Bivariate-Multivariate-Practise.ipynb

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   "execution_count": 1,
   "id": "5b0ba069-2e3f-4e90-8eaf-9da870121101",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Waris Hayyat\\AppData\\Local\\Temp\\ipykernel_6016\\1285016483.py:1: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d9985656-162e-474d-9189-5c1ccba1b563",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HeartDisease</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>AlcoholDrinking</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>PhysicalHealth</th>\n",
       "      <th>MentalHealth</th>\n",
       "      <th>DiffWalking</th>\n",
       "      <th>Sex</th>\n",
       "      <th>AgeCategory</th>\n",
       "      <th>Race</th>\n",
       "      <th>Diabetic</th>\n",
       "      <th>PhysicalActivity</th>\n",
       "      <th>GenHealth</th>\n",
       "      <th>SleepTime</th>\n",
       "      <th>Asthma</th>\n",
       "      <th>KidneyDisease</th>\n",
       "      <th>SkinCancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>16.60</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>55-59</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>20.34</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>80 or older</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>7.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>26.58</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Male</td>\n",
       "      <td>65-69</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fair</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>24.21</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>75-79</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Good</td>\n",
       "      <td>6.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>23.71</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Female</td>\n",
       "      <td>40-44</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>8.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319790</th>\n",
       "      <td>Yes</td>\n",
       "      <td>27.41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Male</td>\n",
       "      <td>60-64</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fair</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>319791</th>\n",
       "      <td>No</td>\n",
       "      <td>29.84</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Male</td>\n",
       "      <td>35-39</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
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       "      <th>319792</th>\n",
       "      <td>No</td>\n",
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       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>45-49</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Good</td>\n",
       "      <td>6.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319793</th>\n",
       "      <td>No</td>\n",
       "      <td>32.81</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>25-29</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Good</td>\n",
       "      <td>12.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319794</th>\n",
       "      <td>No</td>\n",
       "      <td>46.56</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>80 or older</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Good</td>\n",
       "      <td>8.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>319795 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       HeartDisease    BMI Smoking AlcoholDrinking Stroke  PhysicalHealth  \\\n",
       "0                No  16.60     Yes              No     No             3.0   \n",
       "1                No  20.34      No              No    Yes             0.0   \n",
       "2                No  26.58     Yes              No     No            20.0   \n",
       "3                No  24.21      No              No     No             0.0   \n",
       "4                No  23.71      No              No     No            28.0   \n",
       "...             ...    ...     ...             ...    ...             ...   \n",
       "319790          Yes  27.41     Yes              No     No             7.0   \n",
       "319791           No  29.84     Yes              No     No             0.0   \n",
       "319792           No  24.24      No              No     No             0.0   \n",
       "319793           No  32.81      No              No     No             0.0   \n",
       "319794           No  46.56      No              No     No             0.0   \n",
       "\n",
       "        MentalHealth DiffWalking     Sex  AgeCategory      Race Diabetic  \\\n",
       "0               30.0          No  Female        55-59     White      Yes   \n",
       "1                0.0          No  Female  80 or older     White       No   \n",
       "2               30.0          No    Male        65-69     White      Yes   \n",
       "3                0.0          No  Female        75-79     White       No   \n",
       "4                0.0         Yes  Female        40-44     White       No   \n",
       "...              ...         ...     ...          ...       ...      ...   \n",
       "319790           0.0         Yes    Male        60-64  Hispanic      Yes   \n",
       "319791           0.0          No    Male        35-39  Hispanic       No   \n",
