Capstone Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of contents\n",
    "- [Exploraroty Data Analysis](#EDA)\n",
    "\n",
    "- [Balancing the classes](#Balancing)\n",
    "\n",
    "    - [Random Undersampling](#rus)\n",
    "    - [Random Oversampling](#ros) \n",
    "    - [SMOTE](#smote)\n",
    "        - [Checking the difference between random oversampling and SMOTE](#rosxsmote)\n",
    "\n",
    "- [Metrics](#metrics)\n",
    "\n",
    "- [Models and Results](#models)\n",
    "    - [Results using random undersampling](#models-rus)\n",
    "    - [Results using random oversampling](#models-ros)\n",
    "    - [Results using SMOTE](#models-smote)\n",
    "\n",
    "- [Conclusions](#conclusions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
    "from collections import Counter\n",
    "from scipy import interp\n",
    "import itertools\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "table = pd.read_csv('creditcard.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='EDA'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.isnull().sum().all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Null elements are not present in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
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       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <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",
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       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
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       "   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]"
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    "table.head()"
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   "cell_type": "code",
   "execution_count": 5,
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   "outputs": [
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       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
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       "      <th>284802</th>\n",
       "      <td>172786.0</td>\n",
       "      <td>-11.881118</td>\n",
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       "      <td>-9.834783</td>\n",
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       "      <td>-2.606837</td>\n",
       "      <td>-4.918215</td>\n",
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       "      <td>1.914428</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.943651</td>\n",
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       "      <td>0.77</td>\n",
       "      <td>0</td>\n",
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       "      <th>284803</th>\n",
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       "      <td>-0.732789</td>\n",
       "      <td>-0.055080</td>\n",
       "      <td>2.035030</td>\n",
       "      <td>-0.738589</td>\n",
       "      <td>0.868229</td>\n",
       "      <td>1.058415</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>0.294869</td>\n",
       "      <td>0.584800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214205</td>\n",
       "      <td>0.924384</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>-1.016226</td>\n",
       "      <td>-0.606624</td>\n",
       "      <td>-0.395255</td>\n",
       "      <td>0.068472</td>\n",
       "      <td>-0.053527</td>\n",
       "      <td>24.79</td>\n",
       "      <td>0</td>\n",
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       "      <th>284804</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>1.919565</td>\n",
       "      <td>-0.301254</td>\n",
       "      <td>-3.249640</td>\n",
       "      <td>-0.557828</td>\n",
       "      <td>2.630515</td>\n",
       "      <td>3.031260</td>\n",
       "      <td>-0.296827</td>\n",
       "      <td>0.708417</td>\n",
       "      <td>0.432454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.232045</td>\n",
       "      <td>0.578229</td>\n",
       "      <td>-0.037501</td>\n",
       "      <td>0.640134</td>\n",
       "      <td>0.265745</td>\n",
       "      <td>-0.087371</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>-0.026561</td>\n",
       "      <td>67.88</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>-0.240440</td>\n",
       "      <td>0.530483</td>\n",
       "      <td>0.702510</td>\n",
       "      <td>0.689799</td>\n",
       "      <td>-0.377961</td>\n",
       "      <td>0.623708</td>\n",
       "      <td>-0.686180</td>\n",
       "      <td>0.679145</td>\n",
       "      <td>0.392087</td>\n",
       "      <td>...</td>\n",
       "      <td>0.265245</td>\n",
       "      <td>0.800049</td>\n",
       "      <td>-0.163298</td>\n",
       "      <td>0.123205</td>\n",
       "      <td>-0.569159</td>\n",
       "      <td>0.546668</td>\n",
       "      <td>0.108821</td>\n",
       "      <td>0.104533</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>172792.0</td>\n",
       "      <td>-0.533413</td>\n",
       "      <td>-0.189733</td>\n",
       "      <td>0.703337</td>\n",
       "      <td>-0.506271</td>\n",
       "      <td>-0.012546</td>\n",
       "      <td>-0.649617</td>\n",
       "      <td>1.577006</td>\n",
       "      <td>-0.414650</td>\n",
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       "      <td>0.261057</td>\n",
       "      <td>0.643078</td>\n",
       "      <td>0.376777</td>\n",
       "      <td>0.008797</td>\n",
       "      <td>-0.473649</td>\n",
       "      <td>-0.818267</td>\n",
       "      <td>-0.002415</td>\n",
       "      <td>0.013649</td>\n",
       "      <td>217.00</td>\n",
       "      <td>0</td>\n",
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       "            Time         V1         V2        V3        V4        V5  \\\n",
       "284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
       "284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
       "284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
       "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
       "284806  172792.0  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n",
       "\n",
       "              V6        V7        V8        V9  ...       V21       V22  \\\n",
       "284802 -2.606837 -4.918215  7.305334  1.914428  ...  0.213454  0.111864   \n",
       "284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \n",
       "284804  3.031260 -0.296827  0.708417  0.432454  ...  0.232045  0.578229   \n",
       "284805  0.623708 -0.686180  0.679145  0.392087  ...  0.265245  0.800049   \n",
       "284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
       "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
       "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
       "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
       "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n",
       "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
       "\n",
       "        Class  \n",
       "284802      0  \n",
       "284803      0  \n",
       "284804      0  \n",
       "284805      0  \n",
       "284806      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "table.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      "Time      284807 non-null float64\n",
      "V1        284807 non-null float64\n",
      "V2        284807 non-null float64\n",
      "V3        284807 non-null float64\n",
      "V4        284807 non-null float64\n",
      "V5        284807 non-null float64\n",
      "V6        284807 non-null float64\n",
      "V7        284807 non-null float64\n",
      "V8        284807 non-null float64\n",
      "V9        284807 non-null float64\n",
      "V10       284807 non-null float64\n",
      "V11       284807 non-null float64\n",
      "V12       284807 non-null float64\n",
      "V13       284807 non-null float64\n",
      "V14       284807 non-null float64\n",
      "V15       284807 non-null float64\n",
      "V16       284807 non-null float64\n",
      "V17       284807 non-null float64\n",
      "V18       284807 non-null float64\n",
      "V19       284807 non-null float64\n",
      "V20       284807 non-null float64\n",
      "V21       284807 non-null float64\n",
      "V22       284807 non-null float64\n",
      "V23       284807 non-null float64\n",
      "V24       284807 non-null float64\n",
      "V25       284807 non-null float64\n",
      "V26       284807 non-null float64\n",
      "V27       284807 non-null float64\n",
      "V28       284807 non-null float64\n",
      "Amount    284807 non-null float64\n",
      "Class     284807 non-null int64\n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "table.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>1.165980e-15</td>\n",
       "      <td>3.416908e-16</td>\n",
       "      <td>-1.373150e-15</td>\n",
       "      <td>2.086869e-15</td>\n",
       "      <td>9.604066e-16</td>\n",
       "      <td>1.490107e-15</td>\n",
       "      <td>-5.556467e-16</td>\n",
       "      <td>1.177556e-16</td>\n",
       "      <td>-2.406455e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.656562e-16</td>\n",
       "      <td>-3.444850e-16</td>\n",
       "      <td>2.578648e-16</td>\n",
       "      <td>4.471968e-15</td>\n",
       "      <td>5.340915e-16</td>\n",
       "      <td>1.687098e-15</td>\n",
       "      <td>-3.666453e-16</td>\n",
       "      <td>-1.220404e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  1.165980e-15  3.416908e-16 -1.373150e-15  2.086869e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   9.604066e-16  1.490107e-15 -5.556467e-16  1.177556e-16 -2.406455e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.656562e-16 -3.444850e-16  2.578648e-16  4.471968e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   5.340915e-16  1.687098e-15 -3.666453e-16 -1.220404e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(['Non-Fraud','Fraud'], table['Class'].value_counts(), color=['b','r'])\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Number of transactions')\n",
    "plt.annotate('{}\\n({:.4}%)'.format(table['Class'].value_counts()[0], \n",
    "                                         table['Class'].value_counts()[0]/table['Class'].count()*100),\n",
    "             (0.20, 0.45), xycoords='axes fraction')\n",
    "plt.annotate('{}\\n({:.4}%)'.format(table['Class'].value_counts()[1], \n",
    "                                         table['Class'].value_counts()[1]/table['Class'].count()*100),\n",
    "             (0.70, 0.45), xycoords='axes fraction')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has only two classes: fraud or non-fraud. The classes are highly imbalanced with 99.83% of observations belonging to non-fraudulent transactions and only 0.17% of observations labeled as fraudulent. This issue will be adressed later with a method for balancing classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CsBq4Gjirqq5McnKSI7pupwIPBD6b5NIkq6aYnSRpnunzIz6q6lzg3Eltbxq6/LQ+71+SNHd5JAlJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkxb2OfMkhwHvA8aAD1XVKZOufwDwCeBPgJuBF1fVtX3Vs/KkL/Y1a0mad6495dm9zr+3LagkY8BpwLOAfYBjkuwzqdtxwC+q6g+B9wDv6Ksew0mStq2+16t9fsR3ALCuqq6pqjuAM4EjJ/U5Evh4d/lzwJ8lSY81SZLmiD4Dajlw3dD0+q5tZJ+qugv4JfCQyTNKcnySNUnWbNiwoadyJUkt6TOgRm0J1Vb0oapOr6rxqhpftmzZNilOktS2PgNqPbBiaHpP4Iap+iRZCOwC/LzHmiRJc0SfAXURsHeSRyRZDBwNrJrUZxXw8u7yC4CvVdUmW1DbQt97m0jSfNP3erW33cyr6q4kJwCrGexm/pGqujLJycCaqloFfBj4ZJJ1DLacju6rHjCkJGku6fV3UFV1LnDupLY3DV3+LfDCPmuQJM1NHklCktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1KT0dGSh3iTZAPzo95zNbsBN26Cc+xvHZTTHZTTHZTTHZbThcXl4VU175O85F1DbQpI1VTU+23W0xnEZzXEZzXEZzXEZbWvGxY/4JElNMqAkSU2arwF1+mwX0CjHZTTHZTTHZTTHZbQtHpd5+R2UJKl983ULSpLUOANKktSkeRVQSQ5LsjbJuiQnzXY9synJR5LcmOR7Q227Jvlykh92/x88mzVub0lWJDkvydVJrkzymq59vo/LDkm+k+Syblz+rmt/RJILu3H5TJLFs13rbEgyluSSJOd0044LkOTaJFckuTTJmq5ti5aleRNQScaA04BnAfsAxyTZZ3armlUfAw6b1HYS8NWq2hv4ajc9n9wFvK6qHgscBPxl9xqZ7+PyO+CpVfUEYD/gsCQHAe8A3tONyy+A42axxtn0GuDqoWnH5V6HVtV+Q79/2qJlad4EFHAAsK6qrqmqO4AzgSNnuaZZU1XfAH4+qflI4OPd5Y8DR23XomZZVf2kqr7bXf41g5XOchyXqqpbu8lF3V8BTwU+17XPu3EBSLIn8GzgQ910cFw2Z4uWpfkUUMuB64am13dtutceVfUTGKysgd1nuZ5Zk2QlsD9wIY7LxMdYlwI3Al8G/hW4paru6rrM1+XpvcDfAvd00w/BcZlQwJeSXJzk+K5ti5alhT0X2JKMaHMfe20iyQOBfwJeW1W/Grwpnt+q6m5gvyRLgS8Ajx3VbftWNbuSPAe4saouTnLIRPOIrvNqXIYcXFU3JNkd+HKS72/pDObTFtR6YMXQ9J7ADbNUS6t+luShAN3/G2e5nu0uySIG4fSpqvp81zzvx2VCVd0CnM/gO7qlSSbe5M7H5elg4Igk1zL4yuCpDLao5vu4AFBVN3T/b2TwpuYAtnBZmk8BdRGwd7eHzWLgaGDVLNfUmlXAy7vLLwf+eRZr2e667w8+DFxdVe8eumq+j8uybsuJJEuApzH4fu484AVdt3k3LlX1+qras6pWMliffK2qXso8HxeAJDsl2XniMvAM4Hts4bI0r44kkeRwBu9wxoCPVNXbZrmkWZPkDOAQBofA/xnwZuBs4CxgL+DHwAuravKOFPdbSZ4M/AtwBfd+p/BfGXwPNZ/H5fEMvtAeY/Cm9qyqOjnJIxlsOewKXAIcW1W/m71KZ0/3Ed9/qarnOC7QjcEXusmFwKer6m1JHsIWLEvzKqAkSXPHfPqIT5I0hxhQkqQmGVCSpCYZUJKkJhlQkqQmGVDabpI8pDuy8aVJfprk+qHpb23nWs5IcnmSv57UflRrBxFOsjLJS4amx5P8z2007yT5WpIHddNb9DwkOT/J+PQ9t40kJyR55fa6P82u+XSoI82yqrqZwdGwSfIW4Naqetf2riPJHwBPqqqHj7j6KOAc4KoRt1s4dIy17Wkl8BLg0wBVtQZYs43mfThwWVX9qpv3k7bRfH8vSca6wytN9hHgm8BHt3NJmgVuQakJSW7t/h+S5OtJzkrygySnJHlpdz6iK5I8quu3LMk/Jbmo+zt4xDx3SPLR7naXJDm0u+pLwO7dlttThvo/CTgCOLW77lHdFsJ/T/J14DVJntud6+eSJF9Jskd327dkcI6t85Nck+TVXftOSb6YwbmUvpfkxV37m7q6v5fk9O4oFiT5w26+lyX5bvd4TwGe0tX0190YTZx7aNckZ3dbgxd0P6qdsp4RXsrQr/knPQ/nJ/lcku8n+dREjSO8sHt+fjAxnlONfZJXJHn/0P2d0/3IlSS3Jjk5yYXAE7vn/qrusb0LoKpuA65NcsAUtej+pKr882+7/wFvYfDL+4npW7v/hwC3AA8FHgBcD/xdd91rgPd2lz8NPLm7vBeDwxNNvo/XAR/tLv8Rg1+u78Bgi+R7U9T1MeAFQ9PnAx8Ymn4w9/7A/S+A/zH0eL7V1bwbcDOD01L8OfDBodvv0v3fdajtk8Bzu8sXAs/rLu8A7NiNyTlD/TdOA/8AvLm7/FTg0s3VM+Lx/gjYeYrn4ZcMjiW3APj2xHhPuv35Q2NwOPCVacb+FcD7h25/DnBId7mAF02MD7B2aKyXDt3mDQzO2zXrr2P/+v1zC0otuqgG52b6HYPTOnypa7+CQbjA4Hhw78/gFBCrgAelO/bXkCczWPlTVd9nsDJ+9FbU85mhy3sCq5NcAZwIPG7oui9W1e+q6iYGB8Hco6v5aUnekeQpVfXLru+h3ZbYFQyC5XFd/cur6gtdzb+twRbD5gw/xq8BD0myy2bqmWzXGpz7apTvVNX6qroHuJR7x36yiYPqXjzUZ2vG/m4GB+oF+BXwW+BDSZ4PDI/DjcDDppmX7gcMKLVo+Lhl9wxN38O935suAJ5Yg7N17ldVy0esaLfVeTJ+M3T5HxhsAewL/CcGWwWj6r4bWFhVPwD+hEFQvb37aG8H4AMMttT2BT7YzWdr6t3c6R02qWdE37uSTLUemMnth/sN95nqsdzFfdc7w+P32+q+d6rBd30HMAiso4D/N+k2t08xf92PGFCaq74EnDAxkWS/EX2+weA7FpI8msFHgWunme+vgclbYsN2YfCxI9x7VOYpJXkYcFtV/SPwLuDfce9K+aYMzj31AoAa7KiwPslR3W0fkGTHaWoafoyHADd185mptcAjt6D/TE019tcyOK/UgiQrGITQJrpx2aWqzgVeS7dzTefRDI6Mrfs5A0pz1auB8e4L9KuA/zyizweAse5jtM8Ar6jpjyp9JnBi98X+o0Zc/xbgs0n+BbhpBnXuC3yn+yjyDcBba3BOpQ8y2Ko6m8GpYCa8DHh1kssZfIf0B8DlDLZ0Lsuk3eK7esa7/qcwg9Cc5IsMvm/a1qYa+28C/8bgsb8L+O4Ut98ZOKd7XF8Hhh/3wcBXeqhZjfFo5tI8lsFJ4z5RVU+f7VpmIsn+wN9U1ctmuxb1zy0oaR6rqp8AH0z3Q905YDfgv812Edo+3IKSJDXJLShJUpMMKElSkwwoSVKTDChJUpMMKElSk/4/2tv/HWFymAQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(table['Time']/(60*60), table['Class'])\n",
    "plt.xlabel('Time of transaction (in hours)')\n",
    "plt.ylabel('Class')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data was collected over the period of 2 days and, apparently, the 'Time' variable isn't a good predictor for frauds. As seen above, the pattern for both non-fradulent and fraudulent transactions seems to be random regarding the hour of the day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers below the lower bound:  0  (0.0%)\n",
