HaticeCandan_HW5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Library\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "from scipy.stats import norm\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "print(\"Library\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import scale \n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
    "from sklearn.metrics import roc_auc_score,roc_curve\n",
    "import statsmodels.formula.api as smf\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from catboost import CatBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bad</th>\n",
       "      <th>loan</th>\n",
       "      <th>mortdue</th>\n",
       "      <th>value</th>\n",
       "      <th>reason</th>\n",
       "      <th>job</th>\n",
       "      <th>yoj</th>\n",
       "      <th>derog</th>\n",
       "      <th>delinq</th>\n",
       "      <th>clage</th>\n",
       "      <th>ninq</th>\n",
       "      <th>clno</th>\n",
       "      <th>debtinc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>81200</td>\n",
       "      <td>18834.0</td>\n",
       "      <td>108355.0</td>\n",
       "      <td>HomeImp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>139.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>34.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>12600</td>\n",
       "      <td>103960.0</td>\n",
       "      <td>127384.0</td>\n",
       "      <td>DebtCon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>129.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>34.479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>18000</td>\n",
       "      <td>46865.0</td>\n",
       "      <td>61266.0</td>\n",
       "      <td>DebtCon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102.59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>26.354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>10300</td>\n",
       "      <td>57676.0</td>\n",
       "      <td>71027.0</td>\n",
       "      <td>DebtCon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157.52</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>33.992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>9400</td>\n",
       "      <td>56508.0</td>\n",
       "      <td>78358.0</td>\n",
       "      <td>DebtCon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>32.327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bad   loan   mortdue     value   reason  job   yoj  derog  delinq   clage  \\\n",
       "0    0  81200   18834.0  108355.0  HomeImp  NaN  28.0    0.0     0.0  139.14   \n",
       "1    0  12600  103960.0  127384.0  DebtCon  NaN   2.0    0.0     0.0  129.02   \n",
       "2    0  18000   46865.0   61266.0  DebtCon  NaN   5.0    0.0     0.0  102.59   \n",
       "3    0  10300   57676.0   71027.0  DebtCon  NaN  19.0    0.0     0.0  157.52   \n",
       "4    0   9400   56508.0   78358.0  DebtCon  NaN  17.0    0.0     0.0  141.93   \n",
       "\n",
       "   ninq  clno  debtinc  \n",
       "0   0.0  14.0   34.042  \n",
       "1   0.0  25.0   34.479  \n",
       "2   2.0   9.0   26.354  \n",
       "3   1.0  11.0   33.992  \n",
       "4   0.0  11.0   32.327  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "#os.getcwd()\n",
    "os.chdir('C:/Users/asus1/Desktop/bootcamp')\n",
    "#reading data\n",
    "veri=pd.read_csv('hmelq.csv')\n",
    "veri.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>job_Mgr</th>\n",
       "      <th>job_Office</th>\n",
       "      <th>job_Other</th>\n",
       "      <th>job_ProfEx</th>\n",
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      "text/plain": [
       "   reason_DebtCon  reason_HomeImp  job_Mgr  job_Office  job_Other  job_ProfEx  \\\n",
       "0               0               1        0           0          0           0   \n",
       "1               1               0        0           0          0           0   \n",
       "2               1               0        0           0          0           0   \n",
       "3               1               0        0           0          0           0   \n",
       "4               1               0        0           0          0           0   \n",
       "\n",
       "   job_Sales  job_Self  \n",
       "0          0         0  \n",
       "1          0         0  \n",
       "2          0         0  \n",
       "3          0         0  \n",
       "4          0         0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dms=pd.get_dummies(veri[['reason', 'job']])\n",
    "dms.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>bad</th>\n",
       "      <th>loan</th>\n",
       "      <th>mortdue</th>\n",
       "      <th>value</th>\n",
       "      <th>yoj</th>\n",
       "      <th>derog</th>\n",
       "      <th>delinq</th>\n",
       "      <th>clage</th>\n",
       "      <th>ninq</th>\n",
       "      <th>clno</th>\n",
       "      <th>debtinc</th>\n",
