Risk Status Tracking.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1e347aaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c9d81c3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>User_ID</th>\n",
       "      <th>Had_Health_Issue_Within_90_Days</th>\n",
       "      <th>Age</th>\n",
       "      <th>Has_Taken_Rest</th>\n",
       "      <th>Direct_Reached</th>\n",
       "      <th>Taking_Healthy_Diets</th>\n",
       "      <th>Altitude</th>\n",
       "      <th>Weather</th>\n",
       "      <th>Risk_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DS1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>2060</td>\n",
       "      <td>-5</td>\n",
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       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "    </tr>\n",
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       "      <td>1</td>\n",
       "      <td>7851</td>\n",
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       "      <th>...</th>\n",
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       "      <td>0</td>\n",
       "      <td>6947</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>DS352</td>\n",
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       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2803</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>DS353</td>\n",
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       "      <td>36</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>DS354</td>\n",
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       "      <td>23</td>\n",
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       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>355 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    User_ID  Had_Health_Issue_Within_90_Days  Age  Has_Taken_Rest  \\\n",
       "0       DS1                                0   48               0   \n",
       "1       DS2                                0   26               1   \n",
       "2       DS3                                0   28               1   \n",
       "3       DS4                                1   48               1   \n",
       "4       DS5                                1   25               0   \n",
       "..      ...                              ...  ...             ...   \n",
       "350   DS351                                1   37               0   \n",
       "351   DS352                                0   48               0   \n",
       "352   DS353                                1   36               0   \n",
       "353   DS354                                0   23               1   \n",
       "354   DS355                                1   31               1   \n",
       "\n",
       "     Direct_Reached  Taking_Healthy_Diets  Altitude  Weather  Risk_Status  \n",
       "0                 1                     1      2060       -5            0  \n",
       "1                 0                     0      4765       17            1  \n",
       "2                 0                     0      2014        5            1  \n",
       "3                 0                     1      5437       19            1  \n",
       "4                 1                     1      7851        5            1  \n",
       "..              ...                   ...       ...      ...          ...  \n",
       "350               1                     0      6947       13            1  \n",
       "351               1                     1      2803       18            0  \n",
       "352               1                     1      3050        7            1  \n",
       "353               0                     1      4911       14            0  \n",
       "354               0                     1      4159       10            1  \n",
       "\n",
       "[355 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"Tracking Dataset.xlsx\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "171d9c96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "350    0\n",
       "351    0\n",
       "352    0\n",
       "353    1\n",
       "354    1\n",
       "Name: Has_Taken_Rest, Length: 355, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.iloc[: ,1]\n",
    "y = df.iloc[: ,3]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d9362f82",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4dcc0efc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# user_encoded=le.fit_transform(df['User_ID'])\n",
    "# user_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b7741302",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Had_Health_Issue_Within_90_Days_encoded=le.fit_transform(df['Had_Health_Issue_Within_90_Days'])\n",
    "Had_Health_Issue_Within_90_Days_encoded\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2bbcd477",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Taking_Healthy_Diets_encoded=le.fit_transform(df['Taking_Healthy_Diets'])\n",
    "Taking_Healthy_Diets_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e19889ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Risk_Status_encoded=le.fit_transform(df['Risk_Status'])\n",
    "# Risk_Status_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6331a25e",
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Weather_encoded=le.fit_transform(df['Weather'])\n",
    "Weather_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e4117d02",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Age_encoded=le.fit_transform(df['Age'])\n",
    "Age_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "62ce812a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1,\n",
       "       0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0,\n",
       "       0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1,\n",
       "       1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1,\n",
       "       0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0,\n",
       "       0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0,\n",
       "       0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1,\n",
       "       1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Has_Taken_Rest_encoded=le.fit_transform(df['Has_Taken_Rest'])\n",
    "Has_Taken_Rest_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ee8125d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Direct_Reached_encoded=le.fit_transform(df['Direct_Reached'])\n",
    "# Direct_Reached_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "714401af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Altitude_encoded=le.fit_transform(df['Altitude'])\n",
    "# Altitude_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "27cbbb74",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "65b1d349",
   "metadata": {},
   "outputs": [],
   "source": [
    "# features=list(zip(Had_Health_Issue_Within_90_Days_encoded,Taking_Healthy_Diets_encoded,Age_encoded,Weather_encoded,Has_Taken_Rest_encoded,Direct_Reached_encoded,Weather_encoded,df['Had_Health_Issue_Within_90_Days'],df['Taking_Healthy_Diets'],df['Age'],df['Weather'],df['Has_Taken_Rest'],df['Direct_Reached'],df['Altitude']))\n",
    "# features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0149699f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " (1, 1, 22, 1, 1, 1, 17, 1),\n",
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       " (0, 1, 3, 0, 0, 1, -2, 0),\n",
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       " (0, 0, 14, 1, 0, 0, 9, 1),\n",
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       " (1, 0, 11, 1, 1, 0, 6, 1),\n",
