Kalp Hastalığı Tahmini (Sınıflandırma).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heart Disease UCI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Header](https://storage.googleapis.com/kaggle-datasets-images/33180/43520/718447d8c4f7e29f129c25b44b324efa/dataset-cover.jpg?t=2018-06-25-11-44-45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Giriş\n",
    "<a id=\"toc\"></a>\n",
    "\n",
    "Bu kernelda [Heart Disease UCI](https://www.kaggle.com/ronitf/heart-disease-uci) veri setini inceleyeceğiz. Bu veri seti içerisindeki değişkenler üzerinden hastada kalp hastalığı olup olmadığı tahmin etmeye çalışan bir model oluşturacağız.\n",
    "\n",
    "Kalp hastalığı ile ilgili risk faktörlerine baktığımızda\n",
    "* **Değiştirilemeyen başlıca faktörler:**\n",
    "    * artan yaş\n",
    "    * cinsiyet\n",
    "    * kalıtım. \n",
    "    > Bu veri setindeki değişkenlerden biri olan **talaseminin**(thal) kalıtım olduğuna dikkat edin.\n",
    "* **Değiştirilebilecek ana faktörler şunlardır:**\n",
    "    * Sigara içmek\n",
    "    * Yüksek kolesterol\n",
    "    * Yüksek tansiyon\n",
    "    * Fiziksel hareketsizlik\n",
    "    * Fazla kilolu olmak\n",
    "    * Şeker hastası olmak\n",
    "* **Diğer faktörler arasında stres, alkol ve zayıf diyet / beslenme sayılabilir.**\n",
    "\n",
    ">Yukarıdaki veriler göz önüne alındığında, modelimiz tahminde bulunabiliyorsa, yukarıdaki faktörlerin önemli olduğunu göreceğiz.\n",
    "\n",
    " \n",
    "\n",
    "\n",
    "**Hedefimiz**, hastada kalp hastalığının varlığını tespit etmektir.\n",
    "\n",
    "\n",
    "Bu doğrultuda,\n",
    "* [Logistic Regression](#Logistic-Regression), \n",
    "* [K-Nearest Neighbour (KNN) Classification](#K-Nearest-Neighbour-(KNN)-Classification), \n",
    "* [Support Vector Machine (SVM) Algorithm](#Support-Vector-Machine-(SVM)-Algorithm), \n",
    "* [Naive Bayes Algorithm](#Naive-Bayes-Algorithm), \n",
    "* [Decision Tree Algorithm](#Decision-Tree-Algorithm), \n",
    "* [Random Forest Classification](#Random-Forest-Classification) \n",
    "algoritmalarını kullanacağız.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# İçindekiler\n",
    "1. [Giriş](#Giriş)\n",
    "2. [Hazırlık](#Hazırlık)\n",
    "    1. [Kütüphanelerin Yüklenmesi](#Kütüphanelerin-Yüklenmesi)\n",
    "    2. [Verinin Çağırılması](#Verinin-çağırılması)\n",
    "    3. [Değişkenler](#Değişkenler)\n",
    "3. [Keşifçi Veri Analizi ve Veri Görselleştirme](#Keşifçi-Veri-Analizi-ve-Veri-Görselleştirme)\n",
    "    1. [Hasta olanlar ve olmayanların dağılımı](#Hasta-olanlar-ve-olmayanların-dağılımı)\n",
    "        1. [Hasta olanlar ve olmayanların yüzdelik dağılımları](#Hasta-olanlar-ve-olmayanların-yüzdelik-dağılımları)\n",
    "    2. [Veri setimizdeki cinsiyet dağılımı](#Veri-setimizdeki-cinsiyet-dağılımı)\n",
    "    3. [Hastalık durumuna göre diğer değişkenlerin ortalama değerleri](#Hastalık-durumuna-göre-diğer-değişkenlerin-ortalama-değerleri)\n",
    "    4. [Yaşlara göre Kalp Rahatsızlığı olup olmaması](#Yaşlara-göre-Kalp-Rahatsızlığı-olup-olmaması)\n",
    "    5. [Cinsiyete göre Kalp rahatsızlığı olup olmaması](#Cinsiyete-göre-Kalp-rahatsızlığı-olup-olmaması)\n",
    "    6. [Maksimum Kalp Atış Hızı ve Yaş Arasında Hastalık Dağılımı](#Maksimum-Kalp-Atış-Hızı-ve-Yaş-Arasında-Hastalık-Dağılımı)\n",
    "    7. [Slope Değişkenine Göre Hastalık Sıklığı](#Slope-Değişkenine-Göre-Hastalık-Sıklığı)\n",
    "    8. [Açlık Kan Şekerine Göre Kalp Hastalığı Sıklığı](#Açlık-Kan-Şekerine-Göre-Kalp-Hastalığı-Sıklığı)\n",
    "    9. [Göğüs Ağrısı Tipine Göre Halp Hastalığı Sıklığı](#Göğüs-Ağrısı-Tipine-Göre-Halp-Hastalığı-Sıklığı)\n",
    "4. [Kategorik Değişkenlerin Dönüştürülmesi](#Kategorik-Değişkenlerin-Dönüştürülmesi)\n",
    "5. [Modelin Uygulanması](#Modelin-Uygulanması)\n",
    "    1. [Logistic Regression](#Logistic-Regression)\n",
    "        1. [Normelleştirme](#Normelleştirme)\n",
    "    2. [K-Nearest Neighbour (KNN) Classification](#K-Nearest-Neighbour-(KNN)-Classification)\n",
    "        1. [En iyi n_neighbors değerini bulma](#En-iyi-n_neighbors-değerini-bulma)\n",
