Anomaly Detection in Credit Card Fraud.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                                                 Anomaly Detection in Credit Card Fraud "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the necessary libraries for the project\n",
    "\n",
    "import pandas as pd    # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import numpy as np     # linear algebra\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings \n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First we have to read our data\n",
    "\n",
    "df = pd.read_csv('creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
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      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The first five lines from out dataset\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "# The original shape of the dataset\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
       "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
       "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
       "       'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The names of the columns of the dataset\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>3.919560e-15</td>\n",
       "      <td>5.688174e-16</td>\n",
       "      <td>-8.769071e-15</td>\n",
       "      <td>2.782312e-15</td>\n",
       "      <td>-1.552563e-15</td>\n",
       "      <td>2.010663e-15</td>\n",
       "      <td>-1.694249e-15</td>\n",
       "      <td>-1.927028e-16</td>\n",
       "      <td>-3.137024e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.537294e-16</td>\n",
       "      <td>7.959909e-16</td>\n",
       "      <td>5.367590e-16</td>\n",
       "      <td>4.458112e-15</td>\n",
       "      <td>1.453003e-15</td>\n",
       "      <td>1.699104e-15</td>\n",
       "      <td>-3.660161e-16</td>\n",
       "      <td>-1.206049e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  3.919560e-15  5.688174e-16 -8.769071e-15  2.782312e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean  -1.552563e-15  2.010663e-15 -1.694249e-15 -1.927028e-16 -3.137024e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.537294e-16  7.959909e-16  5.367590e-16  4.458112e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   1.453003e-15  1.699104e-15 -3.660161e-16 -1.206049e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The mathematicial description of our dataset\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "# More information about our data for example tha type of it's column\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      0\n",
       "V1        0\n",
       "V2        0\n",
       "V3        0\n",
       "V4        0\n",
       "V5        0\n",
       "V6        0\n",
       "V7        0\n",
       "V8        0\n",
       "V9        0\n",
       "V10       0\n",
       "V11       0\n",
       "V12       0\n",
       "V13       0\n",
       "V14       0\n",
       "V15       0\n",
       "V16       0\n",
       "V17       0\n",
       "V18       0\n",
       "V19       0\n",
       "V20       0\n",
       "V21       0\n",
       "V22       0\n",
       "V23       0\n",
       "V24       0\n",
       "V25       0\n",
       "V26       0\n",
       "V27       0\n",
       "V28       0\n",
       "Amount    0\n",
       "Class     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking if there are any missing data in our dataset, luckily there is none\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The outlier fraction is : 0.1727485630620034\n",
      "The valid transactions are : 284315\n",
      "The fraud transactions are : 492\n"
     ]
    }
   ],
   "source": [
    "# Determine number of fraud cases in the dataset\n",
    "# Valid transaction if Class = 0 and Fraud if Class = 1\n",
    "\n",
    "\n",
    "valid = len(df[df['Class'] == 0])\n",
    "fraud = len(df[df['Class'] == 1])\n",
    "\n",
    "Outlier_Fraction = (fraud/(valid+fraud)) * 100\n",
    "\n",
    "print('The outlier fraction is :' , Outlier_Fraction) \n",
    "print('The valid transactions are :'  , valid ) \n",
    "print('The fraud transactions are :'  , fraud )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amount details of valid transaction\n"
     ]
    },
    {
     "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Information about the valid transactions only\n",
    "\n",
    "print('Amount details of valid transaction')\n",
    "\n",
    "valid_info= df[(df['Class']==0)]\n",
    "valid_info.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amount details of fraud transaction\n"
     ]
    },
    {
     "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Information about the fraud transactions only\n",
    "\n",
    "print('Amount details of fraud transaction')\n",
    "\n",
    "fraud_info = df[df['Class'] ==1]\n",
    "fraud_info.Amount.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Notice how imbalanced is our original dataset. \n",
    "Most of the transactions are non-fraud. If we use this dataframe as the base for our predictive models and analysis we might get a lot of errors and our algorithms will probably overfit since it will \"assume\" that most transactions are not fraud. \n",
    "But we don't want our model to assume, we want our model to detect patterns that give signs of fraud!\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking for the fraud and valid trasactions in a graph for better understanding\n",
    "\n",
    "count_classes = pd.value_counts(df['Class'], sort = True ).sort_index()\n",
    "count_classes.plot(kind = 'bar' ,rot = 0 ,colormap ='viridis')\n",
    "\n",
    "plt.title ( \"Fraud Class Histogram\" )\n",
    "plt.xlabel( \"Class\" )\n",
    "plt.ylabel( \"Frequency\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkiRJO6DXF5I7+ZuPb81n/Rf+6qdaeax7O11z1WtPGkvu1lfNsObo0WdvOWU8uV3Pnrth1Viyd9t3qz9HsxvPNbvZ7C7WbHZzuWY3m93Fmuezn3Tvl4wl++PffQu/8cw3jTz38+uPG3lm28xVK8dVreJ0TUmSJEnqkd528iRJkiT1jwuvLM8jJEmSJEk94iBPkiRJknrE6ZqSJEmSOmOu7FMtxyMkSZIkST3iIE+SJEmSeqQV0zWTbAJeX1UXLNh2DLAKeD7wleHm71TVf2+8QEmSJEmtcGu/r/U+Em3p5K0HphZtmxpu/39VddDw5gBPkiRJkrajLYO8c4DDk+wJkOQAYH/gkkkWJUmSJEld04rpmlV1U5LLgHXAhxl08TZUVSX5uSSbgR8Db6iqc5fKSDINTAOcdtppzRQuSZIkqVGurrm8Nh2hhVM256dqAvxSVa0Ffhd4c5L7LvXiqpqtqrVVtXZ6enr81UqSJElSC7Wikzd0LnBikjXAXlW1BaCqrh9+/dZwgZaHANdOqkhJkiRJk+PCK8trTSevqm4BNgGnM+ziJfnFBefp3R14FPC1SdUoSZIkSW3Xpk4eDAZ3H+K2aZu/BpyWZI7BgPQNVeUgT5IkSZK2oVWDvKraCLf1X6vqc8CvT64iSZIkSW3iwivL8whJkiRJUo84yJMkSZKkHmnVdE1JkiRJ2p5bna65LI+QJEmSJPWIgzxJkiRJ6pFU1aRrGIdeflOSJEnSDuj11cJf/dWntOaz/qsf/OFWHuvenpP38OecOJbcL5xxLGuOPmnkuVtOmRlLrtnbzp67YdXIc3fbd2tnj4fZ4881u9nsLtZsdnO5Zjeb3cWa57Pv+9fj+Ux57R8fy8OOGn32F9997Mgz1T1O15QkSZKkHultJ0+SJElS/7i65vI8QpIkSZLUI3byJEmSJHXGXLVyrZNWsZMnSZIkST3iIE+SJEmSeqQVg7wkm5IctmjbMUlOTvJLST6R5OtJvpbkgAmVKUmSJGnCbmW31tzaqi2VrQemFm2bGm4/A3hjVf0acDBwY8O1SZIkSVJntGWQdw5weJI9AYbduv2BfwHuUFUXAlTVLVX17xOrUpIkSZJarhWra1bVTUkuA9YBH2bQxdsA3B+4OcmHgPsAnwReXlW3TqxYSZIkSRPj6prLa0snD356yub8VM07AI8G/hh4GPArwJFLvTjJdJLNSTbPzs6Ov1pJkiRJaqE2DfLOBQ5NsgbYq6q2ANcBX66qb1XVj4f7rFnqxVU1W1Vrq2rt9PR0UzVLkiRJUqu0YromDM63S7IJOJ1BFw/gi8AvJtmnqv4PcAiweUIlSpIkSZqwuVb1qdqpbUdoPbAaOAtgeO7dHwMXJfkKEOAdkytPkiRJktqtNZ08gKrayGAgt3DbhcCBk6lIkiRJUpvc6sIry2pbJ0+SJEmStAIO8iRJkiSpR1o1XVOSJEmStsfr5C3PTp4kSZIk9YiDPEmSJEnqEadrSpIkSeqMubJPtZxU1aRrGIdeflOSJEnSDuj1SWt/tOVZrfms/7Y172/lse5tJ+/BLztpLLlf/asZ1hw9+uwtp4wn1+xms7ecMsPcDatGnguw275bO3c8uprdxZrNbi7X7Gazu1iz2c3lNpF93w2vG0v2tUf8CY8/7ISR537qguNHnqnu6e0gT5IkSVL/3NrvRuVIOKFVkiRJknrETp4kSZKkzvA6ecuzkydJkiRJPeIgT5IkSZJ6xOmakiRJkjrD6+QtrxVHKMmmJIct2nZMkq8nuXzB7T+SPHVCZUqSJElS67VikAesB6YWbZsCpqvqoKo6CDgE+HfgEw3XJkmSJEmd0ZbpmucAr02yZ1X9MMkBwP7AJQv2eRrw8ar690kUKEmSJGny5rxO3rJa0cmrqpuAy4B1w01TwIaqqgW7TTHo+EmSJEmStqEVg7yhhVM2f2pAl2Q/4NeBC7b14iTTSTYn2Tw7OzvWQiVJkiSprdoyXRPgXODEJGuAvapqy4LnngFsrKr/3NaLq2oWmB/d1VtfdtLYCpUkSZI0Gbd6MfRltaaTV1W3AJuA0/nZaZnPXGKbJEmSJGmRNnXyYDCQ+xALVtocLsJyb+DTE6pJkiRJUkt4nbzltWqQV1Ub4aeXy6mqbwP3nEhBkiRJktQxDoMlSZIkqUda1cmTJEmSpO2Zc+GVZdnJkyRJkqQecZAnSZIkST3idE1JkiRJnTGH0zWXYydPkiRJknokVTXpGsahl9+UJEmStAN63ep61hf+Z2s+67//4e9o5bHu7XTNtc87cSy5m991LGuOPmnkuVtOmRlLrtnNZo+75rkbVo0le7d9t3buWI8zu4s1m91crtnNZnexZrOby20i+5HPeNNYsj/3geM46EWjr/vyt8+MPLNtXF1zeU7XlCRJkqQecZAnSZIkqTPmarfW3HZEknVJvpnkmiQv38Y+j0tyeZKrknx6pceot9M1JUmSJGmSkuwO/A3wBOA64ItJPlJVX1uwz12Ak4F1VfWdJP9lpe9rJ0+SJEmSxuNg4Jqq+lZV/Qg4C3jKon1+F/hQVX0HoKpuXOmb2smTJEmS1BltWnglyTQwvWDTbFXNLnh8T+C7Cx5fBzx8Ucwq4I5JNgE/D7ylqs5YSV0O8iRJkiRpJwwHdLPb2WWpEeniS0DcAXgocCiwF/D5JJdW1dadrasVg7zhqPX1VXXBgm3HMBjV3gI8mcHU0guBl1RPL+4nSZIkqVeuA+694PG9gOuX2Oefq+rfgH9LcjGwGtjpQV5bzslbD0wt2jYFbAAeBRwIPBh4GPDYZkuTJEmS1BZzpDW3HfBF4P5J7pNkDwZjnI8s2ufDwKOT3CHJnRhM5/z6So5RKzp5wDnAa5PsWVU/THIAsD/wI+DngD0YtDrvCPzTxKqUJEmSpB1UVT9O8iLgAmB34PSquirJC4bPn1pVX09yPnAlMAe8s6q+upL3bcUgr6puSnIZsI7BSHYK2FBVn0/yKeB7DAZ5b6+qFY1qJUmSJKkpVXUecN6ibacuevxG4I2jes9WDPKG5qdszg/ynpvkfsCvMZi7CnBhksdU1cWLX7xwZZvTTjutmYolSZIkNapNq2u2VVvOyQM4Fzg0yRpgr6raAvw2cGlV3VJVtwAfBx6x1Iuraraq1lbV2unp6aV2kSRJkqTea80gbziI2wSczqCrB/Ad4LHDkxDvyGDRFadrSpIkSbuouUprbm3VmkHe0HoGy4WeNXx8DnAt8BXgCuCKqvrfE6pNkiRJklqvTefkUVUbWXDBwKq6FXj+5CqSJEmSpG5p1SBPkiRJkranzdMk26Jt0zUlSZIkSSvgIE+SJEmSesTpmpIkSZI6w+may7OTJ0mSJEk94iBPkiRJknokVTXpGsahl9+UJEmStAN6PZ/xSRe/pDWf9T/+mLe08lj39py8Nc8/aSy5W06bYc3Ro8/ecsp4cs1uNruLNc9nz92waizZu+27tXPHpMs/R7PHn2t2s9ldrNns5nKbyH7YUSeOJfuL7z6WRx7xppHnfm7DcSPPVPc4XVOSJEmSeqS3nTxJkiRJ/ePqmsuzkydJkiRJPWInT5IkSVJn2Mlbnp08SZIkSeoRB3mSJEmS1COtmK6ZZBPw+qq6YMG2Y4BVwA+AJw83v6aqNjReoCRJkqRWcLrm8trSyVsPTC3aNgX8E7AGOAh4OPDSJHs3W5okSZIkdUdbBnnnAIcn2RMgyQHA/sC/A5+uqh9X1b8BVwDrJlalJEmSJLVcKwZ5VXUTcBm3DeCmgA0MBnVPSnKnJHcHHg/cezJVSpIkSZq0uUprbm3VinPyhuanbH54+PW5VbUlycOAzwH/B/g88OOlXpxkGpgGOO200xopWJIkSZLaphWdvKFzgUOTrAH2qqotAFX1uqo6qKqeAAS4eqkXV9VsVa2tqrXT09ONFS1JkiRJbdKaTl5V3TJcZfN0Bl09kuwO3KWqbkpyIHAg8InJVSlJkiRpkqrF0yTbojWDvKH1wIe4baXNOwKfSQLwfeD3qmrJ6ZqSJEmSpJYN8qpqI4MpmfOP/wN44OQqkiRJktQmc9jJW06bzsmTJEmSJK2QgzxJkiRJ6pFWTdeUJEmSpO1p8/Xp2sJOniRJkiT1iIM8SZIkSeqRVNWkaxiHXn5TkiRJ0g7o9XzGR114fGs+63/2CSe08lj39py81S8+aSy5V7x1hjVHjz57yynjyTW72ewu1txE9twNq8aSvdu+W/05mt14rtnNZnexZrOby20i+5FHvGks2Z/bcByHPv71I8+96FOvGHmmusfpmpIkSZLUI73t5EmSJEnqH1fXXJ6dPEmSJEnqETt5kiRJkjqj7OQty06eJEmSJPWIgzxJkiRJ6hGna0qSJEnqDBdeWV6jnbwkm5IctmjbMUlOTnJ+kpuTfHTR8/dJ8oUkVyfZkGSPJmuWJEmSpC5perrmemBq0bap4fY3As9e4jUnACdV1f2BfwWeN9YKJUmSJKnDmh7knQMcnmRPgCQHAPsDl1TVRcAPFu6cJMAhw9cBvBd4alPFSpIkSWqXqvbc2qrRQV5V3QRcBqwbbpoCNlRt8xDdDbi5qn48fHwdcM+ldkwynWRzks2zs7OjLFuSJEmSOmMSq2sunLI5P1VzW5Y6q3LJAWFVzVbV2qpaOz09vcISJUmSJKmbJrG65rnAiUnWAHtV1Zbt7PvPwF2S3GHYzbsXcH0DNUqSJElqobkl+0BaqPFOXlXdAmwCTmf7XTyG0zg/BTxtuOn3gQ+Psz5JkiRJ6rJJXQx9PbAaOGt+Q5LPAGcDhya5bsGlFo4Hjk1yDYNz9N7VdLGSJEmS2qEqrbm11UQuhl5VG1l0vl1VPXob+34LOLiJuiRJkiSp6ybVyZMkSZIkjcFEOnmSJEmStDPmWjxNsi3s5EmSJElSjzjIkyRJkqQecbqmJEmSpM6omnQF7Zfq51Hq5TclSZIk7YBen7S2+qN/2prP+lcc/ppWHuvedvKe8KjXjiX3ws++ijVHnzTy3C2nzIwl1+xms7tYc9ez525YNfLc3fbd2tnjYfb4c81uNruLNZvdXG7Xs3/1L0af/c0/mxl5prqnt4M8SZIkSf3T5ouQt4ULr0iSJElSj9jJkyRJktQZdvKWZydPkiRJknrEQZ4kSZIk9YjTNSVJkiR1xpzTNZfVaCcvyaYkhy3adkySk5Ocn+TmJB9d9PyLklyTpJLcvcl6JUmSJKlrmp6uuR6YWrRtarj9jcCzl3jNZ4HfBP5hvKVJkiRJUvc1PV3zHOC1Sfasqh8mOQDYH7ikqirJ4xa/oKq+DJDYlpUkSZJ2dVWTrqD9Gu3kVdVNwGXAuuGmKWBDlT8qSZIkSRqFSayuuXDK5vxUzRVLMp1kc5LNs7Ozo4iUJEmSpM6ZxOqa5wInJlkD7FVVW0YRWlWzwPzors5+72tHEStJkiSpRbwY+vIa7+RV1S3AJuB0RtTFkyRJkiQNTOpi6OuB1cBZ8xuSfAY4Gzg0yXXzl1pI8uIk1wH3Aq5M8s5JFCxJkiRp8qrSmltbTeRi6FW1EciibY/exr5vBd7aRF2SJEmS1HWT6uRJkiRJksZgIp08SZIkSdoZXntteXbyJEmSJKlHHORJkiRJUo84XVOSJElSZ7R5Vcu2sJMnSZIkST2Sql6eutjLb0qSJEnaAb1uda364Gta81l/6+/8aSuPdW+na/7an540ltyvv2aGNUePPnvLKePJNbvZ7C7WbPbSuXM3rBp5LsBu+27t3PHoanYXaza7uVyzm83uYs0/yX7+mLJPm+GJj/iLked+4tI/G3lm67RmiNdeTteUJEmSpB5xkCdJkiRJPdLb6ZqSJEmS+sfVNZdnJ0+SJEmSesRBniRJkqTOqGrPbUckWZfkm0muSfLy7ez3sCS3JnnaSo+RgzxJkiRJGoMkuwN/AzwJeCDwzCQP3MZ+JwAXjOJ9Gx3kJdmU5LBF245JcnKS85PcnOSji55//3Dk+9Ukpye5Y5M1S5IkSdJOOhi4pqq+VVU/As4CnrLEfn8EfBC4cRRv2nQnbz0wtWjb1HD7G4FnL/Ga9wMPAH4d2Av4g3EWKEmSJKm9qtKa2w64J/DdBY+vG277iST3BH4bOHVUx6jpQd45wOFJ9gRIcgCwP3BJVV0E/GDxC6rqvBoCLgPu1WC9kiRJkrSkJNNJNi+4TS/eZYmXLT6b783A8VV166jqavQSClV1U5LLgHXAhxl08TYMB3DbNZym+WzgJeOtUpIkSZKWV1WzwOx2drkOuPeCx/cCrl+0z1rgrCQAdwd+K8mPq+rcna1rEtfJm5+yOT/Ie+4Ovu5k4OKq+sxSTw5HzdMAp5122gjKlCRJktQ63bpO3heB+ye5D/CPDMY/v7twh6q6z/z9JO8BPrqSAR5MZnXNc4FDk6wB9qqqLcu9IMmfA/sAx25rn6qaraq1VbV2enpxl1SSJEmSmlVVPwZexGDVzK8DH6iqq5K8IMkLxvW+jXfyquqWJJuA0xl09bYryR8AhwGHVtXcmMuTJEmSpJGpqvOA8xZtW3KRlao6chTvOanr5K0HVjNYQhSAJJ8BzmbQ5btuwaUWTgXuAXw+yeVJ/qzxaiVJkiS1wqQvgH57L4Y+CZM4J4+q2siilWaq6tHb2HciNUqSJElSFzmAkiRJktQdLe6gtcWkpmtKkiRJksbAQZ4kSZIk9YjTNSVJkiR1RnXrOnkTYSdPkiRJknrEQZ4kSZIk9UiqzRd42Hm9/KYkSZKkHdDr+Yz3OfP1rfms//e/94pWHuvenpP38OecOJbcL5xxLGuOPmnkuVtOmRlLrtnNZnexZrOby53Pnrth1Viyd9t3a+eO9Tizu1iz2c3lmt1sdhdrns9+yAvHk/3lk2dY84IxHOtTZ0aeqe5xuqYkSZIk9UhvO3mSJEmS+sfVNZdnJ0+SJEmSesROniRJkqTuaM2yK+1lJ0+SJEmSesRBniRJkiT1SKODvCSbkhy2aNsxSU5Ocn6Sm5N8dNHz70pyRZIrk5yT5M5N1ixJkiSpTdKiWzs13clbD0wt2jY13P5G4NlLvGamqlZX1YHAd4AXjbdESZIkSequpgd55wCHJ9kTIMkBwP7AJVV1EfCDxS+oqu8P9w2wF55qKUmSJEnb1Oggr6puAi4D1g03TQEbqmq7A7ck7wZuAB4AvG2sRUqSJElqr2rRraUmsfDKwimb81M1t6uqjmLQ8fs6cMRS+ySZTrI5yebZ2dlR1SpJkiRJnTKJQd65wKFJ1gB7VdWWHXlRVd0KbAB+ZxvPz1bV2qpaOz09PbJiJUmSJKlLGh/kVdUtwCbgdJbp4mXgfvP3gf8GfGPcNUqSJElqqUlP0ezAdM07TOh91wMfYsFKm0k+w+CcuzsnuQ54HnAh8N4kezNYo/QK4Ojmy5UkSZKkbpjIIK+qNrLowhJV9eht7P6o8VckSZIkqROqvdena4tJnJMnSZIkSRoTB3mSJEmS1COTOidPkiRJkm637V9hW2AnT5IkSZJ6xUGeJEmSJPWI0zUlSZIkdYfTNZeV6uek1l5+U5IkSdIO6PU1Bn75XX/Vms/6//C8l7XyWPe2k/fII940ltzPbTiONUefNPLcLafMjCXX7Gazu1iz2c3lNpE9d8OqsWTvtu/Wzh2TLv8czR5/rtnNZnex5vns1S8ZT/YVb5lh3eo/HXnu+Ve8ZuSZ6p7eDvIkSZIk9ZAXQ1+WC69IkiRJUo/YyZMkSZLUGWnNGXntZSdPkiRJknrEQZ4kSZIk9YjTNSVJkiR1h9M1l9VoJy/JpiSHLdp2TJKTk5yf5OYkH93Ga9+W5JZmKpUkSZKkbmp6uuZ6YGrRtqnh9jcCz17qRUnWAncZa2WSJEmS1ANND/LOAQ5PsidAkgOA/YFLquoi4AeLX5BkdwYDwJc1WKckSZKkNqq059ZSjQ7yquom4DJg3XDTFLChqrY3s/ZFwEeq6nvjrk+SJEmSum4Sq2sunLI5P1VzSUn2B54OvG250CTTSTYn2Tw7OzuSQiVJkiSpayaxuua5wIlJ1gB7VdWW7ez7EOB+wDVJAO6U5Jqqut/iHatqFpgf3dV7LnrTaKuWJEmSNHmurrmsxgd5VXVLkk3A6Wynizfc92PAvvOPk9yy1ABPkiRJkjQwqYuhrwdWA2fNb0jyGeBs4NAk1y2+1IIkSZIkaXkTuRh6VW0Esmjbo3fgdXceW1GSJEmS2s/pmsuaVCdPkiRJkjQGE+nkSZIkSdJOsZO3LDt5kiRJktQjDvIkSZIkqUecrilJkiSpOyrL77OLS1UvJ7X28puSJEmSdkCvR0EH/M2bWvNZ/9t/eFwrj3VvO3mPW3fCWHI3nX88a44+aeS5W06ZGUuu2c1md7Fms5vL7Xr23A2rxpK9275b/Tma3Xiu2c1md7Hmn2S/YEzZp44ne8upMyPPVPf0dpAnSZIkqX/Smj5ee7nwiiRJkiT1iIM8SZIkSeoRp2tKkiRJ6g6nay7LTp4kSZIk9YiDPEmSJEnqEQd5kiRJktQjjQ7ykmxKctiibcckOTnJ+UluTvLRRc+/J8nfJ7l8eDuoyZolSZIkqUuaXnhlPTAFXLBg2xTwUmAP4E7A85d43Uur6pzxlydJkiSpzbxO3vKanq55DnB4kj0BkhwA7A9cUlUXAT9ouB5JkiRJ6pVGB3lVdRNwGbBuuGkK2FBVy43HX5fkyiQnzQ8QF0synWRzks2zs7MjrFqSJEmSumMSC6/MT9lk+HX9Mvu/AngA8DDgrsDxS+1UVbNVtbaq1k5PT4+qVkmSJEltUmnPraUmMcg7Fzg0yRpgr6rasr2dq+p7NfBD4N3AwQ3UKEmSJEmd1Pggr6puATYBp7N8F48k+w2/Bngq8NUxlidJkiRJndb06prz1gMf4rZpmyT5DINpmXdOch3wvKq6AHh/kn2AAJcDL2i+XEmSJEmt4Oqay5rIIK+qNjIYtC3c9uht7HtII0VJkiRJUg9MqpMnSZIkSbefnbxlTWLhFUmSJEnSmDjIkyRJkqQecbqmJEmSpM6I0zWXZSdPkiRJknokVb0cCvfym5IkSZJ2QJbfpbvue+KJrfmsf+2xx7byWPd2uuZD/vCkseR++W9mWHP06LO3nDKeXLObze5izWY3l2v2trPnblg18tzd9t3a2eNh9vhzzW42u4s1/yT7BWPKPnWGQ57whpHn/t2FLx95Zuu0ZojXXk7XlCRJkqQecZAnSZIkST3S2+makiRJknrI6ZrLspMnSZIkST1iJ0+SJElSZ3idvOXZyZMkSZKkHml0kJdkU5LDFm07JsnJSc5PcnOSjy56Pklel2Rrkq8neXGTNUuSJElSlzTdyVsPTC3aNjXc/kbg2Uu85kjg3sADqurXgLPGWaAkSZKkFqu057YDkqxL8s0k1yT5mQsZJnlWkiuHt88lWb3SQ9T0IO8c4PAkewIkOQDYH7ikqi4CfrDEa44G/qKq5gCq6saGapUkSZKknZZkd+BvgCcBDwSemeSBi3b7e+CxVXUg8BpgdqXv2+ggr6puAi4D1g03TQEbqmp7p0/eFzgiyeYkH09y/3HXKUmSJEkjcDBwTVV9q6p+xGBW4lMW7lBVn6uqfx0+vBS410rfdBILryycsjk/VXN79gT+o6rWAu8ATl9qpyTTw4Hg5tnZFQ9+JUmSJLVRtei2vHsC313w+Lrhtm15HvDxHUrejkkM8s4FDk2yBtirqrYss/91wAeH9zcCBy61U1XNVtXaqlo7PT09smIlSZIkaSkLG03D2+KByFIn7i05PEzyeAaDvONXWlfj18mrqluSbGLQkVuuiweDQeEhw/0fC2wdW3GSJEmStIOqapbtn0N3HYNFJOfdC7h+8U5JDgTeCTxpeIrbikzqYujrgQ+xYKXNJJ8BHgDcOcl1wPOq6gLgDcD7k8wAtwB/MIF6JUmSJLVAxy6G/kXg/knuA/wjg/HP7y7cIckvMRgbPbuqRtLQmsggr6o2sqh1WVWP3sa+NwNPbqAsSZIkSRqZqvpxkhcBFwC7A6dX1VVJXjB8/lTgz4C7AScnAfjxcD2SnTapTp4kSZIk3X7d6uRRVecB5y3aduqC+3/AiGcrTmLhFUmSJEnSmDjIkyRJkqQecbqmJEmSpM7o2MIrE2EnT5IkSZJ6xEGeJEmSJPVIqnrZ7+zlNyVJkiTtgCy/S3etet1Jrfmsv/VPZlp5rHt7Tt4jfu/EseReeuaxrDn6pJHnbjllZiy5Zjeb3cWazW4u1+xms7ecMsPcDatGnguw275bO3c8uprdxZrNbi63ieyHvHA82V8+eYYnPfCVI8/9+Nf+cuSZ6h6na0qSJElSj/S2kydJkiSph1ozWbO97ORJkiRJUo/YyZMkSZLUGV4nb3l28iRJkiSpRxzkSZIkSVKPNDrIS7IpyWGLth2T5OQk5ye5OclHFz3/mSSXD2/XJzm3yZolSZIkqUuaPidvPTAFXLBg2xTwUmAP4E7A8xe+oKoePX8/yQeBD4+/TEmSJEnqpqana54DHJ5kT4AkBwD7A5dU1UXAD7b1wiQ/DxwCnDv+MiVJkiSpmxod5FXVTcBlwLrhpilgQ1XtyBo5vw1cVFXfH1d9kiRJklquWnRrqUksvDI/ZZPh1/U7+Lpnbm/fJNNJNifZPDs7u8ISJUmSJKmbJjHIOxc4NMkaYK+q2rLcC5LcDTgY+Ni29qmq2apaW1Vrp6enR1asJEmSJHVJ4xdDr6pbkmwCTmfHu3hPBz5aVf8xtsIkSZIktZ4XQ1/epK6Ttx5YDZw1vyHJZ4CzGXT5rlt0qYXbM61TkiRJknZZjXfyAKpqI5BF2x69jd2pqseNuyZJkiRJ6oOJDPIkSZIkaac4XXNZk5quKUmSJEkaAzt5kiRJkrrDTt6y7ORJkiRJUo84yJMkSZKkHnG6piRJkqTO8Dp5y0tVL49SL78pSZIkaQdk+V2669f+7KTWfNb/+l/MtPJY97aT96DjTxpL7lUnzLDm6NFnbzllPLlmN5vdxZrNbi7X7Gazx13z3A2rxpK9275bO3esx5ndxZrNbi63iezVLxlP9hVvmeFRT3/TyHM/e/ZxI89U9/R2kCdJkiSph1rTx2svF16RJEmSpB5xkCdJkiRJPeJ0TUmSJEmd4eqay7OTJ0mSJEk9YidPkiRJUnfYyVtWo528JJuSHLZo2zFJTk5yfpKbk3x00fOHJtmS5PIklyS5X5M1S5IkSVKXND1dcz0wtWjb1HD7G4FnL/GaU4BnVdVBwN8CrxpngZIkSZLUZU0P8s4BDk+yJ0CSA4D9gUuq6iLgB0u8poC9h/d/Abi+gTolSZIktVG16NZSjZ6TV1U3JbkMWAd8mEEXb0NVbe8Q/QFwXpL/B3wfeMT4K5UkSZKkbprE6poLp2zOT9Xcnhngt6rqXsC7gROX2inJdJLNSTbPzs6OrFhJkiRJ6pJJDPLOBQ5NsgbYq6q2bGvHJPsAq6vqC8NNG4BHLrVvVc1W1dqqWjs9PT3qmiVJkiS1QKo9t7ZqfJBXVbcAm4DTWb6L96/ALyRZNXz8BODr46tOkiRJkrptUtfJWw98iAUrbSb5DPAA4M5JrgOeV1UXJPmfwAeTzDEY9D13EgVLkiRJUhdMZJBXVRuBLNr26O3su7GJuiRJkiS1XIunSbbFJM7JkyRJkiSNyaSma0qSJEnS7Wcnb1l28iRJkiSpRxzkSZIkSVKPOF1TkiRJUme0+fp0bWEnT5IkSZJ6JFW9HAr38puSJEmSdkCW36W7Hvyyk1rzWf+rfzXTymPd2+maq1980lhyr3jrDGuOHn32llPGk2t2s9ldrNns5nLNbja7izXPZ8/dsGos2bvtu7Vzx6TLP0ezx5/bRPbBv3/iWLIve++xrH7J6Ou+4i0zI89sndYM8drL6ZqSJEmS1CMO8iRJkiSpR3o7XVOSJElS/7i65vLs5EmSJElSj9jJkyRJktQddvKWZSdPkiRJknqk0UFekk1JDlu07ZgkJyc5P8nNST666PlDkmxJ8tUk701i91GSJEmStqHpTt56YGrRtqnh9jcCz174RJLdgPcCU1X1YOAfgN9voE5JkiRJbVQturVU04O8c4DDk+wJkOQAYH/gkqq6CPjBov3vBvywqrYOH18I/E5DtUqSJElS5zQ6yKuqm4DLgHXDTVPAhqra1jj4n4E7Jlk7fPw04N7jrVKSJEmSumsSC68snLI5P1VzScPB3xRwUpLLGHT6frzUvkmmk2xOsnl2dnbEJUuSJElqg7To1laTGOSdCxyaZA2wV1Vt2d7OVfX5qnp0VR0MXAxcvY39ZqtqbVWtnZ6eHnnRkiRJktQFjQ/yquoWYBNwOtvp4s1L8l+GX/cEjgdOHWd9kiRJktRlk7pO3npgNXDW/IYknwHOZtDlu27BpRZemuTrwJXA/66qv2u8WkmSJEntMOkVNTuwuuZErjlXVRtZNI21qh69jX1fCry0ibokSZIkqeu8sLgkSZKkzkiLO2htManpmpIkSZKkMXCQJ0mSJEk94nRNSZIkSd3hdM1l2cmTJEmSpB5JVS+Hwr38piRJkqQdkOV36a7VLzmpNZ/1r3jLTCuPdW+nax585Iljyb3sPcey5uiTRp675ZSZseSa3Wx2F2s2u7lcs5vN7mLNTWTP3bBqLNm77bvVn6PZjec2kX3Ae04YS/a3jzyeg140+rovf/vMyDNbpzVDvPZyuqYkSZIk9YiDPEmSJEnqkd5O15QkSZLUP14MfXl28iRJkiSpR+zkSZIkSeoOO3nLspMnSZIkST3iIE+SJEmSeqTRQV6STUkOW7TtmCTnJfl8kquSXJnkiAXP3yfJF5JcnWRDkj2arFmSJElSe6Tac2urpjt564GpRdumgBOA51TVg4B1wJuT3GX4/AnASVV1f+Bfgec1VKskSZIkdU7Tg7xzgMOT7AmQ5ABgf+DiqroaoKquB24E9kkS4JDh6wDeCzy14ZolSZIkqTMaHeRV1U3AZQy6dTDo4m2oqp80O5McDOwBXAvcDbi5qn48fPo64J5LZSeZTrI5yebZ2dlxfQuSJEmSJqladNsBSdYl+WaSa5K8fInnk+Stw+evTLLm9h2QnzWJhVcWTtmcGj4GIMl+wPuAo6pqDsgSr1/ycFbVbFWtraq109PTIy5ZkiRJkm6fJLsDfwM8CXgg8MwkD1y025OA+w9v08ApK33fSQzyzgUOHY5Q96qqLQBJ9gY+Bryqqi4d7vvPwF2SzF/P717A9Q3XK0mSJEk742Dgmqr6VlX9CDgLeMqifZ4CnFEDlzIY/+y3kjdtfJBXVbcAm4DTGXbxhitmbmTwzZ29YN8CPgU8bbjp94EPN1mvJEmSpPaY9Iqat3N1zXsC313weKnTz3Zkn9tlUtfJWw+sZjCSBXgG8BjgyCSXD28HDZ87Hjg2yTUMztF7V9PFSpIkSdJiC9cFGd4Wnze2I6ef7fApajvqDsvvMnpVtZEF30xVnQmcuY19v8WgzSlJkiRJrVFVs8D2Vn28Drj3gsdLnX62I/vcLpPq5EmSJEnS7TfpFTVv3+qaXwTun+Q+w1PUpoCPLNrnI8BzhqtsPgL4v1X1vdtxRH7GRDp5kiRJktR3VfXjJC8CLgB2B06vqquSvGD4/KnAecBvAdcA/w4ctdL3dZAnSZIkqTtWdLZa86rqPAYDuYXbTl1wv4A/HOV7Ol1TkiRJknrEQZ4kSZIk9UgG3cHe6eU3JUmSJO2ApZbk742HTp/Ums/6X5qdaeWx7u05eauPOWksuVe8eYY1R48+e8sp48k1u9nsLtZsdnO5Zjeb3cWau549d8Oqkefutu/Wzh4Ps8ef20T2A185nuyv/eXMWD6vXvHmmZFnqnucrilJkiRJPdLbTp4kSZKkHmrNZM32spMnSZIkST3iIE+SJEmSesTpmpIkSZI6I/28OsBI2cmTJEmSpB5pdJCXZFOSwxZtOybJeUk+n+SqJFcmOWLB8y9Kck2SSnL3JuuVJEmS1DLVoltLNd3JWw9MLdo2BZwAPKeqHgSsA96c5C7D5z8L/CbwD00VKUmSJEld1fQg7xzg8CR7AiQ5ANgfuLiqrgaoquuBG4F9ho+/XFXfbrhOSZIkSeqkRgd5VXUTcBmDbh0Mungbqm47ezLJwcAewLVN1iZJkiSp/VLtubXVJBZeWThlc2r4GIAk+wHvA46qqrnbE5pkOsnmJJtnZ2dHVqwkSZIkdckkBnnnAocmWQPsVVVbAJLsDXwMeFVVXXp7Q6tqtqrWVtXa6enpkRYsSZIkSV3R+HXyquqWJJuA0xl28ZLsAWwEzqiqs5uuSZIkSVJHtHiaZFtM6jp564HVwFnDx88AHgMcmeTy4e0ggCQvTnIdcC/gyiTvnETBkiRJktQFjXfyAKpqI5AFj88EztzGvm8F3tpQaZIkSZLUaRMZ5EmSJEnSzmjzqpZtManpmpIkSZKkMbCTJ0mSJKk77OQty06eJEmSJPWIgzxJkiRJ6hGna0qSJEnqDBdeWV6qenmUevlNSZIkSTsgy+/SXQ9/zomt+az/hTOObeWx7m0n7wGvPmksud949Qxrjh599pZTxpNrdrPZXazZ7OZyzW42u4s1m7107twNq0