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       "      <td>34.479</td>\n",
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       "      <th>2</th>\n",
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       "      <td>18000</td>\n",
       "      <td>46865.0</td>\n",
       "      <td>61266.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102.59</td>\n",
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       "      <td>9.0</td>\n",
       "      <td>26.354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>10300</td>\n",
       "      <td>57676.0</td>\n",
       "      <td>71027.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157.52</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>33.992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>9400</td>\n",
       "      <td>56508.0</td>\n",
       "      <td>78358.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.93</td>\n",
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      ],
      "text/plain": [
       "   bad   loan   mortdue     value   yoj  derog  delinq   clage  ninq  clno  \\\n",
       "0    0  81200   18834.0  108355.0  28.0    0.0     0.0  139.14   0.0  14.0   \n",
       "1    0  12600  103960.0  127384.0   2.0    0.0     0.0  129.02   0.0  25.0   \n",
       "2    0  18000   46865.0   61266.0   5.0    0.0     0.0  102.59   2.0   9.0   \n",
       "3    0  10300   57676.0   71027.0  19.0    0.0     0.0  157.52   1.0  11.0   \n",
       "4    0   9400   56508.0   78358.0  17.0    0.0     0.0  141.93   0.0  11.0   \n",
       "\n",
       "   debtinc  \n",
       "0   34.042  \n",
       "1   34.479  \n",
       "2   26.354  \n",
       "3   33.992  \n",
       "4   32.327  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_veri = veri.drop([\"reason\",\"job\"],axis=1)\n",
    "n_veri.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
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       "      <td>129.02</td>\n",
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       "      <td>34.479</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",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>18000</td>\n",
       "      <td>46865.0</td>\n",
       "      <td>61266.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102.59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>26.354</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>10300</td>\n",
       "      <td>57676.0</td>\n",
       "      <td>71027.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157.52</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>33.992</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>4</th>\n",
       "      <td>0</td>\n",
       "      <td>9400</td>\n",
       "      <td>56508.0</td>\n",
       "      <td>78358.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>32.327</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bad   loan   mortdue     value   yoj  derog  delinq   clage  ninq  clno  \\\n",
       "0    0  81200   18834.0  108355.0  28.0    0.0     0.0  139.14   0.0  14.0   \n",
       "1    0  12600  103960.0  127384.0   2.0    0.0     0.0  129.02   0.0  25.0   \n",
       "2    0  18000   46865.0   61266.0   5.0    0.0     0.0  102.59   2.0   9.0   \n",
       "3    0  10300   57676.0   71027.0  19.0    0.0     0.0  157.52   1.0  11.0   \n",
       "4    0   9400   56508.0   78358.0  17.0    0.0     0.0  141.93   0.0  11.0   \n",
       "\n",
       "   debtinc  reason_DebtCon  reason_HomeImp  job_Mgr  job_Office  job_Other  \\\n",
       "0   34.042               0               1        0           0          0   \n",
       "1   34.479               1               0        0           0          0   \n",
       "2   26.354               1               0        0           0          0   \n",
       "3   33.992               1               0        0           0          0   \n",
       "4   32.327               1               0        0           0          0   \n",
       "\n",
       "   job_ProfEx  job_Sales  job_Self  \n",
       "0           0          0         0  \n",
       "1           0          0         0  \n",
       "2           0          0         0  \n",
       "3           0          0         0  \n",
       "4           0          0         0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri = pd.concat([n_veri, dms[[\"reason_DebtCon\",\"reason_HomeImp\",\"job_Mgr\",\"job_Office\",\"job_Other\",\"job_ProfEx\",\"job_Sales\",\"job_Self\"]]],axis = 1)\n",
    "veri=veri.dropna()\n",
    "veri.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1473, 18)\n",
      "(632, 18)\n",
      "(1473,)\n",
      "(632,)\n"
     ]
    }
   ],
   "source": [
    "y=veri[\"bad\"]\n",
    "X=veri.drop([\"bad\"],axis=1)\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",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision Tree Yapalım"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "cart=DecisionTreeClassifier()\n",
