Fraud_detection_balanced_kaggle.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "beec93ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA, TruncatedSVD\n",
    "import matplotlib.patches as mpatches\n",
    "import time\n",
    "\n",
    "# Classifier Libraries\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import collections\n",
    "\n",
    "\n",
    "# Other Libraries\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import NearMiss\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report\n",
    "from collections import Counter\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa092aa3",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "<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": [
    "df = pd.read_csv('creditcard.csv')\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "87f21a5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Frauds 99.83 % of the dataset\n",
      "Frauds 0.17 % of the dataset\n"
     ]
    }
   ],
   "source": [
    "print('No Frauds', round(df['Class'].value_counts()[0]/len(df) * 100,2), '% of the dataset')\n",
    "print('Frauds', round(df['Class'].value_counts()[1]/len(df) * 100,2), '% of the dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0b537baa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Class Distributions \\n (0: No Fraud || 1: Fraud)')"
      ]
     },
     "execution_count": 4,
     "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": [
    "colors = [\"#0101DF\", \"#DF0101\"]\n",
    "\n",
    "sns.countplot(x='Class', data=df, palette=colors)\n",
    "plt.title('Class Distributions \\n (0: No Fraud || 1: Fraud)', fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d45de7f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(18,4))\n",
    "\n",
    "amount_val = df['Amount'].values\n",
    "time_val = df['Time'].values\n",
    "\n",
    "sns.distplot(amount_val, ax=ax[0], color='r')\n",
    "ax[0].set_title('Distribution of Transaction Amount', fontsize=14)\n",
    "ax[0].set_xlim([min(amount_val), max(amount_val)])\n",
    "\n",
    "sns.distplot(time_val, ax=ax[1], color='b')\n",
    "ax[1].set_title('Distribution of Transaction Time', fontsize=14)\n",
    "ax[1].set_xlim([min(time_val), max(time_val)])\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a2b10fd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</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",
       "      <th>scaled_amount</th>\n",
       "      <th>scaled_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0.090794</td>\n",
       "      <td>...</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",
       "      <td>1.783274</td>\n",
       "      <td>-0.994983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>-0.166974</td>\n",
       "      <td>...</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",
       "      <td>-0.269825</td>\n",
       "      <td>-0.994983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>0.207643</td>\n",
       "      <td>...</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",
       "      <td>4.983721</td>\n",
       "      <td>-0.994972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>-0.054952</td>\n",
       "      <td>...</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",
       "      <td>1.418291</td>\n",
       "      <td>-0.994972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>0.753074</td>\n",
       "      <td>...</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",
       "      <td>0.670579</td>\n",
       "      <td>-0.994960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9       V10  ...       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  0.090794  ...  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425 -0.166974  ... -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  0.207643  ...  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024 -0.054952  ...  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  0.753074  ...  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Class  scaled_amount  scaled_time  \n",
       "0 -0.189115  0.133558 -0.021053      0       1.783274    -0.994983  \n",
       "1  0.125895 -0.008983  0.014724      0      -0.269825    -0.994983  \n",
       "2 -0.139097 -0.055353 -0.059752      0       4.983721    -0.994972  \n",
       "3 -0.221929  0.062723  0.061458      0       1.418291    -0.994972  \n",
       "4  0.502292  0.219422  0.215153      0       0.670579    -0.994960  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since most of our data has already been scaled we should scale the columns that are left to scale (Amount and Time)\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
    "\n",
    "# RobustScaler is less prone to outliers.\n",
    "\n",
    "std_scaler = StandardScaler()\n",
    "rob_scaler = RobustScaler()\n",
    "\n",
    "df['scaled_amount'] = rob_scaler.fit_transform(df['Amount'].values.reshape(-1,1))\n",
    "df['scaled_time'] = rob_scaler.fit_transform(df['Time'].values.reshape(-1,1))\n",
    "\n",
    "df.drop(['Time','Amount'], axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "be88748d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scaled_amount</th>\n",
       "      <th>scaled_time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>...</th>\n",
       "      <th>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.783274</td>\n",
       "      <td>-0.994983</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251412</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.269825</td>\n",
       "      <td>-0.994983</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.069083</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.983721</td>\n",
       "      <td>-0.994972</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>...</td>\n",
       "      <td>0.524980</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.418291</td>\n",
       "      <td>-0.994972</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208038</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.670579</td>\n",
       "      <td>-0.994960</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>...</td>\n",
       "      <td>0.408542</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   scaled_amount  scaled_time        V1        V2        V3        V4  \\\n",
       "0       1.783274    -0.994983 -1.359807 -0.072781  2.536347  1.378155   \n",
       "1      -0.269825    -0.994983  1.191857  0.266151  0.166480  0.448154   \n",
       "2       4.983721    -0.994972 -1.358354 -1.340163  1.773209  0.379780   \n",
       "3       1.418291    -0.994972 -0.966272 -0.185226  1.792993 -0.863291   \n",
       "4       0.670579    -0.994960 -1.158233  0.877737  1.548718  0.403034   \n",
       "\n",
       "         V5        V6        V7        V8  ...       V20       V21       V22  \\\n",
       "0 -0.338321  0.462388  0.239599  0.098698  ...  0.251412 -0.018307  0.277838   \n",
       "1  0.060018 -0.082361 -0.078803  0.085102  ... -0.069083 -0.225775 -0.638672   \n",
       "2 -0.503198  1.800499  0.791461  0.247676  ...  0.524980  0.247998  0.771679   \n",
       "3 -0.010309  1.247203  0.237609  0.377436  ... -0.208038 -0.108300  0.005274   \n",
       "4 -0.407193  0.095921  0.592941 -0.270533  ...  0.408542 -0.009431  0.798278   \n",
       "\n",
       "        V23       V24       V25       V26       V27       V28  Class  \n",
       "0 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053      0  \n",
       "1  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724      0  \n",
       "2  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752      0  \n",
       "3 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458      0  \n",
       "4 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_amount = df['scaled_amount']\n",
    "scaled_time = df['scaled_time']\n",
    "\n",
    "df.drop(['scaled_amount', 'scaled_time'], axis=1, inplace=True)\n",
    "df.insert(0, 'scaled_amount', scaled_amount)\n",
    "df.insert(1, 'scaled_time', scaled_time)\n",
    "\n",
    "# Amount and Time are Scaled!\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "64ee5403",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Frauds 99.83 % of the dataset\n",
      "Frauds 0.17 % of the dataset\n",
      "Train: [ 30473  30496  31002 ... 284804 284805 284806] Test: [    0     1     2 ... 57017 57018 57019]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [ 30473  30496  31002 ... 113964 113965 113966]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [ 81609  82400  83053 ... 170946 170947 170948]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [150654 150660 150661 ... 227866 227867 227868]\n",
      "Train: [     0      1      2 ... 227866 227867 227868] Test: [212516 212644 213092 ... 284804 284805 284806]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Label Distributions: \n",
      "\n",
      "[0.99827076 0.00172924]\n",
      "[0.99827952 0.00172048]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "print('No Frauds', round(df['Class'].value_counts()[0]/len(df) * 100,2), '% of the dataset')\n",
    "print('Frauds', round(df['Class'].value_counts()[1]/len(df) * 100,2), '% of the dataset')\n",
    "\n",
    "X = df.drop('Class', axis=1)\n",
    "y = df['Class']\n",
    "\n",
    "sss = StratifiedKFold(n_splits=5, random_state=None, shuffle=False)\n",
    "\n",
    "for train_index, test_index in sss.split(X, y):\n",
    "    print(\"Train:\", train_index, \"Test:\", test_index)\n",
    "    original_Xtrain, original_Xtest = X.iloc[train_index], X.iloc[test_index]\n",
    "    original_ytrain, original_ytest = y.iloc[train_index], y.iloc[test_index]\n",
    "\n",
    "# We already have X_train and y_train for undersample data thats why I am using original to distinguish and to not overwrite these variables.\n",
    "# original_Xtrain, original_Xtest, original_ytrain, original_ytest = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Check the Distribution of the labels\n",
    "\n",
    "\n",
    "# Turn into an array\n",
    "original_Xtrain = original_Xtrain.values\n",
    "original_Xtest = original_Xtest.values\n",
    "original_ytrain = original_ytrain.values\n",
    "original_ytest = original_ytest.values\n",
    "\n",
    "# See if both the train and test label distribution are similarly distributed\n",
    "train_unique_label, train_counts_label = np.unique(original_ytrain, return_counts=True)\n",
    "test_unique_label, test_counts_label = np.unique(original_ytest, return_counts=True)\n",
    "print('-' * 100)\n",
    "\n",
    "print('Label Distributions: \\n')\n",
    "print(train_counts_label/ len(original_ytrain))\n",
    "print(test_counts_label/ len(original_ytest))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a891a433",
   "metadata": {},
   "source": [
    "Going to use random under-sampling\n",
    "\n",
    "The main issue with \"Random Under-Sampling\" is that we run the risk that our classification models will not perform as accurate as we would like to since there is a great deal of information loss (bringing 492 non-fraud transaction from 284,315 non-fraud transaction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a6c3163f",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <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>212025</th>\n",
       "      <td>-0.223573</td>\n",
       "      <td>0.634300</td>\n",
       "      <td>1.908539</td>\n",
       "      <td>-0.704038</td>\n",
       "      <td>0.305284</td>\n",
       "      <td>0.690758</td>\n",
       "      <td>-1.021024</td>\n",
       "      <td>0.480507</td>\n",
       "      <td>-1.178722</td>\n",
       "      <td>0.325983</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.201738</td>\n",
       "      <td>-0.036008</td>\n",
       "      <td>0.266674</td>\n",
       "      <td>0.223582</td>\n",
       "      <td>-0.299537</td>\n",
       "      <td>-0.413996</td>\n",
       "      <td>0.232410</td>\n",
       "      <td>0.028374</td>\n",
       "      <td>-0.048537</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30473</th>\n",
       "      <td>-0.105359</td>\n",
       "      <td>-0.572728</td>\n",
       "      <td>-4.194074</td>\n",
       "      <td>4.382897</td>\n",
       "      <td>-5.118363</td>\n",
       "      <td>4.455230</td>\n",
       "      <td>-4.812621</td>\n",
       "      <td>-1.224645</td>\n",
       "      <td>-7.281328</td>\n",
       "      <td>3.332250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.579098</td>\n",
       "      <td>1.550473</td>\n",
       "      <td>0.614573</td>\n",
       "      <td>0.028521</td>\n",
       "      <td>0.013704</td>\n",
       "      <td>-0.149512</td>\n",
       "      <td>-0.131687</td>\n",
       "      <td>0.473934</td>\n",
       "      <td>0.473757</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141055</th>\n",
       "      <td>0.057850</td>\n",
       "      <td>-0.006967</td>\n",
       "      <td>1.261385</td>\n",
       "      <td>-0.120875</td>\n",
       "      <td>0.340058</td>\n",
       "      <td>0.049027</td>\n",
       "      <td>-0.016315</td>\n",
       "      <td>0.678753</td>\n",
       "      <td>-0.541044</td>\n",
       "      <td>0.180165</td>\n",
       "      <td>...</td>\n",
       "      <td>0.078797</td>\n",
       "      <td>-0.044900</td>\n",
       "      <td>-0.147085</td>\n",
       "      <td>-0.190469</td>\n",
       "      <td>-1.340780</td>\n",
       "      <td>0.370455</td>\n",
       "      <td>0.476354</td>\n",
       "      <td>-0.011546</td>\n",
       "      <td>0.003998</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144108</th>\n",
       "      <td>4.030182</td>\n",
       "      <td>0.013804</td>\n",
       "      <td>-3.586964</td>\n",
       "      <td>2.609127</td>\n",
       "      <td>-5.568577</td>\n",
       "      <td>3.631947</td>\n",
       "      <td>-4.543590</td>\n",
       "      <td>-0.157899</td>\n",
       "      <td>-4.089128</td>\n",
       "      <td>2.417305</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.178534</td>\n",
       "      <td>1.024423</td>\n",
       "      <td>0.428756</td>\n",
       "      <td>0.182032</td>\n",
       "      <td>-0.534598</td>\n",
       "      <td>0.168933</td>\n",
       "      <td>-0.149844</td>\n",
       "      <td>0.685517</td>\n",
       "      <td>-0.299728</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276864</th>\n",
       "      <td>4.570391</td>\n",
       "      <td>0.970947</td>\n",
       "      <td>-1.374424</td>\n",
       "      <td>2.793185</td>\n",
       "      <td>-4.346572</td>\n",
       "      <td>2.400731</td>\n",
       "      <td>-1.688433</td>\n",
       "      <td>0.111136</td>\n",
       "      <td>-0.922038</td>\n",
       "      <td>-2.149930</td>\n",
       "      <td>...</td>\n",
       "      <td>0.650196</td>\n",
       "      <td>-0.870779</td>\n",
       "      <td>0.504849</td>\n",
       "      <td>0.137994</td>\n",
       "      <td>0.368275</td>\n",
       "      <td>0.103137</td>\n",
       "      <td>-0.414209</td>\n",
       "      <td>0.454982</td>\n",
       "      <td>0.096711</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",
       "212025      -0.223573     0.634300  1.908539 -0.704038  0.305284  0.690758   \n",
       "30473       -0.105359    -0.572728 -4.194074  4.382897 -5.118363  4.455230   \n",
       "141055       0.057850    -0.006967  1.261385 -0.120875  0.340058  0.049027   \n",
       "144108       4.030182     0.013804 -3.586964  2.609127 -5.568577  3.631947   \n",
       "276864       4.570391     0.970947 -1.374424  2.793185 -4.346572  2.400731   \n",
       "\n",
       "              V5        V6        V7        V8  ...       V20       V21  \\\n",
       "212025 -1.021024  0.480507 -1.178722  0.325983  ... -0.201738 -0.036008   \n",
       "30473  -4.812621 -1.224645 -7.281328  3.332250  ...  0.579098  1.550473   \n",
       "141055 -0.016315  0.678753 -0.541044  0.180165  ...  0.078797 -0.044900   \n",
       "144108 -4.543590 -0.157899 -4.089128  2.417305  ... -0.178534  1.024423   \n",
       "276864 -1.688433  0.111136 -0.922038 -2.149930  ...  0.650196 -0.870779   \n",
       "\n",
       "             V22       V23       V24       V25       V26       V27       V28  \\\n",
       "212025  0.266674  0.223582 -0.299537 -0.413996  0.232410  0.028374 -0.048537   \n",
       "30473   0.614573  0.028521  0.013704 -0.149512 -0.131687  0.473934  0.473757   \n",
       "141055 -0.147085 -0.190469 -1.340780  0.370455  0.476354 -0.011546  0.003998   \n",
       "144108  0.428756  0.182032 -0.534598  0.168933 -0.149844  0.685517 -0.299728   \n",
       "276864  0.504849  0.137994  0.368275  0.103137 -0.414209  0.454982  0.096711   \n",
       "\n",
       "        Class  \n",
       "212025      0  \n",
       "30473       1  \n",
       "141055      0  \n",
       "144108      1  \n",
       "276864      1  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since our classes are highly skewed we should make them equivalent in order to have a normal distribution of the classes.\n",
    "\n",
    "# Lets shuffle the data before creating the subsamples\n",
    "\n",
    "df = df.sample(frac=1)\n",
    "\n",
    "# amount of fraud classes 492 rows.\n",
    "fraud_df = df.loc[df['Class'] == 1]\n",
    "non_fraud_df = df.loc[df['Class'] == 0][:492]\n",
    "\n",
    "normal_distributed_df = pd.concat([fraud_df, non_fraud_df])\n",
    "\n",
    "# Shuffle dataframe rows\n",
    "new_df = normal_distributed_df.sample(frac=1, random_state=42)\n",
    "\n",
    "new_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "60c910f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of the Classes in the subsample dataset\n",
      "0    0.5\n",
      "1    0.5\n",
      "Name: Class, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Distribution of the Classes in the subsample dataset')\n",
    "print(new_df['Class'].value_counts()/len(new_df))\n",
    "\n",
    "\n",
    "\n",
    "sns.countplot('Class', data=new_df, palette=colors)\n",
    "plt.title('Equally Distributed Classes', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "005f5e99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make sure we use the subsample in our correlation\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(2, 1, figsize=(24,20))\n",
    "\n",
    "# Entire DataFrame\n",
    "corr = df.corr()\n",
    "sns.heatmap(corr, cmap='coolwarm_r', annot_kws={'size':20}, ax=ax1)\n",
    "ax1.set_title(\"Imbalanced Correlation Matrix \\n (don't use for reference)\", fontsize=14)\n",
    "\n",
    "\n",
    "sub_sample_corr = new_df.corr()\n",
