train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imported Libraries\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA, TruncatedSVD\n",
    "import matplotlib.patches as mpatches\n",
    "import time\n",
    "\n",
    "# Classifier Libraries\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import collections\n",
    "\n",
    "\n",
    "# Other Libraries\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import NearMiss\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report\n",
    "from collections import Counter\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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       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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"creditcard.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>1.168375e-15</td>\n",
       "      <td>3.416908e-16</td>\n",
       "      <td>-1.379537e-15</td>\n",
       "      <td>2.074095e-15</td>\n",
       "      <td>9.604066e-16</td>\n",
       "      <td>1.487313e-15</td>\n",
       "      <td>-5.556467e-16</td>\n",
       "      <td>1.213481e-16</td>\n",
       "      <td>-2.406331e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.654067e-16</td>\n",
       "      <td>-3.568593e-16</td>\n",
       "      <td>2.578648e-16</td>\n",
       "      <td>4.473266e-15</td>\n",
       "      <td>5.340915e-16</td>\n",
       "      <td>1.683437e-15</td>\n",
       "      <td>-3.660091e-16</td>\n",
       "      <td>-1.227390e-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  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-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   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-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   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-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   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for null values in all columns\n",
    "df.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "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": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1854"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for duplicated rows\n",
    "df.duplicated(keep=False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n"
     ]
    }
   ],
   "source": [
    "# Check unique values of the columns\n",
    "print(df['Class'].unique())\n",
    "\n",
    "# 1 => Fraud; 0 => Not fraud\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Frauds 99.83 % of the dataset\n",
      "Frauds 0.17 % of the dataset\n"
     ]
    }
   ],
   "source": [
    "print('No Frauds', round(df['Class'].value_counts()[0]/len(df) * 100,2), '% of the dataset')\n",
    "print('Frauds', round(df['Class'].value_counts()[1]/len(df) * 100,2), '% of the dataset')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Notice how imbalanced is our original dataset! Most of the transactions are non-fraud. If we use this dataframe as the base for our predictive models and analysis we might get a lot of errors and our algorithms will probably overfit since it will \"assume\" that most transactions are not fraud. But we don't want our model to assume, we want our model to detect patterns that give signs of fraud!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal : To detect patterns nya yang give signs of fraud!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Class Distributions \\n (0: No Fraud || 1: Fraud)')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='Class')\n",
    "plt.title('Class Distributions \\n (0: No Fraud || 1: Fraud)', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen that the distribution is very skewed. There are techniques to reduce the skewedness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# There are other features that we could analyze.\n",
    "# The 'Time' and 'Amount' features because it is the only features that have not been transformed with PCA\n",
    "# Belum di standardize\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(18,4))\n",
    "\n",
    "amount_val = df['Amount'].values\n",
    "time_val = df['Time'].values\n",
    "\n",
    "sns.distplot(amount_val, ax=ax[0], color='r')\n",
    "ax[0].set_title('Distribution of Transaction Amount', fontsize=14)\n",
    "# Set the x limit, so that the plot is neat in height\n",
    "ax[0].set_xlim([min(amount_val), max(amount_val)])\n",
    "\n",
    "sns.distplot(time_val, ax=ax[1], color='b')\n",
    "ax[1].set_title('Distribution of Transaction Time', fontsize=14)\n",
    "ax[1].set_xlim([min(time_val), max(time_val)])\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling and Distributing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to create a sub-sample of the Fraud and Non-fraud cases. Where both categories are equal\n",
    "\n",
    "There are 492 cases of fraud in our dataset so we can randomly get 492 cases of non-fraud to create our new sub dataframe.\n",
    "We concat the 492 cases of fraud and non fraud, creating a new sub-sample.\n",
    "\n",
    "We're going to this later in the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the Amount and Time \n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
    "\n",
    "# RobustScaler is less prone to outliers.\n",
    "# Because our data is not normally distributed, leans heavily to one side. Hence we're going to use RobustScaler instead.\n",
    "\n",
    "std_scaler = StandardScaler()\n",
    "rob_scaler = RobustScaler()\n",
    "\n",
    "# values.reshape(-1, 1) is used to reshape a one-dimensional array or list, values, into a two-dimensional array with a single column\n",
    "# the -1 means that the size of that dimension should be automatically inferred based on the length of the input.\n",
    "\n",
    "# Karena fit_transform harus dalam bentuk array, makanya df['Amount'] and df['Time'] diubah dulu pake .values.reshape(-1, 1)\n",
    "df['scaled_amount'] = rob_scaler.fit_transform(df['Amount'].values.reshape(-1,1))\n",
    "df['scaled_time'] = rob_scaler.fit_transform(df['Time'].values.reshape(-1,1))\n",
    "\n",
    "df.drop(['Time','Amount'], axis=1, inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scaled_amount</th>\n",
       "      <th>scaled_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>...</th>\n",
       "      <th>V20</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>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.783274</td>\n",
       "      <td>-0.994983</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>...</td>\n",
       "      <td>0.251412</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.269825</td>\n",
       "      <td>-0.994983</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>...</td>\n",
       "      <td>-0.069083</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.983721</td>\n",
       "      <td>-0.994972</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>...</td>\n",
       "      <td>0.524980</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.418291</td>\n",
       "      <td>-0.994972</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>...</td>\n",
       "      <td>-0.208038</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.670579</td>\n",
       "      <td>-0.994960</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>...</td>\n",
       "      <td>0.408542</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   scaled_amount  scaled_time        V1        V2        V3        V4  \\\n",
       "0       1.783274    -0.994983 -1.359807 -0.072781  2.536347  1.378155   \n",
       "1      -0.269825    -0.994983  1.191857  0.266151  0.166480  0.448154   \n",
       "2       4.983721    -0.994972 -1.358354 -1.340163  1.773209  0.379780   \n",
       "3       1.418291    -0.994972 -0.966272 -0.185226  1.792993 -0.863291   \n",
       "4       0.670579    -0.994960 -1.158233  0.877737  1.548718  0.403034   \n",
       "\n",
       "         V5        V6        V7        V8  ...       V20       V21       V22  \\\n",
       "0 -0.338321  0.462388  0.239599  0.098698  ...  0.251412 -0.018307  0.277838   \n",
       "1  0.060018 -0.082361 -0.078803  0.085102  ... -0.069083 -0.225775 -0.638672   \n",
       "2 -0.503198  1.800499  0.791461  0.247676  ...  0.524980  0.247998  0.771679   \n",
       "3 -0.010309  1.247203  0.237609  0.377436  ... -0.208038 -0.108300  0.005274   \n",
       "4 -0.407193  0.095921  0.592941 -0.270533  ...  0.408542 -0.009431  0.798278   \n",
       "\n",
       "        V23       V24       V25       V26       V27       V28  Class  \n",
       "0 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053      0  \n",
       "1  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724      0  \n",
       "2  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752      0  \n",
       "3 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458      0  \n",
       "4 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_amount = df['scaled_amount']\n",
    "scaled_time = df['scaled_time']\n",
    "\n",
    "# Cuma untuk mindahin scaled_amount and scaled_time ke depan, to make it easier to see.\n",
    "df.drop(['scaled_amount', 'scaled_time'], axis=1, inplace=True)\n",
    "df.insert(0, 'scaled_amount', scaled_amount)\n",
    "df.insert(1, 'scaled_time', scaled_time)\n",
    "\n",
    "# Amount and Time are Scaled!\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting the Dataset (Original DataFrame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before proceeding with the Random UnderSampling technique we have to separate the orginal dataframe. For testing purposes, we want to test our models on the original testing set not on the testing set created by either of these techniques. \n",
    "\n",
    "The main goal is to fit the model either with the dataframes that were undersample and oversample (in order for our models to detect the patterns), and test it on the original testing set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kenapa dipisah dulu? Karena untuk testing, kita juga mau ngetest-nya pake data yang original dulu. To test the accuracy.\n",
    "\n",
    "Because we want to test the data with both the original dataset and the undersampled dataset, that's why we need to split it first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Frauds 99.83 % of the dataset\n",
      "Frauds 0.17 % of the dataset\n"
     ]
    }
   ],
   "source": [
    "print('No Frauds', round(df['Class'].value_counts()[0]/len(df) * 100,2), '% of the dataset')\n",
    "print('Frauds', round(df['Class'].value_counts()[1]/len(df) * 100,2), '% of the dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data to predictor variable (X) and target variable (Y)\n",
    "X = df.drop('Class', axis=1)\n",
    "y = df['Class']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we want to assess the existing model's performance, and we have an imbalanced dataset (leans heavily towards the No-Fraud). \n",
    "\n",
    "Hence, it is better to use StratifiedKFold(), instead of the train_test_split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: [ 30473  30496  31002 ... 284804 284805 284806] Test: [    0     1     2 ... 57017 57018 57019]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [ 30473  30496  31002 ... 113964 113965 113966]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [ 81609  82400  83053 ... 170946 170947 170948]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [150654 150660 150661 ... 227866 227867 227868]\n",
      "Train: [     0      1      2 ... 227866 227867 227868] Test: [212516 212644 213092 ... 284804 284805 284806]\n"
     ]
    }
   ],
   "source": [
    "# default n_splits = 5\n",
    "# Create the model\n",
    "sss = StratifiedKFold(n_splits=5, random_state=None, shuffle=False)\n",
    "\n",
    "# Split X and y with that\n",
    "# .split() => generates split data into training and testing data\n",
    "for train_index, test_index in sss.split(X, y):\n",
    "    print(\"Train:\", train_index, \"Test:\", test_index)\n",
    "    original_Xtrain, original_Xtest = X.iloc[train_index], X.iloc[test_index]\n",
    "    original_ytrain, original_ytest = y.iloc[train_index], y.iloc[test_index]\n",
    "\n",
    "# The code above is similar to the once below v\n",
    "# We are not using the code below (train_test_split), because we are using another model (sss) to split the data\n",
    "# original_Xtrain, original_Xtest, original_ytrain, original_ytest = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# We already have X_train and y_train for the undersample data \n",
    "# thats why I am using original to distinguish and to not overwrite these variables.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "Label Distributions: \n",
      "\n",
      "[0.99827076 0.00172924]\n",
      "[0.99827952 0.00172048]\n"
     ]
    }
   ],
   "source": [
    "# Turn into an array\n",
    "original_Xtrain = original_Xtrain.values\n",
    "original_Xtest = original_Xtest.values\n",
    "original_ytrain = original_ytrain.values\n",
    "original_ytest = original_ytest.values\n",
    "\n",
    "# See if both the train and test label distribution are similarly distributed\n",
    "train_unique_label, train_counts_label = np.unique(original_ytrain, return_counts=True)\n",
    "test_unique_label, test_counts_label = np.unique(original_ytest, return_counts=True)\n",
    "print('-' * 100)\n",
    "\n",
    "print('Label Distributions: \\n')\n",
    "print(train_counts_label/ len(original_ytrain))\n",
    "print(test_counts_label/ len(original_ytest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Undersampling\n",
    "In order to fix the imbalanced dataset that we have. We have to undersample the 'No-Fraud' entry.\n",
    "Because if we just let the data to lean towards the 'No-Fraud', the model will produce biased prediction. Which is leaning heavily towards a 'No-Fraud' classification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Steps\n",
