covid-impact-stocks-project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "AQMFuQoqiIrY"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler , RobustScaler ,OneHotEncoder ,LabelEncoder\n",
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import  accuracy_score, f1_score, confusion_matrix, recall_score, roc_auc_score,classification_report ,plot_confusion_matrix\n",
    "from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier\n",
    "from sklearn.svm import SVC\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sb\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import xgboost as xgb\n",
    "from math import ceil\n",
    "from xgboost import plot_tree , plot_importance\n",
    "from sklearn import tree\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "_i1AMt82eGVf"
   },
   "outputs": [],
   "source": [
    "\n",
    "pd.options.display.float_format = \"{:,.2f}\".format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "jujtSuoWinrX"
   },
   "outputs": [],
   "source": [
    " #! pip install graphviz\n",
    " # ! pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "TLBUBZk-ircX"
   },
   "outputs": [],
   "source": [
    "raw = pd.read_csv('dataset_raw.csv')\n",
    "data= raw.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 704
    },
    "executionInfo": {
     "elapsed": 23,
     "status": "ok",
     "timestamp": 1636338256714,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "zAsTKJVRkozx",
    "outputId": "b8112567-d2f5-4868-83af-2385c657f75b"
   },
   "outputs": [
    {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number</th>\n",
       "      <th>Ticker</th>\n",
       "      <th>sector</th>\n",
       "      <th>city</th>\n",
       "      <th>industry</th>\n",
       "      <th>profitMargins</th>\n",
       "      <th>priceToBook</th>\n",
       "      <th>bookValue</th>\n",
       "      <th>shortRatio</th>\n",
       "      <th>revenuePerShare</th>\n",
       "      <th>...</th>\n",
       "      <th>COVID DEPENDANCY</th>\n",
       "      <th>CLASS</th>\n",
       "      <th>15/3/2020 0:00</th>\n",
       "      <th>10/4/2020 0:00</th>\n",
       "      <th>10/5/2020 0:00</th>\n",
       "      <th>10/9/2020 0:00</th>\n",
       "      <th>1/1/2021 0:00</th>\n",
       "      <th>20/2/2021 0:00</th>\n",
       "      <th>15/5/2021 0:00</th>\n",
       "      <th>10/9/2021 0:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Cupertino</td>\n",
       "      <td>Consumer Electronics</td>\n",
       "      <td>0.25</td>\n",
       "      <td>36.81</td>\n",
       "      <td>3.88</td>\n",
       "      <td>1.28</td>\n",
       "      <td>20.61</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>66.46</td>\n",
       "      <td>66.00</td>\n",
       "      <td>85.25</td>\n",
       "      <td>113.91</td>\n",
       "      <td>129.93</td>\n",
       "      <td>129.92</td>\n",
       "      <td>129.88</td>\n",
       "      <td>153.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>ADI</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Wilmington</td>\n",
       "      <td>Semiconductors</td>\n",
       "      <td>0.26</td>\n",
       "      <td>5.11</td>\n",
       "      <td>32.99</td>\n",
       "      <td>2.22</td>\n",
       "      <td>17.63</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>93.97</td>\n",
       "      <td>98.06</td>\n",
       "      <td>120.81</td>\n",
       "      <td>114.11</td>\n",
       "      <td>147.92</td>\n",
       "      <td>160.27</td>\n",
       "      <td>167.18</td>\n",
       "      <td>166.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>ADM</td>\n",
       "      <td>Consumer Defensive</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Farm Products</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.70</td>\n",
       "      <td>37.28</td>\n",
       "      <td>1.94</td>\n",
       "      <td>133.43</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>34.21</td>\n",
       "      <td>36.45</td>\n",
       "      <td>40.93</td>\n",
       "      <td>46.05</td>\n",
       "      <td>51.01</td>\n",
       "      <td>56.15</td>\n",
       "      <td>64.36</td>\n",
       "      <td>59.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>ADP</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Roseland</td>\n",
       "      <td>Staffing &amp; Employment Services</td>\n",
       "      <td>0.17</td>\n",
       "      <td>15.41</td>\n",
       "      <td>13.38</td>\n",
       "      <td>2.53</td>\n",
       "      <td>35.20</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>139.93</td>\n",
       "      <td>139.42</td>\n",
       "      <td>152.98</td>\n",
       "      <td>134.62</td>\n",
       "      <td>170.39</td>\n",
       "      <td>169.20</td>\n",
       "      <td>198.52</td>\n",
       "      <td>202.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>ADSK</td>\n",
       "      <td>Technology</td>\n",
       "      <td>San Rafael</td>\n",
       "      <td>Software—Application</td>\n",
       "      <td>0.33</td>\n",
       "      <td>54.38</td>\n",
       "      <td>5.13</td>\n",
       "      <td>2.06</td>\n",
       "      <td>17.74</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>152.20</td>\n",
       "      <td>156.28</td>\n",
       "      <td>229.26</td>\n",
       "      <td>228.20</td>\n",
       "      <td>301.69</td>\n",
       "      <td>302.91</td>\n",
       "      <td>276.97</td>\n",
       "      <td>287.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>418</td>\n",
       "      <td>XRX</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Norwalk</td>\n",
       "      <td>Information Technology Services</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.74</td>\n",
       "      <td>28.26</td>\n",
       "      <td>4.32</td>\n",
       "      <td>36.31</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>24.87</td>\n",
       "      <td>18.99</td>\n",
       "      <td>17.94</td>\n",
       "      <td>18.84</td>\n",
       "      <td>22.93</td>\n",
       "      <td>23.74</td>\n",
       "      <td>24.50</td>\n",
       "      <td>21.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>419</td>\n",
       "      <td>XYL</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Rye Brook</td>\n",
       "      <td>Specialty Industrial Machinery</td>\n",
       "      <td>0.07</td>\n",
       "      <td>7.44</td>\n",
       "      <td>16.32</td>\n",
       "      <td>4.88</td>\n",
       "      <td>28.86</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>70.40</td>\n",
       "      <td>67.45</td>\n",
       "      <td>67.81</td>\n",
       "      <td>82.83</td>\n",
       "      <td>101.20</td>\n",
       "      <td>98.64</td>\n",
       "      <td>116.59</td>\n",
       "      <td>134.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>421</td>\n",
       "      <td>ZBH</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>Medical Devices</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2.46</td>\n",
       "      <td>59.72</td>\n",
       "      <td>2.10</td>\n",
       "      <td>37.95</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>104.27</td>\n",
       "      <td>105.37</td>\n",
       "      <td>130.25</td>\n",
       "      <td>139.09</td>\n",
       "      <td>155.83</td>\n",
       "      <td>158.55</td>\n",
       "      <td>158.52</td>\n",
       "      <td>144.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>422</td>\n",
       "      <td>ZION</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>Banks—Regional</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.41</td>\n",
       "      <td>44.98</td>\n",
       "      <td>3.92</td>\n",
       "      <td>19.58</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>29.15</td>\n",
       "      <td>28.85</td>\n",
       "      <td>37.04</td>\n",
       "      <td>31.74</td>\n",
       "      <td>44.62</td>\n",
       "      <td>51.41</td>\n",
       "      <td>54.11</td>\n",
       "      <td>57.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>423</td>\n",
       "      <td>ZTS</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Parsippany</td>\n",
       "      <td>Drug Manufacturers—Specialty &amp; Generic</td>\n",
       "      <td>0.26</td>\n",
       "      <td>22.99</td>\n",
       "      <td>8.61</td>\n",
       "      <td>3.03</td>\n",
       "      <td>15.59</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>123.16</td>\n",
       "      <td>127.23</td>\n",
       "      <td>137.03</td>\n",
       "      <td>157.28</td>\n",
       "      <td>165.24</td>\n",
       "      <td>164.67</td>\n",
       "      <td>184.60</td>\n",
       "      <td>207.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>396 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Number Ticker              sector            city  \\\n",
       "0         1   AAPL          Technology       Cupertino   \n",
       "1         3    ADI          Technology      Wilmington   \n",
       "2         4    ADM  Consumer Defensive         Chicago   \n",
       "3         5    ADP         Industrials        Roseland   \n",
       "4         6   ADSK          Technology      San Rafael   \n",
       "..      ...    ...                 ...             ...   \n",
       "391     418    XRX          Technology         Norwalk   \n",
       "392     419    XYL         Industrials       Rye Brook   \n",
       "393     421    ZBH          Healthcare          Warsaw   \n",
       "394     422   ZION  Financial Services  Salt Lake City   \n",
       "395     423    ZTS          Healthcare      Parsippany   \n",
       "\n",
       "                                   industry  profitMargins  priceToBook  \\\n",
       "0                      Consumer Electronics           0.25        36.81   \n",
       "1                            Semiconductors           0.26         5.11   \n",
       "2                             Farm Products           0.03         1.70   \n",
       "3            Staffing & Employment Services           0.17        15.41   \n",
       "4                      Software—Application           0.33        54.38   \n",
       "..                                      ...            ...          ...   \n",
       "391         Information Technology Services           0.04         0.74   \n",
       "392          Specialty Industrial Machinery           0.07         7.44   \n",
       "393                         Medical Devices           0.12         2.46   \n",
       "394                          Banks—Regional           0.35         1.41   \n",
       "395  Drug Manufacturers—Specialty & Generic           0.26        22.99   \n",
       "\n",
       "     bookValue  shortRatio  revenuePerShare  ...  COVID DEPENDANCY  CLASS  \\\n",
       "0         3.88        1.28            20.61  ...                 3      0   \n",
       "1        32.99        2.22            17.63  ...                 6      1   \n",
       "2        37.28        1.94           133.43  ...                 3      0   \n",
       "3        13.38        2.53            35.20  ...                 4      0   \n",
       "4         5.13        2.06            17.74  ...                 6      1   \n",
       "..         ...         ...              ...  ...               ...    ...   \n",
       "391      28.26        4.32            36.31  ...                 5      1   \n",
       "392      16.32        4.88            28.86  ...                 3      0   \n",
       "393      59.72        2.10            37.95  ...                 3      0   \n",
       "394      44.98        3.92            19.58  ...                 7      1   \n",
       "395       8.61        3.03            15.59  ...                 2      0   \n",
       "\n",
       "     15/3/2020 0:00  10/4/2020 0:00  10/5/2020 0:00  10/9/2020 0:00  \\\n",
       "0             66.46           66.00           85.25          113.91   \n",
       "1             93.97           98.06          120.81          114.11   \n",
       "2             34.21           36.45           40.93           46.05   \n",
       "3            139.93          139.42          152.98          134.62   \n",
       "4            152.20          156.28          229.26          228.20   \n",
       "..              ...             ...             ...             ...   \n",
       "391           24.87           18.99           17.94           18.84   \n",
       "392           70.40           67.45           67.81           82.83   \n",
       "393          104.27          105.37          130.25          139.09   \n",
       "394           29.15           28.85           37.04           31.74   \n",
       "395          123.16          127.23          137.03          157.28   \n",
       "\n",
       "     1/1/2021 0:00  20/2/2021 0:00  15/5/2021 0:00  10/9/2021 0:00  \n",
       "0           129.93          129.92          129.88          153.71  \n",
       "1           147.92          160.27          167.18          166.42  \n",
       "2            51.01           56.15           64.36           59.69  \n",
       "3           170.39          169.20          198.52          202.84  \n",
       "4           301.69          302.91          276.97          287.28  \n",
       "..             ...             ...             ...             ...  \n",
       "391          22.93           23.74           24.50           21.22  \n",
       "392         101.20           98.64          116.59          134.76  \n",
       "393         155.83          158.55          158.52          144.72  \n",
       "394          44.62           51.41           54.11           57.24  \n",
       "395         165.24          164.67          184.60          207.54  \n",
       "\n",
       "[396 rows x 36 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3MAoS6eckLwJ"
   },
   "source": [
    "# Data Cleaning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "zZOgWXjzkjmg"
   },
   "outputs": [],
   "source": [
    "\n",
    "data.drop('Number',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "pq9i33ixk3hf"
   },
   "outputs": [],
   "source": [
    "data.set_index('Ticker',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 735
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1636338256716,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "rfoyXP75k75j",
    "outputId": "7f9a76db-0d32-4cf7-c4de-1eb3cb4b5835"
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sector</th>\n",
       "      <th>city</th>\n",
       "      <th>industry</th>\n",
       "      <th>profitMargins</th>\n",
       "      <th>priceToBook</th>\n",
       "      <th>bookValue</th>\n",
       "      <th>shortRatio</th>\n",
       "      <th>revenuePerShare</th>\n",
       "      <th>operatingMargins</th>\n",
       "      <th>forwardEps</th>\n",
       "      <th>...</th>\n",
       "      <th>COVID DEPENDANCY</th>\n",
       "      <th>CLASS</th>\n",
       "      <th>15/3/2020 0:00</th>\n",
       "      <th>10/4/2020 0:00</th>\n",
       "      <th>10/5/2020 0:00</th>\n",
       "      <th>10/9/2020 0:00</th>\n",
       "      <th>1/1/2021 0:00</th>\n",
       "      <th>20/2/2021 0:00</th>\n",
       "      <th>15/5/2021 0:00</th>\n",
       "      <th>10/9/2021 0:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Cupertino</td>\n",
       "      <td>Consumer Electronics</td>\n",
       "      <td>0.25</td>\n",
       "      <td>36.81</td>\n",
       "      <td>3.88</td>\n",
       "      <td>1.28</td>\n",
       "      <td>20.61</td>\n",
       "      <td>0.29</td>\n",
       "      <td>5.68</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>66.46</td>\n",
       "      <td>66.00</td>\n",
       "      <td>85.25</td>\n",
       "      <td>113.91</td>\n",
       "      <td>129.93</td>\n",
       "      <td>129.92</td>\n",
       "      <td>129.88</td>\n",
       "      <td>153.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADI</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Wilmington</td>\n",
       "      <td>Semiconductors</td>\n",
       "      <td>0.26</td>\n",
       "      <td>5.11</td>\n",
       "      <td>32.99</td>\n",
       "      <td>2.22</td>\n",
       "      <td>17.63</td>\n",
       "      <td>0.33</td>\n",
       "      <td>6.96</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>93.97</td>\n",
       "      <td>98.06</td>\n",
       "      <td>120.81</td>\n",
       "      <td>114.11</td>\n",
       "      <td>147.92</td>\n",
       "      <td>160.27</td>\n",
       "      <td>167.18</td>\n",
       "      <td>166.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADM</th>\n",
       "      <td>Consumer Defensive</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Farm Products</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.70</td>\n",
       "      <td>37.28</td>\n",
       "      <td>1.94</td>\n",
       "      <td>133.43</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4.45</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>34.21</td>\n",
       "      <td>36.45</td>\n",
       "      <td>40.93</td>\n",
       "      <td>46.05</td>\n",
       "      <td>51.01</td>\n",
       "      <td>56.15</td>\n",
       "      <td>64.36</td>\n",
       "      <td>59.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADP</th>\n",
       "      <td>Industrials</td>\n",
       "      <td>Roseland</td>\n",
       "      <td>Staffing &amp; Employment Services</td>\n",
       "      <td>0.17</td>\n",
       "      <td>15.41</td>\n",
       "      <td>13.38</td>\n",
       "      <td>2.53</td>\n",
       "      <td>35.20</td>\n",
       "      <td>0.22</td>\n",
       "      <td>7.30</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>139.93</td>\n",
       "      <td>139.42</td>\n",
       "      <td>152.98</td>\n",
       "      <td>134.62</td>\n",