    "sns.heatmap(sub_sample_corr, cmap='coolwarm_r', annot_kws={'size':20}, ax=ax2)\n",
    "ax2.set_title('SubSample Correlation Matrix \\n (use for reference)', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "07017f71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(ncols=4, figsize=(20,4))\n",
    "\n",
    "# Negative Correlations with our Class (The lower our feature value the more likely it will be a fraud transaction)\n",
    "sns.boxplot(x=\"Class\", y=\"V17\", data=new_df, palette=colors, ax=axes[0])\n",
    "axes[0].set_title('V17 vs Class Negative Correlation')\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V14\", data=new_df, palette=colors, ax=axes[1])\n",
    "axes[1].set_title('V14 vs Class Negative Correlation')\n",
    "\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V12\", data=new_df, palette=colors, ax=axes[2])\n",
    "axes[2].set_title('V12 vs Class Negative Correlation')\n",
    "\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V10\", data=new_df, palette=colors, ax=axes[3])\n",
    "axes[3].set_title('V10 vs Class Negative Correlation')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0868fb32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(ncols=4, figsize=(20,4))\n",
    "\n",
    "# Positive correlations (The higher the feature the probability increases that it will be a fraud transaction)\n",
    "sns.boxplot(x=\"Class\", y=\"V11\", data=new_df, palette=colors, ax=axes[0])\n",
    "axes[0].set_title('V11 vs Class Positive Correlation')\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V4\", data=new_df, palette=colors, ax=axes[1])\n",
    "axes[1].set_title('V4 vs Class Positive Correlation')\n",
    "\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V2\", data=new_df, palette=colors, ax=axes[2])\n",
    "axes[2].set_title('V2 vs Class Positive Correlation')\n",
    "\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V19\", data=new_df, palette=colors, ax=axes[3])\n",
    "axes[3].set_title('V19 vs Class Positive Correlation')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7a272e17",
   "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>212025</th>\n",
       "      <td>-0.223573</td>\n",
       "      <td>0.634300</td>\n",
       "      <td>1.908539</td>\n",
       "      <td>-0.704038</td>\n",
       "      <td>0.305284</td>\n",
       "      <td>0.690758</td>\n",
       "      <td>-1.021024</td>\n",
       "      <td>0.480507</td>\n",
       "      <td>-1.178722</td>\n",
       "      <td>0.325983</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.201738</td>\n",
       "      <td>-0.036008</td>\n",
       "      <td>0.266674</td>\n",
       "      <td>0.223582</td>\n",
       "      <td>-0.299537</td>\n",
       "      <td>-0.413996</td>\n",
       "      <td>0.232410</td>\n",
       "      <td>0.028374</td>\n",
       "      <td>-0.048537</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30473</th>\n",
       "      <td>-0.105359</td>\n",
       "      <td>-0.572728</td>\n",
       "      <td>-4.194074</td>\n",
       "      <td>4.382897</td>\n",
       "      <td>-5.118363</td>\n",
       "      <td>4.455230</td>\n",
       "      <td>-4.812621</td>\n",
       "      <td>-1.224645</td>\n",
       "      <td>-7.281328</td>\n",
       "      <td>3.332250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.579098</td>\n",
       "      <td>1.550473</td>\n",
       "      <td>0.614573</td>\n",
       "      <td>0.028521</td>\n",
       "      <td>0.013704</td>\n",
       "      <td>-0.149512</td>\n",
       "      <td>-0.131687</td>\n",
       "      <td>0.473934</td>\n",
       "      <td>0.473757</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141055</th>\n",
       "      <td>0.057850</td>\n",
       "      <td>-0.006967</td>\n",
       "      <td>1.261385</td>\n",
       "      <td>-0.120875</td>\n",
       "      <td>0.340058</td>\n",
       "      <td>0.049027</td>\n",
       "      <td>-0.016315</td>\n",
       "      <td>0.678753</td>\n",
       "      <td>-0.541044</td>\n",
       "      <td>0.180165</td>\n",
       "      <td>...</td>\n",
       "      <td>0.078797</td>\n",
       "      <td>-0.044900</td>\n",
       "      <td>-0.147085</td>\n",
       "      <td>-0.190469</td>\n",
       "      <td>-1.340780</td>\n",
       "      <td>0.370455</td>\n",
       "      <td>0.476354</td>\n",
       "      <td>-0.011546</td>\n",
       "      <td>0.003998</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144108</th>\n",
       "      <td>4.030182</td>\n",
       "      <td>0.013804</td>\n",
       "      <td>-3.586964</td>\n",
       "      <td>2.609127</td>\n",
       "      <td>-5.568577</td>\n",
       "      <td>3.631947</td>\n",
       "      <td>-4.543590</td>\n",
       "      <td>-0.157899</td>\n",
       "      <td>-4.089128</td>\n",
       "      <td>2.417305</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.178534</td>\n",
       "      <td>1.024423</td>\n",
       "      <td>0.428756</td>\n",
       "      <td>0.182032</td>\n",
       "      <td>-0.534598</td>\n",
       "      <td>0.168933</td>\n",
       "      <td>-0.149844</td>\n",
       "      <td>0.685517</td>\n",
       "      <td>-0.299728</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276864</th>\n",
       "      <td>4.570391</td>\n",
       "      <td>0.970947</td>\n",
       "      <td>-1.374424</td>\n",
       "      <td>2.793185</td>\n",
       "      <td>-4.346572</td>\n",
       "      <td>2.400731</td>\n",
       "      <td>-1.688433</td>\n",
       "      <td>0.111136</td>\n",
       "      <td>-0.922038</td>\n",
       "      <td>-2.149930</td>\n",
       "      <td>...</td>\n",
       "      <td>0.650196</td>\n",
       "      <td>-0.870779</td>\n",
       "      <td>0.504849</td>\n",
       "      <td>0.137994</td>\n",
       "      <td>0.368275</td>\n",
       "      <td>0.103137</td>\n",
       "      <td>-0.414209</td>\n",
       "      <td>0.454982</td>\n",
       "      <td>0.096711</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",
       "212025      -0.223573     0.634300  1.908539 -0.704038  0.305284  0.690758   \n",
       "30473       -0.105359    -0.572728 -4.194074  4.382897 -5.118363  4.455230   \n",
       "141055       0.057850    -0.006967  1.261385 -0.120875  0.340058  0.049027   \n",
       "144108       4.030182     0.013804 -3.586964  2.609127 -5.568577  3.631947   \n",
       "276864       4.570391     0.970947 -1.374424  2.793185 -4.346572  2.400731   \n",
       "\n",
       "              V5        V6        V7        V8  ...       V20       V21  \\\n",
       "212025 -1.021024  0.480507 -1.178722  0.325983  ... -0.201738 -0.036008   \n",
       "30473  -4.812621 -1.224645 -7.281328  3.332250  ...  0.579098  1.550473   \n",
       "141055 -0.016315  0.678753 -0.541044  0.180165  ...  0.078797 -0.044900   \n",
       "144108 -4.543590 -0.157899 -4.089128  2.417305  ... -0.178534  1.024423   \n",
       "276864 -1.688433  0.111136 -0.922038 -2.149930  ...  0.650196 -0.870779   \n",
       "\n",
       "             V22       V23       V24       V25       V26       V27       V28  \\\n",
       "212025  0.266674  0.223582 -0.299537 -0.413996  0.232410  0.028374 -0.048537   \n",
       "30473   0.614573  0.028521  0.013704 -0.149512 -0.131687  0.473934  0.473757   \n",
       "141055 -0.147085 -0.190469 -1.340780  0.370455  0.476354 -0.011546  0.003998   \n",
       "144108  0.428756  0.182032 -0.534598  0.168933 -0.149844  0.685517 -0.299728   \n",
       "276864  0.504849  0.137994  0.368275  0.103137 -0.414209  0.454982  0.096711   \n",
       "\n",
       "        Class  \n",
       "212025      0  \n",
       "30473       1  \n",
       "141055      0  \n",
       "144108      1  \n",
       "276864      1  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2ad68086",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x12J8Hhlm9k2LnOQxKlpUcSH1Jpfo5DOUyCkSItKDaJ7QPKF5QkSao3lC84Tmie5FySaJp6eA95tZS1nvJrn7LuBTRIrc7SOSxf5cgzZXgN8nUoRuN/BR4O8atNkI/BHwp0T2Nz9MpCBdvP0hkE1kr/jTwA+IvBi3xjUisa4F9gC/Bzzs7seik+77ganRa98k8lwr6zq7eymR0xjOEFkiuxf4B95ervprYDnwKpFvcR5rGIC7O/Du6PUCYBVwHnh39Fosaokse/4xkQKFzxLZ1/7Zem0eA1529xMxjiki3YvmCc0TmidEpDmaJzRPaJ7oJiz2/+8iLTOzDcC33P2/kx2LdC7Rb0AOAY+5++vJjkdEkkPzhDRF84SIgOYJaZrmia5FK5sk3v4E/b2Sxo0EvqyJQaTH0zwhTdE8ISKgeUKapnmiC9HKJhERERERERERiRtljEVEREREREREJG6UbBIRERERERERkbhJSXYA8dSvXz8fNWpUssMQEel0tm3bdsnd+yc7jmTSHCEi0jTNE5onRESa09p5olslm0aNGsXWrVuTHYaISKdjZj3+eFjNESIiTdM8oXlCRKQ5rZ0ntI1ORERERERERETiRskmERERERERERGJGyWbREREREREREQkbpRsEhERERERERGRuFGySURERERERERE4kbJJhERERERERERiRslm0REREREREREJG6UbBIRERERERERkbhRsklEREREREREROJGySYREREREREREYkbJZtERERERERERCRulGwSEREREREREZG4UbJJRERERERERETiJiXZAYhIbMI7C1rdJzB9adzjEBGRnmtl7ek2970jOCyOkYiISGdw/kRtzG0HjQwmMBLpbLSySURERERERERE4kbJJhERERERERERiRslm0REREREREREJG6UbBIRERERERERkbhRsklEREREREREROJGySYREREREREREYkbJZtERERERERERCRulGwSEREREREREZG4SUl2ACIiIiLScVbWnk52CCIiItLNaWWTiIiIiDTK3anyWtw92aGIiIhIF6KVTSIiIiLyDte9ih1+idNeSjm1BIAhZDE50Jvhlp3s8ERERKSTS+jKJjO7x8wOmtlhM/tCI9fHm9kGM6s0s8+1pq+IiIiIxJe7syN8iV+Gj3LMixlqWcy2/ky03lyhkhfDp3k9fJ5arXQSERGRZiRsZZOZBYFvAncCp4EtZva8u++r1+wK8Eng3W3oKyIiIiJxEnanwM9yxIsZaznMtYFk2ttvFfN9AFu8kF1+hRKv4c7AUAJmSYxYREREOqtEbqObAxx296MAZvY08BDwVsLI3S8CF83s/tb2FREREemp4l3k291Z7ec44sXMtv5Msz5Yg0RSwIx8G0BWOIUNfpGNfpH5NjCucYiIiEj3kMhtdEOBU/V+Px29L9F9RURERKQVtvolDnsRs6wf0wN9b0g01Tc50IfJ1pu9fpXjXtyBUYqIiEhXkchkU2PvUmLd4B9zXzP7qJltNbOthYWFMQcnIiIiInDSS9jpl7nFejHd+sbUZ44NoA9pvB6+QKXXJjhCERER6WoSmWw6DQyv9/sw4Gy8+7r7U+4+y91n9e/fv02BioiIiPREZV5DQfgsfUljvg1sdkVTfUEzFgcGU04NW11f9omIiMg7JTLZtAUYZ2ajzSwVeBR4vgP6ioiIiEgM1ocvUI2zLDCEFGvd28L+ls4tlscBv0aRVyUoQhEREemKEpZscvca4BPAS8B+4Bl332tmT5jZEwBmNsjMTgOfBf7WzE6bWW5TfRMVq4iIiEhPc9yLOUYxM60vvS2tTWPMtL4Yxna/FOfoREREpCtL5Gl0uPtyYHmD+75d7+fzRLbIxdRXRERERNqvxsNsDF+kN6lMi7FOU2OyLMREy2OPX2Wm9yPXUuMYpYiIiHRVidxGJyIiIiKd0B6/SjHVzAsMJBBjnaamTLE+AOz1q/EITURERLoBJZtEREREepAKr2GnX2Yk2Qy1rHaPl2UhxlguB/06VTqZTkREREjwNjoRuVF4Z0GyQxARkR7sDb9CDWFmB+J3iu9k680RL+JNv87k6EonERER6bm0sklERESkhyjzGvb6VcZabpuLgjdmgGXQn3QO+nXcPW7jipjZPWZ20MwOm9kXGrk+3sw2mFmlmX2u3v3DzWyVme03s71m9qmOjVxEpGdTsklERESkh9jlVwjj3Gr94j72zdaLK1Rymcq4jy09k5kFgW8C9wITgcfMbGKDZleATwJfbXB/DfDn7j4BmAt8vJG+IiKSIEo2iYiIiPQAVV7LAb/GGMtNyKlxYy2XIMZBvxb3saXHmgMcdvej7l4FPA08VL+Bu1909y1AdYP7z7n79ujPxcB+YGjHhC0iIko2iYiIiPQA+/0a1YSZmqCaSmkWZJRlc8SLqPVwQh5DepyhwKl6v5+mDQkjMxsFzAA2NXLto2a21cy2FhYWtjVOERFpQMkmERERkW6u1p29fpUhZNLP0hP2ODdZLyoJc5qyhD2G9CjWyH2tKgpmZtnAr4FPu3vRDYO5P+Xus9x9Vv/+8SuaLyLS0+k0OhEREZFupLyklHOHjnP1fCGBgJE3qD9lY/tSmlHDosCghD72ULJII8BRL2KkZSf0saRHOA0Mr/f7MOBsrJ3NLEQk0fQzd/9NnGMTEZFmKNkkIiIi0sW5O/vWbWHN/zzHwQ3bqa2pecd1C6XQd9EUyn7/McifkbA4gmaMtByOeTE1HibFtIhe2mULMM7MRgNngEeBD8TS0cwM+D6w392/lrgQRUSkMUo2iYiIiHRhF46d4n+++DWObt9D3sD+LP3ge7hp9jT6Dh2Ih50DJ4+ydstGSlfs4L9e+zwTFszi0S9+mj5DBiYknjGWw5t+nTOUMpKchDyG9AzuXmNmnwBeAoLAD9x9r5k9Eb3+bTMbBGwFcoGwmX2ayMl1U4EPAbvNbGd0yL929+Ud/DRERHokJZtEOhl3hyvn8XPHoPgqVFdCRjbWdwgMvxkLxf8EIRER6Zo2Pvsiz/zfbxBKS+P9f/8p5r7nblJCoXe02TM2xNhlI3jvZz7H+l+8wIpv/Tf//PBH+f1/+hzT71wU95jqttId82JGmpJN0j7R5NDyBvd9u97P54lsr2toHY3XfBIRkQ6gZJNIJ+JFV/D9m+BaIQRDkNcfMnOgrAg/tB2O7YGpC7H+jb2nEhGRnsLdeeE/fsAr33+aW+bO5EP//Hl69e97Q7syr+EkJUy1PqSnp3HbH7yXqbcv4Md/+c98/zP/yEOf/Qh3/OH74hpbwIzhls0pLyXsTsD0eV9EROD8idqY2g0aGUxwJNIRlGwS6WBXi0rYfvAwl64VEQwGGD9yOLeMHErKuSP4/i0QSsUmzoUhY7Dg2/9E/fplfN8GfPsqmJiPDb85ic9CRESSxd35zb8+ScFPn2XBI/fzyN/8GcGUxt+YH/RrOHCL5b11X79hg/nkD77KT//m/+O5r32X2upq7v6T349rjCPI5jBFFFLBQDLiOraIiIh0fko2iXSQffv28eSTT/Lqq69GtsrVMyAnkw/e0o/fX3graTOWYKlpN/S3Xn1h9t34rjX4vk2QGd1aJyIiPcp//dd/UfDTZ1n6wffw8F/+KdbEyiF356BfZzCZ9LJ3bsEOpaXyB//6BYKhFH73jR+RlpXJ0g++J24xDrMszOGklzDQlGwSERHpaZRsEkmwmpoavv3tb/Ptb3+brKws/vjBu1gycwrD+vejvLKK3ete49frd/C1rSd5/nw1/zJwAhNGDW90LEsJwdTF+KYV+BtrYP6DWHpmBz8jERFJlueff55vfetb5L/7rmYTTQBnKKOYamZb/0avB4JBfv+fPkdlaTm/+X/fpv+IIUxanB+XONMsyEAyOOklzKbxxxcREZHuS+fRiiRQZWUln/70p/nmN7/J/fffzyuvvMKnH303M24eS//evRheUci9OZV87yMP8+TnP05RaRkf+tK/se6NfU2OaSkhbPpSqK3B39zWcU9GRESSat++ffzd3/0dc+bM4dEvfrrZRBPAgfA10ggyyrKbbBNMCfL4v/wlQ28Zw4/+4isUnjwTt3hHWDZXqKTEq+M2poiIiHQNSjaJJEhVVRUf+9jHePXVV/mbv/kb/vVf/5Xc3Ny3rnvhafzgVhg4Ahs/m8UzJvPMl/+KkYP68/H/75us3bmnybEtKxdGTYJzx/CrFzri6YiISBKVlJTwmc98ht69e/Pv//7vN5w411CZ13CcYsZZLkFr/u1eWmYGH/n6PxAIBvnh575MdVVVXGIeEU1ynfSSuIwnIiIiXYeSTSIJ4O787d/+LevXr+fLX/4yH/zgB995vaIU3/06ZPfGpix869vp/r178aO//yzjhg/lM//xXXYfOd7kY9iYKZCeiR/cdkMNKBER6V6+8pWvcPr0ab761a/Sp0+fFtsf9us4ML5eYfDm9Bk8gA9++XOc2neI3/3nD9sXbFQeqeQQ4pSSTSIiIj2Okk0iCfDDH/6QF154gU9+8pM8/PDD77jm7pFEU7gWm774HSfOAeRkZvDkX36cPrk5fOpr3+FqUeNv0i2Ygo2eDNcvwbWLCXsuIiKSXAUFBTz77LN85CMfYdasWTH1OeRF9Ced3nbjgRNNmbJsPgvf9wCrfvJrju1sejt3rMyMEZbNGcqo8XC7xxMREZGuQ8kmkTh74403+Pd//3fuvPNOnnjiiRsbnD4EV85j42djWb0aHaN/Xi/+8zN/wpWiEv76yR8TDjfxJn3oTRBKw4/tjeMzEBGRzqK0tJQvfvGLjBs3jo997GMx9bnilVyhkpsst+XGDTz05x8hb1B/fvp3X43LdroRlk0tzlnK2j2WiIiIdB1KNonEUUVFBZ///OcZMGAA//RP/3RD8VYvvoof3AZ9BkUSRc2YMHo4n//Q77Fm5x5+9drrjbaxYAqMuAUKT+OlRXF7HiIi0jl85zvf4eLFi/zjP/4jqampMfU54kUYMKYNyab0rEwe+9KnuXjsFK/98Jet7t/QYDJIwVS3SUREpIdRskkkjr75zW9y8uRJvvKVr9Cr142rlsIFvwSvxSbNa/EUIYDH7lxC/qRb+Lf/+Q3nL19ttI0Nuxkw/OyR9oYvIiKdyIkTJ/jRj37EQw89xPTp02Pq4+4c9usMJYtMS2m5QyMmLJjN9DsX8dJ3f87lM+fbNEadoAUYTCZnvLRd44iIiEjXomSTSJwcPHiQH/7whzz88MPk5+ffcN3PHMH3bYRRk7DMnJjGNDO+9JHfp6a2ln/+yTONt0nPhL6D4exRFQoXEelG/vVf/5VQKMRnP/vZmPtcoJwSatq0ha6+hz//BGbGc1/7brvGARhqWRRRTaGXt3ssERER6RqUbBKJA3fnX/7lX8jOzuYv/uIvGr1e+9rTkN0rUtS7FUYM7M9H330vKzfvZPO+NxttY0PHQkUpXLnQpvhFRKRzWbt2LatWreJP//RPGTBgQMz9DnkRKRijLLYvNZrSe/AAlj3+e+x4aQ0n9zY+98RqqGUBsCd8pV3jiIiISNehZJNIHBQUFLBx40Y+8YlPkJeXd8N1P7oLzh4hsODdWEqo1eP/wf23M7hfH/71J7+ktrFi4QOGQzCkrXTS5ZjZPWZ20MwOm9kXGrluZvb16PVdZjaz3rXPmNleM9tjZj83s/SOjV4kMWpra/mXf/kXRowYweOPPx57P3eOeREjLYeQtf8t3u0ffoTMXjm88J8/aNc4vUklgyC7XMkmERGRnkLJJpF2qq2t5d/+7d8YPXo073//+2+47u6E1z4Lef2xKQva9Bjpqal89rF3c+DEaVas33rDdQumRBJOhafxpk6uE+lkzCwIfBO4F5gIPGZmExs0uxcYF719FHgy2nco8ElglrtPBoLAox0UukhCLV++nKNHj/Lnf/7nMRcFBzhNCZWE272Frk5GThZ3feQxDqzfxsFNO9o8jpkx1LLYE75CWNu9RUREegQlm0Taafny5Rw5coRPfvKThEI3rlrywzvhwkkC8x+MJIXa6J65t3LziKF869f/S01t7Q3XbcBwqK6Ea4VtfgyRDjYHOOzuR929CngaeKhBm4eAn3jERiDPzAZHr6UAGWaWAmQCZzsqcJFEqa2t5Vvf+ha33HILd9xxR6v6HvVi0ggwjKy4xbP4sYfIG9ifF/7jB+2qCziUSN0mnUonIiLSMyjZJNIONTU1fOtb3+Lmm2/mrrvuuuG6uxPeuBx69cMmzW3XYwUCAf7skXdx4vxFXli76cYG/YaABfCLp9r1OCIdaChQ/y/s6eh9LbZx9zPAV4GTwDnguru/3PABzOyjZrbVzLYWFioRK53f8uXLOX78OB/72McIBGJ/m1brYU54CaMsh0AMp53GKpSWyn0ff5wTuw+w69XX2zzOUMsEYLe20omIiPQISjaJtMPy73wt8qHg/qWwaw3hnQXvvBU8A2ePYIPH4LvWEt5Z0K7HW3brVCaNHsFTz714Q+0mSwlB30Fw8ZROpZOuorFPxA3/8jbaxsx6E1n1NBoYAmSZ2QdvaOj+lLvPcvdZ/fv3b3fAIolUW1vLk08+yc0339zqVU2nKaOaMKPbWRi8MXMevJMBo4bx4nd+1ub5JctCDCWL3SoSLiIi0iMo2STSRu7OD154hTFDB3H7rGmNtzm2F0JpMOymuDymmfFHD93NyfOFrNyy88brA4ZDeTGUXo/L44kk2GlgeL3fh3HjVrim2twBHHP3QnevBn4DzE9grCIJt2LFCo4dO8bHP/7xVq1qAjjmRaQSYEgct9DVCaYEuf3Dj3B6/2EObmx77aapgT7s96tUu2oLioiIdHdKNom00fr16zl44jR/+MCdjX4o8LJiKDwNw29uV62mhu6YPZ2Rgwbw/edfvvEb5n7RHUiXz8Xt8UQSaAswzsxGm1kqkQLfzzdo8zzwePRUurlEtsudI7J9bq6ZZZqZAbcD+zsyeJF4CofDbV7VVOvOCS9hpGUTjOMWuvpmP3gHuf37sPL7v2jzGFMCfagizJt+LX6BiYiISKekZJNIG33/+99nQO9e3L9gdqPX/dRBMMOG3xLXxw0GAnz4gTvYe/QE2w4cfsc1y8iGjBxcySbpAty9BvgE8BKRRNEz7r7XzJ4wsyeizZYDR4HDwHeBj0X7bgJ+BWwHdhOZz57q2GcgEj+rV6/m6NGjfOQjH2n1qqazlFJFmNFxOoWuMaHUVJZ96GEObtzOyb1vtmmMCdabAKatdCIiPYCHneuXwpw8WMvZo7WUXNOq1p5GySaRNjh27BgbNmzgsbuWkNrYCXS1NXD6MAwYgaVnxv3x37Uwn9ysDJ5+ZfWNF/sOhisX8LBe0KXzc/fl7n6zu4919y9H7/u2u387+rO7+8ej16e4+9Z6fb/o7uPdfbK7f8jdK5P1PETa6yc/+QmDBg3i7rvvbnXfo15MiADDiP98U9+CRx4gPTuTlT94pk39My2FmyxXRcJFRLo5d+fCqTCXzzkpKVBbAxdPOcVX9fmkJ1GySaQNnn76aUKhEA8vXdB4g/MnoKYKGxHfVU11MtJSefeS+byyeSeF195Zn8n6Dobaaii6nJDHFhGR+Dpw4AAbN27kAx/4AKFGvsBoTtidE14c3UKX2Ld1GTlZLHzfA+x8ZS2FJxuWV4vNFOvDES+i1KvjHJ2IiHQWxVecsiLoM8gYMjbIsHEBMrKg8IxTUaaDjHoKJZtEWqm8vJzf/va33HHHHfTLa3zLgp89Ahk50HtgwuJ4/x2LqKmt5Ter1r/zQp/oY2ornYhIl/CTn/yEjIwM3ve+97W671nKqEzQKXSNWfqhhwkGg6z671+3qf/kQB8c2OdX4xuYiIh0CjXVzuVzTkY29OoXqSNoZgwYGSAYhCvnwjo5u4dQskmklVasWEFRURGPPvpoo9e9vASunMeGjMESVKgVYNTggcybPJ5nXl1Lbb0tc5aaDjl9VLdJRKQLKCws5He/+x3vfve76dWrV6v7H/diUjCGJeAUusb06t+XGXcvZsvzK6koLWt1/5utF2kEVLdJRKSbun7ZcYd+QwLv+CwUDBp5A4yKMigvTWKA0mGUbBJppaeffpqxY8cye3bjhcE5ezTy55AxCY/l0buWcP7yVVZv3/3OC70HwPVLkdpRIiLSaT399NNUV1fzoQ99qNV93Z2TXsIwskhJ8Ba6+hY99iAVpWVs/d2rre6bYgEmWG/2hLWySUSkuwnXOsWXnaxeRijtxi/dc3sbwRBcvaDaTT2Bkk0irbB37152797No48+2uiqJXfHzx6F3gOxzMRvaVg6cwoD++Tx9Ctr3nG/9R4A4Vq4eCrhMYiISNtUVVXxi1/8gqVLlzJ69OhW979MJaXUMNKyExBd00ZNncCwCTex5unn27QVYnKgD2co5YpXJCA6ERFJluKrTjj89va5hixg9OpnVJZBVYW20nV3SjaJtMLTTz9NRkYGDz30UOMNrl+CsiKsA1Y1AaQEg7z3toW8vmsfpy4Uvn0hbwAAfvpQh8QhIiKt99prr3H58mU+8IEPtKn/CS8GYHgHJ5vMjMWPPsi5Q8c5sn1Pq/tPsT4AWt0kItLNFF910jIgPbPpUiLZvSLXSq4p2dTdKdkkEqPy8nKWL1/OvffeS05O46uW/OwRCARh0MgOi+s9S+dhZrywbvNb91l6JmRkK9kkItKJPfPMMwwZMoT58+e3qf8JL2EgGWRYSpwja9mt9y0jIyeLtU8/3+q+IyybHELsdtVtEhHpLqornaoKyOrVfM3alJCRkQ3F11yFwrs5JZtEYvTaa69RVlbW5Komr62Fc8dh4AgsJbXD4hrctw+zJ4zjhXWb3vmCnTcAP3NIL+IiIp3QiRMn2LBhA+9973sJBoOt7l/i1VymssO30NVJzUgn/91388Yr6yi61LqkUcCMyYE+7Alf0RwlItJNlFyPvJ5nt5BsAsjpbdRWQ4UKhXdrSjaJxOiFF15g8ODBzJo1q/EGl05DTRU2ZGzHBgY8uCifk+cL2XX42Fv3We8BUFoE1y52eDwiItK8X/3qVwSDQR5++OE29T/pJQBJSzYBLHr/u6itqWH9r1e0uu9k680VKjlL60+0ExGRzqfkmpOWCSmpLSebMnMNMygt0hcO3ZmSTSIxuHz5MuvWreOBBx4gEGj8n41fOAmhNOgzqIOjgzvnzCAtFOL5tZvevrNXv0hc54410UtERJKhqqqKZ599lqVLlzJw4MA2jXHCS8glRC86biVtQwNGDWPcnOlsevYlwuHWnSw0OVBXt0lb6UREurrqSqe6MrZVTQCBQGQrXVmxttJ1Zx2/yV+kC1qxYgW1tbW8613vavS6h2uh8DQMHIk1kYxKpOzMDG6bNZUXN2zjLx9/hNSUFMjOg5TUyOl4E+d2eEwiItK4VatWcfnyZR555JE29S/3Gs5SxiTLa/Rk1I4099138d9//f84sm0342ZPi7nfQDLoTzq7w1e4Ozg8pj4ra0+3NUzuCA5rc18REWleWUkkYZSZE/uclJFjlBU71VWQmpaoyCSZtLJJJAYvvPACEyZMYNy4cY03uHweaqqxgSM6NrB63rUwn2slpazbuRcgkvQaOAI/fzxpMYmIyI2eeeYZBg8ezMKFC9vUf5dfJowz0ho/rKIjTb9zEelZmWz87Uut6mfRuk37/CphfastItKllZc4KSFoTdnausRUWbHmgO5KySaRFhw7doxdu3bx4IMPNtnGL5yAYAj6Du7AyN5p/tSJ9MnNfuepdIPHwIUTeG1N0uISEZG3nT17lg0bNvDwww+3qTA4wNbwJdIIMJCMOEfXeqkZ6cy8dyk7X15LRWnr6i9NsT6UUsNRL0pQdCIikmjuTnlJZKVSa1bbhlKNUBqUq25Tt6Vkk0gLXnjhBQKBAPfdd1+j1z0chounoP9QLNC2Dw7xEEoJct/82RRs30VR9A2/DR4NNdVw6UzS4hIRkbf97ne/w92bPNm0JbUeZkf4EsMtm0CSt9DVmfvuu6kqr2DHi6tb1a+ubtNuV90mEZGuqqIMPAyZ2a2fkzJzjPIyCIeVcOqOlGwSaYa787//+7/k5+czYMCAxhtduwjVldjAkR0bXCPuXzCbquoaVm3bBUSTTahIuIhIZ+DuPPfcc9x6660MHx5bnaKG3vTrFFOd1FPoGho1bQIDRg9v9Va6XpbKCMtmT/hqgiITEZFEK49ug8tow7SUkWXgUKmDSbslJZtEmnHw4EFOnjzJPffc02Qbv3ASAkHoN6QDI2vclLGjGNS3Ny9v2h65I68/pGdFioSLiEhS7dmzh6NHj7Z5VRPAtvAlghjDyIpjZO1jZsx7z90c3bGXi8dbV8R7ivXhoF+jymsTFJ2IiCRSRZmTlgGBYOtXNqVHp7LyUq1s6o4Smmwys3vM7KCZHTazLzRy3czs69Hru8xsZr1rnzGzvWa2x8x+bmbpiYxVpDEvv/wygUCAO+64o9Hr7g4XTkK/IVhKqIOju5GZceecGazfvZ+SsnLMDBs0Cr94MtmhiYj0eM899xypqanNfoHRkm3hQiZZb1Itedu2GzP7XXcQCAZavbppSqAP1YQ56NcTFJl0dTF8nhhvZhvMrNLMPteaviLSPuFap7IM0rPatq07EDRSM6BCyaZuKWHJJjMLAt8E7gUmAo+Z2cQGze4FxkVvHwWejPYdCnwSmOXuk4Eg8GiiYhVpyssvv8zs2bPp06dP4w2uX4LKsk6xha7OXfkzqaquYfWOPZE7BgyHS2dVJFxEJImqqqpYvnw5t99+Ozk5bTtF7ryXcZYyZgb6xTm69uvVvy+3zLuVbctXRb6IidEEyyOIsTusuk1yoxg/T1wh8rnhq23oKyLtUHLdcYf0zLbXEMzIMipVt6lbSuTKpjnAYXc/6u5VwNNAw3XjDwE/8YiNQJ6Z1R3nlQJkmFkKkAmcTWCsIjc4fPgwR44c4c4772yyjReeAQz6De24wFowfdxo+uf14uXNka10NnAE1NbAlfNJjkxEpOdat24dV69ebdcWup3hywDM6ITJJoBZ9y3jytkLHNuxN+Y+6ZbCTZbLHhUJl8a1+HnC3S+6+xagurV9RaR9iq5EEkTp7djZnZ5luOo2dUuJTDYNBU7V+/109L4W27j7GSLfTpwEzgHX3f3lBMYqcoOXX478lWsu2cSlM5DXD0tN66CoWhYIBLhjznTW7dxLWVkZNiBShNYvaCudiEiyPPfcc/Tt25cFCxa0eYyd4UsMIoNBlhnHyOJn6u0LCKWlsnX5a63qN8X6cNSLKPGGuQKRmD5PtKuvmX3UzLaa2dbCwsI2ByrSExVfCRNKg2BK21c21SWqtJWu+0lksqmxv3EN/wY12sbMehP55mE0MATIMrMPNvogmiAkQV555RVmzpzZ5Cl0XnIdii5j/Yd1cGQtuyt/JhVV1axduxb6DIKUkOo2iYgkSVFREatWreL+++8nJSWlTWNUeS17/SrTO+mqJoD0rEwmL53HjpfWUFsd+9btyYE+OLDPdSqd3CCWzxPt6uvuT7n7LHef1b9//1YFJ9KTuTvFV71dW+gAgkEjlAYV5Uo2dTeJTDadBuqf6zuMG7fCNdXmDuCYuxe6ezXwG2B+Yw+iCUIS4cSJExw4cKD5LXTHdkd+6ERb6OrcOv4m+uRm8/LLL2OBIPQfBhdPtdxRRETi7tVXX6W6upr777+/zWPs92tUEWZ6oG8cI4u/2Q/cRsnV6xzYuD3mPuOsF2kE2RXdJihSTyyfJxLRV0RaUF4KNdWQFofFtumZkbpNran5J51fIpNNW4BxZjbazFKJFPh+vkGb54HHo6fSzSWyXe4cke1zc80s08wMuB3Yn8BYRd4hli10fnQ3pGVATu+OCitmwUCA22dPp6CggIqKCqz/cPziKb2Ai4gkwYoVKxg2bBhTpkxp8xg7w5cIEWCSdb45p74JC2eTmZvD1t/FvpUuxQJMtT5sD1/SPCUNxfJ5IhF9RaQFJdfCQPuKg9dJy4RwLVRXtXso6UQSlmxy9xrgE8BLRBJFz7j7XjN7wsyeiDZbDhwFDgPfBT4W7bsJ+BWwHdgdjfOpRMUq0tArr7zC5MmTGTq08VVLHq7Fj+2BfkOJ5EM7nzvnzKCsrIwNGzZgA4dDeQkUa4uCiEhHunr1KuvXr+e+++5r13yxM3yZSdabVAvGMbr4SwmFmH7XIna99jqVZeUx95sR6MdlKjnlpQmMTrqaWD5PmNkgMzsNfBb4WzM7bWa5TfVNzjMR6X5KrjqBIITiULq2LmFVWaYvHLqTthUOiJG7LyeSUKp/37fr/ezAx5vo+0Xgi4mMT6QxFy9eZPfu3XzqU59qutGZI1BZjnXCLXR1Zk8YR1ZWFgUFBSz9SKTkmV88ieX2SXJkIiI9x8svv0xtbS333Xdfm8e46OWcpYw7A52vRmBjZt13G+t/tZzdBRuYdd9tMfWZEegHtbDdLzGC7ARHKF1JDJ8nzhPZIhdTXxGJj5JrTnaexeWL91AaWAAqyjrlphFpo0RuoxPpklavXg3AsmXLmmwTProLAkHoO7ijwmq11FCIhQsXUlBQQLjvkMidqtskItKhli9fztixY7n55pvbPMaO8CWATl+vqc7YWVPIG9iPHS+uiblPH0tjlOW89VxFRKTzCtc6pUWRZFM8mBnpmVrZ1N0o2STSQEFBAYMHD272g4Ef2YUNG4eFUjswstZbunQpFy9eZP+RY9B7AH5BJ9KJiHSUixcvsmXLFu699952b6EbSAaDiUMV1g4QCASYdsdC9q3bTEVpWcz9Zlo/Dvo1Srw6gdGJiEh7lRY77pCTF790QlqGUVUB4bASTt2Fkk0i9VRUVLB+/XqWLVvW5AcDL74KhaexMW0v9NpRlixZQiAQYNWqVdiAEbhWNomIdJgXX3wRd+fee+9t8xhVXstev8L0QN9OWyOwMdPvWkxNVTV712yOuc/MQD8ceEOn0omIdGol1yIJoXitbIJIsgmgqiJuQ0qSKdkkUs/GjRupqKhodgudH9sD0CWSTb1792bGjBnRZNNwuHYRr4y9YKuIiLTd8uXLmTBhAmPGjGnzGAf8GpWEmR7oF8fIEm/M9Ink9uvDzpdj30o31nLJJcQ211Y6EZHOrOSaE0qF1PT4jZmWEfmzslwrm7oLJZtE6ikoKCAzM5M5c+Y02caP74OsXtCJi4PXt3TpUvbv38/5QHT7ReHp5AYkItIDnDt3jjfeeIN77rmnXePsDF8mRIBJ1rUqpgaCQabdsYC9azfHfCpdwIzpgX68Eb5M2PVhQ0SksyorcrJ6xac4eJ1gKFISV9+Ldx9KNolEuTsFBQUsWLCA1NTGazG5h/ET+7BRE7vMdoa6VVqr9x8HIifSiYhIYr366qsA3Hnnne0aZ0f4EhMsjzQLxiOsDjX9rsVUV1Syb92WmPvMtH6UUM2bfj2BkYmISFuFw05ZsZOZG9/PQmZGWgZUaWVTt6Fkk0jUvn37uHDhQrNb6Cg8DWXF2MiJHRdYO40ZM4YRI0awasMmyMhWkXARkQ7w8ssvc9NNNzF69Og2j3HRyzlLWZfbQldn7MwpZPfJY+fLa2PuMzXQhyDG9nBhAiMTEZG2Ki+JFAfPinOyCd4uEl5bq4RTd6Bkk0hUQUEBZsbixYubbOPH9wFgo7pOssnMWLZsGZs2baK810Bc2+hERBLq8uXLbNu2jbvuuqtd49QVyp4R6BuPsDpcMCXItNsXsGf1RqoqKmPqk2UhJllvNoYv4tpKJyLS6ZQWRV6bs3Ljn0qoKxJeVqTX/+5AySaRqNWrVzNt2jT69m36Tb0f3wd9B2M5Xat2xtKlS6mqqmLTlSq4dBb3cLJDEhHptl599VXC4XC7t9DtDl+hH+kMJjNOkXW86Xcuoqq8gv2vb425z7zAQC5QznEvTmBkIiLSFqXXnUAAMrLjP3ZqxtuPIV2fkk0iRL6F3r17N0uWLGmyjddU46fe7FKrmurMnDmTzMxM1h27ANWVcE0n/YiIJMrKlSsZPnw4t9xyS5vHCLuzx68wJdCny9QIbMy42dPI7JXTqq10swP9CWBsCF9MYGQiItIWpUWRek2JmJtSokXCS5Rs6haUbBIBXn/9dQAWLVrUZBs/ewRqqrpUvaY6qampzJs3j3W7D+Du2konIpIgRUVFbNy4kTvvvLNdb8SPeTGl1DDZ+sQxuo4XDKVEttIVbKCqqiqmPjmWyhTrw8bwBW2lExHpRNw9chJdAuo1wdtFwrWyqXtQskkEWLduHX379mXChAlNtvHje8EC2IjxHRhZ/CxcuJAz5y9w7HpFpNC5iIjEXUFBAdXV1e3eQrfHrwAwOdC1tm03Zvpdi6koLXvri51YzA0M4ALlHNNWOhGRTqOqAmqqE1McvE5ahlFW7IRVJLzLU7JJerxwOMy6detYsGABgUDT/yT8+D4YMgZLy+jA6OKnbtXW61eqtbJJRCRBXnnlFQYMGMDUqVPbNc7u8BWGWxZ5lhanyJLn5vzpZORm8/LLL8fcZ3agP0GMjeELCYxMRERao27FUVavxCWbUjMMdygrVrKpq1OySXq8vXv3cvXq1ea30FWUwvnjXXILXZ2hQ4cyduxY1p0twgvPJDscEZFup6ysjHXr1nHnnXc2++VFS6q8lgN+jSldfAtdnZRQiCnL5vHaa6+1aivdZOujU+lERDqRupPoMnMSubIp8qfqNnV9SjZJj7dmzRrMjAULFjTZxk8eAHcCXbA4eH0LFy5k6/FzlBeexWuqkx2OiEi3sm7dOioqKtq9he5Nv041YaYEmj4dtauZfsdCioqK2LJlS8x95mkrnYhIp1JaFCY9C4IpiUs2pYQiN9Vt6vqUbJIeb926dUyZMoXevZuui+EnD0BKKgwZ04GRxd+iRYuoqqlhy7lrcPlsssMREelWXnnlFXr37s2tt97arnF2h68QxJhgefEJrBO4Ze5M0tPTee2112LuMzswgCDGem2lExHpFEqLnKzcxKYQzIysXqaVTd2Akk3So127do1du3axcOHCZtv5yYPY0JuwYEoHRZYYs2bNIiM9nXWnr6luk4hIHFVVVVFQUMBtt91GSkr75oo9foWbLJcM69pzTn2pGeksWLCAVatWxbwtLttCzLB+rA2fo9bDCY5QRESaU1PtVJYltjh4naxeRlmREw4r4dSVKdkkPdr69esJh8PN12sqK4LC09jIrnkKXX1paWnMyZ/D62evqW6TiEgcbdiwgZKSknZvoSvxao54Ubep11Tfbbfdxrlz59i3b1/MfZYFh3CNKnb65QRGJiIiLSmL1mvqkGRTbqRIeHmJkk1dmZJN0qOtW7eOXr16MWXKlCbb+Kk3AbARXT/ZBLBo0WJOFlVw8sDeZIciItJtrFq1iszMTObNm9eucfb5VRyYHOh+yaalS5cSCARatZVuuvWlF6msqtXWbxGRZKorDp7Ik+jqZPWKpClUt6lrU7JJeix3Z926dcyfP59gMNh0u5MHIJQGg0Z1XHAJVFcIff0be5IciYhI9+DurF69mvnz55OamtqusfaEr5BGkHHWK07RdR59+vRhxowZrUo2pViAxYHBbPdLlHtNAqMTEZHmlBY5KaHIx6JEy8iCQPDtBJd0TUo2SY91+PBhCgsLmT9/frPt/MQBbNi4Ll+vqc7IkSMZ3Lc3G4+fx8tLkh2OiEiXd/DgQc6fP8/SpUvbPdbu8BUmWB4p1j3fot12220cOHCAM2di38q9NDiYWpzDXpTAyEREpL7zJ2rfcbt+KUxKKlw4GX7H/YlgZmTmmFY2dXHd852MSAw2bNgA0OyWBy8tgstnsRG3dFRYCWdmzL91OpvOXaf2wslkhyMi0uWtXr0agMWLF7drnMtewVnKmNINt9DVue222wBatbppmGVzk+Vy0K/HXFxcRETix92pqoTU9MRvoauT1csoLXK97ndh3WOphkgbrF+/npEjRzJ06NAm2/jJA0D3qddUZ/7ipfz65VXs2byB6aMmJjscEZEuraCggMmTJ9O/f/9W9VtZ+85TQd8MXweg1KtvuNZdjBo1irFjx/Laa6/xoQ99KOZ+SwND+F7tAS5RQX8yEhihiIg0VFsNHobUDthCVycr17hwAirLIT2z4x5X4kcrm6RHqq6uZsuWLS0WcvVTByOvqt2kXlOd/KW3A7B+0+YkRyIi0rVdvXqVN954gyVLlrR7rDOUkk6QPnTgu/kkuO2229i6dStFRbFvi1sQGEQQ44BfT2BkIiLSmKqKyJ8durIpeuqdttJ1XVrZJD3SG2+8QVlZ2TvqNYV3FtzQzt/cDrn98F1r6Yovc409J4DewIQBvdjwxl6eaNAmMH1posMSEek21qxZg7u3u16Tu3PWyxhqmZh13Jv5ZLjtttv47ne/y5o1a3jggQdi6pNpKYy1XI54Efnen1Rr+mAPERGJr6rKyCehUHrHPWZmXbKpKEzfwVoj0xXp/5r0SBs2bCAQCJCfn99kG68sh9LrWJ+BHRhZx5k7djg7z12htLw82aGIiHRZq1evpl+/fkyc2L4tydeooowahpAVp8g6r6lTp9KvXz9effXVVvWbYHlUE1ahcBGRDlZVAcEUCAY77suQYNDIyFaR8K5MySbpkdavX8/kyZPJzc1tutGV85E/+wzqmKA62LxJ46gJO9t27U12KCIiXVJ1dTXr1q1j8eLFBALte0t1xksBGGrdvzBFIBBg2bJlrF27lqqqqpj79SedvqSx36+pYKyISAeqqnBSO3BVU526IuHSNSnZJD1OcXExu3fvfscWusb4lQuQEoKc7nkq0MzJk0gNGBt27k52KCIiXdKOHTsoLi6OS72ms15GLiFyLDUOkXV+t912G6WlpWzeHHvtQDNjgvXmCpVcpCKB0YmISB13p7qDT6Krk5VrVFVAdZUSTl2Rkk3S42zevJna2toWi4Nz5Tz0Hoi189vqziq9T39mDsxlw/7DyQ5FRKRLWr16NaFQiAULFrRrnLA7ZyljiHX/LXR15s2bR2ZmZqu30o21HEIE2O9XExSZiIjUV1MF7h17El2drF4qEt6Vdc9P0SLN2LBhAxkZGUyfPr3JNl5RBmVF3bZeE4CFUpk7oj+HLlyh8JpO9xERaa3Vq1cza9YssrLalyQqpIJqwj1iC12dtLQ05s2bx+rVq1u1JS7VgtxkuRz1Yiq8NoERiogIvH0SXShJK5sAbaXropRskh5n/fr1zJo1i9TUZrYqdPN6TXXm3TISgI27DyQ5EhGRruXUqVMcOXKk3afQwdv1mobQc5JNAEuWLOHcuXMcOnSoVf0mWB61OIdcX5SIiCRa3Ul0yVjZFEo1UtO1sqmrUrJJepRz585x7NixGOo1nYeUVMjp3UGRJcf40aPJS0th/e59yQ5Feigzu8fMDprZYTP7QiPXzcy+Hr2+y8xm1ruWZ2a/MrMDZrbfzFrYGysSP6tXrwaIU72mUvqSRrqltHusrqTuv13df8tY9bV0BpKhQuEiIh2gqiJSxjbQgSfR1RcpEh5OymNL+yjZJD3Khg0bAFpMNnH1AvQegFn3/icSzO1N/uBebNx9QG/YpcOZWRD4JnAvMBF4zMwanh9/LzAuevso8GS9a/8JvOju44FpwP6EBy0SVVBQwOjRoxk5cmS7xqn2MBeoYGgPqtdUZ8CAAUyaNImCgoJW951geVynirOUxT8wERF5S7JOoquTlWuUl0BtrT6rdDXd+5O0SAMbNmygX79+jBs3rsk2XlEKZcVYN99CB0BOb+YN6cXFa0UcOXM+2dFIzzMHOOzuR929CngaeKhBm4eAn3jERiDPzAabWS6wGPg+gLtXufu1DoxderC6U9TisarpPGWEcYb0oHpN9S1ZsoSdO3dy9WrrCn6PthzSCLBf/+xFRBLGw8k7ia5OVm4kZVGmuk1djpJN0mOEw2E2bNjAvHnzMGvmBfPKhcifPSHZlJXLvKF5AGzYrUUh0uGGAqfq/X46el8sbcYAhcAPzWyHmX3PrAcuDZGk2LBhA9XV1XGp13TWywhgDOph9ZrqLF26lHA4zLp161rVL8UC3Gy9OO7FlHlNgqITEenZqqsif4aSUK+pzlsn0inZ1OUo2SQ9xqFDh7h8+bLqNdVjgSBDBg5kRF42G/aoSLh0uMayvg3fSTTVJgWYCTzp7jOAUqCxmk8fNbOtZra1sLCwvfGKAJEaQ9nZ2cycObPlxi0446UMJINQN9+23ZRJkybRr1+/Nm2lG2+9ceCgVjeJiCREVUW0OHgSVzalZUAwpCLhXVHPfGcjPVJdvaa5c+c23/DKeegzsPnVT91Jdh75Q3qxbf8hamp1jLR0qNPA8Hq/DwPOxtjmNHDa3TdF7/8VkeTTO7j7U+4+y91n9e/fP26BS8/l7qxevZqFCxcSCoXaNVaF13CZyh67hQ4gEAiwePFi1q1bR01N61Yo5VkqQ8jkgF8jrLqDIiJxV1UZ+TOZK5vMjKxc08qmLkjJJukxNm/ezMiRIxk0qOntcV5eAuUlPaNeU5Tl9GZO/0xKyivYd+xUyx1E4mcLMM7MRptZKvAo8HyDNs8Dj0dPpZsLXHf3c+5+HjhlZrdE290O6FhFSbh9+/ZRWFgYn1PoosWte2Jx8PqWLFlCUVERO3bsaHXfCYE8SqjhFKUJiExEpGe7VHiZVRt/xhf+7gk+8OF7ePC98/n9/3MPf/9Pn+aF5b+kvLxjDmnIyjXKilwHGnUxSjZJj1BTU8OWLVtiWNXUg+o11cnOY9agXAA27zuY5GCkJ3H3GuATwEtETpJ7xt33mtkTZvZEtNly4ChwGPgu8LF6Q/wZ8DMz2wVMB77SUbFLz1VQUICZsWjRonaPdcbLCBGgP0k85qcTWLBgAaFQqE1b6UaRQwZB3gxfi3tc0jmY2T1mdtDMDptZY9ulzcy+Hr2+y8xm1rv2GTPba2Z7zOznZtaz/7GJxKisrIzvfP9r/Nlf38tPf/3/uHDxLBMnTOP2Zfcz/uYpvHloH1/7+j/wyAdv5+e//AE1NdUJjSerlxEOQ3lJQh9G4iwl2QGIdIT9+/dTUlLCnDlzmm3nV85H1olm53VMYJ1BTm/6ZaQyblBfNu09yEeTHY/0KO6+nEhCqf593673swMfb6LvTmBWIuMTaWjNmjVMmTKFvn37tnusM17KEDIJ9JRt203Iyspi1qxZrF69mr/4i79oVd+AGTdZLnv9KhVeS7oFExSlJIOZBYFvAncS2T69xcyed/f6K1nvBcZFb/nAk0C+mQ0FPglMdPdyM3uGyAraH3XgUxDpcnbv3s3nPvc5Tp06Rf6M+3j/7/0R02+9+R1t3J19+9/gZ7/4Lk99/2u8uup/+Ye//XeGDhmRkJiycuuKhIfJzNHrfFehlU3SI2zcuBGg2WSTu/e8ek0A6VkQDDF7+AB2HDxCVVVVsiMSEemULl26xK5du+JyCt1FL6eY6h5dr6m+pUuXcuTIEU6dav127nHWizBwxIviH5gk2xzgsLsfdfcq4GngoQZtHgJ+4hEbgTwzGxy9lgJkmFkKkMmNdQFFpJ6XX36ZD33oQ9TU1PCv//h9PvLYV7jppnE3tDMzJk2czlf+4Zv809//JxcvnuNPP/koO3dtSUhcGdmGBVQkvKtRskl6hM2bN3PTTTfRr1+/phuVl0BFaY+q1wSRyYKcPOYMyqG8sordu3cnOyQRkU5p7dq1AHGp17Q7fAXoWfWaVtaebvIWWjQegKdWPddkm6b0tXT6kMYhv95RT0U6zlCgfgbydPS+Ftu4+xngq8BJ4ByRmn8vN3wAnVoqEvHCCy/w6U9/mgkTJvDLX/6S8TdFFo+ntlAcfOH82/n2N35Bnz79+MLf/SnbdmyMe2yBgJGZoyLhXY2STdLtVVVVsW3bNvLz85tveOV85M8elmwCInWb8lIws7dWgYmIyDsVFBQwYMAAJkyY0O6x9vgVMkkhj9Q4RNb19R8xhAGjh7N3zaaWGzdinOVSSAXXXKtzu5nGlpo3/LTZaBsz601k1dNoYAiQZWYfvKGhTi0VYeXKlfzVX/0Vc+bM4Yc//CF9+vSJnERnsZ1EN2TwcP7j//2IoYOH8zdf+gT7D8b/y+u6E+lUJLzrULJJur09e/ZQXl7eYrLJr5yH1AzI6tVBkXUelt2bXkFnwoihbN68OdnhiIh0OlVVVbz++ussWbKk3Vutw+7sCV9hiGX2rG3bLZi8OJ9Dm9+gsqy81X3HWOSgi2PaStfdnAaG1/t9GDduhWuqzR3AMXcvdPdq4DfA/ATGKtIl7du3j89//vNMmjSJb37zm6SnR+roV1c4qWnEPE/l5fXhq//yPfr07sfffPHjnDvf9IrUtsjKNWqqoKoirsNKAinZJN3exo0bMTNmz57dZJseW6+pTk4eALPHDmPHjh1UVOhVXESkvm3btlFaWhqXek0nvYQiqhlKz9lCF4vJS+ZSU13NgQ3bW90320IMJIOjXpyAyCSJtgDjzGy0maUSKfD9fIM2zwOPR0+lm0tku9w5Itvn5ppZpkXe3N1O5ORTEYm6evUqn/jEJ8jLy+O//uu/yMp6e16qqoBQWus+F/XO68u//NOTVNfU8MX/+1mqqirjFmtWr7oi4VrZ1FUo2STd3ubNmxk/fjx5eXlNN7pyHirLe1y9prdk9wYgf3hfqqur2blzZ3LjERHpZFavXk1qaipz585t91h7PFKvScXB32nMjElk5GS1eSvdGMvhCpVc9fh9uJHkcvca4BPAS0QSRc+4+14ze8LMnog2Ww4cBQ4D3wU+Fu27CfgVsB3YTeRzz1Md+wxEOi9352//9m+5dOkS3/jGN6i/jbS2xqmphtT01o87YvhovvDnX+bQ4X1866n/L27xvn0inZJNXYWSTdKtVVZWsmPHjpa30J08EPmhhyabLDUN0jKY2S+LYDCouk0iIg0UFBSQn59PZmb7E0S7w1cYQibZFopDZN1HMJTC+Pmz2LtmE+FwuNX9395Kp9VN3Ym7L3f3m919rLt/OXrft93929Gf3d0/Hr0+xd231uv7RXcf7+6T3f1D7spEitR5+umnee211/jzP/9zJk2a9I5rZcWRhE5qett2fCyYt4xHHv4Dnvvd02zasrbdsQIEU4z0TCi93vr5QZJDySbp1nbu3ElVVVVsyaa0TMjM6aDIOqHs3mRVlTB58mQ2bWrbt8oiIt3RsWPHOHHiRFxOoavxMPv9GlMCfeIQWfczeUk+RYVXOL3/cKv7ZloKA8nguJJNIiLNOnPmDF/96ldZsGABjz/++A3X30o2xVAcvCl//OFPMnLEWL76H1+kuDg+p4Vm9QpoZVMXomSTdGsbN24kGAwya9asJtu4eyTZ1GdQz6zXVCcnD0qvk58/hz179lBaWprsiEREOoXVq1cDxCXZdMivU0mtkk1NmLBwNmbGntVt+9JjpGVzmUpKvDrOkYmIdA/uzpe+9CUA/uEf/qHRzz9lxY4ZpLTjwNTU1DT+6i++wpWrl/nOD/697QPVk5VrVJZBTbUSTl2Bkk3SrW3evJlJkyaRnZ3ddKNLZ6CsGOszsOMC64QsuzeEw8yZMI6amhq2bduW7JBERDqF1atXM3bsWIYNG9busXaHr2DAROvd/sC6oZw+eYycOr7NdZtGWmS+P+El8QxLRKTbWLlyJevWreMzn/kMQ4cObbRNWbETSo/9JLqm3DJuEu99zwdZ/uKv2bf/jXaNBSoS3tUkNNlkZveY2UEzO2xmX2jkupnZ16PXd5nZzHrX8szsV2Z2wMz2m9m8RMYq3U9paSm7du2KvV5T38EdEFUnFj2RbvqgPEKhkLbSiYgAJSUlbN26NS6n0EGkOPhYyyVL9ZqaNGlxPif3HKTo0tVW982zNHqRygltpRMRuUFVVRVf/epXuemmm3j00UebbFde7KS28iS6pvzB73+Mvn0H8B//9X/bVI+vvreKhF9XsqkrSFiyycyCwDeBe4GJwGNmNrFBs3uBcdHbR4En6137T+BFdx8PTENHlUor7dixg5qamtiSTb36YRnNrH7qCbJ6AUZ6USHTpk1TsklEBFi/fj01NTVx2UJX5jUc8iImm7bQNWfS4si8vW/d5jb1H2nZnKWMKq+NZ1giIl3eT3/6U06ePMkXvvAFUlJSGm1TXeVUVbbtJLrGZGZm8cQf/TmHjuzn1VX/266xUtONUJpWNnUVMSWbzOzXZna/mbUmOTUHOOzuR929CngaeKhBm4eAn0RPkdgI5JnZYDPLBRYD3wdw9yp3v9aKxxZh06ZNhEIhZsyY0WQb9zB+8iA2YnwHRtY5WTAFsnLg0hnmzp3Lvn37uH49PsX8pPtr4zwh0ukVFBSQm5vb7FwSq/1+lTCuek0tGDZ+LL0G9G3zVrrhloUDZymLb2DSLponRJLrypUrPPnkkyxevJgFCxY02a68nSfRNWbZknsYN3YC3//xN6iqqmrXWFm5pmRTFxHri/2TwAeAQ2b2L2YWyyfzocCper+fjt4XS5sxQCHwQzPbYWbfM7OsGGMVASLJpqlTpzZ/TPXF01BRqmRTnezeeOFp8vPzcXe2bt3ach+RiLbMEyKdWjgcZs2aNSxcuLDJb4BbY0/4CiEC3Gy94hBd92VmTFw0hwPrt1FbXdPq/gPJJESA066DLjoZzRMiSfSNb3yD8vJyPv/5zzfbrqyk/SfRNRQIBPjoH32GCxfP8vzyX7RrrKxco7zYCYeVcOrsYko2uftKd/99YCZwHHjFzNab2f8xa7LoQGOp0IZ/I5pqkxJ9rCfdfQZQCtxQ8wnAzD5qZlvNbGthYWEMz0Z6guLiYvbu3RtzvSYlmyIsOw+uFTJl/M2kp6drK53ErI3zhEintmfPHi5fvhy3ek27/QrjLY9UC8ZlvO5s0uJ8KkrKOLJjT6v7Bs0YQianvBR3fRjpLDRPiCTPiRMneOaZZ3j00UcZO3Zss23LipxgCgTj/K9y1sz53DpjLj/9+VOUlrb9EIesXoZ7pIi5dG4xL2M1s77Ah4E/BnYQqak0E3iliS6ngeH1fh8GnI2xzWngtLvXfdL9VfSxbuDuT7n7LHef1b9//1ifjnRzW7duJRwOt5xsOrEfeg/AcrWlAYCcyOlIqUWFzJw5U8kmaZU2zBMindrq1asJBAIsXLiw3WNd80pOeam20MVo/LyZpIRC7F3dtnlomGVRQjXXqY5zZNIemidEkuOpp54iFArxJ3/yJy22LSt2MnOs3SfRNeYjf/gZrl+/yi9+/aM2j5GVG0lhqEh45xdrzabfAGuBTOBd7v6gu//C3f8MaKqq8hZgnJmNNrNU4FHg+QZtngcej55KNxe47u7n3P08cMrMbom2ux3Y17qnJj3Zpk2bSEtLY9q0aU228doa/NRBbMSEDoysk8vOA8ALz5Cfn8+bb77J5cuXkxuTdAltnCdEOrWCggKmT59O79692z3WnnDkZLUpKg4ek7TMDG6aNaXNdZuGRasvnPa2f3su8aV5QiQ5Tp8+zfPPP8/73vc+Wlqc4e6UFTsZ2fFPNAHcMm4SSxffzS9/82OuF11r0xjpWRBMUbKpK4h1ZdP33H2iu/+zu58DMLM0AHef1VgHd68BPgG8ROQkuWfcfa+ZPWFmT0SbLQeOAoeB7wIfqzfEnwE/M7NdwHTgK616ZtKjbdq0iRkzZpCW1sxm43PHoaoCGzWpw+Lq9DJzIJT6Vt0mgM2b23YakPQ4rZ4nRDqzixcvsm/fvricQgew2y+TRQqjLCcu4/UEkxbnc+HYKS6dOtfqvrmWSg4hzrqKhHcimidEkuC73/0uZsYf/dEftdi2ugpqqiEzJzHJJoDHP/AEFRXl/PaFn7epv5mR1csouaZkU2cXa7Lp/zZy34aWOrn7cne/2d3HuvuXo/d9292/Hf3Z3f3j0etT3H1rvb47o9vjprr7u939aoyxSg939epVDhw40PIWuuN7AcNGql5THTODfsPwiyeZNGkSWVlZ2konsWrTPCHSWa1evRogLvWa3J3d4StMtj4EErAtobuatCQyj7d1ddMQy+Q8Zarb1HlonhDpYOfOnePZZ5/l4YcfZuDAgS22rzuJLpHJptGjxjE/fym/ee5nlFe07QuB7F5GqYqEd3rNJpvMbJCZ3QpkmNkMM5sZvS0lsgRWpNPZsmULQIvJpvCJfTBoJJahldv12eBRcOEEwUCAWbNmKdkkzdI8Id3V6tWrGTx4MOPGjWv3WOcp5zKVTFa9plbpP2IoA0YNa3OyaTCZVBLmCpVxjkxaQ/OESPJ8//vfx935yEc+ElP7sg5INgE89v4/oqjoGstf/E2b+mfnGR6OFDOXzqulc3zvJlLEbxjwtXr3FwN/naCYRNpl06ZNZGZmMnny5CbbeGU5nD2Kzbm7AyPrGmzQKHz7a3DlPHPnzmX16tVcuHAhpm9DpEfSPCHdTlVVFRs2bODBBx+MS4HUPeErACoO3gaTFuez9unnqSwrJy0zo1V9B1smONpKl3yaJ0SS4PLly/zyl7/koYceYujQoTH1KSt2UkIQaqYSSTxMnjiDqVNm8cyvf8RDD7yflJTWHX2XnRcAaim57nUlZ6UTanZlk7v/2N2XAR9292X1bg+6e9vSkCIJtmnTJm699VZCoaZftPzUQQjXql5TI2zQaAD8/DHmzJkDoNVN0iTNE9IdbdmyhbKysvjVawpfoR/pDKJ1yRKBSYvnUFNVzZubdrS6b7aFyFXdpqTTPCGSHE8//TRVVVX84R/+Ycx9EnkSXUMfeN8fcbHwPK8WLG9137QMSAmhuk2dXEvb6D4Y/XGUmX224a0D4hNplcLCQo4cOfJWkqQpfnwfpISwoTd1UGRdSN/BEErDzx9n/Pjx9OrVS8kmaZLmCemOCgoKSE9PZ+7cue0eK+zOHr/C5EDvDnnz3t2MvXUK6VmZ7F3TtsMqBkfrNoVVtylpNE+IdLyqqiqefvppFi1axJgxY2LqU3cSXaK30NWZM2shY0bfzM+f+UGra+uZGdl5RolOpOvUWioQnhX9MxvIaeQm0qnUnZzW0gcEP74XG3Yz1solmz2BBQIwcCR+7jiBQIDZs2cr2STN0Twh3Yq7U1BQQH5+Punp6e0e77gXU0oNU6xvHKLreVJCIW6ZN5O9aza3qdD3YDKpIsxJL0lAdBIjzRMiHWzFihVcunSJP/iDP4i5T1UF1NZARgclm8yM9//ehzlx8gjbd2xsdf/sXkZZsVNbq4RTZ9VszSZ3/070z3/omHBE2mfTpk3k5OQwYcKEJtt48VW4fA6bsrADI+tabNAofOcqvLaGuXPnsnLlSk6fPs2wYcOSHZp0MponpLs5evQop0+fjumI6Fjs9ki9psmB3nEZryeatDifN1au4+ybxxh6S2zf0NcZaBngcNCvMUp5jaTQPCHSsdydH//4x4wdO5b58+fH3K+jioPXt3TxPTz53a/y7As/59aZ81rVNzsvAB6mrMjJ6a2Vw51RSyubADCz/2dmuWYWMrNXzexSvSWxIp3Gpk2bmD17NsFgsMk2fnwfgOo1NcMGj4Kaarh0RnWbJCaaJ6S7WL16NQCLFy+Oy3i7w1cYblnkWYKrrXZjExfNBmjTqXQ5hMggyEG/Hu+wpJU0T4h0jG3btrF//34ef/zxVm3fTkayKTU1lQfufS8bNhVw/vyZVvXNzovEqbpNnVdMySbgLncvAh4ATgM3A3+RsKhE2uDs2bOcPHmy5XpNJ/ZBZg4M0CqdptiQsQD4mSPcdNNN9O3bV8kmaYnmCekWCgoKuOWWWxgyZEi7x6ryWg74NaaYTqFrj179+zJ84rg2JZvMjEFk8mb4WvwDk9bSPCHSAf77v/+bXr168a53vatV/cqKnVAahFI7dpXQu+5/BDCe+99ftKpfaroRSlOyqTOLNdlUV9jmPuDn7tE14SKdSCz1mtzDkXpNIydgFutf/x6oVz/I6oWfOYyZkZ+fz8aNG9tUL0N6DM0T0uUVFRWxffv2uJ1C96Zfp5owkwNKNrXXpMVzOPbGfkqvFbW670DLoJAKrnhFAiKTVtA8IZJg586dY+XKlTzyyCNkZLTuBNSOLA5e34D+g1k4/zaWv/hrKitb9zodKRIeTlBk0l6xftp+wcwOALOAV82sP6AZWzqVTZs2kZeXx7hx45pudOEklBZhY6Z0XGBdkJlhQ2/Czx4BID8/n8LCQo4fP57cwKQz0zwhXd66deuora1l6dKlcRlvT/gKAYyJpnpN7TVpcT4eDrP/9a2t7jvQIh+4tJUu6TRPiCTYr3/9a9ydRx99tFX93J3yJCWbAN7zrscoKr7OqwXLW9Uvu1eA8hKoqdYX4p1RTMkmd/8CMA+Y5e7VQCnwUCIDE2kNd2fTpk3k5+cTCDT919qP7ALARivZ1BIbMhauFeIl18nPzwdg48bWnxQhPYPmCekOVq9eTV5eHlOnTo3LeLv9CuMslwxr9jwWicGIybeQ3SevTVvp+pJOiIC20iWZ5gmRxKqpqeHXv/41CxcuZOjQoa3qW1kG4XDH1muqb9rU2YweNY7fPv/zVu2kqKvbVHpdyabOqDX7iCYA7zezx4H3AnclJiSR1jt16hTnzp1rsV5T+OhuGDQKy8rtoMi6Lht6EwB+9jAjRoxg8ODBqtskLdE8IV1WbW0ta9asYdGiRc0eMhGrUq/miBcxWfWa4iIQCDBx4Wz2rdtCuLa2VX2DZoy2HI56cYKik1bQPCGSIGvXruX8+fM88sgjre6bjOLg9ZkZD97/fg4d2c+bh/fF3E9Fwju3WE+j+2/gq8BCYHb0NiuBcYm0Sl0SpG4FTmO8vATOHdUWulgNGgnBFPzMEcyMOXPmsHnzZsJh7YuWG2mekK5u165dXLt2LW5b6Pb5VRyYonpNcTNpcT5l14s5/sb+VvcdY7kc8yLCqj2YNJonRBLrmWeeoX///m2ax+qSTRnZyUk2Adyx7D7S0tJZ/uKvY+4TSjXSM6H4mj6fdEaxruueBUx0VQeWTmrTpk3079+fMWPGNNnGj+0FdwJj4rM9oruzlBAMGoWfehOIFF5/7rnnOHToELfcckuSo5NOSPOEdGmrV68mGAyycOHCuIy3O3yFNAKMs15xGU9g/PxbCQQD7FmziTEzJ7eq7xjL4UXCnPFShlt2giKUFmieEEmQc+fOsWbNGj7ykY8QCoVa7tBAWbGTmgEpoeQlm7Kzc1my6C5eXbWcsrK/JDMzM7Z+vQNcvxTG3TFLXvxyo1i30e0BBiUyEJG2qqvXNHfu3LdeYMI7C268bXsFQmmEL55o9LrcyEbcAueP45Xlb21RVN0maYLmCenSVq9ezcyZM8nNjc82693hK0yw3qTo5NO4yczNZsyMyexds7nVfccGIkm/o9760+wkbjRPiCRIXWHwtmyhAygtcrJyk5+ouf+e36O0rISXX3455j45eUZ1JVTpuIFOJ9Z3QP2AfWb2kpk9X3dLZGAisTpy5AiXLl1qtl6Tu8Ols9B3MKY3/jGzEePBw/jpQwwZMoQRI0aweXPr3+RLj6B5Qrqsc+fOceDAAZYsWRKX8S57BWcp0xa6BJi0OJ+zbx7l6rmLreo3hEzSCXJEyaZk0jwhkgC1tbVtLgwOEK51ykudrCTVa6pvyqSZDB86il/96lcx98npHYm7+KoWTXY2sW6j+1IigxBpj7p6TXPnzm26UdFlqKrA+rf+Bbgns6E3QSCInzwAY6eSn5/PihUrqKmpISVFpyvJO3wp2QGItNWaNWsA4lavaU/4CoCKgyfApCX5PPe177J37WYWvu+BmPsFVCS8M/hSsgMQ6Y7Wr1/P+fPn+au/+qs29S8rcXDI7AQrm8yMe+95mKe+/zU2rzvEiOFNl0ip42HHDC6crKWmuumE06CR7T/8Q1onpiUe7r4aOA6Eoj9vAbYnMC6RmG3cuJGhQ4cybNiwphtdOhv5s++Qjgmqm7BQGgwZG0k2ESnAXlJSwv79rS/OKt2b5gnpygoKChg2bFizdf9aY5dfoRepjFBtoLgbNGYEfYYOYu+a1p+OOsZyOe7F1LgKySaD5gmRxHj22WfJy8tr8xcmb51El9s5dn/cffuDBIMpLH/p2ZjaW8BIzYCKMq1s6mxiPY3uI8CvgO9E7xoK/DZBMYnErLa2li1btjR7Ch2AF56G3L5YWkYHRdZ92Ihb4MIJvKLsra2KdavJROponpCuqqKigo0bN7J06dK4FBYNu7M7fIXJgT4EVKg07syMSYvn8OamnVRXVrWq71jLpZowp700QdFJczRPiMRfUVERr776Kvfffz+pqaltGqOsyAkEICO2etwJ16dPP+bnL+Hllc9RU1MdU5/0TKOqPLLKSTqPWNOXHwcWAEUA7n4IGJCooERideDAAa5fv97sFjqvLIPrl7ABwzswsu4jMGoiuOMn9tO/f3/Gjh17Q7Kp0YLsLdyk29E8IV3Spk2bqKioiFu9ppNewnWqmKotdAkzaXE+VeUVHNryRqv6jQ1Eir+rblPSaJ4QibMVK1ZQVVXFe97znjaPUVrkZOQYFug8X5DcdedDXL12hS3b1sfUPi3TcFeR8M4m1mRTpbu/9fWRmaUAShtK0tUVq252ZdPF05E/lWxqmyFjIS0DPxJ5Uz937ly2bdtGVVXrvlGWbk/zhHRJq1evJjMzk9mzZ8dlvN0eqdc0VcXBE2bc7GmE0tNavZVuIBlkkqIT6ZJH84RInD377LOMGzeOiRMntnmMsuLOcRJdffmzFpGbm8crr70QU/v06OYVbaXrXGJNNq02s78GMszsTuCXQGz/50USaOPGjYwZM4YBA5r+YswvnoSMHMjO67jAuhELpmCjJuFHd+PuzJkzh/Lycvbs2ZPs0KRz0TwhXY67U1BQwLx580hLS4vLmLvClxlmWfSx9LiMJzdKTU/jlvwZ7F2zKXLabIzMjDGWq2RT8mieEImjY8eO8cYbb/Ce97ynzdvAqyqd6krI7AQn0dUXCoVYtvgeXt+wipLSlg92CIYgmAKV5R0QnMQs1mTTF4BCYDfwJ8By4G8TFZRILKqrq9m6detbdYQa4zVVcPk8DBwel1ocPZWNnQal1+HiSWbPno2ZsXHjxmSHJZ2L5gnpcg4dOsS5c+fitoWuymvZ79eYoi10CTdp8Rwunz7PhaMnW9VvrOVwwkuoVpHwZNA8IRJHv/3tbwkGgzzwQOwnczZUVlRXHLzzfU666/Z3UVVVyZp1r7TY1sxIy9TKps4mprPL3T1sZr8FfuvuhYkNSSQ2e/bsoaysrNl6TRSeAQ9jA0Z0XGDdkI2ZDIAf2UXv+e9i/PjxbNq0iY997GNJjkw6C80T0hWtXr0aoM3JppW1p9/x+xkvpZowYfyGaxJfExdHts/vXbOZQWNHxtxvTCCX2rBz0ksYa7mJCk8aoXlCJH5qa2t57rnnWLhwIf3792/zOHUn0WV1spVNABPGT2XY0JG88uoL3Hf3wy22T880yoqc2honmNL5nk9P1OzKJov4kpldAg4AB82s0Mz+vmPCE2laXZHqZlc2XTwFqemQ16+jwuqWLKsXDB5N+FDkhOL8/Hx27txJRYWq8PV0miekK1u9ejUTJ05sdit2a5z2UgLAYDrJkT7dWJ/BAxgybnSr6zbVJZi0la7jaJ4Qib9t27Zx4cIFHnzwwXaNU1rkhNIglNb5kjNmxh23PcDOXVu4cPFci+3TMiPPobIs0ZFJrFraRvdpIqdGzHb3vu7eB8gHFpjZZxIdnEhzNm3axPjx4+ndu3ej1z1cG1nZNGA4ZrHuGJWmBG6ZDedP4FcvkJ+fT1VVFTt37kx2WJJ8n0bzhHRBV69eZceOHXHbQgeRlU0DySCkOadDTFqcz5EdeygvLo25Tz/SySGkZFPH+jSaJ0Tiavny5WRmZrJ06dJ2jVNW1PmKg9d3522RLYIrX/tdi23TVCS802np3dDjwGPufqzuDnc/Cnwwek0kKSorK9m+fXvzp9BdPg+11ZhOoYsLmxA5qckPbGHWrFkEg0HVbRLQPCFd1Jo1awiHwyxbtiwu45V7DZepZKhlxWU8admkJfmEa2o5sH5rzH3MjBGWzUkvSWBk0oDmCZE4qq6u5uWXX2bp0qVkZrZ9Ja2HnbIS73TFwesbMng4kyfO4JXXXmjxQIhAwEhNh8pyJZs6i5aSTSF3v9Twzug+61BiQhJp2c6dO6mqqmo22eQXT0aOJeg7uAMj674sty8MGUt4/2ays7OZNGkSmzdvTnZYknyaJ6RLKigooF+/fkyaNCku453xyOqaYUo2dZhRUyeQmZvDnlZupRth2ZzyEsKtOMlO2qVd84SZ3WNmB83ssJl9oZHrZmZfj17fZWYz613LM7NfmdkBM9tvZvPa/WxEkmzTpk1cvXqV++67r13jlJeChztncfD67rz9XZw4eZQ3D+9rsW1aplFRRqtOKpXEaSnZVNXGayIJtWnTJoLBILNnz270uofDcPEU9B+GBYIdHF33FZgwBwpP44VnmDt3Lrt376a0NPbtC9ItaZ6QLqeqqoq1a9eydOlSAoH4bHk7QxlpBOhLelzGk5YFU4JMWDiL/Wu3EA7HfrrcSMuhkjAX0BnZHaTN84SZBYFvAvcCE4HHzGxig2b3AuOit48CT9a79p/Ai+4+HpgG7G9d6CKdz/Lly8nJyWHRokXtGqesKPK62Zm30QEsW3w3oVCIV159ocW26ZmRBFp1ZQcEJi1q6R3WNDMrauRWDEzpiABFGrNp0yYmTZpEdnZ24w0un4OqCmzwqA6Nq7uzifkQCBJ+YzVz5syhpqaGbdu2JTssSS7NE9LlbNmyhdLSUm677ba4jOfunPFShlgWAevcb9q7m0mL5lB85Ron974Zc5+RFnnvcNKLExWWvFN75ok5wGF3P+ruVcDTwEMN2jwE/MQjNgJ5ZjbYzHKBxcD3Ady9yt2vxfWZiXSwqqoqVq5cye23305qamq7xiotdswgI6tzz1s5Ob2YO2cJrxUsp7a2ptm2dUXCVbepc2g22eTuQXfPbeSW4+7aHiFJUVpayq5du5rfQnfuGKSkQr+hHRhZ92eZudgts/A965kxZRKhUOitUwGlZ9I8IV1RQUEBaWlpzJ07Ny7jXaOKUmoYhrbQdbQJC2djZuxdHftcNMyyMOBEWHWbOkI754mhwKl6v5+O3hdLmzFAIfBDM9thZt8zu3Gfq5l91My2mtnWwsLCVj8/kY60bt06iouL272FDqD0upORbQSCnTvZBHDX7e/i6rUrbNm2vtl2oVQIBHQiXWeh41Kky9m+fTs1NTVNJpu8qhIunoRBI7WFLgEC05dCZRnpJ/Ywbdo0JZtEpEtxd1atWsW8efPIyMiIy5h19ZqGWtsLtUrbZPfuxaipE9jbirpNqRZkMJmcUJHwrqCxT8ENlyw01SYFmAk86e4zgFLghppP7v6Uu89y91n9+/dvb7wiCbV8+XLy8vLi8mVJaZGT1avzJ5oA8mcvJic7l9dWr2i2nZmRlqmVTZ2Fkk3S5WzcuJFQKMTMmTMbve6Hd0JtDTZ4dMcG1lMMvxn6Dia89RXmzJnDvn37uF6iuk0i0jUcOnSIM2fOxO0UOoDTXkouIXKsfVsapG0mLZ7DqX2HuF54OeY+IwM5OpGuazgN1D9WeBhwNsY2p4HT7l6XifwVkeSTSJdUXl7OqlWruOuuuwiF2rd4vKrCqa7s/PWa6oRCIRYtuIPXN7xGZWVFs23TMo3qSgjXKuGUbEo2SZezadMmpk2b1uQ30r5vY6Q6XO+BHRxZz2BmBPLvg4unyB/eD3dn24HDyQ5LRCQmBQUFACxdujQu49V6mHOU6RS6JJq0JLLSed/aLTH3GWnZXKScMm++/ock3RZgnJmNNrNU4FHg+QZtngcej55KNxe47u7n3P08cMrMbom2ux1o+TgrkU5q9erVlJWVxWcLXVEkEdNVVjYB3LbkXsrKStm0ZW2z7dKjdZsqdQZE0inZJF3K9evX2bdvX5NLR72sGD+2BwaNxlSkNWFsYj706sfkKwdJT09n454DyQ5JRCQmq1atYtKkSQwYMCAu452nnBqcYdbEgRWScENvGUvewH6t2ko34q0i4Vrd1Jm5ew3wCeAlIifJPePue83sCTN7ItpsOXAUOAx8F/hYvSH+DPiZme0CpgNf6ajYReJt+fLl9OvXj1mzZrV7rNLr0WRTF1nZBDB92mzyevVh1ZoXm22XFl2PoK10yZeS7ABEWmPLli24O3PmzGn0uh/cCuFabaFLMAumEJh7P6kv/ZiZ429i456DyQ5JRKRFly9f5o033uDjH/943MY85SUEMIagek3JYmZMXDSH7SsKqKmuJiWG7SUjLQeIJJvGk5fgCKU93H05kYRS/fu+Xe9nBxr9R+3uO4H2fzIXSbKSkhLWrFnDI488QjDY/pq0pUVh0jIhJdR1kk3BYApLFt3Fi6/8lvLyMjIyGp93gylGKA0qlWxKOq1ski5l/fr1ZGZmMm3atEavh/dugL5DIKd3B0fW89jUhdBvKHOzazly5hwXr1xLdkgiIs1as2YN7h63LXQAp7yUwWQQMr2lSqbJS+dSUVrG4S27YmrflzSySOGEFyc4MhGR9nvttdeorKyMyxY6iKxs6kqrmurctvReKisrWL+xoNl2aRlGRVnkUBBJHq1ski5lw4YNzJo1i9TUG4uw+qWzcOYwgaWPgLbQJZwFggTu+ABzD34JgA17DvDQ4vgcIy4ikgirVq1i4MCBTJw4MS7jFXsV16hivOXFZTxpu1vyZxBKT2PXqvWMn3/rDddX1p6+4b4cQuwKX2n0Wn13BIfFLU4RkbZYvnw5gwcPbvIL99aoqXYqyqD/8K73JcnkiTPo128gq9a8yO3Lmk68pWdCyTWoqYaQzu5Imq73N0x6rLNnz3L8+HHmzZvX6PXwG2sgEMQmL+jgyHquwMgJjF9wG73TUtiwfWeyw5EuyMzuMbODZnbYzG44kjpa8PXr0eu7zGxmg+tBM9thZr/ruKilK6qsrOT1119n6dKlcavpd8ojJ3EOV3HwpEvNSGf8/FvZs2pDzN9k97E0rlKpb75FpFO7du0a69ev59577yUQaP/H97KirlevqU4gEGDporvZvHUtJSVFTbZLqysSrq10SaVkk3QZ69evB2D+/Pk3XPOaanzvemzcDCwrt6ND69FS7v4Q+cP6sHH3AcLVVckOR7oQMwsC3wTuBSYCj5lZwyUn9wLjorePAk82uP4pIkVjRZq1efNmysrKWLZsWdzGPOWl5BCiF/ratDOYumweV88XcvrAkZja9yWdasIUU53gyERE2m7lypVUV1fHbwtdNNmU3YVOoqvvtqX3Ul1dzbr1rzXZJjU9stGloqwDA5MbKNkkXcaGDRvo168f48aNu+GaH9wK5SXYtCVJiKxns/RM5ubnU1hWyeFV/6tviKU15gCH3f2ou1cBTwMPNWjzEPATj9gI5JnZYAAzGwbcD3yvI4OWrmnVqlVkZGQ0eZppa1V7mLOUMtyydPppJzFpcT5mxu7X1sfUvo+lAXCZykSGJSLSLsuXL2fEiBFx2wJeWuSkpEIoLS7DdbjxN09m8KBhvLZ6RZNtzIy0TK1sSjYlm6RLCIfDbNiwgXnz5t3wpt7dCW9bCX0GYaMmJCnCnm3B3HwANux5Ez+0PcnRSBcyFDhV7/fT0ftibfMfwOeBcFMPYGYfNbOtZra1sLCw3QFL1+TuFBQUMH/+fNLS4vPuer9fpQZnmGXHZTxpv5y+vRk9fSK7CzbE1L43kb8LV7wikWGJiLTZ5cuX2bRpE/fee2/cvtgove5k97Iu+0WJmbFs8d1s27GR69evNtkuLdOorIBwWAmnZFGySbqEgwcPcvXq1Ua30HH2KJw7RuDWOzCdBpQUQ/r3ZcTA/my8VgPH9uInDyQ7JOkaGnuX0/AdQaNtzOwB4KK7b2vuAdz9KXef5e6z+vfv39Y4pYs7ePAg586di+spdDvDlwlgDKHxo5clOaYsm8/p/Ye5cu5ii21DFiCXEFddW8BFpHN66aWXCIfDcdtCFw47ZcVd8yS6+pYtvZdwuJbV615psk16hoFDVXkHBibvoE/m0iXU1WtqrDh4eNsrkJaBTW4kESUdZt6UCWw5eZHqPkPw/ZvxCyeSHZJ0fqeB4fV+HwacjbHNAuBBMztOZPvdbWb208SFKl3ZqlWrAFiyJH5brXeELzGYDEL6kqNTmbIs8j5h96rYttL1JlIkXESkM1q+fDk33XQTN998c1zGKy923CGri9ZrqjN29C2MGD6aVc1spUuLfhdUUa6VTcmid0jSJWzYsIGxY8cycODAd9zvVy/iB7Zg05ZgqelJik4A5k0ZT3llFbszh0Fef3zXWvzqhWSHJZ3bFmCcmY02s1TgUeD5Bm2eBx6Pnko3F7ju7ufc/a/cfZi7j4r2e83dP9ih0UuXUVBQwNSpU4nX6rbzXsZZyhihLXSdzsDRwxkwejh7VsW4lc7SuE4Vtao3KCKdzPnz59m+fTv33ntv3MYseeskuq6dBohspbuHN3Zv5fLlxsskpISMlBBUqkh40nTtv2XSI1RWVrJ169ZGt9CFN62AQJDA7LuSEJnUN2fizQTM2LjvEDZjGWRk49tX4SXXkh2adFLuXgN8AniJyIlyz7j7XjN7wsyeiDZbDhwFDgPfBT6WlGCly7pw4QK7du2K6yl028KRN7ZKNnVOU5bO49CWXZQXl7bYtjepOHAdbaUTkc7lxRdfxN3jmmwqve4EgpCeFbchk2bZknsiNRnXvtRkm7RMU5HwJFKySTq97du3U1lZecMWOi++iu95HZu6CMvOS05w8pZe2VlMGjOSDbsPYKnp2K13QCCIb1uJV7T8hl96Jndf7u43u/tYd/9y9L5vu/u3oz+7u388en2Ku29tZIwCd3+go2OXruHVV18F4I477ojbmFvDlxhh2eRaatzGlPiZumwetTU17Fu3pcW2vaMn0l11baUTkc5lxYoVTJw4kdGjR8dtzK5eHLy+kSPGMnbMLc1upUvPhJpqqKlWwikZlGySTm/Dhg2kpKQwZ86cd9wfXv8CuBOYc0+SIpOG5k0Zz+4jxykuK8cysrFbb4fqanzbq3i1vjUWkY63cuVKRo0axdixY+MyXolXc8Cvcav1i8t4En+jpk0gu09eTHWbepGKgeo2iUincvr0aXbt2hXXVU0edkqve5ev11TfbUvuZe/+Nzh/oWHJz4i0zMhz1Va65FCySTq99evXM23aNLKy3l7v6Vcu4G+swaYvxfJ0wlRnMW/KBGrDYbbsexMAy+2DzVgKpUX4G2tw1cQQkQ50/fp1tmzZwh133BG3b3F3hC8RxpkV0NzTWQWCQSYvzmff2s3UVtc02zbFAuQQ4ppOpBORTmTFishqnXgmm8pKnHAYsrtRsmnZksiig4I1LzZ6PTUdMBUJTxYlm6RTu3r1Kvv27bthC114za8hJURgvnbOdCbTx40mIy2VDbsPvHWf9R2MjZ8Nl8/CiX1JjE5EepqCggJqamriuoVum18ij1TGWG7cxpT4m3LbfMqLSzm8bVeLbXUinYh0NsuXL2f69OkMHTo0bmOWXI8kXLLzuk8KYPCgYYy/eTKrmkg2BQJGWjqq25QkCf2bZmb3mNlBMztsZl9o5LqZ2dej13eZ2cwG14NmtsPMfpfIOKXz2rRpE+7+juLg4RP78YNbCcy5B8vqlcTopKHUUIhbx9/Ehj3733lh+M0wYDj+5g68+GpyghORHufVV19lwIABTJkyJS7j1XiYneFL3BroT6Ab1LvozsbPm0koPY1dr7W8le7tE+nCHRCZiEjzjh49yoEDB1q1qun8idoWb4WnwgRTukdx8PqWLbmHNw/t48zZk41ejxQJj2wjlI6VsGSTmQWBbwL3AhOBx8xsYoNm9wLjorePAk82uP4pIicUSQ+1YcMGsrKy3vqg4LU1hF/5KfTqh+XHb1mpxM+8yRM4dvYC5y5fees+M8MmzYOUEL5vo7bTiUjCVVRUsG7dOm6//XYCgfi83dnnVymnllsDqtfU2aVmpDNh/ix2vfo64XDzSaTepOlEOhHpNFasWIGZcc898a1LW1keqdfUHYqD17d08d0ATa5uSs8Edygr1uePjpbIlU1zgMPuftTdq4CngYcatHkI+En0tKGNQJ6ZDQYws2HA/cD3EhijdGLuzuuvv05+fj4pKSmR+zYuh8vnCNzxASykU4A6owXTJgDw+hvv3DJnqenYLbPgWiGcPpSM0ESkB3n99dcpLy+P8yl0haQSYIr1iduYkjjT71zItQuXOLnnYLPtekdPFbyquk0ikmTuzvLly5k9ezYDBgyI37hhp6qie9VrqjOg/2AmTZzOqtWNJ5vqioQXX1OyqaMlMtk0FDhV7/fT0ftibfMfwOcBrWnuoY4dO8aZM2dYuHAhAH7hBOH1L2AT8gncND25wUmTbho2hIF98lj3RiP1mYaMgd4D8cM78cryjg9ORHqMlStX0qtXL2bPnh2X8dydbeFLTLU+pFowLmNKYk1aMpdgSgo7X1nXbDudSCcincXBgwc5evRoXAuDA1RVRlb31FR7TFvuzp+ojevjJ9qyxfdw9NibnDx19IZrKSEIBKFEyaYOl8hkU2Np04b/hxttY2YPABfdfVuLD2L2UTPbamZbCwsL2xKndFJr164FYNGiRXhVBbUvPAUZ2QTu/P0kRybNMTMWTpvIht37qa6pveGa3XIrVFUQ3vxSkiIUke6upqaGVatWsWTJEkKhUFzGPOJFXKKCOYH4fdMsiZWZm83Nc2fwxsp1zW7fTrEAuaRy1ZVsEpHkWrFiBcFgkLvuuiuu41ZGT2NLy+h+K5sAliy6CzNrdHWTmZGWoWRTMiQy2XQaGF7v92HA2RjbLAAeNLPjRLbf3WZmP23sQdz9KXef5e6z+vfXMcTdydq1axk9ejRDhw4l/OKP4cp5Ag/8MZaRnezQpAULp02ipLyCXYdv/HbBevWDgSPxLS/hpUVJiE5EurutW7dy/fp1br/99riNuTF8kSDGrIDea3Ql0+5YyKVTZzlz8Mb5qL7epHJVNZtEJInqttDNmzePPn3iu127shwCAUjpplVI+vUdwNTJt7JqzYuNfrmQlmmUFTu1NUo4daREJpu2AOPMbLSZpQKPAs83aPM88Hj0VLq5wHV3P+fuf+Xuw9x9VLTfa+7+wQTGKp1MeXk5W7Zsiaxq2vwivn8TgUXvITBqUrJDkxjMnTyeYCDQ+FY6wMZNh+oqwtte6djARKTTWll7us23G8ZauZK0tLS3tmG3l7uzMXyBqdaHLIvPSinpGFOXzcMCAd5Y2fxWut6WRpFOpBORJNq9ezenT5+O+xY6iKxsSsug2xUHr2/Zkns4cfIox47fWBu2bkVXaZGSTR0pYckmd68BPgG8ROREuWfcfa+ZPWFmT0SbLQeOAoeB7wIfS1Q80rVs3ryZqqoqFowcQLjgl9j42djc+5IdlsQoNyuTaeNGs+6NvY1et6xe2M0z8e2vqXaTiMSVu/Pqq6+ycOFCMjMz4zLmES+ikArmBgbGZTzpODl9ezN25uSWk03RE+muaXWTiCTJihUrCIVCcT3YAt4uDp6a2X0TTQCLF95JIBBodCtdWkbkT22l61iJXNmEuy9395vdfay7fzl637fd/dvRn93dPx69PsXdtzYyRoG7P5DIOKXzWbduHelpqdx6Yj0MG0fg/j/GLKF/XSXOFk2fxL5jpyi8dr3R64F590NlOb5jVQdHJiLd2Z49ezh//nxc36xrC13XNv3ORZw7fJwLx0412UYn0olIMoXDYVasWMGiRYvIzc2N69hVFYB333pNdXrn9WXGtDm8tnrFDVvpUkJGarqSTR1Nn96lU1r72qvM6p9F2pARBN/7KSxF2xa6moXTIlse1+/a3+h1GzQKGzmB8PZX8XDXOvFCRDqvl19+mWAwyJIlS+IyXt0WuinWh2xtoeuSpt6+AKDZ1U06kU5Ekmn79u1cuHAhYVvo4O3VPd3ZssX3cPbcKd48fGMpj+w8o+Satkp3JCWbpNM5vuZFTpw9x8JbRhF8359jafHZBiEda/zIYfTNzWlyKx2Azbwdiq/ih3d2XGAi0m25Oy+99BLz5s2jd+/ecRnzqBdrC10X13tQf0ZNHd9ssiloAXrpRDoRSZIVK1aQnp7OsmXL4j52ZTkEgtATvrtftPBOgsGURrfSZecFqCiDmiqtbuooSjZJpxLev5m1P/wGAIv/7O908lwXFggEWDBtIq/v2k9tuPFvEeymaZDTB9/+WgdHJyLd0b59+zh16hR333133MbcGL5AEGO2ttB1adPuWMjJvW9y5eyFJtv0Jk0rm0Skw9XU1PDSSy+xdOlSsrKy4j5+RVn3Lw5eJzenF7NmzqNgzUs3bKXL7hV5/iXXlWzqKEo2SacR3ruB8AvfYd3lKoYPH8boWyYkOyRpp4XTJnG9pJS9R080et0CQQIzluIn9uNXzndscCLS7bz44oukpKRw++23x2W8yBa6i9pC1w1MuyNyMuEbr77eZJvelkox1dToRDoR6UCbN2/m8uXLCdlCF651qishvZsXB69v6eJ7uHDxLPsP7HrH/dl50WST6jZ1GCWbpFMI799M+H+/R9XgsWw5dYlFixYnOySJg/lTJmBmrNvZzFa6KQvBjPCepj8AiIi0pG4L3f/f3n3HR1Wljx//nDuTHtIrEAgdQu9VBEEBUbGXVbDtV1111dVVd3WL67qW9efa21rWLutaUUA6iPTeIdRASEJ6bzNzz++PCQgkgZDMZGaS5/16zSvJvXfOPDeZzJn7zDnPGTFihMum0O3WhWRTwWgjwSXtCc+J7dCOtt07s3nB8nqPiahZka5IVqQTQjSjuXPnEhwczNixrr/+Ob7oc0ArSjaNGXUBfn5+LPnp1Kl0Vj9FYAhSt6kZWT0dgGg9zM1L69yus9LQW3+CiDg22NtQUVnJmHYR9R4vfEdkWCh9u3Rk+ZYd3H31qYtKnvL3jW6L3rQER5vIs646aAwY5/pAhRA+7/gUujvvvNNlbS4zMwnEwjAjzmVtCs8ZcOEY5r7xMUXZuYTHxdTaH6kCQEOBriJaBXogQiFEa1NdXc2CBQuYMGECgYGuf92pLG89xcGPCw1pw9DBY1i6fD6/+b+HMYxfri1Cww2K8yXZ1FxkZJPwKJ2b4Uw0hcegBl3Aiu178LNaGZrS3dOhCRcZ07832/anUVhSWu8xql0XqCyHPJlKJ4RonMZOoVvoSK/zNtd+mJ/NLDqoUH42M+s8RviWgZPOR2vNpvl1j276ZUU6GdkkhGgeK1eupKioyC1T6MC5Ep2fP1isrWdkE8D48yeTm3uM7Ts3nbI9NEJRXQnVlTKVrjlIskl4jC4tRG9ZBqERqMETUFY/lm/ZwdBe3QgODPB0eMJFxvRPQWvNym276j8oNgms/uiM/c0XmBCixdBa8+OPPzJixAgiIiJc0uYhXYINk+4q3CXtCc9L6NyBdj06s3Hu0jr3W5SSFemEEM1q7ty5hIWFMXr0aJe3rbWmqrx1TaE7btTwcfj7B9Rale5E3SYpEt4sJNkkPELbbehNS8CwoAaOR1n9ycjJ48DRLEb3T/F0eMKF+nRJJjw0hOVnqttksUBCR8g+gnbYmzE6IURLsHPnTtLT05k8ebLL2tyri2iDHwm0orkHrcCgyeM4uGVnvavSyYp0QojmUllZyaJFi5g4cSL+/v4ub99hA4e9dU2hOy44OIQRw8ay7Of5OByOE9tDwhUoqdvUXKRmk2iUptRT0lqjd66G8lLUsItQQaEA/LR5OwDnDejtihCFl7AYBmP6p7B88w4cponFqDvHrRKS0el7IeeoM/EkhBANNHfuXJeuQleqbRylnEEqulUsFd2aDJo8ju9ffp+NPy5j4m3X1tofqfw5qEuwaxPrWWoICiFEU/z000+UlZVx8cUXu6X9ylZYHPxk48dO5qefF7Bl23oGDRhOVpoz6eQfAHmZJv6Bjnrvm9DR0lxhtmjSi4rml3UIMg+iuvZHRcaf2Lxs43aS4mLo3FZW/Wlpxg3qS0FJKVv3Haz/oKh48A9EZx1qtriEEL7v+Cp0I0eOdNkUur26CIBuMoWuxYlJSqRj355s/HFpnfsjlXMaf6HUbRJCuNncuXOJiopi+PDhbmm/qlyjFAS00vUORgwbS2BgUK2pdAFBiqoK5/sH4V6SbBLNSldXonethfAY6NznxPbyyipW79jN+YP6yqfILdDofilYDINlG7fVe4xShnNEU0462m5rxuiEEL7syI5U0tPTmTRpkkva01qTqotIIIgw5fppDcLzBk0+nyM795KdVrvIeyTOZJPUbRJCuFNZWRlLly7loosuwmp1z2SjqnKNfyAoo3VeWwUGBjFq+Dh+WrEA+0nXFgFBYDpALjfcT5JNolnpPRvAXo3qPfKUJe5Xb99Ntc3O+MH9PBidcJfw0BAG9ezK0jMkmwBUfLLz1T83o3kCE0L4vE3zf3LpFLoMyinGRk8V4ZL2hPcZOOl8ADb+uKzWvnD8MUDqNgkh3Grp0qVUVla6bQqd1pqqitY7he648edPpri4kI2b157Ydvx3UlUuI5vcTZJNotnoolzI2A/JKag2kafsW7pxK6FBgQzq2dVD0Ql3GzeoL3uPZHA0J6/+gyJiwS8AnX2k+QITQvgsrTUb5/3k0il0O8wCArHQSbVxSXvC+0QmxNJ5UJ86V6UzlCKCABnZJIQAICvN0eDbuZgzZw5xcXEMHjzYLXFXV4LWEBjsluZ9xrAhYwgJDmXJT3NPbPMPABRUVXgurtZCkk2iWWitnaOa/ANRnfuess80TZZt2s7ofin4u2kYqfC8cYOcf/czjW5ShgGx7ZxT6UxZJUIIcWYHN+8k/2iWyz4ZLtU2DlNKDxUuxaFbuMFTxpG57xAZe2vXEoxU/uRLzSYhhJsUFhayfPlyLr74Yox6Fs5pquOjdlr7yCZ//wBGjRzPzysXY7M5580pQxEQCFUVMrLJ3eSdlGgeuUeh4BiqS3+U9dQaGDsOHia3sJhxMoWuRUtOjCc5Me6MdZsAVFwS2KuhMLuZIhNC+Kp1PyzCLzCACy+80CXt7dQFAPSSKXQt3oALx6IMo86pdJEEUIoNm5YPPbyBUmqyUmqPUmqfUuoPdexXSqlXavZvVUoNOm2/RSm1SSn1Q/NFLUT9FixYgM1m45JLLnHbY1SWg2EBq5/bHsJnjB87mdLSYtZvXHFimxQJbx6SbBJup7VG79sCQaHQvlut/Us3bsNQirEDensgOtGczh/Yl7U7UymrqKz/oOi2YBgylU4IcUZ2m41N85bRd/xIQkJCmtxetXawSxeSrNrQRgqDt3hhMZF0G9afjT8urXWxcXxFOqnb5HlKKQvwOjAFSAFuUEqlnHbYFKBbze0O4M3T9t8P7HJzqEI02OzZs0lOTiYl5fSnsutUlmsCQ5CFl4Ahg0bRJjTslFXpAoJBm2CTl3m3kmSTcL+8DCjOQ3Xu65wmdZplG7cxoHtnItqEeiA40ZzGDe6HzW5n1fbd9R6jrH4QlQjZ6fJpgxCiXrtWrKessJihU11TGHy3LqIak/4qyiXtCe83ePI4ctKOkr5r3ynbZUU6rzIM2Ke1PqC1rgZmAtNOO2Ya8JF2Wg1EKKUSAZRS7YGpwLvNGbQQ9cnOzmbt2rVMnTrVbYkgu01jr4bAVj6F7jg/Pz/GjJrAitVLqK52vq4HBNUUCZepdG4lBXKE2+n92yAwBNp2rrUvK6+AXYeO8OANV3ggMlEXc/NSt7U9sHsXwkKCWLJhKxOHDqj3OBWXhN65GsqKIDTCbfEIIXzX+h8WExIRRq/RQ5rclkObbNf5JBJErApyQXTCF/S/cAz/feoVNsxZQlLKLyOv2+CHBUWB1G3yBu2Ak4c6pwPDG3BMOyATeAl4BKi34r9S6g6cI6Lo0KFDkwMW4kzmzJmD1pqpU6e67TEqy51fA0Mk2XTc+PMnM3f+N6xZt5zzRk/ELwBUTZHw09atEi4kI5uEW+miXCjMRiWnoAxLrf1LNmwFYNzgvrX2iZbHz2rhvAF9WLphK3bHGVbtiG3v/CpT6YQQdagoLWPbkpUMmjIOi1/TPzfbrYsow84AI8YF0QlfERIeRu/zhrFu9iIc9l/6JOeKdP4yssk71HW1fPpQhDqPUUpdAmRrrTec6QG01v/WWg/RWg+JjY1tbJxCNMjs2bNJSUmhU6dObnuMqjKNUhAQ6LaH8DmDBgwnPDySJT85p9IppfAPgmoZ2eRWkmwSbqXTdjkr07XrWuf+Res206ltPF3aJTZzZMJTJgwdQGFpGRv37K/3GBUYDGHRUrdJCFGnrYtWYKuqdskUOrs22azzSCCIdrTyNaJboWGXXUhxTj6pazaesj1SBUjNJu+QDiSd9HN7IKOBx4wGLlNKHcI5/e4CpdQn7gtViDM7dOgQ27dvd2thcHDWawoIcq66JpwsFivnj7mQVauXUVHhHPolRcLdT5JNwm10ZTlkHYJ2XZ11eE5TWFrG2p2pTBgyoNljE54zpn8K/n5WFq3bfMbjVFwSFOWiqyqaJzAhhM9Y9/0iYtonkty/V5Pb2qkLKMfOYCNGCqm2Qr3PH05wWBvWzlp4yvZIAijDTpU+wyhc0RzWAd2UUp2UUv7A9cCs046ZBcyoWZVuBFCktc7UWv9Ra91ea51cc7/FWuubmjV6IU4ye/ZslFJMmTLFbY9hmpqqCplCV5eJ4y+hsqqCn1cuAiAgCLSWIuHuJMkm4T7pe0FrVIeede5etnEbDtNkwhlq94iWJyQwkFF9e7Fo/ZYzf5IQ2875Nff0DzCFEK1ZUXYuqWs3M+SSC5qcHCrS1WzUeSQRQlvV9BXthO/x8/dn0JRxbFm0gorSshPbj69IVyijmzxKa20H7gXm4VxR7gut9Q6l1F1KqbtqDpsDHAD2Ae8Ad3skWCHOQGvN7NmzGTJkCAkJCW57nKrj9ZqkOHgtvVMGkBDfjgWLfwCkSHhzkGSTcAutTfTRvRCdiAquuybjonWbSYiKpE9nKcbY2kwcOoDM3Hx2Hjxc/0FtosA/EJ0nySYhxC82zF2KNk2GuGAK3X8d+7FjMsKIc0FkwlcNu2witsoqNs9ffmJbFP4AFGgpEu5pWus5WuvuWusuWut/1Gx7S2v9Vs33Wmt9T83+vlrr9XW0sVRr7d65S0Kcwa5duzh48GCzTKEDCJBZ4bUYhsHE8VPZsGkV+fm5ziLhhrNIuHAPSTYJ98jNgMpyVPvude4ur6zi5y07uWBIfwxDnoatzbhB/TCUYuEZptIppSC6LeRmyFxqIcQJ62cvpkPv7sR3Sjr7wWew0yxgkXmU3iqSiJpRLKJ1Su7Xi7jk9qydteDEtlD8sKLIl5FNQggXmD17NlarlQsvvNCtj1NZpvELAItVRjbVZeIFUzFNk8XL5qKUIiBQRja5k1zlC7fQ6XvBPxDi2te5f8XWnVTZbEwY2r+ZIxPeIDIslMG9urFo3ZYzHqdi2jonUhfnN1NkQghvlrU/jSM79zL0kqaNaqrUDt6y7ySeIIYoWX2qtVNKMezSiexbv5W8o1kntkUSICvSCSGazDRN5s6dy5gxY4iMjHTb42itqSqXek1n0rFDF7p3S2HB4u8B8A9SVEuRcLeRZJNwOV1dCTnp0LYzyrDUecyidZsJDw1hSK9uzRyd8BYXDh3A/qOZHMo8Vv9B0TWrFMpUOiEEsHbWQgyLwaAp4xrdhtaa9x27OUYFd1lT8FPyVkjA0EsnArDuh0UntsmKdEIIV1i3bh2ZmZlun0JnqwLThECZQndGF15wKal7d5J2eL8UCXczeYclXO/YYWdh8MROde6utttZtmkb4wf3w2qpOxklWr4Laka1nXEqXUAQtIlCS5FwIVo9h93BmlnzSTlvGGExUY1uZ76ZzjIzk6uNTqQY7vuEWfiWqLbxdBs2gLWzFpz4hDsSfypwUCx1m4QQTfDtt98SGhrKhAlNrzV4JpVlztcuKQ5+ZhecPwXDMFiw+AcpEu5mkmwSLqczD0BIuLPAcx3W7UyluKyCCUNkCl1rlhgdRZ8uHZm/ZtOZD4xpC4XZaLu82ReiNdu1Yj3FOfmMuGJyo9tY5TjGfxx7GKRiuMrS2YXRiZZg2GUTyUk7yqEtu4BfVqRL12VnupsQQtSrvLyc+fPnM3nyZAIDA936WBVlYLGC1d+tD+PzoqJiGDJoFAsXz8biZzqLhJd7OqqWSZJNwqV0RSkUZKMSO9W7JPW81RsICQpkdL+UZo5OeJtJwwex40AaR47l1HuMimnrHN+an9WMkQkhPElrjV2bVGsHZs0ok9Xf/EhoVAR9xg5vVJtLHRm84thODxXB/da+GPX0UaL1GnDhefgFBpwoFB6JM9l0RJd6MiwhhA9bsGAB5eXlTJs2za2Po7WmskwTFKrqvQYTv5h4wSUcy85gx67NBATJyCZ3kWSTcK3MQ86vZ5hCt2DtZi4Y3I8Af7/mi0t4pckjBgMwb83G+g+KiAWLVabSCdGCVWsHabqEn80svnYc5ENzL/8xU/nQ3Mt75h7ez9nI1qWraH/JSA5ayijVtga3Xapt/Nu+izcdO0lRkfzBOoBAJVO4RW2BIcH0nzCGDT8uxVZVTQhW/DBkZJMQotG+/fZb2rdvz+DBg936OLYqcNghMMStD9NijBl1AYGBQSxY5JxKV10pRcLdQZJNwqV05gEIj0EFt6lz/+ptuykuK2fyyCHNHJnwRm1jo+nXNZl5qzfUe4wyLBCVALkZ0gkI0cIU6Co+se/lLtty5ptH2aeLCcRCDxXOUBXLcBXLIBWDOWcL2u6AywayRGfyubmfmY79LDMzSTULSdel2LV5ol27NjlklvC5fR/321ay2DzKZUZHHrMOIEhZPXjGwtuNuOIiKopL2bLwZ5RSRBEgI5uEEI2SmZnJmjVrmDZtmttHG1WUOt8jB4XKqKaGCAoM5rxRE1i6fB6GtRqtobrS01G1PPKOS7iMLimA0kJUz2H1HvPj6g20CQ5iVN+ezRiZ8GaTRgzm+U++Ii0rm44JcXUeo2LaonPSobykmaMTQrhDpXbwjeMgc8zD2NEMM2KJwJ+2hGA57Q251pp536ykY9+e/KbHOAqoIlOXk6nLSdOlpFLEMlsWCgjFDwNFGTbsaAwUA1Q011u60NGo+0MQIU7WbdgAYtonsvLLOQyZegGRyp90XYbWWqamCCHOyffff4/W2u1T6AAqyjRWP/Dzl9ephpp4waUsWPwDm3csp2P0BVRV6BMFw4VrSLJJuIzOPAhKQULHOvdX22wsXr+FCUMH4O8nU+iE06QRg3j+k6+Yt3oDd1w+pe6Dots6v+ZlNl9gQgi32G7m86Z9J7lUMtpI4FpLZxJUMAsd6XUef3h7Kpn7DnHdX+7HUIpoAolWgfQhCq01BVSTqILJ1OUU4VxIIAQrSSqUvkYUETVFnoVoCMMwGHnVFL5/+X2yD6UT2SGY3RRRRDURyHNJCNEwWmu+/fZbBg8eTFJSktsfq7IUQsIlUXIuBg8cTnRULPMXf8sd111AdYWnI2p5JNkkXEJrDVmHICrRuVx9HVZs3UVJeQWTRwxq3uCEV0uMjmJA9878uHpj/cmm4DYQGIKWZJMQblVfwqehJlra17vPrk2+dBzgW/MQiQTzhHUwvYzIs7a58qs5+AUGMGjyuFr7jk9zOs+S2JSwhTjF8MsvYvZrH7Dyq7kMffBXoOGILpPEpRCiwbZt28bBgwe59dZb3f5YZUUa04SgULc/VItisViZNPEyZn75H266PBv/irpnWIjGk5pNwjVKCqCiFBXfod5Dfly1gbCQYEb06dWMgQlfMHnEYPakpXMwo+4V55RSEJ0I+Zlo09HM0QkhmipPV/I3+wa+MQ8xzmjLM37DG5RoqigtY/3sxQyePI7gMHkXLZpHeGw0fc4fyZrv5tPG5nyrnC51m4QQ5+Dbb78lICCAyZMnu/2xinKd9ZoCQ2Rk07maMulKTNNk1cbZVFWCNqU+rCtJskm4hM4+7Pwmru5hopXV1SzesIULhw3AzyqrAIlTXTTcOdpt7qozFAqPaQt22y8rHgohfMIBs5jHbWs5oku5z9KHu6wpDV4Nbv0Pi6muqGT0dZe4OUohTjXq6imU5heyb8la2uBHmiSbhBANVF1dzZw5c5gwYQJt2ri/XmBRnolfAFj9JNl0rtq360i/PoNZuuIbtKmprvJ0RC2LJJuEaxw7AhFx9U6hW755B+WVVUwa4d5lP4Vvio+KYHDPrsxdub7+FeeiEgDQaTubMTIhRFNsMHN4wr4eCwZPWocy2pLQ4Ptqrfn5v9/TvldXOvbp4cYohait1+ghRCbEsuqruXRUbSTZJIRosKVLl1JUVMTll1/u9scyTU1xvpZV6JpgykVXcDQzjX2HNlFVISObXEmSTaLJdHkJlBaccQrd98vXEB0exvDecsEg6nbJmGEcyMhi58HDde5X/oEQFoV5aEczRyaEaIyFjnSet2+hnQrlKb+hdDDObRrcoS27yNh7kDHXXiKrgIlmZ1gsjLhyCrtXbiA8vYQjuhSHNj0dlhDCB3z99dfExsYycuRItz9WaaHGdECQTKFrtPPHXkRQUDA/r/uWKikS7lKSbBJNd5YpdIUlpSzbtJ2po4ditcgUOlG3ScMH4We1Mmv5mvoPik6Eo/vR1ZXNF5gQ4pzNdRzmHcdu+qto/modTGQjCiv//MUPBIYEM/ji8W6IUIizG331FAyrhX3/XYQNk0zKPR2SEMLLZWVlsXz5cq688kqsVvevxXWiXpOUNWy0oMBgLjh/Cuu2zqewQEaxupIkm0ST6WOHoU0kKrjuOck/rt6A3eHgsjHDmzky4UvCQ0M4f1Af5q5cj91RdxFwFZ0IpgN9ZE8zRyeEaKjvHIf4wJHKUBXLw9b+Da7PdLKyomI2zVvGkKkXEBgS7IYohTi78LgY+k8YzapvfsSsqOaQKRchQogz+/rrrzFNk6uuuqpZHq8g2yQ0QmGxyMimpphy0RVUVVWwcs08KRLuQpJsEk2iqyqgMAcVd4YpdD+vpWv7RHom178kthAAl40ZTl5xCau27ar7gIh4sPqhD0ndJiG80XeOQ3zm2McoI54HrH2xqsa9zVjz7XxsVdWMvnaqiyMU4tyMvWEapcUlFM/dQJou8XQ4QggvZJoa09Q4HA6++uorRowYQVJS3TM+XMlWpSkt1ETGySV9U6X06k9Su878vPY7ZAKF68gzUzRN9hHn13rqNR0+lsPm1ANcMma41NwQZ3XegN6EhQTz/c9r69yvLBZU++6SbBLCC+00C04kmn5r6dPoRJPpcPDT59/ReVAf2vfs6uIohTg3XQb3pVu3bhT+92cOmsWeDkcI4UUqyzRZhxwc2mlyaIfJB68tJyMjgysuv6ZZHr8gx1lHLjJOrrGaSinF5IuuYF/aZvbt2+/pcFoMSTaJJtHZRyAoFEIj6tz/w/I1KKW4ZMzQ5g1M+CR/Pz+mjBzMonWbKauo+2MFlZwCuUfRJQXNHJ0Qoj57zSJW6GMMVjHcY+mN0YQPF7YvW0NeehbjbrrChREK0ThKKW688UaKdx9m++atng5HCOEFtNbkZ5lkHDCpqoCwKEVUgmLlxm8IDY4g2m8sBdnuX1CgIFvjFwAh4ZJscoVJF16CxbAyf9G3ng6lxZBkk2g0ba+GvEyI71DnqCWtNbN+XsvQXt1IjI7yQITCF10yZjiV1TYWrttc536VnAKATpPRTS2BUmqyUmqPUmqfUuoPdexXSqlXavZvVUoNqtmepJRaopTapZTaoZS6v/mjFwCHdSnLdCaJBDdp6txxyz79hsiEWPpdMNpFEQrRNJdccgmBocGkzVxEoa7ydDhCCA/SWpOXoSnM0YRGKpK6G8S0NcCvkHWbljBt2jRCQgLYtc5OXqb7Ek6mqSnMMYmMM2T2iItER8UysO95LF05i+rqak+H0yJIskk0Xs5R0CaqnlXotuw9yJFjOVx2nhQGFw03sHtnkuJi+HbZqroPiEuC4DYyla4FUEpZgNeBKUAKcINSKuW0w6YA3WpudwBv1my3Aw9prXsBI4B76rivcLNcXcki8yjRBHCR0Q7/RhQDP1lG6kFS12zmvBsuw2KV1UuFdwgJCWHC5VMpWrCZjccOeTocIYQHFWZrivM14TGK2HYKo6Yw97yF32Gz2bj+V1fTd7SVNhGK1I12CnPck3AqztM4bBAZL5fzrjRpwtUUl+Qzf/5CT4fSIsizUzSazj4M/oEQEVvn/q+XriAowJ8Lhw1s5siEL1NKcfn5I1m7M5XDx3Lq2G+gOvZCp+1Ea1ktwscNA/ZprQ9orauBmcC0046ZBnyknVYDEUqpRK11ptZ6I4DWugTYBbRrzuBbu1JtY56ZTgAWLjLaNznRBLD002/wCwxg1FUXuyBCIVzn9htvRjtM/vfp554ORQjhIeWlmoJsTWiEc