8 4 11 TÜM MODELLERİN KARŞILAŞTIRILMASI.ipynb

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 "cells": [
  {
   "source": [
    "# TÜM MODELLLERİN KARŞILAŞTIRILMASI"
   ],
   "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": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\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": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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": 4,
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm_tuned = LGBMClassifier(learning_rate=0.01, max_depth=1, n_estimators=500).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_tuned = XGBClassifier(learning_rate=0.001, max_depth=7, n_estimators=500, subsample=0.6).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm_tuned = GradientBoostingClassifier(learning_rate=0.01, max_depth=5, n_estimators=500).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_tuned = SVC(C=2, kernel=\"linear\").fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_final_model_tuned = RandomForestClassifier(max_features=5,min_samples_split=5,n_estimators=200).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cart_tuned = DecisionTreeClassifier(max_depth=5, min_samples_split=20).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlpc_tuned = MLPClassifier(solver = \"lbfgs\", alpha = 5, activation = 'logistic', hidden_layer_sizes = (100, 100)).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_tuned = KNeighborsClassifier(n_neighbors=11).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "loj_model = LogisticRegression(solver=\"liblinear\").fit(X_train, y_train)"
   ]
  },
  {
   "source": [
    "# PRATİK KULLANIM"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SEÇİLEN BİR KAÇ MODEL İLE İŞLEMLERİN OTOMATİKLEŞTİRİLMESİ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "modeller = [loj_model,\n",
    "knn_tuned,\n",
    "svm_tuned,\n",
    "mlpc_tuned,\n",
    "cart_tuned,\n",
    "rf_final_model_tuned,\n",
    "gbm_tuned,\n",
    "xgb_tuned,\n",
    "lgbm_tuned,\n",
    "catb_tuned]\n",
    "\n",
    "sonuc = []\n",
    "sonuclar = pd.DataFrame(columns=[\"Modeller\",\"Accuracy\"])\n",
    "\n",
    "for model in modeller:\n",
    "    isimler = model.__class__.__name__\n",
    "    y_pred = model.predict(X_test)\n",
    "    dogruluk = accuracy_score(y_test, y_pred)\n",
    "    y_pred = model.predict(X_test)\n",
    "    dogruluk = accuracy_score(y_test, y_pred)\n",
    "    sonuc = pd.DataFrame([[isimler, dogruluk * 100]], columns = [\"Modeller\",\"Accuracy\"])\n",
    "    sonuclar = sonuclar.append(sonuc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Modellerin Doğruluk Oranları')"
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     },
     "metadata": {},
     "execution_count": 58
    },
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "sns.barplot(x = 'Accuracy', y = 'Modeller', data = sonuclar, color = 'r')\n",
    "plt.xlabel('Accuracy %')\n",
    "plt.ylabel('Modellerin Doğruluk Oranları')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Kişisel çalışmalarda AUTO ML adı verilen yapı ile bu tür işlemler hiperparametre aramaları dahil, modellerin seçilmesi dahil bir çok işlemi daha otonom daha optimize bir şekilde yapılabilir.\n",
    "# Otonom yaklaşımlar sektörde çok sık kullanılmayan yaklaşımlardır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                     Modeller   Accuracy\n",
       "0          LogisticRegression  75.757576\n",
       "0        KNeighborsClassifier  73.160173\n",
       "0                         SVC  74.458874\n",
       "0               MLPClassifier  74.025974\n",
       "0      DecisionTreeClassifier  75.324675\n",
       "0      RandomForestClassifier  74.891775\n",
       "0  GradientBoostingClassifier  74.025974\n",
       "0               XGBClassifier  75.757576\n",
       "0              LGBMClassifier  75.757576\n",
       "0          CatBoostClassifier  75.324675"
      ],
      "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>Modeller</th>\n      <th>Accuracy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>LogisticRegression</td>\n      <td>75.757576</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>KNeighborsClassifier</td>\n      <td>73.160173</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>SVC</td>\n      <td>74.458874</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>MLPClassifier</td>\n      <td>74.025974</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>DecisionTreeClassifier</td>\n      <td>75.324675</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>RandomForestClassifier</td>\n      <td>74.891775</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>GradientBoostingClassifier</td>\n      <td>74.025974</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>XGBClassifier</td>\n      <td>75.757576</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>LGBMClassifier</td>\n      <td>75.757576</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>CatBoostClassifier</td>\n      <td>75.324675</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 30
    }
   ],
   "source": [
    "sonuclar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RandomForest ın bir sisteme enteğre edilmesi daha kolaydır. Dolayısıyla sonuçlar birbirine çok yakınsa random forest seçilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LogisticRegression basit olması sonuçların birbirine yakın olmasından dolayı seçilebilir. Sisteme entegre edilmeside kolaydır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Yapay sinir ağlarında standartlaştırma yapılmamıştır."
   ]
  },
  {
   "source": [
    "## DAHA BAŞKA NE YAPILABİLİR?"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Değişken türetme / değişken mühendisliği\n",
    "\n",
    "# Elimizdeki verisetinin içerisinden ya da verisetinin içerisinde olmayan ek değişkenler üretme durumu ifade ediliyor. Örneğin elimizde \"boy\" ve \"kilo\" isminde değişkenler olsun. Biz bu değişkenlr üzerinden \"vücut kitle indeksi\" adı verilen bir değişken türetebiliyoruz.\n",
    "\n",
    "# 2. DEĞİŞKEN SEÇME\n",
    "\n",
    "# 3. AUTO ML\n",
    "\n",
    "# 4. MODEL DEPLOYMENT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SON UN SON U"
   ]
  }
 ]
}