04_Credit_Card_Fraud_Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARN: OMP_NUM_THREADS=None =>\n",
      "... If you are using openblas if you are using openblas set OMP_NUM_THREADS=1 or risk subprocess calls hanging indefinitely\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import ppscore as pps\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from hpsklearn import HyperoptEstimator, pca, min_max_scaler, standard_scaler\n",
    "from hpsklearn import xgboost_classification, random_forest, ada_boost, gradient_boosting, extra_trees\n",
    "from hpsklearn import svc, svc_linear, svc_rbf, svc_poly, svc_sigmoid, liblinear_svc\n",
    "from hpsklearn import any_classifier\n",
    "from hpsklearn import any_preprocessing\n",
    "from hyperopt import tpe, hp\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import f1_score, recall_score, classification_report,confusion_matrix,plot_confusion_matrix\n",
    "from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler, PowerTransformer, MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Data/creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <th>V9</th>\n",
       "      <th>...</th>\n",
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       "      <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",
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       "    <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",
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       "    <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",
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      "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       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not Fraud    284315\n",
      "Fraud           492\n",
      "Name: Class, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "class_names = {0:'Not Fraud', 1:'Fraud'}\n",
    "print(df.Class.value_counts().rename(index = class_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0017304750013189597\n"
     ]
    }
   ],
   "source": [
    "Fraud = df[df['Class']==1]\n",
    "Nofraud = df[df['Class']==0]\n",
    "outlier_fraction = len(Fraud)/float(len(Nofraud))\n",
    "print (outlier_fraction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>ppscore</th>\n",
       "      <th>case</th>\n",
       "      <th>is_valid_score</th>\n",
       "      <th>metric</th>\n",
       "      <th>baseline_score</th>\n",
       "      <th>model_score</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Time</td>\n",
       "      <td>Time</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>predict_itself</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Time</td>\n",
       "      <td>V1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>regression</td>\n",
       "      <td>True</td>\n",
       "      <td>mean absolute error</td>\n",
       "      <td>1.389729</td>\n",
       "      <td>1.830853</td>\n",
       "      <td>DecisionTreeRegressor()</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Time</td>\n",
       "      <td>V2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>regression</td>\n",
       "      <td>True</td>\n",
       "      <td>mean absolute error</td>\n",
       "      <td>0.977685</td>\n",
       "      <td>1.439041</td>\n",
       "      <td>DecisionTreeRegressor()</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Time</td>\n",
       "      <td>V3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>regression</td>\n",
       "      <td>True</td>\n",
       "      <td>mean absolute error</td>\n",
       "      <td>1.127297</td>\n",
       "      <td>1.307829</td>\n",
       "      <td>DecisionTreeRegressor()</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Time</td>\n",
       "      <td>V4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>regression</td>\n",
       "      <td>True</td>\n",
       "      <td>mean absolute error</td>\n",
       "      <td>1.078434</td>\n",
       "      <td>1.528329</td>\n",
       "      <td>DecisionTreeRegressor()</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>Class</td>\n",
       "      <td>V26</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>regression</td>\n",
       "      <td>True</td>\n",
       "      <td>mean absolute error</td>\n",
       "      <td>0.379558</td>\n",
       "      <td>0.382253</td>\n",
       "      <td>DecisionTreeRegressor()</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>Class</td>\n",
       "      <td>V27</td>\n",
       "      <td>0.003908</td>\n",
       "      <td>regression</td>\n",
       "      <td>True</td>\n",
       "      <td>mean absolute error</td>\n",
       "      <td>0.175710</td>\n",
       "      <td>0.175024</td>\n",
       "      <td>DecisionTreeRegressor()</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>Class</td>\n",
       "      <td>V28</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>regression</td>\n",
       "      <td>True</td>\n",
       "      <td>mean absolute error</td>\n",
       "      <td>0.128624</td>\n",
       "      <td>0.128884</td>\n",
       "      <td>DecisionTreeRegressor()</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>959</th>\n",
       "      <td>Class</td>\n",
       "      <td>Amount</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>regression</td>\n",
       "      <td>True</td>\n",
       "      <td>mean absolute error</td>\n",
       "      <td>83.454490</td>\n",
       "      <td>106.054629</td>\n",
       "      <td>DecisionTreeRegressor()</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>Class</td>\n",
       "      <td>Class</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>predict_itself</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>961 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         x       y   ppscore            case  is_valid_score  \\\n",
