Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p align=\"center\"><h1><strong>Data Preprocessing for Prediction of future Coronary Heart Disease(CHD) in Framingham,Massachusetts</strong></h1></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sklearn as sk\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The dataset is publicly available and it is from an ongoing cardiovascular study on residents of the town of Framingham, Massachusetts. \n",
    "#### The classification goal is to predict whether the patient has 10-year risk of future $Coronary$ $Heart$ $Disease$ (CHD).The dataset provides the patients’ information. It includes over 4,000 records and 15 attributes(excluding target variable)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>male</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>currentSmoker</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>BPMeds</th>\n",
       "      <th>prevalentStroke</th>\n",
       "      <th>prevalentHyp</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>totChol</th>\n",
       "      <th>sysBP</th>\n",
       "      <th>diaBP</th>\n",
       "      <th>BMI</th>\n",
       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "      <th>TenYearCHD</th>\n",
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       "      <td>65.0</td>\n",
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       "      <td>85.0</td>\n",
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       "      <td>84.0</td>\n",
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       "      <td>210.0</td>\n",
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       "      <td>87.0</td>\n",
       "      <td>19.16</td>\n",
       "      <td>86.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>269.0</td>\n",
       "      <td>133.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>21.47</td>\n",
       "      <td>80.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>4238 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      male  age  education  currentSmoker  cigsPerDay  BPMeds  \\\n",
       "0        1   39        4.0              0         0.0     0.0   \n",
       "1        0   46        2.0              0         0.0     0.0   \n",
       "2        1   48        1.0              1        20.0     0.0   \n",
       "3        0   61        3.0              1        30.0     0.0   \n",
       "4        0   46        3.0              1        23.0     0.0   \n",
       "...    ...  ...        ...            ...         ...     ...   \n",
       "4233     1   50        1.0              1         1.0     0.0   \n",
       "4234     1   51        3.0              1        43.0     0.0   \n",
       "4235     0   48        2.0              1        20.0     NaN   \n",
       "4236     0   44        1.0              1        15.0     0.0   \n",
       "4237     0   52        2.0              0         0.0     0.0   \n",
       "\n",
       "      prevalentStroke  prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  \\\n",
       "0                   0             0         0    195.0  106.0   70.0  26.97   \n",
       "1                   0             0         0    250.0  121.0   81.0  28.73   \n",
       "2                   0             0         0    245.0  127.5   80.0  25.34   \n",
       "3                   0             1         0    225.0  150.0   95.0  28.58   \n",
       "4                   0             0         0    285.0  130.0   84.0  23.10   \n",
       "...               ...           ...       ...      ...    ...    ...    ...   \n",
       "4233                0             1         0    313.0  179.0   92.0  25.97   \n",
       "4234                0             0         0    207.0  126.5   80.0  19.71   \n",
       "4235                0             0         0    248.0  131.0   72.0  22.00   \n",
       "4236                0             0         0    210.0  126.5   87.0  19.16   \n",
       "4237                0             0         0    269.0  133.5   83.0  21.47   \n",
       "\n",
       "      heartRate  glucose  TenYearCHD  \n",
       "0          80.0     77.0           0  \n",
       "1          95.0     76.0           0  \n",
       "2          75.0     70.0           0  \n",
       "3          65.0    103.0           1  \n",
       "4          85.0     85.0           0  \n",
       "...         ...      ...         ...  \n",
       "4233       66.0     86.0           1  \n",
       "4234       65.0     68.0           0  \n",
       "4235       84.0     86.0           0  \n",
       "4236       86.0      NaN           0  \n",
       "4237       80.0    107.0           0  \n",
       "\n",
       "[4238 rows x 16 columns]"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0=pd.read_csv('Dataset.csv')\n",
    "df0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4238 entries, 0 to 4237\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   male             4238 non-null   int64  \n",
      " 1   age              4238 non-null   int64  \n",
      " 2   education        4133 non-null   float64\n",
      " 3   currentSmoker    4238 non-null   int64  \n",
      " 4   cigsPerDay       4209 non-null   float64\n",
      " 5   BPMeds           4185 non-null   float64\n",
      " 6   prevalentStroke  4238 non-null   int64  \n",
      " 7   prevalentHyp     4238 non-null   int64  \n",
      " 8   diabetes         4238 non-null   int64  \n",
      " 9   totChol          4188 non-null   float64\n",
      " 10  sysBP            4238 non-null   float64\n",
      " 11  diaBP            4238 non-null   float64\n",
      " 12  BMI              4219 non-null   float64\n",
      " 13  heartRate        4237 non-null   float64\n",
      " 14  glucose          3850 non-null   float64\n",
      " 15  TenYearCHD       4238 non-null   int64  \n",
      "dtypes: float64(9), int64(7)\n",
      "memory usage: 529.9 KB\n"
     ]
    }
   ],
   "source": [
    "df0.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>male</th>\n",
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       "      <th>education</th>\n",
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       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "      <th>TenYearCHD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4238.000000</td>\n",
       "      <td>4238.000000</td>\n",
       "      <td>4133.000000</td>\n",
       "      <td>4238.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4185.000000</td>\n",
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       "      <td>4238.000000</td>\n",
       "      <td>4238.000000</td>\n",
       "      <td>4188.000000</td>\n",
       "      <td>4238.000000</td>\n",
       "      <td>4238.000000</td>\n",
       "      <td>4219.000000</td>\n",
       "      <td>4237.000000</td>\n",
       "      <td>3850.000000</td>\n",
       "      <td>4238.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.429212</td>\n",
       "      <td>49.584946</td>\n",
       "      <td>1.978950</td>\n",
       "      <td>0.494101</td>\n",
       "      <td>9.003089</td>\n",
       "      <td>0.029630</td>\n",
       "      <td>0.005899</td>\n",
       "      <td>0.310524</td>\n",
       "      <td>0.025720</td>\n",
       "      <td>236.721585</td>\n",
       "      <td>132.352407</td>\n",
       "      <td>82.893464</td>\n",
       "      <td>25.802008</td>\n",
       "      <td>75.878924</td>\n",
       "      <td>81.966753</td>\n",
       "      <td>0.151958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.495022</td>\n",
       "      <td>8.572160</td>\n",
       "      <td>1.019791</td>\n",
       "      <td>0.500024</td>\n",
       "      <td>11.920094</td>\n",
       "      <td>0.169584</td>\n",
       "      <td>0.076587</td>\n",
       "      <td>0.462763</td>\n",
       "      <td>0.158316</td>\n",
       "      <td>44.590334</td>\n",
       "      <td>22.038097</td>\n",
       "      <td>11.910850</td>\n",
