ödev_hafta_5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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/sonuncu_ödeb_hafta_5\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <td>168.96</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",
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       "      <td>0</td>\n",
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       "    <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",
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       "      <td>0</td>\n",
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       "    <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",
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       "    <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",
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      "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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"hmelq.csv\")\n",
    "df=data.copy()\n",
    "df=df.dropna()\n",
    "y=df[\"bad\"]\n",
    "X=df.drop([\"bad\"],axis=1)\n",
    "X=pd.get_dummies(X)\n",
    "X=pd.DataFrame(X)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.30,random_state=42)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1-) Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cart=DecisionTreeClassifier()#hedef değişken kategorik\n",
    "cart_model=cart.fit(X_train,y_train)\n",
    "cart_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skompiler import skompile\n",
    "\n",
    "#print(skompile(cart_model.predict).to(\"python/code\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9306930693069307"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=cart_model.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.97      0.96       554\n",
      "           1       0.62      0.50      0.55        52\n",
      "\n",
      "   micro avg       0.93      0.93      0.93       606\n",
      "   macro avg       0.79      0.74      0.76       606\n",
      "weighted avg       0.93      0.93      0.93       606\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))#default parametrelerle kurulan modelimizin basarisini inceleriz;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.1) Decision Tree Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "?cart_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cart_grid={\"max_depth\":range(1,10),\"min_samples_split\":list(range(2,50))}\n",
    "#max_Depth:ne kadar derinlikte olsun\n",
    "#min_samples_split:yapragı bölerken en az kaça böliyim datayi bilgisidir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 432 candidates, totalling 4320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  39 tasks      | elapsed:    2.4s\n",
      "[Parallel(n_jobs=-1)]: Done 4320 out of 4320 | elapsed:   17.4s finished\n"
     ]
    }
   ],
   "source": [
    "cart=tree.DecisionTreeClassifier()\n",
    "cart_cv=GridSearchCV(cart,cart_grid,cv=10,n_jobs=-1,verbose=2)\n",
    "cart_cv_model=cart_cv.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EN iyi parametreler: {'max_depth': 8, 'min_samples_split': 2}\n"
     ]
    }
   ],
   "source": [
    "print(\"EN iyi parametreler: \"+str(cart_cv_model.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#simdi en iyi parametrelerle tekrar model kurariz ve default degerler ile olan accuracy farklarına bakarız\n",
    "cart=tree.DecisionTreeClassifier(max_depth=8,min_samples_split=2)\n",
    "cart_tuned=cart.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9455445544554455"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=cart_tuned.predict(X_test)#kurdugumuz model ile tahmin edilen y leri y_prede atadık\n",
    "accuracy_score(y_test,y_pred)#gerçek y degerleri ile modelimizin tahmin ettiği y değerlerini kıyaslayarak accuracyi buluruz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.99      0.97       554\n",
      "           1       0.81      0.48      0.60        52\n",
      "\n",
      "   micro avg       0.95      0.95      0.95       606\n",
      "   macro avg       0.88      0.73      0.79       606\n",
      "weighted avg       0.94      0.95      0.94       606\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))\n",
    "#en iyi parametrelerle kurulan modelimizin basarisini inceleriz;\n",
    "#0 lar icin recall ve f1 score iyilesmis\n",
    "#1ler icin precision ve f1 score degeri ciddi oranda iyilesmistir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2-) Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>18200</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <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",
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       "      <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",
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       "      <td>17400</td>\n",
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       "      <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",
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      ],
      "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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"hmelq.csv\")\n",
    "df=data.copy()\n",