    "1. Determine how imbalanced our dataset is. => using value_counts()\n",
    "2. Bring the amount of non-fraud transaction the same as the fraud transaction\n",
    "3. After implementing this technique, we will have a new sub-sample dataset with a 50/50 ratio with regards to our classes\n",
    "4. Then, we want to shuffle our data, so that the models can maintain a certain accuracy everytime we run the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shuffle the data before creating the subsamples\n",
    "# Sampling with a fraction of 1 => The same amount of data\n",
    "df = df.sample(frac=1)\n",
    "\n",
    "# amount of fraud classes 492 rows.\n",
    "fraud_df = df.loc[df['Class'] == 1]\n",
    "non_fraud_df = df.loc[df['Class'] == 0][:492]       # Only take 492 of it\n",
    "\n",
    "normal_distributed_df = pd.concat([fraud_df, non_fraud_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scaled_amount</th>\n",
       "      <th>scaled_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>...</th>\n",
       "      <th>V20</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>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>257254</th>\n",
       "      <td>0.347935</td>\n",
       "      <td>0.862099</td>\n",
       "      <td>-0.243051</td>\n",
       "      <td>1.407894</td>\n",
       "      <td>1.506115</td>\n",
       "      <td>4.280676</td>\n",
       "      <td>0.985939</td>\n",
       "      <td>1.051460</td>\n",
       "      <td>0.669649</td>\n",
       "      <td>0.088972</td>\n",
       "      <td>...</td>\n",
       "      <td>0.444234</td>\n",
       "      <td>-0.391642</td>\n",
       "      <td>-1.221050</td>\n",
       "      <td>0.264886</td>\n",
       "      <td>0.411840</td>\n",
       "      <td>-0.692307</td>\n",
       "      <td>-0.193485</td>\n",
       "      <td>0.160741</td>\n",
       "      <td>0.183927</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150663</th>\n",
       "      <td>4.108992</td>\n",
       "      <td>0.107626</td>\n",
       "      <td>-5.839192</td>\n",
       "      <td>7.151532</td>\n",
       "      <td>-12.816760</td>\n",
       "      <td>7.031115</td>\n",
       "      <td>-9.651272</td>\n",
       "      <td>-2.938427</td>\n",
       "      <td>-11.543207</td>\n",
       "      <td>4.843627</td>\n",
       "      <td>...</td>\n",
       "      <td>0.055684</td>\n",
       "      <td>2.462056</td>\n",
       "      <td>1.054865</td>\n",
       "      <td>0.530481</td>\n",
       "      <td>0.472670</td>\n",
       "      <td>-0.275998</td>\n",
       "      <td>0.282435</td>\n",
       "      <td>0.104886</td>\n",
       "      <td>0.254417</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91797</th>\n",
       "      <td>-0.230699</td>\n",
       "      <td>-0.247418</td>\n",
       "      <td>1.167516</td>\n",
       "      <td>0.169447</td>\n",
       "      <td>0.509497</td>\n",
       "      <td>0.453736</td>\n",
       "      <td>-0.201459</td>\n",
       "      <td>-0.185675</td>\n",
       "      <td>-0.085807</td>\n",
       "      <td>0.055684</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.079758</td>\n",
       "      <td>-0.171226</td>\n",
       "      <td>-0.485855</td>\n",
       "      <td>0.145080</td>\n",
       "      <td>0.024221</td>\n",
       "      <td>0.127454</td>\n",
       "      <td>0.105166</td>\n",
       "      <td>-0.014379</td>\n",
       "      <td>0.006946</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82400</th>\n",
       "      <td>-0.042199</td>\n",
       "      <td>-0.297313</td>\n",
       "      <td>-7.626924</td>\n",
       "      <td>-6.976420</td>\n",
       "      <td>-2.077911</td>\n",
       "      <td>3.416754</td>\n",
       "      <td>4.458758</td>\n",
       "      <td>-5.080408</td>\n",
       "      <td>-6.578948</td>\n",
       "      <td>1.760341</td>\n",
       "      <td>...</td>\n",
       "      <td>2.875260</td>\n",
       "      <td>1.224795</td>\n",
       "      <td>-0.656639</td>\n",
       "      <td>-0.330811</td>\n",
       "      <td>-0.078946</td>\n",
       "      <td>0.270306</td>\n",
       "      <td>0.431119</td>\n",
       "      <td>0.821381</td>\n",
       "      <td>-1.056088</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234633</th>\n",
       "      <td>-0.285195</td>\n",
       "      <td>0.744381</td>\n",
       "      <td>1.261324</td>\n",
       "      <td>2.726800</td>\n",
       "      <td>-5.435019</td>\n",
       "      <td>5.342759</td>\n",
       "      <td>1.447043</td>\n",
       "      <td>-1.442584</td>\n",
       "      <td>-0.898702</td>\n",
       "      <td>0.123062</td>\n",
       "      <td>...</td>\n",
       "      <td>0.313332</td>\n",
       "      <td>0.209086</td>\n",
       "      <td>-0.425938</td>\n",
       "      <td>-0.154440</td>\n",
       "      <td>-0.018820</td>\n",
       "      <td>0.632234</td>\n",
       "      <td>0.192922</td>\n",
       "      <td>0.468181</td>\n",
       "      <td>0.280486</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        scaled_amount  scaled_time        V1        V2         V3        V4  \\\n",
       "257254       0.347935     0.862099 -0.243051  1.407894   1.506115  4.280676   \n",
       "150663       4.108992     0.107626 -5.839192  7.151532 -12.816760  7.031115   \n",
       "91797       -0.230699    -0.247418  1.167516  0.169447   0.509497  0.453736   \n",
       "82400       -0.042199    -0.297313 -7.626924 -6.976420  -2.077911  3.416754   \n",
       "234633      -0.285195     0.744381  1.261324  2.726800  -5.435019  5.342759   \n",
       "\n",
       "              V5        V6         V7        V8  ...       V20       V21  \\\n",
       "257254  0.985939  1.051460   0.669649  0.088972  ...  0.444234 -0.391642   \n",
       "150663 -9.651272 -2.938427 -11.543207  4.843627  ...  0.055684  2.462056   \n",
       "91797  -0.201459 -0.185675  -0.085807  0.055684  ... -0.079758 -0.171226   \n",
       "82400   4.458758 -5.080408  -6.578948  1.760341  ...  2.875260  1.224795   \n",
       "234633  1.447043 -1.442584  -0.898702  0.123062  ...  0.313332  0.209086   \n",
       "\n",
       "             V22       V23       V24       V25       V26       V27       V28  \\\n",
       "257254 -1.221050  0.264886  0.411840 -0.692307 -0.193485  0.160741  0.183927   \n",
       "150663  1.054865  0.530481  0.472670 -0.275998  0.282435  0.104886  0.254417   \n",
       "91797  -0.485855  0.145080  0.024221  0.127454  0.105166 -0.014379  0.006946   \n",
       "82400  -0.656639 -0.330811 -0.078946  0.270306  0.431119  0.821381 -1.056088   \n",
       "234633 -0.425938 -0.154440 -0.018820  0.632234  0.192922  0.468181  0.280486   \n",
       "\n",
       "        Class  \n",
       "257254      0  \n",
       "150663      1  \n",
       "91797       0  \n",
       "82400       1  \n",
       "234633      1  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shuffle again to make sure that the models can maintain a certain accuracy everytime we run the model\n",
    "new_df = normal_distributed_df.sample(frac=1, random_state=42)\n",
    "\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Equally Distributing and Correlating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of the Classes in the subsample dataset\n",
      "0    0.5\n",
      "1    0.5\n",
      "Name: Class, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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XZUqbTVV9lZFLfDV5fgOUtgwvo01UP+M02klqaVF5TkGSNLhJdx/tvPPOtXz58mk3Q5JuUk499dQfVdWcoxPfpENh+fLlrFy5cuMVJUmDJOfPt81zCpKkwURDoY8//6206fRW9rKdkpyU5Oz+9za9PEn+NcmqJKf3AbckSYtoMY4U/rCq9qmqmYk1XgScXFV7ASdzwy8YHw7s1W+HAm9bhLZJkkZMo/voAOCYvnwM7afyM+XHVvNlYMcku85xf0nShEw6FAr4VNrk7of2sl2qamYo5ou4YRjm3WgDaM1Y3cskSYtk0lcf3b+q1vTxS06aPVpjVdVCpwrs4XIowJ57bspc8ZKk+Uz0SKGPcElVXQJ8mDbO/MUz3UL97yW9+hpgj5G7797LZu/zyKpaUVUrli2b8zJbSdImmlgoJPnVJL82sww8lDY14gm0YXfpf4/vyycAB/arkPYDfjrSzSRJWgST7D7aBfhwn1RpO+A/q+oTSb4GfCDJIcD5wON7/RNpc8Suok2F+PQJtk2SNIeJhUJVnQvcc47yHwMPmqO8gGdPqj3zufffHbvYD6mbgFP/+cBpN4EfvPy3pt0EbYH2fOm3Jrp/f9EsSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRpMPBSSbJvkG0k+1tfvmOQrSVYleX+S7Xv5Dn19Vd++fNJtkyStazGOFJ4LnDWy/lrgDVV1Z+Ay4JBefghwWS9/Q68nSVpEEw2FJLsDjwTe2dcDPBD4YK9yDPCYvnxAX6dvf1CvL0laJJM+Ungj8PfA9X3914GfVNW1fX01sFtf3g24AKBv/2mvL0laJBMLhSSPAi6pqlM3834PTbIyycq1a9duzl1L0pI3ySOF+wGPTnIe8D5at9GbgB2TbNfr7A6s6ctrgD0A+vZbAz+evdOqOrKqVlTVimXLlk2w+ZK09EwsFKrqxVW1e1UtB54IfKaqngJ8Fnhcr3YQcHxfPqGv07d/pqpqUu2TJK1vGr9TeCHwgiSraOcMjurlRwG/3stfALxoCm2TpCVtu41XufGq6hTglL58LnCfOepcCfzpYrRHkjQ3f9EsSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkwcRCIcnNk3w1yTeTfDvJy3r5HZN8JcmqJO9Psn0v36Gvr+rbl0+qbZKkuU3ySOEq4IFVdU9gH2D/JPsBrwXeUFV3Bi4DDun1DwEu6+Vv6PUkSYtoYqFQzeV99Wb9VsADgQ/28mOAx/TlA/o6ffuDkmRS7ZMkrW+i5xSSbJvkNOAS4CTgHOAnVXVtr7Ia2K0v7wZcANC3/xT49Um2T5K0romGQlVdV1X7ALsD9wHuemP3meTQJCuTrFy7du2N3Z0kacSiXH1UVT8BPgv8HrBjku36pt2BNX15DbAHQN9+a+DHc+zryKpaUVUrli1bNummS9KSMsmrj5Yl2bEv/wrwEOAsWjg8rlc7CDi+L5/Q1+nbP1NVNan2SZLWt93Gq2yyXYFjkmxLC58PVNXHkpwJvC/JPwLfAI7q9Y8C3pNkFXAp8MQJtk2SNIeJhUJVnQ7ca47yc2nnF2aXXwn86aTaI0nauLG6j5KcPE6ZJOmmbYNHCkluDtwC2DnJbYCZ3w3cihsuJZUkbSU21n30TOB5wO2BU7khFH4GvGVyzZIkTcMGQ6Gq3gS8KclfVdWbF6lNkqQpGetEc1W9Ocl9geWj96mqYyfULknSFIwVCkneA9wJOA24rhcXYChI0lZk3EtSVwB388dkkrR1G/cXzWcAt5tkQyRJ0zfukcLOwJlJvkqbJwGAqnr0RFolSZqKcUPhiEk2QpK0ZRj36qP/mXRDJEnTN+7VRz+nXW0EsD1tFrVfVNWtJtUwSdLiG/dI4ddmlvsUmQcA+02qUZKk6VjwfAp97uWPAA/b/M2RJE3TuN1Hjx1Z3Yb2u4UrJ9IiSdLUjHv10R+NLF8LnEfrQpIkbUXGPafw9Ek3RJI0feNOsrN7kg8nuaTfjkuy+6QbJ0laXOOeaH43cAJtXoXbAx/tZZKkrci4obCsqt5dVdf229HAsgm2S5I0BeOGwo+TPDXJtv32VODHk2yYJGnxjRsKzwAeD1wEXAg8Djh4Qm2SJE3JuJekvhw4qKouA0iyE/A6WlhIkrYS4x4p/PZMIABU1aXAvSbTJEnStIwbCtskuc3MSj9SGPcoQ5J0EzHuB/u/AF9K8l99/U+BV06mSZKkaRn3F83HJlkJPLAXPbaqzpxcsyRJ0zB2F1APAYNAkrZiCx46W5K09TIUJEkDQ0GSNDAUJEkDQ0GSNJhYKCTZI8lnk5yZ5NtJntvLd0pyUpKz+9/b9PIk+dckq5KcnmTfSbVNkjS3SR4pXAv8TVXdDdgPeHaSuwEvAk6uqr2Ak/s6wMOBvfrtUOBtE2ybJGkOEwuFqrqwqr7el38OnAXsRpvb+Zhe7RjgMX35AODYar4M7Jhk10m1T5K0vkU5p5BkOW0Ava8Au1TVhX3TRcAufXk34IKRu63uZZKkRTLxUEhyS+A44HlV9bPRbVVVQC1wf4cmWZlk5dq1azdjSyVJEw2FJDejBcJ/VNWHevHFM91C/e8lvXwNsMfI3XfvZeuoqiOrakVVrVi2zBlBJWlzmuTVRwGOAs6qqtePbDoBOKgvHwQcP1J+YL8KaT/gpyPdTJKkRTDJORHuBzwN+FaS03rZYcBrgA8kOQQ4nzbNJ8CJwCOAVcAVwNMn2DZJ0hwmFgpV9QUg82x+0Bz1C3j2pNojSdo4f9EsSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRoYCpKkgaEgSRpMLBSSvCvJJUnOGCnbKclJSc7uf2/Ty5PkX5OsSnJ6kn0n1S5J0vwmeaRwNLD/rLIXASdX1V7AyX0d4OHAXv12KPC2CbZLkjSPiYVCVX0OuHRW8QHAMX35GOAxI+XHVvNlYMcku06qbZKkuS32OYVdqurCvnwRsEtf3g24YKTe6l4mSVpEUzvRXFUF1ELvl+TQJCuTrFy7du0EWiZJS9dih8LFM91C/e8lvXwNsMdIvd172Xqq6siqWlFVK5YtWzbRxkrSUrPYoXACcFBfPgg4fqT8wH4V0n7AT0e6mSRJi2S7Se04yXuBBwA7J1kNHA68BvhAkkOA84HH9+onAo8AVgFXAE+fVLskSfObWChU1ZPm2fSgOeoW8OxJtUWSNB5/0SxJGhgKkqSBoSBJGhgKkqSBoSBJGhgKkqSBoSBJGhgKkqSBoSBJGhgKkqSBoSBJGhgKkqSBoSBJGhgKkqSBoSBJGhgKkqSBoSBJGhgKkqSBoSBJGhgKkqSBoSBJGhgKkqSBoSBJGhgKkqSBoSBJGhgKkqSBoSBJGhgKkqSBoSBJGhgKkqSBoSBJGhgKkqSBoSBJGmxRoZBk/yTfTbIqyYum3R5JWmq2mFBIsi3wb8DDgbsBT0pyt+m2SpKWli0mFID7AKuq6tyquhp4H3DAlNskSUvKlhQKuwEXjKyv7mWSpEWy3bQbsFBJDgUO7auXJ/nuNNuzldkZ+NG0G7ElyOsOmnYTtC7/bc44PJtjL3eYb8OWFAprgD1G1nfvZeuoqiOBIxerUUtJkpVVtWLa7ZBm89/m4tmSuo++BuyV5I5JtgeeCJww5TZJ0pKyxRwpVNW1SZ4DfBLYFnhXVX17ys2SpCVliwkFgKo6EThx2u1YwuyW05bKf5uLJFU17TZIkrYQW9I5BUnSlBkKcngRbbGSvCvJJUnOmHZblgpDYYlzeBFt4Y4G9p92I5YSQ0EOL6ItVlV9Drh02u1YSgwFObyIpIGhIEkaGAoaa3gRSUuDoSCHF5E0MBSWuKq6FpgZXuQs4AMOL6ItRZL3Al8CfjPJ6iSHTLtNWzt/0SxJGnikIEkaGAqSpIGhIEkaGAqSpIGhIEkaGArSmJLcLsn7kpyT5NQkJya5iyN4amuyRc28Jm2pkgT4MHBMVT2xl90T2GWqDZM2M48UpPH8IXBNVb19pqCqvsnIYIJJlif5fJKv99t9e/muST6X5LQkZyT5/STbJjm6r38ryfMX/ylJ6/NIQRrPPYBTN1LnEuAhVXVlkr2A9wIrgCcDn6yqV/b5K24B7APsVlX3AEiy46QaLi2EoSBtPjcD3pJkH+A64C69/GvAu5LcDPhIVZ2W5FzgN5K8Gfhv4FPTaLA0m91H0ni+Ddx7I3WeD1wM3JN2hLA9DBPF/AFt9NmjkxxYVZf1eqcAfwG8czLNlhbGUJDG8xlghySHzhQk+W3WHXb81sCFVXU98DRg217vDsDFVfUO2of/vkl2BrapquOAlwD7Ls7TkDbM7iNpDFVVSf4YeGOSFwJXAucBzxup9lbguCQHAp8AftHLHwD8XZJrgMuBA2mz2707ycwXsxdP+jlI43CUVEnSwO4jSdLAUJAkDQwFSdLAUJAkDQwFSdLAUJAkDQwFSdLAUJAkDf4PdVhigfn7aToAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Distribution of the Classes in the subsample dataset')\n",
    "print(new_df['Class'].value_counts()/len(new_df))\n",
    "\n",
    "\n",
    "# Countplot is used to visualize the frequency or count of categorical variables\n",
    "sns.countplot(data=new_df, x='Class')\n",
    "plt.title('Equally Distributed Class \\n (0: No Fraud || 1: Fraud)', fontsize=14)\n",
    "plt.show()\n",
    "\n",
    "# The dataset is now equally distributed "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From these heat maps we want to know if there are features that heavily influenced whether a specific transaction is a fraud. It is also important to use the correct dataset (the balanced dataset), instead of the imbalanced one. Because the imbalanced dataset will cause bias. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heatmaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1728x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(2, 1, figsize=(24,20))\n",
    "\n",
    "# Entire DataFrame\n",
    "corr = df.corr()\n",
    "sns.heatmap(corr, cmap='coolwarm_r', annot_kws={'size':20}, ax=ax1)\n",
    "ax1.set_title(\"Imbalanced Correlation Matrix \\n (don't use for reference)\", fontsize=14)\n",
    "\n",
    "\n",
    "sub_sample_corr = new_df.corr()\n",
    "sns.heatmap(sub_sample_corr, cmap='coolwarm_r', annot_kws={'size':20}, ax=ax2)\n",
    "ax2.set_title('SubSample Correlation Matrix \\n (use for reference)', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Blue => Positively influence => The higher the value, higher chance that the transaction is a fraud.\n",
    "* V2, V4, V11, V19\n",
    "\n",
    "Red => Negatively influence => The lower the value, the higher chance that the transaction is a fraud\n",
    "* V10, V12, V14"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(ncols=4, figsize=(20,4))\n",
