obesity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "data= pd.read_csv('D:\\PROJECTS\\project csv files\\obesity.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "analysing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>family_history_with_overweight</th>\n",
       "      <th>FAVC</th>\n",
       "      <th>FCVC</th>\n",
       "      <th>NCP</th>\n",
       "      <th>CAEC</th>\n",
       "      <th>SMOKE</th>\n",
       "      <th>CH2O</th>\n",
       "      <th>SCC</th>\n",
       "      <th>FAF</th>\n",
       "      <th>TUE</th>\n",
       "      <th>CALC</th>\n",
       "      <th>Automobile</th>\n",
       "      <th>Bike</th>\n",
       "      <th>Motorbike</th>\n",
       "      <th>Public_Transportation</th>\n",
       "      <th>Walking</th>\n",
       "      <th>NObeyesdad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>21.000000</td>\n",
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       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>2.054193</td>\n",
       "      <td>0</td>\n",
       "      <td>1.414209</td>\n",
       "      <td>0.646288</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>2.863513</td>\n",
       "      <td>0</td>\n",
       "      <td>1.026452</td>\n",
       "      <td>0.714137</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2111 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender        Age  family_history_with_overweight  FAVC  FCVC  NCP  \\\n",
       "0          0  21.000000                               1     0   2.0  3.0   \n",
       "1          0  21.000000                               1     0   3.0  3.0   \n",
       "2          1  23.000000                               1     0   2.0  3.0   \n",
       "3          1  27.000000                               0     0   3.0  3.0   \n",
       "4          1  22.000000                               0     0   2.0  1.0   \n",
       "...      ...        ...                             ...   ...   ...  ...   \n",
       "2106       0  20.976842                               1     1   3.0  3.0   \n",
       "2107       0  21.982942                               1     1   3.0  3.0   \n",
       "2108       0  22.524036                               1     1   3.0  3.0   \n",
       "2109       0  24.361936                               1     1   3.0  3.0   \n",
       "2110       0  23.664709                               1     1   3.0  3.0   \n",
       "\n",
       "      CAEC  SMOKE      CH2O  SCC       FAF       TUE  CALC  Automobile  Bike  \\\n",
       "0        1      0  2.000000    0  0.000000  1.000000     0           0     0   \n",
       "1        1      1  3.000000    1  3.000000  0.000000     1           0     0   \n",
       "2        1      0  2.000000    0  2.000000  1.000000     2           0     0   \n",
       "3        1      0  2.000000    0  2.000000  0.000000     2           0     0   \n",
       "4        1      0  2.000000    0  0.000000  0.000000     1           0     0   \n",
       "...    ...    ...       ...  ...       ...       ...   ...         ...   ...   \n",
       "2106     1      0  1.728139    0  1.676269  0.906247     1           0     0   \n",
       "2107     1      0  2.005130    0  1.341390  0.599270     1           0     0   \n",
       "2108     1      0  2.054193    0  1.414209  0.646288     1           0     0   \n",
       "2109     1      0  2.852339    0  1.139107  0.586035     1           0     0   \n",
       "2110     1      0  2.863513    0  1.026452  0.714137     1           0     0   \n",
       "\n",
       "      Motorbike  Public_Transportation  Walking  NObeyesdad  \n",
       "0             0                      1        0           0  \n",
       "1             0                      1        0           0  \n",
       "2             0                      1        0           0  \n",
       "3             0                      0        1           0  \n",
       "4             0                      1        0           0  \n",
       "...         ...                    ...      ...         ...  \n",
       "2106          0                      1        0           1  \n",
       "2107          0                      1        0           1  \n",
       "2108          0                      1        0           1  \n",
       "2109          0                      1        0           1  \n",
       "2110          0                      1        0           1  \n",
       "\n",
       "[2111 rows x 19 columns]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how many males and females are there"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "1    1068\n",
       "0    1043\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gids=data['Gender'].value_counts()\n",
    "gids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "checking for the null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                            0\n",
       "Age                               0\n",
       "family_history_with_overweight    0\n",
       "FAVC                              0\n",
       "FCVC                              0\n",
