ML_Credit_Card_Fraud.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing required packages:\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "from matplotlib import pyplot\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, matthews_corrcoef\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline\n",
    "\n",
    "# Import dataset.\n",
    "# Assign to a data frame:\n",
    "df_card = pd.read_csv(\"creditcard.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.425966</td>\n",
       "      <td>0.960523</td>\n",
       "      <td>1.141109</td>\n",
       "      <td>-0.168252</td>\n",
       "      <td>0.420987</td>\n",
       "      <td>-0.029728</td>\n",
       "      <td>0.476201</td>\n",
       "      <td>0.260314</td>\n",
       "      <td>-0.568671</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208254</td>\n",
       "      <td>-0.559825</td>\n",
       "      <td>-0.026398</td>\n",
       "      <td>-0.371427</td>\n",
       "      <td>-0.232794</td>\n",
       "      <td>0.105915</td>\n",
       "      <td>0.253844</td>\n",
       "      <td>0.081080</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.229658</td>\n",
       "      <td>0.141004</td>\n",
       "      <td>0.045371</td>\n",
       "      <td>1.202613</td>\n",
       "      <td>0.191881</td>\n",
       "      <td>0.272708</td>\n",
       "      <td>-0.005159</td>\n",
       "      <td>0.081213</td>\n",
       "      <td>0.464960</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.167716</td>\n",
       "      <td>-0.270710</td>\n",
       "      <td>-0.154104</td>\n",
       "      <td>-0.780055</td>\n",
       "      <td>0.750137</td>\n",
       "      <td>-0.257237</td>\n",
       "      <td>0.034507</td>\n",
       "      <td>0.005168</td>\n",
       "      <td>4.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.644269</td>\n",
       "      <td>1.417964</td>\n",
       "      <td>1.074380</td>\n",
       "      <td>-0.492199</td>\n",
       "      <td>0.948934</td>\n",
       "      <td>0.428118</td>\n",
       "      <td>1.120631</td>\n",
       "      <td>-3.807864</td>\n",
       "      <td>0.615375</td>\n",
       "      <td>...</td>\n",
       "      <td>1.943465</td>\n",
       "      <td>-1.015455</td>\n",
       "      <td>0.057504</td>\n",
       "      <td>-0.649709</td>\n",
       "      <td>-0.415267</td>\n",
       "      <td>-0.051634</td>\n",
       "      <td>-1.206921</td>\n",
       "      <td>-1.085339</td>\n",
       "      <td>40.80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.894286</td>\n",
       "      <td>0.286157</td>\n",
       "      <td>-0.113192</td>\n",
       "      <td>-0.271526</td>\n",
       "      <td>2.669599</td>\n",
       "      <td>3.721818</td>\n",
       "      <td>0.370145</td>\n",
       "      <td>0.851084</td>\n",
       "      <td>-0.392048</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.073425</td>\n",
       "      <td>-0.268092</td>\n",
       "      <td>-0.204233</td>\n",
       "      <td>1.011592</td>\n",
       "      <td>0.373205</td>\n",
       "      <td>-0.384157</td>\n",
       "      <td>0.011747</td>\n",
       "      <td>0.142404</td>\n",
       "      <td>93.20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.0</td>\n",
       "      <td>-0.338262</td>\n",
       "      <td>1.119593</td>\n",
       "      <td>1.044367</td>\n",
       "      <td>-0.222187</td>\n",
       "      <td>0.499361</td>\n",
       "      <td>-0.246761</td>\n",
       "      <td>0.651583</td>\n",
       "      <td>0.069539</td>\n",
       "      <td>-0.736727</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.246914</td>\n",
       "      <td>-0.633753</td>\n",
       "      <td>-0.120794</td>\n",
       "      <td>-0.385050</td>\n",
       "      <td>-0.069733</td>\n",
       "      <td>0.094199</td>\n",
       "      <td>0.246219</td>\n",
       "      <td>0.083076</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 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",
       "5   2.0 -0.425966  0.960523  1.141109 -0.168252  0.420987 -0.029728  0.476201   \n",
       "6   4.0  1.229658  0.141004  0.045371  1.202613  0.191881  0.272708 -0.005159   \n",
       "7   7.0 -0.644269  1.417964  1.074380 -0.492199  0.948934  0.428118  1.120631   \n",
       "8   7.0 -0.894286  0.286157 -0.113192 -0.271526  2.669599  3.721818  0.370145   \n",
       "9   9.0 -0.338262  1.119593  1.044367 -0.222187  0.499361 -0.246761  0.651583   \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",
       "5  0.260314 -0.568671  ... -0.208254 -0.559825 -0.026398 -0.371427 -0.232794   \n",
       "6  0.081213  0.464960  ... -0.167716 -0.270710 -0.154104 -0.780055  0.750137   \n",
       "7 -3.807864  0.615375  ...  1.943465 -1.015455  0.057504 -0.649709 -0.415267   \n",
       "8  0.851084 -0.392048  ... -0.073425 -0.268092 -0.204233  1.011592  0.373205   \n",
       "9  0.069539 -0.736727  ... -0.246914 -0.633753 -0.120794 -0.385050 -0.069733   \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",
       "5  0.105915  0.253844  0.081080    3.67      0  \n",
       "6 -0.257237  0.034507  0.005168    4.99      0  \n",
       "7 -0.051634 -1.206921 -1.085339   40.80      0  \n",
       "8 -0.384157  0.011747  0.142404   93.20      0  \n",
       "9  0.094199  0.246219  0.083076    3.68      0  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore the data frmae; first 10 rows: \n",
    "df_card.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>284797</th>\n",
       "      <td>172782.0</td>\n",
       "      <td>-0.241923</td>\n",
       "      <td>0.712247</td>\n",
       "      <td>0.399806</td>\n",
       "      <td>-0.463406</td>\n",
       "      <td>0.244531</td>\n",
       "      <td>-1.343668</td>\n",
       "      <td>0.929369</td>\n",
       "      <td>-0.206210</td>\n",
       "      <td>0.106234</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.228876</td>\n",
       "      <td>-0.514376</td>\n",
       "      <td>0.279598</td>\n",
       "      <td>0.371441</td>\n",
       "      <td>-0.559238</td>\n",
       "      <td>0.113144</td>\n",
       "      <td>0.131507</td>\n",
       "      <td>0.081265</td>\n",
       "      <td>5.49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284798</th>\n",
       "      <td>172782.0</td>\n",
       "      <td>0.219529</td>\n",
       "      <td>0.881246</td>\n",
       "      <td>-0.635891</td>\n",
       "      <td>0.960928</td>\n",
       "      <td>-0.152971</td>\n",
       "      <td>-1.014307</td>\n",
       "      <td>0.427126</td>\n",
       "      <td>0.121340</td>\n",
       "      <td>-0.285670</td>\n",
       "      <td>...</td>\n",
       "      <td>0.099936</td>\n",
       "      <td>0.337120</td>\n",
       "      <td>0.251791</td>\n",
       "      <td>0.057688</td>\n",
       "      <td>-1.508368</td>\n",
       "      <td>0.144023</td>\n",
       "      <td>0.181205</td>\n",
       "      <td>0.215243</td>\n",
       "      <td>24.05</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284799</th>\n",
       "      <td>172783.0</td>\n",
       "      <td>-1.775135</td>\n",
       "      <td>-0.004235</td>\n",
       "      <td>1.189786</td>\n",
       "      <td>0.331096</td>\n",
       "      <td>1.196063</td>\n",
       "      <td>5.519980</td>\n",
       "      <td>-1.518185</td>\n",
       "      <td>2.080825</td>\n",
       "      <td>1.159498</td>\n",
       "      <td>...</td>\n",
       "      <td>0.103302</td>\n",
       "      <td>0.654850</td>\n",
       "      <td>-0.348929</td>\n",
       "      <td>0.745323</td>\n",
       "      <td>0.704545</td>\n",
       "      <td>-0.127579</td>\n",
       "      <td>0.454379</td>\n",
       "      <td>0.130308</td>\n",
       "      <td>79.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284800</th>\n",
       "      <td>172784.0</td>\n",
       "      <td>2.039560</td>\n",
       "      <td>-0.175233</td>\n",
       "      <td>-1.196825</td>\n",
       "      <td>0.234580</td>\n",
       "      <td>-0.008713</td>\n",
       "      <td>-0.726571</td>\n",
       "      <td>0.017050</td>\n",
       "      <td>-0.118228</td>\n",
       "      <td>0.435402</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.268048</td>\n",
       "      <td>-0.717211</td>\n",
       "      <td>0.297930</td>\n",
       "      <td>-0.359769</td>\n",
       "      <td>-0.315610</td>\n",
       "      <td>0.201114</td>\n",
       "      <td>-0.080826</td>\n",
       "      <td>-0.075071</td>\n",
       "      <td>2.68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284801</th>\n",
       "      <td>172785.0</td>\n",
       "      <td>0.120316</td>\n",
       "      <td>0.931005</td>\n",
       "      <td>-0.546012</td>\n",
       "      <td>-0.745097</td>\n",
       "      <td>1.130314</td>\n",
       "      <td>-0.235973</td>\n",
       "      <td>0.812722</td>\n",
       "      <td>0.115093</td>\n",
       "      <td>-0.204064</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.314205</td>\n",
