DataPrepreocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Necessay libraries for preprocessing:\n",
    "    - Numpy\n",
    "    - Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing The dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"E:\\Courses\\DEPI\\New folder\\creditcard.csv\") #specify the dataset directory\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking For Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      0\n",
       "V1        0\n",
       "V2        0\n",
       "V3        0\n",
       "V4        0\n",
       "V5        0\n",
       "V6        0\n",
       "V7        0\n",
       "V8        0\n",
       "V9        0\n",
       "V10       0\n",
       "V11       0\n",
       "V12       0\n",
       "V13       0\n",
       "V14       0\n",
       "V15       0\n",
       "V16       0\n",
       "V17       0\n",
       "V18       0\n",
       "V19       0\n",
       "V20       0\n",
       "V21       0\n",
       "V22       0\n",
       "V23       0\n",
       "V24       0\n",
       "V25       0\n",
       "V26       0\n",
       "V27       0\n",
       "V28       0\n",
       "Amount    0\n",
       "Class     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking null \n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize The Class Balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Class', ylabel='count'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAAAMIKoAAAAMaFJUPfHEEyovL2+w3e1264knnrjVNQEAALQ4TYqqnTt3qrq6usH2y5cv6//+7/9ueVEAAAAtTaubGT548KD157///e9yuVzW/draWuXk5Ohf/uVfzK0OAACghbipqBowYIBsNptsNts1P+YLDg7W7373O2OLAwAAaCluKqpOnTolj8ejnj17at++fQoLC7P2BQQEKDw8XP7+/sYXCQAA0NzdVFR169ZNklRXV3dbFgMAANBS3VRUXe348eP69NNPde7cuQaRlZmZecsLAwAAaEmaFFW///3vNX36dHXu3FmRkZGy2WzWPpvNRlQBAIB7TpOiavHixXrjjTeUnp5uej0AAAAtUpOuU3XhwgX94he/ML0WAACAFqtJUfWLX/xCn3zyiem1AAAAtFhN+vivV69emj9/vvbs2aPY2Fi1bt3aa/+vfvUrI4sDAABoKZr0TtVbb72ldu3aKT8/X6tXr9by5cut24oVKxp9nF27dmn06NGKioqSzWbT1q1bvfa/8MIL1sVG628jR470mjl//rwmTpwou92u0NBQTZ48WRcvXvSaOXjwoB577DEFBQUpOjpaS5cubbCWzZs3q0+fPgoKClJsbKw++ugjr/0ej0eZmZnq0qWLgoODlZiYqOPHjzf6ZwUAAHe3JkXVqVOnrnv76quvGn2cyspK9e/fX2vWrLnuzMiRI3X27Fnr9qc//clr/8SJE3XkyBHl5uYqOztbu3bt0rRp06z9brdbI0aMULdu3VRYWKhly5Zp4cKFeuutt6yZ3bt3a8KECZo8ebK++OILjRkzRmPGjNHhw4etmaVLl2rVqlVav3699u7dq7Zt28rhcOjy5cuN/nkBAMDdy+bxeDy+XoT0w6UYtmzZojFjxljbXnjhBZWXlzd4B6ve0aNHFRMTo88//1wDBw6UJOXk5GjUqFE6ffq0oqKitG7dOr322mtyuVwKCAiQJM2dO1dbt25VcXGxJGncuHGqrKxUdna2dewhQ4ZowIABWr9+vTwej6KiovTyyy/rlVdekSRVVFQoIiJCWVlZGj9+fKN+RrfbrZCQEFVUVMhut9/sS9RocbP/eNuODbRUhcue9/USALRQjf393aRzql588cWf3P/OO+805bDXtHPnToWHh6tDhw564okntHjxYnXq1EmSVFBQoNDQUCuoJCkxMVF+fn7au3evfv7zn6ugoEDDhg2zgkqSHA6Hfvvb3+rChQvq0KGDCgoK5HQ6vZ7X4XBYMXfq1Cm5XC4lJiZa+0NCQhQfH6+CgoLrRlVVVZWqqqqs+263+5ZfDwAA0Dw1KaouXLjgdb+mpkaHDx9WeXn5Nf+h5aYaOXKkxo4dqx49eujkyZN69dVX9dRTT6mgoED+/v5yuVwKDw/3ekyrVq3UsWNHuVwuSZLL5VKPHj28ZiIiIqx9HTp0kMvlsrZdPXP1Ma5+3LVmrmXJkiVatGhRE35yAADQ0jQpqrZs2dJgW11dnaZPn67777//lhdV7+p3gGJjY/Xwww/r/vvv186dO/Xkk08ae57bJSMjw+sdMLfbrejoaB+uCAAA3C5NOlH9mgfy85PT6dTy5ctNHbKBnj17qnPnzjpx4oQkKTIyUufOnfOauXLlis6fP6/IyEhrprS01Gum/v6NZq7ef/XjrjVzLYGBgbLb7V43AABwdzIWVZJ08uRJXblyxeQhvZw+fVrffvutunTpIklKSEhQeXm5CgsLrZkdO3aorq5O8fHx1syuXbtUU1NjzeTm5urBBx9Uhw4drJm8vDyv58rNzVVCQoIkqUePHoqMjPSacbvd2rt3rzUDAADubU36+O/HJ3V7PB6dPXtW27ZtU0pKSqOPc/HiRetdJ+mHE8KLiorUsWNHdezYUYsWLVJycrIiIyN18uRJzZkzR7169ZLD4ZAk9e3bVyNHjtTUqVO1fv161dTUKC0tTePHj1dUVJQk6dlnn9WiRYs0efJkpaen6/Dhw1q5cqXXO2q//vWv9W//9m968803lZSUpPfee0/79++3Lrtgs9k0c+ZMLV68WL1791aPHj00f/58RUVFeX1bEQAA3LuaFFVffPGF130/Pz+FhYXpzTffvOE3A6+2f/9+DR8+3LpfH2spKSlat26dDh48qA0bNqi8vFxRUVEaMWKEfvOb3ygwMNB6zLvvvqu0tDQ9+eST8vPzU3JyslatWmXtDwkJ0SeffKLU1FTFxcWpc+fOyszM9LqW1c9+9jNt3LhR8+bN06uvvqrevXtr69at6tevnzUzZ84cVVZWatq0aSovL9ejjz6qnJwcBQUFNf6FAwAAd61mc52qewHXqQJ8h+tUAWiq23qdqnplZWU6duyYJOnBBx9UWFjYrRwOAACgxWrSieqVlZV68cUX1aVLFw0bNkzDhg1TVFSUJk+erO+//970GgEAAJq9JkWV0+lUfn6+PvzwQ5WXl6u8vFwffPCB8vPz9fLLL5teIwAAQLPXpI///vKXv+jPf/6zHn/8cWvbqFGjFBwcrP/4j//QunXrTK0PAACgRWjSO1Xff/99g3+yRZLCw8P5+A8AANyTmhRVCQkJWrBggS5fvmxtu3TpkhYtWsTFMAEAwD2pSR//rVixQiNHjlTXrl3Vv39/SdKXX36pwMBAffLJJ0YXCAAA0BI0KapiY2N1/PhxvfvuuyouLpYkTZgwQRMnTlRwcLDRBQIAALQETYqqJUuWKCIiQlOnTvXa/s4776isrEzp6elGFgcAANBSNOmcqv/+7/9Wnz59Gmx/6KGHtH79+lteFAAAQEvTpKhyuVzq0qVLg+1hYWE6e/bsLS8KAACgpWlSVEVHR+uzzz5rsP2zzz5TVFTULS8KAACgpWnSOVVTp07VzJkzVVNToyeeeEKSlJeXpzlz5nBFdQAAcE9qUlTNnj1b3377rX75y1+qurpakhQUFKT09HRlZGQYXSAAAEBL0KSostls+u1vf6v58+fr6NGjCg4OVu/evRUYGGh6fQAAAC1Ck6KqXrt27TRo0CBTawEAAGixmnSiOgAAALwRVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAb4NKp27dql0aNHKyoqSjabTVu3bvXa7/F4lJmZqS5duig4OFiJiYk6fvy418z58+c1ceJE2e12hYaGavLkybp48aLXzMGDB/XYY48pKChI0dHRWrp0aYO1bN68WX369FFQUJBiY2P10Ucf3fRaAADAvcunUVVZWan+/ftrzZo119y/dOlSrVq1SuvXr9fevXvVtm1bORwOXb582ZqZOHGijhw5otzcXGVnZ2vXrl2aNm2atd/tdmvEiBHq1q2bCgsLtWzZMi1cuFBvvfWWNbN7925NmDBBkydP1hdffKExY8ZozJgxOnz48E2tBQAA3LtsHo/H4+tFSJLNZtOWLVs0ZswYST+8MxQVFaWXX35Zr7zyiiSpoqJCERERysrK0vjx43X06FHFxMTo888/18CBAyVJOTk5GjVqlE6fPq2oqCitW7dOr732mlwulwICAiRJc+fO1datW1VcXCxJGjdunCorK5WdnW2tZ8iQIRowYIDWr1/fqLU0htvtVkhIiCoqKmS32428btcSN/uPt+3YQEtVuOx5Xy8BQAvV2N/fzfacqlOnTsnlcikxMdHaFhISovj4eBUUFEiSCgoKFBoaagWVJCUmJsrPz0979+61ZoYNG2YFlSQ5HA4dO3ZMFy5csGaufp76mfrnacxarqWqqkput9vrBgAA7k7NNqpcLpckKSIiwmt7RESEtc/lcik8PNxrf6tWrdSxY0evmWsd4+rnuN7M1ftvtJZrWbJkiUJCQqxbdHT0DX5qAADQUjXbqLobZGRkqKKiwrp9/fXXvl4SAAC4TZptVEVGRkqSSktLvbaXlpZa+yIjI3Xu3Dmv/VeuXNH58+e9Zq51jKuf43ozV++/0VquJTAwUHa73esGAADuTs02qnr06KHIyEjl5eVZ29xut/bu3auEhARJUkJCgsrLy1VYWGjN7NixQ3V1dYqPj7dmdu3apZqaGmsmNzdXDz74oDp06GDNXP089TP1z9OYtQAAgHubT6Pq4sWLKioqUlFRkaQfTggvKipSSUmJbDabZs6cqcWLF+uvf/2rDh06pOeff15RUVHWNwT79u2rkSNHaurUqdq3b58+++wzpaWlafz48YqKipIkPfvsswoICNDkyZN15MgRbdq0SStXrpTT6bTW8etf/1o5OTl68803VVxcrIULF2r//v1KS0uTpEatBQAA3Nta+fLJ9+/fr+HDh1v360MnJSVFWVlZmjNnjiorKzVt2jSVl5fr0UcfVU5OjoKCgqzHvPvuu0pLS9OTTz4pPz8/JScna9WqVdb+kJAQffLJJ0pNTVVcXJw6d+6szMxMr2tZ/exnP9PGjRs1b948vfrqq+rdu7e2bt2qfv36WTONWQsAALh3NZvrVN0LuE4V4DtcpwpAU7X461QBAAC0JEQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAUQVAACAAc06qhYuXCibzeZ169Onj7X/8uXLSk1NVadOndSuXTslJyertLTU6xglJSVKSkpSmzZtFB4ertmzZ+vKlSteMzt37tQjjzyiwMBA9erVS1lZWQ3WsmbNGnXv3l1BQUGKj4/Xvn37bsvPDAAAWqZmHVWS9NBDD+ns2bPW7W9/+5u1b9asWfrwww+1efNm5efn68yZMxo7dqy1v7a2VklJSaqurtbu3bu1YcMGZWVlKTMz05o5deqUkpKSNHz4cBUVFWnmzJmaMmWKtm/fbs1s2rRJTqdTCxYs0IEDB9S/f385HA6dO3fuzrwIAACg2bN5PB6PrxdxPQsXLtTWrVtVVFTUYF9FRYXCwsK0ceNGPfPMM5Kk4uJi9e3bVwUFBRoyZIg+/vhjPf300zpz5owiIiIkSevXr1d6errKysoUEBCg9PR0bdu2TYcPH7aOPX78eJWXlysnJ0eSFB8fr0GDBmn16tWSpLq6OkVHR2vGjBmaO3duo38et9utkJAQVVRUyG63N/VluaG42X+8bccGWqrCZc/7egkAWqjG/v5u9u9UHT9+XFFRUerZs6cmTpyokpISSVJhYaFqamqUmJhozfbp00f33XefCgoKJEkFBQWKjY21gkqSHA6H3G63jhw5Ys1cfYz6mfpjVFdXq7Cw0GvGz89PiYmJ1sz1VFVVye12e90AAMDdqVlHVXx8vLKyspSTk6N169bp1KlTeuyxx/Tdd9/J5XIpICBAoaGhXo+JiIiQy+WSJLlcLq+gqt9fv++nZtxuty5duqRvvvlGtbW115ypP8b1LFmyRCEhIdYtOjr6pl8DAADQMrTy9QJ+ylNPPWX9+eGHH1Z8fLy6deum999/X8HBwT5cWeNkZGTI6XRa991uN2EFAMBdqlm/U/VjoaGheuCBB3TixAlFRkaqurpa5eXlXjOlpaWKjIyUJEVGRjb4NmD9/RvN2O12BQcHq3PnzvL397/mTP0xricwMFB2u93rBgAA7k4tKqouXryokydPqkuXLoqLi1Pr1q2Vl5dn7T927JhKSkqUkJAgSUpISNChQ4e8vqWXm5sru92umJgYa+bqY9TP1B8jICBAcXFxXjN1dXXKy8uzZgAAAJp1VL3yyivKz8/XP/7xD+3evVs///nP5e/vrwkTJigkJESTJ0+W0+nUp59+qsLCQk2aNEkJCQkaMmSIJGnEiBGKiYnRc889py+//FLbt2/XvHnzlJqaqsDAQEnSSy+9pK+++kpz5sxRcXGx1q5dq/fff1+zZs2y1uF0OvX73/9eGzZs0NGjRzV9+nRVVlZq0qRJPnldAABA89Osz6k6ffq0JkyYoG+//VZhYWF69NFHtWfPHoWFhUmSli9fLj8/PyUnJ6uqqkoOh0Nr1661Hu/v76/s7GxNnz5dCQkJatu2rVJSUvT6669bMz169NC2bds0a9YsrVy5Ul27dtXbb78th8NhzYwbN05lZWXKzMyUy+XSgAEDlJOT0+DkdQAAcO9q1teputtwnSrAd7hOFYCmumuuUwUAANASEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFUAAAAGEFU3ac2aNerevbuCgoIUHx+vffv2+XpJAACgGSCqbsKmTZvkdDq1YMECHThwQP3795fD4dC5c+d8vTQAAOBjRNVN+K//+i9NnTpVkyZNUkxMjNavX682bdronXfe8fXSAACAj7Xy9QJaiurqahUWFiojI8Pa5ufnp8TERBUUFFzzMVVVVaqqqrLuV1RUSJLcbvdtXWtt1aXbenygJbrdf+/ulGHz/uTrJQDNzq7FE27r8ev/++HxeH5yjqhqpG+++Ua1tbWKiIjw2h4REaHi4uJrPmbJkiVatGhRg+3R0dG3ZY0Ari/kdy/5egkAbpM79ff7u+++U0hIyHX3E1W3UUZGhpxOp3W/rq5O58+fV6dOnWSz2Xy4MtwJbrdb0dHR+vrrr2W32329HAAG8ff73uLxePTdd98pKirqJ+eIqkbq3Lmz/P39VVpa6rW9tLRUkZGR13xMYGCgAgMDvbaFhoberiWimbLb7fxHF7hL8ff73vFT71DV40T1RgoICFBcXJzy8vKsbXV1dcrLy1NCQoIPVwYAAJoD3qm6CU6nUykpKRo4cKAGDx6sFStWqLKyUpMmTfL10gAAgI8RVTdh3LhxKisrU2ZmplwulwYMGKCcnJwGJ68D0g8f/y5YsKDBR8AAWj7+fuNabJ4bfT8QAAAAN8Q5VQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVcBtsGbNGnXv3l1BQUGKj4/Xvn37fL0kAAbs2rVLo0ePVlRUlGw2m7Zu3errJaEZIaoAwzZt2iSn06kFCxbowIED6t+/vxwOh86dO+frpQG4RZWVlerfv7/WrFnj66WgGeKSCoBh8fHxGjRokFavXi3phyvvR0dHa8aMGZo7d66PVwfAFJvNpi1btmjMmDG+XgqaCd6pAgyqrq5WYWGhEhMTrW1+fn5KTExUQUGBD1cGALjdiCrAoG+++Ua1tbUNrrIfEREhl8vlo1UBAO4EogoAAMAAogowqHPnzvL391dpaanX9tLSUkVGRvpoVQCAO4GoAgwKCAhQXFyc8vLyrG11dXXKy8tTQkKCD1cGALjdWvl6AcDdxul0KiUlRQMHDtTgwYO1YsUKVVZWatKkSb5eGoBbdPHiRZ04ccK6f+rUKRUVFaljx4667777fLgyNAdcUgG4DVavXq1ly5bJ5XJpwIABWrVqleLj4329LAC3aOfOnRo+fHiD7SkpKcrKyrrzC0KzQlQBAAAYwDlVAAAABhBVAAAABhBVAAAABhBVAAAABhBVAAAABhBVAAAABhBVAAAABhBVAAAABhBVANBINptNW7du9fUyADRTRBUA/H8ul0szZsxQz549FRgYqOjoaI0ePdrrH8gGgOvhH1QGAEn/+Mc/NHToUIWGhmrZsmWKjY1VTU2Ntm/frtTUVBUXF/t6iQCaOd6pAgBJv/zlL2Wz2bRv3z4lJyfrgQce0EMPPSSn06k9e/Zc8zHp6el64IEH1KZNG/Xs2VPz589XTU2Ntf/LL7/U8OHD1b59e9ntdsXFxWn//v2SpH/+858aPXq0OnTooLZt2+qhhx7SRx99dEd+VgC3B+9UAbjnnT9/Xjk5OXrjjTfUtm3bBvtDQ0Ov+bj27dsrKytLUVFROnTokKZOnar27dtrzpw5kqSJEyfqX//1X7Vu3Tr5+/urqKhIrVu3liSlpqaqurpau3btUtu2bfX3v/9d7dq1u20/I4Dbj6gCcM87ceKEPB6P+vTpc1OPmzdvnvXn7t2765VXXtF7771nRVVJSYlmz55tHbd3797WfElJiZKTkxUbGytJ6tmz563+GAB8jI//ANzzPB5Pkx63adMmDR06VJGRkWrXrp3mzZunkpISa7/T6dSUKVOUmJio//zP/9TJkyetfb/61a+0ePFiDR06VAsWLNDBgwdv+ecA4FtEFYB7Xu/evWWz2W7qZPSCggJNnDhRo0aNUnZ2tr744gu99tprqq6utmYWLlyoI0eOKCkpSTt27FBMTIy2bNkiSZoyZYq++uorPffcczp06JAGDhyo3/3ud8Z/NgB3js3T1P+LBgB3kaeeekqHDh3SsWPHGpxXVV5ertDQUNlsNm3ZskVjxozRm2++qbVr13q9+zRlyhT9+c9/Vnl5+TWfY8KECaqsrNRf//rXBvsyMjK0bds23rECWjDeqQIASWvWrFFtba0GDx6sv/zlLzp+/LiOHj2qVatWKSEhocF87969VVJSovfee08nT57UqlWrrHehJOnSpUtKS0vTzp079c9//lOfffaZPv/8c/Xt21eSNHPmTG3fvl2nTp3SgQMH9Omnn1r7ALRMnKgOAPrhRPEDBw7ojTfe0Msvv6yzZ88qLCxMcXFxWrduXYP5f//3f9esWbOUlpamqqoqJSUlaf78+Vq4cKEkyd/fX99++62ef/55lZaWqnPnzho7dqwWLVokSaqtrVVqaqpOnz4tu92ukSNHavny5XfyRwZgGB//AQAAGMDHfwAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAYQVQAAAAb8P4hwEDO/3N1AAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking class imbalance \n",
    "# import necessary libraries\n",
    "import seaborn as sns\n",
    "\n",
    "sns.countplot(x = df['Class'], data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Separate The Minor Class From The Major Class To Apply The Resampling Technique"
   ]
  },
  {
   "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>122207</th>\n",
       "      <td>76475.0</td>\n",
       "      <td>-1.004046</td>\n",
       "      <td>1.053270</td>\n",
       "      <td>1.093808</td>\n",
       "      <td>-0.591014</td>\n",
       "      <td>0.516087</td>\n",
       "      <td>-0.415980</td>\n",
       "      <td>0.476134</td>\n",
       "      <td>0.305124</td>\n",
       "      <td>-0.844857</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.198036</td>\n",
       "      <td>-0.798085</td>\n",
       "      <td>-0.105299</td>\n",
       "      <td>-0.624258</td>\n",
       "      <td>-0.033219</td>\n",
       "      <td>-0.054535</td>\n",
       "      <td>-0.066880</td>\n",
       "      <td>0.004584</td>\n",
       "      <td>14.95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147150</th>\n",
       "      <td>88181.0</td>\n",
       "      <td>-0.422831</td>\n",
       "      <td>0.239428</td>\n",
       "      <td>1.512375</td>\n",
       "      <td>-1.075881</td>\n",
       "      <td>-0.809157</td>\n",
       "      <td>-0.089498</td>\n",
       "      <td>0.838381</td>\n",
       "      <td>-0.463100</td>\n",
       "      <td>-0.698558</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.568205</td>\n",
       "      <td>-0.860065</td>\n",
       "      <td>-0.002557</td>\n",
       "      <td>-0.079295</td>\n",
       "      <td>-0.594139</td>\n",
       "      <td>-0.644110</td>\n",
       "      <td>-0.140223</td>\n",
       "      <td>-0.106076</td>\n",
       "      <td>123.75</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132194</th>\n",
       "      <td>79893.0</td>\n",
       "      <td>-0.138855</td>\n",
       "      <td>-0.976921</td>\n",
       "      <td>0.883736</td>\n",
       "      <td>-3.393556</td>\n",
       "      <td>1.142488</td>\n",
       "      <td>3.914621</td>\n",
       "      <td>-1.119220</td>\n",
       "      <td>0.869570</td>\n",
       "      <td>-1.539373</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.228985</td>\n",
       "      <td>-0.147826</td>\n",
       "      <td>-0.093426</td>\n",
       "      <td>0.977747</td>\n",
       "      <td>-0.269920</td>\n",
       "      <td>-0.413884</td>\n",
       "      <td>0.018587</td>\n",
       "      <td>-0.078387</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259649</th>\n",
       "      <td>159214.0</td>\n",
       "      <td>0.256621</td>\n",
       "      <td>0.223809</td>\n",
       "      <td>0.975735</td>\n",
       "      <td>-0.220988</td>\n",
       "      <td>-0.270464</td>\n",
       "      <td>0.568080</td>\n",
       "      <td>-0.394989</td>\n",
       "      <td>-0.637367</td>\n",
       "      <td>0.416232</td>\n",
       "      <td>...</td>\n",
