "25%   -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01 -6.915971e-01   \n",
       "50%    1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02 -5.433583e-02   \n",
       "75%    1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01  6.119264e-01   \n",
       "max    2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01  3.480167e+01   \n",
       "\n",
       "                 V6            V7            V9           V10           V11  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   2.010663e-15 -1.694249e-15 -3.137024e-15  1.768627e-15  9.170318e-16   \n",
       "std    1.332271e+00  1.237094e+00  1.098632e+00  1.088850e+00  1.020713e+00   \n",
       "min   -2.616051e+01 -4.355724e+01 -1.343407e+01 -2.458826e+01 -4.797473e+00   \n",
       "25%   -7.682956e-01 -5.540759e-01 -6.430976e-01 -5.354257e-01 -7.624942e-01   \n",
       "50%   -2.741871e-01  4.010308e-02 -5.142873e-02 -9.291738e-02 -3.275735e-02   \n",
       "75%    3.985649e-01  5.704361e-01  5.971390e-01  4.539234e-01  7.395934e-01   \n",
       "max    7.330163e+01  1.205895e+02  1.559499e+01  2.374514e+01  1.201891e+01   \n",
       "\n",
       "                V12           V14           V16           V17           V18  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean  -1.810658e-15  1.479045e-15  1.392007e-15 -7.528491e-16  4.328772e-16   \n",
       "std    9.992014e-01  9.585956e-01  8.762529e-01  8.493371e-01  8.381762e-01   \n",
       "min   -1.868371e+01 -1.921433e+01 -1.412985e+01 -2.516280e+01 -9.498746e+00   \n",
       "25%   -4.055715e-01 -4.255740e-01 -4.680368e-01 -4.837483e-01 -4.988498e-01   \n",
       "50%    1.400326e-01  5.060132e-02  6.641332e-02 -6.567575e-02 -3.636312e-03   \n",
       "75%    6.182380e-01  4.931498e-01  5.232963e-01  3.996750e-01  5.008067e-01   \n",
       "max    7.848392e+00  1.052677e+01  1.731511e+01  9.253526e+00  5.041069e+00   \n",
       "\n",
       "                V19           V21         Amount          Class  \n",
       "count  2.848070e+05  2.848070e+05  284807.000000  284807.000000  \n",
       "mean   9.049732e-16  1.537294e-16      88.349619       0.001727  \n",
       "std    8.140405e-01  7.345240e-01     250.120109       0.041527  \n",
       "min   -7.213527e+00 -3.483038e+01       0.000000       0.000000  \n",
       "25%   -4.562989e-01 -2.283949e-01       5.600000       0.000000  \n",
       "50%    3.734823e-03 -2.945017e-02      22.000000       0.000000  \n",
       "75%    4.589494e-01  1.863772e-01      77.165000       0.000000  \n",
       "max    5.591971e+00  2.720284e+01   25691.160000       1.000000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cb15240>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15327e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(cc_dataset['Amount'],bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The feature 'Amount' has higher standard deviation of 250, which indicate the spread is very high & also we might have outliers in the data. So let us go for feature scaling for Amount variable using StandardScaler()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>V11</th>\n",
       "      <th>V12</th>\n",
       "      <th>V14</th>\n",
       "      <th>V16</th>\n",
       "      <th>V17</th>\n",
       "      <th>V18</th>\n",
       "      <th>V19</th>\n",
       "      <th>V21</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.919560e-15</td>\n",
       "      <td>5.688174e-16</td>\n",
       "      <td>-8.769071e-15</td>\n",
       "      <td>2.782312e-15</td>\n",
       "      <td>-1.552563e-15</td>\n",
       "      <td>2.010663e-15</td>\n",
       "      <td>-1.694249e-15</td>\n",
       "      <td>-3.137024e-15</td>\n",
       "      <td>1.768627e-15</td>\n",
       "      <td>9.170318e-16</td>\n",
       "      <td>-1.810658e-15</td>\n",
       "      <td>1.479045e-15</td>\n",
       "      <td>1.392007e-15</td>\n",
       "      <td>-7.528491e-16</td>\n",
       "      <td>4.328772e-16</td>\n",
       "      <td>9.049732e-16</td>\n",
       "      <td>1.537294e-16</td>\n",
       "      <td>3.202236e-16</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>1.088850e+00</td>\n",
       "      <td>1.020713e+00</td>\n",
       "      <td>9.992014e-01</td>\n",
       "      <td>9.585956e-01</td>\n",
       "      <td>8.762529e-01</td>\n",
       "      <td>8.493371e-01</td>\n",
       "      <td>8.381762e-01</td>\n",
       "      <td>8.140405e-01</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>1.000002e+00</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>-2.458826e+01</td>\n",
       "      <td>-4.797473e+00</td>\n",
       "      <td>-1.868371e+01</td>\n",
       "      <td>-1.921433e+01</td>\n",
       "      <td>-1.412985e+01</td>\n",
       "      <td>-2.516280e+01</td>\n",
       "      <td>-9.498746e+00</td>\n",
       "      <td>-7.213527e+00</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-3.532294e-01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>-5.354257e-01</td>\n",
       "      <td>-7.624942e-01</td>\n",
