8 4 1 LOJISTIK REGRESYON.ipynb

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 "cells": [
  {
   "source": [
    "# LOGISTIK REGRESYON"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import statsmodels.api as sm \n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.preprocessing import scale, StandardScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, mean_squared_error, r2_score, roc_auc_score, roc_curve, classification_report "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
   ]
  },
  {
   "source": [
    "## Veri Seti Hikayesi ve Problem : Şeker Hastalığı Tahmini"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ],
      "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>Pregnancies</th>\n      <th>Glucose</th>\n      <th>BloodPressure</th>\n      <th>SkinThickness</th>\n      <th>Insulin</th>\n      <th>BMI</th>\n      <th>DiabetesPedigreeFunction</th>\n      <th>Age</th>\n      <th>Outcome</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>6</td>\n      <td>148</td>\n      <td>72</td>\n      <td>35</td>\n      <td>0</td>\n      <td>33.6</td>\n      <td>0.627</td>\n      <td>50</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>85</td>\n      <td>66</td>\n      <td>29</td>\n      <td>0</td>\n      <td>26.6</td>\n      <td>0.351</td>\n      <td>31</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>183</td>\n      <td>64</td>\n      <td>0</td>\n      <td>0</td>\n      <td>23.3</td>\n      <td>0.672</td>\n      <td>32</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>89</td>\n      <td>66</td>\n      <td>23</td>\n      <td>94</td>\n      <td>28.1</td>\n      <td>0.167</td>\n      <td>21</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>137</td>\n      <td>40</td>\n      <td>35</td>\n      <td>168</td>\n      <td>43.1</td>\n      <td>2.288</td>\n      <td>33</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "df = pd.read_csv(\"verisetleri\\diabetes.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hasta bilgileri ve tahlil bilgileri üzerinden tahmin yapılacak."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preganacies-> hamilelik sayısı, Glucose-> gilikoz, BlookPressure-> kan basıncı, BMI-> vücut kitle endesi\n",
    "# Outcome -> şekir hastalığını ifade eder"
   ]
  },
  {
   "source": [
    "## Lojistic Regresyon\n",
    "## Model & Tahmin"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TEST VE TRAIN OLARAK AYRIM YAPILMADIĞINA DİKKAT EDİNİZ. SONRAKİ BÖLÜMLERDE YAPILACAKTIR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0    500\n",
       "1    268\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "df[\"Outcome\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                          count        mean         std     min       25%  \\\n",
       "Pregnancies               768.0    3.845052    3.369578   0.000   1.00000   \n",
       "Glucose                   768.0  120.894531   31.972618   0.000  99.00000   \n",
       "BloodPressure             768.0   69.105469   19.355807   0.000  62.00000   \n",
       "SkinThickness             768.0   20.536458   15.952218   0.000   0.00000   \n",
       "Insulin                   768.0   79.799479  115.244002   0.000   0.00000   \n",
       "BMI                       768.0   31.992578    7.884160   0.000  27.30000   \n",
       "DiabetesPedigreeFunction  768.0    0.471876    0.331329   0.078   0.24375   \n",
       "Age                       768.0   33.240885   11.760232  21.000  24.00000   \n",
       "Outcome                   768.0    0.348958    0.476951   0.000   0.00000   \n",
       "\n",
       "                               50%        75%     max  \n",
       "Pregnancies                 3.0000    6.00000   17.00  \n",
       "Glucose                   117.0000  140.25000  199.00  \n",
