hafta_4_Odev.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd \n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import scale \n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
    "from sklearn.metrics import roc_auc_score,roc_curve\n",
    "import statsmodels.formula.api as smf\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "#from xgboost import XGBClassifier\n",
    "#from lightgbm import LGBMClassifier\n",
    "#from catboost import CatBoostClassifier  \n",
    "os.chdir(\"C:/Users/ekorkmaz/Desktop/EnkTelefonYedek/kodluyoruz/3.hafta\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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>bad</th>\n",
       "      <th>loan</th>\n",
       "      <th>mortdue</th>\n",
       "      <th>value</th>\n",
       "      <th>reason</th>\n",
       "      <th>job</th>\n",
       "      <th>yoj</th>\n",
       "      <th>derog</th>\n",
       "      <th>delinq</th>\n",
       "      <th>clage</th>\n",
       "      <th>ninq</th>\n",
       "      <th>clno</th>\n",
       "      <th>debtinc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>0</td>\n",
       "      <td>18200</td>\n",
       "      <td>94727.0</td>\n",
       "      <td>136877.0</td>\n",
       "      <td>DebtCon</td>\n",
       "      <td>Mgr</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168.96</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>36.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>0</td>\n",
       "      <td>21700</td>\n",
       "      <td>79240.0</td>\n",
       "      <td>96784.0</td>\n",
       "      <td>DebtCon</td>\n",
       "      <td>Mgr</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.51</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>38.079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>0</td>\n",
       "      <td>34100</td>\n",
       "      <td>241931.0</td>\n",
       "      <td>36486.0</td>\n",
       "      <td>DebtCon</td>\n",
       "      <td>Mgr</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>196.01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>42.459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>0</td>\n",
       "      <td>8400</td>\n",
       "      <td>62989.0</td>\n",
       "      <td>76718.0</td>\n",
       "      <td>HomeImp</td>\n",
       "      <td>Mgr</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>131.47</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>29.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>0</td>\n",
       "      <td>17400</td>\n",
       "      <td>25859.0</td>\n",
       "      <td>43684.0</td>\n",
       "      <td>DebtCon</td>\n",
       "      <td>Mgr</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>27.108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     bad   loan   mortdue     value   reason  job   yoj  derog  delinq  \\\n",
       "153    0  18200   94727.0  136877.0  DebtCon  Mgr  15.0    0.0     0.0   \n",
       "154    0  21700   79240.0   96784.0  DebtCon  Mgr   5.0    0.0     0.0   \n",
       "155    0  34100  241931.0   36486.0  DebtCon  Mgr   1.0    0.0     2.0   \n",
       "156    0   8400   62989.0   76718.0  HomeImp  Mgr   3.0    0.0     2.0   \n",
       "157    0  17400   25859.0   43684.0  DebtCon  Mgr  16.0    1.0     0.0   \n",
       "\n",
       "      clage  ninq  clno  debtinc  \n",
       "153  168.96   2.0  26.0   36.056  \n",
       "154   64.51   6.0  24.0   38.079  \n",
       "155  196.01   3.0  50.0   42.459  \n",
       "156  131.47   0.0  22.0   29.200  \n",
       "157   95.36   1.0  17.0   27.108  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes=pd.read_csv(\"hmelq.csv\")\n",
    "df=diabetes.copy()\n",
    "df=df.dropna()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2018 entries, 153 to 3549\n",
      "Data columns (total 13 columns):\n",
      "bad        2018 non-null int64\n",
      "loan       2018 non-null int64\n",
      "mortdue    2018 non-null float64\n",
      "value      2018 non-null float64\n",
      "reason     2018 non-null object\n",
      "job        2018 non-null object\n",
      "yoj        2018 non-null float64\n",
      "derog      2018 non-null float64\n",
      "delinq     2018 non-null float64\n",
      "clage      2018 non-null float64\n",
      "ninq       2018 non-null float64\n",
      "clno       2018 non-null float64\n",
      "debtinc    2018 non-null float64\n",
      "dtypes: float64(9), int64(2), object(2)\n",
      "memory usage: 220.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1840\n",
       "1     178\n",