       "0     Time    Time  1.000000  predict_itself            True   \n",
       "1     Time      V1  0.000000      regression            True   \n",
       "2     Time      V2  0.000000      regression            True   \n",
       "3     Time      V3  0.000000      regression            True   \n",
       "4     Time      V4  0.000000      regression            True   \n",
       "..     ...     ...       ...             ...             ...   \n",
       "956  Class     V26  0.000000      regression            True   \n",
       "957  Class     V27  0.003908      regression            True   \n",
       "958  Class     V28  0.000000      regression            True   \n",
       "959  Class  Amount  0.000000      regression            True   \n",
       "960  Class   Class  1.000000  predict_itself            True   \n",
       "\n",
       "                  metric  baseline_score  model_score                    model  \n",
       "0                   None        0.000000     1.000000                     None  \n",
       "1    mean absolute error        1.389729     1.830853  DecisionTreeRegressor()  \n",
       "2    mean absolute error        0.977685     1.439041  DecisionTreeRegressor()  \n",
       "3    mean absolute error        1.127297     1.307829  DecisionTreeRegressor()  \n",
       "4    mean absolute error        1.078434     1.528329  DecisionTreeRegressor()  \n",
       "..                   ...             ...          ...                      ...  \n",
       "956  mean absolute error        0.379558     0.382253  DecisionTreeRegressor()  \n",
       "957  mean absolute error        0.175710     0.175024  DecisionTreeRegressor()  \n",
       "958  mean absolute error        0.128624     0.128884  DecisionTreeRegressor()  \n",
       "959  mean absolute error       83.454490   106.054629  DecisionTreeRegressor()  \n",
       "960                 None        0.000000     1.000000                     None  \n",
       "\n",
       "[961 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pps.matrix(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Class',axis=1)\n",
    "y = df['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Missing Data Imputation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Statistical Imputation: \n",
    "\n",
    "    results = list()\n",
    "    strategies = ['mean', 'median', 'most_frequent', 'constant']\n",
    "    for s in strategies:\n",
    "        # create the modeling pipeline\n",
    "        pipeline = Pipeline(steps=[('i', SimpleImputer(strategy=s)), ('m', XGBClassifier())])\n",
    "        # evaluate the model\n",
    "        cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "        scores = cross_val_score(pipeline, X, y, scoring='accuracy', cv=cv, n_jobs=-1)\n",
    "        # store results\n",
    "        results.append(scores)\n",
    "        print('>%s %.3f (%.3f)' % (s, mean(scores), std(scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "KNN Imputation:\n",
    "\n",
    "    from sklearn.impute import KNNImputer\n",
    "    # evaluate each strategy on the dataset\n",
    "    results = list()\n",
    "    strategies = [str(i) for i in [1,3,5,7,9,15,18,21]]\n",
    "    for s in strategies:\n",
    "        # create the modeling pipeline\n",
    "        pipeline = Pipeline(steps=[('i', KNNImputer(n_neighbors=int(s))), ('m', XGBClassifier())])\n",
    "        # evaluate the model\n",
    "        cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "        scores = cross_val_score(pipeline, X, y, scoring='accuracy', cv=cv, n_jobs=-1)\n",
    "        # store results\n",
    "        results.append(scores)\n",
    "        print('>%s %.3f (%.3f)' % (s, mean(scores), std(scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Iterative Imputation:\n",
    "\n",
    "    from sklearn.experimental import enable_iterative_imputer\n",
    "    from sklearn.impute import IterativeImputer\n",
    "    # evaluate each strategy on the dataset\n",
    "    results = list()\n",
    "    strategies = [str(i) for i in range(1, 21)]\n",
    "    for s in strategies:\n",
    "        # create the modeling pipeline\n",
    "        pipeline = Pipeline(steps=[('i', IterativeImputer(max_iter=int(s))), ('m', XGBClassifier())])\n",
    "        # evaluate the model\n",
    "        cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "        scores = cross_val_score(pipeline, X, y, scoring='accuracy', cv=cv, n_jobs=-1)\n",
    "        # store results\n",
    "        results.append(scores)\n",
    "        print('>%s %.3f (%.3f)' % (s, mean(scores), std(scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deleting the row: \n",
    "\n",
    "        Lastly, you can delete the row. This is not usually recommended, but it is acceptable when you have an immense amount of data to start with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "preproc = hp.choice('myprepros_name', \n",
    "                    [\n",
    "                        [min_max_scaler('myprepros_name.only_norm')],\n",
    "                        [standard_scaler('myprepros_name.only_std_scaler')],\n",
    "                        [pca('myprepros_name.only_pca')],\n",
    "                        [pca('myprepros_name.pca'), min_max_scaler('myprepros_name.norm')],\n",
    "                        [min_max_scaler('myprepros_name.first_norm'), standard_scaler('myprepros_name.second_std_scaler')],\n",
    "                        []\n",
    "                    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = hp.choice( 'model_name',\n",
