Oscars Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6ff491b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import sqrt\n",
    "import pandas as pd\n",
    "import os\n",
    "import re\n",
    "from scipy.stats import spearmanr\n",
    "from scipy.stats import jarque_bera\n",
    "import sklearn\n",
    "import statsmodels.api as stat\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import tree\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score, roc_curve, roc_auc_score, precision_score, recall_score, precision_recall_curve, f1_score, PrecisionRecallDisplay, plot_precision_recall_curve\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import metrics\n",
    "from sklearn import feature_selection\n",
    "from imblearn.over_sampling import SMOTE \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c402fa8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"C:\\\\Users\\\\Abdullah\\\\Desktop\\\\Studies\\\\IS 407 Intro to Data Science\\\\Project\\\\Model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3af0e992",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_df = pd.read_csv(\"oscars_df_clean.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e4dda8e1",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Film</th>\n",
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       "      <td>1927/28</td>\n",
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       "      <td>The Caddo Company</td>\n",
       "      <td>Nominee</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>The Broadway Melody</td>\n",
       "      <td>1928/29</td>\n",
       "      <td>Metro-Goldwyn-Mayer</td>\n",
       "      <td>Winner</td>\n",
       "      <td>1929</td>\n",
       "      <td>100</td>\n",
       "      <td>Drama,Musical,Romance</td>\n",
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       "      <td>Rotten</td>\n",
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       "      <td>Comedy,Music</td>\n",
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       "      <td>Ceán Chaffin, Eric Roth, and Douglas Urbanski</td>\n",
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       "      <td>2020</td>\n",
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       "      <td>Nominee</td>\n",
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       "      <td>Crime,Drama,Thriller</td>\n",
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       "      <td>Certified-Fresh</td>\n",
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       "      <td>87</td>\n",
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       "      <td>365</td>\n",
       "      <td>40</td>\n",
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       "      <td>Sound of Metal</td>\n",
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       "      <td>Bert Hamelinck and Sacha Ben Harroche</td>\n",
       "      <td>Nominee</td>\n",
       "      <td>2019</td>\n",
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       "      <td>Drama,Music</td>\n",
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       "      <td>The Trial of the Chicago 7</td>\n",
       "      <td>2020</td>\n",
       "      <td>Marc Platt and Stuart M. Besser</td>\n",
       "      <td>Nominee</td>\n",
       "      <td>2020</td>\n",
       "      <td>129</td>\n",
       "      <td>Drama,History,Thriller</td>\n",
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       "<p>533 rows × 19 columns</p>\n",
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      ],
      "text/plain": [
       "     Unnamed: 0                        Film Oscar Year  \\\n",
       "0             0                       Wings    1927/28   \n",
       "1             1                  7th Heaven    1927/28   \n",
       "2             2                  The Racket    1927/28   \n",
       "3             3         The Broadway Melody    1928/29   \n",
       "4             4             Hollywood Revue    1928/29   \n",
       "..          ...                         ...        ...   \n",
       "528         528                        Mank       2020   \n",
       "529         529                      Minari       2020   \n",
       "530         530       Promising Young Woman       2020   \n",
       "531         531              Sound of Metal       2020   \n",
       "532         532  The Trial of the Chicago 7       2020   \n",
       "\n",
       "                               Film Studio/Producer(s)    Award  \\\n",
       "0                                 Famous Players-Lasky   Winner   \n",
       "1                                                  Fox  Nominee   \n",
       "2                                    The Caddo Company  Nominee   \n",
       "3                                  Metro-Goldwyn-Mayer   Winner   \n",
       "4                                  Metro-Goldwyn-Mayer  Nominee   \n",
       "..                                                 ...      ...   \n",
       "528      Ceán Chaffin, Eric Roth, and Douglas Urbanski  Nominee   \n",
       "529                                       Christina Oh  Nominee   \n",
       "530  Ben Browning, Ashley Fox, Emerald Fennell, and...  Nominee   \n",
       "531              Bert Hamelinck and Sacha Ben Harroche  Nominee   \n",
       "532                    Marc Platt and Stuart M. Besser  Nominee   \n",
       "\n",
       "     Year of Release  Movie Time             Movie Genre  IMDB Rating  \\\n",
       "0               1927         144       Drama,Romance,War          7.5   \n",
       "1               1927         110           Drama,Romance          7.7   \n",
       "2               1928          84   Crime,Drama,Film-Noir          6.7   \n",
       "3               1929         100   Drama,Musical,Romance          5.7   \n",
       "4               1929         130            Comedy,Music          5.7   \n",
       "..               ...         ...                     ...          ...   \n",
       "528             2020         131  Biography,Comedy,Drama          6.9   \n",
       "529             2020         115                   Drama          7.5   \n",
       "530             2020         113    Crime,Drama,Thriller          7.5   \n",
       "531             2019         120             Drama,Music          7.8   \n",
       "532             2020         129  Drama,History,Thriller          7.8   \n",
       "\n",
       "    IMDB Votes Tomatometer Status  Tomatometer Rating  Tomatometer Count  \\\n",
       "0       12,221    Certified-Fresh                  93                 46   \n",
       "1        3,439              Fresh                  84                  9   \n",
       "2        1,257              Fresh                 100                  6   \n",
       "3        6,890             Rotten                  33                 24   \n",
       "4        2,004             Rotten                  43                  7   \n",
       "..         ...                ...                 ...                ...   \n",
       "528     65,380    Certified-Fresh                  83                339   \n",
       "529     57,976    Certified-Fresh                  98                310   \n",
       "530    122,269    Certified-Fresh                  90                405   \n",
       "531    102,807    Certified-Fresh                  97                275   \n",
       "532    156,283    Certified-Fresh                  89                331   \n",
       "\n",
       "    Audience Status  Audience Rating  Audience Count  \\\n",
       "0           Upright               78            3530   \n",
       "1           Upright               84             500   \n",
       "2           Upright               72             100   \n",
       "3           Spilled               21            1813   \n",
       "4           Spilled               22             100   \n",
       "..              ...              ...             ...   \n",
       "528         Spilled               59            1000   \n",
       "529         Upright               87            1000   \n",
       "530         Upright               87             500   \n",
       "531         Upright               90            1000   \n",
       "532         Upright               90            2500   \n",
       "\n",
       "     Tomatometer Top Critics Count  Tomatometer Fresh Critics Count  \\\n",
       "0                                9                               43   \n",
       "1                                1                                1   \n",
       "2                                1                                6   \n",
       "3                                7                                8   \n",
       "4                                3                                3   \n",
       "..                             ...                              ...   \n",
       "528                             69                              280   \n",
       "529                             70                              303   \n",
       "530                             70                              365   \n",
       "531                             53                              267   \n",
       "532                             68                              294   \n",
       "\n",
       "     Tomatometer Rotten Critics Count  \n",
       "0                                   3  \n",
       "1                                   9  \n",
       "2                                   0  \n",
       "3                                  16  \n",
       "4                                   4  \n",
       "..                                ...  \n",
       "528                                59  \n",
       "529                                 7  \n",
       "530                                40  \n",
       "531                                 8  \n",
       "532                                37  \n",
       "\n",
       "[533 rows x 19 columns]"
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     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "movies_df"
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   "id": "b13d40c8",
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 533 entries, 0 to 532\n",
      "Data columns (total 19 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   Unnamed: 0                        533 non-null    int64  \n",
      " 1   Film                              533 non-null    object \n",
      " 2   Oscar Year                        533 non-null    object \n",
      " 3   Film Studio/Producer(s)           533 non-null    object \n",
      " 4   Award                             533 non-null    object \n",
      " 5   Year of Release                   533 non-null    int64  \n",
      " 6   Movie Time                        533 non-null    int64  \n",
      " 7   Movie Genre                       533 non-null    object \n",
      " 8   IMDB Rating                       533 non-null    float64\n",
      " 9   IMDB Votes                        533 non-null    object \n",
      " 10  Tomatometer Status                533 non-null    object \n",
      " 11  Tomatometer Rating                533 non-null    int64  \n",
      " 12  Tomatometer Count                 533 non-null    int64  \n",
      " 13  Audience Status                   533 non-null    object \n",
      " 14  Audience Rating                   533 non-null    int64  \n",
      " 15  Audience Count                    533 non-null    int64  \n",
      " 16  Tomatometer Top Critics Count     533 non-null    int64  \n",
      " 17  Tomatometer Fresh Critics Count   533 non-null    int64  \n",
      " 18  Tomatometer Rotten Critics Count  533 non-null    int64  \n",
      "dtypes: float64(1), int64(10), object(8)\n",
      "memory usage: 79.2+ KB\n"
     ]
    }
   ],
   "source": [
    "movies_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0949814c",
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_genre = movies_df['Movie Genre']\n",
    "movies_df = movies_df.drop(columns = ['Movie Genre', 'Unnamed: 0', 'Film', 'Oscar Year', 'Film Studio/Producer(s)', 'Year of Release', 'Movie Time', 'Tomatometer Status', 'Movie Time', 'Audience Status'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7803c651",
   "metadata": {},
   "outputs": [
    {
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       "      <td>3,439</td>\n",
       "      <td>84</td>\n",
       "      <td>9</td>\n",
       "      <td>84</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nominee</td>\n",
       "      <td>6.7</td>\n",
       "      <td>1,257</td>\n",
       "      <td>100</td>\n",
       "      <td>6</td>\n",
       "      <td>72</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Winner</td>\n",
       "      <td>5.7</td>\n",
       "      <td>6,890</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>1813</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nominee</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2,004</td>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>Nominee</td>\n",
       "      <td>6.9</td>\n",
       "      <td>65,380</td>\n",
       "      <td>83</td>\n",
       "      <td>339</td>\n",
       "      <td>59</td>\n",
