HaticeCandan_HW4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Library\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "from scipy.stats import norm\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "print(\"Library\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>0</th>\n",
       "      <td>0</td>\n",
       "      <td>81200</td>\n",
       "      <td>18834.0</td>\n",
       "      <td>108355.0</td>\n",
       "      <td>HomeImp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>139.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>34.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>12600</td>\n",
       "      <td>103960.0</td>\n",
       "      <td>127384.0</td>\n",
       "      <td>DebtCon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>129.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>34.479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>18000</td>\n",
       "      <td>46865.0</td>\n",
       "      <td>61266.0</td>\n",
       "      <td>DebtCon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102.59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>26.354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>10300</td>\n",
       "      <td>57676.0</td>\n",
       "      <td>71027.0</td>\n",
       "      <td>DebtCon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157.52</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>33.992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>9400</td>\n",
       "      <td>56508.0</td>\n",
       "      <td>78358.0</td>\n",
       "      <td>DebtCon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>32.327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bad   loan   mortdue     value   reason  job   yoj  derog  delinq   clage  \\\n",
       "0    0  81200   18834.0  108355.0  HomeImp  NaN  28.0    0.0     0.0  139.14   \n",
       "1    0  12600  103960.0  127384.0  DebtCon  NaN   2.0    0.0     0.0  129.02   \n",
       "2    0  18000   46865.0   61266.0  DebtCon  NaN   5.0    0.0     0.0  102.59   \n",
       "3    0  10300   57676.0   71027.0  DebtCon  NaN  19.0    0.0     0.0  157.52   \n",
       "4    0   9400   56508.0   78358.0  DebtCon  NaN  17.0    0.0     0.0  141.93   \n",
       "\n",
       "   ninq  clno  debtinc  \n",
       "0   0.0  14.0   34.042  \n",
       "1   0.0  25.0   34.479  \n",
       "2   2.0   9.0   26.354  \n",
       "3   1.0  11.0   33.992  \n",
       "4   0.0  11.0   32.327  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "#os.getcwd()\n",
    "os.chdir('C:/Users/asus1/Desktop/bootcamp')\n",
    "#reading data\n",
    "veri=pd.read_csv('hmelq.csv')\n",
    "veri.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3576 entries, 0 to 3575\n",
      "Data columns (total 13 columns):\n",
      "bad        3576 non-null int64\n",
      "loan       3576 non-null int64\n",
      "mortdue    3262 non-null float64\n",
      "value      3512 non-null float64\n",
      "reason     3429 non-null object\n",
      "job        3409 non-null object\n",
      "yoj        3264 non-null float64\n",
      "derog      3149 non-null float64\n",
      "delinq     3225 non-null float64\n",
      "clage      3397 non-null float64\n",
      "ninq       3273 non-null float64\n",
      "clno       3443 non-null float64\n",
      "debtinc    2809 non-null float64\n",
      "dtypes: float64(9), int64(2), object(2)\n",
      "memory usage: 363.3+ KB\n"
     ]
    }
   ],
   "source": [
    "veri.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": [
    "veri=veri.dropna()\n",
    "veri.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>156</th>\n",
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       "      <th>157</th>\n",
       "      <td>1</td>\n",
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       "      <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": [
       "     reason_DebtCon  reason_HomeImp  job_Mgr  job_Office  job_Other  \\\n",
       "153               1               0        1           0          0   \n",
       "154               1               0        1           0          0   \n",
       "155               1               0        1           0          0   \n",
       "156               0               1        1           0          0   \n",
       "157               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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Kategorik değişkenler için dummies oluşturalım\n",
    "dms=pd.get_dummies(veri[['reason', 'job']])\n",
    "dms.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>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>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",
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       "      <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>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>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>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   yoj  derog  delinq   clage  ninq  clno  \\\n",
       "153    0  18200   94727.0  136877.0  15.0    0.0     0.0  168.96   2.0  26.0   \n",
       "154    0  21700   79240.0   96784.0   5.0    0.0     0.0   64.51   6.0  24.0   \n",