       " (1, 1, 23, 0, 1, 1, 18, 0),\n",
       " (1, 1, 23, 0, 1, 1, 18, 0),\n",
       " (1, 0, 19, 1, 1, 0, 14, 1),\n",
       " (1, 0, 23, 1, 1, 0, 18, 1),\n",
       " (0, 0, 22, 1, 0, 0, 17, 1),\n",
       " (1, 0, 9, 1, 1, 0, 4, 1),\n",
       " (0, 1, 17, 0, 0, 1, 12, 0),\n",
       " (0, 0, 12, 0, 0, 0, 7, 0),\n",
       " (0, 0, 13, 1, 0, 0, 8, 1),\n",
       " (0, 1, 23, 1, 0, 1, 18, 1),\n",
       " (1, 1, 14, 0, 1, 1, 9, 0),\n",
       " (1, 0, 6, 1, 1, 0, 1, 1),\n",
       " (1, 0, 5, 0, 1, 0, 0, 0),\n",
       " (0, 0, 2, 1, 0, 0, -3, 1),\n",
       " (1, 0, 20, 1, 1, 0, 15, 1),\n",
       " (0, 0, 25, 0, 0, 0, 20, 0),\n",
       " (1, 0, 3, 0, 1, 0, -2, 0),\n",
       " (1, 0, 7, 0, 1, 0, 2, 0),\n",
       " (0, 1, 3, 0, 0, 1, -2, 0),\n",
       " (1, 1, 24, 0, 1, 1, 19, 0),\n",
       " (0, 1, 17, 1, 0, 1, 12, 1),\n",
       " (0, 1, 25, 1, 0, 1, 20, 1),\n",
       " (1, 0, 1, 1, 1, 0, -4, 1),\n",
       " (1, 0, 7, 0, 1, 0, 2, 0),\n",
       " (0, 1, 0, 1, 0, 1, -5, 1),\n",
       " (0, 1, 11, 0, 0, 1, 6, 0),\n",
       " (1, 1, 5, 1, 1, 1, 0, 1),\n",
       " (1, 1, 9, 0, 1, 1, 4, 0),\n",
       " (0, 0, 18, 0, 0, 0, 13, 0),\n",
       " (0, 0, 12, 1, 0, 0, 7, 1),\n",
       " (1, 0, 19, 1, 1, 0, 14, 1),\n",
       " (1, 0, 13, 0, 1, 0, 8, 0),\n",
       " (1, 0, 14, 0, 1, 0, 9, 0),\n",
       " (1, 0, 17, 1, 1, 0, 12, 1),\n",
       " (0, 0, 24, 1, 0, 0, 19, 1),\n",
       " (0, 0, 17, 0, 0, 0, 12, 0),\n",
       " (1, 0, 16, 1, 1, 0, 11, 1),\n",
       " (1, 0, 2, 1, 1, 0, -3, 1),\n",
       " (1, 0, 7, 0, 1, 0, 2, 0),\n",
       " (1, 1, 8, 1, 1, 1, 3, 1),\n",
       " (1, 1, 22, 0, 1, 1, 17, 0),\n",
       " (1, 0, 4, 1, 1, 0, -1, 1),\n",
       " (1, 0, 17, 1, 1, 0, 12, 1),\n",
       " (1, 0, 4, 0, 1, 0, -1, 0),\n",
       " (0, 1, 4, 0, 0, 1, -1, 0),\n",
       " (0, 0, 21, 0, 0, 0, 16, 0),\n",
       " (0, 0, 6, 1, 0, 0, 1, 1),\n",
       " (1, 1, 9, 0, 1, 1, 4, 0),\n",
       " (1, 0, 3, 0, 1, 0, -2, 0),\n",
       " (1, 0, 8, 1, 1, 0, 3, 1),\n",
       " (1, 1, 0, 0, 1, 1, -5, 0),\n",
       " (0, 0, 13, 0, 0, 0, 8, 0),\n",
       " (1, 0, 18, 0, 1, 0, 13, 0),\n",
       " (0, 0, 20, 1, 0, 0, 15, 1),\n",
       " (0, 0, 8, 1, 0, 0, 3, 1),\n",
       " (1, 1, 5, 1, 1, 1, 0, 1),\n",
       " (0, 1, 8, 1, 0, 1, 3, 1),\n",
       " (0, 1, 6, 0, 0, 1, 1, 0),\n",
       " (0, 1, 21, 0, 0, 1, 16, 0),\n",
       " (1, 1, 10, 0, 1, 1, 5, 0),\n",
       " (0, 1, 4, 1, 0, 1, -1, 1),\n",
       " (0, 0, 3, 1, 0, 0, -2, 1),\n",
       " (0, 0, 23, 0, 0, 0, 18, 0),\n",
       " (1, 0, 17, 0, 1, 0, 12, 0),\n",
       " (0, 0, 13, 0, 0, 0, 8, 0),\n",
       " (1, 0, 4, 0, 1, 0, -1, 0),\n",
       " (1, 0, 14, 1, 1, 0, 9, 1),\n",
       " (0, 0, 12, 1, 0, 0, 7, 1),\n",
       " (0, 0, 13, 0, 0, 0, 8, 0),\n",
       " (0, 0, 20, 1, 0, 0, 15, 1),\n",
       " (0, 0, 20, 0, 0, 0, 15, 0),\n",
       " (1, 0, 0, 1, 1, 0, -5, 1),\n",
       " (0, 1, 22, 1, 0, 1, 17, 1),\n",
       " (0, 1, 20, 0, 0, 1, 15, 0),\n",
       " (1, 0, 0, 0, 1, 0, -5, 0),\n",
       " (0, 0, 1, 0, 0, 0, -4, 0),\n",
       " (1, 0, 18, 0, 1, 0, 13, 0),\n",
       " (0, 1, 23, 0, 0, 1, 18, 0),\n",
       " (1, 1, 12, 0, 1, 1, 7, 0),\n",
       " (0, 1, 19, 1, 0, 1, 14, 1),\n",
       " (1, 1, 15, 1, 1, 1, 10, 1)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features=list(zip(Had_Health_Issue_Within_90_Days_encoded,Taking_Healthy_Diets_encoded,Weather_encoded,Has_Taken_Rest_encoded,df['Had_Health_Issue_Within_90_Days'],df['Taking_Healthy_Diets'],df['Weather'],df['Has_Taken_Rest']))\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2eb18e5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "350    1\n",
       "351    0\n",
       "352    1\n",
       "353    0\n",
       "354    1\n",
       "Name: Risk_Status, Length: 355, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label=df['Risk_Status']\n",
    "label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2539e25c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test,=train_test_split(features,label,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d42e4c1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "29e8a0c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn=KNeighborsClassifier(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "09aca35a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=7)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(x_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d8da6a9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=knn.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c22c6ce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0585ec03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8785046728971962\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c2bd18f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(107,)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "28112efe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(107,)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eed4f0c",
   "metadata": {},
   "source": [
    "# Finding K value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a41e46d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "error=[]\n",
    "for i in range(1,30):\n",
    "    knn = KNeighborsClassifier(n_neighbors= i)\n",
    "    knn.fit(x_train, y_train)\n",
    "    p = knn.predict(x_test)\n",
    "    b = np.mean(p!=y_test)\n",
    "    error.append(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "26b34e70",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0086951c0>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gyAemenyeAvj6DOAaYL+qnq5RVNUSVW1T1XbgSVyPoEwQycivYt6U0YgIy2ZPYL6D4ZF7652sUtY99SGJo2N54duLmeylw1RHT9tN7+Z6OYLvqutbOFhQHdDRRgMhcXQsi5LG8mqGaz7jP2cWoYp1IhvCnATBXiBFRJLcf9mvBdJ9PM9NdHosJCKe0xrdCBzy8ZgmgOqbWzlaUkvq1NFAx/DIs/ul0nbr4WL+4Xf7mDFhOH/89mImjIzxul1HT9undx2ntObsVi++2p1bgergGlZioKxKTSS7tI4jRbWkHyjgvMSRzJgwPNDFMv2kxyBQ1VbgTmArcAR4QVUPi8gGEdkAICKTRCQfuAf4sYjki8hI97phwArg5U6HfkhEDopIJnAF8AO/XZXpd4cKamhXSJ3ytykbFyeP4/IU/1bavnqggO/8fj/nTx7Fc/9wCWN7aMJ59/KZtLYp//12dp/PvSunnNjIcNLcYRdKrr0ggYgw4b/fPkZGfrXdDQxxjvoRqOoWVZ2pqsmq+u/uZZtUdZP7fbGqTlHVkao62v2+xr2uXlXHqWp1p2PeoqoXqOo8VV2lqv3XI8j4Xaa7onjelNFnLL93pf8qbf+49yR3//EAC6eP4X9uu9hRG/np4+P46oKp/OHDk+RV1vfp/Duzy1mYNJaoiNDrdzk2LoolKePZctDVy/g6C4IhLfT+Dzd+cSCvismjY8+arzZt6mhWuittuxuzpidP7zzOj146yBdS4nn6m4sY7kNl7V1XzkBE+IV7JM7eKKpuIKfsFJcN8v4D/anjLmDh9DFe62TM0GFBYHolM7+a1KmjvK67d+UsV6Xtezm9OvYv38nmgdc+4arzJrL51ot8Hu44YVQst15yDi/vzye7tNbn81fVN7Phfz4iIkxCesz9ledNYtrYYdyyeHqgi2L6mQWB8VnlqWZOVtaf9Viow6xJI7ghbTLP7DrhU6WtqvLwtiweeiOL1WmJPH7zfKIjejfm/R1Lk4mNDOeR7Ud92q+8rom1m/dwpKiWTV+/iBkTRvTq/EPB8OgI3vvhFVY/EAIsCIzPOuoHUrsIAoC7l6f4VGmrqvzb60f4r7eyWbtwKo+sSSOiDyNcjhsezW1LkthysJhDBdU97wAUVzey5ondnKg4xW++sYDlc/s+ZIMxwcCCwPgsM78aEbhgivdHQwDnjItjzUJnlbbt7co//+kQv3n/ON+4dDr/+eULCPfDeDa3f+FcRsVGsnFbVo/b5lXW89UndlFa08TvvnUxl6dY50UTOiwIjM8y8qqYET+8xwrcu5alICL8/M2uK21b29q578UMnvvgJN9ZmsxPrp/rt96rI2MiuWNpMu9klfHh8cout8spq2PNE7upaWjl2dsvZlFS6AwnYQxYEBgfqSoZ+dVd1g94mjQqhnWLz+GVj71X2ja3tnPX8x/z8scF3LdyJj+8erbfhzBYt3g68SOi2bg1y+ucCZ8W1/C1J3bT3NrO8+svCck+A8ZYEBifFFY3Ul7XRFoXLYY6u2PpDK+Vto0tbWx49iO2HCzmX66by53LfJts3anYqHC+t2wGH56o5L1j5Wesy8yvYu3mPYSHCX/89mLmJNiAaiY0WRAYn2TmVQFndyTryti4KG67/Fy2HCzmYL6r0vZUUyvfenovb2eV8h83XsBtS5L6qbQuaxdOY8qYWH629dPTdwX7TlTy909+wPDoCF789qU2fIIJaRYExicH8quICg9jdoLzZpW3X57E6GGuStuaxhZufepD9uRW8MiaVG6+eFo/ltYlKiKMu5fP5FBBDW8cKmZndjm3/OZD4kdE8+KGxUwbN6zfy2DMYBY6Y+sav8jMq2ZOwgif2vePjInkji8m859/+ZRV//U+BVUNPH7zfK65IKHnnf3kxgsns+ndHH762idU1jeTNC