bp_bmi_preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>18393</td>\n",
       "      <td>2</td>\n",
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       "      <td>62.0</td>\n",
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       "      <td>156</td>\n",
       "      <td>85.0</td>\n",
       "      <td>140</td>\n",
       "      <td>90</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>17623</td>\n",
       "      <td>2</td>\n",
       "      <td>169</td>\n",
       "      <td>82.0</td>\n",
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       "      <td>100</td>\n",
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       "      <th>4</th>\n",
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       "      <td>17474</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>56.0</td>\n",
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       "      <td>60</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id    age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  \\\n",
       "0   0  18393       2     168    62.0    110     80            1     1      0   \n",
       "1   1  20228       1     156    85.0    140     90            3     1      0   \n",
       "2   2  18857       1     165    64.0    130     70            3     1      0   \n",
       "3   3  17623       2     169    82.0    150    100            1     1      0   \n",
       "4   4  17474       1     156    56.0    100     60            1     1      0   \n",
       "\n",
       "   alco  active  cardio  \n",
       "0     0       1       0  \n",
       "1     0       1       1  \n",
       "2     0       0       1  \n",
       "3     0       1       1  \n",
       "4     0       0       0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data into a Pandas DataFrame\n",
    "df = pd.read_csv('cardio_train.csv', sep=';') \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(69007, 13)\n"
     ]
    }
   ],
   "source": [
    "# code in this cell taken from eda.ipynb\n",
    "# remove outliers in ap_hi and ap_lo\n",
    "cleaned_df = df[df['ap_hi'] < 250]  \n",
    "cleaned_df = cleaned_df[cleaned_df['ap_lo'] < 250] \n",
    "\n",
    "print(cleaned_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
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       "      <td>17623</td>\n",
       "      <td>2</td>\n",
       "      <td>169</td>\n",
       "      <td>82.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id    age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  \\\n",
       "0   0  18393       2     168    62.0    110     80            1     1      0   \n",
       "1   1  20228       1     156    85.0    140     90            3     1      0   \n",
       "2   2  18857       1     165    64.0    130     70            3     1      0   \n",
       "3   3  17623       2     169    82.0    150    100            1     1      0   \n",
       "4   4  17474       1     156    56.0    100     60            1     1      0   \n",
       "\n",
       "   alco  active  cardio  \n",
       "0     0       1       0  \n",
       "1     0       1       1  \n",
       "2     0       0       1  \n",
       "3     0       1       1  \n",
       "4     0       0       0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "      <th>cholesterol_1</th>\n",
       "      <th>cholesterol_2</th>\n",
       "      <th>cholesterol_3</th>\n",
       "      <th>gluc_1</th>\n",
       "      <th>gluc_2</th>\n",
       "      <th>gluc_3</th>\n",
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       "      <td>1</td>\n",
       "      <td>165</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>17623</td>\n",
       "      <td>2</td>\n",
       "      <td>169</td>\n",
       "      <td>82.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>17474</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>56.0</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id    age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  \\\n",
       "0   0  18393       2     168    62.0    110     80            1     1      0   \n",
       "1   1  20228       1     156    85.0    140     90            3     1      0   \n",
       "2   2  18857       1     165    64.0    130     70            3     1      0   \n",
       "3   3  17623       2     169    82.0    150    100            1     1      0   \n",
       "4   4  17474       1     156    56.0    100     60            1     1      0   \n",
       "\n",
       "   alco  active  cardio  cholesterol_1  cholesterol_2  cholesterol_3  gluc_1  \\\n",
       "0     0       1       0              1              0              0       1   \n",
       "1     0       1       1              0              0              1       1   \n",
       "2     0       0       1              0              0              1       1   \n",
       "3     0       1       1              1              0              0       1   \n",
       "4     0       0       0              1              0              0       1   \n",
       "\n",
       "   gluc_2  gluc_3  \n",