       "      <td>4.080111</td>\n",
       "      <td>12.026596</td>\n",
       "      <td>23.959998</td>\n",
       "      <td>0.359023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>83.500000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>15.540000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>23.070000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>25.400000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>89.875000</td>\n",
       "      <td>28.040000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>696.000000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>142.500000</td>\n",
       "      <td>56.800000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              male          age    education  currentSmoker   cigsPerDay  \\\n",
       "count  4238.000000  4238.000000  4133.000000    4238.000000  4209.000000   \n",
       "mean      0.429212    49.584946     1.978950       0.494101     9.003089   \n",
       "std       0.495022     8.572160     1.019791       0.500024    11.920094   \n",
       "min       0.000000    32.000000     1.000000       0.000000     0.000000   \n",
       "25%       0.000000    42.000000     1.000000       0.000000     0.000000   \n",
       "50%       0.000000    49.000000     2.000000       0.000000     0.000000   \n",
       "75%       1.000000    56.000000     3.000000       1.000000    20.000000   \n",
       "max       1.000000    70.000000     4.000000       1.000000    70.000000   \n",
       "\n",
       "            BPMeds  prevalentStroke  prevalentHyp     diabetes      totChol  \\\n",
       "count  4185.000000      4238.000000   4238.000000  4238.000000  4188.000000   \n",
       "mean      0.029630         0.005899      0.310524     0.025720   236.721585   \n",
       "std       0.169584         0.076587      0.462763     0.158316    44.590334   \n",
       "min       0.000000         0.000000      0.000000     0.000000   107.000000   \n",
       "25%       0.000000         0.000000      0.000000     0.000000   206.000000   \n",
       "50%       0.000000         0.000000      0.000000     0.000000   234.000000   \n",
       "75%       0.000000         0.000000      1.000000     0.000000   263.000000   \n",
       "max       1.000000         1.000000      1.000000     1.000000   696.000000   \n",
       "\n",
       "             sysBP        diaBP          BMI    heartRate      glucose  \\\n",
       "count  4238.000000  4238.000000  4219.000000  4237.000000  3850.000000   \n",
       "mean    132.352407    82.893464    25.802008    75.878924    81.966753   \n",
       "std      22.038097    11.910850     4.080111    12.026596    23.959998   \n",
       "min      83.500000    48.000000    15.540000    44.000000    40.000000   \n",
       "25%     117.000000    75.000000    23.070000    68.000000    71.000000   \n",
       "50%     128.000000    82.000000    25.400000    75.000000    78.000000   \n",
       "75%     144.000000    89.875000    28.040000    83.000000    87.000000   \n",
       "max     295.000000   142.500000    56.800000   143.000000   394.000000   \n",
       "\n",
       "        TenYearCHD  \n",
       "count  4238.000000  \n",
       "mean      0.151958  \n",
       "std       0.359023  \n",
       "min       0.000000  \n",
       "25%       0.000000  \n",
       "50%       0.000000  \n",
       "75%       0.000000  \n",
       "max       1.000000  "
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4238, 16)\n",
      "Index(['male', 'age', 'education', 'currentSmoker', 'cigsPerDay', 'BPMeds',\n",
      "       'prevalentStroke', 'prevalentHyp', 'diabetes', 'totChol', 'sysBP',\n",
      "       'diaBP', 'BMI', 'heartRate', 'glucose', 'TenYearCHD'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df0.shape)\n",
    "print(df0.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Firstly, We remove null values and duplicate values by using $dropna()$ and $dropduplicates()$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
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       "      <td>103.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>80.0</td>\n",
       "      <td>19.71</td>\n",
       "      <td>65.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>3656 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      male  age  education  currentSmoker  cigsPerDay  BPMeds  \\\n",
       "0        1   39        4.0              0         0.0     0.0   \n",
       "1        0   46        2.0              0         0.0     0.0   \n",
       "2        1   48        1.0              1        20.0     0.0   \n",
       "3        0   61        3.0              1        30.0     0.0   \n",
       "4        0   46        3.0              1        23.0     0.0   \n",
       "...    ...  ...        ...            ...         ...     ...   \n",
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       "3652     1   68        1.0              0         0.0     0.0   \n",
       "3653     1   50        1.0              1         1.0     0.0   \n",
       "3654     1   51        3.0              1        43.0     0.0   \n",
       "3655     0   52        2.0              0         0.0     0.0   \n",
       "\n",
       "      prevalentStroke  prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  \\\n",
       "0                   0             0         0    195.0  106.0   70.0  26.97   \n",
       "1                   0             0         0    250.0  121.0   81.0  28.73   \n",
       "2                   0             0         0    245.0  127.5   80.0  25.34   \n",
       "3                   0             1         0    225.0  150.0   95.0  28.58   \n",
       "4                   0             0         0    285.0  130.0   84.0  23.10   \n",
       "...               ...           ...       ...      ...    ...    ...    ...   \n",
       "3651                0             1         0    187.0  141.0   81.0  24.96   \n",
       "3652                0             1         0    176.0  168.0   97.0  23.14   \n",
       "3653                0             1         0    313.0  179.0   92.0  25.97   \n",
       "3654                0             0         0    207.0  126.5   80.0  19.71   \n",
       "3655                0             0         0    269.0  133.5   83.0  21.47   \n",
       "\n",
       "      heartRate  glucose  TenYearCHD  \n",
       "0          80.0     77.0           0  \n",
       "1          95.0     76.0           0  \n",
       "2          75.0     70.0           0  \n",
       "3          65.0    103.0           1  \n",
       "4          85.0     85.0           0  \n",
       "...         ...      ...         ...  \n",
       "3651       80.0     81.0           0  \n",
       "3652       60.0     79.0           1  \n",
       "3653       66.0     86.0           1  \n",
       "3654       65.0     68.0           0  \n",
       "3655       80.0    107.0           0  \n",
       "\n",
       "[3656 rows x 16 columns]"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=df0.dropna()\n",
    "df2=df1.drop_duplicates()\n",
    "#df.isnull().sum()\n",
    "df3=df2.reset_index(drop=True)\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We now convert attributes like education,cigsPerDay and BPMeds to their relevent data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
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       "      <td>65.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>23.10</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>24.96</td>\n",
       "      <td>80.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3652</th>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>23.14</td>\n",
       "      <td>60.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3653</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>25.97</td>\n",
