Building Recommendation Engine Using Cosine Similarity Matrix and Haversine.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "import random\n",
    "from bs4 import BeautifulSoup as bs\n",
    "import requests\n",
    "import datetime\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Final Dataset before Normalisation.csv\", index_col=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Name Of Area</th>\n",
       "      <th>Pincode</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Flexibility</th>\n",
       "      <th>Body Composition</th>\n",
       "      <th>Composition Percentage</th>\n",
       "      <th>Interactiveness</th>\n",
       "      <th>Workout Frequency</th>\n",
       "      <th>Workout Mode Preferred</th>\n",
       "      <th>is_sports_enthusiast</th>\n",
       "      <th>like_to_be_mentored</th>\n",
       "      <th>like_to_mentor</th>\n",
       "      <th>fitness_goal_to_be achieved</th>\n",
       "      <th>addons in session</th>\n",
       "      <th>Height_in_cms</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amoli Veer</td>\n",
       "      <td>Female</td>\n",
       "      <td>1982-03-31</td>\n",
       "      <td>Nana Peth S.O</td>\n",
       "      <td>411002</td>\n",
       "      <td>18.51009</td>\n",
       "      <td>73.85672</td>\n",
       "      <td>4</td>\n",
       "      <td>Essential Fat</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>Only Weekends</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>All</td>\n",
       "      <td>Meditation</td>\n",
       "      <td>166</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kaia Subramanian</td>\n",
       "      <td>Female</td>\n",
       "      <td>1994-06-09</td>\n",
       "      <td>Mundhva AV S.O</td>\n",
       "      <td>411036</td>\n",
       "      <td>18.53135</td>\n",
       "      <td>73.91478</td>\n",
       "      <td>5</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>Alternate Days</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Improve endurance/conditioning</td>\n",
       "      <td>Meditation</td>\n",
       "      <td>163</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rushil Vish</td>\n",
       "      <td>Male</td>\n",
       "      <td>1999-02-07</td>\n",
       "      <td>Yerwada S.O</td>\n",
       "      <td>411006</td>\n",
       "      <td>18.56318</td>\n",
       "      <td>73.87742</td>\n",
       "      <td>3</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Both</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Improve athletic skills</td>\n",
       "      <td>Meditation</td>\n",
       "      <td>171</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Atiksh Manohar</td>\n",
       "      <td>Male</td>\n",
       "      <td>1987-07-01</td>\n",
       "      <td>Dapodi S.O</td>\n",
       "      <td>411012</td>\n",
       "      <td>18.57749</td>\n",
       "      <td>73.83138</td>\n",
       "      <td>5</td>\n",
       "      <td>Athletes</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>Alternate Days</td>\n",
       "      <td>Both</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Lose fat</td>\n",
       "      <td>Meditation</td>\n",
       "      <td>173</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ved Kapoor</td>\n",
       "      <td>Male</td>\n",
       "      <td>1988-01-28</td>\n",
       "      <td>Hadapsar S.O</td>\n",
       "      <td>411028</td>\n",
       "      <td>18.50453</td>\n",
       "      <td>73.91255</td>\n",
       "      <td>5</td>\n",
       "      <td>Essential Fat</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3 days of week</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Build muscle</td>\n",
       "      <td>None</td>\n",
       "      <td>161</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>Neysa Rai</td>\n",
       "      <td>Female</td>\n",
       "      <td>1989-12-10</td>\n",
       "      <td>Mohamadwadi S.O</td>\n",
       "      <td>411060</td>\n",
       "      <td>18.47335</td>\n",
       "      <td>73.90957</td>\n",
       "      <td>2</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>Any Day of Week</td>\n",
       "      <td>Both</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Improve athletic skills</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>170</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>Raunak Kapoor</td>\n",
       "      <td>Male</td>\n",
       "      <td>1984-08-12</td>\n",
       "      <td>Masulkar Colony S.O</td>\n",
       "      <td>411018</td>\n",
       "      <td>18.63374</td>\n",
       "      <td>73.81290</td>\n",
       "      <td>3</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>All</td>\n",
       "      <td>None</td>\n",
       "      <td>161</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>Taara Veer</td>\n",
       "      <td>Female</td>\n",
       "      <td>1995-06-06</td>\n",
       "      <td>Masulkar Colony S.O</td>\n",
       "      <td>411018</td>\n",
       "      <td>18.63374</td>\n",
       "      <td>73.81290</td>\n",
       "      <td>5</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>Only Weekends</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Build muscle</td>\n",
       "      <td>Both</td>\n",
       "      <td>166</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>Sarthak Rai</td>\n",
       "      <td>Male</td>\n",
       "      <td>1997-09-03</td>\n",
       "      <td>Guruwar Peth S.O</td>\n",
       "      <td>411042</td>\n",
       "      <td>18.50547</td>\n",
       "      <td>73.85951</td>\n",
       "      <td>5</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Improve athletic skills</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>177</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>Kairav Dutta</td>\n",
       "      <td>Male</td>\n",
       "      <td>1999-11-06</td>\n",
       "      <td>Kasarwadi S.O</td>\n",
       "      <td>411034</td>\n",
       "      <td>18.60373</td>\n",
       "      <td>73.82221</td>\n",
       "      <td>1</td>\n",
       "      <td>Athletes</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Daily</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>All</td>\n",
       "      <td>Both</td>\n",
       "      <td>178</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1387 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name  Gender         DOB         Name Of Area  Pincode  \\\n",
       "0           Amoli Veer  Female  1982-03-31        Nana Peth S.O   411002   \n",
       "1     Kaia Subramanian  Female  1994-06-09       Mundhva AV S.O   411036   \n",
       "2          Rushil Vish    Male  1999-02-07          Yerwada S.O   411006   \n",
       "3       Atiksh Manohar    Male  1987-07-01           Dapodi S.O   411012   \n",
       "4           Ved Kapoor    Male  1988-01-28         Hadapsar S.O   411028   \n",
       "...                ...     ...         ...                  ...      ...   \n",
       "1382         Neysa Rai  Female  1989-12-10      Mohamadwadi S.O   411060   \n",
       "1383     Raunak Kapoor    Male  1984-08-12  Masulkar Colony S.O   411018   \n",
       "1384        Taara Veer  Female  1995-06-06  Masulkar Colony S.O   411018   \n",
       "1385       Sarthak Rai    Male  1997-09-03     Guruwar Peth S.O   411042   \n",
       "1386      Kairav Dutta    Male  1999-11-06        Kasarwadi S.O   411034   \n",
       "\n",
       "      Latitude   Longitude  Flexibility Body Composition  \\\n",
       "0      18.51009   73.85672            4    Essential Fat   \n",
       "1      18.53135   73.91478            5          Fitness   \n",
       "2      18.56318   73.87742            3       Acceptable   \n",
       "3      18.57749   73.83138            5         Athletes   \n",
       "4      18.50453   73.91255            5    Essential Fat   \n",
       "...         ...        ...          ...              ...   \n",
       "1382   18.47335   73.90957            2          Fitness   \n",
       "1383   18.63374   73.81290            3          Fitness   \n",
       "1384   18.63374   73.81290            5          Fitness   \n",
       "1385   18.50547   73.85951            5          Fitness   \n",
       "1386   18.60373   73.82221            1         Athletes   \n",
       "\n",
       "      Composition Percentage  Interactiveness Workout Frequency  \\\n",
       "0                         12                4     Only Weekends   \n",
       "1                         22                5    Alternate Days   \n",
       "2                         22                3  Monday To Friday   \n",
       "3                         10                3    Alternate Days   \n",
       "4                          6                3    3 days of week   \n",
       "...                      ...              ...               ...   \n",
       "1382                      22                4   Any Day of Week   \n",
       "1383                      15                1  Monday To Friday   \n",
       "1384                      22                1     Only Weekends   \n",
       "1385                      15                5  Monday To Friday   \n",
       "1386                      10                1             Daily   \n",
       "\n",
       "     Workout Mode Preferred is_sports_enthusiast  like_to_be_mentored  \\\n",
       "0                   Outdoor                   No                    3   \n",
       "1                   Outdoor                Maybe                    1   \n",
       "2                      Both                  Yes                    4   \n",
       "3                      Both                Maybe                    3   \n",
       "4                    Indoor                   No                    1   \n",
       "...                     ...                  ...                  ...   \n",
       "1382                   Both                Maybe                    2   \n",
       "1383                Outdoor                  Yes                    2   \n",
       "1384                 Indoor                   No                    5   \n",
       "1385                 Indoor                Maybe                    3   \n",
       "1386                Outdoor                  Yes                    4   \n",
       "\n",
       "      like_to_mentor     fitness_goal_to_be achieved addons in session  \\\n",
       "0                  3                             All        Meditation   \n",
       "1                  3  Improve endurance/conditioning        Meditation   \n",
       "2                  5         Improve athletic skills        Meditation   \n",
       "3                  4                        Lose fat        Meditation   \n",
       "4                  1                    Build muscle              None   \n",
       "...              ...                             ...               ...   \n",
       "1382               4         Improve athletic skills              Yoga   \n",
       "1383               5                             All              None   \n",
       "1384               4                    Build muscle              Both   \n",
       "1385               2         Improve athletic skills              Yoga   \n",
       "1386               2                             All              Both   \n",
       "\n",
       "      Height_in_cms  Age  \n",
       "0               166   39  \n",
       "1               163   26  \n",
       "2               171   22  \n",
       "3               173   33  \n",
       "4               161   33  \n",
       "...             ...  ...  \n",
       "1382            170   31  \n",
       "1383            161   36  \n",
       "1384            166   25  \n",
       "1385            177   23  \n",
       "1386            178   21  \n",
       "\n",
       "[1387 rows x 20 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dividing Datasets for Normalisation - Content Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df[['Name', 'Gender', 'Flexibility', 'Pincode', 'Body Composition', 'Composition Percentage', 'Interactiveness', 'Workout Frequency', 'Workout Mode Preferred', 'is_sports_enthusiast', 'like_to_be_mentored', 'like_to_mentor', 'fitness_goal_to_be achieved','addons in session','Height_in_cms','Age']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Gender', 'DOB', 'Name Of Area', 'Pincode', 'Latitude ',\n",
