Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 299 entries, 0 to 298\n",
      "Data columns (total 13 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   age                       299 non-null    float64\n",
      " 1   anaemia                   299 non-null    int64  \n",
      " 2   creatinine_phosphokinase  299 non-null    int64  \n",
      " 3   diabetes                  299 non-null    int64  \n",
      " 4   ejection_fraction         299 non-null    int64  \n",
      " 5   high_blood_pressure       299 non-null    int64  \n",
      " 6   platelets                 299 non-null    float64\n",
      " 7   serum_creatinine          299 non-null    float64\n",
      " 8   serum_sodium              299 non-null    int64  \n",
      " 9   sex                       299 non-null    int64  \n",
      " 10  smoking                   299 non-null    int64  \n",
      " 11  time                      299 non-null    int64  \n",
      " 12  DEATH_EVENT               299 non-null    int64  \n",
      "dtypes: float64(3), int64(10)\n",
      "memory usage: 30.5 KB\n"
     ]
    }
   ],
   "source": [
    "heart_data=pd.read_csv('heart_failure_clinical_records_dataset.csv')\n",
    "heart_data.info()\n",
    "#Verilerimiz kalp krizini etkileyen bazı faktörler üzerine hazırlanmıştır. \n",
    "#Verimizde  cinsiyet ksımı için 0=kadın 1=erkek bireyi ifade etmektedir.\n",
    "# death_event faktöründe 0 = bireyin hayatta kaldığını 1=ise vefat ettiğini ifade edtmektedir.\n",
    "#Bunun dışında verimizde 0 yok 1 var olarak kullanılmıştır. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>582</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>265000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7861</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>263358.03</td>\n",
       "      <td>1.1</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>162000.00</td>\n",
       "      <td>1.3</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>210000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>327000.00</td>\n",
       "      <td>2.7</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>155000.00</td>\n",
       "      <td>1.1</td>\n",
       "      <td>143</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>270</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1820</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.00</td>\n",
       "      <td>1.2</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>271</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2060</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>742000.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>278</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2413</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>140000.00</td>\n",
       "      <td>1.4</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>280</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>196</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>395000.00</td>\n",
       "      <td>1.6</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>285</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>299 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0    75.0        0                       582         0                 20   \n",
       "1    55.0        0                      7861         0                 38   \n",
       "2    65.0        0                       146         0                 20   \n",
       "3    50.0        1                       111         0                 20   \n",
       "4    65.0        1                       160         1                 20   \n",
       "..    ...      ...                       ...       ...                ...   \n",
       "294  62.0        0                        61         1                 38   \n",
       "295  55.0        0                      1820         0                 38   \n",
       "296  45.0        0                      2060         1                 60   \n",
       "297  45.0        0                      2413         0                 38   \n",
       "298  50.0        0                       196         0                 45   \n",
       "\n",
       "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                      1  265000.00               1.9           130    1   \n",
       "1                      0  263358.03               1.1           136    1   \n",
       "2                      0  162000.00               1.3           129    1   \n",
       "3                      0  210000.00               1.9           137    1   \n",
       "4                      0  327000.00               2.7           116    0   \n",