    "            [ random_forest ('model_name.random_forest'),\n",
    "            ada_boost ('model_name.ada_boost'),\n",
    "            gradient_boosting ('model_name.gradient_boosting'),\n",
    "            xgboost_classification ('model_name.xgboost_classification'),\n",
    "            extra_trees('model_name.extra_trees'),\n",
    "            svc_linear('model_name.svc_linear'),\n",
    "            svc_rbf('model_name.svc_rbf'),\n",
    "            svc('model_name.svc')]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#preprocessing=[min_max_scaler('norm_scaler'), pca('my_pca') ]\n",
    "#classifier=xgboost_classification('xgboost')\n",
    "#preprocessing=any_preprocessing('pre')\n",
    "#preprocessing = preproc\n",
    "#classifier=any_classifier('cla')\n",
    "#classifier=clf\n",
    "\n",
    "#01. 100 times with classifier=any_classifier('cla') & preprocessing=any_preprocessing('pre')\n",
    "\n",
    "#02. 100 times with classifier=clf & preprocessing = preproc\n",
    "\n",
    "#03. 100 times with classifier=xgboost_classification('xgboost') & preprocessing = preproc\n",
    "\n",
    "#04. 100 times with classifier=xgboost_classification('xgboost') & preprocessing = any_preprocessing('pre')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "100%|██████████| 100/100 [06:46<00:00,  4.07s/trial, best loss: 0.0004263536729114392]\n",
      "Execution time :  11:34:59.565273\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "accuracy = 0\n",
    "best_model = None\n",
    "#for i in range(100):\n",
    "    #print (i)\n",
    "model = HyperoptEstimator(  classifier=any_classifier('cla'), \n",
    "                                preprocessing=any_preprocessing('pre'), \n",
    "                                algo=tpe.suggest, \n",
    "                                max_evals=100, \n",
    "                                trial_timeout=1500)\n",
    "    # perform the search\n",
    "model.fit(X_train, y_train)\n",
    "acc = model.score(X_test, y_test)\n",
    "    #if acc > accuracy:\n",
    "        #accuracy = acc\n",
    "        #best_model = model\n",
    "        #print (accuracy)\n",
    "\n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.000\n",
      "{'learner': RandomForestClassifier(max_features='sqrt', n_estimators=781, n_jobs=1,\n",
      "                       random_state=0, verbose=False), 'preprocs': (), 'ex_preprocs': ()}\n"
     ]
    }
   ],
   "source": [
    "# summarize the best model\n",
    "print(\"Accuracy: %.3f\" % acc)\n",
    "print(model.best_model())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time :  0:00:26.437672\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "model = ExtraTreesClassifier(criterion='entropy', max_features=0.3901684099941361,\n",
    "                     n_estimators=122, n_jobs=1, random_state=0, verbose=False)\n",
    "model.fit(X_train, y_train)\n",
    "prediction = model.predict(X_test)\n",
    "#print(f\"F-measure of XGB: {mean(scores):.3f}\")\n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity/Recall: 0.77\n",
      "F1 Score: 0.86\n"
     ]
    }
   ],
   "source": [
    "f1_score = round(f1_score(y_test, prediction), 2)\n",
    "recall_score = round(recall_score(y_test, prediction), 2)\n",
    "print(\"Sensitivity/Recall: {recall_score}\".format(recall_score = recall_score))\n",
    "print(\"F1 Score: {f1_score}\".format(f1_score = f1_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85295\n",
      "           1       0.97      0.77      0.86       148\n",
      "\n",
      "    accuracy                           1.00     85443\n",
      "   macro avg       0.99      0.89      0.93     85443\n",
      "weighted avg       1.00      1.00      1.00     85443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     "metadata": {
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     "output_type": "display_data"
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   ],
   "source": [
    "#class_names = {0:'Not Fraud', 1:'Fraud'}\n",
    "plot_confusion_matrix(model, X_test, y_test, values_format='d', display_labels=[\"Not Fraud\",\"Fraud\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time :  0:27:03.944571\n"
     ]
    }
   ],
   "source": [
    "init_time = datetime.now()\n",
    "model = RandomForestClassifier(max_features='sqrt', n_estimators=781, n_jobs=1,\n",
    "                       random_state=0, verbose=False)\n",
    "#pipeline = Pipeline(steps=[('norm', StandardScaler(with_mean=False)), ('m', model)])\n",
    "model.fit(X_train, y_train)\n",
    "prediction = model.predict(X_test)\n",
    "#print(f\"F-measure of XGB: {mean(scores):.3f}\")\n",
    "fin_time = datetime.now()\n",
    "print(\"Execution time : \", (fin_time-init_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity/Recall: 0.77\n",
      "F1 Score: 0.85\n"
     ]
    }
   ],
   "source": [
    "f1_score = round(f1_score(y_test, prediction), 2)\n",
    "recall_score = round(recall_score(y_test, prediction), 2)\n",
    "print(\"Sensitivity/Recall: {recall_score}\".format(recall_score = recall_score))\n",
    "print(\"F1 Score: {f1_score}\".format(f1_score = f1_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85295\n",
      "           1       0.96      0.77      0.85       148\n",
      "\n",
      "    accuracy                           1.00     85443\n",
      "   macro avg       0.98      0.89      0.93     85443\n",
      "weighted avg       1.00      1.00      1.00     85443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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    "plot_confusion_matrix(model, X_test, y_test, values_format='d', display_labels=[\"Not Fraud\",\"Fraud\"]);"
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