       "      <td>-4.055715e-01</td>\n",
       "      <td>-4.255740e-01</td>\n",
       "      <td>-4.680368e-01</td>\n",
       "      <td>-4.837483e-01</td>\n",
       "      <td>-4.988498e-01</td>\n",
       "      <td>-4.562989e-01</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-3.308401e-01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>-9.291738e-02</td>\n",
       "      <td>-3.275735e-02</td>\n",
       "      <td>1.400326e-01</td>\n",
       "      <td>5.060132e-02</td>\n",
       "      <td>6.641332e-02</td>\n",
       "      <td>-6.567575e-02</td>\n",
       "      <td>-3.636312e-03</td>\n",
       "      <td>3.734823e-03</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>-2.652715e-01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>4.539234e-01</td>\n",
       "      <td>7.395934e-01</td>\n",
       "      <td>6.182380e-01</td>\n",
       "      <td>4.931498e-01</td>\n",
       "      <td>5.232963e-01</td>\n",
       "      <td>3.996750e-01</td>\n",
       "      <td>5.008067e-01</td>\n",
       "      <td>4.589494e-01</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>-4.471707e-02</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>2.374514e+01</td>\n",
       "      <td>1.201891e+01</td>\n",
       "      <td>7.848392e+00</td>\n",
       "      <td>1.052677e+01</td>\n",
       "      <td>1.731511e+01</td>\n",
       "      <td>9.253526e+00</td>\n",
       "      <td>5.041069e+00</td>\n",
       "      <td>5.591971e+00</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.023622e+02</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 V1            V2            V3            V4            V5  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   3.919560e-15  5.688174e-16 -8.769071e-15  2.782312e-15 -1.552563e-15   \n",
       "std    1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00  1.380247e+00   \n",
       "min   -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00 -1.137433e+02   \n",
       "25%   -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01 -6.915971e-01   \n",
       "50%    1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02 -5.433583e-02   \n",
       "75%    1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01  6.119264e-01   \n",
       "max    2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01  3.480167e+01   \n",
       "\n",
       "                 V6            V7            V9           V10           V11  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   2.010663e-15 -1.694249e-15 -3.137024e-15  1.768627e-15  9.170318e-16   \n",
       "std    1.332271e+00  1.237094e+00  1.098632e+00  1.088850e+00  1.020713e+00   \n",
       "min   -2.616051e+01 -4.355724e+01 -1.343407e+01 -2.458826e+01 -4.797473e+00   \n",
       "25%   -7.682956e-01 -5.540759e-01 -6.430976e-01 -5.354257e-01 -7.624942e-01   \n",
       "50%   -2.741871e-01  4.010308e-02 -5.142873e-02 -9.291738e-02 -3.275735e-02   \n",
       "75%    3.985649e-01  5.704361e-01  5.971390e-01  4.539234e-01  7.395934e-01   \n",
       "max    7.330163e+01  1.205895e+02  1.559499e+01  2.374514e+01  1.201891e+01   \n",
       "\n",
       "                V12           V14           V16           V17           V18  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean  -1.810658e-15  1.479045e-15  1.392007e-15 -7.528491e-16  4.328772e-16   \n",
       "std    9.992014e-01  9.585956e-01  8.762529e-01  8.493371e-01  8.381762e-01   \n",
       "min   -1.868371e+01 -1.921433e+01 -1.412985e+01 -2.516280e+01 -9.498746e+00   \n",
       "25%   -4.055715e-01 -4.255740e-01 -4.680368e-01 -4.837483e-01 -4.988498e-01   \n",
       "50%    1.400326e-01  5.060132e-02  6.641332e-02 -6.567575e-02 -3.636312e-03   \n",
       "75%    6.182380e-01  4.931498e-01  5.232963e-01  3.996750e-01  5.008067e-01   \n",
       "max    7.848392e+00  1.052677e+01  1.731511e+01  9.253526e+00  5.041069e+00   \n",
       "\n",
       "                V19           V21        Amount          Class  \n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000  \n",
       "mean   9.049732e-16  1.537294e-16  3.202236e-16       0.001727  \n",
       "std    8.140405e-01  7.345240e-01  1.000002e+00       0.041527  \n",
       "min   -7.213527e+00 -3.483038e+01 -3.532294e-01       0.000000  \n",
       "25%   -4.562989e-01 -2.283949e-01 -3.308401e-01       0.000000  \n",
       "50%    3.734823e-03 -2.945017e-02 -2.652715e-01       0.000000  \n",
       "75%    4.589494e-01  1.863772e-01 -4.471707e-02       0.000000  \n",
       "max    5.591971e+00  2.720284e+01  1.023622e+02       1.000000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_dataset['Amount'] = StandardScaler().fit_transform(cc_dataset['Amount'].reshape(-1,1))\n",
    "cc_dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cac5630>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cae5908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(cc_dataset['Amount'][cc_dataset['Class']==0],color='g',label='Genuine Class')\n",
    "sns.distplot(cc_dataset['Amount'][cc_dataset['Class']==1],color='r',label='Fraud Class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of training data 227845\n",