       "319792           0.0          No  Female        45-49  Hispanic       No   \n",
       "319793           0.0          No  Female        25-29  Hispanic       No   \n",
       "319794           0.0          No  Female  80 or older  Hispanic       No   \n",
       "\n",
       "       PhysicalActivity  GenHealth  SleepTime Asthma KidneyDisease SkinCancer  \n",
       "0                   Yes  Very good        5.0    Yes            No        Yes  \n",
       "1                   Yes  Very good        7.0     No            No         No  \n",
       "2                   Yes       Fair        8.0    Yes            No         No  \n",
       "3                    No       Good        6.0     No            No        Yes  \n",
       "4                   Yes  Very good        8.0     No            No         No  \n",
       "...                 ...        ...        ...    ...           ...        ...  \n",
       "319790               No       Fair        6.0    Yes            No         No  \n",
       "319791              Yes  Very good        5.0    Yes            No         No  \n",
       "319792              Yes       Good        6.0     No            No         No  \n",
       "319793               No       Good       12.0     No            No         No  \n",
       "319794              Yes       Good        8.0     No            No         No  \n",
       "\n",
       "[319795 rows x 18 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"C:\\\\Users\\\\Waris Hayyat\\\\Desktop\\\\Machine-Learning-Model\\\\heart_2020_cleaned.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b401fcd8-0b50-40de-a716-46145c635028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='MentalHealth'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df,x=\"Sex\",y=\"MentalHealth\",hue=\"Smoking\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "75b8003a-8df8-4dd2-abda-3928af711276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='PhysicalHealth'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df,x=\"Sex\",y=\"PhysicalHealth\",hue=\"Smoking\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5f630974-669e-48fc-a55f-b3b099bec4be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HeartDisease</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>AlcoholDrinking</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>PhysicalHealth</th>\n",
       "      <th>MentalHealth</th>\n",
       "      <th>DiffWalking</th>\n",
       "      <th>Sex</th>\n",
       "      <th>AgeCategory</th>\n",
       "      <th>Race</th>\n",
       "      <th>Diabetic</th>\n",
       "      <th>PhysicalActivity</th>\n",
       "      <th>GenHealth</th>\n",
       "      <th>SleepTime</th>\n",
       "      <th>Asthma</th>\n",
       "      <th>KidneyDisease</th>\n",
       "      <th>SkinCancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>217969</th>\n",
       "      <td>No</td>\n",
       "      <td>30.90</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>60-64</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>7.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>Yes</td>\n",
       "      <td>20.18</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>80 or older</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Good</td>\n",
       "      <td>8.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122752</th>\n",
       "      <td>No</td>\n",
       "      <td>30.13</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Male</td>\n",
       "      <td>60-64</td>\n",
       "      <td>Black</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>7.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       HeartDisease    BMI Smoking AlcoholDrinking Stroke  PhysicalHealth  \\\n",
       "217969           No  30.90      No              No     No             0.0   \n",
       "975             Yes  20.18      No              No     No             0.0   \n",
       "122752           No  30.13     Yes              No     No             0.0   \n",
       "\n",
       "        MentalHealth DiffWalking     Sex  AgeCategory   Race Diabetic  \\\n",
       "217969           0.0          No  Female        60-64  White       No   \n",
       "975              0.0          No  Female  80 or older  White       No   \n",
       "122752           0.0          No    Male        60-64  Black       No   \n",
       "\n",
       "       PhysicalActivity  GenHealth  SleepTime Asthma KidneyDisease SkinCancer  \n",
       "217969              Yes  Excellent        7.0     No            No         No  \n",
       "975                  No       Good        8.0     No            No         No  \n",
       "122752              Yes  Very good        7.0     No            No         No  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7e92ee19-84d0-48c3-a0b4-e29b41e533fe",
   "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>Sex</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HeartDisease</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>156571</td>\n",
       "      <td>135851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>11234</td>\n",
       "      <td>16139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex           Female    Male\n",
       "HeartDisease                \n",
       "No            156571  135851\n",