       "      <td>1000</td>\n",
       "      <td>69</td>\n",
       "      <td>280</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>Nominee</td>\n",
       "      <td>7.5</td>\n",
       "      <td>57,976</td>\n",
       "      <td>98</td>\n",
       "      <td>310</td>\n",
       "      <td>87</td>\n",
       "      <td>1000</td>\n",
       "      <td>70</td>\n",
       "      <td>303</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>Nominee</td>\n",
       "      <td>7.5</td>\n",
       "      <td>122,269</td>\n",
       "      <td>90</td>\n",
       "      <td>405</td>\n",
       "      <td>87</td>\n",
       "      <td>500</td>\n",
       "      <td>70</td>\n",
       "      <td>365</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>Nominee</td>\n",
       "      <td>7.8</td>\n",
       "      <td>102,807</td>\n",
       "      <td>97</td>\n",
       "      <td>275</td>\n",
       "      <td>90</td>\n",
       "      <td>1000</td>\n",
       "      <td>53</td>\n",
       "      <td>267</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>Nominee</td>\n",
       "      <td>7.8</td>\n",
       "      <td>156,283</td>\n",
       "      <td>89</td>\n",
       "      <td>331</td>\n",
       "      <td>90</td>\n",
       "      <td>2500</td>\n",
       "      <td>68</td>\n",
       "      <td>294</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>533 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Award  IMDB Rating IMDB Votes  Tomatometer Rating  Tomatometer Count  \\\n",
       "0     Winner          7.5     12,221                  93                 46   \n",
       "1    Nominee          7.7      3,439                  84                  9   \n",
       "2    Nominee          6.7      1,257                 100                  6   \n",
       "3     Winner          5.7      6,890                  33                 24   \n",
       "4    Nominee          5.7      2,004                  43                  7   \n",
       "..       ...          ...        ...                 ...                ...   \n",
       "528  Nominee          6.9     65,380                  83                339   \n",
       "529  Nominee          7.5     57,976                  98                310   \n",
       "530  Nominee          7.5    122,269                  90                405   \n",
       "531  Nominee          7.8    102,807                  97                275   \n",
       "532  Nominee          7.8    156,283                  89                331   \n",
       "\n",
       "     Audience Rating  Audience Count  Tomatometer Top Critics Count  \\\n",
       "0                 78            3530                              9   \n",
       "1                 84             500                              1   \n",
       "2                 72             100                              1   \n",
       "3                 21            1813                              7   \n",
       "4                 22             100                              3   \n",
       "..               ...             ...                            ...   \n",
       "528               59            1000                             69   \n",
       "529               87            1000                             70   \n",
       "530               87             500                             70   \n",
       "531               90            1000                             53   \n",
       "532               90            2500                             68   \n",
       "\n",
       "     Tomatometer Fresh Critics Count  Tomatometer Rotten Critics Count  \n",
       "0                                 43                                 3  \n",
       "1                                  1                                 9  \n",
       "2                                  6                                 0  \n",
       "3                                  8                                16  \n",
       "4                                  3                                 4  \n",
       "..                               ...                               ...  \n",
       "528                              280                                59  \n",
       "529                              303                                 7  \n",
       "530                              365                                40  \n",
       "531                              267                                 8  \n",
       "532                              294                                37  \n",
       "\n",
       "[533 rows x 10 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d28a76b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 533 entries, 0 to 532\n",
      "Data columns (total 11 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   IMDB Rating                       533 non-null    float64\n",
      " 1   IMDB Votes                        533 non-null    int64  \n",
      " 2   Tomatometer Rating                533 non-null    int64  \n",
      " 3   Tomatometer Count                 533 non-null    int64  \n",
      " 4   Audience Rating                   533 non-null    int64  \n",
      " 5   Audience Count                    533 non-null    int64  \n",
      " 6   Tomatometer Top Critics Count     533 non-null    int64  \n",
      " 7   Tomatometer Fresh Critics Count   533 non-null    int64  \n",
      " 8   Tomatometer Rotten Critics Count  533 non-null    int64  \n",
      " 9   Award_Nominee                     533 non-null    uint8  \n",
      " 10  Award_Winner                      533 non-null    uint8  \n",
      "dtypes: float64(1), int64(8), uint8(2)\n",
      "memory usage: 38.6 KB\n"
     ]
    }
   ],
   "source": [
    "#Set Award variable as categorical, and remove comma from IMDB votes. Set A dummy variable for Award variable.\n",
    "\n",
    "movies_df['Award'] = movies_df['Award'].astype('category')\n",
    "movies_df = pd.get_dummies(movies_df, columns=['Award'])\n",
    "movies_df['IMDB Votes'] = movies_df['IMDB Votes'].astype('str')\n",
    "movies_df['IMDB Votes'] = movies_df['IMDB Votes'].str.replace(',','')\n",
    "movies_df['IMDB Votes'] = movies_df['IMDB Votes'].astype('int64')\n",
    "\n",
    "movies_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6d487b55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IMDB Rating</th>\n",
       "      <th>IMDB Votes</th>\n",
       "      <th>Tomatometer Rating</th>\n",
       "      <th>Tomatometer Count</th>\n",
       "      <th>Audience Rating</th>\n",
       "      <th>Audience Count</th>\n",
       "      <th>Tomatometer Top Critics Count</th>\n",
       "      <th>Tomatometer Fresh Critics Count</th>\n",
       "      <th>Tomatometer Rotten Critics Count</th>\n",
       "      <th>Award_Nominee</th>\n",
       "      <th>Award_Winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.5</td>\n",
       "      <td>12221</td>\n",
       "      <td>93</td>\n",
       "      <td>46</td>\n",
       "      <td>78</td>\n",
       "      <td>3530</td>\n",
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       "      <td>9</td>\n",
       "      <td>84</td>\n",
       "      <td>500</td>\n",
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       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.7</td>\n",
       "      <td>1257</td>\n",
       "      <td>100</td>\n",
       "      <td>6</td>\n",
       "      <td>72</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.7</td>\n",
       "      <td>6890</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>1813</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2004</td>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>6.9</td>\n",
       "      <td>65380</td>\n",
       "      <td>83</td>\n",
       "      <td>339</td>\n",
       "      <td>59</td>\n",
       "      <td>1000</td>\n",
       "      <td>69</td>\n",
       "      <td>280</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>7.5</td>\n",
       "      <td>57976</td>\n",
       "      <td>98</td>\n",
       "      <td>310</td>\n",
       "      <td>87</td>\n",
       "      <td>1000</td>\n",
       "      <td>70</td>\n",
       "      <td>303</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>7.5</td>\n",
       "      <td>122269</td>\n",
       "      <td>90</td>\n",
       "      <td>405</td>\n",
       "      <td>87</td>\n",
       "      <td>500</td>\n",
       "      <td>70</td>\n",
       "      <td>365</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>7.8</td>\n",
       "      <td>102807</td>\n",
       "      <td>97</td>\n",
       "      <td>275</td>\n",
       "      <td>90</td>\n",
       "      <td>1000</td>\n",
       "      <td>53</td>\n",
       "      <td>267</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>7.8</td>\n",
       "      <td>156283</td>\n",
       "      <td>89</td>\n",
       "      <td>331</td>\n",
       "      <td>90</td>\n",
       "      <td>2500</td>\n",
       "      <td>68</td>\n",
       "      <td>294</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>533 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     IMDB Rating  IMDB Votes  Tomatometer Rating  Tomatometer Count  \\\n",
       "0            7.5       12221                  93                 46   \n",
       "1            7.7        3439                  84                  9   \n",
       "2            6.7        1257                 100                  6   \n",
       "3            5.7        6890                  33                 24   \n",
       "4            5.7        2004                  43                  7   \n",
       "..           ...         ...                 ...                ...   \n",
       "528          6.9       65380                  83                339   \n",
       "529          7.5       57976                  98                310   \n",
       "530          7.5      122269                  90                405   \n",
       "531          7.8      102807                  97                275   \n",
       "532          7.8      156283                  89                331   \n",
       "\n",
       "     Audience Rating  Audience Count  Tomatometer Top Critics Count  \\\n",
       "0                 78            3530                              9   \n",
       "1                 84             500                              1   \n",
       "2                 72             100                              1   \n",
       "3                 21            1813                              7   \n",
       "4                 22             100                              3   \n",
       "..               ...             ...                            ...   \n",
       "528               59            1000                             69   \n",
       "529               87            1000                             70   \n",
       "530               87             500                             70   \n",
       "531               90            1000                             53   \n",
       "532               90            2500                             68   \n",
       "\n",
       "     Tomatometer Fresh Critics Count  Tomatometer Rotten Critics Count  \\\n",
       "0                                 43                                 3   \n",
       "1                                  1                                 9   \n",
       "2                                  6                                 0   \n",
       "3                                  8                                16   \n",
       "4                                  3                                 4   \n",
       "..                               ...                               ...   \n",
       "528                              280                                59   \n",
       "529                              303                                 7   \n",
       "530                              365                                40   \n",
       "531                              267                                 8   \n",
       "532                              294                                37   \n",
       "\n",
       "     Award_Nominee  Award_Winner  \n",
       "0                0             1  \n",
       "1                1             0  \n",
       "2                1             0  \n",
       "3                0             1  \n",
       "4                1             0  \n",
       "..             ...           ...  \n",
       "528              1             0  \n",
       "529              1             0  \n",
       "530              1             0  \n",
       "531              1             0  \n",
       "532              1             0  \n",
       "\n",
       "[533 rows x 11 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c8bc8fe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Making DF for correlation\n",
    "\n",