      "Length of test data 56962\n"
     ]
    }
   ],
   "source": [
    "#Data Preparation\n",
    "x_train,x_test,y_train,y_test = data_preparation(cc_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "----------Accuracy Scores on Train data------------------------------------\n",
      "F1 Score:  0.9191797346200241\n",
      "Precision Score:  0.8698630136986302\n",
      "Recall Score:  0.9744245524296675\n",
      "\n",
      "----------Accuracy Scores on Cross validation data------------------------------------\n",
      "F1 Score:  0.8300395256916996\n",
      "Precision Score:  0.8559782608695652\n",
      "Recall Score:  0.8056265984654731\n",
      "\n",
      "----------Accuracy Scores on Test data------------------------------------\n",
      "F1 Score:  0.8442211055276382\n",
      "Precision Score:  0.8571428571428571\n",
      "Recall Score:  0.8316831683168316\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e227a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let us check the performance of RandomForest after removing few features\n",
    "build_model_train_test(rnd_clf,x_train,x_test,y_train,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can notice that RandomForest has given pretty decent performance on this dataset though it is overfitting little bit. Also notice that same accuracy is maintained even after removing the features ('V28','V27','V26','V25','V24','V23','V22','V20','V15','V13','V8','Time')."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I developed KNN algorithm here, mainly to use it in Voting Classifier. But KNN is giving us pretty good performance as well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "----------Accuracy Scores on Train data------------------------------------\n",
      "F1 Score:  0.8885793871866295\n",
      "Precision Score:  0.9755351681957186\n",
      "Recall Score:  0.8158567774936062\n",
      "\n",
      "----------Accuracy Scores on Cross validation data------------------------------------\n",
      "F1 Score:  0.8523676880222842\n",
      "Precision Score:  0.9357798165137615\n",
      "Recall Score:  0.782608695652174\n",
      "\n",
      "----------Accuracy Scores on Test data------------------------------------\n",
      "F1 Score:  0.8631578947368421\n",
      "Precision Score:  0.9213483146067416\n",
      "Recall Score:  0.8118811881188119\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef34e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_clf = KNeighborsClassifier(n_neighbors=3)\n",
    "build_model_train_test(knn_clf,x_train,x_test,y_train,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us also try AdaBoostClassifier before using voting classifier. \n",
    "Looking at below scores, we can say that AdaBoost is overfitting on the training data. Overall a good performance on the cross validation and test dataset nevertheless."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "----------Accuracy Scores on Train data------------------------------------\n",
      "F1 Score:  1.0\n",
      "Precision Score:  1.0\n",
      "Recall Score:  1.0\n",
      "\n",
      "----------Accuracy Scores on Cross validation data------------------------------------\n",
      "F1 Score:  0.8349249658935881\n",
      "Precision Score:  0.8947368421052632\n",
      "Recall Score:  0.782608695652174\n",
      "\n",
      "----------Accuracy Scores on Test data------------------------------------\n",
      "F1 Score:  0.8307692307692307\n",
      "Precision Score:  0.8617021276595744\n",
      "Recall Score:  0.801980198019802\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb5a780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ada_clf = AdaBoostClassifier(\n",
    "        DecisionTreeClassifier(max_depth=3,class_weight='balanced'), n_estimators=100,\n",
    "        algorithm=\"SAMME.R\", learning_rate=0.5, random_state=0\n",
    "    )\n",
    "build_model_train_test(ada_clf,x_train,x_test,y_train,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RandomForest, AdaBoost & KNN are all providing good accuracy measures. So let us include all of them in a soft voting classifier and see if we can improve the performance even further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "----------Accuracy Scores on Train data------------------------------------\n",
      "F1 Score:  0.9534883720930233\n",
      "Precision Score:  0.9634464751958225\n",
      "Recall Score:  0.9437340153452686\n",
      "\n",
      "----------Accuracy Scores on Cross validation data------------------------------------\n",
      "F1 Score:  0.8583218707015131\n",
      "Precision Score:  0.9285714285714286\n",
      "Recall Score:  0.7979539641943734\n",
      "\n",
      "----------Accuracy Scores on Test data------------------------------------\n",
      "F1 Score:  0.8795811518324607\n",
      "Precision Score:  0.9333333333333333\n",
      "Recall Score:  0.8316831683168316\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x208b9128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "soft_voting_clf = VotingClassifier(\n",