       "      <td>1.080174</td>\n",
       "      <td>0.763228</td>\n",
       "      <td>-0.220929</td>\n",
       "      <td>0.824970</td>\n",
       "      <td>0.789579</td>\n",
       "      <td>-0.064883</td>\n",
       "      <td>0.223809</td>\n",
       "      <td>0.257101</td>\n",
       "      <td>39.95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120126</th>\n",
       "      <td>75718.0</td>\n",
       "      <td>-1.438013</td>\n",
       "      <td>1.298178</td>\n",
       "      <td>0.909734</td>\n",
       "      <td>-0.495798</td>\n",
       "      <td>0.255640</td>\n",
       "      <td>-0.766527</td>\n",
       "      <td>0.449792</td>\n",
       "      <td>0.361424</td>\n",
       "      <td>-0.492433</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.141175</td>\n",
       "      <td>-0.479615</td>\n",
       "      <td>0.105791</td>\n",
       "      <td>0.038759</td>\n",
       "      <td>-0.299486</td>\n",
       "      <td>-0.087374</td>\n",
       "      <td>-0.033205</td>\n",
       "      <td>0.108152</td>\n",
       "      <td>4.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273800</th>\n",
       "      <td>165706.0</td>\n",
       "      <td>2.341158</td>\n",
       "      <td>-1.140193</td>\n",
       "      <td>-1.431234</td>\n",
       "      <td>-1.446218</td>\n",
       "      <td>-0.959278</td>\n",
       "      <td>-1.328559</td>\n",
       "      <td>-0.580484</td>\n",
       "      <td>-0.525896</td>\n",
       "      <td>-1.617138</td>\n",
       "      <td>...</td>\n",
       "      <td>0.204313</td>\n",
       "      <td>1.250873</td>\n",
       "      <td>-0.096074</td>\n",
       "      <td>0.132532</td>\n",
       "      <td>0.408741</td>\n",
       "      <td>0.286812</td>\n",
       "      <td>-0.011742</td>\n",
       "      <td>-0.067825</td>\n",
       "      <td>13.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9384</th>\n",
       "      <td>13744.0</td>\n",
       "      <td>-0.300059</td>\n",
       "      <td>0.364418</td>\n",
       "      <td>1.611098</td>\n",
       "      <td>-1.056658</td>\n",
       "      <td>0.652460</td>\n",
       "      <td>0.112808</td>\n",
       "      <td>0.338364</td>\n",
       "      <td>-0.154589</td>\n",
       "      <td>1.126379</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.051844</td>\n",
       "      <td>0.166848</td>\n",
       "      <td>-0.294988</td>\n",
       "      <td>-0.818076</td>\n",
       "      <td>0.005634</td>\n",
       "      <td>0.907665</td>\n",
       "      <td>-0.267201</td>\n",
       "      <td>-0.249516</td>\n",
       "      <td>20.33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38205</th>\n",
       "      <td>39293.0</td>\n",
       "      <td>0.813744</td>\n",
       "      <td>-0.617420</td>\n",
       "      <td>1.782595</td>\n",
       "      <td>1.696710</td>\n",
       "      <td>-1.515473</td>\n",
       "      <td>0.350754</td>\n",
       "      <td>-0.893978</td>\n",
       "      <td>0.316722</td>\n",
       "      <td>1.186037</td>\n",
       "      <td>...</td>\n",
       "      <td>0.305272</td>\n",
       "      <td>0.919456</td>\n",
       "      <td>-0.114790</td>\n",
       "      <td>0.653273</td>\n",
       "      <td>0.272301</td>\n",
       "      <td>-0.189475</td>\n",
       "      <td>0.091363</td>\n",
       "      <td>0.064843</td>\n",
       "      <td>125.53</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74642</th>\n",
       "      <td>55681.0</td>\n",
       "      <td>-1.833329</td>\n",
       "      <td>0.046103</td>\n",
       "      <td>2.151713</td>\n",
       "      <td>-0.431903</td>\n",
       "      <td>-0.762017</td>\n",
       "      <td>-0.388451</td>\n",
       "      <td>-0.215207</td>\n",
       "      <td>0.714157</td>\n",
       "      <td>-0.162439</td>\n",
       "      <td>...</td>\n",
       "      <td>0.310683</td>\n",
       "      <td>0.559447</td>\n",
       "      <td>-0.071901</td>\n",
       "      <td>0.598248</td>\n",
       "      <td>0.370825</td>\n",
       "      <td>0.617409</td>\n",
       "      <td>0.112006</td>\n",
       "      <td>-0.033303</td>\n",
       "      <td>101.16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90141</th>\n",
       "      <td>62878.0</td>\n",
       "      <td>-0.931864</td>\n",
       "      <td>1.459951</td>\n",
       "      <td>0.581482</td>\n",
       "      <td>0.646880</td>\n",
       "      <td>0.438010</td>\n",
       "      <td>-0.279948</td>\n",
       "      <td>0.717567</td>\n",
       "      <td>-0.147485</td>\n",
       "      <td>-0.539104</td>\n",
       "      <td>...</td>\n",
       "      <td>0.167936</td>\n",
       "      <td>0.488751</td>\n",
       "      <td>-0.234726</td>\n",
       "      <td>-0.414403</td>\n",
       "      <td>-0.179710</td>\n",
       "      <td>-0.370491</td>\n",
       "      <td>-0.652901</td>\n",
       "      <td>-0.088777</td>\n",
       "      <td>15.32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>492 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time        V1        V2        V3        V4        V5        V6  \\\n",
       "122207   76475.0 -1.004046  1.053270  1.093808 -0.591014  0.516087 -0.415980   \n",
       "147150   88181.0 -0.422831  0.239428  1.512375 -1.075881 -0.809157 -0.089498   \n",
       "132194   79893.0 -0.138855 -0.976921  0.883736 -3.393556  1.142488  3.914621   \n",
       "259649  159214.0  0.256621  0.223809  0.975735 -0.220988 -0.270464  0.568080   \n",
       "120126   75718.0 -1.438013  1.298178  0.909734 -0.495798  0.255640 -0.766527   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "273800  165706.0  2.341158 -1.140193 -1.431234 -1.446218 -0.959278 -1.328559   \n",
       "9384     13744.0 -0.300059  0.364418  1.611098 -1.056658  0.652460  0.112808   \n",
       "38205    39293.0  0.813744 -0.617420  1.782595  1.696710 -1.515473  0.350754   \n",
       "74642    55681.0 -1.833329  0.046103  2.151713 -0.431903 -0.762017 -0.388451   \n",
       "90141    62878.0 -0.931864  1.459951  0.581482  0.646880  0.438010 -0.279948   \n",
       "\n",
       "              V7        V8        V9  ...       V21       V22       V23  \\\n",
       "122207  0.476134  0.305124 -0.844857  ... -0.198036 -0.798085 -0.105299   \n",
       "147150  0.838381 -0.463100 -0.698558  ... -0.568205 -0.860065 -0.002557   \n",
       "132194 -1.119220  0.869570 -1.539373  ... -0.228985 -0.147826 -0.093426   \n",
       "259649 -0.394989 -0.637367  0.416232  ...  1.080174  0.763228 -0.220929   \n",
       "120126  0.449792  0.361424 -0.492433  ... -0.141175 -0.479615  0.105791   \n",
       "...          ...       ...       ...  ...       ...       ...       ...   \n",
       "273800 -0.580484 -0.525896 -1.617138  ...  0.204313  1.250873 -0.096074   \n",
       "9384    0.338364 -0.154589  1.126379  ... -0.051844  0.166848 -0.294988   \n",
       "38205  -0.893978  0.316722  1.186037  ...  0.305272  0.919456 -0.114790   \n",
       "74642  -0.215207  0.714157 -0.162439  ...  0.310683  0.559447 -0.071901   \n",
       "90141   0.717567 -0.147485 -0.539104  ...  0.167936  0.488751 -0.234726   \n",
       "\n",
       "             V24       V25       V26       V27       V28  Amount  Class  \n",
       "122207 -0.624258 -0.033219 -0.054535 -0.066880  0.004584   14.95      0  \n",
       "147150 -0.079295 -0.594139 -0.644110 -0.140223 -0.106076  123.75      0  \n",
       "132194  0.977747 -0.269920 -0.413884  0.018587 -0.078387   20.00      0  \n",
       "259649  0.824970  0.789579 -0.064883  0.223809  0.257101   39.95      0  \n",
       "120126  0.038759 -0.299486 -0.087374 -0.033205  0.108152    4.99      0  \n",
       "...          ...       ...       ...       ...       ...     ...    ...  \n",
       "273800  0.132532  0.408741  0.286812 -0.011742 -0.067825   13.99      0  \n",
       "9384   -0.818076  0.005634  0.907665 -0.267201 -0.249516   20.33      0  \n",
       "38205   0.653273  0.272301 -0.189475  0.091363  0.064843  125.53      0  \n",
       "74642   0.598248  0.370825  0.617409  0.112006 -0.033303  101.16      0  \n",
       "90141  -0.414403 -0.179710 -0.370491 -0.652901 -0.088777   15.32      0  \n",
       "\n",
       "[492 rows x 31 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# oversampling or undersampling \n",
    "from sklearn.utils import resample\n",
    "\n",
    "fraud_data = df[df['Class'] == 1 ]\n",
    "nonfraud_data = df[df['Class'] == 0 ]\n",
    "\n",
    "# downsampling the major class -->non-fraud\n",
    "\n",
    "the_resampled_major_class = resample(nonfraud_data, replace=True, random_state=42, n_samples= len(fraud_data))\n",
    "\n",
    "the_resampled_major_class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# balance The Dataset Using Balancing Techniques, Used Libraries: Sklearn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53888.0</td>\n",
       "      <td>1.281120</td>\n",
       "      <td>-0.577976</td>\n",
       "      <td>0.640332</td>\n",
       "      <td>-0.768576</td>\n",
       "      <td>-0.999888</td>\n",
       "      <td>-0.354724</td>\n",
       "      <td>-0.720369</td>\n",
       "      <td>0.084789</td>\n",
       "      <td>-1.022822</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001682</td>\n",
       "      <td>-0.149659</td>\n",
       "      <td>0.167250</td>\n",
       "      <td>0.208051</td>\n",
       "      <td>0.093428</td>\n",
       "      <td>-0.481443</td>\n",
       "      <td>0.020354</td>\n",
       "      <td>0.013129</td>\n",
       "      <td>21.44</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>147501.0</td>\n",
       "      <td>-1.611877</td>\n",
       "      <td>-0.408410</td>\n",
       "      <td>-3.829762</td>\n",
       "      <td>6.249462</td>\n",
       "      <td>-3.360922</td>\n",
       "      <td>1.147964</td>\n",
       "      <td>1.858425</td>\n",
       "      <td>0.474858</td>\n",
       "      <td>-3.838399</td>\n",
       "      <td>...</td>\n",
       "      <td>1.245582</td>\n",
       "      <td>0.616383</td>\n",
       "      <td>2.251439</td>\n",
       "      <td>-0.066096</td>\n",
       "      <td>0.538710</td>\n",
       "      <td>0.541325</td>\n",
       "      <td>-0.136243</td>\n",
       "      <td>-0.009852</td>\n",
       "      <td>996.27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>122772.0</td>\n",
       "      <td>-3.690522</td>\n",
       "      <td>2.875949</td>\n",
       "      <td>-1.575620</td>\n",
       "      <td>-0.991799</td>\n",
       "      <td>-0.373811</td>\n",
       "      <td>-0.380407</td>\n",
       "      <td>-0.894052</td>\n",
       "      <td>-0.799849</td>\n",
       "      <td>-0.854101</td>\n",
       "      <td>...</td>\n",
       "      <td>2.623885</td>\n",
       "      <td>-0.210703</td>\n",
       "      <td>-0.313619</td>\n",
       "      <td>-0.547039</td>\n",
       "      <td>1.340200</td>\n",
       "      <td>-0.002744</td>\n",
       "      <td>-0.247120</td>\n",
       "      <td>-0.148923</td>\n",
       "      <td>19.03</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>135095.0</td>\n",
       "      <td>0.232512</td>\n",
       "      <td>0.938944</td>\n",
       "      <td>-4.647780</td>\n",
       "      <td>3.079844</td>\n",
       "      <td>-1.902655</td>\n",
       "      <td>-1.041408</td>\n",
       "      <td>-1.020407</td>\n",
       "      <td>0.547069</td>\n",
       "      <td>-1.105990</td>\n",
       "      <td>...</td>\n",
       "      <td>0.911373</td>\n",
       "      <td>1.042929</td>\n",
       "      <td>0.999394</td>\n",
       "      <td>0.901260</td>\n",
       "      <td>-0.452093</td>\n",
       "      <td>0.192959</td>\n",
       "      <td>0.180859</td>\n",
       "      <td>-0.029315</td>\n",
       "      <td>345.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>117811.0</td>\n",
       "      <td>2.032471</td>\n",
       "      <td>0.157993</td>\n",
       "      <td>-2.401119</td>\n",
       "      <td>0.841142</td>\n",
       "      <td>1.284617</td>\n",
       "      <td>-0.288681</td>\n",
       "      <td>0.744835</td>\n",
       "      <td>-0.294876</td>\n",
       "      <td>-0.339306</td>\n",
       "      <td>...</td>\n",
       "      <td>0.115705</td>\n",
       "      <td>0.336102</td>\n",
       "      <td>-0.228765</td>\n",
       "      <td>-1.440519</td>\n",
       "      <td>0.599827</td>\n",
       "      <td>-0.418285</td>\n",
       "      <td>-0.041942</td>\n",
       "      <td>-0.080323</td>\n",
       "      <td>47.76</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>41204.0</td>\n",
       "      <td>-8.440284</td>\n",
       "      <td>6.147653</td>\n",
       "      <td>-11.683706</td>\n",
       "      <td>6.702780</td>\n",
       "      <td>-8.155839</td>\n",
       "      <td>-3.716264</td>\n",
       "      <td>-12.407313</td>\n",
       "      <td>5.626571</td>\n",
       "      <td>-6.232161</td>\n",
       "      <td>...</td>\n",
       "      <td>2.192855</td>\n",
       "      <td>-0.282597</td>\n",
       "      <td>0.008068</td>\n",
       "      <td>0.403858</td>\n",
       "      <td>-0.018788</td>\n",
       "      <td>0.522722</td>\n",
       "      <td>0.792691</td>\n",
       "      <td>0.067790</td>\n",