       "0       0       0  \n",
       "1       0       0  \n",
       "2       0       0  \n",
       "3       0       0  \n",
       "4       0       0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# code in this cell taken from eda.ipynb\n",
    "cont_cols = ['age', 'height', 'weight', 'ap_hi', 'ap_lo']\n",
    "ord_cols = ['cholesterol', 'gluc']\n",
    "\n",
    "for col in ord_cols:\n",
    "    dummies = pd.get_dummies(cleaned_df[col])\n",
    "    dummies.columns = ['{0}_{1}'.format(col, ind) for ind in dummies.columns]\n",
    "    cleaned_df = pd.concat([cleaned_df, dummies], axis=1)\n",
    "    \n",
    "cleaned_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "      <th>cholesterol_1</th>\n",
       "      <th>cholesterol_2</th>\n",
       "      <th>cholesterol_3</th>\n",
       "      <th>gluc_1</th>\n",
       "      <th>gluc_2</th>\n",
       "      <th>gluc_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "      <td>64.0</td>\n",
       "      <td>130</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>169</td>\n",
       "      <td>82.0</td>\n",
       "      <td>150</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>156</td>\n",
       "      <td>56.0</td>\n",
       "      <td>100</td>\n",
       "      <td>60</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender  height  weight  ap_hi  ap_lo  smoke  alco  active  cardio  \\\n",
       "0   51       1     168    62.0    110     80      0     0       1       0   \n",
       "1   56       0     156    85.0    140     90      0     0       1       1   \n",
       "2   52       0     165    64.0    130     70      0     0       0       1   \n",
       "3   49       1     169    82.0    150    100      0     0       1       1   \n",
       "4   49       0     156    56.0    100     60      0     0       0       0   \n",
       "\n",
       "   cholesterol_1  cholesterol_2  cholesterol_3  gluc_1  gluc_2  gluc_3  \n",
       "0              1              0              0       1       0       0  \n",
       "1              0              0              1       1       0       0  \n",
       "2              0              0              1       1       0       0  \n",
       "3              1              0              0       1       0       0  \n",
       "4              1              0              0       1       0       0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_df['gender'] -= 1  # convert gender values to be either 1 or 0\n",
    "cleaned_df['age'] //= 356  # convert age values to be in years\n",
    "cleaned_df = cleaned_df.drop(labels=['cholesterol', 'gluc', 'id'], axis=1)\n",
    "cleaned_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "      <th>cholesterol_1</th>\n",
       "      <th>cholesterol_2</th>\n",
       "      <th>cholesterol_3</th>\n",
       "      <th>gluc_1</th>\n",
       "      <th>gluc_2</th>\n",
       "      <th>gluc_3</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>21.967120</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0</td>\n",
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       "      <td>85.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.927679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "      <td>64.0</td>\n",
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       "      <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>0</td>\n",
       "      <td>28.710479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>156</td>\n",
       "      <td>56.0</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>23.011177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender  height  weight  ap_hi  ap_lo  smoke  alco  active  cardio  \\\n",
       "0   51       1     168    62.0    110     80      0     0       1       0   \n",
       "1   56       0     156    85.0    140     90      0     0       1       1   \n",
       "2   52       0     165    64.0    130     70      0     0       0       1   \n",
       "3   49       1     169    82.0    150    100      0     0       1       1   \n",
       "4   49       0     156    56.0    100     60      0     0       0       0   \n",
       "\n",
       "   cholesterol_1  cholesterol_2  cholesterol_3  gluc_1  gluc_2  gluc_3  \\\n",
       "0              1              0              0       1       0       0   \n",
       "1              0              0              1       1       0       0   \n",
       "2              0              0              1       1       0       0   \n",
       "3              1              0              0       1       0       0   \n",
       "4              1              0              0       1       0       0   \n",
       "\n",
       "         bmi  \n",
       "0  21.967120  \n",
       "1  34.927679  \n",
       "2  23.507805  \n",
       "3  28.710479  \n",
       "4  23.011177  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate bmi and add it to the data\n",
    "cleaned_df['bmi'] = cleaned_df['weight']/((cleaned_df['height']/100)**2)\n",
    "cleaned_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "      <th>cholesterol_1</th>\n",