       "Name: bad, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"bad\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20e198cd320>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"bad\"].value_counts().plot.barh() # plot.bar dikey grafiktir, plot.barh yatay grafiktir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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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>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>bad</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>0.088206</td>\n",
       "      <td>0.283665</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>19010.257681</td>\n",
       "      <td>10768.607345</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>12000.0000</td>\n",
       "      <td>16900.000</td>\n",
       "      <td>23600.0000</td>\n",
       "      <td>89900.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mortdue</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>76337.035183</td>\n",
       "      <td>44476.368189</td>\n",
       "      <td>5076.00</td>\n",
       "      <td>49577.2500</td>\n",
       "      <td>67595.000</td>\n",
       "      <td>92922.7500</td>\n",
       "      <td>399412.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>106899.244797</td>\n",
       "      <td>54026.707096</td>\n",
       "      <td>23031.00</td>\n",
       "      <td>71287.2500</td>\n",
       "      <td>94514.000</td>\n",
       "      <td>121220.7500</td>\n",
       "      <td>512650.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yoj</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>9.003964</td>\n",
       "      <td>7.573012</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>41.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>derog</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>0.150149</td>\n",
       "      <td>0.580421</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>10.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delinq</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>0.264123</td>\n",
       "      <td>0.789816</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>10.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clage</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>180.645020</td>\n",
       "      <td>84.420116</td>\n",
       "      <td>0.49</td>\n",
       "      <td>118.2050</td>\n",
       "      <td>176.670</td>\n",
       "      <td>229.6925</td>\n",
       "      <td>1168.230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ninq</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>1.037661</td>\n",
       "      <td>1.578414</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>13.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clno</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>22.265114</td>\n",
       "      <td>9.457238</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>21.000</td>\n",
       "      <td>27.0000</td>\n",
       "      <td>64.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>debtinc</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>34.105829</td>\n",
       "      <td>8.438411</td>\n",
       "      <td>4.03</td>\n",
       "      <td>29.2955</td>\n",
       "      <td>34.949</td>\n",
       "      <td>38.9520</td>\n",
       "      <td>144.189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count           mean           std       min         25%        50%  \\\n",
       "bad      2018.0       0.088206      0.283665      0.00      0.0000      0.000   \n",
       "loan     2018.0   19010.257681  10768.607345   1700.00  12000.0000  16900.000   \n",
       "mortdue  2018.0   76337.035183  44476.368189   5076.00  49577.2500  67595.000   \n",
       "value    2018.0  106899.244797  54026.707096  23031.00  71287.2500  94514.000   \n",
       "yoj      2018.0       9.003964      7.573012      0.00      3.0000      7.000   \n",
       "derog    2018.0       0.150149      0.580421      0.00      0.0000      0.000   \n",
       "delinq   2018.0       0.264123      0.789816      0.00      0.0000      0.000   \n",
       "clage    2018.0     180.645020     84.420116      0.49    118.2050    176.670   \n",
       "ninq     2018.0       1.037661      1.578414      0.00      0.0000      0.000   \n",
       "clno     2018.0      22.265114      9.457238      0.00     16.0000     21.000   \n",
       "debtinc  2018.0      34.105829      8.438411      4.03     29.2955     34.949   \n",
       "\n",
       "                 75%         max  \n",
       "bad           0.0000       1.000  \n",
       "loan      23600.0000   89900.000  \n",
       "mortdue   92922.7500  399412.000  \n",
       "value    121220.7500  512650.000  \n",
       "yoj          13.0000      41.000  \n",
       "derog         0.0000      10.000  \n",
       "delinq        0.0000      10.000  \n",
       "clage       229.6925    1168.230  \n",