       "      <td>-0.808520</td>\n",
       "      <td>0.050343</td>\n",
       "      <td>0.102800</td>\n",
       "      <td>-0.435870</td>\n",
       "      <td>0.124079</td>\n",
       "      <td>0.217940</td>\n",
       "      <td>0.068803</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284802</th>\n",
       "      <td>172786.0</td>\n",
       "      <td>-11.881118</td>\n",
       "      <td>10.071785</td>\n",
       "      <td>-9.834783</td>\n",
       "      <td>-2.066656</td>\n",
       "      <td>-5.364473</td>\n",
       "      <td>-2.606837</td>\n",
       "      <td>-4.918215</td>\n",
       "      <td>7.305334</td>\n",
       "      <td>1.914428</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.111864</td>\n",
       "      <td>1.014480</td>\n",
       "      <td>-0.509348</td>\n",
       "      <td>1.436807</td>\n",
       "      <td>0.250034</td>\n",
       "      <td>0.943651</td>\n",
       "      <td>0.823731</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284803</th>\n",
       "      <td>172787.0</td>\n",
       "      <td>-0.732789</td>\n",
       "      <td>-0.055080</td>\n",
       "      <td>2.035030</td>\n",
       "      <td>-0.738589</td>\n",
       "      <td>0.868229</td>\n",
       "      <td>1.058415</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>0.294869</td>\n",
       "      <td>0.584800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214205</td>\n",
       "      <td>0.924384</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>-1.016226</td>\n",
       "      <td>-0.606624</td>\n",
       "      <td>-0.395255</td>\n",
       "      <td>0.068472</td>\n",
       "      <td>-0.053527</td>\n",
       "      <td>24.79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284804</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>1.919565</td>\n",
       "      <td>-0.301254</td>\n",
       "      <td>-3.249640</td>\n",
       "      <td>-0.557828</td>\n",
       "      <td>2.630515</td>\n",
       "      <td>3.031260</td>\n",
       "      <td>-0.296827</td>\n",
       "      <td>0.708417</td>\n",
       "      <td>0.432454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.232045</td>\n",
       "      <td>0.578229</td>\n",
       "      <td>-0.037501</td>\n",
       "      <td>0.640134</td>\n",
       "      <td>0.265745</td>\n",
       "      <td>-0.087371</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>-0.026561</td>\n",
       "      <td>67.88</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>-0.240440</td>\n",
       "      <td>0.530483</td>\n",
       "      <td>0.702510</td>\n",
       "      <td>0.689799</td>\n",
       "      <td>-0.377961</td>\n",
       "      <td>0.623708</td>\n",
       "      <td>-0.686180</td>\n",
       "      <td>0.679145</td>\n",
       "      <td>0.392087</td>\n",
       "      <td>...</td>\n",
       "      <td>0.265245</td>\n",
       "      <td>0.800049</td>\n",
       "      <td>-0.163298</td>\n",
       "      <td>0.123205</td>\n",
       "      <td>-0.569159</td>\n",
       "      <td>0.546668</td>\n",
       "      <td>0.108821</td>\n",
       "      <td>0.104533</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>172792.0</td>\n",
       "      <td>-0.533413</td>\n",
       "      <td>-0.189733</td>\n",
       "      <td>0.703337</td>\n",
       "      <td>-0.506271</td>\n",
       "      <td>-0.012546</td>\n",
       "      <td>-0.649617</td>\n",
       "      <td>1.577006</td>\n",
       "      <td>-0.414650</td>\n",
       "      <td>0.486180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261057</td>\n",
       "      <td>0.643078</td>\n",
       "      <td>0.376777</td>\n",
       "      <td>0.008797</td>\n",
       "      <td>-0.473649</td>\n",
       "      <td>-0.818267</td>\n",
       "      <td>-0.002415</td>\n",
       "      <td>0.013649</td>\n",
       "      <td>217.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time         V1         V2        V3        V4        V5  \\\n",
       "284797  172782.0  -0.241923   0.712247  0.399806 -0.463406  0.244531   \n",
       "284798  172782.0   0.219529   0.881246 -0.635891  0.960928 -0.152971   \n",
       "284799  172783.0  -1.775135  -0.004235  1.189786  0.331096  1.196063   \n",
       "284800  172784.0   2.039560  -0.175233 -1.196825  0.234580 -0.008713   \n",
       "284801  172785.0   0.120316   0.931005 -0.546012 -0.745097  1.130314   \n",
       "284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
       "284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
       "284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
       "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
       "284806  172792.0  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n",
       "\n",
       "              V6        V7        V8        V9  ...       V21       V22  \\\n",
       "284797 -1.343668  0.929369 -0.206210  0.106234  ... -0.228876 -0.514376   \n",
       "284798 -1.014307  0.427126  0.121340 -0.285670  ...  0.099936  0.337120   \n",
       "284799  5.519980 -1.518185  2.080825  1.159498  ...  0.103302  0.654850   \n",
       "284800 -0.726571  0.017050 -0.118228  0.435402  ... -0.268048 -0.717211   \n",
       "284801 -0.235973  0.812722  0.115093 -0.204064  ... -0.314205 -0.808520   \n",
       "284802 -2.606837 -4.918215  7.305334  1.914428  ...  0.213454  0.111864   \n",
       "284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \n",
       "284804  3.031260 -0.296827  0.708417  0.432454  ...  0.232045  0.578229   \n",
       "284805  0.623708 -0.686180  0.679145  0.392087  ...  0.265245  0.800049   \n",
       "284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
       "284797  0.279598  0.371441 -0.559238  0.113144  0.131507  0.081265    5.49   \n",
       "284798  0.251791  0.057688 -1.508368  0.144023  0.181205  0.215243   24.05   \n",
       "284799 -0.348929  0.745323  0.704545 -0.127579  0.454379  0.130308   79.99   \n",
       "284800  0.297930 -0.359769 -0.315610  0.201114 -0.080826 -0.075071    2.68   \n",
       "284801  0.050343  0.102800 -0.435870  0.124079  0.217940  0.068803    2.69   \n",
       "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
       "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
       "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
       "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n",
       "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
       "\n",
       "        Class  \n",
       "284797      0  \n",
       "284798      0  \n",
       "284799      0  \n",
       "284800      0  \n",
       "284801      0  \n",
       "284802      0  \n",
       "284803      0  \n",
       "284804      0  \n",
       "284805      0  \n",
       "284806      0  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the last 10 rows:\n",
    "df_card.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if there are any NULL values in the data frame:\n",
    "df_card.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarize the 'class' distribution:\n",
    "df_card[\"Class\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the 'class' distribution using a bar chart:\n",
    "sns.countplot(x = df_card[\"Class\"])\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     492.000000\n",
       "mean      122.211321\n",
       "std       256.683288\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         9.250000\n",
       "75%       105.890000\n",
       "max      2125.870000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analyze the fraudulent transactions:\n",
    "fraud_t = df_card[df_card[\"Class\"] == 1]\n",
    "fraud_t.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    284315.000000\n",
       "mean         88.291022\n",
       "std         250.105092\n",
       "min           0.000000\n",
       "25%           5.650000\n",
       "50%          22.000000\n",
       "75%          77.050000\n",
       "max       25691.160000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analyze the normal transactions:\n",
    "normal_t = df_card[df_card[\"Class\"] == 0]\n",
    "normal_t.Amount.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the 'fraudulent' and 'normal' transaction distribution.\n",
    "# Use a histogram:\n",
    "f, (ax1, ax2) = pyplot.subplots(2, 1, sharex = True)\n",
    "f.suptitle(\"Amount of Transactions by Class\")\n",
    "bins = 50\n",
    "ax1.hist(fraud_t.Amount, bins = bins)\n",
    "ax1.set_title(\"Fraudulent\")\n",
    "ax2.hist(normal_t.Amount, bins = bins)\n",
    "ax2.set_title(\"Normal\")\n",
    "pyplot.xlabel(\"Amount($)\")\n",
    "pyplot.ylabel(\"Number of transactions\")\n",
    "pyplot.xlim(0, 26000)\n",
    "pyplot.yscale(\"log\")\n",
    "pyplot.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": [
    "# Overview of the data frame:\n",
    "df_card.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of all feature(input) variables.\n",
    "# Create a histogram of each:\n",