    "cart_model=cart.fit(X_train,y_train)\n",
    "cart_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9240506329113924"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = cart_model.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy'miz çok yüksek çıkmış. Recalllara da bakalım"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.97      0.96       574\n",
      "           1       0.60      0.52      0.56        58\n",
      "\n",
      "    accuracy                           0.92       632\n",
      "   macro avg       0.78      0.74      0.76       632\n",
      "weighted avg       0.92      0.92      0.92       632\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "?cart_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "cart_grid = {\"max_depth\": range(1,10),\n",
    "             \"min_samples_split\" : list(range(2,50))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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  50 tasks      | elapsed:    8.1s\n",
      "[Parallel(n_jobs=-1)]: Done 4320 out of 4320 | elapsed:   25.1s finished\n"
     ]
    }
   ],
   "source": [
    "cart = tree.DecisionTreeClassifier()\n",
    "cart_cv = GridSearchCV(cart, cart_grid, cv=10, n_jobs=-1, verbose=1)\n",
    "cart_cv_model=cart_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "En iyi paramatreler: {'max_depth': 9, 'min_samples_split': 7}\n"
     ]
    }
   ],
   "source": [
    "#En iyi parametreleri alalım\n",
    "print(\"En iyi paramatreler: \" + str(cart_cv_model.best_params_)) #en iyilerini bul"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cart = tree.DecisionTreeClassifier(max_depth=9, min_samples_split=7)\n",
    "cart_tuned=cart.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9382911392405063"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=cart_tuned.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bakıldığında ilk duruma göre accuracy'nin artiği görülmektedir. İlk default durumunda accuracymiz = 0.924 iken, şimdi 0.938'dir. Raporlayalım"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.99      0.97       574\n",
      "           1       0.88      0.38      0.53        58\n",
      "\n",
      "    accuracy                           0.94       632\n",
      "   macro avg       0.91      0.69      0.75       632\n",
      "weighted avg       0.94      0.94      0.93       632\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Precision: 1 için iyileşmiş\n",
    "Recall   : 0 için iyileşmiş, 1 için düşmüştür\n",
    "f1-score : 0 için iyileşmiş, 1 için düşmüştür\n",
    "Accuracy : iyileşmiştir    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RANDOM FOREST YAPALIM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random forest\n",
    "y=veri[\"bad\"]\n",
    "X=veri.drop([\"bad\"],axis=1)\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",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asus1\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: 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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "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=10,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf_model=RandomForestClassifier().fit(X_train,y_train)\n",
    "rf_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9462025316455697"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=rf_model.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      1.00      0.97       574\n",
      "           1       0.96      0.43      0.60        58\n",
      "\n",
      "    accuracy                           0.95       632\n",
      "   macro avg       0.95      0.71      0.78       632\n",
      "weighted avg       0.95      0.95      0.94       632\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "?rf_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_params= {\"max_depth\": [2,5,8],\n",
    "           \"max_features\": [2,5,8],\n",
    "           \"n_estimators\": [10,50,500],\n",
    "           \"min_samples_split\": [2,5,8]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 81 candidates, totalling 810 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  33 tasks      | elapsed:    8.9s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:   21.3s\n",
      "[Parallel(n_jobs=-1)]: Done 357 tasks      | elapsed:   44.7s\n",
      "[Parallel(n_jobs=-1)]: Done 640 tasks      | elapsed:  1.6min\n",
      "[Parallel(n_jobs=-1)]: Done 810 out of 810 | elapsed:  2.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
       "             estimator=RandomForestClassifier(bootstrap=True, class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators='warn', n_jobs=None,\n",
       "                                              oob_score=False,\n",
       "                                              random_state=None, verbose=0,\n",
       "                                              warm_start=False),\n",