       "155    0  34100  241931.0   36486.0   1.0    0.0     2.0  196.01   3.0  50.0   \n",
       "156    0   8400   62989.0   76718.0   3.0    0.0     2.0  131.47   0.0  22.0   \n",
       "157    0  17400   25859.0   43684.0  16.0    1.0     0.0   95.36   1.0  17.0   \n",
       "\n",
       "     debtinc  \n",
       "153   36.056  \n",
       "154   38.079  \n",
       "155   42.459  \n",
       "156   29.200  \n",
       "157   27.108  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_veri = veri.drop([\"reason\",\"job\"],axis=1)\n",
    "n_veri.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>bad</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",
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       "  </thead>\n",
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       "      <th>153</th>\n",
       "      <td>0</td>\n",
       "      <td>18200</td>\n",
       "      <td>94727.0</td>\n",
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       "      <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>0</td>\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>0</td>\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>0</td>\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": [
       "     bad   loan   mortdue     value   yoj  derog  delinq   clage  ninq  clno  \\\n",
       "153    0  18200   94727.0  136877.0  15.0    0.0     0.0  168.96   2.0  26.0   \n",
       "154    0  21700   79240.0   96784.0   5.0    0.0     0.0   64.51   6.0  24.0   \n",
       "155    0  34100  241931.0   36486.0   1.0    0.0     2.0  196.01   3.0  50.0   \n",
       "156    0   8400   62989.0   76718.0   3.0    0.0     2.0  131.47   0.0  22.0   \n",
       "157    0  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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri = pd.concat([n_veri, dms[[\"reason_DebtCon\",\"reason_HomeImp\",\"job_Mgr\",\"job_Office\",\"job_Other\",\"job_ProfEx\",\"job_Sales\",\"job_Self\"]]],axis = 1)\n",
    "veri.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1840\n",
       "1     178\n",
       "Name: bad, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Bad'lerin sayısını bulduk\n",
    "veri[\"bad\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29bf066e2b0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "veri[\"bad\"].value_counts().plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=veri[\"bad\"]\n",
    "X=veri.drop([\"bad\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Mon, 23 Sep 2019</td> <th>  Pseudo R-squ.:     </th>  <td>0.2346</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>23:52:13</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>Covariance Type:</th>     <td>nonrobust</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:                Mon, 23 Sep 2019   Pseudo R-squ.:                  0.2346\n",
       "Time:                        23:52:13   Log-Likelihood:                -460.84\n",
       "converged:                       True   LL-Null:                       -602.11\n",
       "Covariance Type:            nonrobust   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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Lojistik regresyon yapalım \n",
    "import statsmodels.api as sm\n",
    "loj=sm.Logit(y,X)\n",
    "loj_model=loj.fit()\n",
    "loj_model.summary()\n",
    "\n",
    "# Logistic regresyon modeline bakıldığında \n",
    "# derog,delinq,clage,ninq,clno ve debtinc'in anlamlı olduğu söylenir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scikit learn ile logistic regresyon kuralım\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
    "loj=LogisticRegression(solver = \"liblinear\")\n",
    "##lojistik regresyon kursun\n",
    "loj_model = loj.fit(X,y)\n",
    "loj_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=loj_model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1835,    5],\n",
       "       [ 178,    0]], dtype=int64)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr=confusion_matrix(y,y_pred)\n",
    "pr\n",
    "## 1840 tane 0 var, modelim 2013 tane bulmus (1835+178)\n",
    "## 178 tane 1 var, modelim 5 tane bulmuş (5+0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9093161546085233"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y,y_pred)\n",
    "#accuracy çok yüksek ama sensitivity ile specificity bakmak gerekir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "###elle girelim\n",
    "TN = pr[0][0]\n",
    "FP = pr[0][1]\n",
    "FN = pr[1][0]\n",
    "TP = pr[1][1]\n",
    "n=len(veri) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.9093161546085233\n",
      "Mis Rate:  0.09068384539147671\n",
      "Precision Rate (1):  0.0\n",
      "Precision Rate (0):  0.9115747640337805\n"
     ]
    }
   ],
   "source": [
    "###elle girelim\n",
    "acc = (TP + TN)/n\n",
    "mis_rate=(FP+FN)/n\n",
    "precision_rate1 = (TP)/(TP+FP)\n",
    "precision_rate0 = (TN)/(TN+FN)\n",
    "print(\"Accuracy: \" , acc)\n",
    "print(\"Mis Rate: \", mis_rate)\n",
    "print(\"Precision Rate (1): \", precision_rate1)\n",