    "new_df = df_card.drop(columns=[\"Class\"])\n",
    "distr_hist = new_df.hist(bins=100)\n",
    "\n",
    "# Disable axis labels to avoid clutter:\n",
    "for axis in distr_hist.flatten():\n",
    "    axis.set_xticklabels([])\n",
    "    axis.set_yticklabels([])\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    284807.000000\n",
      "mean         88.349619\n",
      "std         250.120109\n",
      "min           0.000000\n",
      "25%           5.600000\n",
      "50%          22.000000\n",
      "75%          77.165000\n",
      "max       25691.160000\n",
      "Name: Amount, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Summarize the total number of transactions ('Amount' variable):\n",
    "print(df_card[\"Amount\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation matrix.\n",
    "# Shows the relationship of feature variables to each other:\n",
    "corrmatrix = df_card.corr()\n",
    "fig = pyplot.figure(figsize = (12, 9))\n",
    "\n",
    "sns.heatmap(corrmatrix, vmax = 1.0, square = True)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training & Test Splitting of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use a sample from the main dataframe:\n",
    "df = df_card.sample(frac = 0.4, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate dataset as features and response variables:\n",
    "input_ds = df.drop(columns=[\"Class\"])\n",
    "output_ds = df[\"Class\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(113923, 30) (113923,) Counter({0: 113737, 1: 186})\n"
     ]
    }
   ],
   "source": [
    "# Dataset summary:\n",
    "print(input_ds.shape, output_ds.shape, Counter(output_ds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training and test splitting of data:\n",
    "input_train, input_test, output_train, output_test = train_test_split(input_ds, output_ds, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying a standard scalar to the data, prior to modeling.\n",
    "# Provides optimized results:\n",
    "sc = StandardScaler()\n",
    "input_train = sc.fit_transform(input_train)\n",
    "input_test = sc.transform(input_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5.34243064e-01 -6.52178790e-01  8.49699395e-01 -2.34775852e-01\n",
      "   4.18304507e-01  7.71409938e-01 -1.03233172e-01  1.58294038e+00\n",
      "  -6.87163795e-01  1.58560731e-01  1.63895454e+00  5.61202751e-01\n",
      "   2.32859005e-01 -4.25734038e-01 -8.64449119e-02 -8.67609855e-01\n",
      "  -1.27454420e+00 -5.57552747e-01  1.26568367e-01  1.43097284e+00\n",
      "   1.55412752e-01 -5.61282210e-02  9.71353394e-01 -8.45690418e-01\n",
      "  -6.45265779e-01  4.40916190e-02 -9.90545283e-01 -7.51302450e-01\n",
      "   8.29674872e-02  2.50123267e-02]\n",
      " [ 1.21519191e+00 -7.06610721e-01  6.35284687e-01 -5.28993679e-01\n",
      "  -6.32826160e-01  2.31764656e-02  7.45116572e-02 -2.73879513e-01\n",
      "   3.63741286e-01  7.29989675e-01  2.07538771e-01  1.16942660e-01\n",
      "   1.34416213e+00  1.16124135e+00  1.89970857e-02 -4.83146325e-01\n",
      "   7.82873172e-01 -1.48716959e+00  1.19200471e+00 -2.40173780e-01\n",
      "  -8.41248488e-01  8.07238888e-01  2.19821573e+00  6.76557601e-01\n",
      "   5.26868581e-01 -3.83494709e+00 -1.57236923e+00 -2.64710065e+00\n",
      "   1.35715699e-01 -3.26135149e-01]\n",
      " [-6.26017495e-01  6.76874128e-01 -3.81550363e-01  4.92383780e-01\n",
      "  -1.18169274e-01 -9.16346693e-01 -3.26436907e-01 -6.78708148e-01\n",
      "  -4.23776170e-02 -4.70929462e-01  6.68424148e-01 -9.74137543e-01\n",
      "  -3.90388678e-01  2.85439635e-01 -1.48941413e-01  1.30656008e+00\n",
      "  -9.85050003e-01 -4.66585963e-01  1.96381881e+00 -1.30448306e+00\n",
      "  -5.95924876e-01 -2.55104224e-01 -9.35808877e-02 -1.11352492e-01\n",
      "   8.75219349e-02  5.81960671e-01  1.31635286e+00  1.40508193e-02\n",
      "   8.07932753e-02 -2.30077827e-01]\n",
      " [-1.10242493e+00  6.73661363e-01 -2.98422390e-03  1.00079314e-01\n",
      "  -1.66805781e-01 -4.18805411e-01 -9.82157705e-01  7.18336798e-02\n",
      "  -2.22808189e-01  1.74790573e-01 -1.48349861e-01 -2.60770121e-01\n",
      "  -9.11474583e-02 -4.61063196e-01  4.23184542e-01  1.18317571e+00\n",
      "   2.73632151e-01 -2.35704229e-01 -8.02194142e-01  2.54722080e-01\n",
      "  -1.12697557e-01 -2.64376942e-01 -8.19096714e-01  1.30709990e-01\n",
      "   7.57148598e-01  3.20419532e-01  2.61800483e+00 -3.21694492e-01\n",
      "  -1.21817316e-02 -3.30813803e-01]\n",
      " [-2.91730456e-01 -9.74338449e-02 -4.69462558e-01  1.04526614e+00\n",
      "  -1.55377885e+00 -9.63025577e-01 -7.98228023e-01  2.90370278e-01\n",
      "  -3.53558267e-01 -1.96638642e+00  8.97955925e-01 -3.96465530e-01\n",
      "  -1.37222417e+00 -1.19682478e-01 -5.87361491e-01  2.32693432e-03\n",
      "  -4.18243740e-01  3.37144690e-01  7.94842982e-03 -1.15047303e+00\n",
      "  -1.09525402e-01 -1.37365509e-01 -2.70070723e-02  3.40675926e-01\n",
      "   1.09580294e+00 -2.26984247e-01 -8.89685966e-01 -3.94466050e-01\n",
      "  -5.45874916e-01  1.42599737e-01]\n",
      " [-1.10316152e+00 -1.08498078e+00 -7.89618777e-01 -1.43751031e-01\n",
      "  -8.66613744e-01 -6.41331582e-01 -4.39652443e-01 -5.42081422e-01\n",
      "   6.16055168e-01 -2.95213439e+00  9.38873847e-01  3.83841839e-02\n",
      "  -1.06419421e+00 -4.50064749e-01  9.54278712e-01  1.93649681e-01\n",
      "  -3.47624635e-01  1.01659785e+00  9.09589050e-01  2.25346664e+00\n",
      "  -5.49437431e-01 -5.06312599e-01 -1.40332457e+00  9.37763173e-01\n",
      "  -9.40983018e-01  2.88097113e-02 -5.86859938e-01 -1.48895338e-02\n",
      "  -1.76295155e+00 -9.68811018e-02]\n",
      " [ 7.32281406e-01  1.06258145e+00 -9.32306983e-02 -1.43355856e+00\n",
      "   2.44131384e-01  5.70371596e-01 -3.59331985e-01  4.06703383e-01\n",
      "  -2.55207570e-01  4.08042331e-01  1.07131798e-01 -1.67288199e+00\n",
      "  -1.50474311e-01 -3.32713913e-01  4.56826436e-01 -3.37052399e-01\n",
      "  -3.34316716e-01 -4.70878037e-01 -5.41429520e-01  5.50710146e-01\n",
      "  -1.74278939e-01 -9.01784803e-02 -1.66110698e-01 -4.76318716e-02\n",
      "   2.55002179e-02  6.11546716e-01  8.02780738e-01 -2.55655968e-01\n",
      "  -2.12064781e-01 -1.64907903e-01]\n",
      " [ 1.19812399e+00 -1.62922134e+00 -1.30007157e-01 -6.42158951e-01\n",
      "  -7.17469655e-01  6.69215724e-01  1.12064757e+00  9.87131393e-02\n",
      "   9.56365539e-01  1.89182412e-01 -6.84680461e-01  2.63421199e-01\n",
      "   8.75871511e-01 -4.94216656e-01  7.27912945e-01 -3.78219425e-01\n",
      "  -7.17346274e-01  4.69269979e-01 -1.19969561e+00 -2.45165470e-01\n",
      "  -1.03432510e+00 -1.26466358e-01  4.79167421e-02  2.60859595e-01\n",
      "  -1.51981958e+00  1.66784492e-01 -7.74852474e-02 -1.41222236e+00\n",
      "   1.08980068e+00 -5.77129009e-02]\n",
      " [-5.95817174e-01 -3.81181426e-01  7.91989470e-01  6.86914363e-01\n",
      "   1.31696216e-01 -3.82438087e-01 -9.11982565e-01  3.24275668e-01\n",
      "   2.97067967e-01 -4.79819130e-01 -6.88333257e-01 -1.42906910e-01\n",
      "  -5.20271283e-02 -3.30525593e-01 -9.04863025e-03  9.65959042e-01\n",
      "   5.00182070e-01  3.74928308e-01 -1.74387817e-01 -2.68364188e-01\n",
      "   3.66496215e-03 -2.67403549e-01 -9.36278960e-01  2.04124054e-01\n",
      "   1.07270435e+00 -3.62263881e-01  1.08207873e-01  2.82148249e-01\n",
      "   1.22142706e-01 -2.76119095e-01]\n",
      " [-4.13099970e-01  6.37631189e-01  2.41164403e-01  2.01724105e-01\n",
      "   7.80733862e-01 -1.57899477e-01 -7.16604272e-01  2.22188717e-01\n",
      "  -2.25285090e-01 -9.97976084e-02 -6.36764734e-03 -2.86709841e-01\n",
      "   5.06353092e-01  5.87054797e-01  2.77070620e-01  9.36520077e-01\n",
      "   2.66004718e-02 -5.54083634e-01 -2.69820207e-01 -4.59370543e-01\n",
      "  -1.39764313e-01  4.82715398e-02  2.40794245e-01 -1.76639173e-01\n",
      "   6.90137615e-01  1.36364998e+00 -6.90822477e-01  4.11813406e-02\n",
      "   7.10966489e-02 -3.36858789e-01]]\n"
     ]
    }
   ],
   "source": [
    "# Print the first 10 rows of the 'input_train' 2D array:\n",
    "print(input_train[:10, :])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Machine Learning Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the first model.\n",
    "# It comprises of 200 trees.\n",
    "# Train it.\n",
    "rfc = RandomForestClassifier(n_estimators = 200)\n",
    "rfc.fit(input_train, output_train)\n",
    "predictions_rfc = rfc.predict(input_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-6.66782667e-01 -7.60866727e-01  3.20373071e-01  7.27193217e-01\n",
      "  -1.22060676e-01  1.16889755e-01 -4.17062522e-01  4.02889384e-01\n",
      "   2.61721833e-01 -2.37345060e-01 -9.53068810e-01 -1.35424328e+00\n",
      "   4.77010223e-01  5.94647777e-01 -8.24533069e-02 -9.12258155e-01\n",
      "   1.26187598e-01 -3.12762704e-01 -5.62344561e-01 -5.57054890e-02\n",
      "  -2.83667236e-01 -1.79473157e-01 -4.96065171e-01  1.82621460e-01\n",