aeC7Dbvls7dzy6mt3FmuezH/6cE8eS/YUzjh3bsZZ6O8iTJEmS1EOt6eO1lwuvSJIkSVKPOMiTJEmSpB5xuqYkSZKkznB1zeXZyZMkSZKkHrGTJ0mSJKk7+nkJuJFqtJOXZFOSwxZtOybJeUk+n+SqJFcmOWLB8+9P8s0kX01yepI7NlmzJEmSJHVJ09M11wNTi7ZNAScAz6mqBwHrgDcnucvw+fcDDwB+HdgL+INmSpUkSZKk7ml6uuY5wGuT7FlVP0xyALA/cHHVoO9aVdcnuRHYB7i5qs6bf3GSy4B7NVyzJEmSpJZw4ZXlNdrJq6qbgMsYdOtg0MXbMD/AA0hyMLAHcO3C1w6naT4bOL+ZaiVJkiSpeyaxuubCKZtTw8cAJNkPeB9wVFXNLXrdyQw6fp9ZKjTJdJLNSTbPzs6OoWxJkiRJar9JDPLOBQ5NsgbYq6q2ACTZG/gY8KqqunThC5L8OYPpm8duK7SqZqtqbVWtnZ6eHlvxkiRJkiaoWnRrqcYvoVBVtyTZBJzOsIuXZA9gI3BGVZ29cP8kfwAcBhy6RHdPkiRJkrTApC6Gvh5YDZw1fPwM4DHAkUkuH94OGj53KnAP4PPD7X/WeLWSJEmS1BETuRh6VW0EsuDxmcCZ29jXC7ZLkiRJAiDO7VvWpDp5kiRJkqQxsEsmSZIkqTtavOBJW9jJkyRJkqQecZAnSZIkST3idE1JkiRJnRGnay7LTp4kSZIk9UiqejkU7uU3JUmSJO2ALL9Ldz3q6W9qzWf9z559XCuPdW+nax547Eljyb3yxBnWHD367C2njCfX7Gazu1iz2c3lmt1sdhdrNru53PnsuRtWjSV7t323du5YjzO7izXPZz/q6W8aS/Znzz6ONS8Yw7E+dWbkma3TzybVSDldU5IkSZJ6xEGeJEmSJPVIb6drSpIkSeofV9dcnp08SZIkSeoRB3mSJEmS1CNO15QkSZLUHU7XXFajnbwkm5IctmjbMUnOS/L5JFcluTLJEQuef1eSK4bbz0ly5yZrliRJkqQuaXq65npgatG2KeAE4DlV9SBgHfDmJHcZPj9TVaur6kDgO8CLmipWkiRJUruk2nNrq6YHeecAhyfZEyDJAcD+wMVVdTVAVV0P3AjsM3z8/eG+AfbCBq0kSZIkbVOjg7yqugm4jEG3DgZdvA1Vt122PsnBwB7AtQu2vRu4AXgA8LbGCpYkSZKkjpnE6poLp2xODR8DkGQ/4H3AUVU1N7+9qo5i0PH7OnAES0gynWRzks2zs7Pjql2SJEnSJFW159ZSkxjknQscmmQNsFdVbQFIsjfwMeBVVXXp4hdV1a3ABuB3lgqtqtmqWltVa6enp8dWvCRJkiS1WeODvKq6BdgEnM6wi5dkD2AjcEZVnT2/bwbuN38f+G/AN5quWZIkSZK6YlLXyVsPfIjbpm0+A3gMcLckRw63HQlcCbx32OULcAVwdKOVSpIkSWqNNq9q2RYTGeRV1UYGg7b5x2cCZ25j90c1UpQkSZIk9cAkzsmTJEmSJI3JpKZrSpIkSdLt53TNZdnJkyRJkqQesZMnSZIkqTNceGV5dvIkSZIkqUcc5EmSJElSj6Sql/3OXn5TkiRJ0g7I8rt012Of/Fet+az/6Y+9rJXHurfn5D10+qSx5H5pdoY1R48+e8sp48k1u9nsLtZsdnO5Zjeb3cWazW4ut4nsuRtWjSV7t323du6YdPnn+JAXjif7yyfPsOYFYzjWp86MPFPd43RNSZIkSeqR3nbyJEmSJPVQayZrtpedPEmSJEnqEQd5kiRJktQjTteUJEmS1BleDH15dvIkSZIkqUcaHeQl2ZTksEXbjklyXpLPJ7kqyZVJjljitW9Lcktz1UqSJElqnar23Fqq6ema64Ep4IIF26aA44Hrq+rqJPsDX0pyQVXdDJBkLXCXhmuVJEmSpM5perrmOcDhSfYESHIAsD9wcVVdDVBV1wM3AvsM99kdeCPwsoZrlSRJkqTOaXSQV1U3AZcB64abpoANVbf1OpMcDOwBXDvc9CLgI1X1vSZrlSRJktQ+qfbc2moSC6/MT9lk+HX9/BNJ9gPeBxxVVXPDqZtPB962XGiS6SSbk2yenZ0dQ9mSJEmS1H6TGOSdCxyaZA2wV1VtAUiyN/Ax4FVVdelw34cA9wOuSfJt4E5JrlkqtKpmq2ptVa2dnp4e9/cgSZIkSa3U+HXyquqWJJuA0xl28ZLsAWwEzqiqsxfs+zFg3/nHSW6pqvs1W7EkSZKk1mjxNMm2mNR18tYDq4Gzho+fATwGODLJ5cPbQROqTZIkSZLGKsldk1yY5Orh119cYp97J/lUkq8PLzf3kh3Jnsggr6o2VlWq6hvDx2dW1R2r6qAFt8uXeN2dGy9WkiRJkkbv5cBFVXV/4KLh48V+DBxXVb8GPAL4wyQPXC648emakiRJkrSz0uKLkN9OTwEeN7z/XmATg+uH/8TwCgPfG97/QZKvA/cEvra94ElN15QkSZKkTlu4wv/wdntWgLzH/GXihl//yzLvdQCDhSm/sFywnTxJkiRJ3TE36QJuU1WzwDav35bkkyxYSHKBP7k975PkzsAHgWOq6vvL7e8gT5IkSZLGoKp+c1vPJfmnJPtV1feG1wu/cRv73ZHBAO/9VfWhHXlfp2tKkiRJUvM+Avz+8P7vAx9evEOSAO8Cvl5VJ+5ocKo/Jy4u1MtvSpIkSdoBmXQB43ToIa9vzWf9i/7uFTt9rJPcDfgA8EvAd4CnV9W/JNkfeGdV/VaS/wp8BvgKt01UfWVVnbe97N5O1/zNx7xuLLmfvPhPWHP0SSPP3XLKzFhyzW42u4s1m91crtnNZnexZrOby+169twNq8aSvdu+W/05Lsp+2FE73Dy5Xb747mPHdqzVDVV1E3DoEtuvB35reP8SdmLQ7nRNSZIkSeqR3nbyJEmSJPVQayZrtpedPEmSJEnqEQd5kiRJktQjTteUJEmS1B39vDrASNnJkyRJkqQesZMnSZIkqTNiI29ZjXbykmxKctiibcckOS/J55NcleTKJEcseP49Sf4+yeXD20FN1ixJkiRJXdJ0J289MAVcsGDbFHA8cH1VXT28wvuXklxQVTcP93lpVZ3TbKmSJEmS1D1ND/LOAV6bZM+q+mGSA4D9gYurBmdQVtX1SW4E9gFubrg+SZIkSW3mwivLanS6ZlXdBFwGrBtumgI2zA/wAJIcDOwBXLvgpa8bTuM8KcmeS2UnmU6yOcnm2dnZMX0HkiRJktRuk1hdc37KJsOv6+efSLIf8D7gqKqaG25+BfAA4GHAXRlM7fwZVTVbVWurau309PS4apckSZKkVpvEIO9c4NAka4C9qmoLQJK9gY8Br6qqS+d3rqrv1cAPgXcDB0+gZkmSJEktkLn23Nqq8UFeVd0CbAJOZ9jFS7IHsBE4o6rOXrj/sLtHkgBPBb7aYLmSJEmS1CmTuk7eeuBD3DZt8xnAY4C7JTlyuO3IqroceH+SfYAAlwMvaLRSSZIkSeqQiQzyqmojg0Hb/OMzgTO3se8hTdUlSZIkqeVcXXNZkzgnT5IkSZI0JpOarilJkiRJt5+NvGXZyZMkSZKkHnGQJ0mSJEk94nRNSZIkSZ0RF15ZVqqfB6mX35QkSZK0A7L8Lt31xN94TWs+63/i83/aymPd207ew5574lhyv3j6saw5+qSR5245ZWYsuWY3m93Fms1uLtfsZrO7WLPZzeWave3suRtWjTx3t323dvZ4rHnBmLJPneEhLxx99pdPnhl5prqnt4M8SZIkST3Uz5mII+XCK5IkSZLUIw7yJEmSJKlHnK4pSZIkqTvmJl1A+9nJkyRJkqQecZAnSZIkST3S6CAvyaYkhy3adkyS85J8PslVSa5McsSC55PkdUm2Jvl6khc3WbMkSZKk9khVa25t1fQ5eeuBKeCCBdumgOOB66vq6iT7A19KckFV3QwcCdwbeEBVzSX5Lw3XLEmSJEmd0fQg7xzgtUn2rKofJjkA2B+4uGowFK6q65PcCOwD3AwcDfxuVc0Nn7+x4ZolSZIktUWLO2ht0eh0zaq6CbgMWDfcNAVsmB/gASQ5GNgDuHa46b7AEUk2J/l4kvs3WbMkSZIkdckkFl6Zn7LJ8Ov6+SeS7Ae8DzhqvnMH7An8R1WtBd4BnL5UaJLp4UBw8+zs7NiKlyRJkqQ2m8R18s4FTkyyBtirqrYAJNkb+Bjwqqq6dMH+1wEfHN7fCLx7qdCqmgXmR3f1jktPHEPpkiRJkibK6ZrLaryTV1W3AJsYdOTWAyTZg8EA7oyqOnvRS84FDhnefyywtZFCJUmSJKmDJnWdvPXAauCs4eNnAI8Bjkxy+fB20PC5NwC/k+QrwOuBP2i6WEmSJEnqiklM16SqNgJZ8PhM4Mxt7Hsz8ORmKpMkSZLUanPL77Krm1QnT5IkSZI0Bg7yJEmSJKlHJjJdU5IkSZJ2Rlxdc1l28iRJkiSpR+zkSZIkSeoOO3nLspMnSZIkST2S6udIuJfflCRJkrQDsvwu3bVu9Z+25rP++Ve8ppXHurfTNde84KSx5G45dYY1R48+e8sp48k1u9nsLtZsdnO5Zjeb3cWazW4u1+xms7ecMsPcDatGnguw275bx3o8Vr94PNlXvHWGg140+uzL3z4z8szW6WeTaqScrilJkiRJPeIgT5IkSZJ6pLfTNSVJkiT1kNM1l2UnT5IkSZJ6xEGeJEmSJPWI0zUlSZIkdcfcpAtov0Y7eUk2JTls0bZjkpyX5PNJrkpyZZIjFjz/mSSXD2/XJzm3yZolSZIkqUua7uStB6aACxZsmwKOB66vqquT7A98KckFVXVzVT16fsckHwQ+3GjFkiRJklojLryyrKbPyTsHODzJngBJDgD2By6uqqsBqup64EZgn4UvTPLzwCHAuQ3WK0mSJEmd0uggr6puAi4D1g03TQEbqm4bjic5GNgDuHbRy38buKiqvt9ErZIkSZLURZNYXXN+yibDr+vnn0iyH/A+4KiqWnxK5TMX7rtYkukkm5Nsnp2dHXHJkiRJklqhqj23lprEIO9c4NAka4C9qmoLQJK9gY8Br6qqSxe+IMndgIOHzy+pqmaram1VrZ2enh5b8ZIkSZLUZo0P8qrqFmATcDrDzlySPYCNwBlVdfYSL3s68NGq+o+m6pQkSZKkLprUdfLWAx/itmmbzwAeA9wtyZHDbUdW1eXD+1PAG5osUJIkSVILzbV3mmRbTGSQV1UbgSx4fCZw5nb2f1wDZUmSJElS503inDxJkiRJ0phMarqmJEmSJN1+LV7Vsi3s5EmSJElSj9jJkyRJktQddvKWZSdPkiRJknrEQZ4kSZIk9Uiqn+3OXn5TkiRJ0g7I8rt015Pu99LWfNb/+DVvbOWx7u05eWuef9JYcrecNsOao0efveWU8eSa3Wx2F2s2u7lcs5vN7mLNZjeXa3az2eOuee6GVWPJ3m3frTzkD8dT95f/ZoaHvHD02V8+eWbkmeoep2tKkiRJUo/0tpMnSZIkqYfmWjNbs7Xs5EmSJElSjzjIkyRJkqQecbqmJEmSpO6ouUlX0Hp28iRJkiSpRxod5CXZlOSwRduOSXJeks8nuSrJlUmOWPD8oUm2JLk8ySVJ7tdkzZIkSZJapKo9t5ZqupO3HphatG0KOAF4TlU9CFgHvDnJXYbPnwI8q6oOAv4WeFUzpUqSJElS9zQ9yDsHODzJngBJDgD2By6uqqsBqup64EZgn+FrCth7eP8XgOubLFiSJEmSuqTRhVeq6qYklzHo1n2YQRdvQ9Vtvc4kBwN7ANcON/0BcF6S/wd8H3hEkzVLkiRJahGvk7esSSy8snDK5tTwMQBJ9gPeBxxV9ZNlc2aA36qqewHvBk5cKjTJdJLNSTbPzs6OrXhJkiRJarNJDPLOBQ5NsgbYq6q2ACTZG/gY8KqqunS4bR9gdVV9YfjaDcAjlwqtqtmqWltVa6enp8f9PUiSJElSKzV+nbyquiXJJuB0hl28JHsAG4EzqursBbv/K/ALSVZV1VbgCcDXGy5ZkiRJUlu0eFXLtpjUxdDXAx/itmmbzwAeA9wtyZHDbUdW1eVJ/ifwwSRzDAZ9z226WEmSJEnqiokM8qpqI5AFj88EztzOvhsbKk2SJEmSOm1SnTxJkiRJuv2crrmsSSy8IkmSJEm7tCR3TXJhkquHX39xO/vunuTLST66I9kO8iRJkiR1R1V7bivzcuCiqro/cNHw8ba8hNuxAKWDPEmSJElq3lOA9w7vvxd46lI7JbkX8GTgnTsa7CBPkiRJkpp3j6r6HsDw63/Zxn5vBl4GzO1osAuvSJIkSeqOuR0e64xdkmlgesGm2aqaXfD8J4F9l3jpn+xg/uHAjVX1pSSP2+G6qp+r0/Tym5IkSZJ2QJbfpbuetN8ftuaz/se/9zc7fayTfBN4XFV9L8l+wKaq+tVF+7weeDbwY+DngL2BD1XV720vu7edvDXPP2ksuVtOm+E3H/uXI8/95KdfyRN2e/rIcwEunDubQw95/ViyL/q7V/CgV4znWF/1+hmedO+XjCX74999C/f96xNHnnvtHx/LfTe8buS5ANce8Sc88hlvGkv25z5wHA87avTHA+CL7z6WRx4xpro3HMeao0f/52/LKTNj/TvkIS8cT/aXT55h9UvGk33FW2ZY84IxHZNTx5O95dTuHuuDf388v4+XvfdYDnjPCWPJ/vaRx/PAV47+mHztL2d4+HPGczy+cMaxPOrp4/n76bNnHzfWP3/j/Dt7XL+Pq188pt+Zt87wkD8c07H+mxnmblg1luzd9t06lj8jXz55ZuSZGpuPAL8PvGH49cOLd6iqVwCvABh28v54uQEeeE6eJEmSpC6Z9Iqao1td8w3AE5JcDTxh+Jgk+yc5byXBve3kSZIkSVJbVdVNwKFLbL8e+K0ltm8CNu1Itp08SZIkSeoRO3mSJEmSuqOfC0eOlJ08SZIkSeoRB3mSJEmS1CM7NMhL8ttJKskDxl3Qdmo4JsmdJvX+kiRJklpgrtpza6kd7eQ9E7gEmBpjLcs5BnCQJ0mSJEnbsewgL8mdgUcBz2M4yEvyuCSfTvKBJFuTvCHJs5JcluQrSe473O+Xk1yU5Mrh118abn9PkqcteI9bFuRuSnJOkm8keX8GXgzsD3wqyadGfhQkSZIkdULVXGtubbUjnbynAudX1VbgX5KsGW5fDbwE+HXg2cCqqjoYeCfwR8N93g6cUVUHAu8H3roD7/cQBl27BwK/Ajyqqt4KXA88vqoev9SLkkwn2Zxk8+zs7A68jSRJkiT1z44M8p4JnDW8f9bwMcAXq+p7VfVD4FrgE8PtXwEOGN7/DeBvh/ffB/zXHXi/y6rquhoMjS9fkLVdVTVbVWurau309PSOvESSJEmSeme718lLcjfgEODBSQrYHSjgPOCHC3adW/B4bju582cn/pjhADNJgD0W7LMw99blapQkSZK0C2nxgidtsVwn72kMplv+clUdUFX3Bv6eHevIAXyO2xZreRaDxVsAvg08dHj/KcAddyDrB8DP7+D7SpIkSdIuablB3jOBjYu2fRD43R3MfzFwVJIrGZy395Lh9ncAj01yGfBw4N92IGsW+LgLr0iSJEnStm13KmRVPW6JbW9l0QIqC/erqk3ApuH9bzOY7rk445+ARyzY9IrFrx0+ftGC+28D3ra9eiVJkiT1XDldczk7ep08SZIkSVIHOMiTJEmSpB5x5UpJkiRJ3THX3ouQt4WdPEmSJEnqETt5kiRJkrrDhVeWlernQerlNyVJkiTtgEy6gHFat/dRrfmsf/73393KY93bTt6a5580ltwtp83wyCPeNPLcz204jgcdP56arzphhsc/8YSxZH/qE8fzgD8fT93f+F8z/MYzR3+sAT6//jgedtSJI8/94ruP5fGHjelYX3A8B71oPMf68reP5881DP5sH/r4148l+6JPvYJf/YvRH5Nv/tkMT3zEX4w8F+ATl/4Za14wpr+fTp1h3eo/HUv2+Ve8Zqx1H/KEN4w89+8ufDlPeuArR54L8PGv/SWPevp4fmc+e/ZxrH7JeI71FW+ZGevfI6uPGX32FW+eYc3RY/qzd8rMWP9cjzV7jMfkIS8cffaXTx7vn71x1AyDuseZPXfDqpHn7rbv1pFnqnt6O8iTJEmS1D/lwivLcuEVSZIkSeoRB3mSJEmS1CNO15QkSZLUHf1cOHKk7ORJkiRJUo84yJMkSZKkHhnLIC/JvknOSnJtkq8lOS/JqiRfHcf7SZIkSdpFzFV7bi018nPykgTYCLy3qqaG2w4C7jHq95IkSZIk/bRxdPIeD/xnVZ06v6GqLge+O/84yQFJPpNky/D2yOH2/ZJcnOTyJF9N8ugkuyd5z/DxV5LMjKFmSZIkSV1Qc+25tdQ4Vtd8MPClZfa5EXhCVf1HkvsD64G1wO8CF1TV65LsDtwJOAi4Z1U9GCDJXcZQsyRJkiT1wqQuoXBH4O3DaZy3AquG278InJ7kjsC5VXV5km8Bv5LkbcDHgE8sFZhkGpgGOO2008ZcviRJkiS10zima14FPHSZfWaAfwJWM+jg7QFQVRcDjwH+EXhfkudU1b8O99sE/CHwzqUCq2q2qtZW1drp6elRfB+SJEmSWqbmqjW3thrHIO/vgD2T/M/5DUkeBvzygn1+AfheVc0BzwZ2H+73y8CNVfUO4F3AmiR3B3arqg8CfwqsGUPNkiRJktQLI5+uWVWV5LeBNyd5OfAfwLeBYxbsdjLwwSRPBz4F/Ntw++OAlyb5T+AW4DnAPYF3J5kfkL5i1DVLkiRJUl+M5Zy8qroeeMYSTz14+PzVwIELtr9iuP29wHuXeJ3dO0mSJEmtXtWyLcZyMXRJkiRJ0mQ4yJMkSZKkHpnUJRQkSZIk6XZr86qWbWEnT5IkSZJ6xE6eJEmSpO5w4ZVl2cmTJEmSpB5JVS/ntPbym5IkSZJ2QCZdwDg9Ybent+az/oVzZ7fzWFfVLn8DpruW3cWau5rdxZrN7kfNZvejZrP7UbPZ/ajZbG+7ys3pmgPTHczuYs1dze5izWY3l2t2s9ldrNns5nLNbja7izWbrV2CgzxJkiRJ6hEHeZIkSZLUIw7yBmY7mN3Fmrua3cWazW4u1+xms7tYs9nN5ZrdbHYXazZbu4S+rq4pSZIkSbskO3mSJEmS1CMO8iRJkiSpR3bJQV6SeyR5V5KPDx8/MMnzJl2XJEmSJK3ULjnIA94DXADsP3y8FThmXG+W5AkrfP3eSe67xPYDV5I7zNg3yb7D+/sk+R9JHrTS3G2811+OKfc+w7ofMIKsX0ryc8P7SXJUkrclOTrJHVaQ+9/nc8chyWOS/Orw/n9N8sdJnjyi7DsneVqSmSR/lGRdkl317w5Jkn4iyUU7sm0ns18y/AyYYXNiS5InjiJb/berflC7e1V9AJgDqKofA7eO8f3etbMvTPIM4BvAB5NcleRhC55+z0qKSvJ84PPApUmOBj4KHA58aKWdzSRvXXR7G/DC+ccrzD53wf2nAH8H/Dfgw0mOXEk2cB63/V68AXgy8AXgYaxsVasNwHVJ3pfkt5LsvrIyb5PkzQxqfV+S1wB/BewFzCR54wqznwF8ClgHvAg4GHg2cHmSX19h9h2SPD/J+UmuTHJFko8neUGSO64ke5n33emfY5LdhzW/JsmjFj33qhXWdackL0vy0iQ/l+TIJB9J8ldJ7ryS7G2839YR5Ry44P4dk7xqWPdfJrnTCrNflOTuw/v3S3JxkpuTfGElf/6SfCjJ743puP5KktOTvHb4HyTvSPLVJGcnOWCF2bsleW6Sjw1/X76U5KwkjxtB3f4+/vTr/X382Wx/H386++eS3BW4e5JfTHLX4e0AbmsirNRzq+r7wBOBfYCjGPx7Ly1rl1xdM8km4HeAC6tqTZJHACdU1WNXkPmRbT0FHFJV/99O5l4OPKmqvpfkYOAM4JVV9aEkX66qh+xcxZDkK8DDGQwI/gG4X1XdkOQXgU9V1UEryL4O2AR8gsExAPhr4I8Bquq9K8j+yfed5HPAs6rq74f/+FxUVatXkP21qnrg8P6XgIdV1dzw8RU7m53ky8AhwNOAKeDBwEZgfVV9emfrHWZfNczbC/hH4J5V9e/DD2ZfrqoHryD7SuARw7y7A++vqsOGHyROrapHriB7PXAz8F7guuHmewG/D9y1qo5YQfZdt/UUcEVV3