    "df=df.dropna()\n",
    "y=df[\"bad\"]\n",
    "X=df.drop([\"bad\"],axis=1)\n",
    "X=pd.get_dummies(X)\n",
    "X=pd.DataFrame(X)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.30,random_state=42)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekorkmaz\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "rf_model=RandomForestClassifier().fit(X_train,y_train)#default degerlerle modelimizi kurduk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9504950495049505"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=rf_model.predict(X_test) # kurdugumuz modelin tahmin ettigi y'lerimizi buluruz\n",
    "accuracy_score(y_test,y_pred)#gercek y degerleri ile modelimizle tahmin edilen y lerden accuracyi buluruz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      1.00      0.97       554\n",
      "           1       0.96      0.44      0.61        52\n",
      "\n",
      "   micro avg       0.95      0.95      0.95       606\n",
      "   macro avg       0.95      0.72      0.79       606\n",
      "weighted avg       0.95      0.95      0.94       606\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred)) #default degerlerle kurulan modelimizin basarisini inceleriz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.1-) Random Forest Model Tuning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "?RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_params={\"max_depth\":[2,5,8,10],\"max_features\":[2,5,8],\"n_estimators\":[10,500,1000],\"min_samples_split\":[2,5,10]}\n",
    "#GridSearchde kullancagımız dictionary yaptık"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_model=RandomForestClassifier()\n",
    "rf_cv_model=GridSearchCV(rf_model,rf_params,cv=10,n_jobs=-1,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 108 candidates, totalling 1080 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    9.1s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:   40.9s\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:  1.8min\n",
      "[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed:  4.0min\n",
      "[Parallel(n_jobs=-1)]: Done 1080 out of 1080 | elapsed:  6.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'max_depth': [2, 5, 8, 10], 'max_features': [2, 5, 8], 'n_estimators': [10, 500, 1000], 'min_samples_split': [2, 5, 10]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=1)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_cv_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "En iyi parametreler: {'max_depth': 10, 'max_features': 5, 'min_samples_split': 10, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "print(\"En iyi parametreler: \"+str(rf_cv_model.best_params_))#random olduğu için paramlar farklı çıkabilir yani her çalıştırdığında"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#simdi en iyi parametrelerle tekrar model kurariz ve default degerler ile olan accuracy farklarına bakarız\n",
    "rf_tuned=RandomForestClassifier(max_depth=10,max_features=5,min_samples_split=10,n_estimators=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=10, max_features=5, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=10,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_tuned.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9455445544554455"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=rf_tuned.predict(X_test) # kurdugumuz modelin tahmin ettigi y'lerimizi buluruz\n",
    "accuracy_score(y_test,y_pred)#gercek y degerleri ile modelimizle tahmin edilen y lerden accuracyi buluruz\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      1.00      0.97       554\n",
      "           1       1.00      0.37      0.54        52\n",
      "\n",
      "   micro avg       0.95      0.95      0.95       606\n",
      "   macro avg       0.97      0.68      0.75       606\n",
      "weighted avg       0.95      0.95      0.93       606\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))\n",
    "#en iyi parametrelerle kurulan modelimizin basarisini inceleriz;\n",
    "#0 lar icin precision düşmüş\n",
    "#1ler icin precision artarken  recall ve f1 score degeri düşmüştür.\n",
    "#Bu durumda default degerler ile kurulan modelin daha basarili olmasinin sebebi data seçerken ki randomluktan kaynaklanıyor olabilir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3-)Gradient Boosting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Normal hızdaki boosting;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Datamızdaki 0ların ve 1lerin oranı unbalaced ise boosting uygulamaları daha uygundur çünkü bu modellerde \n",
    "#daha iyi öğrendiği sampleları seçmeye eğilimlidir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"hmelq.csv\")\n",
    "df=data.copy()\n",
    "df=df.dropna()\n",
    "y=df[\"bad\"]\n",
    "X=df.drop([\"bad\"],axis=1)\n",
    "X=pd.get_dummies(X)\n",
    "X=pd.DataFrame(X)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.30,random_state=42)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm_model=GradientBoostingClassifier().fit(X_train,y_train) #default degerlerle model kurduk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9455445544554455"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=gbm_model.predict(X_test)#default degerlerden kurulan modelden tahmin edilen y lerimizi aldık\n",