       "NCP                               0\n",
       "CAEC                              0\n",
       "SMOKE                             0\n",
       "CH2O                              0\n",
       "SCC                               0\n",
       "FAF                               0\n",
       "TUE                               0\n",
       "CALC                              0\n",
       "Automobile                        0\n",
       "Bike                              0\n",
       "Motorbike                         0\n",
       "Public_Transportation             0\n",
       "Walking                           0\n",
       "NObeyesdad                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "checking for the duplicate data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "2106    False\n",
       "2107    False\n",
       "2108    False\n",
       "2109    False\n",
       "2110    False\n",
       "Length: 2111, dtype: bool"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated(keep='first')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "average age of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.312599908574136"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aad=data['Age'].mean()\n",
    "aad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "average age of obese people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.806180991769548"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aop=data[data['NObeyesdad']==1]['Age']\n",
    "p=aop.mean()\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how many people workout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FAF\n",
       "0.000000    411\n",
       "1.000000    234\n",
       "2.000000    183\n",
       "3.000000     75\n",
       "0.110174      2\n",
       "           ... \n",
       "1.916751      1\n",
       "0.954459      1\n",
       "0.340915      1\n",
       "0.986414      1\n",
       "1.026452      1\n",
       "Name: count, Length: 1190, dtype: int64"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwds=data['FAF'].value_counts()\n",
    "pwds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how many obese people workout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FAF\n",
      "0.000000    196\n",
      "1.000000     41\n",
      "2.000000     18\n",
      "3.000000     13\n",
      "1.399183      2\n",
      "           ... \n",
      "1.066169      1\n",
      "0.368026      1\n",
      "0.592607      1\n",
      "0.966617      1\n",
      "1.026452      1\n",
      "Name: count, Length: 701, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "opw=data[data['NObeyesdad']==1]['FAF']\n",
    "pp=opw.value_counts()\n",
    "print(pp)\n",
    "pp_ind=pp.index\n",
    "pp_val=pp.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how many healthy people workout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FAF\n",
      "0.000000    215\n",
      "1.000000    193\n",
      "2.000000    165\n",
      "3.000000     62\n",
      "1.228136      2\n",
      "           ... \n",
      "1.487987      1\n",
      "0.023574      1\n",
      "0.463949      1\n",
      "1.230441      1\n",
      "0.647798      1\n",
      "Name: count, Length: 499, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "hpw=data[data['NObeyesdad']==0]['FAF']\n",
    "hpp=hpw.value_counts()\n",
    "print(hpp)\n",
    "hpp_ind=hpp.index\n",
    "hpp_val=hpp.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(hpp_ind,hpp_val,color=['b'])\n",
    "plt.bar(pp_ind,pp_val , color=['r'])\n",
    "plt.xlabel(\"workout strenght\")\n",
    "plt.ylabel(\"frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "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(hpp_ind,hpp_val,color=['b'])\n",
    "plt.scatter(pp_ind,pp_val , color=['r'])\n",
    "plt.xlabel(\"workout strenght\")\n",
    "plt.ylabel(\"frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hpp_ind,hpp_val,label='hpp')\n",
    "plt.plot(pp_ind,pp_val,label='pp' )\n",
    "plt.xlabel(\"workout strenght\")\n",
    "plt.ylabel(\"frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how many obese smoke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMOKE\n",
      "0    950\n",
      "1     22\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "ops=data[data['NObeyesdad']==1]['SMOKE']\n",
    "opss=ops.value_counts()\n",
    "print(opss)\n",
    "opss_ind=opss.index\n",
    "opss_val=opss.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how many healthy smoke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMOKE\n",
      "0    1117\n",
      "1      22\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "hps=data[data['NObeyesdad']==0]['SMOKE']\n",
    "hpss=hps.value_counts()\n",
    "print(hpss)\n",
    "hpss_ind=hpss.index\n",
    "hpss_val=hpss.values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.heatmap(hpss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how much healthy people drink water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CH2O\n",
      "2.000000    372\n",
      "1.000000    154\n",
      "3.000000    109\n",