9rc8RFFpmkya/YX9EkZSKhfJ3KOmkQlKqz+sHu9nfS9drLSHCdurpCXaWJYICJWRjW50vBhY4iJasdn8lrvEpJsEo2iHQ7nyKa4JFQdUybKKiqZs3IDU0YOITQ4yAMRCl92+fkjMZTim6Ur69yvOqZAaRHkHm3myISLtQOOnPRzOrUTRmc9RimVDAwE1pz+AEqpO5RS65VS63NyaicvReNUawfzzXRsmEwy2hOi/JrcZllhMet/WMTQSyYQEhHmgiiFcJ1eyV2IHT+AVV/8QHl5uafDEUI0M4ddk3PExC8AYtqpU6aurd+4kozMI1x+6Q0ntlksioSOBlrDscOmSz8g1aYmL8skMt7AYpFkkysFhRiMG3ENmzavZ+/evZ4Ox+dJskk0Tn4mOOyouLpXoZuzcj0VVVVcNV5qbohzlxAdyXkDevPtslXYHbU/AVKdegPIVDrfV9c7pNPfjZ3xGKVUKPAV8IDWutZSUVrrf2uth2ith8TG1j0KU5wbrTU/mVnkU8UEoy3RKvDEvoWO9Ebffv7iB2xV1Zz/q8s9d3JCnMHQm6dRVVzGN9984+lQhBDNLC9L47BDXJKBYZz61uS772cSGRHF2DEXnrLdL8A51a6qAopyXJdsKs7X2KshOkEu5V3N6qcYN+py/Pz8mTlzpqfD8XnyDBWNoo8dBosfRCfUuf+rJSvo2j6R/t06NXNkoqW4avxosguK+HlL7YSSCouGqHhJNvm+dODkom/tgYyGHqOU8sOZaPpUa/21G+MUJ9mm8zlICcNULEkq1CVt2qqqWfbJN/QaPYS23aXfEN5p2MDBBPdN5sMPP8RRxwchQoiWqaJUU1qgiYhVBASdmmjKyjrKqrXLmDr5avz8ao/yDY0wCAmD/GxNdaVrEk65GSaGAZFxMqrJHWLiohg24EK+++47ysrKPB2OT5Nkkzhn2jQhJx1i26GM2jU69qSls23/Ia4aP1pWRxCNNnZgX6LDw/hqyYo696vk3ugje9AOezNHJlxoHdBNKdVJKeUPXA/MOu2YWcCMmlXpRgBFWutM5XxxeQ/YpbX+V/OG3Xpl6DLW6hw60Ya+ynWrjK75bj4l+YVMvP06l7UphKslW9oQM+MCjhw5wuLFiz0djhCiGWitycs0sfpBRB3Jne/nfIFSikunXlNvG9FtDQwDctKbPp3O4dDkZphEJxpYrHKd5Q6BwTB22HWUlZUxe/ZsT4fj09yabJIlrVuoo3uhuhIVX/cUuq+XrsTPauXSMbIKnWg8P6uFaWNHsGzjNnIKimrtVx1TwFYFR/d7IDrhClprO3AvMA9nge8vtNY7lFJ3KaXuqjlsDnAA2Ae8A9xds300MB24QCm1ueYmVaXdqEzbWGRmEI4/Y40El32YYDocLP7gf3To3Z1uQ/u7pE0h3KGjakP4Bf2IbpfABx984OlwhBDNoKRAU10JUQmq1vS56uoqZs/7mlEjxhEXm1hvG1Y/RXRizXS6vKYlm/IzzRPT+YR7BAQpunTsR9cuPfjss89kQaImcNuzVJa0brnM1I1gGBBTe+Gnqmobs5avYeLQ/kSGuWZ6hWi9rho/Codp8u2yVbX2qY49QRmYh3Z4IDLhKlrrOVrr7lrrLlrrf9Rse0tr/VbN91prfU/N/r5a6/U123/WWiutdT+t9YCa2xxPnktLZmrNEjMTOyYXGu1csvLccVsXryTncAYTb79ORsMKrxZHEMFWf/pNn8rGjRvZsGGDp0MSQriRw6EpOKYJCIaQ8Nr909Kf5lFUVMDll9xQx71PFRqhCG4DBVmairLGJy+y000CgiAsWvpLd/EPAsNQTL3oBvbs2cO6des8HZLPcmdKVJa0boG01ujUjRCdiLLWnpc8b81GisvKpTC4cInkxHiG9urG/xb/jMM0T9mnAoIhsZPUbRKiGWzV+WRSzmgVT4QKcFm7WmsWvPdfYju0pf8E6TeEdzOUorMKI+SK4URFRfH22297OiQhhBtlH3aOIopKMGp9GKK15stvPyGpfScGDjj7bA6lFDHtDFCwf6u9UaNlKko1RbmauCSLfDjjRoahCAlTDO13MZGRkXz00UeeDslnuTPZ5PYlrWv2y7LWzenYYSjOq3cVuk/nLaFT23iG9+7RzIGJluqGSeM4mpPHso3bau1Tyb0h6yC6Uor3CeEuObqC9TqHzqoN3VS4S9veu24Lh7fv4YJbrsGwuG60lBDu0lWFcTSwmptmTGf58uXs2CGja4VoiUyH5uh+B4EhEBRSO7GzdfsG9u7bydVXTMcwGnZJfXw6XXGe5thh8+x3OE3GAQfKgPiOMoXO3UIjFbaKAK679joWL15MWlqap0PySe58prp9SWuQZa2bm5m6HpQBcUm19m3dd4jt+9O44aJxDX7RFeJsJgzpT0JUJJ/PX1Zrn5GcAlqj03Z7IDIhWj6bNllsZhCClTHKdXWajvvxrU8Ji4li2GUXnv1gIbxAFyMcB5pRN1xGWFgYb731lqdDEkK4QXa6SXUlRMTVfU3zv68/IiwsgosmXHpO7baJVIRFK9J2OaiqaPjopuoqTXa6SVx7A/8AGdXkbm0iFKYDLr/seqxWK5988omnQ/JJ7swIyJLWLZDesxHVoQfKP7DWvs/mLSEkKJDLx47wQGSipbJaLFw78TxWbtvFgaNZp+5s2xn8A9BSt0kIt1ijsynGxjijLQEurNMEsG/9Vvau3czE26/DP9B1U/OEcKeuKgyAjBA7N954IwsXLiQ1NdXDUQkhXMk0NUf3OQiNUASF1N6ffjSNlauXMG3qdQQGBp1T20opuvazok04sK3h0+ky9jvQJrTtLKOAm0NopDNNEmCJYcqUKXz99dcUF9c59kWcgTuTTbKkdQujc49Cfiaq++Ba+3ILi5m7agOXjx1BSFDtRJQQTXH1BaPxs1prjW5SFisqqSc6Teo2CeFqR3UZu3QhfVUUiSrY5e3PffNjwmKiGH3NVJe3LYS7RBFAJP7s18VMnz6d4OBg3nnnHU+HJYRwodyjJlUV0L5b3bWRvvr2E6xWK5dfevbC4HUJDFF07GWhIFuTefDs0+kqSp3HxbY3CAqVUU3NITAYrH5QWqiZMWMG5eXlfPXVV54Oy+e4LdkkS1q3PDp1IwCq28Ba+/63+GfsDge/mjSumaMSrUF0eBhTRg7m259WUVpaeso+lZwCBdnoolwPRSdEy2PTJsvNLMLwY4iKcXn7+zZsI3XNZibcdq2MahI+RSlFFxXOfrOYyMhIrr/+eubMmcOBAwc8HZoQwgW01qTvcxASpoiMq53YKS4p4sf53zJh3FSiohrfPyYkG0TFO6fTFefXn3DSWnNwhx3DAh17yaim5qKUIjRSUVJg0rt3b4YMGcLHH3+MzWbzdGg+xa2FdWRJ65bF3LMB2nZBtYk8ZbvN7uCLhT8xul8KyYnxHopOtHS/mjSO8soqvv3221O2q+QUAPRBmUonhKus0zmUYGOskYhVuf6twtw3P6ZNdCRjZFST8EFdjTAyKKdM27jtttsIDAzk1Vdf9XRYQggXyM0wqSyrf1TT97O/oLKqgquvmN6kx1FK0XWAlYBg2LXOTllx3QmnI6kOCnM0HXpYpFZTMwuLNKgoBVu15rbbbiMzM5PZs2d7OiyfIlWcRYPowmzIPozRo/YUunlrNpBdUCSjmoRb9e2STL+uyXz88cc4HI5fdkS3hdAIqdskhItk6XJ26AJ6q0i3TJ/bt2Ebqas3MfG2a/GXadfCB3Wpqdu0XxcTHR3NjBkz+PHHH9m5U6Z0C+HLnKOaTIJCFVEJtRM7lZUVfPnNxwwdPJounZu+8rbVT5Ey3A+LBXasspOX9UvCSWvnanjpe53T5xKS5bK9uYVFOZ8DJfmacePG0b17d959911M89xXEmyt5FkrGuTEFLrT6jVprXn/+wV0bpfA2AG9PRGaaEVuu/QiDh8+zPz5809sU0qhknuj03ahtbz4C9EUDq1ZbmYRipWhyvUrvGqt+f6l9wiLiWLMtZe4vH0hmsPJySaAW2+9lbCwMF555RVPhiWEaKL8LE1FiaZ9N6POUU2zf/yKwqJ8brrhDpc9ZmCwos9IPwKCFXvW29n6s439W+1s+clO2i4HUfGKLv3qHmUl3Cs0QqEMKMo3UUrxf//3f+zfv5/Fixd7OjSfIckm0SDmng0Q1wEVcerFx89bdrInLZ3bLr0Iw5Cnk3CvC4b0p2PHjrz33nunrN6hklOgsgyOHfZgdEL4vm06n0KqGWUk4OeG6XPbl67iwKYdTLl7uktHNS10pDf6JsS5ClF+JBLMPtOZbAoLC+P2229n2bJlbNq0ycPRtTxKqclKqT1KqX1KqT/UsV8ppV6p2b9VKTWoZnuSUmqJUmqXUmqHUur+5o9e+AqtNel7HQSGQEzb2v2fzWbjv1/+h359BtOvT+2ZHk0RGKLoO9rqrMmkIS/TxGKFrgMs9BhixTAk0eQJhkXRJkJRnOe85pg8eTJJSUm88847DV5FsLWT7IA4K12YDRn7MXoNq7Xvve/nkxAVydTRQz0QmWhtLIbBbbfdxo4dO1i9evWJ7apjTd0mmUonRKNl6wo26lySCaWjCnV5+w67g1kvvU9ccntGXjHZ5e0L0Zy6GmEnRjYB3HTTTURHR/PSSy/JRYgLKaUswOvAFCAFuEEplXLaYVOAbjW3O4A3a7bbgYe01r2AEcA9ddxXCAAKszVlxZp2XeseRTR/4Xfk5B7jxutdN6rpZIahaNfFQr/z/Bg2yZ++o/2Iay8jmjwtLFpRVqSx2zRWq5Xbb7+drVu3smbNGk+H5hMk2STOSu90/jOplOGnbN+67yDrdqYy/eIL8LdaPRGaaIWmTZtGTEwM77777oltKjQcYtujD0m9DCEaQ2vN+/bdKGCk4Z6FHtbOWkDW/jQuvf82LH7SZwjf1kWFUUAV+boSgODgYO666y7Wrl3LsmXLPBxdizIM2Ke1PqC1rgZmAtNOO2Ya8FHNwkOrgQilVKLWOlNrvRFAa12Cc3Xsds0ZvPANWmuO7HUQEASx7WpfHjscdj774j26d0th6OBRHohQeEpYlPP5UFLg/BDh8ssvJyYmhrffftuTYfkMSTaJM9JaY+5YDUndUWHRp+x7b9Z8wkKCuOaCMR6KTrRGAQEBzJgxg5UrV55SjFV1TEGn70XbqjwYnRC+aZ3OYZPOY7CKJVT5ubz96soq5rz+IR379qT/ROkzhO/rqsIB2HfS6KbrrruO5ORk/vnPf8ry2K7TDjhy0s/p1E4YnfUYpVQyMBCoNRxBKXWHUmq9Ump9Tk6OK2IWPqYoT1Na6BzVVNeUtaU/zScj8wg3XX+HjDRqZdpEKpSC4nxnXdiAgABuv/12Vq9ezfr16z0cnfeTZJM4s2OHIT8TI2XEKZsPHM1i0fot3HDROEJkNSHRzK677jpCQkJ45513TmxTnfqAw44+vNuDkQnhe6q1g4/sqSSpEPqoSLc8xpKPvqLwWC7TfvdreaMuWoSOKhQLir1m0Yltfn5+PPzwwxw8eJAvvvjCg9G1KHW9YJw+T/GMxyilQoGvgAe0Pik7ePxArf+ttR6itR4SG+v6hRGE90vf68A/AOLa1z2q6cNP3qBTcjdGj7zAA9EJT7JYFSHhv9RtAud1SExMDC+//LJMmz4LSTaJMzJ3rgLDguox5JTtb3w1m0B/P26cNM4zgYlWLSwsjJtuuokff/yR1NRUAFSHHuAfgN63xcPRCeFbvjfTyKGSWyw9MNyQCCrIymH+O5/Tb8Joug3r7/L2hfAEf2Whiwpjty48Zfv48eMZMWIEr776KkVFRXXfWZyLdCDppJ/bAxkNPUYp5Ycz0fSp1vprN8YpfFRxvklxnqZtFwuGpY5aTYu+58jRQ9w2415ZDKmVCotWlBZqHA5nYikoKIg777yT9evXs2rVKg9H593kP0bUS5smetdaVOe+qKBfisWmpqby4+oN3DhpPNHhYR6MULRG5ualmJuXcvOQboQGBfLqU3/F3LwUvX0FRMajd63BsWnJiePMzUs9HbIQXitPV/Kd4xDDVCx9jCi3PMasF9/FdDi44uE73dK+EJ7SU0WwXxdTrR0ntimleOSRRyguLubNN988w71FA60DuimlOiml/IHrgVmnHTMLmFGzKt0IoEhrnamcwyjfA3Zprf/VvGELb5eV5iArzcH+rXYsVkDpE9uO3w7vreDDT96gZ/c+MqqpFQuLMtAaSgt+GcV07bXXkpiYyCuvvCKjm85Akk2iXvrIHigtRJ02he61114jJDCAWy+90EORCQHhoSHcfPEEFq7bzM6DhwFQsUlQVQHF+R6OTgjf8KljLyYw3drdLe3v37id9bMXM/G2a4lpn+iWxxDCU3oaETjQp9RtAujVqxdXXnkln376Kfv37/dQdC2D1toO3AvMw1ng+wut9Q6l1F1KqbtqDpsDHAD2Ae8Ad9dsHw1MBy5QSm2uuV3cvGcgvFlluaaiFMJjVJ21mmb/+CXHsjO5/Zb7ZAp4KxYW5fzbF+f/klTy9/fnrrvuYsuWLbIoxBlIsknUS+9YBf6BqK4DTmzbsWMHCxYsYMbFE4gIDfFccEIA0y+eQFhIMK99+YNzQ2w7QKFzjpzxfkII2G0WssI8xqVGR+JUkMvbNx0Ovnz6NSITYrnw9utd3r4QntZDRaCAXbqg1r7f/e53hISE8Le//U0+9W4irfUcrXV3rXUXrfU/ara9pbV+q+Z7rbW+p2Z/X631+prtP2utlda6n9Z6QM1tjifPRXiXwmwTw/JLMuFkFZXlfPz52/TvO4TBA0d6IDrhLax+ipAwRVGeecr2K664gqSkJF555RVM06zn3q2bJJtEnXRVBXrPOlSPISg//xPbX331VcLDw5lx8QQPRieEU5vgIG69ZCLLNm5j676DKP9AiIiF7HRPhyaEVzO15j+OPUQTwDRLslse4+cvfiB9936uePhO/GUhCdEChSo/klQou83CWvuio6N58MEHWbduHd99913zByeEOKOqCk15CYRHqzprNX397acUFOTJqCYB1NRtKvilbhM4F4W499572bVrF7NmnT67V4Akm0Q99K61UF2F0X/siW2bNm1i2bJl3HrrrbQJdv2n4EI0xo2TxxPZJpQXP/8WrTUqrj2U5KMryjwdmhBea4mZwSFdwo2WbgQqi8vbL8jM5vuX3qfnqMEMuGjs2e8ghI/qqSJI1UU4dO1Pta+++mr69+/P888/T2FhYfMHJ4SoV8Ex56im8JjaiaT8/Fw+/e87jBo+jr69B3kgOuFtImINTBNK8k8dqXrJJZfQp08fXnrpJcrLyz0UnfeSZJOok7llGcS0g7ZdnD+bJs8++ywxMTHcdNNNHo5OiF+EBAbym6umsnZnKks2bIXYmgVpZCqdEHUq0zZmOvbRU0Uwyoh3eftaa7546lVM0+S6v9wvnwiLFi1FRVKJg/26pNY+wzB44oknKCoq4sUXX/RAdEKIupQUmM5RTTF1j2p678NXsNmq+c0dD3sgOuGNwqIUyoDCnFM/WDAMg0cffZRjx47xn//8x0PReS9JNoladFYaZB3CGHD+iYuEOXPmsHXrVh588EFCQqRWk/Au1044j85tE/h/n36NLSAYgtugc2QqnRB1+cpxkBJs3GLp7pZE0KZ5P7F92Wqm3nuzFAUXLV5vIxKA7bruhSl69uzJ9OnT+eKLL1i7dm1zhiaEqMfhPQ7nqKbo2n3g3n27mDv/G6647Ebat+vogeiEN7JYFWFRisKc2jX4hgwZwqRJk3jvvffIzs72QHTeS5JNohZzw0LwC0D1HgVARUUFL7zwAikpKUybNs3D0QlRm5/VwsM3XUVaVjYzF/zkHN2Ul4W22zwdmhBeJV2X8aN5hAuMdnQywlzefllRMV8+/Rodenfn/BuvcHn7QnibMOVPsgplu1n/Kqi//e1v6dChA4899hhlZTLFWwhPKs4zKcrVRMTWHtWkteb1t58jLCyCGb+600MRCm8VEWNQXqKprqydcHrwwQex2+28/PLLHojMe0mySZxClxahd61B9R2NCgwG4D//+Q9ZWVn88Y9/xDDkKSO803kDejOqby/e/HoOxaHRoE3Ilql0QhynteYjeyoBWLje0sUtj/G/f7xGWXEJN/ztQSxW19eCEsIb9VFRpOoiqrWjzv3BwcE888wzZGRk8PzzzzdzdEKI47TWHN7jwC/AWfD5dD+tWMiWbeu5bca9hIa6/gOZ47LSHA26Ce8SEed8zpw+lQ6gQ4cOTJ8+na+//ppNmzY1d2heSzIH4hTmpiXgsGMMngjAsWPHePfdd5k0aRJDhgzxcHRC1E8pxSPTr6K0vILX5q+BwGB01iFPhyWE19ioc9mi87ja0pkw5X/2O5yj9bMXs2HOEqb8Zjrte7onmSWEN+pjRGHDZLcurPeYQYMGccstt/Df//6XFStWNF9wQogTCrI1xfma9t0sGMapyaayslJee+tZOnfqztTJV3koQuHNgtso/AMg/1jtZBPA3XffTUJCAk888QQ2m8yuAEk2iZPo6kr0psWoLv1RUQkAPPXUU5imyUMPPeTh6IQ4u25J7bj+wvOZufAnttlDIDcDXV3l6bCE8Lhq7eBDeyrtCGGS0d7l7ednZvPFU6/QaUAKF95+vcvbF8Kb9VIRWFBsO8NUOoD77ruPzp0786c//UlWpxOimZmmJm2XncAQiO9Q+xL4vQ9fIS8vm4fufwKLxeqBCIW3U0oRGW9QmKMxHbWn0oWEhPD444+TmprKxx9/7IEIvY8km8QJetMSqCjFGHkJAAsWLGDhwoXcc889JCUleTg6IRrm/usuIy4ynL/9uBabww7H0jwdkhAe94N5mGNUcIu1O1bl2q7fdDj4+I/PYTpMZjzzB5k+J1qdQGWlp4pgk84983GBgTz77LPk5eXxpz/9Ca1rX6wIIdzj2GGTilJI7mWtNapp155tfPv951x+6Q2k9OznoQiFL4iKNzAdUJRX9+v3hAkTGD9+PK+99hpHjx5t5ui8jySbBADaVoW5dh4quTeqXRdKSkp46qmn6NmzJ7fccounwxOiwUKDg3jsluvYk57Fx3vzZSqdaPVydSXfOA4yXMXRz4h2efsL3p3JvvVbufqPdxOTJKvPidZpoBHDEV1Grq4843F9+/blwQcfZNGiRXz66afNFJ0QrZvdpjmS6iAsWhEZf2qiyW638cLLTxAdHcftN9/noQiFrwiPURgWKKhnKp1SiscffxxwzhBq7R8qSLJJAKDXL4TyYozRlwLw4osvkpOTw5NPPomfn5+HoxPi3EwcOoAJQ/rz5vqDpKcdQpcUeDokITzmI3sqANOt3Vze9u5VG5n9+kcMvng8wy+f5PL2hfAVA40YADaZZx7dBHDzzTczbtw4/vnPf7Jjxw53hyZEq5e+z4G9GpJTLCh1arLpv19+wP4De7j/7scICQn1UITCVxgWRUSMIv+YWW8iqV27dvz2t79l6dKlfPfdd80coXeRZJNAlxVhrp6N6joA1b4769evZ+bMmdx000307dvX0+EJ0SiP3XIdFouFv67Yj2PnGk+HI4RHbDXzWKOzudySTKwKcmnbBZnZfPjI0yR07sANT/yu1ht4IVqTdgQTRxAbG5BsUkrx9NNPEx0dzYMPPkhRUVEzRChE61RRqsk8aBLb3iA0/NRL3737dvHBJ69z/nmTGDNqgociFL4mKtGguhJKC+sftTRjxgwGDx7MP/7xDzIyMpoxOu8iySaBufxbsNswxl1DcXExjzzyCElJSdx///2eDk2IRkuIjuTRGdewNquYDz76yNPhCNHs7NrkP/Y9xBPEpUZH17Zts/H+Q3/HVlXN7S/+hYBg1yayhPA1SikGGtFs1/lU6bMvWR4ZGckLL7xAZmYmv//977Hb7c0QpRCti9aaA9vtGAZ07HlqPcGqqkr+8c9HiQiP4sH7/uKhCIUvioo3UAbkZtQ9lQ7AYrHwzDPPYJomjz/+OKZZ/7EtmSSbWjmdnoresgw16AJUdCJ///vfyc7O5p///CchISGeDk+IJrly3CgmpHTm5aWb2bn6Z0+HI0SzmmseIYNybrZ2x1+5rmi31pr/PvkKh7bu5sanfk98J1lAQgiAIUYs1Zhs0XkNOn7QoEH8+c9/5ueff+aFF15wc3RCtD65R02KcjUde1rwDzx19O3b7/2LtMMHePShpwhrE+6hCIUvsvopImMVeRn1T6UDSEpK4g9/+AOrV6/mk08+acYIvYes69iKabsNx9wPICwa47wr+P777/nhhx+477776N+/v6fDE6LJlFL87Te3cMUjf+fhPz7Gl3PmERQkIzBEy7bQkU65tvOFeYAkQijQVSx0pLus/QXv/ZfV3/zIpDtvZOBFY13WrhC+rreKpA1+rDazGWbENeg+11xzDampqXzwwQd0796dK664ws1RCtE62Ko1B3c6CI1QxHc8dXzF8uXL+WbWZ1w57UaGDBrloQiFL4tua5B/zEFJviYsuv4yAldffTWLFy/mhRdeYPDgwfTu3bsZo/Q8GdnUiplL/gv5WRiTbyb9mLMY+ODBg7njjjs8HZoQLhMZE8s/rp/Mgawcnn3mGU+HI0SzWKmPYaIZacS7tN1N85bx/UvvMfji8Uy992aXti2Er7Mog6FGLBvNHKobMJXuuEcffZQRI0bw17/+ldWrV7sxQiFaj7RdDuw26NLv1KLgR48e5ZFHHqFTcjfuuO13HoxQ+LKoeAPDAjlHzzw97niNvqioKB544IFWV6NPkk2tlLlrLXrjYtTQSVTEdeLee+/FYrHw3HPPYbG4brqFEN5g9FU3cWuftnzxv//x5ZdfejocIdwqTZdwUJcwUEUTrvxd1u6Bjdv56I/P0Xlgb278+++lILgQdRhuxFOBg206v8H3sVqtvPTSSyQnJ3PPPffICnVCNFJWmoOsNAcHttvJPmISHq0oKdAnth/eW87dv7kPW7WdJ//8EgEBgZ4OWfgoi1URnWiQm2HisNc/lQ6cNfpefPFFjh07xh//+MdWVb9Jkk2tkE7fhznnPWjXFTX2Sh5//HH27dvHCy+8QLt27TwdnhAupzr14f7zBzCyc1uefPJJtm7d6umQhHCLcm1nhXmMKALor6Jd1u6RnXt58+7HiUyM49cvP4FfgOuSWEK0JH1UJKH48bOZdU73Cw8P55133iEiIoI777yTtLQ0N0UoRMvmsGty0k38AiAy/tQPRV5+/R+k7t3JHx9+hvbtXLtwhmh94pMMHHbIyzp78mjAgAE88sgjLFmyhHfffbcZovMOkmxqZXT2YRxfvQxtIrFceS/vvv8f5s2bx0MPPcTo0aM9HZ4QbqEMA78hE/jnsLbEx0Rz3333kZOT4+mwhHC5zx37KMPOeUYChotGHmXuO8Trd/yB4LA2/Pbdf9ImKsIl7QrRElmVwSgjnnVmDuX63FaYi4+P591338U0TW6//fZWvVy2EI2htSbnqInDAXFJBobxSz/43Q8zmTPva266/g5GjxzvwShFS9EmShEYDNmHGzZS6cYbb+Tiiy/mpZdeYuHChW6OzjtIsqkV0UdScXz2HPj5Y7n2QRatXMtLL73ExaOGcPPATpiblzb4JoSvUf3HEtEmhJd+NYmioiLuvfdeysvLPR2WEC6z2yxkgZlOHxVJnHJNIfzstHRe+79HsfhZuffd54hMbFjRYyFas/ONRGyYrDKPnfN9O3XqxL///W+Ki4uZMWMGR48edUOEQrRMJQWa8mKIilcEBP2SaFq1ZimvvPE0w4eexy3T7/FghKIlUUoR18FCcb6mrPjsCSelFE899RR9+/bl4YcfbhUzLSTZ1AporTE3LsIx83kICcNy02OsSz3EQw89RN++fXnyjulSe0O0eCooFNVnND0KD/D8U0+yfft2HnjgAWw2m6dDE6LJKrSd1+07iCWIISrWJW0e3XOAl2Y8iGl3cO+7/yS2g0yzFqIhuqgw2hHCUrNxI5P69OnD+++/T0lJCdOnT+fIkSMujlCIlqes2CQvUxMYAuExv1zX7NqzjSeffpiuXXry18de8KnatMdrTTXkJjwjvoOBYUDWoYaNbgoKCuKNN94gJiaGu+++m/R0160W7I0k2dTC6aJczP+9iLngU1Sn3lhuepzdR7O55557SEpK4q233iJIam+IVsIYchGYDsYHV/DXv/6V5cuX86c//alVFeoTLdMHjj3kUME91t74qaZ37Qe37OTlWx/CYrVy/4f/IrGL1LYQoqGUUpxvSSRVF3HYLG1UG8cTTuXl5cyYMYPU1FQXRylEy2Gr1uxeb8cwnNPnjn+IfjTjMI/95R4iI6N55sk3CAoK9nCkoqXx81fEtjfISTexVZ+5UPhx0dHRvP3229hsNu644w5yc3PdHKXnSLKphdLlxTiW/g/HO4+h0/diTPwVxlX3cSgrhzvuuIPQ0FDeeecdIiMjPR2qEM1GRcWjUkagNy3mmksmc9999zFr1iyeffZZtG5YByGEt1ljZrPUzORyI5meRkST29uxfC2v/fpRQiPC+N3HL5LQuUPTgxSilRlvtMUPg3lm40cl9e7dmw8//BCHw8FNN93E2rVrXRihEC2DNjWpG+1UVzpHmVj9fkk0/e6RWzG1yXNPvUVUZIyHIxUtVWInC6YJmQcbPsKsc+fOvPHGG2RlZXHLLbeQl5fnxgg9x+rpAIRr6ZyjmOsXoHesBIcd1XskxtgrUWHRpKamcvvtt2OaJu+++y6JiYmeDleIZmeMvATHjtWYa+dx1113UVRUxIcffkh1dTV/+ctfMAzJwQvfUaCreMe+i06qDVdbOjepLa01yz75hq+ff5t23Tvxm7eeJiwmykWRCtG6hCl/xhgJLDczuUF3JVT5NaqdHj16MHPmTO644w5+/etf88wzzzB16lQXRytaq4ZOv0ro6L1Tzw7tdlCUq+nSz8Lxzw2PJ5qqqqt44dl3SWqf7NEYhe85l/+N4DaKqARF5kGTtp31iYTn2QwePJi33nqLO++8k9tuu40PPvigxQ0EkWSTlzqXItxaa8jNQKfthLxMMCzQtguqY09UaAT6wDa2H0jj/555lQA/K/95/AE6lRzB3Cw1AETro6ITUb1HoNcvgIHjefTRR/H39+edd96hurqav//97z41n1+0XnZt8pJ9G1U4uNfaB2sTps/ZbTa+fPp1VvxvNv0mjGbGM48SEOyaIuNCtFaTjSSWmBksNo9ymSW50e20bduWTz/9lN/+9rf8/ve/Z9euXTzwwANYrfI2XrRuR/c5yDxgkpBsEN/BQlaag/Sjafzu0duw2ar517Pv0aVzD0+HKVqBpO4W8rPsZBxw0KFHw1+bhw0bxhtvvMFvfvMbbr31Vv79738TF9dyFmORj/B9mHbY0UdS0StmoTcugtJCVLeBqPOvwug9AhUaAcDq7bu57amXCAkM4MO/PkTndgmeDVwIDzPGXglKYf70NUopfve733HvvffyzTff8Lvf/Y6KigpPhyjEWX3i2MtuXcidlhTaq5BGt5N3NIuXb36QFf+bzUX/dwO3v/gXSTQJ4QLJRhv6qCh+cBymSjetgG94eDjvvvsu119/Pe+99x6//vWvW+y0CyEaIvOgg7TdDmLaGnTq7fyQcPvOTdzzuxsl0SSaXUiYcWJ0U3XluZXmGDlyJG+88QZHjhzh+uuvZ//+/W6KsvlJsskHaVs1+sA29E9fo3euBosF1XcMauyVqM59Uf6BJ479YuFy7nz2VeKjIvnoiYfoEO+aVYqE8GUqLBo19CL0ztXo9FSUUtxzzz384Q9/YOHChUyfPp1jx859yWohmstyRyZzzSNcbCQxxtL4DxC2LFrBc9f8hqwDh7nthT9x6f23yVRSIVzoaksniqhmoXm0yW35+/vz17/+lWeeeYbNmzdz5ZVXsnLlShdEKYRvOXbYwcEdDqLiFV0HWFBKMW/ePB589HbahIbx+ouf0rlTd0+HKVqZjj2tmA44vOfcP1wYNWoUH330ETabjRtvvJENGza4IcLmJ+8ofYi22zD3bUb/9BV67yYIi0INvQg1YiqqbWeU8cvUH5vdwTMffsHf3vuMUX1T+OzJh0mMltobQhxnjJgKYdE45n6IttsAuPnmm3n99dc5ePAg1157Ldu3b/dwlELUdsAs5t+OXaSoCG60dGtUGxUlZXz213/x7v1PEJvUlkf/9xYDJ53v4kiFEL2MSHqrSGY5DlGp7S5p8/LLL+ezzz4jNDSU22+/naeffprKykqXtC2EN9Nak77Pwf6tDiJiFd0HWQHNm2++yQMPPECPbr15/cVPaddWFrYQzS8oVJHYySD7iElJwbmvdN27d28+//xzIiMjufXWW/n88899fgEjSTb5AG060Gm70cu/gf1bISoRNXIqxuCJqKiEE8t7HpeencvNT77AJz8uYcbFE3jt4d8QKlMihDiF8g/EmDQD8jMxV35/Yvv48eP57LPPsFgs/OpXv+KDDz7ANM+9wxDCHTJ0Gc/YNxGGP/db+zaqTtP2ZWt4+vJfs/qbeUy87Voe+PhFYpJkwQgh3OV6SxcKqeY7R5rL2kxJSeGrr75i+vTpfPzxx1x55ZWsX7/eZe0L4W1MU7N/q4PDux1EtzXoMcRKYVEBd955J6+88gqXXnopLzz7LuHhLavAsvAt7btZ8A+EfVvsOBznnihq3749n3/+OSNGjODJJ5/k0Ucfpby83A2RNg9JNnk5nXvUWZNp91oIjUCNuBhj4DhUWHSdx89esY6r/vAPDhzN4v/ddzuPTr8ai0yJEKJORue+qL5j0KtmY6btOrG9R48efPXVV4wdO5bnnnuOu+66i9zcXA9GKgTk60r+YdsEwON+A4lQAed0/9z0TN773ZO8fc+fCGoTykOfvsK0B/8PP39/d4QrhKjR3YhgjJHA92Ya2dp1NQEDAwN57LHHeO+996iurmb69Om8+eabLmtfCG9RXanZucZO9hGT9t0Mug+0sH79Wq688krWrl3L3/72N5577jn8/c+tXxTC1ax+iq79rVSUNm46HUBERARvvfUW9957Lz/88INPz7aQLISX0hWlmJuWojcsAhRq0ATUkAtR4TF1Hp+Zl89vX3iLR157n65Jbfnq2ceZMnJI8wYthA8yJv4KouIxv/83uqTgxPbIyEheffVV/vKXv7BmzRouueQSvvzySxnlJDyiRFfzD/smyrDxR+tA2p5DQfCK0jK+f/l9/nHZ7ez8eR1Tf3sLD3/xOh37SuFUIZrLjZauGCjese9y+bSIUaNGMWvWLG677TaGDJH3fqLl0FqTm2GyeZmN0gJN1wEWIttW8MQTT3DLLbcQGBjIzJkzufbaa2vN9BDCUyJiDRI6GmQeMMnLbNx1g2EY3HPPPbzzzjuUlJRw/fXX89JLL1FdXe3iaN1Lkk1eRtttmCu/R//8HeRlOFeXG30pKrZdnS+iNruDD2Yv5NKHnmTl1p387obL+fAvD9Iutu6RT0KIUyn/QCyX3w22ShxfvYyu/qXuhVKKG264ga+//pquXbvy5z//mRkzZpCamurBiEVrk6sr+YttPcd0Bb+39qezEdag+1WWlTP/nc95YtJ05r/zOQMvGsuff/gPk++8UUYzCdHMolQgN1m6sVXnu6RY+OmCg4N5+OGHGTp0qMvbFsITyopNdq21k7rRTkCwou8YKxu3L+DSSy/lyy+/5LbbbuObb76hV69eng5ViFqSUyyERij2brZTVtz4D6pHjx7NrFmzuOyyy3j77be58sorWbFihQsjdS+rpwMQvzD3b8Vc9BkUZEN8B1SPIaig0LqPNU3mrtrAq//7niPHcjh/YB8ev/V6STIJ0Qgqtj3GZb/B/OplzK9fw7jqtyi/X4Zid+nShY8++ohvvvmG559/niuuuIJp06Zxzz330K5dOw9GLlq6w2YpT9s3UYWDx6wDSTHOXouiJK+A5f/9geWff0dpQRG9xw7n4run06GPjGQSwpMuNNqxzszmI0cq3VQ4yUYbT4ckBFprTAfYqsFerXE4QJtgOgAFhuH82c8f/IMU/oGKgEBQhutHEmmtKS3UZNSMCLFYnRftxwq38us7/8nmzZvp0aMHr776Kn379nX54wvhKoZF0WOwlW0/29i5xk6fkX4EhTbufyY8PJynn36aSZMm8fTTT/PrX/+a888/n0ceeYTOnTu7OHLXUr5e4fxkQ4YM0b5YHFHnZ2Eumok+sBWi4jEm3ghFddeHsTscLFi7iXe+m8eetHS6d2jHA9dNY+zAPjJ8VIh6GAPGNeg4c9sKzDnvozr2xLjiXlRA7cL6BQUFvPvuu3zyySdorbnmmmu45ZZbSEpKcnHUrqWU2qC1btXzK3ytj9hs5vGKfRv+WHjMOpAORt0fPoDzDfr27duZOXMms77/HrvNRu+xw5l8140k95NPfYVoiomW9i5rq1BX8ZhtLQrF037DCFfeM8pQ+gnf6ycao7pKU1qgKS00ycsyqSqHc60QoBQEBEFgiHLegp1fg0IUAcFgnEMiyjQ1ZUWa/CyT3ExnPBYrJHQ0yCnbzn/+8w6LFy8mJiaG++67jyuvvBKLxVJnW1lpjauRI0RjJXSs+7l4XHmJZvsqG4YBKcOtBLdp2sSy6upqPv74Y958800qKiq4+OKLueOOO+jWrXGrE5+rc+0nJNnkQbqqHHPF9+gNC8HqhzH6MtTgiSiLFXPz0lOOLSwt47ufVvPJ3MVk5ObTMSGOu6+eysUjh2BIAXAhzqihySYAc8cqzDnvQ2Q8lqt+i4qMr/O4zMxM3njjDb777jscDgcXXXQRN910E4MGDfLKxK9cRPhOH2HTJp879jHbPEySCuFR6wBiVd0rimZnZzNnzhy+/vpr9u7dS1BQEIMvm8i4m64gvpN3J0CF8BWuTDYBHDCL+Yt9Pe1UCH+2DiJU+bm0/caSfsJ3+okzOT3hYpqayjLnRW9FqcZW9cs+/0AICFL4B4LVX+HnD4bFeTv+VkZriG1nwVblLNRdVaGpKtdUlEFluaayTOOwnxpDQDAnElCBwQqrFVDONu02sFVrivO0s71KoOZyNCgUAkMd7Ni7gq+/+5hNW9bSJjSMm2+ZwS233EJIyJnrFUqySXijqgpN9hET04Qeg6xExDb92j0vL4/333+fmTNnUl5ezoQJE/jVr37FyJEj3XodIskmH+ggtK0avWUZ5qrZUF6C6jcGY+yVqJDwE8eYm5didzhYs2MP3yxdxaL1m6m22Rncsyu3TJ3IuEF9JckkRAOdS7IJwEzbhfntG2DaMS64AdXvvHpfuLOzs/nkk0+YOXMmJSUlJCcnc+WVV3LppZeSkJDgguhdQy4ifKOPOGSW8JZjJwd1CRcZ7Zlu6Ya/OvVTs7S0NBYuXMiCBQvYsmULAP369eOqq65iypQprAku8kToQrRYrk42AWw2c3nevoX2KpRHrQOIOsfVJd1B+gnf6CfOJvOQHVs1VJRoykuciSatnYmewBAICnUmgPyDGj4C6UyjN7TW2KudiaeKMmfyqbIMKss0FeUah62OOymwWMAvwJnsCgiCgtJ0Fi35njnzviYn9xgx0XFcc+XNXHrxNXTq2bApp5JsEt4qItZg1zo7FSWa9l0N2nezYFianhQqKCjgo48+YubMmRQWFpKcnMy1117LxRdfTHx83R+YN4Ukm7y4g9C2KvTWnzFXz4bSQlSHnhjjr0UlJJ84pqysjLVr17Lgvx+zZMNWCkvLCA8N4ZLRQ7ni/FH0kk+qhThn55psAtDFeZiz30Mf3g3tumIZdy2qfdd6jy8vL2f+/Pl8+eWXbNiwAXAmAC688ELGjRtHly5dPDriSS4ivLuPKNRVzHTsZ6mZQSh+3GntxVAjDnC+kVi3bh1r165l9erV7N+/H4DevXszceJELrzwQrp06XKirYWOdI+cgxAtlTuSTeBMOP3Lvo1grDxo7Ut3I8Itj9NQ0k94dz9xJg67pihXU5BjkpdhYq9J8PgFOJNLwW0UgSHnNr3tZGebKnQmJ+pAaUCDxQ+sfs7E0JH0Q6xYvYSlP/1I6t6dKKUYOng0l0y5mpHDz8dq9Tunx5dkk/BWCR0tOOyaA9sd5KSbBIVCx15WIuOUS64Pqqqq+PHHH5k5cyabN29GKcXgwYOZPHky48aNc1mNWUk2eWEHoQuyMTctQW9dDlXlkNQdY8zlGB16UlFRwZYtW1i9ejVr165l27Zt2O12QoMCGTeoHxOHDeD8gX3w9/OOIdZCtBbGgHFobToTxD99BeUl0LYLxtCLUN0HoYz63/gcOnSI+fPnM3/+fHbs2AFATEwMw4cPZ/jw4QwbNowOHTo0a/JJLiK8s4/I0uXMd6SzyDyKDZMLHYn0O2DnwM497Nixgy1btrBnzx601gQHBzNo0CDGjBnDxIkT633jIMkmIVzLXckmgDSzhOftW8ilkkuMjlxp6USw8sz6PdJPeGc/URdtakqLNIU5mqJck5ICjdbO6W+BIRAcqghqo/Dzd837jKYkm47TWpORkcHmzZtZt24dS5cs51h2BgA9e/Rl/NhJnH/eJOLjEhv9+JJsEt7q5OdwQbbJge12qsohJFyR1M1CZLxrkk4ABw8eZO7cucyZM+fEB5RdunRh9OjRDB8+nCFDhhAW1rCVjU8nySYv6SB0cT56z3rMPevh6D60MshL7EVaVDK7ckrYuXMnu3bt4sCBA5imicVioXfv3icuRgcHVOBvlcUChfCUk0dD6epK9LYVmOsXQGE2BIWiug5wJp069EL51z/94ejRo6xatYo1a9awevVqcnOdxf/Dw8Pp1asXKSkppKSk0K1bNzp06EBgYKBbzkcuIrynjyjR1awqTWfhke1sObgH28FsQg8VwsFc0vYdoLq6GoA2bdrQu3dvhg0bxvDhw+nTpw/+/mcvJizJJiFcy53JJoBybedjRyqLzQza4MdUSwcmGO0Ia+bi4dJPeE8/cTrTdE6JKylwJpeKcn+pkxQSroiIUUTEGrSJUmQfafwy667icDjIOnaUQ2n7OZS2j92p29i5ayv5Bc73QMHBIQwaMJyhg0czbPAYEhJkZV/Rsp2eMDVNTU66Sfo+B1XlzvppMW0NYtsZBIe5JvGktebQoUP89NNP/PTTT6xfv57q6moee+wxpk+f3qg2vSrZpJSaDLwMWIB3tdbPnrZf1ey/GCgHbtFab2zIfeviqQ5Cmw4qs46QvXsr2Xu2cWx/KjnZ2eRUVJNuMzhc7uBwbiEVFRUn7hMXF0dKSgq9evWiX79+DBkyhNDQX1YaOr1AuBCiedU19U6bJnr/FvTudej9W6CqApQBcUmodl1QcR1QUQkQlQDBbWp1FFprDhw4wPr169m5cyc7d+5kz5492Gy/FDRISEggOTmZDh06EB8fT3x8PAkJCcTFxZGYmHjK68S58NaLiObsJ9zdR2itKS8vp6CggIKCAvLz8ykoKCA99xipGWmkZR4lOzOLksxcHMXlp9w3MTGRzp0706NHD3r37k3v3r0bPfpNkk1CuJa7k03H7TeLmenYx1adjwVFLxXJYCOGAUY0CQRjuHk0rPQT3pFsstuchbwryjTlxc4EU1mRPrFinH8QRMQYRMQqwmOMWqOXmmN0j81mo7Awj+ycLLJzs8jJyXJ+n5NFZlY6h48cpLr6l0rk7dt1pFfPfvTu1Z/evQbQKbkrFot8qC5aj/pG52lTk5dlkpNuUpjjHKHoFwDh0QZh0YqQcEVwqMJidc1Uu61bt9KxY0fi4uIa1ca59hNu+y9XSlmA14ELgXRgnVJqltZ650mHTQG61dyGA28Cwxt4X5coLS3lwIEDVFZUUFVZQWV5OVWVFVRV1NyqKqksL6OyvIzS4iJKiospLi6hpKSEkrIyissrKKmsptxW+4Xdz8+PxMREOiZ3Yti4ZDp27EhycjI9evQgJibG1acihHAzZRiobgOh20C0w44+vAd9ZA9k7EdvW4G2Lf7l4IAgiIxDhURASBiEhKFCwukUGEKngd1geB+UXyA2w8L+o1kcOJxO2pEjpB1xfl24YAH5BQWnPP6UKVP417/+1bwn7Ua+0k/s2rWLhYsXUVJWRmlZKSVlpZSVlZ24lZeWUV5WTnlpKbaq6jrbsLQJwi8xkvCEWLoO6ENKYjJ92nWiS+fOJCcnExwc7OqwhRA+posRxuPGIA6bpSw3M9lg5vKhI5UPHRCIhQ4qlPYqhCgVSBQBRCh/grASiIUgZSUIC8FYay0q4Mt8pZ84TmvnxaLWoE1O+d50OGsrOezgsIPdrqmuBFuVpqpSU11hUlFmUl3pHAig0SjlfAsRmQihkYqQMIV/oD7xWNU2qLYdf1znraTEjsPhwO6wY7fbcTjs2O027HY7docdh92OreZnR80xlVUVVFaeequo+VpeXkpxSSFFxUUUFxdSUlpEeXlZrXMPCgomLjaRhPi2DBownI4dupDcsSsdkjoRGtKwAt9CtDbKUMS0tRDT1oKtWpOf5Ry5WJxnkpvxy3EBwTXF9AOdq0f6Byr8AsBiVVis1NwUhuFcDEAp5+fhv3xVBAQEMHTo0GY9P3emlIcB+7TWBwCUUjOBacDJL/DTgI+0c3jVaqVUhFIqEUhuwH1dYuvC2dz+xyfOepyhoI2/lTb+Ftr4WwkL8KNjWAht2sUSFhFFRFwC8cmdie3cnbh45yiE8PBwr1wCXQjRdMpiRXXqDZ16A85RTxTnovOPQX4WOj8LCnPQJfmQdQjKS9C69tB2g1/eIQMQC8SGwqCeVDtMssurOVZeTXaFnZiOLe7Nmk/0E9/tXsuHr72OCvTDEhyIERKAERyIJSQAIzoQo0MclpBAwkMCsUaGEhjZhrDISBKiYmgfHUen6ARSQhPorMIIbEEXgUII9+hghHKj0Y0b6UaWLmeHWUCaLiFNl7LezKGYupb3cppmdORX1m717vdBPtFP/L9n3ubjz9868bPWGo0z06SpmUWinQkkXVMp25tLmSilCAwMIjAwiOCgEMLCIoiKjCa5Q2fCwiIIaxNBREQksTEJxMclEhebQEhI7RHdQoiG8/NXxHewEN/B+RpSVQ5lxc4ptOWlmuoKTXG+SXVlTcH9c6QUoKBzbwvxLqjD1hDuTDa1A46c9HM6zk8bznZMuwbeFwCl1B3AHTU/liql9jQh5uYWA+R6OogmagnnAC3jPFrCOUDLOA83ncN38MCTjb1zR1dG4iJu7yekj/A6ck6+Qc7JN9Q6py+AGxvfnvQT9fcT3v78kfiazttjlPiaRuJrmuPxnVM/4c5kU12p7dNzcPUd05D7Ojdq/W/g3+cWmndQSq33xrnx56IlnAO0jPNoCecALeM8WsI5NBO39xPSR3gXOSffIOfkG1riOdXBK/oJb/9dS3xN5+0xSnxNI/E1TWPjc2eyKR1IOunn9kBGA4/xb8B9hRBC+DbpJ4QQQpyJ9BNCCOGjDDe2vQ7oppTqpJTyB64HZp12zCxghnIaARRprTMbeF8hhBC+TfoJIYQQZyL9hBBC+Ci3jWzSWtuVUvcC83AuN/q+1nqHUuqumv1vAXNwLlO6D+dSpbee6b7uitWDfHJqx2lawjlAyziPlnAO0DLOoyWcg9tJP3FWLfF5JOfkG+ScfENLPKdTeFE/4e2/a4mv6bw9RomvaSS+pmlUfMqbV0IQQgghhBBCCCGEEL7FndPohBBCCCGEEEIIIUQrI8kmIYQQQgghhBBCCOEykmxqZkqp55VSu5VSW5VS3yilIk7a90el1D6l1B6l1CQPhnlWSqlrlFI7lFKmUmrISduTlVIVSqnNNbe3PBnnmdR3DjX7fOZvcTKl1BNKqaMn/f4v9nRMDaWUmlzz+96nlPqDp+NpLKXUIaXUtprf/3pPxyN8T0vpJ07WEvqM07XEPuRkvtyfnK6l9C8nk77G/ZRSf695Hd6slJqvlGp70j6v+B+vr7/wltdWb+/PvL1v8rV+xlv7DW/vA7zt9Vwp9b5SKlsptf2kbVFKqQVKqb01XyMb1JjWWm7NeAMuAqw13z8HPFfzfQqwBQgAOgH7AYun4z3DefQCegBLgSEnbU8Gtns6viaeg0/9LU47pyeA33s6jkbEban5PXfGuVTxFiDF03E18lwOATGejkNuvntrKf3Eaefk833GOZyTz/6dTjs/n+xP6jiPFtO/nHZe0te4/3ccdtL39wFv1XzvNf/jZ+gvvOK11dv7M2/vm3ytn/HGfsMX+gBvez0HxgKDTv4fAP4J/KHm+z8c/18+201GNjUzrfV8rbW95sfVQPua76cBM7XWVVrrgzhX1BjmiRgbQmu9S2u9x9NxNMUZzsGn/hYtxDBgn9b6gNa6GpiJ8+8gRKvTUvqJk7WEPuN00of4DOlfRKNorYtP+jEEOL6qktf8j5+hv/AK3t6feXvfJP2MS0gfcI601j8B+adtngZ8WPP9h8DlDWlLkk2edRswt+b7dsCRk/al12zzRZ2UUpuUUsuUUud5OphG8PW/xb01w5Xfb/AQR8/z9d/5yTQwXym1QSl1h6eDET6vpfYTJ/P1PuN0Lenv5Iv9yela0t/jZNLXNAOl1D+UUkeAG4G/1Gz21ufUyf0FeN9rq6/1Z972+zuZN//+vK3f8Obf1XG+8Hoer7XOBKj5GteQO1ndGlIrpZRaCCTUsetxrfV3Ncc8DtiBT4/frY7jdR3bmk1DzqMOmUAHrXWeUmow8K1Sqvdpnw41m0aeg9f9LU52pnMC3gT+jjPevwMv4OzcvZ1X/87P0WitdYZSKg5YoJTaXfMJgRAntJR+4mQtoc84XUvsQ07WQvuT0/nM3+McSV/jAmf7H9daPw48rpT6I3Av8Fea+TnVyP6i2V5bvb0/8/a+ydf6GR/sN3yhD2ixr+eSbHIDrfXEM+1XSt0MXAJM0DUTH3FmWZNOOqw9kOGeCBvmbOdRz32qgKqa7zcopfYD3QGPFDtrzDnghX+LkzX0nJRS7wA/uDkcV/Hq3/m50Fpn1HzNVkp9g3P4bovoMITrtJR+4mQtoc84XUvsQ07WQvuT0/nM3+NcSF/jGufwP/4ZMBtnsqlZn1ON6S+a87XV2/szb++bfK2f8cF+w+v7AB95PT+mlErUWmcqpRKB7IbcSabRNTOl1GTgUeAyrXX5SbtmAdcrpQKUUp2AbsBaT8TYFEqpWKWUpeb7zjjP44BnozpnPvu3qPnnP+4KYHt9x3qZdUA3pVQnpZQ/cD3Ov4NPUUqFKKXaHP8eZ2FMX/kbCC/R0vuJk7WQPuN0LeLv5MP9yelaRP9yMulrmodSqttJP14G7K753mv+x+vrL7zltdVX+zNv+f2dgVf+/ry03/DqPsCHXs9nATfXfH8zUN+ou1PIyKbm9xrOlQMWKKUAVmut79Ja71BKfQHsxDnM9B6ttcODcZ6RUuoK4FUgFpitlNqstZ6Es3r9k0opO+AA7tJan15gzCvUdw6+9rc4zT+VUgNwDg89BNzp0WgaSGttV0rdC8zDuWrE+1rrHR4OqzHigW9q/retwGda6x89G5LwQS2inzhZS+gzTtdC+5CT+WR/croW1L+cTPqa5vGsUqoHYAJpwF0AXvY/Xmd/gfe8tnp1f+btfZMP9jNe12/4QB/gda/nSqnPgXFAjFIqHeeIzmeBL5RStwOHgWsa1NYvoxmFEEIIIYQQQgghhGgamUYnhBBCCCGEEEIIIVxGkk1CCCGEEEIIIYQQwmUk2SSEEEIIIYQQQgghXEaSTUIIIYQQQgghhBDCZSTZJIQQQgghhBBCCCFcRpJNQgghhBBCCCGEEMJlJNkkhBBCCCGEEEIIIVzm/wNU2bPi9QVimQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "f, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(20, 6))\n",
    "\n",
    "v14_fraud_dist = new_df['V14'].loc[new_df['Class'] == 1].values\n",
    "sns.distplot(v14_fraud_dist,ax=ax1, fit=norm, color='#FB8861')\n",
    "ax1.set_title('V14 Distribution \\n (Fraud Transactions)', fontsize=14)\n",
    "\n",
    "v12_fraud_dist = new_df['V12'].loc[new_df['Class'] == 1].values\n",
    "sns.distplot(v12_fraud_dist,ax=ax2, fit=norm, color='#56F9BB')\n",
    "ax2.set_title('V12 Distribution \\n (Fraud Transactions)', fontsize=14)\n",
    "\n",
    "\n",
    "v10_fraud_dist = new_df['V10'].loc[new_df['Class'] == 1].values\n",
    "sns.distplot(v10_fraud_dist,ax=ax3, fit=norm, color='#C5B3F9')\n",
    "ax3.set_title('V10 Distribution \\n (Fraud Transactions)', fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bab1fe8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quartile 25: -9.692722964972386 | Quartile 75: -4.282820849486865\n",
      "iqr: 5.409902115485521\n",
      "Cut Off: 8.114853173228282\n",
      "V14 Lower: -17.807576138200666\n",
      "V14 Upper: 3.8320323237414167\n",
      "Feature V14 Outliers for Fraud Cases: 4\n",
      "V10 outliers:[-18.0499976898594, -19.2143254902614, -18.8220867423816, -18.4937733551053]\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "V12 Lower: -17.3430371579634\n",
      "V12 Upper: 5.776973384895937\n",
      "V12 outliers: [-18.4311310279993, -18.0475965708216, -18.6837146333443, -18.5536970096458]\n",
      "Feature V12 Outliers for Fraud Cases: 4\n",
      "Number of Instances after outliers removal: 975\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "V10 Lower: -14.89885463232024\n",
      "V10 Upper: 4.92033495834214\n",
      "V10 outliers: [-15.1237521803455, -16.6011969664137, -15.5637913387301, -24.4031849699728, -15.1241628144947, -16.6496281595399, -22.1870885620007, -20.9491915543611, -15.2318333653018, -16.2556117491401, -15.3460988468775, -15.5637913387301, -14.9246547735487, -22.1870885620007, -22.1870885620007, -22.1870885620007, -16.3035376590131, -16.7460441053944, -15.2399619587112, -23.2282548357516, -18.2711681738888, -24.5882624372475, -19.836148851696, -14.9246547735487, -18.9132433348732, -15.2399619587112, -17.1415136412892]\n",
      "Feature V10 Outliers for Fraud Cases: 27\n",
      "Number of Instances after outliers removal: 946\n"
     ]
    }
   ],
   "source": [
    "# # -----> V14 Removing Outliers (Highest Negative Correlated with Labels)\n",
    "v14_fraud = new_df['V14'].loc[new_df['Class'] == 1].values\n",
    "q25, q75 = np.percentile(v14_fraud, 25), np.percentile(v14_fraud, 75)\n",
    "print('Quartile 25: {} | Quartile 75: {}'.format(q25, q75))\n",
    "v14_iqr = q75 - q25\n",
    "print('iqr: {}'.format(v14_iqr))\n",
    "\n",
    "v14_cut_off = v14_iqr * 1.5\n",
    "v14_lower, v14_upper = q25 - v14_cut_off, q75 + v14_cut_off\n",
    "print('Cut Off: {}'.format(v14_cut_off))\n",
    "print('V14 Lower: {}'.format(v14_lower))\n",
    "print('V14 Upper: {}'.format(v14_upper))\n",
    "\n",
    "outliers = [x for x in v14_fraud if x < v14_lower or x > v14_upper]\n",
    "print('Feature V14 Outliers for Fraud Cases: {}'.format(len(outliers)))\n",
    "print('V10 outliers:{}'.format(outliers))\n",
    "\n",
    "new_df = new_df.drop(new_df[(new_df['V14'] > v14_upper) | (new_df['V14'] < v14_lower)].index)\n",
    "print('----' * 44)\n",
    "\n",
    "# -----> V12 removing outliers from fraud transactions\n",
    "v12_fraud = new_df['V12'].loc[new_df['Class'] == 1].values\n",
    "q25, q75 = np.percentile(v12_fraud, 25), np.percentile(v12_fraud, 75)\n",
    "v12_iqr = q75 - q25\n",
    "\n",
    "v12_cut_off = v12_iqr * 1.5\n",
    "v12_lower, v12_upper = q25 - v12_cut_off, q75 + v12_cut_off\n",
    "print('V12 Lower: {}'.format(v12_lower))\n",
    "print('V12 Upper: {}'.format(v12_upper))\n",
    "outliers = [x for x in v12_fraud if x < v12_lower or x > v12_upper]\n",
    "print('V12 outliers: {}'.format(outliers))\n",
    "print('Feature V12 Outliers for Fraud Cases: {}'.format(len(outliers)))\n",
    "new_df = new_df.drop(new_df[(new_df['V12'] > v12_upper) | (new_df['V12'] < v12_lower)].index)\n",
    "print('Number of Instances after outliers removal: {}'.format(len(new_df)))\n",
    "print('----' * 44)\n",
    "\n",
    "\n",
    "# Removing outliers V10 Feature\n",
    "v10_fraud = new_df['V10'].loc[new_df['Class'] == 1].values\n",
    "q25, q75 = np.percentile(v10_fraud, 25), np.percentile(v10_fraud, 75)\n",
    "v10_iqr = q75 - q25\n",
    "\n",
    "v10_cut_off = v10_iqr * 1.5\n",
    "v10_lower, v10_upper = q25 - v10_cut_off, q75 + v10_cut_off\n",
    "print('V10 Lower: {}'.format(v10_lower))\n",
    "print('V10 Upper: {}'.format(v10_upper))\n",
    "outliers = [x for x in v10_fraud if x < v10_lower or x > v10_upper]\n",
    "print('V10 outliers: {}'.format(outliers))\n",
    "print('Feature V10 Outliers for Fraud Cases: {}'.format(len(outliers)))\n",
    "new_df = new_df.drop(new_df[(new_df['V10'] > v10_upper) | (new_df['V10'] < v10_lower)].index)\n",
    "print('Number of Instances after outliers removal: {}'.format(len(new_df)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0e8000fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,(ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20,6))\n",
    "\n",
    "colors = ['#B3F9C5', '#f9c5b3']\n",
    "# Boxplots with outliers removed\n",
    "# Feature V14\n",
    "sns.boxplot(x=\"Class\", y=\"V14\", data=new_df,ax=ax1, palette=colors)\n",
    "ax1.set_title(\"V14 Feature \\n Reduction of outliers\", fontsize=14)\n",
    "ax1.annotate('Fewer extreme \\n outliers', xy=(0.98, -17.5), xytext=(0, -12),\n",
    "            arrowprops=dict(facecolor='black'),\n",
    "            fontsize=14)\n",
    "\n",
    "# Feature 12\n",
    "sns.boxplot(x=\"Class\", y=\"V12\", data=new_df, ax=ax2, palette=colors)\n",
    "ax2.set_title(\"V12 Feature \\n Reduction of outliers\", fontsize=14)\n",
    "ax2.annotate('Fewer extreme \\n outliers', xy=(0.98, -17.3), xytext=(0, -12),\n",
    "            arrowprops=dict(facecolor='black'),\n",
    "            fontsize=14)\n",
    "\n",
    "# Feature V10\n",
    "sns.boxplot(x=\"Class\", y=\"V10\", data=new_df, ax=ax3, palette=colors)\n",
    "ax3.set_title(\"V10 Feature \\n Reduction of outliers\", fontsize=14)\n",
    "ax3.annotate('Fewer extreme \\n outliers', xy=(0.95, -16.5), xytext=(0, -12),\n",
    "            arrowprops=dict(facecolor='black'),\n",
    "            fontsize=14)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1bc875c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-SNE took 5.4 s\n",
      "PCA took 0.035 s\n",
      "Truncated SVD took 0.0082 s\n"
     ]
    }
   ],
   "source": [
    "# New_df is from the random undersample data (fewer instances)\n",
    "X = new_df.drop('Class', axis=1)\n",
    "y = new_df['Class']\n",
    "\n",
    "\n",
    "# T-SNE Implementation\n",
    "t0 = time.time()\n",
    "X_reduced_tsne = TSNE(n_components=2, random_state=42).fit_transform(X.values)\n",
    "t1 = time.time()\n",
    "print(\"T-SNE took {:.2} s\".format(t1 - t0))\n",
    "\n",
    "# PCA Implementation\n",
    "t0 = time.time()\n",
    "X_reduced_pca = PCA(n_components=2, random_state=42).fit_transform(X.values)\n",
    "t1 = time.time()\n",
    "print(\"PCA took {:.2} s\".format(t1 - t0))\n",
    "\n",
    "# TruncatedSVD\n",
    "t0 = time.time()\n",
    "X_reduced_svd = TruncatedSVD(n_components=2, algorithm='randomized', random_state=42).fit_transform(X.values)\n",
    "t1 = time.time()\n",
    "print(\"Truncated SVD took {:.2} s\".format(t1 - t0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c3174502",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(24,6))\n",
    "# labels = ['No Fraud', 'Fraud']\n",
    "f.suptitle('Clusters using Dimensionality Reduction', fontsize=14)\n",
    "\n",
    "\n",
    "blue_patch = mpatches.Patch(color='#0A0AFF', label='No Fraud')\n",
    "red_patch = mpatches.Patch(color='#AF0000', label='Fraud')\n",
    "\n",
    "\n",
    "# t-SNE scatter plot\n",
    "ax1.scatter(X_reduced_tsne[:,0], X_reduced_tsne[:,1], c=(y == 0), cmap='coolwarm', label='No Fraud', linewidths=2)\n",
    "ax1.scatter(X_reduced_tsne[:,0], X_reduced_tsne[:,1], c=(y == 1), cmap='coolwarm', label='Fraud', linewidths=2)\n",
    "ax1.set_title('t-SNE', fontsize=14)\n",
    "\n",
    "ax1.grid(True)\n",
    "\n",
    "ax1.legend(handles=[blue_patch, red_patch])\n",
    "\n",
    "\n",
    "# PCA scatter plot\n",
    "ax2.scatter(X_reduced_pca[:,0], X_reduced_pca[:,1], c=(y == 0), cmap='coolwarm', label='No Fraud', linewidths=2)\n",
    "ax2.scatter(X_reduced_pca[:,0], X_reduced_pca[:,1], c=(y == 1), cmap='coolwarm', label='Fraud', linewidths=2)\n",
    "ax2.set_title('PCA', fontsize=14)\n",
    "\n",
    "ax2.grid(True)\n",
    "\n",
    "ax2.legend(handles=[blue_patch, red_patch])\n",
    "\n",
    "# TruncatedSVD scatter plot\n",
    "ax3.scatter(X_reduced_svd[:,0], X_reduced_svd[:,1], c=(y == 0), cmap='coolwarm', label='No Fraud', linewidths=2)\n",
    "ax3.scatter(X_reduced_svd[:,0], X_reduced_svd[:,1], c=(y == 1), cmap='coolwarm', label='Fraud', linewidths=2)\n",
    "ax3.set_title('Truncated SVD', fontsize=14)\n",
    "\n",
    "ax3.grid(True)\n",
    "\n",
    "ax3.legend(handles=[blue_patch, red_patch])\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57b2bab7",
   "metadata": {},
   "source": [
    "TSNE gave the best result with min loss of information."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a391d783",
   "metadata": {},
   "source": [
    "##UNDERSAMPLING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "78614c4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Undersampling before cross validating (prone to overfit)\n",
    "X = new_df.drop('Class', axis=1)\n",
    "y = new_df['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f4248359",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    946.000000\n",
       "mean       0.483087\n",
       "std        0.499978\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%        0.000000\n",
       "75%        1.000000\n",
       "max        1.000000\n",
       "Name: Class, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "85377055",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# This is explicitly used for undersampling.\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dde01034",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.values\n",
    "X_test = X_test.values\n",
    "y_train = y_train.values\n",
    "y_test = y_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "60f9a678",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "classifiers = {\n",
    "    \"LogisiticRegression\": LogisticRegression(),\n",
    "    \"KNearest\": KNeighborsClassifier(),\n",
    "    \"Support Vector Classifier\": SVC(),\n",
    "    \"DecisionTreeClassifier\": DecisionTreeClassifier()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "110bcd7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifiers:  LogisticRegression Has a training score of 93.0 % accuracy score\n",
      "Classifiers:  KNeighborsClassifier Has a training score of 93.0 % accuracy score\n",
      "Classifiers:  SVC Has a training score of 93.0 % accuracy score\n",
      "Classifiers:  DecisionTreeClassifier Has a training score of 90.0 % accuracy score\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "\n",
    "for key, classifier in classifiers.items():\n",
    "    classifier.fit(X_train, y_train)\n",
    "    training_score = cross_val_score(classifier, X_train, y_train, cv=5)\n",
    "    print(\"Classifiers: \", classifier.__class__.__name__, \"Has a training score of\", round(training_score.mean(), 2) * 100, \"% accuracy score\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0b5a21c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "# Logistic Regression \n",
    "log_reg_params = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
    "\n",
    "\n",
    "\n",
    "grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)\n",
    "grid_log_reg.fit(X_train, y_train)\n",
    "# We automatically get the logistic regression with the best parameters.\n",
    "log_reg = grid_log_reg.best_estimator_\n",
    "\n",
    "knears_params = {\"n_neighbors\": list(range(2,5,1)), 'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute']}\n",
    "\n",
    "grid_knears = GridSearchCV(KNeighborsClassifier(), knears_params)\n",
    "grid_knears.fit(X_train, y_train)\n",
    "# KNears best estimator\n",
    "knears_neighbors = grid_knears.best_estimator_\n",
    "\n",
    "# Support Vector Classifier\n",
    "svc_params = {'C': [0.5, 0.7, 0.9, 1], 'kernel': ['rbf', 'poly', 'sigmoid', 'linear']}\n",
    "grid_svc = GridSearchCV(SVC(), svc_params)\n",
    "grid_svc.fit(X_train, y_train)\n",
    "\n",
    "# SVC best estimator\n",
    "svc = grid_svc.best_estimator_\n",
    "\n",
    "# DecisionTree Classifier\n",
    "tree_params = {\"criterion\": [\"gini\", \"entropy\"], \"max_depth\": list(range(2,4,1)), \n",
    "              \"min_samples_leaf\": list(range(5,7,1))}\n",
    "grid_tree = GridSearchCV(DecisionTreeClassifier(), tree_params)\n",
    "grid_tree.fit(X_train, y_train)\n",
    "\n",
    "# tree best estimator\n",
    "tree_clf = grid_tree.best_estimator_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ca398bda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Cross Validation Score:  93.65%\n",
      "Knears Neighbors Cross Validation Score 93.65%\n",
      "Support Vector Classifier Cross Validation Score 93.65%\n",
      "DecisionTree Classifier Cross Validation Score 91.81%\n"
     ]
    }