       "      <td>66.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3654</th>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>126.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19.71</td>\n",
       "      <td>65.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3655</th>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>133.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>21.47</td>\n",
       "      <td>80.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3656 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      male  age  education  currentSmoker  cigsPerDay  BPMeds  \\\n",
       "0        1   39          4              0           0       0   \n",
       "1        0   46          2              0           0       0   \n",
       "2        1   48          1              1          20       0   \n",
       "3        0   61          3              1          30       0   \n",
       "4        0   46          3              1          23       0   \n",
       "...    ...  ...        ...            ...         ...     ...   \n",
       "3651     1   58          3              0           0       0   \n",
       "3652     1   68          1              0           0       0   \n",
       "3653     1   50          1              1           1       0   \n",
       "3654     1   51          3              1          43       0   \n",
       "3655     0   52          2              0           0       0   \n",
       "\n",
       "      prevalentStroke  prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  \\\n",
       "0                   0             0         0    195.0  106.0   70.0  26.97   \n",
       "1                   0             0         0    250.0  121.0   81.0  28.73   \n",
       "2                   0             0         0    245.0  127.5   80.0  25.34   \n",
       "3                   0             1         0    225.0  150.0   95.0  28.58   \n",
       "4                   0             0         0    285.0  130.0   84.0  23.10   \n",
       "...               ...           ...       ...      ...    ...    ...    ...   \n",
       "3651                0             1         0    187.0  141.0   81.0  24.96   \n",
       "3652                0             1         0    176.0  168.0   97.0  23.14   \n",
       "3653                0             1         0    313.0  179.0   92.0  25.97   \n",
       "3654                0             0         0    207.0  126.5   80.0  19.71   \n",
       "3655                0             0         0    269.0  133.5   83.0  21.47   \n",
       "\n",
       "      heartRate  glucose  TenYearCHD  \n",
       "0          80.0     77.0           0  \n",
       "1          95.0     76.0           0  \n",
       "2          75.0     70.0           0  \n",
       "3          65.0    103.0           1  \n",
       "4          85.0     85.0           0  \n",
       "...         ...      ...         ...  \n",
       "3651       80.0     81.0           0  \n",
       "3652       60.0     79.0           1  \n",
       "3653       66.0     86.0           1  \n",
       "3654       65.0     68.0           0  \n",
       "3655       80.0    107.0           0  \n",
       "\n",
       "[3656 rows x 16 columns]"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['education']=df3['education'].astype(int)\n",
    "df3['cigsPerDay']=df3['cigsPerDay'].astype(int)\n",
    "df3['BPMeds']=df3['BPMeds'].astype(int)\n",
    "df3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', 15)\n",
    "patients=df3.index\n",
    "health_parameters=df3.columns\n",
    "df3=df3.rename_axis(\"Patients\", axis='rows').rename_axis(\"Health Parameters\", axis='columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sampling won't be advisable in this heart diease dataset as it is a public health dataset and the more the data,the better the prediction and lower the risk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.to_csv(\"Dataset_preprocessed.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Subset Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here,we are using Univariate Filter method by removing the features with the least variance,i.e. which are not important for the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Health Parameters\n",
      "prevalentStroke    0.005713\n",
      "diabetes           0.026353\n",
      "BPMeds             0.029447\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# pd.set_option('display.float_format', '{:.6f}'.format)\n",
    "# Assuming 'column_name' is the column from which you want to get the smallest values\n",
    "variance=df3.var()\n",
    "print(variance.nsmallest(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df3.prevalentStroke,df3.TenYearCHD)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "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>Health Parameters</th>\n",
       "      <th>male</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>currentSmoker</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>BPMeds</th>\n",
       "      <th>prevalentHyp</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>totChol</th>\n",
       "      <th>sysBP</th>\n",
       "      <th>diaBP</th>\n",
       "      <th>BMI</th>\n",
       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "      <th>TenYearCHD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patients</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>26.97</td>\n",
       "      <td>80.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>28.73</td>\n",
       "      <td>95.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>127.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>25.34</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>28.58</td>\n",
       "      <td>65.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>23.10</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>24.96</td>\n",
       "      <td>80.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3652</th>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>23.14</td>\n",
       "      <td>60.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3653</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>25.97</td>\n",
       "      <td>66.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3654</th>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>126.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19.71</td>\n",
       "      <td>65.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3655</th>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>133.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>21.47</td>\n",
       "      <td>80.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3656 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Health Parameters  male  age  education  currentSmoker  cigsPerDay  BPMeds  \\\n",
       "Patients                                                                     \n",
       "0                     1   39          4              0           0       0   \n",
       "1                     0   46          2              0           0       0   \n",
       "2                     1   48          1              1          20       0   \n",
       "3                     0   61          3              1          30       0   \n",
       "4                     0   46          3              1          23       0   \n",
       "...                 ...  ...        ...            ...         ...     ...   \n",
       "3651                  1   58          3              0           0       0   \n",
       "3652                  1   68          1              0           0       0   \n",
       "3653                  1   50          1              1           1       0   \n",
       "3654                  1   51          3              1          43       0   \n",
       "3655                  0   52          2              0           0       0   \n",
       "\n",
       "Health Parameters  prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  \\\n",