      "Number of outliers above the upper bound:  31904  (11.2%)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAD8CAYAAAC/1zkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAGr9JREFUeJzt3X10XPWd3/H3x7KMwYHYLI4LfojJxmnkqAcv0QJt3CTKggNsFxOaLNinsTcocdqC6hR6CGF2C82uvFnaOASRQMnaAbZYmO6GYhpY8LLaEDXhQSYGbCsEL4+KvUYEwoOJQJa//WOuzNjI0vXojq7G+rzOmTMz3/ndO985B/Hxvb/7oIjAzMwsCxPybsDMzA4fDhUzM8uMQ8XMzDLjUDEzs8w4VMzMLDMOFTMzy4xDxczMMuNQMTOzzDhUzMwsMxPzbmC0HXfccTF37ty82zAzqyqbNm16KSKmDzdu3IXK3Llz6ezszLsNM7OqIum5NOO8+8vMzDLjUDEzs8w4VMzMLDMOFTMzy4xDxczMMuNQMctZW1sb9fX11NTUUF9fT1tbW94tmZVt3B1SbDaWtLW1USgUWLNmDQsXLqSjo4OmpiYAlixZknN3ZodO4+12wg0NDeHzVGysqK+vp7W1lcbGxn219vZ2mpub2bJlS46dme1P0qaIaBh2nEPFLD81NTX09vZSW1u7r9bX18fkyZPp7+/PsTOz/aUNFc+pmOWorq6Ojo6O/WodHR3U1dXl1JHZyFQsVCTNltQuqUvSVkkrk/pVkn4paXPyOLtkma9J2i7pSUmfLqmfmdS2S7q8pH6ipIckPSVpvaRJlfo9ZpVQKBRoamqivb2dvr4+2tvbaWpqolAo5N2aWVkqOVG/B7g0Ih6VdDSwSdLG5LNvRcT/KB0saT5wAfAR4ATg7yR9KPn4O8AZQDfwiKQNEbEN+ItkXbdJugFoAq6v4G8yy9TAZHxzczNdXV3U1dXR0tLiSXqrWhULlYjYCexMXr8uqQuYOcQii4HbIuIt4BlJ24FTks+2R8TTAJJuAxYn6/sUsDQZczNwFQ4VqzJLlixxiNhhY1TmVCTNBX4HeCgpXSzpcUlrJU1LajOBF0oW605qB6v/FvDriNhzQN3MzHJS8VCR9B7gb4CvRMRrFLckfhtYQHFL5psDQwdZPMqoD9bDCkmdkjp7enoO8ReYmVlaFQ0VSbUUA+XWiPgBQETsioj+iNgLfI93dnF1A7NLFp8F7Bii/hIwVdLEA+rvEhE3RkRDRDRMnz7sPWbMzKxMlTz6S8AaoCsiVpfUjy8Z9hlg4AyvDcAFko6QdCIwD3gYeASYlxzpNYniZP6GKJ5g0w58Nll+OXBnpX6PmZkNr5JHf30M+DzwhKTNSe0KYImkBRR3VT0LfBkgIrZKuh3YRvHIsYsioh9A0sXAvUANsDYitibr+ypwm6Q/A35GMcTMzCwnPqPezMyG5TPqzcxs1DlUzMwsMw4VMzPLjEPFzMwy41AxM7PMOFTMzCwzDhUzM8uMQ8XMzDLjUDEzs8w4VMzMLDMOFTMzy4xDxczMMuNQMTOzzDhUzMwsMw4VMzPLjEPFzMwy41AxM7PMOFTMzCwzDhUzM8uMQ8XMzDLjUDEzs8w4VMzMLDMOFTMzy4xDxczMMuNQMTOzzDhUzMwsMw4VMzPLjEPFzMwy41AxM7PMVCxUJM2W1C6pS9JWSSuT+rGSNkp6KnmeltQl6VpJ2yU9LunkknUtT8Y/JWl5Sf2jkp5IlrlWkir1e8zMbHiV3FLZA1waEXXAacBFkuYDlwP3R8Q84P7kPcBZwLzksQK4HoohBFwJnAqcAlw5EETJmBUly51Zwd9jZmbDqFioRMTOiHg0ef060AXMBBYDNyfDbgbOTV4vBm6JogeBqZKOBz4NbIyIlyPiFWAjcGby2TER8dOICOCWknWZmVkORmVORdJc4HeAh4AZEbETisEDvC8ZNhN4oWSx7qQ2VL17kLqZmeWk4qEi6T3A3wBfiYjXhho6SC3KqA/WwwpJnZI6e3p6hmvZbFS1tbVRX19PTU0N9fX1tLW15d2SWdkqGiqSaikGyq0R8YOkvCvZdUXy/GJS7wZmlyw+C9gxTH3WIPV3iYgbI6IhIhqmT58+sh9llqG2tjYKhQKtra309vbS2tpKoVBwsFjVquTRXwLWAF0Rsbrkow3AwBFcy4E7S+rLkqPATgNeTXaP3QsskjQtmaBfBNybfPa6pNOS71pWsi6zqtDS0sKaNWtobGyktraWxsZG1qxZQ0tLS96tmZVFxTnuCqxYWgj8GHgC2JuUr6A4r3I7MAd4HvhcRLycBMN1FI/gehP4QkR0Juu6MFkWoCUivp/UG4CbgCOBe4DmGOYHNTQ0RGdnZ1Y/02xEampq6O3tpba2dl+tr6+PyZMn09/fn2NnZvuTtCkiGoYbN7FSDUREB4PPewD83iDjA7joIOtaC6wdpN4J1I+gTbNc1dXV0dHRQWNj475aR0cHdXV1OXZlVr6KhYqZDa9QKHD++eczZcoUnn/+eebMmcPu3bv59re/nXdrZmUZdk5F0n1pamY2MpXaFW02mg4aKpImSToGmCHpaEnHJI9ZFOdDzGyEWlpaWL9+Pc888wx79+7lmWeeYf369Z6ot6o11O6vi4BLKJ6cuJV35kdeA26ocF9m40JXVxcLFy7cr7Zw4UK6urpy6shsZA66pRIR34qI2cBXI2JORMxOHh+JiGtGsUezw9bARH0pT9RbNRt2TiUirpF0iqQ/lLR04DEazZkd7gqFAk1NTbS3t9PX10d7eztNTU0UCoW8WzMry7BHf0m6CZgPbAYGDpwPYF3l2jIbH5YsWQJAc3MzXV1d1NXV0dLSsq9uVm2GPflR0s+B+RGxd8iBVcInP5qZHbq0Jz+muUzLVuC4kbdkZmaHuzQnP74X6JL0IPDWQDEizqtYV2ZmVpXShMqfV7wLMzM7LAwbKhFx/2g0YmZm1S/N0V+v887NryYCNcBbEXFMJRszM7Pqk2ZL5eiB15ImAOcBJ1WyKTMzq06HdJOuiNgbEX8NnFGhfszMrIql2f11TsnbCUADB79PipmZjWNpjv76XMnrPcCzwOKKdGNmZlUtzZzK50ejETMzq35pbtJ1gqT/LWln8lgv6YTRaM7MzKpLmon67wP3AXOTx8akZmZmtp80oTIjIr4XEW8lj78EZlS6MTMzqz5pQuVlSRfoHecDL1e6MTMzqz5pQuVCYBnwEtADfB5oqmRTZmZWndIc/fUscHblWzEzs2qX5uTHOcDFFCfp9433pe/NzOxAaU5+3ADcQvGor8Pi7o9mZlYZaULl7YhYXfFOzMys6qUJlVZJfwzcy/53fny8Yl2ZmVlVShMqHwK+CJzFO7u/Avh4pZoyM7PqlCZU/hCYGxFvDTvSzMzGtTTnqTwOHD3sqANIWivpRUlbSmpXSfqlpM3J4+ySz74mabukJyV9uqR+ZlLbLunykvqJkh6S9FRyPbJJh9qjmZllK02o/Bbwc0k/lPSDgUeK5W4Czhyk/q2IWJA87gaQNB+4APhIssx3JdVIqgG+Q3HX23xgSTIW4C+Sdc0DXsEnZJqZ5S7N7q+WclYcEQ9Impty+GLgtmQX2zOStgOnJJ9tj4inASTdBiyW1AV8CliajLkZuAq4vpxezcwsG2nOqL8/4++8WNIyoBO4NCJeAWYCD5aM6U5qAC8cUD+V4tbTryNizyDjzcwsJ2nup/K7kh6U9KqkXklvSXqtzO+7HvhtYAGwE/jmwNcMMjbKqA9K0gpJnZI6e3p6Dq1jMzNLLc2cyneB5cDTFCfsLwauKefLImJXRPRHxF7ge7yzi6sbmF0ydBawY4j6S8BUSRMPqB/se2+MiIaIaJg+fXo5rZuZWQppQmVCRDwJTIyIvoj4HnB6OV8m6fiSt58BBo4M2wBcIOkISScC84CHgUeAecmRXpMoTuZviIgA2oHPJssvB+4spyczM8tOmon63cn/0B+TtIribqv3DLeQpDbgk8BxkrqBK4FPSlpAcVfVs8CXASJiq6TbgW3AHuCiiOhP1nMxxbP5a4C1EbE1+YqvArdJ+jPgZ8CaVL/YzMwqRsV/9A8xQPoAxV1Lk4FLgfcC10XELyrfXvYaGhqis7Mz7zbMzKqKpE0R0TDcuDRHfz2dvOwF/mSkjZmZ2eErzZyKmZlZKg4VMzPLjEPFzMwyk+Z2wh8ELuHdtxNeVLm2zMysGqU5pPivKR6u+7+A/sq2Y2Zm1SxNqOyNiNaKd2JmZlUvzZzKncm1s6ZLOmbgUfHOzMys6qQJlS9SPD/lUWBr8tgy5BJmllpbWxv19fXU1NRQX19PW1tb3i2ZlS3NyY+zhxtjZuVpa2ujUCiwZs0aFi5cSEdHB01NxfvNLVmyJOfuzA5dmsu0TARWAB9PSv8A/GXJvUyqii/TYmNJfX09ra2tNDY27qu1t7fT3NzMli3eIWBjR9rLtKQJlf8JTAFuSUr/DuiNiBUj7jIHDhUbS2pqaujt7aW2tnZfra+vj8mTJ9Pf74MtbezI7NpfwGkRcVLJ+/skPVZ+a2Y2oK6ujo6Ojv22VDo6Oqirq8uxK7PypZmo31t6r/nk9d7KtGM2vhQKBZqammhvb6evr4/29naampooFAp5t2ZWljRbKpcBD0j6BcXb+H4QaKpoV2bjxMBkfHNzM11dXdTV1dHS0uJJeqtaaY7+2ijpnwN1FENlW0T8puKdmZlZ1TloqEj6RET8SNI5B3w0UxIRsaHCvZkd9tra2li5ciVTpkwBYPfu3axcuRLwIcVWnYaaUzkjef7cII/PHmwhM0vvsssuY+LEiaxdu5be3l7Wrl3LxIkTueyyy/JuzawsaQ4pnhMRzw9XqxY+pNjGEkncd999nHHGGftqGzduZNGiRQz3t2k2mtIeUpzm6K//k7JmZmbj3FBzKh+iODn/3gPmVY4BJle6MbPxYNasWSxbtox169btu0zLsmXLmDVrVt6tmZVlqKO/PgKcB0ylOI8y4HXgy5Vsymy8uPrqq1m5ciUXXnghzz33HO9///vp7+9n9erVebdmVpaDhkpE3AHcIWlhRHSMYk9m48bAEV4tLS1IYsqUKaxatcpHflnVSjNRvwa4NCJ+nbyfBlwdEV8ahf4y54l6M7NDl+VE/ckDgQIQEa8AHx1Jc2ZmdnhKEyoTJL134E2ypVI7xHgzMxun0lz76xrgp5LWAwFcAFxd0a7MzKwqpbn21/clPQo0Urz21/kR8UTFOzMzs6qTZvcXEfEYxZt0rQd+JemEinZlNo74HvV2OBk2VCT9fnLZ+27gQeAF4O9TLLdW0ouStpTUjpW0UdJTyfO0pC5J10raLulxSSeXLLM8Gf+UpOUl9Y9KeiJZ5lpJOrSfbpa/gXvUt7a20tvbS2trK4VCwcFiVSvNlkoL8DHgyYiYA5xJ8T71w7kpGVvqcuD+iJgH3J+8BzgLmJc8VgDXQzGEgCuBU4FTgCsHgigZs6JkuQO/y2zMa2lpYenSpTQ3NzN58mSam5tZunQpLS0tebdmVpY0E/V7IqJH0gRJSu6vMux/8RHxQOkdIxOLgU8mr2+mGE5fTeq3RPGkmQclTZV0fDJ2Y0S8DCBpI3CmpH8AjomInyb1W4BzgXtS/B6zMWPbtm3s3r2btWvX7rtMy8DZ9WbVKM2WyquSpgAdwC2Svkn5txOeERE7AZLn9yX1mRR3qw3oTmpD1bsHqZtVlUmTJtHc3ExjYyO1tbU0NjbS3NzMpEmT8m7NrCxpQuVcoBf4CsUti18Cf5BxH4PNh0QZ9cFXLq2Q1Cmps6enp8wWzbL39ttvc9111+13j/rrrruOt99+O+/WzMqSJlT2AHsjoo9iqPwceKXM79uV7NYieX4xqXcDs0vGzQJ2DFOfNUh9UBFxY0Q0RETD9OnTy2zdLHvz588fdE5l/vz5ebdmVpY0ofJj4MgkBH4E/HtgbZnftwEYOIJrOXBnSX1ZchTYacCrye6xe4FFkqYlE/SLgHuTz16XdFpy1NeyknWZVY1CocC6dev2O/pr3bp1FAqFvFszK0uaifoJEfGmpAuB6yLiG5I2D7eQpDaKE+3HSeqmeBTXN4DbJTUBz/POJfXvBs4GtgNvAl8AiIiXJf0p8Egy7usDk/bAf6B4hNmRFCfoPUlvVWfgasTNzc10dXVRV1dHS0uLr1JsVSvNVYo3A18Cvg2siIgtkp6IiH8xGg1mzVcpNjM7dFlepfgS4L8BP0wC5QMUd4mZmZntZ9hQiYi/j4izI6Ilef90RPzHyrdmNj4MTNJL2jdZb1at0lym5YOSvivpbkn3DTxGozmzw11zczM33HADq1atYvfu3axatYobbrjBwWJVK+2cyhpgE9A/UI+IhyrbWmV4TsXGksmTJ7Nq1SouueSSfbXVq1dzxRVX0Nvbm2NnZvtLO6eSJlQejYiThxxURRwqNpZIYvfu3Rx11FH7am+++SZTpkxhuL9Ns9GUNlTSHFJ8p6QVwB3AWwPFiHhtBP2ZGXDEEUewYsUKNm/evO+Q4gULFnDEEUfk3ZpZWdKEyheT5z8pqQUwJ/t2zMaXT3ziE9x6661MmzaNiGDHjh1s3bqVRYsW5d2aWVnS3Plx9nBjzKw827Zt46ijjuKNN94gInjjjTc46qij2LZtW96tmZUl1Z0fJX1Y0nmSlg48Kt2Y2XjQ3d3N6aefzoQJxT/FCRMmcPrpp9Pd3T3MkmZjU5pDiv8YuBG4geLNtK4BPlvhvszGjbvuuoupU6ciialTp3LXXXfl3ZJZ2dJsqZwPNAI7I+LzwEmkm4sxsxQigpdeemm/Z7NqlSZUfhMR/cAeSUcD/wR8oLJtmY0v/f39+z2bVas0Wxw/kzSV4uXuO4HXgEcr2pWZmVWlIbdUknuVXBURv46I7wC/D3w5IpaNSndm48Q555xDT08P55xzTt6tmI3IkFsqERGS/i/w0eT99lHpymwcmTBhAvfccw/Tp0+ntraWCRMmsHfv3rzbMitLmjmVhyUdNpdpMRtr9u7dS19fHwB9fX0OFKtqB91SkTQxIvYAC4EvSfpHYDcgihsxDhozM9vPULu/HgZOBs4dpV7Mxi1JRMS+Z7NqNVSoCCAi/nGUejEbl2bMmMGuXbuA4jkrpe/Nqs1QoTJd0iUH+zAiVlegH7NxZ9euXdTU1NDf309NTY0DxaraUBP1NcB7gKMP8jCzjJx66qns2LGDU089Ne9WzEZkqC2VnRHx9VHrxGwc+8lPfsIJJ5yQdxtmIzbUlopGrQuzcaympmbI92bVZKhQ+b1R68JsHDvwel++/pdVs4OGSkS8PJqNmJlZ9Ut1ky4zM7M0HCpmY8DANb9qa2vzbsVsRHyzLbMxYODaX77ul1U7b6mYmVlmHCpmZpaZXEJF0rOSnpC0WVJnUjtW0kZJTyXP05K6JF0rabukx0svwy9peTL+KUnL8/gtZmb2jjy3VBojYkFENCTvLwfuj4h5wP3Je4CzgHnJYwVwPRRDCLgSOBU4BbhyIIjMzCwfY2n312Lg5uT1zbxzyf3FwC1R9CAwVdLxwKeBjRHxckS8AmwEzhztps3M7B15hUoA90naJGlFUpsRETsBkuf3JfWZwAsly3YntYPV30XSCkmdkjp7enoy/BlmZlYqr0OKPxYROyS9D9go6edDjB3sGmQxRP3dxYgbgRsBGhoafAckM7MKyWVLJSJ2JM8vAndQnBPZlezWInl+MRneDcwuWXwWsGOIupmZ5WTUQ0XSFElHD7wGFgFbgA3AwBFcy4E7k9cbgGXJUWCnAa8mu8fuBRZJmpZM0C9KamZmlpM8dn/NAO6QNPD96yLibyU9AtwuqQl4HvhcMv5u4GxgO/Am8AUoXvBS0p8CjyTjvu6LYJqZ5UsR42uKoaGhITo7O/NuwwyA5B9Xgxpvf5s2tknaVHIKyEGNpUOKzcysyjlUzMwsMw4VMzPLjEPFzMwy41AxM7PMOFTMzCwzDhUzM8uMQ8XMzDLjUDEzs8w4VMzMLDMOFTMzy4xDxczMMuNQMTOzzDhUzMwsMw4VMzPLjEPFzMwy41AxM7PMOFTMzCwzDhUzM8uMQ8XMzDLjUDEzs8w4VMzMLDMOFTMzy4xDxczMMuNQMTOzzDhUzMwsMw4VMzPLjEPFzMwy41AxM7PMVH2oSDpT0pOStku6PO9+zMzGs6oOFUk1wHeAs4D5wBJJ8/Ptysxs/JqYdwMjdAqwPSKeBpB0G7AY2JZrV2aApFFZPiJG9D1mWarqLRVgJvBCyfvupGaWqWOPPRZJh/QYLYfS07HHHjtqfdn4VO1bKoP95b7rn22SVgArAObMmVPpnuww9PJ/6geOybuNDPTn3YAd5qo9VLqB2SXvZwE7DhwUETcCNwI0NDR4X4EduqterdiqB9uq8S4tq1bVvvvrEWCepBMlTQIuADbk3JPZIYmIdz3MqlVVb6lExB5JFwP3AjXA2ojYmnNbZmbjVlWHCkBE3A3cnXcfZmZW/bu/zMxsDHGomJlZZhwqZmaWGYeKmZllxqFiZmaZ0Xg7Jl5SD/Bc3n2YDeI44KW8mzA7iPdHxPThBo27UDEbqyR1RkRD3n2YjYR3f5mZWWYcKmZmlhmHitnYcWPeDZiNlOdUzMwsM95SMTOzzDhUzEZIUr+kzZIek/SopH9Vge94Y5jP50pamvX3mh0qh4rZyP0mIhZExEnA14A/z6GHuYBDxXLnUDHL1jHAKwAq+u+Stkh6QtL5Sf0zkv4u+fx4Sb+Q9M8k/ZGkOyX9raQnJV154MoPtk7gG8C/TraY/vOo/VqzA1T9/VTMxoAjJW0GJgPHA59K6ucBC4CTKJ4t/4ikByLiDkn/FrgIOBO4MiL+Kbmt8ClAPfBmMv6HEdFZ8l2DrhO4HPgvEfFvKvxbzYbkLRWzkRvY/fVhiiFxi4oJsRBoi4j+iNgF/Aj43WSZZoq7yt6KiLaSdW2MiF9FxG+AHyTrKDXUOs1y51Axy1BE/JTiFsR0QEMMnQnsBWZIKv07PPAY/wPfD7VOs9w5VMwyJOnDQA3wK+AB4HxJNZKmAx8HHpY0Efg+xYn1LuCSklWcIelYSUcC5wL/74CvGHSdwOvA0RX8aWapeE7FbOQG5lSguCWxPCL6Jd0B/EvgMYpbHJclcyf/FfhxRPw4We4RST9Mlu8A/gr4ILDugPkUgIOt81fAHkmPATdFxLcq+HvNDspn1JuNEZL+CGiIiIvz7sWsXN79ZWZmmfGWipmZZcZbKmZmlhmHipmZZcahYmZmmXGomJlZZhwqZmaWGYeKmZll5v8DQnKB7aWch0gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(table['Amount'], labels = ['Boxplot'])\n",
    "plt.ylabel('Transaction amount')\n",
    "plt.plot()\n",
    "\n",
    "amount = table[['Amount']].sort_values(by='Amount')\n",
    "q1, q3 = np.percentile(amount,[25,75])\n",
    "iqr = q3 - q1\n",
    "lower_bound = q1 -(1.5 * iqr) \n",
    "upper_bound = q3 +(1.5 * iqr)\n",
    "\n",
    "print('Number of outliers below the lower bound: ', amount[amount['Amount'] < lower_bound].count()[0],\n",
    "     ' ({:.4}%)'.format(amount[amount['Amount'] < lower_bound].count()[0] / amount['Amount'].count() * 100))\n",
    "print('Number of outliers above the upper bound: ', amount[amount['Amount'] > upper_bound].count()[0],\n",
    "      ' ({:.4}%)'.format(amount[amount['Amount'] > upper_bound].count()[0] / amount['Amount'].count() * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The interquartile range method found 31904 outliers, which represents 11.2% of the observations. Removing them from the dataset would be a bad idea due to the loss of a large amount of information for the machine learning models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table[table['Class']==1].where(table['Amount']>upper_bound).count()['Amount']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to that, only 91 out of 31904 outliers are classified as frauds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next two plots show that fraudulent transactions are highly concentrated at smaller values when compared to non-fraudulent transactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(table['Amount'], table['Class'])\n",
    "plt.xlabel('Amount')\n",
    "plt.ylabel('Class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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559qtTKfIfmJmVwB3Strtz3UzOz3Nbnu/srKwXrHCE4xzzmWQaRTZw9H61rYIpMNIJpi33oIjj8xY1TnnOrNMp8jmRusX2i6cDuDAA8N6xYqchuGcc+1dplNkC2jknioAZjYqKxG1d927Q79+nmCcc64JmU6R/b82i6KjKSvzBOOcc03IdIrMR441pqwMXn8911E451y71uh1MJJmReuNkj5quG67ENuh5BFMfX2uI3HOuXYr0xHMsdG6R9uF00GUl8P27fDvf8PHPpbraJxzrl2KM9klksYAxxI6/WeZ2WtZjaq9S70WxhOMc86lFed+MDcADxJuQ9wXeEDSt7MdWLuWmmCcc86lFWc25XOAsWb2HTP7DnAkcF6cxiVNlLREUpWka9JsL5L0aLT9FUllKduujcqXSJoQlQ2SNFPSYkmLJH0tpf6Nkt6TNC9aPh0nxhbxa2Gcc65JcU6RrQCKgZroeRHQ5Hz1kvKBu4BTgGpgjqTpZvZGSrVLgPVmNkTSZOAW4GxJw4HJwKHAx4C/SToYSABXmdmrknoAcyXNSGnzdjPL/swDXbvCfvv5rZOdcy6DTBda/ozQ57INWCRpRvT8FGBWY/ulGAdUmdnyqL2pwCQgNcFMAm6MHk8jzHumqHyqmW0D3pJUBYwzs5eBlQBmtlHSYmBAgzbbhl8L45xzGWU6gqmM1nOBJ1LKn4/Z9gDg3ZTn1cD4xuqYWULSBkJfzwDgnw32HZC6Y3Q67XDglZTiyyRdGMV+lZmtjxlr85WVwdy5WWveOec6ukzDlB/cw7aVrtmYdTLuK6k78HvgCjNLXpNzN/C9qN73gB8DX9wtKGkKMAXggAMOyPwOMikrg8cfD9fC5MXpynLOuc4lziiyoZKmSXpD0vLkEqPtamBQyvOBwPuN1ZFUAPQC1mXaV1IhIbk8YmaPJyuY2SozqzOzeuBXhFN0uzGze8yswswq+vXrF+NtNKK8HGprYeXKlrfhnHN7sTh/et9PODpIAJ8AHmLnVP6ZzAGGSiqX1IXQaT+9QZ3pwEXR47OA58zMovLJ0SizcmAoMDvqn7kXWGxmt6U2JKl/ytPPAtm9r3HqtP3OOed2EyfBlJjZs4DM7G0zuxE4qamdzCwBXAY8AywGHjOzRZJukpS8Wdm9QGnUiX8lcE207yLgMULn/dPApWZWBxwDXACclGY48g8lLZA0n5AIvx7nA2gxvxbGOecyijNMuUZSHrBU0mXAe8C+cRo3s6eApxqU3ZDyuAb4XCP73gzc3KBsFun7ZzCzC+LE1GqS/TeeYJxzLq04RzBXAF2By4EjCEcQF2XcozMoKQm3TPYE45xzaTV5BGNmcwCio5jLzWxj1qPqKMrLPcE451wj4owiq4jubjkfWCDpdUlHZD+0DqCszDv5nXOuEXFOkd0HfNXMysysDLiUMLLMlZXBO+9AXV2uI3HOuXYnToLZaGZ/Tz6JOtr9NBmEBJNIwPsNL+9xzjmXaS6yMdHD2ZJ+CfyWcJX82cSfLmbvVl4e1itWwKBBGas651xnk6mT/8cNnn8n5XHDKV86p9SLLY87LqehOOdce5NpLrJPtGUgHZJfC+Occ42KM4qsl6TbJFVGy48l9WqL4Nq9oqJwy2RPMM45t5u4o8g2Ap+Plo/wUWQ7lZf7UGXnnEsjzlQxg83szJTn35U0L1sBdTjl5fD3vzddzznnOpk4RzBbJR2bfCLpGGBr9kLqYIYMCdfCbNuW60icc65diXME82XgoZR+l/X4XGQ7DR4MZuE02bBhuY7GOefajYwJJpp/7BAzO0xST4CUO0g6CEcwAMuWeYJxzrkUGU+RRXeHvCx6/JEnlzSSCaaqKrdxOOdcOxOnD2aGpG9IGiSpT3LJemQdRWkp9OzpCcY55xqI0wfzxWh9aUqZAQe1fjgdkBSOYjzBOOfcLuLcD6a8LQLp0IYMgddey3UUzjnXrsS5kr9Y0pWSHpf0e0lXSCpui+A6jMGDwyiyRCLXkTjnXLsRpw/mIeBQ4GfAncBw4OE4jUuaKGmJpCpJ16TZXiTp0Wj7K5LKUrZdG5UvkTQhKhskaaakxZIWSfpaSv0+kmZIWhqte8eJsVUMGRKSyzvvtNlLOudcexcnwRxiZpeY2cxomQIc3NROkvKBu4BTCUnpHEnDG1S7BFhvZkOA24Fbon2HA5MJiW0i8POovQRwlZl9HDgSuDSlzWuAZ81sKPBs9LxtDB4c1suWtdlLOudcexcnwbwm6cjkE0njgX/E2G8cUGVmy81sOzAVmNSgziTgwejxNOBkSYrKp5rZNjN7C6gCxpnZSjN7FcDMNgKLgQFp2noQOCNGjK3Dhyo759xu4iSY8cBLklZIWgG8DJwgaYGk+Rn2GwC8m/K8mp3JYLc6ZpYANgClcfaNTqcdDrwSFe1nZiujtlYC+6YLStKU5MzQq1evzhB+M/TvDyUlnmCccy5FnGHKE1vYttKUNbxRWWN1Mu4rqTvwe+CK5l78aWb3APcAVFRUtM6N0/Ly4KCD/BSZc86liDNM+e0Wtl0NpN5HeCDQ8Ob1yTrVkgqAXsC6TPtKKiQkl0fM7PGUOqsk9TezlZL6Ax+0MO6W8WthnHNuF3FOkbXUHGCopHJJXQid9tMb1JnOzokzzwKeMzOLyidHo8zKgaHA7Kh/5l5gsZndlqGti4AnW/0dZTJkSDiCqa9v05d1zrn2qtEEI6loTxqO+lQuA54hdMY/ZmaLJN0k6fSo2r1AqaQq4EqikV9mtgh4DHgDeBq41MzqgGOAC4CTJM2Llk9Hbf0AOEXSUuCU6HnbGTwYampg5co2fVnnnGuvFA4Y0myQXjWzMZIeNrML2jiuNlFRUWGVlZWt09iMGfCpT8Hzz8MJJ7ROm8451w5JmmtmFU3Vy9QH00XSRcDRkv6j4cYG/R8ueS1MVZUnGOecI3OC+TJwHrAPcFqDbQZ4gkl1wAFQUOAjyZxzLtJogjGzWcAsSZVmdm8bxtQxFRRAebmPJHPOuUic62AelnQ5cHz0/AXgF2ZWm72wOqjBgz3BOOdcJM4w5Z8DR0TrnwNjgLuzGVSHlRyq3MjACeec60ziHMGMNbPDUp4/J+n1bAXUoQ0ZAh99BGvWQL9+uY7GOedyKs4RTJ2kwcknkg4C6rIXUgeWOpLMOec6uThHMN8EZkpaTpgj7EDg4qxG1VElZ1VetgyOOiq3sTjnXI7FmYvsWUlDgUMICeZNM9uW9cg6ovJykPwIxjnniHcEQ5RQMk3N7wCKimDQIE8wzjlHdie77JySI8mcc66T8wTT2oYOhSVLfKiyc67TazLBSPq9pM9I8mQUx6GHwvr1Pquyc67Ti5M07gbOBZZK+oGkYVmOqWMbOTKsFyzIbRzOOZdjTSYYM/ubmZ1HuIJ/BTBD0kuSLo7uLulSjRgR1p5gnHOdXKzTXpJKgS8A/wm8BvyUkHBmZC2yjqpvX9h/f08wzrlOr8lhypIeB4YBDwOnmVmyc+FRSa10t669zMiRsHBhrqNwzrmcinME82szG25m308ml+TtlOPc0axTGjkS3ngD6nxGHedc5xUnwfxPmrKX4zQuaaKkJZKqJF2TZnuRpEej7a9IKkvZdm1UvkTShJTy+yR9IGlhg7ZulPSepHnR8uk4MWbFyJFQU+MXXDrnOrVGE4yk/SUdAZRIOlzSmGg5EejaVMOS8oG7gFOB4cA5koY3qHYJsN7MhgC3A7dE+w4HJgOHAhOBn0ftATwQlaVzu5mNjpanmooxa5Id/X6azDnXiWXqg5lA6NgfCNyWUr4RuC5G2+OAKjNbDiBpKjAJeCOlziTgxujxNOBOSYrKp0ZT1LwlqSpq72UzezH1SKddGj48zEm2YAGceWauo3HOuZzIdMvkB4EHJZ1pZr9vQdsDgHdTnlcD4xurY2YJSRuA0qj8nw32HRDjNS+TdCFQCVxlZutbEPee69o1TBnjI8mcc51YplNk50cPyyRd2XCJ0bbSlDWcP6WxOnH2behuYDAwGlgJ/DhtUNIUSZWSKlevXt1Ek3tg5EhPMM65Ti1TJ3+3aN0d6JFmaUo1MCjl+UDg/cbqSCoAegHrYu67CzNbZWZ1ZlYP/IpwSi1dvXvMrMLMKvpl866TI0eGTv6tW7P3Gs45145lOkX2y2j93Ra2PQcYKqkceI/QaX9ugzrTgYsIo9LOAp4zM5M0HfiNpNuAjwFDgdmZXkxS/5RrdD4L5LaHfcSIMOHlG2/AEUfkNBTnnMuFOJNd/lBST0mFkp6VtCbl9FmjzCwBXAY8AywGHjOzRZJuknR6VO1eoDTqxL8SuCbadxHwGGFAwNPApWZWF8XzW0JCOkRStaRLorZ+KGmBpPnAJ4Cvx/4UssHnJHPOdXKyJqaVlzTPzEZL+ixwBuGLe6aZHdYWAWZTRUWFVVZmaTKCujro3h2++lX4cdruIOec65AkzY1zoX2cCy2TE1p+Gvitma3bo8g6i/z8MFzZr4VxznVScRLMHyW9CVQAz0rqB9RkN6y9xIgRforMOddpxZmu/xrgKKDCzGqBzYQLIV1TRo4MNx5buzbXkTjnXJtrcjblyMcJ18Ok1n8oC/HsXVI7+k88MaehOOdcW4szXf/DhAsY5wHJ6YENTzBNSyaYhQs9wTjnOp04RzAVwHBrariZ213//tCnj/fDOOc6pTid/AuB/bMdyF5J8o5+51ynFecIpi/whqTZwLZkoZmd3vguboeRI+Ghh6C+HvJi3aHaOef2CnESzI3ZDmKvNn483HVX6IcZNSrX0TjnXJuJM0z5BWAFUBg9ngO8muW49h7HHx/WL76Y2zicc66NxZmL7EuEm4H9MioaAPwhm0HtVQ48EA44wBOMc67TidMpcClwDPARgJktBfbNZlB7neOPDwnGB+I55zqROAlmm5ltTz6JLrb0b8rmOO44WLUKli7NdSTOOddm4iSYFyRdB5RIOgX4HfDH7Ia1l0n2w/z977mNwznn2lCcBHMNsBpYAPwX8BTw7WwGtdc55BDo18/7YZxznUqTw5TNrF7SH4A/mFkWb2K/F5N29sM451wn0egRjIIbJa0B3gSWSFot6Ya2C28vcvzxsGIFvPNOriNxzrk2kekU2RWE0WNjzazUzPoA44FjJOX2dsQdkffDOOc6mUwJ5kLgHDN7K1lgZsuB86NtTZI0UdISSVWSrkmzvUjSo9H2VySVpWy7NipfImlCSvl9kj6QtLBBW30kzZC0NFr3jhNjmxk5Enr18tNkzrlOI1OCKTSzNQ0Lo36YwjT1dyEpH7gLOBUYDpwjaXiDapcA681sCHA7cEu073BgMnAoMBH4edQewANRWUPXAM+a2VDg2eh5+5GfD8ce60cwzrlOI1OC2d7CbUnjgCozWx5dRzOV3e+EOQl4MHo8DThZkqLyqWa2LTqCqoraw8xeBNaleb3Uth4EzogRY9s67jhYvBg++CDXkTjnXNZlSjCHSfoozbIRGBmj7QHAuynPq6OytHXMLAFsAEpj7tvQfma2MmprJe1xtoFkP8ysWbmNwznn2kCjCcbM8s2sZ5qlh5k1eYoMULpmY9aJs2+LSJoiqVJS5erVbTzq+ogjoKTE+2Gcc51CNm9QUg0MSnk+EHi/sTrRFDS9CKe/4uzb0CpJ/aO2+gNpz0OZ2T1mVmFmFf369Yv5VlpJly5w1FGeYJxznUI2E8wcYKikckldCJ320xvUmQ5cFD0+C3guujXzdGByNMqsHBgKzG7i9VLbugh4shXeQ+s7/niYNw/Wrs11JM45l1VZSzBRn8plwDPAYuAxM1sk6SZJybth3guUSqoCriQa+WVmi4DHgDeAp4FLzawOQNJvgZeBQyRVS7okausHwCmSlgKnRM/bn0mTwqzKjz2W60iccy6rZJ14CvmKigqrrKxs2xc1C3e27NkT/vGPtn1t55xrBZLmmllFU/X8JvFtTYLzz4eXXoLly3MdjXPOZY0nmFw499yQaP7v/3IdiXPOZY0nmFwYNAhOPDEkmE58itI5t3fzBJMr558f7nA5u6nBcc451zF5gsmVM8+E4mI/Teac22t5gsmVXr3g9NNh6lSorc11NM451+o8weTSBRfAmjXwzDO5jsQ551qdJ5hcmjABSkv9NJlzbq8N9N1GAAAWOElEQVTkCSaXCgth8mR48klYvz7X0TjnXKvyBJNrU6bAtm3wv/+b60icc65VeYLJtVGj4OKL4ac/haqqXEfjnHOtxhNMe/A//wNFRXD11bmOxDnnWo0nmPagf3+47jp44gmYOTPX0TjnXKvwBNNefP3rcOCBYV1Xl+tonHNuj3mCaS+Ki+FHP4LXX4f77891NM45t8c8wbQnZ50Fxx4L11/vw5adcx2eJ5j2RAqjydavh89+Fmpqch2Rc861mCeYHFi1Cn75S/joozQbx4yBBx6AF14IU8l4f4xzroPyBNPG/vhHGDkSvvxlGD48DBzb7ZYw554LP/4xTJsGV1zh94xxznVIWU0wkiZKWiKpStI1abYXSXo02v6KpLKUbddG5UskTWiqTUkPSHpL0rxoGZ3N99ZcmzfDf/1XmEB5wAB49FHo2xf+4z/gjDPg3Xcb7HDllXDVVXDnnfCDH+QkZuec2yNmlpUFyAeWAQcBXYDXgeEN6nwV+EX0eDLwaPR4eFS/CCiP2snP1CbwAHBWc2I84ogjrC288orZ0KFmktm3vmVWUxPKt283+9GPzLp2NevWzez2280SiZQd6+rMzj3XDMxuuik8d865HAMqLcZ3bDaPYMYBVWa23My2A1OBSQ3qTAIejB5PA06WpKh8qpltM7O3gKqovThtthuJBHz3u3D00WG6sZkzw8FIUVHYXlgI3/gGLFoExx8fLoEZPx7mzo0ayMsLQ5bPOw9uuCHcpCxtx41zzrU/2UwwA4DUEz/VUVnaOmaWADYApRn2barNmyXNl3S7pKJ0QUmaIqlSUuXq1aub/65iqqoKI45vvDF0qcyfDyeckL5uWRn8+c/htNl778G4cSHZbNoEdOkCDz8Mt98eOnDGj4c338xa3M4511qymWCUpqxhb3VjdZpbDnAtMAwYC/QBvpUuKDO7x8wqzKyiX79+6arsETP49a9h9GhYsiTcsPKhh8INLDOR4POfh8WLwwTLP/lJGATwxz9GG6+4Av72N1i7NmSgqVNbPXbnnGtN2Uww1cCglOcDgfcbqyOpAOgFrMuwb6NtmtnK6PTgNuB+wum0NrV6deiw/9KX4MgjYcECOPvs5rWxzz5w993wj3/svKvymWeGIxtOPBFefRVGjIBzzgmLX5DpnGunsplg5gBDJZVL6kLoxJ/eoM504KLo8VnAc1EH0nRgcjTKrBwYCszO1Kak/tFawBnAwiy+t9089VQYfvzMM3DbbfDXv8LAgS1v7+ijQy75/vdD2x//eBhQVtd/ILz4YpiBedq08KIzZrTeG3HOudYSZyRASxfg08C/CCO/ro/KbgJOjx4XA78jdOLPBg5K2ff6aL8lwKmZ2ozKnwMWEBLL/wHdm4qvNUaRbd5s9pWvhIFeI0eazZ+/x03upqrK7JRTwmuMG2c2b160obLSbNiwsOHyy822bGn9F3fOuQaIOYpM1okv4quoqLDKysoW7z9nDpx/PixdGi5ZSd7WJRvM4Le/DZ3/a9eGy2S+8x3olrcVvvUt+NnPQqfNI4+EDiDnnMsSSXPNrKKpen4lfwskEiGZHH00bNkCzz4bJkLOVnKB0M9/7rlhEMDFF4fXO/RQeGpmCdxxBzz9dOiPGTcOfvhDn2LGOZdznmCaadmycM3Kf/83fO5zYfjxJz7Rdq/fpw/86lehG6ZrV/jMZ8JAgpWjJoRRBaedFo5oTj4Z3nmn7QJzzrkGPMHEZAb33RfOPr3xRjgT9ZvfQO/euYnnuOPgtdfge9+DJ58MgwB+8btS6h+bFi7OnDsXRo0KF9c451wOeIKJYc2aMFT4kkugoiIctZx7bq6jCqfkvv3tEM8RR8BXvgLHHicWVnwB5s0LWWfyZLjwQp8BwDnX5jzBNOHpp8NI4D//GW69NfS3HHBArqPa1cEHh2swH3wQ/vUvOPxwuO7ewWz969/DSIBHHtk5hto559qIJ5hGbNkCl10Gp54KpaUwe3YYKZbXTj8xKRyovPlmGNn2/e/DiNEF/PXoG8NVm926wcSJYYSAX5zpnGsD7fTrMrdefTWccrrrrjAsuLISDjss11HF07dv6IKZORMKCmDCBDjvZ0ey6i+vwnXXhXnNhg+H3/3O7zPjnMsqTzAp6urCX/7jx8PGjeEC+dtug+LiXEfWfCeeCK+/Hs6QTZsGHz+8mF+X30z9K3Ng//3DxGdHHRXunOmcc1ngCSby1lvhS/m668JNwObPh09+MtdR7Zni4jCb8+uvhy6YL30JTvz64bzx4By4916org5v+jOfCUPSnHOuFXX6BGMWOscPOywklYcfDhMV9+mT68haz7Bh8PzzIacsXAijKwr477e+SM2CpXDLLfDSSzBmTDiiue++cPtN55zbQ506wSQS4UzRF74QRl7Nnx86yJXupgAdnARf/GIYBHD22WEmglHjS3iu4mpYvjzcb2bDhjAWu39/+M//hN//3gcEOOdarFPPRdalS4VBJd/7XrizZH5+riNqOzNmhOtmli0Lo89uvRX69bUw4uxXv4InnggdUXl54eKfk04KIx9Gj4aDDmq/w+mcc1kXdy6yTp1gSkoq7KWXKjn88FxHkhtbt8LNN4ezZL16hSRz0UXREVxtLbzySshEM2aEcdrJ+c26dw/3pDn4YBg8GIYMCeuBA8MAgs6UqZ3rhDzBxDBmTIW9+mrLZ1PeWyxaFO6i+dJLoc//F7+AQw5pUKmmJlScNy+MGliwINwXurp613p5eeEU24ABYcx0aWno0CotDdfiFBWF0QfJoXmJRFjq6kLG27Qp9AGlrpPLtm1QX79zKSgId2hLLv36hUQ3dGhY9ttv7zzf6VyOeYKJYU+n69+b1NeHWz1ffXX4nr/++jBnZpMzRG/dGobgLV8eks1774X1+++HOXbWrg3Lpk3xgykoCMmoe/ddl6KicHSUlxeW7dtDv9GGDfDhh+GWoonEznZ69Qqn98aNC2PPx48PR1jOuT3iCSYGTzC7+/e/w8WlU6eGrpaxY2HffcPBwL777r507x7zIKG2NiSjmpqdixSSSX5+WEpKQoNdurQs+EQC3n47HFktXRqOuGbPDqM3konn4IPD9NcnnggnnBCOtpxzzeIJJgZPMI17+ulwW5n33oMPPggHCOkUF6dPPOkSUr9+UFjYtu8DCInttdfCOcAXXgj3OkhO/jlsWEg2yWW//XIQoHMdiyeYGDzBxLdtWzgD9cEHTS+rVoWzV+n07p0+IaVLSvvsk6UulEQi9CXNnLkz4WzcGLYNGQJHHrlzGTEiu3eSc64DahcJRtJE4KdAPvBrM/tBg+1FwEPAEcBa4GwzWxFtuxa4BKgDLjezZzK1KakcmAr0AV4FLjCzRr7mAk8w2WEWvq+TyaaphLR2bfpp0QoKGk9GqUdFxcXhyKhLl13XqY8zDmxLJMIRzvPPw8svh+Xf/w7b8vPDgIERI8IyeDAceGBYPvaxEGRntH17uEZq/frQB7Zly86lpibUSf51kDz92a1buEte9+7hr4c+fZpxjtW1JzlPMJLygX8BpwDVwBzgHDN7I6XOV4FRZvZlSZOBz5rZ2ZKGA78FxgEfA/4GHBztlrZNSY8Bj5vZVEm/AF43s7szxegJpn1IJEKSST0CypSQWjLRQF7e7kknXSIqLIQuhcaAuncYueUVBm9ZQNmmhRywcSH7bVpGHjv/v9Qpn83d9mVL177UdOtLTfe+bO/Wm7qS7tR17YF16059STdUUgwlJaikGBUXkVdSRF5xF/JKisgvLqSga5ewLomW4oId68KuhRQW51NYnI/y8/b8y7i+PhyOpvaHbd4c/iJILhs27Ewe69fDunW7DthYuzYkktZQULBzlGHyL4Z+/cIIxNTHpaU7l65dPSnlWNwEk80/v8YBVWa2PApoKjAJeCOlziTgxujxNOBOSYrKp5rZNuAtSVVRe6RrU9Ji4CQgeRuwB6N2MyYY1z4UFITTY3G7PzZv3nm6bvXq8H25fXsYR1Bbu/Nxy8vEu3Ygy4sOpDbv82zvArU9QDVb2a/mbfbf9jb9t7/NgMTblG5eRZ9Na+jLGvrxOv34iB5spDvZmW4nQT510WLKo458kDC0Yx3+NfKoRxj5ltixpCbIptQpn5qS3tQU92ZL175s7TqQrX0PY+sBpWwr6U1N1z7UlPRme0kvEkXdqCvqSqJLV+oLwylFCfJk5FNHQe1WCmu30KV2M4XbNlFc8yHFW9ZRtGUdxZvXUrxpDSXvr6b4Xwsp3riaos3rUCN//NYVFrG9W29qu+0Tlq5hnejak0TXHiS69qSupAd1xd2oL+66Y21FxdQXFmFdwrJjgEm0lnbmLWGovo68+sTOJbF951K7jfzENvK215BXm7KOtmv7NlSXQPV1YamLBpkkX0TC8vOhoBArCH/ZWGEXrKgI61IMRVGcRcUh1uTzwi47/hqygsIdA2RUEH4fJMBsxzrP6iCR2BGDakNseYntsH07edtrQqzbGqyjekrUhv3rElCXaPRn0phsJpgBwLspz6uB8Y3VMbOEpA1AaVT+zwb7Dogep2uzFPjQzBJp6ru9TLduYSkra+tXLgGGRctOZuEynmSi2rodPtpWT+KjLSQ2bCaxcSt1m2uo37yVui011G/dTn1NWKxmG/XbaqnfVottD0v99gRWm8Bqa2F7LZaoS1kSkKijvq4eEnVQV0d9nVFfb1idYfVGXb1I1OdRZ6KuXtTWF7DdCtleX0BtfT5brJit9cVsri9mS10xH9V348O6HnxU34ON9GCD9WSd9eYj64FtEWwB1rXtJ51HHX1YFyXu1ZSyllLW0od1lNaupfeH69nnww/pzXp6s5qeLKNnlNy70UpHV60g9Q8CYEfyByggQQF1uQwvtloKqCOf+mbOLpbNBJPuGLZh+musTmPl6d5dpvq7ByVNAaZET7dJWpiuXjvQF1iT6yAyaM/xeWwt1y7iq4+CWAO8ubO4XcSWQZr46qIl5/bws0tEyw4HxtkrmwmmGhiU8nwg8H4jdaolFQC9CH8rZdo3XfkaYB9JBdFRTLrXAsDM7gHuAZBUGec8Yi6059igfcfnsbVce46vPccG7Tu+XMWWzRkL5wBDJZVL6gJMBqY3qDMduCh6fBbwnIVRB9OByZKKotFhQ4HZjbUZ7TMzaoOozSez+N6cc841IWtHMFGfymXAM4QhxfeZ2SJJNwGVZjYduBd4OOrEX0dIGET1HiMMCEgAl5pZHUC6NqOX/BYwVdL/AK9FbTvnnMuRrA7iN7OngKcalN2Q8rgG+Fwj+94M3Bynzah8OTtHmsV1TzPrt6X2HBu07/g8tpZrz/G159igfceXk9g69ZX8zjnnssfvGuWccy4rOmWCkTRR0hJJVZKuyVEMgyTNlLRY0iJJX4vKb5T0nqR50fLplH2ujWJeImlCluNbIWlBFENlVNZH0gxJS6N176hcku6IYpsvaUwW4zok5bOZJ+kjSVfk8nOTdJ+kD1KHvLfks5J0UVR/qaSL0r1WK8X2I0lvRq//hKR9ovIySVtTPsNfpOxzRPT7UBXF3yqX0jcSX7N/ltn4P91IbI+mxLVC0ryovE0/uwzfH+3i924HM+tUC2FwwDLgIKAL8DowPAdx9AfGRI97EKbAGU6YgeAbaeoPj2ItAsqj95CfxfhWAH0blP0QuCZ6fA1wS/T408BfCNcjHQm80oY/y38TxuTn7HMDjgfGAAtb+lkR5tBbHq17R497Zym2TwEF0eNbUmIrS63XoJ3ZwFFR3H8BTs3iZ9esn2W2/k+ni63B9h8DN+Tis8vw/dEufu+SS2c8gtkxhY2FyTCTU9i0KTNbaWavRo83AovJPPvAjulzzOwtIHX6nLYyiTAND9H6jJTyhyz4J+GapLa40crJwDIzeztDnax/bmb2Irtf697cz2oCMMPM1pnZemAGMDEbsZnZX23nrBf/JFw31qgovp5m9rKFb6WHUt5Pq8eXQWM/y6z8n84UW3QU8nnCnImNytZnl+H7o1383iV1xgSTbgqbnE4rI6kMOBx4JSq6LDqMvS95iEvbx23AXyXNVZj9AGA/M1sJ4Rcc2DdHsSVNZtf/4O3hc0tq7meVqzi/SPjLNqlc0muSXpB0XFQ2IIqnLWNrzs8yF5/dccAqM1uaUpaTz67B90e7+r3rjAkm9rQybUFSd+D3wBVm9hFhgs7BwGhgJeEwHNo+7mPMbAxwKnCppOMz1G3zz1ThQtvTgd9FRe3lc2tKc6dHyl4g0vWE68weiYpWAgeY2eHAlcBvJPXMQWzN/Vnm4md8Drv+cZOTzy7N90ejVRuJI6vxdcYEE2cKmzYhqZDwy/GImT0OYGarzKzOzOqBX7HzdE6bxm1m70frD4AnojhWJU99ResPchFb5FTgVTNbFcXZLj63FM39rNo0zqgz9/8B50WnbohOPa2NHs8l9GscHMWWehot2797zf1ZtvVnVwD8B/BoSsxt/tml+/6gnf3edcYEE2cKm6yLzuHeCyw2s9tSylP7Lj4LJEewNDZ9TjZi6yapR/IxoVN4IbtO7ZM6Hc904MJopMqRwIbkYXoW7fIXZHv43Bpo7mf1DPApSb2jU0KfispancJN+74FnG5mW1LK+yncxwlJBxE+q+VRfBslHRn93l5IFqdiasHPsq3/T38SeNPMdpz6auvPrrHvD9rb711rjRboSAthRMW/CH9lXJ+jGI4lHIrOB+ZFy6eBh4EFUfl0oH/KPtdHMS+hlUbxNBLbQYSROK8Di5KfEeG2CM8CS6N1n6hcwF1RbAuAiix/dl0Jd0DtlVKWs8+NkOhWArWEvwgvaclnRegPqYqWi7MYWxXhvHvy9+4XUd0zo5/364S7wp6W0k4F4Yt+GXAn0UXaWYqv2T/LbPyfThdbVP4A8OUGddv0s6Px74928XuXXPxKfuecc1nRGU+ROeecawOeYJxzzmWFJxjnnHNZ4QnGOedcVniCcc45lxWeYJxrZZI+K8kkDcthDFdI6pqr13cOPME4lw3nALOIbgGeI1cQrhdyLmc8wTjXiqK5oY4hXDA4OSo7MZoA8TFJ/5L0A0nnSZqtcJ+QwVG9AyU9G03y+KykA6LyBySdlfIam1LafV7SNIX7uzwSXal9OfAxYKakmW38ETi3gycY51rXGcDTZvYvYJ123tjpMOBrwEjgAuBgMxsH/Br4/6I6dxKmVB9FmIDyjhivdzjhaGU4YQaGY8zsDsJ8Up8ws0+0zttyrvk8wTjXus4h3I+EaH1O9HiOhXt4bCNM1/HXqHwB4WZVEG5K9Zvo8cOE6UCaMtvMqi1MDDkvpS3ncq4g1wE4t7eQVAqcBIyQZIQ7LRrwFLAtpWp9yvN6Gv9/mJzHKUH0x2A0yWGXlDqp7dZlaMu5NudHMM61nrMIp7gONLMyMxsEvEW8IxGAl9g5MOA8wkABCLevPiJ6PAkojNHWRsKtdJ3LGU8wzrWecwj3zkn1e+DcmPtfDlwsaT6hn+ZrUfmvgBMkzQbGA5tjtHUP8Bfv5He55LMpO+ecywo/gnHOOZcVnmCcc85lhScY55xzWeEJxjnnXFZ4gnHOOZcVnmCcc85lhScY55xzWeEJxjnnXFb8/wThBSj4jTTmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_0 = table.loc[table['Class'] == 0]\n",
    "target_1 = table.loc[table['Class'] == 1]\n",
    "ax1=sns.distplot(target_0[['Amount']], hist=False, color='b', label='Non-fraud')\n",
    "ax2=sns.distplot(target_1[['Amount']], hist=False, color='r', label='Fraud')\n",
    "ax1.set_xlim(0, max(table[table['Class']==1]['Amount']))\n",
    "ax2.set_xlim(0, max(table[table['Class']==1]['Amount']))\n",
    "plt.legend()\n",
    "plt.xlabel('Amount')\n",
    "plt.ylabel('Density of probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     492.000000\n",
       "mean      122.211321\n",
       "std       256.683288\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         9.250000\n",
       "75%       105.890000\n",
       "max      2125.870000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.loc[table['Class'] == 1]['Amount'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see below that all features have very low correlation coefficients among each other, and especially low correlation with the 'Class' feature. This was already expected since the data was processed using PCA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "heatmap = sns.heatmap(table.corr(method='spearman'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.corrwith(table.Class, method='spearman').plot.bar(\n",
    "        figsize = (20, 10), title = \"Correlation with class\", fontsize = 15,\n",
    "        rot = 45, grid = True, color=['blue'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Balancing'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Balancing the classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before balancing the classes we need to split the observations into a training set and a testing set. ***This is extremely important!*** We can only balance the classes after we set some observations aside to be used as a test set! Otherwise, the models might use part of the test data during the training, which will lead to overfitting. Let's be smart and avoid that! :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = table['Class']\n",
    "X = table.drop(columns=['Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, stratify=y, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data was split in train and test sets maintaining the original rate of frauds to non-frauds observations in each set by using 'stratify=y' in the function train_test_split."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to test three types of resampling methods: [random undersampling](https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.under_sampling.RandomUnderSampler.html#imblearn.under_sampling.RandomUnderSampler), [random oversampling](https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.over_sampling.RandomOverSampler.html#imblearn.over_sampling.RandomOverSampler) and [Synthetic Minority Over-sampling (SMOTE)](https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.over_sampling.SMOTE.html#imblearn.over_sampling.SMOTE). The image below depicts how undersampling and oversampling works and it is very intuitive to understand. SMOTE is similar to oversampling but instead of copying the same original points randomly, the algorithm creates new points close to the original ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"Images/resampling.png\">\n",
    "\n",
    "[Image Source](https://www.kaggle.com/rafjaa/resampling-strategies-for-imbalanced-datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='rus'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random undersampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "rus = RandomUnderSampler(sampling_strategy='auto', random_state=42, replacement=False)\n",
    "X_rus, y_rus = rus.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking If classes are balanced:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(['Non-Fraud','Fraud'], [Counter(y_rus)[0], Counter(y_rus)[1]], color=['b','r'])\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Number of transactions')\n",
    "plt.annotate('{}'.format(Counter(y_rus)[0]), (0.25, 0.45), xycoords='axes fraction')\n",
    "plt.annotate('{}'.format(Counter(y_rus)[1]), (0.75, 0.45), xycoords='axes fraction')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert Counter(y_rus)[1] == Counter(y_train)[1] #Checking if they have the same number of fraud cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='ros'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ros = RandomOverSampler(sampling_strategy='auto', random_state=42)\n",
    "X_ros, y_ros = ros.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking If classes are balanced:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(['Non-Fraud','Fraud'], [Counter(y_ros)[0], Counter(y_ros)[1]], color=['b','r'])\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Number of transactions')\n",
    "plt.annotate('{}'.format(Counter(y_ros)[0]), (0.20, 0.45), xycoords='axes fraction')\n",
    "plt.annotate('{}'.format(Counter(y_ros)[1]), (0.70, 0.45), xycoords='axes fraction')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert Counter(y_ros)[0] == Counter(y_train)[0] #Checking if they have the same number of non-fraud cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='smote'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Synthetic Minority Over-sampling (SMOTE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quick explanation of how SMOTE works: it consists of synthesizing elements for the minority class using the existing ones. It randomly chooses a point from the minority class and computes the k-nearest neighbors (default = 5) for this point. The synthetic points are added between the chosen point and its neighbors by choosing a factor between 0 and 1 to multiply the distance. This process can be seen below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"Images/smote.png\">\n",
    "\n",
    "[Image Source](https://www.kaggle.com/rafjaa/resampling-strategies-for-imbalanced-datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "smote = SMOTE(sampling_strategy='auto', random_state=42)\n",
    "X_smote, y_smote = smote.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(['Non-Fraud','Fraud'], [Counter(y_smote)[0], Counter(y_smote)[1]], color=['b','r'])\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Number of transactions')\n",
    "plt.annotate('{}'.format(Counter(y_smote)[0]), (0.20, 0.45), xycoords='axes fraction')\n",
    "plt.annotate('{}'.format(Counter(y_smote)[1]), (0.70, 0.45), xycoords='axes fraction')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert Counter(y_smote)[0] == Counter(y_train)[0] #Checking if they have the same number of non-fraud cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='rosxsmote'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the difference between random oversampling and SMOTE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the dataset has many features and our graphs will be 2D, we will reduce the size of the dataset using Principal Component Analysis (PCA). But before using PCA, it is recommended to scale the features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a function to perform feature scaling because some models need this prior to fitting and we can use it more than one time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_scaling(X, X_test=X_test):\n",
    "    std_scale = StandardScaler().fit(X)\n",
    "    X_std = std_scale.transform(X)\n",
    "    X_test_std = std_scale.transform(X_test)\n",
    "    return X_std, X_test_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transforming all the sets using different types of sampling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_rus_std, X_test_rus_std = feature_scaling(X_rus)\n",
    "X_ros_std, X_test_ros_std = feature_scaling(X_ros)\n",
    "X_smote_std, X_test_smote_std = feature_scaling(X_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)\n",
    "X_ros_pca = pca.fit_transform(X_ros_std)\n",
    "X_smote_pca = pca.fit_transform(X_smote_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a function to visualize the data distribution after the PCA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_2d_space(X, y, label='Classes'):\n",
    "    '''Plots the data points in a 2D scatterplot.'''\n",
    "    colors = ['blue', 'red']\n",
    "    markers = ['o', 's']\n",
    "    for l, c, m in zip(np.unique(y), colors, markers):\n",
    "        plt.scatter(X[y==l, 0], X[y==l, 1], c=c, label=l, marker=m)\n",
    "    plt.title(label)\n",