   ],
   "source": [
    "# Overfitting Case\n",
    "\n",
    "log_reg_score = cross_val_score(log_reg, X_train, y_train, cv=5)\n",
    "print('Logistic Regression Cross Validation Score: ', round(log_reg_score.mean() * 100, 2).astype(str) + '%')\n",
    "\n",
    "\n",
    "knears_score = cross_val_score(knears_neighbors, X_train, y_train, cv=5)\n",
    "print('Knears Neighbors Cross Validation Score', round(knears_score.mean() * 100, 2).astype(str) + '%')\n",
    "\n",
    "svc_score = cross_val_score(svc, X_train, y_train, cv=5)\n",
    "print('Support Vector Classifier Cross Validation Score', round(svc_score.mean() * 100, 2).astype(str) + '%')\n",
    "\n",
    "tree_score = cross_val_score(tree_clf, X_train, y_train, cv=5)\n",
    "print('DecisionTree Classifier Cross Validation Score', round(tree_score.mean() * 100, 2).astype(str) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4859a025",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scaled_amount    284807\n",
       "scaled_time      284807\n",
       "V1               284807\n",
       "V2               284807\n",
       "V3               284807\n",
       "V4               284807\n",
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       "V10              284807\n",
       "V11              284807\n",
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       "V20              284807\n",
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       "V28              284807\n",
       "Class            284807\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5976d4fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: [ 49837  50049  50886 ... 284804 284805 284806] Test: [    0     1     2 ... 56972 56973 56974]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [ 49837  50049  50886 ... 119007 119636 119850]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [113915 113916 113917 ... 181665 182005 182153]\n",
      "Train: [     0      1      2 ... 284804 284805 284806] Test: [170862 170863 170864 ... 240178 240441 241177]\n",
      "Train: [     0      1      2 ... 240178 240441 241177] Test: [227826 227827 227828 ... 284804 284805 284806]\n",
      "NearMiss Label Distribution: Counter({0: 492, 1: 492})\n"
     ]
    }
   ],
   "source": [
    "# We will undersample during cross \n",
    "from imblearn.under_sampling import NearMiss \n",
    "\n",
    "nm = NearMiss()\n",
    "undersample_X = df.drop('Class', axis=1)\n",
    "undersample_y = df['Class']\n",
    "undersample_y_list = undersample_y.tolist()\n",
    "\n",
    "for train_index, test_index in sss.split(undersample_X, undersample_y):\n",
    "    print(\"Train:\", train_index, \"Test:\", test_index)\n",
    "    undersample_Xtrain, undersample_Xtest = undersample_X.iloc[train_index], undersample_X.iloc[test_index]\n",
    "    undersample_ytrain, undersample_ytest = undersample_y.iloc[train_index], undersample_y.iloc[test_index]\n",
    "    \n",
    "undersample_Xtrain = undersample_Xtrain.values\n",
    "undersample_Xtest = undersample_Xtest.values\n",
    "undersample_ytrain = undersample_ytrain.values\n",
    "undersample_ytest = undersample_ytest.values \n",
    "\n",
    "undersample_accuracy = []\n",
    "undersample_precision = []\n",
    "undersample_recall = []\n",
    "undersample_f1 = []\n",
    "undersample_auc = []\n",
    "\n",
    "# Implementing NearMiss Technique \n",
    "# Distribution of NearMiss (Just to see how it distributes the labels we won't use these variables)\n",
    "\n",
    "X_nearmiss, y_nearmiss = nm.fit_resample(undersample_X.values, undersample_y.values)\n",
    "print('NearMiss Label Distribution: {}'.format(Counter(y_nearmiss)))\n",
    "# Cross Validating the right way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c782d948",
   "metadata": {},
   "outputs": [],
   "source": [
    "for train, test in sss.split(undersample_Xtrain, undersample_ytrain):\n",
    "    undersample_pipeline = imbalanced_make_pipeline(NearMiss(sampling_strategy='majority'), log_reg) # SMOTE happens during Cross Validation not before..\n",
    "    undersample_model = undersample_pipeline.fit(undersample_Xtrain[train], undersample_ytrain[train])\n",
    "    undersample_prediction = undersample_model.predict(undersample_Xtrain[test])\n",
    "    \n",
    "    undersample_accuracy.append(undersample_pipeline.score(original_Xtrain[test], original_ytrain[test]))\n",
    "    undersample_precision.append(precision_score(original_ytrain[test], undersample_prediction))\n",
    "    undersample_recall.append(recall_score(original_ytrain[test], undersample_prediction))\n",
    "    undersample_f1.append(f1_score(original_ytrain[test], undersample_prediction))\n",
    "    undersample_auc.append(roc_auc_score(original_ytrain[test], undersample_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b0a16d4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's Plot LogisticRegression Learning Curve\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "def plot_learning_curve(estimator1, estimator2, estimator3, estimator4, X, y, ylim=None, cv=None,\n",
    "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "    f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize=(20,14), sharey=True)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    # First Estimator\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator1, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    ax1.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"#ff9124\")\n",
    "    ax1.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
    "    ax1.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
    "             label=\"Training score\")\n",
    "    ax1.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
    "             label=\"Cross-validation score\")\n",
    "    ax1.set_title(\"Logistic Regression Learning Curve\", fontsize=14)\n",
    "    ax1.set_xlabel('Training size (m)')\n",
    "    ax1.set_ylabel('Score')\n",
    "    ax1.grid(True)\n",
    "    ax1.legend(loc=\"best\")\n",
    "    \n",
    "    # Second Estimator \n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator2, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    ax2.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"#ff9124\")\n",
    "    ax2.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
    "    ax2.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
    "             label=\"Training score\")\n",
    "    ax2.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
    "             label=\"Cross-validation score\")\n",
    "    ax2.set_title(\"Knears Neighbors Learning Curve\", fontsize=14)\n",
    "    ax2.set_xlabel('Training size (m)')\n",
    "    ax2.set_ylabel('Score')\n",
    "    ax2.grid(True)\n",
    "    ax2.legend(loc=\"best\")\n",
    "    \n",
    "    # Third Estimator\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator3, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    ax3.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"#ff9124\")\n",
    "    ax3.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
    "    ax3.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
    "             label=\"Training score\")\n",
    "    ax3.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
    "             label=\"Cross-validation score\")\n",
    "    ax3.set_title(\"Support Vector Classifier \\n Learning Curve\", fontsize=14)\n",
    "    ax3.set_xlabel('Training size (m)')\n",
    "    ax3.set_ylabel('Score')\n",
    "    ax3.grid(True)\n",
    "    ax3.legend(loc=\"best\")\n",
    "    \n",
    "    # Fourth Estimator\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator4, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    ax4.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"#ff9124\")\n",
    "    ax4.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
    "    ax4.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
    "             label=\"Training score\")\n",
    "    ax4.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
    "             label=\"Cross-validation score\")\n",
    "    ax4.set_title(\"Decision Tree Classifier \\n Learning Curve\", fontsize=14)\n",
    "    ax4.set_xlabel('Training size (m)')\n",
    "    ax4.set_ylabel('Score')\n",
    "    ax4.grid(True)\n",
    "    ax4.legend(loc=\"best\")\n",
    "    return plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1d8e8fb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\Abhinav\\\\anaconda3\\\\envs\\\\tf_gpu\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=42)\n",
    "plot_learning_curve(log_reg, knears_neighbors, svc, tree_clf, X_train, y_train, (0.87, 1.01), cv=cv, n_jobs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0eb33dc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "# Create a DataFrame with all the scores and the classifiers names.\n",
    "\n",
    "log_reg_pred = cross_val_predict(log_reg, X_train, y_train, cv=5,\n",
    "                             method=\"decision_function\")\n",
    "\n",
    "knears_pred = cross_val_predict(knears_neighbors, X_train, y_train, cv=5)\n",
    "\n",
    "svc_pred = cross_val_predict(svc, X_train, y_train, cv=5,\n",
    "                             method=\"decision_function\")\n",
    "\n",
    "tree_pred = cross_val_predict(tree_clf, X_train, y_train, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5c17482c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression:  0.9765126300669166\n",
      "KNears Neighbors:  0.9357951161405599\n",
      "Support Vector Classifier:  0.9657218932838175\n",
      "Decision Tree Classifier:  0.9168903058543251\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "print('Logistic Regression: ', roc_auc_score(y_train, log_reg_pred))\n",
    "print('KNears Neighbors: ', roc_auc_score(y_train, knears_pred))\n",
    "print('Support Vector Classifier: ', roc_auc_score(y_train, svc_pred))\n",
    "print('Decision Tree Classifier: ', roc_auc_score(y_train, tree_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "320b0a69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_fpr, log_tpr, log_thresold = roc_curve(y_train, log_reg_pred)\n",
    "knear_fpr, knear_tpr, knear_threshold = roc_curve(y_train, knears_pred)\n",
    "svc_fpr, svc_tpr, svc_threshold = roc_curve(y_train, svc_pred)\n",
    "tree_fpr, tree_tpr, tree_threshold = roc_curve(y_train, tree_pred)\n",
    "\n",
    "\n",
    "def graph_roc_curve_multiple(log_fpr, log_tpr, knear_fpr, knear_tpr, svc_fpr, svc_tpr, tree_fpr, tree_tpr):\n",
    "    plt.figure(figsize=(16,8))\n",
    "    plt.title('ROC Curve \\n Top 4 Classifiers', fontsize=18)\n",
    "    plt.plot(log_fpr, log_tpr, label='Logistic Regression Classifier Score: {:.4f}'.format(roc_auc_score(y_train, log_reg_pred)))\n",
    "    plt.plot(knear_fpr, knear_tpr, label='KNears Neighbors Classifier Score: {:.4f}'.format(roc_auc_score(y_train, knears_pred)))\n",
    "    plt.plot(svc_fpr, svc_tpr, label='Support Vector Classifier Score: {:.4f}'.format(roc_auc_score(y_train, svc_pred)))\n",
    "    plt.plot(tree_fpr, tree_tpr, label='Decision Tree Classifier Score: {:.4f}'.format(roc_auc_score(y_train, tree_pred)))\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([-0.01, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "    plt.annotate('Minimum ROC Score of 50% \\n (This is the minimum score to get)', xy=(0.5, 0.5), xytext=(0.6, 0.3),\n",
    "                arrowprops=dict(facecolor='#6E726D', shrink=0.05),\n",
    "                )\n",
    "    plt.legend()\n",
    "    \n",
    "graph_roc_curve_multiple(log_fpr, log_tpr, knear_fpr, knear_tpr, svc_fpr, svc_tpr, tree_fpr, tree_tpr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "152fdca0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def logistic_roc_curve(log_fpr, log_tpr):\n",
    "    plt.figure(figsize=(12,8))\n",
    "    plt.title('Logistic Regression ROC Curve', fontsize=16)\n",
    "    plt.plot(log_fpr, log_tpr, 'b-', linewidth=2)\n",
    "    plt.plot([0, 1], [0, 1], 'r--')\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "    plt.axis([-0.01,1,0,1])\n",
    "    \n",
    "    \n",
    "logistic_roc_curve(log_fpr, log_tpr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "03796ad3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precision, recall, threshold = precision_recall_curve(y_train, log_reg_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "91c45ef7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "Overfitting: \n",
      "\n",
      "Recall Score: 0.90\n",
      "Precision Score: 0.84\n",
      "F1 Score: 0.87\n",
      "Accuracy Score: 0.87\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "How it should be:\n",
      "\n",
      "Accuracy Score: 0.76\n",
      "Precision Score: 0.00\n",
      "Recall Score: 0.21\n",
      "F1 Score: 0.00\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score, precision_score, f1_score, accuracy_score\n",
    "y_pred = log_reg.predict(X_train)\n",
    "\n",
    "# Overfitting Case\n",
    "print('---' * 45)\n",
    "print('Overfitting: \\n')\n",
    "print('Recall Score: {:.2f}'.format(recall_score(y_train, y_pred)))\n",
    "print('Precision Score: {:.2f}'.format(precision_score(y_train, y_pred)))\n",
    "print('F1 Score: {:.2f}'.format(f1_score(y_train, y_pred)))\n",
    "print('Accuracy Score: {:.2f}'.format(accuracy_score(y_train, y_pred)))\n",
    "print('---' * 45)\n",
    "\n",
    "# How it should look like\n",
    "print('---' * 45)\n",
    "print('How it should be:\\n')\n",
    "print(\"Accuracy Score: {:.2f}\".format(np.mean(undersample_accuracy)))\n",
    "print(\"Precision Score: {:.2f}\".format(np.mean(undersample_precision)))\n",
    "print(\"Recall Score: {:.2f}\".format(np.mean(undersample_recall)))\n",
    "print(\"F1 Score: {:.2f}\".format(np.mean(undersample_f1)))\n",
    "print('---' * 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7e9bda36",
   "metadata": {},
   "outputs": [],
   "source": [
    "undersample_y_score = log_reg.decision_function(original_Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3c3ce986",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 0.08\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import average_precision_score\n",
    "\n",
    "undersample_average_precision = average_precision_score(original_ytest, undersample_y_score)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "      undersample_average_precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "62d9f431",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'UnderSampling Precision-Recall curve: \\n Average Precision-Recall Score =0.08')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(12,6))\n",
    "\n",
    "precision, recall, _ = precision_recall_curve(original_ytest, undersample_y_score)\n",
    "\n",
    "plt.step(recall, precision, color='#004a93', alpha=0.2,\n",
    "         where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.2,\n",
    "                 color='#48a6ff')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('UnderSampling Precision-Recall curve: \\n Average Precision-Recall Score ={0:0.2f}'.format(\n",
    "          undersample_average_precision), fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "303bef35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of X (train): 227846 | Length of y (train): 227846\n",
      "Length of X (test): 56961 | Length of y (test): 56961\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "accuracy: 0.9500018583927636\n",
      "precision: 0.0636327861922972\n",
      "recall: 0.9162934112301201\n",
      "f1: 0.11723562337509288\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
    "\n",
    "\n",
    "print('Length of X (train): {} | Length of y (train): {}'.format(len(original_Xtrain), len(original_ytrain)))\n",
    "print('Length of X (test): {} | Length of y (test): {}'.format(len(original_Xtest), len(original_ytest)))\n",
    "\n",
    "# List to append the score and then find the average\n",
    "accuracy_lst = []\n",
    "precision_lst = []\n",
    "recall_lst = []\n",
    "f1_lst = []\n",
    "auc_lst = []\n",
    "\n",
    "# Classifier with optimal parameters\n",
    "# log_reg_sm = grid_log_reg.best_estimator_\n",
    "log_reg_sm = LogisticRegression()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "rand_log_reg = RandomizedSearchCV(LogisticRegression(), log_reg_params, n_iter=4)\n",
    "\n",
    "\n",
    "# Implementing SMOTE Technique \n",
    "# Cross Validating the right way\n",
    "# Parameters\n",
    "log_reg_params = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
    "for train, test in sss.split(original_Xtrain, original_ytrain):\n",
    "    pipeline = imbalanced_make_pipeline(SMOTE(sampling_strategy='minority'), rand_log_reg) # SMOTE happens during Cross Validation not before..\n",
    "    model = pipeline.fit(original_Xtrain[train], original_ytrain[train])\n",
    "    best_est = rand_log_reg.best_estimator_\n",
    "    prediction = best_est.predict(original_Xtrain[test])\n",
    "    \n",
    "    accuracy_lst.append(pipeline.score(original_Xtrain[test], original_ytrain[test]))\n",
    "    precision_lst.append(precision_score(original_ytrain[test], prediction))\n",
    "    recall_lst.append(recall_score(original_ytrain[test], prediction))\n",
    "    f1_lst.append(f1_score(original_ytrain[test], prediction))\n",
    "    auc_lst.append(roc_auc_score(original_ytrain[test], prediction))\n",
    "    \n",
    "print('---' * 45)\n",
    "print('')\n",
    "print(\"accuracy: {}\".format(np.mean(accuracy_lst)))\n",
    "print(\"precision: {}\".format(np.mean(precision_lst)))\n",
    "print(\"recall: {}\".format(np.mean(recall_lst)))\n",
    "print(\"f1: {}\".format(np.mean(f1_lst)))\n",
    "print('---' * 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0155c61b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    No Fraud       1.00      0.99      0.99     56863\n",
      "       Fraud       0.11      0.86      0.19        98\n",
      "\n",
      "    accuracy                           0.99     56961\n",
      "   macro avg       0.55      0.92      0.59     56961\n",
      "weighted avg       1.00      0.99      0.99     56961\n",
      "\n"
     ]
    }
   ],
   "source": [
    "labels = ['No Fraud', 'Fraud']\n",
    "smote_prediction = best_est.predict(original_Xtest)\n",
    "print(classification_report(original_ytest, smote_prediction, target_names=labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8317a129",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_score = best_est.decision_function(original_Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3877bac3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 0.74\n"