       "      <th>cholesterol_2</th>\n",
       "      <th>cholesterol_3</th>\n",
       "      <th>gluc_1</th>\n",
       "      <th>gluc_2</th>\n",
       "      <th>gluc_3</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
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       "  <tbody>\n",
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       "      <td>1</td>\n",
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       "      <td>52</td>\n",
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       "      <td>165</td>\n",
       "      <td>64.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.710479</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>156</td>\n",
       "      <td>56.0</td>\n",
       "      <td>100</td>\n",
       "      <td>60</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>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>23.011177</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>151</td>\n",
       "      <td>67.0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29.384676</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>157</td>\n",
       "      <td>93.0</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.729725</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>95.0</td>\n",
       "      <td>130</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29.983588</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>158</td>\n",
       "      <td>71.0</td>\n",
       "      <td>110</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.440955</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>164</td>\n",
       "      <td>68.0</td>\n",
       "      <td>110</td>\n",
       "      <td>60</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.282570</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender  height  weight  ap_hi  ap_lo  smoke  alco  active  cardio  \\\n",
       "0   51       1     168    62.0    110     80      0     0       1       0   \n",
       "1   56       0     156    85.0    140     90      0     0       1       1   \n",
       "2   52       0     165    64.0    130     70      0     0       0       1   \n",
       "3   49       1     169    82.0    150    100      0     0       1       1   \n",
       "4   49       0     156    56.0    100     60      0     0       0       0   \n",
       "5   61       0     151    67.0    120     80      0     0       0       0   \n",
       "6   62       0     157    93.0    130     80      0     0       1       0   \n",
       "7   63       1     178    95.0    130     90      0     0       1       1   \n",
       "8   49       0     158    71.0    110     70      0     0       1       0   \n",
       "9   55       0     164    68.0    110     60      0     0       0       0   \n",
       "\n",
       "   cholesterol_1  cholesterol_2  cholesterol_3  gluc_1  gluc_2  gluc_3  \\\n",
       "0              1              0              0       1       0       0   \n",
       "1              0              0              1       1       0       0   \n",
       "2              0              0              1       1       0       0   \n",
       "3              1              0              0       1       0       0   \n",
       "4              1              0              0       1       0       0   \n",
       "5              0              1              0       0       1       0   \n",
       "6              0              0              1       1       0       0   \n",
       "7              0              0              1       0       0       1   \n",
       "8              1              0              0       1       0       0   \n",
       "9              1              0              0       1       0       0   \n",
       "\n",
       "         bmi bp  \n",
       "0  21.967120  0  \n",
       "1  34.927679  2  \n",
       "2  23.507805  2  \n",
       "3  28.710479  2  \n",
       "4  23.011177  0  \n",
       "5  29.384676  1  \n",
       "6  37.729725  2  \n",
       "7  29.983588  2  \n",
       "8  28.440955  0  \n",
       "9  25.282570  0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create categories for blood pressure based on value of ap_hi\n",
    "labels = [0, 1, 2]  # 0:normal, 1:elevated bp, 2:hypertension\n",
    "cleaned_df['bp'] = pd.cut(cleaned_df['ap_hi'], [0, 120, 130, 250], right=False, labels=labels)\n",
    "cleaned_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save un-normalized data\n",
    "cleaned_df.to_csv('preprocessed_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>gender</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "      <th>cholesterol_1</th>\n",
       "      <th>cholesterol_2</th>\n",
       "      <th>cholesterol_3</th>\n",
       "      <th>gluc_1</th>\n",
       "      <th>gluc_2</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.579487</td>\n",
       "      <td>0.269841</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.576923</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.967120</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.517949</td>\n",
