Credit_Risk.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Credit Rist Analysis and Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Dataset Contains the History of some set of Employees who obtained Loans from an Organisation, it also contains the column that indicates if such Employee defaults the agreement to pay back the Loan or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The aim of this Project is to build a model that can predict whether an Employee will Default or not, and the Model that best Suits the project is the K-Nearest Neighbor since the output is quantitative in nature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Column in the Dataset is as follows: Client ID, Income, Age, Loan and Default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The Analytics Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The Visualisation Libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The Prediction Libraries\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import classification_report, classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('C:/Users/OlADAYO/Documents/raw_data/Credit_Risk.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having a feel of The Dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clientid</th>\n",
       "      <th>income</th>\n",
       "      <th>age</th>\n",
       "      <th>loan</th>\n",
       "      <th>default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>66155.925095</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>34415.153966</td>\n",
       "      <td>48.117153</td>\n",
       "      <td>6564.745018</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>42709.534201</td>\n",
       "      <td>45.751972</td>\n",
       "      <td>6103.642260</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>66952.688845</td>\n",
       "      <td>18.584336</td>\n",
       "      <td>8770.099235</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   clientid        income        age         loan  default\n",
       "0         1  66155.925095  59.017015  8106.532131        0\n",
       "1         2  34415.153966  48.117153  6564.745018        0\n",
       "2         3  57317.170063  63.108049  8020.953296        0\n",
       "3         4  42709.534201  45.751972  6103.642260        0\n",
       "4         5  66952.688845  18.584336  8770.099235        1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 5 columns):\n",
      "clientid    2000 non-null int64\n",
      "income      2000 non-null float64\n",
      "age         1997 non-null float64\n",
      "loan        2000 non-null float64\n",
      "default     2000 non-null int64\n",
      "dtypes: float64(3), int64(2)\n",
      "memory usage: 78.2 KB\n"
     ]
    }
   ],
   "source": [
    "#Checking the Data Type of Each Column\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1000.500000</td>\n",
       "      <td>45331.600018</td>\n",
       "      <td>40.807559</td>\n",
       "      <td>4444.369695</td>\n",
       "      <td>0.141500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>577.494589</td>\n",
       "      <td>14326.327119</td>\n",
       "      <td>13.624469</td>\n",
       "      <td>3045.410024</td>\n",
       "      <td>0.348624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20014.489470</td>\n",
       "      <td>-52.423280</td>\n",
       "      <td>1.377630</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>500.750000</td>\n",
       "      <td>32796.459717</td>\n",
       "      <td>28.990415</td>\n",
       "      <td>1939.708847</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1000.500000</td>\n",
       "      <td>45789.117313</td>\n",
       "      <td>41.317159</td>\n",
       "      <td>3974.719419</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1500.250000</td>\n",
       "      <td>57791.281668</td>\n",
       "      <td>52.587040</td>\n",
       "      <td>6432.410625</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>69995.685578</td>\n",
       "      <td>63.971796</td>\n",
       "      <td>13766.051239</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          clientid        income          age          loan      default\n",
       "count  2000.000000   2000.000000  1997.000000   2000.000000  2000.000000\n",
       "mean   1000.500000  45331.600018    40.807559   4444.369695     0.141500\n",
       "std     577.494589  14326.327119    13.624469   3045.410024     0.348624\n",
       "min       1.000000  20014.489470   -52.423280      1.377630     0.000000\n",
       "25%     500.750000  32796.459717    28.990415   1939.708847     0.000000\n",
       "50%    1000.500000  45789.117313    41.317159   3974.719419     0.000000\n",
       "75%    1500.250000  57791.281668    52.587040   6432.410625     0.000000\n",
       "max    2000.000000  69995.685578    63.971796  13766.051239     1.000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for the Statistical Breakdown of the Dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning The Dataset for Optimize Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for Null Values and Treating it Accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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      "text/plain": [
       "          0\n",
       "clientid  0\n",
       "income    0\n",
       "age       3\n",
       "loan      0\n",
       "default   0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
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   ],
   "source": [
    "df.isnull().sum().to_frame()\n",
    "#Only the Age Column has Three(3) missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping The Rows With Missing Values\n",
    "df = df.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>clientid</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>income</th>\n",
       "      <td>0</td>\n",
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       "      <th>age</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>loan</th>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "          0\n",
       "clientid  0\n",
       "income    0\n",
       "age       3\n",
       "loan      0\n",
       "default   0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Double Checking for Missing Values\n",
    "df.isnull().sum().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting the Age Column from Float Object to String Object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>66155.925095</td>\n",
       "      <td>59</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>34415.153966</td>\n",
       "      <td>48</td>\n",
       "      <td>6564.745018</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>57317.170063</td>\n",
       "      <td>63</td>\n",
       "      <td>8020.953296</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>42709.534201</td>\n",
       "      <td>45</td>\n",
       "      <td>6103.642260</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>66952.688845</td>\n",
       "      <td>18</td>\n",
       "      <td>8770.099235</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>24904.064140</td>\n",
       "      <td>57</td>\n",
       "      <td>15.498598</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>48430.359613</td>\n",
       "      <td>26</td>\n",
       "      <td>5722.581981</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>24500.141984</td>\n",
       "      <td>32</td>\n",
       "      <td>2971.003310</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>40654.892537</td>\n",
       "      <td>55</td>\n",
       "      <td>4755.825280</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>25075.872771</td>\n",
       "      <td>39</td>\n",
       "      <td>1409.230371</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>64131.415372</td>\n",
       "      <td>25</td>\n",
       "      <td>4351.028971</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>59436.847123</td>\n",
       "      <td>60</td>\n",
       "      <td>9254.244538</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>61050.346079</td>\n",