    "\n",
    "# Negative Correlations with our Class (The lower our feature value the more likely it will be a fraud transaction)\n",
    "sns.boxplot(x=\"Class\", y=\"V17\", data=new_df, ax=axes[0])\n",
    "axes[0].set_title('V17 vs Class Negative Correlation \\n (0: No Fraud || 1: Fraud)')\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V14\", data=new_df, ax=axes[1])\n",
    "axes[1].set_title('V14 vs Class Negative Correlation \\n (0: No Fraud || 1: Fraud)')\n",
    "\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V12\", data=new_df, ax=axes[2])\n",
    "axes[2].set_title('V12 vs Class Negative Correlation \\n (0: No Fraud || 1: Fraud)')\n",
    "\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V10\", data=new_df, ax=axes[3])\n",
    "axes[3].set_title('V10 vs Class Negative Correlation \\n (0: No Fraud || 1: Fraud)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6i1kLPD8ijh7laj9CcSbq+Cy6NL6Jmq6bmfn5zPw9ig0mgb8pp/8sM19P0QXvb4Avlwer++Ln5XIHzCmnUVZp35OZv0JxEPvuiKg3cNyIFe7M3EXRRfP15c9/lDumUCSED5cJc+DnoMy8tM6irgKOjoh5w6zqXh4741D7ejbtJdbaaXcB/zcknkMy82V1/u4NFF94Cyh2cOeW0wf+d3ur/A/73mtCGS43HUfxmbguIu6m6A48K4o7jcwdZlkDl4icBvyo3CmhPKNxXmY+k+JM7CsY3DV8wHcpzga9apSxj/gZzswfZuZpFDnm65RdrfchN4xk0Oe/zF2dlNtrZl6Ymb9BcSnF06h/84PRnF27FHh9WfQ6kGInCYpt/dtDtvWDM/P/q7OMn5Tzv6bOc3VfD8X23E+xMzxcvLXteykusXlWTTxPymLAzEGi6AnyaYou8p1ZXI52M2PMPUPfe01YI+4nlQdSZ1GMwbG3OzwOt92MdtsczTZTG9uJFAd+rwMOLz/Tv+CxXFR3f2cfcuNIhm4PQXFwNJCL6u6DDTHaXPTacvt9AcVBMOzDfkdmbufxl+vs7fXU275HykUDMZ0yJKYDM3NQjojikpWvUPQUmln+31Yx9lw06L3XhDHmY7TM/HZm/mZmHkHRo+MZQN3LnTxG8xgNj9FGZOHoMSsp/vn/jyFdIKO4JvRAikpmRMSBUXN9dBTdHw+keD+nls+PNDL75ykS4GupqZpHxB/UJJr7KT50u4f+cWaupRg742sR8RtR3I7ykIh4+zAV8EMourj9IiJmU7MTFhFPj4gXl1/Ov+SxgQaJiDdFxIzyDM0D5Z88Lp69uBQ4JyJmRMSRFONpfK5c/isioqv8Iv8FxdmmesvfAsyN4jr94XyeItG/kcFnIj4NvL2sdEdEPDEiXh4RhwxdQGb+jOLuUJdGxPwoxh84MCJOj4izapLfh8v3+1iKsxifG7qsEfwAeCgi3h8R08uK9bMj4jfrzHsIxUF6H0VPgY8MeX4LxXW8wxn2vdeEMlxuupliB/iE8uePKT4TJ1D/rAoUZ6RfQnGHnNrc86KIOL7MWw9SfPnWyz2/oPgcfTIiXhURB5X575SI+Fid9Q37GS63rzdGxJOyuITgQR7LPaPNDSO5FHhrRJxQ5rePAN/PzA0R8ZtlTphGMSDiL4dZ/hagM0bueryK4sv/Lymu8x9Yzn8AT4uIN5fv0bRyvb82dAHljum7gT+PiLdGxKHl987vRcSKmtdzZkQ8NYpbfA+MKzCqywbLuD5NMT7IkwEiYnbUv3b/iRTfP1vL+d7K4DEhtlDswD2hzt8OxFr3vR9NrGpZI+0nvZHi/7wwi+79e1N3uxnttjnKbabWIRSF1q0U+2l/QTFmzkD8dfd3Rpsb9+KLwMsj4uTydb2HIi/+90j7YENsLacP+52fxeWw91JcrntFZg68jn3Z74CiwPaWiPiziOgEiIjnRsRAL4Xx2L4/RbEvdWy5/BkRcVqd+Z5AcanOVqA/Ik6h+A4bsLccPex7vw+xqnr7c4z26+X376EUBci7MvOKEdblMRoeo40iV7blMZqFo1L5hfffFDvM3xzy9AspNtZVFBXBHRQV6QGfLqe9nmKwqx0Mvk5xqG9S9Ba4OwcP7vabwPcj4uFynmUj7IC9tozn3yk26Jspbv26ts6851HcOekXFN3Dv1rz3AEUA6beS9Fd98kU16tD0aXzljKeC4DT87FrQkfrfIpR/2+iGIjthnIaFO/BWoqE+V3gnzLz6jrL+FL5uy8ibqjzPJn5fYqdzKMoBisbmL6O4ovmExSJvpeRBz48g8e6yT9AMUDsqymue4diQMdtFIOrfYciAV40wvKGxrmL4ozlCRSj9Q/s5NXb6VlJ0XVxE8VdjYZev/xZiuueH4iIr9f5+5Hee00Qw+WmLK7Rvnvgh+KuF7vL9q5hlrWZYlv7HYrcMeApFIOhPkhxycC3KS7RqLeMv6f4Mj6HYmf+LoqeKV+vM/vePsNvBjZE0c337RQ7FTD63DCscuftzynOVm+mGNdg4NKYQyny9v1lfH0U3bKHLuPHFF/ut5fb2eO6T+djgz8uoGaHqDyj9pJynT+nyK9/Q5Fz68X7ZYodqz8q599Csb1+o5zlIor/ybUUueOXFPloX7yfIgd+r3zP11LnmvnM/BHF2ADfLeM4nuJuJAP+k2Lg47sj4t46fz/Se68Jai/7SedT9Dr5YUQ8XP58aoRl1d1uGOW2WS5jb9tMrSsoLs38abncXzK4wD7c/s6oc+MIr/UnFL0IllN8578SeGVmPsrI+2C1y9hOMYDrf5W5qN4YIFC8l0Nz0b7sd5CZ/w28uPy5PSLuA1ZQ7HOO1/Z9AcVn6MqIeIjiu+EFdWJ5iGK/7IsUn4k3MPh7cMQcvZf3XhPEfh6jvY/if38XxaVOr97L6jxGK3iM5jHa40SOaZwrSZIkSZIkTXb2OJIkSZIkSVJdFo4kSZIkSZJUl4UjSZIkSZIk1WXhSJIkSZIkSXVZOJIkSZIkSVJdFo5aQERcGhGvqjqOVhIR10TEHw/z3NyI2DBce7KIiA0RsaB8vDQi/qbqmDS5mYsez1xkLlLzmYsez1xkLlLzmYsez1zUvrnIwlHFIuI5wHOBb9RMe0NE3BER2yLi6xFxxD4sLyNifURMqZl2fkRcPMb4sozj4fLngbEsp0oR8aKIuDoifrGvyatMeFnz+h+OiP9tUKgj+TTwxoh4cgXrVhswFzWeuUjaO3NR45mLpL0zFzWeuWhisXBUvT8BLsnMBIiIZwH/ArwZmAlsB/5pH5d5FHD6OMb43Mw8uPw5bOiTETF1HNfVCNuAi4A/249lHFbzHjx36JONfg8y85fAt4DFjVyP2pq5qPHMRdLemYsaz1wk7Z25qPHMRROIhaPqnQJ8u6b9RuCyzLw2Mx8G/hz4/Yg4ZB+W+THgvOE2lIg4NSJuiYgHouhu+Gv7EnBNhfdtEXEn8J/l9C9FxN1l1fjaMsEO/M2gbo0R8ZaI+E5Ne2FE/Lj8208AsS8xjSQzf5CZ/wbcPl7LjIj5EbExIt4fEXcD/xoRh0fEf0TE1oi4v3x8dM3f7OnWWLbPjYjP1bTfXJ7F6IuID9ZZ7TXAy8frNUhDmIswF5Vtc5GqZC7CXFS2zUWqkrkIc1HZNhdh4ahSEfFE4KnAT2omPwvY080uM28DHgWeVv7NP0XE3qrbXwUeBN5SZ51PAy4F3gXMAFYBl0XEE8bwEk4Cfg14adn+FnAc8GTgBuCS0SwkIo4sYz4HOBK4DfjdMcQzJmXyOGsMf/oU4AjgWGAJxfb0r2V7DrAD+MQoY3gm8M8UZzGOAjqBo4fMditFl1lpXJmL9sRkLjIXqULmoj0xmYvMRaqQuWhPTOYic9EeFo6qdVj5+6GaaQcDvxgy3y+AQwAy808z80/3stykqIL/eZ1k84fA5Zm5JjN3An8HTAd+Z4Tl3VBWvh+IiAtrpp+bmdsyc0cZ20WZ+VBmPgKcCzw3Ip60l1gBXgbckplfLmP6OHD3KP5uXGTmKzLzo3uZ7d6a9+C95bTdwIcy85HM3JGZfZn5lczcnpkPAR+mSNyj8VrgP8qzGI9Q/P92D5nnIWA076e0rw4rf5uLzEXmIlXpsPK3uchcZC5SlQ4rf5uLzEXmolKrX/c42T1Q/j4E+GX5+GHg0CHzHcrgxLVXmbkqIjZSXJ9b6yjgjpr5dkfEXcDsERb3vMzsHWhExNzy4V010zooNsI/oKiSD2xQR/L4JDvUUbXLyswsY2olR2Zm/0AjIuYDW8vrWgemHQT8I7AIOLycfEhEdGTmrr0sf+h7sC0i+obMcwh7fy+lsXig/G0uMheZi1SlB8rf5iJzkblIVXqg/G0uMheZi0r2OKpQZm6j6PL3tJrJt1DT1S0ifgU4APjpGFbxQeADwEE1035O0U1vYPkBHANsGsPys+bxG4DTgAUUFde5A6sof28bEsdTah5vLmMYGlOryyHt9wBPB16QmYcCLyynj+U9OIiiK2StX6Omi6w0XsxFe5iLzEWqkLloD3ORuUgVMhftYS4yF+1h4ah6qxjcVe4S4JURcWJ5fe1fAl8tu9Xtk8y8BrgZ6K6Z/EXg5RFxckRMo9iQHgH+e4zxDzikXE4fxYb3kSHP30gxgNxBEdEFvK3mucuBZ0XE70cxWNwZDN5g90tETImIA4FpRTMOHOP1wntzCMU1sw9EcXvODw15/kbg9IiYFhHzKLo+Dvgy8IqI+L0ytr/k8dvnSRTXKEuNYC4yF4G5SNUzF5mLwFyk6pmLzEVgLtrDwlH1VgBvLCu4ZOYtwNspktM9FB/0PdfLRsSnIuJT+7D8cygGB6Nc/k+ANwHLgXuBVwKvzMxH9/N1rKToXrkJ+BHwvSHP/yPFAHJbgB5qBmXLzHspuk9+lCKpHQf8137GU+uFFMliFY8NiHblwJMR8a2I+MA4rOfjFNci30vx+lcPef7PgV8F7gfOAz4/8ET5f39HOW1zOc/GmhgPpLjOuGcc4pTqMReZi8xFagXmInORuUitwFxkLjIX1YjMoT251GwR8Xngi5n59apjmQiiuH73msycW689GUXEUuCYzHxf1bFo8jIX7RtzkdQY5qJ9Yy6SGsNctG/MRZObg2O3gMx8Q9UxqLVl5vKqY9DkZy7S3piL1AzmIu2NuUjNYC7S3rRTLvJSNU1ED1B0ORyuLUnN8ADmIknVewBzkaTqPYC5aNLyUjVJkiRJkiTVNaEuVTvyyCNz7ty5VYchaT9df/3192bmjKrjGCtzkTQ5mIsktQJzkaRWMFIumlCFo7lz57Ju3bqqw5C0nyLijqpj2B/mImlyMBdJagXmIkmtYKRc5BhHkiRJkiRJqsvCkSRJkiRJkuqycCRJkiRJkqS6LBxJkiRJkiSpLgtHUoX6+vo444wz6OvrqzoUSZKkSrlfJKkVmIser+GFo4i4KCLuiYiba6b9bUT8OCJuioivRcRhjY5DakU9PT2sX7+elStXVh2KJElSpdwvktQKzEWP14weRxcDi4ZMWwM8OzOfA/wUOLsJcUgtpa+vj9WrV5OZrF692oq2JElqW+4XSWoF5qL6pjZ6BZl5bUTMHTLtyprm94DXNjoOqdX09PSwe/duAHbt2sXKlSs588wzK45q8oqIi4BXAPdk5rPLaX8LvBJ4FLgNeGtmPlBZkNIwli9fTm9vb8OWv2nTJgBmz57dsHV0dXWxdOnShi1f0sTmfpGkVmAuqq8Vxjj6I+Bbwz0ZEUsiYl1ErNu6dWsTw5Iaa+3atfT39wPQ39/PmjVrKo5o0rsYez9Kde3YsYMdO3ZUHYakNuZ+kaRWYC6qr+E9jkYSER8E+oFLhpsnM1cAKwDmzZuXTQpNargFCxZw+eWXs2vXLjo6Oli4cGHVIU1q9n7URNbonjrLli0D4IILLmjoeiRpOCeeeCJXXHHFoLYkNduCBQtYtWoV/f39TJ061WO0UmU9jiLiLRSXjbwxMy0Iqe10d3eza9cuoOgGuXjx4oojanv2fpQkqSIeDkhqBd3d3UyZUpRJOjo6PEYrVVI4iohFwPuAUzNzexUxSFW7//77R2yreUbb+zEz52XmvBkzZjQvOEmS2sB3vvOdQe3rrruuokgktbPOzk4WLVpERLBo0SI6OzurDqklNLxwFBGXAt8Fnh4RGyPibcAngEOANRFxY0R8qtFxSK3m/PPPH7Gt5rD3oyRJ1VuwYAEdHR0AXsIvqVLd3d0cf/zx9jaq0Yy7qr2+zuTPNnq9UqvbsGHDiG01Xk3vx5Ps/ShJUnW6u7tZvXo1u3btYurUqR6wSVILaYW7qkltae7cuSO2Nb7s/ShJUuvy8hBJraKnp4f169ezcuXKqkNpGRaOpIqcc845I7Y1vjLz9Zk5KzOnZebRmfnZzOzKzGMy84Ty5+1VxylJUrvy8hBJVevr62P16tVkJqtXr6avr6/qkFqChSOpIocffviIbUnS5BMRx0TE1RHxo4i4JSKWldOPiIg1EfGz8rdfCmo7nZ2dXHjhhfY2klSZnp4edu/eDRR3vrbXUcHCkVSRf/mXfxnUXrFiRUWRSJKaqB94T2Y+E/gt4B0R8UzgLOCqzDwOuKpsS5KkJlq7di39/f0A9Pf3s2bNmoojag0WjqSKXHXVVYPaa9eurSgSSVKzZObmzLyhfPwQcCswGzgN6Cln6wFeVUmAkiS1sQULFjB1anEPsalTp3qHx5KFI6kiA10gh2tLkia3iJgL/DrwfWBmZm4un7obmDnM3yyJiHURsW7r1q3NCVSSpDbR3d3NlClFmaSjo8Mx10oWjqSKZOaIbUnS5BURBwNfAd6VmQ/WPpfFF0LdL4XMXJGZ8zJz3owZM5oQqdQ8fX19nHHGGQ5GK6ky3uGxPgtHUkUGKtnDtSVJk1NETKMoGl2SmV8tJ2+JiFnl87OAe6qKT6qKt8CW1Aq8w+PjeaQqVeTEE08c1H7hC19YUSSSpGaJiAA+C9yamf9Q89Q3ge7ycTfwjWbHJlXJW2BLahXe4fHxLBxJFTnwwAMHtQ844ICKIpEkNdHvAm8GXhwRN5Y/LwM+CiyMiJ8BC8q21Da8BbYktS4LR1JFrrvuuhHbkqTJJzO/k5mRmc/JzBPKn1WZ2ZeZJ2fmcZm5IDPvqzpWqZm8BbYktS4LR1JFFixYMKjtrR4lSVK78hbYktS6LBxJFTn11FMHtV/5yldWFIkkSVK1vAW2JLUuC0dSRb74xS8Oan/pS1+qKBJJkqRqeQtsSWpdFo6kilx11VWD2mvXrq0oEkmSpOp5C2xJak1Tqw5AalfFHZmHb0uSJLWTgVtgS5Jai4UjaQTLly+nt7e3Ics+5JBDuP/++we1ly1bNu7r6erqYunSpeO+XEmSJEnS5OelalJFZs2aNWJb4y8iLoqIeyLi5pppR0TEmoj4Wfn78CpjlCRJkqRW0vAeRxFxEfAK4J7MfHY57Qjg34G5wAbgdZl5/3DLkKrS6J46r371q7n//vt56Utfytlnn93QdQmAi4FPACtrpp0FXJWZH42Is8r2+yuITZIkqWVERAewDtiUma+oOh5pQCOvCgHYtGkTALNnz27YOmBiXRnSjB5HFwOLhkwbOFA7DriqbEttZ9asWTzxiU9kyZIlVYfSFjLzWuC+IZNPA3rKxz3Aq5oZkyRJKvT29vLyl7+8oQeE2ifLgFurDkJqth07drBjx46qw2gpDe9xlJnXRsTcIZNPA+aXj3uAa/AMv9rQtGnT6Orq8paz1ZqZmZvLx3cDM6sMRpKkdnXWWWexbds2zjrrLL785S9XHU5bi4ijgZcDHwbeXXE40iCN7qUzMO7sBRdc0ND1TCRVjXE06gO1iFgSEesiYt3WrVubE52ktpSZCWS958xFkiQ1Tm9vL/feey8A9957r72Oqvdx4H3A7npPul8ktZfKB8ce6UCtfH5FZs7LzHkzZsxoYmSS2sSWiJgFUP6+p95M5iJJkhrnrLPOGrGt5omIgfFprx9uHveLpPZSVeFoVAdqktQE3wS6y8fdwDcqjEWSpLY00NtouLaa6neBUyNiA/AF4MUR8blqQ5JUpaoKRx6oSWq6iLgU+C7w9IjYGBFvAz4KLIyInwELyrYkSVJbysyzM/PozJwLnA78Z2a+qeKwJFWo4YNjlwdq84EjI2Ij8CGKA7MvlgdtdwCva3QckpSZrx/mqZObGogkSZIkTRDNuKuaB2qSJEmSNMFk5jUUd8CW1MYqHxxbkiRJUnubNWvWoPZRRx1VUSSSpKEsHEmSJEmq1D33DL5XzpYtWyqKRJI0lIUjSZKkJoqIiyLinoi4uWbauRGxKSJuLH9eVmWMUrPt3r17xLYkqToWjiRJkprrYmBRnen/mJknlD+rmhyTVKnMHLEtSaqOhSNJkqQmysxrgfuqjkNqJR0dHSO2JUnVsXAkSZLUGt4ZETeVl7IdXnUwUjOdeOKJI7YlSdWxcCRJklS9fwZ+FTgB2Az8fb2ZImJJRKyLiHVbt25tYniSJKldWTiSJEmqWGZuycxdmbkb+DTw/GHmW5GZ8zJz3owZM5obpNRA11133YhtqVX09fVxxhln0NfXV3UoUtNYOJIkSapYRMyqab4auHm4eaXJyLuqaaLo6elh/fr1rFy5supQpKaxcCRJktREEXEp8F3g6RGxMSLeBnwsItZHxE3Ai4AzKw1SarIpU6aM2JZaQV9fH6tXryYzWb16tb2O1DbMyJIkSU2Uma/PzFmZOS0zj87Mz2bmmzPz+Mx8Tmaempmbq45TaqaZM2eO2JZaQU9Pz57ecLt27bLXkdqGhSNJkiRJldqyZcuIbakVrF27lv7+fgD6+/tZs2ZNxRFJzWHhSJIkSZKkvViwYAERAUBEsHDhwoojkprDwpEkSZKkSj35yU8e1PZSNbWiU089lcwEIDN55StfWXFEUnNYOJIkSZJUqaGXpt19990VRSIN75vf/OagHkeXXXZZxRFJzWHhSFLbi4gzI+KWiLg5Ii6NiAOrjkmSJEmtZe3atYN6HDnGkdqFhSNJbS0iZgNnAPMy89lAB3B6tVFJktRepkyZMmJbagULFixg6tSpAEydOtUxjtQ2Ks3InuWX1CKmAtMjYipwEPDziuORJKmtDNypari21Aq6u7v3FDU7OjpYvHhxxRFJzVFZ4ciz/JJaQWZuAv4OuBPYDPwiM68cOl9ELImIdRGxbuvWrc0OU5IkSRXr7Oxk0aJFRASLFi2is7Oz6pCkpqi6D6hn+SVVKiIOB04DngocBTwxIt40dL7MXJGZ8zJz3owZM5odpiRJklpAd3c3xx9/vL2N1FYqKxx5ll9Si1gA/F9mbs3MncBXgd+pOCZJkiS1oM7OTi688EJ7G6mtVHmpmmf5JbWCO4HfioiDori/6snArRXHJEmSJEktocpL1TzLL6lymfl94MvADcB6iry4otKgJElqM0ceeeSIbUlSdaZWuO49Z/mBHRRn+ddVGI+kNpWZHwI+VHUckiS1q3vvvXfEtiSpOlWOceRZfkmSJEmSpBZW6V3VMvNDmfmMzHx2Zr45Mx+pMh5JkiRJzXf00UcPah9zzDEVRSJJGqrSwpEkSZIkvetd7xqxLbWK3t5eXv7yl9Pb21t1KFLTWDiSJEmSVKlrr712xLbUKs4991y2bdvGueeeW3UoUtNYOJIkSZJUqbVr1w5qr1mzpqJIpOH19vayceNGADZu3GivI7WNKu+qJkmSJGmCWL58ecMOlKdPn8727dsHtZctWzbu6+nq6mLp0qXjvly1h6G9jM4991w+97nPVROM1ET2OJIkSWqiiLgoIu6JiJtrph0REWsi4mfl78OrjFFqtpkzZ+55HBGD2lKrGOhtNFxbmqzscSRJktRcFwOfAFbWTDsLuCozPxoRZ5Xt91cQmzSsRvfUec1rXkNfXx+nnnoqZ555ZkPXJY1FRJCZg9pSO7DHkSRJUhNl5rXAfUMmnwb0lI97gFc1MyapFcycOZMnPvGJLF68uOpQpLpOOumkEdvSZGXhSJIkqXozM3Nz+fhuoO51OhGxJCLWRcS6rVu3Ni86qQmmTZtGV1cXnZ2dVYci1bV06dI9vYwiwvGy1DYsHEmSJLWQLK6DyGGeW5GZ8zJz3owZM5ocmSS1t87Ozj29jE466SSLnGobjnEkSZJUvS0RMSszN0fELOCeqgOSJD3e0qVLue++++xtpLZijyNJkqTqfRPoLh93A9+oMBZJbSwijomIqyPiRxFxS0QsqzqmVtLZ2cmFF15obyO1FQtHkiRJTRQRlwLfBZ4eERsj4m3AR4GFEfEzYEHZlqQq9APvycxnAr8FvCMinllxTJIq5KVqkiTtp+XLl9Pb21t1GGM2EPuyZRP7pHJXV9eEuHQgM18/zFMnNzUQSaqjHKh/c/n4oYi4FZgN/KjSwFpEb28vy5Yt44ILLqCrq6vqcKSmsHAkSdJ+6u3t5We3/A9zDt5VdShj8oSdRQfkR+5YV3EkY3fnwx1VhyBJk05EzAV+Hfj+kOlLgCUAc+bMaX5gFTr//PPZtm0b559/PhdffHHV4UhNYeFIUtuLiMOAzwDPpriT0R9l5ncrDUoTzpyDd/GB5z1YdRht6yM3HFp1CJI0qUTEwcBXgHdl5qAvuMxcAawAmDdvXt27QE5Gvb29bNiwAYANGzbQ29trryO1Bcc4kiS4AFidmc8AngvcWnE8kiRJlYmIaRRFo0sy86tVx9Mqzj///BHb0mRljyNJbS0ingS8EHgLQGY+CjxaZUySJElViYgAPgvcmpn/UHU8rWSgt9FwbWmyqrRw5OUhklrAU4GtwL9GxHOB64FlmbmtdqZ2vpZfkiS1ld8F3gysj4gby2kfyMxV1YU0eo28YcUBBxzAI488MqjdiBtLTJSbPah9VH2pmpeHSKraVOB5wD9n5q8D24Czhs6UmSsyc15mzpsxY0azY5QkSWqKzPxOZkZmPiczTyh/JkTRqNGGnjw89thjK4pEaq7Kehx5eYikFrER2JiZA3cL+TJ1CkeSpGr19fVx3nnn8aEPfYjOzs6qw5HUohrdU+elL30pjzzyCHPnzmXFihUNXZfUKqq8VM3LQyRVLjPvjoi7IuLpmfkT4GTgR1XHJUkabMWKFdx0002sWLGCs88+u+pwJLWpOXPmcNttt3HOOedUHUrLauTlgs0wEHsjLkNspvG85LHKwtHA5SFLM/P7EXEBxVn+P6+dqV1v9SipqZYCl0TEE4DbgbdWHE9L8Sy/pKr19fWxZs0aANasWcOSJUvMR5IqcdBBB3H88cfT1dVVdSgtq7e3l5/d8j/MOXhX1aGMyRN2FiP6PHLHuoojGbs7H+4Y1+VVWTjy8hBJLSEzbwTmVR1Hq+rp6WH9+vWsXLmSM888s+pwJLWhFStWsHv3bgB2795tryNJanFzDt7FB573YNVhtK2P3HDouC6vssGxM/Nu4K6IeHo5yctDJKnF9PX1sXr1ajKT1atX09fXV3VIktrQVVddNWJbkiQ1TtV3VRu4POQm4ATgI9WGI0mq1dPTs+cs/65du1i5cmXFEUlqR5k5YluSJDVOpYWjzLyxvL31czLzVZl5f5XxSJIGW7t2Lf39/QD09/fvGWNEkppp1qxZI7YlSVLjVN3jSJLUwhYsWMDUqcVweFOnTmXhwoUVRySpHQ29TNbLZiVJah4LR5KkYXV3dzNlSvFV0dHRweLFiyuOSFI7Glq0fslLXlJRJJIktZ8q76om7Zfly5fT29tbdRj7ZSD+ZcuWVRzJ2HV1dbF06dKqw1CDdHZ28qIXvYgrrriC+fPne/trSZXo7u5m9erVPProozzhCU+wiC1JUhNZONKE1dvby89u+R/mHLyr6lDG7Ak7i54cj9yxruJIxubOhzuqDkFN4CC0kqrW2dnJokWLuOyyyzjllFMsYkuS1EQWjjShzTl4Fx943oNVh9G2PnLDoVWHoAbr6+vjmmuuAeDqq69myZIlHrBJqkR3dzcbNmywt5EkSU3mGEeSpGH19PSwc+dOAHbu3MnKlSsrjkia3CJiQ0Ssj4gbI2JidkdtkM7OTi688EKL15IkNZmFI0nSsNasWbPnUrXM5Morr6w4IqktvCgzT8jMeVUH0kr6+vo444wzvKOaJElN5qVqkqRhzZw5kw0bNgxqS1IVli9fzk033cTy5cs599xzqw6nJU30G4dMhpuGgDcOkTT5jKlwFBErMnPJeAcjSWotd99994htSeMugSsjIoF/ycwVtU9GxBJgCcCcOXMqCK8ateOtXXPNNfT19XnJWh0T/cYhE/2mIdCcG4dExBTgLcBrgKOBXcBPgU9l5jUND0BS2xm2cBQRRwz3FPCyxoQjSWolT3nKUwb1OHrKU55SXTBSe/i9zNwUEU8G1kTEjzPz2oEny0LSCoB58+a1zS0Ply9f/ri2vY7q88Yh1WrSjUM+C9wB/DXwWuBB4DrgnIg4PjOXj/THkrSvRupxtJUiIUXNtCzbT25kUJI0koj4SGZ+oOo42sHmzZtHbEsaX5m5qfx9T0R8DXg+cO3IfzX5ffvb3x6xLbWZ38jMt5aPvxMR38vMv4iIa4EbAQtHksbVSIWj24GTM/POoU9ExF2NC0mSHhMRFw6dBLw5Ig4GyMwzmh9V+5g6dSqPPPLIoLY02UXEocCMzLxtyPTnZOZNDVzvE4EpmflQ+fglwF82an0TycAg/cO1pTazMyJ+NTNvi4jnAY8CZOYj5WWukjSuRrqr2seBw4d57mPjH4ok1fVq4AhgHXB9+Xtn+fj6CuNqC9u2bRuxLU02EfE64MfAVyLiloj4zZqnL27w6mdS9B74X+AHwOWZubrB65wQjj766EHtY445pqJIpJbwZ8DVEfEz4Ctlm4iYAfxHlYFJmpyGPXWcmZ8c4Tm7P0pqlmdRnHFfBLw3M38eER/KzJ7xXElEdFAUpTZl5ivGc9ma/DZt2sS2hzqaNbaF6rjjoQ6euGnTeCzqAxSXgWyOiOcD/xYRZ2fm1xh8+f64y8zbgec2ch0T1bve9S7e+973DmpL7Soz/zMijgU6M/PemulbgfdVF5mkyWqkHkfDioiF4x2IJNWTmQ9m5ruAvwcuiYj3MsbctRfLgFsbsNwJ7aSTThrUnj9/fjWBSM0zNTM3A2TmD4AXUQw4ewbFWI+qwJo1awa1r7zyyooikVpDFu4dOt3jNEmNMNbBKj4LtM89YCVVJiI+CXw+M/8rIl4M/CnwnXFex9HAy4EPA+8ez2VPdG9+85sHDUL7pje9qcJoWtfs2bN5pH+zdzKq0EduOJQDZs8ej0U9ODB2CEDZ82g+8HWKHpCqwFVXXfW49tlnn11RNFJL8zhN0rgbtnAUEd8c7imgszHhSNLj/BT4u4iYBXwRuHSkS2nH6OMUXbsPGW6GiFgCLAGYM6d99sc+85nPDGp/9rOf5a//+q8rikZqigeAWcCegbHLwaoXAa+rKqh25+DY0mM8TlOr8xL+6o3jJfzAyD2OTgTeBDw8ZHpQ3Bp2XDiuiKSRZOYFwAXltfynAxdFxHTgUooi0k/3Z/kR8Qrgnsy8