       "       'Longitude', 'Flexibility', 'Body Composition',\n",
       "       'Composition Percentage', 'Interactiveness', 'Workout Frequency',\n",
       "       'Workout Mode Preferred', 'is_sports_enthusiast', 'like_to_be_mentored',\n",
       "       'like_to_mentor', 'fitness_goal_to_be achieved', 'addons in session',\n",
       "       'Height_in_cms', 'Age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Flexibility</th>\n",
       "      <th>Pincode</th>\n",
       "      <th>Body Composition</th>\n",
       "      <th>Composition Percentage</th>\n",
       "      <th>Interactiveness</th>\n",
       "      <th>Workout Frequency</th>\n",
       "      <th>Workout Mode Preferred</th>\n",
       "      <th>is_sports_enthusiast</th>\n",
       "      <th>like_to_be_mentored</th>\n",
       "      <th>like_to_mentor</th>\n",
       "      <th>fitness_goal_to_be achieved</th>\n",
       "      <th>addons in session</th>\n",
       "      <th>Height_in_cms</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amoli Veer</td>\n",
       "      <td>Female</td>\n",
       "      <td>4</td>\n",
       "      <td>411002</td>\n",
       "      <td>Essential Fat</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>Only Weekends</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>All</td>\n",
       "      <td>Meditation</td>\n",
       "      <td>166</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kaia Subramanian</td>\n",
       "      <td>Female</td>\n",
       "      <td>5</td>\n",
       "      <td>411036</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>Alternate Days</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Improve endurance/conditioning</td>\n",
       "      <td>Meditation</td>\n",
       "      <td>163</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rushil Vish</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>411006</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Both</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Improve athletic skills</td>\n",
       "      <td>Meditation</td>\n",
       "      <td>171</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Atiksh Manohar</td>\n",
       "      <td>Male</td>\n",
       "      <td>5</td>\n",
       "      <td>411012</td>\n",
       "      <td>Athletes</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>Alternate Days</td>\n",
       "      <td>Both</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Lose fat</td>\n",
       "      <td>Meditation</td>\n",
       "      <td>173</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ved Kapoor</td>\n",
       "      <td>Male</td>\n",
       "      <td>5</td>\n",
       "      <td>411028</td>\n",
       "      <td>Essential Fat</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3 days of week</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Build muscle</td>\n",
       "      <td>None</td>\n",
       "      <td>161</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>Neysa Rai</td>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "      <td>411060</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>Any Day of Week</td>\n",
       "      <td>Both</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Improve athletic skills</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>170</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>Raunak Kapoor</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>411018</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>All</td>\n",
       "      <td>None</td>\n",
       "      <td>161</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>Taara Veer</td>\n",
       "      <td>Female</td>\n",
       "      <td>5</td>\n",
       "      <td>411018</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>Only Weekends</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Build muscle</td>\n",
       "      <td>Both</td>\n",
       "      <td>166</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>Sarthak Rai</td>\n",
       "      <td>Male</td>\n",
       "      <td>5</td>\n",
       "      <td>411042</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Improve athletic skills</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>177</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>Kairav Dutta</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>411034</td>\n",
       "      <td>Athletes</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Daily</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>All</td>\n",
       "      <td>Both</td>\n",
       "      <td>178</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1387 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name  Gender  Flexibility  Pincode Body Composition  \\\n",
       "0           Amoli Veer  Female            4   411002    Essential Fat   \n",
       "1     Kaia Subramanian  Female            5   411036          Fitness   \n",
       "2          Rushil Vish    Male            3   411006       Acceptable   \n",
       "3       Atiksh Manohar    Male            5   411012         Athletes   \n",
       "4           Ved Kapoor    Male            5   411028    Essential Fat   \n",
       "...                ...     ...          ...      ...              ...   \n",
       "1382         Neysa Rai  Female            2   411060          Fitness   \n",
       "1383     Raunak Kapoor    Male            3   411018          Fitness   \n",
       "1384        Taara Veer  Female            5   411018          Fitness   \n",
       "1385       Sarthak Rai    Male            5   411042          Fitness   \n",
       "1386      Kairav Dutta    Male            1   411034         Athletes   \n",
       "\n",
       "      Composition Percentage  Interactiveness Workout Frequency  \\\n",
       "0                         12                4     Only Weekends   \n",
       "1                         22                5    Alternate Days   \n",
       "2                         22                3  Monday To Friday   \n",
       "3                         10                3    Alternate Days   \n",
       "4                          6                3    3 days of week   \n",
       "...                      ...              ...               ...   \n",
       "1382                      22                4   Any Day of Week   \n",
       "1383                      15                1  Monday To Friday   \n",
       "1384                      22                1     Only Weekends   \n",
       "1385                      15                5  Monday To Friday   \n",
       "1386                      10                1             Daily   \n",
       "\n",
       "     Workout Mode Preferred is_sports_enthusiast  like_to_be_mentored  \\\n",
       "0                   Outdoor                   No                    3   \n",
       "1                   Outdoor                Maybe                    1   \n",
       "2                      Both                  Yes                    4   \n",
       "3                      Both                Maybe                    3   \n",
       "4                    Indoor                   No                    1   \n",
       "...                     ...                  ...                  ...   \n",
       "1382                   Both                Maybe                    2   \n",
       "1383                Outdoor                  Yes                    2   \n",
       "1384                 Indoor                   No                    5   \n",
       "1385                 Indoor                Maybe                    3   \n",
       "1386                Outdoor                  Yes                    4   \n",
       "\n",
       "      like_to_mentor     fitness_goal_to_be achieved addons in session  \\\n",
       "0                  3                             All        Meditation   \n",
       "1                  3  Improve endurance/conditioning        Meditation   \n",
       "2                  5         Improve athletic skills        Meditation   \n",
       "3                  4                        Lose fat        Meditation   \n",
       "4                  1                    Build muscle              None   \n",
       "...              ...                             ...               ...   \n",
       "1382               4         Improve athletic skills              Yoga   \n",
       "1383               5                             All              None   \n",
       "1384               4                    Build muscle              Both   \n",
       "1385               2         Improve athletic skills              Yoga   \n",
       "1386               2                             All              Both   \n",
       "\n",
       "      Height_in_cms  Age  \n",
       "0               166   39  \n",
       "1               163   26  \n",
       "2               171   22  \n",
       "3               173   33  \n",
       "4               161   33  \n",
       "...             ...  ...  \n",
       "1382            170   31  \n",
       "1383            161   36  \n",
       "1384            166   25  \n",
       "1385            177   23  \n",
       "1386            178   21  \n",
       "\n",
       "[1387 rows x 16 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 =df[['Name', 'Name Of Area', 'Pincode', 'Latitude ','Longitude']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-9599b35390af>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['Gender'] = df1[\"Gender\"].astype('category').cat.codes\n"
     ]
    }
   ],
   "source": [
    "df1['Gender'] = df1[\"Gender\"].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-f42aa0aed271>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['Body Composition'] = df1[\"Body Composition\"].astype('category').cat.codes\n",
      "<ipython-input-9-f42aa0aed271>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['Workout Frequency'] = df1[\"Workout Frequency\"].astype('category').cat.codes\n",
      "<ipython-input-9-f42aa0aed271>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['Workout Mode Preferred'] = df1[\"Workout Mode Preferred\"].astype('category').cat.codes\n",
      "<ipython-input-9-f42aa0aed271>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['is_sports_enthusiast'] = df1[\"is_sports_enthusiast\"].astype('category').cat.codes\n",
      "<ipython-input-9-f42aa0aed271>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['fitness_goal_to_be achieved'] = df1[\"fitness_goal_to_be achieved\"].astype('category').cat.codes\n",
      "<ipython-input-9-f42aa0aed271>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['addons in session'] = df1[\"addons in session\"].astype('category').cat.codes\n"
     ]
    },
    {
     "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>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Flexibility</th>\n",
       "      <th>Pincode</th>\n",
       "      <th>Body Composition</th>\n",
       "      <th>Composition Percentage</th>\n",
       "      <th>Interactiveness</th>\n",
       "      <th>Workout Frequency</th>\n",
       "      <th>Workout Mode Preferred</th>\n",
       "      <th>is_sports_enthusiast</th>\n",
       "      <th>like_to_be_mentored</th>\n",
       "      <th>like_to_mentor</th>\n",
       "      <th>fitness_goal_to_be achieved</th>\n",
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       "      <th>Height_in_cms</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amoli Veer</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>411002</td>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>166</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kaia Subramanian</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>411036</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rushil Vish</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>411006</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
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       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Atiksh Manohar</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>411012</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ved Kapoor</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>411028</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>161</td>\n",