       "      <td>26</td>\n",
       "      <td>5893.264659</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>27267.995458</td>\n",
       "      <td>61</td>\n",
       "      <td>4759.787581</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>63061.960174</td>\n",
       "      <td>39</td>\n",
       "      <td>1850.369377</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>50501.726689</td>\n",
       "      <td>-28</td>\n",
       "      <td>3977.287432</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>43548.654711</td>\n",
       "      <td>39</td>\n",
       "      <td>3935.544453</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>43378.175194</td>\n",
       "      <td>60</td>\n",
       "      <td>3277.737553</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>20542.365073</td>\n",
       "      <td>61</td>\n",
       "      <td>3157.442290</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>58887.357549</td>\n",
       "      <td>26</td>\n",
       "      <td>4965.516066</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>23000.784002</td>\n",
       "      <td>31</td>\n",
       "      <td>1148.118057</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>32197.620701</td>\n",
       "      <td>-52</td>\n",
       "      <td>4244.057136</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>23329.319414</td>\n",
       "      <td>48</td>\n",
       "      <td>222.622299</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>27845.800894</td>\n",
       "      <td>51</td>\n",
       "      <td>4959.921226</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>65301.984029</td>\n",
       "      <td>48</td>\n",
       "      <td>5465.267886</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>47451.630123</td>\n",
       "      <td>27</td>\n",
       "      <td>5361.282716</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>63287.038908</td>\n",
       "      <td>-36</td>\n",
       "      <td>9595.286289</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>45727.459870</td>\n",
       "      <td>55</td>\n",
       "      <td>6376.822949</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>58842.891308</td>\n",
       "      <td>54</td>\n",
       "      <td>10871.186790</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>67252.900607</td>\n",
       "      <td>38</td>\n",
       "      <td>4221.303157</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>1971</td>\n",
       "      <td>68047.925311</td>\n",
       "      <td>47</td>\n",
       "      <td>4023.064765</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>1972</td>\n",
       "      <td>36275.735863</td>\n",
       "      <td>30</td>\n",
       "      <td>644.384195</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>1973</td>\n",
       "      <td>52389.366848</td>\n",
       "      <td>54</td>\n",
       "      <td>10398.820592</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>1974</td>\n",
       "      <td>23678.376111</td>\n",
       "      <td>47</td>\n",
       "      <td>1779.814597</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>1975</td>\n",
       "      <td>37707.642948</td>\n",
       "      <td>31</td>\n",
       "      <td>268.290971</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>1976</td>\n",
       "      <td>57753.568962</td>\n",
       "      <td>54</td>\n",
       "      <td>5067.449964</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>1977</td>\n",
       "      <td>30529.963288</td>\n",
       "      <td>47</td>\n",
       "      <td>6046.840845</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>1978</td>\n",
       "      <td>44022.268737</td>\n",
       "      <td>31</td>\n",
       "      <td>1707.672870</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>1979</td>\n",
       "      <td>58533.884683</td>\n",
       "      <td>40</td>\n",
       "      <td>7832.443321</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>1980</td>\n",
       "      <td>33702.531834</td>\n",
       "      <td>48</td>\n",
       "      <td>922.036590</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>1981</td>\n",
       "      <td>40236.872067</td>\n",
       "      <td>52</td>\n",
       "      <td>4354.314412</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>1982</td>\n",
       "      <td>62619.155988</td>\n",
       "      <td>43</td>\n",
       "      <td>3959.611772</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>1983</td>\n",
       "      <td>50738.362193</td>\n",
       "      <td>45</td>\n",
       "      <td>9719.562798</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>1984</td>\n",
       "      <td>64466.760138</td>\n",
       "      <td>33</td>\n",
       "      <td>8537.369666</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>1985</td>\n",
       "      <td>64636.402186</td>\n",
       "      <td>60</td>\n",
       "      <td>2583.106425</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>1986</td>\n",
       "      <td>22371.522191</td>\n",
       "      <td>39</td>\n",
       "      <td>2291.856428</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>1987</td>\n",
       "      <td>67994.988470</td>\n",
       "      <td>38</td>\n",
       "      <td>7289.014109</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>1988</td>\n",
       "      <td>49640.004702</td>\n",
       "      <td>20</td>\n",
       "      <td>5760.858734</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>1989</td>\n",
       "      <td>42067.246446</td>\n",
       "      <td>24</td>\n",
       "      <td>4601.606183</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>1990</td>\n",
       "      <td>43662.092688</td>\n",
       "      <td>25</td>\n",
       "      <td>7269.596897</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>1991</td>\n",
       "      <td>34237.575419</td>\n",
       "      <td>34</td>\n",
       "      <td>2658.090632</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>1992</td>\n",
       "      <td>26300.446554</td>\n",
       "      <td>45</td>\n",
       "      <td>2317.393678</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>1993</td>\n",
       "      <td>30803.806165</td>\n",
       "      <td>23</td>\n",
       "      <td>623.024153</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>1994</td>\n",
       "      <td>54421.410155</td>\n",
       "      <td>26</td>\n",
       "      <td>3273.631823</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>1995</td>\n",
       "      <td>24254.700791</td>\n",
       "      <td>37</td>\n",
       "      <td>2225.284643</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>1996</td>\n",
       "      <td>59221.044874</td>\n",
       "      <td>48</td>\n",
       "      <td>1926.729397</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>1997</td>\n",
       "      <td>69516.127573</td>\n",
       "      <td>23</td>\n",
       "      <td>3503.176156</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>1998</td>\n",
       "      <td>44311.449262</td>\n",
       "      <td>28</td>\n",
       "      <td>5522.786693</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>1999</td>\n",
       "      <td>43756.056605</td>\n",
       "      <td>63</td>\n",
       "      <td>1622.722598</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>2000</td>\n",
       "      <td>69436.579552</td>\n",
       "      <td>56</td>\n",
       "      <td>7378.833599</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1997 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      clientid        income  age          loan  default\n",
       "0            1  66155.925095   59   8106.532131        0\n",
       "1            2  34415.153966   48   6564.745018        0\n",
       "2            3  57317.170063   63   8020.953296        0\n",
       "3            4  42709.534201   45   6103.642260        0\n",
       "4            5  66952.688845   18   8770.099235        1\n",
       "5            6  24904.064140   57     15.498598        0\n",