    "    3. [Support Vector Machine (SVM) Algorithm](#Support-Vector-Machine-(SVM)-Algorithm)\n",
    "    4. [Naive Bayes Algorithm](#Naive-Bayes-Algorithm)\n",
    "    5. [Decision Tree Algorithm](#Decision-Tree-Algorithm)\n",
    "    6. [Random Forest Classification](#Random-Forest-Classification)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hazırlık\n",
    "<a id=\"toc\"></a>\n",
    "Kütüphanelerin yüklenmesi ve verinin çağırılması."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "collapsed": true
   },
   "source": [
    "## Kütüphanelerin Yüklenmesi\n",
    "<a id=\"toc\"></a>\n",
    "\n",
    "Kullandığımız kütüphanelerin yüklenmesi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Temel Kütüphaneler\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Görselleştirme\n",
    "import seaborn as sns #for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Model Kurma\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn import preprocessing\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verinin çağırılması\n",
    "<a id=\"toc\"></a>\n",
    "\n",
    "> Bu aşamada verimizi içeri alıyoruz ardından değişkenlerin kısaltmalarının anlamlarını ve hangi değerleri aldığını açıklıyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"heart.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
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       "      <td>1</td>\n",
       "      <td>163</td>\n",
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       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <th>5</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
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       "      <td>0</td>\n",
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       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "5   57    1   0       140   192    0        1      148      0      0.4      1   \n",
       "6   56    0   1       140   294    0        0      153      0      1.3      1   \n",
       "7   44    1   1       120   263    0        1      173      0      0.0      2   \n",
       "8   52    1   2       172   199    1        1      162      0      0.5      2   \n",
       "9   57    1   2       150   168    0        1      174      0      1.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  \n",
       "5   0     1       1  \n",
       "6   0     2       1  \n",
       "7   0     3       1  \n",
       "8   0     3       1  \n",
       "9   0     2       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verimize bakalım.\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Değişkenler\n",
    "<a id=\"toc\"></a>\n",
    "\n",
    "Verimize yakından baktığımızda oldukça temiz bir yapısı olduğunu görüyoruz. Eksik değerlerin kontrolü ve boyutlarına bakmadan önce değişkenlerin kısaltmalarını açıklayalım. [Türkçe Açıklamaları](#Türkçeleri)\n",
    "\n",
    "1. **age:** The person's age in years\n",
    "2. **sex:** The person's sex (1 = male, 0 = female)\n",
    "* **cp:** The chest pain experienced (Value 1: typical angina, Value 2: atypical angina, Value 3: non-anginal pain, Value 4: asymptomatic)\n",
    "* **trestbps:** The person's resting blood pressure (mm Hg on admission to the hospital)\n",
    "* **chol:** The person's cholesterol measurement in mg/dl\n",
    "* **fbs:** The person's fasting blood sugar (> 120 mg/dl, 1 = true; 0 = false)\n",
    "* **restecg:** Resting electrocardiographic measurement (0 = normal, 1 = having ST-T wave abnormality, 2 = showing probable or definite left ventricular hypertrophy by Estes' criteria)\n",
    "* **thalach:** The person's maximum heart rate achieved\n",
    "* **exang:** Exercise induced angina (1 = yes; 0 = no)\n",
    "* **oldpeak:** ST depression induced by exercise relative to rest ('ST' relates to positions on the ECG plot. See more here)\n",
    "* **slope:** the slope of the peak exercise ST segment (Value 1: upsloping, Value 2: flat, Value 3: downsloping)\n",
    "* **ca:** The number of major vessels (0-3)\n",