       "      <td>33</td>\n",
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       "      <td>...</td>\n",
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       "      <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>1382</th>\n",
       "      <td>Neysa Rai</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>411060</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>170</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>Raunak Kapoor</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>411018</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>161</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>Taara Veer</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>411018</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>166</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>Sarthak Rai</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>411042</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>177</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>Kairav Dutta</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>411034</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>178</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1387 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name  Gender  Flexibility  Pincode  Body Composition  \\\n",
       "0           Amoli Veer       0            4   411002                 2   \n",
       "1     Kaia Subramanian       0            5   411036                 3   \n",
       "2          Rushil Vish       1            3   411006                 0   \n",
       "3       Atiksh Manohar       1            5   411012                 1   \n",
       "4           Ved Kapoor       1            5   411028                 2   \n",
       "...                ...     ...          ...      ...               ...   \n",
       "1382         Neysa Rai       0            2   411060                 3   \n",
       "1383     Raunak Kapoor       1            3   411018                 3   \n",
       "1384        Taara Veer       0            5   411018                 3   \n",
       "1385       Sarthak Rai       1            5   411042                 3   \n",
       "1386      Kairav Dutta       1            1   411034                 1   \n",
       "\n",
       "      Composition Percentage  Interactiveness  Workout Frequency  \\\n",
       "0                         12                4                  5   \n",
       "1                         22                5                  1   \n",
       "2                         22                3                  4   \n",
       "3                         10                3                  1   \n",
       "4                          6                3                  0   \n",
       "...                      ...              ...                ...   \n",
       "1382                      22                4                  2   \n",
       "1383                      15                1                  4   \n",
       "1384                      22                1                  5   \n",
       "1385                      15                5                  4   \n",
       "1386                      10                1                  3   \n",
       "\n",
       "      Workout Mode Preferred  is_sports_enthusiast  like_to_be_mentored  \\\n",
       "0                          2                     1                    3   \n",
       "1                          2                     0                    1   \n",
       "2                          0                     2                    4   \n",
       "3                          0                     0                    3   \n",
       "4                          1                     1                    1   \n",
       "...                      ...                   ...                  ...   \n",
       "1382                       0                     0                    2   \n",
       "1383                       2                     2                    2   \n",
       "1384                       1                     1                    5   \n",
       "1385                       1                     0                    3   \n",
       "1386                       2                     2                    4   \n",
       "\n",
       "      like_to_mentor  fitness_goal_to_be achieved  addons in session  \\\n",
       "0                  3                            0                  1   \n",
       "1                  3                            3                  1   \n",
       "2                  5                            2                  1   \n",
       "3                  4                            4                  1   \n",
       "4                  1                            1                  2   \n",
       "...              ...                          ...                ...   \n",
       "1382               4                            2                  3   \n",
       "1383               5                            0                  2   \n",
       "1384               4                            1                  0   \n",
       "1385               2                            2                  3   \n",
       "1386               2                            0                  0   \n",
       "\n",
       "      Height_in_cms  Age  \n",
       "0               166   39  \n",
       "1               163   26  \n",
       "2               171   22  \n",
       "3               173   33  \n",
       "4               161   33  \n",
       "...             ...  ...  \n",
       "1382            170   31  \n",
       "1383            161   36  \n",
       "1384            166   25  \n",
       "1385            177   23  \n",
       "1386            178   21  \n",
       "\n",
       "[1387 rows x 16 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Body Composition'] = df1[\"Body Composition\"].astype('category').cat.codes\n",
    "df1['Workout Frequency'] = df1[\"Workout Frequency\"].astype('category').cat.codes\n",
    "df1['Workout Mode Preferred'] = df1[\"Workout Mode Preferred\"].astype('category').cat.codes\n",
    "df1['is_sports_enthusiast'] = df1[\"is_sports_enthusiast\"].astype('category').cat.codes\n",
    "df1['fitness_goal_to_be achieved'] = df1[\"fitness_goal_to_be achieved\"].astype('category').cat.codes\n",
    "df1['addons in session'] = df1[\"addons in session\"].astype('category').cat.codes\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Flexibility</th>\n",
       "      <th>Pincode</th>\n",
       "      <th>Body Composition</th>\n",
       "      <th>Composition Percentage</th>\n",
       "      <th>Interactiveness</th>\n",
       "      <th>Workout Frequency</th>\n",
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       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amoli Veer</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>411002</td>\n",
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       "      <td>Kaia Subramanian</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>411036</td>\n",
       "      <td>3</td>\n",
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       "      <td>163</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rushil Vish</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>411006</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
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       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Atiksh Manohar</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>411012</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
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       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ved Kapoor</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>411028</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>161</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>Neysa Rai</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>411060</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>170</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>Raunak Kapoor</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>411018</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>161</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>Taara Veer</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>411018</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>166</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>Sarthak Rai</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>411042</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>177</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>Kairav Dutta</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>411034</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>178</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1387 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name  Gender  Flexibility  Pincode  Body Composition  \\\n",
       "0           Amoli Veer       0            4   411002                 2   \n",
       "1     Kaia Subramanian       0            5   411036                 3   \n",
       "2          Rushil Vish       1            3   411006                 0   \n",
       "3       Atiksh Manohar       1            5   411012                 1   \n",
       "4           Ved Kapoor       1            5   411028                 2   \n",
       "...                ...     ...          ...      ...               ...   \n",
       "1382         Neysa Rai       0            2   411060                 3   \n",
       "1383     Raunak Kapoor       1            3   411018                 3   \n",
       "1384        Taara Veer       0            5   411018                 3   \n",
       "1385       Sarthak Rai       1            5   411042                 3   \n",
       "1386      Kairav Dutta       1            1   411034                 1   \n",
       "\n",
       "      Composition Percentage  Interactiveness  Workout Frequency  \\\n",
       "0                         12                4                  5   \n",
       "1                         22                5                  1   \n",
       "2                         22                3                  4   \n",
       "3                         10                3                  1   \n",
       "4                          6                3                  0   \n",
       "...                      ...              ...                ...   \n",
       "1382                      22                4                  2   \n",
       "1383                      15                1                  4   \n",
       "1384                      22                1                  5   \n",
       "1385                      15                5                  4   \n",
       "1386                      10                1                  3   \n",
       "\n",
       "      Workout Mode Preferred  is_sports_enthusiast  like_to_be_mentored  \\\n",
       "0                          2                     1                    3   \n",
       "1                          2                     0                    1   \n",
       "2                          0                     2                    4   \n",
       "3                          0                     0                    3   \n",
       "4                          1                     1                    1   \n",
       "...                      ...                   ...                  ...   \n",
       "1382                       0                     0                    2   \n",
       "1383                       2                     2                    2   \n",
       "1384                       1                     1                    5   \n",
       "1385                       1                     0                    3   \n",
       "1386                       2                     2                    4   \n",
       "\n",
       "      like_to_mentor  fitness_goal_to_be achieved  addons in session  \\\n",
       "0                  3                            0                  1   \n",
       "1                  3                            3                  1   \n",
       "2                  5                            2                  1   \n",