      "1.636326      2\n",
      "2.137550      2\n",
      "           ... \n",
      "1.755497      1\n",
      "1.546856      1\n",
      "2.311791      1\n",
      "2.582165      1\n",
      "1.026729      1\n",
      "Name: count, Length: 500, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "hpdw=data[data['NObeyesdad']==0]['CH2O']\n",
    "hpdww=hpdw.value_counts()\n",
    "print(hpdww)\n",
    "hpdww_ind=hpdww.index\n",
    "hpdww_val=hpdww.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how much obese people drink water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CH2O\n",
      "2.000000    76\n",
      "1.000000    57\n",
      "3.000000    53\n",
      "2.184707     2\n",
      "2.530035     2\n",
      "            ..\n",
      "1.430444     1\n",
      "2.165048     1\n",
      "2.117733     1\n",
      "2.141271     1\n",
      "2.863513     1\n",
      "Name: count, Length: 787, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "opdw=data[data['NObeyesdad']==1]['CH2O']\n",
    "opdww=opdw.value_counts()\n",
    "print(opdww)\n",
    "opdww_ind=opdww.index\n",
    "opdww_val=opdww.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how many healthy people drink alcohol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CALC\n",
      "1    682\n",
      "0    402\n",
      "2     54\n",
      "3      1\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "hpda=data[data['NObeyesdad']==0]['CALC']\n",
    "hpdaa=hpda.value_counts()\n",
    "print(hpdaa)\n",
    "hpdaa_ind=hpdaa.index\n",
    "hpdaa_val=hpdaa.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how many obese people drink alcohol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CALC\n",
      "1    719\n",
      "0    237\n",
      "2     16\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "opda=data[data['NObeyesdad']==1]['CALC']\n",
    "opdaa=opda.value_counts()\n",
    "print(opdaa)\n",
    "opdaa_ind=opdaa.index\n",
    "opdaa_val=opdaa.values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "on screen time ratings of obese people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TUE\n",
      "0.000000    204\n",
      "1.000000     47\n",
      "2.000000     27\n",
      "0.002600      3\n",
      "0.630866      3\n",
      "           ... \n",
      "0.340196      1\n",
      "0.731257      1\n",
      "0.687342      1\n",
      "0.157007      1\n",
      "0.714137      1\n",
      "Name: count, Length: 690, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "stop=data[data['NObeyesdad']==1]['TUE']\n",
    "stopp=stop.value_counts()\n",
    "print(stopp)\n",
    "stopp_ind=stopp.index\n",
    "stopp_val=stopp.values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "on screen time ratings of healthy people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TUE\n",
      "0.000000    353\n",
      "1.000000    245\n",
      "2.000000     82\n",
      "0.469735      2\n",
      "0.369134      2\n",
      "           ... \n",
      "1.415536      1\n",
      "0.707044      1\n",
      "0.142056      1\n",
      "0.265790      1\n",
      "0.023564      1\n",
      "Name: count, Length: 452, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "sthp=data[data['NObeyesdad']==0]['TUE']\n",
    "sthpp=sthp.value_counts()\n",
    "print(sthpp)\n",
    "sthpp_ind=sthpp.index\n",
    "sthpp_val=sthpp.values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how many healthy people track their calorie intake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SCC\n",
      "0    1046\n",
      "1      93\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "hpci=data[data['NObeyesdad']==0]['SCC']\n",
    "hpcii=hpci.value_counts()\n",
    "print(hpcii)\n",
    "hpcii_ind=hpcii.index\n",
    "hpcii_val=hpcii.values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how many obese people track their calorie intake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SCC\n",
      "0    969\n",
      "1      3\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "opci=data[data['NObeyesdad']==1]['SCC']\n",
    "opcii=opci.value_counts()\n",
    "print(opcii)\n",
    "opcii_ind=opcii.index\n",
    "opcii_val=opcii.values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "number  of meal taken by the healthy people in a day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NCP\n",
      "3.000000    525\n",
      "1.000000    160\n",
      "4.000000     68\n",
      "1.737620      2\n",
      "3.559841      2\n",
      "           ... \n",
      "3.985442      1\n",
      "1.578521      1\n",
      "3.131032      1\n",
      "1.394539      1\n",
      "1.313403      1\n",
      "Name: count, Length: 383, dtype: int64\n",
      "0 for 1-2 meals, 1 for 3 meals, and 2 for more than 3 meals\n"
     ]
    }
   ],
   "source": [
    "hpmi=data[data['NObeyesdad']==0]['NCP']\n",
    "hpmii=hpmi.value_counts()\n",
    "print(hpmii)\n",
    "print(\"0 for 1-2 meals, 1 for 3 meals, and 2 for more than 3 meals\")\n",
    "hpmii_ind=hpmii.index\n",