       "             iid='warn', n_jobs=-1,\n",
       "             param_grid={'max_depth': [2, 5, 8], 'max_features': [2, 5, 8],\n",
       "                         'min_samples_split': [2, 5, 8],\n",
       "                         'n_estimators': [10, 50, 500]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=2)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model = RandomForestClassifier()\n",
    "rf_cv_model=GridSearchCV(rf_model,rf_params,cv=10,n_jobs=-1,verbose=2)\n",
    "rf_cv_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "En iyi parametreler: {'max_depth': 8, 'max_features': 8, 'min_samples_split': 8, 'n_estimators': 500}\n"
     ]
    }
   ],
   "source": [
    "print(\"En iyi parametreler: \" + str(rf_cv_model.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=8, max_features=8, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=8,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=500,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_tuned = RandomForestClassifier(max_depth = 8,\n",
    "                                 max_features =8,\n",
    "                                 min_samples_split=8,\n",
    "                                 n_estimators=500)\n",
    "rf_tuned.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.939873417721519"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=rf_tuned.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracymiz 0.9367'den 0.9398'e yükselmiştir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      1.00      0.97       574\n",
      "           1       0.92      0.38      0.54        58\n",
      "\n",
      "    accuracy                           0.94       632\n",
      "   macro avg       0.93      0.69      0.75       632\n",
      "weighted avg       0.94      0.94      0.93       632\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Precisionlar ve f1 score düşmüştür"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "Importance = pd.DataFrame({\"Importance\":rf_tuned.feature_importances_*100}, index=X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Importance.sort_values(by=\"Importance\", axis=0, ascending=True).plot(kind=\"barh\", color ='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importance grafiğine ve tablosuna bakılıdğında debtinc ve delinq parametrelerinin anlamlı olduğu çıkmıştır.Importance değerinin \n",
    "10'dan büyük olduğu tek parametreler:debtinc ve delinqdir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>loan</th>\n",
       "      <td>9.331706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mortdue</th>\n",
       "      <td>3.697003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>8.475816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yoj</th>\n",
       "      <td>1.599782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>derog</th>\n",
       "      <td>3.884678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delinq</th>\n",
       "      <td>13.092410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clage</th>\n",
       "      <td>6.048364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ninq</th>\n",
       "      <td>2.497627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clno</th>\n",
       "      <td>5.973161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>debtinc</th>\n",
       "      <td>43.055753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reason_DebtCon</th>\n",
       "      <td>0.125872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reason_HomeImp</th>\n",
       "      <td>0.251311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_Mgr</th>\n",
       "      <td>0.256467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_Office</th>\n",
       "      <td>0.799118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_Other</th>\n",
       "      <td>0.303220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_ProfEx</th>\n",
       "      <td>0.349755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_Sales</th>\n",
       "      <td>0.112963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_Self</th>\n",
       "      <td>0.144994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Importance\n",
       "loan              9.331706\n",
       "mortdue           3.697003\n",
       "value             8.475816\n",
       "yoj               1.599782\n",
       "derog             3.884678\n",
       "delinq           13.092410\n",
       "clage             6.048364\n",
       "ninq              2.497627\n",
       "clno              5.973161\n",
       "debtinc          43.055753\n",
       "reason_DebtCon    0.125872\n",
       "reason_HomeImp    0.251311\n",
       "job_Mgr           0.256467\n",
       "job_Office        0.799118\n",
       "job_Other         0.303220\n",
       "job_ProfEx        0.349755\n",
       "job_Sales         0.112963\n",