    "accuracy_score(y_test,y_pred) #gercek y degerleri ile modelimizin tahmin ettigi y degerlerinin farkından accuracyi bulduk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.99      0.97       554\n",
      "           1       0.85      0.44      0.58        52\n",
      "\n",
      "   micro avg       0.95      0.95      0.95       606\n",
      "   macro avg       0.90      0.72      0.78       606\n",
      "weighted avg       0.94      0.95      0.94       606\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred)) #default degerlerle kurulan modelimizin basarisini inceleriz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.1-) Gradient Boosting Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm_params={\"learning_rate\":[0.001,0.01,0.1,0.05],\"n_estimators\":[10,50,100],\"max_depth\":[3,5,10],\"min_samples_split\":[2,5,10]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm=GradientBoostingClassifier()\n",
    "gbm_cv=GridSearchCV(gbm,gbm_params,cv=10,n_jobs=-1,verbose=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 108 candidates, totalling 1080 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    2.5s\n",
      "[Parallel(n_jobs=-1)]: Done  87 tasks      | elapsed:    4.9s\n",
      "[Parallel(n_jobs=-1)]: Done 267 tasks      | elapsed:   15.8s\n",
      "[Parallel(n_jobs=-1)]: Done 519 tasks      | elapsed:   28.0s\n",
      "[Parallel(n_jobs=-1)]: Done 801 tasks      | elapsed:   47.0s\n",
      "[Parallel(n_jobs=-1)]: Done 1080 out of 1080 | elapsed:  1.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
       "       estimator=GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_sampl...      subsample=1.0, tol=0.0001, validation_fraction=0.1,\n",
       "              verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'learning_rate': [0.001, 0.01, 0.1, 0.05], 'n_estimators': [10, 50, 100], 'max_depth': [3, 5, 10], 'min_samples_split': [2, 5, 10]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=5)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm_cv.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "En iyi parametreler: {'learning_rate': 0.1, 'max_depth': 10, 'min_samples_split': 5, 'n_estimators': 50}\n"
     ]
    }
   ],
   "source": [
    "print(\"En iyi parametreler: \"+str(gbm_cv.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm=GradientBoostingClassifier(learning_rate=0.1,max_depth=10,min_samples_split=5,n_estimators=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm_tuned=gbm.fit(X_train,y_train) #en iyi parametrelerle modelimizi tekrar kurduk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9537953795379538"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=gbm_tuned.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.99      0.98       554\n",
      "           1       0.90      0.52      0.66        52\n",
      "\n",
      "   micro avg       0.95      0.95      0.95       606\n",
      "   macro avg       0.93      0.76      0.82       606\n",
      "weighted avg       0.95      0.95      0.95       606\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred)) #en iyi degerlerle kurulan modelimizin basarisini inceleriz\n",
    "#0 lar icin; precision ve f1 degeri iyilesmis\n",
    "#1 ler icin ;tüm degerler iyilesmis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4-) XGBOOST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"hmelq.csv\")\n",
    "df=data.copy()\n",
    "df=df.dropna()\n",
    "y=df[\"bad\"]\n",
    "X=df.drop([\"bad\"],axis=1)\n",
    "X=pd.get_dummies(X)\n",
    "X=pd.DataFrame(X)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.30,random_state=42)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model=XGBClassifier().fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9455445544554455"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=gbm_model.predict(X_test)#default degerlerden kurulan modelden tahmin edilen y lerimizi aldık\n",
    "accuracy_score(y_test,y_pred) #gercek y degerleri ile modelimizin tahmin ettigi y degerlerinin farkından accuracyi bulduk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.99      0.97       554\n",
      "           1       0.85      0.44      0.58        52\n",
      "\n",
      "   micro avg       0.95      0.95      0.95       606\n",
      "   macro avg       0.90      0.72      0.78       606\n",
      "weighted avg       0.94      0.95      0.94       606\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred)) #default degerlerle kurulan modelimizin basarisini inceleriz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.1-) XGBOOST Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bynode=1, colsample_bytree=1, gamma=0, learning_rate=0.1,\n",
       "       max_delta_step=0, max_depth=3, min_child_weight=1, missing=None,\n",
       "       n_estimators=100, n_jobs=1, nthread=None,\n",
       "       objective='binary:logistic', random_state=0, reg_alpha=0,\n",
       "       reg_lambda=1, scale_pos_weight=1, seed=None, silent=None,\n",
       "       subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_params={\n",