       "ninq          2.0000      13.000  \n",
       "clno         27.0000      64.000  \n",
       "debtinc      38.9520     144.189  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>loan</th>\n",
       "      <th>mortdue</th>\n",
       "      <th>value</th>\n",
       "      <th>yoj</th>\n",
       "      <th>derog</th>\n",
       "      <th>delinq</th>\n",
       "      <th>clage</th>\n",
       "      <th>ninq</th>\n",
       "      <th>clno</th>\n",
       "      <th>debtinc</th>\n",
       "      <th>reason_DebtCon</th>\n",
       "      <th>reason_HomeImp</th>\n",
       "      <th>job_Mgr</th>\n",
       "      <th>job_Office</th>\n",
       "      <th>job_Other</th>\n",
       "      <th>job_ProfEx</th>\n",
       "      <th>job_Sales</th>\n",
       "      <th>job_Self</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>18200</td>\n",
       "      <td>94727.0</td>\n",
       "      <td>136877.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168.96</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>36.056</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>21700</td>\n",
       "      <td>79240.0</td>\n",
       "      <td>96784.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.51</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>38.079</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>34100</td>\n",
       "      <td>241931.0</td>\n",
       "      <td>36486.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>196.01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>42.459</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>8400</td>\n",
       "      <td>62989.0</td>\n",
       "      <td>76718.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>131.47</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>29.200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>17400</td>\n",
       "      <td>25859.0</td>\n",
       "      <td>43684.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>27.108</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      loan   mortdue     value   yoj  derog  delinq   clage  ninq  clno  \\\n",
       "153  18200   94727.0  136877.0  15.0    0.0     0.0  168.96   2.0  26.0   \n",
       "154  21700   79240.0   96784.0   5.0    0.0     0.0   64.51   6.0  24.0   \n",
       "155  34100  241931.0   36486.0   1.0    0.0     2.0  196.01   3.0  50.0   \n",
       "156   8400   62989.0   76718.0   3.0    0.0     2.0  131.47   0.0  22.0   \n",
       "157  17400   25859.0   43684.0  16.0    1.0     0.0   95.36   1.0  17.0   \n",
       "\n",
       "     debtinc  reason_DebtCon  reason_HomeImp  job_Mgr  job_Office  job_Other  \\\n",
       "153   36.056               1               0        1           0          0   \n",
       "154   38.079               1               0        1           0          0   \n",
       "155   42.459               1               0        1           0          0   \n",
       "156   29.200               0               1        1           0          0   \n",
       "157   27.108               1               0        1           0          0   \n",
       "\n",
       "     job_ProfEx  job_Sales  job_Self  \n",
       "153           0          0         0  \n",
       "154           0          0         0  \n",
       "155           0          0         0  \n",
       "156           0          0         0  \n",
       "157           0          0         0  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['bad']\n",
    "X = df.drop(['bad'], axis = 1)\n",
    "X=pd.get_dummies(X)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# statsmodels ile modelleme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.228362\n",
      "         Iterations 9\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>bad</td>       <th>  No. Observations:  </th>  <td>  2018</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>               <td>Logit</td>      <th>  Df Residuals:      </th>  <td>  2001</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>    16</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Sat, 21 Sep 2019</td> <th>  Pseudo R-squ.:     </th>  <td>0.2346</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>22:17:29</td>     <th>  Log-Likelihood:    </th> <td> -460.84</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -602.11</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>1.039e-50</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>loan</th>           <td> -1.73e-05</td> <td> 1.05e-05</td> <td>   -1.640</td> <td> 0.101</td> <td> -3.8e-05</td> <td> 3.37e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mortdue</th>        <td>-4.777e-06</td> <td> 4.76e-06</td> <td>   -1.004</td> <td> 0.315</td> <td>-1.41e-05</td> <td> 