Heart_Attack.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(r'C:\\Users\\shrey\\PycharmProjects\\untitled\\ML\\HeartAttack_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <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",
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       "      <th>slope</th>\n",
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      ],
      "text/plain": [
       "   age  sex  cp trestbps chol fbs restecg thalach exang  oldpeak slope ca  \\\n",
       "0   28    1   2      130  132   0       2     185     0      0.0     ?  ?   \n",
       "1   29    1   2      120  243   0       0     160     0      0.0     ?  ?   \n",
       "2   29    1   2      140    ?   0       0     170     0      0.0     ?  ?   \n",
       "3   30    0   1      170  237   0       1     170     0      0.0     ?  ?   \n",
       "4   31    0   2      100  219   0       1     150     0      0.0     ?  ?   \n",
       "\n",
       "  thal  num         \n",
       "0    ?           0  \n",
       "1    ?           0  \n",
       "2    ?           0  \n",
       "3    6           0  \n",
       "4    ?           0  "
      ]
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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "data.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
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       "      <td>154</td>\n",
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       "      <td>4</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
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       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>23</td>\n",
       "      <td>266</td>\n",
       "      <td>235</td>\n",
       "      <td>29</td>\n",
       "      <td>204</td>\n",
       "      <td>NaN</td>\n",
       "      <td>190</td>\n",
       "      <td>291</td>\n",
       "      <td>266</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>47.826531</td>\n",
       "      <td>0.724490</td>\n",
       "      <td>2.982993</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.447533</td>\n",
       "      <td>0.965117</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.908648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.480977</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>42.000000</td>\n",
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       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
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      "text/plain": [
       "               age         sex          cp trestbps chol  fbs restecg thalach  \\\n",
       "count   294.000000  294.000000  294.000000      294  294  294     294     294   \n",
       "unique         NaN         NaN         NaN       32  154    3       4      72   \n",
       "top            NaN         NaN         NaN      120    ?    0       0     150   \n",
       "freq           NaN         NaN         NaN       65   23  266     235      29   \n",
       "mean     47.826531    0.724490    2.982993      NaN  NaN  NaN     NaN     NaN   \n",
       "std       7.811812    0.447533    0.965117      NaN  NaN  NaN     NaN     NaN   \n",
       "min      28.000000    0.000000    1.000000      NaN  NaN  NaN     NaN     NaN   \n",
       "25%      42.000000    0.000000    2.000000      NaN  NaN  NaN     NaN     NaN   \n",
       "50%      49.000000    1.000000    3.000000      NaN  NaN  NaN     NaN     NaN   \n",
       "75%      54.000000    1.000000    4.000000      NaN  NaN  NaN     NaN     NaN   \n",
       "max      66.000000    1.000000    4.000000      NaN  NaN  NaN     NaN     NaN   \n",
       "\n",
       "       exang     oldpeak slope   ca thal  num         \n",
       "count    294  294.000000   294  294  294  294.000000  \n",
       "unique     3         NaN     4    2    4         NaN  \n",
       "top        0         NaN     ?    ?    ?         NaN  \n",
       "freq     204         NaN   190  291  266         NaN  \n",
       "mean     NaN    0.586054   NaN  NaN  NaN    0.360544  \n",
       "std      NaN    0.908648   NaN  NaN  NaN    0.480977  \n",
       "min      NaN    0.000000   NaN  NaN  NaN    0.000000  \n",
       "25%      NaN    0.000000   NaN  NaN  NaN    0.000000  \n",
       "50%      NaN    0.000000   NaN  NaN  NaN    0.000000  \n",
       "75%      NaN    1.000000   NaN  NaN  NaN    1.000000  \n",
       "max      NaN    5.000000   NaN  NaN  NaN    1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "(294, 14)"
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    "data.shape"
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       "      <th>289</th>\n",
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       "      <td>130</td>\n",
       "      <td>294</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>155</td>\n",
       "      <td>342</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>393</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>275</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>294 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp trestbps chol fbs restecg thalach exang  oldpeak  num       \n",
       "0     28    1   2      130  132   0       2     185     0      0.0           0\n",
       "1     29    1   2      120  243   0       0     160     0      0.0           0\n",
       "2     29    1   2      140    ?   0       0     170     0      0.0           0\n",
       "3     30    0   1      170  237   0       1     170     0      0.0           0\n",
       "4     31    0   2      100  219   0       1     150     0      0.0           0\n",
       "..   ...  ...  ..      ...  ...  ..     ...     ...   ...      ...         ...\n",
       "289   52    1   4      160  331   0       0      94     1      2.5           1\n",
       "290   54    0   3      130  294   0       1     100     1      0.0           1\n",
       "291   56    1   4      155  342   1       0     150     1      3.0           1\n",
       "292   58    0   2      180  393   0       0     110     1      1.0           1\n",