       "..                   ...        ...               ...           ...  ...   \n",
       "294                    1  155000.00               1.1           143    1   \n",
       "295                    0  270000.00               1.2           139    0   \n",
       "296                    0  742000.00               0.8           138    0   \n",
       "297                    0  140000.00               1.4           140    1   \n",
       "298                    0  395000.00               1.6           136    1   \n",
       "\n",
       "     smoking  time  DEATH_EVENT  \n",
       "0          0     4            1  \n",
       "1          0     6            1  \n",
       "2          1     7            1  \n",
       "3          0     7            1  \n",
       "4          0     8            1  \n",
       "..       ...   ...          ...  \n",
       "294        1   270            0  \n",
       "295        0   271            0  \n",
       "296        0   278            0  \n",
       "297        1   280            0  \n",
       "298        1   285            0  \n",
       "\n",
       "[299 rows x 13 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                         0\n",
       "anaemia                     0\n",
       "creatinine_phosphokinase    0\n",
       "diabetes                    0\n",
       "ejection_fraction           0\n",
       "high_blood_pressure         0\n",
       "platelets                   0\n",
       "serum_creatinine            0\n",
       "serum_sodium                0\n",
       "sex                         0\n",
       "smoking                     0\n",
       "time                        0\n",
       "DEATH_EVENT                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_data.isnull().sum()\n",
    "#Bu kod satırı ile verimizde null olan ifadeleri bulmayı ve işleme başlamadan önce doğru bir sonuç almak için veriyi kontrol ediyoruz.\n",
    "#Verimizde null bir ifade olmadığı için herhangi bir işlem yapmayacağız. Ancak eğer null bir ifademiz olsaydı onu temizlememiz ya da bu ifadeleri doldurmamız gerekecekti. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000021E7ABFEE50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000021E7AC5A580>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000021E7AC889D0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000021E7ACB5E50>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000021E7ACEF2E0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000021E7AD1A670>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000021E7AD1A760>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000021E7AD47C10>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000021E7ADAD460>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000021E7ADD88B0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000021E7AE04D30>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000021E7AE3E1C0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000021E7AE6A610>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000021E7AE979A0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000021E7AEB8FA0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000021E7AEEC460>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "heart_data.hist(figsize=(20,10))\n",
    "#Verimizi daha yakından tanımak için her faktör için bir histagran grafiği oluşturuyoruz. \n",
    "#Bu şekilde veri hakkında genel bilgimizi ediniyoruz. Daha sonra ise faktörlerin kalp krizini nasıl ekilediğini tek tek inceleyeceğiz. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Age')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "heart_data['age'].plot.hist(title='age histograms', color='green')\n",
    "plt.xlabel('Age')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x21e7b6666d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_hist = sns.FacetGrid(heart_data, col='DEATH_EVENT', hue='DEATH_EVENT')\n",
    "age_hist.map(plt.hist, 'age', bins=15)\n",
    "#yaş faktörünün kalp krizine etkenenine baktığımızda özellikle 80 yaşından sonrasındaki bireylerin hayatını kaybetmiş olduğunu görüyoruz.\n",
    "#80 yaşından küçük bireylerde kapl kriznin yaşa etkisi ile ilgili bir incelemenin tam manasıyla bir sonuç vermediğini söyleyebiliriz.\n",
    "#Yani bir bireyin yaşı ile kalp krizi hakkıda bir yorumlama yapmamız gerekirse .Ancak öok ileri yaştaki bireylerin bu konudan doğrudan etkilendiğini söyleyebiliriz.\n",