vuxVMFwcK4AVAPPmzWubI5bvfe97g9rf/e53K4pEaporgL8dUqz+n8zcCVxSbWjta9asWWzcuHFQW2pjTTlOk6QBIxWOvgdsz8xvD30iIn4yjjEMjCtiOVLSsDLzDuBvgL+JiF8HLgL+AujYz0X/LnBqRLwMOBA4NCI+l5lekyW1ob0Uqz+fmT+rNMA21dfXN2JbajPNOk6TxsRL+Ks3jpfwAyMXjv6P4pbXj5OZLxyPlTuuiKTRioipwCkUB3InA9cA5+7vcjPzbODsch3zKe7cZtFIanMNLFZPWsuXL6e3t7chyz7ooIPYsWPHoPayZcvGfT1dXV0sXbp03JfbLF4eUr3xvjxkGA0/TpOkWiMVjn5Cna7a47z+j7OXcUUktbfy7iCvB14G/AD4ArAkM7dVGpikSa1RxWqNzcyZM/f0MooIZs6cWXFEUqWacZzWEI0sMDfLQPyNKF4300QvlKu5hi0ctcq4Iu06IK2kPc4GPg+8JzPvb+SKMvMaioNDSW3KYvXYNfoA5DWveQ19fX2ceuqpnHnmmQ1d10Tl5SHVG+/LQ+pp9HFaI/X29nLjzbey66Ajqg5lzKY8Wgx1ef3tWyqOZOw6tt9XdQiaYEbqcQRUP65Iuw5IK6mQmS+uOgZJbaVpxWrtm5kzZ/LLX/6SxYsXVx2K1BIm6iW1uw46gh3PeFnVYbS16T9eVXUImmCm7G2GiJgaEa+MiEuAb1F0jfz9/V1xZp6dmUdn5lyKSvl/Oq6IJEmqUma+ODM/Y9Go9UybNo2uri46O73buASNO06TpKGG7XFkV21JkiRJai0ep0lqtpEuVXNcEbU07xxSvSbdOUQVmjlzJlu2bBnUliRJlfKSWklNNdLg2I4rIkltbuvWrSO2JUlSc3mcJqnZ9jo4ttSqvHNI9Zpx5xBVa/fu3SO2JUmt5c6HJ25v7C3bi+FXZx40cb9r7ny4g+OqDkKSxpmFI0nSsCKCzBzUliS1pq6urqpD2C+P9vYCcMCxE/d1HMfE/z8ARMQi4AKKO7R9JjM/WnFIkipk4UiSNKwpU6awa9euQW1JUmtaunRp1SHsl2XLlgFwwQUXVBxJe4uIDuCTwEJgI/DDiPhmZv6o2sgkVcXCkSRpWCeffDJXXnnlnvaCBQsqjEbSWC1fvpzesjfHRDUQ/0BxYSLq6uqa8MUdtYXnA72ZeTtARHwBOA3Y78LRpk2b6Nj+C6b/eNX+Lkr7oWN7H5s29VcdhiYQC0eSNIE1+mBw586dg9p33XVXQw7aPJiSGqu3t5cbb76VXQcdUXUoYzbl0eKy2etv37KXOVtTx/b7qg5BGq3ZwF017Y3AC2pniIglwBKAOXPmNC8ySZWwcDRGzThzt6m8zfnsBg4+7MGapJFMmzaNjo4Odu3axeGHH860adOqDkma1Bo5rsiug45gxzNeNl6L0z6yh4Umk8xcAawAmDdvXu5l9j1mz57N3Y9MNRdVbPqPVzF79syqw9AEYuGohe3YsaPqECS1uGYUfv/0T/+UO+64g8985jN0dnY2fH1Su3JcEUktYhNwTE376HKapDZl4WiMmnGw5gCBklrBtGnT6OrqsmgkNZ7jikxijimiCeSHwHER8VSKgtHpwBuqDUlSlbw9jiRJUmuoN67IoOvVI2JJRKyLiHVbt25tanCS2kNm9gPvBK4AbgW+mJm3VBuVpCrZ40iSpHFw58MdfOSGQ6sOY0y2bC/OI808aHfFkYzdnQ93cFzVQTSB44pMXI4pookkM1cBdlGUBFg4kiRpv3V1dVUdwn55tLzZwwHHTtzXcRwT//9Ag8cV6dh+34S+VG3KLx8EYPeBE7NAW9xVzcKRpPbgCbVqjfcJNQtHktpaRBwDrKTYm09gRWY6sJj2yUS/O6Vj6rWMho0rMgmKavT2PgRA169M1OLLzEnxf5CkvZnouc4Tao9n4UhSu+sH3pOZN0TEIcD1EbHGuxhJarbM7I+IgXFFOoCLxmtckYle3AQLnJI0UUz07xy/bx7PwpGktpaZm4HN5eOHIuJWisFoLRxJajrHFZE02XnZbPW8dFb7ysKRJJUiYi7w68D36zy3BFgCMGfOnOYGJkmSNAlM9EuYYDJcNgteOqt9ZeFIkoCIOBj4CvCuzHxw6PNjvZORJEmSChP9EibwMia1pylVByBJVYuIaRRFo0sy86tVxyNJkiRJraKywlFEHBMRV0fEjyLilohYVlUsktpXRATwWeDWzPyHquORJEmSpFZSZY+jgTsZPRP4LeAdEfHMCuOR1J5+F3gz8OKIuLH8eVnVQUmSJElSK6hsjCPvZKTxcOfDHXzkhol7R4Mt24va7cyDdlccydjc+XAHx1UdxH7KzO8AUXUckiRJktSKWmJwbO9kpLGYDHcCeLS3F4ADjp2Yr+U4Jsf/QZIkSZJUX+WFI+9kpLHyrgySJLWP7du3c9ttt9Hb2+tJC0mSmqjSwpF3MpIkSZocli9fTm/Zk7YRent7yUze8Y538IxnPKMh6+jq6poUJ6YkSRpPVd5VzTsZSZIkaa+2b99OZtHx/JFHHmHHjh0VRyRJUvuossfRwJ2M1kfEjeW0D2TmqupCkiRJ0lg0sqfOW97ylkHtRx99lBUrVjRsfZIk6TFV3lXNOxlJkiRprzZs2DBiW5IkNU5ll6pJkiRJo3HwwQeP2JYkSY1j4UiSJEktbefOnSO2JUlS41R6VzVJkiRpb570pCdxzz337Gkfdthh1QXTxppx5zyAZcuWNWwd3jlPkvadhSNJaqBG72Q3QzN25BvNAwVpYqstGgFs2bKlokjUSNOnT686BElSHRaOJKmBent7ufHmW9l10BFVhzJmUx4tboF9/e0T80CtY/t9VYcgSZNCowvwfX19nHfeefzFX/wFnZ2dDV2XJGn0LBxJUoPtOugIdjzjZVWH0bam/3hV1SFIkkahp6eH9evXs3LlSs4888yqw5EklRwcW5IkqWIRcW5EbIqIG8sfq801ImLEtia+vr4+Vq9eTWbyrW99i76+vqpDkiSVLBxJkiS1hn/MzBPKH7vK1Zg1a9aIbU18PT09e+6Wt3PnTlauXFlxRJKkAZP2UjUHpG0NDkgrSZL219DBsB0ce/JZs2YNmcWYepnJlVde6eVqktQiJm3hyAFpq+eAtJIk7ZN3RsRiYB3wnsy8f+gMEbEEWAIwZ86cJodXHS9Vm/xmzpzJhg0bBrUlSa1h0haOwAFpq+aAtJooImIRcAHQAXwmMz9acUiSJqGIWAs8pc5THwT+GfgrIMvffw/80dAZM3MFsAJg3rx52bBgW8zJJ5/MFVdcMaitycVeZZLUuiZ14UiS9iYiOoBPAguBjcAPI+Kbmfmj8Vj+pk2b6Nj+CwupFerY3semTf1VhyGRmQtGM19EfBr4jwaHM6EsWbKENWvWsHv3bqZMmcKSJUuqDknjbOHChVx22WVkJhHBS17ykqpDkiSVHBxbUrt7PtCbmbdn5qPAF4DTKo5JUpuJiNrRnl8N3FxVLK2os7OThQsXAkWBobOzs+KINN66u7uZOrU4pz1t2jQWL15ccUSSpAH2OJLU7mYDd9W0NwIvGDrTWMcVmT17Nnc/MtXLZis0/cermD3bsTLU8j4WESdQXKq2AfiTSqNpQUuWLGHz5s32NpqkOjs7OeWUU7jssss45ZRTLA5KUguxcCRJo9Cu44pIao7MfHPVMbS6zs5OLrzwwqrDUAN1d3ezYcMGexuppe3cuZM77riDvr4+C5xqG16qJqndbQKOqWkfXU6TJElNNFAc9GBcrWzz5s1s27aNFStWVB2K1DT2OJLU7n4IHBcRT6UoGJ0OvKHakCRJaj99fX2cd955fOhDH7J4pDFbvnw5vb29DVn2zp07uf/++wG44ooruOuuu5g2bdq4r6erq4ulS5eO+3Klsaq0x1FELIqIn0REb0ScVWUsktpTZvYD7wSuAG4FvpiZt1QblSRJ7efCCy/kpptuYvny5VWHItW1efPmEdvSZFVZj6NG3wJbkkYrM1cBq6qOQ5KkdtXX18e3v/1tAK655hrHj9GYNbKnzsknnzyo/eCDD3LBBRc0bH1Sq6iyx5G3wJYkSZL0uIHP7XVUjYj424j4cUTcFBFfi4jDqo6plezatWvEtjRZVTnGUUNvgb1p0yY6tv+C6T+2E0FVOrb3sWlTf9VhSJKkScDxbya3gd5GA6655ppqAtEa4OzM7I+IvwHOBt5fcUySKtbyg2N7C2xJE13H9vsmdBF7yi8fBGD3gYdWHMnYdGy/D5hZdRiS9lNPTw/r169n5cqVnHnmmVWHI01KmXllTfN7wGuriqUVRQSZOagttYMqC0cNvQX27NmzufuRqex4xsvGa5HaR9N/vIrZsz1YU3vr6uqqOoT91tv7EABdvzJRt+eZk+L/ILWzvr4+Vq9eTWayevVqFi9ebK8jqfH+CPj3qoNoJbVFo3ptabKqsnDkLbAlTXqT4Vaqy5YtA3DwR0mV6enpYffu3UAxpoi9jiaf+fPnD7o8bf78+ZXFMtlFxFrgKXWe+mBmfqOc54NAP3DJMMsY03AiE93BBx/Mww8/PKgttYPKBsf2FtiSJEkajbVr19LfX4yb2N/fz5o1ayqOSONt6ImWyXDipVVl5oLMfHadn4Gi0VuAVwBvzGG61GTmisycl5nzZsyY0cToq/Xud797UPs973lPRZFIzVXlXdXIzFWZ+bTM/NXM/HCVsUiSJKk1LViwgI6ODgA6OjpYuHBhxRFpvHV2djJr1iwAjjrqKC9FrEhELALeB5yamdurjqfVXHvttYPaQwd1lyarSgtHkiRJ0t50d3fvGUskM1m8eHHFEWm89fX1ce+99wJw77330tfXV3FEbesTwCHAmoi4MSI+VXVArWRoocjCkdqFhSNJkiRJlerp6dlTHNy9ezcrV66sOKL2lJldmXlMZp5Q/ry96phaiYNjq11VOTi2JEkaheXLl9Pb29uw5Q8se2Ag9Ebo6upyzBKNWU9PD1OmTGH37t1MmTLFwbEnoXrjWPk/Vqt54hOfyLZt2wa1pXZgjyNJktrc9OnTmT59etVhSMNycOzJb8GCBUydWpzTnjp1quNYqSXt2rVrxLY0WU3qHkcd2+9j+o9XVR3GmE355YMA7D7w0IojGZuO7fcBM6sOQ5ImPHvqqN0tWLCAVatW0d/fb1Fhkuru7mb16tVAMQC641ipFb3whS/kyiuv3NM+6aSTKoxGap5JWzjq6uqqOoT91tv7EABdvzJRiy8zJ8X/QZIkVcuiwuTX2dnJokWLuOyyy1i0aJF3VVNLioiqQ5AqMWkLR5Ph7OzAWBMXXHBBxZFIkiRVx6JCe+ju7mbDhg0WBtWyrrvuuse1zz777IqikZrHMY4kSZKaICL+ICJuiYjdETFvyHNnR0RvRPwkIl5aVYytrLu7m+OPP96iwiTW2dnJhRdeaGFQLWvBggV0dHQARe9HL5tVu7BwJEmS1Bw3A78PXFs7MSKeCZwOPAtYBPxTRHQ0P7zWZlFBUtW6u7vZvXs3ALt377aQrbZh4UhS24qIv42IH0fETRHxtYg4rOqYJE1emXlrZv6kzlOnAV/IzEcy8/+AXuD5zY1OkrQ3999/P5kJQGZy//33VxyR1BwWjiS1szXAszPzOcBPAS9Sl1SF2cBdNe2N5bTHiYglEbEuItZt3bq1KcFJkgrnn3/+iG1psrJwJKltZeaVmdlfNr8HHF1lPJImvohYGxE31/k5bTyWn5krMnNeZs6bMWPGeCxSkjRKGzZsGLEtTVaT9q5qkrSP/gj49+GejIglwBKAOXPmNCsmSRNMZi4Yw59tAo6paR9dTpMktZC5c+cOKhbNnTu3slikZrLHkaRJbTRn/yPig0A/cMlwy/Esv6QG+iZwekQcEBFPBY4DflBxTJKkIc4555wR29JkZY8jSZPa3s7+R8RbgFcAJ+fAaIeS1AAR8WpgOTADuDwibszMl2bmLRHxReBHFEXsd2TmripjlSQ9XldX155eR3PnzqWrq6vqkKSmsHAkjWD58uX09vY2bPkDy162bFnD1tHV1cXSpUsbtvyJLCIWAe8DTsrM7VXHI2lyy8yvAV8b5rkPAx9ubkSSpH11zjnnsGzZMnsbVWgyHKPBxDpOs3AkVWj69OlVh9DuPgEcAKyJCIDvZebbqw1JkiRJraqrq4vLL7+86jDUQB6jPV4lhaOI+FvglcCjwG3AWzPzgSpikUYyUSrAGpvMtH+xJEmSNIF4jNZ8VQ2OvQZ4dmY+B/gpcHZFcUiSJEmSJGkYlRSOMvPKzOwvm9+juO2sJEmSJEmSWkhVPY5q/RHwreGejIglEbEuItZt3bq1iWFJkgB27txJb28vfX19VYciSZIkqckaVjiKiLURcXOdn9Nq5vkgxW1nLxluOZm5IjPnZea8GTNmNCpcSdIwNm/ezLZt21ixYkXVoUiSJEkN1dfXxxlnnOFJ0xoNKxxl5oLMfHadn28ARMRbgFcAb8zMbFQckqSx6+vr4/777wdgzZo1foFKkiRpUuvp6WH9+vWsXLmy6lBaRlV3VVsEvA84KTO3VxGDJE0Gy5cvp7e3t2HLv/POO/c83r17N3/8x3/MnDlzxn09XV1d3iFDkiRJlerr62P16tVkJqtXr2bx4sV0dnZWHVblqhrj6BPAIcCaiLgxIj5VURySpBEM9DYari1JkiRNFj09PezevRuAXbt22euoVEmPo8zsqmK9kjTZNLqXzote9CJqryaOCC644IKGrlOSJEmqwtq1a+nvL24A39/fz5o1azjzzDMrjqp6rXBXNUlSi5oyZcqIbUmSJGmyWLBgAVOnFv1rpk6dysKFCyuOqDV4BCBJGtbJJ588qL1gwYKKIpEkSZIaq7u7e8+J0o6ODhYvXlxxRK3BwpEkaVh/8id/sufLc8qUKSxZsqTiiCRJkqTG6OzsZP78+QDMnz/fgbFLFo4kScPq7OzkBS94AQAveMEL/PKUJEnSpBYRVYfQciwcSZJGtGHDBgDuuOOOagOR1Nb6+vo444wz6OvrqzoUSdIk1dfXx9VXXw3A1Vdf7XdOycKRJGlYvb29bN68GYCf//zn9Pb2VhyRpHbV09PD+vXrvTWyJKlhenp62LlzJwA7d+70O6dk4UiSNKxzzz13UPu8886rJhBJba2vr4/Vq1eTmaxevdozwJKkhlizZg2ZCUBmcuWVV1YcUWuYWnUAE9Xy5csbfuZ9YPnLli1r2Dq6urpYunRpw5YvaWLbuHHjoPZdd91VUSSS2llPTw+7d+8GYNeuXaxcuZIzzzyz4qgkSZPNzJkz9wzTMNCWPY5a2vTp05k+fXrVYUhtISLeExEZEUdWHUsrGTo4oIMFSmMXEX8QEbdExO6ImFczfW5E7IiIG8ufT1UZZytau3Yt/f39APT397NmzZqKI5IkTUZbtmwZsd2u7HE0RvbSkSaPiDgGeAlwZ9WxtJqTTjqJa665ZlBb0pjdDPw+8C91nrstM09objgTx4IFC1i1ahX9/f1MnTqVhQsXVh2SJGkSWrhwIZdddhmZSUTwkpe8pOqQWoI9jiQJ/hF4H5BVB9JqhhbJLZpLY5eZt2bmT6qOYyLq7u5mypRit7Wjo4PFixdXHJEkaTLq7u5m6tSif820adP8vilZOJLU1iLiNGBTZv7vXuZbEhHrImLd1q1bmxRd9To7O5k/fz4A8+fPp7Ozs9qApMnrqRHxPxHx7Yg4cbiZ2jkXLVq0iIhg0aJF5iJJUkN0dnZyyimnEBGccsopft+UvFRN0qQXEWuBp9R56oPAByguUxtRZq4AVgDMmzevrXomLV26lPvuu8/eRtIojJRvMvMbw/zZZmBOZvZFxG8AX4+IZ2Xmg0NnbOdc1N3dzYYNGzz7KzVBRLwH+DtgRmbeW3U8UjP5ffN4Fo4kTXqZuaDe9Ig4Hngq8L/loM9HAzdExPMz8+4mhtjSOjs7ufDCC6sOQ5oQhss3e/mbR4BHysfXR8RtwNOAdeMc3oRmLpKaw7Ef1e78vnk8L1WT1LYyc31mPjkz52bmXGAj8DyLRpKaKSJmRERH+fhXgOOA26uNSlIbc+xHSYNYOJIkSWqCiHh1RGwEfhu4PCKuKJ96IXBTRNwIfBl4e2beV1GYktqYYz9KqsdL1SSpVPY6kqSGyMyvAV+rM/0rwFeaH5GkduTYj5L2lYUjSZIkSWoTjv0oaV9F5sQpEEfEVuCOquNosiMB72QwubXj//jYzJxRdRBjZS7SJNWO/2Nz0cTTjp/TdtOO/+OWzEURsQGYt7e7qpmLNEm14/942Fw0oXoctWJCbbSIWJeZ86qOQ43j/3jiMRdpMvJ/PPGYizQZ+T+eeMxFmoz8Hw82oQpHkiRJkqTGc+xHSQO8q5okSZIkSZLqsnDU+lZUHYAazv+xJgI/p5Of/2NNBH5OJz//x5oI/JxOfv6Pa0yowbElSZIkSZLUPPY4kiRJkiRJUl0WjiRJkiRJklSXhaMWFRGLIuInEdEbEWdVHY/GX0RcFBH3RMTNVcciDcdcNPmZizQRmIsmP3ORJgJz0eRnLqrPwlELiogO4JPAKcAzgddHxDOrjUoNcDGwqOogpOGYi9rGxZiL1MLMRW3jYsxFamHmorZxMeaix7Fw1JqeD/Rm5u2Z+SjwBeC0imPSOMvMa4H7qo5DGoG5qA2YizQBmIvagLlIE4C5qA2Yi+qzcNSaZgN31bQ3ltMkqZnMRZJagblIUiswF6ltWTiSJEmSJElSXRaOWtMm4Jia9tHlNElqJnORpFZgLpLUCsxFalsWjlrTD4HjIuKpEfEE4HTgmxXHJKn9mIsktQJzkaRWYC5S27Jw1IIysx94J3AFcCvwxcy8pdqoNN4i4lLgu8DTI2JjRLyt6pikWuai9mAuUqszF7UHc5FanbmoPZiL6ovMrDoGSZIkSZIktSB7HEmSJEmSJKkuC0eSJEmSJEmqy8KRJEmSJEmS6rJwJEmSJEmSpLosHEmSJEmSJKkuC0dquIh4SkR8ISJui4jrI2JVRDwtIm6uOjZJ7cNcJKkVmIsktQJzkfbF1KoD0OQWEQF8DejJzNPLac8FZlYamKS2Yi6S1ArMRZJagblI+8oeR2q0FwE7M/NTAxMy83+BuwbaETE3Iq6LiBvKn98pp8+KiGsj4saIuDkiToyIjoi4uGyvj4gzm/+SJE1A5iJJrcBcJKkVmIu0T+xxpEZ7NnD9Xua5B1iYmb+MiOOAS4F5wBuAKzLzwxHRARwEnADMzsxnA0TEYY0KXNKkYi6S1ArMRZJagblI+8TCkVrBNOATEXECsAt4Wjn9h8BFETEN+Hpm3hgRtwO/EhHLgcuBK6sIWNKkZC6S1ArMRZJagblIe3ipmhrtFuA39jLPmcAW4LkUVewnAGTmtcALgU3AxRGxODPvL+e7Bng78JnGhC1pkjEXSWoF5iJJrcBcpH1i4UiN9p/AARGxZGBCRDwHOKZmnicBmzNzN/BmoKOc71hgS2Z+miL5PC8ijgSmZOZXgHOA5zXnZUia4MxFklqBuUhSKzAXaZ94qZoaKjMzIl4NfDwi3g/8EtgAvKtmtn8CvhIRi4HVwLZy+nzgzyJiJ/AwsBiYDfxrRAwUPc9u9GuQNPGZiyS1AnORpFZgLtK+isysOgZJkiRJkiS1IC9VkyRJkiRJUl0WjiRJkiRJklSXhSNJkiRJkiTVZeFIkiRJkiRJdVk4kiRJkiRJUl0WjiRJkiRJklSXhSNJkiRJkiTV9f8DDVBXhYfsTXoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(ncols=4, figsize=(20,4))\n",
    "\n",
    "# Positive correlations (The higher the feature the probability increases that it will be a fraud transaction)\n",
    "sns.boxplot(x=\"Class\", y=\"V11\", data=new_df, ax=axes[0])\n",
    "axes[0].set_title('V11 vs Class Positive Correlation \\n (0: No Fraud || 1: Fraud)')\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V4\", data=new_df, ax=axes[1])\n",
    "axes[1].set_title('V4 vs Class Positive Correlation \\n (0: No Fraud || 1: Fraud)')\n",
    "\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V2\", data=new_df, ax=axes[2])\n",
    "axes[2].set_title('V2 vs Class Positive Correlation \\n (0: No Fraud || 1: Fraud)')\n",
    "\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V19\", data=new_df, ax=axes[3])\n",
    "axes[3].set_title('V19 vs Class Positive Correlation \\n (0: No Fraud || 1: Fraud)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Anomaly (Outliers) Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to focus more on the extreme values ,other than just regular outliers. Because we might run the risk of information loss which will cause our models to have a lower accuracy\n",
    "\n",
    "**Steps:**\n",
    "1. Visualize the distributions\n",
    "2. Determining the threshold\n",
    "3. Conditional Dropping: Lastly, we create a conditional dropping stating that if the \"threshold\" is exceeded in both extremes, the instances will be removed.\n",
    "4. Boxplot representations\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Before outliers removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "f, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(20, 6))\n",
    "\n",
    "v14_fraud_dist = new_df['V14'].loc[new_df['Class'] == 1].values\n",
    "sns.distplot(v14_fraud_dist,ax=ax1, fit=norm, color='#FB8861')\n",
    "ax1.set_title('V14 Distribution \\n (Fraud Transactions)', fontsize=14)\n",
    "\n",
    "v12_fraud_dist = new_df['V12'].loc[new_df['Class'] == 1].values\n",
    "sns.distplot(v12_fraud_dist,ax=ax2, fit=norm, color='#56F9BB')\n",
    "ax2.set_title('V12 Distribution \\n (Fraud Transactions)', fontsize=14)\n",
    "\n",
    "\n",
    "v10_fraud_dist = new_df['V10'].loc[new_df['Class'] == 1].values\n",
    "sns.distplot(v10_fraud_dist,ax=ax3, fit=norm, color='#C5B3F9')\n",
    "ax3.set_title('V10 Distribution \\n (Fraud Transactions)', fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The outlier removal method that was made by  aggle solutions are a bit confusing for me\n",
    "# So I decided to use my old KASDD outlier removal method\n",
    "\n",
    "def check_outliers(feature):\n",
    "    q1 = np.percentile(feature, 25)\n",
    "\n",
    "    q3 = np.percentile(feature, 75)\n",
    "\n",
    "    iqr = q3 - q1\n",
    "\n",
    "    lower_bound = q1 - (1.5*iqr)\n",
    "\n",
    "    upper_bound = q3 + (1.5*iqr)\n",
    "\n",
    "    outliers = feature[(feature < lower_bound) | (feature > upper_bound)]\n",
    "\n",
    "    return outliers\n",
    "\n",
    "def remove_outliers(new_df, feature, feature_col):\n",
    "    q1 = np.percentile(feature, 25)\n",
    "\n",
    "    q3 = np.percentile(feature, 75)\n",
    "\n",
    "    iqr = q3 - q1\n",
    "\n",
    "    lower_bound = q1 - (1.5*iqr)\n",
    "\n",
    "    upper_bound = q3 + (1.5*iqr)\n",
    "\n",
    "    new_df = new_df.drop(new_df[(feature_col > upper_bound) | (feature_col < lower_bound)].index)\n",
    "\n",
    "    return new_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature V14 Outliers for Fraud Cases: 4\n",
      "V14 outliers:[-18.49377336 -19.21432549 -18.04999769 -18.82208674]\n",
      "Number of Instances before outliers removal: 984\n",
      "Number of Instances after outliers removal: 980\n"
     ]
    }
   ],
   "source": [
    "# Check outliers on V14\n",
    "v14_fraud = new_df['V14'].loc[new_df['Class'] == 1].values\n",
    "\n",
    "v14_outliers = check_outliers(v14_fraud)\n",
    "\n",
    "print('Feature V14 Outliers for Fraud Cases: {}'.format(len(v14_outliers)))\n",
    "print('V14 outliers:{}'.format(v14_outliers))\n",
    "\n",
    "# Removing outliers on V14\n",