       "6            7  48430.359613   26   5722.581981        0\n",
       "7            8  24500.141984   32   2971.003310        1\n",
       "8            9  40654.892537   55   4755.825280        0\n",
       "9           10  25075.872771   39   1409.230371        0\n",
       "10          11  64131.415372   25   4351.028971        0\n",
       "11          12  59436.847123   60   9254.244538        0\n",
       "12          13  61050.346079   26   5893.264659        0\n",
       "13          14  27267.995458   61   4759.787581        0\n",
       "14          15  63061.960174   39   1850.369377        0\n",
       "15          16  50501.726689  -28   3977.287432        0\n",
       "16          17  43548.654711   39   3935.544453        0\n",
       "17          18  43378.175194   60   3277.737553        0\n",
       "18          19  20542.365073   61   3157.442290        0\n",
       "19          20  58887.357549   26   4965.516066        0\n",
       "20          21  23000.784002   31   1148.118057        0\n",
       "21          22  32197.620701  -52   4244.057136        0\n",
       "22          23  23329.319414   48    222.622299        0\n",
       "23          24  27845.800894   51   4959.921226        0\n",
       "24          25  65301.984029   48   5465.267886        0\n",
       "25          26  47451.630123   27   5361.282716        0\n",
       "26          27  63287.038908  -36   9595.286289        0\n",
       "27          28  45727.459870   55   6376.822949        0\n",
       "29          30  58842.891308   54  10871.186790        0\n",
       "32          33  67252.900607   38   4221.303157        0\n",
       "...        ...           ...  ...           ...      ...\n",
       "1970      1971  68047.925311   47   4023.064765        0\n",
       "1971      1972  36275.735863   30    644.384195        0\n",
       "1972      1973  52389.366848   54  10398.820592        0\n",
       "1973      1974  23678.376111   47   1779.814597        0\n",
       "1974      1975  37707.642948   31    268.290971        0\n",
       "1975      1976  57753.568962   54   5067.449964        0\n",
       "1976      1977  30529.963288   47   6046.840845        0\n",
       "1977      1978  44022.268737   31   1707.672870        0\n",
       "1978      1979  58533.884683   40   7832.443321        0\n",
       "1979      1980  33702.531834   48    922.036590        0\n",
       "1980      1981  40236.872067   52   4354.314412        0\n",
       "1981      1982  62619.155988   43   3959.611772        0\n",
       "1982      1983  50738.362193   45   9719.562798        0\n",
       "1983      1984  64466.760138   33   8537.369666        1\n",
       "1984      1985  64636.402186   60   2583.106425        0\n",
       "1985      1986  22371.522191   39   2291.856428        0\n",
       "1986      1987  67994.988470   38   7289.014109        0\n",
       "1987      1988  49640.004702   20   5760.858734        0\n",
       "1988      1989  42067.246446   24   4601.606183        0\n",
       "1989      1990  43662.092688   25   7269.596897        1\n",
       "1990      1991  34237.575419   34   2658.090632        0\n",
       "1991      1992  26300.446554   45   2317.393678        0\n",
       "1992      1993  30803.806165   23    623.024153        0\n",
       "1993      1994  54421.410155   26   3273.631823        0\n",
       "1994      1995  24254.700791   37   2225.284643        0\n",
       "1995      1996  59221.044874   48   1926.729397        0\n",
       "1996      1997  69516.127573   23   3503.176156        0\n",
       "1997      1998  44311.449262   28   5522.786693        1\n",
       "1998      1999  43756.056605   63   1622.722598        0\n",
       "1999      2000  69436.579552   56   7378.833599        0\n",
       "\n",
       "[1997 rows x 5 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'] = df['age'].astype('int')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rounding up the Income and Loan Columns to Two(2) Decimal Places"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Income\n",
    "df['income'] = round(df['income'], 2)\n",
    "#Loan\n",
    "df['loan'] = round(df['loan'], 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some Values in the Age Column are carrying Negative sign, which is out of place, so we need to get rid of the Negative signs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#first i Convert the Column to a Sting Object for easy manipulation\n",
    "df['age'] = df['age'].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing the negative sign'-' sign with a tab ''\n",
    "df['age'] = df['age'].str.replace('-', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Taking the Age Column back to its proper Integer Data Type\n",
    "df['age'] = df['age'].astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploration and Insight Derivation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The Age Distribution Plot\n",
    "plt.figure(figsize=(8,5))\n",
    "sns.distplot(df['age'], bins = 40, kde= False)\n",
    "plt.title('Age Distribution');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age Distribution of Defaulters and Non-Defaulters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   default\n",
       "0     86.0\n",
       "1     14.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verifying the Default Percentage\n",
    "percentage_default=round((df['default'].value_counts()/df['default'].count() * 100)).to_frame()\n",
    "percentage_default"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen ABOVE, 85% of the Participants Defaulted the Loan Agreement, while 14% did not default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>clientid</th>\n",
       "      <th>income</th>\n",
       "      <th>age</th>\n",
       "      <th>loan</th>\n",
       "      <th>default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>66155.93</td>\n",
       "      <td>59</td>\n",
       "      <td>8106.53</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>34415.15</td>\n",
       "      <td>48</td>\n",
       "      <td>6564.75</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>57317.17</td>\n",
       "      <td>63</td>\n",
       "      <td>8020.95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>42709.53</td>\n",
       "      <td>45</td>\n",
       "      <td>6103.64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>24904.06</td>\n",
       "      <td>57</td>\n",
       "      <td>15.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clientid    income  age     loan  default\n",
       "0         1  66155.93   59  8106.53        0\n",
       "1         2  34415.15   48  6564.75        0\n",
       "2         3  57317.17   63  8020.95        0\n",
       "3         4  42709.53   45  6103.64        0\n",
       "5         6  24904.06   57    15.50        0"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filtering out the Defaulters\n",
    "filt = (df['default'] == 0)\n",
    "defaulters = df.loc[filt]\n",
    "defaulters.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The Age Distribution Plot of Defaulters\n",
    "plt.figure(figsize=(8,5))\n",
    "sns.distplot(defaulters['age'], bins = 40, kde= False)\n",
    "plt.title('Age Distribution of Defaulters');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>clientid</th>\n",
       "      <th>income</th>\n",
       "      <th>age</th>\n",
       "      <th>loan</th>\n",
       "      <th>default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>66952.69</td>\n",
       "      <td>18</td>\n",
       "      <td>8770.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>24500.14</td>\n",
       "      <td>32</td>\n",
       "      <td>2971.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>26578.54</td>\n",
       "      <td>32</td>\n",
       "      <td>3489.84</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>24381.95</td>\n",
       "      <td>25</td>\n",
       "      <td>4227.02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>22248.18</td>\n",
       "      <td>23</td>\n",