       "3                  4                            4                  1   \n",
       "4                  1                            1                  2   \n",
       "...              ...                          ...                ...   \n",
       "1382               4                            2                  3   \n",
       "1383               5                            0                  2   \n",
       "1384               4                            1                  0   \n",
       "1385               2                            2                  3   \n",
       "1386               2                            0                  0   \n",
       "\n",
       "      Height_in_cms  Age  \n",
       "0               166   39  \n",
       "1               163   26  \n",
       "2               171   22  \n",
       "3               173   33  \n",
       "4               161   33  \n",
       "...             ...  ...  \n",
       "1382            170   31  \n",
       "1383            161   36  \n",
       "1384            166   25  \n",
       "1385            177   23  \n",
       "1386            178   21  \n",
       "\n",
       "[1387 rows x 16 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_scaled = df1.copy() \n",
    "names = ['Flexibility','Composition Percentage','Pincode', 'Interactiveness', 'like_to_be_mentored', 'like_to_mentor', 'Height_in_cms', 'Age']\n",
    "\n",
    "df1_scaled[names] = MinMaxScaler().fit_transform(df1_scaled[names])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Flexibility</th>\n",
       "      <th>Pincode</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amoli Veer</td>\n",
       "      <td>0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.000898</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.75</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.956522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kaia Subramanian</td>\n",
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       "      <td>1.00</td>\n",
       "      <td>0.031447</td>\n",
       "      <td>3</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.391304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rushil Vish</td>\n",
       "      <td>1</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.004492</td>\n",
       "      <td>0</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.50</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.217391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Atiksh Manohar</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.009883</td>\n",
       "      <td>1</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.75</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.695652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ved Kapoor</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.024259</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.695652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>Neysa Rai</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.053010</td>\n",
       "      <td>3</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.608696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>Raunak Kapoor</td>\n",
       "      <td>1</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.015274</td>\n",
       "      <td>3</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.826087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>Taara Veer</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.015274</td>\n",
       "      <td>3</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>Sarthak Rai</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.036837</td>\n",
       "      <td>3</td>\n",
       "      <td>0.18</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.260870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>Kairav Dutta</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.029650</td>\n",
       "      <td>1</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.173913</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1387 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name  Gender  Flexibility   Pincode  Body Composition  \\\n",
       "0           Amoli Veer       0         0.75  0.000898                 2   \n",
       "1     Kaia Subramanian       0         1.00  0.031447                 3   \n",
       "2          Rushil Vish       1         0.50  0.004492                 0   \n",
       "3       Atiksh Manohar       1         1.00  0.009883                 1   \n",
       "4           Ved Kapoor       1         1.00  0.024259                 2   \n",
       "...                ...     ...          ...       ...               ...   \n",
       "1382         Neysa Rai       0         0.25  0.053010                 3   \n",
       "1383     Raunak Kapoor       1         0.50  0.015274                 3   \n",
       "1384        Taara Veer       0         1.00  0.015274                 3   \n",
       "1385       Sarthak Rai       1         1.00  0.036837                 3   \n",
       "1386      Kairav Dutta       1         0.00  0.029650                 1   \n",
       "\n",
       "      Composition Percentage  Interactiveness  Workout Frequency  \\\n",
       "0                       0.12             0.75                  5   \n",
       "1                       0.32             1.00                  1   \n",
       "2                       0.32             0.50                  4   \n",
       "3                       0.08             0.50                  1   \n",
       "4                       0.00             0.50                  0   \n",
       "...                      ...              ...                ...   \n",
       "1382                    0.32             0.75                  2   \n",
       "1383                    0.18             0.00                  4   \n",
       "1384                    0.32             0.00                  5   \n",
       "1385                    0.18             1.00                  4   \n",
       "1386                    0.08             0.00                  3   \n",
       "\n",
       "      Workout Mode Preferred  is_sports_enthusiast  like_to_be_mentored  \\\n",
       "0                          2                     1                 0.50   \n",
       "1                          2                     0                 0.00   \n",
       "2                          0                     2                 0.75   \n",
       "3                          0                     0                 0.50   \n",
       "4                          1                     1                 0.00   \n",
       "...                      ...                   ...                  ...   \n",
       "1382                       0                     0                 0.25   \n",
       "1383                       2                     2                 0.25   \n",
       "1384                       1                     1                 1.00   \n",
       "1385                       1                     0                 0.50   \n",
       "1386                       2                     2                 0.75   \n",
       "\n",
       "      like_to_mentor  fitness_goal_to_be achieved  addons in session  \\\n",
       "0               0.50                            0                  1   \n",
       "1               0.50                            3                  1   \n",
       "2               1.00                            2                  1   \n",
       "3               0.75                            4                  1   \n",
       "4               0.00                            1                  2   \n",
       "...              ...                          ...                ...   \n",
       "1382            0.75                            2                  3   \n",
       "1383            1.00                            0                  2   \n",
       "1384            0.75                            1                  0   \n",
       "1385            0.25                            2                  3   \n",
       "1386            0.25                            0                  0   \n",
       "\n",
       "      Height_in_cms       Age  \n",
       "0          0.333333  0.956522  \n",
       "1          0.166667  0.391304  \n",
       "2          0.611111  0.217391  \n",
       "3          0.722222  0.695652  \n",
       "4          0.055556  0.695652  \n",
       "...             ...       ...  \n",
       "1382       0.555556  0.608696  \n",
       "1383       0.055556  0.826087  \n",
       "1384       0.333333  0.347826  \n",
       "1385       0.944444  0.260870  \n",
       "1386       1.000000  0.173913  \n",
       "\n",
       "[1387 rows x 16 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "similarity_cos = cosine_similarity(df1_scaled.drop('Name',axis=1))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.57627794, 0.76418107, ..., 0.93143217, 0.82849821,\n",
       "        0.86062995],\n",
       "       [0.57627794, 1.        , 0.46438112, ..., 0.63526626, 0.78007361,\n",
       "        0.44357132],\n",
       "       [0.76418107, 0.46438112, 1.        , ..., 0.78119403, 0.74796451,\n",
       "        0.76068417],\n",
       "       ...,\n",
       "       [0.93143217, 0.63526626, 0.78119403, ..., 1.        , 0.82159792,\n",
       "        0.81350651],\n",
       "       [0.82849821, 0.78007361, 0.74796451, ..., 0.82159792, 1.        ,\n",
       "        0.64996769],\n",
       "       [0.86062995, 0.44357132, 0.76068417, ..., 0.81350651, 0.64996769,\n",
       "        1.        ]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity_cos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_similarity = pd.DataFrame(similarity_cos, columns=df1_scaled['Name'], index=df1_scaled['Name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Name</th>\n",
       "      <th>Amoli Veer</th>\n",
       "      <th>Kaia Subramanian</th>\n",
       "      <th>Rushil Vish</th>\n",
       "      <th>Atiksh Manohar</th>\n",
       "      <th>Ved Kapoor</th>\n",
       "      <th>Vihaan Chauhan</th>\n",
       "      <th>Tanvi Sandeep</th>\n",
       "      <th>Taara Chauhan</th>\n",
       "      <th>Anaisha Veena</th>\n",
       "      <th>Rebecca Chander</th>\n",
       "      <th>...</th>\n",
       "      <th>Rohan Nagpal</th>\n",
       "      <th>Alisha Roy</th>\n",
       "      <th>Sneha Ranga</th>\n",
       "      <th>Ira Naran</th>\n",
       "      <th>Avni Nath</th>\n",
       "      <th>Neysa Rai</th>\n",
       "      <th>Raunak Kapoor</th>\n",
       "      <th>Taara Veer</th>\n",
       "      <th>Sarthak Rai</th>\n",
       "      <th>Kairav Dutta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amoli Veer</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.576278</td>\n",
       "      <td>0.764181</td>\n",
       "      <td>0.361788</td>\n",
       "      <td>0.475178</td>\n",
       "      <td>0.839872</td>\n",
       "      <td>0.400600</td>\n",
       "      <td>0.643023</td>\n",
       "      <td>0.496650</td>\n",
       "      <td>0.363565</td>\n",
       "      <td>...</td>\n",
       "      <td>0.436768</td>\n",
       "      <td>0.575520</td>\n",
       "      <td>0.834565</td>\n",
       "      <td>0.419347</td>\n",
       "      <td>0.693091</td>\n",
       "      <td>0.647972</td>\n",
       "      <td>0.922747</td>\n",
       "      <td>0.931432</td>\n",
       "      <td>0.828498</td>\n",
       "      <td>0.860630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kaia Subramanian</th>\n",
       "      <td>0.576278</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.464381</td>\n",
       "      <td>0.779724</td>\n",
       "      <td>0.774249</td>\n",
       "      <td>0.790255</td>\n",
       "      <td>0.881326</td>\n",
       "      <td>0.807988</td>\n",
       "      <td>0.856773</td>\n",
       "      <td>0.788733</td>\n",
       "      <td>...</td>\n",
       "      <td>0.437792</td>\n",
       "      <td>0.896195</td>\n",
       "      <td>0.739324</td>\n",
       "      <td>0.591120</td>\n",
       "      <td>0.605904</td>\n",
       "      <td>0.799657</td>\n",
       "      <td>0.625648</td>\n",
       "      <td>0.635266</td>\n",
       "      <td>0.780074</td>\n",