    "#Bunun dışında kalan bireyler için kap krizinin yalnızca yaş faktörü ile doğrudan bir orantısı olduğunu söylemek pek mümkün değildir. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CİNSİYET VE YAŞ FAKTÖRLERİNİN BERABER İNCELENMESİ : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21e7b972880>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heart_data['sex'].value_counts().plot.pie(explode=[0,0.1],shadow=True,autopct='%1.1f%%',\n",
    "                                       title= 'CİNSİYET')\n",
    "#0=female 1=male\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x21e7b256040>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_sex_hist = sns.FacetGrid(heart_data, col='DEATH_EVENT', row='sex', hue='DEATH_EVENT')\n",
    "age_sex_hist.map(plt.hist, 'age', bins=15)\n",
    "#sex= 1:male 0:female   death_event = 0:no 1 :yes \n",
    "#diabetes= 0:no  1:yes \n",
    "#Yaş ve cinsiyet faktörlerinin ölümle alakalı durumunu incelemek istediğimizde . Şu şekilde bir yorum yapabiliriz. \n",
    "#kadın ve erkek hastalar için hayatta kalanlara baktığımızda ; Bu durumun özellikle 50-70 yaş aralığındaki erkeklerde çok daha fazla olduğunu görebiliriyoruz. \n",
    "#Bununla birlikte kadınlarda hayatta kalma durumunun çok ileri yaşlarda bile görülmüş olduğunu fark ediyoruz. \n",
    "#Hayatını kaybeden bireyler için cinsiyetin ve yaşın etkisinin nasıl olduğunu incelediğimizde 40-60 yaş aralığında kadınlardan her yaş aralığında hayatını kaybedenleri görebiliyoruz \n",
    "#Ancak 40-60 yaş aralığındaki erkeklere baktığımızda ise erkek bireylerde yer yer bazı yaşlarda hiç bir bireyin hayatını bkaybetmediğini söyleyebiliz. \n",
    "#Yine de baktığımızda 40-60 yaş aralığındaki hayatını kaybeden erkek ve kadınların sayısının birbirine oldukça yakın olduğunu da tespit edebiliriz. \n",
    "#60-80 yaş aralığında erkek bireylerin daha fazla hayatını kaybetmiş olduğunu görebiliriz. \n",
    "#Aynı şekilde 80 yaştan fazla olan bireylerde de erkek bireylerin daha fazla hayatını kaybetmiş olduğunu görüyoruz.\n",
    "#Bu incelemeler sonucunda şöyle bir çıkarım yapabilmemiz mümkün olabilir. Özellikle yaş arttığında erkek bireylerde kalp krizi sebebiyle hayatını kaybetme oranının daha fazla olduğunu görüyoruz. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diyabet "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21e7b337c10>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heart_data['diabetes'].value_counts().plot.pie(explode=[0,0.1],shadow=True,autopct='%1.1f%%',\n",
    "                                       title= 'diabetes')\n",
    "#Verimize baktığımızda bireylerin %58.2 si diabet hastası değildir ve kalanlar ise diabet hastasıdır .\n",
    "#Peki bir bireyin diyabet hastası olması kalp krizine nasıl bir etki sağlar ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    174\n",
       "1    125\n",
       "Name: diabetes, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_data['diabetes'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diabetes  DEATH_EVENT\n",
       "0         0              118\n",
       "          1               56\n",
       "1         0               85\n",
       "          1               40\n",
       "Name: DEATH_EVENT, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.countplot(data=heart_data, x=heart_data['diabetes'], hue=\"DEATH_EVENT\", palette = \"Set3\", alpha=0.8, edgecolor=\"k\", linewidth=1) \n",
    "diabetes=heart_data.groupby('diabetes')\n",
    "diabetes['DEATH_EVENT'].value_counts() \n",
    "#Diyabet hastası olan bireylerden 85 kişinin hayatta kaldığını ancak 40 kadar bireyin hayatını kaybettiğini görüyoruz. Bu durumda diyabet hastası olup hayatta kalan birey oranı 0.32 oluyor.\n",
    "#Diyabet hastası olmayan bireylerden 56  kişinin hayatını kaybettiğini görüyoruz. 118 kişi ise hayatta kalmıştır .Bu durumda diyabet hastası olmayıp hayatını kaybetme ooranı ise 0.322olduğunu görüyoruz.\n",
    "#Diyabet hastası olan hayatta kalan bireylerin oranı %68\n",
    "#Diyabet hastası olmayıp hayatta kalan bireylerin oranı %67.8\n",
    "#Bu durumda diyabet hastası olmanın kalp krizi ile doğrudan bir ilişkiye sahip olduğunu söylemek pek mümkün değildir. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ANEMİ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "anaemia  DEATH_EVENT\n",
       "0        0              120\n",
       "         1               50\n",
       "1        0               83\n",
       "         1               46\n",
       "Name: DEATH_EVENT, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.countplot(data=heart_data, x=heart_data['anaemia'], hue=\"DEATH_EVENT\", palette = [\"#990303\", \"#9C9999\"], alpha=0.8, edgecolor=\"k\", linewidth=1) \n",
    "Anaemia=heart_data.groupby('anaemia')\n",