    "    'n_estimators':[10,50,100,200],\n",
    "    'subsample':[0.6,0.8,1.0],\n",
    "    'max_depth':[3,4,5,6],\n",
    "    'learning_rate':[0.1,0.01,0.02,0.05],\n",
    "    'min_samples_split':[2,5,10]\n",
    "}\n",
    "#sub sample =agac olustururken traine ne kadar veri cekiyim,bilgisidir. \n",
    "#datamın farklı parametrelerle model kurması icin dictionary olusturduk\n",
    "#overfitting olmamması icin cok fazla derinlikte agac olusturmasak iyi olur cunku her yeni yaprak olusturumunda adtayı \n",
    "#böldugu icin yapraga az veri kaliyor onları ezberlememesi icin optimum derinlikte agac kurdurmalıyız "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb=XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_cv_model=GridSearchCV(xgb,xgb_params,cv=10,n_jobs=-1,verbose=1) \n",
    "#cv=cross validation yani yukardaki parametreler ısıgında 10 tane farklı agac kurar(10 tane farklı sample cekerek)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 576 candidates, totalling 5760 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  54 tasks      | elapsed:    3.6s\n",
      "[Parallel(n_jobs=-1)]: Done 384 tasks      | elapsed:   24.1s\n",
      "[Parallel(n_jobs=-1)]: Done 867 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=-1)]: Done 1327 tasks      | elapsed:  1.8min\n",
      "[Parallel(n_jobs=-1)]: Done 2162 tasks      | elapsed:  2.9min\n",
      "[Parallel(n_jobs=-1)]: Done 2902 tasks      | elapsed:  4.3min\n",
      "[Parallel(n_jobs=-1)]: Done 3851 tasks      | elapsed:  5.6min\n",
      "[Parallel(n_jobs=-1)]: Done 4993 tasks      | elapsed:  7.3min\n",
      "[Parallel(n_jobs=-1)]: Done 5760 out of 5760 | elapsed:  8.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
       "       estimator=XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bynode=1, colsample_bytree=1, gamma=0, learning_rate=0.1,\n",
       "       max_delta_step=0, max_depth=3, min_child_weight=1, missing=None,\n",
       "       n_estimators=100, n_jobs=1, nthread=None,\n",
       "       objective='binary:logistic', random_state=0, reg_alpha=0,\n",
       "       reg_lambda=1, scale_pos_weight=1, seed=None, silent=None,\n",
       "       subsample=1, verbosity=1),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'n_estimators': [10, 50, 100, 200], 'subsample': [0.6, 0.8, 1.0], 'max_depth': [3, 4, 5, 6], 'learning_rate': [0.1, 0.01, 0.02, 0.05], 'min_samples_split': [2, 5, 10]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=1)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_cv_model.fit(X_train,y_train)\n",
    "#modelimize datamızı veririz(fit ederiz) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.1,\n",
       " 'max_depth': 4,\n",
       " 'min_samples_split': 2,\n",
       " 'n_estimators': 200,\n",
       " 'subsample': 0.8}"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_cv_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb=XGBClassifier(learning_rate=0.01,\n",
    "                 max_depth=4,\n",
    "                  min_samples_split=2,\n",
    "                  n_estimators=200,\n",
    "                  subsample=0.6\n",
    "                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_tuned=xgb.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9488448844884488"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=xgb_tuned.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      1.00      0.97       554\n",
      "           1       1.00      0.40      0.58        52\n",
      "\n",
      "   micro avg       0.95      0.95      0.95       606\n",
      "   macro avg       0.97      0.70      0.77       606\n",
      "weighted avg       0.95      0.95      0.94       606\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))#xgboostun best parametrelerle kurdugu modelin accuracysini yorumlarız \n",
    "# 0'lar için;recall degeri iyilesmis\n",
    "# 1'ler için;precision iyilesmis recall kötülesmis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5-) LIGHTGBM "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#light Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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",
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       "      <td>1</td>\n",
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       "      <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": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"hmelq.csv\")\n",
    "df=data.copy()\n",
    "df=df.dropna()\n",
    "y=df[\"bad\"]\n",
    "X=df.drop([\"bad\"],axis=1)\n",
    "X=pd.get_dummies(X)\n",
    "X=pd.DataFrame(X)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.30,random_state=42)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lightgbm import LGBMClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm_model=LGBMClassifier().fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9537953795379538"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=lgbm_model.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      1.00      0.98       554\n",
      "           1       0.96      0.48      0.64        52\n",
      "\n",
      "   micro avg       0.95      0.95      0.95       606\n",
