odev4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"C:\\\\Users\\\\ozlem\\\\OneDrive\\\\Masaüstü\\\\kodluyoruz\\\\2.hafta\\\\titanik\\\\titanik\\\\hmelq.csv\")\n",
    "data=df.dropna()\n",
    "data.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": [
    "data[\"bad\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ozlem\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\frame.py:3940: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    }
   ],
   "source": [
    "data.drop(['reason','job'],inplace = True,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=data[\"bad\"]\n",
    "X=data.drop([\"bad\"],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.261768\n",
      "         Iterations 7\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>  2008</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     9</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Thu, 26 Sep 2019</td> <th>  Pseudo R-squ.:     </th>  <td>0.1227</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>19:17:19</td>     <th>  Log-Likelihood:    </th> <td> -528.25</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>2.621e-27</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>-2.706e-05</td> <td> 1.04e-05</td> <td>   -2.600</td> <td> 0.009</td> <td>-4.74e-05</td> <td>-6.66e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mortdue</th> <td>-3.409e-07</td> <td> 5.22e-06</td> <td>   -0.065</td> <td> 0.948</td> <td>-1.06e-05</td> <td>  9.9e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>value</th>   <td>-3.173e-06</td> <td> 4.29e-06</td> <td>   -0.739</td> <td> 0.460</td> <td>-1.16e-05</td> <td> 5.24e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>yoj</th>     <td>   -0.0342</td> <td>    0.013</td> <td>   -2.727</td> <td> 0.006</td> <td>   -0.059</td> <td>   -0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>derog</th>   <td>    0.5800</td> <td>    0.122</td> <td>    4.758</td> <td> 0.000</td> <td>    0.341</td> <td>    0.819</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>delinq</th>  <td>    0.7398</td> <td>    0.086</td> <td>    8.610</td> <td> 0.000</td> <td>    0.571</td> <td>    0.908</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>clage</th>   <td>   -0.0084</td> <td>    0.001</td> <td>   -6.358</td> <td> 0.000</td> <td>   -0.011</td> <td>   -0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ninq</th>    <td>    0.0266</td> <td>    0.047</td> <td>    0.571</td> <td> 0.568</td> <td>   -0.065</td> <td>    0.118</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>clno</th>    <td>   -0.0505</td> <td>    0.011</td> <td>   -4.635</td> <td> 0.000</td> <td>   -0.072</td> <td>   -0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>debtinc</th> <td>    0.0222</td> <td>    0.007</td> <td>    3.126</td> <td> 0.002</td> <td>    0.008</td> <td>    0.036</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:                     2008\n",
       "Method:                           MLE   Df Model:                            9\n",
       "Date:                Thu, 26 Sep 2019   Pseudo R-squ.:                  0.1227\n",
       "Time:                        19:17:19   Log-Likelihood:                -528.25\n",
       "converged:                       True   LL-Null:                       -602.11\n",
       "Covariance Type:            nonrobust   LLR p-value:                 2.621e-27\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "loan       -2.706e-05   1.04e-05     -2.600      0.009   -4.74e-05   -6.66e-06\n",
       "mortdue    -3.409e-07   5.22e-06     -0.065      0.948   -1.06e-05     9.9e-06\n",
       "value      -3.173e-06   4.29e-06     -0.739      0.460   -1.16e-05    5.24e-06\n",
       "yoj           -0.0342      0.013     -2.727      0.006      -0.059      -0.010\n",
       "derog          0.5800      0.122      4.758      0.000       0.341       0.819\n",
       "delinq         0.7398      0.086      8.610      0.000       0.571       0.908\n",
       "clage         -0.0084      0.001     -6.358      0.000      -0.011      -0.006\n",
       "ninq           0.0266      0.047      0.571      0.568      -0.065       0.118\n",
       "clno          -0.0505      0.011     -4.635      0.000      -0.072      -0.029\n",
       "debtinc        0.0222      0.007      3.126      0.002       0.008       0.036\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loj=sm.Logit(y,X)#y leri x le modelleyeceğiz\n",
    "loj_model=loj.fit()\n",
    "loj_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_=X.drop([\"mortdue\",\"value\",\"ninq\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "loj=LogisticRegression(solver=\"liblinear\")\n",
    "loj_model=loj.fit(X_,y)\n",
    "loj_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.70541302]\n",
      "[[-2.15724966e-05 -3.13104225e-02  5.70133837e-01  7.20738701e-01\n",
      "  -6.86137414e-03 -3.69658767e-02  4.37205190e-02]]\n"
     ]
    }
   ],
   "source": [
    "print(loj_model.intercept_)#beta0\n",
    "print(loj_model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=loj_model.predict(X_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9207135777998018"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y,y_pred) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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       0.75      0.15      0.25       178\n",
      "\n",
      "   micro avg       0.92      0.92      0.92      2018\n",
      "   macro avg       0.84      0.57      0.61      2018\n",
      "weighted avg       0.91      0.92      0.90      2018\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loj_model.predict(X_)[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.93791535, 0.06208465],\n",
       "       [0.81229489, 0.18770511],\n",
       "       [0.98697812, 0.01302188],\n",
       "       [0.83476237, 0.16523763],\n",
       "       [0.82000557, 0.17999443],\n",
       "       [0.98423197, 0.01576803],\n",
       "       [0.85334995, 0.14665005],\n",
       "       [0.96480699, 0.03519301],\n",
       "       [0.75102422, 0.24897578],\n",
       "       [0.96060046, 0.03939954]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loj_model.predict_proba(X)[0:10][:,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5733970444553004"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logit_roc_auc=roc_auc_score(y,loj_model.predict(X_))\n",
    "logit_roc_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.         0.         0.00108696 0.00108696 0.00163043\n",
      " 0.00163043 0.00326087 0.00326087 0.00434783]\n",
      "[0.         0.00561798 0.09550562 0.09550562 0.11797753 0.11797753\n",
      " 0.12921348 0.12921348 0.13483146 0.13483146]\n",
      "[1.99359308 0.99359308 0.78450679 0.74083947 0.68589847 0.65698617\n",
      " 0.65315655 0.61004577 0.60089134 0.58478392]\n"
     ]
    }
   ],
   "source": [
    "fpr,tpr,thresholds=roc_curve(y,loj_model.predict_proba(X_)[:,1])\n",
    "print(fpr[0:10])\n",
    "print(tpr[0:10])\n",
    "print(thresholds[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}