       "      <td>0.443571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rushil Vish</th>\n",
       "      <td>0.764181</td>\n",
       "      <td>0.464381</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.642193</td>\n",
       "      <td>0.400436</td>\n",
       "      <td>0.800647</td>\n",
       "      <td>0.333760</td>\n",
       "      <td>0.789015</td>\n",
       "      <td>0.588003</td>\n",
       "      <td>0.572836</td>\n",
       "      <td>...</td>\n",
       "      <td>0.576720</td>\n",
       "      <td>0.350883</td>\n",
       "      <td>0.849133</td>\n",
       "      <td>0.194214</td>\n",
       "      <td>0.447531</td>\n",
       "      <td>0.600946</td>\n",
       "      <td>0.730432</td>\n",
       "      <td>0.781194</td>\n",
       "      <td>0.747965</td>\n",
       "      <td>0.760684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atiksh Manohar</th>\n",
       "      <td>0.361788</td>\n",
       "      <td>0.779724</td>\n",
       "      <td>0.642193</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.605155</td>\n",
       "      <td>0.686441</td>\n",
       "      <td>0.711210</td>\n",
       "      <td>0.839542</td>\n",
       "      <td>0.796664</td>\n",
       "      <td>0.907987</td>\n",
       "      <td>...</td>\n",
       "      <td>0.600453</td>\n",
       "      <td>0.583284</td>\n",
       "      <td>0.704879</td>\n",
       "      <td>0.281498</td>\n",
       "      <td>0.212589</td>\n",
       "      <td>0.714139</td>\n",
       "      <td>0.394981</td>\n",
       "      <td>0.480231</td>\n",
       "      <td>0.690128</td>\n",
       "      <td>0.293672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ved Kapoor</th>\n",
       "      <td>0.475178</td>\n",
       "      <td>0.774249</td>\n",
       "      <td>0.400436</td>\n",
       "      <td>0.605155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.599109</td>\n",
       "      <td>0.837260</td>\n",
       "      <td>0.685991</td>\n",
       "      <td>0.846541</td>\n",
       "      <td>0.652275</td>\n",
       "      <td>...</td>\n",
       "      <td>0.648820</td>\n",
       "      <td>0.645725</td>\n",
       "      <td>0.510913</td>\n",
       "      <td>0.620360</td>\n",
       "      <td>0.602202</td>\n",
       "      <td>0.768453</td>\n",
       "      <td>0.685620</td>\n",
       "      <td>0.438285</td>\n",
       "      <td>0.727103</td>\n",
       "      <td>0.426002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neysa Rai</th>\n",
       "      <td>0.647972</td>\n",
       "      <td>0.799657</td>\n",
       "      <td>0.600946</td>\n",
       "      <td>0.714139</td>\n",
       "      <td>0.768453</td>\n",
       "      <td>0.821575</td>\n",
       "      <td>0.826965</td>\n",
       "      <td>0.827691</td>\n",
       "      <td>0.859290</td>\n",
       "      <td>0.763485</td>\n",
       "      <td>...</td>\n",
       "      <td>0.726873</td>\n",
       "      <td>0.710704</td>\n",
       "      <td>0.694075</td>\n",
       "      <td>0.588808</td>\n",
       "      <td>0.449129</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.732233</td>\n",
       "      <td>0.674641</td>\n",
       "      <td>0.919988</td>\n",
       "      <td>0.418134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raunak Kapoor</th>\n",
       "      <td>0.922747</td>\n",
       "      <td>0.625648</td>\n",
       "      <td>0.730432</td>\n",
       "      <td>0.394981</td>\n",
       "      <td>0.685620</td>\n",
       "      <td>0.846631</td>\n",
       "      <td>0.542939</td>\n",
       "      <td>0.697051</td>\n",
       "      <td>0.646033</td>\n",
       "      <td>0.421986</td>\n",
       "      <td>...</td>\n",
       "      <td>0.493623</td>\n",
       "      <td>0.629614</td>\n",
       "      <td>0.765830</td>\n",
       "      <td>0.573263</td>\n",
       "      <td>0.808455</td>\n",
       "      <td>0.732233</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.872380</td>\n",
       "      <td>0.844839</td>\n",
       "      <td>0.857150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taara Veer</th>\n",
       "      <td>0.931432</td>\n",
       "      <td>0.635266</td>\n",
       "      <td>0.781194</td>\n",
       "      <td>0.480231</td>\n",
       "      <td>0.438285</td>\n",
       "      <td>0.925836</td>\n",
       "      <td>0.423614</td>\n",
       "      <td>0.667283</td>\n",
       "      <td>0.551617</td>\n",
       "      <td>0.422934</td>\n",
       "      <td>...</td>\n",
       "      <td>0.318198</td>\n",
       "      <td>0.676695</td>\n",
       "      <td>0.830865</td>\n",
       "      <td>0.511681</td>\n",
       "      <td>0.674167</td>\n",
       "      <td>0.674641</td>\n",
       "      <td>0.872380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.821598</td>\n",
       "      <td>0.813507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sarthak Rai</th>\n",
       "      <td>0.828498</td>\n",
       "      <td>0.780074</td>\n",
       "      <td>0.747965</td>\n",
       "      <td>0.690128</td>\n",
       "      <td>0.727103</td>\n",
       "      <td>0.903288</td>\n",
       "      <td>0.737916</td>\n",
       "      <td>0.832712</td>\n",
       "      <td>0.766055</td>\n",
       "      <td>0.700381</td>\n",
       "      <td>...</td>\n",
       "      <td>0.724571</td>\n",
       "      <td>0.693733</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.453048</td>\n",
       "      <td>0.517717</td>\n",
       "      <td>0.919988</td>\n",
       "      <td>0.844839</td>\n",
       "      <td>0.821598</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.649968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kairav Dutta</th>\n",
       "      <td>0.860630</td>\n",
       "      <td>0.443571</td>\n",
       "      <td>0.760684</td>\n",
       "      <td>0.293672</td>\n",
       "      <td>0.426002</td>\n",
       "      <td>0.729393</td>\n",
       "      <td>0.316528</td>\n",
       "      <td>0.557543</td>\n",
       "      <td>0.429603</td>\n",
       "      <td>0.254352</td>\n",
       "      <td>...</td>\n",
       "      <td>0.307119</td>\n",
       "      <td>0.524654</td>\n",
       "      <td>0.764112</td>\n",
       "      <td>0.351944</td>\n",
       "      <td>0.808685</td>\n",
       "      <td>0.418134</td>\n",
       "      <td>0.857150</td>\n",
       "      <td>0.813507</td>\n",
       "      <td>0.649968</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1387 rows × 1387 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Name              Amoli Veer  Kaia Subramanian  Rushil Vish  Atiksh Manohar  \\\n",
       "Name                                                                          \n",
       "Amoli Veer          1.000000          0.576278     0.764181        0.361788   \n",
       "Kaia Subramanian    0.576278          1.000000     0.464381        0.779724   \n",
       "Rushil Vish         0.764181          0.464381     1.000000        0.642193   \n",
       "Atiksh Manohar      0.361788          0.779724     0.642193        1.000000   \n",
       "Ved Kapoor          0.475178          0.774249     0.400436        0.605155   \n",
       "...                      ...               ...          ...             ...   \n",
       "Neysa Rai           0.647972          0.799657     0.600946        0.714139   \n",
       "Raunak Kapoor       0.922747          0.625648     0.730432        0.394981   \n",
       "Taara Veer          0.931432          0.635266     0.781194        0.480231   \n",
       "Sarthak Rai         0.828498          0.780074     0.747965        0.690128   \n",
       "Kairav Dutta        0.860630          0.443571     0.760684        0.293672   \n",
       "\n",
       "Name              Ved Kapoor  Vihaan Chauhan  Tanvi Sandeep  Taara Chauhan  \\\n",
       "Name                                                                         \n",
       "Amoli Veer          0.475178        0.839872       0.400600       0.643023   \n",
       "Kaia Subramanian    0.774249        0.790255       0.881326       0.807988   \n",
       "Rushil Vish         0.400436        0.800647       0.333760       0.789015   \n",
       "Atiksh Manohar      0.605155        0.686441       0.711210       0.839542   \n",
       "Ved Kapoor          1.000000        0.599109       0.837260       0.685991   \n",
       "...                      ...             ...            ...            ...   \n",
       "Neysa Rai           0.768453        0.821575       0.826965       0.827691   \n",
       "Raunak Kapoor       0.685620        0.846631       0.542939       0.697051   \n",
       "Taara Veer          0.438285        0.925836       0.423614       0.667283   \n",
       "Sarthak Rai         0.727103        0.903288       0.737916       0.832712   \n",
       "Kairav Dutta        0.426002        0.729393       0.316528       0.557543   \n",
       "\n",
       "Name              Anaisha Veena  Rebecca Chander  ...  Rohan Nagpal  \\\n",
       "Name                                              ...                 \n",
       "Amoli Veer             0.496650         0.363565  ...      0.436768   \n",
       "Kaia Subramanian       0.856773         0.788733  ...      0.437792   \n",
       "Rushil Vish            0.588003         0.572836  ...      0.576720   \n",
       "Atiksh Manohar         0.796664         0.907987  ...      0.600453   \n",
       "Ved Kapoor             0.846541         0.652275  ...      0.648820   \n",
       "...                         ...              ...  ...           ...   \n",
       "Neysa Rai              0.859290         0.763485  ...      0.726873   \n",
       "Raunak Kapoor          0.646033         0.421986  ...      0.493623   \n",
       "Taara Veer             0.551617         0.422934  ...      0.318198   \n",
       "Sarthak Rai            0.766055         0.700381  ...      0.724571   \n",
       "Kairav Dutta           0.429603         0.254352  ...      0.307119   \n",
       "\n",
       "Name              Alisha Roy  Sneha Ranga  Ira Naran  Avni Nath  Neysa Rai  \\\n",
       "Name                                                                         \n",
       "Amoli Veer          0.575520     0.834565   0.419347   0.693091   0.647972   \n",
       "Kaia Subramanian    0.896195     0.739324   0.591120   0.605904   0.799657   \n",
       "Rushil Vish         0.350883     0.849133   0.194214   0.447531   0.600946   \n",
       "Atiksh Manohar      0.583284     0.704879   0.281498   0.212589   0.714139   \n",
       "Ved Kapoor          0.645725     0.510913   0.620360   0.602202   0.768453   \n",
       "...                      ...          ...        ...        ...        ...   \n",
       "Neysa Rai           0.710704     0.694075   0.588808   0.449129   1.000000   \n",
       "Raunak Kapoor       0.629614     0.765830   0.573263   0.808455   0.732233   \n",
       "Taara Veer          0.676695     0.830865   0.511681   0.674167   0.674641   \n",
       "Sarthak Rai         0.693733     0.829268   0.453048   0.517717   0.919988   \n",
       "Kairav Dutta        0.524654     0.764112   0.351944   0.808685   0.418134   \n",
       "\n",
       "Name              Raunak Kapoor  Taara Veer  Sarthak Rai  Kairav Dutta  \n",
       "Name                                                                    \n",
       "Amoli Veer             0.922747    0.931432     0.828498      0.860630  \n",
       "Kaia Subramanian       0.625648    0.635266     0.780074      0.443571  \n",
       "Rushil Vish            0.730432    0.781194     0.747965      0.760684  \n",
       "Atiksh Manohar         0.394981    0.480231     0.690128      0.293672  \n",
       "Ved Kapoor             0.685620    0.438285     0.727103      0.426002  \n",
       "...                         ...         ...          ...           ...  \n",
       "Neysa Rai              0.732233    0.674641     0.919988      0.418134  \n",
       "Raunak Kapoor          1.000000    0.872380     0.844839      0.857150  \n",
       "Taara Veer             0.872380    1.000000     0.821598      0.813507  \n",
       "Sarthak Rai            0.844839    0.821598     1.000000      0.649968  \n",
       "Kairav Dutta           0.857150    0.813507     0.649968      1.000000  \n",
       "\n",