Wsnc98J3Am4jMFg99NVdezwuS1VtWZncoev/wDwXQY/x18Fvg58gMF/YOxbVc9eQfYPgPm/7Od/H+8E/DtQVbX3CrJ/8n0neRNwN+DdwFOBu1XVc1aQfVVVPWh4/2PAO6tq43BQ87qqetT2Xr+d3H9k8B9chwCfBNYDH6uqH+1srQuyLx7m/QLwewyOxQcYfEh7VlUdsoLsdzP4u/qTDP4u+T7wGeB44MNV9bYVZPv7+NPZ/j7+bLa/jz+d/RIGs8D2Z/Bv7/zP8vvAO6rq7Ttf+U/e48qqOjDJW4BNw+O9os9+2oVU1S53A9YAnwX+7/DrVuDAFWb+K4Ouz2MX3R4H/NMKcr+66PF+wJeAFwNbVljzlxfcv2Jbz+1k9t7Am4G/ZTDoAPjWiH5+Wxbcv2zEdV/AYFAO8EHgl4f377b4GO1szcPH+w5/hp8HvrvCmr86/Ppzwz+Hew0f7w58bYXZX+G2/wzaa9Gfma+uMPub23lu6wqzbwW+Bfz9gtv84x+tIPfKBffvwKC7+yFgzxH82bt8+DXADQuOexa+705mv43BfxDdY8G2v19J5oKchX8mLgfuOMK6v7ng/he39bPY2ZqBn2cwODgP+D8MPgA+cYTH4zvbem4ns69c9PjS4dc9ga+P6lgv8Zy/j/4++vu47ff4o1HkbCP73Qz+s/xqBv8R8PPAl8b1ft76ddvpc4y6rKq2JHksg/+dC4O/uP5zhbGXAv9eS3RlknxzBbnfT3LfqroWoAYdvccB5wIrPXfu1iR3HH7vPzl/K4Nzx1Y0lbcG0wuOSfJQ4Mzh//qNanrwgUm+z+Bn93NJ9q1BB3IPBgOblfgD4Iwkr2bwnwCXD7twvwgcu8Lsn6iqG4C3Am9N8ssrjPtYkksYfLB5J/CBJJcy+E+Gi1eaDZyf5NPAk4Cz4Sf/M5/tvXAH/GuSpwMfrNu6pbsBT2cwWF2JbwGHVtV3Fj+R5LsryN1j/k4NpnlPJ/kzBlOGRzLVqKoqyXlVVQser2jKRVX90fB3cX0G053fzm2dhJX6hSS/zeD3e8/5v0tHUTdwTpL3AH8BbExyDIMP8YcCP/OzvR3mj+0PgPcxmOp8V+AZwMsZfKjaWXNJVjHoHNwpydqq2pzkfqz876f/nP/3IMka4EcAVfXDERxrfx+X4O/jT/H3cQlV9bYkjwQOgNs+V1fVGSOIfx5wEIP/JP/34XE5agS52gXskoO8DM6H+i1u+4V8YhKq6sQVxH6L4T+4i1XVY1aQ+68MpgJcuyDvB0nWMfgLcCWuZDBd85Kqum7B9rsBx60kOMnbgb+tqs8lOQR4IXDJSjIXOG2Y/dlF2+8EPH+F2ccDr2Jw3O/P4LzH6xj8r+XcCnJ/Lskjq+pzi5+oqn9YQS4MPsy8gsH/iH8hg0V6fpvBgO+cFWb/PIOf2w+B/1VVnxxuv5lBR3wlpoATgJOTzH+IvAuDcwCnVpj9ZgYD86U+ePzVCnI3J1lXVefPb6iqv0hyPXDKCnLns+9cVbdU1XPnNw5/nj9YYTZV9aUkv8ng3MpPM+j8jsKngf8+vH9pkntU1T9lsKDTP68kuKr+JIPzbNcD92XwHxnTDP6T61kriL5liff6F+DU4W0lXgb8bwbnfD8VeEWS1QxmN/zPFWa/FPhUkv8A7sjw9yTJPgzOqV4Jfx9/NtvfxwX8fVxakvcxOB6Xc9v6DsWgW7tSv8Ggq/xvSX6Pwb+7bxlBrnYBu+o5eecB/8FgKtpPPrhX1f9aQeZLGPxDuB+DRTbWV9XlK6t0fLlmN5vdxZoXZe8PnDXK7EXvczcGfx+t6ENIXyVJjfAv6yT7AQ+pqvNGlalty+B81n+tqhUv8JUkDM6tGtvvir+P2+fvY7eN8vdxmPd14IGj/DOxIPtKYDVwIIMu57uA/1ErWENCu45ddZB3ZVWt+PID28j+ZQYfiqcY/M/ceuCsqlrRylnbyF1fVVevrOLx1Wx2c7ldzt7G+z2hqi7sUnYXaza7udwuZCfZG9hn/vSABdsPrKor25jdxZrNbi533NnDnLOBF1fV91aatUT2lhosEPhnwD9W1buywkWFtAupFpwY2PSNwZSUFZ3Mu4Pv8xDgy8CtXcg125/jpLMXvMd3upbdxZrN7kfNo8hmMP3/egZTzq5isLLw/HMrXeRrLNldrNnsftS86D0+xeAUjwuAj8zfRpT9aQanY2xlsGDb7sBXRpHtrf+3XfKcPAaLpGwcnlT+nwwWkKhawbLF8zJYtn4dg47HoQx+QXd6Gui4c81uNruLNY8rO9u/7Mjd2pjdxZrNbi63y9nAK4GH1m2X63lfkldW1YdY+SJL48ruYs1m96PmhV49opylHAH8LvC8Giww90vAiq5/q13HrjrIexODk1m/UlUjma+a5AnAMxmsUnkZg/OXpqvq39qYa3az2V2sedzZwKMZXLdo8Un3YXDR9TZmd7Fms5vL7XL2HWo43ayqLkvyeOCjSe7Fyld+HFd2F2s2ux81/0St8Fq3y2TfAJy44PF3GM2CLtoVTLqVOIkbg5b6biPO/BSDlZru2oVcs/05tiD748Djt/HcxW3M7mLNZvej5gayPwfcd9G2nwcuAn7Yxuwu1mx2P2pelPcDBhdA/z6DRf1uBb4/ouxHAF9k8B87Pxpm/99RZHvr/21X7eR9D9iU5OMMloYHoFZwCYWqevwoCmsq1+xms7tY87izGd9lR8aZ3cWazW4ut8vZ/8r4Ltczruwu1mx2c7njzp7P+/mFj5M8lZV31ue9ncFpEmcDa4HnMLi8k7SsUV2cumv+nsH/4uzB4H905m+SmrMV+Osk305yQpKDOpDdxZrNbi63y9mfAP5qcXZV/WdVvb+l2V2s2ex+1LxNVXUucMgI864Bdq+qW6vq3cDjRpWtftslL6EgqT3SwUs/dLFms5vL7WH2OC/Xs+LsLtZsdnO5DWT/jwUPd2PQcXtsVf3GCLIvBn4TeCdwA4OZaEdW1eqVZqv/dqlBXpK3V9WLkvxvljjhtqr++wTKkjSU5CHA6cCBVbV7F7K7WLPZzeWa3Wx2F2s2u7nccWQnefeChz8Gvg28o6puHEH2LwM3AncEZoBfAE4edvek7drVzsl7DvAi4K8nXYikgXTs0g/jzDW72ewu1mx2c7lmN5vdxZoBquqoUeRsI/sfhnf/HyOqV7uOXa2T9+Wqesik65C0zcsznFvju/TDirO7WLPZzeWa3Wx2F2s2u7nccWcveI97AW8DHsVgltglwEuq6roVZH6F7VzioaoO3Nls7Tp2tUHedSy43shiK1ldU9Ltk+RTwN8CH6yqf+lCdhdrNru5XLObze5izWY3lzvu7AXvceHwPd433PR7wLOq6gkryLw/cA/gu4ue+mXgeqdrakfsaoO87wGnMLhw7M+oKlvhkiRJ2iFJLq+qg5bbdjszPwq8sqquXLR9LfDnVfXfdjZbu45d7Zy871XVX0y6CEmSJPXCPyf5PQYrdsJgeuhNK8w8YPEAD6CqNic5YIXZ2kXsatfJW7KDJ0mSJO2E5zK4sPr8JQ6eNty2Ej+3nef2WmG2dhG72nTNu45rTrYkSZK0UknWA39XVe9YtP15wBOr6ojJVKYu2aUGeZIkSdKoJLkP8EfAASw4DWol115Ocg9gI/Aj4EvDzWuBPYDfrqobdjZbuw4HeZIkSdJOSHIF8C7gK8Dc/Paq+vQIsh8PPHj48Kqq+ruVZmrX4SBPkiRJ2glJvlBVD590HdJiDvIkSZKknZDkd4H7A58Afji/vaq2TKwoiV3vEgqSJEnSqPw68GzgEG6brlnDx9LE2MmTJEmSdkKSbwAHVtWPJl2LtNCudp08SZIkaVSuAO4y6SKkxZyuKUmSJO2cewDfSPJFbjsnr6rqKROsSXK6piRJkrQzkjx24UPgvwLPrKoHTagkCXC6piRJkrRThtfD+7/Ak4H3AIcCp06yJgmcrilJkiTdLklWAVPAM4GbgA0MZsg9fqKFSUNO15QkSZJuhyRzwGeA51XVNcNt36qqX5lsZdKA0zUlSZKk2+d3gBuATyV5R5JDGZyTJ7WCnTxJkiRpJyT5/4CnMpi2eQjwXmBjVX1iknVJDvIkSZKkFUpyV+DpwBFVdcik69GuzUGeJEmSJPWI5+RJkiRJUo84yJMkSZKkHnGQJ0mSJEk94iBPkiRJknrEQZ4kSZIk9cj/D8MPfNEkBa4XAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation matrix of the original dataset, we will not use this in order to find our outliers\n",
    "# We use it only for a quick view and understanding the original dataset\n",
    "\n",
    "corrmat = df.corr() \n",
    "fig = plt.figure(figsize = (20, 13)) \n",
    "sns.heatmap(corrmat, cmap='viridis', vmax = 1, vmin=-0.5 , square = True , linewidths= 0.05)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of our dataset in Time\n",
    "\n",
    "sns.kdeplot(df['Time'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of our dataset in Amount of transaction\n",
    "\n",
    "sns.kdeplot(df['Amount'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>scaled_amount</th>\n",
       "      <th>scaled_time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>...</th>\n",
       "      <th>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.244964</td>\n",
       "      <td>-1.996583</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251412</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.342475</td>\n",
       "      <td>-1.996583</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.069083</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.160686</td>\n",
       "      <td>-1.996562</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>...</td>\n",
       "      <td>0.524980</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.140534</td>\n",
       "      <td>-1.996562</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208038</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.073403</td>\n",
       "      <td>-1.996541</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>...</td>\n",
       "      <td>0.408542</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   scaled_amount  scaled_time        V1        V2        V3        V4  \\\n",
       "0       0.244964    -1.996583 -1.359807 -0.072781  2.536347  1.378155   \n",
       "1      -0.342475    -1.996583  1.191857  0.266151  0.166480  0.448154   \n",
       "2       1.160686    -1.996562 -1.358354 -1.340163  1.773209  0.379780   \n",
       "3       0.140534    -1.996562 -0.966272 -0.185226  1.792993 -0.863291   \n",
       "4      -0.073403    -1.996541 -1.158233  0.877737  1.548718  0.403034   \n",
       "\n",
       "         V5        V6        V7        V8  ...       V20       V21       V22  \\\n",
       "0 -0.338321  0.462388  0.239599  0.098698  ...  0.251412 -0.018307  0.277838   \n",
       "1  0.060018 -0.082361 -0.078803  0.085102  ... -0.069083 -0.225775 -0.638672   \n",
       "2 -0.503198  1.800499  0.791461  0.247676  ...  0.524980  0.247998  0.771679   \n",
       "3 -0.010309  1.247203  0.237609  0.377436  ... -0.208038 -0.108300  0.005274   \n",
       "4 -0.407193  0.095921  0.592941 -0.270533  ...  0.408542 -0.009431  0.798278   \n",
       "\n",
       "        V23       V24       V25       V26       V27       V28  Class  \n",
       "0 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053      0  \n",
       "1  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724      0  \n",
       "2  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752      0  \n",
       "3 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458      0  \n",
       "4 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our Vi columns are already scaled that's why we only scaled the columns 'Amount' and 'Time'\n",
    "# After this we must remove the old columns and replace them with the new, making a new dataset with the right values\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "stc = StandardScaler()\n",
    "\n",
    "df['scaled_amount'] = stc.fit_transform(df['Amount'].values.reshape(-1,1))\n",
    "df['scaled_time'] = stc.fit_transform(df['Time'].values.reshape(-1,1))\n",
    "\n",
    "df.drop(['Amount' , 'Time'] , axis = 1 , inplace = True )\n",
    "\n",
    "scaled_amount = df['scaled_amount']\n",
    "scaled_time   = df['scaled_time']\n",
    "\n",
    "df.drop(['scaled_amount' , 'scaled_time'] , axis = 1 , inplace = True )\n",
    "\n",
    "df.insert(0 , 'scaled_amount' , scaled_amount)\n",
    "df.insert(1 , 'scaled_time' , scaled_time)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will produce a sub-sample from the previous dataset which will contain randomly 492 valid transactions \n",
    "and 492 fraud transactions.\n",
    "We must do this because in the beginning of this notebook we saw that the original dataframe was heavily imbalanced.\n",
    "By doing this we help our algorithms better understand patterns that determines whether a transaction is a fraud or not \n",
    "which is our target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>scaled_amount</th>\n",
       "      <th>scaled_time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>...</th>\n",
       "      <th>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>186342</th>\n",
       "      <td>-0.340156</td>\n",
       "      <td>0.679037</td>\n",
       "      <td>0.667110</td>\n",
       "      <td>0.552175</td>\n",
       "      <td>-0.924040</td>\n",
       "      <td>-0.852436</td>\n",
       "      <td>0.711257</td>\n",
       "      <td>-0.626963</td>\n",
       "      <td>-0.127934</td>\n",
       "      <td>-1.805875</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.615246</td>\n",
       "      <td>1.418534</td>\n",
       "      <td>-0.788975</td>\n",
       "      <td>0.093979</td>\n",
       "      <td>-0.393006</td>\n",
       "      <td>-1.097888</td>\n",
       "      <td>-0.132725</td>\n",
       "      <td>0.374955</td>\n",
       "      <td>0.429319</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15204</th>\n",
       "      <td>0.046539</td>\n",
       "      <td>-1.437369</td>\n",
       "      <td>-19.179826</td>\n",
       "      <td>11.817922</td>\n",
       "      <td>-21.919174</td>\n",
       "      <td>6.086236</td>\n",
       "      <td>-14.708845</td>\n",
       "      <td>-4.308888</td>\n",