    "Anaemia['DEATH_EVENT'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "high_blood_pressure  DEATH_EVENT\n",
       "0                    0              137\n",
       "                     1               57\n",
       "1                    0               66\n",
       "                     1               39\n",
       "Name: DEATH_EVENT, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.countplot(data=heart_data, x=heart_data['high_blood_pressure'], hue=\"DEATH_EVENT\", palette = \"pink\", alpha=0.8, edgecolor=\"k\", linewidth=1) \n",
    "HBP=heart_data.groupby('high_blood_pressure')\n",
    "HBP['DEATH_EVENT'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Serum sodium')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.kdeplot(heart_data.loc[(heart_data['DEATH_EVENT']==0), \n",
    "            'serum_sodium'], color='r', shade=True, Label='alive') \n",
    "  \n",
    "sns.kdeplot(heart_data.loc[(heart_data['DEATH_EVENT']==1),  \n",
    "            'serum_sodium'], color='b', shade=True, Label='dead') \n",
    "  \n",
    "plt.xlabel('Serum sodium') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Ejection fraction')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.kdeplot(heart_data.loc[(heart_data['DEATH_EVENT']==0), \n",
    "            'ejection_fraction'], color='r', shade=True, Label='alive') \n",
    "  \n",
    "sns.kdeplot(heart_data.loc[(heart_data['DEATH_EVENT']==1),  \n",
    "            'ejection_fraction'], color='b', shade=True, Label='dead') \n",
    "  \n",
    "plt.xlabel('Ejection fraction')  \n",
    "\n",
    "#Ejeksiyon fraksiyonu (EF) denen bir ölçüm ile her kalp atışında kalbin ne kadar iyi kasıldığı, böylece sistolik veya diyastolik KYayrımı yapılabilir.\n",
    "#Grafiği incelediğimizde kasılmanın en ideal olduğu sayının 20-50 arası olduğunu fark ediyoruz. Hatta bu aralıkta en ideal olan durum ise 40 .Bu durumda bireylerin hayatta kalma durumlarının daha fazla olduğunu gözlemlemek mümkün. \n",
    "#Yine grafiği incelediğimizde kalp kasılması durumunun 20 olduğu durumda ölüm oranının en fazla miktara ulaştığını görebiliyoruz. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MODELLEMELER "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
      "0    75.0        0                       582         0                 20   \n",
      "1    55.0        0                      7861         0                 38   \n",
      "2    65.0        0                       146         0                 20   \n",
      "3    50.0        1                       111         0                 20   \n",
      "4    65.0        1                       160         1                 20   \n",
      "..    ...      ...                       ...       ...                ...   \n",
      "294  62.0        0                        61         1                 38   \n",
      "295  55.0        0                      1820         0                 38   \n",
      "296  45.0        0                      2060         1                 60   \n",
      "297  45.0        0                      2413         0                 38   \n",
      "298  50.0        0                       196         0                 45   \n",
      "\n",
      "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
      "0                      1  265000.00               1.9           130    1   \n",
      "1                      0  263358.03               1.1           136    1   \n",
      "2                      0  162000.00               1.3           129    1   \n",
      "3                      0  210000.00               1.9           137    1   \n",
      "4                      0  327000.00               2.7           116    0   \n",
      "..                   ...        ...               ...           ...  ...   \n",
      "294                    1  155000.00               1.1           143    1   \n",
      "295                    0  270000.00               1.2           139    0   \n",
      "296                    0  742000.00               0.8           138    0   \n",
      "297                    0  140000.00               1.4           140    1   \n",
      "298                    0  395000.00               1.6           136    1   \n",
      "\n",
      "     smoking  time  \n",
      "0          0     4  \n",
      "1          0     6  \n",
      "2          1     7  \n",
      "3          0     7  \n",
      "4          0     8  \n",
      "..       ...   ...  \n",
      "294        1   270  \n",
      "295        0   271  \n",
      "296        0   278  \n",
      "297        1   280  \n",