       "[1387 rows x 1387 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = 'Sarthak Rai'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Amoli Veer          0.828498\n",
       "Kaia Subramanian    0.780074\n",
       "Rushil Vish         0.747965\n",
       "Atiksh Manohar      0.690128\n",
       "Ved Kapoor          0.727103\n",
       "                      ...   \n",
       "Neysa Rai           0.919988\n",
       "Raunak Kapoor       0.844839\n",
       "Taara Veer          0.821598\n",
       "Sarthak Rai         1.000000\n",
       "Kairav Dutta        0.649968\n",
       "Name: Sarthak Rai, Length: 1387, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_user=df1_similarity.loc[name,:]\n",
    "df1_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "Recommendations=df1_user\n",
    "rec =Recommendations.index.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Name Of Area</th>\n",
       "      <th>Pincode</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Flexibility</th>\n",
       "      <th>Body Composition</th>\n",
       "      <th>Composition Percentage</th>\n",
       "      <th>Interactiveness</th>\n",
       "      <th>Workout Frequency</th>\n",
       "      <th>Workout Mode Preferred</th>\n",
       "      <th>is_sports_enthusiast</th>\n",
       "      <th>like_to_be_mentored</th>\n",
       "      <th>like_to_mentor</th>\n",
       "      <th>fitness_goal_to_be achieved</th>\n",
       "      <th>addons in session</th>\n",
       "      <th>Height_in_cms</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amoli Veer</td>\n",
       "      <td>Female</td>\n",
       "      <td>1982-03-31</td>\n",
       "      <td>Nana Peth S.O</td>\n",
       "      <td>411002</td>\n",
       "      <td>18.51009</td>\n",
       "      <td>73.85672</td>\n",
       "      <td>4</td>\n",
       "      <td>Essential Fat</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>Only Weekends</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>All</td>\n",
       "      <td>Meditation</td>\n",
       "      <td>166</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kaia Subramanian</td>\n",
       "      <td>Female</td>\n",
       "      <td>1994-06-09</td>\n",
       "      <td>Mundhva AV S.O</td>\n",
       "      <td>411036</td>\n",
       "      <td>18.53135</td>\n",
       "      <td>73.91478</td>\n",
       "      <td>5</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>Alternate Days</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Improve endurance/conditioning</td>\n",
       "      <td>Meditation</td>\n",
       "      <td>163</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rushil Vish</td>\n",
       "      <td>Male</td>\n",
       "      <td>1999-02-07</td>\n",
       "      <td>Yerwada S.O</td>\n",
       "      <td>411006</td>\n",
       "      <td>18.56318</td>\n",
       "      <td>73.87742</td>\n",
       "      <td>3</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Both</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Improve athletic skills</td>\n",
       "      <td>Meditation</td>\n",
       "      <td>171</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Atiksh Manohar</td>\n",
       "      <td>Male</td>\n",
       "      <td>1987-07-01</td>\n",
       "      <td>Dapodi S.O</td>\n",
       "      <td>411012</td>\n",
       "      <td>18.57749</td>\n",
       "      <td>73.83138</td>\n",
       "      <td>5</td>\n",
       "      <td>Athletes</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>Alternate Days</td>\n",
       "      <td>Both</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Lose fat</td>\n",
       "      <td>Meditation</td>\n",
       "      <td>173</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ved Kapoor</td>\n",
       "      <td>Male</td>\n",
       "      <td>1988-01-28</td>\n",
       "      <td>Hadapsar S.O</td>\n",
       "      <td>411028</td>\n",
       "      <td>18.50453</td>\n",
       "      <td>73.91255</td>\n",
       "      <td>5</td>\n",
       "      <td>Essential Fat</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3 days of week</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Build muscle</td>\n",
       "      <td>None</td>\n",
       "      <td>161</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>Neysa Rai</td>\n",
       "      <td>Female</td>\n",
       "      <td>1989-12-10</td>\n",
       "      <td>Mohamadwadi S.O</td>\n",
       "      <td>411060</td>\n",
       "      <td>18.47335</td>\n",
       "      <td>73.90957</td>\n",
       "      <td>2</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>Any Day of Week</td>\n",
       "      <td>Both</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Improve athletic skills</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>170</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>Raunak Kapoor</td>\n",
       "      <td>Male</td>\n",
       "      <td>1984-08-12</td>\n",
       "      <td>Masulkar Colony S.O</td>\n",
       "      <td>411018</td>\n",
       "      <td>18.63374</td>\n",
       "      <td>73.81290</td>\n",
       "      <td>3</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>All</td>\n",
       "      <td>None</td>\n",
       "      <td>161</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>Taara Veer</td>\n",
       "      <td>Female</td>\n",
       "      <td>1995-06-06</td>\n",
       "      <td>Masulkar Colony S.O</td>\n",
       "      <td>411018</td>\n",
       "      <td>18.63374</td>\n",
       "      <td>73.81290</td>\n",
       "      <td>5</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>Only Weekends</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Build muscle</td>\n",
       "      <td>Both</td>\n",
       "      <td>166</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>Sarthak Rai</td>\n",
       "      <td>Male</td>\n",
       "      <td>1997-09-03</td>\n",
       "      <td>Guruwar Peth S.O</td>\n",
       "      <td>411042</td>\n",
       "      <td>18.50547</td>\n",
       "      <td>73.85951</td>\n",
       "      <td>5</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Improve athletic skills</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>177</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>Kairav Dutta</td>\n",
       "      <td>Male</td>\n",
       "      <td>1999-11-06</td>\n",
       "      <td>Kasarwadi S.O</td>\n",
       "      <td>411034</td>\n",
       "      <td>18.60373</td>\n",
       "      <td>73.82221</td>\n",
       "      <td>1</td>\n",
       "      <td>Athletes</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Daily</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>All</td>\n",
       "      <td>Both</td>\n",
       "      <td>178</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1387 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name  Gender         DOB         Name Of Area  Pincode  \\\n",
       "0           Amoli Veer  Female  1982-03-31        Nana Peth S.O   411002   \n",
       "1     Kaia Subramanian  Female  1994-06-09       Mundhva AV S.O   411036   \n",
       "2          Rushil Vish    Male  1999-02-07          Yerwada S.O   411006   \n",
       "3       Atiksh Manohar    Male  1987-07-01           Dapodi S.O   411012   \n",
       "4           Ved Kapoor    Male  1988-01-28         Hadapsar S.O   411028   \n",
       "...                ...     ...         ...                  ...      ...   \n",
       "1382         Neysa Rai  Female  1989-12-10      Mohamadwadi S.O   411060   \n",
       "1383     Raunak Kapoor    Male  1984-08-12  Masulkar Colony S.O   411018   \n",
       "1384        Taara Veer  Female  1995-06-06  Masulkar Colony S.O   411018   \n",
       "1385       Sarthak Rai    Male  1997-09-03     Guruwar Peth S.O   411042   \n",
       "1386      Kairav Dutta    Male  1999-11-06        Kasarwadi S.O   411034   \n",
       "\n",
       "      Latitude   Longitude  Flexibility Body Composition  \\\n",
       "0      18.51009   73.85672            4    Essential Fat   \n",
       "1      18.53135   73.91478            5          Fitness   \n",
       "2      18.56318   73.87742            3       Acceptable   \n",
       "3      18.57749   73.83138            5         Athletes   \n",
       "4      18.50453   73.91255            5    Essential Fat   \n",
       "...         ...        ...          ...              ...   \n",
       "1382   18.47335   73.90957            2          Fitness   \n",
       "1383   18.63374   73.81290            3          Fitness   \n",
       "1384   18.63374   73.81290            5          Fitness   \n",
       "1385   18.50547   73.85951            5          Fitness   \n",
       "1386   18.60373   73.82221            1         Athletes   \n",
       "\n",
       "      Composition Percentage  Interactiveness Workout Frequency  \\\n",
       "0                         12                4     Only Weekends   \n",
       "1                         22                5    Alternate Days   \n",
       "2                         22                3  Monday To Friday   \n",
       "3                         10                3    Alternate Days   \n",
       "4                          6                3    3 days of week   \n",
       "...                      ...              ...               ...   \n",
       "1382                      22                4   Any Day of Week   \n",
       "1383                      15                1  Monday To Friday   \n",
       "1384                      22                1     Only Weekends   \n",
       "1385                      15                5  Monday To Friday   \n",
       "1386                      10                1             Daily   \n",
       "\n",
       "     Workout Mode Preferred is_sports_enthusiast  like_to_be_mentored  \\\n",
       "0                   Outdoor                   No                    3   \n",
       "1                   Outdoor                Maybe                    1   \n",
       "2                      Both                  Yes                    4   \n",
       "3                      Both                Maybe                    3   \n",
       "4                    Indoor                   No                    1   \n",
       "...                     ...                  ...                  ...   \n",
       "1382                   Both                Maybe                    2   \n",
       "1383                Outdoor                  Yes                    2   \n",
       "1384                 Indoor                   No                    5   \n",
       "1385                 Indoor                Maybe                    3   \n",
       "1386                Outdoor                  Yes                    4   \n",
       "\n",
       "      like_to_mentor     fitness_goal_to_be achieved addons in session  \\\n",
       "0                  3                             All        Meditation   \n",
       "1                  3  Improve endurance/conditioning        Meditation   \n",
       "2                  5         Improve athletic skills        Meditation   \n",
       "3                  4                        Lose fat        Meditation   \n",
       "4                  1                    Build muscle              None   \n",
       "...              ...                             ...               ...   \n",
       "1382               4         Improve athletic skills              Yoga   \n",
       "1383               5                             All              None   \n",
       "1384               4                    Build muscle              Both   \n",
       "1385               2         Improve athletic skills              Yoga   \n",
       "1386               2                             All              Both   \n",
       "\n",
       "      Height_in_cms  Age  \n",
       "0               166   39  \n",
       "1               163   26  \n",
       "2               171   22  \n",
       "3               173   33  \n",
       "4               161   33  \n",
       "...             ...  ...  \n",
       "1382            170   31  \n",
       "1383            161   36  \n",
       "1384            166   25  \n",
       "1385            177   23  \n",
       "1386            178   21  \n",
       "\n",