       "      <td>-15.357952</td>\n",
       "      <td>12.857165</td>\n",
       "      <td>...</td>\n",
       "      <td>1.572950</td>\n",
       "      <td>1.746802</td>\n",
       "      <td>-1.353149</td>\n",
       "      <td>-0.762965</td>\n",
       "      <td>0.117028</td>\n",
       "      <td>1.297994</td>\n",
       "      <td>-0.224825</td>\n",
       "      <td>1.621052</td>\n",
       "      <td>0.484614</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238268</th>\n",
       "      <td>-0.341835</td>\n",
       "      <td>1.153619</td>\n",
       "      <td>2.045338</td>\n",
       "      <td>0.074569</td>\n",
       "      <td>-1.875549</td>\n",
       "      <td>0.649284</td>\n",
       "      <td>0.389128</td>\n",
       "      <td>-0.679788</td>\n",
       "      <td>-0.008083</td>\n",
       "      <td>-0.135733</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.184529</td>\n",
       "      <td>-0.046246</td>\n",
       "      <td>0.140679</td>\n",
       "      <td>0.070449</td>\n",
       "      <td>0.522447</td>\n",
       "      <td>0.098893</td>\n",
       "      <td>0.617067</td>\n",
       "      <td>-0.044819</td>\n",
       "      <td>-0.026088</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44001</th>\n",
       "      <td>2.855315</td>\n",
       "      <td>-1.117562</td>\n",
       "      <td>-2.144411</td>\n",
       "      <td>1.073499</td>\n",
       "      <td>-2.773663</td>\n",
       "      <td>1.384394</td>\n",
       "      <td>-4.015477</td>\n",
       "      <td>0.747235</td>\n",
       "      <td>1.525638</td>\n",
       "      <td>0.192988</td>\n",
       "      <td>...</td>\n",
       "      <td>0.668124</td>\n",
       "      <td>0.144653</td>\n",
       "      <td>-0.885682</td>\n",
       "      <td>0.625099</td>\n",
       "      <td>0.096527</td>\n",
       "      <td>-0.189455</td>\n",
       "      <td>0.532908</td>\n",
       "      <td>0.423045</td>\n",
       "      <td>-0.210266</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43428</th>\n",
       "      <td>1.102834</td>\n",
       "      <td>-1.122574</td>\n",
       "      <td>-16.526507</td>\n",
       "      <td>8.584972</td>\n",
       "      <td>-18.649853</td>\n",
       "      <td>9.505594</td>\n",
       "      <td>-13.793819</td>\n",
       "      <td>-2.832404</td>\n",
       "      <td>-16.701694</td>\n",
       "      <td>7.517344</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.514923</td>\n",
       "      <td>1.190739</td>\n",
       "      <td>-1.127670</td>\n",
       "      <td>-2.358579</td>\n",
       "      <td>0.673461</td>\n",
       "      <td>-1.413700</td>\n",
       "      <td>-0.462762</td>\n",
       "      <td>-2.018575</td>\n",
       "      <td>-1.042804</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        scaled_amount  scaled_time         V1         V2         V3        V4  \\\n",
       "186342      -0.340156     0.679037   0.667110   0.552175  -0.924040 -0.852436   \n",
       "15204        0.046539    -1.437369 -19.179826  11.817922 -21.919174  6.086236   \n",
       "238268      -0.341835     1.153619   2.045338   0.074569  -1.875549  0.649284   \n",
       "44001        2.855315    -1.117562  -2.144411   1.073499  -2.773663  1.384394   \n",
       "43428        1.102834    -1.122574 -16.526507   8.584972 -18.649853  9.505594   \n",
       "\n",
       "               V5        V6         V7         V8  ...       V20       V21  \\\n",
       "186342   0.711257 -0.626963  -0.127934  -1.805875  ... -0.615246  1.418534   \n",
       "15204  -14.708845 -4.308888 -15.357952  12.857165  ...  1.572950  1.746802   \n",
       "238268   0.389128 -0.679788  -0.008083  -0.135733  ... -0.184529 -0.046246   \n",
       "44001   -4.015477  0.747235   1.525638   0.192988  ...  0.668124  0.144653   \n",
       "43428  -13.793819 -2.832404 -16.701694   7.517344  ... -1.514923  1.190739   \n",
       "\n",
       "             V22       V23       V24       V25       V26       V27       V28  \\\n",
       "186342 -0.788975  0.093979 -0.393006 -1.097888 -0.132725  0.374955  0.429319   \n",
       "15204  -1.353149 -0.762965  0.117028  1.297994 -0.224825  1.621052  0.484614   \n",
       "238268  0.140679  0.070449  0.522447  0.098893  0.617067 -0.044819 -0.026088   \n",
       "44001  -0.885682  0.625099  0.096527 -0.189455  0.532908  0.423045 -0.210266   \n",
       "43428  -1.127670 -2.358579  0.673461 -1.413700 -0.462762 -2.018575 -1.042804   \n",
       "\n",
       "        Class  \n",
       "186342      0  \n",
       "15204       1  \n",
       "238268      0  \n",
       "44001       1  \n",
       "43428       1  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sample(frac=1)\n",
    "\n",
    "# We take all the fraud transactions from the original dataset\n",
    "fraud_df =  df[df['Class'] ==1]\n",
    "# We just want to take 492 random choices of valid transactions, in order to create the new dataframe\n",
    "valid_df = df.loc[np.random.choice(df.index, 492, replace=False)]\n",
    "# We join them\n",
    "normal_distributed_df = pd.concat([fraud_df, valid_df],axis=0)\n",
    "\n",
    "# We shuffle the new dataframe and make it normal distributed\n",
    "new_df = normal_distributed_df.sample(frac=1, random_state=42)\n",
    "\n",
    "\n",
    "new_df = pd.DataFrame(new_df)\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(984, 31)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our new dataset which contains 984 transactions in random order (492 valid and 492 fraud)\n",
    "# 492 + 492 = 984, so the shape of the new_df is correct\n",
    "\n",
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of the Classes in the new dataset\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We present the new dataset of the equally possible transactions\n",
    "\n",
    "print('Distribution of the Classes in the new dataset')\n",
    "\n",
    "sns.countplot('Class', data=new_df)\n",
    "sns.color_palette(\"Set2\", 2)\n",
    "print('\\n')\n",
    "plt.title('Equally Distributed Classes',fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage of normal transactions is:  0.49796747967479676\n",
      "The percentage of fraud transactions is:  0.5020325203252033\n",
      "The total number of transactions in resampled data:  984\n"
     ]
    }
   ],
   "source": [
    "# We show the ratio of the new dataframe\n",
    "# Our data are equally likely as we can see below\n",
    "\n",
    "print(\"The percentage of normal transactions is: \", (len(new_df[new_df['Class']==0])/len(new_df)))\n",
    "print(\"The percentage of fraud transactions is: \", (len(new_df[new_df['Class']==1])/len(new_df)))\n",
    "print(\"The total number of transactions in resampled data: \", len(new_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We will not use this heatmap as a reference. We just use it as observation\n",
    "\n",
    "corr = df.corr()\n",
    "fig, ax = plt.subplots(figsize=(20,13))\n",
    "sns.heatmap(corr, cmap='viridis', annot_kws={'size':20},linewidths= 0.05)\n",
    "ax.set_title(\"Old Correlation Matrix \\n (don't use for reference)\", fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This is the heatmap that we will use as a reference in the project\n",
    "\n",
    "corr1 = new_df.corr()\n",
    "fig, ax = plt.subplots(figsize=(20,13))\n",
    "sns.heatmap(corr1, cmap='viridis', annot_kws={'size':20}, linewidths= 0.05)\n",
    "ax.set_title('SubSample Correlation Matrix \\n (use for reference)', fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                                                                                      IQR METHOD "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive Correlation Boxtplos \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find the positive correletaion which can be our outliers\n",
    "# Positive correlation: The higher the feature value the probability increases that it will be a fraudulent transaction\n",
    "\n",
    "f, axes = plt.subplots(ncols=5, figsize=(25,15))\n",
    "\n",
    "sns.boxplot(x='Class' , y ='V2',  data = new_df, ax=axes[0])\n",
    "sns.boxplot(x='Class' , y ='V3',  data = new_df, ax=axes[1])\n",
    "sns.boxplot(x='Class' , y ='V4',  data = new_df, ax=axes[2])\n",
    "sns.boxplot(x='Class' , y ='V7',  data = new_df, ax=axes[3])\n",
    "sns.boxplot(x='Class' , y ='V20', data = new_df, ax=axes[4])\n",
    "\n",
    "print('Positive Correlation Boxtplos', '\\n')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 25th Quartile is : -0.17254810780112\n",
      "The 75th Quantile is : 0.8207682657103028\n",
      "The IQR of V20 is : 0.9933163735114228 \n",
      "\n",
      "The V20 that we will remove is : 1.4899745602671342\n",
      "The min point is : -1.662522668068254\n",
      "The max point is : 2.310742825977437 \n",
      "\n",
      "The number of the outliers is :  41\n",
      "The number of transactions after the outliers removes is : 941\n",
      "The new dataset after we remove the outliers of v20 is : (941, 31)\n"
     ]
    }
   ],
   "source": [
    "# We will remove of the extreme outliers from top two positive correlation\n",
    "# First is the V20\n",
    "\n",
    "V20_fraud = new_df['V20'].loc[new_df['Class'] == 1 ].values\n",
    "\n",
    "q25_V20 = np.percentile(V20_fraud , 25)\n",
    "q75_V20 = np.percentile(V20_fraud , 75)\n",
    "\n",
    "print('The 25th Quartile is :' , q25_V20)\n",
    "print('The 75th Quantile is :' , q75_V20)\n",
    "\n",
    "V20_iqr = q75_V20 - q25_V20\n",
    "\n",
    "print('The IQR of V20 is :' , V20_iqr , '\\n')\n",
    "\n",
    "V20_off = 1.5 * V20_iqr\n",
    "V20_lower = q25_V20 - V20_off\n",
    "V20_upper = q75_V20 + V20_off\n",
    "\n",
    "print('The V20 that we will remove is :' , V20_off)\n",
    "print('The min point is :' , V20_lower)\n",
    "print('The max point is :' , V20_upper , '\\n')\n",
    "\n",
    "outliers_V20 = [i for i in V20_fraud if i < V20_lower or i > V20_upper ]\n",
    "new_df_V20 = new_df.drop(new_df[(new_df['V20'] > V20_upper) | (new_df['V20'] < V20_lower)].index)\n",
    "\n",
    "print('The number of the outliers is : ', len(outliers_V20))\n",