4.55e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value</th>          <td> 4.089e-06</td> <td> 4.06e-06</td> <td>    1.006</td> <td> 0.314</td> <td>-3.87e-06</td> <td> 1.21e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yoj</th>            <td>   -0.0109</td> <td>    0.013</td> <td>   -0.854</td> <td> 0.393</td> <td>   -0.036</td> <td>    0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>derog</th>          <td>    0.5708</td> <td>    0.130</td> <td>    4.382</td> <td> 0.000</td> <td>    0.315</td> <td>    0.826</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>delinq</th>         <td>    0.7985</td> <td>    0.095</td> <td>    8.448</td> <td> 0.000</td> <td>    0.613</td> <td>    0.984</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>clage</th>          <td>   -0.0046</td> <td>    0.001</td> <td>   -3.354</td> <td> 0.001</td> <td>   -0.007</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ninq</th>           <td>    0.1119</td> <td>    0.049</td> <td>    2.303</td> <td> 0.021</td> <td>    0.017</td> <td>    0.207</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>clno</th>           <td>   -0.0286</td> <td>    0.011</td> <td>   -2.627</td> <td> 0.009</td> <td>   -0.050</td> <td>   -0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>debtinc</th>        <td>    0.1143</td> <td>    0.014</td> <td>    8.222</td> <td> 0.000</td> <td>    0.087</td> <td>    0.142</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>reason_DebtCon</th> <td>   -3.7938</td> <td> 2.64e+07</td> <td>-1.44e-07</td> <td> 1.000</td> <td>-5.17e+07</td> <td> 5.17e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>reason_HomeImp</th> <td>   -3.8593</td> <td> 2.64e+07</td> <td>-1.46e-07</td> <td> 1.000</td> <td>-5.17e+07</td> <td> 5.17e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>job_Mgr</th>        <td>   -1.4722</td> <td> 2.64e+07</td> <td>-5.58e-08</td> <td> 1.000</td> <td>-5.17e+07</td> <td> 5.17e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>job_Office</th>     <td>   -2.0716</td> <td> 2.64e+07</td> <td>-7.86e-08</td> <td> 1.000</td> <td>-5.17e+07</td> <td> 5.17e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>job_Other</th>      <td>   -1.5260</td> <td> 2.64e+07</td> <td>-5.79e-08</td> <td> 1.000</td> <td>-5.17e+07</td> <td> 5.17e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>job_ProfEx</th>     <td>   -1.5377</td> <td> 2.64e+07</td> <td>-5.83e-08</td> <td> 1.000</td> <td>-5.17e+07</td> <td> 5.17e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>job_Sales</th>      <td>   -0.2487</td> <td> 2.64e+07</td> <td>-9.43e-09</td> <td> 1.000</td> <td>-5.17e+07</td> <td> 5.17e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>job_Self</th>       <td>   -0.7970</td> <td> 2.64e+07</td> <td>-3.02e-08</td> <td> 1.000</td> <td>-5.17e+07</td> <td> 5.17e+07</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                    bad   No. Observations:                 2018\n",
       "Model:                          Logit   Df Residuals:                     2001\n",
       "Method:                           MLE   Df Model:                           16\n",
       "Date:                Sat, 21 Sep 2019   Pseudo R-squ.:                  0.2346\n",
       "Time:                        22:17:29   Log-Likelihood:                -460.84\n",
       "converged:                       True   LL-Null:                       -602.11\n",
       "                                        LLR p-value:                 1.039e-50\n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "loan            -1.73e-05   1.05e-05     -1.640      0.101    -3.8e-05    3.37e-06\n",
       "mortdue        -4.777e-06   4.76e-06     -1.004      0.315   -1.41e-05    4.55e-06\n",
       "value           4.089e-06   4.06e-06      1.006      0.314   -3.87e-06    1.21e-05\n",
       "yoj               -0.0109      0.013     -0.854      0.393      -0.036       0.014\n",
       "derog              0.5708      0.130      4.382      0.000       0.315       0.826\n",
       "delinq             0.7985      0.095      8.448      0.000       0.613       0.984\n",
       "clage             -0.0046      0.001     -3.354      0.001      -0.007      -0.002\n",
       "ninq               0.1119      0.049      2.303      0.021       0.017       0.207\n",
       "clno              -0.0286      0.011     -2.627      0.009      -0.050      -0.007\n",
       "debtinc            0.1143      0.014      8.222      0.000       0.087       0.142\n",
       "reason_DebtCon    -3.7938   2.64e+07  -1.44e-07      1.000   -5.17e+07    5.17e+07\n",
       "reason_HomeImp    -3.8593   2.64e+07  -1.46e-07      1.000   -5.17e+07    5.17e+07\n",
       "job_Mgr           -1.4722   2.64e+07  -5.58e-08      1.000   -5.17e+07    5.17e+07\n",