      "   macro avg       0.96      0.74      0.81       606\n",
      "weighted avg       0.95      0.95      0.95       606\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))#lightgbm'nindefault parametrelerle kurdugu modelin accuracysi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.1-) LıghtGBM Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "?lgbm_model  \n",
    "#LGBM parametreleri hakkında bilgi verir;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm_params={\n",
    "    'n_estimators':[10,50,100,200],\n",
    "    'subsample':[0.6,0.8,1.0],\n",
    "    'max_depth':[3,4,5,6],\n",
    "    'learning_rate':[0.1,0.01,0.02,0.05],\n",
    "    \"min_child\":[5,10,20]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm=LGBMClassifier()\n",
    "lgbm_cv_model=GridSearchCV(lgbm,lgbm_params,\n",
    "                          cv=10,\n",
    "                          n_jobs=-1,\n",
    "                          verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 576 candidates, totalling 5760 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  59 tasks      | elapsed:    3.8s\n",
      "[Parallel(n_jobs=-1)]: Done 659 tasks      | elapsed:   18.4s\n",
      "[Parallel(n_jobs=-1)]: Done 1573 tasks      | elapsed:   53.0s\n",
      "[Parallel(n_jobs=-1)]: Done 2273 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done 3173 tasks      | elapsed:  1.8min\n",
      "[Parallel(n_jobs=-1)]: Done 4273 tasks      | elapsed:  2.4min\n",
      "[Parallel(n_jobs=-1)]: Done 5573 tasks      | elapsed:  3.1min\n",
      "[Parallel(n_jobs=-1)]: Done 5753 out of 5760 | elapsed:  3.2min remaining:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 5760 out of 5760 | elapsed:  3.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
       "       estimator=LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
       "        importance_type='split', learning_rate=0.1, max_depth=-1,\n",
       "        min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
       "        n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,\n",
       "        random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=True,\n",
       "        subsample=1.0, subsample_for_bin=200000, subsample_freq=0),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'n_estimators': [10, 50, 100, 200], 'subsample': [0.6, 0.8, 1.0], 'max_depth': [3, 4, 5, 6], 'learning_rate': [0.1, 0.01, 0.02, 0.05], 'min_child': [5, 10, 20]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=1)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgbm_cv_model.fit(X_train,y_train)#modeli datamızla  eğitiyoruz ,kuruyoruz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.1,\n",
       " 'max_depth': 3,\n",
       " 'min_child': 5,\n",
       " 'n_estimators': 200,\n",
       " 'subsample': 0.6}"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgbm_cv_model.best_params_#subsample;sample cekerken datanın yüzde kacini kullaniyim bilgisidir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm=LGBMClassifier(learning_rate=0.1,\n",
    "                 max_depth=3,\n",
    "                  min_child=5,#en alttaki yaprakta kac tane veri kalsın bilgisidir.\n",
    "                  n_estimators=200,\n",
    "                  subsample=0.6\n",
    "                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm_tuned=lgbm.fit(X_train,y_train)\n",
    "y_pred=lgbm_tuned.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9488448844884488"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      1.00      0.97       554\n",
      "           1       0.92      0.44      0.60        52\n",
      "\n",
      "   micro avg       0.95      0.95      0.95       606\n",
      "   macro avg       0.94      0.72      0.79       606\n",
      "weighted avg       0.95      0.95      0.94       606\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))#boostun best parametrelerle kurdugu modelin accuracysini yorumlarız \n",
    "# 0 lar için f1 lerin degeri düşmüş \n",
    "# 1 ler için precision ve recall degerleri düşmüş f1 degeri iyilesmis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6-) Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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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",
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       "    <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",
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       "    <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",
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       "      <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"hmelq.csv\")\n",
    "df=data.copy()\n",
    "df=df.dropna()\n",
    "y=df[\"bad\"]\n",
    "X=df.drop([\"bad\"],axis=1)\n",
    "X=pd.get_dummies(X)\n",
    "X=pd.DataFrame(X)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.30,random_state=42)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekorkmaz\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\ekorkmaz\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  \n",