       "Yes            11234   16139"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(pd.crosstab(df[\"HeartDisease\"],df[\"Sex\"]))\n",
    "pd.crosstab(df[\"HeartDisease\"],df[\"Sex\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "765a7a9c-608a-48c0-bbaa-330aff162ad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='AlcoholDrinking'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df[\"AlcoholDrinking\"],df[\"Sex\"])\n",
    "sns.heatmap(pd.crosstab(df[\"AlcoholDrinking\"],df[\"Sex\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a8f1b048-032d-4942-8987-85797268f33d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Cancer patient f/m SkinCancer      No    Yes\n",
      "Sex                      \n",
      "Female      152782  15023\n",
      "Male        137194  14796\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='SkinCancer', ylabel='Sex'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Cancer_patient=pd.crosstab(df[\"Sex\"],df[\"SkinCancer\"])\n",
    "print(\"Total Cancer patient f/m\",Cancer_patient)\n",
    "sns.heatmap(pd.crosstab(df[\"Sex\"],df[\"SkinCancer\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fffae441-2679-48df-b859-72a17f2dbf2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Kidney patient f/m KidneyDisease      No   Yes\n",
      "Sex                        \n",
      "Female         161351  6454\n",
      "Male           146665  5325\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='KidneyDisease', ylabel='Sex'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Kidney=pd.crosstab(df[\"Sex\"],df[\"KidneyDisease\"])\n",
    "print(\"Total Kidney patient f/m\",Kidney)\n",
    "sns.heatmap(pd.crosstab(df[\"Sex\"],df[\"KidneyDisease\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "da1b009a-d649-4333-917d-5d2d31543be0",
   "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>SleepTime</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "      <th>7.0</th>\n",
       "      <th>8.0</th>\n",
       "      <th>9.0</th>\n",
       "      <th>10.0</th>\n",
       "      <th>...</th>\n",
       "      <th>15.0</th>\n",
       "      <th>16.0</th>\n",
       "      <th>17.0</th>\n",
       "      <th>18.0</th>\n",
       "      <th>19.0</th>\n",
       "      <th>20.0</th>\n",
       "      <th>21.0</th>\n",
       "      <th>22.0</th>\n",
       "      <th>23.0</th>\n",
       "      <th>24.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HeartDisease</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",
       "      <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>No</th>\n",
       "      <td>491</td>\n",
       "      <td>660</td>\n",
       "      <td>1645</td>\n",
       "      <td>6570</td>\n",
       "      <td>17154</td>\n",
       "      <td>61294</td>\n",
       "      <td>91426</td>\n",
       "      <td>89244</td>\n",
       "      <td>14345</td>\n",
       "      <td>6631</td>\n",
       "      <td>...</td>\n",
       "      <td>165</td>\n",
       "      <td>202</td>\n",
       "      <td>19</td>\n",
       "      <td>81</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>60</td>\n",
       "      <td>128</td>\n",
       "      <td>347</td>\n",
       "      <td>1180</td>\n",
       "      <td>2030</td>\n",
       "      <td>5427</td>\n",
       "      <td>6325</td>\n",
       "      <td>8358</td>\n",
       "      <td>1696</td>\n",
       "      <td>1165</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "SleepTime     1.0   2.0   3.0   4.0    5.0    6.0    7.0    8.0    9.0   10.0  \\\n",
       "HeartDisease                                                                    \n",
       "No             491   660  1645  6570  17154  61294  91426  89244  14345  6631   \n",
       "Yes             60   128   347  1180   2030   5427   6325   8358   1696  1165   \n",
       "\n",
       "SleepTime     ...  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  \n",
       "HeartDisease  ...                                                              \n",
       "No            ...   165   202    19    81     2    48     2     6     3    24  \n",
       "Yes           ...    24    34     2    21     1    16     0     3     0     6  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df,x=\"HeartDisease\",y=\"SleepTime\")\n",
    "pd.crosstab(df[\"HeartDisease\"],df[\"SleepTime\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "cf859442-1120-4cab-a725-e640aa25bf66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HeartDisease</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>AlcoholDrinking</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>PhysicalHealth</th>\n",
       "      <th>MentalHealth</th>\n",
       "      <th>DiffWalking</th>\n",
       "      <th>Sex</th>\n",
       "      <th>AgeCategory</th>\n",
       "      <th>Race</th>\n",
       "      <th>Diabetic</th>\n",
       "      <th>PhysicalActivity</th>\n",
       "      <th>GenHealth</th>\n",
       "      <th>SleepTime</th>\n",
       "      <th>Asthma</th>\n",
       "      <th>KidneyDisease</th>\n",
       "      <th>SkinCancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>110449</th>\n",
       "      <td>No</td>\n",
       "      <td>25.61</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>35-39</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes (during pregnancy)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>8.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219616</th>\n",