    "    plt.legend(loc='best')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rrQJ+UoG3YQO4j1bg1Rr0n3rqKQ4+ePQd4r29vTz11FTfVy31NDgIq1dDXx+Yhb+rV6vCViRLWwX8vCrwst4ZoFY5xTE4COvXw5494a+CvUi2tgr4eVXg9fb28uSTT74+PDw8zIEHHljbSkVEGqytAn5eFXhvfetbeeyxx3jiiSfYuXMn119/Pe99b+YL7kVECqutAn5eFXjTp0/ni1/8IqeccgpHHHEEZ5xxBkcddVRtKxURabCWeNK2WknZ7cqVoRhn4cIQ7OtRprtkyRKWLFlS+4pERJqkrQI+hOCuSjsRkfHaqkhHRETKU8AXEekQCvgiIh1CAV9EpEMo4IuIdAgF/CosX76c/fffn6OPPrrZSRGRVmdW/pMzBfwqnHPOOdx2223NToaItLIGBfVK2ivg77NP9lVzn31qWu3b3/525s2bV6dEikhHKUCgT7RXwN+2bXLjRUQ6SHsFfBGRokhKHAqk5oBvZgeb2XfM7GEze9DMLojj55nZGjN7LP7dr/bkiogUWLoouYAlC/XI4b8GfMzdjwCOAz5sZkcCFwJ3uvthwJ1xWESk/RQwN5+l5oDv7s+4+w/i/9uAh4GDgNOBK+NsVwLvq3VbzXLmmWdy/PHH8+ijj9Lb28vll1/e7CSJSBEkgb6Aufksde0t08z6gV8G7gEOcPdnIFwUzGz/MsusAFYALKz1TSVz52Yf+Llza1rtddddV9PyItKG9tmnZQJ9om6VtmY2B7gR+Ki7b612OXdf7e4D7j4wf/782hKxdWt4e3npZ2vVyRERqazFcvVpdQn4ZjaDEOyH3P1rcfQmM1sQpy8AnqvHtkREmqoFA32iHq10DLgceNjdv5CadDOwLP6/DLhpqttw96knsEFaIY0iMgWlD3S2sHrk8H8NOBs40czuj58lwMXAO83sMeCdcXjSZs2axcjISKEDqrszMjLCrFmzmp0UEamHdJBvVI6+ATGu5kpbd/93oNxl76Ra19/b28vw8DCbN2+udVW5mjVrFr29vc1OhojUolkVsQ3K0Bb+nbYzZsxg0aJFzU6GiLS7Fi+uqYa6VpAxhoagvx+mTQt/h4aanSKRnDW7bL6BxdUK+PK6oSFYsQI2bAjn4IYNYbjRQX9oCHp6Rn+HPT268EgOmh3ooaHBHhTwJWXlStixY+y4HTvC+EYZGoLly2FkZHTcyAice66CvtRJEQJ9kyjgy+s2bpzc+DysXAk7d44fv2tXYy880qZqfDdGXTWh5aECvryuXM8WtfZ4MRmVLi6NvPBImyna07FNamaugC+vW7UKZs8eO2727DC+USpdXBp54ZE20Iy29AWngC+vGxyE1auhry/8Rvr6wvDgYOPSsGoVzJw5fvyMGY298EiLK3KQb+JDpIVvhy+NNTjY2ACftX2ACy4Yrbjt7oZLLmluuqQFtEJFbJN7DFDAl8Jp9kVHWlCRKmMLTAFfRFpXK/VJX4D+wBTwRaS1tFKQTxQg2IMqbaXA1M2DjFG0ppUtSAFfCimrm4dzzw3dLOgC0IFaNdAnb90rCAV8KaSsbh527Qotd5rZz480ULodfSsqUKBPKOBLIW3YMPE8je7nRxqk1YtuCparT1PAl8IZGqo+U6fuFtpMK1bIJgoc6BNqpSOFs3Jl9b8bdbfQ4lo5wEPhA3wp5fClcKrNtTe6nx/JQSsH+xakgC+FUy7X3t3d3H5+pA7SFbGtXBnbAsU3WRTwpXDK9dp5ySWwfj3s2RP+Kti3CPVaWRgK+FI4Rei1U+qoXYJ8i+bq01RpK4WkDtRaWKtXxJaaOxe2bm12KupCAV9E6qsdgn2L5+TLUZGOiExdaSVsq1bEdgjl8EVk6tohNw9tm6MvpRy+iFSvHZpV1qL0bqbSp4CUwxeRibVbRWza3LnZ4wsatGuhHL7kopa+7JNlzWD69PBX3SE3STu3n0+aWSYtcFogh14rBXypu/PPh7PPHtuXfbmujEsvDOefP9oPPsDu3eFvuj98M+jqGv1d9vToYlBXbR70XtcBAb6UeYEqKwYGBnzt2rXNTobUYGgoBPus06qvLzwhm553xYrx/d5PxYwZcMUVartfFx0S/HLXwNhqZve5+8BE89Ulh29mXzaz58zsx6lx88xsjZk9Fv/uV49tSbFV6umytFO0rJecTNWuXeobf0rUrLKj1KtI55+BU0vGXQjc6e6HAXfGYWlzlXq6nDdv9P+hoepeclKvbUuJVn/JiExJXQK+u38XeL5k9OnAlfH/K4H31WNbUmyV+qffujWU0ff0wFlnNXbbktLOLW6KokBF5Wl5Vtoe4O7PAMS/+2fNZGYrzGytma3dvHlzjsmRRsjq6TKxaxdcdll4L229dXWpb/wJtXOLG6lK01vpuPtqdx9w94H58+c3OzlSo6Sny3Lyyvjsu68qbDO1+ovAW1FBc/eQb8DfZGYLAOLf53LclhTI4GBokVNPXV2Vpz9fWqAogXLzk1dLwC5wsId8A/7NwLL4/zLgphy3JQVT7+KVri645pryFxKV39Myj/cXyty5Y99g5T7141bwYA/1a5Z5HfA94HAzGzazDwIXA+80s8eAd8ZhaRFDQ6MPOaU/06fDySdP/CRsvYtXdu4MzS7LvQ2ro8vvFdyrUxrY00/ZJtr8ONalLx13P7PMpJPqsX5prKEhWL48BNlSu3fDnXeOHYbRp2khv7L0jRtH171yZRheuDAE+44sv1drm4lN5uUltQT7Fsjdg560lQz9/VNvI59+mranp74tckqf1O14bZ4bnbKpxLQWD/YNfdJW2kstDzCllz3jjNrTkrZkSX3XJ20gqwx+slo82E+GAr6MU0sFaLLs0BBceWXleSfryis7tJO0rO4POjV3Xxrga33XbC3HsVy3ygWmgC/j1JKTXrw4BOVly+rXT05ix47x/eXU0g1z4an7g4krWWtR60WzBV9srhegyDi33FJ+WlcXnHACrFuXXc5/551w112jlbn1li4yKu1tsxEVx7lTReyoqRaXNOIYtlhRTkKVtjLOtGnZ57MZ7NkzOjx9en6BvZzubtiyJfxfrnK55Sp3O7V4ppx0y5oiHpsCxcyEKm1lysqV4ZeOb3SwB3jlldFinHItiVqm18ykyEbG2rats+spcqSAL+NU+3DTRN0d5OHll8Obryo1Gy38U7cqm5cmUcCXcZIO0Pr6Qlzq6wvDSbl4ksNuRg4fQq+b5RT+qVsFemkiBXzJNDgYysH37BktD0+6U0jeV1s0pRemwlCPlVIQaqUjEyptDVPAOqtiVdSqpU37KuLJPwnK4cuE6vnu2VrNnBleWJ5WuGIcBXspKAV8mVBRWr10d8OXvwxXXFG+fqHh9ARs52jx3D2oSEeqsHBhMcrsR0bCE7y7d4dAf/XVDQr0CuLSJpTDl0zpLgu2b29OE8wspd0x59qVgnLsnSur7/w2oIAv4ySVtBs2hPN8ZKR5TTAryepbpyYqmpE2pyIdGadIlbQTqal+QUFdsrRJbj6LcvgyxtBQMcrrqzVvXpUzqnK1c9TabXEbnycK+PK6888PD1W1kq1bM8rxFdzbU2mZernArmaxZalIR4AQNC+7rPXuZl/dZdhZwFnNTonUjXv2w2u6cNdMAV+AUG6fR7Dfg1HuZ+rANCa30UrrkzahwJ4bBXwB8nm4aqLgbIBnzJFcCBTcRepLZfgC5NOl8FSDdXIhULAXqS/l8IWhIdi0aWrLKhcu0joU8Dvc0BAsXw47d05teQV7kdahIp0Ot3Ll1IO9iLQW5fA73FQfslJRjnSEVmunPAHl8DtYLR2PKdiLtB4F/DaV7u2yv398cE/K7kWkc6hIpw2VvpIw6UoYRvuPV9m9yATarDgHlMNvS1m9XZZ2JVyUt1iJSOPkHvDN7FQze9TM1pnZhXlvT8oH8/T4qnuZFOlEbZi7h5wDvpl1AV8CTgOOBM40syPz3KaUf2q2Hk/T7sEyu0MQaQtz57ZtsIf8c/jHAuvc/XF33wlcD5ye8zY73pIl4/ufmj0bVq0arcwdGZncOpNAr1AvbW3r1manIFd5V9oeBDyZGh4GfiU9g5mtAFYALMyjQ5cOMzQEV145PpOyYwdccEHocXYqlbUK9NL22jhnn8g7h58VJ8YcVXdf7e4D7j4wf/78nJPT/iq9nnBkRC1zREo5dESwh/wD/jBwcGq4F3g65212NLW+EamOA4azqK8zgj3kH/C/DxxmZovMbCawFLg55212NJWKiVQneflOJ2WScg347v4a8BHgW8DDwA3u/mCe2+x0q1bphUEik9FJmaTc2+G7+y3u/mZ3P9TdV+W9vU43OAgnnlj/9XbOTa90kqT1WqfQk7ZtaN26+q9zGo7hCvzSFhzo6oLVq0e7G+kE6kunDeVZJln60nF1kyytJnlnsu3prGAPyuG3pUaUSepBLGlFSbCHziq7Tyjgt6FVq0LZZFo9K3KVq5dWlQT7Tiu7Tyjgt6HBwVA22dcXAn1fH1x9NXzoQ/UJ/Ar20sr6+jqv7D6hgN8iJnqhSanBQVi/HvbsCX8hu8sFkU7iHn4PnRjsQZW2LaGaF5pMpFKXCyLSGZTDbwHVvNBkIp30NGHT6PZJCk4BvwVU80KTSoaGQlGQNID75D9z5zY71dIhFAZaQLnmY9OmjS/TT5f19/TAnDlw1lmwe3f90tOwfOxkgmYRTLVGfOtWXSRq1UrnSRMp4LeArGaWEIK4+2iZ/sknw9lnh2H30B3yyy/XPz3JU7e1fvr76vjjLMqPulEdGU3lItHsY1Nv7bpfOVLAL5ByLXFKm1l2dY1fdscOuPPO1jn3c20HrUBQXqtfJJJXEBYtXS1CAb8gkpY4Se48ybWng37SzHLPnqYmdcqSC1VD20E3I3C1W3elRaiXSNbb5q8gzJsCfkFMpiVOKz0SPm0aXHNN+K2+9lqT20E3MvC3W9CfrKkWObXCXUYLU8AviEotcUqLehYvHh9PihhfzOCqqwr4kIsCiXQoBfyCKJdrnzdvfFFPaVm9WegDf+bMxqS1Wu4FDPalFPylgyjgF0RWS5xkeKInZN3h/vuLF7P6+pqdgklSk0dpcwr4BZHV4dnq1fD889UtPzICu3blm8bJMGvh3gjT5c8ibcS8QCf1wMCAr127ttnJKJT+/lCM04oKdGrVx2QrStruAEhRmdl97j4w0XzK4RdcuYeuiq7linOqofJ+aXEK+AWVtMw5++zK/eAUMbB2xMsl1JRQWpACfgGVPoS1fXv2fN3do33d56GvL7ShnzEje3pyIeruDp903UPhW+eIdKC2CPiTfTlI0VXbd/3ISNjXvfeufxpmzgy59MFBuOKKsZXJyYNUSV8+W7aET/KyFQV7kWJq+Reg1OPlIEUzmb7rV6zI56GrdInE4GDrHksRGdXyOfx6vBykaCbTdcKOHfn0iLlrV2sfQxEZr+UDfq0vBymiJUuanYKglY+hiIzX8gG/XG64lToYK3XLLc1OQdDKx1BExmv5gF+uS4JWbhZYKWc91Tb53d3woQ+NX37GjPBWrKztpI9hu1WMi3Silg/45bokaOVKxnI5664uWLZscm3vu7pCq5otW+DSS8cfqyuugG3bwjzljuFEffWLSItw98J83vKWt7i4X3ON++zZ2U/zzJ4dpvf1Vd+ZeLJMNdvt63M3C3+TZcptq68vt0MgIpMArPUqYmzL5/DbUXLXUu5VhitXZhdlzZiR/VRuNa2WKuXi27FiXKQT1RTwzex3zOxBM9tjZgMl0z5hZuvM7FEzO6W2ZHaWoaEQoHfvzp6edKaWVTxT7on+dHDOKo+v1Ly1HSvGRTpRrTn8HwPvB76bHmlmRwJLgaOAU4FLzSwjvyql0jntSpKHy5L33CZPuE4UnMvl5Mttb+PG9qwYF+lENQV8d3/Y3R/NmHQ6cL27v+ruTwDrgGNr2VanqLZbhXLFNOV619y+vXJOPqv4CMKFoh0rxkU6UV5l+AcBT6aGh+O4ccxshZmtNbO1mzdvzik5rWMy5eJZufIkOHd3jx0/MlI5J797d+Vc/ODg2LsJUDNNkVYzYcA3szvM7McZn9MrLZYxLrN02d1Xu/uAuw/Mnz+/2nS3rcmUi5fLlQ8OZretr5STT3Lt1eTi1UxTpDVN2Hmau58v3zz+AAAKLUlEQVQ8hfUOAwenhnuBp6ewno6zatXYzuAqKVepC+XvFJKcfHr9SU6+2k7SKlXwqphHpLjyKtK5GVhqZnuZ2SLgMODenLbVVkrLy7u7y78ApdIDWOXuFCaTky9HzTRFWlOtzTJ/08yGgeOBb5rZtwDc/UHgBuAh4Dbgw+5eIT8qaeny8i1b4KqrJt9KplLLmtLy+MnmytVMU6Q11dpK5+vu3uvue7n7Ae5+SmraKnc/1N0Pd/dba09q58rK9b/hDeH1h+UqTPNsWaNmmiKtybxA794cGBjwtWvXNjsZhVb6whcIwbbRzSSTJp4bN4acfXLnICKNZ2b3ufvAhPMp4LeW/v7sppV9ffm+31ZEiqvagK++dOqoEV0Iq8JURKZKAb9OGtU2XRWmIjJVCvh10qh366rCVESmSgG/ThpV1KJ+bURkqiZ80laqs3BhdmVqHkUt1T4RKyKSphx+naioRUSKTgG/TlTUIiJFpyKdOlJRi4gUmXL4DdSIdvoiIuUoh98gpV0iJO30QXcFItIYyuE3SKPa6YuIlKOA3yDqEkFEmk0Bv0HUJYKINJsCfoOonb6INJsCfoOonb6INJta6TSQ2umLSDMphy8i0iEU8EVEOoQCvohIh1DAFxHpEAr4OVP/OSJSFGqlkyP1nyMiRaIcfo7Uf46IFIkCfo7Uf46IFIkCfo7Uf46IFIkCfo7Uf46IFIkCfo7Uf46IFIla6eRM/eeISFEohy8i0iFqCvhm9jkze8TMHjCzr5vZvqlpnzCzdWb2qJmdUntSRUSkFrXm8NcAR7v7LwA/AT4BYGZHAkuBo4BTgUvNrKvGbYmISA1qCvjufru7vxYH7wZ64/+nA9e7+6vu/gSwDji2lm2JiEht6lmGvxy4Nf5/EPBkatpwHDeOma0ws7Vmtnbz5s11TI6IiKRN2ErHzO4A3pQxaaW73xTnWQm8BiRdg1nG/J61fndfDawGGBgYyJxHRERqN2HAd/eTK003s2XAu4GT3D0J2MPAwanZeoGnp5pIERGpXa2tdE4FPg68193T3YTdDCw1s73MbBFwGHBvLdsSEZHa1Prg1ReBvYA1ZgZwt7uf5+4PmtkNwEOEop4Pu/vuGrclIiI1qCngu/viCtNWAeo1RkSkIPSkrYhIh1DAFxHpEAr4IiIdQgFfRKRDKOCLiHQIBXwRkQ6hgF+joSHo74dp08LfoaGJlhARaQ698aoGQ0OwYgXsiM8Yb9gQhkFvuRKR4lEOvwYrV44G+8SOHWG8iEjRKODXYOPGyY0XEWkmBfwaLFw4ufEiIs2kgF+DVatg9uyx42bPDuNFRIpGAb8Gg4OwejX09YFZ+Lt6tSpsRaSY1EqnRoODCvAi0hqUwxcR6RAK+CIiHUIBX0SkQyjgi4h0CAV8EZEOYe7e7DS8zsy2AY82Ox0leoAtzU5EBqWrekVMExQzXUVMExQzXUVKU5+7z59opqI1y3zU3QeanYg0M1tbtDSB0jUZRUwTFDNdRUwTFDNdRUzTRFSkIyLSIRTwRUQ6RNEC/upmJyBDEdMEStdkFDFNUMx0FTFNUMx0FTFNFRWq0lZERPJTtBy+iIjkRAFfRKRDNDzgm9nvmNmDZrbHzAZKpn3CzNaZ2aNmdkqZ5ReZ2T1m9piZfcXMZtY5fV8xs/vjZ72Z3V9mvvVm9qM439p6pqHM9i4ys6dSaVtSZr5T4/FbZ2YXNiBdnzOzR8zsATP7upntW2a+3I/XRPtuZnvF73ddPIf680hHansHm9l3zOzheM5fkDHPCWb2Uup7/VSeaUptt+L3YcHfxmP1gJkd04A0HZ46Dveb2VYz+2jJPLkfLzP7spk9Z2Y/To2bZ2ZrYtxZY2b7lVl2WZznMTNbVu+01czdG/oBjgAOB+4CBlLjjwR+COwFLAJ+CnRlLH8DsDT+fxnwoRzT+nngU2WmrQd6GnjcLgL+aIJ5uuJxOwSYGY/nkTmn613A9Pj/Z4HPNuN4VbPvwPnAZfH/pcBXcj42C4Bj4v9zgZ9kpOkE4F8bdR5V+30AS4BbAQOOA+5pcPq6gGcJDxQ19HgBbweOAX6cGveXwIXx/wuzznNgHvB4/Ltf/H+/Rn+3lT4Nz+G7+8PunvU07enA9e7+qrs/AawDjk3PYGYGnAh8NY66EnhfHumM2zoDuC6P9efkWGCduz/u7juB6wnHNTfufru7vxYH7wZ689xeBdXs++mEcwbCOXRS/J5z4e7PuPsP4v/bgIeBg/LaXp2dDlzlwd3Avma2oIHbPwn4qbtvaOA2AXD37wLPl4xOnzvl4s4pwBp3f97dXwDWAKfmltApKFIZ/kHAk6nhYcb/OLqBF1MBJmueevl1YJO7P1ZmugO3m9l9ZrYipzSU+ki8vf5ymVvKao5hnpYTcoVZ8j5e1ez76/PEc+glwjmVu1h89MvAPRmTjzezH5rZrWZ2VCPSw8TfR7PPpaWUz2w143gd4O7PQLiQA/tnzNPsYzahXLpWMLM7gDdlTFrp7jeVWyxjXGmb0WrmmVCV6TuTyrn7X3P3p81sf2CNmT0ScwZTVildwN8DnyHs72cIxU3LS1eRsWzN7W6rOV5mthJ4DRgqs5q6H6/SZGaMy+X8mSwzmwPcCHzU3beWTP4Bodhie6yX+QZwWN5pYuLvoynHCiDWy70X+ETG5GYdr2o07ZhVK5eA7+4nT2GxYeDg1HAv8HTJPFsIt5bTYw4ta56a02dm04H3A2+psI6n49/nzOzrhCKFmgJYtcfNzP4R+NeMSdUcw7qnK1ZOvRs4yWNhZsY66n68SlSz78k8w/E7fiPjb93rysxmEIL9kLt/rXR6+gLg7reY2aVm1uPuuXbKVcX3kcu5VKXTgB+4+6bSCc06XsAmM1vg7s/Eoq3nMuYZJtQxJHoJdZWFUaQinZuBpbElxSLCVfve9AwxmHwH+O04ahlQ7o6hFicDj7j7cNZEM9vbzOYm/xMqLn+cNW+9lJSf/maZ7X0fOMxCS6aZhNvim3NO16nAx4H3uvuOMvM04nhVs+83E84ZCOfQt8tdoOoh1g9cDjzs7l8oM8+bknoEMzuW8JscyStNcTvVfB83Ax+IrXWOA15KijQaoOzddTOOV5Q+d8rFnW8B7zKz/WKR67viuOJodC0xIVgNA68Cm4BvpaatJLS0eBQ4LTX+FuDA+P8hhAvBOuBfgL1ySOM/A+eVjDsQuCWVhh/Gz4OEoo28j9vVwI+ABwgn34LSdMXhJYTWID9tULrWEcot74+fy0rT1ajjlbXvwJ8TLkYAs+I5sy6eQ4fkfGzeRrilfyB1fJYA5yXnF/CReEx+SKj0/tUGfGeZ30dJugz4UjyWPyLVoi7ntM0mBPA3psY19HgRLjbPALtirPogoa7nTuCx+HdenHcA+KfUssvj+bUOOLcRx2wyH3WtICLSIYpUpCMiIjlSwBcR6RAK+CIiHUIBX0SkQyjgi4h0CAV8EZEOoYAvItIh/gtuRpRAOyfvUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_2d_space(X_ros_pca, y_ros, 'Balanced dataset (2 PCA components) using random oversampling')\n",
    "plot_2d_space(X_smote_pca, y_smote, 'Balanced dataset (2 PCA components) using SMOTE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can clearly see that new the observations tagged as 1 (fraudulent transactions) are present in different coordinates on the graphs above. This is the SMOTE algorithm works.\n",
    "\n",
    "Also, by applying the PCA we can visualize the data in 2D and now we can see that the patterns for fraud and non-fraud transactions are distinct and each class has its own cluster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='metrics'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usually accuracy is the first metric that comes to mind when someone is assessing a model performance. However, we must be careful. The data in this case is highly unbalanced, so accuracy is not a good metric at all. If we created a model that always classifies a transaction as non-fraudulent, we would have an astonishing accuracy of 99.83%! So, what is the solution? We should use other metrics to consider a model as good or bad.\n",
    "\n",
    "The metrics to be used will be the [Area Under the ROC curve](https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve) (also called AUC), and the [recall and precision](https://en.wikipedia.org/wiki/Precision_and_recall) scores obtained from the confusion matrix.\n",
    "\n",
    "The ROC curve is a plot with the true positive rate on the y-axis and false positive rate on the x-axis. The true positive rate answers the question \"When the actual classification is positive, how often does the classifier predict positive?\" and the false positive rate answers the question \"When the actual classification is negative, how often does the classifier incorrectly predict positive?\"\n",
    "\n",
    "The AUC shows how good the classifier is in separating the classes. The closer to 1, the better is the classifier.\n",
    "\n",
    "Precision answers the question \"what proportion of positive identifications was actually correct?\" and recall answers \"what proportion of actual positives was identified correctly?\"\n",
    "\n",
    "With these 3 metrics the we can to tell whether the model performance is good or poor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='models'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models and Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The models to be used are shown below. There are also functions to plot the confusion matrix and the ROC curve for the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifiers = []\n",
    "\n",
    "classifiers.append(('Logistic Regression', LogisticRegression(random_state=42)))\n",
    "classifiers.append(('Naive Bayes', GaussianNB()))\n",
    "classifiers.append(('KNN', KNeighborsClassifier()))\n",
    "#classifiers.append(('SVM', SVC(random_state=42, probability=True))) #This one takes a very long time to run!\n",
    "classifiers.append(('Decision Tree', DecisionTreeClassifier(random_state=42)))\n",
    "classifiers.append(('Random Forest', RandomForestClassifier(random_state=42)))\n",
    "\n",
    "#Ensemble classifier - All classifiers have the same weight\n",
    "eclf = VotingClassifier(estimators=classifiers, voting='soft', weights=np.ones(len(classifiers)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ensemble model used here is a [soft voting classifier](http://www.datajango.com/heterogeneous-ensemble-learning-hard-voting-soft-voting/) which puts the same weight to all individual classifiers' class probabilities. It was already proven in many competitions that classifiers that combine predictions from more than one single classifier can achieve a better performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\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",
    "    #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",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from scipy import interp\n",
    "\n",
    "def plot_CM_and_ROC_curve(classifier, X_train, y_train, X_test, y_test):\n",
    "    '''Plots the ROC curve and the confusion matrix, and calculates AUC, recall and precision.'''\n",
    "    \n",
    "    name = classifier[0]\n",
    "    classifier = classifier[1]\n",
    "\n",
    "    mean_fpr = np.linspace(0, 1, 100)\n",
    "    class_names = ['Non-Fraud', 'Fraud']\n",
    "    confusion_matrix_total = [[0, 0], [0, 0]]\n",
    "    \n",
    "    #Obtain probabilities for each class\n",
    "    probas_ = classifier.fit(X_train, y_train).predict_proba(X_test)\n",
    "    \n",
    "    # Compute ROC curve and area the curve\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, probas_[:, 1])\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    plt.plot(fpr, tpr, lw=1, alpha=1, color='b', label='ROC (AUC = %0.7f)' % (roc_auc))\n",
    "    plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n",
    "             label='Chance', alpha=.8)\n",
    "    plt.xlim([-0.05, 1.05])\n",
    "    plt.ylim([-0.05, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('ROC curve - model: ' + name)\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "    \n",
    "    #Store the confusion matrix result to plot a table later\n",
    "    y_pred=classifier.predict(X_test)\n",
    "    cnf_matrix = confusion_matrix(y_test, y_pred)\n",
    "    confusion_matrix_total += cnf_matrix\n",
    "    \n",
    "    #Print precision and recall\n",
    "    tn, fp = confusion_matrix_total.tolist()[0]\n",
    "    fn, tp = confusion_matrix_total.tolist()[1]\n",
    "    accuracy = (tp+tn)/(tp+tn+fp+fn)\n",
    "    precision = tp/(tp+fp)\n",
    "    recall = tp/(tp+fn)\n",
    "    print('Accuracy = {:2.2f}%'.format(accuracy*100))\n",
    "    print('Precision = {:2.2f}%'.format(precision*100))\n",
    "    print('Recall = {:2.2f}%'.format(recall*100))\n",
    "    \n",
    "    # Plot confusion matrix\n",
    "    plt.figure()\n",
    "    plot_confusion_matrix(confusion_matrix_total, classes=class_names, title='Confusion matrix - model: ' + name)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='models-rus'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results using random undersampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 98.23%\n",
      "Precision = 7.94%\n",