6OZ2+/+Kye0caEIrsjMI61tysHC6pPjzjqi1vdlbaF1Y1svmXBgIYAQHiYcM+KmRTXNDJ70gieX3+JhYAxbnZHYBzLLa+jrqnVcf2Ap9iocJ67/WLa1dXzOBCuOX8ST39zIRedM4YRMb2f5N2YocaCwDh2IM9V2eu0xVBnHdNKBoqIsHRW6I4dZExX7NGQcSwzv4rh0RGcO95a2BgzlFgQGMcy8qq4YPIowvww/IMxZvCwIDCONLW2caSolnm9fCxkjBm8LAiMI58W1dLc1t7tiKPGmODkKAhE5GoRyRKRbBG538v62SKyW0SaROS+TutOuOcmPiAi+zyWjxWR7SJyzP06pu+XY/rL6aGnbSweY4acHoNARMKBx4FrgLnATSIyt9NmlcBdwMYuDnOFqqap6gKPZfcDO1Q1Bdjh/mwGqQN51YwfHkXiqJhAF8UY42dO7ggWAdmqmquqzcDzwGrPDVS1VFX3Ai0+nHs18Iz7/TPADT7sawZYZn4VqVNG+310UGNM4DkJgslAnsfnfPcypxTYJiIfich6j+UTVbUIwP1qDbwHqbqmVrLL6nrVkcwYM/g56VDm7U/Aswd279plqlooIhOA7SLyqaq+53Rnd3isB5g2rf8HKDNnO5hfjSpdTlZvjAluTu4I8oGpHp+nAIVOT6Cqhe7XUuAVXI+aAEpEJAHA/Vraxf6bVXWBqi6Ij7fpAwMhw11RbHcExgxNToJgL5AiIkkiEgWsBdKdHFxE4kRkRMd7YCVwyL06HVjnfr8OeNWXgpuBk5lfxbSxwxgbFxXoohhj+kGPj4ZUtVVE7gS2AuHAU6p6WEQ2uNdvEpFJwD5gJNAuInfjamE0HnjFXcEYATynqm+4D/0g8IKI3AacBL7q1yszfpORV82F00YHuhjGmH7iaNA5Vd0CbOm0bJPH+2Jcj4w6qwFSuzhmBXCl45KagCirbaKgqoFvXjY90EUxxvQT61lsupVp9QPGDHkWBKZbGfnVhAmcP9kmdjdmqLIgMN3KyKti5sQRDIuyqSuMGaosCEyXVPV0j2JjzNBlQWC6lFfZwOf1LTb0tDFDnAWB6VJHRzK7IzBmaLMgMF3KyKsiOiIsYJPNG2MGhgWB6VJmfjXnJY4kMtz+NzFmKLN/4car1rZ2DhZUW/8BY0KABYHxKrusjoaWNtJsRjJjhjwLAuNVRl4VAPOmWIshY4Y6CwLjVUZ+NSNjIpg+Li7QRTHG9DMLAuNVRl4V86aMJizMpqY0ZqizIDBnaWxpI6u41mYkMyZEWBCYs3xSVENru1qLIWNChAXBAGhpa+dbT+/l1QMFgS6KI1sPFwNYiyFjQoQNKTkA/t9H+bz1aSmZ+dWsmDtxUI/k+d9vHeOJd3NZlZrIxJExgS6OMWYA2B1BP2tsaeMXbx5jyphYyuuaeHrXiUAXyStV5aE3PmXjtqN8+cLJPLLG68RyxpghyIKgnz275zOKaxr52d+lcuXsCWx6J4fqhpZAF+sMqspPX/uEX76Tw80XT2PjV1OJsGEljAkZjv61i8jVIpIlItkicr+X9bNFZLeINInIfR7Lp4rI2yJyREQOi8j3PdY9ICIFInLA/XOtfy5p8KhrauVX7+SwZMZ4FieP456VM6lpbOXXf80NdNFOa2tX/tfLB3l61wluW5LEv99wvjUZNSbE9BgEIhIOPA5cA8wFbhKRuZ02qwTuAjZ2Wt4K3Kuqc4BLgO922vdRVU1z/2zp7UUMVr99/zgVp5q576pZAJyXOIrr5iXwm/ePU17XFODSucYTuueFAzy/N4/vLZvBj780BxELAWNCjZM7gkVAtqrmqmoz8Dyw2nMDVS1V1b1AS6flRaq63/2+FjgCTPZLyQe5qvpmNr+Xy8q5E89ofXPPipk0tbbzy7dzAlc4oKm1je8+t59XDxTyw6tnce/KWRYCxoQoJ0EwGcjz+JxPL36Zi8h04ELgA4/Fd4pIpog8JSJjuthvvYjsE5F9ZWVlvp42YDa9m0tdcyv3rpx1xvJz44fzd/On8OyezyisaghI2Rpb2lj/u4/YeriEn1w/l+8snRGQchhjBgcnQeDtz0T15SQiMhx4CbhbVWvci38FJANpQBHwsLd9VXWzqi5Q1QXx8fG+nDZgSmsbeXrXcVanJnqd1OWu5SkA/Ndbxwa6aNQ1tfKN337Ie8fKePDLF/DNy5IGvAzGmMHFSRDkA1M9Pk8BCp2eQEQicYXA71X15Y7lqlqiqm2q2g48iesR1JDw+FvZtLYpdy+f6XX95NGx3HzxNF7Yl8/x8lMDVq7qhhZu+c0H7D3xOT//WhprF00bsHMbYwYvJ0GwF0gRkSQRiQLWAulODi6uh86/AY6o6iOd1iV4fLwROOSsyP3rUEE1e3Irer1/XmU9z314kjULpzJ9fNcjd373ihlEhYfx6PajvT6XL6rrW7j5yT0cKqjm8ZvnszotJKpqjDEO9BgEqtoK3AlsxVXZ+4KqHhaRDSKyAUBEJolIPnAP8GMRyReRkcBlwC3AMi/NRB8SkYMikglcAfzA/5fnu//YcoSbn9zDSx/l92r/x3YcQ0T43rLun7vHj4jmW0umk55RyJGimm639Yff7T7B4cIaNt+6gKvPn9Tv5zPGBA9HYx24m3Zu6bRsk8f7YlyPjDp7H+91DKjqLc6LOXAKqhpQ4N4XM2hoaePrl5zjeN/s0jpe2p/Pty5LImFUbI/br788md/t/oyHtx3l1+sW9KHU3VNVXs0oZNH0sVwxa0K/nccYE5ys+6iH9nalqKqRdYuns2z2BH78p0M+df56dPtRYiPDuWNpsqPtRw2LZMMXk3nzSAn7T37e22L36EhRLdmldVyflthv5zDGBC8LAg/lp5pobmvn3Pg4Nn39Ir50QQL/9voRHttxDNXuG0odKqjm9YNF3LYkiXHDox2f8xuXTmf88Cg2bs3qa/G7lJ5RSHiYcK09EjLGeGFB4KGwqhGAxFGxREWE8Yu1aXx5/mQe2X6U//tGVrdh8PC2LEbFRnL7F8716Zxx0RF894oZ7MqpYGd2eZ/K742q8lpGIUtmjPcpoIwxocOCwEORu4NXwmjX8MsR4WFs/LtU/v7iaWx6N4cH0g/T3n52GOw9UcnbWWXcsTSZkTGRPp/35ounkTgqhp9t7T5semP/yc8pqGpgVao9FjLGeGdB4KHAHQSTR/+tojcsTPi3G87n9iVJPLP7M+5/OZM2jzBQVX62NYv4EdGsWzy9V+eNjgjn+8tTOJBXxZtHSvt0DZ2lHygkOiKMledN9OtxjTFDhwWBh8KqRoZFhTMq9sy/6kWEf/7SHO66MoUX9uXzgz8eoKWtHYC/Hivnw+OVfG/ZDGKjwnt97q/Mn0LS+Dg2bs3yetfRG61t7bx+sIgr50xgRC/uVIwxocGCwENRdQMJo2K8Dr4mItyzYiY/uno26RmFfOf3+2lsaeNnW7OYMiaWtQv71ks3IjyMe1bMJKukltcyHXfc7taunArK65rtsZAxplsWBB4KqxpIHN19+/87libz01Xnsf2TEr702F85WFDN3ctnEhXR9/+UX7oggTkJI3l0+9HTdxx9kZ5RyIjoCJZa3wFjTDcsCDwUVDWeUT/QlXWXTuehr8wjt/wUyfFx3Hihf4ZrCAsT7ls5kxMV9by4r3c9mzs0trSx9VAxK8+bRExk7x9ZGWOGvsE7i/oAa2pto7yuqcc7gg5rFk4lecJwxsVFEe7HGb2WzZ7A/GmjeWzHMb48f3Kvf4m/k1VGbVMrq6wTmTGmB3ZH4FZc7epDkDAqxvE+F50zptuB5XpDRPjHq2ZTXNPIs3s+6/VxXssoZFxcFJclj/Nj6YwxQ5EFgZu3pqOBsjh5HJenjOdX7+RQ19Tq8/61jS28eaSEL81LsEnojTE9st8Sbqd7FQ+CIAC4d+UsKk4189v3j/u87/ZPSmhqbbfWQsYYRywI3DqmjZzkw6Oh/pQ2dTQr505k83u5VNU3+7RvekYhk0fHMn+a19k/jTHmDBYEbkXVDYwfHjWoWtjcu3IWdc2tPPGe8xFQK0818/6xcq5LTSDMj5XYxpihy4LAraCqcdA8Fuowa9IIbkibzG93Hqe0ttHRPlsOFtHarvZYyBjjmAWBW2FVA4kOJpMZaHcvT6G1TXn8rWxH26dnFDJjwnDmJozs55IZY4YKCwJcA8cVVTWcHnV0MDlnXBxrFk7luQ9PkldZ3+22hVUNfHi8klWpiV6HyTDGGG8sCICahlZONbcNiqaj3ty1LAUR4bEdx7rd7s/uMYrssZAxxheOgkBErhaRLBHJFpH7vayfLSK7RaRJRO5zsq+IjBWR7SJyzP0asCYuHX0IBlsdQYdJo2K49ZJzeGl/PtmldV1ul55RyLwpo/zeyc0YM7T1GAQiEg48DlwDzAVuEpG5nTarBO4CNvqw7/3ADlVNAXa4PwdER9NRX3oVD7Q7liYTGxnOo9uPel2fW1bHoYIauxswxvjMyR3BIiBbVXNVtRl4HljtuYGqlqrqXqDFh31XA8+43z8D3NC7S+i7ourB06u4K+OGR3Pb5efy+sEiDhVUn7U+PaMQEbhungWBMcY3ToJgMpDn8TnfvcyJ7vadqKpFAO5Xr2Mli8h6EdknIvvKysocntY3BVWNRIYL4wf5nL63X57E6GGRPLztzInuVZX0jEIuTho7aDrEGWOCh5Mg8Nb8xOkUWn3Z17Wx6mZVXaCqC+Lj433Z1bHCqgYSRsUO+g5YI2Mi2fDFZN7OKmPvicrTyw8X1pBbdopVqf4ZDtsYE1qcBEE+MNXj8xTA6RRa3e1bIiIJAO5X/07W64OOmcmCwbrF04kfEc3P3vjbRPfpGYVEhAnXnD8pwKUzxgQjJ0GwF0gRkSQRiQLWAukOj9/dvunAOvf7dcCrzovtX4UOJ6QZDGKjwrlr2Qw+PFHJe8fKaW9XXsso5Asz4xkTFxXo4hljglCPQaCqrcCdwFbgCPCCqh4WkQ0isgFARCaJSD5wD/BjEckXkZFd7es+9IPAChE5Bqxwfx5wrW3tFNcMvuEluvO1hdOYMiaWjVuz2HuikqLqRmstZIzpNUczlKnqFmBLp2WbPN4X43rs42hf9/IK4EpfCtsfSmubaGvXQdmruCtREWHcvXwm972YwT+9cpCYyDBWzJ0Y6GIZY4JUyPcs7mg6Gkx3BAA3XjiZGROGk1N2iuVzJhIXbbOOGmN6J+SDoMA9IU2w1BF0CA8T7ls5C4Avz7fWQsaY3gv5PyODoVdxV64+fxJv37eUJBtSwhjTByF/R1BU1cCImAhGxEQGuii9YiFgjOmrkA+CgiBqOmqMMf0h5IOgsKoh6CqKjTHGnywIqhtIDKKmo8YY428hHQT1za1U1beQMAinqDTGmIES0kFQGKRNR40xxp9CPAiCszOZMcb4kwUBwdmHwBhj/CW0g6C6ERFsMhdjTEgL7SCoamDiiBgiw0P6P4MxJsSF9G9AVx8CuxswxoS2kA6CoupGEqyi2BgT4kI2CFSVgqoGazpqjAl5IRsEFaeaaW5tJ9Eqio0xIS5kg+B001G7IzDGhLgQDgLrVWyMMeAwCETkahHJEpFsEbnfy3oRkcfc6zNFZL57+SwROeDxUyMid7vXPSAiBR7rrvXrlfXAehUbY4xLjzOUiUg48DiwAsgH9opIuqp+4rHZNUCK++di4FfAxaqaBaR5HKcAeMVjv0dVdaMfrsNnhVUNxESGMWZYcE5IY4wx/uLkjmARkK2quaraDDwPrO60zWrgd+qyBxgtIgmdtrkSyFHVz/pcaj8oqm4kcVQsIhLoohhjTEA5CYLJQJ7H53z3Ml+3WQv8odOyO92Pkp4SkTHeTi4i60Vkn4jsKysrc1BcZwpsQhpjjAGcBYG3P5nVl21EJApYBbzosf5XQDKuR0dFwMPeTq6qm1V1gaouiI+Pd1BcZ6xXsTHGuDgJgnxgqsfnKUChj9tcA+xX1ZKOBapaoqptqtoOPInrEdSAaG5tp6yuySakMcYYnAXBXiBFRJLcf9mvBdI7bZMO3OpuPXQJUK2qRR7rb6LTY6FOdQg3Aod8Ln0vldQ0ompNR40xBhy0GlLVVhG5E9gKhANPqephEdngXr8J2AJcC2QD9cA3O/YXkWG4Whx9u9OhHxKRNFyPkE54Wd9vCqzpqDHGnNZjEACo6hZcv+w9l23yeK/Ad7vYtx4Y52X5LT6V1I/+1ofA6giMMSYkexYXVbt6FVsdgTHGhGgQFFQ1MDYuitio8EAXxRhjAi4kg8CajhpjzN+EbBDYYyFjjHEJySAoqmq0pqPGGOMWckFQ09hCbVOrPRoyxhi3kAsCG37aGGPOFLJBYHUExhjjEoJBYDOTGWOMpxAMggYiwoT4EdGBLooxxgwKIRkEE0fGEB5mE9IYYwyEYhBUW9NRY4zxFHpBYL2KjTHmDCEVBG3tSnF1ozUdNcYYDyEVBGW1TbS2KwkWBMYYc1pIBUFhtasPwWR7NGSMMaeFVhBYr2JjjDlLSAaB9So2xpi/CbEgaGR4dAQjYxzN0GmMMSHBURCIyNUikiUi2SJyv5f1IiKPuddnish8j3UnROSgiBwQkX0ey8eKyHYROeZ+HeOfS+paR9NREetMZowxHXoMAhEJBx4HrgHmAjeJyNxOm10DpLh/1gO/6rT+ClVNU9UFHsvuB3aoagqww/25XxVWN1j9gDHGdOLkjmARkK2quaraDDwPrO60zWrgd+qyBxgtIgk9HHc18Iz7/TPADc6L3TuFVY1WP2CMMZ04CYLJQJ7H53z3MqfbKLBNRD4SkfUe20xU1SIA9+sEbycXkfUisk9E9pWVlTkorneNLW1Unmq2pqPGGNOJkyDw9kBdfdjmMlWdj+vx0XdF5As+lA9V3ayqC1R1QXx8vC+7nsGajhpjjHdOgiAfmOrxeQpQ6HQbVe14LQVewfWoCaCk4/GR+7XU18L7omMeAns0ZIwxZ3ISBHuBFBFJEpEoYC2Q3mmbdOBWd+uhS4BqVS0SkTgRGQEgInHASuCQxz7r3O/XAa/28Vq61XFHYCOPGmPMmXpsUK+qrSJyJ7AVCAeeUtXDIrLBvX4TsAW4FsgG6oFvunefCLzibq4ZATynqm+41z0IvCAitwEnga/67aq8KKxuQAQmjrIJaYwxxpOjnlWqugXXL3vPZZs83ivwXS/75QKpXRyzArjSl8L2RWFVA/HDo4mOCB+oUxpjTFAImZ7FhVWNNuqoMcZ4ETpBUN1gTUeNMcaLkAgCVXUNL2Ethowx5iwhEQSf17fQ2NJuj4aMMcaLkAiCvzUdtUdDxhjTWUgFgfUqNsaYs1kQGGNMiAuNIKhuJCoijHFxUYEuijHGDDohEQTnjo/jhrREm5DGGGO8CIk5G9cumsbaRdMCXQxjjBmUQuKOwBhjTNcsCIwxJsRZEBhjTIizIDDGmBBnQWCMMSHOgsAYY0KcBYExxoQ4CwJjjAlx4pplMjiISBnwmcei8UB5gIrT34bqtdl1BZ+hem2hdF3nqGp8VzsEVRB0JiL7VHVBoMvRH4bqtdl1BZ+hem12XX9jj4aMMSbEWRAYY0yIC/Yg2BzoAvSjoXptdl3BZ6hem12XW1DXERhjjOm7YL8jMMYY00cWBMYYE+KCNghE5GoRyRKRbBG5P9Dl8RcROSEiB0XkgIjsC3R5+kJEnhKRUhE55LFsrIhsF5Fj7tcxgSxjb3RxXQ+ISIH7ezsgItcGsoy9ISJTReRtETkiIodF5Pvu5UH9nXVzXUPhO4sRkQ9FJMN9bT91L/fpOwvKOgIRCQeOAiuAfGAvcJOqfhLQgvmBiJwAFqhq0Hd0EZEvAHXA71T1fPeyh4BKVX3QHeBjVPVHgSynr7q4rgeAOlXdGMiy9YWIJAAJqrpfREYAHwE3AN8giL+zbq5rDcH/nQkQp6p1IhIJvA98H/gyPnxnwXpHsAjIVtVcVW0GngdWB7hMphNVfQ+o7LR4NfCM+/0zuP5BBpUurivoqWqRqu53v68FjgCTCfLvrJvrCnrqUuf+GOn+UXz8zoI1CCYDeR6f8xkiXyyuL3GbiHwkIusDXZh+MFFVi8D1DxSYEODy+NOdIpLpfnQUVI9POhOR6cCFwAcMoe+s03XBEPjORCRcRA4ApcB2VfX5OwvWIBAvy4LvGZd3l6nqfOAa4LvuxxBm8PsVkAykAUXAwwEtTR+IyHDgJeBuVa0JdHn8xct1DYnvTFXbVDUNmAIsEpHzfT1GsAZBPjDV4/MUoDBAZfErVS10v5YCr+B6DDaUlLif2XY8uy0NcHn8QlVL3P8g24EnCdLvzf2c+SXg96r6sntx0H9n3q5rqHxnHVS1CngHuBofv7NgDYK9QIqIJIlIFLAWSA9wmfpMROLclVmISBywEjjU/V5BJx1Y536/Dng1gGXxm45/dG43EoTfm7vi8TfAEVV9xGNVUH9nXV3XEPnO4kVktPt9LLAc+BQfv7OgbDUE4G7q9XMgHHhKVf89sCXqOxE5F9ddAEAE8FwwX5eI/AFYimtY3BLgJ8CfgBeAacBJ4KuqGlQVr11c11JcjxgUOAF8u+MZbbAQkSXAX4GDQLt78T/hep4etN9ZN9d1E8H/nc3DVRkcjusP+xdU9f+IyDh8+M6CNgiMMcb4R7A+GjLGGOMnFgTGGBPiLAiMMSbEWRAYY0yIsyAwxpgQZ0FgjDEhzoLAGGNC3P8H1hEpRymZaosAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,30),error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "975f832c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = KNeighborsClassifier(n_neighbors=5)\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7ca9137",
   "metadata": {},
   "source": [
    "# Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3140e37f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12, 12],\n",
       "       [ 1, 82]], dtype=int64)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ac7fd17",
   "metadata": {},
   "source": [
    "# Precision , Recall , F1-Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0eebd557",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.50      0.65        24\n",
      "           1       0.87      0.99      0.93        83\n",
      "\n",
      "    accuracy                           0.88       107\n",
      "   macro avg       0.90      0.74      0.79       107\n",
      "weighted avg       0.88      0.88      0.86       107\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "273817b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        DS1\n",
       "1        DS2\n",
       "2        DS3\n",
       "3        DS4\n",
       "4        DS5\n",
       "       ...  \n",
       "350    DS351\n",
       "351    DS352\n",
       "352    DS353\n",
       "353    DS354\n",
       "354    DS355\n",
       "Name: User_ID, Length: 355, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_value=df['User_ID']\n",
    "x_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b6660f53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "350    1\n",
       "351    0\n",
       "352    1\n",
       "353    0\n",
       "354    1\n",
       "Name: Risk_Status, Length: 355, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_value=df['Risk_Status']\n",
    "y_value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bc9fc7d",
   "metadata": {},
   "source": [
    "# Visualization of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "eda0c66f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Risk_Status'], df['Age'], color ='green')\n",
    "plt.xlabel(\"risk\")\n",
    "plt.ylabel(\"age\")\n",
    "plt.title(\"TT\")\n",
    "plt.show()\n",
    "\n",
    "# print(df.Age[25:30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c84dafe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(df['Risk_Status'], df['Altitude'], color ='green' , width=0.01)\n",
    "plt.xlabel(\"risk\")\n",
    "plt.ylabel(\"age\")\n",
    "plt.xticks(df.Risk_Status[0:5])\n",
    "plt.yticks(df.Altitude[0:5])\n",
    "plt.title(\"TT\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ddf99350",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(df['Weather'], df['Age'])\n",
    "plt.xlabel(\"weather\")\n",
    "plt.ylabel(\"age\")\n",
    "plt.xticks(df.Weather[10:20])\n",
    "plt.yticks(df.Age[25:30])\n",
    "plt.title(\"TT\")\n",
    "plt.show()\n",
    "\n",
    "# print(df.Age[25:30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f25f63af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(df['Weather'], df['Age'])\n",
    "plt.xlabel(\"weather\")\n",
    "plt.ylabel(\"age\")\n",
    "plt.xticks(df.Weather[10:20])\n",
    "plt.yticks(df.Age[25:30])\n",
    "plt.title(\"TT\")\n",
    "plt.show()\n",
    "\n",
    "# print(df.Age[25:30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2100c273",
   "metadata": {},
   "outputs": [],
   "source": [
    "# no = list(df([df.Risk_Status == 0] & [df.Age >30]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "61cffbb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "no = df.loc[df['Risk_Status'] == 0 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34a4b556",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "26a486c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "Age = df[(df['Age'] < 25) &  (df['Altitude'] <= 5000 ) & (df['Altitude'] >= 3000) & (df['Risk_Status'] == 1 )]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bf394fc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "Ega = df[(df['Age'] < 25)  & (df['Altitude'] <= 5000 ) & (df['Altitude'] >= 3000) & (df['Risk_Status'] == 0 )]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a547b59a",
   "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>User_ID</th>\n",
       "      <th>Had_Health_Issue_Within_90_Days</th>\n",
       "      <th>Age</th>\n",
       "      <th>Has_Taken_Rest</th>\n",
       "      <th>Direct_Reached</th>\n",
       "      <th>Taking_Healthy_Diets</th>\n",
       "      <th>Altitude</th>\n",
       "      <th>Weather</th>\n",
       "      <th>Risk_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>DS165</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3406</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>DS208</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3680</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>DS354</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4911</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    User_ID  Had_Health_Issue_Within_90_Days  Age  Has_Taken_Rest  \\\n",
       "164   DS165                                0   19               1   \n",
       "207   DS208                                0   19               1   \n",
       "353   DS354                                0   23               1   \n",
       "\n",
       "     Direct_Reached  Taking_Healthy_Diets  Altitude  Weather  Risk_Status  \n",
       "164               0                     1      3406        4            0  \n",
       "207               0                     1      3680       15            0  \n",
       "353               0                     1      4911       14            0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ega"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e6f763d6",
   "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>User_ID</th>\n",
       "      <th>Had_Health_Issue_Within_90_Days</th>\n",
       "      <th>Age</th>\n",
       "      <th>Has_Taken_Rest</th>\n",
       "      <th>Direct_Reached</th>\n",
       "      <th>Taking_Healthy_Diets</th>\n",
       "      <th>Altitude</th>\n",
       "      <th>Weather</th>\n",