       "BloodPressure              72.0000   80.00000  122.00  \n",
       "SkinThickness              23.0000   32.00000   99.00  \n",
       "Insulin                    30.5000  127.25000  846.00  \n",
       "BMI                        32.0000   36.60000   67.10  \n",
       "DiabetesPedigreeFunction    0.3725    0.62625    2.42  \n",
       "Age                        29.0000   41.00000   81.00  \n",
       "Outcome                     0.0000    1.00000    1.00  "
      ],
      "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>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Pregnancies</th>\n      <td>768.0</td>\n      <td>3.845052</td>\n      <td>3.369578</td>\n      <td>0.000</td>\n      <td>1.00000</td>\n      <td>3.0000</td>\n      <td>6.00000</td>\n      <td>17.00</td>\n    </tr>\n    <tr>\n      <th>Glucose</th>\n      <td>768.0</td>\n      <td>120.894531</td>\n      <td>31.972618</td>\n      <td>0.000</td>\n      <td>99.00000</td>\n      <td>117.0000</td>\n      <td>140.25000</td>\n      <td>199.00</td>\n    </tr>\n    <tr>\n      <th>BloodPressure</th>\n      <td>768.0</td>\n      <td>69.105469</td>\n      <td>19.355807</td>\n      <td>0.000</td>\n      <td>62.00000</td>\n      <td>72.0000</td>\n      <td>80.00000</td>\n      <td>122.00</td>\n    </tr>\n    <tr>\n      <th>SkinThickness</th>\n      <td>768.0</td>\n      <td>20.536458</td>\n      <td>15.952218</td>\n      <td>0.000</td>\n      <td>0.00000</td>\n      <td>23.0000</td>\n      <td>32.00000</td>\n      <td>99.00</td>\n    </tr>\n    <tr>\n      <th>Insulin</th>\n      <td>768.0</td>\n      <td>79.799479</td>\n      <td>115.244002</td>\n      <td>0.000</td>\n      <td>0.00000</td>\n      <td>30.5000</td>\n      <td>127.25000</td>\n      <td>846.00</td>\n    </tr>\n    <tr>\n      <th>BMI</th>\n      <td>768.0</td>\n      <td>31.992578</td>\n      <td>7.884160</td>\n      <td>0.000</td>\n      <td>27.30000</td>\n      <td>32.0000</td>\n      <td>36.60000</td>\n      <td>67.10</td>\n    </tr>\n    <tr>\n      <th>DiabetesPedigreeFunction</th>\n      <td>768.0</td>\n      <td>0.471876</td>\n      <td>0.331329</td>\n      <td>0.078</td>\n      <td>0.24375</td>\n      <td>0.3725</td>\n      <td>0.62625</td>\n      <td>2.42</td>\n    </tr>\n    <tr>\n      <th>Age</th>\n      <td>768.0</td>\n      <td>33.240885</td>\n      <td>11.760232</td>\n      <td>21.000</td>\n      <td>24.00000</td>\n      <td>29.0000</td>\n      <td>41.00000</td>\n      <td>81.00</td>\n    </tr>\n    <tr>\n      <th>Outcome</th>\n      <td>768.0</td>\n      <td>0.348958</td>\n      <td>0.476951</td>\n      <td>0.000</td>\n      <td>0.00000</td>\n      <td>0.0000</td>\n      <td>1.00000</td>\n      <td>1.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    " # Hamilelik max 17 aykırı gözlem olabilir. ( Diğer keşifçi veri analizine ilişkin bazı değerlendirmeler yapılmalıdır. Ve veri önişlemle adımları uygulanmalıdır. )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"Outcome\"]\n",
    "X = df.drop([\"Outcome\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  \n",
       "0                     0.627   50  \n",
       "1                     0.351   31  \n",
       "2                     0.672   32  \n",
       "3                     0.167   21  \n",
       "4                     2.288   33  "
      ],