      "  -1.30956683e-01  4.76518313e-01  3.16274741e-01 -3.94757338e-01\n",
      "  -4.18491629e-02 -1.58490280e-01]\n",
      " [ 6.56875309e-01  1.06951872e+00  2.49152575e-02 -1.17569196e+00\n",
      "   2.89722388e-01  2.23858776e-01 -6.66867948e-01  7.18041254e-02\n",
      "  -1.22630413e-01  6.66522383e-01 -3.12542989e-01 -8.60782073e-01\n",
      "  -6.87747822e-01 -1.47482131e+00 -5.72433099e-01  4.16929213e-01\n",
      "   4.66139851e-01  6.39484402e-01 -1.22141864e-01  1.04389148e-01\n",
      "  -3.45583914e-01 -5.30124569e-01 -1.49032799e+00  5.73527267e-01\n",
      "   7.32279162e-01 -5.83129860e-01  3.67569158e-01 -1.87806870e-01\n",
      "  -1.05436489e-01 -3.57643606e-01]\n",
      " [ 1.57656453e+00 -3.80947413e-01  2.40156822e-01 -5.27170782e-02\n",
      "  -1.21941758e+00  1.57507092e+00  3.09299882e+00 -4.12897782e-01\n",
      "   1.11009992e+00  2.25467561e-01 -6.51821387e-01 -4.92587911e-01\n",
      "  -5.08963578e-03 -1.76076436e-01 -6.93975844e-02  4.01316167e-01\n",
      "   2.67314234e-01 -6.92093933e-01  6.08325266e-02 -5.61374679e-01\n",
      "  -1.16580183e-01  3.41928488e-01  1.00518283e+00 -2.89676455e-01\n",
      "   1.26291175e+00 -7.21047594e-01  1.06383654e+00 -7.05660280e-01\n",
      "  -4.28791595e-01 -3.14127983e-01]\n",
      " [-1.27184136e+00  7.46898062e-01 -5.96901013e-01 -9.37038316e-02\n",
      "  -1.00888294e+00 -7.68052716e-01 -7.13116246e-01 -3.74030356e-01\n",
      "  -2.95428284e-01 -1.96577932e+00  1.30215664e+00 -7.43043409e-01\n",
      "  -7.90458550e-01  8.33339613e-01 -1.84335295e-01  4.06444191e-01\n",
      "  -4.95085322e-01  4.99236563e-01 -5.28125139e-01  2.47544994e-01\n",
      "  -2.38877191e-01 -7.63670831e-01 -1.92875440e+00  1.44731345e-01\n",
      "  -2.90480132e-01  5.61336084e-01 -1.05178435e+00 -4.69321530e-03\n",
      "   8.29387502e-02 -4.70720683e-02]\n",
      " [ 9.91225484e-01  1.01058986e+00  1.82560003e-01 -1.56985576e+00\n",
      "   8.02136530e-01  1.06107516e+00 -1.04791536e-01  7.26453583e-01\n",
      "  -1.76273610e-01 -2.83473773e-01  2.32327142e-01 -1.56115088e-01\n",
      "   4.48241929e-01 -2.60160798e-01  1.02558697e+00  5.08089839e-01\n",
      "  -2.08238558e+00  8.10746292e-01 -2.32303462e+00 -1.78311625e+00\n",
      "  -5.57163066e-01  5.38504368e-01  2.01640927e+00 -2.07734906e-01\n",
      "  -1.50451920e+00  1.24753734e+00 -2.16234286e-01 -1.47995768e-03\n",
      "  -2.79115064e-01 -3.57602202e-01]\n",
      " [ 1.11253186e+00  8.93733338e-01 -6.68334672e-01 -2.64753420e-01\n",
      "   3.96077614e-01 -7.04275377e-01  2.40569420e-02 -5.60324326e-01\n",
      "   1.98102101e-02 -4.95234826e-01  1.00809391e+00  4.23501604e-01\n",
      "   8.87340419e-01  8.74096152e-01  1.23370781e-02 -9.68381845e-02\n",
      "  -7.95164706e-01 -1.09446363e+00  2.27268737e+00 -1.20728386e+00\n",
      "  -3.83844647e-01 -4.76816512e-01 -1.12081395e+00  4.03505255e-01\n",
      "  -7.14367254e-01 -1.02527915e+00 -1.95540725e+00  1.28162870e-01\n",
      "  -7.89057906e-03  3.21423690e-01]\n",
      " [ 7.53579473e-01  7.89217317e-01  2.28193826e-03 -3.05684854e-01\n",
      "   2.71818553e+00 -2.81701838e-02 -2.17468852e-01  3.19179052e-01\n",
      "  -1.18343782e-01 -9.18664610e-01  1.27562618e+00 -9.27738184e-01\n",
      "  -2.61075622e-01 -5.41763600e-02  4.16330951e-01 -5.34246378e-02\n",
      "   1.52795356e+00 -1.20129779e+00 -8.24511302e-01 -1.90980803e+00\n",
      "   1.35623173e-01 -4.43756975e-01 -2.08135891e+00  6.49181469e-01\n",
      "  -1.40050243e-01 -1.40957347e+00 -1.36686569e+00 -1.59860542e-01\n",
      "   1.48676790e-02  4.62197351e-01]\n",
      " [ 6.11837896e-01  1.02471692e+00 -1.31977432e-01 -1.41096796e+00\n",
      "   1.28425174e-01  3.30247754e-01 -8.33770649e-01  5.27180416e-01\n",
      "  -3.18404265e-01  2.34815154e-02  2.91427141e-01  4.75705046e-01\n",
      "   5.15698750e-01 -7.70850993e-01  9.25191771e-01 -1.01452231e+00\n",
      "  -3.64991096e-01 -4.86058514e-01 -1.47898724e-01  6.86149633e-01\n",
      "  -1.91726930e-01  2.09575423e-01  6.31953389e-01 -1.98274670e-01\n",
      "  -4.25084331e-01  6.24579061e-01  1.70088280e+00 -3.61017928e-01\n",
      "  -2.84095288e-01 -1.15512910e-01]\n",
      " [ 8.12380655e-01 -2.38895407e-01  3.05062951e-01 -3.11808535e-01\n",
      "  -2.56570547e-01  8.90037120e-01 -7.13855446e-01  8.19692030e-01\n",
      "  -2.18602038e-01  1.59935501e-01 -3.06033076e-01  5.24059078e-01\n",
      "  -8.52692476e-01 -1.93407461e+00 -6.40263310e-01  3.24698302e-01\n",
      "   3.98119292e-01  1.20349098e-01  1.94022785e+00  4.43009501e-01\n",
      "   9.77247738e-02  3.49994185e-01  1.30788131e+00 -4.14901301e-01\n",
      "  -1.00727404e+00 -8.91661634e-01 -3.53043837e-01  3.71302325e-01\n",
      "   4.18117331e-01 -1.73644151e-01]\n",
      " [ 6.06597562e-01  1.06499984e+00 -4.77003434e-01 -1.09351319e+00\n",
      "  -3.79836937e-01 -1.65131458e-01 -5.40231315e-01 -4.56571887e-02\n",
      "  -2.34997235e-01 -7.93431835e-01  9.70407957e-01  2.56561181e-01\n",
      "   5.70478788e-01  2.18074707e-01  4.43899590e-01 -9.94048931e-01\n",
      "  -1.69450063e+00 -4.25877986e-01  1.37906298e+00 -1.63901926e-01\n",
      "  -6.55420029e-01 -6.08543628e-01 -1.13331756e+00  2.48556690e-01\n",
      "  -8.15423932e-01 -2.96658792e-01  1.07968688e+00 -2.23925731e-01\n",
      "  -2.20560905e-01 -9.32375482e-02]]\n"
     ]
    }
   ],
   "source": [
    "# Print the first 10 rows of the 'input_test' 2D array:\n",
    "print(input_test[:10, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "# Print the first 10 elements of the 'predictions' 1D array:\n",
    "print(predictions_rfc[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model's performance summary is:\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     22751\n",
      "           1       0.92      0.71      0.80        34\n",
      "\n",
      "    accuracy                           1.00     22785\n",
      "   macro avg       0.96      0.85      0.90     22785\n",
      "weighted avg       1.00      1.00      1.00     22785\n",
      "\n",
      "\n",
      "The total number of errors is:  12\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the model's performance:\n",
    "n_errors = (predictions_rfc != output_test).sum()\n",
    "\n",
    "print(\"The model's performance summary is:\\n\")\n",
    "print(classification_report(output_test, predictions_rfc))\n",
    "print(\"\\nThe total number of errors is: \", n_errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score is:  0.999\n"
     ]
    }
   ],
   "source": [
    "# Calculate the model's accuracy score:\n",
    "acc_score = accuracy_score(output_test, predictions_rfc)\n",
    "print(\"The accuracy score is: \", round(acc_score, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Matthews correlation coefficient is:  0.807\n"
     ]
    }
   ],
   "source": [
    "# Calculate the Matthews correlation coefficient:\n",
    "mcc = matthews_corrcoef(output_test, predictions_rfc)\n",
    "print(\"The Matthews correlation coefficient is: \", round(mcc, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[22749     2]\n",
      " [   10    24]]\n"
     ]
    }
   ],
   "source": [
    "# Calculate the confusion matrix:\n",
    "print(confusion_matrix(output_test, predictions_rfc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAq0AAALJCAYAAACeORrnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAA1bUlEQVR4nO3deZhlVXk37N8jqCDKLMikOKAJGiWOqNGAGgTFOBGjMUoICSZRjBqHJOaVOORN1JDXMRpwABwQ+QSDigLBEROVQWQQIjgQmRVwAqI0tb4/ajdWtz1Ub9i7dlXft9e56px19tl7VWl3P/7qWWtXay0AADBlt1voCQAAwNooWgEAmDxFKwAAk6doBQBg8hStAABMnqIVAIDJU7QCg6iqjavqE1X146o69lac57lVdfJtObeFUFWfrqr9F3oeAIuVohXWc1X1B1V1RlX9rKqu6Iqr37oNTr1fkm2TbNVa+72+J2mtfai1ttdtMJ8VVNUeVdWq6viVxh/UjX9+nuf5+6r64NqOa63t01o7sud0AdZ7ilZYj1XVy5K8Jcn/zWyBefck/5rkqbfB6e+R5FuttWW3wbmG8oMkj6yqreaM7Z/kW7fVBWqWv2sBbiV/kcJ6qqo2S/K6JC9srR3XWru+tXZTa+0TrbVXdMfcsareUlWXd4+3VNUdu/f2qKpLq+qvqurqLqU9oHvvtUlek+T3uwT3wJUTyarauUs0N+xe/1FVfaeqflpV362q584ZP23O5x5VVad3bQenV9Wj5rz3+ap6fVV9uTvPyVW19Rp+DL9I8vEkz+4+v0GS30/yoZV+Vm+tqu9X1U+q6syqekw3vneSv53zfX5jzjz+oaq+nOSGJPfqxv6ke/9dVfWxOed/Y1WdWlU13//+ANY3ilZYfz0yyUZJjl/DMa9OsnuS3ZI8KMnDk/zdnPfvlmSzJDskOTDJO6tqi9baIZlNb49prd25tfbeNU2kqjZJ8rYk+7TW7pLkUUnOXsVxWyb5VHfsVkn+JcmnVkpK/yDJAUm2SXKHJC9f07WTHJXk+d3zJyY5L8nlKx1zemZ/Blsm+XCSY6tqo9baZ1b6Ph805zPPS3JQkrskuWSl8/1Vkt/oCvLHZPZnt39zX22A1VK0wvprqyQ/XMuv75+b5HWttatbaz9I8trMFmPL3dS9f1Nr7cQkP0tyv57zmUnygKrauLV2RWvt/FUc8+QkF7XWPtBaW9ZaOzrJhUmeMueY97fWvtVauzHJRzNbbK5Wa+0/k2xZVffLbPF61CqO+WBr7ZrumocmuWPW/n0e0Vo7v/vMTSud74bM/hz/JckHkxzcWrt0LecDWK8pWmH9dU2SrZf/en41ts+KKeEl3dgt51ip6L0hyZ3XdSKttesz+2v5P0tyRVV9qqp+bR7zWT6nHea8vrLHfD6Q5EVJ9swqkueqenlVXdC1JPwos+nymtoOkuT7a3qztfbVJN9JUpktrgFYA0UrrL/+K8nPkzxtDcdcntkFVcvdPb/6q/P5uj7Jnea8vtvcN1trJ7XWfifJdplNTw+fx3yWz+mynnNa7gNJ/iLJiV0Keovu1/evTPKsJFu01jZP8uPMFptJsrpf6a/xV/1V9cLMJraXd+cHYA0UrbCeaq39OLOLpd5ZVU+rqjtV1e2rap+qelN32NFJ/q6q7totaHpNZn+d3cfZSR5bVXfvFoH9zfI3qmrbqnpq19v688y2Gcys4hwnJrlvt03XhlX1+0l2TfLJnnNKkrTWvpvktzPbw7uyuyRZltmdBjasqtck2XTO+1cl2XlddgioqvsmeUOSP8xsm8Arq2q3frMHWD8oWmE91vVnviyzi6t+kNlfab8osyvqk9nC6owk5yQ5N8lZ3Vifa52S5JjuXGdmxULzdt08Lk9ybWYLyD9fxTmuSbJvZhcyXZPZhHLf1toP+8xppXOf1lpbVYp8UpLPZHYbrEuS/G9W/NX/8hsnXFNVZ63tOl07xgeTvLG19o3W2kWZ3YHgA8t3ZgDgV5XFqgAATJ2kFQCAyVO0AgAweYpWAAAmT9EKAMDkrWlT8QV10w+/Y4UYMC8bb/+YhZ4CsEgs+8VltfajhjWFGuf2W99rwX8O60rSCgDA5ClaAQCYvMm2BwAALEkzNy/0DBYlSSsAAJOnaAUAYPK0BwAAjKnNLPQMFiVJKwAAkydpBQAY04yktQ9JKwAAk6doBQBg8rQHAACMqFmI1YukFQCAyZO0AgCMyUKsXiStAABMnqIVAIDJ0x4AADAmC7F6kbQCADB5klYAgDHN3LzQM1iUJK0AAEyeohUAgMnTHgAAMCYLsXqRtAIAMHmSVgCAMbkjVi+SVgAAJk/RCgDA5GkPAAAYUbMQqxdJKwAAk6doBQBg8rQHAACMye4BvUhaAQCYPEkrAMCYLMTqRdIKAMDkKVoBAJg87QEAAGOauXmhZ7AoSVoBAJg8SSsAwJgsxOpF0goAwOQpWgEAmDztAQAAY3JHrF4krQAATJ6kFQBgTBZi9SJpBQBg8hStAABMnvYAAIAxWYjVi6QVAIDJk7QCAIyotZsXegqLkqQVAIDJU7QCADB52gMAAMZkn9ZeJK0AAEyepBUAYEy2vOpF0goAwOQpWgEAmDztAQAAY7IQqxdJKwAAk6doBQBg8rQHAACMacZtXPuQtAIAMHmSVgCAMVmI1YukFQCAyVO0AgAwedoDAADG5DauvUhaAQCYPEkrAMCYLMTqRdIKAMDkKVoBAJg87QEAAGOyEKsXSSsAAJMnaQUAGJOktRdJKwAAk6doBQBg8rQHAACMqLWbF3oKi5KkFQCAyZO0AgCMyUKsXiStAABMnqIVAIAVVNVOVfW5qvpmVZ1fVX/ZjW9ZVadU1UXd1y268aqqt1XVxVV1TlU9eM659u+Ov6iq9p8z/pCqOrf7zNuqqtY0J0UrAMCY2szCP9ZuWZK/aq3tmmT3JC+sql2T/HWSU1truyQ5tXudJPsk2aV7HJTkXclskZvkkCSPSPLwJIcsL3S7Y/50zuf2XtOEFK0AAKygtXZFa+2s7vlPk1yQZIckT01yZHfYkUme1j1/apKj2qyvJNm8qrZL8sQkp7TWrm2tXZfklCR7d+9t2lr7SmutJTlqzrlWyUIsAIAxTWAhVlUdlNlEdLnDWmuHrebYnZP8ZpKvJtm2tXZF99aVSbbtnu+Q5PtzPnZpN7am8UtXMb5ailYAgPVMV6Cuskidq6runORjSV7SWvvJ3LbT1lqrqjbcLFekPQAAgF9RVbfPbMH6odbacd3wVd2v9tN9vbobvyzJTnM+vmM3tqbxHVcxvlqKVgCAMS30Iqx5LMTqVvK/N8kFrbV/mfPWCUmW7wCwf5J/nzP+/G4Xgd2T/LhrIzgpyV5VtUW3AGuvJCd17/2kqnbvrvX8OedaJe0BAACs7NFJnpfk3Ko6uxv72yT/lOSjVXVgkkuSPKt778QkT0pycZIbkhyQJK21a6vq9UlO7457XWvt2u75XyQ5IsnGST7dPVZL0QoAwApaa6clWd2+qY9fxfEtyQtXc673JXnfKsbPSPKA+c5J0QoAMKYJ7B6wGOlpBQBg8iStAABjmt8dqViJpBUAgMlTtAIAMHnaAwAAxmQhVi+SVgAAJk/SCgAwJklrL5JWAAAmT9EKAMDkaQ8AABiTfVp7kbQCADB5klYAgDFZiNWLpBUAgMlTtAIAMHnaAwAAxmQhVi+SVgAAJk/SCgAwJguxepG0AgAweYpWAAAmT3sAAMCYLMTqRdIKAMDkKVoBAJg87QEAAGOye0AvklYAACZP0goAMCZJay+SVgAAJk/RCgDA5GkPAAAYU2sLPYNFSdIKAMDkSVoBAMZkIVYvklYAACZP0QoAwORpDwAAGJP2gF4krQAATJ6kFQBgTE3S2oekFQCAyVO0AgAwedoDAADGZCFWL5JWAAAmT9IKADCm1hZ6BouSpBUAgMlTtAIAMHnaAwAAxmQhVi+SVgAAJk/SCgAwJklrL5JWAAAmT9EKAMDkaQ8AABhT0x7Qh6QVAIDJU7QCADB52gMAAEbUZtzGtQ9JKwAAkydpBQAYk31ae5G0AgAweYpWAAAmT3sAAMCY7NPai6QVAIDJk7QCAIzJlle9SFoBAJg8RSsAAJOnPQAAYEz2ae1F0goAwOQNkrRW1YPX9H5r7awhrgsAMHmS1l6Gag84dA3vtSSPG+i6AAAsQYMUra21PYc4LwAA66fBF2JV1QOS7Jpko+VjrbWjhr4uAMAkNfu09jFo0VpVhyTZI7NF64lJ9klyWhJFKwAA8zZ00rpfkgcl+Xpr7YCq2jbJBwe+JgDAdFmI1cvQW17d2FqbSbKsqjZNcnWSnQa+JgAAS8zQSesZVbV5ksOTnJnkZ0n+a+BrAgCwxAxatLbW/qJ7+u6q+kySTVtr5wx5TQCASZuxEKuPMXYPeGCSnZdfq6ru01o7bujrAgCwdAy9e8D7kjwwyflJlncdtySKVgBg/dQsxOpj6KR199bargNfg0Xmiqt+kL99/T/nmuuuS6Wy31P3yfOe9bT88zveky98+avZ8PYbZqcdtssb/vZl2fQud84nT/ps3v/hj93y+W99+7s59n1vz6/d9963jL3olX+fSy+/Mh//4LuTJBde9J28/s1vzw03/m+2326bvPGQV+bOm2wy+vcKjG/HHbfPEe97a7bZduu01vKe93wob3/Hexd6WsCtNHTR+l9VtWtr7ZsDX4dFZMMNNsgrDv7T7Hq/++T662/Isw58cR71sN/MIx/2m3nJnx2QDTfcIP/yr+/Nez5wTF72Fwdm3yc+Lvs+cfbOv9/69nfz4r9+3QoF6ymf/3LudKeNV7jGIf/0lrz8RX+Sh/3mA3PcJ0/K+z/0sRx80PNH/T6BhbFs2bK84pWvzdfPPi93vvMm+dpXP5P/OPWLueCCixZ6asCtMPSWV0dltnD976o6p6rOrSoLsdZzd916y+x6v/skSTbZ5E651z12ylU/uCaPfsRDsuGGGyRJHnj/X8tVV//wVz574ilfyD5P+O1bXt9ww4056pjj8oL9n73CcZd8/7I8dLffSJI88mEPzilfOG2obweYmCuvvDpfP/u8JMnPfnZ9Lrzwouyw/d0WeFYwx0xb+MciNHTR+t4kz0uyd5KnJNm3+wpJksuuuCoXXPTtPPD+91th/PhPnZzfeuTDfuX4z5z6hTzpd/a45fXbDz8q+z/7Gdloo41WOO7e97xHPvul2d3VTv7cl3LlVb9aAANL3z3usWN2e9AD8tWvfX2hpwLcSkMXrT9orZ3QWvtua+2S5Y+Br8kiccMNN+alr35DXvXiF6zQb/pvRx6dDTbYIPvutecKx59z/oXZeKONssu9dk6SXPitb+f7l12RJ/z2o3/l3K//25fmI8d9Ms/644Nz/Q035va3H3yjDGBiNtnkTvnoMYfnZS8/JD/96c8WejrArTT0v+Rfr6oPJ/lEkp8vH1zdlldVdVCSg5LkXw99Q/7k+c8ZeHoslJuWLctLXv2GPHmvPfM7e/yy6Pz4p07JF7/8tbznbf+YqlrhM5/+jxVbA84+/4Kcf+FF2euZ++fmm2/ONdf9OH/0olfmiHe8Kfe6x045/C3/N0nyvf+5NF/8z6+N840Bk7Dhhhvm2GMOz9FHH5+Pf/zTCz0dWEFzG9dehi5aN85ssbrXnLHVbnnVWjssyWFJctMPv7M4Gy5Yq9ZaXvOPb8m97rFT9n/2M24ZP+0rZ+R9Hz42R7zjTdl4pV/3z8zM5KTPfilH/uubbxl79tP3zbOfvm+S2TaDF77ikBzxjjclSa657kfZaovNMzMzk3878iN51tOeNMJ3BkzF4YcdmgsuvDhveethCz0V4DYyWNFaVRskuaa19vKhrsHi9PVzzs8nPnNqdrn3znnm/i9MkvzlC/bPP77l3fnFTTflT1/y6iSzi7EOeeXBSZIzzj4vd9tm6+y0w3bzusaJp3w+Hznuk0mSJ/z2o/L0J++1lk8AS8WjH/WwPO8P98s5534zZ5x+cpLk//yff8qnP/PZBZ4ZdBbpQqiFVq0N94Orqv9qrT2yz2clrcB8bbz9YxZ6CsAisewXl9XajxrW9f/w/AWvcTZ59VEL/nNYV0O3B5xdVSckOTbJ9csH3cYVAIB1MXTRulGSa5I8bs6Y27gCAOsvt3HtZdCitbV2wJDnBwBg/TDoPq1VtWNVHV9VV3ePj1XVjkNeEwBg0hb6bliLdCHY0DcXeH+SE5Js3z0+0Y0BAMC8DV203rW19v7W2rLucUSSuw58TQAAlpihF2JdU1V/mOTo7vVzMrswCwBg/eSOWL0MnbT+cZJnJbkyyRVJ9kticRYAAOtk6N0DLknyu0NeAwBgUVmkC6EW2iBFa1W9Zg1vt9ba64e4LgAAS9NQSev1qxjbJMmBSbZKomgFAGDeBilaW2uHLn9eVXdJ8peZ7WX9SJJDV/c5AIAlzx2xehmsp7WqtkzysiTPTXJkkge31q4b6noAACxdQ/W0vjnJM5IcluQ3Wms/G+I6AACLjoVYvQy15dVfZfYOWH+X5PKq+kn3+GlV/WSgawIAsEQN1dM69P6vAACsR4a+IxYAAHM0d8TqRSIKAMDkSVoBAMZkIVYvklYAACZP0QoAwORpDwAAGJP2gF4krQAATJ6iFQCAydMeAAAwpmaf1j4krQAATJ6kFQBgTBZi9SJpBQBg8hStAABMnvYAAIARNe0BvUhaAQCYPEkrAMCYJK29SFoBAJg8RSsAAJOnPQAAYEwz7ojVh6QVAIDJk7QCAIzJQqxeJK0AAEyeohUAgMnTHgAAMCbtAb1IWgEAmDxJKwDAiFqTtPYhaQUAYPIUrQAATJ72AACAMVmI1YukFQCAyVO0AgAwedoDAADGpD2gF0krAACTJ2kFABhRk7T2ImkFAGDyFK0AAKygqt5XVVdX1Xlzxv6+qi6rqrO7x5PmvPc3VXVxVf13VT1xzvje3djFVfXXc8bvWVVf7caPqao7rG1OilYAgDHNtIV/rN0RSfZexfj/a63t1j1OTJKq2jXJs5Pcv/vMv1bVBlW1QZJ3Jtknya5JntMdmyRv7M51nyTXJTlwbRNStAIAsILW2heTXDvPw5+a5COttZ+31r6b5OIkD+8eF7fWvtNa+0WSjyR5alVVkscl+f+6zx+Z5Glru4iiFQBgTDMTePT3oqo6p2sf2KIb2yHJ9+ccc2k3trrxrZL8qLW2bKXxNVK0AgCsZ6rqoKo6Y87joHl87F1J7p1ktyRXJDl0yDmuzJZXAADrmdbaYUkOW8fPXLX8eVUdnuST3cvLkuw059Adu7GsZvyaJJtX1YZd2jr3+NWStAIAjKjNtAV/9FFV2815+fQky3cWOCHJs6vqjlV1zyS7JPlaktOT7NLtFHCHzC7WOqG11pJ8Lsl+3ef3T/Lva7u+pBUAgBVU1dFJ9kiydVVdmuSQJHtU1W5JWpLvJXlBkrTWzq+qjyb5ZpJlSV7YWru5O8+LkpyUZIMk72utnd9d4lVJPlJVb0jy9STvXeucZovd6bnph9+Z5sSAydl4+8cs9BSARWLZLy6rhZ7Dj56z54LXOJsf/bkF/zmsK+0BAABMnqIVAIDJ09MKADCmW7dP6npL0goAwORJWgEARtR3y6n1naQVAIDJU7QCADB52gMAAMZkIVYvklYAACZP0goAMCILsfqRtAIAMHmKVgAAJk97AADAmCzE6kXSCgDA5ClaAQCYPO0BAAAjatoDepG0AgAweZJWAIAxSVp7kbQCADB5ilYAACZPewAAwIgsxOpH0goAwORJWgEAxiRp7UXSCgDA5ClaAQCYPO0BAAAjshCrH0krAACTJ2kFABiRpLUfSSsAAJOnaAUAYPK0BwAAjEh7QD+SVgAAJk/SCgAwplYLPYNFSdIKAMDkKVoBAJg87QEAACOyEKsfSSsAAJMnaQUAGFGbsRCrD0krAACTp2gFAGDytAcAAIzIQqx+JK0AAEyeohUAgMnTHgAAMKLmNq69SFoBAJg8SSsAwIgsxOpH0goAwOQpWgEAmDztAQAAI3Ib134krQAATJ6kFQBgRK0t9AwWJ0krAACTp2gFAGDytAcAAIzIQqx+JK0AAEyepBUAYESS1n4krQAATJ6iFQCAydMeAAAwIvu09iNpBQBg8iStAAAjshCrH0krAACTp2gFAGDytAcAAIyoNe0BfUhaAQCYPEUrAACTpz0AAGBEbWahZ7A4SVoBAJg8SSsAwIhmLMTqRdIKAMDkrbVorap7V9Udu+d7VNWLq2rzwWcGAACd+SStH0tyc1XdJ8lhSXZK8uFBZwUAsES1Vgv+WIzmU7TOtNaWJXl6kre31l6RZLthpwUAAL80n4VYN1XVc5Lsn+Qp3djth5sSAMDS1WYWZ9K50OaTtB6Q5JFJ/qG19t2qumeSDww7LQAA+KW1Jq2ttW8meXGSVNUWSe7SWnvj0BMDAIDl1lq0VtXnk/xud+yZSa6uqi+31l428NwAAJac1hZ6BovTfNoDNmut/STJM5Ic1Vp7RJInDDstAAD4pfksxNqwqrZL8qwkrx54PgAAS5qFWP3MJ2l9XZKTklzcWju9qu6V5KJhpwUAAL80n4VYxyY5ds7r7yR55pCTAgCAueazEGujJAcmuX+SjZaPt9b+eMB5AQAsSTOL9I5UC20+7QEfSHK3JE9M8oUkOyb56ZCTAgCAueazEOs+rbXfq6qnttaOrKoPJ/nS0BMDAFiKmqS1l/kkrTd1X39UVQ9IslmSbYabEgAArGg+Seth3Z2w/k+SE5LcOclrBp0VAADMMZ/dA97TPf1CknsNOx0AgKXNHbH6WW3RWlVrvE1ra+1fbvvpAADAr1pT0nqX0WYBALCesOVVP6stWltrrx1zIgAAsDqr3T2gqt5cVS9YxfgLquqfhp0WAAD80pq2vHpcksNWMX54kn2HmQ4AwNLWWi34YzFaU9F6x9Z+dX1ba20myeL8bgEAWJTWVLTeWFW7rDzYjd043JQAAGBFa9o94DVJPl1Vb0hyZjf20CR/k+QlA88LAGBJsk9rP2vaPeDTVfW0JK9IcnA3fF6SZ7bWzh1hbgAAkGQtd8RqrZ2XZP+R5gIAsOTZp7WfNfW0AgDAJChaAQCYvDW2Byykjbd/zEJPAQDgNrdY90ldaKstWqvq7UlWu76ttfbiQWYEAAArWVPSesZoswAAWE9YiNXPmra8OnLMiQAAwOqstae1qu6a5FVJdk2y0fLx1trjBpwXAADcYj67B3woyQVJ7pnktUm+l+T0AecEALBktQk8FqP5FK1btdbem+Sm1toXWmt/nETKCgDAaOaz5dVN3dcrqurJSS5PsuVwUwIAWLosxOpnPkXrG6pqsyR/leTtSTZN8tJBZwUAAHOstWhtrX2ye/rjJHsOOx0AAPhV89k94P1ZRc9u19sKAMA6cEesfubTHvDJOc83SvL0zPa1AgDAKObTHvCxua+r6ugkpw02IwCAJWxmoSewSM1ny6uV7ZJkm9t6IgAAsDrz6Wn9aVbsab0ys3fIAgCAUcynPeAuY0wEAGB90GIhVh9rbQ+oqlPnMwYAAENZbdJaVRsluVOSratqi+SW/1uwaZIdRpgbAMCSM/MrG4kyH2tqD3hBkpck2T7Jmfll0fqTJO8YdloAAPBLqy1aW2tvTfLWqjq4tfb2EecEAAArmM+WVzNVtfnyF1W1RVX9xXBTAgBYumZSC/5YjOZTtP5pa+1Hy1+01q5L8qeDzQgAAFYyn6J1g6q6pSSvqg2S3GG4KQEAwIrWuk9rks8kOaaq/q17/YJuDACAdWSf1n7mU7S+KslBSf68e31KksMHmxEAAKxkPnfEmkny7u6RqnpMkrcneeGwUwMAWHpmFnoCi9R8ktZU1W8meU6SZyX5bpLjhpwUAADMtaY7Yt03s4Xqc5L8MMkxSaq1tudIcwMAgCRrTlovTPKlJPu21i5Okqp66SizAgBYoizE6mdNW149I8kVST5XVYdX1eMTP2UAAMa3ptu4fjzJx6tqkyRPTfKSJNtU1buSHN9aO3mUGQIALCEWYvWz1psLtNaub619uLX2lCQ7Jvl6ZrfBAgCAUcznjli3aK1d11o7rLX2+KEmBAAAK5vXllcAANw2tAf0s05JKwAALARJKwDAiGx51Y+kFQCAFVTV+6rq6qo6b87YllV1SlVd1H3dohuvqnpbVV1cVedU1YPnfGb/7viLqmr/OeMPqapzu8+8rarWWskrWgEAWNkRSfZeaeyvk5zaWtslyand6yTZJ8ku3eOgJO9KZovcJIckeUSShyc5ZHmh2x3zp3M+t/K1foWiFQBgRDO18I+1aa19Mcm1Kw0/NcmR3fMjkzxtzvhRbdZXkmxeVdsleWKSU1pr17bWrktySpK9u/c2ba19pbXWkhw151yrpWgFAFjPVNVBVXXGnMdB8/jYtq21K7rnVybZtnu+Q5Lvzznu0m5sTeOXrmJ8jSzEAgAY0cwEFmK11g5Lctit+HyrqnYbTmmtJK0AAMzHVd2v9tN9vbobvyzJTnOO27EbW9P4jqsYXyNFKwAA83FCkuU7AOyf5N/njD+/20Vg9yQ/7toITkqyV1Vt0S3A2ivJSd17P6mq3btdA54/51yrpT0AAGBEo/5OvaeqOjrJHkm2rqpLM7sLwD8l+WhVHZjkkiTP6g4/McmTklyc5IYkByRJa+3aqnp9ktO7417XWlu+uOsvMrtDwcZJPt091jyn2UVb07PhHXaY5sQAgEVr2S8uW/CG0