       "Patients                                                                  \n",
       "0                             0         0    195.0  106.0   70.0  26.97   \n",
       "1                             0         0    250.0  121.0   81.0  28.73   \n",
       "2                             0         0    245.0  127.5   80.0  25.34   \n",
       "3                             1         0    225.0  150.0   95.0  28.58   \n",
       "4                             0         0    285.0  130.0   84.0  23.10   \n",
       "...                         ...       ...      ...    ...    ...    ...   \n",
       "3651                          1         0    187.0  141.0   81.0  24.96   \n",
       "3652                          1         0    176.0  168.0   97.0  23.14   \n",
       "3653                          1         0    313.0  179.0   92.0  25.97   \n",
       "3654                          0         0    207.0  126.5   80.0  19.71   \n",
       "3655                          0         0    269.0  133.5   83.0  21.47   \n",
       "\n",
       "Health Parameters  heartRate  glucose  TenYearCHD  \n",
       "Patients                                           \n",
       "0                       80.0     77.0           0  \n",
       "1                       95.0     76.0           0  \n",
       "2                       75.0     70.0           0  \n",
       "3                       65.0    103.0           1  \n",
       "4                       85.0     85.0           0  \n",
       "...                      ...      ...         ...  \n",
       "3651                    80.0     81.0           0  \n",
       "3652                    60.0     79.0           1  \n",
       "3653                    66.0     86.0           1  \n",
       "3654                    65.0     68.0           0  \n",
       "3655                    80.0    107.0           0  \n",
       "\n",
       "[3656 rows x 15 columns]"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "health_parameters=list(health_parameters)\n",
    "health_parameters.remove('prevalentStroke')\n",
    "df3 = df3.drop(['prevalentStroke'], axis=1)#ALso dropped 'prevalentStroke from health_parameters,i.e. list of columns\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now we use $Correlation$ method to remove attributes with high correlation between them,i.e. there is a highj chance that change in one of the attribute may cause a change in the other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Health Parameters</th>\n",
       "      <th>male</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>currentSmoker</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>BPMeds</th>\n",
       "      <th>prevalentHyp</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>totChol</th>\n",
       "      <th>sysBP</th>\n",
       "      <th>diaBP</th>\n",
       "      <th>BMI</th>\n",
       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "      <th>TenYearCHD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health Parameters</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024387</td>\n",
       "      <td>0.017677</td>\n",
       "      <td>0.206778</td>\n",
       "      <td>0.331243</td>\n",
       "      <td>0.052128</td>\n",
       "      <td>0.000806</td>\n",
       "      <td>0.013833</td>\n",
       "      <td>0.070229</td>\n",
       "      <td>0.045484</td>\n",
       "      <td>0.051575</td>\n",
       "      <td>0.072867</td>\n",
       "      <td>0.114923</td>\n",
       "      <td>0.003048</td>\n",
       "      <td>0.091745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.024387</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.158961</td>\n",
       "      <td>0.210862</td>\n",
       "      <td>0.189099</td>\n",
       "      <td>0.134670</td>\n",
       "      <td>0.306693</td>\n",
       "      <td>0.109027</td>\n",
       "      <td>0.267764</td>\n",
       "      <td>0.388551</td>\n",
       "      <td>0.208880</td>\n",
       "      <td>0.137172</td>\n",
       "      <td>0.002685</td>\n",
       "      <td>0.118245</td>\n",
       "      <td>0.233810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>0.017677</td>\n",
       "      <td>0.158961</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025253</td>\n",
       "      <td>0.013527</td>\n",
       "      <td>0.013647</td>\n",
       "      <td>0.079100</td>\n",
       "      <td>0.039547</td>\n",
       "      <td>0.012956</td>\n",
       "      <td>0.124511</td>\n",
       "      <td>0.058502</td>\n",
       "      <td>0.137280</td>\n",
       "      <td>0.064254</td>\n",
       "      <td>0.031874</td>\n",
       "      <td>0.063068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>currentSmoker</th>\n",
       "      <td>0.206778</td>\n",
       "      <td>0.210862</td>\n",
       "      <td>0.025253</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.773819</td>\n",
       "      <td>0.051936</td>\n",
       "      <td>0.107561</td>\n",
       "      <td>0.041859</td>\n",
       "      <td>0.051119</td>\n",
       "      <td>0.134371</td>\n",
       "      <td>0.115748</td>\n",
       "      <td>0.159574</td>\n",
       "      <td>0.050452</td>\n",
       "      <td>0.053346</td>\n",
       "      <td>0.019176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cigsPerDay</th>\n",
       "      <td>0.331243</td>\n",
       "      <td>0.189099</td>\n",
       "      <td>0.013527</td>\n",
       "      <td>0.773819</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.046479</td>\n",
       "      <td>0.069890</td>\n",
       "      <td>0.036934</td>\n",
       "      <td>0.030222</td>\n",
       "      <td>0.094764</td>\n",
       "      <td>0.056650</td>\n",
       "      <td>0.086888</td>\n",
       "      <td>0.063549</td>\n",
       "      <td>0.053803</td>\n",
       "      <td>0.052159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BPMeds</th>\n",
       "      <td>0.052128</td>\n",
       "      <td>0.134670</td>\n",
       "      <td>0.013647</td>\n",
       "      <td>0.051936</td>\n",
       "      <td>0.046479</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.263047</td>\n",
       "      <td>0.049051</td>\n",
       "      <td>0.094011</td>\n",
       "      <td>0.271291</td>\n",
       "      <td>0.199750</td>\n",
       "      <td>0.105603</td>\n",
       "      <td>0.012894</td>\n",
       "      <td>0.054210</td>\n",
       "      <td>0.089116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prevalentHyp</th>\n",
       "      <td>0.000806</td>\n",
       "      <td>0.306693</td>\n",
       "      <td>0.079100</td>\n",
       "      <td>0.107561</td>\n",
       "      <td>0.069890</td>\n",
       "      <td>0.263047</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.080623</td>\n",
       "      <td>0.167074</td>\n",
       "      <td>0.697790</td>\n",
       "      <td>0.617634</td>\n",
       "      <td>0.302917</td>\n",
       "      <td>0.147333</td>\n",
       "      <td>0.087129</td>\n",
       "      <td>0.181556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetes</th>\n",
       "      <td>0.013833</td>\n",
       "      <td>0.109027</td>\n",
       "      <td>0.039547</td>\n",
       "      <td>0.041859</td>\n",
       "      <td>0.036934</td>\n",
       "      <td>0.049051</td>\n",
       "      <td>0.080623</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.048371</td>\n",
       "      <td>0.102574</td>\n",
       "      <td>0.050767</td>\n",
       "      <td>0.088970</td>\n",
       "      <td>0.060996</td>\n",
       "      <td>0.614817</td>\n",
       "      <td>0.093397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totChol</th>\n",
       "      <td>0.070229</td>\n",
       "      <td>0.267764</td>\n",
       "      <td>0.012956</td>\n",
       "      <td>0.051119</td>\n",
       "      <td>0.030222</td>\n",
       "      <td>0.094011</td>\n",