      "Recall = 87.16%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/vt8RGWOKbP58eOwx2LLF/brbvNnviKKCJYVC+Ne/4Ndf4e9/t4RgTMzasweeeQY++sgtN23qGpYbN/Y3rihhSaEQ5syBAQMsIRgTs376yU12s3On6518yy1w/fWFqweOc5YU8jFmzJEOjOAGrLv/fv/iMcYUU1KSazw+7TR48MGonivZL5YUcrFmDezd62YxGz4cypd36wcOhEsv9Tc2Y0whqLpBxc4/H044ASpUgAkToFq1uBnArqRZUsghMxNatIDkZOjQwZUMypb1OypjTKFt3uwakr/7zg1cN3y4W1+jhr9xRTlLCjls2+YSw6JFfkdijCmSrCyYMcPdapqW5noit27td1Qxw5JCDh98AFWq+B2FMaZI1q+HRx6B5cvd8sUXu9sFTzrJ37hiiCUFz9698N//wocfutnPjDExZvNmuO46N21htWquuqhjR7+jijmWFDz/+7/w6qtQs6a768gYE2Nq1YKLLnK3mt59N1Sq5HdEMSmsze8i0kVE1ojIzyIyPJftSSIyV0SWishyEfHtN/rSpa4PwpIl7kYFY0yUO3TItRusWnVk3cMPu1tNLSEUWdiSgoiUBsYDlwLNgT4i0jzHbiOBt1X1dOBa4IVwxZOfjAw3imnwCKbGmCi2dCn06eNmp3rssSNj0tttpsUWzuqj9sDPqroOQESmAd2BlKB9FMgepPxEwJfBR377zfV8v+giP85ujAnZ/v2udDBjhluuX9/dN27JoMSEMymcCmwKWk4FOuTYZxTwqYjcAZwA5Pq1LCKDgEEASWHogfj55+6Oo5o1S/zQxpiS8u238I9/wNatbliKAQPgpptcG4IpMeFMr5LLOs2x3AeYqKq1ga7AJBE5JiZVnaCq7VS1XfXq1Us0yLQ099m68soSPawxpiTt2wcjR7qE0Lw5vPWWG7fIEkKJC2dJIRUInmiyNsdWD/0V6AKgqvNFpAJQDdgWxriOkj3jnt1xZEyUUXWPUqWgYkXX32DHDnfbqQ1gFzbhLCksBBqJSD0RKYdrSJ6VY59fgE4AItIMqAAUYj6z4lu82DUwV6wYybMaY/K1fTsMGwZTphxZ17Ur3HCDJYQwC1tSUNUMYAgwG1iNu8tolYiMFpErvN3uAW4WkR+AqcCNqpqziimsduyAli0jeUZjTJ5UYeZMuOoq+OorePNNd+upiZiwdl5T1Y+Bj3OseyjoeQrg67x3GRnQsKGfERhjADeD1aOPwsKFbvncc91QxdnDFJuISPgezevXu/m6jTE+ycqCadNg/HhXKqhSxbUfdO4Mktv9KiacEj4pHDgAxx3ndxTGJLjPP3cJ4ZJLXFvCn/7kd0QJK+GTwqFDUKdOwfsZY0rQ4cPuF9mJJ7q7ix56CH75xcaYiQIJ3w3wwAE3O58xJkJSUtxdRA8+6BqWAerWtYQQJRK+pJCWZtVHxkTEwYPw8sswebJrRzh4EHbtsrkOokzCJwUrKRgTAYsXuzuLNm1y1UU33OB6JFeo4HdkJoeETwpWUjAmjFTdZCVvv+2WGzZ07QfNcw6YbKJFwicFKykYE0YibriAMmXgr3+FG2+EsmX9jsrkI+GTgpUUjClhu3dDauqRoQIGDoQuXdww1ybqhXT3kYiUE5G47PdrJQVjSogqfPop9O4N99zjJikBN5KpJYSYUWBSEJHLgBXAZ95ysoi8F+7AIsVKCsaUgG3bXCJ44AFXUqhf391dZGJOKNVHo3GT48wFUNVl8VJqyMyE9HQbWsWYIsvKgvffh+eec7OinXAC/O1v0L27DVERo0JJCodVdbcc/QeO6Eim4XLwoCsl2GfXmCJ65BH44AP3/PzzYfhwqFHD35hMsYSSFFaLyNVAKRGpB9wFLAhvWJFh7QnGFNOll7ppMocNg4svtl9YcSCUhuYhwBlAFvAucBCXGGKetScYU0hr18LUqUeW27d38x/YiKZxI5SSwiWqeh9wX/YKEemFSxAxzUoKxoQoPR0mToTXX3eTkDRvDm3auG32yyquhFJSGJnLuhElHYgfrKRgTAhWroTrr4cJE1xC6N3bZqaKY3mWFETkEqALcKqI/DNoU2VcVVLMs5KCMflIS4MXX3TVRaqQlAQjR0Lbtn5HZsIov+qjbcBKXBvCqqD1e4Hh4QwqUqykYEw+XnjBJYTgAezs/u24l2dSUNWlwFIRmayqcdkLxUoKxuRjwAD4+We44w4bwC6BhNKmcKqITBOR5SLyU/Yj7JFFgJUUjAny9ddw552u3QDclJgvvmgJIcGEkhQmAv8CBLgUeBuYFsaYIsZKCsYAO3e64SmGDoV58+DDD/2OyPgolKRwvKrOBlDVtao6EvhLeMOKDCspmISmCh9/7O4m+vRTN+HNsGFwxRV+R2Z8FEo/hUPixrhYKyKDgV+BuOjHbiUFk7C2bIF//MOVDMB1Qhs5EmrV8jcu47tQksLfgIrAncBjwInAgHAGFSlWUjAJa8EClxAqVXID2HXrZj2SDRBCUlDV77yne4EbAESkdjiDipQDB6ByZb+jMCZCgn8Fde/uhrvu1QuqVfM3LhNV8m1TEJEzRaSHiFTzlluIyJvEyYB4VlIwCSEzE958Ey6/HH791a0TgUGDLCGYY+SZFETkcWAy0Bf4j4iMwM2p8APQODLhhZclBRP3fvoJ+veHsWPhjz/gyy/9jshEufyqj7oDbVQ1TUROAjZ7y2siE1r4WUOziVvp6fDaa24Qu8xMqFkTRoyAs8/2OzIT5fJLCgdVNQ1AVXeKyI/xlBDASgomTq1Z4+4kWr/eVRNdfTUMGWK/gExI8ksK9UUke3hsAeoGLaOqvQo6uIh0AZ4DSgOvquoTuexzNTAKN5vbD6p6XejhF4+VFExcKlsWUlPhtNPgwQchOdnviEwMyS8pXJljeVxhDiwipYHxwMVAKrBQRGapakrQPo2A+4E/q+ouEYlo/wcrKZi48eOP0KSJKxnUr+/aENq0gXLl/I7MxJj8BsSbU8xjtwd+VtV1ACIyDddOkRK0z83AeFXd5Z1zWzHPWShWUjAxb88eGDMGZs1yndE6d3brzzzT37hMzAplmIuiOhXYFLSc6q0L1hhoLCLfisgCr7rpGCIySEQWicii7du3l1iAVlIwMW3uXLjqKpcQypWD3bv9jsjEgVB6NBdVbt0jNZfzNwI6ArWBb0Skpaoe9elW1QnABIB27drlPEaRWUnBxKQdO+Cpp2COV5hv08a1HdSt62tYJj6EnBREpLyqHirEsVOBOkHLtXG3tebcZ4GqHgbWi8gaXJJYWIjzFJmVFEzMWb0abr/dVRsdd5yb66B3bzcRjjEloMBPkoi0F5EVwH+95TYi8nwIx14INBKReiJSDrgWmJVjn/fxRlz1ek03BtYVIv5isZKCiTn160OVKq6/wdtvu9tNLSGYEhRKSWEscDnuCxxV/UFEChw6W1UzRGQIMBt3S+rrqrpKREYDi1R1lrets4ikAJnA31V1RxGvpdCspGCiXlYWvP8+XHyxG7yufHl49VU3AY4NYGfCIJSkUEpVN8rRH8DMUA6uqh8DH+dY91DQcwWGeo+Iysx0nT5tylkTtTZuhEcegWXLICXFdUgDOOkkf+MycS2UpLBJRNoD6vU9uAOI+ek4Dx50pQT7sWWiTkYGvPUWTJjgfrlUrQrnnON3VCZBhJIUbsVVISUBW4HPvXUxzdoTTFRaswZGj3b/gpsF7e67bYx3EzGhJIUMVb027JFEmLUnmKiTmgr9+rm6zVq13AB2HTr4HZVJMKEkhYXeraLTgXdVdW+YY4oIKymYqFO7Nlx2mftg3nabfUCNL0KZea2BiJyDu6X0YRFZBkxT1Wlhjy6MrKRgfHfgAIwfD5dcAq1bu3UPPmgNXcZXId3grKrzVPVOoC2wBzf5TkyzkoLx1fz5ro/B9Onw+OOgXkd9SwjGZwWWFESkIm4gu2uBZsBMIOZvhbCSgvHFnj3wzDPw0UduuVkzKx2YqBJKm8JK4APgKVX9JszxRIyVFEzEzZkDTz4JO3e6AewGD4a+faF0ab8jMyYglKRQX1Wzwh5JhFlJwUTU3r3w2GOupNC2reuIlpTkd1TGHCPPpCAiz6jqPcC/ReSYkUlDmXktmllJwYSdqnuUKuWGqBg+3CWFXr1svCITtfIrKUz3/i3UjGuxwkoKJqw2b3YlgzPPhBtvdOuyJ8AxJorlN/Pa997TZqp6VGLwBror7sxsvrKSggmLrCw3eun48e6Xx/r1cN11Ni2miRmhlGEH5LLuryUdSKRZScGUuPXrYeBAePpp9wHr3NmNYWQJwcSQ/NoUrsHdhlpPRN4N2lQJiPl5/w4csOFkTAnJzIQ33oBXXoHDh6F6dbj/fjj/fL8jM6bQ8mtT+B7YgZsxbXzQ+r3A0nAGFQlpaXDyyX5HYeKCCCxY4BJCz55w552uYdmYGJRfm8J6YD1uVNS4Y20KplgOHYL9+93cBqVKuVtMt251DcvGxLA82xRE5Cvv310isjPosUtEdkYuxPCwNgVTZEuWQJ8+ridy9vAUSUmWEExcyK/6KHvKzWqRCCTSDhywpGAKaf9+GDcOZsxwy2XKwO7dbmpMY+JEniWFoF7MdYDSqpoJnA3cApwQgdjCKi3Nqo9MIcyb5wawmzHDDUsxaJC7s8gSgokzoQxz8T5wpog0AN4EPgKmAJeHM7Bws5KCCYkqPPoozJzplps3h4cegoYN/Y3LmDAJJSlkqephEekFjFHVsSISF3cfWUnBFEjE3aZWrpyb+KZPHxvAzsS1kKbjFJGrgBuAHt66suELKTKspGDytH27mxrz9NPd8k03QdeubmY0Y+JcqD2a/4IbOnudiNQDpoY3rPCzkoI5hqqrJrrqKrj3XvjjD7e+bFlLCCZhhDId50oRuRNoKCJNgZ9V9bHwhxZeVlIwR/n1V9d2sHChWz7vPMjI8DcmY3wQysxr5wGTgF8BAWqKyA2q+m24gwsnKykYwA1gN20avPACHDwIVarA3//uxi2y2dBMAgqlTeFZoKuqpgCISDNckmgXzsDCzUoKBnB3Ev3nP+55ly5wzz12m6lJaKEkhXLZCQFAVVeLSEwP+5iZ6YapKV/e70iM73r2dD2Uhw+3AeyMIbSksEREXsaVDgD6EuMD4mUPcWG1AwkoJcW1G/Tv75bPOAPef9+GtzbGE0pSGAzcCdyLa1P4Gng+nEGFm7UnJKCDB+Hll2HyZNeO0Lr1kVtOLSEYE5BvUhCRVkAD4D1VfSoyIYWftSckmMWL4ZFHXN+DUqXghhugWTO/ozImKuU3yc4DuBnWluCGuRitqq9HLLIwspJCgti3D8aOhXe9OaIaNnQNy82b+xuXMVEsv85rfYHWqnoVcCZwa2EPLiJdRGSNiPwsIsPz2a+3iKiIROSOJispJIgXX3QJoUwZGDwYJk2yhGBMAfKrPjqkqvsBVHW7iITS+zlARErjZmy7GEgFForIrOA7mbz9KuHaLL4rVOTFYCWFOKZ65A6Cm2+GzZvhjjugfn1/4zImRuSXFOoHzc0sQIPguZpVtVcBx26P6/28DkBEpgHdgZQc+z0CPAUMK0zgxWElhTikCrNnw3vvuTkPypZ1HdGefdbvyIyJKfklhStzLI8r5LFPBTYFLacCHYJ3EJHTgTqq+qGI5JkURGQQMAggKSmpkGEcy0oKcWbbNnj8cfjmG7f8ySdwxRX+xmRMjMpvjuY5xTx2br0ANLDRVUc9C9xY0IFUdQIwAaBdu3ZawO4FspJCnMjKcn0Mxoxxf9SKFeFvf4Nu3fyOzJiYFUo/haJKxc3alq02sDlouRLQEvhSXB1wTWCWiFyhqovCGJeVFOLBpk1uALvFi93yBRe4XsnVq/sblzExLpxJYSHQyBtq+1fgWuC67I2q+gdB8z+LyJfAsHAnBDjSo9nEsKVLXUI46SQ3zHWnTtZF3ZgSEHJSEJHyqnoo1P1VNUNEhgCzgdLA66q6SkRGA4tUdVbhwy0ZBw5YSSEm7d0LlSq55926we7d0L07nHiiv3EZE0cKvM1URNqLyArgv95yGxEJaZgLVf1YVRuraoPsORhU9aHcEoKqdoxEKQGspBBz0tPdEBWXXw6//OLWiUC/fpYQjClhofQ9GAtcDuwAUNUfcDOxxSyzzjEqAAAd7ElEQVQrKcSQFSvg+uvhlVdg/35YsMDviIyJa6FUH5VS1Y1ydH1tZpjiiYi0NDcXu4liaWmuR/LUqa4PQlISPPjgkUHsjDFhEUpS2CQi7QH1einfAfwU3rDCy0oKUW7lShgxwk2RWaqUqyYaNMgmwDAmAkJJCrfiqpCSgK3A5xRhHKRoYm0KUa5SJdi+HRo3dqUDG9HUmIgpMCmo6jbc7aRxw0oKUWjZMmjTxjUgn3YavPSSG7yuTDjvmjbG5FTg/zgReYWgnsjZVHVQWCKKACspRJGdO+Hpp+HTT+Hhh+Gyy9z61q39jcuYBBXKz7DPg55XAHpy9JhGMceGuYgCqm6Moqefhj17oEIFN3G2McZXoVQfTQ9eFpFJwGdhiygCbJgLn23ZAv/4B8yb55Y7dHANy7Vq+RuXMaZIw1zUA04r6UAiyUoKPlq5Em67zf0RKlWCoUNdpzQbosKYqBBKm8IujrQplAJ2AnnOohYLrKTgo8aNXSeRunXhvvugWrUCX2KMiZx8k4K4HmttcAPaAWSparGHrvablRQiKDMTpk93pYHKlaFcOXjtNffcGBN18h3mwksA76lqpveI+YQAVlKImJ9+gv794Z//dI9slhCMiVqhtCl8LyJtVXVJ2KOJECsphFl6Orz6Krzxhisp1KwJl1zid1TGmBDkmRREpIyqZgDnAjeLyFpgP25GNVXVthGKsURlZro7H23EhDBZvhxGj4YNG1zj8dVXw5AhVjQzJkbkV1L4HmgL9IhQLBGR3XHNbnYJg02bYOBAN03maafBQw+5XsrGmJiRX1IQAFVdG6FYIsLaE8KoTh3o2dPNcTBwoGtUNsbElPySQnURGZrXRlX9Z17bopm1J5SgPXtgzBg3C1r2kNbDh1sxzJgYll9SKA1UxCsxxAsrKZSQL76AJ5+EHTtg9WqYMsUlA0sIxsS0/JLCb6o6OmKRRIiVFIppxw6XDL74wi0nJ7vhrS0ZGBMXCmxTiDdWUigiVfjoI9ffYM8e9ybecQdceaWbCMcYExfySwqdIhZFBFlJoYj27oVnn3UJ4Zxz4P774ZRT/I7KGFPC8kwKqrozkoFEipUUCiEry5UQSpd2vZBHjICDB+HSS626yJg4lXDlfisphGjDBrj5Zpg48ci6Cy+Erl0tIRgTxxIuKVhJoQAZGfD669CnD/zwA8yc6YatMMYkhISbANdKCvlYs8ZNifnTT265e3e46y7rhGZMAkm4pGAlhVxkZMDLL7sB7LKy3AxoI0dC+/Z+R2aMibCESwpWUshF6dJuRjRVV210662WOY1JUAmXFNLSbDh/wGXH/fuhenXXcPzgg/D779C6td+RGWN8lHANzVZSAObPd0NajxzpSgfgqowsIRiT8BKypJCwNSN//OF6JH/0kVv+05/cuipV/I3LGBM1wlpSEJEuIrJGRH4WkeG5bB8qIikislxE5ojIaeGMBxK0pKAKc+bAVVe5hFCuHNx5p+uDYAnBGBMkbCUFESkNjAcuBlKBhSIyS1VTgnZbCrRT1QMicivwFHBNuGKCBCwpqLpqotmz3XLbtm45KcnfuIwxUSmc1UftgZ9VdR2AiEwDugOBpKCqc4P2XwBcH8Z4gAQsKYhA/fouE951l5sExwawM8bkIZxJ4VRgU9ByKtAhn/3/CnyS2wYRGQQMAkgq5i/chCgpbN4MqalH+hn07+8mwqlRw9+4jDFRL5w/GXMbIEdz3VHkeqAd8L+5bVfVCaraTlXbVa9evVhBxXVJISsLpk51dxbdfz/s9MY0LFPGEoIxJiThLCmkAnWClmsDm3PuJCIXASOAC1T1UBjjAeK4pLBuHTz6KCxf7pbPP9+qiYwxhRbOpLAQaCQi9YBfgWuB64J3EJHTgZeBLqq6LYyxBMRdSSEjww1P8eqrcPiw64x2//0uKRhjTCGFLSmoaoaIDAFm4+Z7fl1VV4nIaGCRqs7CVRdVBGaIG475F1W9IlwxgSspxFVSGDHC3W4KrhH5rrugYkV/YzLGxKywdl5T1Y+Bj3Oseyjo+UXhPH9uDhyIs+qjPn3cqKYPPABnnul3NMaYGJdwlc4xX1JYsgQmTDiynJwM77xjCcEYUyISapiLzExX7V6+vN+RFMH+/TB2LPz73265XTvXEQ3cKKfGGFMCEiopZJcSYm42yW+/hcceg23b3O2lAwZAq1Z+R2WMiUMJlRRirj1h92545hn4xOvT16IFPPQQNGjgb1zGmLiVUEkh5toTXnnFJYTy5eG221yjsvU9MMaEUUIlhZgoKageqd+65RbXK/n226F2bX/jMsYkhIRKClFdUlCF99+HWbPcfMnlyrkp4h5/3O/ITDEcPnyY1NRUDh486HcoJkFUqFCB2rVrU7Zs2SK9PuGSQlSWFFJT3RAVixa55c8+g8su8zcmUyJSU1OpVKkSdevWRWLuDgcTa1SVHTt2kJqaSr169Yp0jISqoI66IS6ysmDyZLjmGpcQ/vQn+Mc/oGtXvyMzJeTgwYNUrVrVEoKJCBGhatWqxSqZWknBL+vWwcMPw6pVbvnSS+Gee2wmtDhkCcFEUnE/bwmVFKKqpPDjjy4h1Kjhhqg491y/IzLGmMSqPvK9pLBr15Hnl14K994Lb79tCcGEVenSpUlOTqZly5Z069aN3bt3B7atWrWKCy+8kMaNG9OoUSMeeeQRVI9Me/LJJ5/Qrl07mjVrRtOmTRk2bFiu53j//fcZPXr0UevatGlDnz59jlrXsWNHFmW3nQEbNmygZcuWgeXvv/+e888/nyZNmtC0aVMGDhzIgQMHinX969evp0OHDjRq1IhrrrmG9PT0Y/ZJT0/npptuolWrVrRp04Yvv/zyqJibNGlCcnIyycnJbNvmBnR+6aWXaNWqFcnJyZx77rmkpLhJJSdPnhzYNzk5mVKlSrFs2TIAunTpQps2bWjRogWDBw8mMzMTgAcffJDWrVuTnJxM586d2bzZzTKwa9cuevbsSevWrWnfvj0rV64MxHv++eeTkZFRrPcmV6oaU48zzjhDi2rcONVbby3yy4suLU312WdV//xn1XXrfAjA+CUlJcXvEPSEE04IPO/Xr58++uijqqp64MABrV+/vs6ePVtVVffv369dunTRcePGqarqihUrtH79+rp69WpVVT18+LCOHz8+13OcffbZun379sBySkqKtmzZUmvVqqX79u0LrL/gggt04cKFgeX169drixYtVFV1y5YtmpSUpPPmzVNV1aysLJ0xY4Zu2bKlWNd/1VVX6dSpU1VV9ZZbbtEXXnjhmH3GjRunN954o6qqbt26Vdu2bauZmZm5xpztjz/+CDyfOXOmXnLJJcfss3z5cq1Xr94xr8nKytJevXoF4go+1nPPPae33HKLqqoOGzZMR40apaqqq1ev1gsvvDCw36hRo/Stt97K9Zpz+9zhRqcu8DvWSgrhtmgRXHstvPUWpKfD0qURDsCYI84++2x+/fVXAKZMmcKf//xnOnfuDMDxxx/PuHHjeOKJJwB46qmnGDFiBE2bNgWgTJky3Hbbbccc86effqJ8+fJUq1YtsG7KlCnccMMNdO7cmVmzZoUU2/jx4+nfvz9nn3024OrGe/fuzcknn1zk61VVvvjiC3r37g1A//79ef/994/ZLyUlhU6dOgFQo0YNqlSpclSJJjeVK1cOPN+/f3+udflTp049qrSU/ZqMjAzS09MDr8nrWMFxNW3alA0bNrB161YAevToweTJkwt4BwovoZJCRNsU9u1zdxINHuxuOW3YECZOhF69IhSAiUYiJf8IVWZmJnPmzOGKK9yUJatWreKMM844ap8GDRqwb98+9uzZw8qVK4/Znptvv/2WttmDM3qmT5/ONddcQ58+fZg6dWpI8YV6vjVr1hxVPRP8CK4aA9ixYwdVqlShTBnXfFq7du1AUgzWpk0bZs6cSUZGBuvXr2fx4sVs2nRkivmbbrqJ5OTkY6rXxo8fT4MGDbj33nsZO3bsMcedPn36MVVol1xyCTVq1KBSpUqBZAUwYsQI6tSpw+TJkwNVcW3atOHdd98FXNXaxo0bSU1NBaBly5YsXLiwwPersBIqKUSspLBsmZsn+d133QB2gwfDpEnQvHkETm6imWrJPwqSlpZGcnIyVatWZefOnVx88cVeLJrnnSqFuYPlt99+I3ju9IULF1K9enVOO+00OnXqxJIlS9jltafldtzC3i3TpEkTli1bluujSo679zSXNyi38w0YMIDatWvTrl077r77bs4555xAIpk8eTIrVqzgm2++4ZtvvmHSpEmB191+++2sXbuWJ598kkcfffSoY3733Xccf/zxR7WZAMyePZvffvuNQ4cO8cUXXwTWP/bYY2zatIm+ffsybtw4AIYPH86uXbtITk7m+eef5/TTTw/EVbp0acqVK8fevXsL8/YVKKGSQsRKClWrusHsWrWCKVNg4EAoYu9CY4rruOOOY9myZWzcuJH09HTGjx8PQIsWLY6pIlm3bh0VK1akUqVKtGjRgsWLF4d0/OD74qdOncqPP/5I3bp1adCgAXv27OHf3pDvVatWDSQIgJ07dwaqnUI9X2FKCtWqVWP37t2BBtnU1FRq1ap1zDHLlCnDs88+y7Jly5g5cya7d++mUaNGAJx66qkAVKpUieuuu47vv//+mNdfe+21x1RLTZs27ZhSQrYKFSpwxRVXMHPmzGO2XXfddYH3q3LlyvzrX/9i2bJlvPnmm2zfvv2oTmmHDh2iQoUKeb5XRRJKw0M0PYrT0Hzzzaovv1zkl+ctK0t1/nz3b7aUFFWvocokrmhraF6yZInWqVNH09PT9cCBA1qvXj397LPPVNU1PF922WU6duxYVVX94YcftEGDBrpmzRpVVc3MzNRnnnnmmON/8skn2rdv38A+tWvX1tTU1MD2L774ItBA+vzzz2u/fv00y/u/cuedd+rDDz+sqkcamhcsWBB47aRJk/S3334r1vX37t37qIbm3BrL9+/fH2gQ//TTT/W8885TVde4nt2Anp6erldeeaW++OKLqqr6008/BV4/a9YsDf5uyszM1FNPPVXXrl0bWLd3717dvHlz4LhXX321Pv/888cca+zYsXrllVeqququXbv00KFDqqo6YcIEveGGGwL7/f7779q0adNcr7k4Dc2+f8kX9lGcpNC3r+qbbxb55bnbskX1rrtUzzhDdebMEj64iXXRlhRUVS+//HJ90/uPsHz5cr3gggu0cePG2qBBAx01alTgC1tV9YMPPtC2bdtq06ZNtVmzZjps2LBjjr9//35t3ry5ZmVl6dy5c7VDhw5Hbc/IyNCaNWvq5s2b9dChQ3r77bdrq1attHXr1jpgwADdv39/YN958+bpueeeq40bN9amTZvqoEGDjtpeFGvXrtUzzzxTGzRooL1799aDBw+qqrtj6MEHH1RVdxdU9jk7deqkGzZsUFXVffv2adu2bbVVq1bavHlzvfPOOzUjI0NVXUJr3ry5tmnTRjt27KgrV64MnDO392HLli3arl27wLGGDBmihw8fVlXVXr16aYsWLbRVq1Z6+eWXB5LqvHnztGHDhtqkSRPt2bOn7ty5M3C8GTNm6NChQ3O95uIkBdFQKiWjSLt27bSguwLycuWVcN117t9iy8pyA9iNGePqpSpWhPvuc/0PjPGsXr2aZs2a+R1G2N11111069aNiy6K+LTrCatXr148/vjjNGnS5JhtuX3uRGSxqrYr6LjWo7kofvnFDWC3ZIlb7tjRJYSgxjZjEskDDzzAd99953cYCSM9PZ0ePXrkmhCKK6GSQoncfbR8ububKD0dTjrJ9Uru1CkG5/g0puScfPLJgVtdTfiVK1eOfv36heXYCZUUSqSk0KwZJCVBkyYwdCiceGKJxGaMMdEgoZJCkUoK6emuN3KvXm4E07Jl4fXXo2i4VWOMKTnWTyE/K1bA9dfDCy/AM88cWW8JwRgTp6ykkNeOL74IU6e6LqNJSTY8hTEmISRUUgippPD99+7Oos2boVQp6N8fBg1ycyYbE4O2bNnC3XffzcKFCylfvjx169alR48ezJo1iw8//NDv8EyUSaikkJZWQFL45Re4/XZXOmjcGB56CLwRIo2JRapKz5496d+/P9OmTQNg2bJlfPDBBz5HZqJVwiSFzEw4fBjKl89np6Qk6NPHNSj36+cGszOmJLXLp+/QAw8cqaZ89103ym5eQuzAOXfuXMqWLcvgwYMD67LHCJozZw69e/cOjE761ltvISKMHj2aDz74gLS0NM455xxefvllRISOHTvSoUMH5s6dy+7du3nttdc477zzyMzM5L777mP27NmICDfffDN33HEHixcvZujQoezbt49q1aoxceJETjnllJDiNv5JmIbm7FLCUd0Jdu6E++8/+j/Y0KEwYIAlBBMX8huOeunSpYwZM4aUlBTWrVvHt99+C8CQIUNYuHAhK1euJC0t7agqpoyMDL7//nvGjBnDww8/DMCECRNYv349S5cuZfny5fTt25fDhw9zxx138M4777B48WIGDBjAiBEjwn/BptgS5pvvwIGgRmZV+OQTePpp2LMHNm6EyZOtA5oJv1CHaOnVK+w3N7Rv357atWsDrvSwYcMGzj33XObOnctTTz3FgQMH2LlzJy1atKBbt25eWC6mM844gw0bNgDw+eefM3jw4MCQzieddBIrV65k5cqVgWG6MzMzrZQQI8KaFESkC/AcUBp4VVWfyLG9PPAmcAawA7hGVTeEI5ZAe8KWLa5YPm+e23DWWa7YbgnBxKEWLVrwzjvv5LqtfFBdaunSpcnIyODgwYPcdtttLFq0iDp16jBq1KijhsXOfk32/pD7vAyqSosWLZg/f35JX5IJs7BVH4lIaWA8cCnQHOgjIjlnmfkrsEtVGwLPAk+GK54D+7LomTnDTX4zbx5UrgyjRsHzz0Mu46sbEw8uvPBCDh06xCuvvBJYt3DhQr766qtc989OANWqVWPfvn15JpRgnTt35qWXXgokiZ07d9KkSRO2b98eSAqHDx9m1apVxb0cEwHhbFNoD/ysqutUNR2YBnTPsU934A3v+TtAJynsNEwhOrRjH9fufcXVI114IcyYAZdfbiUEE9dEhPfee4/PPvuMBg0a0KJFC0aNGpXrRDMAVapU4eabb6ZVq1b06NGDM888s8BzDBw4kKSkJFq3bk2bNm2YMmUK5cqV45133uG+++6jTZs2JCcnMy+7dG6iWtiGzhaR3kAXVR3oLd8AdFDVIUH7rPT2SfWW13r7/J7jWIOAQQBJSUlnbNy4sdDxbN0KKS99zV/Oy3BJwZgISJShs010idahs3P7CZ4zA4WyD6o6AZgAbj6FogRz8slw8v+cX5SXGmNMwghn9VEqUCdouTawOa99RKQMcCKwM4wxGWOMyUc4k8JCoJGI1BORcsC1wKwc+8wC+nvPewNfaKxNBWdMAewjbSKpuJ+3sCUFVc0AhgCzgdXA26q6SkRGi0j2bByvAVVF5GdgKDA8XPEY44cKFSqwY8cOSwwmIlSVHTt2UKFChSIfI6HmaDYm0g4fPkxqaupR9/obE04VKlSgdu3alC1b9qj10dDQbEzCK1u2LPXq1fM7DGNCljBjHxljjCmYJQVjjDEBlhSMMcYExFxDs4hsBwrfpdmpBvxe4F7xxa45Mdg1J4biXPNpqlq9oJ1iLikUh4gsCqX1PZ7YNScGu+bEEIlrtuojY4wxAZYUjDHGBCRaUpjgdwA+sGtODHbNiSHs15xQbQrGGGPyl2glBWOMMfmwpGCMMSYgLpOCiHQRkTUi8rOIHDPyqoiUF5Hp3vbvRKRu5KMsWSFc81ARSRGR5SIyR0RO8yPOklTQNQft11tEVERi/vbFUK5ZRK72/tarRGRKpGMsaSF8tpNEZK6ILPU+3139iLOkiMjrIrLNm5kyt+0iImO992O5iLQt0QBUNa4eQGlgLVAfKAf8ADTPsc9twEve82uB6X7HHYFr/gtwvPf81kS4Zm+/SsDXwAKgnd9xR+Dv3AhYCvzJW67hd9wRuOYJwK3e8+bABr/jLuY1nw+0BVbmsb0r8Alu5sqzgO9K8vzxWFJoD/ysqutUNR2YBnTPsU934A3v+TtAJxHJbWrQWFHgNavqXFU94C0uwM2EF8tC+TsDPAI8BcTD2NWhXPPNwHhV3QWgqtsiHGNJC+WaFajsPT+RY2d4jCmq+jX5z0DZHXhTnQVAFRE5paTOH49J4VRgU9Byqrcu133UTQb0B1A1ItGFRyjXHOyvuF8asazAaxaR04E6qvphJAMLo1D+zo2BxiLyrYgsEJEuEYsuPEK55lHA9SKSCnwM3BGZ0HxT2P/vhRKP8ynk9os/5323oewTS0K+HhG5HmgHXBDWiMIv32sWkVLAs8CNkQooAkL5O5fBVSF1xJUGvxGRlqq6O8yxhUso19wHmKiqz4jI2cAk75qzwh+eL8L6/RWPJYVUoE7Qcm2OLU4G9hGRMrgiZ37FtWgXyjUjIhcBI4ArVPVQhGILl4KuuRLQEvhSRDbg6l5nxXhjc6if7ZmqelhV1wNrcEkiVoVyzX8F3gZQ1flABdzAcfEqpP/vRRWPSWEh0EhE6olIOVxD8qwc+8wC+nvPewNfqNeCE6MKvGavKuVlXEKI9XpmKOCaVfUPVa2mqnVVtS6uHeUKVY3luVxD+Wy/j7upABGphqtOWhfRKEtWKNf8C9AJQESa4ZLC9ohGGVmzgH7eXUhnAX+o6m8ldfC4qz5S1QwRGQLMxt258LqqrhKR0cAiVZ0FvIYrYv6MKyFc61/ExRfiNf8vUBGY4bWp/6KqV/gWdDGFeM1xJcRrng10FpEUIBP4u6ru8C/q4gnxmu8BXhGRv+GqUW6M5R95IjIVV/1XzWsn+R+gLICqvoRrN+kK/AwcAG4q0fPH8HtnjDGmhMVj9ZExxpgisqRgjDEmwJKCMcaYAEsKxhhjAiwpGGOMCbCkYKKOiGSKyLKgR9189q2b12iShTznl95InD94Q0Q0KcIxBotIP+/5jSJSK2jbqyLSvITjXCgiySG85m4ROb645zaJwZKCiUZpqpoc9NgQofP2VdU2uMES/7ewL1bVl1T1TW/xRqBW0LaBqppSIlEeifMFQovzbsCSggmJJQUTE7wSwTcissR7nJPLPi1E5HuvdLFcRBp5668PWv+yiJQu4HRfAw2913byxulf4Y1zX95b/4QcmZ/iaW/dKBEZJiK9ceNLTfbOeZz3C7+diNwqIk8FxXyjiDxfxDjnEzQQmoi8KCKLxM2j8LC37k5ccporInO9dZ1FZL73Ps4QkYoFnMckEEsKJhodF1R19J63bhtwsaq2Ba4BxubyusHAc6qajPtSTvWGPbgG+LO3PhPoW8D5uwErRKQCMBG4RlVb4UYAuFVETgJ6Ai1UtTXwaPCLVfUdYBHuF32yqqYFbX4H6BW0fA0wvYhxdsENa5FthKq2A1oDF4hIa1UdixsX5y+q+hdv6IuRwEXee7kIGFrAeUwCibthLkxcSPO+GIOVBcZ5deiZuDF9cpoPjBCR2sC7qvpfEekEnAEs9Ib3OA6XYHIzWUTSgA244ZebAOtV9Sdv+xvA7cA43PwMr4rIR0DIQ3Or6nYRWeeNWfNf7xzfesctTJwn4IZ9CJ5162oRGYT7f30KbsKZ5Tlee5a3/lvvPOVw75sxgCUFEzv+BmwF2uBKuMdMmqOqU0TkO+AyYLaIDMQNM/yGqt4fwjn6Bg+YJyK5zrHhjcfTHjcI27XAEODCQlzLdOBq4EfgPVVVcd/QIceJm4HsCWA80EtE6gHDgDNVdZeITMQNDJeTAJ+pap9CxGsSiFUfmVhxIvCbN0b+DbhfyUcRkfrAOq/KZBauGmUO0FtEanj7nCShz0/9I1BXRBp6yzcAX3l18Ceq6se4Rtzc7gDaixu+OzfvAj1w8wBM99YVKk5VPYyrBjrLq3qqDOwH/hCRk4FL84hlAfDn7GsSkeNFJLdSl0lQlhRMrHgB6C8iC3BVR/tz2ecaYKWILAOa4qYsTMF9eX4qIsuBz3BVKwVS1YO4EShniMgKIAt4CfcF+6F3vK9wpZicJgIvZTc05zjuLiAFOE1Vv/fWFTpOr63iGWCYqv6Am5t5FfA6rkoq2wTgExGZq6rbcXdGTfXOswD3XhkD2CipxhhjglhJwRhjTIAlBWOMMQGWFIwxxgRYUjDGGBNgScEYY0yAJQVjjDEBlhSMMcYE/D80Q+zmt7RHtwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 96.55%\n",
      "Precision = 4.07%\n",
      "Recall = 83.78%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 97.16%\n",
      "Precision = 4.90%\n",