    "corr_df = movies_df.drop(columns = ['Award_Nominee'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eb0dfe5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation matrix')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hhDBg+pMqcbCJCjaEEMKA6U+qxMEmKtgQQggDZjAOXmpVVLAhhBAGzFAe5BQVbAghhAHTSo7hwSoq2DBkvW3s6NJjap118DMnlx4XQCM/W0ncEDpZXIMNIYQQKqB5B9WdN/0SFWwIIYQBo2FRwYYQQgjlGz50L8JGBRtCCGHgRAs2hBBCKJ+GRwUbQgghlG/e6CIOIYQQSjeUBzkN3a8OFZD0NkmT8uNJSf+pW56vxPMsLumLZcXr5TxbStqkYIxRkl7L78Gdkk6TNG9/zitpf0mfKlKOEMIgNXxY648WSPqwpHsk3S/p0Cbbt5T0Qt1n9/daPba/ogXbD3mC8jEAkg4DXrb9iwpOtTjwReC3FcSutyXwMnB9qwdImsf29IbVD9geI2k48C9gV+D0Vs9r+4R+lDmEMISU2YLNn0G/AT4APAZMkDTO9p0Nu15j+2NtHtuyaMEWJGkbSbdJul3SHyTNn9c/LOlwSTdImihpXUmXSHpA0v55n4UlXSbp1nz8DjnsEcDo/O3qSCVHSpqa99stH7+lpKsknS3pXklHSBor6ea83+i830hJf5M0IT82lTQK2J80wfokSZs32y8ff5ikkySNB07r6b2wPQO4GVguH/dxSTfl9+dSSUv3cN7DJH0tH3OlpJ/l13CvpM3z+gXz65wi6awcd/1Sf5khhLlvuFp/9G1D4H7bD9p+AzgT2KGPY8o4tqmoYIsZAZwC7GZ7TVKPwBfqtj9qe2PgmrzfLsBGwA/z9mnATrbXBbYCfilJwKHkVqHtQ4BPkFrOawPbAkdKWibHWBv4MrAmsDewsu0NgZOBA/M+vwKOtr0BsDNwsu2HgRPy+jG2r2m2X91rWQ/YwfaePb0ZkkYA7wNqM5JfC2xkex3SH+vXezhvo3nyazgY+H5e90Xgv7bXAn6Uy9OsDPvlLzQTT7rwjp6KGkLoFP2oYOv/f+fHfg3RlgMerVt+LK9rtLGkyZIukrR6P49tWXQRFzMceMj2vXn5VOBLwDF5eVz+eTuwsO2XgJckTZO0OPAKcLikLYCZpF/m0k3OsxlwRm4hPiXpKmAD4EVggu0nACQ9AIyvO+dW+fm2wGqp7gZgUUmLNDlPb/uNs/1aD+/DaEmTgJWAv9qekte/EzgrfxmYD3ioh+MbnZt/3gKMys83I30BwPZUSVOaHIftk4CTAGaO/9LQTXIawhChfowirv//3VO4Zoc1LN8KrGD7ZUkfAf5O+uxq5dh+iQq2mFf62P56/jmz7nlteR5gLDASWM/2m5IeJrWKG/XWN9IYt/6ctd/vMGDjxgqyriKlhf16e621a7DLAFdK2t72OOBY4Cjb4yRtCRzWS4xmr2lG3WsYukMNQ+hm5U6n8xjwrrrldwKP1+9g+8W65xdK+q2kpVo5tr+ii7iYEcAoSe/Jy3sDV/Xj+MWAp3PluhWwQl7/ElDfwrwa2E3ScEkjgS1I1zpbNR44oLYgaUwP5+lpv5bklvShwDfzqsWA/+Tn+9Tt2njeVlxLGjyFpNVIXeIhhEFOw9XyowUTgJUkrah0Z8fuzO5JTOeT3pEvxSFpQ1I9+Fwrx/ZXVLDFTAM+DZwj6XZSq7E/I2JPB9aXNJHUmr0bZo1Wvi4PajoSOA+YAkwGLiddy3yyH+c5KJ9niqQ7SYOMAM4HdqoNNuplv/74O7BgjncY6b25Bni2bp/G87bit8DI3DX8DdL78UIb5QshdJJhav3Rh3yHwwHAJcBdwNm271C6FbD2ebYLMFXSZODXwO5Omh5b5KXJjstUofMpDaGf1/a0PDr6MtKArjd6OqaKa7BaZ52yQ86OHfPBhsGn8KWbN0/eo+X/p/N+9oxBdakorsGGwWJB4AqlJBYCvtBb5RpCGByGcianqGDDoJBHYMd9ryEMNfMNH+gSVCYq2BBCCAMmWrAhhBBCFWK6uhAGnxkPPVd6zCs/f27fO7Vh25s/gZ85ue8d+ykGToWON4RbsHGbTghDWBWVdghlKvk+2I4SLdgQQggDZ54Y5BRCCCGUr9xUiR0lKtgQQggDJyrYEEIIoQJDeJBTVLAhhBAGTrRgQwghhAoM4Qp26L6yCkl6W54JZpKkJyX9p255vhLPs7ikL5YVr5fzbClpkxLirCzpQkn3S7pL0tmSmk0gX+QcO+bp6kIIQ8E8w1t/DDJRwbbB9nO2x9geQ5qe7ujacskJ6BcHKq9ggS2BflWwkuZpWB4B/BM43vZ7bK8KHE+aUL5MOwJRwYYwVAwb1vpjkBl8Je5QkraRdJuk2yX9QdL8ef3Dkg6XdIOkiZLWlXSJpAdq8xNKWljSZZJuzcfvkMMeAYzOLeMjlRyZ54m9XdJu+fgtJV2VW4z3SjpC0lhJN+f9Ruf9Rkr6m6QJ+bGppFGkeV+/Upuftdl++fjDJJ0kaTxwWsNbsCdwg+3zaytsX2F7qqQRkv6Yy3JbnlweSftKOq7uPbxA0pb5+cuSfiJpsqQbJS2dW9nbA0fmso4u83cYQhgAJc4H22niGmw5RgCnANvYvlfSacAXgGPy9kdtbyzp6LzfpvmYO0gt4GnATrZflLQUcKOkccChwBq5pYyknYExwNrAUsAESVfnc6wNrAo8DzwInGx7Q0lfBg4EDgZ+RWptXytpeeAS26tKOgF42fYv8nn+0rhfjg2wHrCZ7dca3oM1gFt6eH++BGB7TUmrAOMlrdzHe7oQcKPtb0v6OfA52z/O78sFtv/ax/EhhMFgELZMWxUVbDmGAw/Zvjcvn0qqVI7Jy+Pyz9uBhfPUay9JmiZpceAV4HBJWwAzgeWAZtcuNwPOsD0DeErSVcAGwIvABNtPAEh6ABhfd86t8vNtgdWkWd8EF5W0SJPz9LbfuCaVa182A44FsH23pEeAvirYN4AL8vNbgA+0ciJJ+wH7Afx27Pp8dvNo5IbQ0aKCDX14pY/tr+efM+ue15bnAcaSrlWuZ/tNSQ+TWriNeusjaYxbf87a73kYsHFjBVlXkdLCfj291juA9/ewradyT2fOyxT1r/lN287PZ9Di36rtk4CTAN48cXf3sXsIYYA1+fwZMobuV4e5awQwStJ78vLewFX9OH4x4OlcuW4FrJDXvwTUtzCvBnaTNFzSSGAL4OZ+nGc8cEBtQdKYHs7T0369+QuwiaSP1h33YUlr5nKPzetWBpYH7gEeBsZIGibpXcCGLZynsawhhMFsnnlaf7Qgf+7ck+9mOLTJ9rGSpuTH9ZLWrtv2cB4rMknSxKIvLSrYckwDPg2cI+l2UqvxhH4cfzqwfv6FjgXuhjRaGbguD2o6EjgPmAJMBi4Hvm77yX6c56B8nimS7iQNbgI4H9ipNsipl/16lFu7HwMOlHRfPm5f4Gngt8Dw/N6cBexr+3XgOuAhUjf2L4BbW3gNZwKH5MFS0f8bwmBX4iAnScOB3wDbke422ENvva3vIeD9ttcCfkTu8aqzVb4jZP2iL02ze+FCGFqq6CK+8ogXyw4JpPlgqxJzwoYKFe7f9eTvt/z/VGv/oNfzSdoYOMz2h/LyNwFs/7SH/ZcAptpeLi8/DKxv+9lWy9SbaMGGEEIYOP1owUraT+l2x9pjv4ZoywGP1i0/ltf15DPARXXLJt3lcEuT2P0Wg5xCCCEMnH6MIq4fxNiDZi3cpi3kPN7lM6S7HGo2tf24pLcD/5J0t+2rmx3fiqhgQwghDJxyUyA+BryrbvmdwOONO0laCzgZ2C6PdQHA9uP559OSziMNvGy7go0u4hBCCAOn3FSJE4CVJK2olBd+d2bnIQAgJ885F9i7LncBkhaq3e8vaSHgg8DUIi8tWrAhhBAGTomJJmxPl3QAKfvccOAPtu9QTktr+wTge8DbgN/me3Cn5xHDSwPn5XXzAH+xfXGR8sQo4jBkXbHyR0v/497ok9X8f5l/8xX63qkdb7xZSdhh259cSdww6BQfRfzQz1sfRbzi1wdVVopowYYQQhg4kSoxhBBCqICigg0hhBDKN3zoVkND95WFEELofNGCDSGEECoQFWwIIYRQgahgQwghhAoM4VHEQ/eVDRKSdpJkSau0ceyWki7Iz7dvNvdh1XIZXsjTx90t6RctHLNj/RRSkn4oadtqSxpC6Ega1vpjkBl8JR569gCuJaX0apvtcbaPKKdI/XaN7XWAdYCPSdq0j/13JM3VCIDt79m+tMLyhRA61bB5Wn8MMlHBDiBJCwObkmZ02L1u/ayWaV4+TtK++fmHc0vxWuATdfvsK+m4/HykpL9JmpAfm+b1h0n6g6QrJT0o6aC64z+VJ1ifLOlPvcXpSZ50fRJ5eihJn8vHTc5xFpS0CbA9cGSe4H20pFMk7ZKPeVjSDyTdKun2Wss+l+Vfef2Jkh6RtFS7730IoUNECzZUZEfg4pxw+nlJ6/a2s6QRwO+AjwObA+/oYddfAUfb3gDYmTRrRM0qwIdIs0R8X9K8klYHvg1sbXtt4MstxGlWviWAlZg9+8S5tjfIMe8CPmP7elLy7UNsj7H9QJNQz9peFzge+Fpe933g8rz+PGD53soSQhgkyk3231EGX4mHlj2AM/PzM/Nyb1YBHrJ9n1MS6T/3sN+2wHGSJpEqs0Vrs0QA/7T9uu1ngadJCa63Bv6a12H7+Rbi1Ntc0hTgSeAC20/m9WtIukbS7cBYYPU+Xl/NufnnLcCo/Hwz8nuVE3D/t9mB9RMyX/DCv1s8XQhhoEjDW34MNoOvU3uIkPQ2UsW2hiSTZn6wpK8D05nzy8+IuuetJMYeBmycu2zrzwnwet2qGaS/AfUQt2mcJq6x/TFJKwPXSjrP9iTgFGBH25NzF/eWLZS9voy18kGLScXrJ2SuItl/CKFkg7Drt1VD95V1vl2A02yvYHuU7XcBD5Faao8Aq0maX9JiwDb5mLuBFSWNzss9tXjHAwfUFiSN6aMslwG75kofSUu2Eyd3df8U+EZetQjwhKR5SS3Ympfytv64Ftg1l+ODwBL9PD6E0IlikFOowB6ka4n1/gbsaftR4GxgCnA6cBuA7WnAfsA/8yCnR3qIfRCwfh60dCewf28FsX0H8BPgKkmTgaPaiZOdAGwhaUXgu8BNwL9IXw5qzgQOybf2jG4So5kfAB+UdCuwHfAEqaIOIQxmQ/gabMwHGwYFSfMDM/KEyhsDx9se09sxMR8sMR9sqFrx+Vlf+0fr/6kW2CHmgw2hAssDZ0saBrwBfG6AyxNCKMMQvgYbFWwYFGzfR0pkEUIYSoZwBTt0X1kIIYTOV/I12JyM5x5J9zdLH6vk13n7lPr8A30d2++XVjRACCGE0LYSRxEr3Sz7G9JAyNWAPerznmfbkRLirEQaNHp8P47t30srcnAIIYRQSLmpEjcE7rf9oO03SHcs7NCwzw6kWyRt+0ZgcUnLtHhsv8Q12DBkbbxP+X/eN5w6vfSYAFt958OVxH1s75NKj/n2LReC43YtPS7AfAecXUnc0MH6cQ1W0n6kVmfNSTm5TM1ywKN1y48B72sI02yf5Vo8tl+igg0hhDBw+nHjTX2mtn5Ea7wNqKd9Wjm2X6KCDSGEMGD6k4tBfVfGjwHvqlt+J/B4i/vM18Kx/RLXYEMIIQwYM7PlRwsmACtJWlHSfKRpQMc17DMO+FQeTbwR8ILtJ1o8tl+iBRtCCGHAzPSMlvcd1kcLNmd6OwC4hDSByh9s3yFp/7z9BOBC4CPA/cCrwKd7O7a/r6deVLAhhBAGjItd5nxrPPtCUiVav+6EuucGvtTqsUVEBRtCCGHA2C11/Q5KUcGGEEIYMGW3YDtJDHLqIJJ2kmRJq7Rx7JaSLsjPty8jzVc7JG0o6eqcbuxuSSdLWrDkc+wradkyY4YQBkbJg5w6SlSwnWUP0sTiuxcJYnuc7SPKKVLrJC0NnAN8w/Z7gVWBi+n/5Op92ReICjaEIWCmZ7T8GGyigu0QkhYGNgU+Q10FW98yzcvHSdo3P/9wbiVeC3yibp99JR2Xn4+U9DdJE/Jj07z+MEl/kHSlpAclHVR3/KdyEuzJkv7UW5wGXwJOtX0DpMEEtv9q+ylJS0r6e457o6S16srxtbpzT5U0Kj/ukvQ7SXdIGi9pAUm7AOsDp0uaJGmBgm99CGEA2TNbfgw2UcF2jh2Bi23fCzxfP8NDM5JGAL8DPg5sDryjh11/BRxtewNgZ6B+puxVgA+RcnB+X9K8klYHvg1sbXtt4MstxKlZA7ilh3L8ALjN9lrAt4DTent92UrAb2yvDvwP2Nn2X4GJwFjbY2y/Vn+ApP0kTZQ08fcTHm7hFCGEgeR+/BtsYpBT59gDOCY/PzMv39rL/qsAD+V5UpH0Z+bM0VmzLbCaZqdAWVRSrcv2n7ZfB16X9DSwNLA18FfbzwLYfr63OLZfavH1bUaqmLF9uaS3SVqsj2Mesj0pP78FGNXXSepTqU37yQ6D739kCF1mMF5bbVVUsB1A0ttIFdsakky6ydmSvg5MZ86ehhF1z1upQIYBGzdp6QG8XrdqBunvQT3EbRqnwR3AesA/mmzrKc9nb6+vsXzRHRzCENOfVImDTXQRd4ZdSNMnrWB7lO13AQ+RWn2PkFqO8+cW3zb5mLuBFSWNzst79BB7PHBAbUHSmD7Kchmwa670kbRkP+IcB+wj6X11++0l6R3A1cDYvG5L4FnbLwIPA+vm9esCK/ZRPoCXKH/gVAhhAMQo4lC1PYDzGtb9DdjT9qPA2cAU4HTgNgDb00hdwv/Mg5we6SH2QcD6eXDRncD+vRUkpwb7CXCVpMnAUa3Gsf0UaYDWL/JtOneRrg+/CBxWOx44Atin7nUuKWkS8AXg3t7Kl50CnBCDnEIY/IbyKGIN5eZ56G5VXIOtbD7Yyc0unxdX2XywFYn5YAedfkw219zzr/+55f+nS86/V+HzzU1xDTaEEMKAGYy337QqKtgQQggDZjDeftOqqGBDCCEMmME4eKlVUcGGEEIYMINx8FKrooINIYQwYIbyQNuoYMOQNd+qS/a9Uz8tsMCzpccE4I1XKwl77cVvlB5z1wNWKj0mwNMn3QPXb1dJ7Hf85aJK4obioos4hBBCqMDModuAjUQTIYQQBs4Mq+VHEXlGr39Jui//XKLJPu+SdEWeyesOSV+u23aYpP/kBDeTJH2kr3NGBRtCCGHAzHTrj4IOBS6zvRIpJeyhTfaZDvyf7VWBjYAvSVqtbvvReRavMbYv7OuEUcGGEEIYMG/OVMuPgnYATs3PTyVNEToH20/YvjU/fwm4C1iu3RNGBRtCCGHAzLRafhS0tO0nIFWkwNt721nSKGAd4Ka61QfkfOx/aNbF3Cgq2BBCCAOmP13EkvaTNLHuMUcSb0mXSpra5LFDf8okaWHSRCQH51m/AI4HRgNjgCeAX/YVJ0YRhxBCGDD9Gbxk+ySgxxksbG/b0zZJT0laxvYTkpYBnu5hv3lJlevpts+ti/1U3T6/Ay7oq7yVtmAlva1uxNWTDSOw5ivxPItL+mJZ8Xo5z5aSNilw/Jp1r/95SQ/l55cWLNen8re0OyTdKelrPey3v6RP5ef7Slq2btvJDRfz2ynHOySdKemBXI4LJa1cJGaTcxT6HYQQOsvMfjwKGsfsaTL3Af7RuIMkAb8H7rJ9VMO2ZeoWdwKm9nXCSluwtp8jNaeRdBjwsu1fVHCqxYEvAr+tIHa9LYGXgetbPUDSPLanA9i+ndnvxynABbb/WqRAkrYDDgY+aPtxSSOAvXsoxwl1q/Yl/YE8nsv22YLlEGlO21Nt757XjQGWprU5Xlu1Jf38HYQQOlcJ11ZbdQRwtqTPAP8GPgmQGxon2/4IsCnp8/P2PEc1wLfyiOGf5880Aw8Dn+/rhHO9i1jSNsAv8rknAF+w/bqkh4G/AFsB85ImE/8p8B7gSNsn5H7xfwBL5H2+Y/sfpDdudH5D/gV8Hfg5sB3pzfix7bMkbQn8AHiKVNGdC9wOfBlYANjR9gOSRgInAMvnYh8M/Ic0yfgMSXsBBwJ3N+5n+7r8ZWJZYBTwLLBnH+/JHsC3SHMr/tP2N/L6l4ET83vyX2B32880HP5N4Gu2axXlNOB3+fgrSRXRpsA4SYuQKqeHgfWB0yW9BmwMXJTjTJT0YeBwYDjwrO1tJL0f+FU+p4Et8ii7mq2AN+srcduTcjlEz7+Pr9n+WN7vOGCi7VPy38OpwMdJv+tPAtMafwe2r+ntvQ0hdLYSRge3JDf4tmmy/nHgI/n5tfQwx63ttzRc+jK3BzmNAE4BdrO9JqmS/ULd9kdtbwxck/fbhXQv0g/z9mnATrbXJX2g/zJ/eB8KPJDvTToE+ASpAl0b2BY4sq55vzapQl2T9E1lZdsbAieTKk1IFcnRtjcAdiZ9u3mYVJnW7oO6ptl+da9lPWAH231VrssCPwO2zmXeQNKOefNCwK359V4FfL9JiDWAW3o5xeK232971gX53GqeCIzNr+W1uvKMJFXQO9tem/wtD/ga8CXbY4DNgVnHtFCO3n4fvXk2v/bjSRXxw7z1dzCH+kEQJ42/p4VThBAG0ly8D3aum9st2OHAQ7ZrXYanAl8CjsnL4/LP24GFcwvpJUnTJC0OvAIcLmkLUpf8cqQuyEabAWfYngE8JekqYAPgRWBCbai2pAeA8XXn3Co/3xZYLdXdACyaW3+NettvXH3F1YsNgCtrLVNJpwNbAH/Pr/GsvN+fSS3u/jqr713msBFwte2HAGw/n9dfBxyVy3eu7cf6EbO330dvaq/3FlIl3af6QRAzz/30IPwvGUJ3mYtdxHPd3K5gX+lj++v558y657XleYCxwEhgPdtv5m7EEU3i9PYba4xbf87a+zEM2LixgqyrSGlhv75eaytlbdSswriD1Fq+vIdjWi1HfXnech7bR0j6J6kr5UZJ29q+u6Ecu/QSs5npzNmL0vi7rP1uZhAj3kMYkmYM4a/BA9FFPErSe/Ly3qSuz1YtBjydK9etgBXy+peA+hbm1cBukobnLs8tgJv7cZ7xwAG1hXxhu9l5etqvP24C3i9pKUnDgT2Y/Z4MY3altSdwbZPjf0q6+P6OXIb5JR3UwnkbX0vNDbk8K+Z4S+afo23fbvtnpO7lVRqOuxyYX9LnaiskbZCv3fb0+3iE1AMwv6TFaHJ9pB/lDiEMQnMx0cRcN7dbBdOATwPnSKoNcjqh90PmcDpwvqSJwCTSICNsPyfpOklTSYN1vk4auDOZ1Br7uu0nJTVWCj05CPiNpCmk9+hq0uCa84G/Kt20fGAv+7Us35P1TeAKUkvvwjxwC1Lrc3VJtwAvALs1Of5CSUsDl+br0Qb+0MKpTwFOqBvkVIv3jNLN2+dKGka6V+wDwMH5S80M4E7S+1xfDkvaCThG0qGk3/XDpAFiV9Pk9wEg6WxgCnAfcFsL5Z7jdxCDnEIY3N4curPVoaE82e1gJ+ll2wsPdDkGqyquwd78g2rmg93o6l7HwrXtzOVa+a7VP7tesHrpMSHPB1uRmA+2MoWblf967LSW/59+4J2fGlTN2LiuFUIIYcAMxtHBrYoKtoNF6zWEMNQVnee1k0UFG0IIYcBECzaEEEKowGAcHdyqqGDDkDX9kb7yWPTfKy/PKD0mgJ9+uJK4r71a/hBNP/O/0mMCzJhezQftdRdPh0U/WHrcXV8c3/dOoU9zK1XiQIj5YEMIoQ1nV1Bpd6NIlRhCCCFUYChncooKNoQQwoCJCjaEEEKowGDs+m1VVLAhhBAGTLRgQwghhAq8MYRzEUcFG0IIYcAM5S7itm7TkfQ2SZPy40lJ/6lbnq+swklaXNIXy4rXy3m2lLRJwRijJL1W9z4Ufi8kvdzifttJmijpLkl3S/pFD/ttn2e6QdKOklar2/ZDSdsWLO/Ckk6U9ICkOyRdLel9RWI2OccYSR8pM2YIYeDMcOuPIiQtKelfku7LP5foYb+HJd2eP8Mn9vf4em1VsLafsz3G9hjSdHNH15Ztv9FOzB4sDlRewQJbAv2qYPN0e40eqHsf5ngv8lyvpZO0BnAcsJftVYE1gAebldf2ONtH5FU7ArMqWNvfs31pweKcDDwPrGR7dWBfYKmCMRuNIU36HkIYAuZWBQscClxmeyXgsrzck63yZ/j6bR4PlJhoQtI2km7LNf8fJM2f1z8s6XBJN+RW1rqSLsmtnP3zPgtLukzSrfn4HXLYI4DR+ZvEkUqOlDQ177dbPn5LSVdJOlvSvZKOkDRW0s15v9F5v5GS/iZpQn5sKmkUaQ7Xr+TzbN5sv3z8YZJOkjQeOK2F92RLSVdI+gtwu9KE40fmmFMkfT7vt0xu7U3Kr23zuhg/kTRZ0o1K8742+jrwE9u1uXGn2/5tPvYUSUdJugL4maR9JR2XW+vbA0fmc47O++6Sj9tA0vX5vDdLWkTS6vn5pFz2lRpe62jgfcB3bM/MZXnQ9j/z9q/m1zZV0sF53SilOXxrMb4m6bD8/EpJP8vnvDf/XuYDfkiavH1S7fcfQhi85mKiiR2AU/PzU0mNjEqPL+sa7AjSBN7b2L5X0mnAF4Bj8vZHbW8s6ei836b5mDtILeBpwE62X5S0FHCjpHGkbwhr5JYyknYmtWDWJrWMJki6Op9jbWBVUgvqQeBk2xtK+jJpcvSDgV+RWtvXSloeuMT2qpJOAF62/Yt8nr807pdjA6wHbGb7tSbvw2hJk/Lz64BzgA3za3hIaSLzF2xvkL+AXJcr60/ksvxEqaW7YI6xEHCj7W9L+jnwOeDHDedcA/hls19KtjKwre0ZkvYFsH19fn8vsP3X/JrJP+cDzgJ2sz1B0qLAa6QvIb+yfXrep7FFvjowyfZbcglKWg/4NKkCFnCTpKuA//ZSboB58u/wI8D3bW8r6XvA+rYP6OPYEMIgMBcHOS1t+wkA209IensP+xkYL8nAibZP6ufxs5RVwQ4HHrJ9b14+FfgSsyvYcfnn7cDCtl8CXpI0TdLiwCvA4ZK2AGYCywHNWmubAWfkD/Gn8of0BsCLwITai5f0AFBLFHo7sFV+vi2wWq0yARaVtEiT8/S237geKlfIXcS1BUlbAjfbfiiv+iCwVq2lCCwGrARMAP4gaV7g77Yn5e1vABfk57cAH+jhvL05p1ml14v3Ak/YngBg+8X8Wm4Avi3pncC5tu/rR8zNgPNsv5JjnQtszuy/i56cm3/eAoxq5UT5S8x+AL/55Dp8duMV+1HMEMLc1p/p6ur/f2cn1VWASLoUeEeTQ7/djyJtavvxXIH+S9Ldtq/u86gmyqpgX+lj++v558y657XleYCxwEhgPdtvSnqY1MJt1NtvojFu/Tlrr3MYsHFjBVlXkdLCfn291kb1+ws40PYljTvlLxcfBf4k6UjbpwFv2q51jMyg+e/rDlKrenIL52+FSN/g5mD7L5JuymW8RNJnbV/eUI61JQ2rdRE3xGxmOnNepmj8ndd+hz299rfI/9lOAnjj6J2H8PjEEIaG/nT91v//7mF7jwM1JT0laZnc+lwGeLqHGI/nn09LOo/UC3k1qVHX5/H1yroGOwIYJek9eXlv4Kp+HL8Y8HSuXLcCVsjrXwLqW5hXk66/DZc0EtgCuLkf5xkPzOpalDSmh/P0tF9RlwBfyC1VJK0saSFJK5Be/++A3wPr9iPmkcC3JK2cYw6T9NUWjmt8zTV3A8tK2iDHW0TSPJLeDTxo+9ekluda9QfZfgCYCPxA+ZuIpJWUrqdfDewoaUFJCwE7AdcATwFvVxqVPj/wsQLlDiEMQnNxkNM4YJ/8fB/gH4075M/jRWrPSb2OU1s9vlFZFew00jW2cyTdTmo1ntCP408H1lcaEj2W9CGP7edI1ymnSjoSOA+YQmqtXQ583faT/TjPQfk8UyTdSbquCHA+sFMeOLN5L/sVdTJwJ3BrHtxzIqlltiUwSdJtwM6ka8UtsT2FdH35DEl3kf4Ylmnh0DOBQ5QGpo2ui/cGsBtwrKTJwL9IX6B2A6bma8yr0HyQ12dJ3TP357+D3wGP276VdO39ZuAm0vXx22y/SRq0dBOpK/zuFsp9Ban7PgY5hTAEzMUK9gjgA5LuI11uOwJA0rKSLsz7LA1cmz/7bgb+afvi3o7vjWb3QIYwtFTRRXzNcT1dfi9m64s373unNpzy3qJ3Xr3Vp856V+kxAZ48p88et7Zcd/H0SuJCzAlL75ftWvLdCX9p+f/pjzbYc1BNHhuZnEIIIQyYSJUYQgghVGAop0qMCjaEEMKAidl0QgghhApEBRtCCCFUICrYEAaheT/18dJjrnvnX0qPCXDkq8tXEnfnDy3Y9079NHz7T5YeE+Btd/Xnzr7WrfNCNR9zCy0xncd3L3/eiWXPvLDvnYaQuAYbQgghVODNGYPqzpt+iQo2hBDCgJkZFWwIIYRQvpkzo4INIYQQShcVbAghhFCBqGBDCCGECkx/s6w5ZzpPVLAhhBAGzFBuwfb61SHP0zkpP56U9J+65fnKKoSkxSV9sax4vZxnS0mbFIwxStJr+T24U9JptfldeznmYEkL1i1/q0gZmsTfTtJESXdJulvSL3rYb3tJh+bnO0parW7bDyX1OFlxi+VYWNKJkh6QdIekqyW9r0jMJucYI6n8mw9DCANi5szWH4NNrxWs7edsj7E9hjS/69G15TxvaFkWByqvYEnzrvargpXUrJX/QH5P1gTeCezaR5iDgfo7/kurYCWtARwH7GV7VWAN4MEm+81je5zt2hyGOwKzKljb37NddG6zk4HngZVsrw7sCyxVMGajMUBUsCEMETNnqOXHYNPvzm9J2+RJum+X9AdJ8+f1D0s6XNINuTW1rqRLcmtm/7zPwpIuk3RrPn6HHPYIYHRuFR6p5Mg80frttYm1cwv0KklnS7pX0hGSxkq6Oe83Ou83UtLfJE3Ij00ljSJNnP6V2sTqzfbLxx8m6SRJ42k+sTgAtmeQJuVdrqf3RtJBwLLAFZKukHQEsEAuw+n5uL3ya5iUW4DD8/qXJf1E0mRJN0paukkxvg78xHZtkvrptn+bjz9F0lGSrgB+JmlfScflVvz2wJH5nKPzvrvk4zaQdH0+782SFpG0el0Zp0haqeHvYjTwPuA7tmfmsjxo+595+1fz73OqpIPzulFKE8/XYnxN0mH5+ZWSfpbPeW/+fc1HmqB9N8WE6yEMCTNnquXHYNPfa7AjgFOAbWzfK+k04AvAMXn7o7Y3lnR03m/TfMwdpBbwNGAn2y9KWgq4UdI44FBgjdwqRNLOpJbK2qQW0ARJV+dzrA2sSmopPQicbHtDSV8GDiS1Fn9Fam1fK2l54BLbq0o6AXjZ9i/yef7SuF+ODbAesJntHmfYljSCVKl8OT9/y3tj+xhJXwW2sv1sPu6Aute6KrAbsKntNyX9FhhLqtgXAm60/W1JPwc+B/y4oRhrAL/sqYzAysC2tmdI2hfA9vX5fb/A9l9zOWqvaT7gLGA32xMkLQq8Rvpy8ivbp+d9hjecZ3VgUv7S0fg+rQd8Or9XAm6SdBXw317KDTBP/t1+BPi+7W0lfQ9Y3/YBfRwbQhgEBmPF2ar+tmCHAw/ZvjcvnwpsUbd9XP55O3CT7ZdsPwNMk7Q46cP1cElTgEtJLb9mrbLNgDNsz7D9FHAVsEHeNsH2E7ZfBx4Axtedc1R+vi1wnKRJuUyLSlqkyXl6229cL5Xr6HzMc8C/bU8B3tvHe9OTbUiV+YQccxvg3XnbG8AF+fktda+vP85pVun14r3AE7YnANh+0fZ04AbgW5K+AazQ2xePJjYDzrP9iu2XgXOBzVs47tz8s+XXLmk/pR6UiSedemU/ihhCGAhvvjms5cdg098W7Ct9bH89/5xZ97y2PA+pZTYSWC+31h4mtXAb9faVpjFu/Tlrr2cYsHFjJVBrpdXpbb/eXusDtsdIWga4UtL2wL972b83Ak61/c0m2960XUuFPYPmv687SBX05B7i9/U7a1aet6Tftv0XSTcBHwUukfRZ25c3lGNtScNqXcQNMZuZzpxf8hr/Fmq/255e+1vYPgk4CcDPnTKE04iHMDTMrRaspCVJvXOjgIeBXW3/t2Gf9+Z9at4NfC/3RB5G6kV8Jm/7lu1eZ2bo71eCEcAoSe/Jy3uTWpetWgx4OleuWwEr5PUvAfUtzKtJ19mGSxpJagne3I/zjAdmdSFKGtPDeXraryW2nyB1b38TuJue35vG876p2SOPLwN2kfT2XIYlJa1A644ktSxXzscPy13SfWksU83dwLKSNsjxFpE0j6R3Aw/a/jWptb9W/UG2HwAmAj9Q/oYiaSWl6+xXAztKWlDSQsBOwDXAU8DblUarzw98rEC5QwiD0Fy8BnsocJntlUifu4c27mD7nrqBvesBrwLn1e1SP9C3z2mP+lvBTiNdSztH0u2kVmN/5pg6HVhf0kRSa7Y2MOc54Lo8AOZI0guaQmqVXQ583faT/TjPQfk8UyTdSbp+CHA+sFMeILN5L/v1x99JI4Q3oOf35iTgojzYqLY8RdLptu8EvgOMz13n/wKWafXkuXv6YOAMSXcBU1s8/kzgEKVBWaPr4r1BuiZ8rKTJuTwj8rqpuRt7FZoP/vos8A7g/vwe/A543PatpOvTNwM3ka6b32b7TdKgpZtIXeF3t1DuK4DVYpBTCEPDXBxFvAPp0h3554597L8NqbfykXZPqNk9kCEMLVV0Ef/v0Grmg/3dAftUEnfnQ88sPebov7fzPbRv035WzXywj15XTRfkQktMryTuIJsPtvCbu/VJF7X8//Ty/bZr+3yS/md78brl/9peopf9/wDcavu4vHwY6dbDF0m9df/X2MXcaPBdNQ4hhDBkTJ8+rOVH/SDG/NivPpakS+tuB6x/7NDT+ZvJd0psD5xTt/p4YDTpDpcn6P3uDSBSJYYQQhhA/en6rR/E2MP2HrPRSXpK0jK2n8gDVJ/u5VTbkVqvT9XFnvVc0u+YfYdHj6IFG0IIYcDMxUFO44DatZh9gH/0su8ewBn1K3KlXLMTabxLr6IFG0IIYcDMxUQTRwBnS/oM6bbKTwJIWpY08PIjeXlB4APA5xuO/3m+08Sk23wat79FVLAhhBAGzNzKMZzvVtmmyfrHqctvbvtV4G1N9tu7v+eMUcRhyPJ/fl3+H/cb00oPCaAVN+h7pzb48TvKDzqsou/lb7xaTVxVdCVs3mY5cgoaXl2bRyM/W0nYogHW//nlLf8/nfj1rQdVXsVowYYQQhgww6YPwnnoWhQVbAghhAGjGUO3FzUq2BBCCANm2MyoYEMIIYTSRQUbQgghVEBRwYYQQgjlGxbXYEMIIYTyzROjiEMIIYTyDeUu4shFPEhI2kmSJa1S8XmulLR+D9u+LOmYuuUTJV1at3ygpF9LWl/Sr6ssZwhhaBg20y0/BpuoYAePPYBrgd3LCiipvz0Y1wOb1C2PARaTNDwvbwJcZ3ui7YNKKGKP2ih7CKEDDZvhlh+DTVSwg4CkhYFNgc8Au0saLulBJYtLmilpi7zvNZLeI2lDSddLui3/fG/evq+kcySdD4yXtICkMyVNkXQWsEAvRbkNWDkfsxjwKjAJWDNv3wS4XtKWki7I5ztM0h9yy/hBSQfl9aMk3SXpd5LukDRe0gJ522hJF0u6Jb+eVfL6UyQdJekK4Gc9vFez5os86c/Xt/+mhxDmiqHcgo1WwOCwI3Cx7XslPQ+sDdwLrAasCNwCbC7pJuCdtu+XtCiwhe3pkrYFDgd2zvE2Btay/bykrwKv2l5L0lrArT0VIseaBGxAqohvAu4DNpH0NCm39aOSRjccugqwFbAIcI+k4/P6lYA9bH9O0tm5fH8mzfe4v+37JL0P+C2wdT5mZWBb2zN6KOOs+SIryUUcQijV8BjkFAbYHsAx+fmZefkaYAtSBftT4HPAVcCEvN9iwKmSViJNrzRvXbx/2X4+P98C+DWA7SmSpvRRlutILdUFgBtIFey3gGdIXcjN/NP268DruSJeOq9/yPak/PwWYFRurW8CnCPNyus9f12sc3qqXEMIg89g7PptVVSwHU7S20ittzUkGRhOqjD3Js1HuCzwPeAQYEvg6nzoj4ArbO8kaRRwZV3YVxpO05+/8OvzeUcAvyFVrKvln9f1cMzrdc9nMPvvrnH9AqTLFv+zPaaHWI1lDyEMYjGKOAykXYDTbK9ge5TtdwEP5W2bADNtTyNdC/08qWULqQX7n/x8317iXw2MBZC0BrBWH+W5HtgIGGn7aaf5Dp8BdqDnFmzLbL8IPCSpNhmyJK1dNG4IoTMNmzmz5cdgExVs59sDOK9h3d9Io4kfBW7M664hXeO8PS//HPippOtIrd6eHA8snLuGvw7c3FthbP+XVKHWTzR6A/B2YHJfL6ZFY4HPSJqcz7NDSXFDCB1mKA9yignXw5AVE67HhOtATLiedeqE65/c+6yW/5+e86fdYsL1EEIIoRVDeRRxdBGHpiTdJGlSw2PNvo8MIYTWza0uYkmfzPfcz+wpW13e78OS7pF0v6RD69YvKelfku7LP5fo87UVKnEYsmy/z/aYhsftfR8ZQgitm4uZnKYCn2D2nRZvkbPS/QbYjnR3xB6SVsubDwUus70ScFle7v21FS1xCCGE0C7NdMuPImzfZfuePnbbELjf9oO23yDlHagNstwBODU/P5WUAKhXcQ02DFla7qCWB0RI2i9ngSrv/BXE7G9cLbtVJXH7Y6i+t0M1blVl7cmfzt2rX/9Pgf3qVp1UclmXI92dUfMY8L78fGnbTwDYfkLS2/sKFi3YEJL9+t6lI2JG3OpiRtzqYpbC9km21697zFG5SrpU0tQmj1Zv9WtW2bfddI4WbAghhCHB9rYFQzwGvKtu+Z3A4/n5U5KWya3XZYCn+woWLdgQQgghmQCsJGlFSfOREvqMy9vGAfvk5/sA/+grWFSwISRVXHOq6jpWxB1cZR1scefa9de5SdJOkh4jzSb2T0mX5PXLSroQ0oxhwAHAJcBdwNm2a9lajgA+IOk+4AN5ufdzRianEEIIoXzRgg0hhBAqEBVsCCGEUIGoYEOogKRhkhYd6HL0RtKKrazrBJLmb2VdG3E3bWVdG3E/2cq6TokbqhHXYENXkvTrJqtfACba7nN0YA8x/wLsT5o8/hbSnLxH2T6y7YKmuOs2Wf0C8EgelNFu3Fttr9uw7hbb67UbM8dYssnql2y/WSBms7K+ZV3EbSvmcOAI24cUKVt4q7gPNnSrEcAqwDl5eWfS3LOfkbSV7YPbiLma7RcljQUuBL5BqmgLVbDAb4F1gSmkG+HXyM/fJml/2+P7E0zSKsDqwGKSPlG3aVHS+1LUraR7Cf+by7s48ISkp4HP2b6lH2V9Bym7zgKS1mF2IoBFgQXbLaCkjYFNgJGSvlq3aVF6nz+5r7jbAR8Blmv4ErcoUOTLUCVxAWzPkLSeJDlaXKWKCjZ0q/cAW9dagJKOB8aTht+3O6nBvJLmJeUoPc72m5LK+MB6GPhM7XaBnHz8EOBHwLmkcvfHe4GPkSq+j9etfwn4XMGyAlwMnGe7dhvEB4EPA2eTviy8r5djG30I2Jd0w/9RDWX9VoEyzgcsTPoMXKRu/YvALgXiPg5MBLYnfbmqeQn4SgfGrbkN+Iekc4BXaittn1tC7K4VXcShK0m6B9jQ9gt5eTHgJturSLrN9jptxDyI1GqdDHwUWB74s+3NC5Z1ku0xzdY129aPuBvbvqFI2XqIO9H2+s3WtVteSTvb/ltphZwddwXbj1QQd94iXeIDEPePTVbb9v8r+1zdJFqwoVv9HJgk6UpSt+MWwOGSFgIubSeg7V8D9d13j0hqPdt+z+7JLewz8/JuwL15kE+RD9v7JX0LGEXdZ0EJH6rPS/oGc5b3v/laX7uza18gaU/eWtYfFikoML+kk5rE3bpg3A0lHQaskOMqhfW7OzGu7U8XLFdoIlqwoWvlfKIbkj6kbrb9eB+H9BVvaeBwYFnb2+Wu3I1t/75g3AWALwKb5bJeS+pqnQYsaPvlNuNeD1xD6nKcUVtftKUoaSng+w3l/QFpYNbytu9vI+bF+fjGsv6yYFknAyc0idvydeIe4t5N6rptjPtch8ZdGTieNGPMGpLWAra3/eMicbtdVLCha0lajtktAQBs9zgZcwvxLgL+CHzb9tqS5gFus71m4cJWoEj38twmaartNSqIW3jUdA9xb7Ldn2vNAx33KtJ1/RNrl0eqes+7SXQRh64k6Wekrss7mN1taaDtChZYyvbZkr4JKa+ppBl9HdSXfF/mYbz1y0DR7sYLJH3E9oUF48wht4a+RrndrtdLWtN2uwPQenK+pC8C5wGv11bafr5g3CskHUkahFYf99YOjbug7ZulOWZrKzQ6OUQFG7rXjsB7bb/e14798Iqkt5Hnj5S0Ealbs6jf06RbsARfBr4l6XXStdza9byiCTLOIXW7nkx55d0M2FfSQ6SKpVbWtQrGrc2OUn8PqIGiX15qrcz6wV4Gil7brSrus5JGM/tvdxfgiYIxu150EYeulLtzP9nu9cseYq4LHEu6T3UqMDKfY3LBuJV0C1alim5XSSs0W1/FCOBuJOndpFl0NiHdv/wQsJfthweyXINdVLChK0n6G7A2cBlzdrUdVCDm/KQW23tJLax7gGFFW8mSjiAlPyi1W1DSFs3WF7kOneMeRpqMurRuV0nLN1tv+9/txsxxP9VD3NMKxv1eD3ELjXquKm5d/IVIf7MvlRGv20UXcehW45g9kXJZbsgp62rzRyLpVlIWpiKq6has7xYdQRpRfUsJcavodv1njiFSWVckfYFZvUBMgA3qno8AtiFloipUwVKXrCHH/RhpftGiKombvxzuTL5uXrsWW1bF3a2iBRtCQXXp/P4M7Mmc6fxOsL3KQJWtPyS9C/i57T0Guix9yd3xn7f9+ZLjLgb8yfb2JcedHxhn+0OdGLeq26C6XbRgQ1eRdLbtXSXdTh7QUa/NQTOVpPOTtJftPzfkyp3F9lHN1hfwGOn6cVskbW378ob8xrOUmXbP9q2SNuh7z357FVipgrgLUnzgVJVx32n7wyXECXWigg3d5sv558fKCmj7VODUCtL5LZR/LtJkW+GuJ0nH1sUZBowhpXls1/uBy5kzv3GNSdeQ29LwJWMYqdv9mXbj1cU9n9nvwXBgVVLO5KJx67/ADScNeCvc3VpVXKq7DaqrRRdx6EqSfmb7G32t62fMxYHvkdIuAlwF/LCW77hA3E1tX9fXujbi7lO3OB14uGjMHHdF2w/1ta6fMb9ftzidNAHC32xPazdmjvv+hriP2H6sSMwct37U83TgKReYWnAuxL2TNAFG2bdBdbWoYENXUvN5NacU+UDJI5OnAqfmVXsDa9tu2mXaj7iVzC2a48wHrJwX7ykjkXwP5S3l1h1Ji5A++Mu8vWppZg92utn20yXFXRuoTfRwte0pnRo3boOqRnQRh64i6QukvL7vllT/wbQIULT1Ntr2znXLP5A0qd1gqmjO0rr4W5K+DDxMarG8S9I+7d6mowrnmZW0BvAnYMm8/Cywj+2pBePuSpqv90rSe3CspENs/7Vg3C+Tpv6rdYufLukk28d2YlzbjyhNxrA0US+UJt7I0G3+AlwE/BQ4tG79SyWkx3tN0ma2r4VZKQ5fKxCvqjlLa34JfND2PTArxeEZQLstzSrnmT0J+KrtK2DWl4NaYoQivg1sUGu1ShpJmk2pUAULfAZ4n+1XctyfATeQEpF0XFxJB5ImaHiKOVOHRhdxAVHBhq6Sr4e+AOwBIOntpNbVwpIWLpi4YH/gtHyrB6SMOPv0sn9fZb0KuErSKRV11c1bq1zz+e5VmjC+Lbb/QZq0u4p5ZheqVa75XFfmpAhFDWvoEn6ONIiqKDFnmsgZzL59qxPjfpmUOrTQrDxhTlHBhq4k6eOkW2qWJWUdWoF0w36/ExfkASKnA2fmWXQWBbD9YknFfTUneF+duq7WgsnzASZK+j2p6xVgL9J9kEXdJulLvLW8ReaZfVDSd5mzrG0PmqpzsaRLSC13SBNAXFRC3D8CN0k6Ly/vSMop3alxH6WcvNmhTgxyCl1JaR7QrYFLba+jNDH6Hrb3ayPW2sDuwK7As6QP67NdcH7ZuvjjgbNIM9TsT2oVP1NkxHOOOz/wJWbP23oVcHwJqR3PAe4mJd34ITAWuMv2l3s9sPeYS5DmlN0sr7oa+IHt/xYpa479CWa/B1fbPq+PQ1qNu25D3Ns6NW7+ovVeUsas+vSWZd9r3VWigg1dSdJE2+vninYd2zMl3Wx7w4JxNyK1gnYG7gfOsP27gjFvsb1e/ShnSVfZfn9fx/YQbyQw0vadDevXIN32Uej+Ukm35S8tU2yvlbudL2mnxS1pBLBIY5nyyN8X2r1NR9J7SJOLN97+tAXwH9sPtBl3A9K0hRc1rN8+x22rh6CquHVxvt9sve0fFInb7cq41hDCYPQ/SQuTWkKnS/oVJcx/aftG218BPgUsARxXNCZpKjmAJyR9VNI6pKxR7TqWlKCg0XLArwrEramV93+50l6MlOO2Hb9m9i0p9bYFjm4zJsAxpMFXjV7N29p1JM1zA9+Zt3VaXCBVpM0eReN2u2jBhq6UB8hMI3WzjSVVAn8uMpI4tzL2ILVeHwbOBM6x/WzBsn4MuAZ4F6lyXBQ4zPb5bca7w3bTa82SptpuO11ijvFZ4G/AmsAppJHQ37V9Yhux7rS9Wg/benwdLcTt8XVKut32mm3G7fFYSZNtr91hcY+xfbDmzGg1i0vOydxtYpBT6Eq12xyyU/M9nD+jjdtJJB1O6hb+L6lS3bSMbEA1ti/IT18Atsrn3LRAyN5GCrc9irjG9sn56dXkPLk9JTJoQW8jZIv0wPV2X+4CBeL2dmyRUc9Vxa0NGvtFgRihB9FFHLqKpLUkjZc0VdKPJS2dMzBdSupua8frwHa217f9i7IqV0nDJe0h6Wu5qxVJH5N0PcW6nu+T9JEm59sOeLBAXCRtLGmXfPtT7f3+C3BtmyGflvSW6+K5t6DIteIJkt7yZUrSZyg2kvpSST+RNMcXA0k/IOVp7rS4e0v6JHC/7asaHwXiBqKLOHQZSTcBx5Nuzv8w8HVS8onvFs1rWzZJp5C6hW8mzQn7CLAxcKjtvxeIuzJwAXA9syuT9XPsj9m+t824R5ISTUwi5bW9gJQ163DgxHbe31y5nk3qaq4v66eA3W3f1GZZlyZNCP9GQ9z5gJ1sP9lm3IWAk0lz607Kq9cGJgKfbTfFY4VxDyAl66gl7LielNHsemCy7Zk9HRv6FhVs6CqSJtkeU7f8KDDK9oyejxoYkqYCa+URziNItwC9p90P/4bY85Nuo6ldh7wD+EuRLxn5fuB1bU/Lt9U8Tir/fQXL+nbS7UT1ZT2ujJzB+fasWXFtF2kN1sd9N7Pvqb7DdqGegarj5tjLAJuSKtvtgbfbXrSs+N0oKtjQVSTdTRqIVOtqO526SdJt3zpARXsLNSTNb1zuNGpI6N/4ZSZ0ptztvCapYt0UWI3U/X5DjCQuJirY0FUkXdHLZrebHUnSPMAM25b0LlKX7gNFkgBIepV0Ly2kLwCj83JHTiUm6X+kgU01W9Qvx4jUziPpX6RR6ZOAG4EbbTe7HSi0IUYRh65ie6uyY+bBMj8DXpb0I+AQ4FZgHUl/sP2zNkOvWlYZ55IdGpZ/OSClCP3xIOla7kqkPMzPSnqm6K1lIYkWbAgFSbqDlLpuEVIygBVsPytpQWBCu/dqVklparJTbe810GXpD0kLNdxiVTge8Fq+zr0ysApwkQvOiytpNPCY7deVZv5ZCzjN9v86NO6iwEakbuKNSIlIptpue7KKELfphFCGN2z/12kmnvtr3/5tv0oapdpx8qCukUoTrnc8SZvkQVR35eW1Jf22hNBXAyMkLQdcBnyaNGK5qL8BM3JKxt8DK5JGq3dq3NdJWaxey8/fCXTs9f7BIrqIQyhugZy+cBgwX36u/Cg00XjFHgaukzQOmNUq7NAE70cDHwLGAdienPMGFyXbr+b7X4+1/XNJZSTln2l7uqSdgGNsH9uJcSUdTWq1rkS6Dns9cCJpMvv/lVDerhYVbOg6FQxIeoI09R3Ak3XPa8ttq7gr9/H8GMacE7qXouzuXNuPNuRZKOPWKknamJQu8zN5XRmfi29K2oM081Ft8vnCWbIqiPsQaST9bb3dqiZpddt3FDhPV4oKNnSVKgYkVTFwqi72DEkjJc1nu9Tu5totGBVc19yElBRhYWB5pen8Pm/7iwXCPprjOndrH0Tz5Pf9dTDwTeA823fk+0x7G2neqk+Tphb8ie2HJK0I/LnT4tr+dYu7/onoMu63GOQUukpVA5IkvY10P+0qedVdpMQNbU8eUBf7RNKHW6ldubnl9ntgYdtlVYS1bFm7AONsr5PXFZpEQNJSpJl+tiV1vY8Hvmz7uSJlrUoePDWt1irMPRHz5+vyHRe3hfPeVvtdhtbFIKfQbUofkCRpVWAqsB5wL3AfsAEwVWkSgaIeJ6UdrHXl1h5FHUO6rvkcpOuapHtXC7P9aMOqQt25tp+1Pdb20rbfbnuvMipXSf+StHjd8hKSLikalzRgqj5B/wKkfNedGrcv0RJrQ3QRh25TxYCkH5FaU2fXr5S0M/AT0vR1bauqKzfHruK6ZunduZJOJb3H/8vLSwC/tP3/CpZ1ZP1gHtv/zXmKixpRnx/Y9su5l6RT44YKRAs2dJvagKRfMHtA0i/rltuxZmPlCmD7b8zOc9s2pRlqqrhFZY6KUNLXKOe65v6k3MHLAY8BY/JyEWs1VoRAGV2WMyQtX1tQmlavjAT3r0iqT3O5HukWmE6N25eOvN2s00ULNnSVigYk9daqLKPFeQzV3KKyP+m6Zq0iHE+a/aaQ3O0+tmicBsMkLZErViQtSTmfX98GrpVUm5ptC2C/EuIeDJwj6fG8vAxpzuCOiltfWTdTy81te6N2z9HNooINXaeCAUlvl/TVZqciZcQprKKu3PfanqMiVJrI/boiQSvqzv0lcL2kv+blT5K63wuxfXGuZDYi/b6+UkaaQNsT8vX39+a4dxfNDlVR3Fo6yxGk6fom57hrATeRBgSGNkUFG7pKHpB0OXAJcBvpw2QD4FuStrZ9dxthf0fPg45Obqugc6rqFpVjeeutF83W9ddbunPzte622T5N0i3AVqTf2Sds39luPEmr2L67rgVXaxEuL2l5tzmrUv4bulzSJxo2rSQJ2+d2Utxaj46kM4H9bN+el9cAvtZOzDBbVLCh25Q+IMnVT+lValduvj1nE1KqxPqW96LA8ALlrKmqO/du4L+1WLki/Hebsb5K6gpuNiGBgbZmVQLeT/oC9/Em2wy0VRFWGLdmlVrlCmB7qqQxBWN2vbgPNnQVSffYfm9/t/URs9eb9W0f1N+YDfE3tX1dX+v6Ee/9wJakivuEuk0vAee7+ATpnyIlb5ijO9f2nwrEPBD4PvAUqXu8lCn7JI1wwyTzzda1EXdF2w/1ta6D4p4JvExKWmFgL9L90XsUidvtooINXUW9TFre27Y+Yr5Bug/2bFJX4xwXS22f2k5ZeytXu2VtiLGC7UequP1H0urM7s69rEh3bo53P/C+shNLVPjeNos7x4T0HRZ3BPAFZt8HfTVwfNEvGt0uuohDt6liQNIypFbabsB04Czgb7Uu0nbNha7cZSVdRLkpDWvK7M4FeBR4oYRykcvzDlKXe+2+6NqXokWBtu8rzQOQVgcWa7heuigFJn6oKm6OPRy4wPa2pEkVQkmigg3dpvQBSblVdQJwgtK0Z3sAd0j6RpFuUWA+UuU3D3OW+UVSKsKijqGC23966s4ljUxt14PAlZL+SZpODSiULvJDwL6kadnqY7wEfKvNmJBG934MWJw5r5e+BHyuA+PW8l2/Kmkx26V9iQnRRRxCafKI1D2ADwC3kG5NKdQ1muNW0pUr6Sbb71NdnllJk22vXTBu6d25kr7fbH3RAWaSds4JQUolaWPbNwyiuGeTblX6F3Pmuy40fqDbRQs2dJUqBiRJ+gGpdXEXcCbwTdvT2ythU1V15VZ1+0+p3blQfrpISXvZ/jMwqtklg3ZbxpK+bvvnwJ5K08o1xm2rwqoqbp1/5kcoUVSwodvsTy8Dktr0XVIX5tr5cXhOClHKSFfmbianoikNofzu3Dlm/qGcLxkL5Z8Lt1umHtS+oEwcJHGB4gPxQnNRwYZuU8WApBXLKFhvqsjkVFFKQ4B/58d8+VGGYyjxS4btE/Pgnhdtlzawx/b5Oe4atg/p9Lg1klYCfgqsRt2gKdvvLvtc3SQq2NBVqhiQZPuRMsvYRCVduUqTdR8IjKLus8D29kXilt2dWxe31C8ZeXDP9pQ8cjbHLXTbzNyMm/2RNDDtaNLtVZ+mnN6drhYVbOhKDQOSLiINSmo31ks0ny+z1kW8aLuxs6q6cv9O6nY9n3JmkAEq6c6F6q4XXy/pOFJPRv3gnrZSJda5TdI44JyGuEUzLlUVdwHbl0lS/sJ4mKRrSJVuaFOMIg5dpcmApItLHpA0aNRGEVcRl3Qb0bi60clTbbc9dZ+kpUhfMrYlfXEZT0p5WWiksqQrmqy27XZTJdbi/rGHuIXmr60w7nXA5qTsW5cD/wGOaCezWZgtKtjQVSTNJA3Cqc2hWfsPUNaApNJV1ZUraU9gJVJlVT8YqVDrrarbf0J1JG1A+tK5OClf96LAkbZvHMhyDXbRRRy6TeUDkirwdyroygXWBPYmJbavxS2S6L6mtO7c2u0pko6lSTd8gdtevgq8YPv3DesPBIbbPqbNuD8HHrR9QsP6rwDvsP2NTopb5znbL5PyEX+6YKyQRQs2hA5XYVfu3aSp5d4oOW5p3bmSPp5H0O7TbHu7t5dImgqs2/jaJc0PTGi3J0PSnaSRvjMb1g8DprTbTV5V3Lo4V5Ou8U8g5SG+xnWz64T2RAs2dJW5MCCpCr/KmYxK7colTa69OPB0wThzKPP2H9vn559l36fpZl8sbL+uhqHKbcR9Sy+D7ZkdGrcWZ4vc27ABaaalf0pa2PaSRWN3s6hgQ1ex3VMe4k5WVVfu0sDdkiYwZ8Xd1rXdKrpzJZ3fLFZdzLavQ0ta2vZTjevajZe9KmklN0z5l+8zfa2HYwYybi3OZqRBTpuTvnRdAFxTNG63iwo2hM63E/DusrtyKf8WjCqyDf0i//wE8A7SfKWQbrF6uEDcI0mttP8Daj0B6wE/rztnO74HXCTpx8y+9Wt90vy4B3dg3JqrSL+3nwIXVvC31pXiGmwIHU7SWcCBtkvtys2xlyZ1CwLcXMU5yiDpattb9LWunzG3Aw4F1iC1ku8g3ZpyUcGyrgEckuNCSs35i6LXNKuKm2MvDmxKmg92A1JPyQ22v1s0djeLCjaEDifpStJUb6V05dbF3ZXUkruSdA16c+AQ239tM16V3bl3AR+1/WBeXpHU0lq13ZhhTpJWBd5P+jvYBPi37fcPbKkGt+giDqHzVZVN59vABrVWq6SRwKWkZAPtqKo7F+ArpAkEHszLo4DPF4wZMkkPAPeQrrueAJwI7D6ghRoCogUbwiBQRVeupNttr1m3PAyYXL+uzbild+fmGPMDq+TFu22/3tv+oXX5d7826cvQrsBDpEkwjhvQgg1y0YINocM16co9VlLbXbl1LpZ0CXBGXt6NlJe5qJGS3t3QnTuySEBJn2pYtbYkbJ9WJG63k7QyqaW6B/AcKSezbG81oAUbIqIFG0KHkzQZ+EBjV24ZqQclfQLYjFRxX237vBJifhg4iZSSEnJ3ru1LCsQ8tm5xBLANcKvtXdqNmeO+DTiMNMDHwLXAD0vIcTwS+BxvTW9ZNGdwqXFz6tBrgM/Yvj+ve9AxTV0pogUbQucb1tAl/BwwrGhQST/LKfbObbKubbYvzvdnltada/vA+mVJiwFtTS/Y4ExS5qKd8/JYUitu24Jx/0GquC6lhLl7K4y7M6kFe4Wki0nvR0xTV5JowYbQ4SQdSRpFXN+Ve7vtrxeMe6vtdRvWTSk64UGT7lyAUrtzJc1LShFYaBSxpFtsr9ewbqLt9QvGnWR7TJEYcznuQsCOpK7irYFTgfNsjy/7XN0kWrAhdDjbhzR05Z5UpCtX0heALwLvljSlbtMiwHWFCptsUPd8Vncu0HYF23AL0DBgNdKcqEVdIWl34Oy8vAvwzxLiXiDpI7YvLCFW5XFtvwKcDpwuaUngk6R7hKOCLSBasCF0uGbdtkW6cnP36hKkrD2H1m16yfbz7Ze01/P9qeB9sPX3Y04HHrH9WAllewlYiNkpKIcxeyLzfuemrst1rRz3deBNSsp1XVfeN/Kjk3Nod72oYEPocFV15dbFejuppQmA7X+XEbcufinduQ0xNwX2tP2lsmKGULboIg6hQ1XdlSvp48BRwLKkGXVWIOUTXr1g3Eq6cyWNAfZk9n2a5/Z6QOtxtyelCAS40vYFJcTcFJhk+xVJewHrAscU/fKSZ84ZC6xo+0eS3gUsY/vmomUO5YsWbAgdququ3Hz7z9akW37WkbQVsIft/QrGLa07t4f7NL9me4UiZayLfwTpmvHpedUewC22D+35qJbiTiElbliLNNr598AniqYelHQ8qTt7a9urSloCGG97gz4ODQMgKtgQBomyu3Jro2VzRbtOnlv0ZtsbFi1rw3na7s6t+j7NXBGOqc21Kmk4cFsJI6lvtb2upO8B/7H9+2Zd/QXi3mZ7nbxuchn3RIfyRRdxCB2uqq5c4H+SFibdB3q6pKdJLc7CSuzOnRv3aS4O1HoEFisp5kuSvkmax3fzXHHPW0LcN3Msw6zEE2+ZiD10hsI3q4cQKvdjYCPgXtsrkm57KeN2mh1Ik3V/BbgYeAD4eLvBJK0s6Xt55pvjgEfJafdsH9vH4U3ZPs/2bqSkFVfmsi4t6XhJHyxQ1trtJz8FbpN0iqRTSXOtHt5u3Dq7kUYQ/z/bTwLLkdJdFvVr4Dzg7ZJ+Qso89dMS4oYKRBdxCB2u6q5cSYsyZ9q9tq7vzq20e3X3ae5me+s2Y9R3sS5Dug4r4KZcIZZRzhWAlWxfKmlBYLjtl0qIuwrpS5aAy2zf1cchYYBEF3EIna+SrlxJnwd+SGrFziTfUwm0WyHOlbR7+QvAifnRrsVy8o5Gm+RJBAqNUJb0OWA/YElgNKkFewKpYiwS90+29wbubrIudJhowYbQ4XIau2mkymos6Trh6SUkpL8P2Nj2s8VLOUfcjk+7J+k5Ul7fZl8AXEJS/knAhqQWca2lPMf0gG3GnWOgVL4ee7vt1YrEDdWIFmwIHS6nsat15Z5fYugHgFdLjAcMmrR7jxStRPvwuu030m2rIGkeZt8b3G95wNS3gAUkvVhbTcrm9LuCZQ0ViRZsCB2up67cotc2Ja0D/BG4iTQgB1Lgg4rEHQzqr8FWFP/nwP+ATwEHkhKG3Gn72wXj/tT2N4uXMMwNUcGG0OEq7Mq9mTQK9XbqbvWwfWqZ5+lEktawPbXC+AI+C3yQ9IXoEuBkF/zAlXSZ7W36Whc6Q3QRh9D5KunKBabb/moFcTtexZXrMFLu5TUoqftW0ghSkv+lcvam2rXjRUn3R4cOFBVsCJ3vm8D1ksruyr1C0n6k67r1cUufUaeb5NuoJktavsSJEz4PHEyqTG+tW/8i8JuSzhFKFl3EIXS4qrpyJT3UZHXha7uDRR6Be6rtvSqIfTnp3tqbmT39HUWm7MtxD2w3aUeY+6KCDaHDSbre9iYDXY6hSNIlwMdtv1FSvHlsT2+Y8GAW21cVjD8fsD91s/8AJ9p+s0jcUI2oYEPocDkl3iOU3JWb52n9Al38YS3pRNJUcuOYs6V5VJvxZt2nKulY2weWUtDZ8U8m5TSu9V7sDcyw/dkyzxPKEddgQ+h8e+af9bdnFMm4VHM86cP6t3l577yumz6sH8+PYaR5douqT1yxaQnxGm3QMHPO5TmFZuhAUcGG0OFygv/S1LoxiQ9rbP8AUvapWkKPoiFLiNGbGZJG234AQNK7gRkVnzO0KSrYEDpcBV25N5O6Rbv+w1rSxqTJ0BcGlpe0NvB5219sM+QqeY5ZAaPzc5idHKTQPLPAIaTR3w/mmCsAny4YM1QkKtgQOl/ZXbm1bsyvMfvDGmAU3fdhfQzwIdI1WGxPlrRFr0f0btUyCtUT25dJWgl4L+n3eDewfpXnDO2LCjaEDlVhV+5ISbUEEycCw0kDfEYA6wBXFIg96Nh+tJYzOGu7FW/7keIleqt8S9GupFl5LrI9RdLHSF+0FiD93kKHiQo2hM5VVVfucFKXaH2tsnD+WcZAn8HkUUmbAM63wBwEdOL8qr8H3kX6mzhW0iPARsA3bf99IAsWehYVbAidq6qu3Cds/7BIwYaQ/YFfkVqGj5Fm/Gn3+muV1gfWylmiRgDPAu8pa3L4UI2oYEPoXFV15ZY+Cfog9l7bY+tXSNoUuK7dgBVliHrD9kwA29Mk3RuVa+eLCjaEzlVVV27MvDLbsaRu+L7Wtcz2DEkjJc1XVoYoZo9OhjlHKJc1OjlUICrYEDpXJV25kcx/1u05mzBnLwGk2WmGl3CKh4HrJJWSIYqKRyeHakQFG0Lniq7c6sxH6g2Yhzl7A14EdikhfqkZoqoanRyqFbmIQ+hQkpaM1ma1JK1g+5ESMzk1xq8kbhgchg10AUIIzUXlOlcsK+lO8q05ktaW9Ns+jumTpI2riBsGl6hgQwjd7BhSJqfnIGVyYnZKyo6KK2m4pD8XL1qYW6KCDSF0NduPNqwqJR9z2XFtzyANypqvSJww98QgpxBCN6sqk1NVcR+m3NHJoUJRwYYQulmzTE5fqihuGRmiyp6/NlQoRhGHEELJJG1q+7q+1hWIH6OTB4GoYEMIXUvSisCBpPzOs3r0bG9fMO6tttfta10bcWfNX2u7jPlrQ4WiiziE0M3+TqqwzgdmFg02FzJEHUO589eGCkUFG0LoZtNs/7rEeFVniCp1/tpQrahgQwjd7FeSvk8ahPR6baXtW9sJZvsq4CpJp1SUIWqwzF8biAo2hNDd1gT2BrZmdhex83IRy0q6iNSaLfNa6WCZvzYQg5xCCF1M0t2kiczLmlauFvcmUpfwONvr5HVTba9RMG6lo5NDuSKTUwihm00GFq8icEUZoo5tcV3oANFFHELoZksDd0uawJzXYAvdpkPJ10rnwujkUIGoYEMI3ez7FcUtO0NU5aOTQ/niGmwIoatJWhrYIC/ebPvpgSxPb6qevzaUK67BhhC6lqRdgZuBTwK7AjdJKtwilLSipKMknStpXO1RNC4VzV8bqhEt2BBC15I0GfhArdUqaSRwqe21S4j7e+B26jJE5ftki8StZHRyqEZcgw0hdLNhDV3Cz1FOz17ZGaJmiUxOg0dUsCGEbnaxpEuAM/LybsBFJcQtNUNUncjkNIhEF3EIoatJ+gSwGSDgatvnlRDzp6QMUQ9QlyHKdqEMUZKWIo1O3pZU3vHAl20/VyRuqEZUsCGEriXpZ7a/0de6NuJWkiEqDC4xijiE0M0+0GTddiXErSRDVIWjk0MF4hpsCKHrSPoCKUn+uyVNqdu0CFBGXt+qMkT9nRLnrw3Vii7iEELXkbQYsATwU+DQuk0v2X6+hPjvb7a+jNt0bL+vSIww90QFG0LoepLeDoyoLdv+dwkxS88QJWlPYCXKH50cKhBdxCGEriXp48BRwLLA08AKpNteVi8Yd1fgSOBK0mjfYyUdYvuvhQpc3fy1oQLRgg0hdK2ccWlrUvamdSRtBexhe78S4laRISpGJw8iMYo4hNDN3sz3kA6TNMz2FcCYEuJWlSGqsvlrQ/miiziE0M3+J2lh4GrgdElPA9NLiFtVhqiqRieHCkQXcQiha0laCJhGuk46FlgMOL2MzEgVZYiqZHRyqEZUsCGEridpUep69IreqlNVhqgcZ9DMX9vt4hpsCKFrSfq8pKeAKcBE4Jb8s6hKMkRVNX9tqEa0YEMIXUvSfcDGtp8tKd6sDFGkRP81iwDX2d6rYPxKRieHasQgpxBCN3sAeLXEeH8hDWaqJEMU1Y1ODhWIFmwIoWtJWgf4I3ATc47KPaik+KVmiJJ0JLAWc45Ovt3214vEDdWICjaE0LUk3QxcC9xOXfJ826cWjNs0Q5TtQhmicuzSRyeHakQFG0LoWpKut71JBXGryhBV2ejkUL7ouw8hdLMrJO0naRlJS9YeJcStKkNUVfPXhgrEIKcQQjfbM//8Zt06k0YBF1Fqhqi5MH9tqEB0EYcQQsnKzhBV9fy1oRpRwYYQupakeYEvAFvkVVcCJ9p+s6T4pWaIqotb+vy1oXxRwYYQupakk4F5gdqo4b2BGbY/WzDu54EfAq+RRicLsO1CXc9Vjk4O5YsKNoTQdSTNY3u6pMmNWZCarWsjfqkZouriVjI6OVQjRhGHELrRzfnnDEmjayslvRuYUUL8sjNE1VQ1OjlUIEYRhxC6kfLPr5Fu1XkwL48CPl1C/G8C10sqO0NUVfPXhgpEF3EIoetIeox0LRNgAWA48App4NBrto/q6dgW41eVIaqy+WtD+aIFG0LoRsOBhZndkiUvQ7q3tKjptr9aQpw52H4FZo1OPr/s+KFc0YINIXQdSbfaXrfC+D8BHiFVgvVdxEUncq9kdHKoRlSwIYSuI+k22+tUGP+hJqvLuE2nktHJoRrRRRxC6EbbVBnc9ooVha5qdHKoQLRgQwihZFVliKp6/tpQrqhgQwihZBVmiKpkdHKoRlSwIYRQkrmQIaqS+WtDNSKTUwghlKfqDFFVzV8bKhAt2BBCKEltdLKkrYFTgDkyROXUhkXiVzI6OVQjKtgQQihJ1RmiwuASt+mEEEJ5Ks0QVfX8taFc0YINIYSSzIUMUZWMTg7ViBZsCCGUR33v0kbQPDoZ2KBhJPLleY7Y0IFiFHEIIZSnqgxRVY9ODhWIFmwIIZSkaDL/XlQ9f22oQFyDDSGEDhejkwenaMGGEELnq3r+2lCBaMGGEEKHq3p0cqhGDHIKIYTOV8no5FCtaMGGEEKHk7RkhQOoQkWigg0hhBAqEF3EIYQQQgWigg0hhBAqEBVsCCGEUIGoYEMIIYQKRAUbQgghVOD/A99WAOxIufa0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check for Multicollinearity Using Spearman Rank Correlations\n",
    "\n",
    "corrs = corr_df.corr(method = \"spearman\")\n",
    "mask = np.zeros_like(corrs)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "sns.heatmap(corrs, cmap='Spectral_r', mask=mask, square=True, vmin=-1, vmax=1)\n",
    "plt.title('Correlation matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7b0cac29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine Attrition\n",
    "#Create X and Y Variables\n",
    "\n",
    "#x = movies_df.iloc[:, 0:9]\n",
    "y = movies_df.iloc[:, 10]\n",
    "#X = sm.add_constant(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5baeceee",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.DataFrame(data = movies_df)\n",
    "x = x.drop(columns = ['Award_Nominee', 'Award_Winner'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4fe626eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove variables one by one\n",
    "x = x.drop(columns = ['Tomatometer Fresh Critics Count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f47f8e35",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = stat.add_constant(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f4a0d692",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 9\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:           Award_Winner   No. Observations:                  533\n",
      "Model:                          Logit   Df Residuals:                      524\n",
      "Method:                           MLE   Df Model:                            8\n",
      "Date:                Fri, 17 Dec 2021   Pseudo R-squ.:                     inf\n",
      "Time:                        15:42:18   Log-Likelihood:                   -inf\n",
      "converged:                       True   LL-Null:                        0.0000\n",
      "Covariance Type:            nonrobust   LLR p-value:                     1.000\n",
      "====================================================================================================\n",
      "                                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "const                               -6.3176      3.043     -2.076      0.038     -12.282      -0.353\n",
      "IMDB Rating                          1.1402      0.616      1.850      0.064      -0.068       2.348\n",
      "IMDB Votes                        2.385e-08   5.14e-07      0.046      0.963   -9.83e-07    1.03e-06\n",
      "Tomatometer Rating                  -0.0321      0.014     -2.373      0.018      -0.059      -0.006\n",
      "Tomatometer Count                   -0.0043      0.004     -1.171      0.242      -0.012       0.003\n",
      "Audience Rating                     -0.0157      0.023     -0.673      0.501      -0.062       0.030\n",
      "Audience Count                    1.699e-07   1.67e-07      1.020      0.308   -1.57e-07    4.96e-07\n",
      "Tomatometer Top Critics Count        0.0341      0.019      1.818      0.069      -0.003       0.071\n",
      "Tomatometer Rotten Critics Count    -0.0235      0.013     -1.843      0.065      -0.048       0.001\n",
      "====================================================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abdullah\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\Abdullah\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\Abdullah\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  warnings.warn('Inverting hessian failed, no bse or cov_params '\n",
      "C:\\Users\\Abdullah\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  warnings.warn('Inverting hessian failed, no bse or cov_params '\n"
     ]
    }
   ],
   "source": [
    "#Fit a Logistic Regression Model for Explanation\n",
    "\n",
    "logit_mod = stat.Logit(y, x).fit()\n",
    "\n",
    "#Print Logistic Regression Results\n",
    "\n",
    "print(logit_mod.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a53193fe",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.702679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IMDB Rating</th>\n",
       "      <td>0.934355</td>\n",
       "      <td>10.468156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IMDB Votes</th>\n",
       "      <td>0.999999</td>\n",
       "      <td>1.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomatometer Rating</th>\n",
       "      <td>0.943038</td>\n",
       "      <td>0.994430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomatometer Count</th>\n",
       "      <td>0.988527</td>\n",
       "      <td>1.002912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Audience Rating</th>\n",
       "      <td>0.940268</td>\n",
       "      <td>1.030556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Audience Count</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomatometer Top Critics Count</th>\n",
       "      <td>0.997339</td>\n",
       "      <td>1.073337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomatometer Rotten Critics Count</th>\n",
       "      <td>0.952703</td>\n",
       "      <td>1.001486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         0          1\n",
       "const                             0.000005   0.702679\n",
       "IMDB Rating                       0.934355  10.468156\n",
       "IMDB Votes                        0.999999   1.000001\n",
       "Tomatometer Rating                0.943038   0.994430\n",
       "Tomatometer Count                 0.988527   1.002912\n",
       "Audience Rating                   0.940268   1.030556\n",
       "Audience Count                    1.000000   1.000000\n",
       "Tomatometer Top Critics Count     0.997339   1.073337\n",
       "Tomatometer Rotten Critics Count  0.952703   1.001486"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the 95% Confidence Intervals for the Odds Ratios\n",
    "#Exponetializing the 95% confidence interval to convert them into Odds Ratios\n",
    "\n",
    "np.exp(logit_mod.conf_int())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2be076bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   VIF Factor                          features\n",
      "0       604.2                             const\n",
      "1         7.6                       IMDB Rating\n",
      "2         2.7                        IMDB Votes\n",
      "3         1.9                Tomatometer Rating\n",
      "4        12.0                 Tomatometer Count\n",
      "5         5.4                   Audience Rating\n",
      "6         1.2                    Audience Count\n",
      "7        10.8     Tomatometer Top Critics Count\n",
      "8         2.6  Tomatometer Rotten Critics Count\n"
     ]
    }
   ],
   "source": [
    "#VIF\n",
    "\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF Factor\"] = [variance_inflation_factor(x.values, i) for i in range(x.values.shape[1])]\n",
    "vif[\"features\"] = x.columns\n",
    "print(vif.round(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ca5b66fc",
   "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>IMDB Rating</th>\n",
       "      <th>Tomatometer Rating</th>\n",
       "      <th>Tomatometer Top Critics Count</th>\n",
       "      <th>Tomatometer Rotten Critics Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.5</td>\n",
       "      <td>93</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.7</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.7</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.7</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.7</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>6.9</td>\n",
       "      <td>83</td>\n",
       "      <td>69</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>7.5</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>7.5</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>7.8</td>\n",
       "      <td>97</td>\n",
       "      <td>53</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>7.8</td>\n",
       "      <td>89</td>\n",
       "      <td>68</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>533 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     IMDB Rating  Tomatometer Rating  Tomatometer Top Critics Count  \\\n",
       "0            7.5                  93                              9   \n",
       "1            7.7                  84                              1   \n",
       "2            6.7                 100                              1   \n",
       "3            5.7                  33                              7   \n",
       "4            5.7                  43                              3   \n",
       "..           ...                 ...                            ...   \n",
       "528          6.9                  83                             69   \n",
       "529          7.5                  98                             70   \n",