       "      <th>Risk_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DS17</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4890</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>DS92</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3017</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>DS98</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4627</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>DS102</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4148</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>DS107</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4366</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>DS114</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3684</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>DS135</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4005</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>DS144</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3680</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>DS152</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3447</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>DS175</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3557</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>DS227</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4766</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>DS304</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4125</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>DS321</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4573</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>DS329</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3724</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    User_ID  Had_Health_Issue_Within_90_Days  Age  Has_Taken_Rest  \\\n",
       "16     DS17                                1   20               0   \n",
       "91     DS92                                0   20               0   \n",
       "97     DS98                                1   23               0   \n",
       "101   DS102                                0   18               0   \n",
       "106   DS107                                0   21               1   \n",
       "113   DS114                                1   22               0   \n",
       "134   DS135                                0   22               0   \n",
       "143   DS144                                1   20               0   \n",
       "151   DS152                                1   23               1   \n",
       "174   DS175                                1   23               0   \n",
       "226   DS227                                0   20               1   \n",
       "303   DS304                                0   20               1   \n",
       "320   DS321                                0   18               1   \n",
       "328   DS329                                0   23               1   \n",
       "\n",
       "     Direct_Reached  Taking_Healthy_Diets  Altitude  Weather  Risk_Status  \n",
       "16                1                     1      4890       19            1  \n",
       "91                1                     0      3017       15            1  \n",
       "97                1                     1      4627        4            1  \n",
       "101               1                     0      4148        0            1  \n",
       "106               0                     0      4366       17            1  \n",
       "113               1                     1      3684        9            1  \n",
       "134               1                     0      4005        7            1  \n",
       "143               1                     0      3680        0            1  \n",
       "151               0                     0      3447        7            1  \n",
       "174               1                     1      3557        2            1  \n",
       "226               0                     0      4766        5            1  \n",
       "303               0                     0      4125        7            1  \n",
       "320               0                     0      4573        1            1  \n",
       "328               0                     0      3724        3            1  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9c748a63",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df1 = pd.Dataframe(Age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ab529b4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Age.type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "0db12478",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.bar(Age.Risk_Status , Age.Age , width = 0.1)\n",
    "plt.bar(Age.Age , Age.Altitude , width = 0.1)\n",
    "plt.bar(Ega.Age , Ega.Altitude , width = 0.1)\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Altitude\")\n",
    "# plt.xticks(no.Age[:50])\n",
    "# plt.yticks(no.Altitude[5:10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "1fd5d87d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pxc = df[(df['Weather'] > 0) & (df['Weather'] < 19) & (df['Age'] > 25 ) & (df['Age'] < 50 )]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c5675342",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(pxc.Age , pxc.Weather , width = 0.1)\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Weather\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8cf209fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16     20\n",
       "91     20\n",
       "97     23\n",
       "101    18\n",
       "106    21\n",
       "113    22\n",
       "134    22\n",
       "143    20\n",
       "151    23\n",
       "174    23\n",
       "226    20\n",
       "303    20\n",
       "320    18\n",
       "328    23\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Age.Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "aa886a13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        DS1\n",
       "13      DS14\n",
       "15      DS16\n",
       "20      DS21\n",
       "22      DS23\n",
       "       ...  \n",
       "334    DS335\n",
       "346    DS347\n",
       "347    DS348\n",
       "351    DS352\n",
       "353    DS354\n",
       "Name: User_ID, Length: 74, dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no.User_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "887122c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      DS1\n",
       "13    DS14\n",
       "15    DS16\n",
       "20    DS21\n",
       "22    DS23\n",
       "32    DS33\n",
       "36    DS37\n",
       "40    DS41\n",
       "51    DS52\n",
       "54    DS55\n",
       "Name: User_ID, dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no.User_ID.iloc[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2924ce7",
   "metadata": {},
   "source": [
    "# Analysing the Data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef090377",
   "metadata": {},
   "source": [
    "### Seeing which age group has risk or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "920a4015",
   "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>Had_Health_Issue_Within_90_Days</th>\n",
       "      <th>Has_Taken_Rest</th>\n",
       "      <th>Direct_Reached</th>\n",
       "      <th>Taking_Healthy_Diets</th>\n",
       "      <th>Altitude</th>\n",
       "      <th>Weather</th>\n",
       "      <th>Risk_Status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</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>18</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>56342</td>\n",
       "      <td>48</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>58736</td>\n",
       "      <td>95</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>61619</td>\n",