       "293   65    1   4      130  275   0       1     115     1      1.0           1\n",
       "\n",
       "[294 rows x 11 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(columns=['slope', 'ca', 'thal'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['132', '243', '?', '237', '219', '198', '225', '254', '298', '161',\n",
       "       '214', '220', '160', '167', '308', '264', '166', '340', '209',\n",
       "       '260', '211', '173', '283', '194', '223', '315', '275', '297',\n",
       "       '292', '182', '200', '204', '241', '339', '147', '273', '307',\n",
       "       '289', '215', '281', '250', '184', '245', '291', '295', '269',\n",
       "       '196', '268', '228', '358', '201', '249', '266', '186', '207',\n",
       "       '218', '412', '224', '238', '230', '163', '240', '280', '257',\n",
       "       '263', '276', '284', '195', '227', '253', '187', '202', '328',\n",
       "       '168', '216', '129', '190', '188', '179', '210', '272', '180',\n",
       "       '100', '259', '468', '274', '320', '221', '309', '312', '171',\n",
       "       '208', '246', '305', '217', '365', '344', '394', '256', '326',\n",
       "       '277', '270', '229', '85', '347', '251', '222', '287', '318',\n",
       "       '213', '294', '193', '271', '156', '267', '282', '117', '466',\n",
       "       '247', '226', '265', '206', '288', '303', '338', '248', '306',\n",
       "       '529', '392', '231', '329', '355', '233', '242', '603', '255',\n",
       "       '172', '175', '290', '341', '234', '342', '404', '518', '285',\n",
       "       '279', '388', '164', '336', '491', '205', '212', '331', '393'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['chol'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['chol'] = data['chol'].replace({'?' : '235'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['132', '243', '235', '237', '219', '198', '225', '254', '298',\n",
       "       '161', '214', '220', '160', '167', '308', '264', '166', '340',\n",
       "       '209', '260', '211', '173', '283', '194', '223', '315', '275',\n",
       "       '297', '292', '182', '200', '204', '241', '339', '147', '273',\n",
       "       '307', '289', '215', '281', '250', '184', '245', '291', '295',\n",
       "       '269', '196', '268', '228', '358', '201', '249', '266', '186',\n",
       "       '207', '218', '412', '224', '238', '230', '163', '240', '280',\n",
       "       '257', '263', '276', '284', '195', '227', '253', '187', '202',\n",
       "       '328', '168', '216', '129', '190', '188', '179', '210', '272',\n",
       "       '180', '100', '259', '468', '274', '320', '221', '309', '312',\n",
       "       '171', '208', '246', '305', '217', '365', '344', '394', '256',\n",
       "       '326', '277', '270', '229', '85', '347', '251', '222', '287',\n",
       "       '318', '213', '294', '193', '271', '156', '267', '282', '117',\n",
       "       '466', '247', '226', '265', '206', '288', '303', '338', '248',\n",
       "       '306', '529', '392', '231', '329', '355', '233', '242', '603',\n",
       "       '255', '172', '175', '290', '341', '234', '342', '404', '518',\n",
       "       '285', '279', '388', '164', '336', '491', '205', '212', '331',\n",
       "       '393'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['chol'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['chol'] = data['chol'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['chol'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['130', '120', '140', '170', '100', '105', '110', '125', '150',\n",
       "       '98', '112', '145', '190', '160', '115', '142', '180', '132',\n",
       "       '135', '?', '108', '124', '113', '122', '92', '118', '106', '200',\n",
       "       '138', '136', '128', '155'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['trestbps'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['trestbps'] = data['trestbps'].replace({'?' : '131'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['130', '120', '140', '170', '100', '105', '110', '125', '150',\n",
       "       '98', '112', '145', '190', '160', '115', '142', '180', '132',\n",
       "       '135', '131', '108', '124', '113', '122', '92', '118', '106',\n",
       "       '200', '138', '136', '128', '155'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['trestbps'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['trestbps'] = data['trestbps'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['trestbps'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['185', '160', '170', '150', '165', '184', '155', '190', '168',\n",
       "       '180', '178', '172', '130', '142', '98', '158', '129', '146',\n",
       "       '145', '120', '106', '132', '140', '138', '167', '188', '144',\n",
       "       '137', '136', '152', '175', '176', '118', '154', '115', '135',\n",
       "       '122', '110', '90', '116', '174', '125', '?', '148', '100', '164',\n",
       "       '139', '127', '162', '112', '134', '114', '128', '126', '124',\n",
       "       '153', '166', '103', '156', '87', '102', '92', '99', '121', '91',\n",
       "       '108', '96', '82', '105', '143', '119', '94'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['thalach'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['thalach'] = data['thalach'].replace('?','0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['185', '160', '170', '150', '165', '184', '155', '190', '168',\n",
       "       '180', '178', '172', '130', '142', '98', '158', '129', '146',\n",
       "       '145', '120', '106', '132', '140', '138', '167', '188', '144',\n",
       "       '137', '136', '152', '175', '176', '118', '154', '115', '135',\n",
       "       '122', '110', '90', '116', '174', '125', '0', '148', '100', '164',\n",
       "       '139', '127', '162', '112', '134', '114', '128', '126', '124',\n",
       "       '153', '166', '103', '156', '87', '102', '92', '99', '121', '91',\n",