    "    estimators=[('rf', rnd_clf), ('ada', ada_clf), ('knn',knn_clf)], \n",
    "    voting='soft')\n",
    "\n",
    "build_model_train_test(soft_voting_clf,x_train,x_test,y_train,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm pretty happy with the outcome of Voting Classifier here though it is over fitting on the train data set. But a deviation of 5% is acceptable.\n",
    "I'd like to improve the recall score further. So let us use predict_proba to get the probabilities and see if we can improve the recall by using a different threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Probability scores for each record for prediction\n",
    "probs_sv_test = soft_voting_clf.predict_proba(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c68f908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best F1 Score 0.888889\n",
      "Best Precision Score 0.954545\n",
      "Best Recall Score 0.831683\n",
      "Best Epsilon Score 0.5710000000000001\n"
     ]
    }
   ],
   "source": [
    "#Choose the best probability threshold where F1-score is highest\n",
    "SelectThresholdByCV(probs_sv_test[:,1],y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score:  0.888888888888889\n",
      "Precision Score:  0.9545454545454546\n",
      "Recall Score:  0.8316831683168316\n"
     ]
    }
   ],
   "source": [
    "y_pred_test = (probs_sv_test[:,1] > 0.571)\n",
    "Print_Accuracy_Scores(y_test,y_pred_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However we couldn't improve the recall score much on the test data. Precision is going down drastically if we try to improve recall little bit. So I'd imagine that the model has given us best results with 83 recall score and 95 precision score at 0.57 threshold."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion: From adhoc approach, we have seen slightly better results for Voting Classifier than RandomForest. So we could go with Voting Classifier with F1-score of 89 and Recall score of 83. \n",
    "However we are going to check the performance of both Voting Classifier and RandomForest in our next approach using resampling technique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Approach 2 - Resampling Techniques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this approach we are going to balance the data using resampling techniques. The main objective of resampling is to balance classes by either increasing the frequency of minority class or decreasing the frequency of majority class.\n",
    "\n",
    "We can use imbalanced-learn library of python to resample the data. But we are going to use manual approach first for the demonstration purpose and see the synthetic approach later on using oversampling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step1 - Under Sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we randomly choose only a sample of genuine records, which will make sure to balance the instances of majority and minority classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "genuine_data = cc_dataset[cc_dataset['Class']==0]\n",
    "fraud_data = cc_dataset[cc_dataset['Class']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "genuine_indexes = genuine_data.index\n",
    "fraud_indexes = fraud_data.index\n",
    "count_genuine = len(genuine_data)\n",
    "count_fraud = len(fraud_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "492\n"
     ]
    }
   ],
   "source": [
    "#Choose a smaple of genuine records which is equal to number of fraud records \n",
    "genuine_indexes_undersample = np.random.choice(genuine_indexes,count_fraud,replace=False)\n",
    "print(len(genuine_indexes_undersample))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "984\n"
     ]
    }
   ],
   "source": [
    "#Merge under-sampled genuine records with fraud records to get the final under-sampled dataset\n",
    "undersample_indexes = np.concatenate((genuine_indexes_undersample,fraud_indexes),axis=0)\n",
    "print(len(undersample_indexes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "undersample_data = cc_dataset.iloc[undersample_indexes,:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Split X & Y\n",
    "us_data_x = undersample_data[:,0:-1]\n",
    "us_data_y = undersample_data[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "----------Accuracy Scores on Train data------------------------------------\n",
      "F1 Score:  1.0\n",
      "Precision Score:  1.0\n",
      "Recall Score:  1.0\n",
      "\n",
      "----------Accuracy Scores on Cross validation data------------------------------------\n",
      "F1 Score:  0.9282700421940928\n",
      "Precision Score:  0.9649122807017544\n",
      "Recall Score:  0.8943089430894309\n",
      "\n",
      "----------Accuracy Scores on Test data------------------------------------\n",
      "F1 Score:  0.1470160116448326\n",
      "Precision Score:  0.0793401413982718\n",
      "Recall Score:  1.0\n"
     ]
    },
    {
     "data": {
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