       "      <td>170.39</td>\n",
       "      <td>169.20</td>\n",
       "      <td>198.52</td>\n",
       "      <td>202.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADSK</th>\n",
       "      <td>Technology</td>\n",
       "      <td>San Rafael</td>\n",
       "      <td>Software—Application</td>\n",
       "      <td>0.33</td>\n",
       "      <td>54.38</td>\n",
       "      <td>5.13</td>\n",
       "      <td>2.06</td>\n",
       "      <td>17.74</td>\n",
       "      <td>0.17</td>\n",
       "      <td>6.87</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>152.20</td>\n",
       "      <td>156.28</td>\n",
       "      <td>229.26</td>\n",
       "      <td>228.20</td>\n",
       "      <td>301.69</td>\n",
       "      <td>302.91</td>\n",
       "      <td>276.97</td>\n",
       "      <td>287.28</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>XRX</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Norwalk</td>\n",
       "      <td>Information Technology Services</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.74</td>\n",
       "      <td>28.26</td>\n",
       "      <td>4.32</td>\n",
       "      <td>36.31</td>\n",
       "      <td>0.07</td>\n",
       "      <td>2.22</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>24.87</td>\n",
       "      <td>18.99</td>\n",
       "      <td>17.94</td>\n",
       "      <td>18.84</td>\n",
       "      <td>22.93</td>\n",
       "      <td>23.74</td>\n",
       "      <td>24.50</td>\n",
       "      <td>21.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XYL</th>\n",
       "      <td>Industrials</td>\n",
       "      <td>Rye Brook</td>\n",
       "      <td>Specialty Industrial Machinery</td>\n",
       "      <td>0.07</td>\n",
       "      <td>7.44</td>\n",
       "      <td>16.32</td>\n",
       "      <td>4.88</td>\n",
       "      <td>28.86</td>\n",
       "      <td>0.12</td>\n",
       "      <td>3.28</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>70.40</td>\n",
       "      <td>67.45</td>\n",
       "      <td>67.81</td>\n",
       "      <td>82.83</td>\n",
       "      <td>101.20</td>\n",
       "      <td>98.64</td>\n",
       "      <td>116.59</td>\n",
       "      <td>134.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZBH</th>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>Medical Devices</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2.46</td>\n",
       "      <td>59.72</td>\n",
       "      <td>2.10</td>\n",
       "      <td>37.95</td>\n",
       "      <td>0.17</td>\n",
       "      <td>8.77</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>104.27</td>\n",
       "      <td>105.37</td>\n",
       "      <td>130.25</td>\n",
       "      <td>139.09</td>\n",
       "      <td>155.83</td>\n",
       "      <td>158.55</td>\n",
       "      <td>158.52</td>\n",
       "      <td>144.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZION</th>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>Banks—Regional</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.41</td>\n",
       "      <td>44.98</td>\n",
       "      <td>3.92</td>\n",
       "      <td>19.58</td>\n",
       "      <td>0.47</td>\n",
       "      <td>4.34</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>29.15</td>\n",
       "      <td>28.85</td>\n",
       "      <td>37.04</td>\n",
       "      <td>31.74</td>\n",
       "      <td>44.62</td>\n",
       "      <td>51.41</td>\n",
       "      <td>54.11</td>\n",
       "      <td>57.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZTS</th>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Parsippany</td>\n",
       "      <td>Drug Manufacturers—Specialty &amp; Generic</td>\n",
       "      <td>0.26</td>\n",
       "      <td>22.99</td>\n",
       "      <td>8.61</td>\n",
       "      <td>3.03</td>\n",
       "      <td>15.59</td>\n",
       "      <td>0.36</td>\n",
       "      <td>5.07</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>123.16</td>\n",
       "      <td>127.23</td>\n",
       "      <td>137.03</td>\n",
       "      <td>157.28</td>\n",
       "      <td>165.24</td>\n",
       "      <td>164.67</td>\n",
       "      <td>184.60</td>\n",
       "      <td>207.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>396 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    sector            city  \\\n",
       "Ticker                                       \n",
       "AAPL            Technology       Cupertino   \n",
       "ADI             Technology      Wilmington   \n",
       "ADM     Consumer Defensive         Chicago   \n",
       "ADP            Industrials        Roseland   \n",
       "ADSK            Technology      San Rafael   \n",
       "...                    ...             ...   \n",
       "XRX             Technology         Norwalk   \n",
       "XYL            Industrials       Rye Brook   \n",
       "ZBH             Healthcare          Warsaw   \n",
       "ZION    Financial Services  Salt Lake City   \n",
       "ZTS             Healthcare      Parsippany   \n",
       "\n",
       "                                      industry  profitMargins  priceToBook  \\\n",
       "Ticker                                                                       \n",
       "AAPL                      Consumer Electronics           0.25        36.81   \n",
       "ADI                             Semiconductors           0.26         5.11   \n",
       "ADM                              Farm Products           0.03         1.70   \n",
       "ADP             Staffing & Employment Services           0.17        15.41   \n",
       "ADSK                      Software—Application           0.33        54.38   \n",
       "...                                        ...            ...          ...   \n",
       "XRX            Information Technology Services           0.04         0.74   \n",
       "XYL             Specialty Industrial Machinery           0.07         7.44   \n",
       "ZBH                            Medical Devices           0.12         2.46   \n",
       "ZION                            Banks—Regional           0.35         1.41   \n",
       "ZTS     Drug Manufacturers—Specialty & Generic           0.26        22.99   \n",
       "\n",
       "        bookValue  shortRatio  revenuePerShare  operatingMargins  forwardEps  \\\n",
       "Ticker                                                                         \n",
       "AAPL         3.88        1.28            20.61              0.29        5.68   \n",
       "ADI         32.99        2.22            17.63              0.33        6.96   \n",
       "ADM         37.28        1.94           133.43              0.04        4.45   \n",
       "ADP         13.38        2.53            35.20              0.22        7.30   \n",
       "ADSK         5.13        2.06            17.74              0.17        6.87   \n",
       "...           ...         ...              ...               ...         ...   \n",
       "XRX         28.26        4.32            36.31              0.07        2.22   \n",
       "XYL         16.32        4.88            28.86              0.12        3.28   \n",
       "ZBH         59.72        2.10            37.95              0.17        8.77   \n",
       "ZION        44.98        3.92            19.58              0.47        4.34   \n",
       "ZTS          8.61        3.03            15.59              0.36        5.07   \n",
       "\n",
       "        ...  COVID DEPENDANCY  CLASS  15/3/2020 0:00  10/4/2020 0:00  \\\n",
       "Ticker  ...                                                            \n",
       "AAPL    ...                 3      0           66.46           66.00   \n",
       "ADI     ...                 6      1           93.97           98.06   \n",
       "ADM     ...                 3      0           34.21           36.45   \n",
       "ADP     ...                 4      0          139.93          139.42   \n",
       "ADSK    ...                 6      1          152.20          156.28   \n",
       "...     ...               ...    ...             ...             ...   \n",
       "XRX     ...                 5      1           24.87           18.99   \n",
       "XYL     ...                 3      0           70.40           67.45   \n",
       "ZBH     ...                 3      0          104.27          105.37   \n",
       "ZION    ...                 7      1           29.15           28.85   \n",
       "ZTS     ...                 2      0          123.16          127.23   \n",
       "\n",
       "        10/5/2020 0:00  10/9/2020 0:00  1/1/2021 0:00  20/2/2021 0:00  \\\n",
       "Ticker                                                                  \n",
       "AAPL             85.25          113.91         129.93          129.92   \n",
       "ADI             120.81          114.11         147.92          160.27   \n",
       "ADM              40.93           46.05          51.01           56.15   \n",
       "ADP             152.98          134.62         170.39          169.20   \n",
       "ADSK            229.26          228.20         301.69          302.91   \n",
       "...                ...             ...            ...             ...   \n",
       "XRX              17.94           18.84          22.93           23.74   \n",
       "XYL              67.81           82.83         101.20           98.64   \n",
       "ZBH             130.25          139.09         155.83          158.55   \n",
       "ZION             37.04           31.74          44.62           51.41   \n",
       "ZTS             137.03          157.28         165.24          164.67   \n",
       "\n",
       "        15/5/2021 0:00  10/9/2021 0:00  \n",
       "Ticker                                  \n",
       "AAPL            129.88          153.71  \n",
       "ADI             167.18          166.42  \n",
       "ADM              64.36           59.69  \n",
       "ADP             198.52          202.84  \n",
       "ADSK            276.97          287.28  \n",
       "...                ...             ...  \n",
       "XRX              24.50           21.22  \n",
       "XYL             116.59          134.76  \n",
       "ZBH             158.52          144.72  \n",
       "ZION             54.11           57.24  \n",
       "ZTS             184.60          207.54  \n",
       "\n",
       "[396 rows x 34 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o3QfWsLMlOiz"
   },
   "source": [
    "## droping duplicate columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "9xo-quHwk8rq"
   },
   "outputs": [],
   "source": [
    "data.drop(['profitMargins.1','volume.1'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "fEqtA02OlKlo"
   },
   "outputs": [],
   "source": [
    "data.drop(['COVID DEPENDANCY'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WhZVl5o_leuE"
   },
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "EIwBz2EIlih7"
   },
   "outputs": [],
   "source": [
    "OH_encoder =OneHotEncoder(handle_unknown='ignore', sparse=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1636333638554,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "P-ny4XrjSUHL",
    "outputId": "5c5f1bc1-1808-46b0-a838-d35ef3ec3927"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneHotEncoder(handle_unknown='ignore', sparse=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OH_encoder.fit(data['sector'].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DsY9P1XFmkax"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "bz_XB_tqmI73"
   },
   "outputs": [],
   "source": [
    "names = OH_encoder.get_feature_names(['sector'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "S6-Cd4N2mQxH"
   },
   "outputs": [],
   "source": [
    "\n",
    "data[names]=OH_encoder.transform(data['sector'].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1636333641309,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "1kcku9rEmZXy",
    "outputId": "e651a94d-7f2f-48e7-c552-6a1406c3767c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sector', 'city', 'industry', 'profitMargins', 'priceToBook',\n",
       "       'bookValue', 'shortRatio', 'revenuePerShare', 'operatingMargins',\n",
       "       'forwardEps', 'heldPercentInstitutions', 'heldPercentInsiders',\n",
       "       'marketCap', 'volume', 'totalDebt', 'totalCash', 'beta',\n",
       "       'totalCashPerShare', 'enterpriseValue', 'floatShares',\n",
       "       'numberOfAnalystOpinions', 'COVID BEHAVIOUR', 'CLASS', '15/3/2020 0:00',\n",
       "       '10/4/2020 0:00', '10/5/2020 0:00', '10/9/2020 0:00', '1/1/2021 0:00',\n",
       "       '20/2/2021 0:00', '15/5/2021 0:00', '10/9/2021 0:00',\n",
       "       'sector_Basic Materials', 'sector_Communication Services',\n",
       "       'sector_Consumer Cyclical', 'sector_Consumer Defensive',\n",
       "       'sector_Energy', 'sector_Financial Services', 'sector_Healthcare',\n",
       "       'sector_Industrials', 'sector_Real Estate', 'sector_Technology',\n",
       "       'sector_Utilities'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1636333641310,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "PWTOcL1Tmr88",
    "outputId": "0e1e3ada-120a-4314-8dd8-67baa478377e",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['sector_Basic Materials', 'sector_Communication Services',\n",
       "       'sector_Consumer Cyclical', 'sector_Consumer Defensive',\n",
       "       'sector_Energy', 'sector_Financial Services', 'sector_Healthcare',\n",
       "       'sector_Industrials', 'sector_Real Estate', 'sector_Technology',\n",
       "       'sector_Utilities'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "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>profitMargins</th>\n",
       "      <th>priceToBook</th>\n",
       "      <th>bookValue</th>\n",
       "      <th>shortRatio</th>\n",
       "      <th>revenuePerShare</th>\n",
       "      <th>operatingMargins</th>\n",
       "      <th>forwardEps</th>\n",
       "      <th>heldPercentInstitutions</th>\n",
       "      <th>heldPercentInsiders</th>\n",
       "      <th>marketCap</th>\n",
       "      <th>...</th>\n",
       "      <th>sector_Communication Services</th>\n",
       "      <th>sector_Consumer Cyclical</th>\n",
       "      <th>sector_Consumer Defensive</th>\n",
       "      <th>sector_Energy</th>\n",
       "      <th>sector_Financial Services</th>\n",
       "      <th>sector_Healthcare</th>\n",
       "      <th>sector_Industrials</th>\n",
       "      <th>sector_Real Estate</th>\n",
       "      <th>sector_Technology</th>\n",
       "      <th>sector_Utilities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>396.00</td>\n",
       "      <td>396.00</td>\n",
       "      <td>396.00</td>\n",
       "      <td>396.00</td>\n",
       "      <td>396.00</td>\n",
       "      <td>396.00</td>\n",
       "      <td>396.00</td>\n",
       "      <td>396.00</td>\n",
       "      <td>396.00</td>\n",
       "      <td>396.00</td>\n",
       "      <td>...</td>\n",
       "      <td>396.00</td>\n",
       "      <td>396.00</td>\n",
       "      <td>396.00</td>\n",
       "      <td>396.00</td>\n",
       "      <td>396.00</td>\n",
       "      <td>396.00</td>\n",
       "      <td>396.00</td>\n",
       "      <td>396.00</td>\n",
       "      <td>396.00</td>\n",
       "      <td>396.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.12</td>\n",
       "      <td>11.05</td>\n",
       "      <td>34.03</td>\n",
       "      <td>3.63</td>\n",
       "      <td>56.29</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>7.15</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.03</td>\n",
       "      <td>71,915,406,665.05</td>\n",
       "      <td>...</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.19</td>\n",
       "      <td>32.59</td>\n",
       "      <td>34.13</td>\n",
       "      <td>2.36</td>\n",
       "      <td>75.08</td>\n",
       "      <td>3.88</td>\n",
       "      <td>6.55</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.08</td>\n",
       "      <td>182,935,717,149.42</td>\n",
       "      <td>...</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-61.77</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.06</td>\n",
       "      <td>1.90</td>\n",
       "      <td>12.79</td>\n",
       "      <td>2.14</td>\n",
       "      <td>17.79</td>\n",
       "      <td>0.11</td>\n",
       "      <td>3.12</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15,736,608,768.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.12</td>\n",
       "      <td>3.63</td>\n",
       "      <td>24.53</td>\n",
       "      <td>2.95</td>\n",
       "      <td>32.05</td>\n",
       "      <td>0.18</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.01</td>\n",
       "      <td>29,998,278,656.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.20</td>\n",
       "      <td>7.58</td>\n",
       "      <td>42.55</td>\n",
       "      <td>4.24</td>\n",
       "      <td>64.13</td>\n",
       "      <td>0.27</td>\n",
       "      <td>9.08</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.01</td>\n",
       "      <td>61,668,108,288.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.13</td>\n",
       "      <td>331.45</td>\n",
       "      <td>249.02</td>\n",
       "      <td>15.24</td>\n",
       "      <td>600.37</td>\n",
       "      <td>0.65</td>\n",
       "      <td>42.90</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.60</td>\n",
       "      <td>2,384,660,000,000.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       profitMargins  priceToBook  bookValue  shortRatio  revenuePerShare  \\\n",
       "count         396.00       396.00     396.00      396.00           396.00   \n",
       "mean            0.12        11.05      34.03        3.63            56.29   \n",