       "      <td>0.167074</td>\n",
       "      <td>0.048371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.220130</td>\n",
       "      <td>0.174986</td>\n",
       "      <td>0.120799</td>\n",
       "      <td>0.093057</td>\n",
       "      <td>0.049749</td>\n",
       "      <td>0.091127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sysBP</th>\n",
       "      <td>0.045484</td>\n",
       "      <td>0.388551</td>\n",
       "      <td>0.124511</td>\n",
       "      <td>0.134371</td>\n",
       "      <td>0.094764</td>\n",
       "      <td>0.271291</td>\n",
       "      <td>0.697790</td>\n",
       "      <td>0.102574</td>\n",
       "      <td>0.220130</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.786727</td>\n",
       "      <td>0.331004</td>\n",
       "      <td>0.184901</td>\n",
       "      <td>0.134702</td>\n",
       "      <td>0.222885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diaBP</th>\n",
       "      <td>0.051575</td>\n",
       "      <td>0.208880</td>\n",
       "      <td>0.058502</td>\n",
       "      <td>0.115748</td>\n",
       "      <td>0.056650</td>\n",
       "      <td>0.199750</td>\n",
       "      <td>0.617634</td>\n",
       "      <td>0.050767</td>\n",
       "      <td>0.174986</td>\n",
       "      <td>0.786727</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.385611</td>\n",
       "      <td>0.179008</td>\n",
       "      <td>0.063704</td>\n",
       "      <td>0.150342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.072867</td>\n",
       "      <td>0.137172</td>\n",
       "      <td>0.137280</td>\n",
       "      <td>0.159574</td>\n",
       "      <td>0.086888</td>\n",
       "      <td>0.105603</td>\n",
       "      <td>0.302917</td>\n",
       "      <td>0.088970</td>\n",
       "      <td>0.120799</td>\n",
       "      <td>0.331004</td>\n",
       "      <td>0.385611</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.074401</td>\n",
       "      <td>0.083671</td>\n",
       "      <td>0.081931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heartRate</th>\n",
       "      <td>0.114923</td>\n",
       "      <td>0.002685</td>\n",
       "      <td>0.064254</td>\n",
       "      <td>0.050452</td>\n",
       "      <td>0.063549</td>\n",
       "      <td>0.012894</td>\n",
       "      <td>0.147333</td>\n",
       "      <td>0.060996</td>\n",
       "      <td>0.093057</td>\n",
       "      <td>0.184901</td>\n",
       "      <td>0.179008</td>\n",
       "      <td>0.074401</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.097026</td>\n",
       "      <td>0.020523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glucose</th>\n",
       "      <td>0.003048</td>\n",
       "      <td>0.118245</td>\n",
       "      <td>0.031874</td>\n",
       "      <td>0.053346</td>\n",
       "      <td>0.053803</td>\n",
       "      <td>0.054210</td>\n",
       "      <td>0.087129</td>\n",
       "      <td>0.614817</td>\n",
       "      <td>0.049749</td>\n",
       "      <td>0.134702</td>\n",
       "      <td>0.063704</td>\n",
       "      <td>0.083671</td>\n",
       "      <td>0.097026</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.121942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TenYearCHD</th>\n",
       "      <td>0.091745</td>\n",
       "      <td>0.233810</td>\n",
       "      <td>0.063068</td>\n",
       "      <td>0.019176</td>\n",
       "      <td>0.052159</td>\n",
       "      <td>0.089116</td>\n",
       "      <td>0.181556</td>\n",
       "      <td>0.093397</td>\n",
       "      <td>0.091127</td>\n",
       "      <td>0.222885</td>\n",
       "      <td>0.150342</td>\n",
       "      <td>0.081931</td>\n",
       "      <td>0.020523</td>\n",
       "      <td>0.121942</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Health Parameters      male       age  education  currentSmoker  cigsPerDay  \\\n",
       "Health Parameters                                                             \n",
       "male               1.000000  0.024387   0.017677       0.206778    0.331243   \n",
       "age                0.024387  1.000000   0.158961       0.210862    0.189099   \n",
       "education          0.017677  0.158961   1.000000       0.025253    0.013527   \n",
       "currentSmoker      0.206778  0.210862   0.025253       1.000000    0.773819   \n",
       "cigsPerDay         0.331243  0.189099   0.013527       0.773819    1.000000   \n",
       "BPMeds             0.052128  0.134670   0.013647       0.051936    0.046479   \n",
       "prevalentHyp       0.000806  0.306693   0.079100       0.107561    0.069890   \n",
       "diabetes           0.013833  0.109027   0.039547       0.041859    0.036934   \n",
       "totChol            0.070229  0.267764   0.012956       0.051119    0.030222   \n",
       "sysBP              0.045484  0.388551   0.124511       0.134371    0.094764   \n",
       "diaBP              0.051575  0.208880   0.058502       0.115748    0.056650   \n",
       "BMI                0.072867  0.137172   0.137280       0.159574    0.086888   \n",
       "heartRate          0.114923  0.002685   0.064254       0.050452    0.063549   \n",
       "glucose            0.003048  0.118245   0.031874       0.053346    0.053803   \n",
       "TenYearCHD         0.091745  0.233810   0.063068       0.019176    0.052159   \n",
       "\n",
       "Health Parameters    BPMeds  prevalentHyp  diabetes   totChol     sysBP  \\\n",
       "Health Parameters                                                         \n",
       "male               0.052128      0.000806  0.013833  0.070229  0.045484   \n",
       "age                0.134670      0.306693  0.109027  0.267764  0.388551   \n",
       "education          0.013647      0.079100  0.039547  0.012956  0.124511   \n",
       "currentSmoker      0.051936      0.107561  0.041859  0.051119  0.134371   \n",
       "cigsPerDay         0.046479      0.069890  0.036934  0.030222  0.094764   \n",
       "BPMeds             1.000000      0.263047  0.049051  0.094011  0.271291   \n",
       "prevalentHyp       0.263047      1.000000  0.080623  0.167074  0.697790   \n",
       "diabetes           0.049051      0.080623  1.000000  0.048371  0.102574   \n",
       "totChol            0.094011      0.167074  0.048371  1.000000  0.220130   \n",
       "sysBP              0.271291      0.697790  0.102574  0.220130  1.000000   \n",
       "diaBP              0.199750      0.617634  0.050767  0.174986  0.786727   \n",
       "BMI                0.105603      0.302917  0.088970  0.120799  0.331004   \n",
       "heartRate          0.012894      0.147333  0.060996  0.093057  0.184901   \n",
       "glucose            0.054210      0.087129  0.614817  0.049749  0.134702   \n",
       "TenYearCHD         0.089116      0.181556  0.093397  0.091127  0.222885   \n",
       "\n",
       "Health Parameters     diaBP       BMI  heartRate   glucose  TenYearCHD  \n",
       "Health Parameters                                                       \n",
       "male               0.051575  0.072867   0.114923  0.003048    0.091745  \n",
       "age                0.208880  0.137172   0.002685  0.118245    0.233810  \n",
       "education          0.058502  0.137280   0.064254  0.031874    0.063068  \n",
       "currentSmoker      0.115748  0.159574   0.050452  0.053346    0.019176  \n",
       "cigsPerDay         0.056650  0.086888   0.063549  0.053803    0.052159  \n",
       "BPMeds             0.199750  0.105603   0.012894  0.054210    0.089116  \n",
       "prevalentHyp       0.617634  0.302917   0.147333  0.087129    0.181556  \n",
       "diabetes           0.050767  0.088970   0.060996  0.614817    0.093397  \n",
       "totChol            0.174986  0.120799   0.093057  0.049749    0.091127  \n",
       "sysBP              0.786727  0.331004   0.184901  0.134702    0.222885  \n",
       "diaBP              1.000000  0.385611   0.179008  0.063704    0.150342  \n",