    "print(\"Precision Rate (0): \", precision_rate0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bakıldığında modelim 1 leri tahmin etmede hiç hassas değil 0 ları iyi öğrenmiş sadece diyebilirim!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      1.00      0.95      1840\n",
      "           1       0.00      0.00      0.00       178\n",
      "\n",
      "    accuracy                           0.91      2018\n",
      "   macro avg       0.46      0.50      0.48      2018\n",
      "weighted avg       0.83      0.91      0.87      2018\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rapora bakıldığında da modelimin sadece 0 lar için iyi çalıştığını söyleyebilirim, 1 ler için precision\n",
    "0.00 çıkmıştır.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loj_model.predict(X)[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.94378703, 0.05621297],\n",
       "       [0.82295558, 0.17704442],\n",
       "       [0.98250686, 0.01749314],\n",
       "       [0.83375945, 0.16624055],\n",
       "       [0.85176979, 0.14823021],\n",
       "       [0.97589329, 0.02410671],\n",
       "       [0.83935067, 0.16064933],\n",
       "       [0.96791113, 0.03208887],\n",
       "       [0.76821404, 0.23178596],\n",
       "       [0.95386306, 0.04613694]])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loj_model.predict_proba(X)[0:10]\n",
    "# 0 olma olasılığı 1 olma olasılığı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05621297, 0.17704442, 0.01749314, 0.16624055, 0.14823021,\n",
       "       0.02410671, 0.16064933, 0.03208887, 0.23178596, 0.04613694])"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_probs = loj_model.predict_proba(X)\n",
    "y_probs = y_probs[:,1]\n",
    "y_probs[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4986413043478261"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score,roc_curve\n",
    "logit_roc_auc = roc_auc_score(y, loj_model.predict(X))\n",
    "logit_roc_auc "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "false positive rate:  [0.         0.00054348 0.00380435 0.00380435 0.0048913  0.0048913\n",
      " 0.00597826 0.00597826 0.00706522 0.00706522]\n",
      "true positive rate:  [0.         0.         0.         0.00561798 0.00561798 0.02247191\n",
      " 0.02247191 0.02808989 0.02808989 0.03932584]\n",
      "threshold:  [1.53618315 0.53618315 0.47634515 0.46766239 0.45658186 0.44811757\n",
      " 0.40339186 0.40207364 0.37659032 0.37206732]\n"
     ]
    }
   ],
   "source": [
    "fpr,tpr,thresholds = roc_curve(y,loj_model.predict_proba(X)[:,1])\n",
    "print(\"false positive rate: \", fpr[0:10])\n",
    "print(\"true positive rate: \",tpr[0:10])\n",
    "print(\"threshold: \",thresholds[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "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 Oranı')\n",
    "plt.ylabel('True Positive Oranı')\n",
    "plt.title('ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##şimdi o thresholdu 0.3 yapıp yeniden tahmin edelim\n",
    "y_pred = [1 if i > 0.3 else 0 for i in y_probs]\n",
    "y_pred[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1817,   23],\n",
       "       [ 157,   21]], dtype=int64)"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.910802775024777"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.99      0.95      1840\n",
      "           1       0.48      0.12      0.19       178\n",
      "\n",
      "    accuracy                           0.91      2018\n",
      "   macro avg       0.70      0.55      0.57      2018\n",
      "weighted avg       0.88      0.91      0.89      2018\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thresholdu 0.3 yapınca hem accuracy hem de precisionlarımız arttı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05621297, 0.17704442, 0.01749314, 0.16624055, 0.14823021])"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loj_model.predict_proba(X)[:,1][0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5568181818181818"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score,roc_curve\n",
    "logit_roc_auc = roc_auc_score(y_pred, loj_model.predict(X))\n",
    "logit_roc_auc "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "false positive rate:  [0.         0.00054348 0.00380435 0.00380435 0.0048913  0.0048913\n",
      " 0.00597826 0.00597826 0.00706522 0.00706522]\n",
      "true positive rate:  [0.         0.         0.         0.00561798 0.00561798 0.02247191\n",
      " 0.02247191 0.02808989 0.02808989 0.03932584]\n",
      "threshold:  [1.53618315 0.53618315 0.47634515 0.46766239 0.45658186 0.44811757\n",
      " 0.40339186 0.40207364 0.37659032 0.37206732]\n"
     ]
    }
   ],
   "source": [
    "fpr,tpr,thresholds = roc_curve(y,loj_model.predict_proba(X)[:,1])\n",
    "print(\"false positive rate: \", fpr[0:10])\n",
    "print(\"true positive rate: \",tpr[0:10])\n",
    "print(\"threshold: \",thresholds[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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": [
    "### en baştan curve eden ve sonra converge eden bir eğri bekliyorum ki grafiğin altında kalan 1e daha yakın olsun\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 Oranı')\n",
    "plt.ylabel('True Positive Oranı')\n",
    "plt.title('ROC')\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}