    "* **thal:** A blood disorder called thalassemia (3 = normal; 6 = fixed defect; 7 = reversable defect)\n",
    "* **target: Heart disease (0 = no, 1 = yes)**\n",
    "\n",
    "\n",
    "### Türkçeleri\n",
    "<a id=\"toc\"></a>\n",
    "1. Yaş\n",
    "2. Cinsiyet\n",
    "3. Göğüs ağrısı tipi (4 değer)\n",
    "4. Dinlenme kan basıncı\n",
    "5. Serum kolestrolü (mg / dl)\n",
    "6. Açlık kan şekeri (>120 mg / dl)\n",
    "7. Elektrokardiyografik sonuçların dinlenmesi (değerler 0,1,2)\n",
    "8. Elde edilen maksimum kalp atış hızı\n",
    "9. Egzersize bağlı Anjin (Anjin, kalbe kan akışının azalmasından kaynaklanan bir tür göğüs ağrısıdır.)\n",
    "10. Eski tepe noktası = istirahate bağlı egzersiz ile indüklenen ST depresyonu\n",
    "11. Tepe egzersizi ST segmentinin eğimi\n",
    "12. floroskopi ile renklendirilmiş ana damarların sayısı (0-3)\n",
    "13. thal: \n",
    "    * 3 = normal\n",
    "    * 6 = sabit hata\n",
    "    * 7 = tersinir defekt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keşifçi Veri Analizi ve Veri Görselleştirme\n",
    "<a id=\"toc\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu bölümde değişkenlere ait değerleri ve değişkenlerin kendi içerisindeki karşılaştırmalarına bakacağız. Ardından değişken içerisindeki değerlerin karşılaştırmalı grafiğini çizdireceğiz."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hasta olanlar ve olmayanların dağılımı\n",
    "Burada **1** ile gösterilenler kalp hastalığı olanları, **0** ile gösterilenler ise kalp rahatsızlığı olmayanları verir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seaborn\n",
    "color = [\"#58a3bc\",\"#666666\"]\n",
    "plt.figure(figsize=(12,7))\n",
    "sns.set()\n",
    "sns.countplot(x=\"target\",\n",
    "              data=df,\n",
    "              palette=color)\n",
    "plt.ylabel(\"Kişi Sayısı\")\n",
    "plt.xlabel(\"Target (0 = hasta olmayan, 1= hasta olan)\")\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('Hastalık.png',transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hasta olanlar ve olmayanların yüzdelik dağılımları"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kalp Rahatsızlığı olmayan hastaların yüzdesi: 45.54%\n",
      "Kalp Rahatsızlığı olan hastaların yüzdesi: 54.46%\n"
     ]
    }
   ],
   "source": [
    "countNoDisease = len(df[df.target == 0]) # hastalığı olmayanların sayısı\n",
    "countHaveDisease = len(df[df.target == 1]) # hastalığı olanların sayısı\n",
    "\n",
    "print(\"Kalp Rahatsızlığı olmayan hastaların yüzdesi: {:.2f}%\".format((countNoDisease / (len(df.target))*100)))\n",
    "print(\"Kalp Rahatsızlığı olan hastaların yüzdesi: {:.2f}%\".format((countHaveDisease / (len(df.target))*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "color = [\"#58a3bc\",\"#666666\"]\n",
    "plt.pie([countNoDisease,countHaveDisease],\n",
    "        labels=[\"Hasta Olmayan Kişiler\",\"Hasta Olan Kişiler\"],\n",
    "        colors=color,\n",
    "        autopct='%1.2f%%');\n",
    "\n",
    "\n",
    "plt.savefig('Hastalık1.png',transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Veri setimizdeki cinsiyet dağılımı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "sns.set()\n",
    "sns.countplot(x='sex', data=df, palette=color)\n",
    "plt.xlabel(\"Cinsiyet (0 = kadın, 1= erkek)\")\n",
    "plt.ylabel(\"Kişi Sayısı\")\n",
    "plt.savefig('cinsiyet.png',transparent=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kadın hastaların yüzdesi: 31.68%\n",
      "Erkek hastaların yüzdesi: 68.32%\n"
     ]
    }
   ],
   "source": [
    "countFemale = len(df[df.sex == 0]) # kadınların sayısı\n",
    "countMale = len(df[df.sex == 1]) # erkeklerin sayısı\n",
    "print(\"Kadın hastaların yüzdesi: {:.2f}%\".format((countFemale / (len(df.sex))*100)))\n",
    "print(\"Erkek hastaların yüzdesi: {:.2f}%\".format((countMale / (len(df.sex))*100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hastalık durumuna göre diğer değişkenlerin ortalama değerleri"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hastalık durumuna göre bir gruplandırma işlemi yapılmıştır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56.601449</td>\n",