       "std             0.19        32.59      34.13        2.36            75.08   \n",
       "min            -1.50         0.00       0.31        0.82             0.13   \n",
       "25%             0.06         1.90      12.79        2.14            17.79   \n",
       "50%             0.12         3.63      24.53        2.95            32.05   \n",
       "75%             0.20         7.58      42.55        4.24            64.13   \n",
       "max             1.13       331.45     249.02       15.24           600.37   \n",
       "\n",
       "       operatingMargins  forwardEps  heldPercentInstitutions  \\\n",
       "count            396.00      396.00                   396.00   \n",
       "mean              -0.13        7.15                     0.84   \n",
       "std                3.88        6.55                     0.13   \n",
       "min              -61.77       -1.38                     0.31   \n",
       "25%                0.11        3.12                     0.76   \n",
       "50%                0.18        5.33                     0.86   \n",
       "75%                0.27        9.08                     0.93   \n",
       "max                0.65       42.90                     1.25   \n",
       "\n",
       "       heldPercentInsiders            marketCap  ...  \\\n",
       "count               396.00               396.00  ...   \n",
       "mean                  0.03    71,915,406,665.05  ...   \n",
       "std                   0.08   182,935,717,149.42  ...   \n",
       "min                   0.00                 0.00  ...   \n",
       "25%                   0.00    15,736,608,768.00  ...   \n",
       "50%                   0.01    29,998,278,656.00  ...   \n",
       "75%                   0.01    61,668,108,288.00  ...   \n",
       "max                   0.60 2,384,660,000,000.00  ...   \n",
       "\n",
       "       sector_Communication Services  sector_Consumer Cyclical  \\\n",
       "count                         396.00                    396.00   \n",
       "mean                            0.04                      0.14   \n",
       "std                             0.20                      0.34   \n",
       "min                             0.00                      0.00   \n",
       "25%                             0.00                      0.00   \n",
       "50%                             0.00                      0.00   \n",
       "75%                             0.00                      0.00   \n",
       "max                             1.00                      1.00   \n",
       "\n",
       "       sector_Consumer Defensive  sector_Energy  sector_Financial Services  \\\n",
       "count                     396.00         396.00                     396.00   \n",
       "mean                        0.08           0.06                       0.15   \n",
       "std                         0.27           0.24                       0.36   \n",
       "min                         0.00           0.00                       0.00   \n",
       "25%                         0.00           0.00                       0.00   \n",
       "50%                         0.00           0.00                       0.00   \n",
       "75%                         0.00           0.00                       0.00   \n",
       "max                         1.00           1.00                       1.00   \n",
       "\n",
       "       sector_Healthcare  sector_Industrials  sector_Real Estate  \\\n",
       "count             396.00              396.00              396.00   \n",
       "mean                0.11                0.14                0.06   \n",
       "std                 0.32                0.34                0.24   \n",
       "min                 0.00                0.00                0.00   \n",
       "25%                 0.00                0.00                0.00   \n",
       "50%                 0.00                0.00                0.00   \n",
       "75%                 0.00                0.00                0.00   \n",
       "max                 1.00                1.00                1.00   \n",
       "\n",
       "       sector_Technology  sector_Utilities  \n",
       "count             396.00            396.00  \n",
       "mean                0.11              0.07  \n",
       "std                 0.31              0.25  \n",
       "min                 0.00              0.00  \n",
       "25%                 0.00              0.00  \n",
       "50%                 0.00              0.00  \n",
       "75%                 0.00              0.00  \n",
       "max                 1.00              1.00  \n",
       "\n",
       "[8 rows x 38 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dPZe/nXf3X0i6z8yOiwe9TtEX0OTNvZJeZmYHxe+D1yn6HGteXSFpNH48KulbGdZSlZktUvRbjTe6+xNZ11OOu29y9yPdfTA+rrYq+jK1X2RcWto6IovrZWYHm9khxceKvsj0FnXQ8VGHSttyhaRzzGy2mT1X0rGSfpZBfS0p+bzomxW9flKHbV+V64eOf/0qbVu3vHaB1ZO5V0haHH+D/MsUfdTzgXYXmoKa22pmz1H0i50/cvc7MqgxLTW31d2fm7im+Kqk8939m22vtHX1vIe/JelVZtYX30H6UuX7Grqaerb3XkU/J8jM5kk6TtJdba2yPZrKpr7wdVXn7lNm9l5F3xY/S9Kl7n5rxmWV8wpJfyRpk5ndGA/7G3dfnV1JXWGJpC/GB/BdijqFueLuPzWzr0q6XtHHAW6QdEm2VUXM7EuSTpd0uJltlfRhSf8o6Stm9k5FAfj72VX4lAq1/rWibzdfF12/6ifu/p7MilT5Ot39C1nW1A4dlMX1mifpG/H7qk/Sf7n7WjO7Tjk8Pmpp5Fh391vN7CuKGrlTkv4s77f0Vti+083sJEW3b05I+lOpI7ev7PWDuuP1q7Rt53bJaxdMpcw1s/fE4z+r6C8enKnoSzifUA6vkepR57Z+SNKAot/+S9KUuw9nVXOz6tzWrlDPtrr7FjNbK+lmRd8993l3L/snGvOuztf27yVdZmabFH0c4a/iOz06SoVzcr/UWjZZPu9sBgAAAAAA3SAPH7UAAAAAAABdisYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDcsHMJszs8BbmP8nMzkw8f7uZPWRmN5rZbWZ2YR3LeLuZPTvx/PNmdnyzNQFArzKzQTPbGWfwZjP7rJnVfc1hZgeZ2RfNbJOZ3WJmPzSzufFyO/JPsQFAs8zsdDM7LfH8PWa2uIXlTZjZD0qG3ZhmvnIdjVJ9WRcAtMrM+iSdJGlY0d+VLfqyu7/XzAYk3W5mX3X3+6os6u2SbpF0vyS5+5+EqRgA0mPRH703d9+XdS0lfu7uJ8UZ/V1Jb5L09VozxdMvk/Sgu58QDztO0p5WCzKzPnefanU5AJC2Gvl0uqQdkn4kSe7+2RRWeYiZHeXu95nZbzU6s5nNcve9lcZzHY1S3PGAtjOzg83sO2Z2U/ybrLfGo5aY2fXxb7ieH097mJl908xuNrOfmNmJ8fAVZnaJmV0taZWkv5P01rhb+9bk+tx9UlJB0rPieT9kZtfF677EIm9R1Lj4YryMOWb2PTMbjuc5N/Gbt4+1Yz8BQCXxb/63mNmnJV0v6W/jXLvZzD4ST/MxMzs/Mc8KM/vz+PFflJm+uMzPmdmtZna1mc2JxyXz8HAzm4gfzzKzjyeW9aeltcYX0j+SNGRmR5jZ1+LprzOzVyRqS2b6syRtSyzjdnffFT+dVaHGd8XLvClex0Hx8MvM7F/M7BpJHzOz55nZWjPbaGY/KJ5vAKARZva++LrwFjO7IM7Q28xsPM7DryZy6BQz+36cO1eZWfGa9Htm9lEz+76kZWb2O2b2UzO7wczWm9k8MxuU9B5JF8bXqK+KM/P9iWV8zMx+ZmZ3mNmr4uEHmdlX4lq+HC93OLEJX5FUvGY+V9KXEts2GOfj9fG/0+Lhp5vZNWb2X5I2mdkBZvbpOI+vNLPV8TV16Xljh5n93ziff2Jm8+Lhvx/vv5vM7NpALxVygsYDsrBI0v3u/iJ3f6GktfHwX7r7yZI+I+n98bCPSLrB3U+U9DeKLkiLTpF0trv/gaQPKbrD4SR3/3JyZWb2HElPk3RzPOjf3P0l8brnSHqDu39V0gZJb4uXsTMx/7MlfUzSaxXdWfESM3tTGjsCAFpwnKJM/CtJ8yWdqiijTjGzV0u6XE9dVErS/yfpv83s9ZKOLTO94uGfcvcXSPq1pN+rUcM7JT3i7i+R9BJJ7zKz5yYniC+8Xydpk6SLJH0ynv73JH0+MWky0y+V9Fdm9mMzGzOzYxPTVarx63G2v0jSlri2ot+UtNDd/1zSJZKWuPspis41n66xjQAwg5mdIukdkl4q6WWS3iXpmYpy+ZL4uvVRSeebWb+klZLeEufOpZL+b2Jxz3D3/+Pu/yzph5Je5u4vVpThf+nuE5I+qyg7T3L3GR+RiPW5+6mSLpD04XjY+ZJ+Fdfy94oyNumrkn43fvw7kr6dGLdd0hnxdflbJV2cGHeqpA+6+/Hx/IOSTpD0J5JeXmGXHSzpJ3E+XxvvLym6fv/tePgbK8yLLsFHLZCFTZI+YdGdA1e6+w/MTHrqFtyNeioIX6n4otLdv2tmA2b29HjcFckGQRlvNbPXKDoJvMvdn4yHv8bM/lLSQZIOk3SrZoZtqZdI+p67PyRJZvZFSa+W9M16NxgAArjH3X9iZp+Q9HpJN8TD50o61t2/YGZHxs3TIxRdgN5rZkvLTS/pXkl3u/uN8fCNii4oq3m9pBOLv+GS9PR4WXdIep6Z3SjJJX3L3deY2bik4+PMl6RDzeyQ+PF0prv7jWZ2TLz8hZKuM7OXS9pZpcYXmtmYpGfE23RVos7/dve9ZjZX0mmKGjDFcbNrbCMAlHqlpG+4++OSZGZfl/QqSfe5+//G0/ynpKWKfsH2Qknr4tyZJemBxLKSvzBbIOnL8R0RB0q6u856ktfQg4kaL5Ikd7/FzG4umedhSb8ys3MUNWufSIzrl/RvZnaSpL2KmrdFP3P3Yl2vVJSv+yT9Ir6zrJzdkq5M1HhG/Ph/JV1mZl9RHR/FQ2ej8YC2c/c74k7xmZL+waJbayWpeBvtXj313rTS+RVdxErS4zVWVfyOh5dL+o6ZrVH027FPSxqOP9O2QtHdENWUqwEAslbMQJP0D+7+72Wm+aqkt0j6DUW/Pas4fXw7767EoL2K7gqTpCk9dZdkMjNN0d0DyR/yi8v6ubufVFLPAZJeXto0ji/GZ2S6u+9QdCH6dTPbp+ic8bUqNV4m6U3ufpOZvV3RZ6KLiss+QNKvy9QFAI2odG3oZZ6bpFvdvdLdAMnsWynpX9z9CjM7XdKKOuup9xq61JclfUrR95wlXSjpQUkvUpSbTybGJeut9xp5j7sX9810je7+HjN7qaSzJN1oZifFH5FGF+KjFmi7+LdvT7j7f0r6hKSTq0x+raS3xfOdrujjGI+Wme4xSYeUGS53/7Gk/1D0ZWXFC+Zfxr/5ekti0krL+Kmk/2PR55pnKfoc3Per1AwA7XSVpD+OM01mNt/MjozHXS7pHEVZ99U6pq9kQk/dppvMzasknRffSiwz+00zO7jKcq6W9N7ik/i3afsxs1eY2TPjxwdKOl7SPTVqPETSA3Etbys3QXz+uNvMfj9etpnZi2osFwBKXSvpTfH3KBws6c2SfiDpOfEvvKToevGHkm6XdERxuJn1m9kLKiz36Xrq+21GE8MrXudW8UNFH7GTRX9d4oQy03xD0j9p5h1ixToeiO9k+CNFd2lUWsfvxd/1ME8zG741mdnz3P2n7v4hSb+UdFQj86Oz0HhAFk6Q9LP4FtwPShqrMu0KScPx7WH/qJkhnHSNott39/tyydjHFH0Wb6+kzyn6uMc3JV2XmOYySZ+Nl1H8DZrc/QFJfx2v4yZJ17v7t6pvIgC0h7tfLem/JP3YzDYpajAcEo+7NX68Lc6yqtNX8QlFDYYfSUr+6ePPS9os6XqL/gzbv6v63ZRLFWe6mW1W9IVp5TxP0vfj+m5Q9B08X6tR498qahSvk3RbleneJumdZnaToo/anV1juQAwg7tfr+i68WeKcufzkn6l6CMLo/F162GSPuPuuxU1bD8W586Nij7yVc4KRR8F+4GiH8SLvi3pzfE16qvqLPPTihoeNyv6LqCbJT1Ssh2PufvH4hpL5x01s58o+phFpbuMvyZpq6K/CvfvivbFIxWmLefjFn95u6Jmzk0NzIsOY0/d9QIAAAAAaFT8EbMr4y8vz1x8l26/uz9pZs+T9D+SfrNMk6HV9cx19x0W/fn6n0l6hbv/Is11oDvwHQ8AAAAA0F0OknRN/PEzk3Re2k2H2JVm9gxFX4b59zQdUAl3PAAAAAAAgGD4jgcAAAAAABAMjQcAAAAAABAMjQcAAAAAABAMjQcAAAAAABAMjQcAAAAAABBMrv6c5uGHH+6Dg4NZlwEAM2zcuPGX7n5E1nW0AzkMII96KYclshhAPrWSxblqPAwODmrDhg1ZlwEAM5jZPVnX0C7kMIA86qUclshiAPnUShbzUQsAAAAAABAMjQcAAAAAABAMjQcAAAAAABAMjQcAAAAAABAMjQcAAAAAABAMjQcAAAAAABAMjQcAAAAAABBMX9YFZGHlypUqFAqpLW/btm2SpPnz56e2zHoMDQ1pyZIlbV0nACCfWjm3NXIe49wDAJ2t1vmiHT/bcC7pPT3ZeCgUCrrxli3ae9BhqSxv1hOPSJJ+sat9u3PWEw+3bV0AgPxr5dxW73mMcw8AdL5a54vQP9twLulNPdl4kKS9Bx2mnc8/M5VlzblttSSltrxG1gkAQFGz57Z6z2OcewCgO1Q7X4T+2YZzSW/iOx4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwuWo8bN++PesS0OFWrlyplStXZl0GgB5D9qSD/QgA6SJX08O+bE1f1gUk7dq1K+sS0OEKhULWJQDoQWRPOtiPAJAucjU97MvW5OqOBwAAAAAA0F1oPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGCCNh7MbJGZ3W5mBTP7QMh1AWmYnJzU0qVLNTk5WXZYrfGNLH9yclLnnXeezj///LrmrWddjdRSaz2N1haynk5ZXh71Sg43+1qWztfqMQ4A5fRKFgNoTqVrjTSv/YvTFgqFTK5rgjUezGyWpE9JGpF0vKRzzez4UOsD0jA+Pq5NmzZp1apVZYfVGt/I8sfHx7VlyxZt3ry5rnnrWVcjtdRaT6O1haynU5aXN72Uw82+lqXztXqMA0CpXspiAM2pdK2R5rV/cdqxsbFMrmtC3vFwqqSCu9/l7rslXS7p7IDrA1oyOTmptWvXyt21du3a6bsSisPWrFmjNWvWVBxfHFbP8tesWaPVq1dPj1uzZk3NrmOtdTVSS631rFmzpqHamqm325aXUz2Rw82+lqXzFQqFqhnQxe8TAGH1RBYDaE6la400r/2T005MTGRyXdMXcNnzJd2XeL5V0kurzfDkk09q2bJlAUuKFAoFHbDbg68npAOefFSFwmNt2V+dpFAoaM6cOU3NOz4+rn379kmS9u7dq1WrVsndp4ft2bNnetpy44vDLrzwwprL37Nnj9yfeg/u2bOn6ryV6ktOX2t8I/thamqqodqaqbeZuvK8vJxqOIc7UbOvZel8Y2NjVTOg2rK3bdumnTt3ZprJ7Ti3hT73tJLhQI71RBYjn8qdn7L+WahTf44JdY6qdB2T5rV/ctqidl//hrzjwcoM2+8dbmbvNrMNZrYh+YMY0G7r16+f/oF7ampK69atmzHM3aebBeXGF4fVs/zS97q7V523Un2NjK/X+vXrZ9RXT23N1Ntty8uphnP4oYceakNZ6Wr2tSydb2JiomoGdPH7BEBYPZHFAJpT6VojzWv/5LRF7b6uCXnHw1ZJRyWeL5B0f+lE7n6JpEskad68eX7RRRcFLCmybNkybbzrweDrCWnf0w7V0DHz1I791Ula6ZwuXLhQq1ev1tTUlPr6+nTGGWfI3aeHmUXXDe5ednxxWD3LN7MZP9ybWdV5K9XXyPhG9sO3v/3t6frqqa2ZertteTnVcA4PDw93XAe42deydL4FCxZo69atFTOg2rLnz58vSZlmcjvObaHPPZ322y+gTj2RxcincuenrH8W6tSfY0Kdoypdx6R57Z+ctqjd178h73i4TtKxZvZcMztQ0jmSrgi4PqAlo6OjOuCA6JCYNWuWFi9ePGNYf3+/+vr6Ko4vDqtn+cllFZ9Xm7dSfY2Mr9fo6GjDtTVTb7ctL6d6IoebfS1L51u+fHnVDOji9wmAsHoiiwE0p9K1RprX/slpi9p9XROs8eDuU5LeK+kqSVskfcXdbw21PqBVAwMDWrRokcxMixYt0sDAwIxhIyMjGhkZqTi+OKye5Y+MjOjMM8+cHjcyMlJ13kr1NTK+kf0wMjLSUG3N1Ntty8ujXsnhZl/L0vmGhoaqZkC3vk8AhNUrWQygOZWuNdK89k9OOzg4mMl1TciPWsjdV0taXXNCICdGR0c1MTExo/tXOqzW+EaWf+edd8rMGvoNbbV1NVJLrfU0WlvIejpleXnUKznc7GtZOl89GQAAjeqVLAbQnErXGmle+xenXbp0qS6++OK2X9cEbTwAnWZgYEAXX3xx1WG1xjey/M985jMt19dsLbXW02htIevplOUhO82+lqXz1ZMBAAAAaap0rZHmtX9y2iyua0J+xwMAAAAAAOhxNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwfVkXkDR79uysS0CHGxoayroEAD2I7EkH+xEA0kWupod92ZpcNR6OPPLIrEtAh1uyZEnWJQDoQWRPOtiPAJAucjU97MvW8FELAAAAAAAQDI0HAAAAAAAQDI0HAAAAAAAQDI0HAAAAAAAQDI0HAAAAAAAQDI0HAAAAAAAQDI0HAAAAAAAQDI0HAAAAAAAQDI0HAAAAAAAQDI0HAAAAAAAQDI0HAAAAAAAQDI0HAAAAAAAQTF+1kWZ2kKQ97r4nfn6cpDMl3ePuX29DfQDQ08hhAMgeWQwAral1x8NaSYOSZGZDkn4s6RhJf2Zm/xC2NACAyGEAyAOyGABaUKvx8Ex3vzN+PCrpS+6+RNKIpDcErQwAIJHDAJAHZDEAtKDqRy0keeLxayV9XJLcfbeZ7QtWVRvMeuJhzbltdUrLmpSk1JZX3zofljSvbesDkJmuzWGkr9lzW73nMc496GFkMbpKtfNF6J9tOJf0plqNh5vN7BOStkkaknS1JJnZMwLXFdTQ0FCqy9u2bUqSNH9+Ow+gealvB4Bc6socRvpaOSfUfx7j3IOeRRaja9TK8fA/23Au6UW1Gg/vkrRM0WfaXu/uT8TDj5f0iYB1BbVkyZKsSwCAenVlDiN9nNuAoMhidA3OF8hC1caDu++U9I/F52bWL+mFkgru/qPAtQFAzyOHASB7ZDEAtKbql0ua2WfN7AXx46dLuknSKkk3mNm5bagPAHoaOQwA2SOLAaA1tf6qxavc/db48Tsk3eHuJ0g6RdJfBq0MACCRwwCQB2QxALSgVuNhd+LxGZK+KUnu/otQBQEAZiCHASB7ZDEAtKBW4+HXZvYGM3uxpFdIWitJZtYnaU7o4gAA5DAA5ABZDAAtqPVXLf5U0sWSfkPSBYmu7uskfSdkYQAASeQwAOQBWQwALaj1Vy3ukLSozPCrzOzhYFUBACSRwwCQB2QxALSm1h0PM5jZ8ZLOkXSupEckDYcoCgBQHjkMANkjiwGgMTUbD2Z2tKJQPVfSlKSjJQ27+0TY0gAAEjkMAHlAFgNA86p+uaSZ/UjSakn9kt7i7qdIeoyABYD2IIcBIHtkMQC0ptZftXhI0iGS5kk6Ih7mQSsCACSRwwCQPbIYAFpQtfHg7mdLOkHS9ZI+YmZ3S3qmmZ3ajuIAoNeRwwCQPbIYAFpT8zse3P0RSZdKutTMjpT0Vkn/amZHuftRoQsEgF5HDgNA9shiAGherY9azODu2919pbufJumiQDUBACoghwEge2QxADSmocZDiaWpVQEAaAY5DADZI4sBoIZWGg+WWhUAgGaQwwCQPbIYAGqo+R0PVeTym3xXrlypQqGQ+nK3bdsmSZo/f37qy65laGhIS5Ysaft6AeReLnMY2Wv0XNjoOY7zEjADWYyOVu2ckeXPQGnj3JWtqo0HM9uk8mFqiv6cUO4UCgXdeesNes7cvaku9/HHZkmSdk09kOpya7l3x6y2rg9AvnRiDiN7jZ4LGznHcV5CLyKL0c2qnTOy+hkobZy7slfrjoffVRSm95UMP1rS/UEqSsFz5u7V35z8aKrL/Oj1h0pS6sutd70AelZH5jCy18i5sJFzHOcl9CiyGF2t0jkjq5+B0sa5K3u1vuPhk5Iedfd7kv8kPRGPAwCERQ4DQPbIYgBoQa3Gw6C731w60N03SBoMUhEAIIkcBoDskcUA0IJajYenVRk3J81CAABlkcMAkD2yGABaUKvxcJ2Zvat0oJm9U9LGMCUBABLIYQDIHlkMAC2o9eWSF0j6hpm9TU+F6rCkAyW9OWBdAIDIBSKHASBrF4gsBoCmVW08uPuDkk4zs9dIemE8+Dvu/t3glQEAyGEAyAGyGABaU+uOB0mSu18j6ZrAtQAAKiCHASB7ZDEANKfWdzwAAAAAAAA0jcYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDOtLKlSu1cuXKrMsAgP2QT7WxjwCgvchdNCPN901fKksB2qxQKGRdAgCURT7Vxj4CgPYid9GMNN833PEAAAAAAACCofEAAAAAAACCofEAAAAAAACCofEAAAAAAACCofEAAAAAAACCofEAAAAAAACCofEAAAAAAACCofEAAAAAAACCofEAAAAAAACCofEAAAAAAACCofEAAAAAAACCofEAAAAAAACCofEAAAAAAACCCdZ4MLNLzWy7md0Sah1Au01OTmrp0qWanJwMOm8r6wmh2XrS3o4s9svk5KQOOuig49q2wpT1WhZn8Z6bnJzUeeedp/PPPz83xyzKy1u2ojf0Wg4DQDkh73i4TNKigMsH2m58fFybNm3SqlWrgs7bynpCaLaetLcji/0yPj6uAw44YG7bVpi+y9RDWZzFe258fFxbtmzR5s2bc3PMory8ZSt6xmXqoRwGgHKCNR7c/VpJD4daPtBuk5OTWrt2rdxda9eubeg3Zo3M28p6Qmi2nrS3I4v9UlxnJ+ulLM7iPTc5Oak1a9ZMP1+zZo327NnT0noRRt6yFb2jl3IYACrpy7qApPvuu0/Lli1raRmFQkEH7umer6548IkDtLtQaHm/dJtCoaA5c+a0dZ3j4+Pat2+fJGnv3r1atWqVLrzwwtTnbWU9ITRbT9rbkcV+Sa4T+ZfFe258fFxTU1PTz/fs2aOf//zn6uvryzS3Q54L0zgvdVqGA0Cn27Ztm3bu3Fk2u7vt56dy+JmqOWmerzN/h5nZu81sg5lt4LdEyLP169dP/4AxNTWldevWBZm3lfWE0Gw9aW9HFvsluc5ulszhhx56KOtympbFe279+vVy9+nn7q7du3e3tF6EkbdsBUp1SxYDQDmZ3/Hg7pdIukSShoeH/aKLLmppecuWLdOuezakUVouzDton2YfPaRW90u3yaJbuXDhQq1evVpTU1Pq6+vTGWecEWTeVtYTQrP1pL0dWeyX4jq7XWkOZ1xO07J4zy1cuFDf/va3p5sPZqbDDjtMCxYsyDS3Q54L0zgvdVqGA+3QLVmMfJo/f74klc3ubvv5qRx+pmpOmufrzO94ADrF6OioDjggOmRmzZqlxYsXB5m3lfWE0Gw9aW9HFvsluU7kXxbvudHRUfX1PdXD7+/v17x581paL8LIW7YCANBLQv45zS9J+rGk48xsq5m9M9S6gHYYGBjQokWLZGZatGiRBgYGgszbynpCaLaetLcji/1SXGcn66UszuI9NzAwoJGRkennIyMj6u/vb2m9CCNv2Yre0Us5DACVBPuohbufG2rZQFZGR0c1MTHR1G/KGpm3lfWE0Gw9aW9HFvtldHRUf/u3f7ujbStMWa9lcRbvudHRUd15550yMy1evFh/93d/l8q6kb68ZSt6Q6/lMACUk/l3PACdZGBgQBdffHHweVtZTwjN1pP2dmSxXwYGBvTEE0/c3taVomlZvOcGBgb0mc98JrV1Ipy8ZSsAAL2CDy8DAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBg+rIuAGjG0NBQ1iUAQFnkU23sIwBoL3IXzUjzfUPjAR1pyZIlWZcAAGWRT7WxjwCgvchdNCPN9w0ftQAAAAAAAMHQeAAAAAAAAMHQeAAAAAAAAMHQeAAAAAAAAMHQeAAAAAAAAMHQeAAAAAAAAMHQeAAAAAAAAMHQeAAAAAAAAMHQeAAAAAAAAMHQeAAAAAAAAMHQeAAAAAAAAMHQeAAAAAAAAMHQeAAAAAAAAMHQeAAAAAAAAMHQeAAAAAAAAMHQeAAAAAAAAMH0ZV1ACPfumKWPXn9oqsu857FZkpT6cmu5d8csHdvWNQIAukEj58JGznGclwCg+1Q6Z2T1M1DaOHdlr+saD0NDQ0GWe/C2bZKk2fPnB1l+Jccq3DYBALpTo+eNRs5xnJcAoLtUy/SsfgZKG+eu7HVd42HJkiVZlwAAQKY4FwIA6sU5A+3AdzwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgzN2zrmGamT0m6fas66jhcEm/zLqIGqgxHdSYjm6o8Wh3P6JdxWQppzmcx/cQNdWHmupDTbUd5+6HZF1Eu5jZQ5LuafNq8/aah8S2die2Nbymr4n70q6kRbe7+3DWRVRjZhuosXXUmA5qTEcn1NhGucvhPL4+1FQfaqoPNdVmZhuyrqGdsmh25+01D4lt7U5sa77xUQsAAAAAABAMjQcAAAAAABBM3hoPl2RdQB2oMR3UmA5qTEcn1NguedwX1FQfaqoPNdUnbzXlrZ5u1Ev7mG3tTmxrjuXqyyUBAAAAAEB3ydsdDwAAAAAAoIvkovFgZovM7HYzK5jZB7KupxwzmzCzTWZ2Y16+WdnMLjWz7WZ2S2LYYWa2zszujP9/Zg5rXGFm2+J9eaOZnZlxjUeZ2TVmtsXMbjWzZfHw3OzLKjXmZl+a2dPM7GdmdlNc40fi4Xnaj5VqzM1+DKlW1lrk4nj8zWZ2cr3zBqrnbXEdN5vZj8zsRYlxQTK5jppON7NHEu+VD9U7b8Ca/iJRzy1mttfMDovHhdpP+2V7yfi2vpfqrCmL91Otmtr6fqqjnizeS2XPbyXTtP391K2sznNyHbnzfjNzMzs8fNXNaXVbzezjZnZb/J77hpk9o23F16mO16lrjp1mt7WejMmbVl7XePwsM7vBzK5sX9V1cPdM/0maJennko6RdKCkmyQdn3VdZeqckHR41nWU1PRqSSdLuiUx7J8kfSB+/AFJH8thjSskvT/r/Zeo51mSTo4fHyLpDknH52lfVqkxN/tSkkmaGz/ul/RTSS/L2X6sVGNu9mPAba+ZtZLOlLQm3k8vk/TTeucNVM9pkp4ZPx4p1hM/Tz2T66zpdElXNjNvqJpKpv8dSd8NuZ/i5e6X7Vm9lxqoqa3vpzpravf7qWo9Gb2Xyp7fsn4/des/1XFOrrVfJR0l6SpJ94R4T+RlWyW9XlJf/Phj5ebPePtydV7P8bbWzJg8/WtlWxPj3yfpv1Tm/JLlvzzc8XCqpIK73+XuuyVdLunsjGvqCO5+raSHSwafLWk8fjwu6U3trKlUhRpzxd0fcPfr48ePSdoiab5ytC+r1JgbHtkRP+2P/7nytR8r1dgL6snasyWtivfTTyQ9w8yeVee8qdfj7j9y91/FT38iaUGL62y5pkDzprnccyV9KYX1VlVHtrfzvVRXTRm8n1o5BwbZTw3W0673Uj3nt7a/n7pYPefkWvv1k5L+Uvk/f7a0re5+tbtPxdO1JTMalLfzekhNb2snXEOXaOV1lZktkHSWpM+3s+h65KHxMF/SfYnnW5XPN4NLutrMNprZu7Mupop57v6AFJ3MJR2ZcT2VvDe+NejSSre+ZcHMBiW9WNFvwnO5L0tqlHK0L+Nbu26UtF3SOnfP3X6sUKOUo/0YSD1ZW2maEDnd6DLfqai7XxQik+ut6eUWfVxnjZm9oMF5Q9UkMztI0iJJX0sMzurc1c73UjPa8X6qVzvfT3XJ6r1U5vxWlPf3Uyep55xccb+a2RslbXP3m0IXmoKWtrXEH2tmZuRB3s7rIbWyrdOqZEyetLqt/6qoMbgvUH1N68u6AEW3iJTKYwf1Fe5+v5kdKWmdmd0W/+YAjfuMpL9X9Dr/vaR/VhTomTKzuYousi5w90fNyr01s1WmxlztS3ffK+mk+HOQ3zCzF2ZVSyUVaszVfgyknqytNE2InK57mWb2GkU/KL4yMThEJtdT0/WSjnb3HRZ9F8g3JR1b57yhair6HUn/6+7J32hnde5q53upIW18P9Wj3e+nerX9vVR6fisdXWaWXLyf8sjM1kv6jTKjPljvIsoM87gh9UFFH0HIhVDbWrKOD0qakvTFxqoLLm/n9ZBa2dZoZPWMyZOmt9XM3iBpu7tvNLPT0y6sVXm442Gros+KFS2QdH9GtVTk7vfH/2+X9A1Ft8Hk0YOJW22epei3urni7g+6+1533yfpc8rBvjSzfkVh9EV3/3o8OFf7slyNedyXkuTuv5b0PUW/McvVfixK1pjX/ZiyerK20jQhcrquZZrZiYpuFzzb3SeLwwNlcs2a3P1Rjz+u4+6rJfVb9OVqoc5ljSz3HJXcGp/huaud76W6tfn9VFMG76d6tfW9VOEcnJTL91NeuftCd39hmX/fUn3n5Er79XmSnivpJjObiIdfb2blfvBvi4Dbqni+UUlvkPQ2d8/bD+Z5O6+H1Mq21pMxedLKtr5C0hvj4/NySa81s/8MV2pj8tB4uE7SsWb2XDM7UNHJ7oqMa5rBzA42s0OKjxV1est+I3QOXCFpNH48KulbGdZSVvEkEHuzMt6XFt3a8AVJW9z9XxKjcrMvK9WYp31pZkfEdxHIzOZIWijpNuVrP5atMU/7MaB6svYKSYst8jJJj8S3p4bI6ZrLNLPnSPq6pD9y9zsSw0Nlcj01/UZ8PMrMTlV0Hp2sZ95QNcW1PF3S/1Hi+Mr43NXO91JdMng/1VNTu99P9dTU1vdSlXNwUu7eTx2snnNy2f3q7pvc/Uh3H3T3QUU//Jzs7r9oR+FNaHpbpegvC0j6K