    "print('The number of transactions after the outliers removes is :' , len(new_df_V20))\n",
    "print('The new dataset after we remove the outliers of v20 is :' , new_df_V20.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before and After we apply the IQR method and remove the outliers of V20 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can compare our boxplots before and after we remove the outliers of V20. \n",
    "\n",
    "f,(ax1, ax2) = plt.subplots(1, 2, figsize=(10,10))\n",
    "\n",
    "sns.boxplot( x=\"Class\", y=\"V20\", data=new_df, ax=ax1 )\n",
    "sns.boxplot( x=\"Class\", y=\"V20\", data=new_df_V20, ax=ax2 )\n",
    "\n",
    "print('Before and After we apply the IQR method and remove the outliers of V20', '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 25th Quartile is : -8.606982274478735\n",
      "The 75th Quantile is :  -2.2747127743259696\n",
      "The IQR of V3 is : 6.332269500152766 \n",
      "\n",
      "The V3 tha we will remove is : 9.498404250229148\n",
      "The min point is : -18.105386524707882\n",
      "The max point is : 7.223691475903179 \n",
      "\n",
      "The number of the outliers is :  53\n",
      "The number of transactions after the outliers removes is : 931\n",
      "The new dataset after we remove the outliers of V3 is : (931, 31)\n"
     ]
    }
   ],
   "source": [
    "# We will remove of the extreme outliers from top two positive correlation\n",
    "# Second is the V3\n",
    "\n",
    "V3_fraud = new_df['V3'].loc[new_df['Class'] == 1 ].values\n",
    "\n",
    "q25_V3 = np.percentile(V3_fraud , 25)\n",
    "q75_V3 = np.percentile(V3_fraud , 75)\n",
    "\n",
    "print('The 25th Quartile is :'  , q25_V3)\n",
    "print('The 75th Quantile is : ' , q75_V3)\n",
    "\n",
    "V3_iqr = q75_V3 - q25_V3\n",
    "\n",
    "print('The IQR of V3 is :' , V3_iqr , '\\n')\n",
    "V3_off = 1.5 * V3_iqr\n",
    "V3_lower = q25_V3 - V3_off\n",
    "V3_upper = q75_V3 + V3_off\n",
    "\n",
    "print('The V3 tha we will remove is :' , V3_off)\n",
    "print('The min point is :' , V3_lower)\n",
    "print('The max point is :' , V3_upper ,'\\n')\n",
    "\n",
    "outliers_V3 = [i for i in V3_fraud if i < V3_lower or i > V3_upper ]\n",
    "new_df_V3 = new_df.drop(new_df[(new_df['V3'] > V3_upper) | (new_df['V3'] < V3_lower)].index)\n",
    "\n",
    "print('The number of the outliers is : ', len(outliers_V3))\n",
    "print('The number of transactions after the outliers removes is :' , len(new_df_V3))\n",
    "print('The new dataset after we remove the outliers of V3 is :' , new_df_V3.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before and After we apply the IQR method and remove the outliers of V3 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can compare our boxplots before and after we remove the outliers of V3 \n",
    "\n",
    "f,(ax1, ax2) = plt.subplots(1, 2, figsize=(10,10))\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V3\", data=new_df, ax=ax1)\n",
    "sns.boxplot(x=\"Class\", y=\"V3\", data=new_df_V3, ax=ax2)\n",
    "\n",
    "print('Before and After we apply the IQR method and remove the outliers of V3' , '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative Correlation Boxtplos \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Negative correlation: The lower the feature value,the probability decreases that it will be a fraudulent transaction.\n",
    "\n",
    "f, axes = plt.subplots(ncols=5, figsize=(25,15))\n",
    "\n",
    "sns.boxplot( x='Class' , y ='V10', data = new_df, ax=axes[0] )\n",
    "sns.boxplot( x='Class' , y ='V12', data = new_df, ax=axes[1] )\n",
    "sns.boxplot( x='Class' , y ='V14', data = new_df, ax=axes[2] )\n",
    "sns.boxplot( x='Class' , y ='V16', data = new_df, ax=axes[3] )\n",
    "sns.boxplot( x='Class' , y ='V17', data = new_df, ax=axes[4] )\n",
    "\n",
    "print('Negative Correlation Boxtplos' , '\\n')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 25th Quartile is : -7.744127897412915\n",
      "The 75th Quantile is : -2.5676682503671873\n",
      "The IQR of V10 is : 5.1764596470457285 \n",
      "\n",
      "The V10 tha we will remove is : 7.764689470568593\n",
      "The min point is : -15.508817367981507\n",
      "The max point is : 5.197021220201405 \n",
      "\n",
      "The number of the outliers is :  19\n",
      "The number of transactions after the outliers removes is : 965\n",
      "The new dataset after we remove the outliers of V10 is : (965, 31)\n"
     ]
    }
   ],
   "source": [
    "# We will remove of the extreme outliers from top two negative correlation\n",
    "# First the V10\n",
    "\n",
    "V10_fraud = new_df['V10'].loc[new_df['Class'] == 1 ].values\n",
    "\n",
    "q25_V10 = np.percentile(V10_fraud , 25)\n",
    "q75_V10 = np.percentile(V10_fraud , 75)\n",
    "\n",
    "print('The 25th Quartile is :' , q25_V10)\n",
    "print('The 75th Quantile is :' , q75_V10)\n",
    "\n",
    "V10_iqr = q75_V10 - q25_V10\n",
    "\n",
    "print('The IQR of V10 is :' , V10_iqr , '\\n')\n",
    "\n",
    "V10_off = 1.5* V10_iqr\n",
    "V10_lower = q25_V10 - V10_off\n",
    "V10_upper = q75_V10 + V10_off\n",
    "\n",
    "print('The V10 tha we will remove is :' , V10_off)\n",
    "print('The min point is :' , V10_lower)\n",
    "print('The max point is :' , V10_upper , '\\n')\n",
    "\n",
    "outliers_V10 = [i for i in V10_fraud if i < V10_lower or i > V10_upper ]\n",
    "new_df_V10 = new_df.drop(new_df[(new_df['V10'] > V10_upper) | (new_df['V10'] < V10_lower)].index)\n",
    "\n",
    "print('The number of the outliers is : ', len(outliers_V10))\n",
    "print('The number of transactions after the outliers removes is :' , len(new_df_V10))\n",
    "print('The new dataset after we remove the outliers of V10 is :' ,new_df_V10.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before and After we apply the IQR method and remove the outliers of V10 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can compare our boxplots before and after we remove the outliers of V10\n",
    "\n",
    "f,(ax1, ax2) = plt.subplots(1, 2, figsize=(10,10))\n",
    "\n",
    "sns.boxplot( x=\"Class\", y=\"V10\", data=new_df, ax = ax1 )\n",
    "sns.boxplot( x=\"Class\", y=\"V10\", data=new_df_V10, ax = ax2 )\n",
    "\n",
    "print('Before and After we apply the IQR method and remove the outliers of V10' , '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 25th Quartile is : -9.651892373984154\n",
      "The 75th Quantile is :  -4.272470994416755\n",
      "The IQR of V14 is : 5.379421379567399 \n",
      "\n",
      "The V14 tha we will remove is : 8.069132069351099\n",
      "The min point is : -17.721024443335253\n",
      "The max point is : 3.7966610749343435 \n",
      "\n",
      "The number of the outliers is :  5\n",
      "The number of transactions after the outliers removes is : 979\n",
      "The new dataset after we remove the outliers of V14 is : (979, 31)\n"
     ]
    }
   ],
   "source": [
    "# We will remove of the extreme outliers from top two negative correlation\n",
    "# First the V14\n",
    "\n",
    "V14_fraud = new_df['V14'].loc[new_df['Class'] == 1 ].values\n",
    "\n",
    "q25_V14 = np.percentile(V14_fraud , 25)\n",
    "q75_V14 = np.percentile(V14_fraud , 75)\n",
    "\n",
    "print('The 25th Quartile is :' , q25_V14)\n",
    "print('The 75th Quantile is : ' , q75_V14)\n",
    "\n",
    "V14_iqr = q75_V14 - q25_V14\n",
    "\n",
    "print('The IQR of V14 is :' , V14_iqr, '\\n')\n",
    "\n",
    "V14_off = 1.5 * V14_iqr\n",
    "V14_lower = q25_V14 - V14_off\n",
    "V14_upper = q75_V14 + V14_off\n",
    "\n",
    "print('The V14 tha we will remove is :' , V14_off)\n",
    "print('The min point is :' , V14_lower)\n",
    "print('The max point is :' , V14_upper,'\\n')\n",
    "\n",
    "outliers_V14 = [i for i in V14_fraud if i < V14_lower or i > V14_upper ]\n",
    "new_df_V14 = new_df.drop(new_df[(new_df['V14'] > V14_upper) | (new_df['V14'] < V14_lower)].index)\n",
    "\n",
    "print('The number of the outliers is : ', len(outliers_V14))\n",
    "print('The number of transactions after the outliers removes is :' , len(new_df_V14))\n",
    "print('The new dataset after we remove the outliers of V14 is :' ,new_df_V14.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before and After we apply the IQR method and remove the outliers of V14 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can compare our boxplots before and after we remove the outliers of V14\n",
    "\n",
    "f,(ax1, ax2) = plt.subplots(1, 2, figsize=(10,10))\n",
    "\n",
    "sns.boxplot( x=\"Class\", y=\"V14\", data=new_df, ax=ax1)\n",
    "sns.boxplot( x=\"Class\", y=\"V14\", data=new_df_V14, ax=ax2)\n",
    "\n",
    "print('Before and After we apply the IQR method and remove the outliers of V14' , '\\n' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['Class'], axis=1)\n",
    "y = df['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spliting the dataset in train set and test set, using sklearn libraly\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state = 42 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import (confusion_matrix, roc_curve, classification_report, precision_score, recall_score, accuracy_score, f1_score, roc_auc_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The errors of the Isolation Forest model is  711 \n",
      "\n",
      "Model Accuracy: 1.0\n",
      "Model Precision: 0.37\n",
      "Model Recall: 0.31\n",
      "Model F1-Score: 0.34\n",
      "Model ROC: 0.66 \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    284226\n",
      "           1       0.37      0.31      0.34       581\n",
      "\n",
      "    accuracy                           1.00    284807\n",
      "   macro avg       0.68      0.66      0.67    284807\n",
      "weighted avg       1.00      1.00      1.00    284807\n",
      " \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ISOLATION FOREST TREE MODEL\n",
    "\n",
    "from sklearn.ensemble import IsolationForest\n",
    "\n",
    "isf = IsolationForest(random_state = 42, max_samples = len(X)).fit(X)    # Fitting the model\n",