       "      <td>0.391534</td>\n",
       "      <td>0.743590</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.927679</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.564103</td>\n",
       "      <td>0.280423</td>\n",
       "      <td>0.717949</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.507805</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.527778</td>\n",
       "      <td>0.584615</td>\n",
       "      <td>0.375661</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.653846</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.710479</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.527778</td>\n",
       "      <td>0.517949</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.641026</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.011177</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.861111</td>\n",
       "      <td>0.492308</td>\n",
       "      <td>0.296296</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.576923</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.384676</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.523077</td>\n",
       "      <td>0.433862</td>\n",
       "      <td>0.717949</td>\n",
       "      <td>0.576923</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.729725</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.630769</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.717949</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.983588</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.527778</td>\n",
       "      <td>0.528205</td>\n",
       "      <td>0.317460</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.440955</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.694444</td>\n",
       "      <td>0.558974</td>\n",
       "      <td>0.301587</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.282570</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age    height    weight     ap_hi     ap_lo  gender  smoke  alco  \\\n",
       "0  0.583333  0.579487  0.269841  0.666667  0.576923     1.0    0.0   0.0   \n",
       "1  0.722222  0.517949  0.391534  0.743590  0.615385     0.0    0.0   0.0   \n",
       "2  0.611111  0.564103  0.280423  0.717949  0.538462     0.0    0.0   0.0   \n",
       "3  0.527778  0.584615  0.375661  0.769231  0.653846     1.0    0.0   0.0   \n",
       "4  0.527778  0.517949  0.238095  0.641026  0.500000     0.0    0.0   0.0   \n",
       "5  0.861111  0.492308  0.296296  0.692308  0.576923     0.0    0.0   0.0   \n",
       "6  0.888889  0.523077  0.433862  0.717949  0.576923     0.0    0.0   0.0   \n",
       "7  0.916667  0.630769  0.444444  0.717949  0.615385     1.0    0.0   0.0   \n",
       "8  0.527778  0.528205  0.317460  0.666667  0.538462     0.0    0.0   0.0   \n",
       "9  0.694444  0.558974  0.301587  0.666667  0.500000     0.0    0.0   0.0   \n",
       "\n",
       "   active  cardio  cholesterol_1  cholesterol_2  cholesterol_3  gluc_1  \\\n",
       "0     1.0     0.0            1.0            0.0            0.0     1.0   \n",
       "1     1.0     1.0            0.0            0.0            1.0     1.0   \n",
       "2     0.0     1.0            0.0            0.0            1.0     1.0   \n",
       "3     1.0     1.0            1.0            0.0            0.0     1.0   \n",
       "4     0.0     0.0            1.0            0.0            0.0     1.0   \n",
       "5     0.0     0.0            0.0            1.0            0.0     0.0   \n",
       "6     1.0     0.0            0.0            0.0            1.0     1.0   \n",
       "7     1.0     1.0            0.0            0.0            1.0     0.0   \n",
       "8     1.0     0.0            1.0            0.0            0.0     1.0   \n",
       "9     0.0     0.0            1.0            0.0            0.0     1.0   \n",
       "\n",
       "   gluc_2        bmi bp  \n",
       "0     0.0  21.967120  0  \n",
       "1     0.0  34.927679  2  \n",
       "2     0.0  23.507805  2  \n",
       "3     0.0  28.710479  2  \n",
       "4     0.0  23.011177  0  \n",
       "5     1.0  29.384676  1  \n",
       "6     0.0  37.729725  2  \n",
       "7     0.0  29.983588  2  \n",
       "8     0.0  28.440955  0  \n",
       "9     0.0  25.282570  0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalize continuous variables\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "df_scaled = min_max_scaler.fit_transform(cleaned_df[cont_cols])\n",
    "df_normalized = pd.DataFrame(df_scaled)\n",
    "df_normalized.columns = cont_cols\n",
    "cols = ['gender', 'smoke', 'alco', 'active', 'cardio', 'cholesterol_1', \n",
    "        'cholesterol_2', 'cholesterol_3', 'gluc_1', 'gluc_2', 'bmi', 'bp']\n",
    "df_normalized = pd.concat([df_normalized, cleaned_df[cols]], axis=1)\n",
    "df_normalized.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save normalized data\n",
    "df_normalized.to_csv('normalized_data.csv', index=False)"
   ]
  }
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