o/f7Q8WvMZ52pUfXvCfw7rSHgAAwORpDwAAGNHMQk9gkZK0AgAweZJWAIARzdSiayedBEkrAACTp2gFAGDytAcAAIxowfe7WqQkrQAATJ6kFQBgRLa86kfSCgDA5ClaAQCYPO0BAAAjmrFNay+SVgAAJk/SCgAwopmIWvuQtAIAMHmKVgAAJk97AADAiNwRqx9JKwAAkydpBQAYkS2v+pG0AgAweYpWAAAmT3sAAMCIZhZ6AouUpBUAgMmTtAIAjMiWV/1IWgEAmDxFKwAAk6c9AABgRPZp7UfSCgDA5ClaAQCYPO0BAAAjsk9rP5JWAAAmT9IKADAiSWs/klYAACZP0QoAwORpDwAAGFGzT2svklYAACZP0goAMCILsfqRtAIAMHmKVgAAJk97AADAiLQH9CNpBQBg8iStAAAjags9gUVK0goAwOQpWgEAmDztAQAAI5pxR6xeJK0AAEyepBUAYES2vOpH0goAwOQpWgEAmDztAQAAI9Ie0I+kFQCAyZO0AgCMyB2x+pG0AgAweYpWAAAmT3sAAMCI3BGrH0krAACTp2gFAGDytAcAAIzIPq39SFoBAJg8SSsAwIjs09qPpBUAgMlTtAIAMHnaAwAARjSjQaAXSSsAAJMnaQUAGJEtr/qRtAIAMHmKVgAAJk97AADAiCzD6kfSCgDA5ElaAQBGZCFWP5JWAAAmT9EKAMDkaQ8AABjRTC30DBYnSSsAAJMnaQUAGNGMTa96kbQCADB5ilYAACZPewAAwIg0B/QjaQUAYPIkrQAAI3JHrH4krQAATJ6iFQCAydMeAAAwIvu09iNpBQBg8hStAABMnvYAAIARaQ7oR9IKAMDkSVoBAEZkn9Z+JK0AAEyeohUAgMnTHgAAMCL7tPYjaQUAYPIkrQAAI5Kz9jNo0lpVvzefMQAAWJOh2wP+Zp5jAACwWoO0B1TVPkmelGSHqnrbnLc2TbJsiGsCACwG9mntZ6ie1suTnJHkd5OcOWf8p0leOtA1AQBYogYpWltr30jyjar6cGvtpiGuAQCwGDVLsXoZeveAh1fV3ye5R3etStJaa/ca+LoAACwhQxet781sO8CZSW4e+FoAACxRQxetP26tfXrgawAALBoWYvUzdNH6uap6c5Ljkvx8+WBr7ayBrwsAwBIydNH6iO7rQ+eMtSSPG/i6AACTNGMhVi+DFq2ttT2HPD8AAOuHoW/jum1VvbeqPt293rWqDhzymgAALD1D38b1iCQnJdm+e/2tJC8Z+JoAAJPVJvBYjIYuWrdurX003UK51tqy2PoKAIB1NHTRen1VbZWuqK+q3ZP8eOBrAgCwxAy9e8DLkpyQ5N5V9eUkd02y38DXBACYLLsH9DP07gFnVdVvJ7lfZm/h+t+ttZuGvCYAAEvPIEVrVT1jNW/dt6rSWjtuiOsCAEydO2L1M1TS+pQ1vNcye4csAACYl0GK1tbaAUOcFwCA9dPQNxd4zaoeQ16TpePwww7N5Zd+I2d//dRbxrbYYvN85sSjc8H5p+UzJx6dzTffbAFnCEzBjjtun/84+dic843P5RtnfzYHv2jFe9i89CUvyLJfXJatttpigWYIK2oT+M9iNPiWV3MeNyfZJ8nOA1+TJeKooz6aJ+/73BXGXvXKF+aznzstv37/38pnP3daXvXKFy7Q7ICpWLZsWV7xytfmgQ/aM4/+rafkz//8j/Lrv75LktmC9nee8NhccsmlCzxL4NYatGhtrR065/EPSfZIcq8hr8nS8aXTvpprr/vRCmNPecoTc9QHjk2SHPWBY/O7v7v3AswMmJIrr7w6Xz/7vCTJz352fS688KLssP3dkiSH/vPf56//9h/S2uJMlliaZibwWIyGTlpXdqckO458TZaQbbfZOldeeXWS2X+ott1m6wWeETAl97jHjtntQQ/IV7/29TzlKXvlssuuyDnnfHOhpwXcBgbdp7Wqzs0vb3G7QWZvLvC6Ia/J+kV6Aiy3ySZ3ykePOTwve/khWbZsWf7mVQdn7yf9wUJPC7iNDH1HrH3nPF+W5KrW2rLVHVxVByU5KElqg81yu9ttMvD0WGyuuvqHudvdtsmVV16du91tm1z9g2sWekrABGy44YY59pjDc/TRx+fjH/90HvCAX8vOO989Z51xSpJkxx23y+lfPSmPfPSTc9VVP1jg2bK+W6wLoRbaIO0BVbVlVW2Z5KdzHjcm2bQbX6XW2mGttYe21h6qYGVVPvmJk/P85/1ekuT5z/u9fOITJy3wjIApOPywQ3PBhRfnLW89LEly3nkXZvsdH5T73Hf33Oe+u+fSS6/Iwx7xRAUrLGJD9bSemeSM7usPknwryUXd8zMHuiZLzAc/8M6c9sUTcr/73jvf+84ZOeCPnp03vvmdecLjH5sLzj8tj3/cY/LGN71zoacJLLBHP+phed4f7pc993xUzjj95Jxx+snZZ+/HLfS0YLUWehHWYl2IVUP2BFbV4UmOb62d2L3eJ8nTWmsvWNtnN7zDDrJzAOA2tewXl9VCz2H/nZ+54DXOkd/72IL/HNbV0LsH7L68YE2S1tqnkzxq4GsCALDEDL0Q6/Kq+rskH+xePzfJ5QNfEwBgsmbsfNPL0EnrczK7zdXx3WObbgwAAOZt0KS1tXZtkr8c8hoAAIuJnLWfoW8u8Lms4r+b1pplnQAAzNvQPa0vn/N8oyTPzOxNBgAAYN6Gbg9YeU/WL1fV14a8JgDAlM1oEOhl6PaAuXe/ul2ShyTZbMhrAgCw9AzdHnBmZntaK7NtAd9NcuDA1wQAmKwmae1l6PaAew55fgAA1g9DJ62pqgck2TWzC7GSJK21o4a+LgAAS8fQPa2HJNkjs0XriUn2SXJaEkUrALBemlnoCSxSQ98Ra78kj09yZWvtgCQPioVYAACTV1Xfq6pzq+rsqjqjG9uyqk6pqou6r1t041VVb6uqi6vqnKp68Jzz7N8df1FV7d93PkMXrTe21maSLKuqTZNcnWSnga8JAMBtY8/W2m6ttYd2r/86yamttV2SnNq9TmZ/m75L9zgoybuSW3aSOiTJI5I8PMkhywvddTV00XpGVW2e5PDM7iRwVpL/GviaAACTNZO24I9b4alJjuyeH5nkaXPGj2qzvpJk86raLskTk5zSWru2tXZdklOS7N3nwoP1tFZVJfnH1tqPkry7qj6TZNPW2jlDXRMAgLWrqoMym4gud1hr7bCVDmtJTq6qluTfuve3ba1d0b1/ZZJtu+c7JPn+nM9e2o2tbnydDVa0ttZaVZ2Y5De6198b6loAAIvFFPZp7QrQlYvUlf1Wa+2yqtomySlVdeFK52hdQTuKodsDzqqqhw18DQAAbmOttcu6r1cnOT6zPalXdb/2T/f16u7wy7LiuqUdu7HVja+zoYvWRyT5SlV9u1tJdm5VaQ8AAJiwqtqkqu6y/HmSvZKcl+SEJMt3ANg/yb93z09I8vxuF4Hdk/y4ayM4KcleVbVFtwBrr25snQ3SHlBVd2+t/U9mm28BAOgskn1at01y/OwSpWyY5MOttc9U1elJPlpVBya5JMmzuuNPTPKkJBcnuSHJAUnSWru2ql6f5PTuuNe11q7tM6Fq7bZvRaiqs1prD+6ef6y19sx1PceGd9hh4Rs+AIAlZdkvLquFnsMz7vG7C17jHHfJCQv+c1hXQy3EmvuDuNdA1wAAWHSGCAzXB0P1tLbVPAcAgHU2VNL6oKr6SWYT14275+let9bapgNdFwCAJWiQorW1tsEQ5wUAWOxu5R2p1ltDb3kFAAC32mB3xAIA4Fctki2vJkfSCgDA5ClaAQCYPO0BAAAjahZi9SJpBQBg8iStAAAjsuVVP5JWAAAmT9EKAMDkaQ8AABhRa9oD+pC0AgAweZJWAIARuSNWP5JWAAAmT9EKAMDkaQ8AABiRO2L1I2kFAGDyFK0AAEye9gAAgBG5jWs/klYAACZP0goAMCJ3xOpH0goAwOQpWgEAmDztAQAAI7IQqx9JKwAAkydpBQAYkTti9SNpBQBg8hStAABMnvYAAIARzdintRdJKwAAkydpBQAYkZy1H0krAACTp2gFAGDytAcAAIzIHbH6kbQCADB5klYAgBFJWvuRtAIAMHmKVgAAJk97AADAiJo7YvUiaQUAYPIUrQAATJ72AACAEdk9oB9JKwAAkydpBQAYUZO09iJpBQBg8hStAABMnvYAAIAR2ae1H0krAACTJ2kFABiRLa/6kbQCADB5ilYAACZPewAAwIgsxOpH0goAwORJWgEARmQhVj+SVgAAJk/RCgDA5GkPAAAYUdMe0IukFQCAyZO0AgCMaMaWV71IWgEAmDxFKwAAk6c9AABgRBZi9SNpBQBg8iStAAAjshCrH0krAACTp2gFAGDytAcAAIzIQqx+JK0AAEyeohUAgMnTHgAAMCK7B/QjaQUAYPIkrQAAI7IQqx9JKwAAk6doBQBg8rQHAACMyEKsfiStAABMnqQVAGBEFmL1I2kFAGDyFK0AAEye9gAAgBG1NrPQU1iUJK0AAEyepBUAYEQzFmL1ImkFAGDyFK0AAEye9gAAgBE1d8TqRdIKAMDkSVoBAEZkIVY/klYAACZP0QoAwORpDwAAGJGFWP1IWgEAmDxJKwDAiGYkrb1IWgEAmDxFKwAAk6c9AABgRM0+rb1IWgEAmDxFKwAAk6c9AABgRPZp7UfSCgDA5ElaAQBGNGMhVi+SVgAAJk/RCgDA5GkPAAAYkYVY/UhaAQCYPEkrAMCIZiStvUhaAQCYPEUrAACTpz0AAGBEFmL1I2kFAGDyJK0AACNyR6x+JK0AAEyeohUAgMnTHgAAMCILsfqRtAIAMHmSVgCAEbkjVj+SVgAAJk/RCgDA5GkPAAAYUbNPay+SVgAAJk/RCgDA5GkPAAAYkd0D+pG0AgAweZJWAIARuSNWP5JWAAAmT9EKAMDkaQ8AABiRfVr7kbQCADB5klYAgBFZiNWPpBUAgMlTtAIAMHnaAwAARqQ9oB9JKwAAkydpBQAYkZy1H0krAACTp2gFAGDySjMwi0lVHdRaO2yh5wFMn78vYGmRtLLYHLTQEwAWDX9fwBKiaAUAYPIUrQAATJ6ilcVGfxowX/6+gCXEQiwAACZP0goAwOQpWgEAmDxFK6OpqlZVh855/fKq+vuR5/D5qnromNcEVlRVN1fV2XMeOw9wje9V1dZrOeaIqtqv5/l3q6on9Zsd0IeilTH9PMkz1vYPyepU1Ya38XyAhXFja223OY/vLX+jZi2Gf5t2S6JohREthr8YWDqWZXY170tXfqOqdq6qz1bVOVV1alXdvRs/oqreXVVfTfKm7vW7quorVfWdqtqjqt5XVRdU1RFzzveuqjqjqs6vqteO9Q0C66778//fVXVUkvOS7LS6P8NzE9SqemhVfb57vlVVndwd/54kNefc5835/Cp/w1NVD6mqL1TVmVV1UlVt141/vqreWFVfq6pvVdVjquoOSV6X5Pe7pPj3B/vhALdQtDK2dyZ5blVtttL425Mc2Vp7YJIPJXnbnPd2TPKo1trLutdbJHlkZovfE5L8vyT3T/IbVbVbd8yrW2sPTfLAJL9dVQ8c4psBetl4TmvA8d3YLkn+tbV2/9baJVn3P8OHJDmttXb/JMcnuft8J1NVt8/s30H7tdYekuR9Sf5hziEbttYenuQlSQ5prf0iyWuSHNMlxcfM91pAf37dyqhaaz/p0pQXJ7lxzluPTPKM7vkHkrxpznvHttZunvP6E621VlXnJrmqtXZuklTV+Ul2TnJ2kmdV1UGZ/d/4dkl2TXLObf8dAT3c2FrbbfmLrqf1ktbaV+Ycs65/hh+b7u+Q1tqnquq6dZjP/ZI8IMkpVZUkGyS5Ys77x3Vfz8zs3zHAAlC0shDekuSsJO+f5/HXr/T6593XmTnPl7/esKrumeTlSR7WWruuaxvYqPdsgTHc8ud8LX+Gl+WXvyWcz5/rucev7jOV5PzW2iNXc47lf8/cHP9uwoLRHsDoWmvXJvlokgPnDP9nkmd3z5+b5Eu34hKbZvYfwB9X1bZJ9rkV5wLGt6Y/w99L8pDu+TPnjH8xyR8kSVXtk9k2oiS5Ksk2Xc/rHZPsu4rr/XeSu1bVI7vP376q7r+WOf40yV3m/R0Bt5qilYVyaJK5uwgcnOSAqjonyfOS/GXfE7fWvpHk60kuTPLhJF++FfMERraWP8OvTfLWqjojs8nn3PHHdm1Cz0jyP925bsrsoqmvJTmlO+fK1/tFkv2SvLGqvpHZFqNHrWWan0uyq4VYMB63cQUAYPIkrQAATJ6iFQCAyVO0AgAweYpWAAAmT9EKAMDkKVqBdVJVN3fb/JxXVcdW1Z1uxbmOqKr9uufvqapd13DsHlW1tm2IVvW5W+5Vv9L4navq36rq29395j9fVY/o3vvZul4HgGEpWoF1dWN3v/UHJPlFkj+b+2ZV9bpjUGvtT1pr31zDIXtk7Xtnrov3JLk2yS7d/eYPyIp7BwMwIYpW4Nb4UpL7dCnol6rqhCTfrKoNqurNVXV6VZ1TVS9Ikpr1jqr676r6jyTbLD9Rl3Q+tHu+d1WdVVXfqKpTu3vT/1mSl3Yp72Oq6q5V9bHuGqdX1aO7z25VVSdX1flV9Z7M3qJzBVV17ySPSPJ3rbWZJGmtfbe19qmVjrtzd/2zqurcqnpqN75JVX2qm995yzeXr6p/qqpvdt/zP9/GP2uA9Zp7KAO9dInqPkk+0w09OMkDWmvfraqDkvy4tfaw7taZX66qk5P8ZpL7Jdk1ybZJvpnkfSud965JDk/y2O5cW7bWrq2qdyf5WWvtn7vjPpzk/7XWTququyc5KcmvJzkkyWmttddV1ZOz4u2Cl7t/krNbazev4r25/jfJ01trP+laDL7SFeZ7J7m8tfbkbi6bVdVWSZ6e5Ndaa62qNp/XDxKAeVG0Autq46o6u3v+pSTvzeyv7b/WWvtuN75Xkgcu71dNslmSXZI8NsnRXbF4eVV9dhXn3z3JF5efq7V27Wrm8YTM3kZz+etNq+rO3TWe0X32U1V1Xb9vM8lsSvt/q+qxSWaS7JDZYvvcJIdW1RuTfLK19qWuiP/fJO+tqk8m+eStuC4AK1G0AuvqxtbabnMHusLx+rlDSQ5urZ200nFPug3ncbsku7fW/ncVc1mb85M8qKo2WEva+twkd03ykNbaTVX1vSQbtda+VVUPTvKkJG+oqlO7ZPfhSR6f2fvYvyjJ49b5uwJglfS0AkM4KcmfV9Xtk6Sq7ltVmyT5YpLf73pet0uy5yo++5Ukj62qe3af3bIb/2mSu8w57uQkBy9/UVW7dU+/mOQPurF9kmyx8gVaa99OckaS11ZX5VbVzl07wVybJbm6K1j3THKP7tjtk9zQWvtgkjcneXCX8m7WWjsxyUuTPGgtPyMA1oGkFRjCe5LsnOSsrij8QZKnJTk+s+njN5P8T5L/WvmDrbUfdD2xx1XV7ZJcneR3knwiyf/XLYY6OMmLk7yzqs7J7N9lX8zsYq3XJjm6qs5P8p/ddVblT5IcmuTiqroxyQ+TvGKlYz6U5BNVdW5mi9wLu/HfSPLmqppJclOSP89sQf3vVbVRZpPml83rJwXAvFRrbaHnAAAAa6Q9AACAyVO0AgAweYpWAAAmT9EKAMDkKVoBAJg8RSsAAJOnaAUAYPL+f48qKf36vbc4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the confusion matrix.\n",
    "# Use a seaborn heatmap:\n",
    "labels = [\"Normal\", \"Fraudulent\"]\n",
    "conf_matrix = confusion_matrix(output_test, predictions_rfc)\n",
    "pyplot.figure(figsize = (12, 12))\n",
    "\n",
    "sns.heatmap(conf_matrix, xticklabels = labels, yticklabels = labels, annot = True, fmt = \"d\")\n",
    "pyplot.title(\"Confusion Matrix\")\n",
    "pyplot.xlabel(\"Predicted Class\")\n",
    "pyplot.ylabel(\"Actual Class\")\n",
    "pyplot.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Credit Card Fraud Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Case 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The transaction's class is:  [1]\n",
      "Warning!!! The credit card transaction is fraudulent.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Miketobbz\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Predict the 'class' of a transaction with the following attributes:\n",
    "new_input_test = [[428, -2.31445, 1.75223, -1.7199, 3.9928, -0.42319,\n",
    "                   -1.56255, -2.5281, 1.42771, -2.87112, -2.8127, 3.27033, \n",
    "                   -2.79812, -0.66622, -4.55259, 0.559247, -1.75015, -2.95306, \n",
    "                   -0.21628, 0.81569, 0.16921, 0.72352, -0.23506, -0.35261, \n",