       "      <td>0.826087</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>134.398551</td>\n",
       "      <td>251.086957</td>\n",
       "      <td>0.159420</td>\n",
       "      <td>0.449275</td>\n",
       "      <td>139.101449</td>\n",
       "      <td>0.550725</td>\n",
       "      <td>1.585507</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>2.543478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52.496970</td>\n",
       "      <td>0.563636</td>\n",
       "      <td>1.375758</td>\n",
       "      <td>129.303030</td>\n",
       "      <td>242.230303</td>\n",
       "      <td>0.139394</td>\n",
       "      <td>0.593939</td>\n",
       "      <td>158.466667</td>\n",
       "      <td>0.139394</td>\n",
       "      <td>0.583030</td>\n",
       "      <td>1.593939</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>2.121212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age       sex        cp    trestbps        chol       fbs  \\\n",
       "target                                                                    \n",
       "0       56.601449  0.826087  0.478261  134.398551  251.086957  0.159420   \n",
       "1       52.496970  0.563636  1.375758  129.303030  242.230303  0.139394   \n",
       "\n",
       "         restecg     thalach     exang   oldpeak     slope        ca      thal  \n",
       "target                                                                          \n",
       "0       0.449275  139.101449  0.550725  1.585507  1.166667  1.166667  2.543478  \n",
       "1       0.593939  158.466667  0.139394  0.583030  1.593939  0.363636  2.121212  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('target').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Yaşlara göre Kalp Rahatsızlığı olup olmaması"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.age,df.target).plot(kind=\"bar\",figsize=(20,9),color=color)\n",
    "plt.title('Yaşlara Göre Kalp Rahatsızlığı')\n",
    "plt.xlabel('Yaş')\n",
    "plt.ylabel('Sıklık')\n",
    "plt.savefig('heartDiseaseAndAges.png',transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cinsiyete göre Kalp rahatsızlığı olup olmaması"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.sex,df.target).plot(kind=\"bar\",figsize=(15,6),color=color)\n",
    "plt.title('Cinsiyete Göre Kalp Hastalığı Sıklığı')\n",
    "plt.xlabel('Cinsiyet (0 = Kadın, 1 = Erkek)')\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend([\"Hasta Olmayanlar\", \"Hasta Olanlar\"])\n",
    "plt.ylabel('Sıklık')\n",
    "plt.savefig('cinsiyetegörehastalık.png',transparent=True)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Maksimum Kalp Atış Hızı ve Yaş Arasında Hastalık Dağılımı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.scatter(x=df.age[df.target==1], y=df.thalach[(df.target==1)], c=\"red\")\n",
    "plt.scatter(x=df.age[df.target==0], y=df.thalach[(df.target==0)])\n",
    "plt.legend([\"Hasta\", \"Hasta Değil\"])\n",
    "plt.xlabel(\"Yaş\")\n",
    "plt.ylabel(\"Maksimum Kalp Atış Hızı\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Slope Değişkenine Göre Hastalık Sıklığı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color = [\"#58a3bc\",\"#666666\"]\n",
    "pd.crosstab(df.slope,df.target).plot(kind=\"bar\",figsize=(15,6),color=color)\n",
    "plt.title('Slope Değişkenine Göre Hastalık Sıklığı')\n",
    "plt.xlabel('The Slope of The Peak Exercise ST Segment ')\n",
    "plt.xticks(rotation = 0)\n",
    "plt.ylabel('Sıklık')\n",
    "plt.savefig('slope.png',transparent=True)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Açlık Kan Şekerine Göre Kalp Hastalığı Sıklığı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.fbs,df.target).plot(kind=\"bar\",figsize=(15,6),color=color)\n",
    "plt.title('Açlık Kan Şekerine Göre Kalp Hastalığı Sıklığı')\n",
    "plt.xlabel('FBS - (Açlık Kan Şekeri > 120 mg/dl) (1 = true; 0 = false)')\n",
    "plt.xticks(rotation = 0)\n",
    "plt.legend([\"Hasta Değil\", \"Hasta\"])\n",
    "plt.ylabel('Hasta veya Hasta Olmayanların Sıklığı')\n",