       "job_Office        -2.0716   2.64e+07  -7.86e-08      1.000   -5.17e+07    5.17e+07\n",
       "job_Other         -1.5260   2.64e+07  -5.79e-08      1.000   -5.17e+07    5.17e+07\n",
       "job_ProfEx        -1.5377   2.64e+07  -5.83e-08      1.000   -5.17e+07    5.17e+07\n",
       "job_Sales         -0.2487   2.64e+07  -9.43e-09      1.000   -5.17e+07    5.17e+07\n",
       "job_Self          -0.7970   2.64e+07  -3.02e-08      1.000   -5.17e+07    5.17e+07\n",
       "==================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loj = sm.Logit(y, X)\n",
    "loj_model = loj.fit()\n",
    "loj_model.summary()\n",
    "# p değerinin alpha'dan küçük olduğu değerler bizim için y yi aciklama da anlam ifade etmektedir. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# scikit-learn -  bir de scikitlearn ile modelleyelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='liblinear',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "loj=LogisticRegression(solver=\"liblinear\")\n",
    "\n",
    "X = X.drop(['loan','mortdue','value','yoj','reason_DebtCon','reason_HomeImp','job_Mgr','job_Office','job_Other','job_ProfEx','job_Sales','job_Self'],axis=1)\n",
    "loj_model=loj.fit(X,y)\n",
    "loj_model\n",
    "#liblinear algoritma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-4.30376684])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loj_model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.59979687,  0.73321048, -0.00540006,  0.08741424, -0.03544782,\n",
       "         0.08599255]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loj_model.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Tahmin ve Model tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = loj_model.predict(X)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counfusion matrix: \n",
      " [[1831    9]\n",
      " [ 143   35]]\n",
      "\n",
      " Accuracy:  0.9246778989098117\n"
     ]
    }
   ],
   "source": [
    "y_pred = loj_model.predict(X)\n",
    "confusion_mat = confusion_matrix(y, y_pred)\n",
    "accuracy = accuracy_score(y, y_pred)\n",
    "print(\"Counfusion matrix: \\n\",confusion_mat)\n",
    "print(\"\\n Accuracy: \",accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuracy: 0.9246778989098117\n",
      "Misclasification 0.0753221010901883\n",
      "True positive rate 0.19662921348314608\n",
      "precision 0.7954545454545454\n",
      "F score 0.49604187946884576\n"
     ]
    }
   ],
   "source": [
    "print(\"Acuracy:\",(35+1831)/2018)          # Accuracy : (TP+TN) / total  modelin genel dogruluk \n",
    "print(\"Misclasification\",(9+143)/2018)    # Missclassification rate(recall yada sensivity) : (FP+FN) / total      \n",
    "print(\"True positive rate\",35/178)        # TP Rate : TP/actual yes \n",
    "print(\"precision\",35/44)                  # Precision: TP / predicted yes  \n",
    "print(\"F score\",((35/178)+(35/44))/2)     # (Tp+Precision)/2\n",
    "#benim icin 1 leri mi tahmin etmek önemli yoksa 0 larımı ona göre bu parametrelerden hangisi iyiyse onu seçerim \n",
    "#ama accuracy e de bakarım genelolarak bi "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      1.00      0.96      1840\n",
      "           1       0.80      0.20      0.32       178\n",
      "\n",
      "   micro avg       0.92      0.92      0.92      2018\n",
      "   macro avg       0.86      0.60      0.64      2018\n",
      "weighted avg       0.92      0.92      0.90      2018\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#presicion 0 dediklerimizin %79 ü dogru(0 dediğimiz precisiıonların gerçekte de degeri 0 yani )\n",
    "#recal sıfırların yüzde %90 doğru tahmin yapıyor 1 lerde %55\n",
    "#f1 score overal olarak 0'ları %84 doğru tahmin ediyor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loj_model.predict(X)[0:10]\n",
    "# X verileri ile tahmin etme (X = df.drop(['Outcome'], axis = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.94595761, 0.05404239],\n",
       "       [0.84604041, 0.15395959],\n",
       "       [0.85255757, 0.14744243],\n",
       "       [0.86010494, 0.13989506],\n",
       "       [0.91705827, 0.08294173],\n",
       "       [0.93958986, 0.06041014],\n",
       "       [0.96161392, 0.03838608],\n",
       "       [0.96126402, 0.03873598],\n",
       "       [0.88599036, 0.11400964],\n",
       "       [0.97446522, 0.02553478]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loj_model.predict_proba(X)[0:10]\n",
    "# 0 olma ve 1 olma ihtimallerini(olasılıgını) veriyor\n",
    "#sağ 1 olma ihtimali sol 0 olma ihtimalidir. aşağıdakiler 0,50 trashholdu içindir. trashold değiştirilebilir bu arada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "153    0\n",