       "      <td>94</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>41634</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>46889</td>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>70365</td>\n",
       "      <td>82</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>48464</td>\n",
       "      <td>55</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>62868</td>\n",
       "      <td>78</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>73698</td>\n",
       "      <td>95</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>45139</td>\n",
       "      <td>104</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>51356</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>61291</td>\n",
       "      <td>96</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>34866</td>\n",
       "      <td>78</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>64307</td>\n",
       "      <td>84</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>35672</td>\n",
       "      <td>77</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>35122</td>\n",
       "      <td>63</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>39646</td>\n",
       "      <td>83</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>35255</td>\n",
       "      <td>60</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>55872</td>\n",
       "      <td>82</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>61189</td>\n",
       "      <td>103</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>23620</td>\n",
       "      <td>59</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>50637</td>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>50169</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>64179</td>\n",
       "      <td>90</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7189</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>54627</td>\n",
       "      <td>51</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>56290</td>\n",
       "      <td>44</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>38453</td>\n",
       "      <td>82</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>41720</td>\n",
       "      <td>55</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>27045</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>34572</td>\n",
       "      <td>91</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>47623</td>\n",
       "      <td>92</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>29177</td>\n",
       "      <td>44</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>58288</td>\n",
       "      <td>114</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>70057</td>\n",
       "      <td>51</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>31098</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9397</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>45758</td>\n",
       "      <td>125</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Had_Health_Issue_Within_90_Days  Has_Taken_Rest  Direct_Reached  \\\n",
       "Age                                                                    \n",
       "18                                 5               6               5   \n",
       "19                                 5               8               3   \n",
       "20                                 4               5               7   \n",
       "21                                 4               6               2   \n",
       "22                                 4               3               5   \n",
       "23                                 8               6               9   \n",
       "24                                 2               3               4   \n",
       "25                                 4               5               6   \n",
       "26                                 5               7               7   \n",
       "27                                 6               4               6   \n",
       "28                                 6               7               4   \n",
       "29                                 4               5               6   \n",
       "30                                 3               5               3   \n",
       "31                                 4               9               3   \n",
       "32                                 4               2               5   \n",
       "33                                 1               4               3   \n",
       "34                                 6               5               4   \n",
       "35                                 3               6               2   \n",
       "36                                 8               8               4   \n",
       "37                                10               7               6   \n",
       "38                                 4               2               3   \n",
       "39                                 4               3               6   \n",
       "40                                 8               5               7   \n",
       "41                                 5               5               6   \n",
       "42                                 1               0               2   \n",
       "43                                 5               6               5   \n",
       "44                                 5               7               4   \n",
       "45                                 4               4               3   \n",
       "46                                 3               3               5   \n",
       "47                                 4               2               4   \n",
       "48                                 5               6               4   \n",
       "49                                 5               4               5   \n",
       "50                                 4               2               4   \n",
       "51                                 6               5               7   \n",
       "52                                 9               7               8   \n",
       "53                                 1               1               4   \n",
       "54                                 0               1               1   \n",
       "55                                 4               8               1   \n",
       "\n",
       "     Taking_Healthy_Diets  Altitude  Weather  Risk_Status  \n",
       "Age                                                        \n",
       "18                      3     56342       48           11  \n",
       "19                      6     58736       95            7  \n",
       "20                      5     61619       94           10  \n",
       "21                      7     41634       70            5  \n",
       "22                      5     46889       37            6  \n",
       "23                      8     70365       82           11  \n",
       "24                      2     48464       55            6  \n",
       "25                      3     62868       78           10  \n",
       "26                      4     73698       95           11  \n",
       "27                      7     45139      104            8  \n",
       "28                      5     51356       64           10  \n",
       "29                      4     61291       96            9  \n",
       "30                      4     34866       78            6  \n",
       "31                      8     64307       84            7  \n",