    "v14_column = df['V14']\n",
    "print('Number of Instances before outliers removal: {}'.format(len(new_df)))\n",
    "new_df = remove_outliers(new_df, v14_fraud, v14_column)\n",
    "print('Number of Instances after outliers removal: {}'.format(len(new_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature V12 Outliers for Fraud Cases: 4\n",
      "V12 outliers:[-18.04759657 -18.68371463 -18.55369701 -18.43113103]\n",
      "Number of Instances before outliers removal: 980\n",
      "Number of Instances after outliers removal: 976\n"
     ]
    }
   ],
   "source": [
    "# Check outliers on V12\n",
    "v12_fraud = new_df['V12'].loc[new_df['Class'] == 1].values\n",
    "\n",
    "v12_outliers = check_outliers(v12_fraud)\n",
    "\n",
    "print('Feature V12 Outliers for Fraud Cases: {}'.format(len(v12_outliers)))\n",
    "print('V12 outliers:{}'.format(v12_outliers))\n",
    "\n",
    "# Removing outliers on V12\n",
    "v12_column = df['V12']\n",
    "print('Number of Instances before outliers removal: {}'.format(len(new_df)))\n",
    "new_df = remove_outliers(new_df, v12_fraud, v12_column)\n",
    "print('Number of Instances after outliers removal: {}'.format(len(new_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature V10 Outliers for Fraud Cases: 27\n",
      "V10 outliers:[-15.56379134 -16.74604411 -14.92465477 -22.18708856 -14.92465477\n",
      " -17.14151364 -22.18708856 -15.34609885 -24.40318497 -22.18708856\n",
      " -15.12375218 -20.94919155 -16.25561175 -16.60119697 -18.27116817\n",
      " -16.30353766 -15.12416281 -24.58826244 -15.23996196 -16.64962816\n",
      " -22.18708856 -15.23996196 -15.23183337 -19.83614885 -18.91324333\n",
      " -23.22825484 -15.56379134]\n",
      "Number of Instances before outliers removal: 976\n",
      "Number of Instances after outliers removal: 945\n"
     ]
    }
   ],
   "source": [
    "# Check outliers on V10\n",
    "v10_fraud = new_df['V10'].loc[new_df['Class'] == 1].values\n",
    "\n",
    "v10_outliers = check_outliers(v10_fraud)\n",
    "\n",
    "print('Feature V10 Outliers for Fraud Cases: {}'.format(len(v10_outliers)))\n",
    "print('V10 outliers:{}'.format(v10_outliers))\n",
    "\n",
    "# Removing outliers on V10\n",
    "v10_column = df['V10']\n",
    "print('Number of Instances before outliers removal: {}'.format(len(new_df)))\n",
    "new_df = remove_outliers(new_df, v10_fraud, v10_column)\n",
    "print('Number of Instances after outliers removal: {}'.format(len(new_df)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### After outliers removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,(ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20,6))\n",
    "\n",
    "colors = ['#B3F9C5', '#f9c5b3']\n",
    "# Boxplots with outliers removed\n",
    "# Feature V14\n",
    "sns.boxplot(x=\"Class\", y=\"V14\", data=new_df,ax=ax1, palette=colors)\n",
    "ax1.set_title(\"V14 Feature \\n Reduction of outliers\", fontsize=14)\n",
    "ax1.annotate('Fewer extreme \\n outliers', xy=(0.98, -17.5), xytext=(0, -12),\n",
    "            arrowprops=dict(facecolor='black'),\n",
    "            fontsize=14)\n",
    "\n",
    "# Feature 12\n",
    "sns.boxplot(x=\"Class\", y=\"V12\", data=new_df, ax=ax2, palette=colors)\n",
    "ax2.set_title(\"V12 Feature \\n Reduction of outliers\", fontsize=14)\n",
    "ax2.annotate('Fewer extreme \\n outliers', xy=(0.98, -17.3), xytext=(0, -12),\n",
    "            arrowprops=dict(facecolor='black'),\n",
    "            fontsize=14)\n",
    "\n",
    "# Feature V10\n",
    "sns.boxplot(x=\"Class\", y=\"V10\", data=new_df, ax=ax3, palette=colors)\n",
    "ax3.set_title(\"V10 Feature \\n Reduction of outliers\", fontsize=14)\n",
    "ax3.annotate('Fewer extreme \\n outliers', xy=(0.95, -16.5), xytext=(0, -12),\n",
    "            arrowprops=dict(facecolor='black'),\n",
    "            fontsize=14)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA (Dimensionality Reduction) and Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-SNE took 2.0 s\n",
      "PCA took 0.054 s\n",
      "Truncated SVD took 0.0081 s\n"
     ]
    }
   ],
   "source": [
    "# t-SNE\n",
    "# A dimensionality reduction techniques that is particularly useful for visualizing clusters\n",
    "\n",
    "# New_df is from the random undersample data (fewer instances)\n",
    "X = new_df.drop('Class', axis=1)\n",
    "y = new_df['Class']\n",
    "\n",
    "# T-SNE Implementation\n",
    "t0 = time.time()\n",
    "X_reduced_tsne = TSNE(n_components=2, random_state=42).fit_transform(X.values)\n",
    "t1 = time.time()\n",
    "print(\"T-SNE took {:.2} s\".format(t1 - t0))\n",
    "\n",
    "# PCA Implementation\n",
    "t0 = time.time()\n",
    "X_reduced_pca = PCA(n_components=2, random_state=42).fit_transform(X.values)\n",
    "t1 = time.time()\n",
    "print(\"PCA took {:.2} s\".format(t1 - t0))\n",
    "\n",
    "# TruncatedSVD\n",
    "t0 = time.time()\n",
    "X_reduced_svd = TruncatedSVD(n_components=2, algorithm='randomized', random_state=42).fit_transform(X.values)\n",
    "t1 = time.time()\n",
    "print(\"Truncated SVD took {:.2} s\".format(t1 - t0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1728x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(24,6))\n",
    "# labels = ['No Fraud', 'Fraud']\n",
    "f.suptitle('Clusters using Dimensionality Reduction', fontsize=14)\n",
    "\n",
    "\n",
    "blue_patch = mpatches.Patch(color='#0A0AFF', label='No Fraud')\n",
    "red_patch = mpatches.Patch(color='#AF0000', label='Fraud')\n",
    "\n",
    "\n",
    "# t-SNE scatter plot\n",
    "ax1.scatter(X_reduced_tsne[:,0], X_reduced_tsne[:,1], c=(y == 0), cmap='coolwarm', label='No Fraud', linewidths=2)\n",
    "ax1.scatter(X_reduced_tsne[:,0], X_reduced_tsne[:,1], c=(y == 1), cmap='coolwarm', label='Fraud', linewidths=2)\n",
    "ax1.set_title('t-SNE', fontsize=14)\n",
    "\n",
    "ax1.grid(True)\n",
    "\n",
    "ax1.legend(handles=[blue_patch, red_patch])\n",
    "\n",
    "\n",
    "# PCA scatter plot\n",
    "ax2.scatter(X_reduced_pca[:,0], X_reduced_pca[:,1], c=(y == 0), cmap='coolwarm', label='No Fraud', linewidths=2)\n",
    "ax2.scatter(X_reduced_pca[:,0], X_reduced_pca[:,1], c=(y == 1), cmap='coolwarm', label='Fraud', linewidths=2)\n",
    "ax2.set_title('PCA', fontsize=14)\n",
    "\n",
    "ax2.grid(True)\n",
    "\n",
    "ax2.legend(handles=[blue_patch, red_patch])\n",
    "\n",
    "# TruncatedSVD scatter plot\n",
    "ax3.scatter(X_reduced_svd[:,0], X_reduced_svd[:,1], c=(y == 0), cmap='coolwarm', label='No Fraud', linewidths=2)\n",
    "ax3.scatter(X_reduced_svd[:,0], X_reduced_svd[:,1], c=(y == 1), cmap='coolwarm', label='Fraud', linewidths=2)\n",
    "ax3.set_title('Truncated SVD', fontsize=14)\n",
    "\n",
    "ax3.grid(True)\n",
    "\n",
    "ax3.legend(handles=[blue_patch, red_patch])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion**\n",
    "* t-SNE algorithm can pretty accurately cluster the cases that were fraud and non-fraud in our dataset\n",
    "* Although it took quite longer than the other dimensionality reduction algorithm, but it has the most accurate clustering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classifiers (Undersampling):\n",
    "In this section we will train four types of classifiers and decide which classifier will be more effective in detecting fraud transactions. Before we have to split our data into training and testing sets and separate the features from the labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using the new undersampled dataset\n",
    "X = new_df.drop('Class', axis=1)\n",
    "y = new_df['Class']\n",
    "\n",
    "# Split the undersampled dataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# This is explicitly used for undersampling.\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Turn the values into an array for feeding the classification algorithms.\n",
    "X_train = X_train.values\n",
    "X_test = X_test.values\n",
    "y_train = y_train.values\n",
    "y_test = y_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifiers = {\n",
    "    \"LogisiticRegression\": LogisticRegression(),\n",
    "    \"KNearest\": KNeighborsClassifier(),\n",
    "    \"Support Vector Classifier\": SVC(),\n",
    "    \"DecisionTreeClassifier\": DecisionTreeClassifier()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifiers:  LogisticRegression Has a training score of 93.0 % accuracy score\n",
      "Classifiers:  KNeighborsClassifier Has a training score of 92.0 % accuracy score\n",
      "Classifiers:  SVC Has a training score of 93.0 % accuracy score\n",
      "Classifiers:  DecisionTreeClassifier Has a training score of 90.0 % accuracy score\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Good iterations to evaluate different classifiers all at once\n",
    "for key, classifier in classifiers.items():\n",
    "    classifier.fit(X_train, y_train)\n",
    "    training_score = cross_val_score(classifier, X_train, y_train, cv=5)\n",
    "    print(\"Classifiers: \", classifier.__class__.__name__, \"Has a training score of\", round(training_score.mean(), 2) * 100, \"% accuracy score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion**\n",
    "* LogisticRegression and SVC has the best training score accuracy, and DecisionTreeClassifier has the worst one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression best parameter:  LogisticRegression(C=0.1)\n",
      "K-Neighbors Classifier best parameter:  KNeighborsClassifier(n_neighbors=3)\n",
      "SVC best parameter:  SVC(C=0.5, kernel='linear')\n",
      "Decision Tree best parameter:  DecisionTreeClassifier(max_depth=2, min_samples_leaf=5)\n"
     ]
    }
   ],
   "source": [
    "# Use GridSearchCV to find the best parameters.\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "# Logistic Regression \n",
    "log_reg_params = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
    "\n",
    "\n",
    "\n",
    "grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)\n",
    "grid_log_reg.fit(X_train, y_train)\n",
    "# We automatically get the logistic regression with the best parameters.\n",
    "log_reg = grid_log_reg.best_estimator_\n",
    "print(\"Logistic Regression best parameter: \", log_reg)\n",
    "\n",
    "knears_params = {\"n_neighbors\": list(range(2,5,1)), 'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute']}\n",
    "\n",
    "grid_knears = GridSearchCV(KNeighborsClassifier(), knears_params)\n",
    "grid_knears.fit(X_train, y_train)\n",
    "# KNears best estimator\n",
    "knears_neighbors = grid_knears.best_estimator_\n",
    "print(\"K-Neighbors Classifier best parameter: \", knears_neighbors)\n",
    "\n",
    "# Support Vector Classifier\n",
    "svc_params = {'C': [0.5, 0.7, 0.9, 1], 'kernel': ['rbf', 'poly', 'sigmoid', 'linear']}\n",
    "grid_svc = GridSearchCV(SVC(), svc_params)\n",
    "grid_svc.fit(X_train, y_train)\n",
    "\n",
    "# SVC best estimator\n",
    "svc = grid_svc.best_estimator_\n",
    "print(\"SVC best parameter: \", svc)\n",
    "\n",
    "# DecisionTree Classifier\n",
    "tree_params = {\"criterion\": [\"gini\", \"entropy\"], \"max_depth\": list(range(2,4,1)), \n",
    "              \"min_samples_leaf\": list(range(5,7,1))}\n",
    "grid_tree = GridSearchCV(DecisionTreeClassifier(), tree_params)\n",
    "grid_tree.fit(X_train, y_train)\n",
    "\n",
    "# tree best estimator\n",
    "tree_clf = grid_tree.best_estimator_\n",
    "print(\"Decision Tree best parameter: \", tree_clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Cross Validation Score:  92.99%\n",
      "Knears Neighbors Cross Validation Score 92.46%\n",
      "Support Vector Classifier Cross Validation Score 92.99%\n",
      "DecisionTree Classifier Cross Validation Score 92.33%\n"
     ]
    }
   ],
   "source": [
    "# Overfitting Case\n",
    "\n",
    "log_reg_score = cross_val_score(log_reg, X_train, y_train, cv=5)\n",
    "print('Logistic Regression Cross Validation Score: ', round(log_reg_score.mean() * 100, 2).astype(str) + '%')\n",
    "\n",
    "\n",
    "knears_score = cross_val_score(knears_neighbors, X_train, y_train, cv=5)\n",
    "print('Knears Neighbors Cross Validation Score', round(knears_score.mean() * 100, 2).astype(str) + '%')\n",
    "\n",
    "svc_score = cross_val_score(svc, X_train, y_train, cv=5)\n",
    "print('Support Vector Classifier Cross Validation Score', round(svc_score.mean() * 100, 2).astype(str) + '%')\n",
    "\n",
    "tree_score = cross_val_score(tree_clf, X_train, y_train, cv=5)\n",
    "print('DecisionTree Classifier Cross Validation Score', round(tree_score.mean() * 100, 2).astype(str) + '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion**\n",
    "\n",
    "Based on the cross validation, too, LogisticRegression and SVC has the best score. It means that LogisticRegression and SVC is also the best model to use to predict unseen data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: [ 51084  51116  51960 ... 284804 284805 284806] Test: [    0     1     2 ... 56970 56971 56972]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [ 51084  51116  51960 ... 114699 114834 115830]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [113920 113921 113922 ... 172726 172793 173899]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [170880 170881 170882 ... 227844 227845 227846]\n",
      "Train: [     0      1      2 ... 227844 227845 227846] Test: [226341 227847 227848 ... 284804 284805 284806]\n",
      "NearMiss Label Distribution: Counter({0: 492, 1: 492})\n"
     ]
    }
   ],
   "source": [
    "# We will undersample during cross validating\n",
    "undersample_X = df.drop('Class', axis=1)\n",
    "undersample_y = df['Class']\n",
    "\n",
    "for train_index, test_index in sss.split(undersample_X, undersample_y):\n",
    "    print(\"Train:\", train_index, \"Test:\", test_index)\n",
    "    undersample_Xtrain, undersample_Xtest = undersample_X.iloc[train_index], undersample_X.iloc[test_index]\n",
