8 4 10 CATBOOST.ipynb

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   "file_extension": ".py",
   "mimetype": "text/x-python",
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 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "# CATBOOST"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import statsmodels.api as sm \n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.preprocessing import scale, StandardScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, mean_squared_error, r2_score, roc_auc_score, roc_curve, classification_report "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from catboost import CatBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"verisetleri\\diabetes.csv\")\n",
    "y = df[\"Outcome\"]\n",
    "X = df.drop([\"Outcome\"], axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# catb_model = CatBoostClassifier().fit(X_train, y_train)\n",
    "catb_model = CatBoostClassifier().fit(X_train, y_train, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.7402597402597403"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "y_pred = catb_model.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MODEL TUNNING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "catb = CatBoostClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iterations --> Ağaç sayısı "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "catb_params = {\"iterations\":[200, 500, 1000], \"learning_rate\":[0.01, 0.03,0.1], \"depth\":[4,5,8]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Fitting 10 folds for each of 27 candidates, totalling 270 fits\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   19.4s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.7min\n",
      "[Parallel(n_jobs=-1)]: Done 270 out of 270 | elapsed:  5.0min finished\n",
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     ]
    }
   ],
   "source": [
    "catb_cv_model = GridSearchCV(catb, catb_params, cv=10, n_jobs=-1, verbose=2).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'depth': 8, 'iterations': 200, 'learning_rate': 0.01}"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "catb_cv_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "catb_tuned = CatBoostClassifier(depth=8, iterations=200,learning_rate=0.01).fit(X_train, y_train, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = catb_tuned.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.7532467532467533"
      ]
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DEĞİŞKENLERİN ÖNEM SIRALAMASI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "feature_imp = pd.Series(catb_tuned.feature_importances_, index=X_train.columns).sort_values(ascending=False)\n",
    "\n",
    "sns.barplot(x=feature_imp, y=feature_imp.index)\n",
    "plt.xlabel('Değişken Önem Skorları')\n",
    "plt.ylabel('Değişkenler')\n",
    "plt.title(\"Değişken Önem Düzeyleri\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SON"
   ]
  }
 ]
}