       "      <td>30.26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>167338.0</td>\n",
       "      <td>-1.374424</td>\n",
       "      <td>2.793185</td>\n",
       "      <td>-4.346572</td>\n",
       "      <td>2.400731</td>\n",
       "      <td>-1.688433</td>\n",
       "      <td>0.111136</td>\n",
       "      <td>-0.922038</td>\n",
       "      <td>-2.149930</td>\n",
       "      <td>-2.027474</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.870779</td>\n",
       "      <td>0.504849</td>\n",
       "      <td>0.137994</td>\n",
       "      <td>0.368275</td>\n",
       "      <td>0.103137</td>\n",
       "      <td>-0.414209</td>\n",
       "      <td>0.454982</td>\n",
       "      <td>0.096711</td>\n",
       "      <td>349.08</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>36746.0</td>\n",
       "      <td>-0.561639</td>\n",
       "      <td>0.767676</td>\n",
       "      <td>0.816832</td>\n",
       "      <td>1.073118</td>\n",
       "      <td>-0.608695</td>\n",
       "      <td>0.073306</td>\n",
       "      <td>0.355362</td>\n",
       "      <td>0.616014</td>\n",
       "      <td>-0.836737</td>\n",
       "      <td>...</td>\n",
       "      <td>0.231941</td>\n",
       "      <td>0.322369</td>\n",
       "      <td>0.325059</td>\n",
       "      <td>0.171376</td>\n",
       "      <td>-0.411887</td>\n",
       "      <td>-0.422552</td>\n",
       "      <td>-0.066977</td>\n",
       "      <td>-0.019168</td>\n",
       "      <td>112.67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>2287.0</td>\n",
       "      <td>1.121937</td>\n",
       "      <td>0.221341</td>\n",
       "      <td>0.533384</td>\n",
       "      <td>1.316220</td>\n",
       "      <td>-0.143069</td>\n",
       "      <td>0.028984</td>\n",
       "      <td>-0.006480</td>\n",
       "      <td>0.091659</td>\n",
       "      <td>-0.054707</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.035520</td>\n",
       "      <td>0.138123</td>\n",
       "      <td>-0.049103</td>\n",
       "      <td>0.245035</td>\n",
       "      <td>0.624553</td>\n",
       "      <td>-0.343122</td>\n",
       "      <td>0.040969</td>\n",
       "      <td>0.007551</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>122369.0</td>\n",
       "      <td>-2.016733</td>\n",
       "      <td>-2.062420</td>\n",
       "      <td>0.669418</td>\n",
       "      <td>-0.754785</td>\n",
       "      <td>-1.754428</td>\n",
       "      <td>0.783723</td>\n",
       "      <td>2.422263</td>\n",
       "      <td>-0.060040</td>\n",
       "      <td>0.116561</td>\n",
       "      <td>...</td>\n",
       "      <td>0.281532</td>\n",
       "      <td>-0.700149</td>\n",
       "      <td>1.661817</td>\n",
       "      <td>0.486404</td>\n",
       "      <td>0.345349</td>\n",
       "      <td>0.594226</td>\n",
       "      <td>-0.268105</td>\n",
       "      <td>0.132783</td>\n",
       "      <td>750.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>984 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Time        V1        V2         V3        V4        V5        V6  \\\n",
       "0     53888.0  1.281120 -0.577976   0.640332 -0.768576 -0.999888 -0.354724   \n",
       "1    147501.0 -1.611877 -0.408410  -3.829762  6.249462 -3.360922  1.147964   \n",
       "2    122772.0 -3.690522  2.875949  -1.575620 -0.991799 -0.373811 -0.380407   \n",
       "3    135095.0  0.232512  0.938944  -4.647780  3.079844 -1.902655 -1.041408   \n",
       "4    117811.0  2.032471  0.157993  -2.401119  0.841142  1.284617 -0.288681   \n",
       "..        ...       ...       ...        ...       ...       ...       ...   \n",
       "979   41204.0 -8.440284  6.147653 -11.683706  6.702780 -8.155839 -3.716264   \n",
       "980  167338.0 -1.374424  2.793185  -4.346572  2.400731 -1.688433  0.111136   \n",
       "981   36746.0 -0.561639  0.767676   0.816832  1.073118 -0.608695  0.073306   \n",
       "982    2287.0  1.121937  0.221341   0.533384  1.316220 -0.143069  0.028984   \n",
       "983  122369.0 -2.016733 -2.062420   0.669418 -0.754785 -1.754428  0.783723   \n",
       "\n",
       "            V7        V8        V9  ...       V21       V22       V23  \\\n",
       "0    -0.720369  0.084789 -1.022822  ...  0.001682 -0.149659  0.167250   \n",
       "1     1.858425  0.474858 -3.838399  ...  1.245582  0.616383  2.251439   \n",
       "2    -0.894052 -0.799849 -0.854101  ...  2.623885 -0.210703 -0.313619   \n",
       "3    -1.020407  0.547069 -1.105990  ...  0.911373  1.042929  0.999394   \n",
       "4     0.744835 -0.294876 -0.339306  ...  0.115705  0.336102 -0.228765   \n",
       "..         ...       ...       ...  ...       ...       ...       ...   \n",
       "979 -12.407313  5.626571 -6.232161  ...  2.192855 -0.282597  0.008068   \n",
       "980  -0.922038 -2.149930 -2.027474  ... -0.870779  0.504849  0.137994   \n",
       "981   0.355362  0.616014 -0.836737  ...  0.231941  0.322369  0.325059   \n",
       "982  -0.006480  0.091659 -0.054707  ... -0.035520  0.138123 -0.049103   \n",
       "983   2.422263 -0.060040  0.116561  ...  0.281532 -0.700149  1.661817   \n",
       "\n",
       "          V24       V25       V26       V27       V28  Amount  Class  \n",
       "0    0.208051  0.093428 -0.481443  0.020354  0.013129   21.44      0  \n",
       "1   -0.066096  0.538710  0.541325 -0.136243 -0.009852  996.27      1  \n",
       "2   -0.547039  1.340200 -0.002744 -0.247120 -0.148923   19.03      0  \n",
       "3    0.901260 -0.452093  0.192959  0.180859 -0.029315  345.00      1  \n",
       "4   -1.440519  0.599827 -0.418285 -0.041942 -0.080323   47.76      0  \n",
       "..        ...       ...       ...       ...       ...     ...    ...  \n",
       "979  0.403858 -0.018788  0.522722  0.792691  0.067790   30.26      1  \n",
       "980  0.368275  0.103137 -0.414209  0.454982  0.096711  349.08      1  \n",
       "981  0.171376 -0.411887 -0.422552 -0.066977 -0.019168  112.67      0  \n",
       "982  0.245035  0.624553 -0.343122  0.040969  0.007551    1.00      0  \n",
       "983  0.486404  0.345349  0.594226 -0.268105  0.132783  750.00      0  \n",
       "\n",
       "[984 rows x 31 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_dataset = pd.concat([fraud_data,the_resampled_major_class])\n",
    "\n",
    "balanced_dataset = balanced_dataset.sample(frac=1).reset_index(drop = True)\n",
    "\n",
    "balanced_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check The Class Imbalance After Using One Of The Balancing Techniques Using seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Class', ylabel='count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = balanced_dataset['Class'], data=balanced_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x2000 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_worst=balanced_dataset.hist(bins=10, figsize=(30, 20),grid=False) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking For Outliers To Decide The Best Scaling Approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import necessary libraries\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ta9asifXr10dTU1NERDQ1NcWqVati06ZN5TFLly6NUqkUEyZM+CzHAgAAAAC9LnWGWmtra9x7773x0EMPxYgRI8qfeVZfXx8HH3xw1NfXx2WXXRZz5syJUaNGRalUiquuuiqamprirLPOioiIqVOnxoQJE+LSSy+NhQsXRnt7e1x//fXR2trqLDQAAAAA+r1UULvrrrsiIuKrX/1qxfa77747vv3tb0dExG233RY1NTUxbdq02Lp1azQ3N8edd95ZHjt48OBYsmRJzJw5M5qammL48OExY8aMuPnmmz/bkQAAAABAHxhUFEVR7UlkdXd3/+Zqn7Pvj5q6YRX71i1oqdKsAAAAAKiG3a2oq6srSqVSrz/fPl/lEwAAAAAORIIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACSkg9rTTz8d3/zmN2PMmDExaNCg+PnPf16xvyiKuOGGG+LII4+Mgw8+OKZMmRJvvPFGxZj33nsvpk+fHqVSKUaOHBmXXXZZfPDBB5/pQAAAAACgL6SD2ubNm+PUU0+NO+64Y4/7Fy5cGLfffnssWrQoVqxYEcOHD4/m5ubYsmVLecz06dNj9erVsXTp0liyZEk8/fTTccUVV+z7UQAAAABAHxlUFEWxz3ceNCgefPDBuOCCCyLiN2enjRkzJq655pr43ve+FxERXV1d0dDQEIsXL45LLrkkXnvttZgwYUI8//zzccYZZ0RExGOPPRZ//Md/HL/61a9izJgxn/i83d3dUV9fH2Nn3x81dcMq9q1b0LKvhwMAAADAALS7FXV1dUWpVOr15+vRz1Bbu3ZttLe3x5QpU8rb6uvrY/LkydHW1hYREW1tbTFy5MhyTIuImDJlStTU1MSKFSv2+Lhbt26N7u7uihsAAAAAVEOPBrX29vaIiGhoaKjY3tDQUN7X3t4eo0ePrtg/ZMiQGDVqVHnM75o/f37U19eXb2PHju3JaQMAAADApzYgrvI5b9686OrqKt82bNhQ7SkBAAAAcIDq0aDW2NgYEREdHR0V2zs6Osr7GhsbY9OmTRX7d+zYEe+99155zO+qq6uLUqlUcQMAAACAaujRoDZ+/PhobGyMZcuWlbd1d3fHihUroqmpKSIimpqaorOzM1auXFke88QTT8SuXbti8uTJPTkdAAAAAOhxQ7J3+OCDD+LNN98sf7127dp46aWXYtSoUTFu3LiYPXt2/PVf/3Ucf/zxMX78+PjBD34QY8aMKV8J9KSTTopzzz03Lr/88li0aFFs3749Zs2aFZdccsmnusInAAAAAFRTOqi98MIL8bWvfa389Zw5cyIiYsaMGbF48eL4/ve/H5s3b44rrrgiOjs748tf/nI89thjcdBBB5Xv85Of/CRmzZoV55xzTtTU1MS0adPi9ttv74HDAQAAAIDeNagoiqLak8jq7u7+zdU+Z98fNXXDKvatW9BSpVkBAAAAUA27W1FXV1effPb+gLjKJwAAAAD0F4IaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAwpNoT6GnHXPfIHrevW9DSxzMBAAAAYH/kDDUAAAAASBDUAAAAACBBUAMAAACABEENAAAAABIENQAAAABIENQAAAAAIEFQAwAAAIAEQQ0AAAAAEgQ1AAAAAEgQ1AAAAAAgQVADAAAAgARBDQAAAAASBDUAAAAASBDUAAAAACBBUAMAAACABEENAAAAABIENQAAAABIENQAAAAAIEFQAwAAAIAEQQ0AAAAAEgQ1AAAAAEgQ1AAAAAAgQVADAAAAgARBDQAAAAASBDUAAAAASBDUAAAAACBBUAMAAACABEENAAAAABIENQAAAABIENQAAAAAIEFQAwAAAIAEQQ0AAAAAEgQ1AAAAAEgQ1AAAAAAgQVADAAAAgARBDQAAAAASBDUAAAAASBDUAAAAACBBUAMAAACABEENAAAAABIENQAAAABIENQAAAAAIEFQAwAAAIAEQQ0AAAAAEgQ1AAAAAEgQ1AAAAAAgQVADAAAAgARBDQAAAAASBDUAAAAASBDUAAAAACBBUAMAAACABEENAAAAABIENQAAAABIENQAAAAAIEFQAwAAAIAEQQ0AAAAAEgQ1AAAAAEgQ1AAAAAAgQVADAAAAgARBDQAAAAASBDUAAAAASBDUAAAAACBBUAMAAACABEENAAAAABKGVHsC1XbMdY98bNu6BS1VmAkAAAAAA4Ez1AAAAAAgQVADAAAAgARBDQAAAAASBDUAAAAASDhgLkqwp4sPAAAAAECWM9QAAAAAIEFQAwAAAIAEQQ0AAAAAEgQ1AAAAAEgQ1AAAAAAgQVADAAAAgIQh1Z7A/uyY6x7Z4/Z1C1r6eCYAAAAA9BRnqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAyp9gT6o2Oue2SP29ctaOnjmQAAAADQ3zhDDQAAAAASBDUAAAAASBDUAAAAACBBUAMAAACABEENAAAAABIENQAAAABIENQAAAAAIEFQAwAAAIAEQQ0AAAAAEoZUewL7g2Oue6TaUwAAAACgjzhDDQAAAAASBDUAAAAASBDUAAAAACBBUAMAAACABBclqII9XcRg3YKWKswEAAAAgCxnqAEAAABAgqAGAAAAAAmCGgAAAAAkCGoAAAAAkCCoAQAAAECCoAYAAAAACYIaAAAAACQIagAAAACQIKgBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAwpBqT4DfOOa6R/a4fd2Clj6eCQAAAAC/jzPUAAAAACBBUAMAAACABEENAAAAABIENQAAAABIENQAAAAAIEFQAwAAAIAEQQ0AAAAAEgQ1AAAAAEgQ1AAAAAAgQVADAAAAgARBDQAAAAASBDUAAAAASBDUAAAAACBBUAMAAACAhCHVnsBAcsx1j1R7CgAAAABUmTPUAAAAACDBGWr9XPasuHULWnppJgAAAABEOEMNAAAAAFKcoXYA29vZb85yAwAAANg7Z6gBAAAAQIKgBgAAAAAJghoAAAAAJAhqAAAAAJAgqAEAAABAgqAGAAAAAAmCGgAAAAAkDKn2BOh/jrnukdT4dQtaemkmvWtvxzlQjwcAAADoG85QAwAAAIAEZ6jtZ5x1BQAAANC7nKEGAAAAAAnOUKPP7eksOmfQAQAAAAOFM9QAAAAAIEFQAwAAAIAEb/k8QOztYgUAAAAA5DhDDQAAAAASnKFGv7C3M+h66mIFztDjQNXbawsAAOBA5Aw1AAAAAEgQ1AAAAAAgwVs+6df609vV+tNcAAAAgOpxhhoAAAAAJDhDjc+sP525VY2LD/TE8VfjNazGhSCczQcAAMD+wBlqAAAAAJDgDDV6TTXOFutPqnGGVn86WzBjoM47YuDOfaDOO2Jgz51Pz/cZAID+zBlqAAAAAJBQ1aB2xx13xDHHHBMHHXRQTJ48OZ577rlqTgcAAAAAPlHV3vL5s5/9LObMmROLFi2KyZMnx49+9KNobm6ONWvWxOjRo6s1LQaI3nw76UB4q2p2jpnx/em1zb7lK/M2297+PvfEXPrTW9t64vWq1vci85wD+UIg/eU5ydnfvkc98f/t3zc+8xh7MxDWXH+5sE9/em37k/60bvvTXNi/+LNFf7enP6O7tn7Yp3OoWlC79dZb4/LLL4/vfOc7ERGxaNGieOSRR+Kf/umf4rrrrqsYu3Xr1ti6dWv5666urojo+xcLPqvu7u49bt/bn+Xs+ANF5nXpT69hT33/M7LH2ZuvV29/LzKvV2++5gf6c/aUgTz3jP3tOHvq51lPrOe9GQhrLvPzrDf1p9e2P+lP67Y/zYX9iz9b9Hd7+jO6e1tRFH0yh0FFXz3Tb9m2bVsMGzYs/uVf/iUuuOCC8vYZM2ZEZ2dnPPTQQxXjb7rppvjhD3/Yx7MEAAAAYCB566234thjj+3156nKGWq//vWvY+fOndHQ0FCxvaGhIV5//fWPjZ83b17MmTOn/HVnZ2ccffTRsX79+qivr+/1+QI53d3dMXbs2NiwYUOUSqVqTwf4LdYn9G/WKPRf1if0b11dXTFu3LgYNWpUnzxf1d7ymVFXVxd1dXUf215fX+9/ZNCPlUolaxT6KesT+jdrFPov6xP6t5qavrn+ZlWu8nn44YfH4MGDo6Ojo2J7R0dHNDY2VmNKAAAAAPCpVCWo1dbWxqRJk2LZsmXlbbt27Yply5ZFU1NTNaYEAAAAAJ9K1d7yOWfOnJgxY0acccYZceaZZ8aPfvSj2Lx5c/mqn79PXV1d3HjjjXt8GyhQfdYo9F/WJ/Rv1ij0X9Yn9G99vUarcpXP3f7u7/4ubrnllmhvb4/TTjstbr/99pg8eXK1pgMAAAAAn6iqQQ0AAAAABpqqfIYaAAAAAAxUghoAAAAAJAhqAAAAAJAgqAEAAABAwoAManfccUccc8wxcdBBB8XkyZPjueeeq/aUYL930003xaBBgypuJ554Ynn/li1borW1NQ477LA45JBDYtq0adHR0VHxGOvXr4+WlpYYNmxYjB49OubOnRs7duzo60OBAe/pp5+Ob37zmzFmzJgYNGhQ/PznP6/YXxRF3HDDDXHkkUfGwQcfHFOmTIk33nijYsx7770X06dPj1KpFCNHjozLLrssPvjgg4oxL7/8cnzlK1+Jgw46KMaOHRsLFy7s7UOD/cInrdFvf/vbH/uZeu6551aMsUahd8yfPz+++MUvxogRI2L06NFxwQUXxJo1ayrG9NTvtU899VScfvrpUVdXF8cdd1wsXry4tw8PBrRPsz6/+tWvfuxn6JVXXlkxpq/W54ALaj/72c9izpw5ceONN8Yvf/nLOPXUU6O5uTk2bdpU7anBfu8LX/hCvP322+XbL37xi/K+q6++Oh5++OF44IEHYvny5bFx48a48MILy/t37twZLS0tsW3btnjmmWfinnvuicWLF8cNN9xQjUOBAW3z5s1x6qmnxh133LHH/QsXLozbb789Fi1aFCtWrIjhw4dHc3NzbNmypTxm+vTpsXr16li6dGksWbIknn766bjiiivK+7u7u2Pq1Klx9NFHx8qVK+OWW26Jm266Kf7+7/++148PBrpPWqMREeeee27Fz9Sf/vSnFfutUegdy5cvj9bW1nj22Wdj6dKlsX379pg6dWps3ry5PKYnfq9du3ZttLS0xNe+9rV46aWXYvbs2fHnf/7n8fjjj/fp8cJA8mnWZ0TE5ZdfXvEz9Lf/QalP12cxwJx55plFa2tr+eudO3cWY8aMKebPn1/FWcH+78YbbyxOPfXUPe7r7Owshg4dWjzwwAPlba+99loREUVbW1tRFEXxb//2b0VNTU3R3t5eHnPXXXcVpVKp2Lp1a6/OHfZnEVE8+OCD5a937dpVNDY2Frfcckt5W2dnZ1FXV1f89Kc/LYqiKF599dUiIornn3++PObRRx8tBg0aVPzv//5vURRFceeddxaHHnpoxfq89tprixNOOKGXjwj2L7+7RouiKGbMmFGcf/75e72PNQp9Z9OmTUVEFMuXLy+Koud+r/3+979ffOELX6h4rosvvrhobm7u7UOC/cbvrs+iKIr/9//+X/Hd7353r/fpy/U5oM5Q27ZtW6xcuTKmTJlS3lZTUxNTpkyJtra2Ks4MDgxvvPFGjBkzJo499tiYPn16rF+/PiIiVq5cGdu3b69YmyeeeGKMGzeuvDbb2tpi4sSJ0dDQUB7T3Nwc3d3dsXr16r49ENiPrV27Ntrb2yvWY319fUyePLliPY4cOTLOOOOM8pgpU6ZETU1NrFixojzm7LPPjtra2vKY5ubmWLNmTfzf//1fHx0N7L+eeuqpGD16dJxwwgkxc+bMePfdd8v7rFHoO11dXRERMWrUqIjoud9r29raKh5j9xh/b4VP73fX524/+clP4vDDD4+TTz455s2bFx9++GF5X1+uzyGp0VX261//Onbu3FnxwkRENDQ0xOuvv16lWcGBYfLkybF48eI44YQT4u23344f/vCH8ZWvfCVeeeWVaG9vj9ra2hg5cmTFfRoaGqK9vT0iItrb2/e4dnfvA3rG7vW0p/X22+tx9OjRFfuHDBkSo