      "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>Pregnancies</th>\n      <th>Glucose</th>\n      <th>BloodPressure</th>\n      <th>SkinThickness</th>\n      <th>Insulin</th>\n      <th>BMI</th>\n      <th>DiabetesPedigreeFunction</th>\n      <th>Age</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>6</td>\n      <td>148</td>\n      <td>72</td>\n      <td>35</td>\n      <td>0</td>\n      <td>33.6</td>\n      <td>0.627</td>\n      <td>50</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>85</td>\n      <td>66</td>\n      <td>29</td>\n      <td>0</td>\n      <td>26.6</td>\n      <td>0.351</td>\n      <td>31</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>183</td>\n      <td>64</td>\n      <td>0</td>\n      <td>0</td>\n      <td>23.3</td>\n      <td>0.672</td>\n      <td>32</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>89</td>\n      <td>66</td>\n      <td>23</td>\n      <td>94</td>\n      <td>28.1</td>\n      <td>0.167</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>137</td>\n      <td>40</td>\n      <td>35</td>\n      <td>168</td>\n      <td>43.1</td>\n      <td>2.288</td>\n      <td>33</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"liblinear\" --> Lojistik regresyonda katsayıları bulabilmek adına kullanılan birden fazla minimizasyon yaklaşımı var. Yani gerçek değerler ile tahmin edilen değerler arasındaki farkların karelerinin toplamını ifade etmek adına bu sefer loglost gibi veya klasik regresyondan alışık olduğumuz yöntemler gibi bazı birbirinden farklı yöntemler var. Hatta ritch ve lasso dan alışık olduğumuz düzenlileştirme yöntemlerinin de işin içine katıldığı bazı katsayı bulma yöntemleri var. Bu sebeple bunu liblinear olarak ifade ettik.\n",
    "loj_model = LogisticRegression(solver=\"liblinear\").fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([-5.88581574])"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "# B0 katsayısı\n",
    "loj_model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[ 1.17109287e-01,  2.83637114e-02, -1.69030150e-02,\n",
       "         7.35724571e-04, -6.42784045e-04,  5.96466920e-02,\n",
       "         6.85700638e-01,  7.19299389e-03]])"
      ]
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "# Bağımısız değişkenlerin katsayıları\n",
    "loj_model.coef_"
   ]
  },
  {
   "source": [
    "### TAHMİN İŞLEMLERİ"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0, 1, 0, 0, 1, 1, 0], dtype=int64)"
      ]
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "# Tahmin edilen değerler\n",
    "loj_model.predict(X)[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "5    0\n",
       "6    1\n",
       "7    0\n",
       "8    1\n",
       "9    1\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "# Gerçek değerler\n",
    "y[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Başarısının değerlendirilmesi\n",
    "y_pred = loj_model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[448,  52],\n",
       "       [120, 148]], dtype=int64)"
      ]
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "# Karmaşıklık matrisi\n",
    "confusion_matrix(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.7760416666666666"
      ]
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "# Karmaşıklık matrisinin değerlerini yorumlamak kolay olmayacağızndan accuracy_score kullanacağız.\n",
    "# accuracy_score() --> doğruluk oranı, (başarılı işler) / (tüm durum)\n",
    "accuracy_score(y, y_pred)"
   ]
  },
  {
   "source": [
    "### Detaylı sınıflandırma raporu"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n\n           0       0.79      0.90      0.84       500\n           1       0.74      0.55      0.63       268\n\n    accuracy                           0.78       768\n   macro avg       0.76      0.72      0.74       768\nweighted avg       0.77      0.78      0.77       768\n\n"
     ]
    }
   ],
   "source": [
    "# DAHA DETAYLI BİR SINIFLANDIRMA RAPORU ALMAK İÇİN classification_report KULLANILIR\n",
    "print(classification_report(y, y_pred))"
   ]
  },
  {
   "source": [
    "### Olasılık Değerlerinin Bulunması"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[0.35043779, 0.64956221],\n",
