Week6-lab.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "ad45037c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np #çok boyutlu diziler,matrislerle çalışmak ve ileri düzey matematiksel işlem yapmak için kütüphane.\n",
    "import pandas as pd #veri işlemesi ve ver analizi için kullanılan kütüphane.\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "37b3c458",
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes = pd.read_csv('/Users/bestecetin/Desktop/machine_learning/diabetes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "852bdf15",
   "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>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",
       "      <th>Intercept</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",
       "      <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",
       "      <td>1</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",
       "      <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",
       "      <td>1</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",
       "      <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  Intercept  \n",
       "0                     0.627   50        1          1  \n",
       "1                     0.351   31        0          1  \n",
       "2                     0.672   32        1          1  \n",
       "3                     0.167   21        0          1  \n",
       "4                     2.288   33        1          1  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "d852d73f",
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes['Intercept'] = 1\n",
    "# Adding intercept\n",
    "\n",
    "y = diabetes['Outcome'] #features\n",
    "x= diabetes.drop('Outcome', axis=1)#label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "4947d1e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "62e1d7ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression \n",
    "model = LogisticRegression(solver='liblinear')\n",
    "# instantiate the model \n",
    "\n",
    "model.fit(x_train, y_train) #fit\n",
    "y_predict = model.predict(x_test) #predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "fdb78a8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[118,  12],\n",
       "       [ 25,  37]])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
    "cnf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "f4cef103",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 257.44, 'Predicted label')"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_names=[0,1] # name  of classes\n",
    "fig, ax = plt.subplots()\n",
    "tick_marks = np.arange(len(class_names))\n",
    "plt.xticks(tick_marks, class_names)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "# create heatmap\n",
    "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
    "ax.xaxis.set_label_position(\"top\")\n",
    "plt.tight_layout()\n",
    "plt.title('Confusion matrix', y=1.1)\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "9ee14168",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8072916666666666\n",
      "Precision: 0.7551020408163265\n",
      "Recall: 0.5967741935483871\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
    "print(\"Precision:\",metrics.precision_score(y_test, y_pred))\n",
    "print(\"Recall:\",metrics.recall_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "7b2c2d1c",
   "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": [
    "y_pred_proba = model.predict_proba(x_test)[::,1]\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)\n",
    "auc = metrics.roc_auc_score(y_test, y_pred_proba)\n",
    "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "1134abbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8125\n"
     ]
    }
   ],
   "source": [
    "#Model Score\n",
    "model_score = model.score(x_test, y_test)\n",
    "\n",
    "print(model_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4557942d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}