    "    undersample_ytrain, undersample_ytest = undersample_y.iloc[train_index], undersample_y.iloc[test_index]\n",
    "    \n",
    "undersample_Xtrain = undersample_Xtrain.values\n",
    "undersample_Xtest = undersample_Xtest.values\n",
    "undersample_ytrain = undersample_ytrain.values\n",
    "undersample_ytest = undersample_ytest.values \n",
    "\n",
    "undersample_accuracy = []\n",
    "undersample_precision = []\n",
    "undersample_recall = []\n",
    "undersample_f1 = []\n",
    "undersample_auc = []\n",
    "\n",
    "# Implementing NearMiss Technique \n",
    "# Distribution of NearMiss (Just to see how it distributes the labels we won't use these variables)\n",
    "X_nearmiss, y_nearmiss = NearMiss().fit_resample(undersample_X.values, undersample_y.values)\n",
    "print('NearMiss Label Distribution: {}'.format(Counter(y_nearmiss)))\n",
    "# Cross Validating the right way\n",
    "\n",
    "for train, test in sss.split(undersample_Xtrain, undersample_ytrain):\n",
    "    undersample_pipeline = imbalanced_make_pipeline(NearMiss(sampling_strategy='majority'), log_reg) # SMOTE happens during Cross Validation not before..\n",
    "    undersample_model = undersample_pipeline.fit(undersample_Xtrain[train], undersample_ytrain[train])\n",
    "    undersample_prediction = undersample_model.predict(undersample_Xtrain[test])\n",
    "    \n",
    "    undersample_accuracy.append(undersample_pipeline.score(original_Xtrain[test], original_ytrain[test]))\n",
    "    undersample_precision.append(precision_score(original_ytrain[test], undersample_prediction))\n",
    "    undersample_recall.append(recall_score(original_ytrain[test], undersample_prediction))\n",
    "    undersample_f1.append(f1_score(original_ytrain[test], undersample_prediction))\n",
    "    undersample_auc.append(roc_auc_score(original_ytrain[test], undersample_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning Curves\n",
    "\n",
    "***Why do we need to learn the curves?***\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's Plot LogisticRegression Learning Curve\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "def plot_learning_curve(estimator1, estimator2, estimator3, estimator4, X, y, ylim=None, cv=None,\n",
    "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "    f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize=(20,14), sharey=True)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "        \n",
    "    # First Estimator\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator1, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    ax1.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                        train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                        color=\"#ff9124\")\n",
    "    ax1.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                        test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
    "    ax1.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
    "                label=\"Training score\")\n",
    "    ax1.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
    "                label=\"Cross-validation score\")\n",
    "    ax1.set_title(\"Logistic Regression Learning Curve\", fontsize=14)\n",
    "    ax1.set_xlabel('Training size (m)')\n",
    "    ax1.set_ylabel('Score')\n",
    "    ax1.grid(True)\n",
    "    ax1.legend(loc=\"best\")\n",
    "\n",
    "    # Second Estimator \n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator2, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    ax2.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                        train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                        color=\"#ff9124\")\n",
    "    ax2.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                        test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
    "    ax2.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
    "                label=\"Training score\")\n",
    "    ax2.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
    "                label=\"Cross-validation score\")\n",
    "    ax2.set_title(\"Knears Neighbors Learning Curve\", fontsize=14)\n",
    "    ax2.set_xlabel('Training size (m)')\n",
    "    ax2.set_ylabel('Score')\n",
    "    ax2.grid(True)\n",
    "    ax2.legend(loc=\"best\")\n",
    "\n",
    "    # Third Estimator\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator3, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    ax3.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                        train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                        color=\"#ff9124\")\n",
    "    ax3.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                        test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
    "    ax3.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
    "                label=\"Training score\")\n",
    "    ax3.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
    "                label=\"Cross-validation score\")\n",
    "    ax3.set_title(\"Support Vector Classifier \\n Learning Curve\", fontsize=14)\n",
    "    ax3.set_xlabel('Training size (m)')\n",
    "    ax3.set_ylabel('Score')\n",
    "    ax3.grid(True)\n",
    "    ax3.legend(loc=\"best\")\n",
    "\n",
    "    # Fourth Estimator\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator4, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    ax4.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                        train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                        color=\"#ff9124\")\n",
    "    ax4.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                        test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
    "    ax4.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
    "                label=\"Training score\")\n",
    "    ax4.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
    "                label=\"Cross-validation score\")\n",
    "    ax4.set_title(\"Decision Tree Classifier \\n Learning Curve\", fontsize=14)\n",
    "    ax4.set_xlabel('Training size (m)')\n",
    "    ax4.set_ylabel('Score')\n",
    "    ax4.grid(True)\n",
    "    ax4.legend(loc=\"best\")\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1008 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=42)\n",
    "plot_learning_curve(log_reg, knears_neighbors, svc, tree_clf, X_train, y_train, (0.87, 1.01), cv=cv, n_jobs=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion**\n",
    "* The wider the gap between the training score and the cross validation score, the more likely your model is overfitting (high variance).\n",
    "* If the score is low in both training and cross-validation sets this is an indication that our model is underfitting (high bias)\n",
    "* Logistic Regression Classifier shows the best score in both training and cross-validating sets.\n",
    "* Logistic Regression also has the narrowest gap between the training score and the cross validation score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "# Create a DataFrame with all the scores and the classifiers names.\n",
    "\n",
    "log_reg_pred = cross_val_predict(log_reg, X_train, y_train, cv=5,\n",
    "                            method=\"decision_function\")\n",
    "\n",
    "knears_pred = cross_val_predict(knears_neighbors, X_train, y_train, cv=5)\n",
    "\n",
    "svc_pred = cross_val_predict(svc, X_train, y_train, cv=5,\n",
    "                            method=\"decision_function\")\n",
    "\n",
    "tree_pred = cross_val_predict(tree_clf, X_train, y_train, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression:  0.9773083928821634\n",
      "KNears Neighbors:  0.923392181588903\n",
      "Support Vector Classifier:  0.9715146420064452\n",
      "Decision Tree Classifier:  0.9216897856242118\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "print('Logistic Regression: ', roc_auc_score(y_train, log_reg_pred))\n",
    "print('KNears Neighbors: ', roc_auc_score(y_train, knears_pred))\n",
    "print('Support Vector Classifier: ', roc_auc_score(y_train, svc_pred))\n",
    "print('Decision Tree Classifier: ', roc_auc_score(y_train, tree_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion**\n",
    "* Based on the prediction score, Logistic Regression also gives out the best score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The visualization of the models' prediction scores comparison\n",
    "log_fpr, log_tpr, log_thresold = roc_curve(y_train, log_reg_pred)\n",
    "knear_fpr, knear_tpr, knear_threshold = roc_curve(y_train, knears_pred)\n",
    "svc_fpr, svc_tpr, svc_threshold = roc_curve(y_train, svc_pred)\n",
    "tree_fpr, tree_tpr, tree_threshold = roc_curve(y_train, tree_pred)\n",
    "\n",
    "\n",
    "def graph_roc_curve_multiple(log_fpr, log_tpr, knear_fpr, knear_tpr, svc_fpr, svc_tpr, tree_fpr, tree_tpr):\n",
    "    plt.figure(figsize=(16,8))\n",
    "    plt.title('ROC Curve \\n Top 4 Classifiers', fontsize=18)\n",
    "    plt.plot(log_fpr, log_tpr, label='Logistic Regression Classifier Score: {:.4f}'.format(roc_auc_score(y_train, log_reg_pred)))\n",
    "    plt.plot(knear_fpr, knear_tpr, label='KNears Neighbors Classifier Score: {:.4f}'.format(roc_auc_score(y_train, knears_pred)))\n",
    "    plt.plot(svc_fpr, svc_tpr, label='Support Vector Classifier Score: {:.4f}'.format(roc_auc_score(y_train, svc_pred)))\n",
    "    plt.plot(tree_fpr, tree_tpr, label='Decision Tree Classifier Score: {:.4f}'.format(roc_auc_score(y_train, tree_pred)))\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([-0.01, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "    plt.annotate('Minimum ROC Score of 50% \\n (This is the minimum score to get)', xy=(0.5, 0.5), xytext=(0.6, 0.3),\n",
    "                arrowprops=dict(facecolor='#6E726D', shrink=0.05),\n",
    "                )\n",
    "    plt.legend()\n",
    "    \n",
    "graph_roc_curve_multiple(log_fpr, log_tpr, knear_fpr, knear_tpr, svc_fpr, svc_tpr, tree_fpr, tree_tpr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion** <p>\n",
    "As it can be seen in the graph above that Logistic Regression has the highest line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deeper into Logistic Regression\n",
    "The previous classifier models evaluation, it can be concluded that Logistic Regression is the best classifier model for our dataset. Hence, in this section, we will delve into Logistic Regression Classifier more further"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Things to remember*** <p>\n",
    "In this case, Fraud is the Positive case, and Non-fraud is the Negative case\n",
    "* Precision : depicts  **how precise (how sure)** is our model in detecting fraud transactions\n",
    "* Recall : **the amount** of fraud cases our model is able to detect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def logistic_roc_curve(log_fpr, log_tpr):\n",
    "    plt.figure(figsize=(12,8))\n",
    "    plt.title('Logistic Regression ROC Curve', fontsize=16)\n",
    "    plt.plot(log_fpr, log_tpr, 'b-', linewidth=2)\n",
    "    plt.plot([0, 1], [0, 1], 'r--')\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "    plt.axis([-0.01,1,0,1])\n",
    "    \n",
    "    \n",
    "logistic_roc_curve(log_fpr, log_tpr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precision, recall, threshold = precision_recall_curve(y_train, log_reg_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "Overfitting: (Our original dataset) \n",
      "\n",
      "Recall Score: 0.91\n",
      "Precision Score: 0.75\n",
      "F1 Score: 0.82\n",
      "Accuracy Score: 0.81\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "How it should be: (Our undersampled dataset)\n",
      "\n",
      "Accuracy Score: 0.77\n",
      "Precision Score: 0.00\n",
      "Recall Score: 0.22\n",
      "F1 Score: 0.00\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score, precision_score, f1_score, accuracy_score\n",
    "y_pred = log_reg.predict(X_train)\n",
    "\n",
    "# Overfitting Case \n",
    "# Our original dataset\n",
    "print('---' * 45)\n",
    "print('Overfitting: (Our original dataset) \\n')\n",
    "print('Recall Score: {:.2f}'.format(recall_score(y_train, y_pred)))\n",
    "print('Precision Score: {:.2f}'.format(precision_score(y_train, y_pred)))\n",
    "print('F1 Score: {:.2f}'.format(f1_score(y_train, y_pred)))\n",
    "print('Accuracy Score: {:.2f}'.format(accuracy_score(y_train, y_pred)))\n",
    "print('---' * 45)\n",
    "\n",
    "# How it should look like\n",
    "# The undersampled dataset\n",
    "print('---' * 45)\n",
    "print('How it should be: (Our undersampled dataset)\\n')\n",
    "print(\"Accuracy Score: {:.2f}\".format(np.mean(undersample_accuracy)))\n",
    "print(\"Precision Score: {:.2f}\".format(np.mean(undersample_precision)))\n",
    "print(\"Recall Score: {:.2f}\".format(np.mean(undersample_recall)))\n",
    "print(\"F1 Score: {:.2f}\".format(np.mean(undersample_f1)))\n",
    "print('---' * 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be honest, this part below is quite too intricate. In a way I've found a simpler way to test whether it is better to undersample the dataset or to just let it be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "undersample_y_score = log_reg.decision_function(original_Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 0.07\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import average_precision_score\n",
    "\n",
    "undersample_average_precision = average_precision_score(original_ytest, undersample_y_score)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "    undersample_average_precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'UnderSampling Precision-Recall curve: \\n Average Precision-Recall Score =0.07')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(12,6))\n",