      "C:\\Users\\ekorkmaz\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "scaler.fit(X_train)\n",
    "X_train_scaled=scaler.transform(X_train)\n",
    "X_test_scaled=scaler.transform(X_test) #aralarındaki ilişki lineer oldugu düşünüldüğü için normalize ederiz veriyi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.66119779,  0.41857973,  0.45688065, -1.1976411 ,  1.5763105 ,\n",
       "        -0.32687081,  0.08335509,  0.56846498,  1.52185726,  0.11670154,\n",
       "         0.6374751 , -0.6374751 , -0.40385294, -0.43767703, -0.80952709,\n",
       "         1.69994328, -0.11678881, -0.18350739],\n",
       "       [ 0.20052987,  0.76904412,  1.29483717, -0.4082825 , -0.26772465,\n",
       "        -0.32687081,  0.38153842,  1.79767255,  0.15796644,  1.03330214,\n",
       "         0.6374751 , -0.6374751 , -0.40385294,  2.28478978, -0.80952709,\n",
       "        -0.58825492, -0.11678881, -0.18350739],\n",
       "       [-0.19690911,  1.05263158,  0.975908  , -0.67140204, -0.26772465,\n",
       "        -0.32687081, -0.28954975, -0.6607426 , -0.1567776 , -0.97373513,\n",
       "         0.6374751 , -0.6374751 , -0.40385294,  2.28478978, -0.80952709,\n",
       "        -0.58825492, -0.11678881, -0.18350739],\n",
       "       [-0.26917075, -1.19611852,  1.06360535, -0.01360321, -0.26772465,\n",
       "        -0.32687081,  1.45586509, -0.6607426 ,  0.47271047, -0.76194009,\n",
       "        -1.56868872,  1.56868872, -0.40385294, -0.43767703, -0.80952709,\n",
       "        -0.58825492, -0.11678881,  5.44937172],\n",
       "       [-1.09114683,  0.50169215,  0.16329071,  1.69667375, -0.26772465,\n",
       "        -0.32687081, -0.72640122, -0.6607426 , -1.20592439,  0.39597918,\n",
       "        -1.56868872,  1.56868872, -0.40385294,  2.28478978, -0.80952709,\n",
       "        -0.58825492, -0.11678881, -0.18350739]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_scaled[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ekorkmaz\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "mlpc=MLPClassifier().fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9438943894389439"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=mlpc.predict(X_test_scaled)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.99      0.97       554\n",
      "           1       0.88      0.40      0.55        52\n",
      "\n",
      "   micro avg       0.94      0.94      0.94       606\n",
      "   macro avg       0.91      0.70      0.76       606\n",
      "weighted avg       0.94      0.94      0.93       606\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred)) \n",
    "#default parametrelerle kurdugumuz nn modeli"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.1-) Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlpc_params = {\"alpha\": [0.1, 0.01, 0.02, 0.001, 0.005,0.0001],\n",
    "              \"hidden_layer_sizes\": [(10,10,10),#2 layer olsun her layerda max 10 node olsun \n",
    "                                     (100,100),\n",
    "                                     (100,100,100),#2 layer olsun her layerda max 100 node olsun \n",
    "                                     (3,5), #single layer\n",
    "                                     (5, 3)],\n",
    "              \"solver\" : [\"lbfgs\",\"adam\",\"sgd\"],\n",
    "              \"activation\": [\"relu\",\"logistic\"]}\n",
    "#alpha :duracagi error sınırı bilgisidir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 180 candidates, totalling 1800 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   29.6s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:  2.4min\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:  5.4min\n",
      "[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed:  8.8min\n",
      "[Parallel(n_jobs=-1)]: Done 1242 tasks      | elapsed: 13.1min\n",
      "[Parallel(n_jobs=-1)]: Done 1792 tasks      | elapsed: 18.4min\n",
      "[Parallel(n_jobs=-1)]: Done 1800 out of 1800 | elapsed: 18.4min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
       "       estimator=MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
       "       random_state=None, shuffle=True, solver='adam', tol=0.0001,\n",
       "       validation_fraction=0.1, verbose=False, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'alpha': [0.1, 0.01, 0.02, 0.001, 0.005, 0.0001], 'hidden_layer_sizes': [(10, 10, 10), (100, 100), (100, 100, 100), (3, 5), (5, 3)], 'solver': ['lbfgs', 'adam', 'sgd'], 'activation': ['relu', 'logistic']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlpc=MLPClassifier()\n",
    "mlpc_cv_model=GridSearchCV(mlpc,mlpc_params,\n",
    "                          cv=10,\n",
    "                          n_jobs=-1,\n",
    "                          verbose=1)\n",
    "mlpc_cv_model.fit(X_train_scaled,y_train)#modelimizi kurarız"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "En iyi parametreler:{'activation': 'relu', 'alpha': 0.1, 'hidden_layer_sizes': (100, 100), 'solver': 'lbfgs'}\n"
     ]
    }
   ],
   "source": [
    "print(\"En iyi parametreler:\" + str(mlpc_cv_model.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlpc_tuned=MLPClassifier(activation=\"relu\",\n",