0lvdPcn2lBvo/J2Xg+p6W2tM2PypOltdfe/dvcF8fF5jqIvCP7DtlZfjefgGy4VfTPnHYq+wfODWddTpr5jFH2j6E2Sbs1LjYp+G/GApD2Kwv+dkgYk/Y+kO+P/D8thjf8haZOkmxUdOM/KuMZXKrqF6WZJN8b/zszTvqxSY272paQTJd0Q13KLpA/Fw/O0HyvVmJv9GHj798taSe+R9J74sUn6VDx+k6ThavO2oZ7PS/pV4j2/IR4eLJPrqOm98TpvUvRlY6eF3Ef11BQ/f7uky0vmC7mfymV7Zu+lOmvK4v1Uq6a2vp9q1ZPRe6nS+S3T91O3/lOFc7KkZ0ta3ch+VQ7/4lua2yqpoOhz9MX35Wez3qYy25ir83oet7VSxmS9PaFe18QyTlfO/qqFxYUBAAAAAACkLg8ftQAAAAAAAF2KxgMAAAAAAAiGxgMAAAAAAAiGxgMAAAAAAAiGxgMAAAAAAAiGxgPaxsyWmtkWM/tixnWsMLP3x48vM7O7zezG+N+PsqwNQO8ws0Ezu6WB6aezq9JyzOx0M3vEzG6I8/bDadZcLzN7u5k9O/H8e2Y23MRyTjKzMxPP32hmH4gfv8nMjm9lGQBQC1ld13KGzeziCuMmzOzwVupEd6DxgHY6X9HfzX1brQnNrC+NFVqk1vv8L9z9pPjfaWmsFwAy9AN3f7GkYUl/aGan1DNTWrkbe7ukZ9eaqA4nKfp75pIkd7/C3f8xfvomSTUbDzWWAQBZ6ZqsdvcN7r609XIkM5uVxnKQPzQe0BZm9llJx0i6wsz+3My+aWY3m9lPzOzEeJoVZnaJmV0taZWZrU6Mu8HMPhQ//nsz+xMzm2tm/2Nm15vZJjM7Ox4/GHePPy3peklHmdkHzex2M1sv6bg66l1hZv9hZt81szvN7F3x8GeZ2bXx3RG3mNmrQuwvAD1jlpl9zsxuNbOrzWyOmT3PzNaa2UYz+4GZPb90JjM7xcxuMrMfS/qzcgt298clbZT0vErLjO/6+hczu0bSx8xsyMzWx8u+3syeF0/3F2Z2XZzbH4mHFbO2tP63KLqQ/mKclXNKat9hZv83XsdPzGxePPz341y9Kc7ZAyX9naS3xst5a/zbuX8zs9MkvVHSx+Nxz0v+ps7MDo9/y1ZxGfF0R8fnkZvj/5+T2C8Xm9mPzOyueJs4BwC9i6yukNXxsNPN7Mr48UC8jhvM7N8lWWKZf2hmP4vX9+8WNxnidf2dmf1U0svN7B/NbHO8HZ9o5YVDftB4QFu4+3sk3S/pNZIGJd3g7idK+htJqxKTniLpbHf/A0nXSnqVmR0qaUrSK+JpXinpB5KelPRmdz85Xu4/m1kx3I6TtCruJB8u6RxJL5b0u5JeUlJe8cL1Rpv5MZATJZ0l6eWSPmTRrWh/IOkqdz9J0osk3dj0TgEA6VhJn3L3F0j6taTfk3SJpCXufoqk90v6dJn5/p+kpe7+8koLNrMBSS+TdGuNZf6mpIXu/ueSvhjX8yJJp0l6wMxeH9d5qqK7B04xs1dXqt/dvyppg6S3xXeS7Swp7WBJP4nXca2kd8XDPyTpt+Phb3T33fGwL8fL+XJxAe7+I0lX6Kk71n5ebh9UW0bs3xSdK06Mtz15q/CzFJ1v3iCpeIcE5wCgN5HVFbK6zCZ9WNIP42vwKyQVG7q/Jemtkl4RZ+heScW7oA+WdIu7v1TSZklvlvSCOJvHKu07dJY0b9UB6vVKRYEtd/9u3Bl9ejzuikTw/UDSUkl3S/qOpDPM7CBJg+5+u5n1S/poHKr7JM2XNC+e9x53/0n8+FWSvuHuT0iSmV1RUs9fxOFb6ltxLTvjDvOpkq6TdGm87m+6+40t7AcAuDuRIxsVNWZPk/TfT/VRNTs5Q5yXz3D378eD/kPSSGKSV5nZDYpy8R8l3VNjmf/t7nvN7BBJ8939G5Lk7k/G63u9pNdLuiGefq6ii9h7K9Rfy25JVybmOSN+/L+SLjOzr0j6eh3LScPLFTWkpWg//lNi3DfdfZ+kzcXf9IlzANCryOr6s/rVinPV3b9jZr+Kh79O0S8Yr4u3b46k7fG4vZK+Fj9+VNEvFz9vZt9J1IAOR+MBWbAywzz+//HEsOsU3QJ2l6R1iu5ceJei8JOiLukRkk5x9z1mNiHpaWWWk1x+I0rncXe/Nm50nCXpP8zs4+6+qsy8AFCPXYnHexU1T38d/zaoElP1TPuBu79heuLorrFqyyzmZblsLg7/B3f/9xkDzQa1f/0zbtWtYI+7F+vfq/haxN3fY2YvVZSvN5pZpXormdJTd3I+rdqEVST3a3LbLK6RcwDQm8jqxrK63HabpHF3/+sy4550973x8qfM7FRFjYpzJL1X0mvrqBc5x0ctkIVrFd9aZWanS/qluz9aOlF8i+x9kv4/ST9RdAfE++P/JenpkrbHTYfXSDq6yvreHH+e7RBJv1NnnWeb2dPiW+BOV9ShPTpe5+ckfUHSyXUuCwDq8aiku83s96XpL8h9UXICd/+1pEfM7JXxoKpf2Bvna9VlJqbbamZviqebHd9ldpWkPzazufHw+WZ2ZI3teEzSITWmmcHMnufuP3X3D0n6paSjaiyndNyEot+mSdJb6qzlR4oubKVoP/6wRo2cAwBIZHVpViclr/NHJD0zHv4/kt5SrMnMDosztXT5cyU93d1XS7pA0cdG0AVoPCALKyQNm9nNim4tG60y7Q8kPRh/TOIHkhboqcbDF+PlbFAUcLeVW4C7Xy/py4o+i/u1xPxFye94uNGiLyOTpJ8p+ojHTyT9vbvfr6gBcWN8a9zvSbqo3o0GgDq9TdI7zewmRZ/5PbvMNO+Q9CmLvrCs9HO5zS5Tkv5I0tI4n38k6Tfc/WpJ/yXpx2a2SdJXVftC9TJJn7UyX1hWxcct+qLgWxRduN4k6RpJx8fLeWvJ9JdL+guLvsDseZI+Iek8i/4scvJPt1VbxlJJ74i3948kLatR4+niHAAgQlY/ldVJH5H0ajO7XtFHP+6VJHffLGm5pKvjutcp+i6dUodIujKe5vuSLqyzLuScPXUHDYAiM1shaYe78026AAAAANAC7ngAAAAAAADBcMcDAAAAAAAIhjseAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMH1ZF5B0+OGH++DgYNZlAMAMGzdu/KW7H5F1He1ADgPIo17KYYksBpBPrWRxrhoPg4OD2rBhQ9ZlAMAMZnZP1jW0CzkMII96KYclshhAPrWSxXzUAgAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABJOrP6cZ0sqVK1UoFFpezrZt2yRJ8+fPb3lZIQwNDWnJkiVZlwGgxzSTsVnlKTkJoFuldb1blPfr3nLIeCCfeqbxUCgUdOMtW7T3oMNaWs6sJx6RJP1iV/523awnHs66BAA9qpmMzSJPyUkA3Syt692iPF/3lkPGA/nVGSmSkr0HHaadzz+zpWXMuW21JLW8nBCKtQFAFhrN2CzylJwE0O3SuN4tyvN1bzlkPJBffMcDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIJveNh5UrV2rlypVZl4Eew/sOvWr79u2897sMeQZ0nu3bt2ddAlA3zjOoR1/WBdRSKBSyLgE9iPcdetWuXbt4/3cZXk+g8+zatSvrEoC6cZ5BPXJ/xwMAAAAAAOhcNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwQRsPZrbIzG43s4KZfSDkuoA8mJyc1NKlSzU5OVn2eb3zNbqetKYvN12j21QcXygUqs5XaTnJ4a0uq5JCoaCzzjpLhUKhruk7GTmMpGrHSrXjv/QY7ASN5kKz8zW7nlbnTUszNaRxrkijjk5CFqNX1JMP9czTyPhW6wydP42ee9NYdh4zNVjjwcxmSfqUpBFJx0s618yOD7U+IA/Gx8e1adMmrVq1quzzeudrdD1pTV9uuka3qTh+bGys6nyVlpMc3uqyKhkbG9Pjjz+usbGxuqbvVOQwSlU7Vqod/6XHYCdoNBeana/Z9bQ6b1qaqSGNc0UadXQKshi9pJ58qGeeRsa3Wmfo/Gn03JvGsvOYqSHveDhVUsHd73L33ZIul3R2wPUBmZqcnNTatWvl7lq7dq0KhcKM59W6uPVMF3r6ctM1uk3J6ScmJirOV2k5yfnXrFmjNWvWNL2sSgqFgiYmJiRJExMT3X7XAzmMadWyoNbxnzwG8/Tbk0oazclm52t2Pa3Om5ZmakjjXJFGHR2GLEZPqCcfql07NjO+1TrXrFkTNH8aPfemsey8ZmpfwGXPl3Rf4vlWSS9tdCHbtm3Tzp07tWzZspaKKRQKOmC3t7SMvDvgyUdVKDzW8r5C9H6ZM2dOQ/OMj49r3759kqS9e/dqbGxsxvNVq1bpwgsvrDlfpelCT19uOndvaJuSyygqN1+l5STn37Nnz341NrKsSkrvchgbG9Nll11WcfoO13AO7969W4VCoeEc6ZSM7cWcLOZZtSyodfwX1XOM5UGjOdnsfM2up9V509JMDWmcK9Koo8M0nMVPPvlkUznVKVkcSi9mfB5UO8+U5kO1a8dmxjej0vVmiPxp9NzbyLorzZ/XTA15x4OVGbZfEprZu81sg5lteOihhwKWA4S1fv16TU1NSZKmpqY0MTEx4/m6devqmq/SdKGnLzddo9uUnL6o3HyVlpOc393l7k0vq5Li3Q6VnneZhnO49AdNdI9qWVDr+C+q5xjLg0Zzstn5ml1Pq/OmpZka0jhXpFFHh2k4i0vPf0AnqCcfql07NjO+1TqT15sh8qfRc28ay85rpoa842GrpKMSzxdIur90Ine/RNIlkjQ8PLxfys6fP1+SdNFFF7VUzLJly7TxrgdbWkbe7XvaoRo6Zl7L+wpqqlO+cOFCrV69WlNTU+rr69OCBQu0devW6ednnHFGXfNVmi709OWmc/eGtim5jKJy81VaTnJ+s+g6LXnx1ciyKhkcHJzRbBgcHKw6fYdrOIfnzZvnQ0NDDedIp2RsL+ZkMc+OPvroillQ6/gvqucYy4NGc7LZ+ZpdT6vzpqWZGtI4V6RRR4dpKoubyalOyeJQejHj86DaeaY0H6pdOzYzvhmVrjdD5E+1+lvdtkrz5zVTQ97xcJ2kY83suWZ2oKRzJF0RcH1ApkZHR3XAAdEhNWvWLC1fvnzG88WLF9c1X6XpQk9fbrpGtyk5fVG5+SotJzl/f3+/+vr6ml5WJcuXL6/6vMuQw5hWLQtqHf9F9RxjedBoTjY7X7PraXXetDRTQxrnijTq6DBkMXpCPflQ7dqxmfGt1tnf36/+/v5Ul19pXfWce9NYdl4zNVjjwd2nJL1X0lWStkj6irvfGmp9QNYGBga0aNEimZkWLVqkoaGhGc8HBgbqmq/SdKGnLzddo9uUnH5wcLDifJWWk5x/ZGREIyMjTS+rkqGh/7+9u4+zoy7v//++2F1CuBMMEGkCrHYRi4gIK/X+iwo0i1Z6gxWqZbXUG6hJoFVrbb7e1K3VaqsktrVUKcm3ij/vK5oNBsVivSXhLpBwc4qLJCDBRe5DyCbX74+Zs0xOzs2cm8+ZmXNez8cjj5wzZ86ca845857PXjszOzJ7lMPw8LBGRkbqzl9k5DCS6mVBo+0/uQ022sbyoNmcbPV5rb5Ou8/tlFZq6MS+ohN1FAlZjH6RJh/qjR1bebzdOsfGxoLmT7P73k4sO6+ZGvJUC7n7akmrQ74GkCfj4+OamprareOYvJ/2ec2+TqfmrzZfs+tUfnzJkiVavnx5zefVWk7l9HaWVcuyZcu0dOnSXj/aQRI5jN3V21bqbf+V22ARNJsLrT6v1ddp97md0koNndhXdKKOIiGL0S/S5EOa5zTzeCfqDJk/ze57O7HsPGaq5eniNaOjo75u3brdppXPGerUNR62PeeMtpYz99Zon9HuckKYe+tqncR5bR3Rqe8deoOZrXf30azr6Ib58+f72Wef3fI1HprJxizytB9zkjxDL+inHJaiLL7vvuav1dCp8W5Znse91fRjxucB+5n+0U4Wh7zGAwAAAAAA6HM0HgAAAAAAQDA0HgAAAAAAQDA0HgAAAAAAQDA0HgAAAAAAQDA0HgAAAAAAQDA0HgAAAAAAQDA0HgAAAAAAQDA0HgAAAAAAQDA0HgAAAAAAQDA0HgAAAAAAQDA0HgAAAAAAQDCDWRfQyMjISNYloA/xvUO/mjNnDt//HsPnCRTPnDlzsi4BSI39DNLIfeNh8eLFWZeAPsT3Dv3qsMMO4/vfY/g8geI57LDDsi4BSI39DNLgVAsAAAAAABAMjQcAAAAAABAMjQcAAAAAABAMjQcAAAAAABAMjQcAAAAAABAMjQcAAAAAABAMjQcAAAAAABAMjQcAAAAAABAMjQcAAAAAABAMjQcAAAAAABAMjQcAAAAAABAMjQcAAAAAABDMYL0HzWxfSTvcfUd8/xhJZ0i6y92/1oX6AKCvkcMAkD2yGADa0+iIhzWShiXJzEYk/VjSsyT9uZn9fdjSAAAihwEgD8hiAGhDo8bDwe5+R3x7XNLl7r5Y0pik1watDAAgkcMAkAdkMQC0oVHjwRO3XyVprSS5+5OSdoUqCgAwixwGgOyRxQDQhrrXeJB0k5l9QtIWSSOSviNJZnZQ4LqCGHj8Ac29dXWby5iWpLaXE8LA4w9Imp91GQA6qzA53GzGZpGn5CSAFvVsFtdfVn7HvdWQ8UB+NWo8vFXSUkXntJ3u7o/H04+V9ImAdXXcyMhIR5azZcuMJGnBgjyG2vyOrSeA3ChEDreSPdnkKTkJoCU9m8X15HvcWw0ZD+SVuXvjucozmw1JOk7SFnff2uliRkdHfd26dZ1eLAC0xczWu/to1nVI5DCA/pSnHJbIYgD9qZ0srnuNBzP7jJk9N779NEk3Slol6XozO6eVFwQApEcOA0D2yGIAaE+ji0u+3N1viW+/RdLt7v48SSdJek/QygAAEjkMAHlAFgNAGxo1Hp5M3D5N0jckyd1/GaogAMBuyGEAyB5ZDABtaNR4eNDMXmtmL5D0UklrJMnMBiXNDV0cAIAcBoAcIIsBoA2N/qrF2yUtl/QMSRcmurqvlvTtkIUBACSRwwCQB2QxALShbuPB3W+XtKjK9CvN7IFgVQEAJJHDAJAHZDEAtKfREQ+7MbNjJZ0t6RxJD0nKzZ81AoB+QA4DQPbIYgBoTsPGg5kdpShUz5E0I+koSaPuPhW2NACARA4DQB6QxQDQuroXlzSzH0laLWlI0lnufpKkRwhYAOgOchgAskcWA0B7Gv1Vi/slHSBpvqRD42ketCIAQBI5DADZI4sBoA11Gw/ufqak50m6TtKHzOznkg42s5O7URwA9DtyGACyRxYDQHsaXuPB3R+SdKmkS83sMElvkPQpMzvC3Y8IXSAA9DtyGACyRxYDQOsanWqxG3ff6u4r3P0lki4OVBMAoAZyGACyRxYDQHOaajxUWNKxKgAArSCHASB7ZDEANNBO48E6VgUAoBXkMABkjywGgAbaaTxwJV8AyBY5DADZI4sBoIG6F5c0sw2qHqam6M8JZWrFihUqlUqp5t2yZYskacGCBSFLkiSNjIxo8eLFwV8HQO/Lew5X00w2N6ObOZ4F9h1AfhUhi/M6Lm6E7AP6Q6O/avEHisL07orpR0m6J0hFTSiVSrrjlut15P47G8772CMDkqTtM/cGrekXjw4EXT6AvpPrHK6mmWxuRrdyPAvsO4Dcy30W53Fc3AjZB/SPRo2HT0p6n7vflZxoZofGj/1uqMLSOnL/nXrfiQ83nO8j1x0oSanmbUf5dQCgQ3Kfw9WkzeZmdCvHs8C+A8i9QmRx3sbFaesA0PsaXeNh2N1vqpzo7uskDQepCACQRA4DQPbIYgBoQ6PGwz51HpvbyUIAAFWRwwCQPbIYANrQqPFwrZm9tXKimZ0naX2YkgAACeQwAGSPLAaANjS6xsOFkr5uZm/UU6E6KmlvSb8fsC4AQORCkcMAkLULRRYDQMvqNh7c/T5JLzGzV0o6Lp78bXf/XvDKAADkMADkAFkMAO1pdMSDJMndr5Z0deBaAAA1kMMAkD2yGABa0+gaDwAAAAAAAC2j8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAILJVeNh69atWZeAOlasWKEVK1ZkXQaALmB7R57x/USvY0zc+8gx9JvBrAtI2r59e9YloI5SqZR1CQC6hO0decb3E72OMXHvI8fQb3J1xAMAAAAAAOgtNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwNB4AAAAAAEAwwRoPZnapmW01s5tDvQayMT09rSVLlmh6erqpx0qlkl7zmteoVCq1tOxW60q7zMr5at0vlUot1xhaq+9fo3VHcZHFQL7V2zd2MovT7MOy2M8VYd/aLnIY7Zientb555+vCy64oO72UW8sV2+MfMEFF+jP/uzP6i4/zRg79NixmeWHqKVTy+znMXbIIx4uk7Qo4PKRkZUrV2rDhg1atWpVU49NTEzoscce08TEREvLbrWutMusnK/W/YmJiZZrDK3V96/RuqPQLhNZDORWvX1jJ7M4zT4si/1cEfatHXCZyGG0aOXKldq0aZM2btxYd/uoN5arN0beuHGjSqVS3eWnGWOHHjs2s/wQtXRqmf08xg7WeHD3ayQ9EGr5yMaOHTu0Zs0aubvWrFmzW7duenq65mOlUklTU1OSpKmpqZq/2an1/EZqPTftMivnK5VKNe9PTU21VGNorb5/jdY9T+uYFTMbyrqGVpHFQH7V2ze2s0+slFxWrX1Ymnk6LYvXzAI5jFZNT09rcnJy9v7k5GTNo5VqjeUmJyfrjpGTqi0/zRh7cnJSk5OTwbbhZvKwk9nZ6WWGqK1IBrMuIOmJJ57Q0qVLU89fKpW09458Xabivsf30pOlUlPrURSlUkkzMzPatWuXJGnnzp1atWqVLrroIklRB6/WY5W/yZmYmNBll12227R6z2+k1nPTLrNyvomJiZr3y5qtMbRW379G656ndczKnDlzDs+6hm7bsmWLtm3b1lKW5TGb866X9x0hlEolzZ07N+sy2lZv39jOPrFSclll9fbhtebptCxesyiaHRNLxczefs6+NDm2cuVKzczMzN7fsWNH1e2j3lhux44ds/NVjpGTj9Vafpoxdq3X6JRm8rCT2dnpZYaorUgyTycze5uZrTOzde6edTlo4Mknn5wNwJmZGa1du3b2sauuuqrmY+Xf6NS63+j5jdR6btplVs43NTVV835ZszWG1ur712jd87SOWRkcHHx61jWElMzh+++/P+tygL5Rb9/Yzj6xUnJZZfX24bXm6bQsXjPPGBOj0lVXXaXkd8Hdq24f9cZy7j67jMoxcuX3rNry04yxa71GpzSTh53Mzk4vM0RtRZL5EQ/ufomkSyRp/vz5fvHFF6d+7tKlS7X9rnWhSmvJ/H13ac5RI2pmPYpi6dKl2rx5sx566CHNzMxocHBQp5122uzjp556qlavXl31seHh4d0GVMPDw3ssv97zG6n13LTLrJxv4cKF2rx5c9X7Zc3WGFqr71+jdc/TOmZlZmampw+RTebw6OioS9KCBQskqaUsy2M2510v7ztC6JXfjtbbN7azT6yUXFZZvX14rXk6LYvXzLN2xsRSMbO3n7MvTY6deuqpuuKKK2Z/qDezqttHvbGcmUmKmgOVY+TksmstP80Yu9ZrdEozedjJ7Oz0MkPUViSZH/GAYpk/f7722iv62gwMDOjcc8+dfWx8fLzmY8uWLdttOZX3Gz2/kVrPTbvMyvmWLVtW835ZszWG1ur712jd87SOWdm+ffu9WdcAoPfU2ze2s0+slFxWWb19eK15Oi2L1wSKZHx8XIODT/2eeGhoqOr2UW8sNzQ0pKGhodnHkmPk8vR6y08zxh4aGpqtM8Q23EwedjI7O73MELUVScg/p3m5pB9LOsbMNpvZeaFeC90zNDSkRYsWycy0aNEizZs3b/axefPm1XxsZGRk9jc5w8PDGhkZ2WPZ9Z7fSK3npl1m5XwjIyM17w8PD7dUY2itvn+N1j1P65gVd9/ReK58IouB/Kq3b2xnn1gpuaxa+7A083RaFq+ZBXIYrZo3b57GxsZm74+NjVXdPuqN5cbGxuqOkZOqLT/NGHtsbExjY2PBtuFm8rCT2dnpZYaorUiCnWrh7ueEWjayNT4+rqmpqZod11qPLVu2TEuXLq16tEOa57daV9plVs5X6/6SJUu0fPnyXHYpW33/Gq07iossBvKt3r6xk1mcZh+WxX6uCPvWdpHDaMf4+LjuuOMOmVnD3/TXG8vVGiOXSiU9+eST2nvvveseGZxmjB1y7NhMHoYYx3Zqmf08xrY8Xbxm/vz5ft9996Wev3wu2/tOfLjhvB+57kBJSjVvOz5y3YGac9RoT56rVj4XrRfXDajHzNa7+2jWdXTD6Oior1u3rq3tvZlsbka3cjwLvbzvCIH9Uf/ppxyWmh8TS/kcF6epo1+zjxxDEbWTxVzjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABDOYdQFJc+bMyboE1DEyMpJ1CQC6hO0decb3E72OMXHvI8fQb3LVeDjssMOyLgF1LF68OOsSAHQJ2zvyjO8neh1j4t5HjqHfcKoFAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIhsYDAAAAAAAIZjDrAtr1i0cH9JHrDmw4312PDEhSqnnbrefooK8AAPmXNpub0a0czwL7DgCdkLdxcSNkH9A/Ct14GBkZST3vflu2SJLmLFgQqhxJ0tFqri4A6DWhMrBbOZ4F9h0A2pXHcXEjZB/QPwrdeFi8eHHWJQAAKpDNANB9ZC+APOMaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBhz96xrmGVmj0i6Les6AjpE0q+yLiKwXl/HXl8/qffXsZX1O8rdDw1RTN4UKIeL8j2lzs4pQo0SdXZauc6+yWGpMFlchO8QNXYGNXZGL9TYchYPtlZPMLe5+2jWRYRiZut6ef2k3l/HXl8/qffXsdfXrwMKkcNF+Ryps3OKUKNEnZ1WlDoDyH0WF+GzocbOoMbO6PcaOdUCAAAAAAAEQ+MBAAAAAAAEk7fGwyVZFxBYr6+f1Pvr2OvrJ/X+Ovb6+rWrKO8PdXZWEeosQo0SdXZaUerstCKsNzV2BjV2BjV2RrAac3VxSQAAAAAA0FvydsQDAAAAAADoITQeAAAAAABAMF1vPJjZIjO7zcxKZvbeKo+bmS2PH7/JzE7sdo3tSrGOp5jZQ2Z2Q/zv/VnU2Sozu9TMtprZzTUeL/RnmGL9Cv35SZKZHWFmV5vZJjO7xcyWVpmnsJ9jyvUr/OfYjqJkcYo63xjXd5OZ/cjMnp+3GhPzvdDMdprZWd2sL/H6DeuMt4sb4u3mv7tdY1xDo8/8aWZ2hZndGNf5lgxqLMR+MEWdmW8/cR1160zMl+k21A1p8yRLaT+vrKQZA2TNzPYxs58lcuxDWddUi5kNmNn1ZvatrGupxsymzGxDvO9al3U91ZjZQWb2FTO7Nf5evjjrmpLM7JjEePgGM3vYzC7s+Au5e9f+SRqQ9L+SniVpb0k3Sjq2Yp4zJE1KMkkvkvTTbtbYpXU8RdK3sq61jXV8haQTJd1c4/Gif4aN1q/Qn1+8DodLOjG+fYCk23tpW0y5foX/HNt4fwqRxSnrfImkg+PbY92uM02Nifm+J2m1pLNy+l4eJGmjpCPj+4fltM73SfpYfPtQSQ9I2rvLdRZiP5iizky3n7R1Jr4bmW1DXXofUuVJ1v/SfF4Z19dwDJD1vzgb9o9vD0n6qaQXZV1XjVr/QtIX8jpmkjQl6ZCs62hQ40pJfxbf3lvSQVnXVKfWAUm/lHRUp5fd7SMeTpZUcvc73f1JSV+UdGbFPGdKWuWRn0g6yMwO73Kd7UizjoXm7tcoGujVUujPMMX6FZ673+vu18W3H5G0SdKCitkK+zmmXL9+VpQsblinu//I3X8d3/2JpIV5qzG2WNJXJW3tZnEJaer8Y0lfc/dfSJK7Z1Frmjpd0gFmZpL2V5TXM90ssij7wUZ15mD7KdeRZr+b9TbUDYUYQ+Z9nFSEMUCcDY/Gd4fif7m74r+ZLZT0GkmfzbqWojKzAxU16z4nSe7+pLs/mGlR9b1a0v+6+12dXnC3Gw8LJN2duL9ZewZBmnnyLG39L44Pr5o0s+d2p7SuKfpnmEbPfH5mNizpBYq67Uk98TnWWT+phz7HJhUli5ut4TxFv2XupoY1mtkCSb8v6TNdrKtSmvfy2ZIONrPvm9l6Mzu3a9U9JU2dn5b0W5LukbRB0lJ339Wd8lLLw/bTrCy2n1Rysg11QxG/N7nWYAyQqfgUhhsUNdPWunvuapT0KUnvkZS3jE1ySd+J91tvy7qYKp4l6X5J/xGfsvJZM9sv66LqOFvS5SEW3O3Gg1WZVtndSzNPnqWp/zpFh688X9IKSd8IXVSXFf0zbKRnPj8z21/Rb5AudPeHKx+u8pRCfY4N1q9nPscWFCWLU9dgZq9U9IPTXwWtqMpLV5lWWeOnJP2Vu+8MX05NaeoclHSSot9u/Y6k/2tmzw5dWIU0df6OpBsk/YakEyR9Ov6NUp7kYftJLcPtJ61PKfttqBsK9b3JuwZjgMy5+053P0HRkUYnm9lxGZe0GzN7raSt7r4+61oaeKm7n6jodLE/N7NXZF1QhUFFpyb9q7u/QNJjkvJ6/Za9Jb1O0pdDLL/bjYfNko5I3F+o6DcWzc6TZw3rd/eHy4dXuftqSUNmdkj3Sgyu6J9hXb3y+ZnZkKId8ufd/WtVZin059ho/Xrlc2xRUbI4VQ1mdryiw0DPdPfpLtVWlqbGUUlfNLMpSWdJ+hcz+72uVPeUtJ/5Gnd/zN1/JekaSd2+2GCaOt+i6JQQd/eSpJ9Lek6X6ksrD9tPKhlvP2nlYRvqhsJ8b/IuxRgnN+LD7r8vaVG2lezhpZJeF293X5T0KjP7z2xL2pO73xP/v1XS1xWdspQnmyVtThzR8hVFjYg8GpN0nbvfF2Lh3W48XCvpaDN7ZtxROVvSNyvm+aakc+MrQr9I0kPufm+X62xHw3U0s2fE56bKzE5W9DnkdWffiqJ/hnX1wucX1/85SZvc/Z9qzFbYzzHN+vXC59iGomRxmjw9UtLXJP2Ju9/e5fpS1ejuz3T3YXcfVjTguMDdv5G3OiX9l6SXm9mgme0r6bcVnRudtzp/oegcVJnZfEnHSLqzq1U2loftp6EcbD+p5GQb6oY03380kHKMkykzO9TMDopvz5V0qqRbMy2qgrv/tbsvjLe7syV9z93flHFZuzGz/czsgPJtSadLytVfW3H3X0q628yOiSe9WtGFnPPoHAU6zUKKDv3oGnefMbN3SrpS0RUzL3X3W8zsHfHjn1F0teIzJJUkPa7oNxuFkXIdz5J0vpnNSNom6Wx3L8yhdGZ2uaK/CHCImW2W9AFFF8Xpic8wxfoV+vOLvVTSn0jaEJ9fKEVXij9S6onPMc369cLn2JKiZHHKOt8vaZ6i34BK0oy7j+asxsylqdPdN5nZGkk3KTqf97Pu3tUBXMr388OSLjOzDYoOTf+r+AiNrinKfjBFnZluP03U2Rdqff8zLmsP1T4vd/9ctlXtpuoYID66MS8Ol7TSzAYU/eLjS+6eyz9XmXPzJX09zq9BSV9w9zXZllTVYkmfjxuKdyqHY+r4Fw6nSXp7sNfok3E2AAAAAADIQLdPtQAAAAAAAH2ExgMAAAAAAAiGxgMAAAAAAAiGxgMAAAAAAAiGxgOAwjOzS81sq5k1vAK/mb3CzK4zsxkzO6visTVm9qCZcWVpAOiQJjP6k2Z2Q/zvdjN7sAslAkBP6OCYeNzM7oj/jXeiNhoPKCwzO6WZHxDN7M1m9huJ+0Nm9tF4g7rZzH5mZmNhqkVgl0lalHLeX0h6s6QvVHns44r+BBeAlMxsOM0AB33tMqXMaHe/yN1PcPcTJK2Q9LWAdQG5YGYHmdkFDeYZNrM/TrGs2UyOx8oPmdn1ZnabmV1jZq9NsYwPmtm7Wq0BmbpMbY6Jzezpiv6s8W9LOlnSB8zs4HYLo/GAQjKzwRae9mZJv5G4/2FFf0f5OHc/TtLvSjqg/erQbe5+jaQHktPM7DfjIxjWm9kPzOw58bxT7n6TpF1VlvNdSY90pWgA6BPNZHSFcyRd3pUigWwdJKlu40HSsKRWfuj/gbu/wN2PkbRE0qfN7NUtLKedGtAlHRoT/46kte7+gLv/WtJapW9m1ETjAV0Vd0pvNbPPxkcZfN7MTjWzH8ZHHpwc//tR3J39kZkdEz/3zWb2ZTO7QtJ3Kpb7wnj+Z5nZSWb23/HGdaWZHR4fPjQq6fPx4Zv7SXqrpMXuvl2S3P0+d/9SvLx/NbN1ZnaLmX0o8TpTZvax+OiIn5nZSJfeOjTvEkWf70mS3iXpXzKuByiMOOcuSNz/oJn9pZl9PM7uDWb2hirPe7OZfTpx/1tmdkp8+9F4uevN7Ko4679vZnea2evieQbi17jWzG4ys7eHX1tkpG5Gm9lRkp4p6XsZ1AZ020cl/WY8Rv14jaz9qKSXx/NcFI+pf2DRofLXmdlLGr2Iu98g6W8lvVOSzOxQM/tqnLnXmtlLE7M/38y+F4/P31qtho6tPUJrdky8QNLdifub42ltaeW3xkC7RiS9XtLbJF2rqHP6Mkmvk/Q+SedKeoW7z5jZqZI+IukP4+e+WNLx7v5AYjD7EkWHY54p6V5J/ynpTHe/Pw7rv3P3PzWzd0p6l7uvM7PjJf3C3R+uUePfxK8xIOm7ZnZ83BGUpIfd/WQzO1fSpyQ1PGQN3WVm+0t6iaQvm1l58pzsKgIK54uK8q08OPkjSR9T9BuP50s6RNK1ZnZNE8vcT9L33f2vzOzrkiYknSbpWEkrJX1T0nmSHnL3F5rZHEk/NLPvuPvPO7BOyImUGX22pK+4+85u1gZk5L2KjsA9wcz+UNI7tGfWvlfROPa1kmRm+0o6zd2fMLOjFR0dNJrita6T9O749sWSPunu/2NmR0q6UtJvxY8dL+lFirL7ejP7dmUNyL8Wx8RWZZq3WwuNB2Th5+6+QZLM7BZJ33V3N7MNig7hepqklXGIuqShxHPXunvy8KHfUtTFO93d7zGz4yQdJ2ltvHENKGpGNOuPzOxtiraRwxUNjMuNh8sT/3+yhWUjvL0kPRifIwygSe5+vZkdZtF1cQ6V9GtJJ0i6PP5B8D4z+29JL9RT2djIk5LWxLc3SNru7jsS2S9Jp0s63p66yNXTJB0ticZDb0mT0WdL+vPulAPkystUPWsrf1k2pOi0iRMk7ZT07JTLT/5QeaqkYxM/kB5oZuXTjv/L3bdJ2mZmVys61//BJtcF2WtlTLxZ0imJ+wslfb8ThQDdtj1xe1fi/i5FP+h/WNLViesu7JOY/7GKZd0r6QlJL4jvm6RbyhemcvfnufvpVWooSToyEa6zzOyZig5DerW7Hy/p2xU1eI3byIn4SJafm9nrJckiz8+4LKBoviLpLElvUHQERLXfgFSa0e5ji2R27nD3cmbOZr+7l7Nf8WssTmT4M919t1PrUHyNMtqiUywPlvTjjEoEspQmayXpIkn3KToyYlTS3imf9wJJm+Lbe0l6cSJzF7h7+VpXlWNcxrwF1OKY+EpJp5vZwRZdVPL0eFpbaDwgj54maUt8+80N5n1Q0mskfSQ+9eI2SYea2Yul2b9c8dx43kcUXzzS3R+X9DlJy81s73jew83sTZIOVNTgeMjM5kuq/EsXb0j8z6AoB8zsckWfxTFmttnMzpP0RknnmdmNkm5RdCpO+XogmxWd7vNv8VE35eX8QNKXJb06Xs7vdHtdgBz5oqLfOp+lqAlxjaQ3xNdhOFTSKyT9rOI5U5JOMLO9zOwIRb8ha8aVks43syFJMrNnW3RNHhRYMxkdO0fSFxONKqDXzY5RVTtrk/NI0Xj53rh5+yeKjvKtKz7V+P9K+ud40ncUX+8hfvyExOxnmtk+ZjZP0W+/r61SA3KmE2Pi+OjyDyv6zK+V9LcVR5y3hFMtkEf/oOhUi79QiotKuft9Zva7kiYl/amiQfJyM3uaou/4pxRtZJdJ+oyZbVN0rYhlis4x3mhmTyhqNrzf3W80s+vj59wp6YcVLznHzH6qqHF3Tpvrig5w91qfwx5X4HX3axUdMlZtOS/vZF1Akbn7LfFRYVvc/d74ugwvlnSjot98vcfdf2lmw4mn/VDRaREbJN2s6FziZnxW0WkX11l07O/9kn6vnfVA9prJ6Hj+D4arBsgfd5+26ELrNysaz96kPbN2WtJM/MPjZYquwfPV+DfZV2vPo4LLXh6Pa/eVtFXSkviveEnRX7n4ZzO7SdGY+RpF15eQombHtyUdKenD8SnN9ydrcHdOOc6ZDo6JL5V0aQdLk9FMBtIzsylJo+7+q6xrAQAAAIAi4FQLAAAAAAAQDEc8AAAAAACAYDjiAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABDOYdQFJhxxyiA8PD2ddBgDsZv369b9y90OzrqMbyGEAedRPOSyRxQDyqZ0szlXjYXh4WOvWrcu6DADYjZndlXUN3UIOA8ijfsphiSwGkE/tZDGnWgAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBy9ec0K61YsUKlUqmp52zZskWStGDBghAldcTIyIgWL16cdRkAUHit7CeqCbXvIO8BdFoRx8dkIYBcNx5KpZJuuHmTdu779NTPGXj8IUnSL7fnc9UGHn8g6xIAoGe0sp+oJsS+g7wHEELRxsdkIQAp540HSdq579O17TlnpJ5/7q2rJamp53RTuT4AQGc0u5+oJsS+g7wHEEqRxsdkIQCJazwAAAAAAICAaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgctV42Lp1a9YlIAdWrFihFStWZF0GAKAKMhoIjzFxbyE3AWkw6wKStm/fnnUJyIFSqZR1CQCAGshoIDzGxL2F3ARydsQDAAAAAADoLTQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMEEbD2a2yMxuM7OSmb035Guhv0xPT2vJkiWanp5u+Fj5fqlU0vnnn6+3ve1tOv/88/d4br1l1lturfmbrbuTpqendcEFF1Rdz06/TuX6dGsd02rlc8vbOrSDHEZe9dJ2ltSr64X2kMWoJZkZ9cZVpVJp9rFSqaTXvOY1KpVKVZdTNLXW+/zzz9cFF1zQ1ti7U/V0Y3lF/gzTCNZ4MLMBSf8saUzSsZLOMbNjQ70e+svKlSu1YcMGrVq1quFj5fsTExPatGmTbr/9dm3atGmP59ZbZr3l1pq/2bo7aeXKldq4cWPV9ez061SuT7fWMa1WPre8rUOryGHkWa9sZ5V6db3QOrIY9SQzo964amJiYvaxiYkJPfbYY5qYmKi6nKKptd6bNm3Sxo0b2xp7d6qebiyvyJ9hGiGPeDhZUsnd73T3JyV9UdKZAV8PfWJ6elpr1qyRu2vNmjV7dEeTj5VKpdn7U1NTuy1ncnJytw5qrWU2Wm61+Zutu5PKr1OWXM8Qr5Ncn26tY6s1pvnc8rYObSKHkUs9tp3N6tX1QtvIYlSVzIzJyUlNTk