      "Recall = 83.78%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RFGVmuqeL09L8jsSYJLZrFzz5JPznP266ZUvXsNy8ub9xxQhLClG0ZImrpkyQp+GNiT/ffusGu9m2zT2dfO21MHiwPeEZxJJCFFnVkTE+S011bQWNGsG998b0WMl+saQQRenpbsxuY0yUqMJ//wtnneU69apYESZOdN0EWJE9JPtWomjxYispGBM1mza5qqJ774Vnnjk0v3ZtSwhFsJJClOzc6X6jLVr4HYkxCS4vD956C5591g0OUrWqG+/AhMWSQpQsXep+lzY8pDERtG4dPPggLF/upi+4AP74R9d3kQmLnaKiJD3djV1gjImQTZvgt791A4CnpLgO7Hr29DuquGNJIUrS0+Gcc/yOwpgEVr8+nH++u9X097+HKlX8jiguRbS1RUR6i8gaEfleRO4MsTxVROaKyBIRWS4ifSIZj5/sdlRjStmBA67dYOXKQ/Puv981LFtCOGoRSwoiUgaYAFwEtAYGiUjrAqvdA7ypqqcAVwHPRSoeP2VluarO1gWP3hhzdJYsgUGD3Ehof/mLa1wGu6uoFESy+qgb8L2qrgUQkenApcCqoHUUyO+k/EQgITsfWb7cJQQb3N6YY5SV5UoHb73lpps2hbvusmRQiiKZFE4CNgZNZwDdC6xzH/CBiNwMnACcH2pDIjIaGA2QGodPIFrVkTGlYN48ePhh+OUX1y3FiBHwu9/Z1VYpi2R6DTWMjBaYHgRMVtUGQB/gNREpFJOqTlTVLqrapVatWhEINbIsKRhzjPbsgXvucQmhdWs3yPm111pCiIBIlhQygIZB0w0oXD10DdAbQFW/FJGKQArwawTjirr0dPf7NcaUgKp7HXccVK7snjfIzHS3nVoHdhETyZLCQqCZiDQRkfK4huRZBdb5ETgPQERaARWBLRGMKeoOHHBDvbZr53ckxsSRLVtgzBiYOvXQvD59YMgQSwgRFrGkoKo5wE3A+8Bq3F1GK0XkARHp5632B2CUiCwDpgHDVbVgFVNc+/prOPlkqFTJ70iMiQOqMHMmXHEFfPopvPqqu7IyURPRh9dU9T3gvQLzxgW9XwWcHskY/GbtCcaE6aef4KGHYOFCN33GGXD33VChgr9xJRl7ojnCrHsLY4qRlwfTp8OECa5UUK2aaz/o1Qsk1P0qJpLs5t4Is5KCMWH46COXEC680D2DcOGFlhB8YiWFCMrOhhUroEMHvyMxJsZkZ8PevXDiie7uonHj4Mcf3WA4xldWUoig1avdqH+VK/sdiTExZNUqdxfRvfe6hmWAxo0tIcQIKylEkFUdGRNk/3548UWYMsW1I+zfD9u321gHMcaSQgRZUjDGs3ixu7No40ZXXTRkiHuis2JFvyMzBVhSiKD0dLj8cr+jMMZHqvB//wdvvummTz7ZtR9Yl8Exy5JChOTmwrJlcMopfkdijI9EXKNa2bJwzTUwfDiUK+d3VKYIlhQi5LvvoHZtd8u1MUllxw7IyIC2bd30yJHQu7fr5trEvLDuPhKR8iJycqSDSSSLF1t7gkkyqvDBBzBgAPzhD7Brl5tfvrwlhDhSbFIQkYuBr4EPvemOIvKvSAcW76yR2SSVX391ieDuu11JoWlTd3eRiTvhlBQewA2OswNAVZcCVmoohnVvYZJCXh68/bbrwO6zz+CEE9y4B8895+pPTdwJp00hW1V3yOGPnCdUT6alLS/PDSFrjcwm4T34ILz7rnt/1llw552WDOJcOElhtYhcCRwnIk2AW4H5kQ0rvq1bB1WrQhwOEmdMyVx0kRsmc8wYuOAC668oAYRTfXQT0BnIA94G9uMSgzkCa08wCeuHH2DatEPT3bq58Q+sR9OEEU5J4UJVvQO4I3+GiPTHJQgTgiUFk3AOHoTJk2HSJMjJcQ+f5ff0aCNIJZRwSgr3hJg3trQDSSSWFExCWbECBg+GiRNdQhgwwD2ZbBLSEUsKInIh0Bs4SUT+GrSoKq4qyYSgaknBJIh9++D55111kSqkpro7i+zHndCKqj76FViBa0NYGTR/N3BnJIOKZxkZblzxevX8jsSYY/Tccy4hBHdgZ0NjJrwjJgVVXQIsEZEpqmpPoYQpv5RgbW4m7o0YAd9/DzffbB3YJZFw2hROEpHpIrJcRL7Nf0U8sjhlVUcmbn32Gdxyi2s3AKhe3VUfWUJIKuEkhcnA3wEBLgLeBKZHMKa4Zn0embizbZvrnuL22+GLL+Df//Y7IuOjcJLC8ar6PoCq/qCq9wDnRDas+GXdW5i4oQrvvefuJvrgAzfgzZgx0K+f35EZH4XznMIBcX1c/CAi1wE/AfYcewg//wwHDribNIyJaZs3w8MPu5IBuIfQ7rkH6tf3Ny7ju3CSwm1AZeAW4C/AicCISAYVr5YssUZmEyfmz3cJoUoVuO026NvXfrgGCCMpqOpX3tvdwBAAEWkQyaDilTUym5i2b9+hp48vvdR1d92/P6Sk+BuXiSlFtimISFcRuUxEUrzpNiLyKtYhXkiWFExMys2FV1+FSy6Bn35y80Rg9GhLCKaQIyYFEXkEmAJcDfxXRMYCc4FlQPPohBdfLCmYmPPttzBsGDz9NOzcCZ984ndEJsYVVX10KdBBVfeJSA1gkze9JjqhxZfMTHdnX1qa35EYg+vA7pVXXCd2ublQty6MHQunneZ3ZCbGFZUU9qvqPgBV3SYi31hCOLL8QXWOC2vUa2MiaM0adyfRunWumujKK+Gmm+D44/2OzMSBopJCUxHJ7x5bgMZB06hq/+I2LiK9gb8BZYCXVfXREOtcCdyHG81tmar+NvzwY4dVHZmYUa6c64SrUSO4917o2NHviEwcKSop/KbA9LMl2bCIlAEmABcAGcBCEZmlqquC1mkG3AWcrqrbRSRun39IT4eLL/Y7CpO0vvkGWrRwJYOmTV0bQocOUL6835GZOFNUh3hzjnHb3YDvVXUtgIhMx7VTrApaZxQwQVW3e/v89Rj36ZvFi91FmTFRtWsXjB8Ps2a5h9F69XLzu3b1Ny4TtyJZA34SsDFoOsObF6w50FxE5onIfK+6qRARGS0ii0Rk0ZYtWyIU7tHbudM9zdyypd+RmKQydy5ccYVLCOXLw44dfkdkEkA4TzQfrVCPR2qI/TcDegINgM9FpK2qHvbrVtWJwESALl26FNyG75Yuhfbt3TgKxkRcZiY8/jjM8QrzHTq4Ymrjxr6GZRJD2ElBRCqo6oESbDsDaBg03QB3W2vBdearajawTkTW4JLEwhLsx3fWyGyiZvVquPFGV21UqZIb62DAALvtzZSaYn9JItJNRL4GvvOmO4jIM2FseyHQTESaiEh54CpgVoF13sHrcdV7aro5sLYE8ccESwomapo2hWrV3PMGb77pbje1hGBKUTi/pqeBS4BMAFVdRhhdZ6tqDnAT8D6wGnhTVVeKyAMikt837/tApoiswj0t/UdVzSz5YfjLkoKJmLw8ePtt2L3bTVeoAC+/7O4usjFfTQSEU310nKpukMN7UMwNZ+Oq+h7wXoF544LeK3C794pLWVnuGSEbnMqUug0b4MEHXaPVqlXugTSAGjX8jcsktHCSwkYR6Qao9+zBzYANx+lZvtwlBLsd3JSanBx4/XWYONF1V1GzJvTo4XdUJkmEkxSux1UhpQK/AB958wxWdWRK2Zo18MAD7l9wo6D9/vdQtaq/cZmkEU5SyFHVqyIeSZxKT7fnhEwpyciAoUNdB3b167sO7Lp39zsqk2TCSQoLvVtF3wDeVtXdEY4prqSnw7XX+h2FSQgNGri+Uo4/Hm64wTqwM74IZ+S1NBHpgbul9H4RWQpMV9XpEY8uxh044Er57dr5HYmJS3v3woQJcOGF7ulHcA+h2bCYxkdh3eCsql+o6i1AJ2AXbvCdpPf119Cs2aERDo0J25dfumcM3ngDHnkE1HtQ3xKC8VmxJQURqYzryO4qoBUwE7BbIbBGZnMUdu2CJ5+E//zHTbdqZaUDE1PCaVNYAbwLPK6qn0c4nrhiScGUyJw58Nhjboi+8uXhuuvg6qut0ywTU8JJCk1VNS/ikcSh9HQYMsTvKExc2L0b/vIXV1Lo1Mk9iJaa6ndUxhRyxKQgIk+q6h+Af4pIoZ5Jwxl5LZFlZ8OKFa6DSmNCUnWv446DKlXgzjtdUujf3/orMjGrqJLCG96/JRpxLVmsXu1GO6xc2e9ITEzatMmVDLp2heHD3bz8AXCMiWFFjby2wHvbSlUPSwwichNwrCOzxTVrTzAh5eW53ksnTIB9+1zHWL/9rfWDYuJGOGXYESHmXVPagcQbSwqmkHXrYORIeOIJlxB69XJ9GFlCMHGkqDaFgbjbUJuIyNtBi6oAST/uX3o6XH6531GYmJCbC//4B7z0kmtsqlUL7roLzjrL78iMKbGi2hQW4MZQaABMCJq/G1gSyaBiXW4uLFsGp5zidyQmJojA/PkuIVx+Odxyi2tYNiYOFdWmsA5Yh+sV1QT57juoXdsNgGWS1IEDbjCNGjXcnUT33AO//GK9I5q4d8Q2BRH51Pt3u4hsC3ptF5Ft0Qsx9ixeDJ07+x2F8U16Ogwa5J5Ezu+eIjXVEoJJCEVVH+UPuZkSjUDiiTUyJ6msLHj2WXjrLTddtizs2AHVq/sblzGl6IglhaCnmBsCZVQ1FzgNuBY4IQqxxSxLCknoiy9cB3ZvveW6pRg92t1ZZAnBJJhwurl4B+gqImnAq8B/gKnAJZEMLFbl5cGSJdbInDRU4aGHYOZMN926NYwbByef7G9cxkRIOEkhT1WzRaQ/MF5VnxaRpL37aN06NzJirVp+R2KiQgTq1HHPGtxwg2tLsA7sTAILazhOEbkCGAJc5s0rF7mQYptVHSWBLVvc0Jj5xcHf/Q769HEjoxmT4MJ9ovkcXNfZa0WkCTAtsmHFLksKCUzVVRNdcQX86U+wc6ebX66cJQSTNIpNCqq6ArgFWCQiLYGNqvqXiEcWoywpJKiffnLVQw8+CHv2QNu2kJPjd1TGRF04I6+dCbwG/AQIUFdEhqjqvEgHF2tULSkknLw8mD4dnnsO9u93TyT+8Y+u3yIbDc0koXDaFJ4C+qjqKgARaYVLEl0iGVgsyshwbYz16vkdiSk148bBf//r3vfuDX/4g91mapJaOEmhfH5CAFDV1SKSlN0+5pcS7AIygVx+ufuPvfNO68DOGMJLCuki8iKudABwNUnaIV56unVvEfdWrYKFC2HYMDfduTO88451b22MJ5ykcB2uoflPuDaFz4BnIhlUrFq8GK5J+pEk4tT+/fDiizBlimtHaN/+0C2nlhCMCSgyKYhIOyAN+JeqPh6dkGJXerobUMvEmcWL3V1FGRmuR9MhQ6BVK7+jMiYmFTXIzt24EdbScd1cPKCqk6IWWYz5+WfXW3Jqqt+RmLDt2QNPPw1ve2NEnXyya1hu3drfuIyJYUU9p3A10F5VrwC6AteXdOMi0ltE1ojI9yJyZxHrDRARFZGYvaNpyRJrZI47zz/vEkLZsnDddfDaa5YQjClGUdVHB1Q1C0BVt4hIOE8/B4hIGdyIbRcAGcBCEZkVfCeTt14VXJvFVyWKPMrs+YQ4oXooc48aBZs2wc03Q9Om/sZlTJwo6kTfVETe9l7/AtKCpt8u4nP5ugHfq+paVT0ITAcuDbHeg8DjwP4SRx9FlhRinKp73uC669ywmOAeRHvqKUsIxpRAUSWF3xSYfraE2z4J2Bg0nQF0D15BRE4BGqrqv0VkzJE2JCKjgdEAqT5V6qenw2OP+bJrU5xff4VHHoHPP3fTs2dDv37+xmRMnCpqjOY5x7jtULXvGljoqqOeAoYXtyFVnQhMBOjSpYsWs3qpy8yEbdsgLS3aezZFystzzxiMHw9790LlynDbbdC3r9+RGRO3wnlO4Whl4EZty9cA2BQ0XQVoC3wirg64LjBLRPqp6qIIxlVi+YPqHFeiVhUTURs3usFvFi9202ef7Z5KtoEujDkmkUwKC4FmXlfbPwFXAb/NX6iqOwka/1lEPgHGxFpCAGtPiElLlriEUKOG6+b6vPPs1jBjSkHYSUFEKqjqgXDXV9UcEbkJeB8oA0xS1ZUi8gCwSFVnlTxcf6Snw8UX+x2FYfduqFLFve/bF3bsgEsvhRNP9DcuYxJIsRUiItJNRL4GvvO3u/WuAAAfJUlEQVSmO4hIWN1cqOp7qtpcVdPyx2BQ1XGhEoKq9ozFUgK4C1Lr88hHBw+6LiouuQR+/NHNE4GhQy0hGFPKwqklfxq4BMgEUNVluJHYksLOne5p5hYt/I4kSX39NQweDC+9BFlZMH++3xEZk9DCqT46TlU3yOH1tbkRiifmLF3q+k6zsdqjbN8+90TytGnuGYTUVLj33kOd2BljIiKcpLBRRLoB6j2lfDPwbWTDih3WyOyDFStg7Fg3ROZxx7lqotGjoUIFvyMzJuGFkxSux1UhpQK/AB9xFP0gxav0dDgnaSrLYkSVKrBlCzRv7koH1qOpMVFTbFJQ1V9xt5MmpfR0N0KjibClS6FDB9eA3KgRvPCC67yubCTvmjbGFFTsX5yIvETQk8j5VHV0RCKKIVlZsG6ddawZUdu2wRNPwAcfwP33H7r3t317f+MyJkmFcxn2UdD7isDlHN6nUcJavtwlBBuYKwJUXR9FTzwBu3ZBxYqHOrIzxvgmnOqjN4KnReQ14MOIRRRDrJE5QjZvhocfhi++cNPdu7uG5fr1/Y3LGHNU3Vw0ARqVdiCxKD0dunb1O4oEs2IF3HCD68CuShW4/Xb3UJp1UWFMTAinTWE7h9oUjgO2AUccRS2RpKfDtdf6HUWCad4c6tSBxo3hjjsgJaXYjxhjoqfIpCDuibUOuA7tAPJUNepdV/vhwAFYs8baO49Zbi688YYrDVSt6hpoXnnFvTfGxJwiu7nwEsC/VDXXeyVFQgDXu0KzZq790xylb7+FYcPgr391r3yWEIyJWeG0KSwQkU6qmh7xaGKINTIfg4MH4eWX4R//cCWFunXhwgv9jsoYE4YjJgURKauqOcAZwCgR+QHIwo2opqqa0KdMSwpHaflyeOABWL/eNR5feSXcdBMcf7zfkRljwlBUSWEB0Am4LEqxxJT0dBgyxO8o4szGjTBypBsms1EjGDfOPaVsjIkbRSUFAVDVH6IUS8zIznZ3Ttr5rIQaNoTLL3djHIwcaU/9GROHikoKtUTk9iMtVNW/HmlZvFu92l3oVq7sdyQxbtcuGD/ejYKW36X1nXfaMwfGxLGikkIZoDJeiSGZWHtCGD7+GB57DDIzXRadOtUlA0sIxsS1opLCz6r6QNQiiSGWFIqQmemSwccfu+mOHV331pYMjEkIxbYpJKP0dFc1boKown/+45432LXL3U10883wm9+4gXCMMQmhqKRwXtSiiCG5ubBsmY36WMju3fDUUy4h9OgBd90F9er5HZUxppQdMSmo6rZoBhIrvvvOdc1TrZrfkcSAvDxXQihTxj2FPHYs7N8PF11k1UXGJCgr9xeweLG1JwDu4bNRo2Dy5EPzzj0X+vSxhGBMArOkUEDSNzLn5MCkSTBokKtHmznTdVthjEkKlhQKSOqksGYNDB0Kzz3nnuC79FJ4/XV7CM2YJGKjogfJy4MlS5KwkTknB1580XVgl5fnRkC75x7o1s3vyIwxUWZJIci6da49tVYtvyOJsjJlXL8eqq7a6PrrrQM7Y5KUJYUgSVV1tHcvZGW5DCjiHkDbutVGFTImyVmbQpCkSQpffum6tL7nHlc6AFdlZAnBmKRnSSFIwieFnTvhz392TyJv3uxKCzt3+h2VMSaGRDQpiEhvEVkjIt+LyJ0hlt8uIqtEZLmIzBGRRpGMpyiqCZwUVGHOHLjiCtdVRfnycMst7hkEe0rPGBMkYm0KIlIGmABcAGQAC0VklqquClptCdBFVfeKyPXA48DASMVUlIwM196acD03qLpqovffd9OdOrnp1FR/4zLGxKRINjR3A75X1bUAIjIduBQIJAVVnRu0/nxgcATjKVJ6OnTunIAP64pA06bubqJbb3U9/VkHdsaYI4hkUjgJ2Bg0nQF0L2L9a4DZoRaIyGhgNEBqhK5wE6rqaNMmV/TJf85g2DA3EE7t2v7GZYyJeZG8ZAx1za0hVxQZDHQB/i/UclWdqKpdVLVLrQg9RJAQfR7l5cG0ae7Oorvugm1en4Zly1pCMMaEJZJJIQNoGDTdANhUcCUROR8YC/RT1QMRjKdIcV9SWLvWjYv85JOuJ9Pu3a2ayBhTYpGsPloINBORJsBPwFXAb4NXEJFTgBeB3qr6awRjKdLPP8OBA3Ha9pqT47qnePll119RrVqulHDWWX5HZoyJQxFLCqqaIyI3Ae/jxnuepKorReQBYJGqzsJVF1UG3hLXwvujqvaLVExHsmSJKyXEZSPz2LHudlNwjci33gqVK/sbkzEmbkW0mwtVfQ94r8C8cUHvz4/k/sMV11VHgwbBt9/C3XdD165+R2OMiXNW6UycJYX0dJg48dB0x44wY4YlBGNMqbAO8XDn2cce8zuKYmRlwdNPwz//6aa7dDmUycqU8S8uY0xCSfqkkJnp7txMS/M7kiLMmwd/+Qv8+qu7vXTECGjXzu+ojDEJKOmTQv6gOjF59+aOHe4W09neM31t2sC4cTGewYwx8Szpk0J+9xYx6aWXXEKoUAFuuME1Ksdk9jLGJApLCulwySV+RxFE9dC9sdde6+q2brwRGjTwNy5jTFJI+qSweLGrkfGdKrzzDsya5cZLLl/ejQ36yCN+R2aOQXZ2NhkZGezfv9/vUEySqFixIg0aNKBcuXJH9fmkTgo7d7qnmVu08DmQjAx46CFYtMhNf/ghXHyxvzGZUpGRkUGVKlVo3LgxEpdPR5p4oqpkZmaSkZFBkyZNjmobSV1BvXSpG4HStzs68/JgyhQYONAlhOrV4eGHoU8fnwIypW3//v3UrFnTEoKJChGhZs2ax1QyTeqSgq8Pra1dC/ffDytXuumLLoI//MFGQktAlhBMNB3r7y3pk8I55/i082++cQmhdm3XRcUZZ/gUiDHGHJLU1UdRLyls337o/UUXwZ/+BG++aQnBRFSZMmXo2LEjbdu2pW/fvuzYsSOwbOXKlZx77rk0b96cZs2a8eCDD6J6aNiT2bNn06VLF1q1akXLli0ZM2ZMyH288847PPDAA4fN69ChA4MGDTpsXs+ePVmU33YGrF+/nrZt2wamFyxYwFlnnUWLFi1o2bIlI0eOZO/evcd0/OvWraN79+40a9aMgQMHcvDgwULrZGdnM2zYMNq1a0erVq14JOgGj6eeeoo2bdrQtm1bBg0aFKia+fjjj+nUqRNt27Zl2LBh5OTkADBlyhTat29P+/bt6dGjB8uWLQNgzZo1dOzYMfCqWrUq48ePB2DgwIGB+Y0bN6Zjx44AHDx4kN/97ne0a9eODh068MknnwTiOv/889kefE4pLaoaV6/OnTtradizR7VSJdUDB0plc0Xbt0/1qadUTz9dde3aKOzQxIpVq1b5HYKecMIJgfdDhw7Vhx56SFVV9+7dq02bNtX3339fVVWzsrK0d+/e+uyzz6qq6tdff61NmzbV1atXq6pqdna2TpgwIeQ+TjvtNN2yZUtgetWqVdq2bVutX7++7tmzJzD/7LPP1oULFwam161bp23atFFV1c2bN2tqaqp+8cUXqqqal5enb731lm7evPmYjv+KK67QadOmqarqtddeq88991yhdaZMmaIDBw5UVfc9NGrUSNetW6cZGRnauHFj3bt3b2Bbf//73zU3N1cbNGiga9asUVXVe++9V19++WVVVZ03b55u27ZNVVXfe+897datW6H95eTkaJ06dXT9+vWFlt1+++16//33q6rqs88+q8OHD1dV1V9++UU7deqkubm5qqo6efLkwP9lQaF+d7jeqYs9xyZtSWH5cmjd2t35GVGLFsFVV8Hrr8PBg+4RamN8ctppp/HTTz8BMHXqVE4//XR69eoFwPHHH8+zzz7Lo48+CsDjjz/O2LFjadmyJQBly5blhhtuKLTNb7/9lgoVKpCSkhKYN3XqVIYMGUKvXr2YNWtWWLFNmDCBYcOGcdpppwGubnzAgAHUqVPnqI9XVfn4448ZMGAAAMOGDeOdd94ptJ6IkJWVRU5ODvv27aN8+fJUrVoVIDAvJyeHvXv3Ur9+fTIzM6lQoQLNmzcH4IILLuCfXr9kPXr0oHr16gCceuqpZGRkFNrfnDlzSEtLo1GjRoXiffPNNwMlrFWrVnHeeecBULt2bapVqxYoafXr149p06Yd9XdzJEmbFCJedbRnj7uT6Lrr3C2nJ58MkydD//4R3KmJdSKl/wpXbm4uc+bMoV8/N2TJypUr6Vzgcf60tDT27NnDrl27WLFiRaHlocybN49OBf6Y3njjDQYOHMigQYPCPnGFu7+C1TDBr+CqMYDMzEyqVatG2bKu+bRBgwaBpBhswIABnHDCCdSrV4/U1FTGjBlDjRo1OOmkkxgzZgypqanUq1ePE088kV69epGSkkJ2dnbgBD1jxgw2btxYaLuvvPIKF110UaH506dPL1S1BvD5559Tp04dmjVrBrgquJkzZ5KTk8O6detYvHhxYD/Vq1fnwIEDZGZmFvudlUTSNjSnpx8a177ULV3qGo/zO7AbORKGDYOjfJjEJA4NOUp5ZO3bt4+OHTuyfv16OnfuzAUXXODFoke8U6Ukd7D8/PPPBI+dvnDhQmrVqkWjRo1o0KABI0aMYPv27VSvXj3kdkt6t0yLFi1YunRpWOtqiC881P4WLFhAmTJl2LRpE9u3b+fMM8/k/PPPp3r16sycOZN169ZRrVo1rrjiCl5//XUGDx7M9OnTue222zhw4AC9evUKJJ58c+fO5ZVXXuF///vfYfMPHjzIrFmzDmu3yDdt2rTDksWIESNYvXo1Xbp0oVGjRvTo0eOw/dSuXZtNmzZRs2bNsL6PcFhJIRJq1nSd2bVrB1OnuqRgCcH4pFKlSixdupQNGzZw8OBBJkyYAECbNm0Oa/QFWLt2LZUrV6ZKlSq0adOGxYsXh7X94Pvip02bxjfffEPjxo1JS0tj165dgaqVmjVrHtY4um3btkC1U7j7K0lJISUlhR07dgQagTMyMqhfv36hbU6dOpXevXtTrlw5ateuzemnn86iRYv46KOPaNKkCbVq1aJcuXL079+fL774AnBVcZ9//nmgcTz/6h5g+fLljBw5kpkzZxY6Yc+ePZtOnToVqhbLycnh7bffZuDAgYF5ZcuW5amnnmLp0qXMnDmTHTt2HLaf/fv3U6lSpWK/sxIJp+Ehll6l0dC8f79rZN6375g35eTlqX75pfs336pVql6DkElesdbQnJ6erg0bNtSDBw/q3r17tUmTJvrhhx+qqmt4vvjii/Xpp59WVdVly5ZpWlpaoDE1NzdXn3zyyULbnz17tl599dWBdRo0aKAZGRmB5R9//LGee+65qqr6zDPP6NChQzXP+1u55ZZbAo2q+Q3N8+fPD3z2tdde059//vmYjn/AgAGHNTSHaix/9NFHdfjw4ZqXl6d79uzRVq1a6bJly3T+/PnaunVrzcrK0ry8PB06dGjg+/nll19UVXX//v167rnn6pw5c1RVdcOGDZqWlqbz5s0LGc/AgQN10qRJhebPnj1bzzrrrMPmZWVlBRrqP/jgAz3zzDMDy/Ly8rR+/fqanZ1daFvH0tDs+0m+pK/SSAoLF6q2b3/Mm3E2b1a99VbVzp1VZ84spY2aRBFrSUFV9ZJLLtFXX31VVVWXL1+uZ599tjZv3lzT0tL0vvvuC5ywVVXfffdd7dSpk7Zs2VJbtWqlY8aMKbT9rKwsbd26tebl5encuXO1e/fuhy3PycnRunXr6qZNm/TAgQN64403art27bR9+/Y6YsQIzcrKCqz7xRdf6BlnnKHNmzfXli1b6ujRow9bfjR++OEH7dq1q6alpemAAQN0//79qqo6c+ZMvffee1VVdffu3TpgwABt3bq1tmrVSh9//PHA58eNG6ctWrTQNm3a6ODBgwOfHzNmjLZs2VKbN2+uTz31VGD9a665RqtVq6YdOnTQDh06aPA5KysrS2vUqKE7duwoFOewYcP0+eefP2zeunXrAt/Feeedd9jdSgsXLtT+/fuHPOZjSQqiflRyHoMuXbpowSJvSU2cCF9+CX//+zFsJC/PdWA3fjzs3QuVK8Mdd7jnD4zxrF69mlatWvkdRsTdeuut9O3bl/PPj4lh15PCrbfeSr9+/QJ3JwUL9bsTkcWq2qW47SZlQ/Mxtyf8+KPrwC493U337OkSQlBjmzHJ5O677+arr77yO4yk0rZt25AJ4VglbVIYMuQoP7x8ubvN9OBBqFHDPZV83nkluzfQmARTp06dwK2uJjpGjRoVke0mXVLIzoYVK6BDh6PcQKtWkJrq+tu+/XY48cRSjc8YY/yUdElh9Wpo1Mg1AYTl4EH3NHL//q4H03LlYNIkOP74iMZpjDF+SLrnFErUnvD11zB4MDz3HDz55KH5lhCMMQkq6UoKYSWFffvg+edh2jT3CGpqqnVPYYxJCklZUiiye5UFC9xIaFOnusbj4cNh+nQ45ZRohWhMqdq8eTNXXXUVaWlptG7dmj59+jBx4kQuueQSv0MzMSipSgq5ubBsGXhdlRf2449w442udNC8OYwbB14PkcbEI1Xl8ssvZ9iwYUyfPh2ApUuX8u677/ocmYlVSZUUvvsO6tQpYsTL1FQYNMitMHSo68zOmNLUpYhnh+6++1A15dtvu152jyTMBzjnzp1LuXLluO666wLz8vsImjNnDgMGDAj0Tvr6668jIjzwwAO8++677Nu3jx49evDiiy8iIvTs2ZPu3bszd+5cduzYwSuvvMKZZ55Jbm4ud9xxB++//z4iwqhRo7j55ptZvHgxt99+O3v27CElJYXJkydTr169sOI2/kmq6qPFiwu0J2zbBnfddfgf2O23w4gRlhBMQiiqO+olS5Ywfvx4Vq1axdq1a5k3bx4AN910EwsXLmTFihXs27ePf//734HP5OTksGDBAsaPH8/9998PwMSJE1m3bh1Llixh+fLlXH311WRnZ3PzzTczY8YMFi9ezIgRIxg7dmzkD9gcs6Q68wUamVVh9mx44gnYtQs2bIApU+wBNBN54XbR0r9/xG9u6NatGw0aNAAIdK19xhlnMHfuXB5//HH27t3Ltm3baNOmDX379vXCcjF17tyZ9evXA/DRRx9x3XXXBbp0rlGjBitWrGDFihWBbrpzc3OtlBAnIpoURKQ38DegDPCyqj5aYHkF4FWgM5AJDFTV9ZGKJz0d/nztZrj1YfC6v+XUU12x3RKCSUBt2rRhxowZIZdVqFAh8L5MmTLk5OSwf/9+brjhBhYtWkTDhg257777DusWO/8z+etD6HEZVJU2bdrw5ZdflvYhmQiLWPWRiJQBJgAXAa2BQSLSusBq1wDbVfVk4CngsUjFk5eTR+qCt+jxtytdQqhaFe67D555BkL0r25MIjj33HM5cOAAL730UmDewoUL+fTTT0Oun58AUlJS2LNnzxETSrBevXrxwgsvBJLEtm3baNGiBVu2bAkkhezsbFauXHmsh2OiIJJtCt2A71V1raoeBKYDlxZY51LgH977GcB5UtJhmMK0fsUersl9ifLZe+Hcc+Gtt+CSS6yEYBKaiPCvf/2LDz/8kLS0NNq0acN9990XcqAZgGrVqjFq1CjatWvHZZddRteuXYvdx8iRI0lNTaV9+/Z06NCBqVOnUr58eWbMmMEdd9xBhw4d6NixY2BwGhPbItZ1togMAHqr6khvegjQXVVvClpnhbdOhjf9g7fO1gLbGg2MBkhNTe28YcOGEsezeTOseekzzj49xyUFY6IgWbrONrElVrvODnUJXjADhbMOqjoRmAhuPIWjCaZuXah771lH81FjjEkakaw+ygAaBk03ADYdaR0RKQucCGyLYEzGGGOKEMmksBBoJiJNRKQ8cBUwq8A6s4Bh3vsBwMcab0PBGVMM+0mbaDrW31vEkoKq5gA3Ae8Dq4E3VXWliDwgIvmjcbwC1BSR74HbgTsjFY8xfqhYsSKZmZmWGExUqCqZmZlUrFjxqLeRlGM0GxMt2dnZZGRkHHavvzGRVLFiRRo0aEC5cuUOmx8LDc3GJL1y5crRpEkTv8MwJmxJ1feRMcaYollSMMYYE2BJwRhjTEDcNTSLyBag5I80OynA1mLXSix2zMnBjjk5HMsxN1LVWsWtFHdJ4ViIyKJwWt8TiR1zcrBjTg7ROGarPjLGGBNgScEYY0xAsiWFiX4H4AM75uRgx5wcIn7MSdWmYIwxpmjJVlIwxhhTBEsKxhhjAhIyKYhIbxFZIyLfi0ihnldFpIKIvOEt/0pEGkc/ytIVxjHfLiKrRGS5iMwRkUZ+xFmaijvmoPUGiIiKSNzfvhjOMYvIld7/9UoRmRrtGEtbGL/tVBGZKyJLvN93Hz/iLC0iMklEfvVGpgy1XETkae/7WC4inUo1AFVNqBdQBvgBaAqUB5YBrQuscwPwgvf+KuANv+OOwjGfAxzvvb8+GY7ZW68K8BkwH+jid9xR+H9uBiwBqnvTtf2OOwrHPBG43nvfGljvd9zHeMxnAZ2AFUdY3geYjRu58lTgq9LcfyKWFLoB36vqWlU9CEwHLi2wzqXAP7z3M4DzRCTU0KDxothjVtW5qrrXm5yPGwkvnoXz/wzwIPA4kAh9V4dzzKOACaq6HUBVf41yjKUtnGNWoKr3/kQKj/AYV1T1M4oegfJS4FV15gPVRKReae0/EZPCScDGoOkMb17IddQNBrQTqBmV6CIjnGMOdg3uSiOeFXvMInIK0FBV/x3NwCIonP/n5kBzEZknIvNFpHfUoouMcI75PmCwiGQA7wE3Ryc035T0771EEnE8hVBX/AXvuw1nnXgS9vGIyGCgC3B2RCOKvCKPWUSOA54ChkcroCgI5/+5LK4KqSeuNPi5iLRV1R0Rji1SwjnmQcBkVX1SRE4DXvOOOS/y4fkiouevRCwpZAANg6YbULg4GVhHRMriipxFFddiXTjHjIicD4wF+qnqgSjFFinFHXMVoC3wiYisx9W9zorzxuZwf9szVTVbVdcBa3BJIl6Fc8zXAG8CqOqXQEVcx3GJKqy/96OViElhIdBMRJqISHlcQ/KsAuvMAoZ57wcAH6vXghOnij1mryrlRVxCiPd6ZijmmFV1p6qmqGpjVW2Ma0fpp6rxPJZrOL/td3A3FSAiKbjqpLVRjbJ0hXPMPwLnAYhIK1xS2BLVKKNrFjDUuwvpVGCnqv5cWhtPuOojVc0RkZuA93F3LkxS1ZUi8gCwSFVnAa/gipjf40oIV/kX8bEL85j/D6gMvOW1qf+oqv18C/oYhXnMCSXMY34f6CUiq4Bc4I+qmulf1McmzGP+A/CSiNyGq0YZHs8XeSIyDVf9l+K1k/wZKAegqi/g2k36AN8De4Hfler+4/i7M8YYU8oSsfrIGGPMUbKkYIwxJsCSgjHGmABLCsYYYwIsKRhjjAmwpGBijojkisjSoFfjItZtfKTeJEu4z0+8njiXeV1EtDiKbVwnIkO998NFpH7QspdFpHUpx7lQRDqG8Znfi8jxx7pvkxwsKZhYtE9VOwa91kdpv1eragdcZ4n/V9IPq+oLqvqqNzkcqB+0bKSqriqVKA/F+Rzhxfl7wJKCCYslBRMXvBLB5yKS7r16hFinjYgs8EoXy0WkmTd/cND8F0WkTDG7+ww42fvseV4//V97/dxX8OY/KofGp3jCm3efiIwRkQG4/qWmePus5F3hdxGR60Xk8aCYh4vIM0cZ55cEdYQmIs+LyCJx4yjc7827BZec5orIXG9eLxH50vse3xKRysXsxyQRSwomFlUKqjr6lzfvV+ACVe0EDASeDvG564C/qWpH3Ek5w+v2YCBwujc/F7i6mP33Bb4WkYrAZGCgqrbD9QBwvYjUAC4H2qhqe+Ch4A+r6gxgEe6KvqOq7gtaPAPoHzQ9EHjjKOPsjevWIt9YVe0CtAfOFpH2qvo0rl+cc1T1HK/ri3uA873vchFwezH7MUkk4bq5MAlhn3diDFYOeNarQ8/F9elT0JfAWBFpALytqt+JyHlAZ2Ch171HJVyCCWWKiOwD1uO6X24BrFPVb73l/wBuBJ7Fjc/wsoj8Bwi7a25V3SIia70+a77z9jHP225J4jwB1+1D8KhbV4rIaNzfdT3cgDPLC3z2VG/+PG8/5XHfmzGAJQUTP24DfgE64Eq4hQbNUdWpIvIVcDHwvoiMxHUz/A9VvSuMfVwd3GGeiIQcY8Prj6cbrhO2q4CbgHNLcCxvAFcC3wD/UlUVd4YOO07cCGSPAhOA/iLSBBgDdFXV7SIyGdcxXEECfKiqg0oQr0kiVn1k4sWJwM9eH/lDcFfJhxGRpsBar8pkFq4aZQ4wQERqe+vUkPDHp/4GaCwiJ3vTQ4BPvTr4E1X1PVwjbqg7gHbjuu8O5W3gMtw4AG9480oUp6pm46qBTvWqnqoCWcBOEakDXHSEWOYDp+cfk4gcLyKhSl0mSVlSMPHiOWCYiMzHVR1lhVhnILBCRJYCLXFDFq7CnTw/EJHlwIe4qpViqep+XA+Ub4nI10Ae8ALuBPtvb3uf4koxBU0GXshvaC6w3e3AKqCRqi7w5pU4Tq+t4klgjKouw43NvBKYhKuSyjcRmC0ic1V1C+7OqGnefubjvitjAOsl1RhjTBArKRhjjAmwpGCMMSbAkoIxxpgASwrGGGMCLCkYY4wJsKRgjDEmwJKCMcaYgP8H8KBiLy3+7mwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 88.67%\n",
      "Precision = 1.35%\n",
      "Recall = 89.19%\n"
     ]
    },
    {
     "data": {