    "\n",
    "precision, recall, _ = precision_recall_curve(original_ytest, undersample_y_score)\n",
    "\n",
    "plt.step(recall, precision, color='#004a93', alpha=0.2,\n",
    "        where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.2,\n",
    "                color='#48a6ff')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('UnderSampling Precision-Recall curve: \\n Average Precision-Recall Score ={0:0.2f}'.format(\n",
    "        undersample_average_precision), fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*A good precision and recall score depends on what the models are for. For example, what the models are for, etc*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SMOTE Technique (Over-sampling)\n",
    "SMOTE stands for Synthetic Minority Over-sampling Technique. Unlike Random UnderSampling, SMOTE creates new synthetic points in order to have an equal balance of the classes. This is another alternative for solving the \"class imbalance problems\".\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Right now, we are just going to evaluate the accuracy, precision, and recall score of the oversampled data. Then, we are going to compare the classification score between the undersampled and oversampled data.\n",
    "\n",
    "***Is it better to undersample the majority class or to oversample the minority class?***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note : If you want to undersample or oversample your data you should not do it before cross validating. Why because you will be directly influencing the validation set before implementing cross-validation causing a \"data leakage\" problem\n",
    "\n",
    "> **Cross-validate before oversampling or undersampling your data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of X (train): 227846 | Length of y (train): 227846\n",
      "Length of X (test): 56961 | Length of y (test): 56961\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "accuracy: 0.9990344358286567\n",
      "precision: 0.8592974481658693\n",
      "recall: 0.5814670561506003\n",
      "f1: 0.6569868385194537\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
    "\n",
    "\n",
    "print('Length of X (train): {} | Length of y (train): {}'.format(len(original_Xtrain), len(original_ytrain)))\n",
    "print('Length of X (test): {} | Length of y (test): {}'.format(len(original_Xtest), len(original_ytest)))\n",
    "\n",
    "# List to append the score and then find the average\n",
    "accuracy_lst = []\n",
    "precision_lst = []\n",
    "recall_lst = []\n",
    "f1_lst = []\n",
    "auc_lst = []\n",
    "\n",
    "# Classifier with optimal parameters\n",
    "# log_reg_sm = grid_log_reg.best_estimator_\n",
    "log_reg_sm = LogisticRegression()\n",
    "\n",
    "# Search for the best parameter\n",
    "# RandomizedSearchCV is better when dealing with larger dataset. \n",
    "# Meanwhile GridSearchCV searches every grid of parameters, making it more suitable for smaller datasets\n",
    "\n",
    "rand_log_reg = RandomizedSearchCV(LogisticRegression(), log_reg_params, n_iter=4)\n",
    "\n",
    "# grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)\n",
    "# grid_log_reg.fit(X_train, y_train)\n",
    "# # We automatically get the logistic regression with the best parameters.\n",
    "# log_reg = grid_log_reg.best_estimator_\n",
    "\n",
    "# Implementing SMOTE Technique \n",
    "# Cross Validating the right way\n",
    "# Parameters\n",
    "# Using pipeline to concatinate multiple transformers and an estimator into a single object. \n",
    "# It helps streamline the preprocessing steps and the model training process.\n",
    "# log_reg_params = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
    "\n",
    "log_reg_params = {\"penalty\": ['l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
    "for train, test in sss.split(original_Xtrain, original_ytrain):\n",
    "    smote = SMOTE(sampling_strategy='minority') # SMOTE happens during Cross Validation not before..\n",
    "    model = rand_log_reg.fit(original_Xtrain[train], original_ytrain[train])\n",
    "    best_est = rand_log_reg.best_estimator_\n",
    "    prediction = best_est.predict(original_Xtrain[test])\n",
    "    \n",
    "    accuracy_lst.append(model.score(original_Xtrain[test], original_ytrain[test]))\n",
    "    precision_lst.append(precision_score(original_ytrain[test], prediction))\n",
    "    recall_lst.append(recall_score(original_ytrain[test], prediction))\n",
    "    f1_lst.append(f1_score(original_ytrain[test], prediction))\n",
    "    auc_lst.append(roc_auc_score(original_ytrain[test], prediction))\n",
    "    \n",
    "print('---' * 45)\n",
    "print('')\n",
    "print(\"accuracy: {}\".format(np.mean(accuracy_lst)))\n",
    "print(\"precision: {}\".format(np.mean(precision_lst)))\n",
    "print(\"recall: {}\".format(np.mean(recall_lst)))\n",
    "print(\"f1: {}\".format(np.mean(f1_lst)))\n",
    "print('---' * 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    No Fraud       1.00      1.00      1.00     56863\n",
      "       Fraud       0.84      0.43      0.57        98\n",
      "\n",
      "    accuracy                           1.00     56961\n",
      "   macro avg       0.92      0.71      0.78     56961\n",
      "weighted avg       1.00      1.00      1.00     56961\n",
      "\n"
     ]
    }
   ],
   "source": [
    "labels = ['No Fraud', 'Fraud']\n",
    "smote_prediction = best_est.predict(original_Xtest)\n",
    "print(classification_report(original_ytest, smote_prediction, target_names=labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_score = best_est.decision_function(original_Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 0.66\n"
     ]
    }
   ],
   "source": [
    "average_precision = average_precision_score(original_ytest, y_score)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "    average_precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'OverSampling Precision-Recall curve: \\n Average Precision-Recall Score =0.66')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "\n",
    "precision, recall, _ = precision_recall_curve(original_ytest, y_score)\n",
    "\n",
    "plt.step(recall, precision, color='r', alpha=0.2,\n",
    "        where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.2,\n",
    "                color='#F59B00')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('OverSampling Precision-Recall curve: \\n Average Precision-Recall Score ={0:0.2f}'.format(\n",
    "        average_precision), fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SMOTE Technique (OverSampling) After splitting and Cross Validating\n",
    "# sm = SMOTE(str='minority', random_state=42)\n",
    "sm = SMOTE(sampling_strategy='minority', random_state=42)\n",
    "\n",
    "Xsm_train, ysm_train = sm.fit_resample(original_Xtrain, original_ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting oversample data took : 3.1674182415008545 sec\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "t0 = time.time()\n",
    "log_reg_sm = grid_log_reg.best_estimator_\n",
    "log_reg_sm.fit(Xsm_train, ysm_train)\n",
    "t1 = time.time()\n",
    "print(\"Fitting oversample data took : {} sec\".format(t1 - t0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Data with Logistic Regression\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix:\n",
    "\n",
    "* Positive/Negative: Type of Class (label) [\"Yes\", \"No\"] \n",
    "* True/False: Correctly or Incorrectly classified by the model.\n",
    "\n",
    "\n",
    "True Negatives (Top-Left Square): The number of correctly classifications of the \"No\" (No Fraud Detected) class.\n",
    "\n",
    "\n",
    "False Negatives (Top-Right Square): The number of incorrectly classifications of the \"No\"(No Fraud Detected) class.\n",
    "\n",
    "\n",
    "False Positives (Bottom-Left Square): The number of incorrectly classifications of the \"Yes\" (Fraud Detected) class\n",
    "\n",
    "\n",
    "True Positives (Bottom-Right Square): The number of correctly classifications of the \"Yes\" (Fraud Detected) class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1584x864 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Logistic Regression fitted using SMOTE technique\n",
    "y_pred_log_reg = log_reg_sm.predict(X_test)\n",
    "\n",
    "# Other models fitted with UnderSampling\n",
    "y_pred_knear = knears_neighbors.predict(X_test)\n",
    "y_pred_svc = svc.predict(X_test)\n",
    "y_pred_tree = tree_clf.predict(X_test)\n",
    "\n",
    "\n",
    "log_reg_cf = confusion_matrix(y_test, y_pred_log_reg)\n",
    "kneighbors_cf = confusion_matrix(y_test, y_pred_knear)\n",
    "svc_cf = confusion_matrix(y_test, y_pred_svc)\n",
    "tree_cf = confusion_matrix(y_test, y_pred_tree)\n",
    "\n",
    "fig, ax = plt.subplots(2, 2,figsize=(22,12))\n",
    "\n",
    "\n",
    "sns.heatmap(log_reg_cf, ax=ax[0][0], annot=True, cmap=plt.cm.copper)\n",
    "ax[0, 0].set_title(\"Logistic Regression \\n Confusion Matrix\", fontsize=14)\n",
    "ax[0, 0].set_xticklabels(['', ''], fontsize=14, rotation=90)\n",
    "ax[0, 0].set_yticklabels(['', ''], fontsize=14, rotation=360)\n",
    "\n",
    "sns.heatmap(kneighbors_cf, ax=ax[0][1], annot=True, cmap=plt.cm.copper)\n",
    "ax[0][1].set_title(\"KNearsNeighbors \\n Confusion Matrix\", fontsize=14)\n",
    "ax[0][1].set_xticklabels(['', ''], fontsize=14, rotation=90)\n",
    "ax[0][1].set_yticklabels(['', ''], fontsize=14, rotation=360)\n",
    "\n",
    "sns.heatmap(svc_cf, ax=ax[1][0], annot=True, cmap=plt.cm.copper)\n",
    "ax[1][0].set_title(\"Suppor Vector Classifier \\n Confusion Matrix\", fontsize=14)\n",
    "ax[1][0].set_xticklabels(['', ''], fontsize=14, rotation=90)\n",
    "ax[1][0].set_yticklabels(['', ''], fontsize=14, rotation=360)\n",
    "\n",
    "sns.heatmap(tree_cf, ax=ax[1][1], annot=True, cmap=plt.cm.copper)\n",
    "ax[1][1].set_title(\"DecisionTree Classifier \\n Confusion Matrix\", fontsize=14)\n",
    "ax[1][1].set_xticklabels(['', ''], fontsize=14, rotation=90)\n",
    "ax[1][1].set_yticklabels(['', ''], fontsize=14, rotation=360)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.98      0.96        98\n",
      "           1       0.98      0.93      0.96        91\n",
      "\n",
      "    accuracy                           0.96       189\n",
      "   macro avg       0.96      0.96      0.96       189\n",
      "weighted avg       0.96      0.96      0.96       189\n",
      "\n",
      "KNears Neighbors:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.98      0.95        98\n",
      "           1       0.98      0.91      0.94        91\n",
      "\n",
      "    accuracy                           0.95       189\n",
      "   macro avg       0.95      0.95      0.95       189\n",
      "weighted avg       0.95      0.95      0.95       189\n",
      "\n",
      "Support Vector Classifier:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.97      0.95        98\n",
      "           1       0.97      0.93      0.95        91\n",
      "\n",
      "    accuracy                           0.95       189\n",
      "   macro avg       0.95      0.95      0.95       189\n",
      "weighted avg       0.95      0.95      0.95       189\n",
      "\n",
      "Support Vector Classifier:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.97      0.93        98\n",
      "           1       0.96      0.87      0.91        91\n",
      "\n",
      "    accuracy                           0.92       189\n",
      "   macro avg       0.93      0.92      0.92       189\n",
      "weighted avg       0.92      0.92      0.92       189\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "\n",
    "print('Logistic Regression:')\n",
    "print(classification_report(y_test, y_pred_log_reg))\n",
    "\n",
    "print('KNears Neighbors:')\n",
    "print(classification_report(y_test, y_pred_knear))\n",
    "\n",
    "print('Support Vector Classifier:')\n",
    "print(classification_report(y_test, y_pred_svc))\n",
    "\n",
    "print('Support Vector Classifier:')\n",
    "print(classification_report(y_test, y_pred_tree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Technique</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random UnderSampling</td>\n",
       "      <td>0.957672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Oversampling (SMOTE)</td>\n",
       "      <td>0.998876</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Technique     Score\n",
       "0  Random UnderSampling  0.957672\n",
       "1  Oversampling (SMOTE)  0.998876"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Final Score in the test set of logistic regression\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Logistic Regression with Under-Sampling\n",
    "y_pred = log_reg.predict(X_test)\n",
    "undersample_score = accuracy_score(y_test, y_pred)\n",
    "\n",
    "\n",
    "# Logistic Regression with SMOTE Technique (Better accuracy with SMOTE t)\n",
    "y_pred_sm = best_est.predict(original_Xtest)\n",
    "oversample_score = accuracy_score(original_ytest, y_pred_sm)\n",
    "\n",
    "\n",
    "d = {'Technique': ['Random UnderSampling', 'Oversampling (SMOTE)'], 'Score': [undersample_score, oversample_score]}\n",
    "final_df = pd.DataFrame(data=d)\n",
    "\n",
    "# Move column\n",
    "score = final_df['Score']\n",
    "final_df.drop('Score', axis=1, inplace=True)\n",
    "final_df.insert(1, 'Score', score)\n",
    "\n",
    "# Note how high is accuracy score it can be misleading! \n",
    "final_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion\n",