0aNqhgzfvz4jz3G7n2HHnpor8wfDgTnnntuXHjhhTF+/Ph466234i//8i/jvPPOi7a2thg8eLA1Cn1k165dMXv27PjSl74UJ598ckREj/1eu7cx3d3d8dFHH8XBBx/cG4cE+409rc+IiD/90z+No48+OsaMGRMvv/xyXHvttbFmzZr413/914jo2/U5oIIaUD3nnXde+b9POeWUmDx5chx99NFx//33+4UAABIuueSS8n9PnDgxTjnllPj85z8fTz31VJxzzjlVnBkcWFpbW+OVV16p+FxgoH/Y2/r87c8TnThxYhx55JFxzjnnxFtvvRWf//zn+3SOA+otn4cffngMHjz4Y1dY6ejoiMbGxirNCg5MI0eOjD/4gz+IN998MxobG2Pbtm3R2dlZMea312ZjY+Me1+7ufUDP2L2eft/PysbGxo9dzGfHjh3x3nvvWbNQBccee2wcfvjh8eabb0aENQp9YdasWbFkyZJ48skn46ijjipv76nfa/c2plQq+cdo+AR7W597Mnny5IiIip+hfbU+B1RQq62tjUmTJsWyZcvK23bt2hXLli2LpqamKs4MDjwffPBBvPXWW3HkkUfGpEmTYujQoRVrc82aNbF+/fry2mxqaopVq1ZV/AVh6dKlUSqVYsKECX0+f9hfjR8/PhobGyvWY3d3d6xYsaJiPXZ2dsbKlSvLY5544onYtWtX+ZeSpqamePrpp2P79u3lMUuXLo0TTjjBW8mgh/3qV7+Kd999N4488siIsEahNxVFEbNmzYoHH3wwnnjiiY+9dbqnfq9tamqqeIzdY/y9Ffbuk9bnnrz00ksRERU/Q/tsfaYuYdAP3HfffUVdXV2xePHi4tVXXy2uuOKKYuTIkRVXcAB63jXXXFM89dRTxdq1a4v//M//LKZMmVIcfvjhxaZNm4qiKIorr7yyGDduXPHEE08UL7zwQtHU1FQ0NTWV779jx47i5JNPLqZOnVq89NJLxWOPPVYcccQRxbx586p1SDBgvf/++8WLL75YvPjii0VEFLfeemvx4osvFv/zP/9TFEVRLFiwoBg5cmTx0EMPFS+//HJx/vnnF+PHjy8++uij8mOce+65xR/+4R8WK1asKH7xi18Uxx9/fPGtb32rvL+zs7NoaGgoLr300uKVV14p7rvvvmLYsGHFj3/84z4/Xhhoft8aff/994vvfe97RVtbW7F27driP/7jP4rTTz+9OP7444stW7aUH8Mahd4xc+bMor6+vnjqqaeKt99+u3z78MMPy2N64vfa//7v/y6GDRtWzJ07t3jttdeKO+64oxg8eHDx2GOP9enxwkDySevzzTffLG6++ebihRdeKNauXVs89NBDxbHHHlucffbZ5cfoy/U54IJaURTF3/7t3xbjxo0ramtrizPPPLN49tlnqz0l2O9dfPHFxZFHHlnU1tYWn/vc54qLL764ePPNN8v7P/roo+Iv/uIvikMPPbQYNmxY8Sd/8ifF22+/XfEY69atK84777zi4IMPLg4//PDimmuuKbZv397XhwID3pNPPllExMduM2bMKIqiKHbt2lX84Ac/KBoaGoq6urrinHPOKdasWVPxGO+++27xrW99qzjkkEOKUqlUfOc73ynef//9ijH/9V//VXz5y18u6urqis997nPFggUL+uoQYUD7fWv0ww8/LKZOnVocccQRxdChQ4ujjz66uPzyyz/2j8PWKPSOPa3NiCjuvvvu8pie+r32ySefLE477bSitra2OPbYYyueA/i4T1qf69evL84+++xi1KhRRV1dXXHccccVc+fOLbq6uioep6/W56D/f9IAAAAAwKcwoD5DDQAAAACqTVADAAAAgARBDQAAAAASBDUAAAAASBDUAAAAACBBUAMAAACABEENAAAAABIENQAAAABIENQAAAAAIEFQAwAAAIAEQQ0AAAAAEv4/Rn5XL0LxgcQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "balanced_dataset['Amount'].hist(bins= 200, figsize=(15,15), grid=False)\n",
    "plt.xlim([0,2500])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ubAFME5dddtnoFaB77rknF198ce644478/d///eh9K6985Stz2GGH5X3ve19+/vOf57nPfW6++93v5pvf/GZOPfXU0asEK1euzE033ZQrr7wyu+66a5qamnLWWWelra0txxxzzKNenRrx4IMP5mUve1le97rX5fbbb8/q1avzohe9aNznNI1429veln/5l3/JCSeckBtuuCFPf/rT8//9f/9ffvCDH+T8888fvRq30047Zb/99suXv/zlPOtZz0pdXV2e85zn5DnPec64r3vAAQekuro6H/7whzMwMJDZs2fnpS99afbYY4+ceeaZWbFiRf76r/86r3rVq0ZnfcELXjBm2/zxrFixIt/5zndy6KGHprW1dTQMFy5cOPqxuuQPV19WrlyZM888Mz//+c/zmte8Jrvuumt6e3vzjW98I29729vy7ne/O0ly4IEH5stf/nJOP/30vOAFL8guu+ySV77ylTnuuOPyla98Je94xzty1VVX5ZBDDsnQ0FBuu+22fOUrX8nll1+e5z//+XnGM56R973vffnABz6QQw89NEcddVRmz56dH/3oR5k/f37OPffc0Z9z4YUXZuXKlXnGM56RPfbYY8L7npLk85//fF72spfl1a9+dd74xjfm0EMPzebNm/P1r389V199dV7/+tfnPe95z+j62tra/M3f/E0+8YlPZMaMGdl7773z7W9/e/Tesi0deOCBSf7wgOSlS5emuro6xx577KP+3o+oqanJhRdemOOOOy7Pe97zcuyxx2b33XfPnXfemUsvvTSHHHLII/5y4U9973vfy8knn5y/+Zu/ybOe9aw8/PDD+cIXvpDq6uocffTRWzUHQBFTuBMiAJXxt37fcccdKwcccEDlwgsvHLPdd6Xyh62yTzvttMr8+fMrO+ywQ+WZz3xm5Z/+6Z9G191www2VmTNnVk455ZQx3/fwww9XXvCCF1Tmz5//qFuAj8xzzTXXVN72trdVdtttt8ouu+xSaW5urvzmN78Zs/ZPt36vVCqVDRs2VN785jdX/uIv/qIya9asyv777z9mm/AR1157beXAAw+szJo1a6u2gf/0pz9daWxsHN2WfcstyT/5yU9W9tlnn8oOO+xQmTdvXqWlpeVR3+OWrrnmmtE5GhsbK5/61Kcq7e3t425l/rWvfa3yohe9qLLzzjtXdt5558o+++xTWb58eeX2228fXXP//fdX3vjGN1bmzp1bSTJmG/gHH3yw8uEPf7iycOHCyuzZsyu77bZb5cADD6ysWLGiMjAwMOZn/du//Vtl0aJFo+te/OIXV6644orR8319fZVly5ZVdt1110qSrdoG/r777qucffbZlYULF1Z22mmnyq677lo55JBDKmvWrHnEv2eVyh+26D/66KMrc+bMqey2226Vt7/97ZVbbrnlEVu/P/zww5VTTjmlsvvuu1dmzJgx5vfuT/+//dOt30dcddVVlaVLl1Zqa2srO+64Y2XvvfeunHDCCZUf//jHo2uOP/74ys477/yIOX/2s59V3vKWt1T23nvvyo477lipq6urHHbYYZX/+I//eMzfE4CSZlQqf/J5CAC2a2vWrMmb3/zm/OhHPxqzOyIAMDnu2QIAAChAbAEAABQgtgAAAAqY0tg6++yzM2PGjDFf++yzz+j53//+91m+fHme8pSnZJdddsnRRx+dDRs2jHmNO++8M8uWLcucOXOyxx575D3vec8jtn+9+uqr87znPS+zZ8/OM57xjKxZs+aJeHsA26QTTjghlUrF/VoA8H805Ve2Fi5cmPXr149+ff/73x89d9ppp+Vb3/pWvvrVr+aaa67J3XffnaOOOmr0/NDQUJYtW5YHH3ww1157bS666KKsWbMmZ5111uia3t7eLFu2LIcddlhuuummnHrqqTnppJMe8QwVAACAx9OU7kZ49tln55JLLslNN930iHMDAwPZfffdc/HFF+eYY45Jktx2223Zd99909XVlYMPPjiXXXZZjjzyyNx9992ZN29ekuRTn/pUzjjjjNx7772ZNWtWzjjjjFx66aW55ZZbRl/72GOPzcaNG/Od73znCXmfAADA9mfKH2p8xx13ZP78+dlxxx2zePHinHvuudlrr71yww035KGHHsrhhx8+unafffbJXnvtNRpbXV1d2X///UdDK/nDk+hbWlrS09OTRYsWpaura8xrjKw59dRTJ5xp8+bNY544Pzw8nP7+/jzlKU/JjBkzHr83DwAAbFMqlUruu+++zJ8/P1VVj/5BwSmNrYMOOihr1qzJs5/97Kxfvz4rVqzIoYcemltuuSV9fX2ZNWtW5s6dO+Z75s2bl76+viRJX1/fmNAaOT9y7tHWDA4O5oEHHshOO+30iLnOPffcrFix4vF6mwAAwJPMXXfdlT333PNR10xpbL385S8f/eempqYcdNBBedrTnpavfOUr40bQE+XMM8/M6aefPvrrgYGB7LXXXrnrrrtSU1MzZXMBAABTa3BwME996lOz6667PubaKf8Y4Zbmzp2bZz3rWfnf//3f/NVf/VUefPDBbNy4cczVrQ0bNqS+vj5JUl9fn+uvv37Ma4zsVrjlmj/dwXDDhg2pqamZMOhmz56d2bNnP+J4TU2N2AIAALbq9qIp341wS/fff39++tOfpqGhIQceeGB22GGHXHnllaPnb7/99tx5551ZvHhxkmTx4sW5+eabc88994yuueKKK1JTU5P99ttvdM2WrzGyZuQ1AAAASpjS2Hr3u9+da665Jj//+c9z7bXX5rWvfW2qq6vzhje8IbW1tTnxxBNz+umn56qrrsoNN9yQN7/5zVm8eHEOPvjgJMkRRxyR/fbbL8cdd1z++7//O5dffnna2tqyfPny0StT73jHO/Kzn/0s733ve3Pbbbdl9erV+cpXvpLTTjttKt86AADwJDelHyP85S9/mTe84Q35zW9+k9133z0vetGL8sMf/jC77757kuS8885LVVVVjj766GzevDlLly7N6tWrR7+/uro63/72t9PS0pLFixdn5513zvHHH59zzjlndM2CBQty6aWX5rTTTssFF1yQPffcM5/5zGeydOnSJ/z9AgAA248pfc7WtmJwcDC1tbUZGBhwzxYAAGzHJtMG0+qeLQAAgCcLsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoIBpE1sf+tCHMmPGjJx66qmjx37/+99n+fLlecpTnpJddtklRx99dDZs2DDm++68884sW7Ysc+bMyR577JH3vOc9efjhh8esufrqq/O85z0vs2fPzjOe8YysWbPmCXhHAADA9mxaxNaPfvSj/Mu//EuamprGHD/ttNPyrW99K1/96ldzzTXX5O67785RRx01en5oaCjLli3Lgw8+mGuvvTYXXXRR1qxZk7POOmt0TW9vb5YtW5bDDjssN910U0499dScdNJJufzyy5+w9wcAAGx/ZlQqlcpUDnD//ffnec97XlavXp2VK1fmgAMOyPnnn5+BgYHsvvvuufjii3PMMcckSW677bbsu+++6erqysEHH5zLLrssRx55ZO6+++7MmzcvSfKpT30qZ5xxRu69997MmjUrZ5xxRi699NLccsstoz/z2GOPzcaNG/Od73xnq2YcHBxMbW1tBgYGUlNT8/j/JgAAANuEybTBlF/ZWr58eZYtW5bDDz98zPEbbrghDz300Jjj++yzT/baa690dXUlSbq6urL//vuPhlaSLF26NIODg+np6Rld86evvXTp0tHXGM/mzZszODg45gsAAGAyZk7lD//Sl76U//qv/8qPfvSjR5zr6+vLrFmzMnfu3DHH582bl76+vtE1W4bWyPmRc4+2ZnBwMA888EB22mmnR/zsc889NytWrPiz3xcAAMCUXdm666678nd/93fp6OjIjjvuOFVjjOvMM8/MwMDA6Nddd9011SMBAADbmCmLrRtuuCH33HNPnve852XmzJmZOXNmrrnmmnz84x/PzJkzM2/evDz44IPZuHHjmO/bsGFD6uvrkyT19fWP2J1w5NePtaampmbcq1pJMnv27NTU1Iz5AgAAmIwpi62Xvexlufnmm3PTTTeNfj3/+c9Pc3Pz6D/vsMMOufLKK0e/5/bbb8+dd96ZxYsXJ0kWL16cm2++Offcc8/omiuuuCI1NTXZb7/9Rtds+Roja0ZeAwAAoIQpu2dr1113zXOe85wxx3beeec85SlPGT1+4okn5vTTT09dXV1qampyyimnZPHixTn44IOTJEcccUT222+/HHfccfnIRz6Svr6+tLW1Zfny5Zk9e3aS5B3veEc++clP5r3vfW/e8pa35Hvf+16+8pWv5NJLL31i3zAAALBdmdINMh7Leeedl6qqqhx99NHZvHlzli5dmtWrV4+er66uzre//e20tLRk8eLF2XnnnXP88cfnnHPOGV2zYMGCXHrppTnttNNywQUXZM8998xnPvOZLF26dCreEgAAsJ2Y8udsbQs8ZwsAAEi2sedsAQAAPBmJLQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABUxpbF144YVpampKTU1Nampqsnjx4lx22WWj53//+99n+fLlecpTnpJddtklRx99dDZs2DDmNe68884sW7Ysc+bMyR577JH3vOc9efjhh8esufrqq/O85z0vs2fPzjOe8YysWbPmiXh7AADAdmxKY2vPPffMhz70odxwww358Y9/nJe+9KV59atfnZ6eniTJaaedlm9961v56le/mmuuuSZ33313jjrqqNHvHxoayrJly/Lggw/m2muvzUUXXZQ1a9bkrLPOGl3T29ubZcuW5bDDDstNN92UU089NSeddFIuv/zyJ/z9AgAA248ZlUqlMtVDbKmuri7/9E//lGOOOSa77757Lr744hxzzDFJkttuuy377rtvurq6cvDBB+eyyy7LkUcembvvvjvz5s1LknzqU5/KGWeckXvvvTezZs3KGWeckUsvvTS33HLL6M849thjs3HjxnznO9/ZqpkGBwdTW1ubgYGB1NTUPP5vGgAA2CZMpg2mzT1bQ0ND+dKXvpTf/e53Wbx4cW644YY89NBDOfzww0fX7LPPPtlrr73S1dWVJOnq6sr+++8/GlpJsnTp0gwODo5eHevq6hrzGiNrRl5jPJs3b87g4OCYLwAAgMmY8ti6+eabs8suu2T27Nl5xzvekW984xvZb7/90tfXl1mzZmXu3Llj1s+bNy99fX1Jkr6+vjGhNXJ+5NyjrRkcHMwDDzww7kznnntuamtrR7+e+tSnPh5vFQAA2I5MeWw9+9nPzk033ZTrrrsuLS0tOf7443PrrbdO6UxnnnlmBgYGRr/uuuuuKZ0HAADY9syc6gFmzZqVZzzjGUmSAw88MD/60Y9ywQUX5PWvf30efPDBbNy4cczVrQ0bNqS+vj5JUl9fn+uvv37M643sVrjlmj/dwXDDhg2pqanJTjvtNO5Ms2fPzuzZsx+X9wcAAGyfpvzK1p8aHh7O5s2bc+CBB2aHHXbIlVdeOXru9ttvz5133pnFixcnSRYvXpybb74599xzz+iaK664IjU1Ndlvv/1G12z5GiNrRl4DAACghCm9snXmmWfm5S9/efbaa6/cd999ufjii3P11Vfn8ssvT21tbU488cScfvrpqaurS01NTU455ZQsXrw4Bx98cJLkiCOOyH777ZfjjjsuH/nIR9LX15e2trYsX7589MrUO97xjnzyk5/Me9/73rzlLW/J9773vXzlK1/JpZdeOpVvHQAAeJKb0ti655578qY3vSnr169PbW1tmpqacvnll+ev/uqvkiTnnXdeqqqqcvTRR2fz5s1ZunRpVq9ePfr91dXV+fa3v52WlpYsXrw4O++8c44//vicc845o2sWLFiQSy+9NKeddlouuOCC7LnnnvnMZz6TpUuXPuHvFwAA2H5Mu+dsTUeeswUAACTb6HO2AAAAnkzEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAGTjq0777wzlUrlEccrlUruvPPOx2UoAACAbd2kY2vBggW59957H3G8v78/CxYseFyGAgAA2NZNOrYqlUpmzJjxiOP3339/dtxxx8dlKAAAgG3dzK1dePrppydJZsyYkfe///2ZM2fO6LmhoaFcd911OeCAAx73AQEAALZFWx1bN954Y5I/XNm6+eabM2vWrNFzs2bNynOf+9y8+93vfvwnBAAA2AZtdWxdddVVSZI3v/nNueCCC1JTU1NsKAAAgG3dVsfWiM997nMl5gAAAHhSmXRs/e53v8uHPvShXHnllbnnnnsyPDw85vzPfvazx204AACAbdWkY+ukk07KNddck+OOOy4NDQ3j7kwIAACwvZt0bF122WW59NJLc8ghh5SYBwAA4Elh0s/Z2m233VJXV1diFgAAgCeNScfWBz7wgZx11lnZtGlTiXkAAACeFCb9McKPfexj+elPf5p58+bl6U9/enbYYYcx5//rv/7rcRsOAABgWzXp2HrNa15TYAwAAIAnlxmVSqUy1UNMd4ODg6mtrc3AwICHOQMAwHZsMm0w6Xu2AAAAeGyT/hhhVVXVoz5ba2ho6P80EAAAwJPBpGPrG9/4xphfP/TQQ7nxxhtz0UUXZcWKFY/bYAAAANuyx+2erYsvvjhf/vKX881vfvPxeLlpxT1bAABAMkX3bB188MG58sorH6+XAwAA2KZN+mOE43nggQfy8Y9/PP/v//2/x+PlAGDaGRoaSnd3d/r7+1NXV5empqZUV1dP9VgATGOTjq3ddtttzAYZlUol9913X+bMmZO1a9c+rsMBwHTQ2dmZ1atXp6+vb/RYfX19Wltbs2TJkimcDIDpbNL3bF100UVjfl1VVZXdd989Bx10UHbbbbfHdbjpwj1bANuvzs7OtLe3Z/HixWlubs6CBQvS29ubjo6OdHV1ZcWKFYILYDsymTbwUOOtILYAtk9DQ0Npbm5OY2NjVq5cmaqqP97qPDw8nLa2tvT29mbt2rU+UgiwnSi+QcbGjRvzsY99LCeddFJOOumknHfeeRkYGPizhgWA6aq7uzt9fX1pbm4eE1rJHz7Z0dzcnPXr16e7u3uKJgRgOpt0bP34xz/O3nvvnfPOOy/9/f3p7+/PP//zP2fvvffOf/3Xf5WYEQCmRH9/f5JkwYIF454fOT6yDgC2NOnYOu200/KqV70qP//5z/P1r389X//619Pb25sjjzwyp556aoERAWBq1NXVJUl6e3vHPT9yfGQdAGzpz7qydcYZZ2TmzD9uZDhz5sy8973vzY9//OPHdTgAmEpNTU2pr69PR0dHhoeHx5wbHh5OR0dHGhoa0tTUNEUTAjCdTTq2ampqcueddz7i+F133ZVdd931cRkKAKaD6urqtLa2pqurK21tbenp6cmmTZvS09OTtra2dHV1paWlxeYYAIxr0rsRvvOd78w3vvGNfPSjH80LX/jCJMkPfvCDvOc978nRRx+d888/v8ScU8puhADbt/Ges9XQ0JCWlhbbvgNsZybTBpN+qPFHP/rRzJgxI29605vy8MMPJ0l22GGHtLS05EMf+tCfNzEATGNLlizJIYccku7u7vT396euri5NTU2uaAHwqP7s52xt2rQpP/3pT5Mke++9d+bMmfO4DjaduLIFAAAkha9sjZgzZ07233//P/fbAQAAntQmHVu///3v84lPfCJXXXVV7rnnnkfszuRZWwAAAH9GbJ144on57ne/m2OOOSZ/+Zd/mRkzZpSYCwAAYJs26dj69re/nXXr1uWQQw4pMQ8AAMCTwqSfs/X//t//8zwtAACAxzDp2PrYxz6WM844I7/4xS9KzAMAAPCkMOmPET7/+c/P73//+zQ2NmbOnDnZYYcdxpzv7+9/3IYDAADYVk06tt7whjfkV7/6Vf7xH/8x8+bNs0EGAADAOCYdW9dee226urry3Oc+t8Q8AAAATwqTvmdrn332yQMPPFBiFgAAgCeNScfWhz70obzrXe/K1Vdfnd/85jcZHBwc8wUAAEAyo1KpVCbzDVVVf+izP71Xq1KpZMaMGRkaGnr8ppsmBgcHU1tbm4GBgdTU1Ez1OAAAwBSZTBtM+p6tq666asJzN99882RfDgAA4Elp0le2/tR9992XL37xi/nMZz6TG264wZUtAADgSWsybTDpe7ZGdHZ25vjjj09DQ0M++tGP5qUvfWl++MMf/rkvBwAA8KQyqY8R9vX1Zc2aNfnsZz+bwcHBvO51r8vmzZtzySWXZL/99is1IwAAwDZnq69svfKVr8yzn/3sdHd35/zzz8/dd9+dT3ziEyVnAwAA2GZt9ZWtyy67LO985zvT0tKSZz7zmSVnAgAA2OZt9ZWt73//+7nvvvty4IEH5qCDDsonP/nJ/PrXvy45GwAAwDZrq2Pr4IMPzqc//emsX78+b3/72/OlL30p8+fPz/DwcK644orcd999JecEAADYpvyftn6//fbb89nPfjZf+MIXsnHjxvzVX/1V/v3f//3xnG9asPU7AACQPEFbvyfJs5/97HzkIx/JL3/5y3zxi1/8v7wUAADAk8r/+aHG2wNXtgAAgOQJvLIFAADA+MQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFzJzqAQBgWzA0NJTu7u709/enrq4uTU1Nqa6unuqxAJjGxBYAPIbOzs6sXr06fX19o8fq6+vT2tqaJUuWTOFkAExnPkYIAI+is7Mz7e3taWxszKpVq7Ju3bqsWrUqjY2NaW9vT2dn51SPCMA0NaNSqVSmeojpbnBwMLW1tRkYGEhNTc1UjwPAE2RoaCjNzc1pbGzMypUrU1X1x7+jHB4eTltbW3p7e7N27VofKQTYTkymDVzZAoAJdHd3p6+vL83NzWNCK0mqqqrS3Nyc9evXp7u7e4omBGA6E1sAMIH+/v4kyYIFC8Y9P3J8ZB0AbElsAcAE6urqkiS9vb3jnh85PrIOALYktgBgAk1NTamvr09HR0eGh4fHnBseHk5HR0caGhrS1NQ0RRMCMJ2JLQCYQHV1dVpbW9PV1ZW2trb09PRk06ZN6enpSVtbW7q6utLS0mJzDADGZTfCrWA3QoDt23jP2WpoaEhLS4vnbAFsZybTBmJrK4gtAIaGhtLd3Z3+/v7U1dWlqanJFS2A7dBk2mDmEzQTAGzTqqurs2jRoqkeA4BtiHu2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAVMaW+eee25e8IIXZNddd80ee+yR17zmNbn99tvHrPn973+f5cuX5ylPeUp22WWXHH300dmwYcOYNXfeeWeWLVuWOXPmZI899sh73vOePPzww2PWXH311Xne856X2bNn5xnPeEbWrFlT+u0BAADbsSmNrWuuuSbLly/PD3/4w1xxxRV56KGHcsQRR+R3v/vd6JrTTjst3/rWt/LVr34111xzTe6+++4cddRRo+eHhoaybNmyPPjgg7n22mtz0UUXZc2aNTnrrLNG1/T29mbZsmU57LDDctNNN+XUU0/NSSedlMsvv/wJfb8AAMD2Y0alUqlM9RAj7r333uyxxx655pprsmTJkgwMDGT33XfPxRdfnGOOOSZJctttt2XfffdNV1dXDj744Fx22WU58sgjc/fdd2fevHlJkk996lM544wzcu+992bWrFk544wzcumll+aWW24Z/VnHHntsNm7cmO985zuPOdfg4GBqa2szMDCQmpqaMm8eAACY9ibTBtPqnq2BgYEkSV1dXZLkhhtuyEMPPZTDDz98dM0+++yTvfbaK11dXUmSrq6u7L///qOhlSRLly7N4OBgenp6Rtds+Roja0Ze409t3rw5g4ODY74AAAAmY9rE1vDwcE499dQccsghec5znpMk6evry6xZszJ37twxa+fNm5e+vr7RNVuG1sj5kXOPtmZwcDAPPPDAI2Y599xzU1tbO/r11Kc+9XF5jwAAwPZj2sTW8uXLc8stt+RLX/rSVI+SM888MwMDA6Nfd91111SPBAAAbGNmTvUASXLyySfn29/+djo7O7PnnnuOHq+vr8+DDz6YjRs3jrm6tWHDhtTX14+uuf7668e83shuhVuu+dMdDDds2JCamprstNNOj5hn9uzZmT179uPy3gAAgO3TlF7ZqlQqOfnkk/ONb3wj3/ve97JgwYIx5w888MDssMMOufLKK0eP3X777bnzzjuzePHiJMnixYtz880355577hldc8UVV6Smpib77bff6JotX2NkzchrAAAAPN6mdDfC1tbWXHzxxfnmN7+ZZz/72aPHa2trR684tbS0ZN26dVmzZk1qampyyimnJEmuvfbaJH/Y+v2AAw7I/Pnz85GPfCR9fX057rjjctJJJ+Uf//Efk/xh6/fnPOc5Wb58ed7ylrfke9/7Xt75znfm0ksvzdKlSx9zTrsRAgAAyeTaYEpja8aMGeMe/9znPpcTTjghyR8eavyud70rX/ziF7N58+YsXbo0q1evHv2IYJL84he/SEtLS66++ursvPPOOf744/OhD30oM2f+8VOSV199dU477bTceuut2XPPPfP+979/9Gc8FrEFAAAk21BsbSvEFgAAkGzDz9kCAAB4shBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUMDMqR4AALYFQ0ND6e7uTn9/f+rq6tLU1JTq6uqpHguAaUxsAcBj6OzszOrVq9PX1zd6rL6+Pq2trVmyZMkUTgbAdOZjhADwKDo7O9Pe3p7GxsasWrUq69aty6pVq9LY2Jj29vZ0dnZO9YgATFMzKpVKZaqHmO4GBwdTW1ubgYGB1NTUTPU4ADxBhoaG0tzcnMbGxqxcuTJVVX/8O8rh4eG0tbWlt7c3a9eu9ZFCgO3EZNrAlS0AmEB3d3f6+vrS3Nw8JrSSpKqqKs3NzVm/fn26u7unaEIApjOxBQAT6O/vT5IsWLBg3PMjx0fWAcCWxBYATKCuri5J0tvbO+75keMj6wBgS2ILACbQ1NSU+vr6dHR0ZHh4eMy54eHhdHR0pKGhIU1NTVM0IQDTmdgCgAlUV1entbU1XV1daWtrS09PTzZt2pSenp60tbWlq6srLS0tNscAYFx2I9wKdiME2L6N95ythoaGtLS0eM4WwHZmMm0gtraC2AJgaGgo3d3d6e/vT11dXZqamlzRAtgOTaYNZj5BMwHANq26ujqLFi2a6jEA2Ia4ZwsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFDBzqgcAgG3B0NBQuru709/fn7q6ujQ1NaW6unqqxwJgGhNbAPAYOjs7s3r16vT19Y0eq6+vT2tra5YsWTKFkwEwnfkYIQA8is7OzrS3t6exsTGrVq3KunXrsmrVqjQ2Nqa9vT2dnZ1TPSIA09SMSqVSmeohprvBwcHU1tZmYGAgNTU1Uz0OAE+QoaGhNDc3p7GxMStXrkxV1R//jnJ4eDhtbW3p7e3N2rVrfaQQYDsxmTZwZQsAJtDd3Z2+vr40NzePCa0kqaqqSnNzc9avX5/u7u4pmhCA6UxsAcAE+vv7kyQLFiwY9/zI8ZF1ALAlsQUAE6irq0uS9Pb2jnt+5PjIOgDYktgCgAk0NTWlvr4+HR0dGR4eHnNueHg4HR0daWhoSFNT0xRNCMB0JrYAYALV1dVpbW1NV1dX2tra0tPTk02bNqWnpydtbW3p6upKS0uLzTEAGJfdCLeC3QgBtm/jPWeroaEhLS0tnrMFsJ2ZTBuIra0gtgAYGhpKd3d3+vv7U1dXl6amJle0ALZDk2mDmU/QTACwTauurs6iRYumegwAtiHu2QIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgAJmTvUAALAtGBoaSnd3d/r7+1NXV5empqZUV1dP9VgATGNiCwAeQ2dnZ1avXp2+vr7RY/X19Wltbc2SJUumcDIApjMfIwSAR9HZ2Zn29vY0NjZm1apVWbduXVatWpXGxsa0t7ens7NzqkcEYJqaUalUKlM9xHQ3ODiY2traDAwMpKamZqrHAeAJMjQ0lObm5jQ2NmblypWpqvrj31EODw+nra0tvb29Wbt2rY8UAmwnJtMGrmwBwAS6u7vT19eX5ubmMaGVJFVVVWlubs769evT3d09RRMCMJ25ZwsAJtDf358kWbBgwbgbZCxYsGDMOgDYktgCgAnU1dUlSb7xjW/kW9/61iM2yDjyyCPHrAOALYktAJhAU1NT5s6dm09/+tNZvHhx3v/+92fBggWj92l95jOfyW677ZampqapHhWAacg9WwDwf2CfKQAm4soWAEygu7s7GzduzFvf+tZ861vfyvLly0fPNTQ05K1vfWs+/elPp7u7O4sWLZrCSQGYjsQWAExgZOOL1772tTn22GMfsUHG5s2b8+lPf9oGGQCMS2wBwARGNr7o7e3NwoULH3H1qre3d8w6ANiSe7YAYAJNTU2pr69PR0dHhoeHx5wbHh5OR0dHGhoabJABwLjEFgBMoLq6Oq2trenq6kpbW1t6enqyadOm9PT0pK2tLV1dXWlpaUl1dfVUjwrANDSjYhulxzQ4OJja2toMDAykpqZmqscB4AnW2dmZ1atXj3nOVkNDQ1paWrJkyZIpnAyAJ9pk2kBsbQWxBcDQ0NAjNshwRQtg+zOZNrBBBgBsherqatu7AzAp7tkCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAXMnOoBAGBbMDQ0lO7u7vT396euri5NTU2prq6e6rEAmMbEFgA8hs7OzqxevTp9fX2jx+rr69Pa2polS5ZM4WQATGc+RggAj6KzszPt7e1pbGzMqlWrsm7duqxatSqNjY1pb29PZ2fnVI8IwDQ1o1KpVKZ6iOlucHAwtbW1GRgYSE1NzVSPA8ATZGhoKM3NzWlsbMzKlStTVfXHv6McHh5OW1tbent7s3btWh8pBNhOTKYNXNkCgAl0d3enr68vzc3NY0IrSaqqqtLc3Jz169enu7t7iiYEYDoTWwAwgf7+/iTJggULxj0/cnxkHQBsSWwBwATq6uqSJL29veOeHzk+sg4AtiS2AGACTU1Nqa+vT0dHR4aHh8ecGx4eTkdHRxoaGtLU1DRFEwIwnYktAJhAdXV1Wltb09XVlba2tvT09GTTpk3p6elJW1tburq60tLSYnMMAMZlN8KtYDdCgO3beM/ZamhoSEtLi+dsAWxnJtMGYmsriC0AhoaG0t3dnf7+/tTV1aWpqckVLYDt0GTaYOYTNBMAbNOqq6uzaNGiqR4DgG2I2AKAreDKFgCTJbYA4DGMd89WfX19Wltb3bMFwITsRggAj6KzszPt7e1pbGzMqlWrsm7duqxatSqNjY1pb29PZ2fnVI8IwDRlg4ytYIMMgO3T0NBQmpub09jYmJUrV6aq6o9/Rzk8PJy2trb09vZm7dq1PlIIsJ2YTBu4sgUAE+ju7k5fX1+am5vHhFaSVFVVpbm5OevXr093d/cUTQjAdCa2AGAC/f39SZIFCxaMe37k+Mg6ANiS2AKACdTV1SVJent7xz0/cnxkHQBsSWwBwASamppSX1+fjo6ODA8Pjzk3PDycjo6ONDQ0pKmpaYomBGA6E1sAMIHq6uq0tramq6srbW1t6enpyaZNm9LT05O2trZ0dXWlpaXF5hgAjMtuhFvBboQA27fxnrPV0NCQlpYWz9kC2M5Mpg3E1lYQWwAMDQ2lu7s7/f39qaurS1NTkytaANuhybTBzCdoJgDYplVXV2fRokVTPQYA2xD3bAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKMBDjQFgKwwNDaW7uzv9/f2pq6tLU1NTqqurp3osAKYxsQUAj6GzszOrV69OX1/f6LH6+vq0trZmyZIlUzgZANOZjxECwKPo7OxMe3t7Ghsbs2rVqqxbty6rVq1KY2Nj2tvb09nZOdUjAjBNzahUKpWpHmK6GxwcTG1tbQYGBlJTUzPV4wDwBBkaGkpzc3MaGxuzcuXKVFX98e8oh4eH09bWlt7e3qxdu9ZHCgG2E5NpA1e2AGAC3d3d6evrS3NzcyqVSm688cZceeWVufHGG1OpVNLc3Jz169enu7t7qkcFYBpyzxYATKC/vz9Jcvfdd+cDH/jAI+7ZOvHEE8esA4AtiS0AmEBdXV2S5IMf/GBe+MIX5v3vf38WLFiQ3t7edHR05IMf/OCYdQCwJR8jBIAJLFy4MNXV1dltt91yzjnnZOHChZkzZ04WLlyYc845J7vttluqq6uzcOHCqR4VgGlIbAHABHp6ejI0NJSNGzfmrLPOSk9PTzZt2pSenp6cddZZ2bhxY4aGhtLT0zPVowIwDfkYIQBMYORerH/4h3/IZz/72Sxfvnz0XENDQ/7hH/4hH/zgB92zBcC4xBYATGDkXqz58+eno6Mj3d3d6e/vT11dXZqamnLbbbeNWQcAW/IxQgCYQFNTU+rr69PR0ZEZM2Zk0aJFednLXpZFixZlxowZ6ejoSENDQ5qamqZ6VACmIbEFABOorq5Oa2trurq60tbWNuaerba2tnR1daWlpcUDjQEY14xKpVKZ6iGmu8k8JRqAJ5/Ozs6sXr16zHO2Ghoa0tLSkiVLlkzhZAA80SbTBu7ZAoDHsGTJkhx88MH55je/mbvvvjvz58/Pq1/96syaNWuqRwNgGhNbAPAYxruy9bWvfS2tra2ubAEwIfdsAcCj6OzsTHt7exobG7Nq1aqsW7cuq1atSmNjY9rb29PZ2TnVIwIwTblnayu4Zwtg+zQ0NJTm5uY0NjZm5cqVqar6499RDg8Pp62tLb29vVm7dq1NMgC2E5NpA1e2AGAC3d3d6evrS3Nz85jQSpKqqqo0Nzdn/fr16e7unqIJAZjOxBYATKC/vz9JsmDBgnHPjxwfWQcAW7JBBgBMoK6uLknS29ubffbZJ93d3env709dXV2amprS29s7Zh0AbElsAcAEmpqaUl9fn49//OMZGBgYsxthfX19amtr09DQkKampimcEoDpyscIAWAC1dXVeclLXpLbb789mzdvzrvf/e587Wtfy7vf/e5s3rw5t99+e1784hfbHAOAcdmNcCvYjRBg+zSyG2FtbW02btyYDRs2jJ4bubI1ODhoN0KA7chk2sDHCAFgAiO7Eb7//e8f956t2267LcuXL093d3cWLVo01eMCMM2ILQCYwJa7EVZXVz8iqOxGCMCjcc8WAExgy90Ix2M3QgAejdgCgAmM7EbY0dGR4eHhMeeGh4fT0dFhN0IAJiS2AGAC1dXVaW1tTVdXV9ra2tLT05NNmzalp6cnbW1t6erqSktLi80xABiX3Qi3gt0IAbZvnZ2dWb169ZjnbDU0NKSlpSVLliyZwskAeKJNpg3E1lYQWwAMDQ09YjdCV7QAtj+2fgeAx9l4uxECwKNxzxYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACZk71AACwLRgaGkp3d3f6+/tTV1eXpqamVFdXT/VYAExjYgsAHkNnZ2dWr16dvr6+0WP19fVpbW3NkiVLpnAyAKYzHyMEgEfR2dmZ9vb2NDY2ZtWqVVm3bl1WrVqVxsbGtLe3p7Ozc6pHBGCamlGpVCpTPcR0Nzg4mNra2gwMDKSmpmaqxwHgCTI0NJTm5uY0NjZm5cqVqar6499RDg8Pp62tLb29vVm7dq2PFAJsJybTBq5sAcAEuru709fXl+bm5jGhlSRVVVVpbm7O+vXr093dPUUTAjCdiS0AmEB/f3+SZMGCBeOeHzk+sg4AtiS2AGACdXV1SZLe3t5xz48cH1kHAFsSWwAwgaamptTX16ejoyPDw8Njzg0PD6ejoyMNDQ1pamqaogkBmM7EFgBMoLq6Oq2trenq6kpbW1t6enqyadOm9PT0pK2tLV1dXWlpabE5BgDjshvhVrAbIcD2bbznbDU0NKSlpcVztgC2M5NpA7G1FcQWAENDQ+nu7k5/f3/q6urS1NTkihbAdmgybTDzCZoJALZp1dXVWbRo0VSPAcA2xD1bAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABYgsAAKAAsQUAAFCA2AIAAChAbAEAABQgtgAAAAoQWwAAAAWILQAAgALEFgAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAmZO9QAAsC0YGhpKd3d3+vv7U1dXl6amplRXV0/1WABMY2ILAB5DZ2dnVq9enb6+vtFj9fX1aW1tzZIlS6ZwMgCmM7EFAI+is7Mz7e3tOfjgg/P6178+O+64Y37/+9/n+uuvT3t7e1asWCG4ABjXjEqlUpnqIaa7wcHB1NbWZmBgIDU1NVM9DgBPkKGhoTQ3N6e2tjYbN27Mhg0bRs/Nmzcvc+fOzeDgYNauXesjhQDbicm0gQ0yAGAC3d3d6evry+2335699947q1atyrp167Jq1arsvffeuf3227N+/fp0d3dP9agATENiCwAm8Otf/zpJctBBB2XlypVZuHBh5syZk4ULF2blypU56KCDxqwDgC2JLQCYwMaNG5Mkhx56aKqqxv4ns6qqKi960YvGrAOALYktAJjA3LlzkyT/+Z//meHh4THnhoeH8/3vf3/MOgDYktgCgAn8xV/8RZLk+uuvT1tbW3p6erJp06b09PSkra0t119//Zh1ALAlW78DwASamppSX1+f2tra/OxnP8vy5ctHzzU0NORZz3pWBgcH09TUNIVTAjBdiS0AmEB1dXVaW1vHPGdr9uzZ2bx5c66//vr88Ic/zIoVK2z7DsC4PGdrK3jOFsD2rbOzM6tXr05fX9/osYaGhrS0tHigMcB2ZjJtILa2gtgCYGhoKN3d3env709dXV2amppc0QLYDk2mDXyMEAC2QnV1dRYtWjTVYwCwDbEbIQAAQAFiCwAAoACxBQAAUIDYAgAAKEBsAQAAFCC2AAAAChBbAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACxBYAAEABUxpbnZ2deeUrX5n58+dnxowZueSSS8acr1QqOeuss9LQ0JCddtophx9+eO64444xa/r7+9Pc3JyamprMnTs3J554Yu6///4xa7q7u3PooYdmxx13zFOf+tR85CMfKf3WAHiSGRoayo033pgrr7wyN954Y4aGhqZ6JACmuZlT+cN/97vf5bnPfW7e8pa35KijjnrE+Y985CP5+Mc/nosuuigLFizI+9///ixdujS33nprdtxxxyRJc3Nz1q9fnyuuuCIPPfRQ3vzmN+dtb3tbLr744iTJ4OBgjjjiiBx++OH51Kc+lZtvvjlvectbMnfu3LztbW97Qt8vANumzs7OrF69On19faPH6uvr09ramiVLlkzhZABMZzMqlUplqodIkhkzZuQb3/hGXvOa1yT5w1Wt+fPn513velfe/e53J0kGBgYyb968rFmzJscee2x+8pOfZL/99suPfvSjPP/5z0+SfOc738krXvGK/PKXv8z8+fNz4YUX5n3ve1/6+voya9asJMnf//3f55JLLsltt922VbMNDg6mtrY2AwMDqampefzfPADTVmdnZ9rb27N48eI0NzdnwYIF6e3tTUdHR7q6urJixQrBBbAdmUwbTNt7tnp7e9PX15fDDz989FhtbW0OOuigdHV1JUm6uroyd+7c0dBKksMPPzxVVVW57rrrRtcsWbJkNLSSZOnSpbn99tvz29/+dtyfvXnz5gwODo75AmD7MzQ0lNWrV2fx4sVZuXJlFi5cmDlz5mThwoVZuXJlFi9enAsvvNBHCgEY17SNrZGPasybN2/M8Xnz5o2e6+vryx577DHm/MyZM1NXVzdmzXivseXP+FPnnntuamtrR7+e+tSn/t/fEADbnO7u7vT19aW5uTlVVWP/k1lVVTX6Ufbu7u4pmhCA6WzaxtZUOvPMMzMwMDD6ddddd031SABMgf7+/iTJggULxj0/cnxkHQBsadrGVn19fZJkw4YNY45v2LBh9Fx9fX3uueeeMecffvjh9Pf3j1kz3mts+TP+1OzZs1NTUzPmC4DtT11dXZI/fLR9PCPHR9YBwJambWwtWLAg9fX1ufLKK0ePDQ4O5rrrrsvixYuTJIsXL87GjRtzww03jK753ve+l+Hh4Rx00EGjazo7O/PQQw+Nrrniiivy7Gc/O7vtttsT9G4A2BY1NTWlvr4+HR0dGR4eHnNueHg4HR0daWhoSFNT0xRNCMB0NqWxdf/99+emm27KTTfdlOQPf0N400035c4778yMGTNy6qmnZuXKlfn3f//33HzzzXnTm96U+fPnj+5YuO++++av//qv89a3vjXXX399fvCDH+Tkk0/Osccem/nz5ydJ3vjGN2bWrFk58cQT09PTky9/+cu54IILcvrpp0/RuwZgW1FdXZ3W1tZ0dXWlra0tPT092bRpU3p6etLW1paurq60tLSkurp6qkcFYBqa0q3fr7766hx22GGPOH788cdnzZo1qVQqaW9vz7/+679m48aNedGLXpTVq1fnWc961uja/v7+nHzyyfnWt76VqqqqHH300fn4xz+eXXbZZXRNd3d3li9fnh/96Ef5i7/4i5xyyik544wztnpOW78DbN/Ge85WQ0NDWlpabPsOsJ2ZTBtMm+dsTWdiC4ChoaF0d3env78/dXV1aWpqckULYDs0mTaY+QTNBADbtOrq6ixatGiqxwBgGzJtN8gAAADYloktAACAAsQWAABAAWILAACgALEFAABQgNgCAAAoQGwBAAAUILYAAAAKEFsAAAAFiC0AAIACZk71AACwLRgaGkp3d3f6+/tTV1eXpqamVFdXT/VYAExjYgsAHkNnZ2dWr16dvr6+0WP19fVpbW3NkiVLpnAyAKYzHyMEgEfR2dmZ9vb2NDY2ZtWqVVm3bl1WrVqVxsbGtLe3p7Ozc6pHBGCamlGpVCpTPcR0Nzg4mNra2gwMDKSmpmaqxwHgCTI0NJTm5uY0NjZm5cqVqar6499RDg8Pp62tLb29vVm7dq2PFAJsJybTBq5sAcAEuru709fXl+bm5jGhlSRVVVVpbm7O+vXr093dPUUTAjCdiS0AmEB/f3+SZMGCBeOeHzk+sg4AtiS2AGACdXV1SZLe3t5xz48cH1kHAFsSWwAwgaamptTX16ejoyPDw8Njzg0PD6ejoyMNDQ1pamqaogkBmM7EFgBMoLq6Oq2trenq6kpbW1t6enqyadOm9PT0pK2tLV1dXWlpabE5BgDjshvhVrAbIcD2bbznbDU0NKSlpcVztgC2M5NpA7G1FcQWAENDQ+nu7k5/f3/q6urS1NTkihbAdmgybTDzCZoJALZp1dXVWbRo0VSPAcA2xD1bAAAABYgtAACAAsQWAABAAWILAACgALEFAABQgN0IAWAr2PodgMkSWwDwGMZ7qHF9fX1aW1s91BiACfkYIQA8is7OzrS3t6exsTGrVq3KunXrsmrVqjQ2Nqa9vT2dnZ1TPSIA09SMSqVSmeohprvJPCUagCePoaGhNDc3p7GxMStXrkxV1R//jnJ4eDhtbW3p7e3N2rVrfaQQYDsxmTZwZQsAJtDd3Z2+vr40NzePCa0kqaqqSnNzc9avX5/u7u4pmhCA6UxsAcAE+vv7kyQLFiwY9/zI8ZF1ALAlsQUAE6irq0uS9Pb2jnt+5PjIOgDYktgCgAk0NTWlvr4+HR0dGR4eHnNueHg4HR0daWhoSFNT0xRNCMB0JrYAYALV1dVpbW1NV1dX2tra0tPTk02bNqWnpydtbW3p6upKS0uLzTEAGJfdCLeC3QgBtm/jPWeroaEhLS0tnrMFsJ2ZTBt4qDEAPIYlS5bk4IMPzje/+c3cfffdmT9/fl796ldn1qxZUz0aANOY2AKAxzDela2vfe1raW1tdWULgAmJLQB4FJ2dnWlvb8/BBx+c17/+9Zk9e3Y2b96c66+/Pu3t7VmxYoXgAmBc7tnaCu7ZAtg+DQ0Npbm5efS/AVte2aqvr09tbW0GBwezdu1am2QAbCfcswUAj4Pu7u709fWlr68vs2fPHnPut7/97Wh8dXd3Z9GiRVMxIgDTmNgCgAn8+te/Hv3n5z3vefnbv/3bLFiwIL29vVm7dm26uroesQ4ARnjOFgBMoL+/P0my995754Mf/GAWLlyYOXPmZOHChfngBz+YxsbGMesAYEtiCwAmMDg4mCSP+AjhiB133HHMOgDYktgCgAlUVf3hP5O33npr2tra0tPTk02bNqWnpydtbW259dZbx6wDgC25ZwsAJnDAAQfkC1/4Qvbaa6/89Kc/zfLly0fP1dfXZ6+99sqdd96ZAw44YOqGBGDaElsAMIEDDjggc+fOzZ133pmDDjooL3rRi7J58+bMnj07v/zlL3Pddddl7ty5YguAcYktAJhAdXV1Tj/99Jx11lm57rrrct111z1izemnn+4ZWwCMy4fMAeAxzJgxI7NmzRpzbNasWZkxY8YUTQTAtsCVLQCYwNDQUFavXp3FixdnxYoVueWWW9Lf35+6uro85znPSXt7ey688MIccsghrm4B8AiubAHABLq7u9PX15fm5ubssMMOWbRoUV72spdl0aJF2WGHHdLc3Jz169enu7t7qkcFYBoSWwAwgZGHFS9YsGDc8yPHPdQYgPGILQCYQF1dXZKkt7d33PMjx0fWAcCWxBYATKCpqSn19fXp6OjI8PDwmHPDw8Pp6OhIQ0NDmpqapmhCAKYzG2QAwASqq6vT2tqa9vb2/7+9+w2yujzvBv5lj+wiGnZBdFcaqTDRQcCNVIshmUFDbZ1MCLXjNKThyeN0qkkLM+2M8iIZQUbGmb5ok7Z20DTSvkgkE4w0FYkxzcjMQ2fKn1GhqxiIjWvBP4sa3F0isLvd3eeFc07PEX6oz8PxrO7n8ybnd+7rxTVOZpbvuX/3defOO+/MwoUL09LSkoGBgezZsye7du3K3XffbTgGAKclbAHAGSxevDjLly/PD3/4w+zcubPyfalUyvLly7N48eIGdgfAWCZsAcAZ7NixI5s3b86nPvWpU3a2Nm/enLlz5wpcAJyWM1sAUKD6nq3169fn0ksvTUtLSy699NKsX78+ixYtyv3335/h4eFGtwrAGGRnCwAKlO/Z+sIXvpCvfOUr6enpqax1dHRk6dKl+fd///d0dXVlwYIFDewUgLFI2AKAAuX7sx544IF8+tOfztq1azNr1qx0d3dn06ZN2bhxY00dAFTzGiEAFGhra0uSXHnllbnnnnsyb968TJ48OfPmzcs999yTK6+8sqYOAKoJWwAAAHUgbAFAgd7e3iTJs88+mzVr1mT//v05fvx49u/fnzVr1uTZZ5+tqQOAas5sAUCBadOmJUluvfXWbN26NatWraqsdXR05NZbb80DDzxQqQOAana2AKBAZ2dnOjo6smPHjkyYMOGU9R07duTiiy9OZ2dnA7oDYKwTtgCgQKlUyvXXX5+DBw9mYGAgq1evzpYtW7J69eoMDAzk4MGDue6661IqlRrdKgBj0ITR0dHRRjcx1vX396e1tTV9fX2ZMmVKo9sB4AMyPDycFStWpLW1Nb29vTly5EhlraOjI62trenv78+DDz4ocAGME+8nGzizBQAFypcar127NnPmzElXV1eOHj2aadOmpbOzMwcOHMiqVatcagzAaXmNEAAKlC8rnjVr1mnXy9+71BiA07GzBQAFylMGf/SjH2Xr1q01rxG2t7fnC1/4Qk0dAFRzZus9cGYLYHwaHh7OzTffnN7e3jQ3N2dwcLCyVn6eOnVqHn74YWe2AMYJZ7YA4CwpB6zJkydn7ty5GRkZSVNTU1588cUMDg5mYGCgwR0CMFYJWwBQYN++fTl+/HgmTZqU3t7e7Nu3r2Z90qRJOX78ePbt25err766MU0CMGYZkAEABcrh6uTJk6ddL3//zhAGAImwBQCFqs9onXNO7csg1c/VdQBQ5jVCAChw6NChyudrrrkmX/nKVzJr1qx0d3fne9/7Xnbt2nVKHQCU2dkCgAJvvPHGWa0DYHyxswUABSZMmFD5vHfv3spOVpK0tLSctg4AyuxsAUCBBQsWJEmamppOuUultbU1TU1NNXUAUE3YAoACCxcuTJKMjIykt7c3S5YsyZ/92Z9lyZIlefPNNzMyMlJTBwDVvEYIAAWuuuqqtLW1pbe3N0NDQ9m+fXu2b99eU9PW1parrrqqMQ0CMKbZ2QKAAqVSKbfffnuS4tHvt99+e0ql0gfeGwBjn7AFAGewePHirF+/PhdccEHN99OnT8/69euzePHiBnUGwFg3YXR0dLTRTYx1/f39aW1tTV9f3ykHpAEYH4aHh9PV1ZWjR49m2rRp6ezstKMFMA69n2zgzBYAvAelUsnUQQDeF68RAgAA1IGwBQAAUAfCFgAAQB04swUA74EBGQC8X8IWALyLHTt25L777ktPT0/lu46OjqxcudLodwAKeY0QAM5gx44dWbduXWbPnp0NGzbksccey4YNGzJ79uysW7cuO3bsaHSLAIxR7tl6D9yzBTA+DQ8PZ8WKFZk9e3buueeeNDX9z2+UIyMjWbNmTbq7u/Pggw96pRBgnHg/2cDOFgAU6OrqSk9PT1asWFETtJKkqakpK1asyKuvvpqurq4GdQjAWCZsAUCBo0ePJklmzZp12vXy9+U6AKhmQAYAFJg2bVqSpLu7O3PmzDllGmF3d3dNHQBUE7YAoEBnZ2c6Ojpy7733pre3N0eOHKmstbe3p62tLRdffHE6Ozsb2CUAY5XXCAGgQKlUyvXXX5+DBw9mcHAwd9xxRx5++OHccccdGRwczMGDB3PdddcZjgHAaZlG+B6YRggwPpWnEZb/BlTfs3XxxRdnypQp6e/vN40QYBx5P9nAa4QAUKA8jXDt2rWZPXt2/uEf/iEvvfRSPv7xj+drX/taXnjhhaxatSpdXV1ZsGBBo9sFYIwRtgCgQHnK4Pbt2/Pnf/7nGR4eTpI8+eSTefTRR3PTTTfV1AFANWELAAqUpwxu2bIlU6dOzZ/8yZ9k0aJF2blzZ/7xH/8xW7ZsqakDgGrCFgAUmDNnTpJk4sSJ2bx5c5qbm5MkS5cuze/93u/l85//fIaGhip1AFBN2AKAAtu2bUuSDA0NZd26dVm4cGFaWloyMDCQPXv2ZGhoqFL3h3/4h41sFYAxSNgCgAKvvPJKkuT3f//3s23btuzcubOyViqVsmzZsmzdurVSBwDVhC0AKDBjxowkySOPPJJFixadsrO1devWmjoAqOaerffAPVsA49OJEyfyuc99LhMnTsyPf/zjypmtJBkcHKyc2frJT36Sc889t4GdAvBBeT/ZoOkD6gkAPnQOHDiQ5O0zW8uXL8+jjz6aN954I48++miWL19eObNVrgOAal4jBIAC5fuzbr755vzLv/xLvvnNb1bWSqVSbr755mzZssU9WwCclp0tAChQvj9ryZIl2bZtW2666aZcc801uemmm7Jt27YsWbKkpg4AqtnZAoACnZ2d6ejoyL333pu+vr709PQkSZ588sns2rUrra2tufjii9PZ2dngTgEYi+xsAUCBUqmU66+/PgcPHszAwEBWr16dLVu2ZPXq1RkYGMjBgwdz3XXXpVQqNbpVAMYg0wjfA9MIAcan4eHhrFixIq2trXnzzTfz2muvVdba29vT1taW/v7+PPjggwIXwDhhGiEAnAVdXV3p6enJ4sWL09RU+ydzwoQJWbx4cV599dV0dXU1qEMAxjJntgCgQHnK4MaNG7No0aKsXbs2s2bNSnd3dzZt2pSNGzfW1AFANTtbAFCgra0tSTJ//vzcc889mTdvXiZPnpx58+blnnvuyfz582vqAKCasAUAAFAHwhYAFOjt7U2SPPPMM1mzZk3279+f48ePZ//+/VmzZk2eeeaZmjoAqObMFgAUKF9WfNttt2Xr1q1ZtWpVZa2joyO33nprNm7c6FJjAE7LzhYAFChfarxjx45T1kZHR/Nv//ZvLjUGoJCwBQAFqi81HhwczB133JGHH344d9xxRwYHB11qDMAZudT4PXCpMcD4VH2pcV9fX3p6eiprF198caZMmeJSY4Bx5v1kA2e2AKBA+VLjtWvXZs6cOenq6srRo0czbdq0dHZ25sCBA1m1alW6urqyYMGCRrcLwBgjbAFAgfJlxbNmzUqpVDolUM2aNaumDgCqObMFAAXKUwa7u7tPu17+3jRCAE5H2AKAAuVphJs2bcrQ0FD27t2bJ554Inv37s