7WHFdNTU3J3bV69erZMe3U1JRKpVKhs6fWeHJycnJ2nsnJyZbG3p2qpxvLK/JnmNZgwGUvkHR34v5mSb9d7wlPPPGEli5dOnu/VCppryc9THUZ2euJh1UqPbLbemJ3pVJJc+fOrfn4ypUrtWvXLknSzp07tWrVKl100UVVH5uYmJi9X2nHjh2zz623zEbLrTZ/s3V30sqVK7Vjx47Z+8n17PTrVK6Pu3dlHVutMc3nVm29CqzpHL777rsLlU953k/0at43yug0upWH3dar64W2tT0mTspz7lXTq1nYjFq5mcyM5Nit2riqLDmfJE1MTOj4448vbPbUGk/OzMzMzrNjx46Wxt6dqqed10m7vH7Yf4Q84sGqTNsjJc3sbWa2zszWuRcnRJGdq666ajaMZmZmtHbt2pqPTU1N7RZcSe4++9x6y2y03GrzN1t3J1111VVKbkvJ9ez061SuT7fWMa1WPre8rUObms7hygENEEKPbWezenW90DbGxKgqmRnuPjt+qzauqmVqaqrQ2VNrPFk5lm1l7N2perqxvCJ/hmmFPOJhs6QjEvcXSrqnciZ3v0TSJZI0f/58v/jii2cfW7p0qdbfeV/AErtv1z4HauRZ85VcT+yuUUf81FNP1erVqzUzM6PBwUGddtppNR9buHChNm/eXDW0zWz2ufWW2Wi51eZvtu5OOvXUU3XFFVfMBnZyPTv9OpXrUz4EMPQ6tlpjms+t2nolD/crmKZzeHR01IuUT3neT/Rq3nfit5bdysNu69X1QtvaHhMn5Tn3qunVLGxGrdxMZoZZ1J9y96rjqlqGh4d1/PHHFzZ7ao0nK8eyRx11VNNj707V043l9cP+I+QRD9dKOtrMnmlme0s6W9I3A74e+sT4+Lj22iv66g4MDOjcc8+t+diyZctm71caGhqafW69ZTZabrX5m627k8bHxzU0NDR7P7menX6dyvXp1jqm1crnlrd1aBM5jFzqse1sVq+uF9pGFqOqZGYMDQ1pcDD6nXC1cVVZcownScuWLSt09tQaT5bfCyla51bG3p2qpxvLK/JnmFawxoO7z0h6p6QrJW2S9CV3vyXU66F/zJs3T4sWLZKZadGiRZo3b17Nx0ZGRmbvDw8P77acsbGx2efWW2aj5Vabv9m6O6n8OmXJ9QzxOsn16dY6tlpjms8tb+vQDnIYedVL21lSr64X2kMWo5ZkZoyNjWlsbKzmuGp4eFhmpjPOOGN2TDs8PKyRkZFCZ0+t8eTY2NjsPGNjYy2NvTtVTzeWV+TPMK2Qp1rI3VdLWh3yNdCfxsfHNTU1VfM31snHyveXLFmif/zHf9TOnTtrHtVQa5n1lttMR7KV57RifHxcpVJJ7h70taqtT7fWMa1WPre8rUM7yGHkVS9tZ0m9ul5oD1mMWiozo9a4asmSJVq+fLnOPfdc/frXv9bSpUu1bNmymsspklrjyTvuuENm1tbYu1P1dGN5Rf4M07A8Xbxm/vz5ft99T52zVj6Hbdtzzki9jLm3RpnezHO6ae6tq3VSn5/n1kj5PDjeI+SFma1399Gs6+iG0dFRX7duXdZlpNbKfqKaEPuOXs17MhpZ6KcclvYcEycVbXzcq1nYDHITvaKdLA55jQcAAAAAANDnaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgaDwAAAAAAIBgBrMuIGnOnDlZl4AcGBkZyboEAEANZDQQHmPi3kJuAjlrPBx22GFZl4AcWLx4cdYlAABqIKOB8BgT9xZyE+BUCwAAAAAAEBCNBwAAAAAAEAyNBwAAAAAAEAyNBwAAAAAAEAyNBwAAAAAAEAyNBwAAAAAAEAyNBwAAAAAAEAyNBwAAAAAAEAyNBwAAAAAAEAyNBwAAAAAAEAyNBwAAAAAAEAyNBwAAAAAAEMxgvQfNbF9JO9x9R3z/GElnSLrL3b/WhfoAoK+RwwCQPbIYANrT6IiHNZKGJcnMRiT9WNKzJP25mf192NIAACKHASAPyGIAaEOjxsPB7n5HfHtc0uXuvljSmKTXBq0MACCRwwCQB2QxALShUePBE7dfJWmtJLn7k5J2hSoKADCLHAaA7JHFANCGutd4kHSTmX1C0hZJI5K+I0lmdlDgumYNPP6A5t66uon5pyWpqed008DjD0ian3UZAIoj8xzOu2b3E9WX0fl9B3kP9JRcZXGRxsdkIQCpcePhrZKWKjqn7XR3fzyefqykTwSsS5I0MjLS9HO2bJmRJC1YkNeAm9/SegHoW5nmcN51Kk/D7DvIe6CH5CaLizc+JgsBSObujecqz2w2JOk4SVvcfWunixkdHfV169Z1erEA0BYzW+/uo1nXIZHDAPpTnnJYIosB9Kd2srjuNR7M7DNm9tz49tMk3ShplaTrzeycVl4QAJAeOQwA2SOLAaA9jS4u+XJ3vyW+/RZJt7v78ySdJOk9QSsDAEjkMADkAVkMAG1o1Hh4MnH7NEnfkCR3/2WoggAAuyGHASB7ZDEAtKFR4+FBM3utmb1A0kslrZEkMxuUNDd0cQAAchgAcoAsBoA2NPqrFm+XtFzSMyRdmOjqvlrSt0MWBgCQRA4DQB6QxQDQhrqNB3e/XdKiKtOvNLMHglUFAJBEDgNAHpDFANCeRkc87MbMjpV0tqRzJD0kKTd/1ggA+gE5DADZI4sBoDkNGw9mdpSiUD1H0oykoySNuvtU2NIAABI5DAB5QBYDQOvqXlzSzH4kabWkIUlnuftJkh4hYAGgO8hhAMgeWQwA7Wn0Vy3ul3SApPmSDo2nedCKAABJ5DAAZI8sBoA21G08uPuZkp4n6TpJHzKzn0s62MxO7kZxANDvyGEAyB5ZDADtaXiNB3d/SNKlki41s8MkvUHSp8zsCHc/InSBANDvyGEAyB5ZDACta3SqxW7cfau7r3D3l0i6OFBNAIAayGEAyB5ZDADNaarxUGFJx6oAALSCHAaA7JHFANBAO40H61gVAIBWkMMAkD2yGAAaaHiNhzoyu5LvihUrVCqVWn7+li1bJEkLFizoVEmZGxkZ0eLFi7MuA0B3cUX1Aml331VL6H0a+xegodxmcYjc6cVxdC3kH9A5dRsPZrZB1cPUFP05oUyUSiXdccv1OnL/nS09/7FHBiRJ22fu7WRZmfnFowNZlwAgkLzmMJrX7r6rlpD7NPYvQKSoWRwid3ptHF0L+Qd0VqMjHv5AUZjeXTH9KEn3BKkopSP336n3nfhwS8/9yHUHSlLLz8+b8voA6Em5zWE0r519Vy0h92nsX4BZhc3iTudOr42jayH/gM5qdI2HT0p62N3vSv6T9Hj8GAAgLHIYALJHFgNAGxo1Hobd/abKie6+TtJwkIoAAEnkMABkjywGgDY0ajzsU+exuZ0sBABQFTkMANkjiwGgDY0aD9ea2VsrJ5rZeZLWhykJAJBADgNA9shiAGhDo4tLXijp62b2Rj0VqqOS9pb0+wHrAgBELhQ5DABZu1BkMQC0rG7jwd3vk/QSM3ulpOPiyd929+8FrwwAQA4DQA6QxQDQnkZHPEiS3P1qSVcHrgUAUAM5DADZI4sBoDWNrvEAAAAAAADQMhoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgmFw1HrZu3Zp1CehzK1as0IoVK7IuA0CT2HYRAt8rZIUxMdA+MjxfBrMuIGn79u1Zl4A+VyqVsi4BQAvYdhEC3ytkhTEx0D4yPF9ydcQDAAAAAADoLTQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMDQeAAAAAABAMMEaD2Z2qZltNbObQ70GkHfT09NasmSJpqenaz5WKpWqzlPvudWWdcEFF+j888/fbf5a05tdfhbyXl9RkMVAd1RmVq0MC51tafY75Gp3kcNAflTLwaJmY9HqDnnEw2WSFgVcPpB7K1eu1IYNG7Rq1aqaj01MTFSdp95zqy1r48aN2rRp027z15re7PKzkPf6CuQykcVAcJWZVSvDQmdbmv0Oudp1l4kcBnKhWg4WNRuLVnewxoO7XyPpgVDLB/Juenpaa9askbtrzZo1e3RWy49NTU3tMU+959Z6nbLJyUlNT0/XnN7s8rOQ9/qKhCwGwqvMrFKpVDXDQmdb2v0Oudpd5DCQD9VysKjZWMS6B7MuIOmJJ57Q0qVLG85XKpW09w4uT1F23+N76clSKdV7h/pKpZLmzp3bkWWtXLlSu3btkiTt3LlTq1at0kUXXbTHY2XJeeo9t9rr7NixY/b+jh07tGrVKrl71enNLj8Lea8P+bNlyxZt27Yt1zlYxH1Xv+9f0u4TKjNrYmKiaoaFzra0+x1yNf/SjokbKWLu5EW/518vqMzwajno7oXMxiJmeuZJZGZvM7N1ZrbO3bMuB+iYq666SjMzM5KkmZkZrV27tupjZcl56j232usktx1319q1a2tOb3b5Wch7fb0mmcP3339/1uUAhVOZWVNTU1UzLHS2pd3vkKv5xJgYCKtaDhY1G4tYd+ZHPLj7JZIukaT58+f7xRdf3PA5S5cu1fa71oUurTDm77tLc44aUZr3DvV1sqt96qmnavXq1ZqZmdHg4KBOO+20qo+VJeep99xqr3PFFVfMNhnMTKeddprcver0ZpefhbzX12uSOTw6OlrI0e6CBQskKdc5WMR9V7/vX9LuEyoza+HChdq8efMeGRY629Lud8jVfGplTNxIEXMnL/o9/3pBZYZXy0F3L2Q2FjHTMz/iAehV4+Pj2muvaBMbGBjQueeeW/WxsuQ89Z5b7XWGhoZm7w8NDencc8+tOb3Z5Wch7/UBQFJlZi1btqxqhoXOtrT7HXIVQD+qloNFzcYi1h3yz2leLunHko4xs81mdl6o1wLyaN68eVq0aJHMTIsWLdK8efOqPjY8PLzHPPWeW+t1ysbGxjRv3rya05tdfhbyXl+RkMVAeJWZNTIyUjXDQmdb2v0Oudpd5DCQD9VysKjZWMS6g51q4e7nhFo2UBTj4+Oampqq2oUsP7ZkyRItX758j3nqPbfaskqlktx9j99wVZve7PKzkPf6ioIsBrqjMrNqZVjobEuz3yFXu4scBvKjWg4WNRuLVnfm13gAetm8efO0fPnyho9Vm6fec6vN+y//8i+ppze7/CzkvT4ASKrMrFoZFjrb0u53AKAfVcvBomZj0ermGg8AAAAAACAYGg8AAAAAACAYGg8AAAAAACAYGg8AAAAAACAYGg8AAAAAACAYGg8AAAAAACAYGg8AAAAAACAYGg8AAAAAACAYGg8AAAAAACAYGg8AAAAAACAYGg8AAAAAACAYGg8AAAAAACCYwawLSJozZ07WJaDPjYyMZF0CgBaw7SIEvlfICmNioH1keL7kqvFw2GGHZV0C+tzixYuzLgFAC9h2EQLfK2SFMTHQPjI8XzjVAgAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABEPjAQAAAAAABDOYdQGt+sWjA/rIdQe29Ny7HhmQpJafnze/eHRAR2ddBACgoXb2XbWE3KexfwGKr9O502vj6FrIP6CzCtl4GBkZaev5+23ZIkmas2BBJ8rJ3NFq/z0BAIQVKqdD7tPYvwDFFmL77bVxdC3kH9BZhWw8LF68OOsSAABoCvsuAN1G7gDIC67xAAAAAAAAgqHxAAAAAAAAgqHxAAAAAAAAgqHxAAAAAAAAgqHxAAAAAAAAgqHxAAAAAAAAgqHxAAAAAAAAgjF3z7qGWWb2iKTbsq4jA4dI+lXWRWSA9e4vRV7vo9z90KyL6Iac5nAevzvUlA41pUNNjR3j7gdkXUS35DSLW5W371K7WJ98Y33CanlMPNjpStp0m7uPZl1Et5nZOta7f7DeyLnc5XAevzvUlA41pUNNjZnZuqxr6LLcZXGr8vZdahfrk2+sT35xqgUAAAAAAAiGxgMAAAAAAAgmb42HS7IuICOsd39hvZFnefycqCkdakqHmtLJW015qye0XlrfXloXifXJO9Ynp3J1cUkAAAAAANBb8nbEAwAAAAAA6CFdbzyY2SIzu83MSmb23iqPm5ktjx+/ycxO7HaNIaRY71PM7CEzuyH+9/4s6uw0M7vUzLaa2c01Hu/Vz7vRevfc521mR5jZ1Wa2ycxuMbOlVebpyc+7iPKYxXnLyTzmVx6zJW/bfsp6uv1d2sfMfmZmN8Y1fajKPF39PqWsKZN9lZkNmNn1ZvatKo/19H6kUQ7mXbWMMrOnm9laM7sj/v/gLGtsRq08KeI61drmi7guSZV5UeT1MbMpM9sQ5+26eFph12cP7t61f5IGJP2vpGdJ2lvSjZKOrZjnDEmTkkzSiyT9tJs1Zrjep0j6Vta1Blj3V0g6UdLNNR7vuc875Xr33Oct6XBJJ8a3D5B0ez9s30X8l8cszmNO5jG/8pgtedv2U9bT7e+SSdo/vj0k6aeSXpTl9yllTZnsqyT9haQvVHvtXt6PpMnBvP+rllGS/kHSe+Pb75X0sazrbGJ9quZJEdep1jZfxHWpWK/d8qLI6yNpStIhFdMKuz6V/7p9xMPJkkrufqe7Pynpi5LOrJjnTEmrPPITSQeZ2eFdrrPT0qx3T3L3ayQ9UGeWXvy806x3z3H3e939uvj2I5I2SVpQMVtPft4FlMcszl1O5jG/8pgtedv2U9bTVfF6PxrfHYr/VV5kq6vfp5Q1dZ2ZLZT0GkmfrTFLL+9HcpeDzaqRUWdKWhnfXinp97pZUzvq5Enh1qnONl+4dSmrkReFXZ8aemZ9ut14WCDp7sT9zdpzMJBmnqJJu04vjg9/mjSz53antMz14uedVs9+3mY2LOkFirrpSf38eedJHrO4iDmZ1+9zZu9R3rb9OvVIXX6f4sOBb5C0VdJad8/8PUpRk9T979OnJL1H0q4aj+d1u+uEXl23+e5+rxT9IC/psIzraUlFnhRynWps84Vcl9intGdeFHl9XNJ3zGy9mb0tnlbk9dnNYJdfz6pMq+yup5mnaNKs03WSjnL3R83sDEnfkHR06MJyoBc/7zR69vM2s/0lfVXShe7+cOXDVZ7SD5933uQxi4uYk3n8Pmf2HuVt229QT9ffJ3ffKekEMztI0tfN7Dh3T16ro+vvUYqauvo+mdlrJW119/Vmdkqt2apMy3q765ReXrdCq8wTs2ofVf5V2+YzLqllKfOiaF7q7veY2WGS1prZrVkX1EndPuJhs6QjEvcXSrqnhXmKpuE6ufvD5cOf3H21pCEzO6R7JWamFz/vhnr18zazIUU75s+7+9eqzNKXn3cO5TGLi5iTufs+Z/Ue5W3bb1RPlt8ld39Q0vclLap4KLPvU62aMnifXirpdWY2peg0g1eZ2X9WzJO77a6DenXd7iufDhP/vzXjeppSI08KvU4V23xR16VWXhR1feTu98T/b5X0dUWnXxV2fSp1u/FwraSjzeyZZra3pLMlfbNinm9KOtciL5L0UPnwkgJruN5m9gyL26dmdrKiz2a665V2Xy9+3g314ucdr8/nJG1y93+qMVtfft45lMcsLmJO5u77nMV7lLdtP0093X6fzOzQ+DeMMrO5kk6VVPmbrK5+n9LU1O33yd3/2t0Xuvuwogz4nru/qWK23G13HZQmm4vom5LG49vjkv4rw1qaUidPCrdOdbb5wq2LVDcvCrk+ZrafmR1Qvi3pdEk3q6DrU01XT7Vw9xkze6ekKxVdufdSd7/FzN4RP/4ZSasVXbG4JOlxSW/pZo0hpFzvsySdb2YzkrZJOtvdC394nZldruiq2IeY2WZJH1B0MZue/bylVOvdi5/3SyX9iaQNFp0/KEnvk3Sk1Nufd9HkMYvzmJN5zK+cZkvetv009XT7fTpc0kozG1D0w/uX3P1bGY9/0tSUi31Vr48Ty2rlYMZlNaVGRn1U0pfM7DxJv5D0+uwqbFqtPCniOtXa5n+s4q1LPUX8bCRpvqLTX6ToZ/QvuPsaM7tWxVyfPVjxf9YBAAAAAAB51e1TLQAAAAAAQB+h8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QCg8MzsUjPbamY3p5j3FWZ2nZnNmNlZFY+Nm9kd8b/xWssAAAAAkB6NB2TGzA4yswsazDNsZn+cYlnDyR86zexkM7vGzG4zs1vN7LNmtm+T9Q2n+UEWuXCZpEUp5/2FpDdL+kJyopk9XdGf/fptSSdL+oCZHdy5EoHe0Gw2mtmbzew3QtYEAKEVedxqZpeZ2c/N7Ib4ly8vbnLZrzWz683sRjPbaGZvTyz3rEbPByQaD8jWQZLqBrikYUkNAzzJzOZL+rKkv3L3YyT9lqQ1kg5ovkQUgbtfI+mB5DQz+00zW2Nm683sB2b2nHjeKXe/SdKuisX8jqS17v6Au/9a0lqlb2YAqO3Nkmg8ACi6g1Tsceu73f0ESe+V9G9N1DdH0iWSftfdny/pBZK+324xFuFn0T7Ch40sfVTSb8bd14/H/242sw1m9obEPC+P57ko7ub+IO7WXmdmL6my3D+XtNLdfyxJHvmKu98Xd5R/FHdtf2Rmx0iSmT3XzH4Wv85NZnZ0vKwBM/t3M7vFzL5jZnNDvynomEskLXb3kyS9S9K/NJh/gaS7E/c3x9MA7GnQzFbGefkVM9vXzE4ys/+Om31Xmtnh8W/CRiV9Ps7XuWb2fjO7Ns77S8zMsl4ZAEihV8at10gaiZfzpsRy/s3MBuLpj5rZ35rZTxUdCTooaTqub7u735ZY3ivi2u4sH/1gZvub2Xfjdd5gZmfG04fNbJOZ/Yuk6yQdYWbvjvcJN5nZh1r7aFAENB6QpfdK+t+4+/oTSSdIer6kUyV93MwOj+f5gbuf4O6flLRV0mnufqKkN0haXmW5x0laX+M1b5X0Cnd/gaT3S/pIPP0dki6OaxlV9EOnJB0t6Z/d/bmSHpT0h62uLLrHzPaX9BJJXzazGxR19g9v9LQq07zDpQG94hhJl7j78ZIeVjRwXiHprLjZd6mkv3P3r0haJ+mNcY5vk/Rpd3+hux8naa6k12azCgDQlF4Zt/6upA1m9ltxTS+Nl7NT0hvjefaTdLO7/3Z8VOk3Jd1lZpeb2RsrjlQ4XNLLFGX5R+NpT0j6/Xi9XynpHxNN5mMkrYrX6Zi45pMVvZ8nmdkrarwXKLjBrAsAYi+TdLm775R0n5n9t6QXKhrQJg1J+rSZnaAoIJ/d5Os8TdLKuDPs8fIk6ceS/sbMFkr6mrvfEefjz939hnie9YoOoUP+7SXpwXhHmtZmSack7i9UBw4lBHrU3e7+w/j2f0p6n6LB89o4Owck3Vvjua80s/dI2lfS0yXdIumKsOUCQEcVcdz6cTNbJul+SedJerWkkyRdGz93rqJGieJav1p+orv/mZk9T1GT5V2STlN0Gp0kfcPdd0naaNFpI1L0y5yPxE2EXYqOIC0/dpe7/yS+fXr87/r4/v6KGhHXpH+LUBQc8YC8SHuo7UWS7lPUYR6VtHeVeW5RFKTVfFjS1fFv2n5X0j6S5O5fkPQ6SdskXWlmr4rn35547k7RrCsEd39Y0s/N7PXS7HmEz2/wtCslnW5mB1t0UcnT42kA9lR5NNAjkm6Jf8t3grs/z91Pr3ySme2j6LSns9z9eZL+XXEOA0CBFHHc+u44n09z95vjdViZyO1j3P2D8bxPxE2VWe6+IT6K4zTtfiRF8jXL78sbJR0q6aT4l0D36amsf6xi/r9P1DDi7p+r8V6g4Gg8IEuP6KkL51wj6Q1mNmBmh0p6haSfVcwjRZ3fe+PO6p8o+q1apU9LGjez3y5PiM9he0b8/C3x5DcnHn+WpDvdfbmiw8mOb3/10C1mdrmi7v8xZrbZzM5TtNM7z8xuVLRTL59f+EIz2yzp9ZL+zcxukSR3f0DRDv7a+N/fxtMA7OlIe+qq6OcoOuz40PI0Mxsys+fGjydzvDzw/FV8ShRXQwdQFL02bv2upLPM7LB4mU83s6MqZ4qv13BKYtIJku5qsOynSdrq7jvM7JWS9lhu7EpJfxrvD2RmC8r1oPfw21tkxt2nzeyHFv3pn0lJN0m6UdFv0t7j7r80s2lJM/EPj5cp+k3ZV+PfZF+t3bum5eXeZ2ZnS/pEHF67FO0gvibpHxQdsvYXkr6XeNobJL3JzHZI+qWkv5V0YIj1Rue5+zk1Htrjr1K4+7WKTqOotpxLFZ2bDqC+TYoGyv8m6Q5F13e4UtJyM3uaovHFpxQ1/S6T9Bkz2ybpxYqOctggaUpRkw8Acq/Xxq3uvjE+9eI78TUbdii6Xk9lU8EkvSfO+23xOry5weI/L+kKM1sn6QZF16qoVsN34mtN/Dg+3eNRSW/SU6d8oIeYO9dOAwAAAAAAYXCqBQAAAAAACIbGAwAAAAAACIbGAwAAAAAACIbGAwAAAAAACIbGAwAAAAAACIbGAwAAAAAACIbGAwAAAAAACGYw6wKSDjnkEB8eHs66DADYzfr163/l7odmXUc3kMMA8qifclgiiwHkUztZnKvGw/DwsNatW5d1GQCwGzO7K+sauoUcBpBH/ZTDElkMIJ/ayWJOtQAAAAAAAMHQeAAAAAAAAMHQeAAAAAAAAMHQeAAAAAAAAMHQeAAAAAAAAMHQeAAAAAAAAMHQeAAAAAAAAMEMZl1At6xYsUKlUqmtZWzZskWStGDBgk6U1HUjIyNavHhx1mUA6BPt5m5eM5csBYDm5XWfQKYD3dE3jYdSqaQbbt6knfs+veVlDDz+kCTpl9uL97YNPP5A1iUA6DPt5m4eM5csBYDW5HGfQKYD3ZOf0VwX7Nz36dr2nDNafv7cW1dLUlvLyEq5dgDopnZyN4+ZS5YCQOvytk8g04Hu4RoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPQBesWLFCK1asyLoMIDW+s+gEvkdA+9iO0Cy+M8ijwawLAPpBqVTKugSgKXxn0Ql8j4D2sR2hWXxnkEcc8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIIJ2ngws0VmdpuZlczsvSFfC+gV09PTuuCCC3T++edrenpaklQqlfSa17xGpVJpdp4lS5Zoeno61e1qr1HtsfL0Uqm02+O1pre7np1aVojl9QpyGEVQuf1WZk6tTGq0vbeSg80up5Oqvc709LTOP/98XXDBBV3Lt0b7iFbqaGVflWRmQ02/aI6Qxci7yrFmpVo5nWas2imM9YotWOPBzAYk/bOkMUnHSjrHzI4N9XpAr1i5cqU2btyoTZs2adWqVZKkiYkJPfbYY5qYmJidZ8OGDVq1alWq29Veo9pj5ekTExO7PV5rervr2allhVheLyCHURSV229l5tTKpEbbeys52OxyOqna66xcuVKbNm3Sxo0bu5ZvjfYRrdTRyr4qac6cOYc3/aI5QRajCCrHmpVq5XSasWqnMNYrtpBHPJwsqeTud7r7k5K+KOnMgK8HFN709LTWrFkze39yclLr1q3T1NSUJGlqakrr1q3TmjVr5O6anJzU5OTk7O3k9PLtNWvW7PHbs2qPJadPTU3NPl4qlapOb6fbXKuGvCyvh5DDyL3K7bda5tTKpHrbe71caCYzupUv1V5nenpak5OTs/NMTk525aiLRvuIZt+H5HPT7qsqnz84OHhIR1YwG2Qxcq1UKu021qw86qFeTq9Zs0br16/f7fnr16/veG4y1iu+wYDLXiDp7sT9zZJ+u94T7r77bi1dujRIMaVSSXs96UGWXQR7PfGwSqVHgr2/qK9UKmnu3LkN51u5cqV27Ngxe3/Hjh364Ac/uNs8H/zgB7Vr167Zx5PzVru9c+dOrVq1ShdddNHsa5Sfn3wsOT353ImJiarTk8tsVq0aWtXp5fWQpnO4bMuWLdq2bVtbmdGLuUuWNidN9lVuv9Uyp6zy8Xrbe71caCYzupUv1V7H3TUzMzM7z44dO4LnW5p9RLPvQ/K5afdVlc8vuJazWOpMHudBHvcJvZrpacedZZVHOUxMTOiyyy6bvV8vp3fu3KkPfOADuz3/Ax/4QMdzk7Fe8YU84sGqTNsjbczsbWa2zszWJXdAQD+66qqr5P7UZuLuevTRR3eb59FHH50diLr77Py1bs/MzGjt2rW7vUb5+cnHktPLZmZmNDU1VXV6cpmtrGe1GvKyvB7SdA7ff//9XSgLeErl9lstc8oqH6+3vdfLhWYyo1v5Uu11qu0TQudbmn1Es+9D8rlp91WVz1f1PCsKshi5Vj5aodb9ejk9MzNTd6zaqdxkrFd8IY942CzpiMT9hZLuqZzJ3S+RdIkkjY6O+sUXXxykmKVLl2r9nfcFWXYR7NrnQI08a75Cvb+oL20n/dRTT9UVV1wxOxAzM+233367Bfr++++vJ554QjMzMzKLxjLuXvP24OCgTjvttN1eY/Xq1ZqZmdntseT0ssHBQS1cuFCbN2/eY3pymc2qVUNeltdDWsphSVqwYIEktZUZvZi7ZGlz0mRf5fZbLXPKKh+vt73Xy4VmMqNb+VLtddx9j31C6HxLs49o9n1IPjftvqry+T/4wQ/y9avy5rScxVJn8jgP8rhP6NVMb/YIjuHh4d2aDcPDw7s9Xi+nBwcHtc8++9Qcq3YqNxnrFV/IIx6ulXS0mT3TzPaWdLakbwZ8PaDwxsfHNTT01IW7h4aGqp5qsddee80+Pjg4OHu7/Nzk7YGBAZ177rm7vUb5+cnHktPLBgYGtGzZsqrTk8tsZT2r1ZCX5fUQchi5V7n9VsucssrH623v9XKhmczoVr5Ue53x8fHZjJeibA+db2n2Ec2+D8nnpt1XVT6/4Mhi5NqyZcvq3q+X0wMDA/rQhz602/wf+tCHOp6bjPWKL1jjwd1nJL1T0pWSNkn6krvfEur1gF4wb948LVq0aPb+2NiYRkdHZzvPw8PDGh0d1aJFi2RmGhsb09jY2Ozt5PTy7UWLFmnevHl7vEblY8npw8PDs4+PjIxUnZ5cZqvr2YllhVheryCHUQSV22+1zKmVSfW293q50ExmdCtfqr3OvHnzNDY2NjvP2NhY8HxLs49o9n1IPjftvqry+TMzM7/qyApmgCxG3o2MjOw21hwZGdnt8Xo5vWjRIp100km7Pf+kk07qeG4y1iu+kKdayN1XS1od8jWAXjM+Pq5SqSR3n+3mLlu2TEuXLp3tQI+Pj2tqamr28TS3K1+j2mPl6UuWLNHy5ct3+01Xtentrmet+vKwvF5BDqMIKrffysyplUmNtvd68zWTGd3Kl2qvMz4+rjvuuENm1rV8a7SPaKWOtPutWrZv335v0y+aI2Qx8q5yrFmpVk6nHat2AmO9YrPkRYuyNjo66uvWrQuy7PJ5Zduec0bLy5h7a7S/aGcZWZl762qd1IPnsBVF+Vw73v9iMrP17j6adR3dUM7hTnxn283dPGYuWdocsg+d0k85LO0+Ju6V7SiP+4RezfRe+c4gf9rJ4pDXeAAAAAAAAH2OxgMAAAAAAAiGxgMAAAAAAAiGxgMAAAAAAAiGxgMAAAAAAAiGxgMAAAAAAAiGxgMAAAAAAAiGxgMAAAAAAAiGxgMAAAAAAAiGxgMAAAAAAAiGxgMAAAAAAAiGxgMAAAAAAAiGxgMAAAAAAAhmMOsCgH4wMjKSdQlAU/jOohP4HgHtYztCs/jOII9oPABdsHjx4qxLAJrCdxadwPcIaB/bEZrFdwZ5xKkWAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgmMF6D5rZvpJ2uPuO+P4xks6QdJe7f60L9QFAXyOHASB7ZDEAtKfREQ9rJA1LkpmNSPqxpGdJ+nMz+/uwpQEARA4DQB6QxQDQhkaNh4Pd/Y749riky919saQxSa8NWhkAQCKHASAPyGIAaEPdUy0keeL2qyR9XJLc/Ukz2xWsqkAGHn9Ac29d3cbzpyWprWVkZeDxByTNz7oMAM0rdA63k7t5zFyyFOhbhc7ivMjbPoFMB7qnUePhJjP7hKQtkkYkfUeSzOygwHV13MjISNvL2LJlRpK0YEERA2p+R94DAF1X2BxuN3PymblkKdCnCpvFeZHPfQKZDnRLo8bDWyUtVXRO2+nu/ng8/VhJnwhYV8ctXrw46xIAoBWFzWFyF0APKWwW5wX7BKC/1W08uPs2SR8t3zezIUnHSSq5+48C1wYAfY8cBoDskcUA0J66F5c0s8+Y2XPj20+TdKOkVZKuN7NzulAfAPQ1chgAskcWA0B7Gv1Vi5e7+y3x7bdIut3dnyfpJEnvCVoZAEAihwEgD8hiAGhDo8bDk4nbp0n6hiS5+y9DFQQA2A05DADZI4sBoA2NGg8PmtlrzewFkl4qaY0kmdmgpLmhiwMAkMMAkANkMQC0odFftXi7pOWSniHpwkRX99WSvh2yMACAJHIYAPKALAaANjT6qxa3S1pUZfqVZvZAsKoAAJLIYQDIA7IYANrT6IiH3ZjZsZLOlnSOpIckjYYoCgBQHTkMANkjiwGgOQ0bD2Z2lKJQPUfSjKSjJI26+1TY0gAAEjkMAHlAFgNA6+peXNLMfiRptaQhSWe5+0mSHiFgAaA7yGEAyB5ZDADtafRXLe6XdICk+ZIOjad50IoAAEnkMABkjywGgDbUbTy4+5mSnifpOkkfMrOfSzrYzE7uRnEA0O/IYQDIHlkMAO1peI0Hd39I0qWSLjWzwyS9QdKnzOwIdz8idIEA0O/IYQDIHlkMAK1rdKrFbtx9q7uvcPeXSLo4UE0AgBrIYQDIHlkMAM1pqvFQYUnHqgAAtIIcBoDskcUA0EA7jQfrWBUAgFaQwwCQPbIYABpoeI2HOjK9ku+KFStUKpUazrdlyxZJ0oIFC4LWMzIyosWLFwd9DQCokLsrqqfN5nq6ldsS2Q2gI3KXxVnrxL6gkU7tK9gPAN1Rt/FgZhtUPUxN0Z8TykypVNIdt1yvI/ffWXe+xx4ZkCRtn7k3WC2/eHQg2LIB9Lc853A1abO5nm7ktkR2A0ivaFmctU7sCxrpxL6C/QDQPY2OePgDRWF6d8X0oyTdE6SiJhy5/06978SH687zkesOlKSG87Wj/BoAEECuc7iaNNlcTzdyO/k6AJBC4bI4a+3uCxrpxL6C/QDQPY2u8fBJSQ+7+13Jf5Iejx8DAIRFDgNA9shiAGhDo8bDsLvfVDnR3ddJGg5SEQAgiRwGgOyRxQDQhkaNh33qPDa3k4UAAKoihwEge2QxALShUePhWjN7a+VEMztP0vowJQEAEshhAMgeWQwAbWh0cckLJX3dzN6op0J1VNLekn4/YF0AgMiFIocBIGsXiiwGgJbVbTy4+32SXmJmr5R0XDz52+7+veCVAQDIYQDIAbIYANrT6IgHSZK7Xy3p6sC1AABqIIcBIHtkMQC0ptE1HgAAAAAAAFpG4wEAAAAAAARD4wEAAAAAAARD4wEAAAAAAARD4wEAAAAAAARD4wEAAAAAAARD4wEAAAAAAARD4wEAAAAAAARD4wEAAAAAAARD4wGSpBUrVmjFihVZlwEgh8iH3sFnCRQf2zEq8Z1AEQxmXQDyoVQqZV0CgJwiH3oHnyVQfGzHqMR3AkXAEQ8AAAAAACAYGg8AAAAAACAYGg8AAAAAACAYGg8AAAAAACAYGg8AAAAAACAYGg8AAAAAACAYGg8AAAAAACAYGg8AAAAAACAYGg8AAAAAACAYGg8AAAAAACAYGg8AAAAAACAYGg8AAAAAACAYGg8AAAAAACCYYI0HM7vUzLaa2c2hXgPdNT09rSVLlmh6ejr1/Oeff74uuOCC1M+p9TrJaY3qqHy80f1ma+klvb5+Sf20rklkMVpVL8Pb2Z7SZHJ5WqlUapj3559/vv70T/9UY2NjKpVKudvWu1FP3tYZuyOHgerIrv4S8oiHyyQtCrh8dNnKlSu1YcMGrVq1KvX8mzZt0saNG1M/p9brJKc1qqPy8Ub3m62ll/T6+iX107pWuExkMVpQL8Pb2Z7SZHJ52sTERMO837Rpk+68805t27ZNExMTudvWu1FP3tYZe7hM5DCwB7KrvwRrPLj7NZIeCLV8dNf09LTWrFkjd9eaNWsadianp6c1OTk5e39ycjL1EQaVr5OcNjk5WbeOyueXSqW69+vV1Ow6F02vr19SP61rJbIYraiX4e1sT40yujLzp6am6uZ9skZJmpqa0uTkZG629W5kTz/nW1GQw8CeyK7+M5h1AUl33323li5dmmreUqmkvXfk4xIV9z2+l54slVLXnkelUklz586t+fjKlSu1a9cuSdLOnTu1atUqXXTRRXXnn5mZmb2/Y8eOhs+p9TruPjttx44ds/NWq6Py+RMTE3Xv16up2XUuml5fv6R+WtcQtmzZom3btqXKuDxlcyO9kN3NapT1ZfUyvJ3tqVFGV2Z+Wa28T9aYrLWV2kLoRvaQb/2nmUzOSlH2Bb2yH0ib7XlCdvWfzBPBzN5mZuvMbF3yh0rky1VXXTU7wJuZmdHatWsbzu/us/fdveFzar1Ocpq7zy63Wh2Vz5+amqp7v15Nza5z0fT6+iX107q2IpnD999/f9blIAfqZXg721OjjK7M/LJaeZ+ssVIetvVuZA/51jvIYvQTsqv/ZH7Eg7tfIukSSRodHfWLL7441fOWLl2q7XetC1laavP33aU5R40obe151KjTe+qpp2r16tWamZnR4OCgTjvttIbzX3HFFbODQjNr+Jxar+Pus9PMTFI0CK5WR+XzFy5cqM2bN9e8X6+mZte5aHp9/ZL6aV1bUZnDlY8vWLBAklJlXJ6yuZFeyO5mpf2tXr0Mb2d7apTRlZlfVivvkzVWysO23o3sId96R6MsLmsmk7NSlH1Br+wHinjEBtnVfzI/4gHFMD4+rr32ir4uAwMDOvfccxvOPzj4VF9raGio4XNqvU5y2tDQkIaGhmrWUfn8ZcuW1b1fr6Zm17loen39kvppXYFOqJfh7WxPjTK6MvPLauV9ssZkra3UFkI3sod8A1BEZFf/CfnnNC+X9GNJx5jZZjM7L9RrIbx58+Zp0aJFMjMtWrRI8+bNazj/2NjY7P2xsbGGz6n1OslpY2NjdeuofP7IyEjd+/Vqanadi6bX1y+pn9a1ElmMVtTL8Ha2p0YZXZn5w8PDdfM+WaMkDQ8Pa2xsLDfbejeyp5/zrSjIYWBPZFf/CXaqhbufE2rZyMb4+LimpqZSdyTHx8d1xx13yMya/o1Y5etUTqtXR+W8je43W0sv6fX1S+qndU0ii9GqehnezvaUJpPL05YsWaLly5fXzfs77rhD27dv17333qtly5bp4IMPztW23o3s6dd8KwpyGKiO7OovmV/jAcUxb948LV++vKn5//Vf/7Ujr1M5rV4dlfM2ut9sLb2k19cvqZ/WFeiEehnezvaUJpOT0xrlfbUa87StdyN7yDcARUR29Reu8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIIZzLoA5MPIyEjWJQDIKfKhd/BZAsXHdoxKfCdQBDQeIElavHhx1iUAyCnyoXfwWQLFx3aMSnwnUAScagEAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIIZzLqAdvzi0QF95LoD685z1yMDktRwvnbrODrY0gGgWNJkcz3dyG2J7AaAkNrdFzTSiX0F+wGgewrbeBgZGUk1335btkiS5ixYEKyWo5uoBwB6WSeysBu5LZHdABBKN7K1E/sK9gNA9xS28bB48eKsSwAAVCCbAQDsCwBU4hoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGBoPAAAAAAAgGHP3rGuYZWaPSLot6zpacIikX2VdRIuKWntR65aKW3tR65bar/0odz+0U8XkWYFzuBVF/k43i3XtTf20rse4+wFZF9EtBcnionz/ilBnEWqUqLOTilCjtGedLY+JBztTT8fc5u6jWRfRLDNbV8S6peLWXtS6peLWXtS6pWLXnoFC5nAr+ul7wbr2pn5b16xr6LLcZ3FRvn9FqLMINUrU2UlFqFHqbJ2cagEAAAAAAIKh8QAAAAAAAILJW+PhkqwLaFFR65aKW3tR65aKW3tR65aKXXu39dN7xbr2Jta1N/XTukrFWN8i1CgVo84i1ChRZycVoUapg3Xm6uKSAAAAAACgt+TtiAcAAAAAANBDut54MLNFZnabmZXM7L1VHjczWx4/fpOZndjtGmtJUfspZvaQmd0Q/3t/FnVWMrNLzWyrmd1c4/Fcvucp6s7r+32EmV1tZpvM7BYzW1plnry+52lqz937bmb7mNnPzOzGuO4PVZknl+95Voqcxc0qanY3q6hZ34qi7h9aUeR9SrOKug/qpEZ5lZVq25yZPd3M1prZHfH/B2dcY9XvTw7rrDpmyVudcU0DZna9mX0rxzVOmdmGOA/W5bjOg8zsK2Z2a/wdfXGe6jSzYxK5eoOZPWxmF3a0Rnfv2j9JA5L+V9KzJO0t6UZJx1bMc4akSUkm6UWSftrNGtus/RRJ38q61iq1v0LSiZJurvF4Xt/zRnXn9f0+XNKJ8e0DJN1eoO95mtpz977H7+P+8e0hST+V9KIivOcZvV+FzeJA65q773SL61rIrA+0rj3xmcbrUth9SqB17ZnPtsr6N8yrDGvbY5uT9A+S3hvffq+kj+Xx+5PDOquOWfJWZ1zHX0j6Qnmby2mNU5IOqZiWxzpXSvqz+Pbekg7KY51xLQOSfinpqE7W2O0jHk6WVHL3O939SUlflHRmxTxnSlrlkZ9IOsjMDu9yndWkqT2X3P0aSQ/UmSWX73mKunPJ3e919+vi249I2iRpQcVseX3P09SeO/H7+Gh8dyj+V3kBm1y+5xkpchY3q7DZ3ayiZn0rirp/aEWR9ynNKuo+qINym1c1trkzFf0wpfj/3+tmTZXqfH/yVmetMUuu6jSzhZJeI+mzicm5qrGOXNVpZgcqat59TpLc/Ul3f1A5qzPh1ZL+193vUgdr7HbjYYGkuxP3N2vPHUqaebKQtq4Xx4dOTZrZc7tTWtvy+p6nkev328yGJb1AUTc7KffveZ3apRy+7/HhgDdI2ipprbsX7j3voiJncbN6Obub1SufaVo995kWeZ/SrKLtgzqkaJ/jfHe/V4p+6Jd0WMb1zKr4/uSuzhpjlrzV+SlJ75G0KzEtbzVKUdPmO2a23szeFk/LW53PknS/pP+IT135rJntp/zVWXa2pMvj2x2rcbADhTXDqkyr/K1kmnmykKau6yQd5e6PmtkZkr4h6ejQhXVAXt/zRnL9fpvZ/pK+KulCd3+48uEqT8nNe96g9ly+7+6+U9IJZnaQpK+b2XHunjz/O9fveZcVOYub1cvZ3axe+UzT6LnPtMj7lGYVcR/UIT31OWal8vtjVu1tzVa1MUvGJe3GzF4raau7rzezUzIup5GXuvs9ZnaYpLVmdmvWBVUxqOhUpcXu/lMzu1jRaQu5Y2Z7S3qdpL/u9LK7fcTDZklHJO4vlHRPC/NkoWFd7v5w+dApd18tacjMDuleiS3L63teV57fbzMbUrTT+7y7f63KLLl9zxvVnuf3XZLiQ9e+L2lRxUO5fc8zUOQsblYvZ3ezeuUzbajXPtMi71OaVfR9UJuK9jneVz6lJ/5/a8b11Pr+5K7OsooxS57qfKmk15nZlKJTfl5lZv+pfNUoSXL3e+L/t0r6uqJTlvJW52ZJmxNH435FUSMib3VK0pik69z9vvh+x2rsduPhWklHm9kz427K2ZK+WTHPNyWda5EXSXqofHhHxhrWbmbPsLitamYnK3p/p7teafPy+p7Xldf3O67pc5I2ufs/1Zgtl+95mtrz+L6b2aHxbw1kZnMlnSqpsuOdy/c8I0XO4mb1cnY3q1c+04Z66TMt8j6lWUXdB3VQmmzOk29KGo9vj0v6rwxrqff9yVudtcYsuanT3f/a3Re6+7Ci7+H33P1NylGNkmRm+5nZAeXbkk6XdLNyVqe7/1LS3WZ2TDzp1ZI2Kmd1xs7RU6dZSB2ssaunWrj7jJm9U9KViq6Weam732Jm74gf/4yk1YquzlyS9Likt3SzxlpS1n6WpPPNbEbSNklnu3vmh8iZ2eWKrgJ9iJltlvQBRReyyfV7nqLuXL7firrEfyJpg0Xn70nS+yQdKeX7PVe62vP4vh8uaaWZDSgahH7J3b9VhGzJQpGzuFlFzu5mFTXrW1Hg/UMrirxPaVZR90EdUSuvMi5LUs1t7qOSvmRm50n6haTXZ1ehpNrfn7zVWWvM8mPlq85q8vZezld0qooU/Vz7BXdfY2bXKl91StJiSZ+Pm4p3KsrpvZSjOs1sX0mnSXp7YnLHPnPrkawGAAAAAAA51O1TLQAAAAAAQB+h8QAAAAAAAIKh8QAAAAAAAIKh8QAAAAAAAIKh8QCg8MzsUjPbamY3p5j3FWZ2nZnNmNlZFY+tMbMHzexb4aoFgP7SZEYfZWbfNbObzOz7ZrawGzUCAMKi8YCuMbPfM7NjA7/Gj1p4zgfN7O8rpp1gZpsaPOddrdSIIC6TtCjlvL+Q9GZJX6jy2McV/SkuoGeZ2RIz22RmW8zs0y0u4xQze0ni/jHxD4k3xMu+JJ7+5lZfAz3lMqXP6E9IWuXux0v6W0l/32B+IBNx5o22uYznmtn3zOx2M7vDzP6vxX8b0szmmNlVca6+IZ52qJntMLO3119yw9dtKZsrsz+e9jYzuzX+9zMze1mK5bzDzM5tMM+omS1vtkbkF40HdNPvSWqq8WBmgynnG5Akd39Jo3mruFzSGyqmna3qP5gih9z9GkkPJKeZ2W/GRzCsN7MfmNlz4nmn3P0mSbuqLOe7kh7pStFAdi6QdIakv2ljGadISubtckmfdPcT3P23JK1oY9mzytmOYmsmoxWNE74b375a0pldLBXoCjMbNLO5kr4p6aPu/mxJz1eUqxfEs71A0lCcq/9fPO31kn4i6Zxu1xw7RYnsN7PXSnq7pJe5+3MkvUPSF8zsGfUW4u6fcfdVDeZZ5+5L2i8ZeUHjAW0xszfF3c0bzOzfzGzAzB41s78zsxvN7CdmNj/ujr5O0sfjeX+z1qDDzC4zs38ys6slfSw+uuD/xR3hO8zsrfF8p5jZ1Wb2BUkb4mmPxv8fbmbXxK91s5m9PJ5+upn9OD7U/stmtr+73ybpQTP77cSq/ZGkL5rZW83s2nhdvmpm+1Z5D2Y73mZ2iJlNxbcHzOzj8fNvarc7jaZdImmxu58k6V2S/iXjeoDMmdlnJD1L0WD34MT05OHt3zWzI+Ppv2tmPzWz6+PfvM03s2FFg8uL4ox9uaTDJW0uL8/dNyRe9jfirL/DzP4h8Zr/ambrzOwWM/tQYvqUmb3fzP5H0uur5XY830fNbGNc8ydCvF8IqlZG3yjpD+Pbvy/pADObl0F96BFmNmzRkVj/HufNd8xsbp3x25vN7BtmdoWZ/dzM3mlmfxHn4E/M7OmJxb/JzH4UjzVPjp+/n0WnF10bP+fMxHK/bGZXSPqOpD+W9EN3/44kufvjkt4p6b1mdpik/5R0QnncHL/eOZL+UtJCM1uQWMc9xt7x9D0yvOK9OSBex6H4/oFxBg9ZdHRcOWO/WCP7/0rSu939V/E6XCdppaQ/j5c3ZWYfs+hnhZ+Z2Ug8ffbI4fhzKM9ze2LMforFp76a2dPjz+SmeP2OTyzn0ngZd5rZksRn8O34/bjZ4iNGkC0aD2iZmf2WoiMFXuruJ0jaKemNkvaT9BN3f76kayS91d1/pGig++64c/u/qv+D4bMlnerufxnfP17SayS9WNL7zew34uknS/obd688kuKPJV0Z1/V8STeY2SGSlsXLPVHSOkl/Ec9/uaKjHGRmL5I07e53SPqau78wXpdNks5r4i06T9JD7v5CSS+U9FYze2YTz0eL4h9MXiLpy2Z2g6R/U/SDEdDX3P0dku6R9EpJv0489Gk9dXj75xUdwSBJ/yPpRe7+AklflPQed5+S9Bk9dYTDDyR9UtL3zGzSzC4ys4MSyz5B0b7ieZLeYGZHxNP/xt1HFeX7/ykPJGNPuPvLJF2lKrkdD/x/X9Jz45on2n1v0D0NMvpdir4P10v6P5K2SJrJok70lKMl/bO7P1fSg3qquVXLcYrGkidL+jtJj8c5+GNJyVME9ouPtr1A0qXxtL+R9L14/PdKRb902y9+7MWSxt39VZKeK2l98kXj8fH+kp6Q9GeSflAeN8fZ+Qx3/5mkL2n3o3X3GHvH0/fI8IrXe0TS9xWNsaVoLPxVd98h6b2SXhBn7DtqZP8e66Aop5+buP+wu5+saD/zKVU3GM9zoaQPVHn8Q5Kuj2t5n6Tk0RLPkfQ7ij6rD8RNlEWS7nH357v7cZLW1HhddFGqw9iBGl4t6SRJ11p0OtpcSVslPSmpfHG+9ZJOq3xixaCjPHlOYpYvu/vOxP3/cvdtkrZZdCTEyYp2HD9z959Xqe1aSZfG4fMNd7/BzP6PokM4fxi/5t6KdiBSFMY/MrO/VBS6l8fTjzOzCUkHKdoRXNngPUk6XdLx9tQFDJ+maMdXrV501l6SHowbTwAae7GkP4hv/z9J5SMTFkr6/8zscEWZWTW/3P0/zOxKRYO9MyW93cyeHz/8XXd/SJLMbKOkoyTdLemPzOxtisYihyvK55vi55QPK36Rquf2w4oG5p81s2/rqX0OiqFmRrv7PYq/i/FY4Q/L3x+gDT939xvi2+slDTeY/+r4h/JHzOwhSVfE0zcoapaWXS5FpxPFRwscpGj89zp76lpg+0g6Mr691t3Lpx2ZJK/x+tWmn62o4SBF49bPSfqn+H6tsXeaDP+soobENyS9RU81LW6S9Hkz+0b8WFqV63V54v9P1njO1xK1D1d5/GWKm0Xu/j0zm2dmT4sf+7a7b5e03cy2Spqv6HP6hJl9TNK34iYJMsYRD2iHSVoZdz1PcPdj3P2Dkna4ezlwdqp6g2t20JH491uJxx+rmL8ygL3GfNGD0fmkr1D0m5L/Z9EFbExR4Jdf71h3Py+e/25JU4p+u/KHeirYL5P0Tnd/nqJu6z5VXm5GT21LycdN0REd5dd7ZvlwOoTl7g9L+rmZvV6SLPL8Bk8D8JRyxq6Q9Ok4A9+u6hkYPcH9Hne/1N3PVJSLx8UPbU/MtlPSYHz017skvTr+Dda3K5Zdzvaque3uM4oa0F9VdP0gfptVIPUy2qJD3sv71L/WU79FBtqxRw6p9vitcv5difu7tPu4ttr41BQ1zMq5daS7ly9Ynhy33iJpt4tTmtmzJD0aNz0qnSPpzRadEvJNSc83s6Pjx2qNvRtmuLv/UNJw/Au6AXcv//WZ10j6Z0W/ZFxv1a+7tjF+POnEePrsS9S4nVR+f2v93GBVppWXtcdn6+63x3VtkPT3Zvb+Gq+LLqLxgHZ8V9JZFp2HVj7/6qg68z8i6QCppR8MzzSzfSw6z/MURUc01BTXsdXd/11RR/hERRfjeWni/LJ9zezZiaeVO7H/6+7lc5UPkHRvfOTEG2u83JSeCt3kn2e8UtL5ifPmnp041A4dZGaXK/ot6DFmttnMzlP0eZ1nZjcq2rmXz7F8oZltVnSBpn8zs1sSy/mBpC9LenW8nN/p9roAGfmR4tPNFG07/xPffpqiBq4kjSfmn81zSTKzRYmse4akeYnnVXOgogH4QxadczxWY76quR3/Jvxp7r5a0aG5J6RYR2SkmYxWtI+/zcxuV/Sby7/LoGT0hylVH781o/zXJl6m6PTahxSN/xabzf51ihfUeO7nJb3MzE6N55ur6DS3f6ic0cyOUXRaxwJ3H3b3YUV/8eXsynkr1MrwSqsUjYP/I369vSQd4e5XKzoa4iBFR/7ulv1xrR+Lx+cysxMU/eWw5OnTb0j8/2O15hrF43AzO0XSr+KfJaqKT8l+3N3/U9FfyjmxxddFB3GqBVrm7hvNbJmk78QBtUPxxWRq+KKkf7fowi9nKQqQf42XMRQ/fmON5/5M0W/EjpT0YXe/p6JpUOkUSe82sx2SHpV0rrvfb2ZvlnS5mZVP61gm6fb49pclXSxpcWI5/1fSTyXdpahrmgzbsk9I+pKZ/Ymk7yWmf1bR4WLXxTuf+xX9Zg4d5u61ru68x59vc/drFR16WG05L+9kXUCBLFF0etq7FWXVW+LpH1R0StwWRU2A8nVqrpD0FYsumrZY0aHFF5vZE/Hj73b3XyZOpduNu98Yn8N/i6Q7Jf2wxny1cvsRSf9lZvso+k3YRS2tNbqiyYz+iqSvhK0IkFR7/NaMX1v0p9wPlPSn8bQPK7qWwU3x+G9K0msrn+ju2+IMXWFm/yxpQNGpbtX+zOU5kr5eMe2risbOH65T3wdVPcMrfV7RtXLKp0UMSPrP+HQGU3RdhwctujDmbPa7+zctusjlj8zMFWXzm9z93sSy55jZTxX9wrvVv8bxQUn/YWY3SXpc9ZsoUnRNoY+b2S5FP5+c3+LrooPsqaNygHwysw8qOuyMq5YDAAAAHWTR9cjOdPc/6fBypySNevxXL9DfOOIBAAAAAPqQma1QdLrbGVnXgt7GEQ8AAAAAACAYLi4JAAAAAACCofEAAAAAAACCofEAAAAAAACCofEAAAAAAACCofEAAAAAAACCofEAAAAAAACC+f8BAf6vsMeCaYQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x1728 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(6, 3, figsize=(18, 24))\n",
    "\n",
    "counter=1\n",
    "hor=0\n",
    "ver=0\n",
    "for n in data:\n",
    "    if(counter>=4 and counter<=21):\n",
    "        bpDF = pd.concat([data[n], data['CLASS']], axis = 1).reindex(data[n].index)\n",
    "        theplot=sb.boxplot(x = n, y = \"CLASS\", data = bpDF, orient = \"h\",ax = axes[ver,hor])\n",
    "        stat=pd.DataFrame(data[n].describe())\n",
    "        pmax=(stat.iloc[6]+1.5*(stat.iloc[6]-stat.iloc[4]))*2\n",
    "        del stat\n",
    "        if(counter!=4):\n",
    "            theplot.set_xlim(0,int(pmax))\n",
    "        del bpDF\n",
    "        hor+=1\n",
    "        if(hor==3):\n",
    "            hor=0\n",
    "            ver+=1\n",
    "    counter+=1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_B_izUGZuqUX"
   },
   "source": [
    "# Univariate vs Multivariate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "a6Tn1LnGuXqU"
   },
   "outputs": [],
   "source": [
    "X = pd.DataFrame(data['beta'])\n",
    "y = pd.DataFrame(data['CLASS'])\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "KL_R6QgGuXqU"
   },
   "outputs": [],
   "source": [
    "RS = RobustScaler()\n",
    "X_train = RS.fit_transform(X_train)\n",
    "X_test = RS.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "HalRG5eiuXqV",
    "outputId": "7c895770-e2a1-4806-97fb-bb09ff5abb30"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gb=GaussianNB()\n",
    "gb.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "2xxxteI8uXqV",
    "outputId": "82864d14-4472-42af-f79e-7078e236b640"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy is 76.25 %\n",
      "std is 4.99 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "accuracies = cross_val_score(estimator=gb, X=X_train ,y=y_train,cv=10)\n",
    "print(\"accuracy is {:.2f} %\".format(accuracies.mean()*100))\n",
    "print(\"std is {:.2f} %\".format(accuracies.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "M1MDFwj_uXqW"
   },
   "outputs": [],
   "source": [
    "pre4=gb.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "uCMeikLWuXqW",
    "outputId": "457f81c1-b545-4659-e3d9-2f94045a99dd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7375\n",
      "[[43  3]\n",
      " [18 16]]\n"
     ]
    }
   ],
   "source": [
    "Naive_bayes_Gaussian_nb=accuracy_score(pre4,y_test)\n",
    "print(accuracy_score(pre4,y_test))\n",
    "print(confusion_matrix(y_test,pre4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "susHjFn_uXqX"
   },
   "outputs": [],
   "source": [
    "X = pd.DataFrame(data[['beta','sector_Healthcare','marketCap','heldPercentInsiders']])\n",
    "y = pd.DataFrame(data['CLASS'])\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "oNJGmN02uXqX"
   },
   "outputs": [],
   "source": [
    "RS = RobustScaler()\n",
    "X_train = RS.fit_transform(X_train)\n",
    "X_test = RS.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "BBVngrZBuXqX",
    "outputId": "61fa9d21-173f-4887-86d5-a8e170e0980a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gb=GaussianNB()\n",
    "gb.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "dzywLsYouXqY",
    "outputId": "61ccfffe-e36a-439e-a4d3-c8d9dd662eeb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy is 71.22 %\n",
      "std is 6.44 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "accuracies = cross_val_score(estimator=gb, X=X_train ,y=y_train,cv=10)\n",
    "print(\"accuracy is {:.2f} %\".format(accuracies.mean()*100))\n",
    "print(\"std is {:.2f} %\".format(accuracies.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "FGy1TpB6uXqZ"
   },
   "outputs": [],
   "source": [
    "pre4=gb.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "B-khv11ouXqZ",
    "outputId": "2e497bf7-01d6-4e93-92ca-dc600d9effea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7375\n",
      "[[28 18]\n",
      " [ 3 31]]\n"
     ]
    }
   ],
   "source": [
    "Naive_bayes_Gaussian_nb=accuracy_score(pre4,y_test)\n",
    "print(accuracy_score(pre4,y_test))\n",
    "print(confusion_matrix(y_test,pre4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DYe-Zj15vaNa"
   },
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "llGMflGQnmVs"
   },
   "outputs": [],
   "source": [
    "X= pd.DataFrame(data[['profitMargins',\n",
    " 'priceToBook' ,'bookValue' ,'shortRatio' ,'revenuePerShare',\n",
    " 'operatingMargins', 'forwardEps' ,'heldPercentInstitutions',\n",
    " 'heldPercentInsiders', 'marketCap', 'volume', 'totalDebt',\n",
    " 'totalCash', 'beta', 'totalCashPerShare' ,'enterpriseValue' ,'floatShares'\n",
    " , 'numberOfAnalystOpinions' ,'sector_Basic Materials', 'sector_Communication Services',\n",
    "       'sector_Consumer Cyclical', 'sector_Consumer Defensive',\n",
    "       'sector_Energy', 'sector_Financial Services', 'sector_Healthcare',\n",
    "       'sector_Industrials', 'sector_Real Estate', 'sector_Technology',\n",
    "       'sector_Utilities']])\n",
    "y = pd.DataFrame(data['CLASS'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D-WUf1F8oYSs"
   },
   "source": [
    "# Data preprocessing -*Scaling* the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "lxDIUFomoZlA"
   },
   "outputs": [],
   "source": [
    "RS = RobustScaler()\n",
    "X_train = RS.fit_transform(X_train)\n",
    "X_test = RS.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NCrP7dtnogT8"
   },
   "source": [
    "# LOGISTIC REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1636333648366,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "Ogbv2TejokcG",
    "outputId": "b20b3744-8c28-48ec-9aa9-21d290b6115c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg=LogisticRegression()\n",
    "lg.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "Vm2fn9XordWP"
   },
   "outputs": [],
   "source": [
    "pre=lg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "RaE3Ah6NaG7d"
   },
   "outputs": [],
   "source": [
    "lg_score=accuracy_score(y_test,pre)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1636333649336,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "XUrii3JhrgMR",
    "outputId": "dda2199c-d620-475f-ea2a-e64f1e0c8a7c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.80      0.78        46\n",
      "           1       0.71      0.65      0.68        34\n",
      "\n",
      "    accuracy                           0.74        80\n",
      "   macro avg       0.73      0.73      0.73        80\n",
      "weighted avg       0.74      0.74      0.74        80\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pre))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 516,
     "status": "ok",
     "timestamp": 1636333650788,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "swPN4Mj6sEKg",
    "outputId": "8508046d-96da-4374-8e62-e2dea09c1b46"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1score :  0.6769230769230768\n"
     ]
    }
   ],
   "source": [
    "print(\"f1score : \" ,f1_score(y_test,pre))\n",
    "lg_accuracies = cross_val_score(estimator=lg, X=X_train ,y=y_train,cv=10).mean()*100\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1636333650789,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "7veNzQcIf_t4",
    "outputId": "075a1ff2-3647-4766-a234-3e1229423a92"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x18265efac70>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(lg,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 302
    },
    "executionInfo": {
     "elapsed": 812,
     "status": "ok",
     "timestamp": 1636333652537,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "l3rOuqKAVFBF",
    "outputId": "165f66fd-75e9-43dd-fed1-dc660e357728"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7375\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(accuracy_score(y_test,pre))\n",
    "sb.heatmap(confusion_matrix(y_test,pre), \n",
    "           annot = True, fmt=\".0f\", annot_kws={\"size\": 18})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "T-bg4yrBHw6P"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TWBcyHsnH2pl"
   },
   "source": [
    "# NAIVE BAYES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "pnCgB5Cs-ner"
   },
   "outputs": [],
   "source": [
    "\n",
    "nb_accuracies = cross_val_score(estimator=gb, X=X_train ,y=y_train,cv=10).mean()*100\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "executionInfo": {
     "elapsed": 793,
     "status": "aborted",
     "timestamp": 1636333190796,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "hvBlwrGF-nZQ",
    "outputId": "93bdf9be-e7e9-4c01-c3ed-d50d2b19655f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gb=GaussianNB()\n",
    "gb.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "yQP6oivZ-nhx"
   },
   "outputs": [],
   "source": [
    "pre4=gb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "executionInfo": {
     "elapsed": 794,
     "status": "aborted",
     "timestamp": 1636333190798,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "FWr3Frqz-3yS",
    "outputId": "61306a73-20e4-4259-a689-b87a02a76547"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Naive_bayes_Gaussian_nb=accuracy_score(pre4,y_test)\n",
    "nb_scores=accuracy_score(y_test,pre4)\n",
    "sb.heatmap(confusion_matrix(y_test,pre4 ), \n",
    "           annot = True, fmt=\".0f\", annot_kws={\"size\": 18})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "executionInfo": {
     "elapsed": 793,
     "status": "aborted",
     "timestamp": 1636333190798,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "G5spHZu_-34I",
    "outputId": "42343b51-dcfc-4ec6-ea57-44fd054ef02a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.96      0.86        46\n",
      "           1       0.92      0.65      0.76        34\n",
      "\n",
      "    accuracy                           0.82        80\n",
      "   macro avg       0.85      0.80      0.81        80\n",
      "weighted avg       0.84      0.82      0.82        80\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(classification_report(y_test,pre4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "IpTuS7fxH6ja"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A52pogkiH725"
   },
   "source": [
    "# DECISION TREE CLASSIFIER\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "executionInfo": {
     "elapsed": 791,
     "status": "aborted",
     "timestamp": 1636333190799,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "opihaM5J-38u",
    "outputId": "aa283005-14d3-40e2-85bd-b3101119668b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=5)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dc=DecisionTreeClassifier(criterion='gini',max_depth=5)\n",
    "dc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xrCz126xkyHK"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "wV5jjcNw-3_8"
   },
   "outputs": [],
   "source": [
    "\n",