       "       [0.91696356, 0.08303644],\n",
       "       [0.22433864, 0.77566136],\n",
       "       [0.92141996, 0.07858004],\n",
       "       [0.16538713, 0.83461287],\n",
       "       [0.79904414, 0.20095586],\n",
       "       [0.88018381, 0.11981619],\n",
       "       [0.27825923, 0.72174077],\n",
       "       [0.32175629, 0.67824371],\n",
       "       [0.9226324 , 0.0773676 ]])"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "# Bazı durmlarda sonuçları 1-bir ve 0-sıfır olarak değilde, direk olasılık değerlerinin kendisinden almak istersek - ki logistik fonksiyonu bize birinci sınıfın yani ilgilenmiş oluduğumuz 1-bir sınıfının gerçekleşme olasılığını veriyordu. - predict_proba() fonksiyonunu kullanırız.\n",
    "loj_model.predict_proba(X)[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict() fonksiyonu arka planda bu değerleri 0-sıfır ve 1-dönüştürmektedir.\n",
    "# Bu sonuçlarda ilgileneceğimiz yani 1-bir ve 0-sıfır değerlerinin gösterildiği bölüm ikinci sutün dur. "
   ]
  },
  {
   "source": [
    "### ROC eğrisinin oluşturulması "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ROC EGRİSİ : False positive rate ve true positive rate ler üzerinden oluşturulan bir grafik aracılığı ile bize bilgi sunan bir değerdir. Eğri altındaki alan ile ilgileniyorduk. Bu ne kadar büyük ise modelimiz o kadar başarılı şeklinde yorumlar yapabiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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ceWbYUSUcSxTmkFhSMElJFUTc/NRVqsC//gX//GfKjM1U0CxRmHyJ1fhsScEktOnT3fhMr70GTZrAgAFhR5TwLFGYPIv0YwBLCiaJbNwIDz4IAwfCMce418aXQBOFiLQEXgSKAINV9V9Z1tcA3gTKe9v0UtXPgozJHJroJGH9GEzSGDUK7rwT1q93kwo9/jiUKRN2VEkjsEQhIkWAAcAFQCYwS0TGqmpG1GYPA6NV9RURqQd8BqQFFZM5NJYkTNL64QdIS4Px46Fx47CjSTpBlihOBZaq6jIAERkJtAWiE4UCkSEXywG/BRiPyaOsDdXWI9okjR074JlnXBtEmzauyunhhwvFAH5BCDJRVAVWRr3OBJpl2aY38LmI3AGUAs7P7kAi0hXoClCjhn1ABSWnxBBpqLb2CJMUvvgCunVzg/f16OESRdGiYUeV1MJuzO4IDFXV50SkOfC2iDRQ1X3RG6nqIGAQQNOmTTWEOFOOn9taLTGYpPJ//wf33APDh0OtWvD553DBBWFHlRKCTBSrgOpRr6t5y6J1AVoCqOq3IlICqASsDTCuQs1uazUpa+JEeO89ePRRN191iRJhR5QygkwUs4DaIpKOSxBXAVdn2WYFcB4wVEROAEoA6wKMqVCyMZVMypo/31UxtW8P11wDZ5wB6elhR5VyAksUqrpHRLoDE3C3vr6hqotFpA8wW1XHAj2A10Tkn7iG7c6qalVLBShrnwdLECYlbN0Kjz3mpiBNS4N27Vwva0sSgQi0jcLrE/FZlmWPRj3PAM4IMobCLlKSsDuVTMoYMwbuuAMyM6FrV3j6aZckTGDs6qaw4TNWMGP57zZNqEkdCxfCpZfCiSe6TnSnnx52RIWCJYoUlLXB2qYJNUlt9243quu557oE8emn7m4mu+U1bixRpJDs7miy9giT1L75Bm69FRYvhiVL3G2vrVqFHVWhY4kihXw0bxUZqzdbgjDJ7/ffoVcvN8Jr9erwwQcuSZhQWKJIMfWqlGXULc3DDsOY/NuxAxo1gt9+cz2re/eG0qXDjqpQs0SRAiJVThmrN1OvStncdzAmEWVmunmqS5SAJ55wyeKkk8KOygCHhR2AOXTRScIark3S2b7d9aauWdPNXQ1w/fWWJBKIlSiSVHRv60iSsConk3Q+/9wN4Pfzz3DttXDqqWFHZLLhu0QhIkcEGYjJm0gpArCShElOd9wBF14Ihx3mRnx9+204+uiwozLZyLVEISKnA4OB0kANETkJuEVVuwUdnInNShEm6ezd634WKQKnnQaVKsH999sAfgnOT4ni38CFwAYAVZ0PtAgyKBNbpMe1MUll7lxo3hxeftm9vuYaN16TJYmE56vqSVVXZlm0N4BYjA/Rg/xZdZNJClu2wD//CaecAitWQJUqYUdk8shPY/ZKr/pJRaQocBfwfbBhmZzYIH8mqXz+Odx4o+sTceut8NRTUL582FGZPPKTKG4FXsRNbboK+Byw9okQ2CB/JukUKwZHHQXvvw/Nss6EbJKFn0RRV1WviV4gImcA04IJyeQkUpqwKieTsHbvhuefh82b4ckn4eyzYfZsd2eTSVp+fnv/8bnMxIGVJkzC+vpraNzYjdH000+wb59bbkki6eVYohCR5sDpQGURuSdqVVncjHXGGAMbNrhbXF9/HWrUcL2rW7cOOypTgGKl+mK4vhOHA2WiHpuB9sGHZoxJChs2wMiRcN99kJFhSSIF5ViiUNUpwBQRGaqqv8YxJmNMovv+exg92vWDqFPH3fZaoULYUZmA+GnM3iYi/YD6wP6eMap6bmBRGWMS07ZtrpG6Xz839HeXLm7EV0sSKc1PK9Mw4AcgHXgc+AWYFWBMJhvWG9uEbvx4aNDA9YW4+mo341y1amFHZeLAT4mioqq+LiJ3RVVHWaKII+uNbUK3dStcdx1UrAiTJrnbXk2h4adEsdv7uVpELhaRxoCVM+PIemObUOzdC++8436WLu1GeJ0/35JEIeSnRNFXRMoBPXD9J8oCdwcZlHGiZ66z/hMmrubMgVtucT9LloTLL7eJhAqxXEsUqvqJqm5S1UWqeo6qngxYZXkc2Mx1Ju42bYI773QTCK1a5W57veyysKMyIYvV4a4IcCVujKfxqrpIRFoDDwIlgcbxCbFwszknTFxdfjl8+SXcfjv07QvlyoUdkUkAsaqeXgeqAzOBl0TkN6Ap0EtVx8QhNmNMPCxbBpUrQ5ky7tbXww5zQ4Ib44mVKJoCDVV1