3Q0FA2bdpkGiEAhbxGCAAFSqVSVq5cmXXr1mXp0qUZGBiorLW0tGRwcDB333234RgAnJadLQB4F0WDew30BeBM7GwBQIHh4eHcd999+fSnP5277747zz77bGUa4fz587Nu3brcf//9+cxnPmN3C4BT2NkCgALl0e8rVqzIxIkTs2DBgvzO7/xOFixYkIkTJ2bFihV59dVX09XV1ehWARiDhC0AKFA9+v10jH4H4Ey8RggABapHv5/uUmOj3wE4E2ELAAqUR7/fe++96e3tzZEjRypr7e3taWtrM/odgEJeIwSAAqVSKddff30OHjyY119/vWbt9ddfz8GDB3PdddcZjgHAaQlbAFBgeHg4jz/+eJKcEqjOOeftl0Mef/zxDA8Pf+C9ATD2CVsAUGDfvn3p7e3NzJkzc8EFF9SsTZs2LTNnzkxvb2/27dvXmAYBGNOELQAoUA5Rhw8fzuzZs7Nhw4Y89thj2bBhQ2bPnp3Dhw/X1AFANWELAAqMjIwkSa644orcddddee655/LAAw/kueeey1133ZUrrriipg4AqplGCAAFpkyZkiR5+eWX8/nPf77mbNa3v/3tnH/++TV1AFDNzhYAFCjfn9XX15empqZ8+ctfzoMPPpgvf/nLaWpqSl9fX00dAFSzswUABdra2mqev//97+f73/9+kmTixImFdQCQ2NkCgEIvvPBCkrcvMJ46dWrN2rRp09Le3l5TBwDV7GwBQIGenp4kyZEjR9LS0lKz1tvbm4GBgZo6AKhmZwsACsyYMeOs1gEwvghbAFBg6dKlSd4+n7Vly5asWrUqf/AHf5BVq1Zly5YtlXNb5ToAqOY1QgAocODAgSTJ0NBQli1bVnOf1v333195PnDgQBYsWNCQHgEYu+xsAUCBo0ePVj6/8+Li6ufqOgAoE7YAoEB5pPvMmTNz4YUX1qxdeOGFmTlzZk0dAFQTtgDgXRw6dCif+MQnsmHDhjz22GPZsGFDPvGJT+TQoUONbg2AMUzYAoAC7/X1QK8RAnA6BmQAQIHe3t4kybJly7J79+6sWrWqstbR0ZFly5Zl69atlToAqGZnCwAKlM9i/fznP8+ECRNOWf/5z39eUwcA1YQtACgwffr0JMnzzz+fgYGBrF69Olu2bMnq1aszMDCQ559/vqYOAKp5jRAACsybNy+lUimTJk1Kc3Nz/vqv/7qy1t7envPOOy8nT57MvHnzGtglAGOVnS0AKLB///4MDw/nrbfeOmUIxtGjR/PWW29leHg4+/fvb1CHAIxlwhYAFKgOWENDQzVr1c+mEQJwOl4jBIAC1YMvrr322nz84x/P4OBgmpub89JLL2X37t2n1AFAmbAFAAVGRkaSJOeee266u7sr4SpJLrroopx77rk5ceJEpQ4AqglbAFCgq6srSXLixIlMmjQpX/ziFzNjxoy88sor+dnPfpYTJ05U6n77t3+7ka0CMAYJWwBQoLxjdcEFF+TNN9/MQw89VFkrlUq54IIL8qtf/crOFgCnJWwBQIEpU6YkSX71q1/lU5/6VK699tq0tLRkYGAgu3fvzq5du2rqAKCaaYQAUOCdgy8uv/zyXH/99bn88svPWAcAiZ0tACjU399f+bx3797KTlaStLS0nLYOAMrsbAFAgfKO1WWXXXbK7tXUqVNz2WWX1dQBQDU7WwBQYPr06UmS559/Ps3NzTVrR48eTU9PT00dAFSzswUABTo7Oyu7VoODgzVr5ee2trZ0dnZ+0K0B8CFgZwsAzmBoaChJ0tramhtvvLFyz9ZPf/rT9PX1VdYB4J2ELQAosG/fvrz11luZOXNmBgYGau7Z6ujoSGtraw4dOpR9+/bl6quvbmCnAIxFXiMEgAL79u1Lknz2s5/NhAkTTln/7Gc/W1MHANXsbAHAu/jud7+bRYsWZe3atZk1a1a6u7uzadOmfPe73210awCMYXa2AKBAefDF+eefn/Xr12fevHmZPHly5s2bl/Xr1+f888+vqQOAana2AKBAU9Pbv0keO3Ysa9asybXXXpuWlpYMDAxk9+7dOXbsWE0dAFQTtgCgQG9vb+Xzrl27smvXrnetA4AyP8UBQIFp06ad1ToAxhc7WwBQYN68eWlqasrIyEgWLlyYSy65JIODg2lubs7hw4ezZ8+eNDU1Zd68eY1uFYAxSNgCgALPPPNMRkZGkrw93n3Pnj2Vtebm5iTJyMhInnnmGfdsAXAKrxECQIHq+7Peec9W9bN7tgA4HTtbAFCgvKs1d+7c/N3f/V2effbZHD16NNOmTcv8+fPzF3/xF3nuuecqdQBQTdgCgAJTpkxJkgwMDKRUKmXBggWVtZGRkZw8ebKmDgCqeY0QAAqUpwz+8pe/zJ133pn9+/fn+PHj2b9/f+6888688MILNXUAUM3OFgAUmD59euXz008/nZ07d1aeW1paTlsHAGXCFgAU6OzsTEdHR1pbW9PX15eenp7K2tSpU9Pa2pr+/v50dnY2sEsAxiphCwAKlEqlrFy5MuvWrcu1116bz3zmMxkYGEhLS0tefvnl7N69O3fffXdKpVKjWwVgDBK2AOAMFi9enOXLl+eHP/xhdu3aVfm+VCpl+fLlWbx4cQO7A2AsMyADAM5gx44d2bx58ym7V6VSKZs3b86OHTsa1BkAY52wBQAFhoeH861vfSujo6MZHBysWRscHMzo6Gj+5m/+JsPDww3qEICxTNgCgAL79u1Lb29vkqSpqfZPZvn5zTffzL59+z7gzgD4MHBmCwAKPPXUU5XPCxcuzLXXXpuWlpYMDAxk9+7dlTNcTz31VK6++upGtQnAGCVsAUCBAwcOJEna2trS3d1dMyCjvb09bW1t6e3trdQBQDVhCwAKlM9plV8lrHbkyJFT6gCgmjNbAFCgvb298rlUKmXJkiVZuXJllixZUjOdsLoOAMrsbAFAgVmzZlU+Dw8PZ/v27dm+ffsZ6wCgzM4WABR48cUXz2odAOOLsAUABU6cOHFW6wAYX4QtACgwbdq0JMnEiRNz0UUX1ay1t7dn4sSJNXUAUM2ZLQAo8LGPfSxJMjQ0lEsvvTRz5szJsWPH8rGPfSwnT56sTCQs1wFANWELAApUTxzcs2fPe6oDgDKvEQJAgauuuuqs1gEwvghbAFBg7ty5Z7UOgPFF2AKAAv/8z/98VusAGF+ELQAo8K//+q9ntQ6A8cWADAAo8MYbb9Q8X3PNNVmwYEH27t2bJ598srAOABJhCwAKjYyMVD5v27Yt559/fpJkxYoV+fWvf52lS5eeUgcAZcIWABQYHh6ufP6jP/qjfO5zn8uMGTPyyiuv5Cc/+clp6wCgTNgCgAKTJ0/O4OBgkuTYsWN56KGHCusA4J0MyACAAldcccVZrQNgfBG2AKDA17/+9bNaB8D4ImwBQIH//M//PKt1AIwvwhYAFHj66aeTJC0tLaddL39frgOAasIWABR47bXXkiRf+9rX8sgjj2T+/Pm58MILM3/+/DzyyCP56le/WlMHANVMIwSAAhdddFGS5Ec/+lF+8IMfVELV66+/nttuuy3Nzc01dQBQzc4WABT4rd/6rSTJ4cOHT9m9eu211/LSSy/V1AFANWELAApceeWVZ7UOgPFF2AKAAv/xH/9xVusAGF+ELQAo8PjjjydJJk6cmOnTp9esXXjhhZk4cWJNHQBUE7YAoMAvf/nLJMlv/uZv5s0336xZO3r0aGbOnFlTBwDVTCMEgALnnXdekrcvLZ46dWp+93d/N7/xG7+Rl19+OT/72c8qIatcBwDVhC0AKLBo0aLs378/SdLf35+HHnqoslYqlWrqAOCdvEYIAAXOOed/fpMcHh6uWat+rq4DgDJhCwAK9PT0nNU6AMYXYQsACrxzN+v/tw6A8cV7DwBQ4Ne//nXl89atW/PTn/40r7zySmbMmJEbb7wxy5YtO6UOAMrsbAFAgRdffLHy+ZZbbsmkSZOyYsWKTJo0Kbfccstp6wCgzM4W8KF38uTJHDp0qNFt8BHU1PT2b5KTJ09OX19fvvnNb9asTZ48OcePH09TU1N+8YtfNKpNPsJmzpyZSZMmNboN4P/RhNHR0dFGNzHW9ff3p7W1NX19fZkyZUqj2wHe4Re/+EW++tWvNroNgLPuO9/5Ti6//PJGtwFUeT/ZwM4W8KE3c+bMfOc732l0G3wE/fd//3dWrlyZJJkwYUKqf5+sfr7vvvuMf6cuZs6c2egWgP8P/jIAH3qTJk3yyy9186UvfSk/+MEP8s4XQcrPX/rSlzJ37txGtAbAGCdsAcAZ/Omf/mmSZPPmzafsbC1fvryyDgDv5MzWe+DMFgCDg4PZuHFjHnrooXzxi1/Mrbfemubm5ka3BcAH7P1kA6PfAeA9aG5uzg033JAkueGGGwQtAN6VsAUAAFAHwhYAAEAdCFsAAAB1IGwBAADUgbAFAABQB8IWAABAHQhbAAAAdSBsAQAA1IGwBQAAUAfCFgAAQB0IWwAAAHUgbAEAANSBsAUAAFAHwhYAAEAdCFsAAAB1IGwBAADUgbAFAABQB8IWAABAHQhbAAAAdSBsAQAA1IGwBQAAUAfCFgAAQB0IWwAAAHUgbAEAANSBsAUAAFAHwhYAAEAdCFsAAAB1IGwBAADUgbAFAABQB8IWAABAHQhbAAAAdSBsAQAA1IGwBQAAUAfCFgAAQB0IWwAAAHUgbAEAANSBsAUAAFAHwhYAAEAdnNPoBvhoO3LkSPr6+hrdBsBZ8V//9V81/wvwUdDa2pr29vZGt/GRNGF0dHS00U2Mdf39/WltbU1fX1+mTJnS6HY+NI4cOZL/9ZX/naHBgUa3AgBAgYnNLXnwe98VuN6j95MN7GxRN319fRkaHMiJ2ddlZFJro9sBAOAdmk72JS/8n/T19QlbdSBsUXcjk1ozct70RrcBAAAfKAMyAAAA6kDYAgAAqANhCwAAoA6ELQAAgDoQtgAAAOpA2AIAAKgDYQsAAKAOhC0AAIA6ELYAAADqQNgCAACoA2ELAACgDoQtAACAOhC2AAAA6kDYAgAAqANhCwAAoA6ELQAAgDoQtgAAAOpA2AIAAKgDYQsAAKAOhC0AAIA6ELYAAADq4JxGN8BHX9OJ3ka3AADAafh3Wn0JW9Tdud07Gt0CAAB84IQt6u7ErMUZObet0W0AAPAOTSd6/TBeR+MqbG3YsCF/9Vd/lZ6ennzyk5/M3//932fhwoWNbusjb+TctoycN73RbQAAwAdq3AzI2Lx5c26//fasW7cuTz/9dD75yU/mxhtvzGuvvdbo1gAAgI+gcbOz9a1vfSu33XZb/viP/zhJ8u1vfzs//vGP80//9E/5+te/3uDuPtqaTvY1ugUAAE7Dv9Pqa1yErcHBwTz11FP5xje+UfmuqakpN9xwQ3bu3HlK/cDAQAYGBirPfX1v/5+wv7+//s1+hDQ1NWVCUykTf/FEo1sBAKDAhOaWNDU1+bfue1T+7zQ6OvquteMibL3xxhsZHh5Oe3t7zfft7e05cODAKfV/+Zd/mbvvvvuU7y+55JK69QgAAI1y2WWXNbqFD51jx46ltbX1jDXjImy9X9/4xjdy++23V55HRkZy9OjRXHDBBZkwYUIDOwOgkfr7+3PJJZfk8OHDmTJlSqPbAaABRkdHc+zYscyYMeNda8dF2Jo+fXpKpVKOHDlS8/2RI0fS0dFxSn1LS0taWlpqvmtra6tniwB8iEyZMkXYAhjH3m1Hq2xcTCNsbm7O1VdfnSee+J+zQyMjI3niiSeyaNGiBnYGAAB8VI2Lna0kuf3223PLLbfkmmuuycKFC/O3f/u3eeuttyrTCQEAAM6mcRO2li9fntdffz133XVXenp6ctVVV+Xxxx8/ZWgGABRpaWnJunXrTnnVHABOZ8Loe5lZCAAAwPsyLs5sAQAAfNCELQAAgDoQtgAAAOpA2AIAAKgDYQsAAKAOhC0AAIA6ELYAAADqQNgCAACoA2ELAACgDoQtAACAOhC2AAAA6uD/Ak7CKot59TFjAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box plot to check for outliers \n",
    "\n",
    "plt.figure(figsize= (10,20))\n",
    "sns.boxplot(data=balanced_dataset['Amount'])\n",
    "plt.ylim([0,5000])\n",
    "plt.title(\"Box plot to detect Outliers\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standard Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "x = balanced_dataset.drop(columns=['Class']) # the rest of dataset\n",
    "y = balanced_dataset['Class'] # target column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create object from StandardScaler\n",
    "\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.692570</td>\n",
       "      <td>0.684420</td>\n",
       "      <td>-0.681072</td>\n",
       "      <td>0.661313</td>\n",
       "      <td>-0.956476</td>\n",
       "      <td>0.132650</td>\n",
       "      <td>0.162292</td>\n",
       "      <td>0.345529</td>\n",
       "      <td>-0.037786</td>\n",
       "      <td>0.116353</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.085553</td>\n",
       "      <td>-0.132785</td>\n",
       "      <td>-0.130266</td>\n",
       "      <td>0.156352</td>\n",
       "      <td>0.470278</td>\n",
       "      <td>0.074176</td>\n",
       "      <td>-1.042174</td>\n",
       "      <td>-0.050446</td>\n",
       "      <td>-0.045423</td>\n",
       "      <td>-0.334524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.271345</td>\n",
       "      <td>0.157499</td>\n",
       "      <td>-0.634418</td>\n",
       "      <td>-0.055032</td>\n",
       "      <td>1.255515</td>\n",
       "      <td>-0.425593</td>\n",
       "      <td>0.997861</td>\n",
       "      <td>0.783638</td>\n",
       "      <td>0.041772</td>\n",
       "      <td>-1.102214</td>\n",
       "      <td>...</td>\n",
       "      <td>2.073614</td>\n",
       "      <td>0.309792</td>\n",
       "      <td>0.521508</td>\n",
       "      <td>1.952374</td>\n",
       "      <td>-0.022174</td>\n",
       "      <td>0.742493</td>\n",
       "      <td>1.105254</td>\n",
       "      <td>-0.202731</td>\n",
       "      <td>-0.095967</td>\n",
       "      <td>3.822321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.752553</td>\n",
       "      <td>-0.221098</td>\n",
       "      <td>0.269237</td>\n",
       "      <td>0.306200</td>\n",
       "      <td>-1.026833</td>\n",
       "      <td>0.280679</td>\n",
       "      <td>0.148012</td>\n",
       "      <td>0.316023</td>\n",
       "      <td>-0.218214</td>\n",
       "      <td>0.189374</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.847338</td>\n",
       "      <td>0.800190</td>\n",
       "      <td>-0.182205</td>\n",
       "      <td>-0.258031</td>\n",
       "      <td>-0.886093</td>\n",
       "      <td>1.945438</td>\n",
       "      <td>-0.037087</td>\n",
       "      <td>-0.310553</td>\n",
       "      <td>-0.401827</td>\n",
       "      <td>-0.344800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.011078</td>\n",
       "      <td>0.493430</td>\n",
       "      <td>-0.263709</td>\n",
       "      <td>-0.186122</td>\n",
       "      <td>0.256494</td>\n",
       "      <td>-0.080800</td>\n",
       "      <td>-0.219538</td>\n",
       "      <td>0.294556</td>\n",
       "      <td>0.056499</td>\n",
       "      <td>0.080358</td>\n",
       "      <td>...</td>\n",
       "      <td>0.902495</td>\n",
       "      <td>0.190881</td>\n",
       "      <td>0.884429</td>\n",
       "      <td>0.873441</td>\n",
       "      <td>1.715494</td>\n",
       "      <td>-0.744589</td>\n",
       "      <td>0.373816</td>\n",
       "      <td>0.105638</td>\n",
       "      <td>-0.138772</td>\n",
       "      <td>1.045192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.648476</td>\n",
       "      <td>0.821269</td>\n",
       "      <td>-0.478579</td>\n",
       "      <td>0.173912</td>\n",
       "      <td>-0.449114</td>\n",
       "      <td>0.672798</td>\n",
       "      <td>0.199015</td>\n",
       "      <td>0.594452</td>\n",
       "      <td>-0.115222</td>\n",
       "      <td>0.412175</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.269011</td>\n",
       "      <td>-0.092216</td>\n",
       "      <td>0.283036</td>\n",
       "      <td>-0.184909</td>\n",
       "      <td>-2.491057</td>\n",
       "      <td>0.834222</td>\n",
       "      <td>-0.909566</td>\n",
       "      <td>-0.111027</td>\n",
       "      <td>-0.250955</td>\n",
       "      <td>-0.222291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>-0.958669</td>\n",
       "      <td>-1.086204</td>\n",
       "      <td>1.169410</td>\n",
       "      <td>-1.313648</td>\n",
       "      <td>1.398395</td>\n",
       "      <td>-1.559302</td>\n",
       "      <td>-1.706891</td>\n",
       "      <td>-1.639955</td>\n",
       "      <td>1.092501</td>\n",
       "      <td>-2.138221</td>\n",
       "      <td>...</td>\n",
       "      <td>0.336225</td>\n",
       "      <td>0.646830</td>\n",
       "      <td>-0.243375</td>\n",
       "      <td>0.019179</td>\n",
       "      <td>0.822008</td>\n",
       "      <td>-0.094248</td>\n",
       "      <td>1.066193</td>\n",
       "      <td>0.700618</td>\n",
       "      <td>0.074792</td>\n",
       "      <td>-0.296914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>1.687507</td>\n",
       "      <td>0.200748</td>\n",
       "      <td>0.246465</td>\n",
       "      <td>-0.137852</td>\n",
       "      <td>0.042447</td>\n",
       "      <td>-0.030150</td>\n",
       "      <td>0.421334</td>\n",
       "      <td>0.311268</td>\n",
       "      <td>-0.493573</td>\n",
       "      <td>-0.318455</td>\n",
       "      <td>...</td>\n",
       "      <td>0.454301</td>\n",
       "      <td>-0.443205</td>\n",
       "      <td>0.426612</td>\n",
       "      <td>0.131141</td>\n",
       "      <td>0.758089</td>\n",
       "      <td>0.088747</td>\n",
       "      <td>-0.901007</td>\n",
       "      <td>0.372210</td>\n",
       "      <td>0.138400</td>\n",
       "      <td>1.062590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>-1.052193</td>\n",
       "      <td>0.348786</td>\n",
       "      <td>-0.310831</td>\n",
       "      <td>0.689597</td>\n",
       "      <td>-0.375999</td>\n",
       "      <td>0.225143</td>\n",
       "      <td>0.400298</td>\n",
       "      <td>0.528284</td>\n",
       "      <td>0.070561</td>\n",
       "      <td>0.196890</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.146820</td>\n",
       "      <td>-0.050859</td>\n",
       "      <td>0.271351</td>\n",
       "      <td>0.292341</td>\n",
       "      <td>0.404398</td>\n",
       "      <td>-0.684244</td>\n",
       "      <td>-0.918525</td>\n",
       "      <td>-0.135372</td>\n",
       "      <td>-0.116456</td>\n",
       "      <td>0.054497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>-1.775112</td>\n",
       "      <td>0.655427</td>\n",
       "      <td>-0.461149</td>\n",
       "      <td>0.644174</td>\n",
       "      <td>-0.299376</td>\n",
       "      <td>0.335236</td>\n",
       "      <td>0.375653</td>\n",
       "      <td>0.466811</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.535348</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.294210</td>\n",