      "298        1   285  \n",
      "\n",
      "[299 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "x=heart_data.iloc[:,:12]\n",
    "print(x) \n",
    "#verilerim dataframe olarak tutulmakta. Bu framin içerisinde bağımsız değişkenlerim mevcut. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     DEATH_EVENT\n",
      "0              1\n",
      "1              1\n",
      "2              1\n",
      "3              1\n",
      "4              1\n",
      "..           ...\n",
      "294            0\n",
      "295            0\n",
      "296            0\n",
      "297            0\n",
      "298            0\n",
      "\n",
      "[299 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "y=heart_data.iloc[:,12:13]\n",
    "print(y) \n",
    "#verilerim dataframe olarak tutulmakta. Bu framin içerisinde bağımlı değişkenim mevcut. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=x.values\n",
    "Y=y.values\n",
    "#Verilerim numpy array olarak tutuluyor.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc=StandardScaler()\n",
    "x_ölçekli=sc.fit_transform(X)\n",
    "y_ölçekli=sc.fit_transform(Y)\n",
    "#Verimizi ortak bir sayı dünyasına taşımız olduk .Böylece veriyi  modellemede daha doğru sonuçlar alıcaz.\n",
    "#Ayrıa burada X bağımsız değişkenlerini tutan bir kısım olarak ayarlandı .Daha sonra dilediğimiz faktörleri bağımsız değişken olarak atayıp denemeler yapıcaz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test,y_train,y_test = train_test_split(x,y,test_size=0.33, random_state=0)\n",
    "#Verilerimizi test ve eğitim olarak ayırdık . %33 lük bir değer kullandık "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PREDİCTİON (TAHMİN MODELLERİ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.06271572]\n",
      " [ 0.17301446]\n",
      " [ 0.55225623]\n",
      " [ 0.09257809]\n",
      " [ 0.27625464]\n",
      " [ 0.17797458]\n",
      " [ 0.614855  ]\n",
      " [ 0.01437581]\n",
      " [ 0.74885798]\n",
      " [ 0.45820389]\n",
      " [ 0.17935865]\n",
      " [ 0.63028966]\n",
      " [ 0.25066903]\n",
      " [ 0.21220872]\n",
      " [ 0.53504392]\n",
      " [ 0.71504694]\n",
      " [-0.10402952]\n",
      " [ 0.3724829 ]\n",
      " [ 0.09992641]\n",
      " [-0.06910342]\n",
      " [ 0.42915291]\n",
      " [ 0.52341557]\n",
      " [ 0.52535103]\n",
      " [ 0.32852216]\n",
      " [ 0.46306305]\n",
      " [ 0.1339507 ]\n",
      " [ 0.2448582 ]\n",
      " [-0.12610875]\n",
      " [-0.18344699]\n",
      " [ 0.13029216]\n",
      " [ 0.12305361]\n",
      " [ 0.61490588]\n",
      " [ 0.42749378]\n",
      " [ 0.38504944]\n",
      " [ 0.21555226]\n",
      " [ 0.48466236]\n",
      " [ 0.34727416]\n",
      " [ 0.38926092]\n",
      " [-0.27376697]\n",
      " [ 0.83479992]\n",
      " [-0.15632133]\n",
      " [ 0.37866878]\n",
      " [ 0.3461496 ]\n",
      " [ 0.36412534]\n",
      " [ 0.89345686]\n",
      " [ 0.33197217]\n",
      " [-0.09088394]\n",
      " [-0.01376701]\n",
      " [ 0.35302165]\n",
      " [ 0.49731182]\n",
      " [ 0.33599588]\n",
      " [-0.02955884]\n",
      " [ 0.26296023]\n",
      " [ 0.29042283]\n",
      " [ 0.71858741]\n",
      " [ 0.38132152]\n",
      " [ 0.34235502]\n",
      " [ 0.81126005]\n",
      " [ 0.04671444]\n",
      " [ 0.31729105]\n",
      " [ 0.7356578 ]\n",
      " [ 0.24044728]\n",
      " [ 0.27160223]\n",
      " [-0.19046999]\n",
      " [-0.2461393 ]\n",
      " [-0.10216863]\n",
      " [ 0.15266255]\n",
      " [ 0.36927461]\n",
      " [ 0.76807967]\n",
      " [ 0.64454498]\n",
      " [ 0.33611169]\n",
      " [ 0.21291688]\n",
      " [ 0.15694286]\n",
      " [ 0.51115333]\n",
      " [ 0.52447665]\n",
      " [-0.01645884]\n",
      " [ 0.54274114]\n",
      " [ 0.29221968]\n",
      " [ 0.04270144]\n",
      " [ 0.43772166]\n",
      " [ 0.15246432]\n",
      " [ 0.2809358 ]\n",
      " [-0.27193281]\n",
      " [ 0.10072092]\n",
      " [-0.09561485]\n",
      " [ 0.25341139]\n",
      " [ 0.36430995]\n",
      " [ 0.14718993]\n",
      " [ 0.25900885]\n",
      " [-0.0108947 ]\n",
      " [ 0.13938747]\n",
      " [ 0.31690758]\n",
      " [ 0.2030897 ]\n",
      " [-0.06697127]\n",
      " [ 0.12094633]\n",
      " [ 0.40552139]\n",
      " [ 0.40710167]\n",
      " [ 0.65373229]\n",
      " [ 0.30717359]]\n",
      "     DEATH_EVENT\n",
      "206            0\n",
      "188            0\n",
      "12             1\n",
      "219            0\n",
      "237            0\n",
      "..           ...\n",
      "152            0\n",
      "158            0\n",
      "101            0\n",
      "54             1\n",