       "530          7.5                  90                             70   \n",
       "531          7.8                  97                             53   \n",
       "532          7.8                  89                             68   \n",
       "\n",
       "     Tomatometer Rotten Critics Count  \n",
       "0                                   3  \n",
       "1                                   9  \n",
       "2                                   0  \n",
       "3                                  16  \n",
       "4                                   4  \n",
       "..                                ...  \n",
       "528                                59  \n",
       "529                                 7  \n",
       "530                                40  \n",
       "531                                 8  \n",
       "532                                37  \n",
       "\n",
       "[533 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2 = pd.DataFrame(data = movies_df)\n",
    "x2 = x2.drop(columns = ['IMDB Votes', 'Tomatometer Count',\t'Audience Rating',\t'Audience Count', 'Tomatometer Fresh Critics Count', 'Award_Nominee', 'Award_Winner'])\n",
    "x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ae3d18aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train/test split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(x2, y, test_size=0.2, random_state=42)\n",
    "\n",
    "#SMOTE\n",
    "\n",
    "sm = SMOTE(random_state=42)\n",
    "\n",
    "X_sm, y_sm = sm.fit_resample(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "976fc042",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: inf\n",
      "         Iterations 6\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:           Award_Winner   No. Observations:                  698\n",
      "Model:                          Logit   Df Residuals:                      693\n",
      "Method:                           MLE   Df Model:                            4\n",
      "Date:                Fri, 17 Dec 2021   Pseudo R-squ.:                     inf\n",
      "Time:                        15:43:03   Log-Likelihood:                   -inf\n",
      "converged:                       True   LL-Null:                        0.0000\n",
      "Covariance Type:            nonrobust   LLR p-value:                     1.000\n",
      "====================================================================================================\n",
      "                                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "const                               -4.7560      1.231     -3.863      0.000      -7.169      -2.343\n",
      "IMDB Rating                          1.2742      0.202      6.310      0.000       0.878       1.670\n",
      "Tomatometer Rating                  -0.0575      0.011     -5.391      0.000      -0.078      -0.037\n",
      "Tomatometer Top Critics Count        0.0218      0.006      3.715      0.000       0.010       0.033\n",
      "Tomatometer Rotten Critics Count    -0.0483      0.011     -4.516      0.000      -0.069      -0.027\n",
      "====================================================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abdullah\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1810: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n",
      "C:\\Users\\Abdullah\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1863: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "C:\\Users\\Abdullah\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  warnings.warn('Inverting hessian failed, no bse or cov_params '\n",
      "C:\\Users\\Abdullah\\anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  warnings.warn('Inverting hessian failed, no bse or cov_params '\n"
     ]
    }
   ],
   "source": [
    "#Fit a Logistic Regression Model for Explanation\n",
    "x_logit_new_sm = stat.add_constant(X_sm)\n",
    "logit_mod = stat.Logit(y_sm, x_logit_new_sm).fit()\n",
    "\n",
    "#Print Logistic Regression Results\n",
    "\n",
    "print(logit_mod.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "58bc43aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const                               0.008600\n",
       "IMDB Rating                         3.575767\n",
       "Tomatometer Rating                  0.944161\n",
       "Tomatometer Top Critics Count       1.022070\n",
       "Tomatometer Rotten Critics Count    0.952880\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(logit_mod.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c39d8eae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit a Logistic Regression Model for Explaination\n",
    "\n",
    "#lr = LogisticRegression()\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_sm, y_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "c5e20070",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.22456259 -0.05589325  0.02185041 -0.0479139 ]]\n",
      "[-4.51614466]\n"
     ]
    }
   ],
   "source": [
    "print(lr.coef_)\n",
    "print(lr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "f3749cad",
   "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>Nominee/Predicted Negative</th>\n",
       "      <th>Winner/Predicted Positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Nominee/Actual Negative</th>\n",
       "      <td>72</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Winner/Actual Positive</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Nominee/Predicted Negative  Winner/Predicted Positive\n",
       "Nominee/Actual Negative                          72                         21\n",
       "Winner/Actual Positive                            6                          8"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Confusion Matrix\n",
    "y_pred = lr.predict(X_test)\n",
    "conf_mat = metrics.confusion_matrix(y_test, y_pred)\n",
    "conf_mat_df = pd.DataFrame(conf_mat, columns = ['Nominee/Predicted Negative', 'Winner/Predicted Positive'],\n",
    "            index = ['Nominee/Actual Negative', 'Winner/Actual Positive'])\n",
    "\n",
    "# Showing the confusion matrix\n",
    "conf_mat_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "id": "89bf6cd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting Confusion Matrix\n",
    "\n",
    "ax = sns.heatmap(conf_mat, annot=True, cmap='YlGn', fmt = 'd')\n",
    "\n",
    "ax.set_title('Confusion Matrix of Predicted Oscar Winners vs Actual\\n With Logistic Regression\\n')\n",
    "ax.set_xlabel('\\nPredicted Values')\n",
    "ax.set_ylabel('Actual Values ')\n",
    "\n",
    "# Ticket labels - List must be in alphabetical order\n",
    "ax.xaxis.set_ticklabels(['Nominee','Winner'])\n",
    "ax.yaxis.set_ticklabels(['Nominee','Winner'])\n",
    "\n",
    "# Display the visualization of the Confusion Matrix.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "id": "fa91552b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7476635514018691"
      ]
     },
     "execution_count": 527,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_test, y_test, sample_weight=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "01ba8955",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.52695553, 0.47304447],\n",
       "       [0.55608267, 0.44391733],\n",
       "       [0.36323285, 0.63676715],\n",
       "       [0.59468545, 0.40531455],\n",
       "       [0.46327875, 0.53672125],\n",
       "       [0.39138586, 0.60861414],\n",
       "       [0.68649955, 0.31350045],\n",
       "       [0.26380328, 0.73619672],\n",
       "       [0.57863452, 0.42136548],\n",
       "       [0.55615856, 0.44384144],\n",
       "       [0.44399213, 0.55600787],\n",
       "       [0.43399636, 0.56600364],\n",
       "       [0.47252316, 0.52747684],\n",
       "       [0.23920561, 0.76079439],\n",
       "       [0.47644385, 0.52355615],\n",
       "       [0.6237445 , 0.3762555 ],\n",
       "       [0.46141425, 0.53858575],\n",
       "       [0.3726965 , 0.6273035 ],\n",
       "       [0.42038864, 0.57961136],\n",
       "       [0.33581213, 0.66418787],\n",
       "       [0.53940051, 0.46059949],\n",
       "       [0.73519535, 0.26480465],\n",
       "       [0.39998426, 0.60001574],\n",
       "       [0.39407657, 0.60592343],\n",
       "       [0.45660722, 0.54339278],\n",
       "       [0.41946418, 0.58053582],\n",
       "       [0.69148748, 0.30851252],\n",
       "       [0.41884573, 0.58115427],\n",
       "       [0.33164628, 0.66835372],\n",
       "       [0.63319471, 0.36680529],\n",
       "       [0.61899095, 0.38100905],\n",
       "       [0.47286351, 0.52713649],\n",
       "       [0.51020478, 0.48979522],\n",
       "       [0.7240299 , 0.2759701 ],\n",
       "       [0.53857874, 0.46142126],\n",
       "       [0.42198168, 0.57801832],\n",
       "       [0.53195987, 0.46804013],\n",
       "       [0.80380481, 0.19619519],\n",
       "       [0.50543692, 0.49456308],\n",
       "       [0.55619923, 0.44380077],\n",
       "       [0.52542212, 0.47457788],\n",
       "       [0.5918    , 0.4082    ],\n",
       "       [0.54167441, 0.45832559],\n",
       "       [0.53245302, 0.46754698],\n",
       "       [0.53558184, 0.46441816],\n",
       "       [0.32895528, 0.67104472],\n",
       "       [0.54106593, 0.45893407],\n",
       "       [0.47047009, 0.52952991],\n",
       "       [0.35352819, 0.64647181],\n",
       "       [0.59611049, 0.40388951],\n",
       "       [0.47667503, 0.52332497],\n",
       "       [0.22095706, 0.77904294],\n",
       "       [0.51459051, 0.48540949],\n",
       "       [0.50456264, 0.49543736],\n",
       "       [0.65971383, 0.34028617],\n",
       "       [0.82641294, 0.17358706],\n",
       "       [0.48736068, 0.51263932],\n",
       "       [0.65113837, 0.34886163],\n",
       "       [0.43724124, 0.56275876],\n",
       "       [0.57010133, 0.42989867],\n",
       "       [0.54080646, 0.45919354],\n",
       "       [0.49369989, 0.50630011],\n",
       "       [0.62466412, 0.37533588],\n",
       "       [0.66140403, 0.33859597],\n",
       "       [0.54022794, 0.45977206],\n",
       "       [0.6784968 , 0.3215032 ],\n",
       "       [0.7684693 , 0.2315307 ],\n",
       "       [0.62612668, 0.37387332],\n",
       "       [0.45480126, 0.54519874],\n",
       "       [0.68929552, 0.31070448],\n",
       "       [0.60894051, 0.39105949],\n",
       "       [0.62679933, 0.37320067],\n",
       "       [0.51356167, 0.48643833],\n",
       "       [0.60907075, 0.39092925],\n",
       "       [0.57181047, 0.42818953],\n",
       "       [0.52000703, 0.47999297],\n",
       "       [0.501176  , 0.498824  ],\n",
       "       [0.50868355, 0.49131645],\n",
       "       [0.55450235, 0.44549765],\n",
       "       [0.64568942, 0.35431058],\n",
       "       [0.71675608, 0.28324392],\n",
       "       [0.48332412, 0.51667588],\n",
       "       [0.27849993, 0.72150007],\n",
       "       [0.47291869, 0.52708131],\n",
       "       [0.76004215, 0.23995785],\n",
       "       [0.60234789, 0.39765211],\n",
       "       [0.57448816, 0.42551184],\n",
       "       [0.47521041, 0.52478959],\n",
       "       [0.42540054, 0.57459946],\n",
       "       [0.46999223, 0.53000777],\n",
       "       [0.44717318, 0.55282682],\n",
       "       [0.50646834, 0.49353166],\n",
       "       [0.82323119, 0.17676881],\n",
       "       [0.40645561, 0.59354439],\n",
       "       [0.62846616, 0.37153384],\n",
       "       [0.6243315 , 0.3756685 ],\n",
       "       [0.56388087, 0.43611913],\n",
       "       [0.69909686, 0.30090314],\n",
       "       [0.5935    , 0.4065    ],\n",
       "       [0.48310624, 0.51689376],\n",
       "       [0.4053025 , 0.5946975 ],\n",
       "       [0.48216361, 0.51783639],\n",
       "       [0.44604914, 0.55395086],\n",
       "       [0.3960456 , 0.6039544 ],\n",
       "       [0.60793558, 0.39206442],\n",
       "       [0.6377607 , 0.3622393 ],\n",
       "       [0.43570783, 0.56429217],\n",
       "       [0.43216736, 0.56783264],\n",
       "       [0.21464288, 0.78535712],\n",
       "       [0.25191352, 0.74808648],\n",
       "       [0.65707286, 0.34292714],\n",
       "       [0.2375852 , 0.7624148 ],\n",
       "       [0.62891026, 0.37108974],\n",
       "       [0.37692738, 0.62307262],\n",
       "       [0.56198643, 0.43801357],\n",
       "       [0.4359091 , 0.5640909 ],\n",
       "       [0.46535141, 0.53464859],\n",
       "       [0.48491498, 0.51508502],\n",
       "       [0.41570814, 0.58429186],\n",
       "       [0.67337038, 0.32662962],\n",
       "       [0.5887003 , 0.4112997 ],\n",
       "       [0.56745145, 0.43254855],\n",
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       "       [0.21020924, 0.78979076],\n",
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       "       [0.6234828 , 0.3765172 ],\n",
       "       [0.58131367, 0.41868633],\n",
       "       [0.59021147, 0.40978853],\n",
       "       [0.53137389, 0.46862611],\n",
       "       [0.59452436, 0.40547564],\n",
       "       [0.70471493, 0.29528507],\n",
       "       [0.57103555, 0.42896445],\n",
       "       [0.52498486, 0.47501514],\n",
       "       [0.29406282, 0.70593718],\n",