       "BMI                0.385611  1.000000   0.074401  0.083671    0.081931  \n",
       "heartRate          0.179008  0.074401   1.000000  0.097026    0.020523  \n",
       "glucose            0.063704  0.083671   0.097026  1.000000    0.121942  \n",
       "TenYearCHD         0.150342  0.081931   0.020523  0.121942    1.000000  "
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = df3.corr().abs()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_dims = (14, 14)\n",
    "fig, ax = plt.subplots(figsize=fig_dims)\n",
    "sns.heatmap(corr_matrix, ax=ax, annot=True, fmt=\".2f\", cmap='coolwarm')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This shows the correlation coefficient between each unique pair of features in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('diaBP', 'sysBP'): 0.7867271218652642,\n",
       " ('cigsPerDay', 'currentSmoker'): 0.7738189372482994,\n",
       " ('sysBP', 'prevalentHyp'): 0.6977899528655885,\n",
       " ('diaBP', 'prevalentHyp'): 0.617634221677018,\n",
       " ('glucose', 'diabetes'): 0.6148174441322215,\n",
       " ('sysBP', 'age'): 0.3885505988687022,\n",
       " ('BMI', 'diaBP'): 0.3856106779899647,\n",
       " ('cigsPerDay', 'male'): 0.3312428456443405,\n",
       " ('BMI', 'sysBP'): 0.33100358987482476,\n",
       " ('prevalentHyp', 'age'): 0.3066929974598801,\n",
       " ('BMI', 'prevalentHyp'): 0.3029168279134485,\n",
       " ('sysBP', 'BPMeds'): 0.27129113074113065,\n",
       " ('totChol', 'age'): 0.26776368401057793,\n",
       " ('prevalentHyp', 'BPMeds'): 0.26304685599848165,\n",
       " ('TenYearCHD', 'age'): 0.23381045046891855,\n",
       " ('TenYearCHD', 'sysBP'): 0.22288534193220752,\n",
       " ('sysBP', 'totChol'): 0.22012958127064888,\n",
       " ('currentSmoker', 'age'): 0.2108623680670528,\n",
       " ('diaBP', 'age'): 0.20888036151774653,\n",
       " ('currentSmoker', 'male'): 0.2067779295380288,\n",
       " ('diaBP', 'BPMeds'): 0.1997503069579374,\n",
       " ('cigsPerDay', 'age'): 0.18909948960681483,\n",
       " ('heartRate', 'sysBP'): 0.18490117053406235,\n",
       " ('TenYearCHD', 'prevalentHyp'): 0.1815564019418955,\n",
       " ('heartRate', 'diaBP'): 0.1790082157336661,\n",
       " ('diaBP', 'totChol'): 0.17498559214937795,\n",
       " ('totChol', 'prevalentHyp'): 0.16707443195552618,\n",
       " ('BMI', 'currentSmoker'): 0.15957357771373532,\n",
       " ('education', 'age'): 0.15896134087672995,\n",
       " ('TenYearCHD', 'diaBP'): 0.1503417291777987,\n",
       " ('heartRate', 'prevalentHyp'): 0.14733267262899255,\n",
       " ('BMI', 'education'): 0.1372800602987169,\n",
       " ('BMI', 'age'): 0.13717210444376637,\n",
       " ('glucose', 'sysBP'): 0.13470173199898103,\n",
       " ('BPMeds', 'age'): 0.13467017040199544,\n",
       " ('sysBP', 'currentSmoker'): 0.13437097937053885,\n",
       " ('sysBP', 'education'): 0.12451062052801438,\n",
       " ('TenYearCHD', 'glucose'): 0.12194204256568018,\n",
       " ('BMI', 'totChol'): 0.12079900635510689,\n",
       " ('glucose', 'age'): 0.1182447325493982,\n",
       " ('diaBP', 'currentSmoker'): 0.11574796248122303,\n",
       " ('heartRate', 'male'): 0.11492340018837795,\n",
       " ('diabetes', 'age'): 0.10902650994031485,\n",
       " ('prevalentHyp', 'currentSmoker'): 0.10756095042424986,\n",
       " ('BMI', 'BPMeds'): 0.10560316444597584,\n",
       " ('sysBP', 'diabetes'): 0.10257418558198873,\n",
       " ('glucose', 'heartRate'): 0.0970258536630028,\n",
       " ('sysBP', 'cigsPerDay'): 0.09476370827324672,\n",
       " ('totChol', 'BPMeds'): 0.09401050082670137,\n",
       " ('TenYearCHD', 'diabetes'): 0.09339741726940773,\n",
       " ('heartRate', 'totChol'): 0.09305742536847582,\n",
       " ('TenYearCHD', 'male'): 0.09174488519402932,\n",
       " ('TenYearCHD', 'totChol'): 0.09112675396256961,\n",
       " ('TenYearCHD', 'BPMeds'): 0.0891157035902126,\n",
       " ('BMI', 'diabetes'): 0.08897003793348875,\n",
       " ('glucose', 'prevalentHyp'): 0.08712918816476456,\n",
       " ('BMI', 'cigsPerDay'): 0.08688806189837403,\n",
       " ('glucose', 'BMI'): 0.0836711028585024,\n",
       " ('TenYearCHD', 'BMI'): 0.08193118314905609,\n",
       " ('diabetes', 'prevalentHyp'): 0.08062311043071686,\n",
       " ('prevalentHyp', 'education'): 0.07909965774868888,\n",
       " ('heartRate', 'BMI'): 0.07440123546618904,\n",
       " ('BMI', 'male'): 0.0728673292416173,\n",
       " ('totChol', 'male'): 0.07022852911030325,\n",
       " ('prevalentHyp', 'cigsPerDay'): 0.06988957176763909,\n",
       " ('heartRate', 'education'): 0.06425396178591473,\n",
       " ('glucose', 'diaBP'): 0.06370364437445744,\n",
       " ('heartRate', 'cigsPerDay'): 0.06354908319198474,\n",
       " ('TenYearCHD', 'education'): 0.06306772728034205,\n",
       " ('heartRate', 'diabetes'): 0.060995532359999945,\n",
       " ('diaBP', 'education'): 0.05850150792660592,\n",
       " ('diaBP', 'cigsPerDay'): 0.056650119215150264,\n",
       " ('glucose', 'BPMeds'): 0.054210369977739606,\n",
       " ('glucose', 'cigsPerDay'): 0.05380272271775479,\n",
       " ('glucose', 'currentSmoker'): 0.053346007861696076,\n",
       " ('TenYearCHD', 'cigsPerDay'): 0.05215872747033031,\n",
       " ('BPMeds', 'male'): 0.052128120464253996,\n",
       " ('BPMeds', 'currentSmoker'): 0.051935824187553666,\n",
       " ('diaBP', 'male'): 0.05157518762069545,\n",
       " ('totChol', 'currentSmoker'): 0.0511193921699002,\n",
       " ('diaBP', 'diabetes'): 0.05076727461654903,\n",
       " ('heartRate', 'currentSmoker'): 0.050451822446580634,\n",
       " ('glucose', 'totChol'): 0.04974866619745286,\n",
       " ('diabetes', 'BPMeds'): 0.04905099817406069,\n",
       " ('totChol', 'diabetes'): 0.04837074532562219,\n",
       " ('BPMeds', 'cigsPerDay'): 0.04647919913317124,\n",
       " ('sysBP', 'male'): 0.04548441091921163,\n",
       " ('diabetes', 'currentSmoker'): 0.04185871232594628,\n",
       " ('diabetes', 'education'): 0.03954682613847677,\n",
       " ('diabetes', 'cigsPerDay'): 0.036934056609152714,\n",
       " ('glucose', 'education'): 0.03187418715833285,\n",
       " ('totChol', 'cigsPerDay'): 0.03022238192801455,\n",
       " ('currentSmoker', 'education'): 0.025252851787677954,\n",
       " ('age', 'male'): 0.024386991232773277,\n",
       " ('TenYearCHD', 'heartRate'): 0.020523423651053353,\n",
       " ('TenYearCHD', 'currentSmoker'): 0.019176196282060416,\n",
       " ('education', 'male'): 0.01767684303341394,\n",
       " ('diabetes', 'male'): 0.013833026672329227,\n",
       " ('BPMeds', 'education'): 0.013646791202537933,\n",
       " ('cigsPerDay', 'education'): 0.013527109342933918,\n",
       " ('totChol', 'education'): 0.0129556315931882,\n",
       " ('heartRate', 'BPMeds'): 0.012893623960414196,\n",
       " ('glucose', 'male'): 0.003048178551977972,\n",
       " ('heartRate', 'age'): 0.0026854264314094254,\n",
       " ('prevalentHyp', 'male'): 0.0008057437268776252}"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation={}#Dictionary with attributes and their correlation in descending order\n",
    "for i in range(0, len(health_parameters)):\n",
    "    for j in range(0, i):\n",
    "            correlation[(health_parameters[i], health_parameters[j])]=corr_matrix.loc[health_parameters[i], health_parameters[j]]\n",
    "sorted_correlation = dict(sorted(correlation.items(), key=lambda item: item[1],reverse=True))\n",
    "sorted_correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Though Systole and Diastole blood pressure are highly correlated, we can't remove any of them as both are important to determine the disease and hence,needed for the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Since the correlation of currentSmoker and cigsPerDay is 0.77(high), we can remove one of the features by $Correlation$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df3.currentSmoker,df3.cigsPerDay)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "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>Health Parameters</th>\n",