       "       '108', '96', '82', '105', '143', '119', '94'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['thalach'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['thalach'] = data['thalach'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "138.656462585034"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['thalach'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['thalach'] = data['thalach'].replace(0,138)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([185, 160, 170, 150, 165, 184, 155, 190, 168, 180, 178, 172, 130,\n",
       "       142,  98, 158, 129, 146, 145, 120, 106, 132, 140, 138, 167, 188,\n",
       "       144, 137, 136, 152, 175, 176, 118, 154, 115, 135, 122, 110,  90,\n",
       "       116, 174, 125, 148, 100, 164, 139, 127, 162, 112, 134, 114, 128,\n",
       "       126, 124, 153, 166, 103, 156,  87, 102,  92,  99, 121,  91, 108,\n",
       "        96,  82, 105, 143, 119,  94])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['thalach'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of 0      185\n",
       "1      160\n",
       "2      170\n",
       "3      170\n",
       "4      150\n",
       "      ... \n",
       "289     94\n",
       "290    100\n",
       "291    150\n",
       "292    110\n",
       "293    115\n",
       "Name: thalach, Length: 294, dtype: int32>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['thalach'].describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2', '0', '1', '?'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['restecg'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['restecg'] = data['restecg'].replace('?','1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2', '0', '1'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['restecg'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of 0      2\n",
       "1      0\n",
       "2      0\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "289    0\n",
       "290    1\n",
       "291    0\n",
       "292    0\n",
       "293    1\n",
       "Name: restecg, Length: 294, dtype: object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['restecg'].describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['restecg'] = data['restecg'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of 0      2\n",
       "1      0\n",
       "2      0\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "289    0\n",
       "290    1\n",
       "291    0\n",
       "292    0\n",
       "293    1\n",
       "Name: restecg, Length: 294, dtype: int32>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['restecg'].describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['oldpeak'] = data['oldpeak'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of 0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "289    2\n",
       "290    0\n",
       "291    3\n",
       "292    1\n",
       "293    1\n",
       "Name: oldpeak, Length: 294, dtype: int32>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['oldpeak'].describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['age'] = data['age'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of 0      28\n",
       "1      29\n",
       "2      29\n",
       "3      30\n",
       "4      31\n",
       "       ..\n",
       "289    52\n",
       "290    54\n",
       "291    56\n",
       "292    58\n",
       "293    65\n",
       "Name: age, Length: 294, dtype: int32>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['age'].describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of 0      1\n",
       "1      1\n",
       "2      1\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "289    1\n",
       "290    0\n",
       "291    1\n",
       "292    0\n",
       "293    1\n",
       "Name: sex, Length: 294, dtype: int32>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['sex'] = data['sex'].astype(int)\n",
    "data['sex'].describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of 0      2\n",
       "1      2\n",
       "2      2\n",
       "3      1\n",
       "4      2\n",
       "      ..\n",
       "289    4\n",
       "290    3\n",
       "291    4\n",
       "292    2\n",
       "293    4\n",
       "Name: cp, Length: 294, dtype: int32>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['cp'] = data['cp'].astype(int)\n",
    "data['cp'].describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 3, 4])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['cp'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '?', '1'], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['fbs'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['fbs'] = data['fbs'].replace('?','1')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '1'], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['fbs'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['fbs'] = data['fbs'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of 0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "289    0\n",
       "290    0\n",
       "291    1\n",
       "292    0\n",
       "293    0\n",
       "Name: fbs, Length: 294, dtype: int32>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['fbs'].describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '1', '?'], dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['exang'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['exang'] = data['exang'].replace('?','0')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '1'], dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['exang'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['exang'] = data['exang'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of 0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "289    1\n",
       "290    1\n",
       "291    1\n",
       "292    1\n",
       "293    1\n",
       "Name: exang, Length: 294, dtype: int32>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['exang'].describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "import time\n",