       "job_Self          0.144994"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XGBOOST YAPALIM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "###Xgboost\n",
    "y=veri[\"bad\"]\n",
    "X=veri.drop([\"bad\"],axis=1)\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
       "              learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
       "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
       "              nthread=None, objective='binary:logistic', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "              silent=None, subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "xgb_model=XGBClassifier().fit(X_train,y_train)\n",
    "xgb_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9462025316455697"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=xgb_model.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      1.00      0.97       574\n",
      "           1       0.96      0.43      0.60        58\n",
      "\n",
      "    accuracy                           0.95       632\n",
      "   macro avg       0.95      0.71      0.78       632\n",
      "weighted avg       0.95      0.95      0.94       632\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "?xgb_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_params = {\"n_estimators\":[100,500],\n",
    "              'subsample':[0.6,0.8,1.0],\n",
    "             \"max_depth\": [3,4,5],\n",
    "             \"learning_rate\":[0.1,0.01,0.2,0.05],\n",
    "             \"min_samples_split\":[2,5,10]}\n",
    "xgb=XGBClassifier()\n",
    "xgb_cv_model = GridSearchCV(xgb, xgb_params, cv=10, n_jobs = -1, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 216 candidates, totalling 2160 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  33 tasks      | elapsed:   12.5s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:   47.9s\n",
      "[Parallel(n_jobs=-1)]: Done 357 tasks      | elapsed:  2.1min\n",
      "[Parallel(n_jobs=-1)]: Done 640 tasks      | elapsed:  4.0min\n",
      "[Parallel(n_jobs=-1)]: Done 1005 tasks      | elapsed:  6.7min\n",
      "[Parallel(n_jobs=-1)]: Done 1450 tasks      | elapsed:  9.7min\n",
      "[Parallel(n_jobs=-1)]: Done 1977 tasks      | elapsed: 13.5min\n",
      "[Parallel(n_jobs=-1)]: Done 2160 out of 2160 | elapsed: 14.9min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
       "             estimator=XGBClassifier(base_score=0.5, booster='gbtree',\n",
       "                                     colsample_bylevel=1, colsample_bynode=1,\n",
       "                                     colsample_bytree=1, gamma=0,\n",
       "                                     learning_rate=0.1, max_delta_step=0,\n",
       "                                     max_depth=3, min_child_weight=1,\n",
       "                                     missing=None, n_estimators=100, n_jobs=1,\n",
       "                                     nthread=None, objective='binary:logistic',\n",
       "                                     random_state=0, reg_alpha=0, reg_lambda=1,\n",
       "                                     scale_pos_weight=1, seed=None, silent=None,\n",
       "                                     subsample=1, verbosity=1),\n",
       "             iid='warn', n_jobs=-1,\n",
       "             param_grid={'learning_rate': [0.1, 0.01, 0.2, 0.05],\n",
       "                         'max_depth': [3, 4, 5],\n",
       "                         'min_samples_split': [2, 5, 10],\n",
       "                         'n_estimators': [100, 500],\n",
       "                         'subsample': [0.6, 0.8, 1.0]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=2)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_cv_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "En iyi parametreler : {'learning_rate': 0.2, 'max_depth': 3, 'min_samples_split': 2, 'n_estimators': 500, 'subsample': 0.6}\n"
     ]
    }
   ],
   "source": [
    "print(\"En iyi parametreler : \" + str(xgb_cv_model.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb=XGBClassifier(learning_rate=0.2, max_depth=3, min_samples_split=2, n_estimators=500,subsample=0.6)\n",
    "xgb_tuned=xgb.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9414556962025317"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=xgb_tuned.predict(X_test)\n",
    "\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracymiz düşmüş,xgb_params da çeşitli parametreler(n_estimators gibi )değiştirilerek model yeniden fit edilebilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.99      0.97       574\n",
      "           1       0.89      0.41      0.56        58\n",
      "\n",
      "    accuracy                           0.94       632\n",
      "   macro avg       0.92      0.70      0.77       632\n",
      "weighted avg       0.94      0.94      0.93       632\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Precision, recall, f1 score ve accuracy düşmüş"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "YAPAY SİNİR AĞLARI "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#neural network\n",