      "181            1\n",
      "\n",
      "[99 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "r_ml = LinearRegression()\n",
    "r_ml.fit(x_train,y_train) #Xten yyi öğretiyoruz. Yani x kullanarak y yi eğitiyoruz.\n",
    "\n",
    "tahmin1 = r_ml.predict(x_test)\n",
    "print(tahmin1)\n",
    "print(y_test)\n",
    "#MultiLinear Regression modeli ile tahminlerimizi yapıyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decision tree regression predict\n",
      "[0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 0. 0.\n",
      " 1. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 1. 1. 0. 0. 0.\n",
      " 0. 1. 0. 0. 0. 0. 1. 1. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 1. 1. 0. 1.\n",
      " 0. 1. 1. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1.\n",
      " 0. 1. 0.]\n",
      "     DEATH_EVENT\n",
      "206            0\n",
      "188            0\n",
      "12             1\n",
      "219            0\n",
      "237            0\n",
      "..           ...\n",
      "152            0\n",
      "158            0\n",
      "101            0\n",
      "54             1\n",
      "181            1\n",
      "\n",
      "[99 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "r_dst=DecisionTreeRegressor(random_state=0) \n",
    "r_dst.fit(x_train,y_train)\n",
    "tahmin2=r_dst.predict(x_test)\n",
    "print(\"decision tree regression predict\")\n",
    "print(tahmin2)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random forest regression predict\n",
      "[0.  0.  0.8 0.  0.  0.  0.1 0.  0.4 0.  0.6 0.7 0.  0.  0.8 0.9 0.  0.\n",
      " 0.  0.2 0.8 0.  0.2 0.3 0.5 0.  0.3 0.1 0.  0.  0.  0.7 0.8 0.  0.  0.6\n",
      " 0.  0.  0.1 0.9 0.  0.5 0.2 0.4 0.9 0.  0.  0.6 0.9 0.9 0.3 0.  0.2 0.\n",
      " 1.  0.1 0.  0.9 0.  0.  0.7 0.  0.7 0.  0.  0.1 0.4 0.1 0.8 0.9 0.  0.1\n",
      " 0.  1.  0.6 0.2 0.8 0.  0.  0.2 0.  0.  0.1 0.4 0.  0.  0.4 0.  0.  0.2\n",
      " 0.1 0.6 0.  0.  0.  0.9 0.3 1.  0.1]\n",
      "     DEATH_EVENT\n",
      "206            0\n",
      "188            0\n",
      "12             1\n",
      "219            0\n",
      "237            0\n",
      "..           ...\n",
      "152            0\n",
      "158            0\n",
      "101            0\n",
      "54             1\n",
      "181            1\n",
      "\n",
      "[99 rows x 1 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-95057a1ead0a>:6: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  r_rf.fit(x_train,y_train)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    " #Tahmin için kullandığımızda Regressor olarak kuulanıyoruz.\n",
    " #Bu algoritmalar aynı zamanda sınıflandırma da da kullandığından \n",
    "r_rf = RandomForestRegressor(n_estimators=10,random_state=0) \n",
    "# n_restimator kaç tane karar ağacı kullanağımızı ayarladığımız parametredir \n",
    "r_rf.fit(x_train,y_train)\n",
    "tahmin3=r_rf.predict(x_test)\n",
    "print(\"random forest regression predict\")\n",
    "print(tahmin3)\n",
    "print(y_test)\n",
    "#Random forest algoritması ensemble yapılı bir algoritmadır .Yani birden fazla görüşten oluşur.Burda amaç baskın olan görüşü almaktır.\n",
    "#Yani oy çokluğu ile sonuca karar verilir. n_estimators :algoritmada kullanılmasını istediğimiz karar ağacı sayısını belirlediğimiz bir parametredir.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SINIFLANDIRMA MODELLERİ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#BU KISIMDA MODELLERİMİZİN BAŞARISINI CONFUSSİON MATRİX KULLANARAK DA ANALİZ EDECEĞİZ. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0\n",
      " 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1\n",
      " 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0]\n",
      "     DEATH_EVENT\n",
      "206            0\n",
      "188            0\n",
      "12             1\n",
      "219            0\n",
      "237            0\n",
      "..           ...\n",
      "152            0\n",
      "158            0\n",
      "101            0\n",
      "54             1\n",
      "181            1\n",
      "\n",
      "[99 rows x 1 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:73: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "c_lr = LogisticRegression(random_state=0)\n",
    "c_lr.fit(x_train,y_train) #Xten yyi öğretiyoruz. Yani x kullanarak y yi eğitiyoruz.\n",
    "\n",
    "tahmin4 = c_lr.predict(x_test)\n",
    "print(tahmin4)\n",
    "print(y_test)\n",
    "#Öncelikle logistic Regression ile model oluşturup tahminimize bakıyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes\n",