    "\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings(action=\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>243</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>235</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "      <td>237</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>219</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0   28    1   2       130   132    0        2      185      0        0   \n",
       "1   29    1   2       120   243    0        0      160      0        0   \n",
       "2   29    1   2       140   235    0        0      170      0        0   \n",
       "3   30    0   1       170   237    0        1      170      0        0   \n",
       "4   31    0   2       100   219    0        1      150      0        0   \n",
       "\n",
       "   num         \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>243</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>235</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "      <td>237</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>219</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  target\n",
       "0   28    1   2       130   132    0        2      185      0        0       0\n",
       "1   29    1   2       120   243    0        0      160      0        0       0\n",
       "2   29    1   2       140   235    0        0      170      0        0       0\n",
       "3   30    0   1       170   237    0        1      170      0        0       0\n",
       "4   31    0   2       100   219    0        1      150      0        0       0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = data['target'].values\n",
    "X = data.drop('target', axis=1).values\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "models_list = []\n",
    "models_list.append(('CART', DecisionTreeClassifier()))\n",
    "models_list.append(('SVM', SVC()))\n",
    "models_list.append(('NB', GaussianNB()))\n",
    "models_list.append(('KNN', KNeighborsClassifier()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_folds = 10\n",
    "\n",
    "results = []\n",
    "names = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CART: 0.674474 (0.118620) (run time: 0.021941)\n",
      "SVM: 0.662105 (0.150889) (run time: 0.021941)\n",
      "NB: 0.806579 (0.129629) (run time: 0.010971)\n",
      "KNN: 0.611316 (0.124716) (run time: 0.021941)\n"
     ]
    }
   ],
   "source": [
    "for name, model in models_list:\n",
    "    kfold = KFold(n_splits=num_folds, random_state=123)\n",
    "    startTime = time.time()\n",
    "    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')\n",
    "    endTime = time.time()\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    print(\"%s: %f (%f) (run time: %f)\" % (name, cv_results.mean(), cv_results.std(), endTime - startTime))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": [
    "fig = plt.figure()\n",
    "fig.suptitle('Performance Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipelines = []\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipelines.append(('ScaledCART', Pipeline([('Scaler', StandardScaler()), ('CART', DecisionTreeClassifier())])))\n",
    "pipelines.append(('ScaledSVM', Pipeline([('Scaler', StandardScaler()), ('SVM', SVC())])))\n",
    "pipelines.append(('ScaledNB', Pipeline([('Scaler', StandardScaler()), ('NB', GaussianNB())])))\n",
    "pipelines.append(('ScaledKNN', Pipeline([('Scaler', StandardScaler()), ('KNN', KNeighborsClassifier())])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "names = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "Accuracies of algorithm after scaled dataset\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\\n\\nAccuracies of algorithm after scaled dataset\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ScaledCART: 0.689474 (0.119839) (run time: 0.028923)\n",
      "ScaledSVM: 0.780789 (0.101506) (run time: 0.028923)\n",
      "ScaledNB: 0.806579 (0.129629) (run time: 0.026928)\n",
      "ScaledKNN: 0.801579 (0.103714) (run time: 0.029920)\n"
     ]
    }
   ],
   "source": [
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    kfold = KFold(n_splits=num_folds, random_state=123)\n",
    "    for name, model in pipelines:\n",
    "        start = time.time()\n",
    "        cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')\n",
    "        end = time.time()\n",
    "        results.append(cv_results)\n",
    "        names.append(name)\n",
    "        print(\"%s: %f (%f) (run time: %f)\" % (name, cv_results.mean(), cv_results.std(), end - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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": [
    "fig = plt.figure()\n",
    "fig.suptitle('Performance Comparison after Scaled Data')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Gaussian Training Completed. It's Run Time: 0.000998\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler().fit(X_train)\n",
    "\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "model = GaussianNB()\n",
    "start = time.time()\n",
    "model.fit(X_train_scaled, Y_train)  # Training of algorithm\n",
    "end = time.time()\n",
    "print(\"\\n\\nGaussian Training Completed. It's Run Time: %f\" % (end - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All predictions done successfully by GausianNB Machine Learning Algorithms\n",
      "\n",
      "\n",
      "Accuracy score 0.857143\n",
      "\n",
      "\n",
      "\n",
      "confusion_matrix = \n",
      "\n",
      "[[54  4]\n",
      " [10 30]]\n"
     ]
    }
   ],
   "source": [
    "X_test_scaled = scaler.transform(X_test)\n",
    "predictions = model.predict(X_test_scaled)\n",
    "print(\"All predictions done successfully by GausianNB Machine Learning Algorithms\")\n",
    "print(\"\\n\\nAccuracy score %f\" % accuracy_score(Y_test, predictions))\n",
    "\n",
    "print(\"\\n\\n\")\n",
    "print(\"confusion_matrix = \\n\")\n",
    "print(confusion_matrix(Y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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