    "hpmii_val=hpmii.values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "number  of meal taken by the obese people in a day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NCP\n",
      "3.000000    678\n",
      "1.000000     39\n",
      "1.213431      1\n",
      "2.986637      1\n",
      "1.924168      1\n",
      "           ... \n",
      "1.000414      1\n",
      "1.135278      1\n",
      "2.463113      1\n",
      "1.782109      1\n",
      "1.089048      1\n",
      "Name: count, Length: 257, dtype: int64\n",
      "0 for 1-2 meals, 1 for 3 meals, and 2 for more than 3 meals\n"
     ]
    }
   ],
   "source": [
    "opmi=data[data['NObeyesdad']==1]['NCP']\n",
    "opmii=opmi.value_counts()\n",
    "print(opmii)\n",
    "print(\"0 for 1-2 meals, 1 for 3 meals, and 2 for more than 3 meals\")\n",
    "opmii_ind=opmii.index\n",
    "opmii_val=opmii.values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how many healthy people eats vegetable in their meals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FCVC\n",
      "2.000000    425\n",
      "3.000000    276\n",
      "1.000000     29\n",
      "2.392665      2\n",
      "2.714447      2\n",
      "           ... \n",
      "2.673638      1\n",
      "1.118436      1\n",
      "1.649974      1\n",
      "1.036414      1\n",
      "2.076689      1\n",
      "Name: count, Length: 406, dtype: int64\n",
      "0 for not 1 for eats vegetable\n"
     ]
    }
   ],
   "source": [
    "hpev=data[data['NObeyesdad']==0]['FCVC']\n",
    "hpevv=hpev.value_counts()\n",
    "print(hpevv)\n",
    "print(\"0 for not 1 for eats vegetable\")\n",
    "hpevv_ind=hpevv.index\n",
    "hpevv_val=hpevv.values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how many obese people eats vegetable in their meals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FCVC\n",
      "3.000000    376\n",
      "2.000000    175\n",
      "1.000000      4\n",
      "2.758394      2\n",
      "2.967300      2\n",
      "           ... \n",
      "2.490776      1\n",
      "2.312825      1\n",
      "2.348745      1\n",
      "2.393837      1\n",
      "2.680375      1\n",
      "Name: count, Length: 416, dtype: int64\n",
      "0 for not 1 for eats vegetable\n"
     ]
    }
   ],
   "source": [
    "opev=data[data['NObeyesdad']==1]['FCVC']\n",
    "opevv=opev.value_counts()\n",
    "print(opevv)\n",
    "print(\"0 for not 1 for eats vegetable\")\n",
    "hpevv_ind=opevv.index\n",
    "hpevv_val=opevv.values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how many healthy people eats high calorie food"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FAVC\n",
      "1    913\n",
      "0    226\n",
      "Name: count, dtype: int64\n",
      "0 for not 1 for eat\n"
     ]
    }
   ],
   "source": [
    "hpehcf=data[data['NObeyesdad']==0]['FAVC']\n",
    "hpehcff=hpehcf.value_counts()\n",
    "print(hpehcff)\n",
    "print(\"0 for not 1 for eat\")\n",
    "hpechff_ind=hpehcff.index\n",
    "hpechff_val=hpehcff.values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how many obese people eats high calorie food"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FAVC\n",
      "1    953\n",
      "0     19\n",
      "Name: count, dtype: int64\n",
      "0 for not 1 for eat\n"
     ]
    }
   ],
   "source": [
    "opehcf=data[data['NObeyesdad']==1]['FAVC']\n",
    "opehcff=opehcf.value_counts()\n",
    "print(opehcff)\n",
    "print(\"0 for not 1 for eat\")\n",
    "opechff_ind=opehcff.index\n",
    "opechff_val=opehcff.values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how many healthy people have overweight history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "family_history_with_overweight\n",
      "1    762\n",
      "0    377\n",
      "Name: count, dtype: int64\n",
      "0 for not 1 for have overweight history\n",
      "family overweight percentage=  66.900790166813\n"
     ]
    }
   ],
   "source": [
    "hpov=data[data['NObeyesdad']==0]['family_history_with_overweight']\n",
    "hpovv=hpov.value_counts()\n",
    "print(hpovv)\n",
    "print(\"0 for not 1 for have overweight history\")\n",
    "hpovv_ind=hpovv.index\n",
    "hpovv_val=hpovv.values\n",
    "hpovv_per=hpovv_val[0]*100/sum(hpovv_val)\n",
    "print(\"family overweight percentage= \",hpovv_per)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how many obese people have overweight history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "family_history_with_overweight\n",
      "1    964\n",
      "0      8\n",
      "Name: count, dtype: int64\n",
      "0 for not 1 for have overweight history\n",
      "family overweight percentage=  99.17695473251028\n"
     ]
    }
   ],
   "source": [
    "opov=data[data['NObeyesdad']==1]['family_history_with_overweight']\n",
    "opovv=opov.value_counts()\n",
    "print(opovv)\n",
    "print(\"0 for not 1 for have overweight history\")\n",
    "opovv_ind=opovv.index\n",
    "opovv_val=opovv.values\n",
    "opovv_per=opovv_val[0]*100/sum(opovv_val)\n",
    "print(\"family overweight percentage= \",opovv_per)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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