    "                   0.42198, 0.14519, 0.27784, 0.271154, -0.14823, 478]]\n",
    "                   \n",
    "new_input_test = sc.transform(new_input_test)\n",
    "predictions_rfc = rfc.predict(new_input_test)\n",
    "print(\"The transaction's class is: \", predictions_rfc)\n",
    "\n",
    "if (predictions_rfc == 1):\n",
    "    print(\"Warning!!! The credit card transaction is fraudulent.\")\n",
    "else:\n",
    "    print(\"The credit card transaction is normal.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Case 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The transaction's class is:  [1]\n",
      "Warning!!! The credit card transaction is fraudulent.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Miketobbz\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Predict the 'class' of a transaction with the following attributes:\n",
    "new_input_test = [[406, -2.31223, 1.951992, -1.6098507, 3.9979056, -0.522188,\n",
    "                   -1.426545, -2.537387, 1.3916578, -2.770089, -2.7722721, 3.2020332, \n",
    "                   -2.8999073, -0.5952219, -4.289254, 0.3897241, -1.1407471, -2.8300557, \n",
    "                   -0.0168225, 0.4169557, 0.12691056, 0.517232, -0.03504936, -0.4652110, \n",
    "                   0.3201982, 0.0445192, 0.1778397, 0.2611450, -0.1432759, 0]]\n",
    "                   \n",
    "new_input_test = sc.transform(new_input_test)\n",
    "predictions_rfc = rfc.predict(new_input_test)\n",
    "print(\"The transaction's class is: \", predictions_rfc)\n",
    "\n",
    "if (predictions_rfc == 1):\n",
    "    print(\"Warning!!! The credit card transaction is fraudulent.\")\n",
    "else:\n",
    "    print(\"The credit card transaction is normal.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Case 3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The transaction's class is:  [1]\n",
      "Warning!!! The credit card transaction is fraudulent.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Miketobbz\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Predict the 'class' of a transaction with the following attributes:\n",
    "new_input_test = [[7627, 1.231223, 3.0197404, -4.304596884, 4.73279513, 3.62420083,\n",
    "                   -1.357745663, 1.713444987, -0.496358487, -1.282857820, -2.44746925511, 2.101343865, \n",
    "                   -4.6096284, 1.46437762, -6.07933719, -0.339237372, 2.5818509, -6.73938438, \n",
    "                   -3.04249318, 2.72185312, 0.00906084, -0.37906831, -0.70418103, -0.65680475, \n",
    "                   -1.63265295, 1.488901448, 0.5667973, -0.01001622, 0.14679273, 378.66]]\n",
    "                   \n",
    "new_input_test = sc.transform(new_input_test)\n",
    "predictions_rfc = rfc.predict(new_input_test)\n",
    "print(\"The transaction's class is: \", predictions_rfc)\n",
    "\n",
    "if (predictions_rfc == 1):\n",
    "    print(\"Warning!!! The credit card transaction is fraudulent.\")\n",
    "else:\n",
    "    print(\"The credit card transaction is normal.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Isolation Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the second model. It detects anomalies in the dataset.\n",
    "# It comprises of 200 trees.\n",
    "# Train it:\n",
    "outlier_fraction = len(fraud_t)/float(len(normal_t))\n",
    "\n",
    "ifc = IsolationForest(n_estimators = 200, max_samples = len(input_train), contamination = outlier_fraction, random_state = 1)\n",
    "ifc.fit(input_train)\n",
    "# Calculates the mean anomaly score:\n",
    "scores_pred = ifc.decision_function(input_train)\n",
    "predictions_ifc = ifc.predict(input_test)\n",
    "\n",
    "# Reshape the prediction values to 0 for 'normal' and 1 for 'fraud':\n",
    "predictions_ifc[predictions_ifc == 1] = 0\n",
    "predictions_ifc[predictions_ifc == -1] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "# Print the first 10 elements of the 'predictions' 1D array:\n",
    "print(predictions_ifc[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model's performance summary is:\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     22751\n",
      "           1       0.28      0.35      0.31        34\n",
      "\n",
      "    accuracy                           1.00     22785\n",
      "   macro avg       0.64      0.68      0.66     22785\n",
      "weighted avg       1.00      1.00      1.00     22785\n",
      "\n",
      "\n",
      "The total number of errors is:  53\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model's performance:\n",
    "n_errors = (predictions_ifc != output_test).sum()\n",
    "\n",
    "print(\"The model's performance summary is:\\n\")\n",
    "print(classification_report(output_test, predictions_ifc))\n",
    "print(\"\\nThe total number of errors is: \", n_errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score is:  0.998\n"
     ]
    }
   ],
   "source": [
    "# Calculate the model's accuracy score:\n",
    "acc_score = accuracy_score(output_test, predictions_ifc)\n",
    "print(\"The accuracy score is: \", round(acc_score, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Matthews correlation coefficient is:  0.313\n"
     ]
    }
   ],
   "source": [
    "# Calculate the Matthews correlation coefficient:\n",
    "mcc = matthews_corrcoef(output_test, predictions_ifc)\n",
    "print(\"The Matthews correlation coefficient is: \", round(mcc, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[22720    31]\n",
      " [   22    12]]\n"
     ]
    }
   ],
   "source": [
    "# Calculate the confusion matrix:\n",
    "print(confusion_matrix(output_test, predictions_ifc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the confusion matrix.\n",
    "# Use a seaborn heatmap:\n",
    "labels = [\"Normal\", \"Fraudulent\"]\n",
    "conf_matrix = confusion_matrix(output_test, predictions_ifc)\n",
    "pyplot.figure(figsize = (12, 12))\n",
    "\n",
    "sns.heatmap(conf_matrix, xticklabels = labels, yticklabels = labels, annot = True, fmt = \"d\")\n",
    "pyplot.title(\"Confusion Matrix\")\n",
    "pyplot.xlabel(\"Predicted Class\")\n",
    "pyplot.ylabel(\"Actual Class\")\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Credit Card Fraud Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Case 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The transaction's class is:  [1]\n",
      "Warning!!! The credit card transaction is fraudulent.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Miketobbz\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Predict the 'class' of a transaction with the following attributes:\n",
    "new_input_test = [[428, -2.31445, 1.75223, -1.7199, 3.9928, -0.42319,\n",
    "                   -1.56255, -2.5281, 1.42771, -2.87112, -2.8127, 3.27033, \n",
    "                   -2.79812, -0.66622, -4.55259, 0.559247, -1.75015, -2.95306, \n",
    "                   -0.21628, 0.81569, 0.16921, 0.72352, -0.23506, -0.35261, \n",
    "                   0.42198, 0.14519, 0.27784, 0.271154, -0.14823, 478]]\n",
    "                   \n",
    "new_input_test = sc.transform(new_input_test)\n",
    "predictions_rfc = rfc.predict(new_input_test)\n",
    "print(\"The transaction's class is: \", predictions_rfc)\n",
    "\n",
    "if (predictions_rfc == 1):\n",
    "    print(\"Warning!!! The credit card transaction is fraudulent.\")\n",
    "else:\n",
    "    print(\"The credit card transaction is normal.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Case 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The transaction's class is:  [1]\n",
      "Warning!!! The credit card transaction is fraudulent.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Miketobbz\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Predict the 'class' of a transaction with the following attributes:\n",
    "new_input_test = [[406, -2.31223, 1.951992, -1.6098507, 3.9979056, -0.522188,\n",
    "                   -1.426545, -2.537387, 1.3916578, -2.770089, -2.7722721, 3.2020332, \n",
    "                   -2.8999073, -0.5952219, -4.289254, 0.3897241, -1.1407471, -2.8300557, \n",
    "                   -0.0168225, 0.4169557, 0.12691056, 0.517232, -0.03504936, -0.4652110, \n",
    "                   0.3201982, 0.0445192, 0.1778397, 0.2611450, -0.1432759, 0]]\n",
    "                   \n",
    "new_input_test = sc.transform(new_input_test)\n",
    "predictions_rfc = rfc.predict(new_input_test)\n",
    "print(\"The transaction's class is: \", predictions_rfc)\n",
    "\n",
    "if (predictions_rfc == 1):\n",
    "    print(\"Warning!!! The credit card transaction is fraudulent.\")\n",
    "else:\n",
    "    print(\"The credit card transaction is normal.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Case 3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The transaction's class is:  [1]\n",
      "Warning!!! The credit card transaction is fraudulent.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Miketobbz\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Predict the 'class' of a transaction with the following attributes:\n",