      "[0 0 1 0 0 0 0 0 1 0 0 1 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0\n",
      " 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0\n",
      " 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      "     DEATH_EVENT\n",
      "206            0\n",
      "188            0\n",
      "12             1\n",
      "219            0\n",
      "237            0\n",
      "..           ...\n",
      "152            0\n",
      "158            0\n",
      "101            0\n",
      "54             1\n",
      "181            1\n",
      "\n",
      "[99 rows x 1 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:73: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "c_gnb=GaussianNB()\n",
    "c_gnb.fit(x_train,y_train)\n",
    "tahmin5=c_gnb.predict(x_test)\n",
    "print(\"Naive Bayes\")\n",
    "print(tahmin5)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier \n",
      "[0 1 1 0 0 0 1 0 1 0 0 1 1 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0\n",
      " 0 0 1 0 1 0 1 1 0 0 0 1 1 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0\n",
      " 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0]\n",
      "     DEATH_EVENT\n",
      "206            0\n",
      "188            0\n",
      "12             1\n",
      "219            0\n",
      "237            0\n",
      "..           ...\n",
      "152            0\n",
      "158            0\n",
      "101            0\n",
      "54             1\n",
      "181            1\n",
      "\n",
      "[99 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier \n",
    "c_dst=DecisionTreeClassifier(criterion='entropy')\n",
    "c_dst.fit(x_train,y_train)\n",
    "tahmin6=c_dst.predict(x_test)\n",
    "print(\"DecisionTreeClassifier \")\n",
    "print(tahmin6)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Clacssifier \n",
      "[1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 0 1 1 1 0 1 0 1\n",
      " 0 1 1 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 1 1 0 1 0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0]\n",
      "     DEATH_EVENT\n",
      "206            0\n",
      "188            0\n",
      "12             1\n",
      "219            0\n",
      "237            0\n",
      "..           ...\n",
      "152            0\n",
      "158            0\n",
      "101            0\n",
      "54             1\n",
      "181            1\n",
      "\n",
      "[99 rows x 1 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-153-0f9677a25fd7>:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  c_knn.fit(x_train,y_train)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "c_knn=KNeighborsClassifier(n_neighbors=5,metric=\"minkowski\")\n",
    "c_knn.fit(x_train,y_train)\n",
    "tahmin7=c_knn.predict(x_test)\n",
    "print(\"KNN Clacssifier \")\n",
    "print(tahmin7)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier \n",
      "[0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 1 1 0 0 1 0\n",
      " 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1\n",
      " 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0]\n",
      "     DEATH_EVENT\n",
      "206            0\n",
      "188            0\n",
      "12             1\n",
      "219            0\n",
      "237            0\n",
      "..           ...\n",
      "152            0\n",
      "158            0\n",
      "101            0\n",
      "54             1\n",
      "181            1\n",
      "\n",
      "[99 rows x 1 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-154-0b81e105de1d>:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  c_rf.fit(x_train,y_train)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "c_rf=RandomForestClassifier(n_estimators=10,criterion='entropy')\n",
    "c_rf.fit(x_train,y_train)\n",
    "tahmin8=c_rf.predict(x_test)\n",
    "print(\"Random Forest Classifier \")\n",
    "print(tahmin8)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:73: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Classification \n",
      "[0 0 1 0 0 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0\n",
      " 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1\n",
      " 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0]\n",
      "     DEATH_EVENT\n",
      "206            0\n",
      "188            0\n",
      "12             1\n",
      "219            0\n",
      "237            0\n",
      "..           ...\n",
      "152            0\n",
      "158            0\n",
      "101            0\n",
      "54             1\n",
      "181            1\n",
      "\n",
      "[99 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc=SVC(kernel='linear')\n",