    "plt.savefig('açlık.png',transparent=True)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Göğüs Ağrısı Tipine Göre Halp Hastalığı Sıklığı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.cp,df.target).plot(kind=\"bar\",figsize=(15,6),color=color)\n",
    "plt.title('Göğüs Ağrısı Tipine Göre Halp Hastalığı Sıklığı')\n",
    "plt.xlabel('Göğüs Ağrısı Tipi (4 Değer)')\n",
    "plt.xticks(rotation = 0)\n",
    "plt.ylabel('Hasta veya Hasta Olmayanların Sıklığı')\n",
    "plt.savefig('göğüs.png',transparent=True)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kategorik Değişkenlerin Dönüştürülmesi\n",
    "\n",
    "Makine öğrenmesi kısmına geçmeden önce veri setimiz içerisinde bulunan kategorik değişkenlerin dönüştürülmesi gerekmektedir.\n",
    "\n",
    "Bunun için hangi değişkenlerin kategorik olduğunu ve ölçek türlerini saptamalıyız."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "İlk bakışta **sex, cp, fbs, testecg, exang, slope, ca ve thal** değişkenlerinin kategorik olduklarını görüyoruz. \n",
    "\n",
    "Şimdi bu değişkenlerin değerlerine **.uniqe()** metodu yardımıyla bakalım. Eğer **0 - 1** haricinde değerler almışlarsa onları **Nominal ve Ordinal** olarak ayıralım. **Nominal** ölçek türüne sahip değişkenleri **get_dummies() metoduyla** tekrardan şekillendirelim. \n",
    "\n",
    "Çünkü örneğin **0 ve 1** makine için bir ast-üst oluşturmazken içerisinde **0, 1, 2** değerlerini almış bir nominal değişken makinede, **2** değerinin **1** değerinin iki katı gibi bir intiba bırakabilir. Oysa burada **0, 1 ve 2** ile anlatılmak istenen hepsinin ayrı birer **kategorik değişken** olduğudur yani bizim için bunlar arasında bir *ast-üst* ilişkisi yoktur. \n",
    "\n",
    "**Bu sebeple eğer dönüştürme işlemini yapmazsak modelimizin yanlış eğitilmesi söz konusu olacaktır.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Sex değişkeni eşsiz değerleri: [1 0]\n",
      "Cp değişkeni eşsiz değerleri: [3 2 1 0]\n",
      "Fbs değişkeni eşsiz değerleri: [1 0]\n",
      "Restecg değişkeni eşsiz değerleri: [0 1 2]\n",
      "Exang değişkeni eşsiz değerleri: [0 1]\n",
      "Slope değişkeni eşsiz değerleri: [0 2 1]\n",
      "Ca değişkeni eşsiz değerleri: [0 2 1 3 4]\n",
      "Thal değişkeni eşsiz değerleri: [1 2 3 0]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\"\"\n",
    "Sex değişkeni eşsiz değerleri: {}\n",
    "Cp değişkeni eşsiz değerleri: {}\n",
    "Fbs değişkeni eşsiz değerleri: {}\n",
    "Restecg değişkeni eşsiz değerleri: {}\n",
    "Exang değişkeni eşsiz değerleri: {}\n",
    "Slope değişkeni eşsiz değerleri: {}\n",
    "Ca değişkeni eşsiz değerleri: {}\n",
    "Thal değişkeni eşsiz değerleri: {}\n",
    "\"\"\".format(df.sex.unique(),\n",
    "           df.cp.unique(),\n",
    "           df.fbs.unique(),\n",
    "           df.restecg.unique(),\n",
    "           df.exang.unique(),\n",
    "           df.slope.unique(),\n",
    "           df.ca.unique(),\n",
    "           df.thal.unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Burada **cp, restecg, slope, ca ve thal** değişkenlerinin **0 - 1** haricinde değerlere sahip olduklarını görüyoruz. Bu değişkenlere **get_dummies()** metodunu uygulamadan önce *tiplerine* bakalım. **get_dummies()** metodu obje yada categorik tipleri dönüştürdüğü için içerisinde numerik tipe sahip değişkenlerin dönüşümlerini gerçekleştirelim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age           int64\n",
       "sex           int64\n",
       "cp            int64\n",
       "trestbps      int64\n",
       "chol          int64\n",
       "fbs           int64\n",
       "restecg       int64\n",
       "thalach       int64\n",
       "exang         int64\n",
       "oldpeak     float64\n",
       "slope         int64\n",
       "ca            int64\n",
       "thal          int64\n",
       "target        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Değişkenlerin Kategorik tiplere dönüştürülmesi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.cp = pd.Categorical(df.cp)\n",
    "df.restecg = pd.Categorical(df.restecg)\n",