    "                        alpha=0.1,\n",
    "                        hidden_layer_sizes=(100,100),\n",
    "                         solver=\"lbfgs\"\n",
    "                        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.1, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100, 100), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
       "       random_state=None, shuffle=True, solver='lbfgs', tol=0.0001,\n",
       "       validation_fraction=0.1, verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlpc_tuned.fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9504950495049505"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=mlpc_tuned.predict(X_test_scaled)\n",
    "accuracy_score(y_test,y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.99      0.97       554\n",
      "           1       0.81      0.56      0.66        52\n",
      "\n",
      "   micro avg       0.95      0.95      0.95       606\n",
      "   macro avg       0.88      0.77      0.82       606\n",
      "weighted avg       0.95      0.95      0.95       606\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))\n",
    "#en iyi parametrelerle NN modeli kurdugumuzda;\n",
    "# 0 lar icin ; precision degerimiz iyileşmiş\n",
    "# 1 ler icin;recall ve f1 iyilesirken precisionımız düşmüş\n",
    "#accuracymiz artmis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7-) SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"hmelq.csv\")\n",
    "df=data.copy()\n",
    "df=df.dropna()\n",
    "y=df[\"bad\"]\n",
    "X=df.drop([\"bad\"],axis=1)\n",
    "X=pd.get_dummies(X)\n",
    "X=pd.DataFrame(X)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.30,random_state=42)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-e0c87f41db18>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msvm_model\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mSVC\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"linear\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0msvm_model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# ylerimi cemer olarak ayrıstırmaya calisirsam \"rbf\" kullanılırım\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    210\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    211\u001b[0m         \u001b[0mseed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrnd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'i'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 212\u001b[1;33m         \u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msolver_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    213\u001b[0m         \u001b[1;31m# see comment on the other call to np.iinfo in this file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36m_dense_fit\u001b[1;34m(self, X, y, sample_weight, solver_type, kernel, random_seed)\u001b[0m\n\u001b[0;32m    269\u001b[0m                 \u001b[0mcache_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcache_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoef0\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    270\u001b[0m                 \u001b[0mgamma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_gamma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsilon\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 271\u001b[1;33m                 max_iter=self.max_iter, random_seed=random_seed)\n\u001b[0m\u001b[0;32m    272\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    273\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_warn_from_fit_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "svm_model=SVC(kernel=\"linear\").fit(X_train,y_train)\n",
    "svm_model\n",
    "# ylerimi cemer olarak ayrıstırmaya calisirsam \"rbf\" kullanılırım"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9207920792079208"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=svm_model.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      1.00      0.96       554\n",
      "           1       1.00      0.08      0.14        52\n",
      "\n",
      "   micro avg       0.92      0.92      0.92       606\n",
      "   macro avg       0.96      0.54      0.55       606\n",
      "weighted avg       0.93      0.92      0.89       606\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))\n",
    "#default parametrelerle kurdumuz SVM modelimizin basarisi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7.1-) SVM Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_params={\"C\":np.arange(1,10)} #C=error ratelerdir yani bu error seviyesinde dur bilgisidi\n",
    "svc=SVC(kernel=\"linear\")\n",
    "svc_cv_model=GridSearchCV(svc,svc_params,\n",
    "                         cv=10,\n",
    "                         n_jobs=-1,\n",
    "                         verbose=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    }
   ],
   "source": [
    "svc_cv_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hocam bu svm tuningi 2 gündür en az 5 kere denedim ve hep bilgisayar dondu en sonunda ise kapandı \n",
    "#ve şirket bilgisayarı oldugu icin daha fazla zorlamak istemedim svm model tuningini yapamadan teslim etmek zorunda kaldım"
   ]
  }
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