      "image/png": 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fIw5eWebXc+fQ+9i+XHX93/LymvIiuz7hBWbWOc3+2cBsM3s7Pn+cEIy/kbRpbBVvCsxLyt826fg2wJyY3q1C+tiY3iZF/kp5MC4hM76YR9e+gwAoKxOfjLqBEa9M4pQj9+LWB17hlvvHrJa/Vq0yhlzfjzOvuo8pH39F8yYbsLx8BfXq1uaW+8bw2oQZ1Kldi+f/eSHdf7UjL775IX/6dU+eGP0u9/z7DbbfehP+c+t5bH/YgHy83KIy/aOpPPrAUJ564TXq1K3L6cf3ptshPfl6zle89PyzPDf2HerVq8eC+eH/fnl5OZf85kz+cfu/2KHDLixetJDadepQr359nn3l7ZXlHnnw3vQ4rHe+Xlb1U26GtpnZ15JmSWpvZtOBg4AP49YPGBR/Ph0PGQFcIOkRwsW6pTFgjwL+nHTRrjtwmZktkrRMUlfgbeBU4NZ0dfJgXKIO6NKez2bP58u5iyvNc/Be2/PBjK+Y8vFXACxa+h0AP/y4nNcmzABgefkK3p82i9atmgJgZjTeoD4ATRo1YO78pVX5MkrGJzOms3unPWjQsCEAXfbehxefG8EHk97l3N/+nnr16gHQcqPQRfn62JfYfscO7NBhFwCaNW+xRpmffTqThQvms0fXX1XTqygMObwd+kLgQUl1gU+B0wldt49JOhP4EugT844EDgVmAt/HvMSgex0wPua71swWxcfnAcOABsDzcauUB+MS1adHJx57YeLK5+f23Y8TD+/Cux9+Sf+bnmTJsh9ot3krzGDE7efTslkjHh81kZuGv7RaOU0aNeDQ/XbmtofGAnDDP0fyzB0XcF7f/WnYoB6HnZv2y95F222/I//480AWL1pI/foNePWlUXTYrSOffTKD8W+9yT9uHEi9evW5bOCf2WX3znz+yUwkcdpxR7Jo4XwOO6oP51x4yWplPvvkYxzW+9iSvQmiUjl6uWb2PpCqK+OgFHkNOL+ScoYAQ1KkTwA6ZFufKruAJ8kk/SPp+aWSBuao7IGSvpL0ftwG5aLcFOcZJunYqii7KtWpXYvD9t+ZJ0e/B8A9/36dHY8YyJ59B/H1gv8x6JJjAKhdqxZ77741p18xjIPOuIkjD9yVbl22W1lOrVplDB90Gnc8PJbPv1oIwHE9O/PAM2+xbc+rOPrCO7n3+lNrXjBYB9tutz3nXHgJ/foczul9e7P9TjtTu3ZtylesYOnSJTzx/Kv0H3ADF551CmZGeXk5E975LzfdOYRHnxnD6JEjePO1V1Yr89n/PM4Rx/Sp5IylKxdD2wpRVY6m+Ak4RlLLKir/ZjPbLW5rXAWVVKuKzlvweuyzI+9Pm8W8RcsAmLdoGb/8YpgZQ558k84dtgDgq3lLeH3iTBYu+Y4fflzOC29MZfftV12juP3KE/jky/krW8UA/Y7aiydefBeAtyd/Rv26dWjZdINqe23F7LiTTmPEmHE8MmI0TZs1Y8uttmGTTTejx2G9kcSuHfegTGUsWriATTZrTZe99qV5i5Y0aNiQ/Q/uwdTJ768s66MPJlNeXs7Ou3ZMc8bSI+XuDrxCU5W1LgfuBn5XcYekLSSNibcVjpG0eUwfFm8b/K+kT9e2VSrpc0lXS3oD6CPpLEnjJU2S9ISkhknnOTbpuG/jT0m6TdKHkp4DWqU+U2E7rmfn1booNmnZeOXj3gfuyoefzAVg9H8/pEO71jSoX4datcrYt9O2fPTp1wAM+M3hNNmwAZf+7YnVyp719SK6dWkPQPutNqZ+vTorR2m49BIX5+bMnsWo50ZwxDHH0b3XEYx7fSwAn30yg5+X/0zzFi3Z74CDmf7hFH74/nvKy8t5579v0K799ivLeuapf3PE0TWvVQyl2zKu6j7j24HJkv5aIf02wt0swyWdAQxm1d0pmwL7ANsTrmA+XknZv5N0cnz8JzMbFR//aGb7AEhqYWb3xMfXA2eS/orm0UB7YGdgY8KV1TX6guKYxTBusU6jNMVVvwb163DgnttzwfUPr0y74aKj2KV9G8yML+Yu4sK4b8myHxj8wMu88cAfMTNGvTGVF96YSutWTel/Vk+mffo14x7+EwB3Pfoqw54aR/+bnuKOq07gwpMPwAzOuvr+vLzOYnT+GSeyZPEiateuw8BBN9OkaTOOPbEf/S86l577daZunTr87dZ7kESTps0449zfcnSPfUGi20E9OOCQXivLGvn0E9z78FN5fDV5VJyxNiOFfukqKFj61swaSboWWA78ADQys4GSFgCbmtlySXWAuWbWUtIwwq2FD8YylpnZhinKHgh8a2Z/r5D+ObC/mX0Rn+8PXA80BRoBo8zs3HieZ83s8Qp1vQWYHDvkkfQk8FAiXyplDVtZvfbHrfP75HJr6os1aLxtEdimVcOJGcb7rpV6G7ez1if9X8Z8n918WE7PWx2qo3PlFkKLNF3HYvI3wk9JjwUg6YbExboszvdd0uNhwAVmtjNwDVA/ppcTX7vC3zR1K6mLc66ASGEMfaatGFV5MI5j7h4jBOSE/wJ94+OTgDcylHFF4mLdWp5+Q2BubH2flJT+OdApPu4N1ImPXwP6SqoV7745YC3P55yrUlnfDl10quuy4z+A5FEVvwVOlzQZOIUwQUdVuIpw98toYFpS+j3A/pLeIdxNk2hNPwXMAKYQpr97tYrq5ZxbR1LmrRhV2QU8M2uU9PgboGHS88+BA1Mcc1plZVRIH1hJ+pYVnt9JnHe0Qvo3QNekpMtiugEXpCrbOVcAYjdFKfI78JxzRUN4MHbOuYJQrN0QmXgwds4VlWK9QJeJB2PnXNGQ9xk751whKN6ha5l4MHbOFZUSjcUejJ1zRcS7KZxzLv+EX8BzzrmCUKKxuNpuh3bOuZzI1dwUcf7zKXESsgkxrbmk0ZJmxJ/NYrriXOsz4zzsHZPK6Rfzz5DULym9Uyx/Zjw2bcU8GDvnikfuZ207IE5Clphusz8wxszaAWPic4BeQLu4nU2cZkFSc2AAYY6bLsAArVop+s6YN3Fcz3QV8WDsnCsaoc+4SicK6g0Mj4+Hs2rRi96EBTHMzN4CmsaZHXsQ5mBfZGaLCZOS9Yz7GpvZuDjnzX1JZaXkwdg5V0SynkKzpaQJSdvZKQoz4EVJE5P2b2xmcwHiz8TSa62BWUnHzo5p6dJnp0ivlF/Ac84VlSy7IRZksdLHr8xsjqRWwGhJ09LkTXVSW4f0SnnL2DlXPLLoosi2m8LM5sSf8whzmXcBvoldDMSf82L22UDbpMPbAHMypLdJkV4pD8bOuaKRGGe8vqMpJG0gacPEY6A78AFhEeTEiIh+wNPx8Qjg1DiqoiuwNHZjjAK6S2oWL9x1J6y1ORdYJqlrHEVxalJZKXk3hXOuqOTopo+NgadiWbUJCw+/IGk88JikM4EvgT4x/0jgUGAm8D1wOoRl5SRdB4yP+a6NS80BnEdYh7MB8HzcKuXB2DlXVHJxO7SZfQrsmiJ9IXBQinQDzq+krCHAkBTpE4AO2dbJg7FzrngU8Rp3mXgwds4VDfkUms45Vxhq1bRZ2yQ1Tnegmf0v99Vxzrn0SrRhnLZlPJU1By8nnhuweRXWyznn1hDGEZdmNK40GJtZ28r2OedcvpRoL0V2N31I6ivp8vi4jaROVVst55xLLcezthWMjMFY0m3AAcApMel74K6qrJRzzqUi4oiKDP+KUTajKfY2s46S3oOVd5zUreJ6OedcSkXa8M0om2C8XFIZccYhSS2AX6q0Vs45l4qKtxsik2z6jG8HngA2knQN8AbwlyqtlXPOpSCgTMq4FaOMLWMzu0/SRODgmNTHzD6o2mo551xqRRprM8r2DrxawHJCV4VPu+mcy5tSHWeczWiKK4CHgc0IEyQ/JOmyqq6Yc85VJIXboTNtxSiblvHJQCcz+x5A0g3ARODGqqyYc86lUpyhNrNsgvEXFfLVBj6tmuo451x6pdpNkW6ioJsJfcTfA1MljYrPuxNGVDjnXLWSircbIpN0fcYfECYLeg4YCIwD3gKuBV6u8po551wKuVqQVFItSe9JejY+30rS25JmSHo0cXObpHrx+cy4f8ukMi6L6dMl9UhK7xnTZkrqn0190k0UdG92L8k556pPDrspLgI+AhLTBf8FuNnMHpF0F3AmcGf8udjMtpXUN+Y7XtKOQF9gJ8IAh5ckbRfLuh04hLBK9HhJI8zsw3SVyWY0xTaSHpE0WdLHiW1tX7Vzzq2vcNNH5i1jOVIb4DDgX/G5gAOBx2OW4cBR8XHv+Jy4/6CYvzfwiJn9ZGafERYr7RK3mWb2qZn9DDwS86aVzZjhYcBQwvvQC3gsFu6cc9UuyzvwWkqakLSdXaGYW4A/smpqhxbAEjMrj89nA63j49bALIC4f2nMvzK9wjGVpaeVzWiKhmY2StLfzewT4EpJr2dxnHPO5ZREtrc7LzCzzqnL0OHAPDObKKlbIjlFVsuwr7L0VI1cS5G2mmyC8U+xSf6JpHOBr4BWWRznnHM5l4Mu418BR0o6FKhP6DO+BWgqqXZs/bYB5sT8s4G2wGxJtYEmwKKk9ITkYypLr1Q23RS/AxoBv40v4izgjCyOc865nFvfyeXN7DIza2NmWxIuwL1sZicBrwDHxmz9gKfj4xHxOXH/y2ZmMb1vHG2xFdAOeAcYD7SLozPqxnOMyPS6spko6O34cBmrJph3zrlqJ6p0VrY/AY9Iuh54D0iMKLsXuF/STEKLuC+AmU2V9BjwIVAOnG9mKwAkXQCMIszrM8TMpmY6ebqbPp4iTT+HmR2T+bU551wOrcU44myY2VhgbHz8KWEkRMU8PwJ9Kjn+BuCGFOkjgZFrU5d0LePb1qagmmr3HTbnzbf9rXKuutS426HNbEx1VsQ55zIRUKumBWPnnCtEJTo1hQdj51xxqfHBWFI9M/upKivjnHPpJCaXL0XZzE3RRdIUYEZ8vqukW6u8Zs45l0KuZm0rNNnc9DEYOBxYCGBmk4ADqrJSzjmXSo1eHRooM7MvKgwnWVFF9XHOubRKdUXkbILxLEldAJNUC7gQ8Ck0nXPVrpRX+sgmGJ9H6KrYHPgGeCmmOedctSvSXoiMspmbYh7xXmznnMu3Em0YZw7Gku4hxRwVZlZxsmbnnKtSonSHtmXTTfFS0uP6wNGsPou9c85VjyyXVSpG2XRTPJr8XNL9wOgqq5FzzqWhlAtsFL91uR16K2CLXFfEOecySSxIWoqy6TNezKo+4zLC5Mr9q7JSzjlXmRrZZxzXvtuVsO4dwC9xuRHnnKt2pdwyTnszSwy8T5nZirh5IHbO5U8W81IU6zjkbO4sfEdSxyqviXPOZSEXc1NIqi/pHUmTJE2VdE1M30rS25JmSHo0LihKXHT0UUkz4/4tk8q6LKZPl9QjKb1nTJspKWPXbqXBOC5JDbAPISBPl/SupPckvZvx1TrnXI6FccaZtyz8BBxoZrsCuwE9JXUF/gLcbGbtgMXAmTH/mcBiM9sWuDnmQ9KOhJvidgJ6AndIqhWnjrgd6AXsCJwQ81YqXZ/xO0BH4KisXppzzlU5UZaDoW2xy/Xb+LRO3Aw4EDgxpg8HBgJ3Ar3jY4DHgdviNbXewCNxrvfP4grSiUVNZ8ZFTpH0SMz7YWV1SheMFSv9Sdav0DnnqpDIuk+4paQJSc/vNrO7VysrtF4nAtsSWrGfAEvMrDxmmQ20jo9bE292M7NySUuBFjH9raRik4+ZVSF9z3QVTheMN5J0SWU7zeymdAU751zOCWpnN5xigZl1TpfBzFYAu0lqCjwF7JAq26ozp9xXWXqqzpK0AyDSBeNaQKNKTuacc9VuLVrGWTOzJZLGAl2BppJqx9ZxG2BOzDYbaAvMjtfTmhDuuUikJyQfU1l6SumC8Vwzuza7l+Occ9UjFyt5SNoIWB4DcQPgYMJFuVeAY4FHgH7A0/GQEfH5uLj/ZTMzSSOAhyTdBGwGtCNcbxPQTtJWhPs0+rKqLzqljH3GzjlXSHLUMt4UGB77jcuAx8zsWUkfAo9Iuh54D7g35r8XuD9eoFtEnFbYzKZKeoxwYa4cOD92fyDpAmAUoZdhiJlNTVehdMH4oHV8kc45VyUkqJWDaGxmk4HdU6R/yqrREMnpPwJ9KinrBuCGFOkjgZHZ1qnSYGw8DmzFAAAT0ElEQVRmi7ItxDnnqkup/sm+LrO2OedcXiRWhy5FHoydc0WlVCcK8mDsnCsiQt4yds65/BLZzW5WjDwYO+eKireMnXMu3+QX8JxzLu+8m8I55wqEd1M451wB8KFtzjmXZ6GbojSjsQdj51xRKdFeCg/GzrliIuQtY+ecyy+Rm1nbCpEHY+dc8ZB3UzjnXEEo1WBcquOnXRrn/PoMNt+sFZ1267Ba+h233couO7Wn4647cXn/P+apdjVDqt/BNQOuYo/dd2HPTrtxeK/uzJkTlkx7+KEH2WP3Xdhj913otu/eTJ40KV/VzrtEN0WmrRh5MK6BTul3Gk8/+8Jqaa+OfYVnn3ma8e9O5t1JU7n4kkvzVLuaIdXv4He//wPj35vM2xPfp9ehh3Pj9WEJyi233IoXX36V8e9N5rIrruL8887OR5ULhrL4l7EMqa2kVyR9JGmqpItienNJoyXNiD+bxXRJGixppqTJkjomldUv5p8hqV9SeidJU+Ixg5XhbhUPxjXQPvvuR/PmzVdLu/ufd3LpH/tTr149AFq1apWPqtUYqX4HjRs3Xvn4+++/W3mn2V57702zZs0A6LJnV776anb1VbQASZm3LJQDvzezHQirQp8vaUegPzDGzNoBY+JzgF6ExUbbAWcDd4a6qDkwANiTsFzTgEQAj3nOTjquZ7oKeTB2AMz8+GPefON19t17Tw45cH8mjB+f7yrVSAOuuoJtt2rLIw8/yFUD11ycfdjQe+nRo1cealY4ctEyNrO5ZvZufLwM+AhoDfQGhsdsw4Gj4uPewH0WvAU0lbQp0AMYbWaLzGwxMBroGfc1NrNxZmbAfUllpVS0wVjSCknvJ21bVsE5tpT0Qa7LLUTlK8pZvHgxr735Fn8e9DdOPvE4wmfIVadrrruBmZ/Nou8JJ3HXHbettu/Vsa8wfOi9XH/jX/JUu/wTmfuLY59xS0kTkrZK+3Zi7NgdeBvY2MzmQgjYQOJPxNbArKTDZse0dOmzU6RXqmiDMfCDme2WtH2evFOSjxRZC61bt+Goo49BEnt06UJZWRkLFizId7VqrOP6nsh/nnpi5fMpkydz3jm/5t9PPE2LFi3yWLM8y6KLInZTLDCzzknb3SmLkxoBTwAXm9n/0p95DbYO6ZUq5mC8BkmnSfq3pGeAFyU1kjRG0ruxI713zLdai1fSpZIGxsedJE2SNA44Py8vJA+OOPIoxr7yMgAzPv6Yn3/+mZYtW+a5VjXLzBkzVj5+7pkRbNd+ewC+/PJL+h53DPcOvZ92222Xr+oVDGWxZVWOVIcQiB80sydj8jexi4H4c15Mnw20TTq8DTAnQ3qbFOmVKubWYwNJ78fHn5nZ0fHxXsAuZrYoto6PNrP/SWoJvCVpRIZyhwIXmtmrkv6WKkP8k+dsgLabb77+r6SanXryCbz+6lgWLFjANlu24aqrr6Hf6Wdwzq/PoNNuHahbpy7/GjK8ZKcqLASpfgcvvDCSGR9Pp0xlbL7FFgy+/S4Abrz+WhYtXMjFF/4GgNq1a/Pm2xPyWf28ydUdeHFkw73AR2Z2U9KuEUA/YFD8+XRS+gWSHiFcrFtqZnMljQL+nHTRrjtwWYw/yyR1JXR/nArcmrZOxdovKOlbM2tUIe00YH8zOz0+rwPcDOwH/AK0B7YC6gPPmlmHmO9SoFHMO8XMNo/puwAPJfKl0qlTZ6up/zGcy6RBHU00s865Km+HnXe3of95JWO+vbZtlva8kvYBXgemEGIDwOWEwPkYsDnwJdAnBlYBtxFGRHwPnG5mE2JZZ8RjAW4ws6ExvTMwDGgAPE9o5FUacIu5ZVyZ75IenwRsBHQys+WSPicE4nJW76KpH3+KDP06zrn8ysVEQWb2BpX3aByUIr9RSbelmQ0BhqRInwBU2pCrqKT6jFNoAsyLgfgAYIuY/g3QSlILSfWAwwHMbAmwNH5rQgjmzrkCkqNxxgWnFFvGyR4EnpE0AXgfmAYQg/O1hD9JPkukR6cDQyR9D4yq5vo65zIo1mCbSdEG44r9xTFtGKGPJvF8AeGCXqrjBwODU6RPBHZNShq4fjV1zuVKGC1RmtG4aIOxc64GKuJuiEw8GDvniooHY+ecyztfdsk55wqCt4ydcy7P1uZ252Ljwdg5V1RK9TZ9D8bOuaJSorHYg7FzrriUaCz2YOycKyLybgrnnMs74d0UzjlXEEo0Fnswds4VmRKNxh6MnXNFpaxE+yk8GDvnikpphuLSn1zeOVdqcrAiqaQhkuZVWJi4uaTRkmbEn81iuiQNljRT0mRJHZOO6Rfzz5DULym9U1wEeWY8NmOtPBg754qGFLopMm1ZGEZYzy5Zf2CMmbUDxsTnAL2AdnE7G7gz1EXNgQGEBUq7AAOSFia9M+ZNHFfxXGvwYOycKyo5aBhjZq8Biyok9waGx8fDgaOS0u+z4C2gqaRNgR7AaDNbZGaLgdFAz7ivsZmNi2vn3ZdUVqW8z9g5V1yy6zRuGZdbS7jbzO7OcMzGZjYXwMzmSmoV01sDs5LyzY5p6dJnp0hPy4Oxc66IZD2f8QIz65yzk67J1iE9Le+mcM4VDQFlyryto29iFwPx57yYPhtom5SvDTAnQ3qbFOlpeTB2zhWXXHQapzYCSIyI6Ac8nZR+ahxV0RVYGrszRgHdJTWLF+66A6PivmWSusZRFKcmlVUp76ZwzhWVXCy7JOlhoBuhb3k2YVTEIOAxSWcCXwJ9YvaRwKHATOB74HQAM1sk6TpgfMx3rZklLgqeRxix0QB4Pm5peTB2zhWV9eiGWMnMTqhk10Ep8hpwfiXlDAGGpEifAHRYmzp5MHbOFQ/5rG3OOVcgSjMaezB2zhUNn8/YOecKRC76jAuRB2PnXFHJxWiKQuTB2DlXXEozFnswds4VD63fHXYFzYOxc66oeDeFc84VgtKMxR6MnXPFpURjsQdj51wxyXolj6Ljwdg5VzRK+aYPn0LTOecKgLeMnXNFxbspnHMu33zWNuecy7/1W8ijsHkwds4VlxKNxh6MnXNFxfuMnXOuAJRmKPZg7JwrNiUajT0YO+eKSqlOFKSw8KlbV5LmA1/kux450BJYkO9KuJVK5fexhZltlKvCJL1AeG8yWWBmPXN13urgwdgBIGmCmXXOdz1c4L+Pmsdvh3bOuQLgwdg55wqAB2OXcHe+K+BW47+PGsb7jJ1zrgB4y9g55wqAB2PnnCsAHozdepHUMN91cK4UeDB260xSB+BiSdvnuy41gaQd810HV3X8dmi3vnYGfpS0wsxm5LsypUpSPeDvkhaZ2cn5ro/LPW8Zu7UmhTkMzewD4A3gcOA3krbIa8VKlKQyM/sJOAHYSNLN+a6Tyz0Pxm6tWRwPKek3wBHAM0A34CRJ2+axaiXJzH6JDw8GpgNHSbo1j1VyVcCDsctaokUsqUxSc0KL+EozuxnoB+wOnCtpmzxWsyRJOh64DrgHuAjYUtJd+a2VyyUPxi5riRaxmf1iZosIs9V1k7SBmU0mBIrTgB6S6uavpiWpNjDEzKYAI4FLgK6S/pnfarlc8WDs1oqkIyVNjE9HApsTuigAVgAvAk+Z2c95qF7JkdRZUmtgPvBbSW3NrDxeLH0d2EbSJvmtpcsFvx3apSVJVuFDIuk1YImZHSnpLEIw3hRoBpxgZtOqv6alR1IboD+wEBgEnAlcAJwDbEvoQ77AzBbmrZIuZzwYu6zEMcU/mtnM+HwMsNzMekqqDXQGvjSzOfmsZ6mR1AM4CPgWuBU4lvDl1xS4LHYPuRLgwdilJakM2AwYDLwGPG1mn8V904CFZvarPFax5Eg6BtjbzC6Nzw8mjFr5Bvg/M/tOUh0zW57Perrc8j5jt4bEqAlYebFuNvBXwmiJQ5NGS9wKNJbUNg/VLBnJ73c0g3Bh9BoAM3sppvUljOeuA5RXby1dVfM78NxqkvuIJZ0B7AB8AIwAbgZ+B7SOoyW2AQ42s2/yVd9iV+H93gTAzKZIOgW4W1JtM7sC+BJ4GxjuLeLS5N0ULiVJFwHHEFq/FwEfAwOBeoQ+y32Bv5rZ1DxVsaRIupTQN9wCuMfM7pG0HfAs8Anhi+8IM5uex2q6KuTdFA5Y/U9lSe2BNkD3+PMXYC5wDVBmZv8CTvNAvO4qvN/nEAJtL2AacJOk35vZx8CuwF+AAz0QlzYPxi4x90HiT+UTCF0TtxD6iI80s/2BscAewEVx0hq3jip0TWwETAJOkXQx0BDoAVwl6Woz+8HMxsZ+e1fCPBjXcPGq/C/xcWfgSGCMmX0FNAaWxKxNgFeAa8zsp4pjj132kgLxmcBwYDLwHWHc8JVm9l9CH/2hkprkraKuWvkFvBpM0oHAXpL+TpgK80XCBaJlMcvLwB2SRhJay4ea2bz81Lb4SdrEzL6Oj/cl9MmfYmbfS/qZ0Dd8vKRFgAF9zGxp/mrsqpO3jGuoeDPBTcBrsaU7AbgR6CWpHYCZlQPtCRPU7GNmH+WtwkVO0mHACEmtJDUD9gE6ES6EJt7r14A6wImEi6Oz8lVfV/18NEUNJGlv4HFCq2yMpM2Bjc1svKRrCV0VxybutnPrR1JP4ArgBjN7IaY1BH4LbA08ZGZjk/JvYGbf5aOuLn+8ZVzDxKv4uxAmmVksaUvgaSDRGr4aeBIYI2nrPFWzZMSpRkcC/zCzFyRtK2k4IMIX4kfAcZIOSRzjgbhm8mBcw8SLR/cTVug4m3BRbqiZPSSpVsxzLeBz5eZAnGr0COBqSbsA/wQmmdl38S+P/wCzCRfrGuSxqi7PvJuihkkMq5K0AXAy4Qr+3WY2Ou73OQ+qQOyqGAlcbmaD4p115XHfFsCyGLhdDeXBuAaK44p/kdSIMBl8e0K3xeNJS/y4HItdEbcCe5rZUv/ic8m8m6LEVZyEJs7CltDAzG4DpgKHEC7cuSoS//r4HfCOpOYeiF0yH2dcwirc6dUI+CkRAOKIiicldQUeJNzyPC5vla0hzOz5OMnSS/EmG/MbaBx4N0XJqhCILyWMa60HnGFmcyU9DDxgZs/ls541laRGZvZtvuvhCocH4xIX77K7GjiXsGxPH6AjsDheyBOsukXXOZcf3k1RwiR1I6yZNiauS/eHGHzfIUyDORs8EDtXCPwCXglJsWLEZ4RVhXeQtCtAXMrnBeCFxLhi51z+eTdFiajQR3wEYVmeJcBEwnSYi4B/m9mkmKeVT/rjXOHwlnGJkfQb4FrCBbshwMWE4VRNgVPjKs8QWszOuQLhwbjISdo8TixjkloRLtCdGNdN2xs4h7C8+w1ALeBr8H5i5wqNB+MiJmlj4PfAeXGo1DxgAfAzgJktJrSKdzGzucAfzGxB3irsnKuUB+PiNh8YD2wGnB4v4H0KPCIpMVJmC6BNvFjny7s7V6D8Al4RipO/l5nZ9BiADwd6Ae+b2d2S7iQsZDkZ2BM4ycw+zF+NnXOZeDAuMpJaEFrECwirNa8A7iasDrEtMNfM/ilpT6AB8IWZfZav+jrnsuM3fRQZM1so6WDgJUI3067Ao8C3hL7inWNreaiZ/ZS/mjrn1oa3jItUnI5xMCEYbwwcCPQFugBzgV/5YpbOFQ8PxkUsLnJ5M9DVzBbFhS7rAA3N7PO8Vs45t1a8m6KImdlzkn4B3pK0l5ktzHednHPrxoNxkaswP24nX6nDueLk3RQlwufHda64eTB2zrkC4HfgOedcAfBg7JxzBcCDsXPOFQAPxs45VwA8GLv1ImmFpPclfSDp35IarkdZ3SQ9Gx8fKal/mrxN40T6a3uOgXG17KzSK+QZJunYtTjXlpI+WNs6uprJg7FbXz+Y2W5m1oEwN8a5yTsVrPXnzMxGmNmgNFmaAmsdjJ0rVB6MXS69DmwbW4QfSboDeBdoK6m7pHGS3o0t6EYAknpKmibpDeCYREGSTpN0W3y8saSnJE2K297AIGCb2Cr/W8z3B0njJU2WdE1SWVdImi7pJaB9phch6axYziRJT1Ro7R8s6XVJH0s6POavJelvSec+Z33fSFfzeDB2OREns+8FTIlJ7YH7zGx34DvgSuBgM+sITAAukVQfuAc4AtgX2KSS4gcDr5rZrkBHYCrQH/gktsr/IKk70I4wUdJuQCdJ+0nqRJhAaXdCsN8ji5fzpJntEc/3EXBm0r4tgf2Bw4C74ms4E1hqZnvE8s+StFUW53FuJb8d2q2vBpLej49fB+4lrDzyhZm9FdO7AjsCb4bZPakLjAO2Bz4zsxkAkh4Azk5xjgOBUwHMbAWwNE6KlKx73N6LzxsRgvOGwFNm9n08x4gsXlMHSdcTukIaAaOS9j0WbzmfIenT+Bq6A7sk9Sc3ief+OItzOQd4MHbr7wcz2y05IQbc75KTgNFmdkKFfLsBuboFVMCNZvbPCue4eB3OMQw4yswmSToN6Ja0r2JZFs99oZklB20kbbmW53U1mHdTuOrwFvArSdsCSGooaTtgGrCVpG1ivhMqOX4McF48tpakxsAyQqs3YRRwRlJfdOu4WvZrwNGSGkjakNAlksmGwFxJdYCTKuzrI6ks1nlrYHo893kxP5K2k7RBFudxbiVvGbsqZ2bzYwvzYUn1YvKVZvaxpLOB5yQtAN4AOqQo4iLgbklnEpaZOs/Mxkl6Mw4dez72G+8AjIst82+Bk83sXUmPAu8DXxC6UjK5Cng75p/C6kF/OvAqYUL/c83sR0n/IvQlvxtXWZkPHJXdu+Nc4BMFOedcAfBuCuecKwAejJ1zrgB4MHbOuQLgwdg55wqAB2PnnCsAHoydc64AeDB2zrkC8P8BfoKtpjSw1A0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 97.64%\n",
      "Precision = 6.07%\n",
      "Recall = 87.16%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for clf in classifiers:\n",
    "    plot_CM_and_ROC_curve(clf, X_rus_std, y_rus, X_test_rus_std, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 98.09%\n",
      "Precision = 7.36%\n",
      "Recall = 86.49%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_CM_and_ROC_curve(('Ensemble model', eclf), X_rus_std, y_rus, X_test_rus_std, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='models-ros'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results using oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 97.84%\n",
      "Precision = 6.65%\n",
      "Recall = 87.84%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 97.41%\n",
      "Precision = 5.32%\n",
      "Recall = 83.11%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 99.90%\n",
      "Precision = 68.55%\n",
      "Recall = 73.65%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 99.91%\n",
      "Precision = 75.37%\n",
      "Recall = 68.24%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 99.95%\n",
      "Precision = 97.25%\n",
      "Recall = 71.62%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for clf in classifiers:\n",
    "    plot_CM_and_ROC_curve(clf, X_ros_std, y_ros, X_test_ros_std, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 99.94%\n",
      "Precision = 85.19%\n",
      "Recall = 77.70%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_CM_and_ROC_curve(('Ensemble model', eclf), X_ros_std, y_ros, X_test_ros_std, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='models-smote'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results using SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 99.14%\n",
      "Precision = 14.95%\n",
      "Recall = 85.14%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 97.69%\n",
      "Precision = 5.91%\n",
      "Recall = 82.43%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 99.82%\n",
      "Precision = 48.13%\n",
      "Recall = 78.38%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 99.78%\n",
      "Precision = 41.60%\n",