    "df.slope = pd.Categorical(df.slope)\n",
    "df.ca = pd.Categorical(df.ca)\n",
    "df.thal = pd.Categorical(df.thal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age            int64\n",
       "sex            int64\n",
       "cp          category\n",
       "trestbps       int64\n",
       "chol           int64\n",
       "fbs            int64\n",
       "restecg     category\n",
       "thalach        int64\n",
       "exang          int64\n",
       "oldpeak      float64\n",
       "slope       category\n",
       "ca          category\n",
       "thal        category\n",
       "target         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Artık **get_dummies()** metodunu kullanabiliriz. \n",
    "\n",
    "> `drop_first=True` paremetresi dönüştürülen değişkenlerin çıkartılması işini üstlenecek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['age', 'sex', 'trestbps', 'chol', 'fbs', 'thalach', 'exang', 'oldpeak',\n",
    "         'cp_1', 'cp_2', 'cp_3', 'restecg_1', 'restecg_2', 'slope_1',\n",
    "         'slope_2', 'ca_1', 'ca_2', 'ca_3', 'ca_4', 'thal_1', 'thal_2',\n",
    "         'thal_3','target']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>cp_1</th>\n",
       "      <th>cp_2</th>\n",
       "      <th>...</th>\n",
       "      <th>slope_1</th>\n",
       "      <th>slope_2</th>\n",
       "      <th>ca_1</th>\n",
       "      <th>ca_2</th>\n",
       "      <th>ca_3</th>\n",
       "      <th>ca_4</th>\n",
       "      <th>thal_1</th>\n",
       "      <th>thal_2</th>\n",
       "      <th>thal_3</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  trestbps  chol  fbs  thalach  exang  oldpeak  cp_1  cp_2  ...  \\\n",
       "0   63    1       145   233    1      150      0      2.3     0     0  ...   \n",
       "1   37    1       130   250    0      187      0      3.5     0     1  ...   \n",
       "2   41    0       130   204    0      172      0      1.4     1     0  ...   \n",
       "3   56    1       120   236    0      178      0      0.8     1     0  ...   \n",
       "4   57    0       120   354    0      163      1      0.6     0     0  ...   \n",
       "\n",
       "   slope_1  slope_2  ca_1  ca_2  ca_3  ca_4  thal_1  thal_2  thal_3  target  \n",
       "0        0        0     0     0     0     0       1       0       0       1  \n",
       "1        0        0     0     0     0     0       0       1       0       1  \n",
       "2        0        1     0     0     0     0       0       1       0       1  \n",
       "3        0        1     0     0     0     0       0       1       0       1  \n",
       "4        0        1     0     0     0     0       0       1       0       1  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelin Uygulanması"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Makine öğrenmesi kısmına geldik. Bu kısımda `sklearn` kütüphanesi içerisindeki çeşitli sınıflandırma modellerini uygulayacağız ve bunlar arasında bir karşılaştırma yapıcaz.\n",
    "\n",
    "Modellerin uygulanmasından önce veri setimizi **train ve test** olarak ayıralım.\n",
    "\n",
    "Bu aşamada %80 eğitim ve %20 test olarak veri setimizi rassal olarak ayırıyoruz.\n",
    "\n",
    "> Aynı rassal değişkenleri yakalamak için bir `random_state` atıyoruz."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy 88.52%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tolga/.local/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "                                   df.drop('target', axis=1), # target hariç veri setinin tamamı X\n",
    "                                   df['target'], # target Y\n",
    "                                   test_size = .2, # %80 train %20 test olmak üzere ayırdık.\n",
    "                                   random_state=1905)\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train,y_train)\n",
    "print(\"Test Accuracy {:.2f}%\".format(lr.score(X_test,y_test)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Modelimin doğruluğu **88.52%** olarak görülüyor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normelleştirme\n",
    "\n",