n4iUANYANVV1Q3xCK3yi58GGA3NhGxOIXbugf3944glX3fTMMzbCq8lWrDaKXaq6D0BVdwDLLEkEK3oebLC5sE2Apk6FRo3goYfckBt33hl2RCaBxSpRHC8iC7znAtT0Xgugqtow8OgKIWuTMIH797/hnnsgLQ0+/RRatQo7IpPgYiWKE+IWRSEXfRusVTWZQOzbB3/+6dohLr4Y1q2Dhx+GI44IOzKTBGINCmgDAQYskiAiQ3M0S69gVU2m4C1e7KYhjcw0V6eOG4bDGJ/8dLjLNxFpiZtGtQgwWFX/lc02VwK9AQXmq+rVQcYUpqyN1VkThHWoMwVq2zbXUN2/v7vN9cYbQRVEwo7MJJnAEoXXD2MAcAGQCcwSkbGqmhG1TW3gAeAMVd0oIkcFFU/YosdrapZeYf9PSxAmEN995zrK/fIL3HADPPssVKoUdlQmSflKFCJSEqihqkvycOxTgaWqusw7xkigLZARtc3NwABV3QigqmvzcPykEZ0kbLwmE6hIiaFGDfd4801o0SLsqEySyzVRiEgboD9uxrt0EWkE9FHVS3LZtSqwMup1JpD1Ju063jmm4aqneqvqeH+hJ66cqpgsSZjA7NkD//0vjB0LEye6UV6nTAk7KpMi/Iwe2xtXOvgDQFXn4eamKAiHA7WBs4GOwGsiUj7rRiLSVURmi8jsdevWFdCpg5O1P0Sz9AqWJExwZs50YzP9859QogRs3pz7PsbkgZ+qp92qukkObgBTH/utwg0BElHNWxYtE5ihqruB5SLyIy5xHDTfhaoOAgYBNG3a1M+5QxOZYKhZegXrD2GCtXUr3H8/vPIKVKkC777rxmqyxmpTwPyUKBaLyNVAERGpLSL/Ab7xsd8soLaIpItIMeAqYGyWbcbgShOISCVcVdQyn7EnHJtgyMRV0aIweTLccYebw7p9e0sSJhB+EsUduPmydwLDgU34mI9CVfcA3YEJwPfAaFVdLCJ9RCTSvjEB2CAiGcAkoGcyDxNiEwyZwC1dCp06wZYtULy4my/ixRehrHXUNMER1dg1OSLSRFXnximeXDVt2lRnz54ddhjZ6jDwWwCrcjIFb+dOd4vrk09CsWJu6I2zzgo7KpNERGSOqjbNz75+2iieE5G/Ae8Bo1R1UX5OlKqi73CyIThMICZNgttugyVLoEMHeP55OOaYsKMyhYifGe7OAc4B1gEDRWShiDwceGRJIvoOJxvt1RQ4VVeK2L0bxo93M85ZkjBx5qvDnaquwU1eNAm4D3gU6BtkYMnA7nAygdi3D15/HVq2hOrV4e23oXx5N3e1MSHItUQhIieISG8RWQhE7niqFnhkCc7ucDKBWLAAzjwTunaFwYPdsipVLEmYUPkpUbwBjAIuVNXfAo4nKdiQHKbAbd0Kjz/u5oo48kgYOtTd3WRMAsg1Uahqoa9TsSE5TOB694bnnoObboJ//csNwWFMgsgxUYjIaFW90qtyir6HttDNcJd1UiEb9dUUiJUr3WRCxx8PvXpBu3au2smYBBOrRHGX97N1PAJJVNZgbQrcnj3w0kvw6KNw8slu8L5KlSxJmISVY2O2qq72nnZT1V+jH0C3+IQXvkiVkzVYmwIxfTo0bQo9esDZZ7thwI1JcH6G8Lggm2UXFXQgiaxZegWrZjKH7tNP4fTTYf16+OAD+PhjSEsLOypjcpVjohCR27z2iboisiDqsRxYEL8QwxOpdjIm31RhlXcjxPnnQ58+bgC/Sy+1AfxM0ojVRjEcGAc8DfSKWr5FVQvFp6dVO5lD8uOP0K2b+5mRAaVLw8M2qIFJPrGqnlRVfwFuB7ZEPRCRCsGHlhis2snk2Y4d7nbXE0+E2bPhgQesw5xJarmVKFoDc3C3x0aXkxU4LsC4jElOa9a4Oap/+gk6dnQD+P3tb2FHZcwhyTFRqGpr72dBTXuaNCId7Gw0WOPb7t1uIqGjj3aJYsAAuCC7+0CMST5+xno6Q0RKec+vFZHnRSSl62Kik4S1T5iY9u2DV1+FmjUhM9M1UA8ebEnCpBQ/t8e+AmwTkZOAHsDPwNuBRhWiyJ1O9aqUZdQtza19wuRs/nx3u+ttt0Ht2q5UYUwK8pMo9qibBq8t8F9VHQCUCTascNiIsMYXVbj3XteretkyNwz4F19AeqGrpTWFhJ/RY7eIyAPAdcBZInIYUDTYsMJhc14bX0Rg40bo0sUN4HfkkWFHZEyg/JQoOgA7gRu9CYyqAf0CjSpEdjusydavv7pB++Z608e/9hoMHGhJwhQKfqZCXQMMA8qJSGtgh6q+FXhkxiSC3bvh2WehXj2YONHNWw1wmJ/vWMakBj93PV0JzASuAK4EZohI+6ADMyZ033wDTZrA/fe7u5i+/971jTCmkPHTRvEQcIqqrgUQkcrAF8B7QQZmTOi++AI2bYIxY6Bt27CjMSY0fsrPh0WShGeDz/2MSS6q8NZbMG6ce33//W6MJksSppDz84E/XkQmiEhnEekMfAp8FmxYxsTZDz/AuefC9dfDkCFuWfHibiA/Ywo5P3Nm9xSRy4DI9FuDVPXDYMOKLxuyoxDbvh2eegqeeQZKlXJ3Mt10U9hRGZNQYs2ZXRvoD9QEFgL3quqqeAUWTzZkRyH28cfQty9cey307+/GajLGHCRWieIN4C1gKtAG+A9wWTyCCkNkyA5TCKxZA/PmQcuWcMUVbpa5U08NOypjElasNooyqvqaqi5R1f5AWpxiiiubxa4Q2bsXXn4Z6taF665z1U4iliSMyUWsEkUJEWnMgXkoSka/VtW5QQcXNBvbqRCZOxduvRVmzXJTkr78sk0mZIxPsRLFauD5qNdrol4rcG5QQQUt0ngdKUnY2E4pbvlyV2qoVAmGD4errrL5qo3Jg1gTF50Tz0DiJboU0Sy9Am0bVbUkkYpUYeFCaNjQjeo6ZAi0aQPly4cdmTFJx0/P7JRiI8QWAsuXQ/fuMH48fPedSxbXXRd2VMYkrUB7WItISxFZIiJLRaRXjO0uFxEVkaZBxhNhI8SmqF273LDf9evDlCnudtd69cKOypikF1iJQkSKAAOAC4BMYJaIjFXVjCzblQHuAmYEFYspBPbudbPNzZkDl10GL7wA1auHHZUxKcHP6LHizZX9qPe6hoj4uZ/wVGCpqi5T1V3ASNwseVk9ATwD7MhD3MY4mze7n0WKwI03ug50779vScKYAuSn6ulloDkQGV95C66kkJuqwMqo15nesv1EpAlQXVU/jXUgEekqIrNFZPa6det8nNqkPFUYOhSOOw4++sgt69YNWrcONSxjUpGfRNFMVW/H+8avqhuBYod6Ym9K1eeBHrltq6qDVLWpqjatXLlyvs9pnetSREYGnH023HADHH881KwZdkTGpDQ/iWK3196gsH8+in0+9lsFRJf/q3nLIsoADYDJIvILcBowNqgGbetclyKefRZOOgkWLYLBg2HqVGjQIOyojElpfhqzXwI+BI4SkSeB9sDDPvabBdQWkXRcgrgKuDqyUlU3AZUir0VkMm7gwdm+o/fBOtelCFXXSe5vf4NrroF+/eAQSpfGGP/8DDM+TETmAOfhhu9op6rf+9hvj4h0ByYARYA3VHWxiPQBZqvq2EOM3ZfIyLDWuS5J/fYb3HUXnHUW3HkndOrkHsaYuMk1UYhIDWAb8HH0MlVdkdu+qvoZWSY5UtVHc9j27NyOl182MmwSigzg99BDsHu3u/XVGBMKP1VPn+LaJwQoAaQDS4D6AcZlCrN589zkQXPmwD/+4RKGNVgbExo/VU8nRr/2bmntFlhExmza5KqcRo1y80XYAH7GhCrPPbNVda6INAsiGFNIqcK778JPP7mqpr//HZYtgxIlwo7MGIO/Nop7ol4eBjQBfgssIlO4/PzzgQH8TjkF7rsPiha1JGFMAvHTj6JM1KM4rs0iu6E4jPFv50548knXB2LaNHjxRfjmG5ckjDEJJWaJwutoV0ZV741TPKawWLkSnnjCzRHxwgtQ1TpBGpOocixRiMjhqroXOCOO8ZhUtm4d/Pe/7nmtWm4ojnfftSRhTIKLVaKYiWuPmCciY4F3gT8jK1X1g4BjM6li3z43w9x998GWLXDBBVC3rhvQzxiT8Py0UZQANuDmyG4NtPF+GpO7RYvcXUw33eQmFJo3zyUJY0zSiFWiOMq742kRBzrcRWigURWAyBhPGas3U69K2bDDKZx27XId5nbtgjfegM6drU+EMUkoVqIoApTm4AQRkfCJIjpJ2Gixcfbll64UUawYjB7thgKvVCn3/YwxCSlWolitqn3iFkkAbIynOMvMdAP4ffCBK0HccAOceWbYURljDlGsNoqkrCMYPmMFHQZ+S8bqzWGHUnjs2eNucT3hBBg3Dp5+2g0FboxJCbFKFOfFLYoCZFVOIbjuOhg5Ei66CAYMgPT0sCMyxhSgHBOFqibtnKFW5RQHf/wBhx8OpUvD7bfD5Ze7hzVWG5Ny/NwemzRsTuw4UHWlhxNOgEceccvOPBPat7ckYUyKSqlE8dE8NyW3VTkFZOlSuPBC6NgRqlWDa68NOyJjTBykVKIAaJZewaY7DcLw4W4Avxkz3DAc06fDySeHHZUxJg5SJlFYtVNAdu92P5s2ddVL33/v2iSKFAk3LmNM3KRMorBqpwK2dq27m6lDB/e6Th145x045phw4zLGxF3KJAqwaqcCsW8fDBrkxmMaNcqNz7R3b9hRGWNClBKJwqqdCsiyZe4OpltugUaNYMECN2eEVTMZU6jlec7sRGTVTgWkXDnXP+LNN121k93uaowhRUoUYNVO+TZ2LFx2mateqljRDQveqZMlCWPMfimTKEwerVgB7dpB27bw44+werVbfpj9SRhjDmafCoXNnj3Qv7/rWf355/DMM/Ddd64DnTHGZCMl2ihMHuzdC4MHw7nnwn/+A2lpYUdkjElwSV+isDuefNi4Ee6/381XXbw4TJvm2iYsSRhjfEj6RGF3PMWgCsOGuRnmnnsOJk1yyytWtMZqY4xvSZ8owO54ytaPP8IFF7iB+9LSYPZsuOSSsKMyxiQha6NIVXff7ZLDyy9D167Wac4Yk2+WKFLJxImumql6dXjlFdce8be/hR2VMSbJBVr1JCItRWSJiCwVkV7ZrL9HRDJEZIGI/E9Ejg0ynpS1Zg1cfTX84x/udleAY4+1JGGMKRCBJQoRKQIMAC4C6gEdRaRels2+A5qqakPgPeDZoOJJSfv2wauvulLE++/DY4+5PhLGGFOAgixRnAosVdVlqroLGAm0jd5AVSep6jbv5XTAen3lxdNPw223uQmEFiyA3r2hRImwozLGpJgg2yiqAiujXmcCzWJs3wUYl90KEekKdAWoUaOQ3920ZQusXw/p6XDrre5nx452u6sxJjAJcXusiFwLNAX6ZbdeVQepalNVbVq5cuX9ywtVZztV+PBDqFfPTSak6vpDXH21JQljTKCCTBSrgOpRr6t5yw4iIucDDwGXqOrOvJyg0HS2+/VX1wfissugQgV46SVLDsaYuAmy6mkWUFtE0nEJ4irg6ugNRKQxMBBoqapr83OSlO9s9+23cP757nn//nDXXXC43dVsjImfwEoUqroH6A5MAL4HRqvqYhHpIyKRLsL9gNLAuyIyT0TGBhVP0tm82f1s0gRuvBG+/x569LAkYYyJu0A/dVT1M+CzLMsejXp+fpDnT0obNkCvXm4I8MWLoXRpN8qrMcaEJCEasw2ucfqtt1yfiCFDXIO1tUMYYxKA1WMkgk2b3GxzkydD8+auE13DhmFHZYwxgCWKcKm6UkPZslCpEgwaBF262HSkxpiEYp9IYZkwwTVUZ2a6ZPHuu3DzzZYkjDEJxz6V4m31arjqKmjZErZtg7X5uivYGGPixhJFPA0Y4Bqrx4yBxx934zM1aRJ2VMYYE5O1UcTTnDnQrJlLGLVrhx2NMcb4YiWKIG3e7GaamzPHvX75Zdc2YUnCGJNELFEEQRXeew9OOMGNyzRlilteooT1jTDGJB1LFAVt+XJo3RquuAKOOsqN1XTPPWFHZYwx+WaJoqANGwZTp8K//w2zZrk2CWOMSWLWmF0QvvoKdu50o7z27AmdO0M1m6zPGJMarERxKNavdyO7tmgBffq4ZcWLW5IwxqSUpC1RRGa3a5ZeIf4nV4WhQ13pYdMmuP9+eOSR+MdhTD7s3r2bzMxMduzYEXYoJgAlSpSgWrVqFC1atMCOmbSJItTZ7T77zJUkzjjDDeDXoEH8YzAmnzIzMylTpgxpaWmI3YWXUlSVDRs2kJmZSXp6eoEdN6mrnuI6u922bTBtmnveqhV89JFrtLYkYZLMjh07qFixoiWJFCQiVKxYscBLi0mdKOJm3DiXEC66CP74w/WFuOQSG8DPJC1LEqkriN+tfdLFsmqV6w/RqpVrpP74YyhfPuyojDEmrpIyUUQasgO1di3UqweffAJ9+8L8+fD3vwd7TmMKkTFjxiAi/PDDD/uXTZ48mdatWx+0XefOnXnvvfcA1xDfq1cvateuTZMmTWjevDnjxo3L9vjt27dn2bJlwb0Bn55++mlq1apF3bp1mTBhQrbbnHXWWTRq1IhGjRpxzDHH0K5dOwCGDRtGw4YNOfHEEzn99NOZP38+ALt27aJFixbs2bMnLu8hKRNFoA3Zq9yxOeooeOIJWLQIHnoIihUr+HMZU4iNGDGCM888kxEjRvje55FHHmH16tUsWrSIuXPnMmbMGLZs2fKX7RYvXszevXs57rjjfB977969vrf1KyMjg5EjR7J48WLGjx9Pt27dsj3PV199xbx585g3bx7NmzfnsssuAyA9PZ0pU6awcOFCHnnkEbp27QpAsWLFOO+88xg1alSBx5ydpL3rqcAbsjdtgocfhoEDYfp0N/z3nXcW3PGNSUCPf7yYjN82F+gx6x1Tlsfa1I+5zdatW/n666+ZNGkSbdq04fHHH8/1uNu2beO1115j+fLlFC9eHICjjz6aK6+88i/bDhs2jLZt2+5/fdtttzFr1iy2b99O+/bt958vLS2NDh06MHHiRO677z4qVKjAY489xs6dO6lZsyZDhgyhdOnS9OnTh48//pjt27dz+umnM3DgQF9tAR999BFXXXUVxYsXJz09nVq1ajFz5kyaN2+e7fabN2/myy+/ZMiQIQCcfvrp+9eddtppZGZm7n/drl07HnjgAa655ppc4zhUSVmiKFCqMHq0G8BvwAC49VaoWTPsqIxJaR999BEtW7akTp06VKxYkTmREZZjWLp0KTVq1KBs2bK5bjtt2jROPvnk/a+ffPJJZs+ezYIFC5gyZQoLFizYv65ixYrMnTuX888/n759+/LFF18wd+5cmjZtyvPPPw9A9+7dmTVrFosWLWL79u188sknAPTr129/lVH0407vS+aqVauoXr36/nNVq1aNVZFai2yMGTOG8847L9v3+Prrr3PRRRftf92gQQNmzZqV67UoCElXovj9z12sL6iOdqpw2WVuIqEmTWDsWGja9NCPa0ySyO2bf1BGjBjBXXfdBcBVV13FiBEjOPnkk3P8lp7XO3lWr15N5cqV978ePXo0gwYNYs+ePaxevZqMjAwaNmwIQIcOHQCYPn06GRkZnHHGGYBrB4h88580aRLPPvss27Zt4/fff6d+/fq0adOGnj170rNnz7y9+RhGjBjBTTfd9JflkyZN4vXXX+frr7/ev6xIkSIUK1aMLVu2UKZMmQKLITtJlyj+2Labshxi+8Tu3VC0qLvN9cwz4dxzoVs3KFKkwOI0xmTv999/58svv2ThwoWICHv37kVE6NevHxUrVmTjxo1/2b5SpUrUqlWLFStWsHnz5lxLFSVLltzfl2D58uX079+fWbNmceSRR9K5c+eD+hmUKlUKcJ3VLrjggr+0mezYsYNu3boxe/ZsqlevTu/evffv369fP4YNG/aX87do0YKXXnqJqlWrsnLlyv3LMzMzqVo1+8+u9evXM3PmTD788MODli9YsICbbrqJcePGUbFixYPW7dy5kxIlSsS8FgVCVZPqcWSN4/XKV7/RfJs0SfX441XHjMn/MYxJYhkZGaGef+DAgdq1a9eDlrVo0UKnTJmiO3bs0LS0tP0x/vLLL1qjRg39448/VFW1Z8+e2rlzZ925c6eqqq5du1ZHjx79l3N06NBBJ06cqKqq8+bN04YNG+revXt1zZo1etRRR+mQIUNUVfXYY4/VdevW7T9W9erV9aefflJV1a1bt+qSJUt048aNetRRR+m2bdt0y5YtWr9+fX3sscd8vddFixZpw4YNdceOHbps2TJNT0/XPXv2ZLvtK6+8op06dTpo2a+//qo1a9bUadOm/WX79evXa926dbM9Vna/Y2C25vNzt/C0UaxbB9dfD+ec40Z6DbioZozJ3ogRI7j00ksPWnb55ZczYsQIihcvzjvvvMMNN9xAo0aNaN++PYMHD6ZcuXIA9O3bl8qVK1OvXj0aNGhA69atsy1dXHzxxUyePBmAk046icaNG3P88cdz9dVX769ayqpy5coMHTqUjh070rBhQ5o3b84PP/xA+fLlufnmm2nQoAEXXnghp5xyiu/3Wr9+fa688krq1atHy5YtGTBgAEW8motWrVrx22+/7d925MiRdOzY8aD9+/Tpw4YNG+jWrRuNGjWiaVTV+KRJk7j44ot9x3JI8pthwnrkq0QxfLjqkUeqFi2q+uCDqn/+mbf9jUkhYZco4mHbtm3arFmzHL+9p4JLL71UlyxZku06K1Hkx549bgiOefPgySfhiCPCjsgYE6CSJUvy+OOPx7zDKJnt2rWLdu3aUadOnbicL+kas33580/XWa5GDddIfe217mHj2xhTaFx44YVhhxCYYsWK0alTp7idL/VKFJ98AvXrwzPPwI8/umUiliSMieJqIkwqCuJ3mzqJIjPT9Ylo0wZKlXJDgL/wQthRGZNwSpQowYYNGyxZpCD15qMo6FtmU6fqadkymDABnn4a7rnHxmYyJgfVqlUjMzOTdevWhR2KCUBkhruClHSJ4s9dUaMlzpwJ334Ld93l5q1esQKydEgxxhysaNGiBTr7mUl9gVY9iUhLEVkiIktFpFc264uLyChv/QwRSfNz3PY1S7tG6tNOg+efd43XYEnCGGMCEFiiEJEiwADgIqAe0FFE6mXZrAuwUVVrAf8GnsntuEfv+ZMrrr3AjfJ6552wcKFrkzDGGBOIIEsUpwJLVXWZqu4CRgJts2zTFnjTe/4ecJ7kMvpX5fVroHp1mDXLNVb7GEnSGGNM/gXZRlEVWBn1OhNoltM2qrpHRDYBFYH10RuJSFegq/dyp8yevYioIYQLsUpkuVaFmF2LA+xaHGDX4oC6+d0xKRqzVXUQMAhARGarqo0Fjl2LaHYtDrBrcYBdiwNEZHZ+9w2y6mkVUD3qdTVvWbbbiMjhQDlgQ4AxGWOMyaMgE8UsoLaIpItIMeAqYGyWbcYC13vP2wNfqvUCMsaYhBJY1ZPX5tAdmAAUAd5Q1cUi0gc3iuFY4HXgbRFZCvyOSya5GRRUzEnIrsUBdi0OsGtxgF2LA/J9LcS+wBtjjIkldcZ6MsYYEwhLFMYYY2JK2EQR1PAfycjHtbhHRDJEZIGI/E9Ejg0jznjI7VpEbXe5iKiIpOytkX6uhYhc6f1tLBaR4fGOMV58/I/UEJFJIvKd93/SKow4gyYib4jIWhFZlMN6EZGXvOu0QESa+DpwfqfGC/KBa/z+GTgOKAbMB+pl2aYb8Kr3/CpgVNhxh3gtzgGO8J7fVpivhbddGWAqMB1oGnbcIf5d1Aa+A470Xh8VdtwhXotBwG3e83rAL2HHHdC1aAE0ARblsL4VMA4Q4DRghp/jJmqJIpDhP5JUrtdCVSep6jbv5XRcn5VU5OfvAuAJ3LhhO+IZXJz5uRY3AwNUdSOAqq6Nc4zx4udaKBAZ76cc8Fsc44sbVZ2Ku4M0J22Bt9SZDpQXkSq5HTdRE0V2w39UzWkbVd0DRIb/SDV+rkW0LrhvDKko12vhFaWrq+qn8QwsBH7+LuoAdURkmohMF5GWcYsuvvxci97AtSKSCXwG3BGf0BJOXj9PgCQZwsP4IyLXAk2Bv4cdSxhE5DDgeaBzyKEkisNx1U9n40qZU0XkRFX9I8ygQtIRGKqqz4lIc1z/rQaqui/swJJBopYobPiPA/xcC0TkfOAh4BJV3Rmn2OItt2tRBmgATBaRX3B1sGNTtEHbz99FJjBWVXer6nLgR1ziSDV+rkUXYDSAqn4LlMANGFjY+Po8ySpRE4UN/3FArtdCRBoDA3FJIlXroSGXa6Gqm1S1kqqmqWoarr3mElXN92BoCczP/8gYXGkCEamEq4paFscY48XPtVgBnAcgIifgEkVhnAt2LNDJu/vpNGCTqq7ObaeErHrS4Ib/SDo+r0U/oDTwrteev0JVLwkt6ID4vBaFgs9rMQH4h4hkAHuBnqqacqVun9eiB/CaiPwT17DdORW/WIrICNyXg0pee8xjQFEAVX0V1z7TClgKbANu8HXcFLxWxhhjClCiVj0ZY4xJEJYojDHGxGSJwhhjTEyWKIwxxsRkicIYY0xMlihMQhKRvSIyL+qRFmPbrQVwvqEistw711yv925ejzFYROp5zx/Msu6bQ43RO07kuiwSkY9FpHwu2zdK1ZFSTfzY7bEmIYnIVlUtXdDbxjjGUOATVX1PRP4B9FfVhodwvEOOKbfjisibwI+q+mSM7TvjRtDtXtCxmMLDShQmKYhIaW+ujbkislBE/jJqrIhUEZGpUd+4z/KW/0NEvvX2fVdEcvsAnwrU8va9xzvWIhG521tWSkQ+FZH53vIO3vLJItJURP4FlPTiGOat2+r9HCkiF0fFPFRE2otIERHpJyKzvHkCbvFxWb7FG9BNRE713uN3IvKNiNT1ein3ATp4sXTwYn9DRGZ622Y3+q4xBwt7/HR72CO7B64n8Tzv8SFuFIGy3rpKuJ6lkRLxVu9nD+Ah73kR3NhPlXAf/KW85fcDj2ZzvqFAe+/5FcAM4GRgIVAK1/N9MdAYuBx4LWrfct7PyXjzX0RiitomEuOlwJve82K4kTxLAl2Bh73lxYHZQHo2cW6Nen/vAi2912WBw73n5wPve887A/+N2v8p4FrveXnc+E+lwv592yOxHwk5hIcxwHZVbRR5ISJFgadEpAWwD/dN+mhgTdQ+s4A3vG3HqOo8Efk7bqKaad7wJsVw38Sz009EHsaNAdQFNzbQh6r6pxfDB8BZwHjgORF5Bldd9VUe3tc44EURKQ60BKaq6navuquhiLT3tiuHG8