       "       [0.57894887, 0.42105113]])"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict_proba(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "03d033ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#AUC-ROC\n",
    "\n",
    "auc_score = roc_auc_score(y_test, y_pred)\n",
    "fpr, tpr, thresholds = roc_curve(y_test,lr.predict_proba(X_test)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % auc_score)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "id": "45bee14a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6728110599078341"
      ]
     },
     "execution_count": 528,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "28b08fcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5714285714285714"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Recall\n",
    "\n",
    "recall_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "7078e5bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.27586206896551724"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "3507b121",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37209302325581395"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# F-score\n",
    "\n",
    "f1_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "066ad83c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.92      0.84        78\n",
      "           1       0.57      0.28      0.37        29\n",
      "\n",
      "    accuracy                           0.75       107\n",
      "   macro avg       0.67      0.60      0.61       107\n",
      "weighted avg       0.72      0.75      0.71       107\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "5cce99f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x23e1e400100>"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_precision_recall_curve(lr, X_test, y_test, name = 'Logistic Regression')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bcfc64d",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "id": "a3c5611d",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier(max_depth = 14, random_state = 0, criterion='entropy', min_samples_split= 2 ).fit(X_train_sm, y_train_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "id": "dc88a8cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9345794392523364"
      ]
     },
     "execution_count": 569,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy\n",
    " \n",
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "id": "dd261382",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predictions by DT\n",
    "pred_dt = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 571,
   "id": "86052b48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.95      0.96        93\n",
      "           1       0.71      0.86      0.77        14\n",
      "\n",
      "    accuracy                           0.93       107\n",
      "   macro avg       0.84      0.90      0.87       107\n",
      "weighted avg       0.94      0.93      0.94       107\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_dt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "id": "5d80c019",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_mat_dt_train = metrics.confusion_matrix(y_train_sm, clf.predict(X_train_sm))\n",
    "#Plotting Confusion Matrix of DT\n",
    "\n",
    "ax = sns.heatmap(conf_mat_dt_train, annot=True, cmap='YlGn', fmt = 'd')\n",
    "\n",
    "ax.set_title('Confusion Matrix of Predicted Oscar winners vs Actual by DT\\n\\n')\n",
    "ax.set_xlabel('\\nPredicted Values')\n",
    "ax.set_ylabel('Actual Values ')\n",
    "\n",
    "# Ticket labels - List must be in alphabetical order\n",
    "ax.xaxis.set_ticklabels(['Nominee','Winner'])\n",
    "ax.yaxis.set_ticklabels(['Nominee','Winner'])\n",
    "\n",
    "# Display the visualization of the Confusion Matrix.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "id": "3cb63429",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99       346\n",
      "           1       1.00      0.99      0.99       361\n",
      "\n",
      "    accuracy                           0.99       707\n",
      "   macro avg       0.99      0.99      0.99       707\n",
      "weighted avg       0.99      0.99      0.99       707\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train_sm, clf.predict(X_train_sm)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "id": "103b1aa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_mat_dt = metrics.confusion_matrix(y_test, pred_dt)\n",
    "#Plotting Confusion Matrix of DT\n",
    "\n",
    "ax = sns.heatmap(conf_mat_dt, annot=True, cmap='YlGn', fmt = 'd')\n",
    "\n",
    "ax.set_title('Confusion Matrix of Predicted Oscar Winners vs Actual\\n Using Classification Tree\\n\\n')\n",
    "ax.set_xlabel('\\nPredicted Values')\n",
    "ax.set_ylabel('Actual Values ')\n",
    "\n",
    "# Ticket labels - List must be in alphabetical order\n",
    "ax.xaxis.set_ticklabels(['Nominee','Winner'])\n",
    "ax.yaxis.set_ticklabels(['Nominee','Winner'])\n",
    "\n",
    "# Display the visualization of the Confusion Matrix.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 573,
   "id": "3bc61949",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x23e1f1a1a30>"
      ]
     },
     "execution_count": 573,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_precision_recall_curve(clf, X_test, y_test, name = 'Decision Tree', pos_label = 1, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "id": "e60a2622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23e1f2279a0>"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot with baseline\n",
    " \n",
    "baseline = np.sum(y_test) / len(y_test)\n",
    "baseline_y = clf.predict_proba(X_test)[:,1]\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "pre_s, rec_s, _ = precision_recall_curve(y_test, baseline_y)\n",
    "ax.step(rec_s, pre_s, color='b', alpha=0.5, where='post', label='PR curve')\n",
    "ax.set_xlabel('recall')\n",
    "ax.set_ylabel('precision')\n",
    "ax.set_title('Scikit Precision-Recall Curve')\n",
    "ax.plot((0, 1), (baseline, baseline), 'r--', alpha=0.3, label='baseline')\n",
    "ax.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "id": "981c0415",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ROC for train DT\n",
    "\n",
    "train_pred = clf.predict(X_sm)\n",
    "auc_score_dt = roc_auc_score(y_sm, train_pred)\n",
    "fpr, tpr, thresholds = roc_curve(y_sm,train_pred)\n",
    "#auc_score_dt = roc_auc_score(y_sm, clf.predict_proba(X_sm)[:,1])\n",
    "#fpr, tpr, thresholds = roc_curve(y_sm,clf.predict_proba(X_sm)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Decision Tree (area = %0.2f)' % auc_score_dt)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 572,
   "id": "e6913f59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MZ58mcwIlNiSEjFRn1EcFi8gwDIO6X34mZchQnDk59L38SlJHjMSenBLt0JqQZCHintvjb5wKukn3TzeV1d4mr+2uO2j9xT4/O5m9+wU3Bic16RGUJN1BRRDPju3sXPgstet+ZMCf5pAyeDDp4yZEO6wWSbIQ3VKT7qD19f5BpYHg19ye3XQHTW2cCrpvXpo1JfSuU0OkJHWPUcEicgJeD6VL36DszTewOZ30PPd8kgsKoh1WqyRZiJgRMAxctd5dpoIO7hZa7fZRWlG320Vi0pITGhqDC3pnNOkB1NgtNIkM6Q4qwsQIBNh8919xb9pIxqTJ5J9+JglZ2dEOq02SLERU1S8SU9HSnX+zFcMqd9cdNMHeUN/fLz+dvftkNlscJqkhEUh3UBEt/qoq7Onp2Ox2cqYdhSMri7SR+0Q7rJBJshBh4fU1zg5a2WxCuAqXO6gx2IOnle6g9b1/BvRK32VxmMbZQRtHBUsPINHVGIEAFR9+QPErL9Hz7HPJPGAKmRGcpqOzSLIQIQsEDKpqmk8F7W5SEqhPAjW76Q6anuJsqPYZ0j9rl8Vh6pNAWhgXiREiUup+3UThwvnUbdhA6oiRJO+1d7RD6jBJFnHOMAxq3L6m4wF2mRra/L9qN4vEJActEtMvP52Rg5pe+OuriKQ7qIgnpW++QfEri3GkZ9D74kvJmHRATHeEkGTRTbmtRWIqXU0bgpu2AdQvEtNSd1BbQ9fPvMxk9rLaAYKXisxMTyQrNZGkROkOKgSYN18YBja7ncQ+fRsn/UtLi3Zoe0ySRQzx+a1RwdbEcA1TQPgNdhS7mpQC6lrqDgpkBA0C65OX1lj3H9wtND2RVOkOKkS7eIuLKFy0kOS9BpN3/Imkjx1H+thx0Q6r00iyiLKAYVBd622hMXjXbqG77Q6a4iTTmhpiUO+MJtNFB3cLTQ9hkRghRPsYPh9lby+n5PUlYLORus+oaIcUFpIswqC+O2jzqaB37RZqTgvR0qhgZ0LjqOBeuakMHRA8O2jTNoG+fbKlB5AQUVC3cSM7np6HZ+sW0saNp+eZ5+DMy4t2WGEhyaIdvL7ALlNBN20Mblw/2OPdtTuo3WYjM83ZcMc/oGd60DKRTbuFBncHFUJ0UTYw3G76Xn1Nt6pyakncJ4tAwKCq1ttkSujgbqHB6wa02h3U6vPfuExkswFh6dYiMZIAhIhZhmFQteoT3Fs3kz/zTJIHFVBw593YHN2/k0dcJ4slH23g9U82ttgdNCm4O2iPNEYMymm48w8uCWSmJUp3UCHigHvbNgoXzqf2J03y3kMIeL3Ync64SBQQ58li3a/l5GelcOT+A3ZZL1gWiRFCAAQ8HkrfeJ3SZUuxJyXR87wLyDroYGxx1lkkrq+I5S43BX0yOGJC/2iHIoToovwuF+Xvvk3GxEnkzzyThMzMaIcUFXGbLAzDoNzlJju9R7RDEUJ0Mb7ycipWriB3xvE4c3Mp+MvdJGRnRzusqIrbZFHr9uPxBshOT4p2KEKILsIIBKj44D2KX30Zw+slfew4kvoPiPtEAXGcLMpdbgCyMxKjHIkQoiuo27SRnQvm4974P1JH7kPPc84nsVevaIfVZcRtsqiwkkWOlCyEiHuG38+2Rx/C8HjofenlZOw/ScY5NRO3yaLc5QGQaigh4pRhGFR//RWp+4zC7nTS94qrcebn40iN/Un/wiG++n4Fqa+GykqXaigh4o23qIitD/yDbQ89QOXKFQAkDyqQRNGKsJYslFJnA7cATuCfWuuHm70+HngcSAQ2A+dqrcvDGVO9Mpeb5ESHjKcQIo4YPh9ly5dR8t/XwGYn/4yzyDr40GiHFRPCVrJQSvUD7gSmAmOBS5VSI5u97QFgjtZ6DKCB68MVT3PlLo9UQQkRZ3Y8/STFrywmbd/RFNzxV3KOPDpuRmDvqXDeVk8D3tNalwIopRYDpwG3B73HAdSPcEkFSsMYTxPmGAupghKiu/NXVeFNNn/OOfoYMiZNIn302KjGFIvCmSz6AtuDHm8HJjZ7z7XAcqXUP4FqYFIY42mivMrNkP5ZkTqcECLCDMOg8uOVFC1+garJk8g+8zySBw6CgYOiHVpMCmeysAPBU/TZgIZ5u5VSKcCTwDSt9WdKqWuBZ4EZoR4gLy+9Q4EZhkFFtYe++Rnk52d0aB9dTXf5HJ1BzkWjeD0XNb/+yi+P/h+VP/xIxojh9D3hONLi9Fx0lnAmiy3AQUGPewPbgh6PAmq11p9Zjx8H7mjPAUpKXARaWDioLa5aL15fgESHrVssGpSfn9EtPkdnkHPRKF7PReVnq9nx5BPYk5Pp9ZtZZE45iLReWXF5Lpqz220dvskOZ7J4B5irlMrHrGI6Fbg06PWfgQFKKaW11sCJwOdhjKdBw+htabMQotsIeDzYExNJGTKMrClTyTv5VBIy4nPSv3AIW28orfVW4GbgfeArYJFV3bRUKbWf1roMuAB4USn1DXAhMCtc8QRrTBbSG0qIWOctK2Pbow+x9cF/YBgGztxcep0/SxJFJwvrIAOt9SJgUbPnpgf9/CbwZjhjaEl5lTV6O0OShRCxyggEKH/vHYpffQUCfnJnHA+BAEhX2LCIyxFpDSWLNKmGEiIWeYuK2PboQ7h/3UTqPqPMSf969ox2WN1a3CaLtOQEEp1yByJELHJkZGBLcNDnsitJ329/mfQvAuI0WcjobSFiiWEYuD7/jPIVH9DvmmuxJycz4KZbJUlEUJwmCxm9LUSs8BQWUvjcs9R8/x1JAwfhr6rEnpsniSLC2kwWSql04B5gODATuAu4TmvtCnNsYVPuctNnYE60wxBCtMLw+ShdtpTSN17H5nCQf+Y5ZB9+BDZ73E6WHVWhlCwexJyqoxdQhzmX0/8BZ4cxrrAJGAYVLo/0hBKiq7PbcX31JWljxpJ/xtk4c+QGL5pCSdHjtNY3A16tdQ1wDuYssjHJVePFHzCkzUKILshXVcnO557F73Jhs9sZcP0N9L38KkkUXUAoJQt/s8cOguZ4ijUyeluIrscIBKhc+RFFi18k4K4jdcQ+ZIyfgD05OdqhCUsoyWKFUuoeIEUpdTRwNfBBWKMKIxm9LUTX4t66hZ0L5lP383pShil6nns+SX37RTss0UwoyeIG4EagAnMxo7douiZFTJG1t4XoWkpeW4Jnx3Z6zbqIzAOnSi+nLiqUZDFda30HQTPCKqXOAxaELaowKq+StbeFiDbX11+R2Ks3ib170/Psc7HZHTgyZArxrmy3yUIpdTzm2tn3KqXsmOtRYD13G7GaLFxuMlKdJDik+50QkeYtLaHo+UW4vlxL1sGH0uv8C0jIyo52WCIErZUsxgKHAz2B2UHP+4B/hDGmsJLR20JEnuH3U/7uOxT/51UwAvQ45TRyjjom2mGJdthtsqivelJKXam1fiSCMYWVOXpbkoUQkVT29lsUL36RtH1H0/Ps83Dm50c7JNFOobRZzFNKnQykY1ZFOYAh1tiLmFPucjOgZ8dWihJChM5fU42vvJykvv3IPvRwEnv1Im3seGnAjlGhJIsXgMFAH+BLYBIx2nU2EDDX3s6SkoUQYWMYBlWffUrRC4twpGcwaO4d2JOTSR83IdqhiT0QSrIYCwwFHgX+jjnq+9EwxhQ2lTUeDANypCeUEGHh2bmDwoULqPnxe5IK9qLXeb+RuZy6iVB+i9u11j7gJ2CU1vp7ICu8YYWHDMgTInxqN2xg059voW7jBnqefS4D/3QryYMKoh2W6CShlCxcSqmzga+BS5RS6zDbL2KOLKcqROfzVVWSkJFJckEBOUcdQ/bh00jIzo52WKKThVKyuAqzKuptzDmhVgD3hjGmsJGShRCdx1dRwfZ5j7Pp1psbJv7rccppkii6qTZLFlrr9cAfrYdnACilRoYzqHApd7mxAZlpzmiHIkTMMgIBKj76kOKXXyLgdpN77AxsidIO2N21NoK7AHMuqFLgBq11jbUQ0m2YkwnG3O15uctNZloiDmlwE6JDAnV1bPn7vdRt+IUUNZxe555PYp++0Q5LREBrJYungO+AvsBNSqnXgcVAFRCTQy9l9LYQHWMEAtjsduzJyST170/2YYeTMflAGTMRR1q7xR6gtZ4NnAecCrwOPAKM0Vq/H4ngOlt5lay9LUR7ub78go233oRnxw4Aep0/i8wDpkiiiDOtlSxcAFrrWqVULnCe1np5ZMIKj3KXm736ZkY7DCFigrekhMLnF1L91Zck9utPwOOOdkgiikLpOgtQGOuJwucPUFnjlWooIUJQ9vZbFL/6MgA9TjudnGlHYUsI9XIhuqPWfvtG0M++cAcSbpXV9YseSTWUEG3xlpaSOmIkPc8+F2dej2iHI7qA1pLFaKVUpfVzatDPNsDQWsdUfU6ZjLEQYrf81dUUv/ISGftPInX4CPJPOx3sdmmXEA1aSxZ7RyyKCGgYvS3JQogGhmFQtXoVRS/+G3+1i8RefUgdPgKbwxHt0EQX09p6FpsiGUi4NYzelqk+hADAs2M7Oxc+S+26H0kePJh+v7+O5IGDoh2W6KLipsWq3OXGbrORkSqjt4UAqP7+O9ybNtLz3PPJOvhQmR1WtCqukkVWeiJ2qYMVcaz6++8wPG7Sx00g+7AjyNhvIglZMTmJtIiwsCYLa7baWwAn8E+t9cPNXlfA40AOsAM4U2tdFo5YZPS2iGe+inKKXvg3VZ+tJnnvIeaKdXa7JAoRsjaThVKqN/Ak5gJIBwHPAhdorbe3sV0/zLmlJgBu4BOl1Pta6x+s123Aa8A1WutlSqm7gRuBG/bg8+xWuctNz+yUcOxaiC7LCASo+PADil95CcPrJff4E8mdPkN6OYl2C6WS8hFgCVCLOangV8C8ELabBryntS7VWldjzit1WtDr44FqrfUy6/FfgYcJk/IqtzRui7hTq9dR+NyzJBfsxaC5f6HHiSdjd8pYI9F+oSSLAq31E0BAa+3VWt8ADAxhu75AcOljO9A/6PEQYIdS6kml1BeYS7W6Qoy7Xbw+P9V1PqmGEnEhUFdL9fffAZA6YiT9r7+Bftf+gcTevaMcmYhlobRZBJRSDUlFKZVBaEnGTtNR4DbMxZOCj30ocLDWeo1S6g7MNb4vCGHfAOTlhbZg346SagAG9skkPz8j1N3HlO76uToiXs+FYRiUrv6UjU88ia/KRb/x+5Cfnwn5E6MdWpcQr9+LzhJKsngFeA7IUkpdBlwMvBjCdlsw2zjq9Qa2BT3eAazXWq+xHj+PWVUVspISF4GA0eb7NmwpB8BhGBQVVbXnEDEhPz+jW36ujojXc+EtLqJw0UKqv/mapAED6HfplTgzM+PyXLQkXr8XzdnttpBvspsLZaW8vyqlzsMsKRwJ/B+htVm8A8xVSuUD1ZjTnF8a9PonQL5SaozW+mvgeGBtO+MPSYVLRm+L7svvcrFp7q0YhkH+6WeSfcSRMgJbdLpQekNdBizSWi9oz4611luVUjcD7wOJwDyt9WdKqaXAHKvq6WTgCaVUGmZJ5Lz2f4S21c8LlSWTCIpuxLNzB4m9euNITyf/rHNIHT4SZ15etMMS3VQo1VCHAX9RSr0GPKG1Xh3qzrXWi4BFzZ6bHvTzp0DYK1TLXW4cdhvpKTJ6W8Q+v8tF0eIXqfz4I/r/4UZShymyphzU9oZC7IE2G6q11mcCwzCriB5USn2nlLom7JF1ovIqc0Ce9C0XscwwDCo+XsnGW26i8pOV5Bx1jMzlJCImpBHcWusypdT/YXZ/vQFz8NwD4QysM5W73GRnSBWUiF2GYbDtoQeo/vorkvceQv9zf0PSgAHRDkvEkVDaLMYBFwIzgS+Av2GOvI4Z5S43fXukRTsMIdot4PVgS3Bis9lIHzOOtDFjyZp6sEz6JyIulJLFf4CngIla61/DHE9YlLs8jCzIjXYYQrRL9XffUvjcs+QdfxKZB04h6+BDoh2SiGOhJItBWuu2BzN0UW6Pn1q3T5ZTFTHDV15G4b+fx7XmM5y9epMgPZxEF7DbZKGUWqm1ngpUKqWaj8SOmWVVy6tlOVUROypXfULhogUYXi95J55MzjHTsTulF5+IvtZKFjOt/0e18FrMdCsqr5IV8kTssKekkLzXYHqecz6JvXpFOxwhGrS2rGr9JICPaa2PDX5NKbUamBzOwDpLuYzeFl2Yv7aWkv+8giM9g7zjTiB9rNmILd28RVfTWjXUYszxFXsrpb4JesmJuT5FTKhfeztH2ixEF2IYBq61ayj893P4KyrInnZUw2uSKERX1Fo11PVAAfAE8Nug533AD2GMqVOVu9wkJthJSYqbFWRFF+ctLmLnwgXUfPcNSQMG0vfK2aQMHhztsIRoVWvVUBuBjUqpYbHcG6p+OVW5WxNdhb+6mrpf1pN/xllkHz5NJv0TMaH794aqcku3WRF1NT9pavU68o4/keRBBQy+9+/Yk2WZXxE7OtobKmaUu9wM6i2Lnojo8FdVNUz6l9CjB9nTjsKRkiKJQsScUHpDFQGjtdarlVKXA/sDt0UiuD1lGAblLg9jpCeUiDDDMKj8eCVFi18gUFtLzjHTyTv+ROxJ8l0UsSmUVt+ngQ1KKT/wR+BZzEbvo8MZWGeo8/hxe/3SbVZEnL+yksLnnyNpwAB6nfcbkvr1b3sjIbqwUGYjG6y1vglzJbtntNZzgZiYaKm+26y0WYhICLjdlH/4PoZhkJCVxcCbb2XAH2+SRCG6hVBKFvVzDRwNXKeUcgAdW8Q1whpGb0vJQoSZ65uvKVy0AF9xMUl9+5MydChJfftFOywhOk0oyeITpdQPmOMrPgHexVxfu8trGL0tU32IMPGWlVH07+dwrV1DYp++9P/DjaQMHRrtsITodKEki98CBwDfaK0DSqn7gDfDG1bnqK+GykqTaijR+QzDYOs/7sNbVEjeyaeSe/Sx2BJk8Kfontr8Zmut/UqpvsBFSikn8LbWOhD+0PZcmctNcqJDRm+LTlW3cSOJ/fphdzrpdd4FOLKySOzZM9phCRFWbTZwK6WuB/4EfI25Ut7vlVK3hDuwzlA/eluIzuCvqWHncwv49c7bKH/nbQBShg6VRCHiQii33OcDU7XWlQBKqSeB1cBfwhlYZ6hwyehtsecMw8D1+WcUvrAIf2Ul2YcdQdahh0U7LCEiKqT6mfpEYf1coZTyhi+kzlPucrN336xohyFiXPFLL1C2fBlJgwro99vfkVywV7RDEiLiQkkWG5VS1wCPWI+vArr8Wtz1o7elGkp0RMDrxfD5cKSkkDFpMgl5eWQfdgQ2eyhDk4TofkL55l8BnAzUWP9OBa4MZ1Cdocbtw+sLSDWUaLeadT+y6bZbKXphEQDJgwrIOeJISRQiroXSG2orcKhSKhWwa61d4Q9rz8lyqqK9fJWVFL/0ApWrPsbZI5+MCftHOyQhuozWpigfCiwCFPAecKnWujBSge0pWU5VtEf1D9+z/bFHCLjryJ1+HLkzjpdJ/4QI0lrJ4mFgPuaI7YuAe4HfRCKoziDzQolQGIEANrudxN59SNl7b3rMPEOm6RCiBa0li15a64cAlFI3YI6ziBkNo7elZCFaEHC7KXn9P7g3/0q/312HMzeXftdcG+2whOiyWmux89X/oLX2AzHRXbZeeZWH1KQEkpyyZKVoyvX1V2yc8yfKli0lIScHwxtTX20hoqK1kkXzRatjah3ucpdbGrdFE76qSgqfnY/ry7Uk9u1L/z/eROowFe2whIgJrSWL/kqpB3f3WGs9O3xh7blyGb0tmrE7E3Fv30qPU04j56hjZNI/IdqhrQbu1h53aeUuN2pgTrTDEFFWu+EXyt56k94XX4Y9OZmC2+7E5pCqSSHaq7U1uPd4nW2l1NnALZgLKP1Ta91iwlFKzQAe0lp3yjwKARm9Hff8NdUUv/IyFR++jyMrC29hIUn9+kmiEKKDwlYOV0r1A+4EJgBuzEWU3tda/9Dsfb2A+9i1jaTDXLVe/AFDqqHikGEYVH66iqIXnsdfVUX2EUfS46STsSenRDs0IWJaOOcvmAa8p7Uu1VpXA4uB01p43zxgj0sxwWQ51TgWCFC2/C0ScvMYeOtcep55tiQKITpBOFv4+gLbgx5vByYGv0EpNRtzjYzVHTlAXl7LS4FvKq4BYK8BOeTnZ3Rk1zEnXj5nSwJeL9v+8zq9jpqGzeFg9G234MzMlCon4vt70Zyciz3TZrJQStmB64BRwNXWv79ZYy9aY6dpd1sb0LDCnlJqFOakhEcA/dsXtqmkxEUgsGuP3k1bywEwfD6Kiqo6suuYkp+fERefsyU1P/7AzoXP4t25g1oSGHraCVT4EqC0JtqhRV08fy+ak3Nhstttu73JbksoJYt7gXxgf8wL/jFAH6CtrrNbgIOCHvcGtgU9nmntZw2QCPRVSn2ktQ7epkMa196WaqjuyldRQdGL/6bq01U483vS7/fXk7bPqGiHJUS3FUqyOAIYD6zVWlcqpY4Cvgphu3eAuUqpfKAasxRxaf2LWus/A38GUEoVAB90RqIAcxLB9BQnzgSZUrq7Klr8AlVrPiP3uBPInX4c9kTpzCBEOIVyNfVqrRuqj7TWboKmAtkda2rzm4H3MZPLIq31Z0qppUqp/ToYb0jKq9zSuN0NuTf/imfnTgB6nDKTgrl30OOkUyRRCBEBoZQsvlNKXQU4lFIKuJbQShZorRdhTnMe/Nz0Ft63ESgIZZ+hMKf6kAtIdxGoq6PktSWUvbOc9HHj6XvF1ThzcgAZdClEpISSLK4B/gH0Aj4G3qLt9oqoKne56d+zY404omtxffkFhc8vxFdaStbBh9DjlJnRDkmIuBTKSnmVmOtZxIRAwKCiWkZvdwcVK1ew85mnSOzXnwE3XkHKkKHRDkmIuBVK19kHW3q+q04kWFnjwTAgR0ZvxyTD58NXXmYua7rfRAJuN9mHHCaT/gkRZaH8BZYE/ZwIHAd8EJZoOkHjCnlSsog1tT+vZ+eC+Rh+HwVz/4I9OZmcI46MdlhCCEKrhmoyFYdS6m7gtbBFtIfq196WFfJih9/loviVxVSs+ICEnFx6nn0OyOhrIbqUdpfttdZV1iSBXZKsvR1bPDu2s/mev+KvribnyKPJO/Fk7MnJ0Q5LCNFMKG0W/6Jx2g4b5iyyP4YzqD1RXuXGBmSmSbLoygIeD/bERJw9e5E+bjxZhx5O8sBB0Q5LCLEboZQsioN+NoAFwHPhCWfPlbs8ZKQlkuCQ0dtdUcDroXTpG1SuXMGgP9+BIz2dXufPinZYQog2hJIs9tZanx/2SDqJLKfadVV//x2Fzy3AW7iTjEkHgBFTy7oLEddCSRZjlFI2rXVM/GWbyUIat7sSw+djx1PzqPpsNc5eveh/3R9JHTEy2mEJIdohlGSxHfheKbUacNU/2VXHWZS7PBT0zox2GCKIOUbCIPf4E8mdPgO7U0p+QsSa3SYLpVSSNWngKutfl+fzB6iq9kg1VBdQ9+smip5/jl6/uZDE3r3pfcnl2GydtnKuECLCWitZrALGNx9n0ZVVVnswgOwMqYaKlkBdLcVLXqX83bdxpGfgKyslsXdvSRRCxLjWkkXM/XXXD8iTNovocH25lsJFC/GVl5N18KH0OOU0HGlp0Q5LCNEJWksWyUqpcewmaWitvwhPSB1XPyAvR5JFVNT89BOO9HT6XH4VKXsPiXY4QohO1FqyGAy8TMvJwrBe71Jk9HZkGT4fZW8vJ3nwYFLVcHqcfCo2x+nYZKoOIbqd1pLFD1rrcRGLpBOUu9zYbTYyUiVZhFvt+p/YuWA+nm1byTnyaFLVcFmxTohurFvN+1xe5SErPRG7PeaaW2KG3+WiaPGLVK5cQUJuHn2vvob0sTF1TyGE6IDWksWKiEXRSWT0dvhVff4plZ+sJOfoY8k74STsSdI+JEQ82G2y0FpfE8lAOkO5y01+dkq0w+h23Nu24SstJm3UaLIOOYwUNYKkvn2jHZYQIoK6VzWUy8PQ/tnRDqPbCHg8lL7xOqXLluLskU/BHaOw2e2SKISIQ90mWXh9AVy1XqmG6iTV331jTvpXVETmAVPoMfMMbHaZyVeIeNVtkkWFLKfaaeo2/o+t//w7zt696X/9DaQOHxHtkIQQUdZtkkXD6G2Z6qNDjECAuo0bSRk8mOSCvehz6RWkjRuP3emMdmhCiC6gGyULKVl0VN3Gjexc8AyerVsouPMenHl5ZEycFO2whBBdSLdJFmUyervd/DU1lCx5hfL338WRmUmvCy8mITc32mEJIbqgbpMsyl1uHHYb6SlSbRKKgNvNprm34CsrI+vQw+lx8ik4UmXSPyFEy7pPsqjykJ2eJFNht8HvcuFIT8eelETOUceSsvfeJO/V5ab5EkJ0Md2mL2RFtYzebo3h81G69L9s+OO11Oh1AORMO1IShRAiJN2nZOHy0Cc3NdphdEk1P2kKF8zHs30b6RP2w5nfM9ohCSFiTPdJFlVuRgzMiXYYXU7h889R/u7bJPToQd/ZvyN99NhohySEiEHdIlm4vX5q3D6yM6QaCswxE9hs2Gw2Env3JueY6eQdf6JM+ieE6LCwJgul1NnALYAT+KfW+uFmr58I3Ia5wNL/gFla67L2HkdGbzdyb91K4cL5ZB10CJkHTiH7sCOiHZIQohsIWwO3UqofcCcwFRgLXKqUGhn0eibwKDBDaz0G+AaY25FjydrbZlfYopdfYtPtc3Bv2wqyWp0QohOFs2QxDXhPa10KoJRaDJwG3G697gSu0lpvtR5/A5zTkQPF+3Kq5V9/w8YHH8ZXXEzmgVPJn3kGjoyMaIclhOhGwpks+gLbgx5vBybWP9BalwCvAiilUoAbgX915EDlVVayiNN5ofx1buzORPr/4UZS1fBohyOE6IbCmSzsgBH02AYEmr9JKZWFmTS+1lrPb88B8vLSAXAHIDHBzqD+OXExKM/w+9m+9E0Mn59+J58I+fuTu994bFL1BEB+vpSq6sm5aCTnYs+EM1lsAQ4Ketwb2Bb8BqVUH+At4D3g9+09QEmJi0DAYHthFVnpiRQXu/Yk3phQu2EDhQvn4/51E2njxuOcchg9e2ZSXFoT7dC6hPz8DIqKqqIdRpcg56KRnAuT3W5ruMlur3Ami3eAuUqpfKAaOBW4tP5FpZQDeB14UWv9lz05kLn2dveugvLX1FD86stUfPAejqws+lx+FekT9ouLkpQQIvrCliy01luVUjcD7wOJwDyt9WdKqaXAHGAAMB5IUEqdZm22Rmt9cXuPVebyMLBnx7JlrPAWF1Gx4gOyDzuCvJNPxZEia40LISInrOMstNaLgEXNnptu/biGTuq6W+5yM3pwXmfsqkvx7NxJ9bdfkzPtKJIHDmLwPfeRkC2j1IUQkRfzI7hr3T7cHn+3Gr0d8HopW7aU0jdex+Z0kjFxMgmZmZIohBBRE/PJorutkFez7kd2LpyPd8cOMvafSP4ZZ5GQmRntsIQQca4bJIvuM3rbX1PNtocewJGRQb/fXUvaqNHRDkkIIYBukSxie/S2EQhQ/fWXpI0djyM1jX6/u46kgYOwJ8bm5xFCdE8xv/hRLFdDubdsZvM9f2Xbw/+i+puvAUgZMlQShRCiy4n9kkWVh6REBylJsfNRAm43Ja8toeztt7CnptJr1sWkjR4T7bCEEGK3YucKuxuxOCBv6z/vp3b9T2ROPZj8007Hkd69x4gIIWJft0gWOTHQXuEtLcWRkY7dmUjeCSdhS0ggZeiwaIclhBAh6RZtFl25ZGH4/ZQtX8bGW2+ibNmbAKSOGCmJQggRU2K6ZGEYBuUuT5dNFrUbfqFwwTO4N28mbfQYMg84MNohCSFEh8R0sqjz+PH6Al2y22zZ8mUUvfQCCdnZ9LniatLHT5BJ/4QQMSumk0VltTkgL6uLlCwMw8DwebE7E0lRw8k+4kh6nHQy9mSZ9E8IEdtiO1nU1I/ejn7JwrNjB4XPPUtCbh69Z11E8qACkgcVRDss0Un8fh9lZUX4fJ5ohxKywkI7gcAu643FpXg7FwkJieTk5ONwdN4lPraThVWyiOZyqgGvh7I3l1K69L/YnE56jJ8QtVhE+JSVFZGcnEpaWu+YqU5MSLDj88XPBbI18XQuDMOgurqSsrIievTo02n77R7JIi06yaLu101sf/wRvDt3kjFxMvlnnElCVnZUYhHh5fN5YipRiPhls9lIS8vE5Srv1P3GdrKo8ZKSlEBSYnTWnnakZ2BPSqbf768nbZ9RUYlBRI4kChErwvFdje1kUe2JaHuFEQhQ8dGH1Hz/HX2uuBpnbi4Db50rFxEhRLcX28miJnJjLNybf2XngvnUbfiFlOEjCNTW4EhNk0QhIm779m2cddYpFBQMBsDtrmPffcdw+eVXk5vb/hUj5817jOHDRzB16iEtvn733Xdw0kmnMnz4yA7HfP/99/Dtt1/j83nZsmVzQ+wzZ57JjBkndHi/LXnppX/Tp0+f3X6eaKiqquL2229h27atZGfncPvtd5GX16PJe2pra7nvvr+i9ToSE5O48MJLmTr1YACef34hr7/+KoGAwRVXXM0hhxxOYeFO/u//HuGWW26LyGeI7WTh8pCbmRzWYwTcbkr+8ypl7yzHkZZG74suJWPyAZIkRFT16JHPM8+YKxYbhsHjjz/MLbfcwCOPzGv3vi6++PJWX7/xxls7FGOw6667ATAT3W9/e1lD7J2ttLSElStX8MADj4Rl/x31xBOPMHr0OO699wGWLXuDBx64n9tvv6vJexYseBqHI4Fnn32BqqpKLrvsQpQaTnFxEcuXL+XppxdRU1PNZZfNYty4CfTs2Yvc3FxWrVrJAQdMDftniO1kUeNhcL8wryJnGFSt/ZysqQfR45SZMumf6HJsNhsXXXQZxx9/FD//vJ4hQ4ayYMEzvP/+O/j9fiZNmswVV8zGZrPxwgvPsWTJyzgcDg488CCuvHI2d945l3HjJnDIIYcxd+7NlJSUAHDhhZcwdeohXH31pVx44aWMH78fzz77FMuXv4ndbmf//Sdz5ZWzKSzcyZ/+dD2DB+/NTz9pcnPzuOOOu8nMzAop/tNOO56RI0exfr3mkUfmsXr1J7z00vMEAgZKDefaa28gKSmJ1as/4cknH8Pn89GnTz9uuOFmspp1KHnllZc47LDDAfD5fNx//91s2PALZWWl7L33EObOvZPS0lKuu+63ZGVlk5SUxP33/4tHHnmAL79ci98fYPr04zjjjHOabF9aWsqQIeb2SUmNN6jff/8d99771yYxpKam7pK0V636mIce+j8Apk07mr///W/4fD4SEhovwevXa04+eSZ2u52srGyGDh3Gp5+uorBwJ4cccjhJSUkkJSUxbtwEPv74I4499jiOOWYGf//73yRZtMUfMMJSDeUtKaZ02VLyTz8Le3IyBbf9RQbWiSY+/nY7K7/ZHpZ9Tx3dhyn7tq/Lo9PpZMCAAWzatJHi4iK0/pGnn16A329wxx1zWL78TQYOHMSrry5m3rwFJCcnc911s1m37seGfaxY8QG9e/fl3nsfYP16zfLly5pU5axa9TErV65g3rwFJCQkcMstf2TJkpc58MCp/Pzzem66aQ7Dhg3n5pv/wPLlb3LaaWeGHP/kyQdy++13sWHDL7z++hIeffQpkpKSeOyxh3j++QWceOKpPPbYQzz44GNkZmayZMnLPProv3Yp9axcuYI///kvAHz33TckJDh5/PGnsdvhyisvZdWqj1FqBL/+uomXXvoXffr0ZcmSxQA89dRzeDwerr32aoYPH4lhGA3bBwIBZs++nFWrPubQQ49oON4++4wKqZRUXFzUUO2UkJBAWloa5eVl9OiR3/CeYcOG8/777zBx4mTKykr59tuvGTp0GMXFRYwYsU/D+/LyelBUVAjA4MFD2LhxA5WVFSEn546K6WQBkNOJycLw+Sh7Zzklry0BIHPygaTsPUQShYgRNpKSkliz5jN++OE7LrjgHAzDbNPo1as3JSUlTJlyEOlW6bh5Vc2oUaN5/PGHKS4u5IADpnLBBRc1eX3t2s+ZNu1okpPNO+sZM07gzTff4MADp5KTk8uwYcMB8wJWWVnZrshHjjR7E3755Rq2bNnMZZfNAsDn8zJs2HB++OE7du7cwezZZpVZIOBv8eK4Zcuv9OzZE4CxY8eTmZnFyy+/yObNm9iyZTO1tbUA5OTk0qdPXwDWrPmM9et/Yu3aNQDU1tbwyy8/c8opMxu2//XXjU22rxdqycIwjF0eN6/KPu+8WTz44P1ceOE5DBxYwMSJk0lIcFrvbb5t4xyw+fk92bZtqySLtnRWyaL25/XsXDAfz9YtpI0dR8+zzsWZ1/7GQhEfpuzb/rv/cPJ6vWzevIm99hrMF198zumnn8W5556PzxegqqoKh8PBf//7H6DxqlNcXNSkSmXAgIEsWrSY1atX8fHHK/j3vxeycOFLDa8bRtNBbYZhjmwHSGy2umPzi2NbkpLMv2O/P8Dhh0/jd7/7AwA1NTX4/X6++moto0eP4Z57/gGA2+3e5cINYLPZG6p2Vq78kHnzHmfmzDM57rgTKCsra4ir/nj1x7zyytkccohZfVVeXk5KSkqT7adPP4Hy8vJdPleoJYv8/J6UlpbQs2cvfD4fNTU1u1ShuVwuLrvsqobn//CHa5gy5WDq6mopLi5ueF9paQkDBw5qeOxwJDRJHuES81OUd0bXWcMwKHrxeQK1NfS9ajb9rr5GEoWIGYFAgCeffJyRI/elX7/+jB+/P2+9tZSamhp8Ph833XQdH3zwLmPGjGP16o8bnp8792bWrfuhYT8vv/wCTz75OIcfPo3rrruRsrIyqqurG14fP35/3nnnLdzuOnw+H0uXvsb48ft16mcZN24CK1Z8QFlZKYZhcP/9d/Hii4sYOXIU33//Lb/+ugmAZ56Zx8MP/3OX7fv168/27Wb14Jo1n3H44dOYMeMEMjIy+PLLtQQC/l22mTBhP157bUnDRfzKKy/i+++/bbJ9enr6brcPxeTJU1i27A0A3nvvbcaMGdukvQLggw/eYd68xwH4+ef1/PTTOvbffyKTJx/Ihx++R11dHWVlZaxd+zn77TexYbuiop0NpaRwivmSRUcnETQMg6pPV5G2z744MjLoc+kV5iC75PD2rhKiMxQXF3HBBWcDZpXM0KGKuXPvBGDq1IP5+eefuOii860G7gM59tjjsNlsnHLK6Vx++SwCAYNDDjmM/fefxPLl5jorxxwzg7lzb+b888/A4XBw1VWzycjIaDjmlCkHsX69tvbrY+LEyZx66hkN9eedYejQYcyadQmzZ1+OYRgMGTKMc8+9gKSkJG68cQ5z5txEIOAnP78Xc+bcvsv2U6YcxBdfrKGgYC+OP/5kbrvtZt555y2cTif77juabdu2MaHZjDwnnXQaW7ZsZtass/H7/Uyffjzjx+9HVlZ2w/YJCY3bd8Qll1zOnXfO5dxzTycjI505c8x2lZUrP2TlyhXceOOtHH/8ydxxx62cf/4ZJCQkcPvtd5OamsbIkaM46qjpXHyxed4vvvhy8vPNqrYNG35m4MACMjPD3NEHsLW3uNhFFAD/++197zPnN+2/s/Fs38bOhc9Sq9eRd9Ip5B3Xuf28Iy0/P4Oioqpoh9ElhOtc7Nixid69B7X9xi4knuZDqldSUsycOTfx8MNPNHm+u56LBx+8n/32m8SBB+7aG6ql76zdbiMvLx1gL2Bje44V0yWLzDRnu94f8HgoXfo6pW8uxZ6URM/zLiDroIPDFJ0QItLy8npw8MGHsmLFBxx88KHRDiesdu7cQUlJSYuJIhxiO1mktq+9onjxC5S/9y4Zkw8gf+aZJGSFt/eAECLyzjjjnGiHEBG9evXmttv+2vYbO0lsJ4u0tpOFr6Icw+vF2SOfnGNmkD5uAqkjOj5tgRBCxKNumyyMQICKD9+n+JXFJO89lP6/uxZnbi7O3NwIRii6k5b6xgvRFYWjLTq2k8VuqqHqNm1k54L5uDf+j9QR+9DzrLMjHJnobhISEqmuriQtLVMShujS6hc/Skjo3Bm5YztZtFCycH31JdsefhBHRga9L7mcjImT5I9b7LGcnHzKyoo6fUGZcLLb42sp0dbE27moX1a1U/fZqXuLsPqShWEYBFwuHBkZpA4fQc7Rx5I7fQaO1LQoRyi6C4cjoVOXqIwE6VLdSM7FngtrslBKnQ3cAjiBf2qtH272+lhgHpAJrAAu11r7Qt1/ZpoTb3ERhYsW4incyaA/34E9OZn8007vvA8hhBAifNN9KKX6AXcCU4GxwKVKqebdkBYCV2uth2FOWnNJe45hfP4xG+fcTI1eR/bBh2Kzx/zsJUII0SWFs2QxDXhPa10KoJRaDJwG3G49HgSkaK1XW+9/BrgNeDSEfTsAatZ8SvaUqeQdfyIJ2dmdG32MsdulXaaenItGci4ayblocg4c7d02nMmiLxA84f92YGIbr/cPcd99AEbffeeexNetWEP4BXIugsm5aCTnook+wC/t2SCcycIOBHf2tQGBdrzems+BgzATTMemgRRCiPjjwEwUn7d3w3Amiy2YF/R6vYFtzV7v08rrrXEDK/coOiGEiE/tKlHUC2eL8DvAEUqpfKVUKnAqsKz+Ra31JqBOKTXFeuo84M0wxiOEEKKDwpYstNZbgZuB94GvgEVa68+UUkuVUvXzip8D/EMptQ5IBx4MVzxCCCE6LlbXsxBCCBFBMjBBCCFEmyRZCCGEaJMkCyGEEG2SZCGEEKJNXX7W2XBPRhhLQjgXJ2JOmWID/gfM0lqXRTzQCGjrXAS9bwbwkNZ6r0jGF0khfC8U8DiQA+wAzozX74VSajzmuUgENgPnaq3LIx1nJCilMoFPgOO01hubvTaWdl43u3TJIhKTEcaKts6F9cV4FJihtR4DfAPMjXyk4Rfi9wKlVC/gPszvRbcUwvfCBrwG3G19L74EboxCqGEX4vfiAWCOdS40cH1Eg4wQpdQkzIHLw3bzlnZfN7t0siBoMkKtdTVQPxkhsNvJCGdGPMrIaPVcYN5JXWWNbwEzWQyMcIyR0ta5qDcPs6TVnbV1LsYD1Vrr+gGxfwVaLIV1A6F8LxyYd9MAqUBtBOOLpEuAq2hhVoyOXje7ejVUOCcjjDWtngutdQnwKoBSKgXz7vFfkQwwgtr6XqCUmg18Aayme2vrXAwBdiilngTGAT8Cv41ceBHV5vcCuBZYrpT6J1ANTIpMaJGltb4YwKyB3EWHrptdvWQRzskIY01In1UplQW8AXyttZ4fodgirdVzoZQahTm9zB0Rjisa2vpeJACHAo9qrccDG4C/Ryy6yGrre5ECPAlM01r3AR4Bno1ohF1Dh66bXT1ZtDXZ4J5MRhhr2vysSqk+wEeYVVAXRy60iGvrXMy0Xl8DLAX6KqU+ilx4EdXWudgBrNdar7EeP8+ud9vdRVvnYhRQq7X+zHr8OGYijTcdum529WQhkxE2avVcKKUcwOvAi1rr32mtu/M8Lm19L/6stR6mtR4LTAe2aa0PanlXMa/Vc4HZGyZfKTXGenw8sDbCMUZKW+fiZ2CAaqybOZEOTNUd6zp63ezSyUImI2wUwrk4AbMx8zSl1FfWv3nRizh8QvxexIW2zoXWuhY4GXhCKfU9cDhwXdQCDqMQzkUZcAHwolLqG+BCYFa04o20Pb1uykSCQggh2tSlSxZCCCG6BkkWQggh2iTJQgghRJskWQghhGiTJAshhBBt6urTfYg4opQygO8Af9DTa+qnLtjNNhcAp2mtj+uE48/FnE9nK+YIVwdQCFyptf6pA/vrCyzWWh+olNoLuE9rfWrw850QcwHwC/Bt0NPpmAOvLtRab2hj+zmYo/3/s6exiO5NkoXoag7TWhdH8fgvaK2vrn+glPotsAho9/gNrfU2oD4hDAJUC893hlprACLQMNPsg5gzsJ7VxraHAz90Yiyim5JkIWKCUupC4DLMdQhyMafcfrTZe07BXMsggFk6+YPWeoU1X9YDwL6Ys/O+a70Wyron7wJ3WfvvjzkNfAHmfDrztdb3KqUSMCdtnAJ4MedfmgX0wCwpZWHOgNtPKfWW9Tnqn98InKS1Xmsd4wXgA631o0qpmzFHIdut911pJZq2JGNOFrfD2ucwzJlmMzCnefgKOAO4CDMJ3quU8mPOKXYPcAhmqepLYLbWujKEY4puTtosRFfzftAI9K+UUj2VUumYUy5P11qPw7zQ/a2Fbe/FvKDuB9xK47w//wDWaq0nYM682gNz9tFWWUngIswRwQDPAe9rrffFTAznKqXOBA6wjjXGOsYGYHT9frTWfsy5un7RWh/d7PmnsEYRK6VyMKfZXqSUOh8zuU20Sg1LMRNOS1Ksc/WtUmon5my764AbrNcvwUxskzFnod0Lc92ThzHnz/qD1vpVzJmKfcAEa72HbcDdbZ0nER+kZCG6mharoZRSxwEzlFJDMRe2SW9h238Dryql3gDepjGhHAdMVEpdZD1OaeX4Zyilplo/J2LOo3SJUioNM0EcBaC1rlBKPQMcC1yDWZL51Co5vGxNM1EQwud9CvhcKXUtZpXRa9a+j8Oc8G+NNZWRA3P9hZY0VEMppY7GXNjmda21y3r9BuBIpdQfMRfD6UvL5+84INt6b/3nLwzhM4g4ICUL0eVZ1T9fYdb7r8SsatqF1vpmzFXS1mDOAbTCeskBzNRaj7UuqpOAq1vaB2abxVjr30it9Xla6x2YfyvNV9yzA05rWc4xmKuu+YEXlFJXhvLZrEndvsC8UM+isfTgAO4Jink/zGTV1v7ewpyC/CVr9UQwZ5q9FNiEWcr6ooXPUn/Ma4KOOZGWF5UScUiShYgF+wFFwF+A5ZgX1vqZdrF+TlBKbQRStdaPAVcCo5VSScBbwO+VUjbr8WvsPlm0SGtdhbmQ0lXW8bKA84G3rVLAu8AnWuu5mGsk7N9sFz7M9pKWPIF595+mtf7Yeu4t4OKgC/7twIIQw70PqKJxlcCjgdu11i9YjydhJobmcb0FXK2USlRK2a247grxmKKbk2QhYsFyzK6gGnOlt4GYyWNI/RusxurfYdb3fwG8hNl11A3MBtIwu5d+Y/3fUptHW87BnAL7W+Az4BXMJSnfBL4HvlNKrcHs6dR8OdcfMKeF/oxd7+pfw2w0D26TmAf8F1htzRY7GrO01CattRczGV5tLQT1J8zquW8x13D4kMZz9xpwl1LqN5iLRW3EbNj+wYqzW85QK9pPZp0VQgjRJilZCCGEaJMkCyGEEG2SZCGEEKJNkiyEEEK0SZKFEEKINkmyEEII0SZJFkIIIdokyUIIIUSb/h/vUIB4UjZbOQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ROC for test DT\n",
    "\n",
    "auc_score_dt = roc_auc_score(y_test, pred_dt)\n",
    "fpr, tpr, thresholds = roc_curve(y_test,pred_dt)\n",
    "#auc_score_dt = roc_auc_score(y_test, clf.predict_proba(X_test)[:,1])\n",
    "#fpr, tpr, thresholds = roc_curve(y_test,clf.predict_proba(X_test)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Decision Tree (area = %0.2f)' % auc_score_dt)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "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": 5
}