    "Bir de değişkenlerimi normalleştirerek sonuçları görelim.\n",
    "\n",
    "> Normalleştirme her bir değişkenin maksimum ve minimum değerlerini **0-1** arasında gösterir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.339623</td>\n",
       "      <td>0.178082</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.770992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.225806</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.562500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.245283</td>\n",
       "      <td>0.251142</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.816794</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.129032</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.245283</td>\n",
       "      <td>0.520548</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.702290</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.096774</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0    1         2         3    4         5    6         7    8    9   \\\n",
       "0  0.708333  1.0  0.481132  0.244292  1.0  0.603053  0.0  0.370968  0.0  0.0   \n",
       "1  0.166667  1.0  0.339623  0.283105  0.0  0.885496  0.0  0.564516  0.0  1.0   \n",
       "2  0.250000  0.0  0.339623  0.178082  0.0  0.770992  0.0  0.225806  1.0  0.0   \n",
       "3  0.562500  1.0  0.245283  0.251142  0.0  0.816794  0.0  0.129032  1.0  0.0   \n",
       "4  0.583333  0.0  0.245283  0.520548  0.0  0.702290  1.0  0.096774  0.0  0.0   \n",
       "\n",
       "   ...   12   13   14   15   16   17   18   19   20   21  \n",
       "0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  \n",
       "1  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "2  ...  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "3  ...  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "4  ...  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop('target',axis=1) # X\n",
    "y = df.target # y\n",
    "\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "np_scaled = min_max_scaler.fit_transform(X)\n",
    "X_normalized = pd.DataFrame(np_scaled)\n",
    "X_normalized.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy 90.16%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tolga/.local/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "                                   X_normalized, # target hariç veri setinin tamamı (X)\n",
    "                                   y, # target (Y)\n",
    "                                   test_size = .2, # %80 train %20 test olmak üzere ayırdık.\n",
    "                                   random_state=1905)\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train,y_train)\n",
    "\n",
    "lr_score = lr.score(X_test,y_test)\n",
    "print(\"Test Accuracy {:.2f}%\".format(lr_score*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normelleştirme sonrasında **88.52%** olan sonucum **90.16%** yükseldi."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Nearest Neighbour (KNN) Classification\n",
    "\n",
    "Tahminlerin gözlem benzerliğine göre yapılmasıdır.\n",
    "\n",
    "> **Erkan Şirin'in** benzetmesiyle **\"Bana arkadaşını söyle sana kim olduğunu söyleyeyim.\"** yaklaşımıdır.\n",
    "> \n",
    "> Via: Vahit Keskin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 NN Score: 81.97%\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "                                   X_normalized, # target hariç veri setinin tamamı (X)\n",
    "                                   y, # target (Y)\n",
    "                                   test_size = .2, # %80 train %20 test olmak üzere ayırdık.\n",
    "                                   random_state=1905)\n",
    "# KNN Model\n",
    "knn = KNeighborsClassifier(n_neighbors = 2)  # n_neighbors means k\n",
    "knn.fit(X_train, y_train)\n",
    "prediction = knn.predict(X_test)\n",
    "\n",
    "knn_score = knn.score(X_test, y_test)\n",
    "print(\"{} NN Score: {:.2f}%\".format(knn.n_neighbors, knn_score*100))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `n_neighbors = 2` olduğunda skorum: 81.97%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### En iyi n_neighbors değerini bulma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN Score is 86.89%\n"