      "Recall = 73.65%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MG68oBr+VxXxSWLoU3noL/vgjeI+bwBt6zZonVt1YjxtjotShQ2400/ffh3/+E3r0cOvPPDOycUVQzN7K1q6FBx6Ab76BG2+EDh2sx40xMeX77+HRR2HbNjeg1datkY6oUIi5pJCUBA89BDNnwpgxbhDD006LdFTGmAKzfz88/TR88olbbtbMNSw3aRLZuAqJmEoKy5a5HmUjRriOBZUrRzoiY0yBWrvWTXaze7d7Ovn66+HKK23oWz8xlRQ++QSuucYNamiMiUFxca7xuG5dV39ciOdKjpSYSgrffAM33BDpKIwxBUYV/u//4JxzXD1xmTIwebLrRWKNhkHF1KeyeLEbfdMYEwO2bnVVRQ88AP/+97H11atbQshGzJQUjhxx7Us1akQ6EmNMWGVkwIwZrqvp4cPuSeTWrSMdVdSImaSQnOxKj/YFwZgibMMGePhhWL7cLffsCXfeab1KciFmkkJqqhtHyBhTRG3dCn/7m/vPXrWqG8CuW7dIRxV1YiYpqFopwZgirVYtOP9819X01lvdCJEm18J6mxSR3iKyRkTWicg9QbbHich8EVkqIstFpE+4YsnIsBFEjSlSjhxx7QYrVx5b99BDrmHZEkKehS0piEhxYBJwIdACGCIiLQJ2ux94X1X/AlwOvBCueKykYEwRsnQpDBniZkJ79FH3rQ/sP3k+CGf1UUdgnaquBxCR6UB/YJXfPgpkDlJeAQjb4CNWUjCmCEhOdqWDGTPccoMGcO+9lgzyUTiTwhnAZr/lJKBTwD4PAp+LyM3AacD5wQ4kIiOAEQBxeXwC0UoKxkS5hQvhscfcMMbFi8Pw4W6IglKlIh1ZkRLO22Sw7+UasDwEmKKqtYE+wNsickJMqjpZVRNUNaFatWp5CsZKCsZEsYMH4f77XUJo0QLeeceNW2QJId+Fs6SQBNTxW67NidVDfwd6A6jq9yJSBqgK7MjvYFQtKRgTVVSPFfHLlnXPG+za5bqd2gB2YRPOkkIi0FhE6otIKVxD8uyAfX4HegCISHOgDLAzHMGkp9vfkTFRY+dOuOMOmDr12Lo+feCqq+w/cpiFLSmoahowCpgDrMb1MlopIuNFpJ+32+3AdSLyMzANGKaqgVVM+cKSgjFRQBVmzYJLL4UFC9y0iEeORDqqmBLWh9dU9VPg04B14/zerwIKZN67jAxraDamUNuyBR55BBIT3fJZZ8F999mk5gXMnmg2xkRWRgZMnw6TJrlSQcWKrv2gVy9rCIyAmEkKVlIwphD78kuXEC64wLUlVKoU6YhiVkwlBfvSYUwhkZoKhw5BhQru29q4cfD7724yHBNRMfPd2bqkGlNIrFrlehE98ID7jwlQr54lhEIiZkoK1qZgTISlpMDLL8O777qie0oK7Nljcx0UMjGTFIwxEbRkietZtHmz+3Z21VXuieQyZSIdmQlgScEYEz6q8K9/wfvvu+VGjVz7QYvAAZNNYWFJwRgTPiJuiIoSJeDvf4dhw2wKxELOkoIxJn/t3QtJSdCypVu+9lro3dsNc20KvZCaXkWklIg0Cncwxpgopgqffw6DBsHtt8P+/W59qVKWEKJIjklBRC4CfgG+8JbbishH4Q7MGBNFduxwieC++1xJoUED17vIRJ1Qqo/G4ybHmQ+gqsus1GCMAVzX0pkz4bnn3Kxop50Gt90G/fvbg0FRKpSkkKqqe+X4X3BYRjI1xkSZhx+Gjz927885B+65B6pXj2xM5qSEkhRWi8hlQDERqQ/cAiwKb1jGmKhw4YVumsw77oCePa10UASE0tA8CmgPZAAfAim4xGCMiTW//QbTph1b7tjRzX9gI5oWGaGUFC5Q1buBuzNXiMhAXIKIGuGZuseYGHH0KEyZAq+/Dmlp7uGzNm3ctlNOiWhoJn+FUlK4P8i6sfkdSEGwLzLG5MGKFXDllTB5sksIgwa5J5NNkZRlSUFELgB6A2eIyDN+m8rjqpKMMUXZ4cPw4ouuukgV4uLg/vuhXbtIR2bCKLvqox3AClwbwkq/9QeAe8IZVDhY9ZExufTCCy4h+A9gZ1NjFnlZJgVVXQosFZF3VbVIPIVi1UfG5MLw4bBuHdx8sw1gF0NCaVM4Q0Smi8hyEVmb+Qp7ZPnMSgrG5ODrr2H0aNduAG5KzBdftIQQY0JJClOANwABLgTeB6aHMaawsZKCMUHs3u2GpxgzBr77Dv7730hHZCIolKRwqqrOAVDV31T1fuC88IaV/6ykYEwAVfj0U9eb6PPP3YQ3d9wB/fpFOjITQaE8p3BE3BgXv4nISGALEJXPsVtJwRjPtm3w2GOuZADuIbT774datSIbl4m4UJLCbUBZYDTwKFABGB7OoIwxYbZokUsI5cq5Aez69rVvTQYIISmo6g/e2wPAVQAiUjucQYWDVR+ZmHf48LGnj/v3d8NdDxwIVatGNi5TqGTbpiAiHURkgIhU9ZbjReQtonRAPPsiZGJSejq89RZcfDFs2eLWicCIEZYQzAmyTAoi8jjwLnAF8H8iMhY3p8LPQJOCCc8Yc1LWroWhQ2HiRNi3D776KtIRmUIuu+qj/kAbVT0sIpWBrd7ymoIJzRiTZ0ePwmuvuUHs0tPh9NNh7Fjo0iXSkZlCLrukkKKqhwFUdbeI/GoJwZgosGaN60m0YYOrJrrsMhg1Ck49NdKRmSiQXVJoICKZw2MLUM9vGVUdmNPBRaQ38BxQHHhVVZ8Iss9lwIO42dx+VtW/hR6+MeYEJUtCUhLUrQsPPABt20Y6IhNFsksKfw1Yfj43BxaR4sAkoCeQBCSKyGxVXeW3T2PgXuBMVd0jIlH5/IMxEffrr9C0qSsZNGjg2hDatIFSpSIdmYky2Q2IN/ckj90RWKeq6wFEZDqunWKV3z7XAZNUdY93zh0neU5jYsv+/TBhAsye7R5G69XLre/QIbJxmagVyjAXeXUGsNlvOclb568J0EREForIIq+66QQiMkJEFovI4p07d4YpXGOizPz5cOmlLiGUKgV790Y6IlMEhPJEc14Feyog8BGyEkBjoBtQG/hGRFqq6nF/3ao6GZgMkJCQYI+hmdi2axc8+STM9Qrzbdq4toN69SIalikaQk4KIlJaVY/k4thJQB2/5dq4bq2B+yxS1VRgg4iswSWJxFycx5jYsXo13HSTqzY65RQ318GgQW4iHGPyQY5/SSLSUUR+Af7nLbcRkX+HcOxEoLGI1BeRUsDlwOyAfWbijbjqPTXdBFifi/iNiS0NGkDFiu55g/ffd91NLSGYfBRKSWEicDHuBo6q/iwiOQ6drappIjIKmIPrkvq6qq4UkfHAYlWd7W3rJSKrgHTgTlXdlcdrMaboyciAmTOhZ083eF3p0vDqq24CHBu3xYRBKEmhmKpukuP/ANNDObiqfgp8GrBunN97BcZ4L2OMv02b4OGHYdkyWLXKPZAGULlyZOMyRVooSWGziHQE1Hv24GYg6qbjNCZqpKXBO+/A5MluuIoqVaBr10hHZWJEKEnhBlwVUhywHfjSW2eMyW9r1sD48e5fcLOg3XorlC8f2bhMzAglKaSp6uVhj8SYWJeUBFdf7Qawq1XLDWDXqVOkozIxJpSkkOh1FX0P+FBVD4Q5JmNiU+3acNFFbuC6G2+0AexMRIQy81pDEemK61L6kIgsA6ar6vSwR2dMUXboEEyaBBdcAK1bu3UPPGC9ikxEhdTBWVW/U9XRQDtgP27yHWNMXn3/vXvG4L334PHHj80XawnBRFiOJQURKYsbyO5yoDkwC7CuEMbkxf798PTT8Mknbrl5cysdmEIllDaFFcDHwJOq+k2Y4zGm6Jo7F/75T9i92w1gN3IkXHEFFC8e6ciM8QklKTRQ1YywR2JMUXbgADz6qCsptGvnHkSLi4t0VMacIMukICJPq+rtwH9E5ISRSUOZec2YmKbqXsWKuSEq7rnHJYWBA228IlNoZVdSeM/7N1czrhljgK1bXcmgQwcYNsyty5wAx5hCLLuZ13703jZX1eMSgzfQ3cnOzGZM0ZOR4UYvnTQJDh+GDRvgb3+zaTFN1AilDDs8yLq/53cgxkS9DRvg2mvhqadcQujVy41hZAnBRJHs2hQG47qh1heRD/02lQNs3j9jMqWnw5tvwiuvQGoqVKsG994L55wT6ciMybXs2hR+BHbhZkyb5Lf+ALA0nEEZE1VEYNEilxAuuQRGj3YNy8ZEoezaFDYAG3Cjohpj/B05AsnJbm6DYsVcF9Pt213DsjFRLMs2BRFZ4P27R0R2+732iMjuggvRmELmp59gyBD3JHLm8BRxcZYQTJGQXfVR5pSbVQsiEGMKveRkeP55mDHDLZcoAXv3uqkxjSkisiwp+D3FXAcorqrpQBfgeuC0AojNmMLju+/cAHYzZrhhKUaMcD2LLCGYIiaUYS5mAh1EpCHwFvAJMBW4OJyBGVMoqMIjj8CsWW65RQsYNw4aNYpsXMaESShJIUNVU0VkIDBBVSeKiPU+MrFBBGrUcM8a3Hija0uwAexMERbSdJwicilwFTDAW1cyfCEZE2E7d7qpMf/yF7d8zTXQp4+bGc2YIi7UJ5rPww2dvV5E6gPTwhuWMRGg6qqJLr0U7roL9u1z60uWtIRgYkYo03GuEJHRQCMRaQasU9VHwx+aMQVoyxbXdpCY6JbPPhvS0iIbkzEREMrMa2cDbwNbAAFOF5GrVHVhuIMzJuwyMmD6dHjhBUhJgYoV4c473bhFNhuaiUGhtCk8C/RR1VUAItIclyQSwhmYMQVi3Dj4v/9z73v3httvt26mJqaFkhRKZSYEAFVdLSI27KMpGi65xD2hfM89NoCdMYSWFH4SkZdxpQOAK7AB8Uy0WrXKtRsMHeqW27eHmTNteGtjPKEkhZHAaOAuXJvC18C/wxmUMfkuJQVefhnefde1I7RufazLqSUEY3yyTQoi0gpoCHykqk8WTEjG5LMlS+Dhh92zB8WKwVVXQfPmkY7KmEIpu0l27sPNsPYTbpiL8ar6eoFFZszJOngQJk6ED705oho1cg3LLVpENi5jCrHsHl67AmitqpcCHYAbcntwEektImtEZJ2I3JPNfoNEREXEejSZ/PPiiy4hlCgBI0fC229bQjAmB9lVHx1R1WQAVd0pIqE8/ewjIsVxM7b1BJKARBGZ7d+TyduvHK7N4odcRW5MMKrHni+47jrYuhVuvhkaNIhsXMZEieySQgO/uZkFaOg/V7OqDszh2B1xTz+vBxCR6UB/YFXAfg8DTwJ35CZwY46jCnPmwEcfuTkPSpZ0D6I9+2ykIzMmqmSXFP4asPx8Lo99BrDZbzkJ6OS/g4j8Baijqv8VkSyTgoiMAEYAxMXF5TIMU+Tt2AGPPw7ffOOWP/sM+vWLbEzGRKns5miee5LHDjZGgPo2uuqoZ4FhOR1IVScDkwESEhI0h91NrMjIcM8YTJgAhw5B2bJw223Qt2+kIzMmaoXynEJeJeFmbctUG9jqt1wOaAl8Ja4O+HRgtoj0U9XFYYzLFAWbN7sB7JYsccvnnuueSq5WLbJxGRPlwpkUEoHG3lDbW4DLgb9lblTVffjN/ywiXwF3WEIwIVm61CWEypXdMNc9etgAdsbkg5CTgoiUVtUjoe6vqmkiMgqYAxQHXlfVlSIyHlisqrNzH66JaQcOQLly7n3fvrB3L/TvDxUqRDYuY4qQHLuZikhHEfkF+J+33EZEQhrmQlU/VdUmqtowcw4GVR0XLCGoajcrJZigjh51Q1RcfDH8/rtbJwJXX20JwZh8FsqzBxOBi4FdAKr6M24mNmPC75df4Mor4ZVXIDkZFi2KdETGFGmhVB8VU9VNcnx9bXqY4jHGOXzYPZE8bZp7BiEuDh544NggdsaYsAglKWwWkY6Aek8p3wysDW9YJqatWAFjx7opMosVc9VEI0ZA6dKRjsyYIi+UpHADrgopDtgOfEkexkEyJmTlysHOndCkiSsd2IimxhSYHJOCqu7AdSc1JnyWLYM2bVwDct268NJLbvC6EuHsNW2MCZTj/zgReQW/J5EzqeqIsERkYsvu3fDUU/D55/DQQ3DRRW5969aRjcuYGBXK17Av/d6XAS7h+DGNjMk9VTdG0VNPwf79UKYMpKZGOipjYl4o1Ufv+S+LyNvAF2GBK3nLAAAcDklEQVSLyBR927bBY4/Bd9+55U6dXMNyrVqRjcsYk6dhLuoDdfM7EBMjVqyAG290A9iVKwdjxriH0myICmMKhVDaFPZwrE2hGLAbyHIWNWOy1aQJ1KgB9erB3XdD1ao5/ogxpuBkmxTEPbHWBjegHUCGqtrQ1SZ06enw3nuuNFC+PJQqBa+95t4bYwqdbIe58BLAR6qa7r0sIZjQrV0LQ4fCM8+4VyZLCMYUWqG0KfwoIu1U9aewR2OKhqNH4dVX4c03XUnh9NPhggsiHZUxJgRZJgURKaGqacBZwHUi8huQjJtRTVW1XQHFaKLJ8uUwfjxs3Ogajy+7DEaNglNPjXRkxpgQZFdS+BFoBwwooFhMtNu8Ga691k2TWbcujBvnnlI2xkSN7JKCAKjqbwUUi4l2derAJZe4OQ6uvdY1Khtjokp2SaGaiIzJaqOqPpPVNhMj9u+HCRPcLGiZQ1rfc489c2BMFMsuKRQHyuKVGIw5zrx58M9/wq5dsHo1TJ3qkoElBGOiWnZJ4Q9VHV9gkZjosGuXSwbz5rnltm3d8NaWDIwpEnJsUzAGcAPYffKJe95g/37Xm+jmm+Gvf3UT4RhjioTskkKPAovCFH4HDsCzz7qE0LUr3Hsv1KwZ6aiMMfksy6SgqrsLMhBTCGVkuBJC8eLuKeSxYyElBS680KqLjCmirNxvgtu4Ea67DqZMObaue3fo08cSgjFFmCUFc7y0NHj9dRgyBH7+GWbNcsNWGGNigk2Aa45Zs8ZNibl2rVvu3x9uucUeQjMmhlhSMK508PLLbgC7jAw3A9r990PHjpGOzBhTwCwpGNeQvGKFa1QeMgRuuMEGsDMmRllSiFWHDkFyMlSr5hqOH3gA/vwTWreOdGTGmAiyhuZY9P33bkjr++93pQNwVUaWEIyJeVZSiCX79rknkj/5xC1XquTWVawY2biMMYVGWEsKItJbRNaIyDoRuSfI9jEiskpElovIXBGpG854YpYqzJ0Ll17qEkKpUjB6tHsGwRKCMcZP2EoKIlIcmAT0BJKARBGZraqr/HZbCiSo6iERuQF4EhgcrphikqqrJpozxy23a+eW4+IiG5cxplAKZ/VRR2Cdqq4HEJHpQH/AlxRUdb7f/ouAK8MYT2wSgQYNXG+iW25xk+DYAHbGmCyEMymcAWz2W04COmWz/9+Bz4JtEJERwAiAOPuGm7OtWyEp6dhzBkOHuolwqlePbFzGmEIvnF8Zgw2Qo0F3FLkSSAD+FWy7qk5W1QRVTahWrVo+hljEZGTAtGmuZ9G998Jub0zDEiUsIRhjQhLOkkISUMdvuTawNXAnETkfGAucq6pHwhhP0bZ+PTzyCCxf7pbPOceqiYwxuRbOpJAINBaR+sAW4HLgb/47iMhfgJeB3qq6I4yxFF1paW54ildfhdRU9zDavfe6pGCMMbkUtqSgqmkiMgqYg5vv+XVVXSki44HFqjobV11UFpghbjjm31W1X7hiKpLGjnXdTcE1It9yC5QtG9mYjDFRK6wPr6nqp8CnAevG+b0/P5znjwlDhrhRTe+7Dzp0iHQ0xpgoZ5XO0eann2Dy5GPLbdvCBx9YQjDG5Asb5iJaJCfDxInwn/+45YQE9yAauFFOjTEmH1hSiAYLF8Kjj8KOHa576fDh0KpVpKMyxhRBlhQKs7174emn4TPvmb74eBg3Dho2jGxcxpgiy5JCYfbKKy4hlC4NN97oGpXt2QNjTBhZUihsVN14RQDXX++eSr7pJqhdO7JxGWNigiWFwkIVZs6E2bPdfMmlSkH58vD445GOzJyE1NRUkpKSSElJiXQoJkaUKVOG2rVrU7JkyTz9vCWFwiApyQ1RsXixW/7iC7joosjGZPJFUlIS5cqVo169eogEGw7MmPyjquzatYukpCTq16+fp2NYBXUkZWTAu+/C4MEuIVSqBI89Bn36RDoyk09SUlKoUqWKJQRTIESEKlWqnFTJ1EoKkbJ+PTz0EKxc6ZYvvBBuv91mQiuCLCGYgnSyf2+WFCLl119dQqhe3Q1RcdZZkY7IGGOs+qhA7dlz7P2FF8Jdd8H771tCMGFVvHhx2rZtS8uWLenbty979+71bVu5ciXdu3enSZMmNG7cmIcffhjVY9OefPbZZyQkJNC8eXOaNWvGHXfcEfQcM2fOZPz48ceta9OmDUOGDDluXbdu3Vic2XYGbNy4kZYtW/qWf/zxR8455xyaNm1Ks2bNuPbaazl06NBJXf+GDRvo1KkTjRs3ZvDgwRw9evSEfY4ePco111xDq1ataNOmDV999ZVvW+/evWnTpg3x8fGMHDmS9PR0AHbv3k3Pnj1p3LgxPXv2ZI/3/3vfvn307dvX9zNvvPGG71h33XUX8fHxNG/enNGjR/s+67Fjx1KnTh3KBgxmedttt9G2bVvatm1LkyZNqOjVJOzcuZPevXuf1OeSJVWNqlf79u01L378UTUhIU8/evIOH1Z99lnVM89UXb8+QkGYSFi1alWkQ9DTTjvN9/7qq6/WRx55RFVVDx06pA0aNNA5c+aoqmpycrL27t1bn3/+eVVV/eWXX7RBgwa6evVqVVVNTU3VSZMmBT1Hly5ddOfOnb7lVatWacuWLbVWrVp68OBB3/pzzz1XExMTfcsbNmzQ+Ph4VVXdtm2bxsXF6XfffaeqqhkZGTpjxgzdtm3bSV3/pZdeqtOmTVNV1euvv15feOGFE/Z5/vnnddiwYaqqun37dm3Xrp2mp6erquq+fft88QwcONB3rDvvvFMff/xxVVV9/PHH9a677lJV1UcffdT3fseOHVqpUiU9cuSILly4ULt27appaWmalpamnTt31vnz56uq6vfff69bt2497ncVaOLEiXrNNdf4locNG6bffvtt0H2D/d3hRqfO8R5rJYVwW7wYLr8c3nkHjh6FpUsjHZGJYV26dGHLli0ATJ06lTPPPJNevXoBcOqpp/L888/zxBNPAPDkk08yduxYmjVrBkCJEiW48cYbTzjm2rVrKV26NFWrVvWtmzp1KldddRW9evVi9uzZIcU2adIkhg4dSpcuXQBXNz5o0CBq1KiR5+tVVebNm8egQYMAGDp0KDNnzjxhv1WrVtGjRw8AqlevTsWKFX0lmvLlywOQlpbG0aNHfXX2s2bNYujQoSccV0Q4cOAAqsrBgwepXLkyJUqUQERISUnh6NGjHDlyhNTUVN+1de7cmZo1a2Z7LdOmTTuu5DVgwADefffdPH82WbGkEC4HD7qeRCNHui6njRrBlCkwcGCkIzMRJJL/r1Clp6czd+5c+vVzU5asXLmS9u3bH7dPw4YNOXjwIPv372fFihUnbA9m4cKFtMscnNHz3nvvMXjwYIYMGcK0adNCii/U861Zs8ZXpRL48q8aA9i1axcVK1akRAnXfFq7dm1fUvTXpk0bZs2aRVpaGhs2bGDJkiVs3nxsivkLLriA6tWrU65cOV+C2b59u+9GXrNmTXbscPOEjRo1itWrV1OrVi1atWrFc889R7FixejSpQvnnXceNWvWpGbNmlxwwQU0b948pM9m06ZNbNiwge7du/vWJSQk8M0334T087lhDc3hsGyZazzOHMDu2mth6FDI48MkpujQoLOUh9fhw4dp27YtGzdupH379vTs2dOLRbPsqZKbHix//PEH/nOnJyYmUq1aNerWrUvt2rUZPnw4e/bsoVKlSkGPm9veMk2bNmXZsmUh7atBPvBg5xs+fDirV68mISGBunXr0rVrV18iAZgzZw4pKSlcccUVzJs3z/cZBjNnzhzatm3LvHnz+O233+jZsydnn302O3bsYPXq1SQlJQHQs2dPvv76a84JYZbE6dOnM2jQIIr7jYhcvXp1tm49YYbjk2YlhXCoUsUNZteqFUyd6pKCJQQTIaeccgrLli1j06ZNHD16lEmTJgEQHx9/XKMvwPr16ylbtizlypUjPj6eJUuWhHR8/37x06ZN49dff6VevXo0bNiQ/fv38x9vyPcqVar4GmTBNdZmVjuFer7clBSqVq3K3r17SUtLA9zDhLVq1TrhmCVKlODZZ59l2bJlzJo1i71799K4cePj9ilTpgz9+vVj1qxZANSoUYM//vgDcImxevXqALzxxhsMHDgQEaFRo0bUr1+fX3/9lY8++ojOnTtTtmxZypYty4UXXsiiRYtyvF5wSSGw0T4lJYVTTjklpJ/PDUsK+UEVFi069jWwTh147TX3atAgsrEZ46lQoQITJ07kqaeeIjU1lSuuuIJvv/2WL7/8EnAlitGjR3PXXXcBcOedd/LYY4+xdu1aADIyMnjmmWdOOG7z5s1Zt26db58ZM2awfPlyNm7cyMaNG5k1a5avCqlbt2688847vm/wb775Jueddx7gql3efPNNfvjhB9+x33nnHbZt23bc+TJLCsFeFQOe8xERzjvvPD744APf+fr373/CNRw6dIjk5GQAvvjiC0qUKEGLFi04ePCg78aflpbGp59+6mtj6devH2+++eYJx42Li2OuN0Xu9u3bWbNmDQ0aNCAuLo4FCxaQlpZGamoqCxYsCKn6aM2aNezZs8fX1pJp7dq1x/XcyjehtEYXpleh6320bZvqLbeotm+vOmtWGE5gollh632kqnrxxRfrW2+9paqqy5cv13PPPVebNGmiDRs21AcffFAzMjJ8+3788cfarl07bdasmTZv3lzvuOOOE46fnJysLVq00IyMDJ0/f7526tTpuO1paWl6+umn69atW/XIkSN60003aatWrbR169Y6fPhwTU5O9u373Xff6VlnnaVNmjTRZs2a6YgRI47bnhe//fabdujQQRs2bKiDBg3SlJQUVVWdNWuWPvDAA6rqekFlnrNHjx66ceNGVXU9ohISErRVq1baokULHTVqlKampqqq6p9//qndu3fXRo0aaffu3XXXrl2qqrplyxbt2bOntmzZUuPj4/Xtt9/2fQ4jRozwfZa33XabL8Y777xTzzjjDBURPeOMM/Qf//iHb9s//vEPvfvuu0+4rn/96186ceLEoNd8Mr2PRCNRyXkSEhISNLDIG4rERDf6dGJiPgWSkeEGsJswAQ4dgrJl4e673fMHxnhWr14dcmNiNLvlllvo27cv559v064XlHPOOYdZs2ZRqVKlE7YF+7sTkSWqmpDTca2hOS9+/90NYPfTT265WzeXEPwa24yJJffdd99x1T4mvHbu3MmYMWOCJoSTZUkht5Yvd91Mjx6FypXdU8k9euSub6AxRUyNGjV8XV1N+FWrVo0BAwaE5diWFHKreXOIi4OmTWHMGKhQIdIRGWNMvrGkkJOjR93TyAMHuhFMS5aE11+HU0+NdGTGGJPvrEtqdn75Ba68El54AZ5++th6SwjGmCLKSgrBHD4ML74I06a5Zw/i4mx4CmNMTLCkEOjHH13Poq1boVgxNzzFiBFuzmRjotS2bdu49dZbSUxMpHTp0tSrV48BAwYwe/Zs/vvf/0Y6PFOIWFLw9/vvcNNNrnTQpAmMGwfe04vGRCtV5ZJLLmHo0KFMnz4dgGXLlvHxxx9HODJTGFlS8BcXB0OGuAblq692g9kZk58Ssnl26L77jlVTfvihG2U3K7l4gHP+/PmULFmSkSNH+tZljhM0d+5cBg0a5Buh9J133kFEGD9+PB9//DGHDx+ma9euvPzyy4gI3bp1o1OnTsyfP5+9e/fy2muvcfbZZ5Oens7dd9/NnDlzEBGuu+46br75ZpYsWcKYMWM4ePAgVatWZcqUKTkOEW0iK7YbmnfvhnvvPf4/2JgxMHy4JQRTZGQ3JPXSpUuZMGECq1atYv369SxcuBBw4xAlJiayYsUKDh8+fFwVU1paGj/++CMTJkzgoYceAmDy5Mls2LCBpUuXsnz5cq644gpSU1O5+eab+eCDD1iyZAnDhw9n7Nix4b9gc1Ji886nCp99Bk89Bfv3w6ZN8O679gCaCb9Qv+EPHFggnRs6duxI7dq1AXzDa5911lnMnz+fJ598kkOHDrF7927i4+Pp27evF5qLq3379mzcuBGAL7/8kpEjR/qGm65cuTIrVqxgxYoVvmGm09PTrZQQBcKaFESkN/AcUBx4VVWfCNheGngLaA/sAgar6sZwxsS2ba5Y/t13brlzZ1dst4Rgiqj4+HjfKKGBSpcu7XtfvHhx0tLSSElJ4cYbb2Tx4sXUqVOHBx988LihsTN/JnN/CD43g6oSHx/P999/n9+XZMIobNVHIlIcmARcCLQAhohIi4Dd/g7sUdVGwLPAP8MVDxkZ9Ng9Ay67zCWE8uXhwQfh3/+GIOOrG1NUdO/enSNHjvDKK6/41iUmJrJgwYKg+2cmgKpVq3Lw4MEsE4q/Xr168dJLL/mSxO7du2natCk7d+70JYXU1FRWrlx5spdjwiycbQodgXWqul5VjwLTgcCBzPsDb3rvPwB6SG6nYQpRsUMHGfDnK25E0+7dYcYMuPhiKyGYIk9E+Oijj/jiiy9o2LAh8fHxPPjgg0EnmwGoWLEi1113Ha1atWLAgAF06NAhx3Nce+21xMXF0bp1a9q0acPUqVMpVaoUH3zwAXfffTdt2rShbdu2fJdZQjeFVtiGzhaRQUBvVb3WW74K6KSqo/z2WeHtk+Qt/+bt82fAsUYAIwDi4uLab9q0KdfxbN8Oq176mvPOTnNJwZgCECtDZ5vCpbAOnR3sK3hgBgplH1R1MjAZ3HwKeQmmRg2o8Y+c50I1xphYFs7qoySgjt9ybSBwlmnfPiJSAqgA7A5jTMYYY7IRzqSQCDQWkfoiUgq4HJgdsM9sYKj3fhAwT6NtKjhjcmB/0qYgnezfW9iSgqqmAaOAOcBq4H1VXSki40UkczaO14AqIrIOGAPcE654jImEMmXKsGvXLksMpkCoKrt27aJMmTJ5PkbMzNFsTCSkpqaSlJR0XD9/Y8KpTJky1K5dm5IlSx63vjA0NBsT80qWLEn9+vUjHYYxIYvtsY+MMcYcx5KCMcYYH0sKxhhjfKKuoVlEdgK5f6TZqQr8meNeRYtdc2ywa44NJ3PNdVW1Wk47RV1SOBkisjiU1veixK45Ntg1x4aCuGarPjLGGONjScEYY4xPrCWFyZEOIALsmmODXXNsCPs1x1SbgjHGmOzFWknBGGNMNiwpGGOM8SmSSUFEeovIGhFZJyInjLwqIqVF5D1v+w8iUq/go8xfIVzzGBFZJSLLRWSuiNSNRJz5Kadr9ttvkIioiER998VQrllELvN+1ytFZGpBx5jfQvjbjhOR+SKy1Pv77hOJOPOLiLwuIju8mSmDbRcRmeh9HstFpF2+BqCqReoFFAd+AxoApYCfgRYB+9wIvOS9vxx4L9JxF8A1nwec6r2/IRau2duvHPA1sAhIiHTcBfB7bgwsBSp5y9UjHXcBXPNk4AbvfQtgY6TjPslrPgdoB6zIYnsf4DPczJWdgR/y8/xFsaTQEVinqutV9SgwHegfsE9/4E3v/QdADxEJNjVotMjxmlV1vqoe8hYX4WbCi2ah/J4BHgaeBIrC2NWhXPN1wCRV3QOgqjsKOMb8Fso1K1Dee1+BE2d4jCqq+jXZz0DZH3hLnUVARRGpmV/nL4pJ4Qxgs99ykrcu6D7qJgPaB1QpkOjCI5Rr9vd33DeNaJbjNYvIX4A6qvrfggwsjEL5PTcBmojIQhFZJCK9Cyy68Ajlmh8ErhSRJOBT4OaCCS1icvv/PVeK4nwKwb7xB/a7DWWfaBLy9YjIlUACcG5YIwq/bK9ZRIoBzwLDCiqgAhDK77kErgqpG640+I2ItFTVvWGOLVxCueYhwBRVfVpEugBve9ecEf7wIiKs96+iWFJIAur4LdfmxOKkbx8RKYErcmZXXCvsQrlmROR8YCzQT1WPFFBs4ZLTNZcDWgJfichGXN3r7ChvbA71b3uWqqaq6gZgDS5JRKtQrvnvwPsAqvo9UAY3cFxRFdL/97wqikkhEWgsIvVFpBSuIXl2wD6zgaHe+0HAPPVacKJUjtfsVaW8jEsI0V7PDDlcs6ruU9WqqlpPVevh2lH6qWo0z+Uayt/2TFynAkSkKq46aX2BRpm/Qrnm34EeACLSHJcUdhZolAVrNnC11wupM7BPVf/Ir4MXueojVU0TkVHAHFzPhddVdaWIjAcWq+ps4DVcEXMdroRweeQiPnkhXvO/gLLADK9N/XdV7RexoE9SiNdcpIR4zXOAXiKyCkgH7lTVXZGL+uSEeM23A6+IyG24apRh0fwlT0Sm4ar/qnrtJP8ASgKo6ku4dpM+wDrgEHBNvp4/ij87Y4wx+awoVh8ZY4zJI0sKxhhjfCwpGGOM8bGkYIwxxseSgjHGGB9LCqbQEZF0EVnm96qXzb71shpNMpfn/MobifNnb4iIpnk4xkgRudp7P0xEavlte1VEWuRznIki0jaEn7lVRE492XOb2GBJwRRGh1W1rd9rYwGd9wpVbYMbLPFfuf1hVX1JVd/yFocBtfy2Xauqq/IlymNxvkBocd4KWFIwIbGkYKKCVyL4RkR+8l5dg+wTLyI/eqWL5SLS2Ft/pd/6l0WkeA6n+xpo5P1sD2+c/l+8ce5Le+ufkGPzUzzlrXtQRO4QkUG48aXe9c55ivcNP0FEbhCRJ/1iHiYi/85jnN/jNxCaiLwoIovFzaPwkLduNC45zReR+d66XiLyvfc5zhCRsjmcx8QQSwqmMDrFr+roI2/dDqCnqrYDBgMTg/zcSOA5VW2LuyknecMeDAbO9NanA1fkcP6+wC8iUgaYAgxW1Va4EQBuEJHKwCVAvKq2Bh7x/2FV/QBYjPtG31ZVD/tt/gAY6Lc8GHgvj3H2xg1rkWmsqiYArYFzRaS1qk7EjYtznqqe5w19cT9wvvdZLgbG5HAeE0OK3DAXpkg47N0Y/ZUEnvfq0NNxY/oE+h4YKyK1gQ9V9X8i0gNoDyR6w3ucgkswwbwrIoeBjbjhl5sCG1R1rbf9TeAm4Hnc/AyvisgnQMhDc6vqThFZ741Z8z/vHAu94+YmztNwwz74z7p1mYiMwP2/rombcGZ5wM929tYv9M5TCve5GQNYUjDR4zZgO9AGV8I9YdIcVZ0qIj8AFwFzRORa3DDDb6rqvSGc4wr/AfNEJOgcG954PB1xg7BdDowCuufiWt4DLgN+BT5SVRV3hw45TtwMZE8Ak4CBIlIfuAPooKp7RGQKbmC4QAJ8oapDchGviSFWfWSiRQXgD2+M/Ktw35KPIyINgPVelclsXDXKXGCQiFT39qksoc9P/StQT0QaectXAQu8OvgKqvoprhE3WA+gA7jhu4P5EBiAmwfgPW9druJU1VRcNVBnr+qpPJAM7BORGsCFWcSyCDgz85pE5FQRCVbqMjHKkoKJFi8AQ0VkEa7qKDnIPoOBFSKyDGiGm7JwFe7m+bmILAe+wFWt5EhVU3AjUM4QkV+ADOAl3A32v97xFuBKMYGmAC9lNjQHHHcPsAqoq6o/eutyHafXVvE0cIeq/oybm3kl8DquSirTZOAzEZmvqjtxPaOmeedZhPusjAFslFRjjDF+rKRgjDHGx5KCMcYYH0sKxhhjfCwpGGOM8bGkYIwxxseSgjHGGB9LCsYYY3z+H8uTKOipUrXhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 99.94%\n",
      "Precision = 84.06%\n",
      "Recall = 78.38%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for clf in classifiers:\n",
    "    plot_CM_and_ROC_curve(clf, X_smote_std, y_smote, X_test_smote_std, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 99.91%\n",
      "Precision = 72.02%\n",
      "Recall = 81.76%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_CM_and_ROC_curve(('Ensemble model', eclf), X_smote_std, y_smote, X_test_smote_std, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we used random undersampling the best model was a simple logistic regression with AUC = 0.9722, recall = 87.16% and precision = 7.94%. The number of false negatives, the worst possible classification regarding frauds, for the logistic regression was 19 out of 148 cases present in the test set. However, we can notice that the ensemble model (a soft voting combination of all simple models with the same weights) was the second best in this case with AUC = 0.9671 and 20 false negative cases, only 1 more than the logistic regression. We can also notice that in all models the number of false positives was pretty high. The best result was with logistic regression (1496 false positive cases). It's good to remember that when we use undersampling method, a lot of information is lost due to discarding part of the observations. Then, it was already expected a not very good result.\n",
    "\n",
    "With the random oversampling method, we can verify the true power of ensemble models. Our ensemble model combined the best part of the simple models and could get a very high AUC (0.967), high precision (85.19%) and high recall (77.7%). Looking at the confusion matrix we can see the cases for false negative and false positive as 33 and 20, respectively. The logistic regression was the model with the lowest number of false negatives (only 18) and the random forest was the model with the lowest number of false positives (an astonishing result of only 3!). So, if we try to tweak the weights for each model in the ensemble model, we can achieve an even better result.\n",
    "\n",
    "The analysis for SMOTE method is very similar to the random oversampling method. Again, we can see that the ensemble model could get the best parts of all simple models, resulting in a good performance. Once again, tweaking the weights for each simple model might be a way to enhance the result.\n",
    "\n",
    "An interesting result is that in all cases of resampling, the highest AUC score among the simple models was using logistic regression. In my opinion, binary classification problems should have at least this model as a benchmark when the objective is to maximize the AUC."
   ]
  }
 ],
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   "language": "python",
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