BveZb9S4rIPO/9fw9MjNr+TRGpjRuiomgO5/8HcImI3Ou9LgHU8I5lTLYsUZhkcQ1QGThZVXeLGx22RPQGqjrVSyQXA0NF5HlgIzBRVTv6OEdPVX0v8kJEzstuI1X9Udy8F62AviLyP1Xt4+dNqOoOEZkMXAh0wE2yA27GsTtUdUIuh9iuqo1E5Ajc2Ea3Ay/hJmuapKqXeg3/k3PYX4DLVXWJn3iNAWujMMmjHLDWSxLnAH+ZF1zcXOH/p6qvAYNxU0JOB84QkUibQykRqePznF8B7UTkCBEphas2+kpEjgG2qeo7uAEZs5t3eLdXssnOKNxgbJHSCbgP/dsi+4hIHe+c2VI3o+GdQA85MMx+ZLjozlGbbsFVwUVMAO4Qr3glbuRhY2KyRGGSxTCgqYgsBDoBP2SzzdnAfBH5Dvdt/UVVXYf74BwhIgtw1U7H+zmhqs7FtV3MxLVZDFbV74ATgZleFdBjQN9sdh8ELIg0ZmfxOW5yqS/UTd0JLrFlAHNFZBFu2PiYJX4vlgW4SXmeBZ723nv0fpOAepHGbFzJo6gX22LvtTEx2e2xxhhjYrIShTHGmJgsURhjjInJEoUxxpiYLFEYY4yJyRKFMcaYmCxRGGOMickShTHGmJj+HzDBXaHePnJdAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "logit_roc_auc = roc_auc_score(y, loj_model.predict(X))\n",
    "fpr, tpr, thresholds = roc_curve(y, loj_model.predict_proba(X)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='AUC (area=%0.2f)' % logit_roc_auc)\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()"
   ]
  },
  {
   "source": [
    "### MODEL TUNNING ( MODEL VALIDATION - MODEL DOĞRULAMA )"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regression bölümünde olduğu gibi bir model tunning işlemi ele alınmayacaktır. Model doğrulama, model validation işlemi yapılacaktır. Konu bütünlüğü olması açısından model tunning şeklinde isimlendirdik."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fakat yinede logjistik regresyon yöntemlerinde tune edilmesi gereken hipeparametreler bulunmaktadır. Örneğin ritch, lasso ve elasticnet ten alışık olduğumuz l1 normu yani bir düzenlileştirme katsayısı modelde bulunduğunda buna yönelik olarak bir lojistik regresyon kullanıldığında parametre tahminleri için bu durumda aslına bakarsanız optimize edilmesi gereken bir hiperparametre elimizde olmuş oluyor. Fakat bizim kullanmış olduğumuz linear lojistic regresyon modeli klasik lojistik regresyon modeli bu sebeple extra bir hiperparametremiz yok."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "loj_model = LogisticRegression(solver=\"liblinear\").fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = loj_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.7532467532467533\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0.78723404, 0.82608696, 0.82608696, 0.69565217, 0.73913043])"
      ]
     },
     "metadata": {},
     "execution_count": 35
    }
   ],
   "source": [
    "cross_val_score(loj_model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Birden fazla hata hesaplama işlemi gerçekleştirmektedir. Biz bunu dilediğimiz katlı örneğin 10 katlı olacak şekilde gerçekleştirebiliriz. Öntanımlı değeri itibariyle 5 tane gelmiş oldu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0.79166667, 0.82608696, 0.73913043, 0.82608696, 0.73913043,\n",
       "       0.91304348, 0.69565217, 0.65217391, 0.65217391, 0.82608696])"
      ]
     },
     "metadata": {},
     "execution_count": 37
    }
   ],
   "source": [
    "cross_val_score(loj_model, X_test, y_test, cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean ile bunların ortalamasını alabiliriz. Böylece test setine ilişkin daha doğru bir test hatası değerine erişmiş oluruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.7661231884057972"
      ]
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "source": [
    "cross_val_score(loj_model, X_test, y_test, cv=10).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SONRAKİ UYGULAMALARDA SADECE accuracy_score() DEĞERLENDİRMESİ YAPACAĞIZ. Hiper parametrelerin optimum değerlerine ulaşmak için de cross validation yöntemini kullanmış olacağız.\n",
    "# Yani modelleri train seti ile eğitirken cross validation yöntemini kullanacağız. Ve dışarda bıraktığımız test setini performansı test etmek için kullanmış olacağız."
   ]
  }
 ]
}