    "new_input_test = [[7627, 1.231223, 3.0197404, -4.304596884, 4.73279513, 3.62420083,\n",
    "                   -1.357745663, 1.713444987, -0.496358487, -1.282857820, -2.44746925511, 2.101343865, \n",
    "                   -4.6096284, 1.46437762, -6.07933719, -0.339237372, 2.5818509, -6.73938438, \n",
    "                   -3.04249318, 2.72185312, 0.00906084, -0.37906831, -0.70418103, -0.65680475, \n",
    "                   -1.63265295, 1.488901448, 0.5667973, -0.01001622, 0.14679273, 378.66]]\n",
    "                   \n",
    "new_input_test = sc.transform(new_input_test)\n",
    "predictions_rfc = rfc.predict(new_input_test)\n",
    "print(\"The transaction's class is: \", predictions_rfc)\n",
    "\n",
    "if (predictions_rfc == 1):\n",
    "    print(\"Warning!!! The credit card transaction is fraudulent.\")\n",
    "else:\n",
    "    print(\"The credit card transaction is normal.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Multi-layer Perceptron Classifier(Neural Network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.31906388\n",
      "Iteration 2, loss = 0.05206952\n",
      "Iteration 3, loss = 0.02168681\n",
      "Iteration 4, loss = 0.01271103\n",
      "Iteration 5, loss = 0.00887408\n",
      "Iteration 6, loss = 0.00694980\n",
      "Iteration 7, loss = 0.00587023\n",
      "Iteration 8, loss = 0.00520531\n",
      "Iteration 9, loss = 0.00476715\n",
      "Iteration 10, loss = 0.00447167\n",
      "Iteration 11, loss = 0.00426864\n",
      "Iteration 12, loss = 0.00410940\n",
      "Iteration 13, loss = 0.00399314\n",
      "Iteration 14, loss = 0.00390049\n",
      "Iteration 15, loss = 0.00382171\n",
      "Iteration 16, loss = 0.00374382\n",
      "Iteration 17, loss = 0.00365966\n",
      "Iteration 18, loss = 0.00359691\n",
      "Iteration 19, loss = 0.00352013\n",
      "Iteration 20, loss = 0.00344898\n",
      "Iteration 21, loss = 0.00337534\n",
      "Iteration 22, loss = 0.00330260\n",
      "Iteration 23, loss = 0.00323158\n",
      "Iteration 24, loss = 0.00316901\n",
      "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n"
     ]
    }
   ],
   "source": [
    "# Create the third model-a neural network.\n",
    "# The neural network is trained using standardized data.\n",
    "\n",
    "mlpc = MLPClassifier(hidden_layer_sizes = (16,), activation = \"logistic\", solver = \"adam\", verbose = True)\n",
    "mlpc.fit(input_train, output_train)\n",
    "predictions_mlpc = mlpc.predict(input_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "# Show the first 10 elements of the 'predictions' 1D array:\n",
    "print(predictions_mlpc[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The performance summary is:\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     22751\n",
      "           1       0.89      0.74      0.81        34\n",
      "\n",
      "    accuracy                           1.00     22785\n",
      "   macro avg       0.95      0.87      0.90     22785\n",
      "weighted avg       1.00      1.00      1.00     22785\n",
      "\n",
      "\n",
      "The total number of errors is:  12\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the neural network's performance:\n",
    "n_errors = (predictions_mlpc != output_test).sum()\n",
    "\n",
    "print(\"The performance summary is:\\n\")\n",
    "print(classification_report(output_test, predictions_mlpc))\n",
    "print(\"\\nThe total number of errors is: \", n_errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score is:  0.999\n"
     ]
    }
   ],
   "source": [
    "# Calculate the neural network's accuracy score:\n",
    "acc_score = accuracy_score(output_test, predictions_mlpc)\n",
    "print(\"The accuracy score is: \", round(acc_score, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Matthews correlation coefficient is:  0.81\n"
     ]
    }
   ],
   "source": [
    "# Calculate the Matthews correlation coefficient:\n",
    "mcc = matthews_corrcoef(output_test, predictions_mlpc)\n",
    "print(\"The Matthews correlation coefficient is: \", round(mcc, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[22748     3]\n",
      " [    9    25]]\n"
     ]
    }
   ],
   "source": [
    "# Calculate the confusion matrix:\n",
    "print(confusion_matrix(output_test, predictions_mlpc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the confusion matrix.\n",
    "# Use a seaborn heatmap:\n",
    "labels = [\"Normal\", \"Fraudulent\"]\n",
    "conf_matrix = confusion_matrix(output_test, predictions_mlpc)\n",
    "pyplot.figure(figsize = (12, 12))\n",
    "\n",
    "sns.heatmap(conf_matrix, xticklabels = labels, yticklabels = labels, annot = True, fmt = \"d\")\n",
    "pyplot.title(\"Confusion Matrix\")\n",
    "pyplot.xlabel(\"Predicted Class\")\n",
    "pyplot.ylabel(\"Actual Class\")\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Credit Card Fraud Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Case 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The transaction's class is:  [1]\n",
      "Warning!!! The credit card transaction is fraudulent.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Miketobbz\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Predict the 'class' of a transaction with the following attributes:\n",
    "new_input_test = [[428, -2.31445, 1.75223, -1.7199, 3.9928, -0.42319,\n",
    "                   -1.56255, -2.5281, 1.42771, -2.87112, -2.8127, 3.27033, \n",
    "                   -2.79812, -0.66622, -4.55259, 0.559247, -1.75015, -2.95306, \n",
    "                   -0.21628, 0.81569, 0.16921, 0.72352, -0.23506, -0.35261, \n",
    "                   0.42198, 0.14519, 0.27784, 0.271154, -0.14823, 478]]\n",
    "                   \n",
    "new_input_test = sc.transform(new_input_test)\n",
    "predictions_rfc = rfc.predict(new_input_test)\n",
    "print(\"The transaction's class is: \", predictions_rfc)\n",
    "\n",
    "if (predictions_rfc == 1):\n",
    "    print(\"Warning!!! The credit card transaction is fraudulent.\")\n",
    "else:\n",
    "    print(\"The credit card transaction is normal.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Case 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The transaction's class is:  [1]\n",
      "Warning!!! The credit card transaction is fraudulent.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Miketobbz\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Predict the 'class' of a transaction with the following attributes:\n",
    "new_input_test = [[406, -2.31223, 1.951992, -1.6098507, 3.9979056, -0.522188,\n",
    "                   -1.426545, -2.537387, 1.3916578, -2.770089, -2.7722721, 3.2020332, \n",
    "                   -2.8999073, -0.5952219, -4.289254, 0.3897241, -1.1407471, -2.8300557, \n",
    "                   -0.0168225, 0.4169557, 0.12691056, 0.517232, -0.03504936, -0.4652110, \n",
    "                   0.3201982, 0.0445192, 0.1778397, 0.2611450, -0.1432759, 0]]\n",
    "                   \n",
    "new_input_test = sc.transform(new_input_test)\n",
    "predictions_rfc = rfc.predict(new_input_test)\n",
    "print(\"The transaction's class is: \", predictions_rfc)\n",
    "\n",
    "if (predictions_rfc == 1):\n",
    "    print(\"Warning!!! The credit card transaction is fraudulent.\")\n",
    "else:\n",
    "    print(\"The credit card transaction is normal.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Case 3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The transaction's class is:  [1]\n",
      "Warning!!! The credit card transaction is fraudulent.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Miketobbz\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Predict the 'class' of a transaction with the following attributes:\n",
    "new_input_test = [[7627, 1.231223, 3.0197404, -4.304596884, 4.73279513, 3.62420083,\n",
    "                   -1.357745663, 1.713444987, -0.496358487, -1.282857820, -2.44746925511, 2.101343865, \n",
    "                   -4.6096284, 1.46437762, -6.07933719, -0.339237372, 2.5818509, -6.73938438, \n",
    "                   -3.04249318, 2.72185312, 0.00906084, -0.37906831, -0.70418103, -0.65680475, \n",
    "                   -1.63265295, 1.488901448, 0.5667973, -0.01001622, 0.14679273, 378.66]]\n",
    "                   \n",
    "new_input_test = sc.transform(new_input_test)\n",
    "predictions_rfc = rfc.predict(new_input_test)\n",
    "print(\"The transaction's class is: \", predictions_rfc)\n",
    "\n",
    "if (predictions_rfc == 1):\n",
    "    print(\"Warning!!! The credit card transaction is fraudulent.\")\n",
    "else:\n",
    "    print(\"The credit card transaction is normal.\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.0 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "26de051ba29f2982a8de78e945f0abaf191376122a1563185a90213a26c5da77"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}