       "      <th>male</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>BPMeds</th>\n",
       "      <th>prevalentHyp</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>totChol</th>\n",
       "      <th>sysBP</th>\n",
       "      <th>diaBP</th>\n",
       "      <th>BMI</th>\n",
       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "      <th>TenYearCHD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patients</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>26.97</td>\n",
       "      <td>80.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>28.73</td>\n",
       "      <td>95.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>127.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>25.34</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>28.58</td>\n",
       "      <td>65.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>23.10</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>24.96</td>\n",
       "      <td>80.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3652</th>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>23.14</td>\n",
       "      <td>60.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3653</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>25.97</td>\n",
       "      <td>66.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3654</th>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>126.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19.71</td>\n",
       "      <td>65.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3655</th>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>133.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>21.47</td>\n",
       "      <td>80.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3656 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Health Parameters  male  age  education  cigsPerDay  BPMeds  prevalentHyp  \\\n",
       "Patients                                                                    \n",
       "0                     1   39          4           0       0             0   \n",
       "1                     0   46          2           0       0             0   \n",
       "2                     1   48          1          20       0             0   \n",
       "3                     0   61          3          30       0             1   \n",
       "4                     0   46          3          23       0             0   \n",
       "...                 ...  ...        ...         ...     ...           ...   \n",
       "3651                  1   58          3           0       0             1   \n",
       "3652                  1   68          1           0       0             1   \n",
       "3653                  1   50          1           1       0             1   \n",
       "3654                  1   51          3          43       0             0   \n",
       "3655                  0   52          2           0       0             0   \n",
       "\n",
       "Health Parameters  diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  \\\n",
       "Patients                                                                        \n",
       "0                         0    195.0  106.0   70.0  26.97       80.0     77.0   \n",
       "1                         0    250.0  121.0   81.0  28.73       95.0     76.0   \n",
       "2                         0    245.0  127.5   80.0  25.34       75.0     70.0   \n",
       "3                         0    225.0  150.0   95.0  28.58       65.0    103.0   \n",
       "4                         0    285.0  130.0   84.0  23.10       85.0     85.0   \n",
       "...                     ...      ...    ...    ...    ...        ...      ...   \n",
       "3651                      0    187.0  141.0   81.0  24.96       80.0     81.0   \n",
       "3652                      0    176.0  168.0   97.0  23.14       60.0     79.0   \n",
       "3653                      0    313.0  179.0   92.0  25.97       66.0     86.0   \n",
       "3654                      0    207.0  126.5   80.0  19.71       65.0     68.0   \n",
       "3655                      0    269.0  133.5   83.0  21.47       80.0    107.0   \n",
       "\n",
       "Health Parameters  TenYearCHD  \n",
       "Patients                       \n",
       "0                           0  \n",
       "1                           0  \n",
       "2                           0  \n",
       "3                           1  \n",
       "4                           0  \n",
       "...                       ...  \n",
       "3651                        0  \n",
       "3652                        1  \n",
       "3653                        1  \n",
       "3654                        0  \n",
       "3655                        0  \n",
       "\n",
       "[3656 rows x 14 columns]"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.drop(['currentSmoker'], axis=1, inplace=True)\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Since the correlation of prevalentHyp with sysBP is 0.69(high), we can remove one of the features by $Correlation$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df3.prevalentHyp,df3.TenYearCHD)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Since the correlation of $education$ attribute is less than the threshold for almost all attributes and doesn't have any meaningful impact on the target variable, we can drop it from the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "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>Health Parameters</th>\n",
       "      <th>male</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>BPMeds</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>totChol</th>\n",
       "      <th>sysBP</th>\n",
       "      <th>diaBP</th>\n",
       "      <th>BMI</th>\n",
       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "      <th>TenYearCHD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patients</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>26.97</td>\n",
       "      <td>80.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>28.73</td>\n",
       "      <td>95.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>127.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>25.34</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>28.58</td>\n",
       "      <td>65.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>23.10</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0</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>3651</th>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>24.96</td>\n",
       "      <td>80.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3652</th>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>23.14</td>\n",
       "      <td>60.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3653</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>25.97</td>\n",
       "      <td>66.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3654</th>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>126.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19.71</td>\n",
       "      <td>65.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3655</th>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>133.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>21.47</td>\n",
       "      <td>80.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3656 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Health Parameters  male  age  education  cigsPerDay  BPMeds  diabetes  \\\n",
       "Patients                                                                \n",
       "0                     1   39          4           0       0         0   \n",
       "1                     0   46          2           0       0         0   \n",
       "2                     1   48          1          20       0         0   \n",