       "32                      4     35672       77            6  \n",
       "33                      2     35122       63            5  \n",
       "34                      5     39646       83            8  \n",
       "35                      3     35255       60            6  \n",
       "36                      7     55872       82           10  \n",
       "37                      7     61189      103           10  \n",
       "38                      1     23620       59            4  \n",
       "39                      2     50637       21            7  \n",
       "40                      6     50169       64           10  \n",
       "41                      6     64179       90            8  \n",
       "42                      1      7189       15            2  \n",
       "43                      8     54627       51            7  \n",
       "44                      4     56290       44            8  \n",
       "45                      1     38453       82            7  \n",
       "46                      3     41720       55            7  \n",
       "47                      4     27045       21            4  \n",
       "48                      6     34572       91            7  \n",
       "49                      5     47623       92            8  \n",
       "50                      2     29177       44            5  \n",
       "51                      4     58288      114           10  \n",
       "52                      9     70057       51           11  \n",
       "53                      1     31098       37            4  \n",
       "54                      0      9397        8            2  \n",
       "55                      3     45758      125            8  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = df.groupby('Age').sum()\n",
    "\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "92df2ff1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Age = range(18,20)\n",
    "\n",
    "# plt.bar(Age , results)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a2331342",
   "metadata": {},
   "outputs": [],
   "source": [
    "Wt = df[(C & (df['Age'] < 35) & (df['Altitude'] > 6000 ) & (df['Direct_Reached'] == 1 ) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2f1ceaf0",
   "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>User_ID</th>\n",
       "      <th>Had_Health_Issue_Within_90_Days</th>\n",
       "      <th>Age</th>\n",
       "      <th>Has_Taken_Rest</th>\n",
       "      <th>Direct_Reached</th>\n",
       "      <th>Taking_Healthy_Diets</th>\n",
       "      <th>Altitude</th>\n",
       "      <th>Weather</th>\n",
       "      <th>Risk_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>DS30</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7794</td>\n",
       "      <td>-5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>DS65</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7234</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>DS123</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7473</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>DS162</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6505</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>DS172</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6455</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>DS223</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7960</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>DS242</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7899</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>DS293</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7685</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>DS310</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6280</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>DS343</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7449</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    User_ID  Had_Health_Issue_Within_90_Days  Age  Has_Taken_Rest  \\\n",
       "29     DS30                                0   26               0   \n",
       "64     DS65                                1   32               0   \n",
       "122   DS123                                0   30               0   \n",
       "161   DS162                                0   26               0   \n",
       "171   DS172                                1   27               0   \n",
       "222   DS223                                1   29               0   \n",
       "241   DS242                                0   33               0   \n",
       "292   DS293                                0   29               0   \n",
       "309   DS310                                0   26               0   \n",
       "342   DS343                                0   27               0   \n",
       "\n",
       "     Direct_Reached  Taking_Healthy_Diets  Altitude  Weather  Risk_Status  \n",
       "29                1                     0      7794       -5            1  \n",
       "64                1                     1      7234       17            1  \n",
       "122               1                     1      7473       12            0  \n",
       "161               1                     0      6505       16            1  \n",
       "171               1                     1      6455       13            1  \n",
       "222               1                     1      7960       10            1  \n",
       "241               1                     0      7899       19            1  \n",
       "292               1                     1      7685       -2            0  \n",
       "309               1                     0      6280       12            1  \n",
       "342               1                     0      7449        8            1  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Wt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "29f43e5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wt3 = df[(df['Had_Health_Issue_Within_90_Days'] == 0 ) & (df['Taking_Healthy_Diets'] == 0 )  & (df['Risk_Status'] == 1 ) & (df['Age'] > 18) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "f1b8ecbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\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>User_ID</th>\n",
       "      <th>Had_Health_Issue_Within_90_Days</th>\n",
       "      <th>Age</th>\n",
       "      <th>Has_Taken_Rest</th>\n",
       "      <th>Direct_Reached</th>\n",
       "      <th>Taking_Healthy_Diets</th>\n",
       "      <th>Altitude</th>\n",
       "      <th>Weather</th>\n",
       "      <th>Risk_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DS2</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4765</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DS3</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DS7</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4660</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DS12</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5344</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>DS15</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7152</td>\n",
       "      <td>16</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>DS342</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>DS343</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7449</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>DS344</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7594</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>DS345</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2507</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>DS350</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4691</td>\n",