       "154    0\n",
       "155    0\n",
       "156    0\n",
       "157    0\n",
       "158    0\n",
       "159    0\n",
       "160    0\n",
       "161    0\n",
       "162    0\n",
       "Name: bad, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gerçek y değerlerim;\n",
    "y[0:10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05404239, 0.15395959, 0.14744243, 0.13989506, 0.08294173,\n",
       "       0.06041014, 0.03838608, 0.03873598, 0.11400964, 0.02553478])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gerçek y değerlerim ile yukarıdaki tahmin değerleri  ile kıyaslamak için;\n",
    "y_probs = loj_model.predict_proba(X)\n",
    "y_probs = y_probs[:,1]\n",
    "y_probs[0:10]\n",
    "# değerlerin 1 olma olasılıkları"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# changing treshold of the predicting model\n",
    "y_pred = [1 if i > 0.7 else 0 for i in y_probs]\n",
    "y_pred[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1840,    0],\n",
       "       [ 155,   23]], dtype=int64)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.923191278493558"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      1.00      0.96      1840\n",
      "           1       1.00      0.13      0.23       178\n",
      "\n",
      "   micro avg       0.92      0.92      0.92      2018\n",
      "   macro avg       0.96      0.56      0.59      2018\n",
      "weighted avg       0.93      0.92      0.90      2018\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05404239, 0.15395959, 0.14744243, 0.13989506, 0.08294173])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loj_model.predict_proba(X)[:,1][0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5958689545676599"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#roc curve ün 1e yakın olmasını bekliyoruz modelin doğruluğunu test ediyoruz bunlar ne kadar fit ediyor\n",
    "logit_roc_auc=roc_auc_score(y,loj_model.predict(X))\n",
    "logit_roc_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fpr:  [0.         0.         0.         0.00108696 0.00108696 0.00217391\n",
      " 0.00217391 0.00271739 0.00271739 0.00543478]\n",
      "tpr:  [0.         0.00561798 0.12921348 0.12921348 0.15168539 0.15168539\n",
      " 0.15730337 0.15730337 0.19662921 0.19662921]\n",
      "thresholds:  [1.99963644 0.99963644 0.72302555 0.68878706 0.66981111 0.644311\n",
      " 0.63431299 0.63039984 0.52925964 0.48258788]\n"
     ]
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y, loj_model.predict_proba(X)[:,1])\n",
    "print(\"fpr: \",fpr[0:10])\n",
    "print(\"tpr: \",tpr[0:10])\n",
    "print(\"thresholds: \",thresholds[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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": [
    "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 Ratio')\n",
    "plt.ylabel('True Positive Ratio')\n",
    "plt.title('ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#iyi bir modelde mavinin altında kalan alanın 1 olması gerekiyor\n",
    "#güzel bi modelde roc curve başlarda pik yapar sonra sabitlenip converge eder\n",
    "#ödev de bad i modelliycez acuracy ve predictioni modelle anlamsız olanları at \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df[\"bad\"]\n",
    "X = df.drop(['bad'], axis = 1)\n",
    "X=pd.get_dummies(X)\n",
    "X = X.drop(['loan','mortdue','value','yoj','reason_DebtCon','reason_HomeImp','job_Mgr','job_Office','job_Other','job_ProfEx','job_Sales','job_Self'],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": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='liblinear',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loj=LogisticRegression(solver=\"liblinear\")  #lojistik model kurduk\n",
    "loj_model=loj.fit(X_train,y_train)\n",
    "loj_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9240924092409241"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,loj_model.predict(X_test))#gerçek degelr ile modelden gelen tahmin degerlerinin kıyasladık"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9240924092409241"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict=loj_model.predict(X_test)\n",
    "accuracy_score(y_test,y_predict)#ustteki kodun alternatifi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      1.00      0.96       554\n",
      "           1       0.80      0.15      0.26        52\n",
      "\n",
      "   micro avg       0.92      0.92      0.92       606\n",
      "   macro avg       0.86      0.58      0.61       606\n",
      "weighted avg       0.92      0.92      0.90       606\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## overall da 0.92 tahmin etmişş "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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