       "      <td>-0.146022</td>\n",
       "      <td>0.114588</td>\n",
       "      <td>-0.030088</td>\n",
       "      <td>0.536713</td>\n",
       "      <td>0.871333</td>\n",
       "      <td>-0.751752</td>\n",
       "      <td>-0.030399</td>\n",
       "      <td>-0.057691</td>\n",
       "      <td>-0.421683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>0.744098</td>\n",
       "      <td>0.083760</td>\n",
       "      <td>-1.089501</td>\n",
       "      <td>0.665974</td>\n",
       "      <td>-0.952129</td>\n",
       "      <td>-0.045753</td>\n",
       "      <td>0.795325</td>\n",
       "      <td>0.879428</td>\n",
       "      <td>-0.067325</td>\n",
       "      <td>0.609472</td>\n",
       "      <td>...</td>\n",
       "      <td>1.559751</td>\n",
       "      <td>-0.033215</td>\n",
       "      <td>-0.598643</td>\n",
       "      <td>1.444275</td>\n",
       "      <td>0.970285</td>\n",
       "      <td>0.452280</td>\n",
       "      <td>1.216325</td>\n",
       "      <td>-0.330961</td>\n",
       "      <td>0.217733</td>\n",
       "      <td>2.772183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>984 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Time        V1        V2        V3        V4        V5        V6  \\\n",
       "0   -0.692570  0.684420 -0.681072  0.661313 -0.956476  0.132650  0.162292   \n",
       "1    1.271345  0.157499 -0.634418 -0.055032  1.255515 -0.425593  0.997861   \n",
       "2    0.752553 -0.221098  0.269237  0.306200 -1.026833  0.280679  0.148012   \n",
       "3    1.011078  0.493430 -0.263709 -0.186122  0.256494 -0.080800 -0.219538   \n",
       "4    0.648476  0.821269 -0.478579  0.173912 -0.449114  0.672798  0.199015   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "979 -0.958669 -1.086204  1.169410 -1.313648  1.398395 -1.559302 -1.706891   \n",
       "980  1.687507  0.200748  0.246465 -0.137852  0.042447 -0.030150  0.421334   \n",
       "981 -1.052193  0.348786 -0.310831  0.689597 -0.375999  0.225143  0.400298   \n",
       "982 -1.775112  0.655427 -0.461149  0.644174 -0.299376  0.335236  0.375653   \n",
       "983  0.744098  0.083760 -1.089501  0.665974 -0.952129 -0.045753  0.795325   \n",
       "\n",
       "           V7        V8        V9  ...       V20       V21       V22  \\\n",
       "0    0.345529 -0.037786  0.116353  ... -0.085553 -0.132785 -0.130266   \n",
       "1    0.783638  0.041772 -1.102214  ...  2.073614  0.309792  0.521508   \n",
       "2    0.316023 -0.218214  0.189374  ... -0.847338  0.800190 -0.182205   \n",
       "3    0.294556  0.056499  0.080358  ...  0.902495  0.190881  0.884429   \n",
       "4    0.594452 -0.115222  0.412175  ... -0.269011 -0.092216  0.283036   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "979 -1.639955  1.092501 -2.138221  ...  0.336225  0.646830 -0.243375   \n",
       "980  0.311268 -0.493573 -0.318455  ...  0.454301 -0.443205  0.426612   \n",
       "981  0.528284  0.070561  0.196890  ... -0.146820 -0.050859  0.271351   \n",
       "982  0.466811 -0.036385  0.535348  ... -0.294210 -0.146022  0.114588   \n",
       "983  0.879428 -0.067325  0.609472  ...  1.559751 -0.033215 -0.598643   \n",
       "\n",
       "          V23       V24       V25       V26       V27       V28    Amount  \n",
       "0    0.156352  0.470278  0.074176 -1.042174 -0.050446 -0.045423 -0.334524  \n",
       "1    1.952374 -0.022174  0.742493  1.105254 -0.202731 -0.095967  3.822321  \n",
       "2   -0.258031 -0.886093  1.945438 -0.037087 -0.310553 -0.401827 -0.344800  \n",
       "3    0.873441  1.715494 -0.744589  0.373816  0.105638 -0.138772  1.045192  \n",
       "4   -0.184909 -2.491057  0.834222 -0.909566 -0.111027 -0.250955 -0.222291  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "979  0.019179  0.822008 -0.094248  1.066193  0.700618  0.074792 -0.296914  \n",
       "980  0.131141  0.758089  0.088747 -0.901007  0.372210  0.138400  1.062590  \n",
       "981  0.292341  0.404398 -0.684244 -0.918525 -0.135372 -0.116456  0.054497  \n",
       "982 -0.030088  0.536713  0.871333 -0.751752 -0.030399 -0.057691 -0.421683  \n",
       "983  1.444275  0.970285  0.452280  1.216325 -0.330961  0.217733  2.772183  \n",
       "\n",
       "[984 rows x 30 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scaling the data\n",
    "\n",
    "x_scale = scaler.fit_transform(x)\n",
    "\n",
    "x_scale_df = pd.DataFrame(x_scale,columns=x.columns)\n",
    "\n",
    "x_scale_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.840000e+02</td>\n",
       "      <td>9.840000e+02</td>\n",
       "      <td>9.840000e+02</td>\n",
       "      <td>9.840000e+02</td>\n",
       "      <td>9.840000e+02</td>\n",
       "      <td>9.840000e+02</td>\n",
       "      <td>9.840000e+02</td>\n",
       "      <td>9.840000e+02</td>\n",
       "      <td>9.840000e+02</td>\n",
       "      <td>9.840000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>9.840000e+02</td>\n",
       "      <td>9.840000e+02</td>\n",
       "      <td>9.840000e+02</td>\n",
       "      <td>9.840000e+02</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>9.840000e+02</td>\n",
       "      <td>9.840000e+02</td>\n",
       "      <td>9.840000e+02</td>\n",
       "      <td>9.840000e+02</td>\n",
       "      <td>9.840000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-3.610481e-17</td>\n",
       "      <td>5.776770e-17</td>\n",
       "      <td>-2.888385e-17</td>\n",
       "      <td>-7.220963e-18</td>\n",
       "      <td>-1.155354e-16</td>\n",
       "      <td>2.888385e-17</td>\n",
       "      <td>-2.888385e-17</td>\n",
       "      <td>-2.166289e-17</td>\n",
       "      <td>5.415722e-18</td>\n",
       "      <td>-9.026203e-17</td>\n",
       "      <td>...</td>\n",
       "      <td>7.220963e-18</td>\n",
       "      <td>9.026203e-18</td>\n",
       "      <td>9.026203e-18</td>\n",
       "      <td>3.159171e-18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.624717e-17</td>\n",
       "      <td>1.083144e-17</td>\n",
       "      <td>5.415722e-18</td>\n",
       "      <td>1.083144e-17</td>\n",
       "      <td>-5.054674e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000509e+00</td>\n",
       "      <td>1.000509e+00</td>\n",
       "      <td>1.000509e+00</td>\n",
       "      <td>1.000509e+00</td>\n",
       "      <td>1.000509e+00</td>\n",
       "      <td>1.000509e+00</td>\n",
       "      <td>1.000509e+00</td>\n",
       "      <td>1.000509e+00</td>\n",
       "      <td>1.000509e+00</td>\n",
       "      <td>1.000509e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000509e+00</td>\n",
       "      <td>1.000509e+00</td>\n",
       "      <td>1.000509e+00</td>\n",
       "      <td>1.000509e+00</td>\n",
       "      <td>1.000509</td>\n",
       "      <td>1.000509e+00</td>\n",
       "      <td>1.000509e+00</td>\n",
       "      <td>1.000509e+00</td>\n",
       "      <td>1.000509e+00</td>\n",
       "      <td>1.000509e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.814573e+00</td>\n",
       "      <td>-5.113626e+00</td>\n",
       "      <td>-3.439183e+00</td>\n",
       "      <td>-4.425752e+00</td>\n",
       "      <td>-1.783834e+00</td>\n",
       "      <td>-4.857564e+00</td>\n",
       "      <td>-3.202666e+00</td>\n",
       "      <td>-6.931987e+00</td>\n",
       "      <td>-8.426358e+00</td>\n",
       "      <td>-5.255168e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.614217e+00</td>\n",
       "      <td>-8.244725e+00</td>\n",
       "      <td>-7.564317e+00</td>\n",
       "      <td>-1.657994e+01</td>\n",
       "      <td>-3.546395</td>\n",
       "      <td>-7.242691e+00</td>\n",
       "      <td>-2.852243e+00</td>\n",
       "      <td>-7.133667e+00</td>\n",
       "      <td>-6.685599e+00</td>\n",
       "      <td>-4.259477e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-8.465722e-01</td>\n",
       "      <td>-8.430701e-02</td>\n",
       "      <td>-5.389634e-01</td>\n",
       "      <td>-2.607280e-01</td>\n",
       "      <td>-7.423674e-01</td>\n",
       "      <td>-4.692982e-02</td>\n",
       "      <td>-4.969575e-01</td>\n",
       "      <td>-6.007330e-02</td>\n",
       "      <td>-9.649578e-02</td>\n",
       "      <td>-4.499786e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.258517e-01</td>\n",
       "      <td>-1.966413e-01</td>\n",
       "      <td>-4.676870e-01</td>\n",
       "      <td>-1.940722e-01</td>\n",
       "      <td>-0.644511</td>\n",
       "      <td>-5.073803e-01</td>\n",
       "      <td>-6.616442e-01</td>\n",
       "      <td>-1.352862e-01</td>\n",
       "      <td>-2.124736e-01</td>\n",
       "      <td>-4.195621e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-1.708728e-01</td>\n",
       "      <td>2.895100e-01</td>\n",
       "      <td>-2.488108e-01</td>\n",
       "      <td>3.423867e-01</td>\n",
       "      <td>-3.026934e-01</td>\n",
       "      <td>2.621727e-01</td>\n",
       "      <td>2.854134e-02</td>\n",
       "      <td>3.594579e-01</td>\n",
       "      <td>-2.891101e-02</td>\n",
       "      <td>2.732425e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.308281e-01</td>\n",
       "      <td>-7.583642e-02</td>\n",
       "      <td>1.988724e-02</td>\n",
       "      <td>-2.138524e-02</td>\n",
       "      <td>0.089352</td>\n",
       "      <td>4.654507e-02</td>\n",
       "      <td>-1.001242e-01</td>\n",
       "      <td>-7.866057e-03</td>\n",
       "      <td>1.020775e-02</td>\n",
       "      <td>-3.552903e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.548856e-01</td>\n",
       "      <td>6.168477e-01</td>\n",
       "      <td>2.609558e-01</td>\n",
       "      <td>6.234861e-01</td>\n",
       "      <td>6.017774e-01</td>\n",
       "      <td>5.017606e-01</td>\n",
       "      <td>4.200007e-01</td>\n",
       "      <td>5.200127e-01</td>\n",
       "      <td>1.237917e-01</td>\n",
       "      <td>6.072266e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.190464e-01</td>\n",
       "      <td>1.029309e-01</td>\n",
       "      <td>4.889016e-01</td>\n",
       "      <td>1.705584e-01</td>\n",
       "      <td>0.741031</td>\n",
       "      <td>5.827751e-01</td>\n",
       "      <td>6.091879e-01</td>\n",
       "      <td>3.539436e-01</td>\n",
       "      <td>4.117415e-01</td>\n",
       "      <td>4.270674e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.796849e+00</td>\n",
       "      <td>8.781958e-01</td>\n",
       "      <td>5.546890e+00</td>\n",
       "      <td>1.046412e+00</td>\n",
       "      <td>3.104150e+00</td>\n",
       "      <td>2.992384e+00</td>\n",
       "      <td>4.449436e+00</td>\n",
       "      <td>2.482606e+00</td>\n",
       "      <td>4.025538e+00</td>\n",
       "      <td>3.568416e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>9.947565e+00</td>\n",
       "      <td>9.545332e+00</td>\n",
       "      <td>7.111738e+00</td>\n",
       "      <td>4.722678e+00</td>\n",
       "      <td>2.078802</td>\n",
       "      <td>3.248220e+00</td>\n",
       "      <td>5.732686e+00</td>\n",
       "      <td>2.898048e+00</td>\n",
       "      <td>5.832242e+00</td>\n",
       "      <td>1.023449e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Time            V1            V2            V3            V4  \\\n",
       "count  9.840000e+02  9.840000e+02  9.840000e+02  9.840000e+02  9.840000e+02   \n",
       "mean  -3.610481e-17  5.776770e-17 -2.888385e-17 -7.220963e-18 -1.155354e-16   \n",
       "std    1.000509e+00  1.000509e+00  1.000509e+00  1.000509e+00  1.000509e+00   \n",
       "min   -1.814573e+00 -5.113626e+00 -3.439183e+00 -4.425752e+00 -1.783834e+00   \n",
       "25%   -8.465722e-01 -8.430701e-02 -5.389634e-01 -2.607280e-01 -7.423674e-01   \n",
       "50%   -1.708728e-01  2.895100e-01 -2.488108e-01  3.423867e-01 -3.026934e-01   \n",
       "75%    9.548856e-01  6.168477e-01  2.609558e-01  6.234861e-01  6.017774e-01   \n",
       "max    1.796849e+00  8.781958e-01  5.546890e+00  1.046412e+00  3.104150e+00   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  9.840000e+02  9.840000e+02  9.840000e+02  9.840000e+02  9.840000e+02   \n",
       "mean   2.888385e-17 -2.888385e-17 -2.166289e-17  5.415722e-18 -9.026203e-17   \n",
       "std    1.000509e+00  1.000509e+00  1.000509e+00  1.000509e+00  1.000509e+00   \n",
       "min   -4.857564e+00 -3.202666e+00 -6.931987e+00 -8.426358e+00 -5.255168e+00   \n",
       "25%   -4.692982e-02 -4.969575e-01 -6.007330e-02 -9.649578e-02 -4.499786e-01   \n",
       "50%    2.621727e-01  2.854134e-02  3.594579e-01 -2.891101e-02  2.732425e-01   \n",
       "75%    5.017606e-01  4.200007e-01  5.200127e-01  1.237917e-01  6.072266e-01   \n",
       "max    2.992384e+00  4.449436e+00  2.482606e+00  4.025538e+00  3.568416e+00   \n",
       "\n",
       "       ...           V20           V21           V22           V23  \\\n",
       "count  ...  9.840000e+02  9.840000e+02  9.840000e+02  9.840000e+02   \n",
       "mean   ...  7.220963e-18  9.026203e-18  9.026203e-18  3.159171e-18   \n",
       "std    ...  1.000509e+00  1.000509e+00  1.000509e+00  1.000509e+00   \n",
       "min    ... -5.614217e+00 -8.244725e+00 -7.564317e+00 -1.657994e+01   \n",