     ]
    }
   ],
   "source": [
    "average_precision = average_precision_score(original_ytest, y_score)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "      average_precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "41534144",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'OverSampling Precision-Recall curve: \\n Average Precision-Recall Score =0.74')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "\n",
    "precision, recall, _ = precision_recall_curve(original_ytest, y_score)\n",
    "\n",
    "plt.step(recall, precision, color='r', alpha=0.2,\n",
    "         where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.2,\n",
    "                 color='#F59B00')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('OverSampling Precision-Recall curve: \\n Average Precision-Recall Score ={0:0.2f}'.format(\n",
    "          average_precision), fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5f8ecf60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SMOTE Technique (OverSampling) After splitting and Cross Validating\n",
    "sm = SMOTE(random_state=42)\n",
    "# Xsm_train, ysm_train = sm.fit_sample(X_train, y_train)\n",
    "\n",
    "# This will be the data were we are going to \n",
    "Xsm_train, ysm_train = sm.fit_resample(original_Xtrain, original_ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d9e5e153",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting oversample data took :8.879320621490479 sec\n"
     ]
    }
   ],
   "source": [
    "# We Improve the score by 2% points approximately \n",
    "# Implement GridSearchCV and the other models.\n",
    "\n",
    "# Logistic Regression\n",
    "t0 = time.time()\n",
    "log_reg_sm = grid_log_reg.best_estimator_\n",
    "log_reg_sm.fit(Xsm_train, ysm_train)\n",
    "t1 = time.time()\n",
    "print(\"Fitting oversample data took :{} sec\".format(t1 - t0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "edce9ba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x864 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Logistic Regression fitted using SMOTE technique\n",
    "y_pred_log_reg = log_reg_sm.predict(X_test)\n",
    "\n",
    "# Other models fitted with UnderSampling\n",
    "y_pred_knear = knears_neighbors.predict(X_test)\n",
    "y_pred_svc = svc.predict(X_test)\n",
    "y_pred_tree = tree_clf.predict(X_test)\n",
    "\n",
    "\n",
    "log_reg_cf = confusion_matrix(y_test, y_pred_log_reg)\n",
    "kneighbors_cf = confusion_matrix(y_test, y_pred_knear)\n",
    "svc_cf = confusion_matrix(y_test, y_pred_svc)\n",
    "tree_cf = confusion_matrix(y_test, y_pred_tree)\n",
    "\n",
    "fig, ax = plt.subplots(2, 2,figsize=(22,12))\n",
    "\n",
    "\n",
    "sns.heatmap(log_reg_cf, ax=ax[0][0], annot=True, cmap=plt.cm.copper)\n",
    "ax[0, 0].set_title(\"Logistic Regression \\n Confusion Matrix\", fontsize=14)\n",
    "ax[0, 0].set_xticklabels(['', ''], fontsize=14, rotation=90)\n",
    "ax[0, 0].set_yticklabels(['', ''], fontsize=14, rotation=360)\n",
    "\n",
    "sns.heatmap(kneighbors_cf, ax=ax[0][1], annot=True, cmap=plt.cm.copper)\n",
    "ax[0][1].set_title(\"KNearsNeighbors \\n Confusion Matrix\", fontsize=14)\n",
    "ax[0][1].set_xticklabels(['', ''], fontsize=14, rotation=90)\n",
    "ax[0][1].set_yticklabels(['', ''], fontsize=14, rotation=360)\n",
    "\n",
    "sns.heatmap(svc_cf, ax=ax[1][0], annot=True, cmap=plt.cm.copper)\n",
    "ax[1][0].set_title(\"Suppor Vector Classifier \\n Confusion Matrix\", fontsize=14)\n",
    "ax[1][0].set_xticklabels(['', ''], fontsize=14, rotation=90)\n",
    "ax[1][0].set_yticklabels(['', ''], fontsize=14, rotation=360)\n",
    "\n",
    "sns.heatmap(tree_cf, ax=ax[1][1], annot=True, cmap=plt.cm.copper)\n",
    "ax[1][1].set_title(\"DecisionTree Classifier \\n Confusion Matrix\", fontsize=14)\n",
    "ax[1][1].set_xticklabels(['', ''], fontsize=14, rotation=90)\n",
    "ax[1][1].set_yticklabels(['', ''], fontsize=14, rotation=360)\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5a1df415",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.98      0.96        98\n",
      "           1       0.98      0.93      0.96        92\n",
      "\n",
      "    accuracy                           0.96       190\n",
      "   macro avg       0.96      0.96      0.96       190\n",
      "weighted avg       0.96      0.96      0.96       190\n",
      "\n",
      "KNears Neighbors:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.97      0.93        98\n",
      "           1       0.96      0.87      0.91        92\n",
      "\n",
      "    accuracy                           0.92       190\n",
      "   macro avg       0.93      0.92      0.92       190\n",
      "weighted avg       0.92      0.92      0.92       190\n",
      "\n",
      "Support Vector Classifier:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.96      0.94        98\n",
      "           1       0.96      0.92      0.94        92\n",
      "\n",
      "    accuracy                           0.94       190\n",
      "   macro avg       0.94      0.94      0.94       190\n",
      "weighted avg       0.94      0.94      0.94       190\n",
      "\n",
      "Decision Tree Classifier:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.96      0.92        98\n",
      "           1       0.95      0.86      0.90        92\n",
      "\n",
      "    accuracy                           0.91       190\n",
      "   macro avg       0.92      0.91      0.91       190\n",
      "weighted avg       0.91      0.91      0.91       190\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "\n",
    "print('Logistic Regression:')\n",
    "print(classification_report(y_test, y_pred_log_reg))\n",
    "\n",
    "print('KNears Neighbors:')\n",
    "print(classification_report(y_test, y_pred_knear))\n",
    "\n",
    "print('Support Vector Classifier:')\n",
    "print(classification_report(y_test, y_pred_svc))\n",
    "\n",
    "print('Decision Tree Classifier:')\n",
    "print(classification_report(y_test, y_pred_tree))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7f5ad50a",
   "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>Technique</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random UnderSampling</td>\n",
       "      <td>0.957895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Oversampling (SMOTE)</td>\n",
       "      <td>0.987781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Technique     Score\n",
       "0  Random UnderSampling  0.957895\n",
       "1  Oversampling (SMOTE)  0.987781"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Final Score in the test set of logistic regression\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Logistic Regression with Under-Sampling\n",
    "y_pred = log_reg.predict(X_test)\n",
    "undersample_score = accuracy_score(y_test, y_pred)\n",
    "\n",
    "\n",
    "\n",
    "# Logistic Regression with SMOTE Technique (Better accuracy with SMOTE t)\n",
    "y_pred_sm = best_est.predict(original_Xtest)\n",
    "oversample_score = accuracy_score(original_ytest, y_pred_sm)\n",
    "\n",
    "\n",
    "d = {'Technique': ['Random UnderSampling', 'Oversampling (SMOTE)'], 'Score': [undersample_score, oversample_score]}\n",
    "final_df = pd.DataFrame(data=d)\n",
    "\n",
    "# Move column\n",
    "score = final_df['Score']\n",
    "final_df.drop('Score', axis=1, inplace=True)\n",
    "final_df.insert(1, 'Score', score)\n",
    "\n",
    "# Note how high is accuracy score it can be misleading! \n",
    "final_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "2bed36fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Activation\n",
    "from keras.layers.core import Dense\n",
    "from keras.optimizers import Adam\n",
    "from keras.metrics import categorical_crossentropy\n",
    "\n",
    "n_inputs = X_train.shape[1]\n",
    "\n",
    "undersample_model = Sequential([\n",
    "    Dense(n_inputs, input_shape=(n_inputs, ), activation='relu'),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(2, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a07ae473",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_9 (Dense)             (None, 30)                930       \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 32)                992       \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 2)                 66        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,988\n",
      "Trainable params: 1,988\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "undersample_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "234fd6dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "undersample_model.compile(Adam(lr=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a0647ab0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "25/25 - 2s - loss: 0.7133 - accuracy: 0.5430 - val_loss: 0.4481 - val_accuracy: 0.7105 - 2s/epoch - 67ms/step\n",
      "Epoch 2/20\n",
      "25/25 - 0s - loss: 0.4055 - accuracy: 0.8046 - val_loss: 0.3310 - val_accuracy: 0.8684 - 266ms/epoch - 11ms/step\n",
      "Epoch 3/20\n",
      "25/25 - 0s - loss: 0.2985 - accuracy: 0.9156 - val_loss: 0.2876 - val_accuracy: 0.9013 - 295ms/epoch - 12ms/step\n",
      "Epoch 4/20\n",
      "25/25 - 0s - loss: 0.2418 - accuracy: 0.9470 - val_loss: 0.2569 - val_accuracy: 0.9079 - 269ms/epoch - 11ms/step\n",
      "Epoch 5/20\n",
      "25/25 - 0s - loss: 0.2011 - accuracy: 0.9470 - val_loss: 0.2443 - val_accuracy: 0.9013 - 264ms/epoch - 11ms/step\n",
      "Epoch 6/20\n",
      "25/25 - 0s - loss: 0.1705 - accuracy: 0.9520 - val_loss: 0.2386 - val_accuracy: 0.9013 - 290ms/epoch - 12ms/step\n",
      "Epoch 7/20\n",
      "25/25 - 0s - loss: 0.1478 - accuracy: 0.9586 - val_loss: 0.2370 - val_accuracy: 0.9013 - 275ms/epoch - 11ms/step\n",
      "Epoch 8/20\n",
      "25/25 - 0s - loss: 0.1324 - accuracy: 0.9553 - val_loss: 0.2387 - val_accuracy: 0.9013 - 284ms/epoch - 11ms/step\n",
      "Epoch 9/20\n",
      "25/25 - 0s - loss: 0.1188 - accuracy: 0.9603 - val_loss: 0.2416 - val_accuracy: 0.9013 - 271ms/epoch - 11ms/step\n",
      "Epoch 10/20\n",
      "25/25 - 0s - loss: 0.1091 - accuracy: 0.9652 - val_loss: 0.2425 - val_accuracy: 0.9013 - 279ms/epoch - 11ms/step\n",
      "Epoch 11/20\n",
      "25/25 - 0s - loss: 0.1016 - accuracy: 0.9669 - val_loss: 0.2530 - val_accuracy: 0.9079 - 281ms/epoch - 11ms/step\n",
      "Epoch 12/20\n",
      "25/25 - 0s - loss: 0.0951 - accuracy: 0.9669 - val_loss: 0.2625 - val_accuracy: 0.9145 - 278ms/epoch - 11ms/step\n",
      "Epoch 13/20\n",
      "25/25 - 0s - loss: 0.0899 - accuracy: 0.9685 - val_loss: 0.2666 - val_accuracy: 0.9145 - 301ms/epoch - 12ms/step\n",
      "Epoch 14/20\n",
      "25/25 - 0s - loss: 0.0864 - accuracy: 0.9685 - val_loss: 0.2704 - val_accuracy: 0.9079 - 281ms/epoch - 11ms/step\n",
      "Epoch 15/20\n",
      "25/25 - 0s - loss: 0.0812 - accuracy: 0.9685 - val_loss: 0.2754 - val_accuracy: 0.9079 - 389ms/epoch - 16ms/step\n",
      "Epoch 16/20\n",
      "25/25 - 0s - loss: 0.0772 - accuracy: 0.9719 - val_loss: 0.2866 - val_accuracy: 0.9145 - 297ms/epoch - 12ms/step\n",
      "Epoch 17/20\n",
      "25/25 - 0s - loss: 0.0719 - accuracy: 0.9752 - val_loss: 0.2838 - val_accuracy: 0.9145 - 286ms/epoch - 11ms/step\n",
      "Epoch 18/20\n",
      "25/25 - 0s - loss: 0.0688 - accuracy: 0.9752 - val_loss: 0.2962 - val_accuracy: 0.9211 - 284ms/epoch - 11ms/step\n",
      "Epoch 19/20\n",
      "25/25 - 0s - loss: 0.0655 - accuracy: 0.9768 - val_loss: 0.3029 - val_accuracy: 0.9211 - 283ms/epoch - 11ms/step\n",
      "Epoch 20/20\n",
      "25/25 - 0s - loss: 0.0629 - accuracy: 0.9818 - val_loss: 0.3051 - val_accuracy: 0.9145 - 300ms/epoch - 12ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1bca3696a70>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "undersample_model.fit(X_train, y_train, validation_split=0.2, batch_size=25, epochs=20, shuffle=True, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "381a7b83",
   "metadata": {},
   "outputs": [],
   "source": [
    "undersample_predictions = undersample_model.predict(original_Xtest, batch_size=200, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "51fa9a98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1781/1781 [==============================] - 8s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "undersample_fraud_predictions = np.argmax(undersample_model.predict(original_Xtest), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "900f00bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "# Create a confusion matrix\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title, fontsize=14)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "a06b5465",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[55127  1736]\n",
      " [    7    91]]\n",
      "Confusion matrix, without normalization\n",
      "[[56863     0]\n",
      " [    0    98]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "undersample_cm = confusion_matrix(original_ytest, undersample_fraud_predictions)\n",
    "actual_cm = confusion_matrix(original_ytest, original_ytest)\n",
    "labels = ['No Fraud', 'Fraud']\n",
    "\n",
    "fig = plt.figure(figsize=(16,8))\n",
    "\n",
    "fig.add_subplot(221)\n",
    "plot_confusion_matrix(undersample_cm, labels, title=\"Random UnderSample \\n Confusion Matrix\", cmap=plt.cm.Reds)\n",
    "\n",
    "fig.add_subplot(222)\n",
    "plot_confusion_matrix(actual_cm, labels, title=\"Confusion Matrix \\n (with 100% accuracy)\", cmap=plt.cm.Greens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "e2a3590e",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_inputs = Xsm_train.shape[1]\n",
    "\n",
    "oversample_model = Sequential([\n",
    "    Dense(n_inputs, input_shape=(n_inputs, ), activation='relu'),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(2, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "be4d24b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "oversample_model.compile(Adam(lr=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "94ebe440",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1214/1214 - 12s - loss: 0.0811 - accuracy: 0.9696 - val_loss: 0.0391 - val_accuracy: 0.9877 - 12s/epoch - 10ms/step\n",
      "Epoch 2/20\n",
      "1214/1214 - 10s - loss: 0.0172 - accuracy: 0.9955 - val_loss: 0.0142 - val_accuracy: 0.9985 - 10s/epoch - 9ms/step\n",
      "Epoch 3/20\n",
      "1214/1214 - 10s - loss: 0.0087 - accuracy: 0.9980 - val_loss: 0.0067 - val_accuracy: 0.9997 - 10s/epoch - 8ms/step\n",
      "Epoch 4/20\n",
      "1214/1214 - 10s - loss: 0.0058 - accuracy: 0.9988 - val_loss: 0.0025 - val_accuracy: 1.0000 - 10s/epoch - 9ms/step\n",
      "Epoch 5/20\n",
      "1214/1214 - 11s - loss: 0.0043 - accuracy: 0.9991 - val_loss: 0.0038 - val_accuracy: 0.9998 - 11s/epoch - 9ms/step\n",
      "Epoch 6/20\n",
      "1214/1214 - 11s - loss: 0.0034 - accuracy: 0.9993 - val_loss: 0.0033 - val_accuracy: 0.9998 - 11s/epoch - 9ms/step\n",
      "Epoch 7/20\n",
      "1214/1214 - 11s - loss: 0.0028 - accuracy: 0.9994 - val_loss: 7.8580e-04 - val_accuracy: 1.0000 - 11s/epoch - 9ms/step\n",
      "Epoch 8/20\n",
      "1214/1214 - 10s - loss: 0.0025 - accuracy: 0.9995 - val_loss: 5.8283e-04 - val_accuracy: 1.0000 - 10s/epoch - 8ms/step\n",
      "Epoch 9/20\n",
      "1214/1214 - 10s - loss: 0.0021 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 1.0000 - 10s/epoch - 9ms/step\n",
      "Epoch 10/20\n",
      "1214/1214 - 7s - loss: 0.0021 - accuracy: 0.9996 - val_loss: 0.0011 - val_accuracy: 0.9999 - 7s/epoch - 6ms/step\n",
      "Epoch 11/20\n",
      "1214/1214 - 6s - loss: 0.0016 - accuracy: 0.9996 - val_loss: 6.3940e-04 - val_accuracy: 1.0000 - 6s/epoch - 5ms/step\n",
      "Epoch 12/20\n",
      "1214/1214 - 5s - loss: 0.0018 - accuracy: 0.9996 - val_loss: 0.0016 - val_accuracy: 0.9999 - 5s/epoch - 4ms/step\n",
      "Epoch 13/20\n",
      "1214/1214 - 5s - loss: 0.0015 - accuracy: 0.9997 - val_loss: 8.1393e-04 - val_accuracy: 1.0000 - 5s/epoch - 4ms/step\n",
      "Epoch 14/20\n",
      "1214/1214 - 5s - loss: 0.0012 - accuracy: 0.9997 - val_loss: 7.2440e-04 - val_accuracy: 1.0000 - 5s/epoch - 4ms/step\n",
      "Epoch 15/20\n",
      "1214/1214 - 5s - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.0128 - val_accuracy: 0.9990 - 5s/epoch - 4ms/step\n",
      "Epoch 16/20\n",
      "1214/1214 - 5s - loss: 0.0011 - accuracy: 0.9997 - val_loss: 3.1773e-04 - val_accuracy: 1.0000 - 5s/epoch - 5ms/step\n",
      "Epoch 17/20\n",
      "1214/1214 - 6s - loss: 0.0010 - accuracy: 0.9998 - val_loss: 4.2414e-04 - val_accuracy: 1.0000 - 6s/epoch - 5ms/step\n",
      "Epoch 18/20\n",
      "1214/1214 - 7s - loss: 0.0011 - accuracy: 0.9997 - val_loss: 9.6542e-04 - val_accuracy: 1.0000 - 7s/epoch - 6ms/step\n",
      "Epoch 19/20\n",
      "1214/1214 - 6s - loss: 0.0011 - accuracy: 0.9998 - val_loss: 4.1096e-04 - val_accuracy: 1.0000 - 6s/epoch - 5ms/step\n",
      "Epoch 20/20\n",
      "1214/1214 - 6s - loss: 9.4218e-04 - accuracy: 0.9998 - val_loss: 0.0049 - val_accuracy: 0.9994 - 6s/epoch - 5ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1bd97c600a0>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oversample_model.fit(Xsm_train, ysm_train, validation_split=0.2, batch_size=300, epochs=20, shuffle=True, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "dac0bf31",
   "metadata": {},
   "outputs": [],
   "source": [
    "oversample_predictions = oversample_model.predict(original_Xtest, batch_size=200, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "97a898d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1781/1781 [==============================] - 4s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "oversample_fraud_predictions = np.argmax(oversample_model.predict(original_Xtest), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "55d970cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[56857     6]\n",
      " [   34    64]]\n",
      "Confusion matrix, without normalization\n",
      "[[56863     0]\n",
      " [    0    98]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "oversample_smote = confusion_matrix(original_ytest, oversample_fraud_predictions)\n",
    "actual_cm = confusion_matrix(original_ytest, original_ytest)\n",
    "labels = ['No Fraud', 'Fraud']\n",
    "\n",
    "fig = plt.figure(figsize=(16,8))\n",
    "\n",
    "fig.add_subplot(221)\n",
    "plot_confusion_matrix(oversample_smote, labels, title=\"OverSample (SMOTE) \\n Confusion Matrix\", cmap=plt.cm.Oranges)\n",
    "\n",
    "fig.add_subplot(222)\n",
    "plot_confusion_matrix(actual_cm, labels, title=\"Confusion Matrix \\n (with 100% accuracy)\", cmap=plt.cm.Greens)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}