    "svc.fit(x_train,y_train)\n",
    "tahmin9=svc.predict(x_test)\n",
    "print(\"Support Vector Classification \")\n",
    "print(tahmin9)\n",
    "print(y_test)\n",
    "#kernel support vector algoritması için farklı şekilde çalıştırması için kullanılan bir parametredir.Burada polinomial gibi bazı seçenekler mevcuttur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lojistic  Regresyon\n",
      "[[64  4]\n",
      " [17 14]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "cm_lr=confusion_matrix(y_test,tahmin4)\n",
    "print('Lojistic  Regresyon')\n",
    "print(cm_lr)\n",
    "#78 doğru 21 yanlış tahmin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes\n",
      "[[64  4]\n",
      " [19 12]]\n"
     ]
    }
   ],
   "source": [
    "cm_gnb=confusion_matrix(y_test,tahmin5)\n",
    "print('Naive Bayes')\n",
    "print(cm_gnb)\n",
    "#76 doğru 23 yanlış \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dession Tree\n",
      "[[58 10]\n",
      " [ 8 23]]\n"
     ]
    }
   ],
   "source": [
    "cm_dst=confusion_matrix(y_test,tahmin6)\n",
    "print('Dession Tree')\n",
    "print(cm_dst) \n",
    "#81 doğru 18 yanlış \n",
    "\n",
    "#Modelimizin ne kadar doğru tahmin yaptığını incelemek amacıyla confusion matrix kullanıyoruz. \n",
    "#Ve görüyoruz ki Modelimiz 78 doğru tahmin 21 yanlış tahmin yapmış. Modelimizin %78 lik bir başarı elde ettiğini görüyoruz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN\n",
      "[[47 21]\n",
      " [21 10]]\n"
     ]
    }
   ],
   "source": [
    "cm_knn=confusion_matrix(y_test,tahmin7)\n",
    "print('KNN')\n",
    "print(cm_knn)\n",
    "#57 doğru 42 yanlış "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "[[64  4]\n",
      " [12 19]]\n"
     ]
    }
   ],
   "source": [
    "cm_rf=confusion_matrix(y_test,tahmin8)\n",
    "print('Random Forest')\n",
    "print(cm_rf)\n",
    "#83 doğru 14 yanlış \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC\n",
      "[[65  3]\n",
      " [15 16]]\n"
     ]
    }
   ],
   "source": [
    "cm_svc=confusion_matrix(y_test,tahmin9)\n",
    "print('SVC')\n",
    "print(cm_svc)\n",
    "#81 doğru 18 yanlış"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "models=pd.Series(['Logistic Regression','Naive Bayes','Decision Tree',\n",
    "                  'K Nearest Neighbors','Random Forest' ,'Support Vector'])\n",
    "Accuracy_score=pd.Series([accuracy_score(y_test,tahmin4),accuracy_score(y_test,tahmin5),accuracy_score(y_test,tahmin6),\n",
    "                         accuracy_score(y_test,tahmin7),accuracy_score(y_test,tahmin8),accuracy_score(y_test,tahmin9)])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "Leaderboard=pd.DataFrame({'Models':models,'Accuracy':Accuracy_score})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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>Models</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.787879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>0.767677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.828283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>K Nearest Neighbors</td>\n",
       "      <td>0.575758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.838384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Support Vector</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Models  Accuracy\n",
       "0  Logistic Regression  0.787879\n",
       "1          Naive Bayes  0.767677\n",
       "2        Decision Tree  0.828283\n",
       "3  K Nearest Neighbors  0.575758\n",
       "4        Random Forest  0.838384\n",
       "5       Support Vector  0.818182"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Leaderboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-164-24f3da805eb9>:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  model.fit(x_train,y_train)\n"
     ]
    }
   ],
   "source": [
    "model=RandomForestClassifier(class_weight='balanced', random_state=0)\n",
    "model.fit(x_train,y_train)\n",
    "\n",
    "y_pred=model.predict(x_test)\n",
    "y_proba=model.predict_proba(x_test)\n",
    "y_proba=[p[1] for p in y_proba]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "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": [
    "df_feat=pd.DataFrame({'Feature':x.columns,\n",
    "                      'Importance':model.feature_importances_})\n",
    "df_feat.sort_values(by='Importance', ascending=False, inplace=True)\n",
    "df_feat.reset_index(inplace=True)\n",
    "\n",
    "sns.barplot(x='Importance',y='Feature', data=df_feat, orient='h')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}