       "      <td>No</td>\n",
       "      <td>22.59</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>55-59</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Good</td>\n",
       "      <td>8.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62068</th>\n",
       "      <td>No</td>\n",
       "      <td>23.41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>60-64</td>\n",
       "      <td>Asian</td>\n",
       "      <td>No, borderline diabetes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>8.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       HeartDisease    BMI Smoking AlcoholDrinking Stroke  PhysicalHealth  \\\n",
       "110449           No  25.61      No              No     No             0.0   \n",
       "219616           No  22.59      No              No     No             0.0   \n",
       "62068            No  23.41     Yes              No     No             0.0   \n",
       "\n",
       "        MentalHealth DiffWalking     Sex AgeCategory   Race  \\\n",
       "110449           0.0          No  Female       35-39  White   \n",
       "219616           0.0          No  Female       55-59  White   \n",
       "62068            3.0          No  Female       60-64  Asian   \n",
       "\n",
       "                       Diabetic PhysicalActivity  GenHealth  SleepTime Asthma  \\\n",
       "110449   Yes (during pregnancy)              Yes  Very good        8.0     No   \n",
       "219616                       No              Yes       Good        8.0     No   \n",
       "62068   No, borderline diabetes              Yes  Very good        8.0     No   \n",
       "\n",
       "       KidneyDisease SkinCancer  \n",
       "110449            No         No  \n",
       "219616            No         No  \n",
       "62068             No         No  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b5295abe-4269-4c69-8c71-40c2435f97a9",
   "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>PhysicalActivity</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HeartDisease</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>61954</td>\n",
       "      <td>230468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>9884</td>\n",
       "      <td>17489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PhysicalActivity     No     Yes\n",
       "HeartDisease                   \n",
       "No                61954  230468\n",
       "Yes                9884   17489"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"HeartDisease\"],df[\"PhysicalActivity\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "01c4b1e4-cf53-4308-9e64-07765b84b73e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='count', ylabel='AgeCategory'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df[\"AgeCategory\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6bf908d7-c9e6-4b57-9c03-82388755f313",
   "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>GenHealth</th>\n",
       "      <th>Excellent</th>\n",
       "      <th>Fair</th>\n",
       "      <th>Good</th>\n",
       "      <th>Poor</th>\n",
       "      <th>Very good</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HeartDisease</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>No</th>\n",
       "      <td>65342</td>\n",
       "      <td>27593</td>\n",
       "      <td>83571</td>\n",
       "      <td>7439</td>\n",
       "      <td>108477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>1500</td>\n",
       "      <td>7084</td>\n",
       "      <td>9558</td>\n",
       "      <td>3850</td>\n",
       "      <td>5381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "GenHealth     Excellent   Fair   Good  Poor  Very good\n",
       "HeartDisease                                          \n",
       "No                65342  27593  83571  7439     108477\n",
       "Yes                1500   7084   9558  3850       5381"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"HeartDisease\"],df[\"GenHealth\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "eee2d2aa-4653-4b50-9846-724cb2698498",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now drop unnecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e584de15-a30a-47b1-b44a-3da3de943531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HeartDisease</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>AlcoholDrinking</th>\n",
       "      <th>PhysicalHealth</th>\n",
       "      <th>MentalHealth</th>\n",
       "      <th>Sex</th>\n",
       "      <th>AgeCategory</th>\n",
       "      <th>SleepTime</th>\n",
       "      <th>PhysicalActivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>16.60</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>55-59</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>20.34</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>80 or older</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>26.58</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>65-69</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>24.21</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>75-79</td>\n",