    "> LogisticRegression has the best score and oversampling (SMOTE) has a higher accuracy score. However we cannot just count on the accuracy score since it is not a sole determinant whether that classifier algorithm is the best one or not. Because sometimes the accuracy score is misleading and tend to be always high. Hence, we are using the other metrics as a comparison, such as precision, recall, and f1 score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network Testing Random Undersampling and Oversampling (SMOTE)\n",
    "In this section we will implement a simple Neural Network (with one hidden layer) in order to see which of the two logistic regressions models we implemented in the (undersample or oversample(SMOTE)) has a better accuracy for detecting fraud and non-fraud transactions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> So, our main goal is to explore how our simple neural network behaves in both the random undersample and oversample dataframes and see whether they can predict accuractely both non-fraud and fraud cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to fit this model into our undersample and oversample dataset to predict ***the original outcome*** from the original ***testing*** data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Undersampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Activation\n",
    "from keras.layers.core import Dense\n",
    "from keras.optimizers import Adam\n",
    "from keras.metrics import categorical_crossentropy\n",
    "\n",
    "n_inputs = X_train.shape[1]\n",
    "\n",
    "# Input layer => The number of features\n",
    "# Hidden layer => 32 nodes\n",
    "# Output layer => 2 nodes => Fraud or Non-fraud\n",
    "undersample_model = Sequential([\n",
    "    Dense(n_inputs, input_shape=(n_inputs, ), activation='relu'),\n",
    "    Dense(32, activation='relu'), \n",
    "    Dense(2, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 30)                930       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 32)                992       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 2)                 66        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,988\n",
      "Trainable params: 1,988\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Check the model\n",
    "undersample_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configures the model for training\n",
    "undersample_model.compile(Adam(lr=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-03 20:48:38.326930: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25/25 - 0s - loss: 0.4977 - accuracy: 0.6672 - val_loss: 0.3728 - val_accuracy: 0.8355 - 334ms/epoch - 13ms/step\n",
      "Epoch 2/20\n",
      "25/25 - 0s - loss: 0.3254 - accuracy: 0.9007 - val_loss: 0.3259 - val_accuracy: 0.9013 - 26ms/epoch - 1ms/step\n",
      "Epoch 3/20\n",
      "25/25 - 0s - loss: 0.2529 - accuracy: 0.9272 - val_loss: 0.2739 - val_accuracy: 0.8947 - 27ms/epoch - 1ms/step\n",
      "Epoch 4/20\n",
      "25/25 - 0s - loss: 0.2054 - accuracy: 0.9338 - val_loss: 0.2377 - val_accuracy: 0.9079 - 27ms/epoch - 1ms/step\n",
      "Epoch 5/20\n",
      "25/25 - 0s - loss: 0.1741 - accuracy: 0.9421 - val_loss: 0.2193 - val_accuracy: 0.9079 - 31ms/epoch - 1ms/step\n",
      "Epoch 6/20\n",
      "25/25 - 0s - loss: 0.1504 - accuracy: 0.9470 - val_loss: 0.2096 - val_accuracy: 0.9013 - 34ms/epoch - 1ms/step\n",
      "Epoch 7/20\n",
      "25/25 - 0s - loss: 0.1355 - accuracy: 0.9487 - val_loss: 0.2187 - val_accuracy: 0.9145 - 31ms/epoch - 1ms/step\n",
      "Epoch 8/20\n",
      "25/25 - 0s - loss: 0.1257 - accuracy: 0.9553 - val_loss: 0.1941 - val_accuracy: 0.9211 - 27ms/epoch - 1ms/step\n",
      "Epoch 9/20\n",
      "25/25 - 0s - loss: 0.1128 - accuracy: 0.9536 - val_loss: 0.1961 - val_accuracy: 0.9145 - 28ms/epoch - 1ms/step\n",
      "Epoch 10/20\n",
      "25/25 - 0s - loss: 0.1113 - accuracy: 0.9553 - val_loss: 0.2333 - val_accuracy: 0.9145 - 26ms/epoch - 1ms/step\n",
      "Epoch 11/20\n",
      "25/25 - 0s - loss: 0.1068 - accuracy: 0.9586 - val_loss: 0.2333 - val_accuracy: 0.9211 - 27ms/epoch - 1ms/step\n",
      "Epoch 12/20\n",
      "25/25 - 0s - loss: 0.0910 - accuracy: 0.9652 - val_loss: 0.2052 - val_accuracy: 0.9145 - 32ms/epoch - 1ms/step\n",
      "Epoch 13/20\n",
      "25/25 - 0s - loss: 0.0867 - accuracy: 0.9652 - val_loss: 0.2074 - val_accuracy: 0.9145 - 49ms/epoch - 2ms/step\n",
      "Epoch 14/20\n",
      "25/25 - 0s - loss: 0.0810 - accuracy: 0.9685 - val_loss: 0.2067 - val_accuracy: 0.9145 - 47ms/epoch - 2ms/step\n",
      "Epoch 15/20\n",
      "25/25 - 0s - loss: 0.0782 - accuracy: 0.9669 - val_loss: 0.2177 - val_accuracy: 0.9145 - 40ms/epoch - 2ms/step\n",
      "Epoch 16/20\n",
      "25/25 - 0s - loss: 0.0723 - accuracy: 0.9719 - val_loss: 0.2198 - val_accuracy: 0.9079 - 42ms/epoch - 2ms/step\n",
      "Epoch 17/20\n",
      "25/25 - 0s - loss: 0.0692 - accuracy: 0.9719 - val_loss: 0.2223 - val_accuracy: 0.9145 - 45ms/epoch - 2ms/step\n",
      "Epoch 18/20\n",
      "25/25 - 0s - loss: 0.0654 - accuracy: 0.9735 - val_loss: 0.2415 - val_accuracy: 0.9013 - 61ms/epoch - 2ms/step\n",
      "Epoch 19/20\n",
      "25/25 - 0s - loss: 0.0626 - accuracy: 0.9735 - val_loss: 0.2386 - val_accuracy: 0.9013 - 39ms/epoch - 2ms/step\n",
      "Epoch 20/20\n",
      "25/25 - 0s - loss: 0.0589 - accuracy: 0.9752 - val_loss: 0.2297 - val_accuracy: 0.9079 - 35ms/epoch - 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x17ff48070>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "undersample_model.fit(X_train, y_train, validation_split=0.2, batch_size=25, epochs=20, shuffle=True, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "undersample_predictions = undersample_model.predict(original_Xtest, batch_size=200, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# undersample_fraud_predictions = undersample_model.predict_classes(original_Xtest, batch_size=200, verbose=0)\n",
    "undersample_fraud_predictions = np.argmax(undersample_model.predict(original_Xtest, batch_size=200, verbose=0), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "# Create a confusion matrix\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                            normalize=False,\n",
    "                            title='Confusion matrix',\n",
    "                            cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title, fontsize=14)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                horizontalalignment=\"center\",\n",
    "                color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[55015  1848]\n",
      " [    8    90]]\n",
      "Confusion matrix, without normalization\n",
      "[[56863     0]\n",
      " [    0    98]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "undersample_cm = confusion_matrix(original_ytest, undersample_fraud_predictions)\n",
    "actual_cm = confusion_matrix(original_ytest, original_ytest)\n",
    "labels = ['No Fraud', 'Fraud']\n",
    "\n",
    "fig = plt.figure(figsize=(16,8))\n",
    "\n",
    "fig.add_subplot(221)\n",
    "plot_confusion_matrix(undersample_cm, labels, title=\"Random UnderSample \\n Confusion Matrix\", cmap=plt.cm.Reds)\n",
    "\n",
    "fig.add_subplot(222)\n",
    "plot_confusion_matrix(actual_cm, labels, title=\"Confusion Matrix \\n (with 100% accuracy)\", cmap=plt.cm.Greens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Oversampling (SMOTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_inputs = Xsm_train.shape[1]\n",
    "\n",
    "oversample_model = Sequential([\n",
    "    Dense(n_inputs, input_shape=(n_inputs, ), activation='relu'),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(2, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "oversample_model.compile(Adam(lr=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1214/1214 - 1s - loss: 0.0761 - accuracy: 0.9720 - val_loss: 0.0294 - val_accuracy: 0.9911 - 1s/epoch - 1ms/step\n",
      "Epoch 2/20\n",
      "1214/1214 - 1s - loss: 0.0155 - accuracy: 0.9964 - val_loss: 0.0135 - val_accuracy: 0.9991 - 644ms/epoch - 530us/step\n",
      "Epoch 3/20\n",
      "1214/1214 - 1s - loss: 0.0083 - accuracy: 0.9984 - val_loss: 0.0035 - val_accuracy: 1.0000 - 656ms/epoch - 540us/step\n",
      "Epoch 4/20\n",
      "1214/1214 - 1s - loss: 0.0057 - accuracy: 0.9989 - val_loss: 0.0028 - val_accuracy: 0.9999 - 652ms/epoch - 537us/step\n",
      "Epoch 5/20\n",
      "1214/1214 - 1s - loss: 0.0044 - accuracy: 0.9992 - val_loss: 0.0044 - val_accuracy: 1.0000 - 641ms/epoch - 528us/step\n",
      "Epoch 6/20\n",
      "1214/1214 - 1s - loss: 0.0035 - accuracy: 0.9993 - val_loss: 0.0018 - val_accuracy: 1.0000 - 642ms/epoch - 529us/step\n",
      "Epoch 7/20\n",
      "1214/1214 - 1s - loss: 0.0030 - accuracy: 0.9994 - val_loss: 0.0029 - val_accuracy: 0.9996 - 642ms/epoch - 529us/step\n",
      "Epoch 8/20\n",
      "1214/1214 - 1s - loss: 0.0025 - accuracy: 0.9995 - val_loss: 0.0123 - val_accuracy: 0.9984 - 645ms/epoch - 531us/step\n",
      "Epoch 9/20\n",
      "1214/1214 - 1s - loss: 0.0024 - accuracy: 0.9995 - val_loss: 7.8994e-04 - val_accuracy: 1.0000 - 634ms/epoch - 522us/step\n",
      "Epoch 10/20\n",
      "1214/1214 - 1s - loss: 0.0019 - accuracy: 0.9997 - val_loss: 8.3086e-04 - val_accuracy: 1.0000 - 637ms/epoch - 525us/step\n",
      "Epoch 11/20\n",
      "1214/1214 - 1s - loss: 0.0022 - accuracy: 0.9996 - val_loss: 5.5110e-04 - val_accuracy: 1.0000 - 639ms/epoch - 526us/step\n",
      "Epoch 12/20\n",
      "1214/1214 - 1s - loss: 0.0016 - accuracy: 0.9997 - val_loss: 0.0016 - val_accuracy: 1.0000 - 637ms/epoch - 524us/step\n",
      "Epoch 13/20\n",
      "1214/1214 - 1s - loss: 0.0016 - accuracy: 0.9997 - val_loss: 0.0017 - val_accuracy: 0.9999 - 645ms/epoch - 531us/step\n",
      "Epoch 14/20\n",
      "1214/1214 - 1s - loss: 0.0014 - accuracy: 0.9997 - val_loss: 3.7024e-04 - val_accuracy: 1.0000 - 637ms/epoch - 525us/step\n",
      "Epoch 15/20\n",
      "1214/1214 - 1s - loss: 0.0013 - accuracy: 0.9997 - val_loss: 6.8727e-04 - val_accuracy: 1.0000 - 636ms/epoch - 524us/step\n",
      "Epoch 16/20\n",
      "1214/1214 - 1s - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.0028 - val_accuracy: 0.9994 - 633ms/epoch - 522us/step\n",
      "Epoch 17/20\n",
      "1214/1214 - 1s - loss: 0.0015 - accuracy: 0.9997 - val_loss: 6.5072e-04 - val_accuracy: 1.0000 - 634ms/epoch - 522us/step\n",
      "Epoch 18/20\n",
      "1214/1214 - 1s - loss: 9.7596e-04 - accuracy: 0.9998 - val_loss: 1.1921e-04 - val_accuracy: 1.0000 - 634ms/epoch - 522us/step\n",
      "Epoch 19/20\n",
      "1214/1214 - 1s - loss: 9.6385e-04 - accuracy: 0.9998 - val_loss: 5.8018e-04 - val_accuracy: 1.0000 - 641ms/epoch - 528us/step\n",
      "Epoch 20/20\n",
      "1214/1214 - 1s - loss: 0.0011 - accuracy: 0.9998 - val_loss: 2.6557e-04 - val_accuracy: 1.0000 - 637ms/epoch - 525us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2880b0310>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oversample_model.fit(Xsm_train, ysm_train, validation_split=0.2, batch_size=300, epochs=20, shuffle=True, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> From here, we could already see that the model which was trained by the oversample dataset has a higher accuracy than the model which is trained by the undersample dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "oversample_predictions = oversample_model.predict(original_Xtest, batch_size=200, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "oversample_fraud_predictions = np.argmax(oversample_model.predict(original_Xtest, batch_size=200, verbose=0), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[56853    10]\n",
      " [   27    71]]\n",
      "Confusion matrix, without normalization\n",
      "[[56863     0]\n",
      " [    0    98]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "oversample_smote = confusion_matrix(original_ytest, oversample_fraud_predictions)\n",
    "actual_cm = confusion_matrix(original_ytest, original_ytest)\n",
    "labels = ['No Fraud', 'Fraud']\n",
    "\n",
    "fig = plt.figure(figsize=(16,8))\n",
    "\n",
    "fig.add_subplot(221)\n",
    "plot_confusion_matrix(oversample_smote, labels, title=\"OverSample (SMOTE) \\n Confusion Matrix\", cmap=plt.cm.Oranges)\n",
    "\n",
    "fig.add_subplot(222)\n",
    "plot_confusion_matrix(actual_cm, labels, title=\"Confusion Matrix \\n (with 100% accuracy)\", cmap=plt.cm.Greens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Then again, we could see that the model which was trained by the oversampled dataset results in a higher accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Overall, the model which was trained by the oversampled dataset (SMOTE) results in a higher accuracy prediction than the model which was trained by the undersampled dataset.  Amongst the classifier algorithm, LogisticRegression has the highest accuracy to predict the fraud and non-fraud cases.\n",
    "- However, one thing to note is that, we removed the outliers from the undersampled dataset, but we did not remove the outliers from the oversampled dataset. This may have affect the accuracy and prediction score result. So, I have to note that in my next exploration, I will try to calculate the accuracy and prediction score of a model that was trained by an oversampled dataset which has been cleared from outliers.\n",
    "- In my opinion, logically, in these kinds of financial cases, it is preferable to use oversampling instead of undersampling. Why? Because if we use undersampling, we cut off the Non-fraud cases. If we cut off the non-fraud cases, it will decrease the possibility of the case being detected as non-fraud. Hence, it will increase the chance of a non-fraud cases being classified as a fraud cases.\n",
    "- In a real world case, it must be frustratingly confusing for a customer if their card suddenly got blocked because it was being classified as a fraud case. This kind of misclassification can increase the number of customer complaints and customer disatisfaction."
   ]
  }
 ],
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   "language": "python",
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