     ]
    }
   ],
   "source": [
    "scoreList = []\n",
    "for i in range(1,20):\n",
    "    knn2 = KNeighborsClassifier(n_neighbors = i)  # n_neighbors means k\n",
    "    knn2.fit(X_train, y_train)\n",
    "    scoreList.append(knn2.score(X_test, y_test))\n",
    "\n",
    "sns.set()    \n",
    "plt.plot(range(1,20), scoreList)\n",
    "plt.xticks(np.arange(1,20,1))\n",
    "plt.xlabel(\"K value\")\n",
    "plt.ylabel(\"Skor\")\n",
    "plt.savefig('kvalue.png',transparent=True)\n",
    "plt.show()\n",
    "\n",
    "knn_score = max(scoreList)\n",
    "print(\"Maximum KNN Score is {:.2f}%\".format((max(scoreList))*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Maksimum KNN skorum 86.89% "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machine (SVM) Algorithm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy of SVM Algorithm: 86.89%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tolga/.local/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "svm = SVC(random_state = 1)\n",
    "svm.fit(X_train, y_train)\n",
    "svm_score = svm.score(X_test,y_test)\n",
    "print(\"Test Accuracy of SVM Algorithm: {:.2f}%\".format(svm_score*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Naive Bayes: 88.52%\n"
     ]
    }
   ],
   "source": [
    "nb = GaussianNB()\n",
    "nb.fit(X_train, y_train)\n",
    "\n",
    "nb_score = nb.score(X_test,y_test)\n",
    "print(\"Accuracy of Naive Bayes: {:.2f}%\".format(nb_score*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Algorithm\n",
    "\n",
    "Normalleştirilmemiş veri setiyle daha yüksek skor yakalandı."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Test Accuracy 78.69%\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df.drop('target', axis=1), # target hariç veri setinin tamamı (X)\n",
    "                                   y, # target (Y)\n",
    "                                   test_size = .2, # %80 train %20 test olmak üzere ayırdık.\n",
    "                                   random_state=1905)\n",
    "dtc = DecisionTreeClassifier()\n",
    "dtc.fit(X_train, y_train)\n",
    "\n",
    "dtc_score = dtc.score(X_test, y_test)\n",
    "print(\"Decision Tree Test Accuracy {:.2f}%\".format(dtc_score*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Algorithm Accuracy Score : 78.69%\n"
     ]
    }
   ],
   "source": [
    "# Random Forest Classification\n",
    "rf = RandomForestClassifier(n_estimators = 1000, random_state = 1)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "rf_score = rf.score(X_test, y_test)\n",
    "print(\"Random Forest Algorithm Accuracy Score : {:.2f}%\".format(rf_score*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "methods_accuracy = {\n",
    "    \"Logistic Regression\":lr_score,\n",
    "    \"KNN\" : knn_score,\n",
    "    \"SVM\" : svm_score,\n",
    "    \"Naive Bayes\" : nb_score,\n",
    "    \"Decision Tree\" : dtc_score,\n",
    "    \"Random Forest\" : rf_score\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "methods = [\"Logistic Regression\",\"Naive Bayes\", \"KNN\", \"SVM\", \"Decision Tree\", \"Random Forest\"]\n",
    "accuracy = [lr_score, nb_score, knn_score, svm_score, dtc_score, rf_score]\n",
    "\n",
    "sns.set()\n",
    "plt.figure(figsize=(14,6))\n",
    "plt.ylabel(\"Başarı %\")\n",
    "plt.xlabel(\"Algoritmalar\")\n",
    "sns.barplot(x=methods, y=accuracy, palette=\"deep\")\n",
    "\n",
    "# Kırılımlar Üzerine Değerlerini Yazmak\n",
    "for line in range(len(methods)):\n",
    "     plt.text(line-0.15, # x\n",
    "              0.70, # y\n",
    "             \"{:.2f}%\".format(accuracy[line]*100), # yazdırılacak değer\n",
    "             horizontalalignment='left',\n",
    "              size='large',\n",
    "             color=\"white\",\n",
    "             )\n",
    "\n",
    "        \n",
    "plt.savefig('karşılaştır.png',transparent=True)\n",
    "\n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}