       "[1387 rows x 20 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Name.isin(rec)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding Similarity Based on location Dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>Name</th>\n",
       "      <th>Name Of Area</th>\n",
       "      <th>Pincode</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude_rad</th>\n",
       "      <th>Longitude_rad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amoli Veer</td>\n",
       "      <td>Nana Peth S.O</td>\n",
       "      <td>411002</td>\n",
       "      <td>18.51009</td>\n",
       "      <td>73.85672</td>\n",
       "      <td>0.323062</td>\n",
       "      <td>1.289043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kaia Subramanian</td>\n",
       "      <td>Mundhva AV S.O</td>\n",
       "      <td>411036</td>\n",
       "      <td>18.53135</td>\n",
       "      <td>73.91478</td>\n",
       "      <td>0.323433</td>\n",
       "      <td>1.290056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rushil Vish</td>\n",
       "      <td>Yerwada S.O</td>\n",
       "      <td>411006</td>\n",
       "      <td>18.56318</td>\n",
       "      <td>73.87742</td>\n",
       "      <td>0.323989</td>\n",
       "      <td>1.289404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Atiksh Manohar</td>\n",
       "      <td>Dapodi S.O</td>\n",
       "      <td>411012</td>\n",
       "      <td>18.57749</td>\n",
       "      <td>73.83138</td>\n",
       "      <td>0.324238</td>\n",
       "      <td>1.288601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ved Kapoor</td>\n",
       "      <td>Hadapsar S.O</td>\n",
       "      <td>411028</td>\n",
       "      <td>18.50453</td>\n",
       "      <td>73.91255</td>\n",
       "      <td>0.322965</td>\n",
       "      <td>1.290017</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>Neysa Rai</td>\n",
       "      <td>Mohamadwadi S.O</td>\n",
       "      <td>411060</td>\n",
       "      <td>18.47335</td>\n",
       "      <td>73.90957</td>\n",
       "      <td>0.322421</td>\n",
       "      <td>1.289965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>Raunak Kapoor</td>\n",
       "      <td>Masulkar Colony S.O</td>\n",
       "      <td>411018</td>\n",
       "      <td>18.63374</td>\n",
       "      <td>73.81290</td>\n",
       "      <td>0.325220</td>\n",
       "      <td>1.288278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>Taara Veer</td>\n",
       "      <td>Masulkar Colony S.O</td>\n",
       "      <td>411018</td>\n",
       "      <td>18.63374</td>\n",
       "      <td>73.81290</td>\n",
       "      <td>0.325220</td>\n",
       "      <td>1.288278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>Sarthak Rai</td>\n",
       "      <td>Guruwar Peth S.O</td>\n",
       "      <td>411042</td>\n",
       "      <td>18.50547</td>\n",
       "      <td>73.85951</td>\n",
       "      <td>0.322981</td>\n",
       "      <td>1.289092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>Kairav Dutta</td>\n",
       "      <td>Kasarwadi S.O</td>\n",
       "      <td>411034</td>\n",
       "      <td>18.60373</td>\n",
       "      <td>73.82221</td>\n",
       "      <td>0.324696</td>\n",
       "      <td>1.288441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1387 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name         Name Of Area  Pincode  Latitude   Longitude  \\\n",
       "0           Amoli Veer        Nana Peth S.O   411002   18.51009   73.85672   \n",
       "1     Kaia Subramanian       Mundhva AV S.O   411036   18.53135   73.91478   \n",
       "2          Rushil Vish          Yerwada S.O   411006   18.56318   73.87742   \n",
       "3       Atiksh Manohar           Dapodi S.O   411012   18.57749   73.83138   \n",
       "4           Ved Kapoor         Hadapsar S.O   411028   18.50453   73.91255   \n",
       "...                ...                  ...      ...        ...        ...   \n",
       "1382         Neysa Rai      Mohamadwadi S.O   411060   18.47335   73.90957   \n",
       "1383     Raunak Kapoor  Masulkar Colony S.O   411018   18.63374   73.81290   \n",
       "1384        Taara Veer  Masulkar Colony S.O   411018   18.63374   73.81290   \n",
       "1385       Sarthak Rai     Guruwar Peth S.O   411042   18.50547   73.85951   \n",
       "1386      Kairav Dutta        Kasarwadi S.O   411034   18.60373   73.82221   \n",
       "\n",
       "      Latitude_rad  Longitude_rad  \n",
       "0         0.323062       1.289043  \n",
       "1         0.323433       1.290056  \n",
       "2         0.323989       1.289404  \n",
       "3         0.324238       1.288601  \n",
       "4         0.322965       1.290017  \n",
       "...            ...            ...  \n",
       "1382      0.322421       1.289965  \n",
       "1383      0.325220       1.288278  \n",
       "1384      0.325220       1.288278  \n",
       "1385      0.322981       1.289092  \n",
       "1386      0.324696       1.288441  \n",
       "\n",
       "[1387 rows x 7 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import haversine_distances\n",
    "from math import radians\n",
    "from sklearn.neighbors import DistanceMetric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-74-8cedbfb38188>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2['Latitude_rad'] = np.radians(df2['Latitude '])\n",
      "<ipython-input-74-8cedbfb38188>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2['Longitude_rad'] = np.radians(df2['Longitude'])\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Name Of Area</th>\n",
       "      <th>Pincode</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude_rad</th>\n",
       "      <th>Longitude_rad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amoli Veer</td>\n",
       "      <td>Nana Peth S.O</td>\n",
       "      <td>411002</td>\n",
       "      <td>18.51009</td>\n",
       "      <td>73.85672</td>\n",
       "      <td>0.323062</td>\n",
       "      <td>1.289043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kaia Subramanian</td>\n",
       "      <td>Mundhva AV S.O</td>\n",
       "      <td>411036</td>\n",
       "      <td>18.53135</td>\n",
       "      <td>73.91478</td>\n",
       "      <td>0.323433</td>\n",
       "      <td>1.290056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rushil Vish</td>\n",
       "      <td>Yerwada S.O</td>\n",
       "      <td>411006</td>\n",
       "      <td>18.56318</td>\n",
       "      <td>73.87742</td>\n",
       "      <td>0.323989</td>\n",
       "      <td>1.289404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Atiksh Manohar</td>\n",
       "      <td>Dapodi S.O</td>\n",
       "      <td>411012</td>\n",
       "      <td>18.57749</td>\n",
       "      <td>73.83138</td>\n",
       "      <td>0.324238</td>\n",
       "      <td>1.288601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ved Kapoor</td>\n",
       "      <td>Hadapsar S.O</td>\n",
       "      <td>411028</td>\n",
       "      <td>18.50453</td>\n",
       "      <td>73.91255</td>\n",
       "      <td>0.322965</td>\n",
       "      <td>1.290017</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>Neysa Rai</td>\n",
       "      <td>Mohamadwadi S.O</td>\n",
       "      <td>411060</td>\n",
       "      <td>18.47335</td>\n",
       "      <td>73.90957</td>\n",
       "      <td>0.322421</td>\n",
       "      <td>1.289965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>Raunak Kapoor</td>\n",
       "      <td>Masulkar Colony S.O</td>\n",
       "      <td>411018</td>\n",
       "      <td>18.63374</td>\n",
       "      <td>73.81290</td>\n",
       "      <td>0.325220</td>\n",
       "      <td>1.288278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>Taara Veer</td>\n",
       "      <td>Masulkar Colony S.O</td>\n",
       "      <td>411018</td>\n",
       "      <td>18.63374</td>\n",
       "      <td>73.81290</td>\n",
       "      <td>0.325220</td>\n",
       "      <td>1.288278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>Sarthak Rai</td>\n",
       "      <td>Guruwar Peth S.O</td>\n",
       "      <td>411042</td>\n",
       "      <td>18.50547</td>\n",
       "      <td>73.85951</td>\n",
       "      <td>0.322981</td>\n",
       "      <td>1.289092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>Kairav Dutta</td>\n",
       "      <td>Kasarwadi S.O</td>\n",
       "      <td>411034</td>\n",
       "      <td>18.60373</td>\n",
       "      <td>73.82221</td>\n",
       "      <td>0.324696</td>\n",
       "      <td>1.288441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1387 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name         Name Of Area  Pincode  Latitude   Longitude  \\\n",
       "0           Amoli Veer        Nana Peth S.O   411002   18.51009   73.85672   \n",
       "1     Kaia Subramanian       Mundhva AV S.O   411036   18.53135   73.91478   \n",
       "2          Rushil Vish          Yerwada S.O   411006   18.56318   73.87742   \n",
       "3       Atiksh Manohar           Dapodi S.O   411012   18.57749   73.83138   \n",
       "4           Ved Kapoor         Hadapsar S.O   411028   18.50453   73.91255   \n",
       "...                ...                  ...      ...        ...        ...   \n",
       "1382         Neysa Rai      Mohamadwadi S.O   411060   18.47335   73.90957   \n",
       "1383     Raunak Kapoor  Masulkar Colony S.O   411018   18.63374   73.81290   \n",
       "1384        Taara Veer  Masulkar Colony S.O   411018   18.63374   73.81290   \n",
       "1385       Sarthak Rai     Guruwar Peth S.O   411042   18.50547   73.85951   \n",
       "1386      Kairav Dutta        Kasarwadi S.O   411034   18.60373   73.82221   \n",
       "\n",
       "      Latitude_rad  Longitude_rad  \n",
       "0         0.323062       1.289043  \n",
       "1         0.323433       1.290056  \n",
       "2         0.323989       1.289404  \n",
       "3         0.324238       1.288601  \n",
       "4         0.322965       1.290017  \n",
       "...            ...            ...  \n",
       "1382      0.322421       1.289965  \n",
       "1383      0.325220       1.288278  \n",
       "1384      0.325220       1.288278  \n",
       "1385      0.322981       1.289092  \n",
       "1386      0.324696       1.288441  \n",
       "\n",
       "[1387 rows x 7 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Latitude_rad'] = np.radians(df2['Latitude '])\n",
    "df2['Longitude_rad'] = np.radians(df2['Longitude'])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist = DistanceMetric.get_metric('haversine')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Amoli Veer</th>\n",
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       "      <td>7.125896</td>\n",
       "      <td>3.248009</td>\n",
       "      <td>8.659993</td>\n",
       "      <td>7.757147</td>\n",
       "      <td>1.814675</td>\n",
       "      <td>...</td>\n",
       "      <td>6.640876</td>\n",
       "      <td>13.115020</td>\n",
       "      <td>9.167550</td>\n",
       "      <td>8.140429</td>\n",
       "      <td>30.016782</td>\n",
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       "      <td>10.382359</td>\n",
       "      <td>6.689094</td>\n",
       "      <td>7.361470</td>\n",
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       "      <th>Atiksh Manohar</th>\n",
       "      <td>7.956407</td>\n",
       "      <td>10.179151</td>\n",
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       "      <td>9.191280</td>\n",
       "      <td>...</td>\n",
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       "      <td>14.214540</td>\n",
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       "      <td>4.686601</td>\n",
       "      <td>3.194399</td>\n",
       "      <td>12.087887</td>\n",
       "      <td>...</td>\n",
       "      <td>9.201000</td>\n",
       "      <td>21.473476</td>\n",
       "      <td>5.798780</td>\n",
       "      <td>9.997217</td>\n",
       "      <td>39.960902</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.540652</td>\n",
       "      <td>20.540652</td>\n",
       "      <td>6.373775</td>\n",
       "      <td>17.175652</td>\n",
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       "      <th>Raunak Kapoor</th>\n",
       "      <td>14.504325</td>\n",
       "      <td>15.650144</td>\n",