       "25%    ... -3.258517e-01 -1.966413e-01 -4.676870e-01 -1.940722e-01   \n",
       "50%    ... -1.308281e-01 -7.583642e-02  1.988724e-02 -2.138524e-02   \n",
       "75%    ...  2.190464e-01  1.029309e-01  4.889016e-01  1.705584e-01   \n",
       "max    ...  9.947565e+00  9.545332e+00  7.111738e+00  4.722678e+00   \n",
       "\n",
       "              V24           V25           V26           V27           V28  \\\n",
       "count  984.000000  9.840000e+02  9.840000e+02  9.840000e+02  9.840000e+02   \n",
       "mean     0.000000  1.624717e-17  1.083144e-17  5.415722e-18  1.083144e-17   \n",
       "std      1.000509  1.000509e+00  1.000509e+00  1.000509e+00  1.000509e+00   \n",
       "min     -3.546395 -7.242691e+00 -2.852243e+00 -7.133667e+00 -6.685599e+00   \n",
       "25%     -0.644511 -5.073803e-01 -6.616442e-01 -1.352862e-01 -2.124736e-01   \n",
       "50%      0.089352  4.654507e-02 -1.001242e-01 -7.866057e-03  1.020775e-02   \n",
       "75%      0.741031  5.827751e-01  6.091879e-01  3.539436e-01  4.117415e-01   \n",
       "max      2.078802  3.248220e+00  5.732686e+00  2.898048e+00  5.832242e+00   \n",
       "\n",
       "             Amount  \n",
       "count  9.840000e+02  \n",
       "mean  -5.054674e-17  \n",
       "std    1.000509e+00  \n",
       "min   -4.259477e-01  \n",
       "25%   -4.195621e-01  \n",
       "50%   -3.552903e-01  \n",
       "75%    4.270674e-04  \n",
       "max    1.023449e+01  \n",
       "\n",
       "[8 rows x 30 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_scale_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class</th>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>984 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Class\n",
       "0        0\n",
       "1        1\n",
       "2        0\n",
       "3        1\n",
       "4        0\n",
       "..     ...\n",
       "979      1\n",
       "980      1\n",
       "981      0\n",
       "982      0\n",
       "983      0\n",
       "\n",
       "[984 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_datafram = pd.DataFrame(balanced_dataset['Class'])\n",
    "y_datafram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = x_scale_df\n",
    "df2 = y_datafram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.reset_index(drop=True, inplace=True)\n",
    "df2.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.692570</td>\n",
       "      <td>0.684420</td>\n",
       "      <td>-0.681072</td>\n",
       "      <td>0.661313</td>\n",
       "      <td>-0.956476</td>\n",
       "      <td>0.132650</td>\n",
       "      <td>0.162292</td>\n",
       "      <td>0.345529</td>\n",
       "      <td>-0.037786</td>\n",
       "      <td>0.116353</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.132785</td>\n",
       "      <td>-0.130266</td>\n",
       "      <td>0.156352</td>\n",
       "      <td>0.470278</td>\n",
       "      <td>0.074176</td>\n",
       "      <td>-1.042174</td>\n",
       "      <td>-0.050446</td>\n",
       "      <td>-0.045423</td>\n",
       "      <td>-0.334524</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.271345</td>\n",
       "      <td>0.157499</td>\n",
       "      <td>-0.634418</td>\n",
       "      <td>-0.055032</td>\n",
       "      <td>1.255515</td>\n",
       "      <td>-0.425593</td>\n",
       "      <td>0.997861</td>\n",
       "      <td>0.783638</td>\n",
       "      <td>0.041772</td>\n",
       "      <td>-1.102214</td>\n",
       "      <td>...</td>\n",
       "      <td>0.309792</td>\n",
       "      <td>0.521508</td>\n",
       "      <td>1.952374</td>\n",
       "      <td>-0.022174</td>\n",
       "      <td>0.742493</td>\n",
       "      <td>1.105254</td>\n",
       "      <td>-0.202731</td>\n",
       "      <td>-0.095967</td>\n",
       "      <td>3.822321</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.752553</td>\n",
       "      <td>-0.221098</td>\n",
       "      <td>0.269237</td>\n",
       "      <td>0.306200</td>\n",
       "      <td>-1.026833</td>\n",
       "      <td>0.280679</td>\n",
       "      <td>0.148012</td>\n",
       "      <td>0.316023</td>\n",
       "      <td>-0.218214</td>\n",
       "      <td>0.189374</td>\n",
       "      <td>...</td>\n",
       "      <td>0.800190</td>\n",
       "      <td>-0.182205</td>\n",
       "      <td>-0.258031</td>\n",
       "      <td>-0.886093</td>\n",
       "      <td>1.945438</td>\n",
       "      <td>-0.037087</td>\n",
       "      <td>-0.310553</td>\n",
       "      <td>-0.401827</td>\n",
       "      <td>-0.344800</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.011078</td>\n",
       "      <td>0.493430</td>\n",
       "      <td>-0.263709</td>\n",
       "      <td>-0.186122</td>\n",
       "      <td>0.256494</td>\n",
       "      <td>-0.080800</td>\n",
       "      <td>-0.219538</td>\n",
       "      <td>0.294556</td>\n",
       "      <td>0.056499</td>\n",
       "      <td>0.080358</td>\n",
       "      <td>...</td>\n",
       "      <td>0.190881</td>\n",
       "      <td>0.884429</td>\n",
       "      <td>0.873441</td>\n",
       "      <td>1.715494</td>\n",
       "      <td>-0.744589</td>\n",
       "      <td>0.373816</td>\n",
       "      <td>0.105638</td>\n",
       "      <td>-0.138772</td>\n",
       "      <td>1.045192</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.648476</td>\n",
       "      <td>0.821269</td>\n",
       "      <td>-0.478579</td>\n",
       "      <td>0.173912</td>\n",
       "      <td>-0.449114</td>\n",
       "      <td>0.672798</td>\n",
       "      <td>0.199015</td>\n",
       "      <td>0.594452</td>\n",
       "      <td>-0.115222</td>\n",
       "      <td>0.412175</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.092216</td>\n",
       "      <td>0.283036</td>\n",
       "      <td>-0.184909</td>\n",
       "      <td>-2.491057</td>\n",
       "      <td>0.834222</td>\n",
       "      <td>-0.909566</td>\n",
       "      <td>-0.111027</td>\n",
       "      <td>-0.250955</td>\n",
       "      <td>-0.222291</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>-0.958669</td>\n",
       "      <td>-1.086204</td>\n",
       "      <td>1.169410</td>\n",
       "      <td>-1.313648</td>\n",
       "      <td>1.398395</td>\n",
       "      <td>-1.559302</td>\n",
       "      <td>-1.706891</td>\n",
       "      <td>-1.639955</td>\n",
       "      <td>1.092501</td>\n",
       "      <td>-2.138221</td>\n",
       "      <td>...</td>\n",
       "      <td>0.646830</td>\n",
       "      <td>-0.243375</td>\n",
       "      <td>0.019179</td>\n",
       "      <td>0.822008</td>\n",
       "      <td>-0.094248</td>\n",
       "      <td>1.066193</td>\n",
       "      <td>0.700618</td>\n",
       "      <td>0.074792</td>\n",
       "      <td>-0.296914</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>1.687507</td>\n",
       "      <td>0.200748</td>\n",
       "      <td>0.246465</td>\n",
       "      <td>-0.137852</td>\n",
       "      <td>0.042447</td>\n",
       "      <td>-0.030150</td>\n",
       "      <td>0.421334</td>\n",
       "      <td>0.311268</td>\n",
       "      <td>-0.493573</td>\n",
       "      <td>-0.318455</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.443205</td>\n",
       "      <td>0.426612</td>\n",
       "      <td>0.131141</td>\n",
       "      <td>0.758089</td>\n",
       "      <td>0.088747</td>\n",
       "      <td>-0.901007</td>\n",
       "      <td>0.372210</td>\n",
       "      <td>0.138400</td>\n",
       "      <td>1.062590</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>-1.052193</td>\n",
       "      <td>0.348786</td>\n",
       "      <td>-0.310831</td>\n",
       "      <td>0.689597</td>\n",
       "      <td>-0.375999</td>\n",
       "      <td>0.225143</td>\n",
       "      <td>0.400298</td>\n",
       "      <td>0.528284</td>\n",
       "      <td>0.070561</td>\n",
       "      <td>0.196890</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.050859</td>\n",
       "      <td>0.271351</td>\n",
       "      <td>0.292341</td>\n",
       "      <td>0.404398</td>\n",
       "      <td>-0.684244</td>\n",
       "      <td>-0.918525</td>\n",
       "      <td>-0.135372</td>\n",
       "      <td>-0.116456</td>\n",
       "      <td>0.054497</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>-1.775112</td>\n",
       "      <td>0.655427</td>\n",
       "      <td>-0.461149</td>\n",
       "      <td>0.644174</td>\n",
       "      <td>-0.299376</td>\n",
       "      <td>0.335236</td>\n",
       "      <td>0.375653</td>\n",
       "      <td>0.466811</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.535348</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.146022</td>\n",
       "      <td>0.114588</td>\n",
       "      <td>-0.030088</td>\n",
       "      <td>0.536713</td>\n",
       "      <td>0.871333</td>\n",
       "      <td>-0.751752</td>\n",
       "      <td>-0.030399</td>\n",
       "      <td>-0.057691</td>\n",
       "      <td>-0.421683</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>0.744098</td>\n",
       "      <td>0.083760</td>\n",
       "      <td>-1.089501</td>\n",
       "      <td>0.665974</td>\n",
       "      <td>-0.952129</td>\n",
       "      <td>-0.045753</td>\n",
       "      <td>0.795325</td>\n",
       "      <td>0.879428</td>\n",
       "      <td>-0.067325</td>\n",
       "      <td>0.609472</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.033215</td>\n",
       "      <td>-0.598643</td>\n",
       "      <td>1.444275</td>\n",
       "      <td>0.970285</td>\n",
       "      <td>0.452280</td>\n",
       "      <td>1.216325</td>\n",
       "      <td>-0.330961</td>\n",
       "      <td>0.217733</td>\n",
       "      <td>2.772183</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>984 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Time        V1        V2        V3        V4        V5        V6  \\\n",
       "0   -0.692570  0.684420 -0.681072  0.661313 -0.956476  0.132650  0.162292   \n",
       "1    1.271345  0.157499 -0.634418 -0.055032  1.255515 -0.425593  0.997861   \n",
       "2    0.752553 -0.221098  0.269237  0.306200 -1.026833  0.280679  0.148012   \n",
       "3    1.011078  0.493430 -0.263709 -0.186122  0.256494 -0.080800 -0.219538   \n",
       "4    0.648476  0.821269 -0.478579  0.173912 -0.449114  0.672798  0.199015   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "979 -0.958669 -1.086204  1.169410 -1.313648  1.398395 -1.559302 -1.706891   \n",
       "980  1.687507  0.200748  0.246465 -0.137852  0.042447 -0.030150  0.421334   \n",
       "981 -1.052193  0.348786 -0.310831  0.689597 -0.375999  0.225143  0.400298   \n",
       "982 -1.775112  0.655427 -0.461149  0.644174 -0.299376  0.335236  0.375653   \n",
       "983  0.744098  0.083760 -1.089501  0.665974 -0.952129 -0.045753  0.795325   \n",
       "\n",
       "           V7        V8        V9  ...       V21       V22       V23  \\\n",
       "0    0.345529 -0.037786  0.116353  ... -0.132785 -0.130266  0.156352   \n",
       "1    0.783638  0.041772 -1.102214  ...  0.309792  0.521508  1.952374   \n",
       "2    0.316023 -0.218214  0.189374  ...  0.800190 -0.182205 -0.258031   \n",
       "3    0.294556  0.056499  0.080358  ...  0.190881  0.884429  0.873441   \n",
       "4    0.594452 -0.115222  0.412175  ... -0.092216  0.283036 -0.184909   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "979 -1.639955  1.092501 -2.138221  ...  0.646830 -0.243375  0.019179   \n",
       "980  0.311268 -0.493573 -0.318455  ... -0.443205  0.426612  0.131141   \n",
       "981  0.528284  0.070561  0.196890  ... -0.050859  0.271351  0.292341   \n",
       "982  0.466811 -0.036385  0.535348  ... -0.146022  0.114588 -0.030088   \n",
       "983  0.879428 -0.067325  0.609472  ... -0.033215 -0.598643  1.444275   \n",
       "\n",
       "          V24       V25       V26       V27       V28    Amount  Class  \n",
       "0    0.470278  0.074176 -1.042174 -0.050446 -0.045423 -0.334524      0  \n",
       "1   -0.022174  0.742493  1.105254 -0.202731 -0.095967  3.822321      1  \n",
       "2   -0.886093  1.945438 -0.037087 -0.310553 -0.401827 -0.344800      0  \n",
       "3    1.715494 -0.744589  0.373816  0.105638 -0.138772  1.045192      1  \n",
       "4   -2.491057  0.834222 -0.909566 -0.111027 -0.250955 -0.222291      0  \n",
       "..        ...       ...       ...       ...       ...       ...    ...  \n",
       "979  0.822008 -0.094248  1.066193  0.700618  0.074792 -0.296914      1  \n",
       "980  0.758089  0.088747 -0.901007  0.372210  0.138400  1.062590      1  \n",
       "981  0.404398 -0.684244 -0.918525 -0.135372 -0.116456  0.054497      0  \n",
       "982  0.536713  0.871333 -0.751752 -0.030399 -0.057691 -0.421683      0  \n",
       "983  0.970285  0.452280  1.216325 -0.330961  0.217733  2.772183      0  \n",
       "\n",
       "[984 rows x 31 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleansed_dataset = pd.concat([df1, df2], axis=1)\n",
    "cleansed_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Export The Cleansed Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleansed_dataset.to_csv(\"cleansed_dataset.csv\",index=False)"
   ]
  }
 ],
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