       "      <td>4364.98</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    clientid    income  age     loan  default\n",
       "4          5  66952.69   18  8770.10        1\n",
       "7          8  24500.14   32  2971.00        1\n",
       "39        40  26578.54   32  3489.84        1\n",
       "43        44  24381.95   25  4227.02        1\n",
       "56        57  22248.18   23  4364.98        1"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filtering out the Non-Defaulters\n",
    "filt = (df['default'] == 1)\n",
    "non_defaulters = df.loc[filt]\n",
    "non_defaulters.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The Age Distribution Plot of Non_Defaulters\n",
    "plt.figure(figsize=(8,5))\n",
    "sns.distplot(non_defaulters['age'], bins = 25, kde= False)\n",
    "plt.title('Age Distribution of Non_Defaulters');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter plot between age and income\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.scatter(x= 'age', y = 'income', data = df)\n",
    "plt.title('Scatter Plot of Age vs Income');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The KDE Plot\n",
    "plt.figure(figsize=(8,5))\n",
    "df_credit['age'].plot(kind = 'kde')\n",
    "plt.title('Kernel Density Estimation');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>loan</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>loan</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.441539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income</th>\n",
       "      <td>0.441539</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            loan    income\n",
       "loan    1.000000  0.441539\n",
       "income  0.441539  1.000000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for Correlation between Loan and Income\n",
    "df[['loan', 'income']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter plot between age and loan\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.scatter(x= 'age', y = 'loan', data = df)\n",
    "plt.title('Scatter Plot of Age vs Loan');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the Default column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining the x and y variables\n",
    "X = df.drop(['default','clientid'], axis = 1)\n",
    "y = df['default']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standerdazing the Independent Variables X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a standard scaler object called scaler\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fit-Transforming the Xvariable into the Standard Scalar\n",
    "X = scaler.fit_transform(X)\n",
    "y = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the X and y variables into train and test sections\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Working on the Prediction Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Determining the Number of Nearest Neighbore(K)\n",
    "error_rate = []\n",
    "for i in range(1,40):\n",
    "    \n",
    "    \n",
    "    knn = KNeighborsClassifier(n_neighbors = i)\n",
    "    knn.fit(X_train, y_train)\n",
    "    pred_i = knn.predict(X_test)\n",
    "    error_rate.append(np.mean(pred_i != y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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x4x//GPfccw9+8YtfYMWKFWhvb0cgEMDjjz+On/70pzhx4gTa29sxPEzxS7Fw+Owz0kwAQHF+MeSMPOsxX4c3HIJs3XOAdjD2DtpByNadwLc2HkrrmExidVlhTDLeK9VrEmIsqnUmuAMujPlHEz5mPqT49yUIIncR07OWT+KKr3PnzmHTpk0AgLVr16KjoyO67fz582htbYVKpYJOp0NNTQ0uXLiA73znO7jrrrsQDocxODgIg8GAnp4e1NTUoKioCCqVCjfeeCM++OAD4a4sh3F6HTMSrAKAjJGJosRQuaYCexv3QbXlaMztii1HsK12K/rGPsGfrr2PP03GM22t2QbFliMxj1FtOYp9jftQPmndyyTeoBfDE0NJW75SGQehxoJbpcnHikep/X0JgshtxPSs5ZO4bke32w2tdqrYsFwuRzAYhEKhgNvthk43FZek0WjgdrvBMAyCwSDuvvtu+Hw+fOMb38Dg4GDMfYm52L12GHVzs62LJdHq4Q2HcLJrA3D2AcBdObVBO4jgmn/Hr/om8Ku+03MPXJMPnH1wzjGRrw/+yvQkQ3+KOb6ANMYB4HUsOKud1WVBS+nqZC9jDlL6+xIEkfuI5VnLJ3HFl1arhcfjif47HA5DoVDE3ObxeKICS6lU4o033sAf/vAHPPDAA/jnf/7nefedD7mcgV6vTvhi5HJZUvuLlbGAE0t0rXOupaqwAvaJkYSuUcix0Ovr8JUb2vCTzY8h9PoPo78rthzB5+vvwN9t/FrM455+7yf4zy1HEHztqehvqq1H8ZW1bVhZtUyQvgILj4XDERGzqypWJD1e3Dgc33oU/len4grijQPA71g0KRoAALbgtQWvIdF7gruun245gsC0/on175sKUnlW8AGNxRQ0FhHENg7ZfNYKNRZxxde6detw9uxZ3Hnnnfjoo4/Q0NAQ3bZmzRo8+eST8Pl88Pv96OnpQUNDAx5++GHcfvvt2LhxIzQaDRiGwfLly3H16lU4nU6o1Wp88MEH+NrX5h8wAAiFWDid4wlfjF6vTmp/sWIbt0EjK5xzLXqlAeahroSuUeix+Mbab+DHa1uBd74T+aLQDkKx7nkc3fz+vGbc2lvrcdOVDQhyXyHaQchaT+Cbre8L2teFxuLiYE9kH2ZJSn34Zus38exfNgBvP5DwOAD8joWK1SJfno9Lw70LHpvMPfHN1m/ipx+un/piFPHfNxWk8qzgAxqLKWgsIohxHLL1rE12LMrKFjYqccSN+dq+fTtUKhX27t2Lxx9/HP/0T/+E48eP46233kJZWRna2tqwf/9+fOUrX8H999+PvLw8tLW14emnn0ZbWxuOHTuGhx9+GEqlEg8++CC+9rWvYe/evbj33ntRXk4xILPxBr0YD47PCbgHIukmhieGRFFiaMDTDxZByDc/DiAx/znnu+d88IrNj2c9Fsji7oOMkaFSU5XS8bPjERKNI0j1uFgwDINqnZHXEkPlmgo0lzUBn/2XhPsX/ftu5v6+R7L+9yUIQhpwzxfZ5kcBZOdZyydxLV8ymQyPPPLIjN+WL18e/f/du3fPSRmxfPlynDhxYs65tm7diq1bt6ba10UBVz5odsA9EIn58oV8cPnHUJhXlOmuzeB0dztUKoBZdwKhD76asP/88IZDeKHrZgT/dBBo/Rm+tfHPwnd2AawuCyo1VVDKlSmfIxqP8KevJBVHkOpxsTBqTbxnuTdoC8Gsexbsn/9ncn9f880IfnAQTOvP8K2N53jtE0EQi5fDGw7h2Y61wAdfy9qzli8ow73IcHg58RXL8lUGIPu5vgKhAP7j8ku4fdkXsK9pP2Rf3Zbwl0S5pgL7GvcDB7fAqKvO+teH1RVJsJoO0S+yJMYhneNiYdSZeK/v2Dt6GTWFpqT/vntW7QUObkFTaVPW/74EQUiHck0FNKoC4ODWrD1r+YLEl8iIZ/kCsl/f8R3LWxiZGMHOhj04vOEQNhhvTCpXyuENh1Cs0QIKn4C9TAyr25rSSsfZpDIO6Rw3G6POhOGJIXiD3rTOw+EOuPHJ6BV8seG2pPv3j7d8CyoVUFtSGX9ngiCIBAmFQ5hgnaguKsvas5YvSHyJDK6u43wxX0D2LV+nu9tRnFeMrTWfQ7mmAq/sejmpL4lyTQW+1tqGPtdVjAeyF9QZCocw4LbCpKtJ+1ypjEM6x82GSzcxwFONx4v2LrBgcXPlLSn9fW+oWgXbxAgvfSEIggCA6+PXEGJD+L83/F3WnrV8QeJLZDi98S1fQ1nM9eXyj+HXV36Fu1fsgEquSvk8TYYWhNkwuh0XeOxdclwfv4ZgOMiL5SvbcAKSL9ej2dYJAGgyNKfYHxOsbn7doARBLG6455tJAs9sEl8iw+6LWL70MWK+DPkGyBhZVi1fv+p9Dd6QF7tW7k3rPM2lLQCmXvLZwOqKWImMacZ8iYHqyaS8fK14NNs6oFXqUKOrjb9zDIzaGgy4+xFmw7z0hyAIYiopdvreimxD4ktkOL0OKGVKaBSaOdvkMjlK8g1ZzXJ/uvsUaguXYn35zWmdp7ZwKdQKDcy2jvg7C4TVHVkdKIWJXKWphoyR8WZtMts60WRoTrlQt1FnQiAcwHXPNV76QxAEwX1cVseoAJNrkPgSGQ6vHcX5JfO+9MoKsldiaNA9gP+yvoOdDXtSfilzyBgZGg1N6BzJoviS0ERWypWoUFfyYvliWRadIx0puxwBRMtj8b0CkyCIxYvFZUFxXjG0Sm38nUUOiS+R4fA5Ygbbc5Spl2TN7fjypdNgwWJXwx5eztdkaIHZ1pG1pLFSmsgAeEu02u+2Ysw/iiZDS8rn4KyJ/RT3RRAET1hdfZLwVAAkvkSH0+uIGWzPUVZQlrVUE6e723Fj+XrU6Vfwcr4mQzMcPgeueQZ5OV+y9LsskpnIQCQI1cKD2OFcwemJr0hALFm+CILgi36eUgOJARJfIsPutccMtufgLF+ZthaZbZ3otH2MnTxZvQCg2cAF3WfH9Wh1WyQzkYFIkPugux+hcCit83CLIBoNjSmfQ6vUojivmPes+wRBLE5YloXFZZHESkeAxJfocPocKMlbwPKlXoKJ4AQ8AXcGewW81H0KckaOu1fcy9s5Gw1NAIDOLKx4lNpEBqaC3IfGr6d1HrOtAzWFS6FTFaZ1nmqdidd6kwRBLF6cPgc8ATeqtdJ4ZpP4EhmOOJavJZOJVocyGPcVZsN4qfsUttZ8DqUFpbydtyhPD6PWlBXLl9QmMsBfkDu30jH9/pjQz1PSV4IgFjfWyWeJVLwVJL5ExERwAt6QN27APZDZEkN/GPg9Bjz9vLocOZoMzejKguVLahMZ4CfI3Rv04rLzEi/iy6SN1JvM1oIKgiCkg1VCCVYBEl+iwsGVFloo4D4qvjJn+Tp9sR1apQ63Lb2T93M3GVpwydkNXyizdR6lNpEBfoLcux0XEGbD0Xi8dKjWmeAOuDDqc6Z9LoIgFjdc/KhUvBUkvkSEI1paKL7bMVPpJiaCE3it9xV8cfldUCvVvJ+/ubQFwXAQlxzdvJ97IbiJLKXVjnwEuXPB9nyIL07YWsn1SBBEmlhcFhQoCngNfckmJL5EhMMX3/JlKCgFAyZjlq///OQ3cPnHBHE5AlPpDDId98VNZEO+IaPtCk26Qe6dIx9DrVCjtnBZ2n3hin1T0D1BEOnS77aiWmtMO8G3WCDxJSI4y5d+gZgvhUyBkvwSDE9kJubrdHc7KjSV+HTVJkHOv6yoDvny/IxnupfaROZIN8jdbOvEqpJGyGVyHvoSsSpSugmCINIlkmBVGi5HgMSXqHD6IuKrZAHLFzCZ6ysDli/bhA1n+t7EjvpdvLyMY6GQKbCypDHjli+pTWSOdILcWZZFp+3jtJKrTqe0oBT58nxKtEoQRNpYXJaoNV0KkPgSEVzA/UKWL2CyvmMGYr5e6XkZwXAQuxr2CtpOk6E5GmuUKaQ2kTnSCXIfGr8Ou9fOy0pHAGAYBtU6I6WbIAgiLSaCExiZGJbUBzOJLxHh8DqQL8+PG9hepi7LiOXr9MV2NJY0o7mUH0vIfDQZmjE8MYShDMWxSXEic3BB7qmUGerkoazQbIxaE7kdCYJIiwEJpgYi8SUi4iVY5YhYvoSN+boy2osPrv83dq4UJtB+OtzLPlP5vqQ4kTk4a16/K3lr01RZoSbe+mPS1ZDbkSCItLBEUwNJZ3U6iS8R4fA5FkywylGmXgJPwI3xwLhgfXmp+xQYMLi3fpdgbXA0Trq5MuV6lOJE5kgnyN1s60CVpnrB1bbJUq0zYnhiCN6gl7dzEgSxuOBWTFdrjVnuCX+Q+BIRDq89oRdfmcC5vliWxenudnym+rOo0lYL0sZ0SgtKUa6uyFjQvRQnMkc6Qe58lRWaDmeJG6C4L4IgUsTq6oOMkaFSU5XtrvAGiS8R4fQ54gbbA5GYL0C4LPd/HvoAvaM9guX2ikUmg+6tbovkJjIHF+RuTTLmyx/y45LjIq/xXsCUdZFcjwRBpIrVbUWlpgpKuTLbXeENEl8iwu61x00zAUy3fAkT93W6ux358nx8oe5Lgpw/Fs2lq3HR3oVgOCh4W1aXRXITeTpGrQn9SYqdS45uBMIB3hdXcHF1lGiVIIhUsboskovRJfElEliWhdPrSCzgXsD6joFQAP9x6SXctvROFOYV8X7++WgyNMMf9qPHeVnwtqQ4kaeTSpC7WYCVjgBQqamCjJElbYkjCILgsLoskgsTIfElEsaD4/CH/QnFfJUWTLodBYj5esfyFmxeW0ZWOU6He+l32j4WvC0pTuTppBLkbrZ1QiVTYbl+Ba99UcqVqFBXkuWLIIiUCIVDGPD0S26BFIkvkcAlWE1ktaNKroI+T4+h8eu89+N0dztK8kuw1fQ53s+9ECv09VDKlDCPCBv3JdWJPJ1ouokkrE1mWwdWljRCIVPw3580600SBLF4uT5+DcFwUHLeCv6ftCJn6/O3oWPsj3H3aym8BW8f+G3KxySLY7K0UKLL/MsKlmB4PPWYr3jXVP2/IwWn07mmZFDJVagvXin4ikepTuTpmKLpJqxYrq9P6BizrRObTVsF6Y9RZ8QH1z8Q5NwEQUgbLoTCKDFvxaITX+urWtF99mb4X3ty3n1UXzqEm74cTOuYZEnG8gVM1ndMw+2YiWtKliZDM/7Q/3tB25DqRJ5OtS5ybYlam0YmRnB9/Brv8V4cRm0NXut5BaFwSLAaoQRBSBMuZ6FRYt6KRed2PLzhEGTrngO0g7F30A5Ctu4EvrXxUFrHJIvTG7F8JRJwD3CWr9TFVyauKVmaDC0Y8PRHhagQSHUiT6dKUw0ZI4PFnViiVa6yAN85vjiMOhMC4YAgbnKCIKRNv0Qrkiw68VWuqcDexn1QbTkac7tyyxHcUXcHxnyjuOToxiVHN8b8Y7ij7k4otxyJeYxqy1Hsa9yHcnV5yv2yTwqORFJNAMASdXolhuKNAx/XlCzcy7/LZhasDalO5OlwQe6JlhgSaqUjR7TeJMV9EQSRJBaXBSX5JdAoNdnuCq8sOrcjELH6nOzaAJx9AHBXTm3QDiKw5t/xyysT+OWVn889cE0+cPbBOcdELETvp9Un52TMVyJJVoGI29HlH4M36EW+Ij+lNhcaBz6uKVmaJ1/+ZlsHPlX9GUHakOpEnk0yQe5mWyfKCpZEk/fyTTWX68vdh5uxQZA2CIKQJlZXH6q10vtYXpTii7P6/GLL0RkxT/LNj+Mzpluxf/XumMf9/ONTeHfz4wi9/v9Ff+PLQmT32qFWqBMWUtNLDKW6cm++cciG1QsAlqjLYcg3CJrpXqoTeTbJBLl32joEczlG+sIlWqUSQwRBJIfVZUEdzylwxMCiFF9ADKuPdhDKG3+OH93+/ryi41NVn8FNgxsQeuefosfwZSFKtLQQx/QSQ+mkTYg1DtmwegGR0jhNpasFXfHY77ZiWdFywc4vFozaGrza8x9xg9yD4SAu2rvwtdX3CdYXrVKL4rzilIp9EwSxeGFZFla3FbeatmS7K7yz6GK+OGbHPCVi7eGOkW9+LOFjEsXpdSScZgLgr8RQuaYCu1ftBW79FwDZs3pxNBmaccHehVA4xPu5WZaFxWWJxiBJGaPOhGA4GDfIvdfZA1/IJ6jlK9JA30TbAAAgAElEQVSfGsr1RRBEUjh9DngCbknG6C5a8QVMW/FX8WHCK/sObzgEWWvkGKb1Od5WA9q9dhQnuNIR4LfE0I5VdwFrjyc1DkLRbGjBeHAcn4z18n5uKU/k2SQa5C50sD1Htc4YXexAEASRCNwHmxRDReKKr3A4jIceegh79uxBW1sbrl69OmP7qVOnsGPHDuzevRtnz54FAAwMDODgwYNoa2vDgQMH0NsbeZG++uqruOeee3DvvffiF7/4hQCXkxycJUv21W0JW3vKNRXYWrsVOLgFW2o382YhStbtGC0xxIP4Ghq/BjBhMAcTHweh4CwwQsR9SXkiz2Z6kPtCmG2dUMgUqC9uELQ/Jq0JFpcFLMsK2g5BENKB+3iUorcirvg6c+YM/H4/2tvbcfjwYRw5MpVuYXh4GCdOnMDJkyfxzDPP4NixY/D7/Xjqqadw4MABnDhxAvfddx+OHTsGAHjiiSdw/PhxvPDCCzh+/DhGR0eFu7IEObzhEDYYb0zK2vMPN38DkPvwmRr+Vm5FLF+Jux3zFfkoVBXxUt/RbOuEXBnCzcbWrFq9AKCheBVkjAydAsR9WSctL1KcyLOZCnKPb/mq1zcgT54ncH9q4A64MOpzCtoOQRDSgSuRJsW8jHED7s+dO4dNmzYBANauXYuOjqmX4vnz59Ha2gqVSgWVSoWamhpcuHABDzzwAHQ6HQAgFAohLy/yYF+5ciVcLhcUCgVYlgXDMEJcU1KUayrwyq6XkzpmTdlaQOnFqJ8f8ciyLJw+R8LZ7TnK1GVplRjiMNs60FC8Eq/t/o+0z5Uu+Yp8rNDXC2T5kn6CVY6pIPd44qsTN1duFLw/nBi0uC0JJxImCGJxY3FZUKAogCHfkO2u8E5c8eV2u6HVaqP/lsvlCAaDUCgUcLvdUZEFABqNBm63GyUlEQtOb28vjh49iqeffhoAUF9fj3vvvRcFBQXYvn07CgsLF2xbLmeg16sTvhi5XJbU/qmjRqW2EkO+QV7aG/ONIRgOoqq4PKnzVeoq4AiMxDwmmbG44DDjU6ZPZ2js4nNDxQ34YPBPvPWHG4th/yAKFAVYXmEShfAXmhp9La57B6LjOPuecHqdsLot+Lrx64L/7RsrI0vFneFhUdxnmXtWiB8aiyloLCKIZRyuewdgKqxBcXH28jIKNRZxxZdWq4XH44n+OxwOQ6FQxNzm8XiiYuy9997D9773PTzxxBOoq6vDhQsX8M477+Ctt96CWq3GP/7jP+LXv/417rjjjnnbDoVYOJ3jCV+MXq9Oav90qNIY0Wv/hJf2+sb6AQD5rDap8xWrSnHBZo55TKJjMepzom+sD3+t+1rGxi4e9YWr8GLXKViGrkGnWligJwI3FpdHrqBaa8To6AQPvRQ/lepqXHH0Rv+us++J9wYiecDq1A2C/+2LEFkgcvHaJWxakv37LJPPCrFDYzEFjUUEsYzDFfsVVGmqs9qXZMeirEwXfyckEPO1bt06/O53vwMAfPTRR2homArMXbNmDc6dOwefzweXy4Wenh40NDTgvffew6OPPoqf/OQnWL16NQBAp9MhPz8feXl5kMvlKCkpwdjYWMIXJDZMOhNveYuiRbWTiPkCgLKCsrRjvrhSPs0CpxpIhqkyQ128nrffbVkUKx054gW5d2ZopSMAlBaUIl+eTyWGCIJIGKvbmlYeSzET1/K1fft2vPvuu9i7dy9YlsVjjz2G48ePo6amBtu2bUNbWxv2798PlmVx//33Iy8vD4899hgCgQAefPBBAMCyZcvwyCOPYM+ePdi/fz+USiVqampwzz33CH6BQlGtNeGN3tcRZsOQMell7HBMlhZKPuZrCZw+J/whP1RyVUptd9o+BpCZF3CiNE0rM3RzJX+LGiwuC1pK1/B2PrFj1NXAE3Bj1OeMGWdltnWgOK8YFZrKGEfzC8MwqNYZYXWT+CIIIj4TwQmMTAyjWmvMdlcEIa74kslkeOSRR2b8tnz5VIbw3bt3Y/fumeV4Xn311Zjn2rdvH/bt25dKP0WHUWeCP+zH8PgQyjUVaZ3L6Z0UX0lbviKunJGJYVRpq1Nq22zrzNgLOFGqtUYU5el5zXQv9YkcC6Mucq3zBbmbbR1oLl2dsfg3o9aEfrJ8EQSRAP2T5cik6q1Y1ElW08EUzaOU/svE7ou4HZNdBcZHolWzrQNNhhZRBaAzDIMmQzOvKx6lPpFjYdTOn24izIbRZTMLntl+OiZdDbkdCYJICMtkWI9U3Y4kvlKkeoEXW7JELV/Juh25RKspxn1l4wWcKJz4CrNhXs7HiWSpTuRYcCk1YlmbPhm7gvHgeEbdzUadCcMTQ/AGvRlrkyCI3ISriCHVD2YSXymSaPmWRHB47dAotUnHbU1ZvlLL9cW9gJtLV6d0vJA0GVrgDriiXz/pwolkqU7kWCwU5G4eiVgVMym8OZfvAJUZIggiDlZXH2SMDBVq8YTE8AmJrxQpzCtCoaqIlxWPjhQSrAJTMV/xiifPRzZewInCd5khqU/kWCwU5G62dUDGyNBQvCpj/eGsjuR6JAgiHhaXBZWaKijlymx3RRBIfKWBUWfipViwI8nSQhxqpRoapTZlt2M2XsCJsrKkEQwY3oLurW6rpCfyfBi1sVOimG2dqCtaDrUyc4kUEy15RBAE0e+2StpTQeIrDYxaI09uR0fKJVeWqJekHHCfjRdwomiVWiwtWsaj5Wtx5fjiMOlqYHXN/UDgFlpkkkpNFWSMDJY4xb4JgiCsLkt00ZAUIfGVBkadiZeveIfPjpK85C1fQMT1ODyRWsxXNl7AydBkaOHP8uWyLKo0ExzVOuOcIHd3wI1Pxq5k3N2slCtRoa6MrjwlCIKIRSgcwoCnX9IfzCS+0sCoq8GYfxRjvvQKbDvTsHyVpWj5ytYLOBmaDM3odfZgPJBeaQluIi+mlY4c3Jdj/7S4ry4bF+uXeeHN1wcLQRDS5ZpnEMFwkMQXEZupXF+pf8mH2XDKAfdA6iWGsvkCTpQmQwtYsLhoT6/M0KBb+hN5PmIFuZtt2VtoYdSZYKEs9wRBLAD3TjVJ+JlN4isNODdWOiseXf4xhNlwSgH3QMTyZffaEQgFkjoumy/gRGmOlhlKL+6rb/QqAGlP5PngBOd0V5/Z1gGdqjArlkCTrgYDbitC4VDG2yYIIjfg3qlGrXS9FSS+0oCPpfOOaGmhVC1fkXQTNu9IUseZbR3QKnWidsXVFNZCo9RG60+mytWxyESulnDw5nzECnI32zrRZGjOSlWDaq0RwXAw5fQoBEFIHy40oVon3ThdEl9pUKZeApVMlVa6CYc3UlooHcsXkHyJoWy+gBNFxsjQWNLEm+VrMboduSB37mHGsmz0b58N+ExOTBCENLG4LCjJL4FGqcl2VwSDxFcayBhZJIllGm5Hhy9i+dKnHPM1Kb6SiPviXsDNpeKN9+LgVjyyLJvyOSyj0p/ICzE9yN3qtsDlH8tarB9X8shK6SYIgpiHfrcl+qyQKiS+0sSoNaXpdoxYvkpStnxF6jsOJWH5yvYLOBmaSpvh9Dkx6BlI+Rx9Y1clP5EXwqgzRQNYsx3rx7kRaMUjQRDzsRhSA5H4SpN0l84707V8cW7HJHJ9ZfsFnAxN0aD71PN9XR3tk/xEXgij1hQNcjePRMaxsaQpK33RKrUozism8UUQRExYloXFZZH8AikSX2li1JlwffwafCFfSsenG3CvVWqhVqiTivnK9gs4GZom+5hq3BfLsugbvSr5ibwQRp0JwXAQg+5BmG2dqC1cCq1Kl8X+1JD4IggiJg6fHeNBj+RjdEl8pQm3WnDA3Z/S8Q6vHTpVIRQyRcp9KE0y0aoYXsCJUphXBJOuJmXLl9PngCcg/Ym8EJzw7Bu9ik7bx1l3N0fcoCS+CIKYC5cWR8ppJgASX2kzlesrtZdJOglWOSKJVpNxO4q7rNBsmgzNKVu+okuWF2GaCY7qSfF10XYRvaM9WXc3czVR01lEQRCENOFiqI0STjMBkPhKG86ikrL48tpTTjPBkUyJoYngBHpGL2f9BZwMzYYWXHJ0p+Ta5SbyYnc7AsBven6DMBtGs2F1lvtTA0/AjVGfM6v9IAhCfEQTrEp8kRSJrzSp0laDAZOyG8Xpc0Cfp0+rD5Hi2omJr4v2LoTZcI5ZvloQYkPodlxM+liupqHUJ/JCcEHu/3nlTQCRFaTZhBODVGaIIIjZWN1WFCgKYMg3ZLsrgpJ6oBEBAMiT52GJujxly5fda0eNrjatPpSpy2D32hAKhyCXyRfcl3PfNYvc8rX1+dvQMfbHGb9tO/WZOfu1FN6Ctw/8dsHjAKDpeN2Cx0mN+cYBADb+vDX6/9kYB9M0a/Hq0jUZbVtoFhr36fAx7plqK5PXROQOQt0XVpcFRq1J1AnA+YDEFw+kk27C6XVAn+JKR46ygiUIs2HYvDYsmUw9MR9mWwfUCjVqC5el1abQrK9qRffZm+F/7cl591F96RBu+nKQl+OkhpjHgYu/65fgisdMjnum2hLzvURkD6HuC6urT9JlhTjI7cgDphRXb4XCITh9zrRjvpYkUWLIbOvEqpLGuBaybHN4wyHI1j0HaAdj76AdhGzdCXxr4yFejpMaYh6H0oJSFCgKJFliKJPjnqm2xHwvEdlDqPvC6raIuuYwX5D44gGjrgb9LivCbDip48b8o2DB8rDaMbESQ5GyQh1oLs1uwHUilGsqsLdxH1RbjsbcrtxyBHev+CsAwPXx69H/wDC4a8VfQbnlSMzjVFuOYl/jPpSrywXruxiIN37ZHAeGYVCtNUoy3UQmxz1TbYn5XiKyhxD3xURwAiMTIzAugtXpJL54oFprhD/sT7q4NVfXMf3VjpESQ/Havz5+DXavPWdWOs77ZaUdRGDNv6O956dY/bP6Of+d6jmOwJofxzxuMX2hLzR+2R6HiKtemvUdMznumWpLzPcSkT34vi+iOb4Wwep0El88wAUQW5J8mXB1HdO1fC2Z/LKIV9+RS1SaKysd5/uykm9+HLdU34InPvv/zvvfxupPQb758RnHLbYv9PnGTwzjYNSaYJ180EqNTI4715bQ97qY7yUie3D3xWxPQ6r3hSWaZkL64osC7nmAS2PQ77ZiPW5O+DjnZGmhdAPutUod8uX5cd2OnZMrHXOhrBDH4Q2HcLJrA3D2AcBdCWgHobzx5/jxne8vOLHvWPYF3HR9A0Lv/FP0uMiX2PsZ7H32iTV+YhgHo86E4YkheINe5Cvys9oXITi84RBeMN+ckXE/vOEQTnTeCAh8r4v1XiKyy+ENh/Bz83rg7INp3xfWaGog6YsvsnzxwJTlK7kYFvuk5askTbcjwzAJJVrtHPkY1Vpj2mIvk8z+4k70iyrV46SGWMeBe7j2SzDuC4iM+/al24HP/guASIyiUONerqlAtbYy2pZQf+NyTQV2r9oL3BppR7b5UVHcS0R2KddUoLm0OXr/pXNf9LsskDEyVGqqeO6l+CDxxQM6VSEKVUVJx7A4J2O+9HnpiS+AKzG0sPjqsnXmTLzXdKJxBRUfJhVHkOpxUkOM48AF1ErV9QgAW+o+C7QeByo+BNY+K+i4h+UT0bZkrc8J1taOVXcBa7lr+pko7iUi+5TpisCsexao+BDhG36Kb6z/25TOY3FZUKmpSqvWca5A4osnUsn1xVm+0s1wD3Alhuav7+gL+XDJ2Z0z8V7T4aw3sq9uS+qLKtXjpIYYxyHdsly5gCfgApgwcHALVhkaBBt3d8ANq9uCUnUpcHALttZuE6ytofFrk9e0FQq5DEsKFs4rSCwOekcvw1RoAnNwG8CE8JfhD1M6j9VtWRQuR4DEF29Ecn0l9xXv9DlQlKfnJedWvBJDlxzdCIaDOWn5AiLWmw3GG5P+0j684RA+vfSmRf+FLrZxqNRUQcbIYHFLc8UjEBGWBXlyqPMVKNIUCNbOBZsZAPA/1/1fgNyHddXCfWCZbZ2QK0OoLa6En3Fh0DMgWFtEbjAeGEevswdfqP88NhjXoVRXhBcvnkzpXFx2+8UAiS+eSMXy5fDa017pyFGmLoNtYmTeXGO5ttJxNuWaCryy6+Wkv+jLNRV466tviMLak03ENg5KuRKVmipJW74sLgtqCmvxhYbt6HFeFqwdrmTYjvqdKNLmC5q81mzrQEPxSvzw9iej/yYWNxftXWDB4ubKjXh19y+xq2EvzvS9CduELanzhMIhDLj7F0WCVYDEF29Ua00Y849izDea8DEOrwPFPAW/lxUsQYgNRV2ZszHbOpEnz8Ny/Qpe2iOIdKnWGqN5faSI1RVxoTQZWjDoGYDdm9zLKFHMtg5olTrU6GrRZGgWVBCZbZ1oNDSjaXLFNCf8iMULdw9wXpVdK/ciGA7i1Z5fJnWea55BhNjQoigtBJD44o1UVjw6fQ7oebN8LVxiyGzrwMqSxkURyEjkBkadCRaJrnYEIis5jbqa6Eupa9I9yDfmyYU0DMOgydCMLps56WobiTDqc8LqtqDJ0ILCvCKYdDVk+SIm6wVrUFu4FADQbGhBY0kTTne3J3Ue7llgopgvIhlSWTpv99rTzm7PEa/EkDlHVzoS0sWkq8GA24pQOJTtrvCOJ+CB3WuHUWtEU2nE1S+EUImUDJua202GFrgDrqQTPicCZ+FojrbVTJYvYtIa2gQZE5ETDMPg3oY9+NO19/HJ6JWEz8NlCzBqye1IJAEXJJic5cvJn9txAcvX8Pgwhsavk/giRIVRZ0IwHMT18WvZ7grvcLFsRp0JSwqWoLSgFJ0j/Isvq9uCMf9oNJaTm+NCtDU7brTJ0IxLjm74Qj7e2yJyA5Zl0Wn7eE4s8b31uwAgKesXF4JAbsdJwuEwHnroIezZswdtbW24evXqjO2nTp3Cjh07sHv3bpw9exYAMDAwgIMHD6KtrQ0HDhxAb28vAOD8+fPYv38/9u3bh3/4h3+AzyedSVumXgKVTJVwAHEwHMSoz4liHnJ8AZE8X0Bsy1eXnfPJ52awPSFNjNrIQ1aKub6iX/G6GjAMg0ZDiyCWr6l4m8jcXlnSCAaMYG0V5xWjQlMZbTPEhtBtv8B7W0RuMOgZgNPnnPNhX60z4tNVm3C6ux0syyZ0LovLgpL8EmiUGiG6Kjriiq8zZ87A7/ejvb0dhw8fxpEjUzWchoeHceLECZw8eRLPPPMMjh07Br/fj6eeegoHDhzAiRMncN999+HYsWNgWRbf/e538fjjj+OFF17Apk2b0N/fL+jFZRIZI0O1zpiw23F0MjCfL8tXUZ4eKpkqZq4v7iuYxBchJriyXFYJppvg0s5w8StNhmZcsHfx7mI1R+d2JABeq9RiadEyQdyBZlsHmktXg2GYyTYjz5NOivtatHAiv7l09Zxtu1buRe9oDz4cOpfQuazuvugzYTEQV3ydO3cOmzZtAgCsXbsWHR1TE+38+fNobW2FSqWCTqdDTU0NLly4gAceeAC33norACAUCiEvLw9XrlyBXq/Hs88+iwMHDsDpdKKurk6gy8oORl1Nwm7HaFFtnmK+GIZB6TxZ7s22DixRl6O0oJSXtgiCD6ScaNXqskAhU6BcXQEgEoTsDXlxZbSX13bMtk7UFi6FVqWL/tZsWM275SvMhtFlM8+wcNQVLUe+PJ/ivhYxUctrjHrBX6y7C3nyvIRdj/0u66LJ8QUkUFjb7XZDq9VG/y2XyxEMBqFQKOB2u6HTTU16jUYDt9uNkpKIoOjt7cXRo0fx9NNPw+Fw4MMPP8R3v/td1NbW4m//9m/R0tKCW265Zd625XIGer064YuRy2VJ7c83dSVL8WbvbxPqQ9A9DgAwGip463OFrgLOgA16vXrGWFx0duGG8jVZHZtsku37QiyIbRz0UMNQYMCw/1rG+yX0WFz3DcCoM8JQEnk+bli6HgDwifcS1utv4K2dC04zbqi4Yca1rKtei1/1vgqlmoVGFd+Fk8hYXLZfxnhwHOtN62bs27ykBZdGu0R1X6WD2OZItkh0HC6NXUBtUS1qyivnbNNDjS/Wfwmv9LyMp+58Ekq5ct7zsCwLq9uC21Z8XnTjL9Q9EVd8abVaeDye6L/D4TAUCkXMbR6PJyrG3nvvPXzve9/DE088gbq6OvT09KC2thYrVkTyTG3atAkdHR0Liq9QiIXTOZ7wxej16qT255sleZUYdA/ius2BPHnegvv2jUQyQ6uCGt76XKIyYGDsGpzO8ehYBMNBmIc78T/W/G1WxyabZPu+EAtiHIcqjRE9I70Z75fQY3HF/gmqNMZoG5WKWsgZOT7oO4fPVd7JSxsTwQlcsnfji8vunnEtdZqVYMHi/SvnsK58fdzzJDIW7135AACwtKB+xr4rixrx5tXfiO6+ShUxzpFskOg4/OXaX7CquGnefe9ethMvXTiNVzpex+dqb5v3PHavDZ6AB6WqCtGNf7L3RFmZLv5OSMDtuG7dOvzud78DAHz00UdoaGiIbluzZg3OnTsHn88Hl8uFnp4eNDQ04L333sOjjz6Kn/zkJ1i9OuILNplM8Hg80YD9Dz74APX19QlfUC7AZeYdcMePZXN4J4tq8xTzBXD1HWe6HXucl+EP+2mlIyFKjDoTrBLM9WV1WVCtnVq1la/Ixwp9Pa8uum77BYTZ8JxYTm6u89mW2dYBGSNDQ/GqOW2NTAxjaJ78goR08YV8uOToXvDdsrXmcyjJL4lbbii6OniRpJkAErB8bd++He+++y727t0LlmXx2GOP4fjx46ipqcG2bdvQ1taG/fv3g2VZ3H///cjLy8Njjz2GQCCABx98EACwbNkyPPLII3j00Udx+PBhsCyL1tZWbN68WejryyjTY1iWFS0cz+b0RcQXX+WFgEiur5GJ4RmrS3K9rBAhbUw6E35nfQcsy0YDuXOdQCiAQc/AnGSRTYZmnLv+AW/tzM67xVFTWAuNUstr3JfZ1om6ouVQK2e6X6bnMFui3spbe4T46XZcRIgNLfhuUclVuHvFDrzQ9Txc/jHoVIUx9+NWPC+WBKtAAuJLJpPhkUcemfHb8uXLo/+/e/du7N69e8b2V199Nea5brnlFpw+fTqVfuYE1dGl8/G/5B1eOxgwKMrT89Z+mboMgXAATp8DxYjEephtnVDIFGgoXslbOwTBF9VaEzwBN0Z9Tl6twNlk0DOAMBues3KrydCCX15+CWO+URTmFaXdTqftY6gVatQWLpvxu4yRobGkiVfLV6ftY9xQ1jrn98aSKSvbZhOJr8WEOcFV9Dsb9uB4x0/wq97XsHfVl2Puw6VmqV5E4ouSrPJItdYIBkxC2aUdPgf0efpoVmA+mEq0OpVuwmzrQL1+JVRyFW/tEARfcNZiKZUZ6p9MM2GMYfkCgC57Fy/tmG2dWFXSCLlMPmdb02ResURzLC2E2+/C1bFPYrqXDAUGVGgqqczQIqTT1oF8eX5cL8/68ptRW7gUp7tPzbuPxW1BgaIAhnwD390ULSS+eEQlV6FcUxF9+C6Eg8fSQhxL1OUAZiZapbJChJgxSTDdhCVaJmW2+OKvzFCkrFDHvFaHptJmOH1ODHoG0m6ryx6pSTlvW1RmaFFitnUmVC+YYRjsbNiD/7K+g2uewZj7WF0WGLUmyYQeJAKJL54xak0Juh0dvCVY5eDqOw6NX59sw45+tzUal0EQYiOaaFWAWoTZgpv/s8ukVGmrUZSn56X0z/Xxa7B77WieZ25HE6COfJx2W9HYsgXa6rZfQCAUSLstIncw2zrQnGAs8a6GPWDB4qXuF2Nu73dZ5liKpQ6JL54x6owJux35Ki3EUaaeLDE0ufKoyxb5Yp0dkEsQYsGQb0CBokBSJYasLgtKC8pQoCiY8TvDMJNWovTFV7yFNFzSSz4sUmZbB3SqwnkTYDYZmuEP+3HZeSnttojcYGh8CCMTwwl7Ver0K3Bj+fp5E65a3SS+iDQx6mow4O5HmA0vuJ/T6+A9wFifVwyFTIHhiUjMF610JMQOwzCo1hollW7C6rbMu2qr2dCCLrs57vMhHp2ToqoxRmZxACjMK0KNrpYnoRcJXZjPJdRsWD25H8V9LRai75YkvCo7G/ag0/bxnA+C8cA4RiZGFlV2e4DEF+8YdSb4w/45+bZmY/faUcJzzJeMkUVKDE22bbZ1oiS/JFrihCDEiFFnkpzbsXpeK1ELPAE3+sauptWGeaQD1Vrjgh9wfMRiRWLLFo4bXaGvh1KmpLivRYQ5Kv4T96rcveJeyBk5XpoVeD/fAhWpQ+KLZ4yT6SYWcj0GQgG4Ay7oeczxxVFWsCQacM8F5C6mIEYi9zDpaiTjdmRZFv1u67wvEr4SoCaykKbJ0IzLzkvwBr0pt2N1W+Dyjy1oPVfKlWgoXkWWr0WE2daBCk0lDAWJr04sLSjF1prP4aXuUzMsv1yMpGkRFdUGSHzxzlQA8fxuFKfPCQC8B9wDkbiv4fEhhMIhXLB30UpHQvRUa40YnhhKSySIBZvXhongxLxux5UljWDApCVU/CE/Ljkvxg0naDK0IMSGcMlxMeW2ooWTExB6ZPlaPKS6in5Xw14MePrxx4F3o79xIQezF6hIHRJfPGNKIG+Rw2sHAN5TTQCc5WsYvc5ejAfHKd6LED2clahfAnFfnPt0doJVDo1Sg2VFdWkJlW7HRQTDwQQE0eSKxzSEHrdacr7YsultDXoGYPfaUm6LyA0CoQC67RdSerd8fukd0Cp1M8oNWV19kDNyVGqq+Oym6CHxxTM6VSGK8vToX8Dy5ZgsLSSI23GyvuP56+cBxP9iJYhsw7kbLBLI9WWJ1qib/yueS4CaKokupFlWVId8eX5aQs9s60Rt4VJoVQsXC44mkJ1cYU1Il57R1OsFq5VqfHH5XXit9xVMBCcAROZMpaYqbr4wqUHiSwCqtcYF3Y6c5YvvgHsgYvnyh/141/J7yBgZVpY08t4GQfBJ1PIlgbivaIHgBYKHmwzNuDLaC0/Ak1IbZhzJx5sAACAASURBVFsn8uR5WK5fseB+cpkcq0oa0xRf8ydync70Go+EtOGsoal6VXY27IHLP4b//OQ3ALBgjKSUIfElACadacGveKeglq9Irq8zV85gedGKObmGCEJsVKgrIWNksLhzf8Vjv9sCjVK74NxuMrSABYuLKZYZMts6EsoszrWVqiAaD4yjd7QnoUSaSwqWoLSglOK+FgFmWyeUMiXq9Q0pHf/pqk2o1FRFc35FVgcvrngvgMSXIBh1pgVLDNkFtnwBwAVbF8V7ETmBUq5EpaZKEiWGLK5Ijq+FVhhz7ppUY7GSCXZuMjRjZGIY1yerXiRDt+MCwmw4oecIwzBoTNOdSuQGZlsHGopXQSlXpnS8XCbHjvpdONP3JobGhzDg7l90Kx0BYHE5WTOEUVuDMf8oxnyjKMwrmrPd6XVAzsihUxXy0t7W529Dx9gf5/z+Ss/LeOXfXo7+u6XwFrx94Le8tEkQ6TL7vj118QWcuvjCnP1y6b5N5Cu+prAWGqU2JaEyPD6MofHriYsvzh040oHymvKk2oqudCxNXOg91/lThMKhmMW+CWlgtnXiM9WfTenY2XO+5WcR1/mTf/4BnvzzD6Z+z6E5nyokvgTAqONyfVnQHEN82b126PP0vOXfWl/Viu6zN8P/2pPz7qP60iHc9OUgL+0RBB9I8b61uvqwrnz9gvvIGBkaS5pSctF12bnUD4lZtafnFdtSsy2ptsy2DqgVaiwtXJbQ/s2GFkwEJ3BltBcriuuTaovIDexeGwY9Ayl7VaQ451OF3I4CwAUPzlcyxelz8Jpm4vCGQ5Ctew7Qxq4YD+0gZOtO4FsbD/HWJkGki9TuW3fADYfPMW+Or+k0l66G2dYJlmWTaiPZkmEl+QZUaqpSsrKZbZ1oNDRBxiT2muBiw8j1KF241ayprqKX2pxPBxJfAhAv0arD5+A12L5cU4G9jfug2nI05nbVlqPY17gP5erk3A4EISRSu2+51ZqJBA83GZox6nNiwN2fVBtmWyeWqMtRWlCa8DGpJECNlBVKbKUjR33xSsgZOYkvCZNKTcfpSG3OpwOJLwEoKyhDnjxvfvElQF3Heb8oFtGXBJF7SOm+jZdgdTpNKVqJOkc6krY6NBla0O24gEAokPAx1zyDsHvtSbWVr8jHCn09rXiUMGZbJ0oLSrFkcmFXKkhpzqcDiS8BkDEyVGmr5xVfTq9jwYK4qTDfF8Vi+pIgcg/uvlVK4L61Tq5wTsTt2DiZfy8ZoRIMB3HRnvwq5iZDMwLhAC47LyV8TLLuzeltkfiSLmZbBxrTrBdM76oIJL4EwqirgXWevEV2r12Q0kJzvigW2ZcEkZsc3nAIcgnct1aXBQqZAuXqirj7FuYVoUZXm5Tlq8eZWmbxVKxsnZMCKl5ZoVht9bmuYsw3mtRxhPjhs14wvatIfAmGSWuCNUbGbl/Ih/GgB8UCJFid/UWx2L4kiNyEu28Vmx8HACg2P56T963F1YcqTXXCaRaStRJx4qnZsDqpfq3Q10MpUybdllFrStpCHy0zlGICWUK8XBntxURwIqGku/GgdxWJL8Go1hlxffwafCHfjN+d3kh2eyEsX8C0L4qKDxfdlwSRuxzecAjyG58HKj5EeO3xnLxvrS5LUmVSmgzNuOy8BG/Qm9D+ZlsnFDIF6ouTyyyulCvRULwqKctXVxKJXKeTaiwbIX6mxD8/ybsX+7uKxJdAcBl7Z2e654pqC2H5Aqa+KGRf3bboviSI3KVcU4F9jfuAg1vBMGGoc7AsVrI16poMLQixIXQ7LiS0v9nWgXr9SqjkqqT71mRoTjijvi/kwyVnd0q5nKq01SjK01PclwQx2zogZ+SoL17Jy/kW+7uKxJdARHN9zQq65yxffAfcT+fwhkP49NKbFt2XBJHbHN5wCC3lKxGSj+P1nlez3Z2kCIQCGPQMJC2+gMSD7pMpKxSrrWueQdgmbHH3veToRjAcTKkthmEm3alk+ZIaZlsnVujrka/I5+2chzccwgbjjYvyXUXiSyCM2shDuH9W3JeQdR05yjUVeOurbyy6LwkitynXVOCt/W9iWVFdtOhurjDoGUCYDUfnfSIsK6pDvjw/IYuU0+tAv9uacn6lqVis+EIv1ZWO09sy2zoRZsMpHU+Ik3TE/3yUayrwyq6XF+W7isSXQFRpq8GAgcU1c8Wjc9LtyGeSVYKQCgzDYGfDHvy+/3dJJyDNJpyFOxnLl1wmx6qSxoQsX132SGbx5lQtX9NqPMbDbOtEnjwPdfrlqbVlaIEn4Ebf2NWUjifEx5hvFH2uqykLcmIuJL4EQiVXoVxTMafEUCYsXwSRy+xs2AMWLF6+dDrbXUkYbp4nkuNrOs2lq2Ee+ThumaHOkY8BpG6NKleXo7SgLCGh1znyMVaWNEIhS630b3OS7lRC/HCrV/m2fC1mSHwJiFFrmuN2dHodUMgU0Ci1WeoVQYibZUV1WF9+M168eDLbXUkYzvJVlUBpoek0GZph89owNDG04H5mWydK8ksSyiE2f1stCcVipeteWlnSCAYMxX1JiHRd0cRcSHwJiElnmuN2dPjsKM4rSStDMEFInZ0r96DL3onOBNxkYsDqsqC0oAwFSa7SjAbdx7lOrs5iOs+NJkMzLti7EAqH5t1naHwIwxNDaYkvjVKDZUV1ZPmSEGZbJ4ry9KjSVme7K5KBxJeAVOtMGHD3zwg8dXgdKBZwpSNBSIG7l++AQqbImcB7q9uStMsRABoNkQzyCwkVvjKLNxma4Q15cWW0d959uib7ka6FI1ErG5EbRMR/MxkNeITEl4AYdSb4w34MjV+P/uYQqLQQQUgJQ4EB22q24+VLLy5oqRELkQSr8Qtqz6Yk34BKTdWCQuXq2BWMB8eTzmw/m+YEEqCaeRNfzbgy2gtPwJPWeYjsE2bDMNs6eUuuSkQg8SUgJu3cXF8On0OwBKsEISV2NuzBoGcAfxj4fba7siAsy0YSrCaRZmI68coMdUYFUXqWr/rilZAz8jjiqwPl6gqUFpSm1VaToQUsWFykMkM5T9/YVXgCbor34hkSXwLCfQlPF19Or0PQBKsEIRU+v/QO6FSFeLFb3IH3IxMjmAhOwKhLLtieo8nQgm7HBQRCgZjbzbYOyBgZGkpWpdNN5CvysUJfv6DQ4yuXE3cOivvKfcw8iX9iJiS+BIR7GFvc0y1fkYB7giAWpkBRgC/V3Y3Xe17FeGA8292Zl343l+MrebcjEHmpBcIBXHJ2x9xutnViedGKpIP552trvqSuwXAQF+1dvFg4agproVFqKe5LAphtHWDAYGVJY7a7IilIfAmITlWIojw9rJMrHieCE5gITlDAPUEkyM6Ve+AOuPDmJ7/OdlfmxZJCgtXpxCtGza105IMmQwssrj6M+UbnbOtxXoY/7OfFwiFjZGgsaUq4niQhXsy2TiwrqoNGqcl2VyQFiS+BmZ7ri6vrSAH3BJEYn6r6DKo01aJe9RjNbp9kji+OFfp6qGSqmC46l38MV8c+4c3lE3UHTmbMn06nLb1ErnPbaoHZ1hk3gSwhbvgU/8QUJL4EJpLrK/JwdkyWFqKAe4JIDBkjw46GXXjbcgYjEyPZ7k5MrK4+aJTalEuGKeVKNJSsimn56rJNZhZPsabjbJpLIysmY7VlHumEQqZAfXEDT221YNTnzKkyUcRMPAEProz2UryXAMQVX+FwGA899BD27NmDtrY2XL06s17XqVOnsGPHDuzevRtnz54FAAwMDODgwYNoa2vDgQMH0Ns7M6/Md7/7XfzgBz/g8TLES7XOGC094pgsLUSWL4JInF0NexEMB/HK5Zey3ZWYWN1WmHSmtBOgxrJ8TWUW5+flV6mpgj5PD/NI7Lbq9Suhkqt4aSueO5UQPxftXWDBkuVLAOKKrzNnzsDv96O9vR2HDx/GkSNHotuGh4dx4sQJnDx5Es888wyOHTsGv9+Pp556CgcOHMCJEydw33334dixY9FjTp48ie7u2IGlUsSorYHLP4ZRnxOOSbcjrXYkiMRpNDSh2bBatK5Hq8uScpoJjiZDC655BmGbsM343WzrQKGqKO3zczAMM28CVL5WOnI0TgZo04rH3IVWOgpHXPF17tw5bNq0CQCwdu1adHRMTdrz58+jtbUVKpUKOp0ONTU1uHDhAh544AHceuutAIBQKIS8vDwAwIcffoi//OUv2LNnjxDXIkq4rNdWlxUO36Tli9yOBJEUOxv24Nz1D9DrvJztrszB6upDdYrB9hzcy63LPlOocIKIz8ziTYZmdNnNsypv2DHg6Y+6JfmgMK8INbpasnzlMGZbB7RKHWoKa7PdFckRt2y92+2GVjtVBFoulyMYDEKhUMDtdkOn00W3aTQauN1ulJRE3Gq9vb04evQonn76aQwNDeFHP/oRfvSjH+HXv05s5ZJczkCvVyd8MXK5LKn9M8GqyhUAAAd7HV7GDQBYVm6ERiVsP8U4FtmCxiJCLo/DwfVteOSP38Xrfb/EQ0v/n7TPx9dYuP1uOHwO1JfVpXW+WxQ3AQCueLrxRf3tACLJW7vsnTiwuo3Xv9t60zr85OP/DSeGUKevg1wug8XXAwC4uXYdr22tqViDC46unLnvcnmO8Ak3DhedZqwub0FJsTb+QRJFqHsirvjSarXweKZKRITDYSgUipjbPB5PVIy99957+N73vocnnngCdXV1eO655+BwOPA3f/M3GB4ehtfrRV1dHXbs2DFv26EQC6cz8fw+er06qf0zQRGzBABw8dplDLqHoJKp4PcAgXFh+ynGscgWNBYRcnkcNCjGZ4y34vnzz+PvV387bUsQX2Nx0R4JoShV/v/t3XtYlHXeP/D3HBhOwyFAQQUUkDFAXUWNWjNPkWVrqSCgidtpt+3arl3KWu3kU56tHq+6etqevbayjerB45Xm1uOzmv3spK1srM2MgIIHEDwwgDAgzDAzvz+QARSYYU733MP79Zdwnz73x/sePvO9v/f3G+PU/vwRgqjAYSiu/gmNqs79nG86h2ZDM5KU41z6/zYmMBkAcPTMcURIYhAeHoQfzxUDAEb7J7v0WKrQW/Hl6S9wsa4eAfIAl+3XXcR8j7hSeHgQGhpacOLyz1g4NmtI52Sw18SwYSG2V4Idjx3T09Nx5MgRAEBJSQlUqu43YSZOnIji4mK0t7ejubkZFRUVUKlUOHr0KDZs2ID33nsPEyZ0NmOvWLECe/bsQWFhIX7729/iV7/61YCFl68YFjgM/jL/zseO1+d15OSkRIO3RJWLs01ncPzSj0KHYtU1hl+s0rEBVnu6sS+Wu/rbjItIgQSSm44VGRCJ4UHRLj1WauR4mCwmnGooc+l+yf1q9Bdwtb2R/b3cxGbxlZmZCYVCgby8PGzatAnPP/88tm3bhkOHDmHYsGHIz8/HsmXL8Otf/xpPP/00/P39sXHjRhiNRqxevRr5+flYs2aNJ87FK0klUoxSxqK6uQoNbQ0cYJXIQfcnLkCgPNCrOt53D7Dq2BhfPaVGpqG0/iQ6zB0Aut8SvDUy1el99xTsF4yEsMReA6B2jeXk6i+GXW/JcbBV8el+05ZvOrqDzceOUqkUa9eu7fW7pKQk679zcnKQk5PTa/m+ffsG3OdQaPHqaVRIHKr156GQ+XOYCSIHhShCce+Y+dh7eg/WTd/ssiERnHFBXw25VI7ooBin95UamYY2UxvOXK1E8i0qaHRqjAlNgNLP9f1tUiPHWwdVNZlNOFmvxYrUR1x+nISwRATIAvjGowh1/Z+lcFoht+Agqx4Qp+wcaLWxrcHhgRiJqPOtx/q2ehyuOiR0KACAqubzGKmMhUwqc3pfaTeMi+XOkcVTI9Nw9uoZ6I16VDRU4FrHNbccSyaV4daIFLZ8iZBWp0Z8yGiE+ocJHYpPYvHlAbEhcbjcegkXW2oRwZYvIofNipuLyIBI7CwrEjoUAJ1jfMW5aAyu5FvGQSaRQatTo9XYisrGCrf1t0mNHA8LLCirP4mfL3dNK+S+Y2nrfuY0QyLj6nHfqDcWXx7QNeFuQztbvoic4Sfzw8LkLBw4+0Wfk0N7WnVzFUa5oL8XAATIA5B8iwpancbtI4unRXW1smnw8+UTkEqkUEXc6rZj6dp0uHztslv2T67X1tGG042nWHy5EYsvD4jtMQAjO9wTOWeJKg/tpnbsrxy4b6m7GU1GXGyt7XV/O6trmqGu/jZpLprT8UZxIfFQ+oVAq1Pj5ys/Y2x4MgLlgW45lnWaoTo+ehSLk3UnYbKYXDroLvXG4ssDek4Nwg73RM6ZPHwKEsOSBH/rsbalBmaLGXEuGGaiS2rkeFQ1n8fR2u8RJA/G6NAxLtt3T1KJFCmRqdDqNFBf/tmtLRwp19/WZKd78XD3o2gCJBYvfhBvNJpEO8jqnI/nQd30g831xofega+WH3D58b0pF0JjLjr5Qh4cua/cdS9+f+FbLNw7HzsX7MXMuNl2b3cjT35W+OqxXEXs94g33R++wl2DrNocaoIcM3XkZJQfvg2Gz9/sdx3FggJMe6jDg1ERiZsj95W77sVqvWvG+PLkZ4WvHos6edP9QQPjY0c3WZlRAGn6R4Cytu8VlLWQphfimdsLPBsYkYg5cl+5616svj7A6ign33b05GeFrx6LOnnT/UEDY/HlJtHBMchLWQrF7C19LlfM3oKlKUsR7eLpPIh8mSP3lbvuxermKgwLHO70nIWe/Kzw1WNRJ2+6P2hg7PPlRpdaLmLahxlo+08NoB/RvUBZi4CV4/HPR4657YL2tlwIibno5Ct5GOi+wh+T4O9vvmmaHIvFgvZ2KfBWhcvuxSX7HkST4SoOZH/txNl08uRnha8eyxV84R7xlvvDV7DPlwh1faP4dPaWXs/T+U2CyHH93VfSWRuROmw8ZiVM73O7r898C+2sjTDvf9v6O2fuxQv6aqS46G0wT35W+OqxqJO33B80MLZ8udlN30I89E3CG3MhFOaiky/lwZH7ypX3osViwZi/xuDhtMfx6vQNrjglj35W+OqxnOUr98illouY+mEG2gW6P3yJu1q+2OfLzW58ns5vEkTOc+S+uqlvy8x1WDJuiUP3Yt21OlzruIY4Fw6w6snPCiGOJZ+9GQAgm7WRn4FuFh0cg/sS7gPuWgfAsfuDf6vci8WXB1jfJon5iW+NELmII/dVz20w6UP8YkSKQ8eubj4PABjlwuIL8OxnhaePJU8vBGJ+gnnSNn4GesAv46cCk7c5fH/wb5V7sfjygK5vFNJH5vKbBJGLOHJf9dwm0C8A/zjn2KCR1fpqAHDp1EKAZz8rhDiW5OE5sMCIFqPebceiThf0FyCRAJKHHbs/+LfKvVh8ecjKjAJkxE7hNwkiF3LkvlqZUYDpY6YhN20xDp3/B3TXdIM+btcYX3FOjvHVF09+VnTlwlPHmjxyAiA3YFeZsFNDDQVanRpJUXG4Pc6x+4N/q9yLHe59FHPRjbnoxDx0Cw8Pwrenj2HOjunYfNd/4tHxvxnU9i9+8yf8T+knqHi8+qbX9sXG09dF1t4FqGo+j2MPlXhd7nzpHpn8USoyRtyB/858f9Db+lIenMUO90RELjQ+agJSItIcaoWp1lcjNiTW64oHMchW5eJs0xkUX/qn0KH4rMa2BlzQVyMtaoLQoVA/WHwR0ZCVPS4Xxy/9iMqrFYParrq5CrFueOQ4FPwq6QEEyAKwq5yPHt3lZL0WAJDmonHoyPVYfBHRkLV4bDYkkGB3+Y5BbVfdfN7lne2HihBFKO5NmI/PTu+G0WQUOhyfpNWpAQCpkeMFjoT6w+KLiIasUSGxmD5qBnaVb4e93V/1Rj0a2hsQGxLv5uh8V7YqF/Vt9fiq6qDQofgkTZ0aEQERiA6KEToU6geLLyIa0pao8nDmaiX+dfm4XetfaO4aZiLWnWH5tNlxdyMyIJJvPbqJVqdGauR49kn0Yiy+iGhIuz9xAQJkAdhZVmTX+l0DrMYq2fLlKD+ZHxYmZ+HA2S/Q1H5V6HB8islsQmn9SaSyv5dXY/FFRENaqH8Y5o2xvw9SVdcYX+zz5ZRsVS7aTG34e+XnQofiU841nUFrRyv7e3k5Fl9ENORlj+vsg3TYjj5I1c1VkEvlGM7Rv52SPnwqEsIS+daji2l0GgBgy5eXY/FFREPe7Li5iAiIsKsQqNZXYaQyFjKpzAOR+S6JRIJsVS6+vXAENfoLQofjM7Q6NaQSKVS33Cp0KDQAFl9ENOQpZAosHJuF/z3zBZoNTQOuW91c5ZZphYaiLFUOLLBg96mdQofiM7Q6DRLDkhDkFyR0KDQAFl9EROjug7S/Yt+A61U3V3GMLxdJDEvC1Ojb+NajC2l1aqRFcmR7b8fii4gIwJToaRgTmjDgo0ejyYiLrbUYxWEmXCZ7XC5O1mugqVMLHYro6Q3NONd0lv29RIDFFxER7OuDVNtSA7PFjDgOM+EyDyYthlwqZ8d7F+iaVig1im86ejsWX0RE12Vf74O059SuPpdXXx9mgo8dXScyMBJz4zOx+9QOmMwmocMRta7WQ7Z8eT8WX0RE1yWGj8WU6Gn9DrhadX2AVY7x5VpLVHm42FKL72q+EToUUdPq1AhRhHLSdxFg8UVE1EO2qv8+SBf0nVMLjVSyz5crZY65FyGKUD56dJJWp0FqZBqnFRIBFl9ERD08OLb/PkjVzVUYFjgcAfIAASLzXYHyQCxIfBD7K/ah1dgqdDiiZLFYrMUXeT8WX0REPUQFRmFO3N3Yc2rnTX2QqprP85Gjm2SPy4Xe2Iz/O/ul0KGIUlXzeeiNzZxWSCRYfBER3WDJuDzUttTg+5pve/2+Wl+F2BC+6egOvxx5J0YGj8LOcvsmOKfetJxWSFRsFl9msxlr1qxBbm4u8vPzce7cuV7Ld+zYgcWLFyMnJweHDx8GANTU1ODhhx9Gfn4+li9fjsrKSgDA/v37sWTJEuTl5WHNmjUwm81uOCUiIufcM+Y+KP1CehUCFosFF5qrMYr9vdxCKpEiS5WDr84fRN21OqHDER2trrOPYgqLL1GwWXwdPHgQBoMB27dvx8qVK7F582brsitXrqCwsBBFRUV4//33sXXrVhgMBrz11ltYvnw5CgsL8cQTT2Dr1q1oa2vDm2++iY8++ghFRUXQ6/XWYo2IyJsEygOxIKl3H6S6a3VoM7XxsaMbZatyYbKYsPf0bqFDER2tToMxoQlQ+imFDoXsYLP4Ki4uxowZMwAAkyZNglrd/QbQiRMnMHnyZCgUCoSEhCA+Ph6lpaVYtWoVZs6cCQAwmUzw9/eHQqFAUVERAgMDAQAdHR3w9/d3xzkRETktW9W7D1L19WEm+NjRfVIiU5EWOYFvPTpAq1Ozv5eIyG2toNfroVR2V9IymQwdHR2Qy+XQ6/UICQmxLgsODoZer0dERAQAoLKyElu2bME777wDqVSKqKgoAEBhYSFaW1sxffr0AY8tk0kQHm7/5KAymXRQ6/sy5qIbc9GJeehmTy7mh96DUYdH4bMzu/DwtHw0XLwCAEgZmexTefS26yL/F8ux+qtVuGyqhipS5dFje1su7NVqbEXl1Qrkjc9zSfxizYM7uCsXNosvpVKJlpYW689msxlyubzPZS0tLdZi7OjRo3j11Vfx2muvITEx0brt66+/jjNnzuDtt9+2ORaJyWRBY6P9rx2HhwcNan1fxlx0Yy46MQ/d7M3ForFL8N///i+crj2PsounAQBhliifyqO3XRfz4xbieazGtuK/YdVtL3r02N6WC3v9dKkYZosZicHjXBK/WPPgDoPNxbBhIbZXgh2PHdPT03HkyBEAQElJCVSq7m8iEydORHFxMdrb29Hc3IyKigqoVCocPXoUGzZswHvvvYcJE7pnV1+zZg3a29vx5z//2fr4kYjIW2WrctFh7sDe07tR3XweSr8QhPmHCx2WT4sJHoEZsbOwq3w7LBaL0OGIgvVNxyh2thcLicXG1W02m/HKK6+gvLwcFosFGzduxJEjRxAfH4+5c+dix44d2L698yZ54oknMG/ePDzwwAMwGAwYNmwYACAhIQG5ubnIysrC1KlTrS1eK1asQGZmZr/HNhpNbPlyEHPRjbnoxDx0sycXcz6eB3XTDzb3NT70Dny1/ICrQvM4b7ouhM65O3LhiXN68Zs/4ZOTH6HyNzWQSpwfQcqbrgmhuavly+ZjR6lUirVr1/b6XVJSkvXfOTk5yMnJ6bV83759fe6rtLTUrqCIiIQ2deRklB++DYbP3+x3HcWCAkx7qMODUfk2X8y5J85Jq9MgJTLVJYUXeQb/p4iI+rAyowDS9I8AZW3fKyhrIU0vxDO3F3g2MB/mizl39zl1TivENx3FhsUXEVEfooNjkJeyFIrZW/pcrpi9BUtTliI6KNrDkfkuX8y5u8/pYkstGtobOLK9yLD4IiLqR7+tFiJsgRELX8y5O8+pa2T7tMgJNtYkb8Lii4ioH/21WoixBUYsfDHn7jwnzfU3HVMiU52KkTyLxRcR0QBuarUQcQuMWPhizt11TlqdGrHKOA6BIjIsvoiIBnBjq4WYW2DE4sacS2auF33Ou84JM9d3/uKudZifON/pczqp07C/lwix+CIissHaahHzk+hbYMSiZ84tk7Zh6fglQofktCen/BaY9AEQ8xMw+UMMDw1zan/tpnacaiznm44ixOKLiMiGrlYL6SNzRd8CIxZdOZc8PBeQmHCk+v8JHZLTGtrqAYkZkofnYoQyBl+e+btTo/iXN5Shw9zBli8RYvFFRGSHlRkFyIidwlYvD1qZUYDb46Zg0og07CovEv10Q1qdBpAbMGlEGn4/9QmcazqL45d+dHx/dZ1vOrLlS3xYfBER2SE6OAZ7l+xhq5cHdeV8aUo+SutPQnN9WAWx0urUCPZT4su8/ViashyB8kDsKt/uxP408Jf5IzE8yfbK5FVYfBERkVd7cOwi+En9sLOsSOhQnKLVEWgSgQAAFUpJREFUaZAS0TkNUIgiFPeOmY/PTu2GwWRwcH9qjItIgVxqc6ZA8jIsvoiIyKtFBERi7uh7sOfUTpjMJqHDcUhf0wBlq3LR0N6Ar84fdGifWp0GaXzkKEosvoiIyOstUeXiUutFfHvhiNChOKRGfwGN7Y1IjeruHD8rbi6iAqMcevR4ufUyrly7zM72IsXii4iIvF7m6HsRogh1qo+UkLqmAerZ8uUn88PCsVk4cPYLNLVfHdT+Tl4f2Z6d7cWJxRcREXm9AHkAHkhaiP2V+9BqbBU6nEHTdhVLEb2nAcpW5aLd1I7PK/Y6tL8UtnyJEosvIiIShWxVLlqMevzv2b8LHcqgaXVqxIeMRqh/74FVJw+fgsSwpEG36Gl1akQHxSAqMMqVYZKHsPgiIiJRuGPkdIxSxmJXmfgePWr7mQZIIpEgW5WL72q+QXVzldP7I3Fg8UVERKIglUiRlZyDw1WHcKX1itDh2K2tow2nG0/1WyxlqXIAAHtO7bJrf0aTEWX1J9nfS8RYfBERkWhkj8uFyWLC3tO7hQ7FbqcaymCymPotlhLCEjEtJsPuUfwrrp6GwWxgy5eIsfgiIiLRuDUiBeOjJmJnuXgGXNX08abjjbJVuSitPwm17meb++vrzUkSFxZfREQkKtmqXPx0+V843XBK6FDsotVpECALQEJYYr/rPDh2EeRSuV392bR1GvhJ/ZB8i8qVYZIHsfgiIiJRWZycDalEil2nxNHxXqvT4NaIFMiksn7XiQiIxN3x9o3ir9WpkXzLOChkCleHSh7C4ouIiEQlJngEZoyaiV3lO+zqIyWkzmmFfrbrEWG2naP4801H8WPxRUREopOtysX5prP458UfhQ5lQJevXUbdtTq7iqV7xtyHEEXogP3ZGtrqUdNygf29RI7FFxERic79iQsQKA/0+o732rrrneOjbBdLXaP4/73yc7QYW/pc56RO27k/tnyJGosvIiISHaUiBPcl3I+9p3fDYDIIHU6/rNMK2VksdY3if+DsF/3sr7OYS2PLl6ix+CIiIlFaospDY3sjDp3/h9Ch9EurU2NE8