    "y_prediction = isf.predict(X)                                            # Prediction using trained model\n",
    "\n",
    "# The isolation forest use (-1,1) in order to predict the result\n",
    "# But we have valid = 0 and fraud = 1 , so we must make some changes  before we run it\n",
    "\n",
    "y_prediction[y_prediction ==  1] = 0                                    # Valid transactions are labelled as 0, but we change it to valid = 1 \n",
    "y_prediction[y_prediction == -1] = 1                                    # Fraudulent transactions are labelled as 1, but we change it to fraud = -1\n",
    "\n",
    "errors = (y_prediction != y).sum()                                      # Total number of errors is calculated\n",
    "\n",
    "print('The errors of the Isolation Forest model is ', errors , '\\n')\n",
    "\n",
    "print(\"Model Accuracy:\", round(accuracy_score(y_prediction , y),2))\n",
    "print(\"Model Precision:\", round(precision_score(y_prediction , y),2))\n",
    "print(\"Model Recall:\", round(recall_score(y_prediction , y),2))\n",
    "print(\"Model F1-Score:\", round(f1_score(y_prediction , y),2))\n",
    "print(\"Model ROC:\", round(roc_auc_score(y_prediction , y),2) , '\\n')\n",
    "\n",
    "\n",
    "conf_matrix=confusion_matrix(y_prediction,y)\n",
    "labels= ['Valid', 'Fraud'] \n",
    "plt.figure(figsize=(6, 6)) \n",
    "\n",
    "sns.heatmap(pd.DataFrame(conf_matrix), xticklabels= labels, yticklabels= labels, \n",
    "            linewidths= 0.05 ,annot=True, fmt=\"d\" , cmap='BuPu')\n",
    "\n",
    "print(classification_report(y_prediction , y) , '\\n')\n",
    "\n",
    "plt.title(\"Isolation Forest Classifier - Confusion Matrix\") \n",
    "plt.ylabel('True Value') \n",
    "plt.xlabel('Predicted Value') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy: 1.0\n",
      "Model Precision: 0.95\n",
      "Model Recall: 0.77\n",
      "Model F1-Score: 0.85\n",
      "Model ROC: 0.88 \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Valid       1.00      1.00      1.00     56859\n",
      "       Fraud       0.95      0.77      0.85       103\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "   macro avg       0.98      0.88      0.92     56962\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      " \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# RANDOM FOREST CLASSIFIER MODEL \n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rfc=RandomForestClassifier( random_state = 42 ) \n",
    "rfc.fit(X_train, y_train) \n",
    "Y_pred=rfc.predict(X_test) \n",
    "\n",
    "print(\"Model Accuracy:\", round(accuracy_score(y_test, Y_pred),2))\n",
    "print(\"Model Precision:\", round(precision_score(y_test, Y_pred),2))\n",
    "print(\"Model Recall:\", round(recall_score(y_test, Y_pred),2))\n",
    "print(\"Model F1-Score:\", round(f1_score(y_test, Y_pred),2))\n",
    "print(\"Model ROC:\", round(roc_auc_score(y_test, Y_pred),2) , '\\n')\n",
    "\n",
    "conf_matrix=confusion_matrix(y_test, Y_pred) \n",
    "labels= ['Valid', 'Fraud'] \n",
    "plt.figure(figsize=(6, 6)) \n",
    "\n",
    "sns.heatmap(pd.DataFrame(conf_matrix), xticklabels= labels, yticklabels= labels, \n",
    "            linewidths= 0.05 ,annot=True, fmt=\"d\" , cmap='BuPu')\n",
    "\n",
    "print(classification_report(y_test, Y_pred, target_names=labels) , '\\n')\n",
    "\n",
    "plt.title(\"Random Forest Classifier - Confusion Matrix\") \n",
    "plt.ylabel('True Value') \n",
    "plt.xlabel('Predicted Value') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy: 1.0\n",
      "Model Precision: 0.86\n",
      "Model Recall: 0.64\n",
      "Model F1-Score: 0.73 \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Valid       1.00      1.00      1.00     56859\n",
      "       Fraud       0.86      0.64      0.73       103\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "   macro avg       0.93      0.82      0.87     56962\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      " \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LOGISTIC REGRESSION MODEL\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression(random_state = 42)\n",
    "logreg.fit(X_train, y_train)\n",
    "Y_pred1 = logreg.predict(X_test)\n",
    "\n",
    "print(\"Model Accuracy:\", round(accuracy_score(y_test, Y_pred1),2))\n",
    "print(\"Model Precision:\", round(precision_score(y_test, Y_pred1),2))\n",
    "print(\"Model Recall:\", round(recall_score(y_test, Y_pred1),2))\n",
    "print(\"Model F1-Score:\", round(f1_score(y_test, Y_pred1),2) , '\\n')\n",
    "\n",
    "\n",
    "conf_matrix1 = confusion_matrix(y_test, Y_pred1)\n",
    "plt.figure(figsize=(6, 6)) \n",
    "labels= ['Valid', 'Fraud'] \n",
    "\n",
    "sns.heatmap(pd.DataFrame(conf_matrix1),annot=True, fmt='d',\n",
    "            linewidths= 0.05 ,cmap='BuPu',xticklabels= labels, yticklabels= labels)\n",
    "\n",
    "print(classification_report(y_test, Y_pred1, target_names=labels) , '\\n')\n",
    "\n",
    "plt.title('Logistic Regression - Confusion Matrix')\n",
    "plt.ylabel('True Value')\n",
    "plt.xlabel('Predicted Value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy: 1.0\n",
      "Model Precision: 0.72\n",
      "Model Recall: 0.75\n",
      "Model F1-Score: 0.73 \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Valid       1.00      1.00      1.00     56859\n",
      "       Fraud       0.72      0.75      0.73       103\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "   macro avg       0.86      0.87      0.87     56962\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      " \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DECISSION TREE CLASSIFIER MODEL\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dtc = DecisionTreeClassifier(random_state = 42)\n",
    "dtc.fit(X_train,y_train)\n",
    "Y_pred2 = dtc.predict(X_test)\n",
    "conf_matrix2 = confusion_matrix(y_test , Y_pred2)\n",
    "\n",
    "print(\"Model Accuracy:\", round(accuracy_score(y_test, Y_pred2),2))\n",
    "print(\"Model Precision:\", round(precision_score(y_test, Y_pred2),2))\n",
    "print(\"Model Recall:\", round(recall_score(y_test, Y_pred2),2))\n",
    "print(\"Model F1-Score:\", round(f1_score(y_test, Y_pred2),2) , '\\n')\n",
    "\n",
    "conf_matrix2 = confusion_matrix(y_test, Y_pred2)\n",
    "plt.figure(figsize=(6, 6))\n",
    "labels= ['Valid', 'Fraud'] \n",
    "\n",
    "sns.heatmap(pd.DataFrame(conf_matrix2),annot=True, fmt='d',linewidths= 0.05 ,cmap='BuPu',\n",
    "            xticklabels= labels, yticklabels= labels)\n",
    "\n",
    "print(classification_report(y_test,Y_pred2,target_names=labels) , '\\n')\n",
    "\n",
    "plt.title('Decission Tree Classifier - Confusion Matrix')\n",
    "plt.ylabel('True Value') \n",
    "plt.xlabel('Predicted Value') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy: 1.0\n",
      "Model Precision: 0.82\n",
      "Model Recall: 0.61\n",
      "Model F1-Score: 0.7 \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Valid       1.00      1.00      1.00     56859\n",
      "       Fraud       0.72      0.75      0.73       103\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "   macro avg       0.86      0.87      0.87     56962\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      " \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NAIVE BAYES CLASSIFIER\n",
    "\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "\n",
    "NB = BernoulliNB()\n",
    "NB.fit(X_train,y_train)\n",
    "Y_pred3 = NB.predict(X_test)\n",
    "conf_matrix_nb = confusion_matrix(y_test , Y_pred3)\n",
    "\n",
    "print(\"Model Accuracy:\", round(accuracy_score(y_test, Y_pred3),2))\n",
    "print(\"Model Precision:\", round(precision_score(y_test, Y_pred3),2))\n",
    "print(\"Model Recall:\", round(recall_score(y_test, Y_pred3),2))\n",
    "print(\"Model F1-Score:\", round(f1_score(y_test, Y_pred3),2) , '\\n')\n",
    "\n",
    "conf_matrix2 = confusion_matrix(y_test, Y_pred3)\n",
    "plt.figure(figsize=(6, 6))\n",
    "labels= ['Valid', 'Fraud'] \n",
    "\n",
    "sns.heatmap(pd.DataFrame(conf_matrix2),annot=True, fmt='d',linewidths= 0.05 ,cmap='BuPu',\n",
    "            xticklabels= labels, yticklabels= labels)\n",
    "\n",
    "print(classification_report(y_test,Y_pred2,target_names=labels) , '\\n')\n",
    "\n",
    "plt.title('Naive Bayes Classifier - Confusion Matrix')\n",
    "plt.ylabel('True Value') \n",
    "plt.xlabel('Predicted Value') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy: 1.0\n",
      "Model Precision: 0.96\n",
      "Model Recall: 0.68\n",
      "Model F1-Score: 0.8 \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Valid       1.00      1.00      1.00     56859\n",
      "       Fraud       0.72      0.75      0.73       103\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "   macro avg       0.86      0.87      0.87     56962\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      " \n",
      "\n"
     ]
    },
    {
     "data": {