       "3                     0   61          3          30       0         0   \n",
       "4                     0   46          3          23       0         0   \n",
       "...                 ...  ...        ...         ...     ...       ...   \n",
       "3651                  1   58          3           0       0         0   \n",
       "3652                  1   68          1           0       0         0   \n",
       "3653                  1   50          1           1       0         0   \n",
       "3654                  1   51          3          43       0         0   \n",
       "3655                  0   52          2           0       0         0   \n",
       "\n",
       "Health Parameters  totChol  sysBP  diaBP    BMI  heartRate  glucose  \\\n",
       "Patients                                                              \n",
       "0                    195.0  106.0   70.0  26.97       80.0     77.0   \n",
       "1                    250.0  121.0   81.0  28.73       95.0     76.0   \n",
       "2                    245.0  127.5   80.0  25.34       75.0     70.0   \n",
       "3                    225.0  150.0   95.0  28.58       65.0    103.0   \n",
       "4                    285.0  130.0   84.0  23.10       85.0     85.0   \n",
       "...                    ...    ...    ...    ...        ...      ...   \n",
       "3651                 187.0  141.0   81.0  24.96       80.0     81.0   \n",
       "3652                 176.0  168.0   97.0  23.14       60.0     79.0   \n",
       "3653                 313.0  179.0   92.0  25.97       66.0     86.0   \n",
       "3654                 207.0  126.5   80.0  19.71       65.0     68.0   \n",
       "3655                 269.0  133.5   83.0  21.47       80.0    107.0   \n",
       "\n",
       "Health Parameters  TenYearCHD  \n",
       "Patients                       \n",
       "0                           0  \n",
       "1                           0  \n",
       "2                           0  \n",
       "3                           1  \n",
       "4                           0  \n",
       "...                       ...  \n",
       "3651                        0  \n",
       "3652                        1  \n",
       "3653                        1  \n",
       "3654                        0  \n",
       "3655                        0  \n",
       "\n",
       "[3656 rows x 13 columns]"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.drop(['prevalentHyp'], axis=1, inplace=True)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df3.education,df3.TenYearCHD)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "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>Health Parameters</th>\n",
       "      <th>male</th>\n",
       "      <th>age</th>\n",
       "      <th>cigsPerDay</th>\n",
       "      <th>BPMeds</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>totChol</th>\n",
       "      <th>sysBP</th>\n",
       "      <th>diaBP</th>\n",
       "      <th>BMI</th>\n",
       "      <th>heartRate</th>\n",
       "      <th>glucose</th>\n",
       "      <th>TenYearCHD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patients</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>26.97</td>\n",
       "      <td>80.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>28.73</td>\n",
       "      <td>95.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>127.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>25.34</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>28.58</td>\n",
       "      <td>65.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>23.10</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>24.96</td>\n",
       "      <td>80.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3652</th>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>23.14</td>\n",
       "      <td>60.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3653</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>25.97</td>\n",
       "      <td>66.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3654</th>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>126.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19.71</td>\n",
       "      <td>65.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3655</th>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>133.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>21.47</td>\n",
       "      <td>80.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3656 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Health Parameters  male  age  cigsPerDay  BPMeds  diabetes  totChol  sysBP  \\\n",
       "Patients                                                                     \n",
       "0                     1   39           0       0         0    195.0  106.0   \n",
       "1                     0   46           0       0         0    250.0  121.0   \n",
       "2                     1   48          20       0         0    245.0  127.5   \n",
       "3                     0   61          30       0         0    225.0  150.0   \n",
       "4                     0   46          23       0         0    285.0  130.0   \n",
       "...                 ...  ...         ...     ...       ...      ...    ...   \n",
       "3651                  1   58           0       0         0    187.0  141.0   \n",
       "3652                  1   68           0       0         0    176.0  168.0   \n",
       "3653                  1   50           1       0         0    313.0  179.0   \n",
       "3654                  1   51          43       0         0    207.0  126.5   \n",
       "3655                  0   52           0       0         0    269.0  133.5   \n",
       "\n",
       "Health Parameters  diaBP    BMI  heartRate  glucose  TenYearCHD  \n",
       "Patients                                                         \n",
       "0                   70.0  26.97       80.0     77.0           0  \n",
       "1                   81.0  28.73       95.0     76.0           0  \n",
       "2                   80.0  25.34       75.0     70.0           0  \n",
       "3                   95.0  28.58       65.0    103.0           1  \n",
       "4                   84.0  23.10       85.0     85.0           0  \n",
       "...                  ...    ...        ...      ...         ...  \n",
       "3651                81.0  24.96       80.0     81.0           0  \n",
       "3652                97.0  23.14       60.0     79.0           1  \n",
       "3653                92.0  25.97       66.0     86.0           1  \n",
       "3654                80.0  19.71       65.0     68.0           0  \n",
       "3655                83.0  21.47       80.0    107.0           0  \n",
       "\n",
       "[3656 rows x 12 columns]"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.drop(['education'], axis=1, inplace=True)\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wrapper Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Backward Subset Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df3.drop('TenYearCHD', axis=1)\n",
    "target = df3['TenYearCHD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['male', 'age', 'cigsPerDay', 'sysBP', 'diaBP', 'heartRate', 'glucose'], dtype='object', name='Health Parameters')\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.feature_selection import SequentialFeatureSelector\n",
    "linear_regression = LinearRegression()\n",
    "\n",
    "backward_feature_selector = SequentialFeatureSelector(linear_regression, n_features_to_select=7, direction='backward')\n",
    "\n",
    "backward_feature_selector.fit(data, target)\n",
    "\n",
    "selected_features = data.columns[backward_feature_selector.get_support()]\n",
    "\n",
    "print(selected_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3.to_csv('Dataset_cleaned.csv')"
   ]
  }
 ],
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