3 - Sınıflandırma Modelleri/1 - Lojistik Regresyon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lojistik Regresyon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, roc_auc_score, roc_curve\n",
    "from warnings import filterwarnings\n",
    "filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>"
      ],
      "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  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"diabetes.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    500\n",
       "1    268\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Outcome\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>"
      ],
      "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  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"Outcome\"]\n",
    "X = df.drop([\"Outcome\"], axis=1)\n",
    "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7532467532467533"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(solver=\"liblinear\").fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Karmaşıklık Matrisi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[126,  25],\n",
       "       [ 32,  48]], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.83      0.82       151\n",
      "           1       0.66      0.60      0.63        80\n",
      "\n",
      "    accuracy                           0.75       231\n",
      "   macro avg       0.73      0.72      0.72       231\n",
      "weighted avg       0.75      0.75      0.75       231\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logit_roc_auc = roc_auc_score(y_test, y_pred)\n",
    "fpr, tpr, thresholds = roc_curve(y, model.predict_proba(X)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label=\"AUC (area = %0.2f)\" % logit_roc_auc) #mavi\n",
    "plt.plot([0,1], [0,1], \"r--\") #kırmızı\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(\"Loc_ROC\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7704710144927536"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(model, X_test, y_test, cv=10).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  },
  "nbTranslate": {
   "displayLangs": [
    "*"
   ],
   "hotkey": "alt-t",
   "langInMainMenu": true,
   "sourceLang": "en",
   "targetLang": "fr",
   "useGoogleTranslate": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}