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fpbWRcWPH0qtXbz634Ya1rsryoaCZQB7NQaukv58DtgJGp8dfB8aXW1HScGA4wMUXX5xD1Wrrp9udwKzps+ixZg9+ee+vmfriVOo71dNttW78ZJvjGLjVBvx01E85dN0fsMGQDVi0cBEH9z2Qbqt14zcPnsVT9z3FSt1Xos/6fbns2Evp9dlei23/n7dNYPw1D7DgowXs9sPd+fHIY/jZzqfU6GgtTx988AGXXHwxF116aa2rYh1cqweBiDgdQNI9wOYRMSc9Pg24vsyqRMQIYETDw9t/OLrc4h3OrOmzgKzJ59GbH2HgkA2YOfVtHrnpEQBe/ue/WbQo6L5Gd7b/zpd54u+Ps3DBQmbPmM2LD7/A+lsOpPvqq7DeFutxyeTLqO9UT49ePTjj/t9wyo4nMWfWnE/2dc8ld3PQWQfX4jCtDUx97TVef30q39x3HwDefPNNhu63H1dfdx1rrLlmbSvXUXXgJp1q5Nkn8Bngo5LHHwEDctxfu9Z5pc507db1k/kv7LoZrz77Co/e8iib7LQJAH0H9qXTip147+33mPHqjE/KO6/UmQ22+RyvvziVuy66i0P6HcSh6wzjxC+ewLR/T+OUHU8CYLW1Pr0yZMheWzP1hdfa+CitrQzcYAPGPfQwd903hrvuG0Pv3r259sYbHQCqUafqpg4qz6uDrgImSLoZCGBf4Moc99eurdp7VU6++WcA1Heq44G/PcATdz9BpxU6cdTlR/OnZ/7Mgo8+5g8HnQvAnX++g6P/8mPOf/bPIDHmL/cx5ZkpZffx9aP2YsheQ1i4YBFzZs3hvIPPy/morK389PjjmDhhAu+++y677LgDhx1xBN/Yb/9aV2v50nHP41VRROS3cWlz4Evp4fiIeHIpVo+9tGcOtbKObHTczvyFi2pdDWtnutRX/1X8rF3+UtXJ8Kf3HtIhw0geVwd1j4j3JPUEpqSp4bmeETGrtfdpZla1gvYJ5NEc9DdgT+BxsmagBkqP181hn2ZmVVEHbtevRh5XB+2Z/q7T2ts2M8tNMWNALs1Bm5d7PiKeaO19mplVzc1BreacMs8FsFMO+zQzs2WQR3PQjq29TTOz3LlPoPVJ2ggYBHRpKIuIwt4rYGbtWDFjQH53DEs6FfhTmnYEfgvsldf+zMyqIlU3Vbwb1Ut6UtLt6XFPSfdKejn9Xa1k2ZMkTZL0kqSvlpRvIemZ9NwflYYxldRZ0nWp/DFJA1qqT57DRuwP7Ay8ERGHAJsCnXPcn5nZsqurcqrc0cALJY9PBMZExEBgTHqMpEHAUGAwsBtwgaT6tM6FZINtDkzTbql8GPBORKwPnAucVclh52V+RCwCFkjqDryF7xEwswKT1B/YAygd/nVvYGSaHwnsU1J+bUR8GBGTgUnAEEl9gO4R8UhkQz5c2Widhm3dAOzckCU0p9WDgKTzJW1HNm7QqsAlZDeOPQFMaO39mZm1iiqbgyQNlzSxZBrexF7OA04ASsc+6R0R0wHS34Yx4vsBpaNATk1l/dJ84/LF1omIBcBsYPVyh51Hx/DLwO+AvsBc4BpgF7LI9XQO+zMzq1q1vw7WaCj8pra/J/BWRDwuaYdKqtTUbsqUl1unWa2eCUTEHyJiW2B7YBbwF+AuYB9JA1t7f2ZmrUJVTi3bDthL0hTgWmAnSX8F3kxNPKS/b6XlpwJrl6zfH5iWyvs3Ub7YOpI6AT3IzsPNyq1PICJeiYizImIz4DtkQ0m/mNf+zMzas4g4KSL6R8QAsg7fsRHxPbJfXzwoLXYQcGuaHw0MTVf8rEPWATwhNRnNkbRNau8/sNE6DdvaP+2jbCaQ230CklYg67EeSnaV0APA6Xntz8ysKrW7WexMYJSkYcCrwAEAEfGcpFHA88AC4PCIWJjWOQy4AuhK1tJyVyq/DLhK0iSyDGBoSzvPY+ygXYBvk/WATyBLe4ZHxLzW3peZWatpw7GDImIcMC7NzyT7otzUcmcAZzRRPhHYqIny+aQgUqk8MoGTyYaTPt6/HWBmHUZB7xj22EFmZlDYsYPyvFnMzMzauVwHkDMz6zCKmQg4CJiZAf5RGTOzIvNvDJuZFVkxY4A7hs3MisyZgJkZuE/AzKzQ3CdgZlZgxYwB7hMwMysyZwJmZuA+ATOzQitou4iDgJkZOBMwMyuyan9juKMqaAJkZmbgTMDMLFPQr8QOAmZm4D4BM7NCcxAwMyuwgjYHFfSwzcwMnAmYmWXcHGRmVmAOAmZmBVbQxvGCHraZmYEzATOzjJuDzMwKzEHAzKzACto47iBgZgaFzQQKGvvMzAycCZiZZQqaCTgImJlBYdtFHATMzMCZgJlZoRUzBhQ1ATIzM6gwE5D0WWBgRNwnqSvQKSLm5Fs1M7M2VFfMVKDFTEDSocANwMWpqD9wS451MjNre1J1UwdVSXPQ4cB2wHsAEfEy0CvPSpmZtTlVOXVQlQSBDyPio4YHkjoBkV+VzMysrVTSJ/CApJOBrpJ2Af4HuC3fapmZtTH3CTTrRGAG8AzwQ+BO4Gd5VsrMrM0VtE+gxUwgIhYBl6TJzGz51HHP41VpMQhImkwTfQARsW4uNTIzq4WCNgdV0iewZcl8F+AAoGc+1TEzs7bUYp9ARMwsmV6PiPOAnfKvmplZG3KfQNMkbV7ysI4sM1gltxqZmdVCxz2PV6WS5qBzSuYXAFOAb+ZSGzOzWnGfQNMiYse2qIiZWU3l3KQjqQswHuhMdu69ISJOldQTuA4YQPqSHRHvpHVOAoYBC4GjIuLuVL4FcAXQleyy/aMjIiR1Bq4EtgBmAt+KiCnl6tVsEJB0bLkVI+L3ZY/YzMxKfQjsFBFzJa0APCTpLuAbwJiIOFPSiWT3Zv1U0iBgKDAY6AvcJ2mDiFgIXAgMBx4lCwK7AXeRBYx3ImJ9SUOBs4BvlatUuY7hVVqYzMyWH3VVTi2IzNz0cIU0BbA3MDKVjwT2SfN7A9dGxIcRMRmYBAyR1AfoHhGPRESQffMvXadhWzcAO0vlU5xmM4GIOL3lwzIzW060wRU+kuqBx4H1gT9HxGOSekfEdICImC6pYYDOfmTf9BtMTWUfp/nG5Q3rvJa2tUDSbGB14O3m6lTJ1UFdyFKMwWT3CZB28P2W1jUz6zCqDAKShpM10TQYEREjSpdJTTlfkLQqcLOkjcptsomyKFNebp1mVTJ20FXAWsBXgQfIfk/APyhjZlYiIkZExJYl04gyy74LjCNry38zNfGQ/r6VFpsKrF2yWn9gWirv30T5YuukEZ97ALPK1buSILB+RPwcmBcRI4E9gI0rWM/MrOPIuU9A0popAyD9QuNXgBeB0cBBabGDgFvT/GhgqKTOktYBBgITUtPRHEnbpPb+Axut07Ct/YGxqd+gWZXcJ/Bx+vtuSl3eILuUycxs+ZF/n0AfYGTqF6gDRkXE7ZIeAUZJGga8SjY0DxHxnKRRwPNk92gdnpqTAA7j00tE70oTwGXAVZImkWUAQ1uqVCVBYISk1YCfk0WZbmnezGz5kXMQiIingc2aKJ8J7NzMOmcAZzRRPhFYoj8hIuaTgkilyt0n8DxwNdklSu+Q9Qd45FAzWz5V0ji+HCp32N8m+9Z/j6THJP24ofPCzMyWD80GgYj4V0ScFBHrAUcDnwUekzRW0qFtVkMzs7bgUUSbFxGPAo9KuhU4FzifNvilsdFxe967sA6oS31B83bLVwc+kVejkpvFtiJrGtqPbHCjEcD1+VYrM3/horbYjXUgXerr/LmwJbTKF4OCfrco1zH8v2QDD70DXAtsFxFTm1vezKwja2GIneVWuUzgQ2D3iPh3W1XGzMzalgeQMzMD9wmYmRVZQWOAg4CZGRS3T6DF/nBlvifpF+nxZyQNyb9qZmaWt0ouiroA2JbsMlHIhpH+c241MjOrhZxHEW2vKmkO2joiNpf0JEBEvCNpxZzrZWbWporaHFTRUNJp6NOAbExswHfrmNnyxUGgWX8EbgZ6STqD7IcKfpZrrczM2lhBY0DLQSAirpb0ONl41wL2iYgXcq+ZmZnlrpKxgz4DvA/cVloWEa/mWTEzszZV0FSgkuagO/j0F+67AOsALwGDc6yXmVmbUp2DQJMiYrEflZe0OfDD3GpkZlYLxYwBS391a0Q8AWyVQ13MzKyNVdIncGzJwzpgc2BGbjUyM6sB3yfQvFVK5heQ9RHcmE91zMxqo6AxoHwQSDeJdYuIn7RRfczMaqOgUaDcL4t1iogFqSPYzGy55uagJU0ga/9/StJost8VntfwZETclHPdzMwsZ5X0CfQEZgI78en9AgE4CJjZ8qMDjwRajXJBoFe6MuhZPj35N4hca2Vm1sbcHLSkeqAbTd9C4SBgZssXB4ElTI+IX7ZZTczMaqigMaBsK1hBXxIzs+Iolwns3Ga1MDOrMfcJNBIRs9qyImZmNeWrg8zMiquomUBBY5+ZmYEzATOzTEEzAQcBMzMKGwMcBMzMgMJGAQcBMzOK+xvD7hg2MyswZwJmZhS2NchBwMwMKGwUcBAwM6O4N4s5CJiZQWGHzHTHsJlZgTkTMDOjuJeIOgiYmVHY1iAHATMzKG7HsPsEzMwKzJmAmRmFvU3AmYCZGWRBoJqp5e1rbUn3S3pB0nOSjk7lPSXdK+nl9He1knVOkjRJ0kuSvlpSvoWkZ9Jzf1Rqy5LUWdJ1qfwxSQNaqpeDgJkZoCr/VWABcFxEfB7YBjhc0iDgRGBMRAwExqTHpOeGAoOB3YALJNWnbV0IDAcGpmm3VD4MeCci1gfOBc5qqVIOAmZm5J8JRMT0iHgizc8BXgD6AXsDI9NiI4F90vzewLUR8WFETAYmAUMk9QG6R8QjERHAlY3WadjWDcDOaqHH20HAzKwVSBouaWLJNLzMsgOAzYDHgN4RMR2yQAH0Sov1A14rWW1qKuuX5huXL7ZORCwAZgOrl6u3O4bNzKi+YzgiRgAjWt6PugE3Aj+OiPfKfFFv6okoU15unWY5EzAzI7tPoJqpwn2sQBYAro6Im1Lxm6mJh/T3rVQ+FVi7ZPX+wLRU3r+J8sXWkdQJ6AHMKlcnBwEzM7Kv0NVMLW4/ixSXAS9ExO9LnhoNHJTmDwJuLSkfmq74WYesA3hCajKaI2mbtM0DG63TsK39gbGp36BZbg4yM6NN7hjeDvhv4BlJT6Wyk4EzgVGShgGvAgcARMRzkkYBz5NdWXR4RCxM6x0GXAF0Be5KE2RB5ipJk8gygKEtVUotBIlaivkLF9W6DtbOdKmvw58La6xLffWjv1330OSqTobf+uI6HfJ2M2cCZmYU945hBwEzMzyKqJlZoXkUUTMzKxxnAmZmuE/AzKzQitoc5CBgZoY7hs3MCq2giYA7hs3MisyZgJkZ7hMwMyu0YoYABwEzM6C4fQIOAmZmFLc5yB3DZmYF5kzAzAw3B5mZFZoK2jXsIGBmRnEzAfcJmJkVmDMBMzOKmwk4CJiZAXXuEzAzKy5nAmZmBVbUIOCOYTOzAnMm0A4tXLiQbx9wAL169+L8Cy9i9rvvcsJxxzLt9dfp268fZ//+XLr36FHralobeu+99zj9Fz9n0ssvI4nTf/1rHhw/nnFjx1KnOlZbvSe/+t/f0KtXr1pXtcPysBHWblx91VWsu966nzy+/NJLGLLNttz297sZss22XHbpJTWsndXCb3/zv2z3xS9y6x13cv1NN7POuutx8PeHccMttzLq5pvZ/ss7cPEFF9S6mh2aqpw6KgeBdubNN97gwQceYN/99v+k7P6xY9lrn70B2Gufvbl/zJhaVc9qYO7cuTw+ceInn4kVVlyR7t27061bt0+Wmf/BB4Vt024tkqqaOqpcmoMk9Sz3fETMymO/y4Pfnvkbjjn+eObNm/dJ2ayZM1lzzSzNX3PNXsya5ZevSKa+9hqr9ezJL045mZdefIlBgwdxwkkns9JKK/Gn887jttG30q1bNy69YmStq9qhdeDzeFXyygQeByamvzOAfwMvp/nHm1tJ0nBJEyVNHDFiRE5Va78eGHc/PXv2ZNDgwbWuirUjCxcu5MXnn+eAbw1l1E030bXrSlyemgSP/PGPuWfs/eyx59e59uqra1xT64hyyQQiYh0ASRcBoyPizvR4d+ArZdYbATSc/WP+wkV5VK/deuqJJxl3//08NH48H374EfPmzeWkE06g5+qrM2PGW6y5Zi9mzHiLnj3LJlq2nOnduze9e/dmk003BWCXXXf9JAg02H2PPTjisB/xP0ceWYsqLhecCeRjq4YAABARdwFfznmfHdbRxx7LvfeP4677xnDWOeew1dZb85vf/pYddtyJ0bfcCsDoW25lx512qnFNrS2tseaa9F6rD1MmTwbgsUcfZd311ueVKVM+WWbc/fezzrrrNrMFq4Sq/NdR5X2J6NuSfgb8FQjge8DMnPe53Pn+oT/gJ8ccyy033sBaffryu3PPrXWVrI2deMopnHTCT/j444/p339tfnnGGZz2i58zZfJk6urq6NO3Lz879bRaV7NDK2omoIjIb+NZB/GpwPapaDxweoUdw4VrDrKWdamvw58La6xLfV3Vp/AHn3+jqpPhlwat1SHDSK6ZQDrZH53nPszMWkNHvsyzGrkGAUn3kzUDLSYi3KhtZu1KQWNA7n0Cx5fMdwH2AxbkvE8zs6XmTCAHEdH4noCHJT2Q5z7NzJZFMUNA/s1BpRe01wFbAGvluU8zM6tc3s1Bj5P1CYisGWgyMCznfZqZLbWCtgbl3hy0Tp7bNzNrLe4TyImkjYBBZB3DAETElXnv18xsaRQ0BuTeJ3AqsANZELgT2B14CHAQMLN2pSMP/VCNvMcO2h/YGXgjIg4BNgU657xPMzOrUN7NQR9ExCJJCyR1B94CPMqVmbU7bg7Kx0RJqwKXkF0pNBeYkPM+zcyWmjuGW5myV/Q3EfEucJGkvwPdI+LpvPZpZrasChoD8usTiGx40ltKHk9xADAza1/y7hh+VNJWOe/DzKxqUnVTR5V3n8COwI8kTQHmkd05HBGxSc77NTNbKr5EtBVJ+kya3Z3saqCdgK8De6a/ZmbtSt6ZgKTLJb0l6dmSsp6S7pX0cvq7WslzJ0maJOklSV8tKd9C0jPpuT+m/lckdZZ0XSp/TNKASo47r+agWwAi4hXg9xHxSumU0z7NzJaZpKqmClwB7Nao7ERgTEQMBMakx0gaBAwFBqd1LpBUn9a5EBgODExTwzaHAe9ExPrAucBZlVQqryBQ+or4vgAzK7yIGA80/mndvYGRaX4ksE9J+bUR8WFETAYmAUMk9SG7yvKRdPHNlY3WadjWDcDOqiA65RUEopl5M7N2qdrmIEnDJU0smYZXsNveETEdIP3tlcr7Aa+VLDc1lfVL843LF1snIhYAs4HVW6pAXh3Dm0p6jywj6Jrm4dOO4e457dfMbJlU2zEcESOAEa1TmyYrE2XKy61TVi5BICLqW17KzKz9qNFlnm9K6hMR01NTz1upfCqwdsly/YFpqbx/E+Wl60yV1AnowZLNT0vI+z4BMzNr3mjgoDR/EHBrSfnQdMXPOmQdwBNSk9EcSduk9v4DG63TsK39gbGp36Cs3H9PwMysI6jLORWQdA3Z0PprSJoKnAqcCYySNAx4FTgAICKekzQKeJ7sVxkPj4iFaVOHkV1p1BW4K00AlwFXSZpElgEMraheFQSKWon5CxfVug7WznSpr8OfC2usS31d1WfwF6fNrupkuGHfHh3ybjNnAmZmdOyhH6rhIGBmhoeNMDOzAnImYGaGm4PMzArNvyxmZlZgBY0BDgJmZlDcTMAdw2ZmBeZMwMyMpkdfKwIHATMzitsc5CBgZkZxO4bdJ2BmVmDOBMzMcJ+AmVmxFbQ9yEHAzAxnAmZmhVbQRMAdw2ZmReZMwMwMNweZmRVbQduDHATMzHAmYGZWaAVNBNwxbGZWZM4EzMyAojYIOQiYmVHc5iAHATMzipoHuE/AzKzQnAmYmeHmIDOzgitmFHAQMDPDmYCZWaEVNAa4Y9jMrMicCZiZQWFTAQcBMzNABY0CDgJmZrhj2Mys0AoaA9wxbGZWZM4EzMygsO1BDgJmZhS3OchBwMyMwiYC7hMwMysyZwJmZrg5yMys2AraHuQgYGaGMwEzs0IraCLgjmEzsyJzJmBmBhS1QchBwMyM4jYHOQiYmVHUPMBBwMwMKG4m4I5hM7MCcyZgZgYUtUGoXQeBLvVOVGxJ/lxYHoraHKSIqHUdrAWShkfEiFrXw9oXfy6sNfgrVccwvNYVsHbJnwurmoOAmVmBOQiYmRWYg0DH4HZfa4o/F1Y1dwybmRWYMwEzswJzEKgBSeMkfbVR2Y8lXVBm+S3T/J2SVm1imdMkHZ9LhS13khZKeqpkGpDDPqZIWqO1t2sdW7u+WWw5dg0wFLi7pGwo8JOWVoyIr+VVKaupDyLiC009IUlkTbeL2rZKVgTOBGrjBmBPSZ0B0re+vsB3JE2U9Jyk05tasfTbnKRTJL0k6T7gc21Ud2sDkgZIeiFlh08Aa0u6sKnPR6PPxJaSxqX51SXdI+lJSRdT1HERrCwHgRqIiJnABGC3VDQUuA44JSK2BDYBvixpk+a2IWmLtN5mwDeArXKttOWta0lT0M2p7HPAlRGxWUS8wlJ8PpJTgYciYjNgNPCZ3GpvHZaDQO00NAmR/l4DfFPSE8CTwGBgUJn1vwTcHBHvR8R7ZP/JreP6ICK+kKZ9U9krEfFoyTJL8/kA2B74K0BE3AG809qVto7PQaB2bgF2lrQ50JXsP+jxwM4RsQlwB9ClhW34+t7l27yGGUnr0PznYwGf/l9u/JnxZ8TKchCokYiYC4wDLifLArqT/aefLak3sHsLmxgP7Cupq6RVgK/nWF2rvXKfjynAFml+v5Ly8cB3ASTtDqyWfzWto/HVQbV1DXATMDQiXpT0JPAc8B/g4XIrRsQTkq4DngJeAR7Mua5WQxHxrzKfj9OByySdDDzWqPya1IT0APBqW9XXOg7fMWxmVmBuDjIzKzAHATOzAnMQMDMrMAcBM7MCcxAwMyswBwFrUsmols9Kul7SSlVs6wpJ+6f5SyU1e6erpB0k/dcy7GOJETLTfn/YqGwfSXdWUlezInAQsOY0DGOwEfAR8KPSJyXVL8tGI+IHEfF8mUV2AJY6CDSjdGiOBg1DdJgZDgJWmQeB9dO39Psl/Q14RlK9pLMl/VPS0w3fupU5X9Lzku4AejVsqNFvI+wm6QlJ/5I0Jo2m+iPgmJSFfEnSmpJuTPv4p6Tt0rqVjJB5H7ChpD5pnZWArwC3SPpF2t6zkkak4ZoXU2Z0zpUlXZ7Wf1LS3q3zMpu1PQcBK0tSJ7IhCp5JRUPIRrMcBAwDZkfEVmSjmB6axrjZl2wEzI2BQ2nim72kNYFLgP0iYlPggIiYAlwEnJuykAeBP6THW5ENiXBp2kSLI2RGxEKyO7K/mYr2Au6PiDnA+RGxVcp0ugJ7LsXLcgowNtVpR+BsSSsvxfpm7YaHjbDmdJX0VJp/ELiM7GQ+ISImp/JdgU1K2tB7AAPJRq+8Jp2Ep0ka28T2twHGN2wrImY1U4+vAINKvqh3T2MlbU82hDYRcYek5kbIvAY4myyYDAWuTOU7SjoBWAnoSTYcw23NbKOxXYG99OkvuXUhC0IvVLi+WbvhIGDNWeKXrtKJeF5pEXBkRNzdaLmv0fLolapgGciy1W0j4oMm6lLJ+g8DfSRtShbEhkrqAlwAbBkRr0k6jaZHbG1udE6RZTAvVbB/s3bNzUFWjbuBwyStACBpg9QsMp7sZFuf2uN3bGLdR8h+GGWdtG7PVD4HWKVkuXuAIxoeSPpCmq1ohMzIBscaBYwE7oyI+Xx6Qn9bUjeguauBptD06Jx3A0c29CNI2qyZ9c3aPQcBq8alwPPAE5KeBS4myy5vBl4m60e4kGwEy8VExAxgOHCTpH+R/bIaZE0y+zZ0DANHAVumjufn+fQqpdOB7dMImbtSfoTMa4BNgWvTvt8l6494hux3Hf7ZzHqnA3+Q9CCwsKT8V8AKwNPpuH9VZt9m7ZpHETUzKzBnAmZmBeYgYGZWYA4CZmYF5iBgZlZgDgJmZgXmIGBmVmAOAmZmBeYgYGZWYP8Ph3hZVgztjt0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SVM CLASSIFIER MODEL\n",
    "# SVM = Support Vector Machine\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "svm_clf = SVC()\n",
    "svm_clf.fit(X_train,y_train)\n",
    "\n",
    "Y_pred4 = svm_clf.predict(X_test)\n",
    "\n",
    "conf_matrix_svm = confusion_matrix(y_test,Y_pred4)\n",
    "\n",
    "print(\"Model Accuracy:\", round(accuracy_score(y_test, Y_pred4),2))\n",
    "print(\"Model Precision:\", round(precision_score(y_test, Y_pred4),2))\n",
    "print(\"Model Recall:\", round(recall_score(y_test,Y_pred4),2))\n",
    "print(\"Model F1-Score:\", round(f1_score(y_test, Y_pred4),2) , '\\n')\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "labels= ['Valid', 'Fraud'] \n",
    "\n",
    "sns.heatmap(pd.DataFrame(conf_matrix2),annot=True, fmt='d',linewidths= 0.05 ,cmap='BuPu',\n",
    "            xticklabels= labels, yticklabels= labels)\n",
    "\n",
    "print(classification_report(y_test,Y_pred2,target_names=labels) , '\\n')\n",
    "\n",
    "plt.title('SVM Classifier - Confusion Matrix')\n",
    "plt.ylabel('True Value') \n",
    "plt.xlabel('Predicted Value') \n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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