Veri Ön İşleme.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "import numpy as np\n",
    "from pandas import read_csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 9)\n"
     ]
    }
   ],
   "source": [
    "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n",
    "names = [\"preg\", \"plas\", \"pres\", \"skin\", \"test\", \"mass\", \"pedi\", \"age\", \"class\"]\n",
    "data = read_csv(url, names = names)\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "veri = data.values\n",
    "X = veri[:, 0:8]\n",
    "Y = veri[:, 8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 8)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768,)\n"
     ]
    }
   ],
   "source": [
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = data.iloc[:, 0:8]\n",
    "Y1 = data.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 8)\n"
     ]
    }
   ],
   "source": [
    "print(X1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768,)\n"
     ]
    }
   ],
   "source": [
    "print(Y1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import Normalizer\n",
    "from sklearn.preprocessing import Binarizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.64   0.848  0.15   0.907 -0.693  0.204  0.468  1.426]\n",
      " [-0.845 -1.123 -0.161  0.531 -0.693 -0.684 -0.365 -0.191]\n",
      " [ 1.234  1.944 -0.264 -1.288 -0.693 -1.103  0.604 -0.106]\n",
      " [-0.845 -0.998 -0.161  0.155  0.123 -0.494 -0.921 -1.042]\n",
      " [-1.142  0.504 -1.505  0.907  0.766  1.41   5.485 -0.02 ]]\n"
     ]
    }
   ],
   "source": [
    "from numpy import set_printoptions\n",
    "\n",
    "sc = StandardScaler()\n",
    "sc.fit(X)\n",
    "rescaledX = sc.transform(X)\n",
    "set_printoptions(precision = 3)\n",
    "print(rescaledX[0:5, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.353 0.744 0.59  0.354 0.    0.501 0.234 0.483]\n",
      " [0.059 0.427 0.541 0.293 0.    0.396 0.117 0.167]\n",
      " [0.471 0.92  0.525 0.    0.    0.347 0.254 0.183]\n",
      " [0.059 0.447 0.541 0.232 0.111 0.419 0.038 0.   ]\n",
      " [0.    0.688 0.328 0.354 0.199 0.642 0.944 0.2  ]]\n"
     ]
    }
   ],
   "source": [
    "sc = MinMaxScaler()\n",
    "sc.fit(X)\n",
    "rescaledX = sc.transform(X)\n",
    "set_printoptions(precision = 3)\n",
    "print(rescaledX[0:5, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 1. 1. 0. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 0. 1. 1. 1.]\n",
      " [1. 1. 1. 0. 0. 1. 1. 1.]\n",
      " [1. 1. 1. 1. 1. 1. 1. 1.]\n",
      " [0. 1. 1. 1. 1. 1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "sc = Binarizer(threshold = 0.0)\n",
    "sc.fit(X)\n",
    "rescaledX = sc.transform(X)\n",
    "set_printoptions(precision = 3)\n",
    "print(rescaledX[0:5, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"ÖZNİTELİK SEÇİMİ\"\"\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#İSTATİSTİKSEL SEÇİM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 111.52  1411.887   17.605   53.108 2175.565  127.669    5.393  181.304] \n",
      " ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age']\n",
      "\n",
      "\n",
      "[[148.    0.   33.6  50. ]\n",
      " [ 85.    0.   26.6  31. ]\n",
      " [183.    0.   23.3  32. ]\n",
      " [ 89.   94.   28.1  21. ]\n",
      " [137.  168.   43.1  33. ]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "# chi2 ye göre okuma yap ve (k = en iyi değerler) kullanıcının belirlediği en iyi 4 değişkeni seç\n",
    "test = SelectKBest(score_func = chi2, k=4)\n",
    "fit = test.fit(X, Y)\n",
    "\n",
    "set_printoptions(precision = 3)\n",
    "print(fit.scores_, \"\\n\", names[0:8])\n",
    "#eğitim verisini kullanacak ise _ ile yazılır, test verisini kullanacak ise _ yazılmaz.\n",
    "print(\"\\n\")\n",
    "features = fit.transform(X)\n",
    "print(features[0:5, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Değişken, Öznitelik seçim döngüsü"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Öznitelik sayısı: 3\n",
      "Seçilen öznitelikler: [ True False False False False  True  True False]\n",
      "['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age']\n",
      "Öznitelik Sıralaması: [1 2 4 6 5 1 1 3]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE #Recursive Feature Elimination\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression(solver= \"lbfgs\", max_iter = 3000)\n",
    "rfe = RFE(model, 3) #RFE fonksiyonunu seçilen makine öğrenmesi yöntemi ile çalıştır, kullanıcının belirlediği en iyi 3 değişkeni kullan(sonuca göre sayı değiştirilebilir)\n",
    "fit = rfe.fit(X, Y)\n",
    "print(\"Öznitelik sayısı: %d\" % fit.n_features_)\n",
    "print(\"Seçilen öznitelikler: %s\" % fit.support_)\n",
    "print(names[0:8])\n",
    "print(\"Öznitelik Sıralaması: %s\" % fit.ranking_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ÖZNİTELİK ÖNEM DERECELERİ (Genellikle ağaç tabanlı yöntemler kullanılır)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.109 0.225 0.089 0.073 0.083 0.152 0.122 0.147]\n",
      "['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\batuh\\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"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "model = ExtraTreesClassifier()\n",
    "model.fit(X, Y)\n",
    "print(model.feature_importances_)#Sona koyulan _ eğitim verisinin kullanılacağını belirtir.\n",
    "print(names[0:8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.109 0.225 0.089 0.073 0.083 0.152 0.122 0.147]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 8 artists>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UOE/dSFLjDL0kNc7QS1LjDL0kNc7QS1LjDL0kNc7QS1Lj/h9dpsu8Kb59LAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "print(model.feature_importances_)\n",
    "plt.bar(range(len(model.feature_importances_)), model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imp_feat = pandas.Series(model.feature_importances_, index=names[0:8]).sort_values(ascending = False)\n",
    "imp_feat.plot(kind = \"bar\", title = \"Extra Trees ile Öznitelik Önemleri\", figsize = (12,8))\n",
    "plt.ylabel = (\"Öznitelik Önem Derecesi\")\n",
    "plt.subplots_adjust(bottom = 0.25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}