EhEBETatb6tUfy1Og0iAyIxPCjalWGSh7H4IiIiUZoZNwdRgVGDnhfRkwbbOd7WKP6a6533JRKJK8MkD2PxRUREoiSXyrFobDb+7+yXuNreKHQ4NzGajChvKB105/iuUfw/O917uiGT2YTS+pPs7+UDWHwREZFoZaty0W5qx+cVe4UO5SanG0/BaDYOuljqGsX/xha9s02VuNZxjW86+gAWX0REJFqThqcjKXysVz56dGYaoL5G8R9s533yXiy+iIhItCQSCZao8vB9zbeobq4SOpxetLrOaYDGhicPetu+RvHX6NSQSqQYF5HiyjBJACy+iIhI1BYnLwEA7Dm1U+BIetPq1FDdciv8ZH6D3ravUfy1Og3GhicjQB7g6lDJw1h8ERGRqI0JS8BtMbdjZ1mRV003pNGpnXpE2DWK/48XjwHgtEK+hMUXERGJXrYqF2UNpVDXnRA6FACA7poOF1tqneoc3zWK/67y7Wg2NOF801l2tvcRcqEDICIictScj+dB3fSD9ee5O2f0ud740Dvw1fID/W7Xnxu3s9fJeuc6x/eM72+a9/E3zfsAgI3H1mLjsbVOx0fCsll8mc1mvPLKKygrK4NCocD69esxevRo6/IdO3agqKgIcrkcTz75JGbPno2amhq88MILMJlMsFgsWLt2LRITE/HVV1/hnXfegVwuR1ZWFnJyctx6ckRE5NumjpyM8sO3wfD5m/2uo1hQgGkPdbhkO3t1zemYFjXBoe3dHR8Jy+Zjx4MHD8JgMGD79u1YuXIlNm/ebF125coVFBYWoqioCO+//z62bt0Kg8GAt956C8uXL0dhYSGeeOIJbN26FUajEZs2bcIHH3yAwsJCbN++HVeu3Dx6LxERkb1WZhRAmv4RoKztewVlLaTphXjm9gKXbGcvrU6DqMBhGB403KHt3R0fCctm8VVcXIwZMzqbcSdNmgS1Wm1dduLECUyePBkKhQIhISGIj49HaWkpVq1ahZkzZwIATCYT/P39UVFRgfj4eISFhUGhUGDKlCk4fvy4m06LiIiGgujgGOSlLIVi9pY+lytmb8HSlKWIvmEuREe3s5dWp3aqf5a74yNh2XzsqNfroVQqrT/LZDJ0dHRALpdDr9cjJCTEuiw4OBh6vR4REREAgMrKSmzZsgXvvPMO6uvr+1yXiIjIGSszClB0MgM4vArQj+heoKyFceJf8XVNDKZ/evPk2x3mDhh/cbHP7TpblY45FE/XNEAPj3/coe27DHRezsRHwrNZfCmVSrS0tFh/NpvNkMvlfS5raWmxFlhHjx7Fq6++itdeew2JiYkwGAz9rtsfmUyC8PAgu09GJpMOan1fxlx0Yy46MQ/dmItuvpCL8PBE/PoX+dg2ZwsM+7r7SElnbcCYiDFIH9V/p3eJRY2zszbAvP+/rL9TzNmCX0/Kx7iRCQ7FU6YrQ5upDVPj0p3KbX/n5Wx8tvjCNeEq7sqFzeIrPT0dhw8fxvz581FSUgKVSmVdNnHiRLz55ptob2+HwWBARUUFVCoVjh49ig0bNuC9997DqFGjAABJSUk4d+4cGhsbERQUhOPHj+Oxxx4b8NgmkwWNja12n0x4eNCg1vdlzEU35qIT89CNuejmK7l4avJT+Nu/M4CvrrcSKWuhmPI/2Jt1bMBHc5daLmJaUwbavn7Rup10ciGemnzM4bwcO9PZpSYhMNnp3PZ1Xs7GZ4uvXBOuMNhcDBs2cKNSF5vFV2ZmJr777jvk5eXBYrFg48aN2LZtG+Lj4zF37lzk5+dj2bJlsFgsePrpp+Hv74+NGzfCaDRi9erVAICEhASsXbsWq1evxmOPPQaLxYKsrCxER/NZNREROa+rj9Sns7fA8PmbdveJ6tru49mb0fH5W5DN2uh0XyqtTg2ZRIbkW8Y5vI8b4xvseZF3k1i8aTjgGxiNJrZ8OYi56MZcdGIeujEX3XwpF5daLmLahxlo+8shBDxxN/75yMCtXn1tJ3l8Bk48XuJUcZP/RS7OXj2Db5b+6PA++otvMOflKF+6JpzlrpYvjnBPREQ+oauVSPrI3EG1DnVtJ3l4DiwwosXQ7FQcrp4GyNHzIu/F4ouIiHzGyowCZMROGfT4VyszCjAt/heA3IBd5TscPn5T+1VUNZ93+TRAjp4XeScWX0RE5DOig2Owd8meQbcORQfH4NvHD2FG7CzsKt/u8ATd2notACAtyrXFl6PnRd6JxRcREdF1S1S5ONt0BscvOdZfS6vrHIicE2DTQFh8ERERXXd/4gIEyAKwq3y7Q9tr6zQI9w/HiOCRLo6MfAmLLyIioutCFKG4N2E+9p7eA4PJMOjtu6YVkkgkboiOfAWLLyIioh6WqPJQ31aPw1WHBrWd2WLGyXqtS990JN/E4ouIiKiHWXFzERkQiZ1lRYPa7nzTObQY9ezvRTax+CIiIurBT+aHhclZOHD2CzS1X7V7O61OAwBs+SKbWHwRERHdIFuVi3ZTO/ZX7rN7G61ODQkkGBeR4sbIyBew+CIiIrpB+vCpSAxLGtRbjxqdGglhiQj2C3ZjZOQLWHwRERHdQCKRIFuVi+8ufIMLzdV2bdP1piORLSy+iIiI+pClyoEFFuw+tdPmunqjHmevnmF/L7ILiy8iIqI+JIQlYmr0bdhVXmRzuqGy+pOwwIK0qAkeio7EjMUXERFRP7LH5aK0/iQ016cN6g/fdKTBYPFFRETUj4VjF0MuldvseK/VqaH0C0FcSLyHIiMxY/FFRETUj4iASNwdfw/2nNoJk9nU73panQYpkamQSvhnlWzjVUJERDSAbFUuLrbU4tsLR/pcbrFYoNVp+KYj2Y3FFxER0QAyx9yLEEVov48ea/QXcLW9kf29yG4svoiIiAYQKA/EgsQHsb9yH1qNrTct117vjM+WL7IXiy8iIiIblozLQ4tRjwNnv7hpWdebkCmcVojsxOKLiIjIhjtGTsfI4FHYWVZ00zKtTo34kNEI9Q8TIDISIxZfRERENkglUmSpcnC46hCutF7ptayzsz37e5H9WHwRERHZIVuVC5PFhL2nd1t/19bRhtONp5Aaxf5eZD8WX0RERHZIiUxFWuSEXm89ljeUwmwxI42d7WkQWHwRERHZacm4PPzrcjEqGk8B4LRC5BgWX0RERHZanJwNCSTYeb31S6NTI1AeiDGhiQJHRmLC4ouIiMhOMcEjMCN2FnaV77CObH9rRApkUpnQoZGIyIUOgIiISAzmfDwP6qYfrD9Hv9s9tMTwP4da/z0+9A58tfyAR2MjcWHxRUREZIepIyej/PBtMHz+Zr/rKBYUYNpDHR6MisSIjx2JiIjssDKjANL0jwBlbd8rKGshTS/EM7cXeDYwEh0WX0RERHaIDo5BXspSKGZv6XO5YvYWLE1ZiuigaA9HRmLD4ouIiMhO/bZ+sdWLBoHFFxERkZ36a/1iqxcNBosvIiKiQbip9YutXjRILL6IiIgG4cbWL7Z60WCx+CIiIhoka+tXzE9s9aJBs1l8mc1mrFmzBrm5ucjPz8e5c+d6Ld+xYwcWL16MnJwcHD58uNeyDz/8EG+88Yb153379mHRokXIysrCp59+6qJTICIi8qyu1i/pI3PZ6kWDZnOQ1YMHD8JgMGD79u0oKSnB5s2b8e677wIArly5gsLCQuzevRvt7e1YtmwZpk+fDrPZjJdeegknTpzAPffcY93Xa6+9hv379yMoKAj3338/7r//foSFhfV3aCIiIq+1MqMAZbpytnrRoNksvoqLizFjxgwAwKRJk6BWq63LTpw4gcmTJ0OhUEChUCA+Ph6lpaUYPXo0Fi5ciF/+8peorKy0rj9u3Dg0NzdDLpfDYrFAIpG44ZSIiIjcLzo4BnuX7BE6DBIhm8WXXq+HUqm0/iyTydDR0QG5XA69Xo+QkBDrsuDgYOj1eoSFheHOO+/Enj29L8rk5GRkZWUhMDAQmZmZCA0NxUBkMgnCw4PsPhmZTDqo9X0Zc9GNuejEPHRjLroxF92Yi07MQzd35cJm8aVUKtHS0mL92Ww2Qy6X97mspaWlVzHWU2lpKb7++mscOnQIQUFBeO655/Dll1/ivvvu6/fYJpMFjY2tdp9MeHjQoNb3ZcxFN+aiE/PQjbnoxlx0Yy46MQ/dBpuLYcP6roFuZLPDfXp6Oo4cOQIAKCkpgUqlsi6bOHEiiouL0d7ejubmZlRUVPRa3lNISAgCAgLg7+8PmUyGiIgINDU12RUkERERka+w2fKVmZmJ7777Dnl5ebBYLNi4cSO2bduG+Ph4zJ07F/n5+Vi2bBksFguefvpp+Pv797mfUaNGITc3F8uWLYOfnx/i4+OxaNEil58QERERkTeTWCwWi9BB9MdoNPGxo4OYi27MRSfmoRtz0Y256MZcdGIeugn22JGIiIiIXIfFFxEREZEHsfgiIiIi8iCv7vNFRERE5GvY8kVERETkQSy+iIiIiDyIxRcRERGRB7H4IiIiIvIgFl9EREREHsTii4iIiMiDbM7tKAZmsxmvvPIKysrKoFAosH79eowePVrosASzcOFChIR0TnEQGxuLTZs2CRyRZ/373//GG2+8gcLCQpw7dw6rV6+GRCJBcnIy/uM//gNS6dD5ztEzFxqNBr/73e8wZswYAMDSpUsxf/58YQP0AKPRiBdeeAEXLlyAwWDAk08+ibFjxw7J66KvXMTExAzJ68JkMuGll17CmTNnIJPJsGnTJlgsliF3XfSVh+bm5iF5TXTR6XRYvHgxPvjgA8jlcrdcEz5RfB08eBAGgwHbt29HSUkJNm/ejHfffVfosATR3t4OACgsLBQ4EmH89a9/xb59+xAYGAgA2LRpEwoKCpCRkYE1a9bg0KFDyMzMFDhKz7gxF1qtFo888ggeffRRgSPzrH379iE8PByvv/46GhoasGjRItx6661D8rroKxe///3vh+R1cfjwYQBAUVERjh07Zi2+htp10Vce5syZMySvCaDzC8qaNWsQEBAAwH1/Q3yipC8uLsaMGTMAAJMmTYJarRY4IuGUlpbi2rVrePTRR7FixQqUlJQIHZJHxcfH4+2337b+rNFocNtttwEA7rrrLnz//fdCheZxN+ZCrVbj66+/xkMPPYQXXngBer1ewOg8595778Uf//hH688ymWzIXhd95WKoXhd333031q1bBwCoqalBVFTUkLwu+srDUL0mAGDLli3Iy8vD8OHDAbjvb4hPFF96vR5KpdL6s0wmQ0dHh4ARCScgIACPPfYY3n//fbz66qt49tlnh1Qu5s2bB7m8u0HXYrFAIpEAAIKDg9Hc3CxUaB53Yy4mTpyIP/3pT/jkk08QFxeHd955R8DoPCc4OBhKpRJ6vR5/+MMfUFBQMGSvi75yMVSvCwCQy+VYtWoV1q1bh3nz5g3Z6+LGPAzVa2LPnj2IiIiwNuYA7vsb4hPFl1KpREtLi/Vns9nc64/OUJKQkIAHHngAEokECQkJCA8Px5UrV4QOSzA9n823tLQgNDRUwGiElZmZifHjx1v/rdVqBY7Ic2pra7FixQo8+OCDWLBgwZC+Lm7MxVC+LoDOlo4DBw7g5ZdftnbbAIbeddEzD3feeeeQvCZ2796N77//Hvn5+Th58iRWrVqF+vp663JXXhM+UXylp6fjyJEjAICSkhKoVCqBIxLOrl27sHnzZgDApUuXoNfrMWzYMIGjEk5qaiqOHTsGADhy5AimTp0qcETCeeyxx3DixAkAwA8//IC0tDSBI/KMuro6PProo3juueeQnZ0NYOheF33lYqheF5999hn+8pe/AAACAwMhkUgwfvz4IXdd9JWHp556akheE5988gk+/vhjFBYWIiUlBVu2bMFdd93llmvCJybW7nrbsby8HBaLBRs3bkRSUpLQYQnCYDDg+eefR01NDSQSCZ599lmkp6cLHZZHVVdX45lnnsGOHTtw5swZvPzyyzAajUhMTMT69eshk8mEDtFjeuZCo9Fg3bp18PPzQ1RUFNatW9frcb2vWr9+Pb788kskJiZaf/fiiy9i/fr1Q+666CsXBQUFeP3114fcddHa2ornn38edXV16OjowG9+8xskJSUNuc+LvvIwYsSIIflZ0VN+fj5eeeUVSKVSt1wTPlF8EREREYmFTzx2JCIiIhILFl9EREREHsTii4iIiMiDWHwREREReRCLLyIiIiIPYvFFRERE5EEsvoiIiIg8iMUXERERkQf9fzv1QCyuV1SNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creeating a plot using the info in the for loop\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,40), error_rate, color='g', linestyle='-', marker ='v', \n",
    "         markerfacecolor = 'b', markersize= 10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating the KNN Variable with 29 Nearest Neighbors as indicated in the above graph\n",
    "knn = KNeighborsClassifier(n_neighbors= 29)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=29, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Validating The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[509   2]\n",
      " [ 11  78]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99       511\n",
      "           1       0.97      0.88      0.92        89\n",
      "\n",
      "   micro avg       0.98      0.98      0.98       600\n",
      "   macro avg       0.98      0.94      0.96       600\n",
      "weighted avg       0.98      0.98      0.98       600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Using Confusion Matrix\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print('\\n')\n",
    "#Using Classification Report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9799634456568483\n",
      "\n",
      "\n",
      "0.010020770717953584\n"
     ]
    }
   ],
   "source": [
    "#Cross Validation\n",
    "accuracies = cross_val_score(estimator = knn, X = X_train, y = y_train, cv = 10)\n",
    "#mean of the 10model evaluation\n",
    "print(accuracies.mean())\n",
    "print('\\n')\n",
    "#standard deviation of the accuracies vector.\n",
    "print(accuracies.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the ABOVE Cross Validation, the Model is 98% accurate with 0.01% of Standard Deviation which are good indications of accurate ML Model"
   ]
  }
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