    "dtc_accuracies = cross_val_score(estimator=dc, X=X_train ,y=y_train,cv=10).mean()*100\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "F1NvY3Jk-4Dh"
   },
   "outputs": [],
   "source": [
    "pre5=dc.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "executionInfo": {
     "elapsed": 793,
     "status": "aborted",
     "timestamp": 1636333190802,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "MQctRNsI-4Gy",
    "outputId": "8fc398b7-385d-4e0e-934c-1759d85cf35b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVoAAAD8CAYAAAA2Y2wxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAWC0lEQVR4nO3deXhU9b3H8fcXSCAssliWEKBayuKCRa872GqxiktFUeryKNTLvdGr3rpVS+tdtGjLVdG6YqNYQHvRuLLcWosoUguKWJFFXABZgiGIlH1N5nv/yMgTSchkYH45k+Pn5XOeZM6Z+c03PvDJj+/5nTPm7oiISDiNoi5ARCTuFLQiIoEpaEVEAlPQiogEpqAVEQlMQSsiEpiCVkSkFmbW2MzeN7OpycftzGyamX2a/No21RgKWhGR2l0PLK7yeAQw3d17ANOTj2uloBUR2Qcz6wKcAzxRZfcgYHzy+/HA+anGaZLxyvaye90yXXom1eR1PiXqEiQLle9abQc6RjqZk9u++1VAYZVdRe5eVOXx74BbgVZV9nV091IAdy81sw6p3id40IqIZKtkqBbVdMzMzgXWuvt7ZnbqgbyPglZE4iVRkamR+gHnmdnZQDPgIDN7Gigzs/zkbDYfWJtqIPVoRSReKsrrvtXC3X/p7l3c/RDgEuB1d78cmAwMSz5tGDApVUma0YpIrLgnQr/FKKDYzIYDK4EhqV6goBWReElkPmjdfQYwI/n9l8CAdF6voBWReAk/o02bglZE4iVzJ8MyRkErIvGiGa2ISFieYjVBFBS0IhIvAU6GHSgFrYjEi1oHIiKB6WSYiEhgmtGKiASmk2EiIoHpZJiISFju6tGKiISlHq2ISGBqHYiIBKYZrYhIYBW7o66gGgWtiMSLWgciIoGpdSAiEphmtCIigSloRUTCcp0MExEJTD1aEZHA1DoQEQksC2e0jaIuQEQkoxKJum+1MLNmZjbHzD4ws0Vmdkdy/+1mttrM5iW3s1OVpBmtiMRL5ma0O4EfuvsWM8sB3jKzV5LH7nf3e+s6kIJWROKlPDM3/nZ3B7YkH+YkN9+fsdQ6EJF48USdNzMrNLO5VbbCqkOZWWMzmwesBaa5+zvJQ9eZ2Xwze9LM2qYqSUErIvGSRo/W3Yvc/dgqW1HVody9wt37Al2A483sSGAM0B3oC5QCo1OVpKAVkXhJY0Zb5yHdNwAzgIHuXpYM4ATwOHB8qtcraEUkXjK36qC9mbVJfp8HnA58ZGb5VZ52AbAwVUk6GSYi8ZK5VQf5wHgza0zlpLTY3aea2VNm1pfKE2PLgatSDaSgFZF4ydyqg/nA0TXsvyLdsRS0IhIvvl8rsIJS0IpIvOheByIigSloRUQCy8KbyihoRSReKiqirqAaBa2IxItaByIigSloRUQCU49WRCQsT2gdrYhIWFnYOtBNZQLZvmMHZ170U47sdxZ3jX602vHPVpTwsxG/5uSBQzhuwPkM/bef88578+q/UIlM27ZtuHvUf/LRh2+xZdNSSlfP57W/PEf/filvBiW1qaio+1ZPNKMN5OEnnuIfGzbVeGxlyedcfvVNNG7cmCsvu4hWLVvw/ORXuOrG/2DM6JGcdFy1y6slZrp1K2D6tOdp2bIFfxg3kU8+WUbr1gfRp89hdC7oFHV5DVsWzmgVtAF8+PESni5+mZuuGc49Dz1e7fgDvx/H5i1bKR77IL17dgfgvIEDGHT51dw1+hGmTHwcM6vvsqUeTRj3EE2aNOHofzqdNWvWRl1OvGRh0Kp1kGEVFRXc/j8P0O+EYzn9B/2qHd+2fQdvvPU2xx3dZ0/IAjRvnseFPz6T5atWs3DxJ/VZstSzU/qfQP/+J3Dv6EdZs2YtTZo0IS+vWdRlxYd73bd6oqDNsAnPvsRnK1Zx203X1Hj8k6WfsWvXbr535GHVjh11RG8ABW3MnXXWDwFYuWo1L780ji2blrJ541I+XPRXLrtscMTVxUCGbvydSSlbB2bWGxgEFFB5o9vPgcnuvjhwbQ1OyedreHTs01x95WUU5HdkdWlZted88cWXAHRof3C1Yx2T+8qSz5F46pn8l8zvx9zDkiWfceXwG2iam8sNNxQyYdxD5DRpwvgJxRFX2YA1tOVdZvYL4FLgGWBOcncXYKKZPePuowLX16CMvPdhCvI7MfSSfc9Ktu/cCUBuTk61Y7m5uQDs2LEjTIGSFVq1bAnA5s1bGPCjIezevRuAlyf9mU8/nsWdI0cw4ann8Cy8r2qDkIX3OkjVOhgOHOfuo9z96eQ2isoPIxu+rxdV/QjfJyZMzGS9WWvKq68za87f+a9briOnyb5/f+U1bQrAruRfrqp27doFQLNm6tfF2fbkL9JniyftCVmADRs2MmXqNPLzO9KrV/d9vVxS8ESizlt9SdU6SACdgRV77c9PHqtR8iN7iwB2r1sW+1/Lu3bt4p4HizjlpOP41sHtWFnyOQBlX6wDYPPWraws+Zw2rQ+ifbI9sLaG9sBXLYOONbQVJD5KSkoBalxtsGZNZbupbZs29VlSvDS01gFwAzDdzD4FViX3dQO+C1wXsK4GZcfOXazfsJGZs+Ywc9acasenvvo6U199nZuvHc7F559Dbm4OHyys3uKev+gjAI7o3SN4zRKdd999n6uvGkqXLvnVjhUUVO5bm/wlLfuhod3rwN3/bGY9qWwVFAAGlADvunv2NUIikpfXjPvu/FW1/es3bOTOex+h/4nHMvjcM+jZ/VCaN8/j1H4n8Nqbs/jo02X07vEdALZt284LU17l210L6HN4r/r+EaQeTZr8Kvdv2sxll17IXb95gK1btwHQqVMHBp03kE8+XcbSpcujLbIha4AzWtw9AbxdD7U0WDlNmnDGaadU2//VqoOunfO/dvyGq6/k7bnzKLzxNoZefAEtWzTn+cmvsHbdOh6959e6WCHmNmzYyK2/GMljY+7mb29NYdy4Z8nNzeGqwqHk5uZw/fW3RV1iw1aefXNAXRkWgW5dOvPUY6P53Zg/MPbpYnbvLuewXt15bPSduvz2G+KJsX9k3ZfrueXma7jj9ltIJBK8/fZ7XDH0WmbNnht1eQ1bFrYOLPQSkm/CyTBJX17n6v8CECnftfqA/zm39bYhdc6cFnc9t8/3M7NmwEygKZWT0ufd/b/NrB3wLHAIsBz4ibv/o7b30ZVhIhIrGVzetRP4obt/D+gLDDSzE4ERwHR37wFMTz6ulYJWROIl4XXfauGVtiQf5iQ3p/JK2fHJ/eOB81OVpKAVkXhJI2irXlyV3AqrDmVmjc1sHrAWmObu7wAd3b0UIPm1Q6qSdDJMROIljUtwq15ctY/jFUBfM2sDvGRmR+5PSQpaEYmVEJ8Z5u4bzGwGMBAoM7N8dy81s3wqZ7u1UutAROIlQz1aM2ufnMliZnnA6cBHwGRgWPJpw4BJqUrSjFZE4iVzN4vJB8abWWMqJ6XF7j7VzGYDxWY2HFgJDEk1kIJWROIlQ60Dd58PVLuCyN2/BAakM5aCVkTipSHe60BEpCHxiuy7BFdBKyLxohmtiEhYIZZ3HSgFrYjEi4JWRCSw7GvRKmhFJF68PPuSVkErIvGSfTmroBWReNHJMBGR0DSjFREJSzNaEZHQNKMVEQnLy6OuoDoFrYjEShZ+2riCVkRiRkErIhKWZrQiIoEpaEVEAvMKi7qEahS0IhIrmtGKiATmCc1oRUSC0oxWRCQwd81oRUSCysYZbaOoCxARyaREhdV5q42ZdTWzN8xssZktMrPrk/tvN7PVZjYvuZ2dqibNaEUkVjJ4MqwcuNnd/25mrYD3zGxa8tj97n5vXQdS0IpIrGQqaN29FChNfr/ZzBYDBfszlloHIhIr7nXfzKzQzOZW2QprGtPMDgGOBt5J7rrOzOab2ZNm1jZVTQpaEYkVT1jdN/cidz+2yla093hm1hJ4AbjB3TcBY4DuQF8qZ7yjU9Wk1oGIxEoml3eZWQ6VIftHd3+xcnwvq3L8cWBqqnEUtCISKxUZuteBmRkwFljs7vdV2Z+f7N8CXAAsTDWWglZEYiWDM9p+wBXAAjObl9z3K+BSM+sLOLAcuCrVQApaEYmVDK46eAuoabA/pTuWglZEYsWz70NwFbQiEi+6e5eISGAViexbtaqgFZFYUetARCSwhG6TKCISlu5HKyIS2DeydXDRMT8L/RbSAL3U7vtRlyAxpdaBiEhgWnUgIhJYFnYOFLQiEi9qHYiIBKZVByIigWXhh+AqaEUkXrzGG25FS0ErIrFSrtaBiEhYmtGKiASmHq2ISGCa0YqIBKYZrYhIYBWa0YqIhJWFn2SjoBWReEloRisiElY23lQm++4nJiJyABJpbLUxs65m9oaZLTazRWZ2fXJ/OzObZmafJr+2TVWTglZEYiVhVucthXLgZnc/DDgRuNbMDgdGANPdvQcwPfm4VgpaEYmVijS22rh7qbv/Pfn9ZmAxUAAMAsYnnzYeOD9VTQpaEYmVhNV9M7NCM5tbZSusaUwzOwQ4GngH6OjupVAZxkCHVDXpZJiIxEo6qw7cvQgoqu05ZtYSeAG4wd03WeqWQzWa0YpIrHgaWypmlkNlyP7R3V9M7i4zs/zk8XxgbapxFLQiEivptA5qY5VT17HAYne/r8qhycCw5PfDgEmpalLrQERiJYP3OugHXAEsMLN5yX2/AkYBxWY2HFgJDEk1kIJWRGKlIkMXhrn7W7DPhu+AdMZS0IpIrOjuXSIigSloRUQCy8KPDFPQiki8aEYrIhJYqktro6CgFZFY0Y2/RUQCU+tARCQwBa2ISGDZ+AkLCloRiRX1aEVEAtOqAxGRwBJZ2DxQ0IpIrOhkmIhIYNk3n1XQikjMaEYrIhJYuWXfnFZBKyKxkn0xq6AVkZhR60BEJDAt7xIRCSz7YlZBKyIxo9aBiEhgFVk4p1XQZsiF1w6h+5Hd6d7nu3Tq1omyVWUU9hte43MH/esFHHf68RR0L6BV61Zs3riZ1UtKmPqHKbz96ux6rlxCafGdTnS5qD/tf3AUzQ/pSOOmOWxdXkbplHdY9vgrVGzbuee5bY7uTsGF/Wlz1KEcdMS3adKiGe9fP4aSZ2dG+BM0TNk4o20UdQFxMfQXw+hz8lGsWVHK5g2ba31uz749WFtSxuQnJjHmtkeZVPQSTfOa8svHb+MnP7ukniqW0LpeeirfKTybrcvL+OS+F/nw1//LlqWl9P7lxfSbcgeNmuXseW6HAX059MozyGndgk2LVkRYdcPnafyXipk9aWZrzWxhlX23m9lqM5uX3M5ONY5mtBlS2H84ZSvLAHhw2iM0a95sn8+959q7q+2bPHYS9/3fAwy+ejDPP1xMIpGNv5clHaVT57DkwUmUb96+Z9+KCa+xddkaet54Ad0uO43lT/4FgOXjXmPpo1Op2LaT/HOPp93xvaIqu8HL8N+cccDDwIS99t/v7vfWdRDNaDPkq5DdX4mKBF+u+ZKmzZvROKdxhqqSKG38YNnXQvYrn0+qbA+16tV1z75d6zZ+rZUg+y+B13lLxd1nAusPtCbNaCPUsnVLGjVuxEHtDqLfOf055tRjWDB7Abt37o66NAkor3M7AHau2xhxJfGUzqkwMysECqvsKnL3ojq89DozGwrMBW5293/U9mQFbYTGvPl7DmrXGoDy3eXMfmUWj932aMRVSVCNjB43DSaxu5zVL/4t6mpiqTyNqE2Gal2CtaoxwEgqM30kMBr459peoKCN0G8Lf0Nu01wO7nQwJ5/Tn9xmuTRv1ZxN6zdFXZoEcuTIYbQ7tieL73qGrUtLoy4nlupykuuAxnff0yc0s8eBqales989WjO7spZjhWY218zmLt+ycn/fIvY+nLOIeX99n+nPvcbIn97O9i3b+e0Ld9OidYuoS5MAet06hEOHn8mKCa+x5KFJUZcTW4k0tv1hZvlVHl4ALNzXc79yICfD7tjXAXcvcvdj3f3YQ1p2O4C3+GZ5/fnptOvQjpMGnhx1KZJhPX9+IT1vGszKiTOYf+vYqMuJtQwv75oIzAZ6mVmJmQ0H7jazBWY2HzgNuDHVOLW2DpID1XgI6JiySklL02ZNAWjVplXElUgm9bz5Qnr9/CJWFc/kg5vSbQdKujK5vMvdL61hd9q/KVP1aDsCZwJ7n1EzYFa6bybQNK8pZsaObTu+tr9Ro0acPewcAD5+/+MoSpMAetw0mF63XMSq52Yy7/rHwLPv8tC4qcjC/8epgnYq0NLd5+19wMxmhCiooTp18Gm0L+gAQOt2B9EkN4ch/34xAF+sXsuMF98AoPOhnbmreBSz/vQ3Vi9bzZYNm2nX6WC+f9736fLdrkx/7jU+nLMosp9DMueQK39E71uHsK3kC9bNXEjB4H5fO77zi42sm7kAgLwu36LLRacA0KpXFwA6/egY8vIPBqDk+b+yvWRdPVbfcDW42yS6e80X61ceuyzz5TRcp198Bn1O6vO1fZffcgUAC2Yv2BO060q/5M2X3uCw447gxIEnkdcij22bt7Js0TKKH3yWN1+eUd+lSyBt+nYHoHmX9hz90DXVjq+b9eGeoG3erT29R/zka8fzzz2B/HNPAGD9nI8UtHUUetXB/jAPPM0e1O3c7PupJXL/skt9aKnux2sm2oGOcfG3z69z5jy74uUDfr+60DpaEYmVBtc6EBFpaLKxdaCgFZFYaYirDkREGhS1DkREAsvGOzkraEUkVtSjFREJTK0DEZHAQl8bsD8UtCISK/q4cRGRwNQ6EBEJTK0DEZHANKMVEQlMy7tERALTJbgiIoGpdSAiEpiCVkQkMK06EBEJTDNaEZHAsnHVQaOoCxARyaQKT9R5S8XMnjSztWa2sMq+dmY2zcw+TX5tm2ocBa2IxIq713mrg3HAwL32jQCmu3sPYHryca0UtCISKwm8zlsq7j4TWL/X7kHA+OT344HzU42joBWRWPE0/jOzQjObW2UrrMNbdHT3UoDk1w6pXqCTYSISK4k0lne5exFQFK6aSprRikispDOj3U9lZpYPkPy6NtULFLQiEiuZXHWwD5OBYcnvhwGTUr1ArQMRiZV0WgepmNlE4FTgW2ZWAvw3MAooNrPhwEpgSKpxFLQiEiuZvGDB3S/dx6EB6YyjoBWRWMnkjDZTFLQiEivZeAmuglZEYqXCK6IuoRoFrYjEim6TKCISmG6TKCISmGa0IiKBadWBiEhgWnUgIhLYAVxaG4yCVkRiRT1aEZHA1KMVEQlMM1oRkcC0jlZEJDDNaEVEAtOqAxGRwHQyTEQkMLUOREQC05VhIiKBaUYrIhJYNvZoLRvTP67MrNDdi6KuQ7KL/lzEX6OoC/iGKYy6AMlK+nMRcwpaEZHAFLQiIoEpaOuX+nBSE/25iDmdDBMRCUwzWhGRwBS0IiKBKWjriZkNNLOPzWyJmY2Iuh6Jnpk9aWZrzWxh1LVIWAraemBmjYFHgLOAw4FLzezwaKuSLDAOGBh1ERKegrZ+HA8scfdl7r4LeAYYFHFNEjF3nwmsj7oOCU9BWz8KgFVVHpck94nIN4CCtn5YDfu0rk7kG0JBWz9KgK5VHncBPo+oFhGpZwra+vEu0MPMDjWzXOASYHLENYlIPVHQ1gN3LweuA14FFgPF7r4o2qokamY2EZgN9DKzEjMbHnVNEoYuwRURCUwzWhGRwBS0IiKBKWhFRAJT0IqIBKagFREJTEErIhKYglZEJLD/B6xDiHCBoMZfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Decisiontress_classifier=accuracy_score(y_test,pre5)\n",
    "dt_scores=accuracy_score(y_test,pre5)\n",
    "sb.heatmap(confusion_matrix(y_test,pre5 ), \n",
    "           annot = True, fmt=\".0f\", annot_kws={\"size\": 18})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "executionInfo": {
     "elapsed": 41,
     "status": "aborted",
     "timestamp": 1636333190803,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "sBPUHvDw-4JF",
    "outputId": "8e58ad4a-02fb-42fc-cdcb-a8ae6ca10636"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.87      0.81        46\n",
      "           1       0.78      0.62      0.69        34\n",
      "\n",
      "    accuracy                           0.76        80\n",
      "   macro avg       0.77      0.74      0.75        80\n",
      "weighted avg       0.76      0.76      0.76        80\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(classification_report(y_test,pre5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "d4-188pZjj7L"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h4gByD99IAUb"
   },
   "source": [
    "# RANDOM FOREST CLASSIFIER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "executionInfo": {
     "elapsed": 41,
     "status": "aborted",
     "timestamp": 1636333190804,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "r9s_WFN3-4My",
    "outputId": "dd2d2a9b-77bb-4f69-dc19-4b47e61901a4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', random_state=0)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "\n",
    "rc=RandomForestClassifier(criterion='entropy',random_state=0)\n",
    "rc.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "yoXqKq2k_ztM"
   },
   "outputs": [],
   "source": [
    "\n",
    "pre6 = rc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "7ntfpjKT-nj2"
   },
   "outputs": [],
   "source": [
    "\n",
    "#rfc_accuracies = cross_val_score(estimator=rc, X=X_train ,y=y_train,cv=10).mean()*100\n",
    "\n",
    "rc_scores=accuracy_score(y_test,pre6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "executionInfo": {
     "elapsed": 41,
     "status": "aborted",
     "timestamp": 1636333190805,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "yl1-Hy8s_z1A",
    "outputId": "16e0c07d-9630-4533-ffd7-59c3c2edd89a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Random_forest=accuracy_score(pre6,y_test)\n",
    "randomf=f1_score(y_test,pre6)\n",
    "#print(accuracy_score(y_test,pre6))\n",
    "sb.heatmap(confusion_matrix(y_test,pre6 ), \n",
    "           annot = True, fmt=\".0f\", annot_kws={\"size\": 18})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "executionInfo": {
     "elapsed": 42,
     "status": "aborted",
     "timestamp": 1636333190805,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "8JCfgXUo_z4k",
    "outputId": "492ede78-ab80-472c-cf9e-f490b95fe600"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.93      0.85        46\n",
      "           1       0.88      0.65      0.75        34\n",
      "\n",
      "    accuracy                           0.81        80\n",
      "   macro avg       0.83      0.79      0.80        80\n",
      "weighted avg       0.82      0.81      0.81        80\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,pre6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_8zKt5xqu4Sw"
   },
   "source": [
    "# XGBOOST "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "XI4WrJtoQ2R-"
   },
   "outputs": [],
   "source": [
    "xgb_model = xgb.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 476,
     "status": "ok",
     "timestamp": 1636333658554,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "V-u1NLTcJklw",
    "outputId": "e6719087-ea59-4476-b6f4-d81148cae622"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-aucpr:0.75112\n",
      "[1]\tvalidation_0-aucpr:0.80572\n",
      "[2]\tvalidation_0-aucpr:0.78966\n",
      "[3]\tvalidation_0-aucpr:0.78564\n",
      "[4]\tvalidation_0-aucpr:0.79727\n",
      "[5]\tvalidation_0-aucpr:0.78589\n",
      "[6]\tvalidation_0-aucpr:0.80745\n",
      "[7]\tvalidation_0-aucpr:0.79436\n",
      "[8]\tvalidation_0-aucpr:0.78995\n",
      "[9]\tvalidation_0-aucpr:0.79142\n",
      "[10]\tvalidation_0-aucpr:0.78415\n",
      "[11]\tvalidation_0-aucpr:0.77547\n",
      "[12]\tvalidation_0-aucpr:0.78309\n",
      "[13]\tvalidation_0-aucpr:0.77601\n",
      "[14]\tvalidation_0-aucpr:0.78884\n",
      "[15]\tvalidation_0-aucpr:0.79000\n",
      "[16]\tvalidation_0-aucpr:0.78877\n",
      "[17]\tvalidation_0-aucpr:0.79048\n",
      "[18]\tvalidation_0-aucpr:0.78770\n",
      "[19]\tvalidation_0-aucpr:0.78897\n",
      "[20]\tvalidation_0-aucpr:0.78716\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.300000012,\n",
       "              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=100, n_jobs=8,\n",
       "              num_parallel_tree=1, predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "xgb_model.fit(X_train,y_train,early_stopping_rounds=15, \n",
    "             eval_set=[(X_test, y_test)],\n",
    "             verbose=True,eval_metric='aucpr')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1636333658555,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "RXsNQK4fgbH6",
    "outputId": "5afea99a-28b4-44c6-c8e4-ad18a0aadd06"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8\n"
     ]
    }
   ],
   "source": [
    "pre7=xgb_model.predict(X_test)\n",
    "xgb_score=accuracy_score(y_test,pre7)\n",
    "print(accuracy_score(y_test,pre7))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1636333658556,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "pFr0YtjATZTC",
    "outputId": "60eab2bd-6b28-4e8b-9a24-2f3ac3722c93"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.300000012,\n",
       "              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=100, n_jobs=8,\n",
       "              num_parallel_tree=1, predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model.fit(X_train,y_train,early_stopping_rounds=15, \n",
    "             eval_set=[(X_test, y_test)],\n",
    "             verbose=False,eval_metric='aucpr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "86B8HUXFTwGB"
   },
   "outputs": [],
   "source": [
    "pre7=xgb_model.predict(X_test)\n",
    "xgb_score=accuracy_score(y_test,pre7)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 302
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1636333659116,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "FnIODY8ETZjF",
    "outputId": "e89b97c4-4b47-4e38-c647-6e92b48f6fee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x18263c42f10>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(xgb_model,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "ra2pxNq8TF4Z"
   },
   "outputs": [],
   "source": [
    "#xgboost_test_cv = cross_val_score(estimator=rc, X=X_test ,y=y_test,cv=10).mean()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1636333675035,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "GFqmtREMiOZs",
    "outputId": "f109b0c6-85d6-4f17-b0e4-d61c1a6e6f53"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.300000012,\n",
       "              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=100, n_jobs=8,\n",
       "              num_parallel_tree=1, predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sEwHiPWdpBIS"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6zc6cURqiPZJ"
   },
   "source": [
    "#  preliminary Accuracy summary of all models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 361
    },
    "executionInfo": {
     "elapsed": 486,
     "status": "error",
     "timestamp": 1636333686419,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "RBg2zpjY_0BY",
    "outputId": "32f9aa6b-72fa-4dbe-f45b-7857af14572b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression: 0.74      f1 score: 0.68  recall: 0.65\n",
      "naive bayes:       0.82       f1 score: 0.76 recall: 0.65\n",
      "Random forest Acurracy: 0.81   f1 score: 0.75        recall: 0.65\n",
      "XGBOOST:             0.80                 f1 score: 0.74 recall: 0.68\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print('Logistic regression: {:.2f} '.format(lg_score) ,'    f1 score: {:.2f} '.format(f1_score(y_test,pre)),'recall: {:.2f}'.format(recall_score(y_test,pre)))\n",
    "#print('svmlinear:             ',svm_accuracies,'   f1 score:', f1_score(y_test,pre1),'precision:',recall_score(y_test,pre1))\n",
    "print('naive bayes:       {:.2f}    '.format(nb_scores),'  f1 score: {:.2f}'.format(f1_score(y_test,pre4)),'recall: {:.2f}'.format(recall_score(y_test,pre4)))\n",
    "#print('Decision tress:        ',dtc_accuracies,'  f1 score:',f1_score(y_test,pre5),'precision:',recall_score(y_test,pre5))\n",
    "print('Random forest Acurracy: {:.2f}'.format(rc_scores), '  f1 score: {:.2f}'.format(f1_score(y_test,pre6)),'       recall: {:.2f}'.format(recall_score(y_test,pre6)))\n",
    "print('XGBOOST:             {:.2f}  '.format(xgb_score),'              f1 score: {:.2f}'.format(f1_score(y_test,pre7)),'recall: {:.2f}'.format(recall_score(y_test,pre7)))\n",
    "#print('Hyperparameter tuned XGBOOST:  {:.2f}'.format(improved_xgb_score) ,'f1 score: {:.2f}'.format(f1_score(y_test,improved_xgb_preds)),'precision: {:.2f}'.format(recall_score(y_test,improved_xgb_preds)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YkPP36LR9BXL"
   },
   "source": [
    "# Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "id": "86w3iQMK-moR"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "HxkDAz8A_PHc"
   },
   "outputs": [],
   "source": [
    "xgb_tune= xgb.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "5Fk2NQhn9FBJ"
   },
   "outputs": [],
   "source": [
    "params = { 'max_depth': [3, 5, 6, 10, 15, 20],\n",