       "      <td>-4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    User_ID  Had_Health_Issue_Within_90_Days  Age  Has_Taken_Rest  \\\n",
       "1       DS2                                0   26               1   \n",
       "2       DS3                                0   28               1   \n",
       "6       DS7                                0   25               1   \n",
       "11     DS12                                0   27               0   \n",
       "14     DS15                                0   46               0   \n",
       "..      ...                              ...  ...             ...   \n",
       "341   DS342                                0   30               1   \n",
       "342   DS343                                0   27               0   \n",
       "343   DS344                                0   55               1   \n",
       "344   DS345                                0   46               0   \n",
       "349   DS350                                0   43               0   \n",
       "\n",
       "     Direct_Reached  Taking_Healthy_Diets  Altitude  Weather  Risk_Status  \n",
       "1                 0                     0      4765       17            1  \n",
       "2                 0                     0      2014        5            1  \n",
       "6                 0                     0      4660        3            1  \n",
       "11                1                     0      5344        5            1  \n",
       "14                1                     0      7152       16            1  \n",
       "..              ...                   ...       ...      ...          ...  \n",
       "341               0                     0      2013        7            1  \n",
       "342               1                     0      7449        8            1  \n",
       "343               0                     0      7594       15            1  \n",
       "344               1                     0      2507       15            1  \n",
       "349               1                     0      4691       -4            1  \n",
       "\n",
       "[101 rows x 9 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Wt3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1e1668f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "Wt1 = df[(df['Age'] > 45) & (df['Altitude'] > 6000 ) & (df['Weather'] <= 0) & (df['Direct_Reached'] == 1 ) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "9d91b354",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Had_Health_Issue_Within_90_Days</th>\n",
       "      <th>Age</th>\n",
       "      <th>Has_Taken_Rest</th>\n",
       "      <th>Direct_Reached</th>\n",
       "      <th>Taking_Healthy_Diets</th>\n",
       "      <th>Altitude</th>\n",
       "      <th>Weather</th>\n",
       "      <th>Risk_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>DS130</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7472</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>DS149</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6063</td>\n",
       "      <td>-2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>DS340</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7781</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    User_ID  Had_Health_Issue_Within_90_Days  Age  Has_Taken_Rest  \\\n",
       "129   DS130                                1   50               0   \n",
       "148   DS149                                1   46               0   \n",
       "339   DS340                                1   53               0   \n",
       "\n",
       "     Direct_Reached  Taking_Healthy_Diets  Altitude  Weather  Risk_Status  \n",
       "129               1                     0      7472       -1            1  \n",
       "148               1                     0      6063       -2            1  \n",
       "339               1                     0      7781       -1            1  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Wt1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "65b46c36",
   "metadata": {},
   "outputs": [],
   "source": [
    "Wt5 = df[(df['Age'] < 25) & (df['Altitude'] < 3000 ) & (df['Weather'] > 0)  & (df['Risk_Status'] == 1 ) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "55ebe884",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Had_Health_Issue_Within_90_Days</th>\n",
       "      <th>Age</th>\n",
       "      <th>Has_Taken_Rest</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DS6</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2315</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>DS53</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2579</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>DS54</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2471</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>DS63</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2880</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>DS158</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2641</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>DS159</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2922</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>DS230</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2176</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>DS246</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2491</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>DS334</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2846</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    User_ID  Had_Health_Issue_Within_90_Days  Age  Has_Taken_Rest  \\\n",
       "5       DS6                                1   19               0   \n",
       "52     DS53                                1   23               1   \n",
       "53     DS54                                1   19               1   \n",
       "62     DS63                                1   18               1   \n",
       "157   DS158                                0   18               1   \n",
       "158   DS159                                0   18               1   \n",
       "229   DS230                                0   18               0   \n",
       "245   DS246                                0   23               1   \n",
       "333   DS334                                1   21               0   \n",
       "\n",
       "     Direct_Reached  Taking_Healthy_Diets  Altitude  Weather  Risk_Status  \n",
       "5                 1                     0      2315       11            1  \n",
       "52                0                     0      2579       14            1  \n",
       "53                0                     0      2471        4            1  \n",
       "62                0                     1      2880       20            1  \n",
       "157               0                     0      2641        2            1  \n",
       "158               0                     0      2922        1            1  \n",
       "229               1                     0      2176        7            1  \n",
       "245               0                     0      2491       14            1  \n",
       "333               1                     1      2846        5            1  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Wt5"
   ]
  }
 ],
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