       "      <td>6.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>23.71</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>40-44</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Yes</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319790</th>\n",
       "      <td>Yes</td>\n",
       "      <td>27.41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>60-64</td>\n",
       "      <td>6.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319791</th>\n",
       "      <td>No</td>\n",
       "      <td>29.84</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>35-39</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319792</th>\n",
       "      <td>No</td>\n",
       "      <td>24.24</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>45-49</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319793</th>\n",
       "      <td>No</td>\n",
       "      <td>32.81</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>25-29</td>\n",
       "      <td>12.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319794</th>\n",
       "      <td>No</td>\n",
       "      <td>46.56</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>80 or older</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>319795 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       HeartDisease    BMI Smoking AlcoholDrinking  PhysicalHealth  \\\n",
       "0                No  16.60     Yes              No             3.0   \n",
       "1                No  20.34      No              No             0.0   \n",
       "2                No  26.58     Yes              No            20.0   \n",
       "3                No  24.21      No              No             0.0   \n",
       "4                No  23.71      No              No            28.0   \n",
       "...             ...    ...     ...             ...             ...   \n",
       "319790          Yes  27.41     Yes              No             7.0   \n",
       "319791           No  29.84     Yes              No             0.0   \n",
       "319792           No  24.24      No              No             0.0   \n",
       "319793           No  32.81      No              No             0.0   \n",
       "319794           No  46.56      No              No             0.0   \n",
       "\n",
       "        MentalHealth     Sex  AgeCategory  SleepTime PhysicalActivity  \n",
       "0               30.0  Female        55-59        5.0              Yes  \n",
       "1                0.0  Female  80 or older        7.0              Yes  \n",
       "2               30.0    Male        65-69        8.0              Yes  \n",
       "3                0.0  Female        75-79        6.0               No  \n",
       "4                0.0  Female        40-44        8.0              Yes  \n",
       "...              ...     ...          ...        ...              ...  \n",
       "319790           0.0    Male        60-64        6.0               No  \n",
       "319791           0.0    Male        35-39        5.0              Yes  \n",
       "319792           0.0  Female        45-49        6.0              Yes  \n",
       "319793           0.0  Female        25-29       12.0               No  \n",
       "319794           0.0  Female  80 or older        8.0              Yes  \n",
       "\n",
       "[319795 rows x 10 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df[[\"HeartDisease\",\"BMI\",\"Smoking\",\"AlcoholDrinking\",\"PhysicalHealth\",\"MentalHealth\",\"Sex\",\"AgeCategory\",\"SleepTime\",\"PhysicalActivity\"]]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "6fcc2809-676c-427f-8bcb-9877416fb7dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HeartDisease        0\n",
       "BMI                 0\n",
       "Smoking             0\n",
       "AlcoholDrinking     0\n",
       "PhysicalHealth      0\n",
       "MentalHealth        0\n",
       "Sex                 0\n",
       "AgeCategory         0\n",
       "SleepTime           0\n",
       "PhysicalActivity    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "24d6e3d4-7722-4077-8bfa-bebee333e014",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 319795 entries, 0 to 319794\n",
      "Data columns (total 10 columns):\n",
      " #   Column            Non-Null Count   Dtype  \n",
      "---  ------            --------------   -----  \n",
      " 0   HeartDisease      319795 non-null  object \n",
      " 1   BMI               319795 non-null  float64\n",
      " 2   Smoking           319795 non-null  object \n",
      " 3   AlcoholDrinking   319795 non-null  object \n",
      " 4   PhysicalHealth    319795 non-null  float64\n",
      " 5   MentalHealth      319795 non-null  float64\n",
      " 6   Sex               319795 non-null  object \n",
      " 7   AgeCategory       319795 non-null  object \n",
      " 8   SleepTime         319795 non-null  float64\n",
      " 9   PhysicalActivity  319795 non-null  object \n",
      "dtypes: float64(4), object(6)\n",
      "memory usage: 24.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "df617499-89c5-49ff-942b-ed5aac93c412",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0f35109-6cd5-4fdc-804f-e84d1f64ad63",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a96ed771-b61f-4668-8271-262f09693df0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0789f397-b5eb-4711-a1ab-1d97e9004ccf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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