    "           'learning_rate': [0.01, 0.1, 0.2, 0.3],\n",
    "           'subsample': np.arange(0.5, 1.0, 0.1),\n",
    "           'colsample_bytree': np.arange(0.4, 1.0, 0.1),\n",
    "           'colsample_bylevel': np.arange(0.4, 1.0, 0.1),\n",
    "           'n_estimators':[100, 500, 1000],\n",
    "         'gamma':np.arange(0,2.0,0.1),\n",
    "    'reg_lambda' : np.arange(0,2.0,0.1),\n",
    "    'scale_pos_weight':[0.3,0.5,0.6,1,2]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "id": "U45kQQd_iADB"
   },
   "outputs": [],
   "source": [
    "clf = RandomizedSearchCV(estimator=xgb_tune,\n",
    "                         param_distributions=params,\n",
    "                      scoring='recall',\n",
    "                         n_iter=50,  # WE USED 200 ITERATIONS setting 50 for smaller runtime\n",
    "\n",
    "                         verbose=1,cv=7,n_jobs=-1\n",
    "                        \n",
    "                        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "executionInfo": {
     "elapsed": 49,
     "status": "aborted",
     "timestamp": 1636333190821,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "DR8Yvzfd-9ab",
    "outputId": "d7c46387-1745-4af7-818c-25b0c3e5198f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 7 folds for each of 50 candidates, totalling 350 fits\n",
      "[23:48:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.0/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=7,\n",
       "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None,\n",
       "                                           enable_categorical=False, gamma=None,\n",
       "                                           gpu_id=None, importance_type=None,\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate=None,\n",
       "                                           max_delta_step=None, max_depth=None,\n",
       "                                           min_child_weight=None, missing=nan,\n",
       "                                           monotone_constraints...\n",
       "                                        'gamma': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,\n",
       "       1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9]),\n",
       "                                        'learning_rate': [0.01, 0.1, 0.2, 0.3],\n",
       "                                        'max_depth': [3, 5, 6, 10, 15, 20],\n",
       "                                        'n_estimators': [100, 500, 1000],\n",
       "                                        'reg_lambda': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,\n",
       "       1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9]),\n",
       "                                        'scale_pos_weight': [0.3, 0.5, 0.6, 1,\n",
       "                                                             2],\n",
       "                                        'subsample': array([0.5, 0.6, 0.7, 0.8, 0.9])},\n",
       "                   scoring='recall', verbose=1)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train,y_train,early_stopping_rounds=10, \n",
    "             eval_set=[(X_test, y_test)],\n",
    "             verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "executionInfo": {
     "elapsed": 49,
     "status": "aborted",
     "timestamp": 1636333190821,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "d_tkgid4_B2r",
    "outputId": "da4deb28-a622-4b28-f688-f650aed1f580"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'subsample': 0.8999999999999999, 'scale_pos_weight': 2, 'reg_lambda': 0.8, 'n_estimators': 1000, 'max_depth': 5, 'learning_rate': 0.1, 'gamma': 0.4, 'colsample_bytree': 0.7999999999999999, 'colsample_bylevel': 0.4} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(clf.best_params_,'\\n')\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "id": "NwID19u5AONy"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=0.4,\n",
       "              colsample_bynode=1, colsample_bytree=0.7999999999999999,\n",
       "              enable_categorical=False, gamma=0.4, gpu_id=-1,\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.1, max_delta_step=0, max_depth=5,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=1000, n_jobs=8, num_parallel_tree=1,\n",
       "              predictor='auto', random_state=0, reg_alpha=0, reg_lambda=0.8,\n",
       "              scale_pos_weight=2, subsample=0.8999999999999999,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_estimator_ # use to create a new XGBOOST model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4cdp1XKI_1Gf"
   },
   "source": [
    "## improved xgboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k56ZzpmEv4GL"
   },
   "source": [
    "Create a new xgboost model using parameters resulted from hyperparamter tuning .\n",
    "Change improved_xgb below accordingly\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "LNxJq2CU_wI5"
   },
   "outputs": [],
   "source": [
    "improved_xgb= xgb.XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=0.5,\n",
    "              colsample_bynode=1, colsample_bytree=0.4,\n",
    "              gamma=1.4000000000000001, learning_rate=0.01, max_delta_step=0,\n",
    "              max_depth=3, min_child_weight=1,  n_estimators=1000,\n",
    "              n_jobs=1, nthread=None, objective='binary:logistic',\n",
    "              random_state=0, reg_alpha=0, reg_lambda=0.5, scale_pos_weight=2,\n",
    "              seed=None, silent=None, subsample=0.6, verbosity=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1636333697699,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "Zedfs1Sz_3lR",
    "outputId": "5e419e86-a136-46eb-f5a0-43d02944ae28"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=0.5,\n",
       "              colsample_bynode=1, colsample_bytree=0.4,\n",
       "              enable_categorical=False, gamma=1.4000000000000001, gpu_id=-1,\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.01, max_delta_step=0, max_depth=3,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=1000, n_jobs=1, nthread=1, num_parallel_tree=1,\n",
       "              predictor='auto', random_state=0, reg_alpha=0, reg_lambda=0.5,\n",
       "              scale_pos_weight=2, seed=0, silent=None, subsample=0.6,\n",
       "              tree_method='exact', validate_parameters=1, ...)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "improved_xgb.fit(X_train,y_train,early_stopping_rounds=15, \n",
    "             eval_set=[(X_test, y_test)],\n",
    "             verbose=False,eval_metric='aucpr')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 592,
     "status": "ok",
     "timestamp": 1636333700536,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "bB0BlYGc_3px",
    "outputId": "c57ed4c6-17eb-4f04-e7bb-380ada175867"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.775\n"
     ]
    }
   ],
   "source": [
    "improved_xgb_preds=improved_xgb.predict(X_test)\n",
    "improved_xgb_score=accuracy_score(y_test,improved_xgb_preds)\n",
    "print(improved_xgb_score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "executionInfo": {
     "elapsed": 647,
     "status": "ok",
     "timestamp": 1636333703076,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "jrmN0xNj_3vL",
    "outputId": "0677d129-0f28-492d-f9d3-3c72ad471f04"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x18264ab4a90>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(improved_xgb,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1636333704330,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "FLncfHMp_3ya",
    "outputId": "79c57053-c456-430b-d40a-81e43abb27eb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Hyperparameter tuned XGBOOST:   0.775     f1 score: 0.7499999999999999 recall: 0.7941176470588235\n"
     ]
    }
   ],
   "source": [
    "print(' Hyperparameter tuned XGBOOST:  ', improved_xgb_score,'    f1 score:',f1_score(y_test,improved_xgb_preds),'recall:',recall_score(y_test,improved_xgb_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 675,
     "status": "ok",
     "timestamp": 1636333735039,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "Ak4eNQCX_30t",
    "outputId": "8d2e66b1-10f2-4205-bf04-0e334a6c43ef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=0.5,\n",
       "              colsample_bynode=1, colsample_bytree=0.4,\n",
       "              enable_categorical=False, gamma=1.4000000000000001, gpu_id=-1,\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.01, max_delta_step=0, max_depth=3,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=1000, n_jobs=1, nthread=1, num_parallel_tree=1,\n",
       "              predictor='auto', random_state=0, reg_alpha=0, reg_lambda=0.5,\n",
       "              scale_pos_weight=2, seed=0, silent=None, subsample=0.6,\n",
       "              tree_method='exact', validate_parameters=1, ...)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "improved_xgb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8WU00xMGopu-"
   },
   "source": [
    "## feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "id": "XTiz6wDQrJYR"
   },
   "outputs": [],
   "source": [
    "xgnames= X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1636333677871,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "nWiil0MGrOjk",
    "outputId": "6eda20e2-c7c7-499b-b7f2-9d1c4a80fbe4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['profitMargins',\n",
       " 'priceToBook',\n",
       " 'bookValue',\n",
       " 'shortRatio',\n",
       " 'revenuePerShare',\n",
       " 'operatingMargins',\n",
       " 'forwardEps',\n",
       " 'heldPercentInstitutions',\n",
       " 'heldPercentInsiders',\n",
       " 'marketCap',\n",
       " 'volume',\n",
       " 'totalDebt',\n",
       " 'totalCash',\n",
       " 'beta',\n",
       " 'totalCashPerShare',\n",
       " 'enterpriseValue',\n",
       " 'floatShares',\n",
       " 'numberOfAnalystOpinions',\n",
       " 'sector_Basic Materials',\n",
       " 'sector_Communication Services',\n",
       " 'sector_Consumer Cyclical',\n",
       " 'sector_Consumer Defensive',\n",
       " 'sector_Energy',\n",
       " 'sector_Financial Services',\n",
       " 'sector_Healthcare',\n",
       " 'sector_Industrials',\n",
       " 'sector_Real Estate',\n",
       " 'sector_Technology',\n",
       " 'sector_Utilities']"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(xgnames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1636333678545,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "V9Aim_x7rPIW",
    "outputId": "e7e90eff-de60-4d59-cf62-bb4a35f07ff4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Feature Importance : XGBOOST')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#plt.yticks(['beta','shortRatio','marketCap','bookValue','priceToBook','totalCashPerShare','operatingMargins','floatShares','revenueheldPercentInsiderser',,'totalDebt','heldPercentInstitutions','volume','profitMargins','heldPercentInstitutions','sector_Technology','sector_Industrials','volume','totalCash'])\n",
    "feat_importances = pd.Series(xgb_model.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.title('Feature Importance : XGBOOST')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 211
    },
    "executionInfo": {
     "elapsed": 681,
     "status": "error",
     "timestamp": 1636333681023,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "5sXHTUhptqrX",
    "outputId": "9d5936ab-c828-4355-8b2c-880b74d3eb8c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Feature Importance : Random Forest ')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances = pd.Series(rc.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.title('Feature Importance : Random Forest ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "executionInfo": {
     "elapsed": 47,
     "status": "aborted",
     "timestamp": 1636333190816,
     "user": {
      "displayName": "Haresh",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgoXsUrwqrdYieaCTJs4eifEU7OEbL4VqCuKgzY=s64",
      "userId": "03605754073230125307"
     },
     "user_tz": -480
    },
    "id": "5flQcbDkZrWm",
    "outputId": "34d84259-7d58-4ca3-87c2-1dd834f5254b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Feature Importance : improved XGB ')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances = pd.Series(improved_xgb.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.title('Feature Importance : improved XGB ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IZXYIO9VvIom"
   },
   "source": [
    "# FULL METRICS SUMMARY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "id": "_hcsIZ6JvNtz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression: 0.7375      f1 score: 0.68  recall: 0.6471\n",
      "naive bayes:       0.8250       f1 score: 0.76 reacall: 0.6471\n",
      "Random forest Acurracy: 0.8125   f1 score: 0.75        recall: 0.6471\n",
      "XGBOOST:             0.8000                 f1 score: 0.74   recall: 0.6765\n",
      "Hyperparameter tuned XGBOOST:  0.7750 f1 score: 0.75 recall: 0.7941\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print('Logistic regression: {:.4f} '.format(lg_score) ,'    f1 score: {:.2f} '.format(f1_score(y_test,pre)),'recall: {:.4f}'.format(recall_score(y_test,pre)))\n",
    "#print('svmlinear:             ',svm_accuracies,'   f1 score:', f1_score(y_test,pre1),'precision:',recall_score(y_test,pre1))\n",
    "print('naive bayes:       {:.4f}    '.format(nb_scores),'  f1 score: {:.2f}'.format(f1_score(y_test,pre4)),'reacall: {:.4f}'.format(recall_score(y_test,pre4)))\n",
    "#print('Decision tress:        ',dtc_accuracies,'  f1 score:',f1_score(y_test,pre5),'precision:',recall_score(y_test,pre5))\n",
    "print('Random forest Acurracy: {:.4f}'.format(rc_scores), '  f1 score: {:.2f}'.format(f1_score(y_test,pre6)),'       recall: {:.4f}'.format(recall_score(y_test,pre6)))\n",
    "print('XGBOOST:             {:.4f}  '.format(xgb_score),'              f1 score: {:.2f}'.format(f1_score(y_test,pre7)),'  recall: {:.4f}'.format(recall_score(y_test,pre7)))\n",
    "print('Hyperparameter tuned XGBOOST:  {:.4f}'.format(improved_xgb_score) ,'f1 score: {:.2f}'.format(f1_score(y_test,improved_xgb_preds)),'recall: {:.4f}'.format(recall_score(y_test,improved_xgb_preds)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CBn9NF3TvOVZ"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B6exmsJMN4-L"
   },
   "source": [
    "# CONFUSION MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "id": "Y9c4jeJkSgvt"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(30\n",
    "                          ,30))\n",
    "ax1 = fig.add_subplot(431)\n",
    "ax2 = fig.add_subplot(432)\n",
    "ax3 = fig.add_subplot(433)\n",
    "ax4 = fig.add_subplot(434)\n",
    "ax5 = fig.add_subplot(435)\n",
    "\n",
    "ax1.title.set_text('LOGISTIC REGRESSION')\n",
    "\n",
    "ax2.title.set_text('NAIVE BAYES')\n",
    "\n",
    "ax3.title.set_text('RANDOM FOREST CLASSIFIER')\n",
    "ax4.title.set_text('XGBOOST')\n",
    "ax5.title.set_text(' HYPER PARAMETER TUNED XGBOOST')\n",
    "plot_confusion_matrix(improved_xgb,X_test,y_test,ax=ax2)\n",
    "\n",
    "plot_confusion_matrix(xgb_model,X_test,y_test,ax=ax4)\n",
    "\n",
    "plot_confusion_matrix(rc,X_test,y_test,ax=ax5)\n",
    "\n",
    "plot_confusion_matrix(gb,X_test,y_test,ax=ax3)\n",
    "\n",
    "plot_confusion_matrix(lg,X_test,y_test,ax=ax1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "InFr9G3TN8yv"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9p7b-iVw2h4D"
   },
   "source": [
    "# CODE AS A TOOL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sample company IDs to try: AEE,DFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "id": "mrnWcxSV2mH4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter company ID: \n",
      "AEE\n",
      "\n",
      "Company Information: \n",
      "SECTOR  -  Utilities\n",
      "CITY  -  Saint Louis\n",
      "INDUSTRY  -  Utilities—Regulated Electric\n",
      "\n",
      "Stock Behaviour: \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COVID Dependancy:  0\n"
     ]
    }
   ],
   "source": [
    "print(\"Enter company ID: \")\n",
    "ID=input()\n",
    "\n",
    "print(\"\\nCompany Information: \")\n",
    "\n",
    "i=0\n",
    "for COL in data:\n",
    "    if(i<3):\n",
    "        print(COL.upper(),\" - \",data.loc[ID][COL])\n",
    "    i+=1\n",
    "    \n",
    "print(\"\\nStock Behaviour: \")\n",
    "listvar=[data.loc[ID]['15/3/2020 0:00'],data.loc[ID]['10/4/2020 0:00'],data.loc[ID]['10/5/2020 0:00'],data.loc[ID]['10/9/2020 0:00'],data.loc[ID]['1/1/2021 0:00'],data.loc[ID]['20/2/2021 0:00'],data.loc[ID]['15/5/2021 0:00'],data.loc[ID]['10/9/2021 0:00']]\n",
    "listvar\n",
    "\n",
    "x=[43905,43931,43961,44084,44197,44247,44331,44449]\n",
    "\n",
    "maxval=max(listvar)\n",
    "\n",
    "img = plt.imread(\"COVIDUS.jpg\")\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "\n",
    "ax.imshow(img, extent=[43905, 44449, 0,300])\n",
    "p1 = ax.plot(x,listvar)\n",
    "\n",
    "ax.axvline(x=44025,color='grey', linewidth=0.8)\n",
    "ax.axvline(x=43930,color='grey', linewidth=0.8)\n",
    "ax.axvline(x=44120,color='grey', linewidth=0.8)\n",
    "ax.axvline(x=44331,color='grey', linewidth=0.8)\n",
    "ax.set_ylabel('Stock Price')\n",
    "ax.set_title('STOCK vs DATE')\n",
    "ax.set_xticklabels(('Mar-20',\n",
    "'Mar-20',\n",
    "'Jul-20',\n",
    "'Oct-20',\n",
    "'Jan-21',\n",
    "'Apr-21',\n",
    "'Jul-21'))\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(\"COVID Dependancy: \",data.loc[ID]['CLASS'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "FuLRefZjHxI6",
    "8WU00xMGopu-",
    "IZXYIO9VvIom"
   ],
   "name": "Finals with univariate and multivariate.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}