    "y=veri[\"bad\"]\n",
    "X=veri.drop([\"bad\"],axis=1)\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler \n",
    "scaler =StandardScaler()\n",
    "\n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asus1\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "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)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "mlpc = MLPClassifier().fit(X_train_scaled, y_train)\n",
    "mlpc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9588607594936709"
      ]
     },
     "execution_count": 86,
     "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": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98       574\n",
      "           1       1.00      0.55      0.71        58\n",
      "\n",
      "    accuracy                           0.96       632\n",
      "   macro avg       0.98      0.78      0.84       632\n",
      "weighted avg       0.96      0.96      0.95       632\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "?mlpc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlpc_params = {\"alpha\": [0.1, 0.01, 0.02, 0.005, 0.0001,0.00001],\n",
    "              \"hidden_layer_sizes\": [(10,10,10),\n",
    "                                     (100,100),\n",
    "                                     (3,5), \n",
    "                                     (5, 3)],\n",
    "              \"solver\" : [\"lbfgs\",\"adam\",\"sgd\"],\n",
    "              \"activation\": [\"relu\",\"logistic\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 144 candidates, totalling 1440 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  33 tasks      | elapsed:   20.7s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 357 tasks      | elapsed:  2.9min\n",
      "[Parallel(n_jobs=-1)]: Done 640 tasks      | elapsed:  4.7min\n",
      "[Parallel(n_jobs=-1)]: Done 1005 tasks      | elapsed:  7.3min\n",
      "[Parallel(n_jobs=-1)]: Done 1440 out of 1440 | elapsed:  9.7min finished\n",
      "C:\\Users\\asus1\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
       "             estimator=MLPClassifier(activation='relu', alpha=0.0001,\n",
       "                                     batch_size='auto', beta_1=0.9,\n",
       "                                     beta_2=0.999, early_stopping=False,\n",
       "                                     epsilon=1e-08, hidden_layer_sizes=(100,),\n",
       "                                     learning_rate='constant',\n",
       "                                     learning_rate_init=0.001, max_iter=200,\n",
       "                                     momentum=0.9, n_iter_no_change=10,\n",
       "                                     nesterovs_momentum=True, power_t=0.5,\n",
       "                                     random_st...\n",
       "                                     solver='adam', tol=0.0001,\n",
       "                                     validation_fraction=0.1, verbose=False,\n",
       "                                     warm_start=False),\n",
       "             iid='warn', n_jobs=-1,\n",
       "             param_grid={'activation': ['relu', 'logistic'],\n",
       "                         'alpha': [0.1, 0.01, 0.02, 0.005, 0.0001, 1e-05],\n",
       "                         'hidden_layer_sizes': [(10, 10, 10), (100, 100),\n",
       "                                                (3, 5), (5, 3)],\n",
       "                         'solver': ['lbfgs', 'adam', 'sgd']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=2)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlpc= MLPClassifier()\n",
    "mlpc_cv_model = GridSearchCV(mlpc, mlpc_params, cv=10,n_jobs=-1, verbose=2)\n",
    "mlpc_cv_model.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "En iyi parametreler : {'activation': 'relu', 'alpha': 0.1, 'hidden_layer_sizes': (100, 100), 'solver': 'adam'}\n"
     ]
    }
   ],
   "source": [
    "print(\"En iyi parametreler : \" + str(mlpc_cv_model.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlpc_tuned = MLPClassifier(activation = \"relu\", alpha=0.1, hidden_layer_sizes=(100,100), solver = \"adam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asus1\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9572784810126582"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlpc_tuned.fit(X_train_scaled, y_train)\n",
    "y_pred=mlpc_tuned.predict(X_test_scaled)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98       574\n",
      "           1       1.00      0.53      0.70        58\n",
      "\n",
      "    accuracy                           0.96       632\n",
      "   macro avg       0.98      0.77      0.84       632\n",
      "weighted avg       0.96      0.96      0.95       632\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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