       "      <td>10.382359</td>\n",
       "      <td>6.550890</td>\n",
       "      <td>17.797557</td>\n",
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       "      <td>13.286032</td>\n",
       "      <td>17.404263</td>\n",
       "      <td>18.037327</td>\n",
       "      <td>8.618179</td>\n",
       "      <td>...</td>\n",
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       "      <td>19.634914</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>3.478182</td>\n",
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       "    <tr>\n",
       "      <th>Taara Veer</th>\n",
       "      <td>14.504325</td>\n",
       "      <td>15.650144</td>\n",
       "      <td>10.382359</td>\n",
       "      <td>6.550890</td>\n",
       "      <td>17.797557</td>\n",
       "      <td>14.129378</td>\n",
       "      <td>13.286032</td>\n",
       "      <td>17.404263</td>\n",
       "      <td>18.037327</td>\n",
       "      <td>8.618179</td>\n",
       "      <td>...</td>\n",
       "      <td>13.094386</td>\n",
       "      <td>6.245470</td>\n",
       "      <td>17.315909</td>\n",
       "      <td>17.368142</td>\n",
       "      <td>19.634914</td>\n",
       "      <td>20.540652</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.085411</td>\n",
       "      <td>3.478182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sarthak Rai</th>\n",
       "      <td>0.591993</td>\n",
       "      <td>6.499336</td>\n",
       "      <td>6.689094</td>\n",
       "      <td>8.539714</td>\n",
       "      <td>5.593817</td>\n",
       "      <td>2.038662</td>\n",
       "      <td>4.067700</td>\n",
       "      <td>2.319391</td>\n",
       "      <td>5.616184</td>\n",
       "      <td>7.515995</td>\n",
       "      <td>...</td>\n",
       "      <td>2.833156</td>\n",
       "      <td>15.217746</td>\n",
       "      <td>2.491584</td>\n",
       "      <td>11.256827</td>\n",
       "      <td>34.058992</td>\n",
       "      <td>6.373775</td>\n",
       "      <td>15.085411</td>\n",
       "      <td>15.085411</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.611988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kairav Dutta</th>\n",
       "      <td>11.029489</td>\n",
       "      <td>12.648496</td>\n",
       "      <td>7.361470</td>\n",
       "      <td>3.073650</td>\n",
       "      <td>14.572734</td>\n",
       "      <td>10.665042</td>\n",
       "      <td>10.021975</td>\n",
       "      <td>13.931293</td>\n",
       "      <td>14.798930</td>\n",
       "      <td>5.547164</td>\n",
       "      <td>...</td>\n",
       "      <td>9.638736</td>\n",
       "      <td>6.409745</td>\n",
       "      <td>13.838860</td>\n",
       "      <td>14.986422</td>\n",
       "      <td>22.829519</td>\n",
       "      <td>17.175652</td>\n",
       "      <td>3.478182</td>\n",
       "      <td>3.478182</td>\n",
       "      <td>11.611988</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1387 rows × 1387 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Amoli Veer  Kaia Subramanian  Rushil Vish  Atiksh Manohar  \\\n",
       "Amoli Veer          0.000000          6.562216     6.293802        7.956407   \n",
       "Kaia Subramanian    6.562216          0.000000     5.295139       10.179151   \n",
       "Rushil Vish         6.293802          5.295139     0.000000        5.107074   \n",
       "Atiksh Manohar      7.956407         10.179151     5.107074        0.000000   \n",
       "Ved Kapoor          5.919329          2.991502     7.499887       11.791663   \n",
       "...                      ...               ...          ...             ...   \n",
       "Neysa Rai           6.910185          6.472663    10.548165       14.214540   \n",
       "Raunak Kapoor      14.504325         15.650144    10.382359        6.550890   \n",
       "Taara Veer         14.504325         15.650144    10.382359        6.550890   \n",
       "Sarthak Rai         0.591993          6.499336     6.689094        8.539714   \n",
       "Kairav Dutta       11.029489         12.648496     7.361470        3.073650   \n",
       "\n",
       "                  Ved Kapoor  Vihaan Chauhan  Tanvi Sandeep  Taara Chauhan  \\\n",
       "Amoli Veer          5.919329        1.698209       3.878664       2.904949   \n",
       "Kaia Subramanian    2.991502        8.189562       3.364122       7.124112   \n",
       "Rushil Vish         7.499887        7.125896       3.248009       8.659993   \n",
       "Atiksh Manohar     11.791663        7.631597       7.249081      10.859106   \n",
       "Ved Kapoor          0.000000        7.603035       4.555992       5.216033   \n",
       "...                      ...             ...            ...            ...   \n",
       "Neysa Rai           3.481271        8.302981       7.348445       4.686601   \n",
       "Raunak Kapoor      17.797557       14.129378      13.286032      17.404263   \n",
       "Taara Veer         17.797557       14.129378      13.286032      17.404263   \n",
       "Sarthak Rai         5.593817        2.038662       4.067700       2.319391   \n",
       "Kairav Dutta       14.572734       10.665042      10.021975      13.931293   \n",
       "\n",
       "                  Anaisha Veena  Rebecca Chander  ...  Rohan Nagpal  \\\n",
       "Amoli Veer             5.965892         7.027572  ...      2.342975   \n",
       "Kaia Subramanian       3.279127         7.104329  ...      8.420922   \n",
       "Rushil Vish            7.757147         1.814675  ...      6.640876   \n",
       "Atiksh Manohar        11.999226         3.436508  ...      6.627419   \n",
       "Ved Kapoor             0.289263         9.191280  ...      8.195036   \n",
       "...                         ...              ...  ...           ...   \n",
       "Neysa Rai              3.194399        12.087887  ...      9.201000   \n",
       "Raunak Kapoor         18.037327         8.618179  ...     13.094386   \n",
       "Taara Veer            18.037327         8.618179  ...     13.094386   \n",
       "Sarthak Rai            5.616184         7.515995  ...      2.833156   \n",
       "Kairav Dutta          14.798930         5.547164  ...      9.638736   \n",
       "\n",
       "                  Alisha Roy  Sneha Ranga  Ira Naran  Avni Nath  Neysa Rai  \\\n",
       "Amoli Veer         14.641492     2.973433  11.291795  33.467366   6.910185   \n",
       "Kaia Subramanian   18.165245     8.209169   4.763670  35.281101   6.472663   \n",
       "Rushil Vish        13.115020     9.167550   8.140429  30.016782  10.548165   \n",
       "Atiksh Manohar      8.020077    10.772929  13.199227  25.746387  14.214540   \n",
       "Ved Kapoor         19.514164     6.449006   6.916275  37.390595   3.481271   \n",
       "...                      ...          ...        ...        ...        ...   \n",
       "Neysa Rai          21.473476     5.798780   9.997217  39.960902   0.000000   \n",
       "Raunak Kapoor       6.245470    17.315909  17.368142  19.634914  20.540652   \n",
       "Taara Veer          6.245470    17.315909  17.368142  19.634914  20.540652   \n",
       "Sarthak Rai        15.217746     2.491584  11.256827  34.058992   6.373775   \n",
       "Kairav Dutta        6.409745    13.838860  14.986422  22.829519  17.175652   \n",
       "\n",
       "                  Raunak Kapoor  Taara Veer  Sarthak Rai  Kairav Dutta  \n",
       "Amoli Veer            14.504325   14.504325     0.591993     11.029489  \n",
       "Kaia Subramanian      15.650144   15.650144     6.499336     12.648496  \n",
       "Rushil Vish           10.382359   10.382359     6.689094      7.361470  \n",
       "Atiksh Manohar         6.550890    6.550890     8.539714      3.073650  \n",
       "Ved Kapoor            17.797557   17.797557     5.593817     14.572734  \n",
       "...                         ...         ...          ...           ...  \n",
       "Neysa Rai             20.540652   20.540652     6.373775     17.175652  \n",
       "Raunak Kapoor          0.000000    0.000000    15.085411      3.478182  \n",
       "Taara Veer             0.000000    0.000000    15.085411      3.478182  \n",
       "Sarthak Rai           15.085411   15.085411     0.000000     11.611988  \n",
       "Kairav Dutta           3.478182    3.478182    11.611988      0.000000  \n",
       "\n",
       "[1387 rows x 1387 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " distance_similarity_df2 = pd.DataFrame(dist.pairwise(df2[['Latitude_rad','Longitude_rad']].to_numpy())*6371,  columns=df2.Name.unique(), index=df2.Name.unique())\n",
    "distance_similarity_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[df.Name.isin([name])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Longitude</th>\n",
       "      <th>Flexibility</th>\n",
       "      <th>Body Composition</th>\n",
       "      <th>Composition Percentage</th>\n",
       "      <th>Interactiveness</th>\n",
       "      <th>Workout Frequency</th>\n",
       "      <th>Workout Mode Preferred</th>\n",
       "      <th>is_sports_enthusiast</th>\n",
       "      <th>like_to_be_mentored</th>\n",
       "      <th>like_to_mentor</th>\n",
       "      <th>fitness_goal_to_be achieved</th>\n",
       "      <th>addons in session</th>\n",
       "      <th>Height_in_cms</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amoli Veer</td>\n",
       "      <td>Female</td>\n",
       "      <td>1982-03-31</td>\n",
       "      <td>Nana Peth S.O</td>\n",
       "      <td>411002</td>\n",
       "      <td>18.51009</td>\n",
       "      <td>73.85672</td>\n",
       "      <td>4</td>\n",
       "      <td>Essential Fat</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>Only Weekends</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>All</td>\n",
       "      <td>Meditation</td>\n",
       "      <td>166</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kaia Subramanian</td>\n",
       "      <td>Female</td>\n",
       "      <td>1994-06-09</td>\n",
       "      <td>Mundhva AV S.O</td>\n",
       "      <td>411036</td>\n",
       "      <td>18.53135</td>\n",
       "      <td>73.91478</td>\n",
       "      <td>5</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>Alternate Days</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Improve endurance/conditioning</td>\n",
       "      <td>Meditation</td>\n",
       "      <td>163</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rushil Vish</td>\n",
       "      <td>Male</td>\n",
       "      <td>1999-02-07</td>\n",
       "      <td>Yerwada S.O</td>\n",
       "      <td>411006</td>\n",
       "      <td>18.56318</td>\n",
       "      <td>73.87742</td>\n",
       "      <td>3</td>\n",
       "      <td>Acceptable</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Both</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Improve athletic skills</td>\n",
       "      <td>Meditation</td>\n",
       "      <td>171</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Atiksh Manohar</td>\n",
       "      <td>Male</td>\n",
       "      <td>1987-07-01</td>\n",
       "      <td>Dapodi S.O</td>\n",
       "      <td>411012</td>\n",
       "      <td>18.57749</td>\n",
       "      <td>73.83138</td>\n",
       "      <td>5</td>\n",
       "      <td>Athletes</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>Alternate Days</td>\n",
       "      <td>Both</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Lose fat</td>\n",
       "      <td>Meditation</td>\n",
       "      <td>173</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ved Kapoor</td>\n",
       "      <td>Male</td>\n",
       "      <td>1988-01-28</td>\n",
       "      <td>Hadapsar S.O</td>\n",
       "      <td>411028</td>\n",
       "      <td>18.50453</td>\n",
       "      <td>73.91255</td>\n",
       "      <td>5</td>\n",
       "      <td>Essential Fat</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3 days of week</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Build muscle</td>\n",
       "      <td>None</td>\n",
       "      <td>161</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>Neysa Rai</td>\n",
       "      <td>Female</td>\n",
       "      <td>1989-12-10</td>\n",
       "      <td>Mohamadwadi S.O</td>\n",
       "      <td>411060</td>\n",
       "      <td>18.47335</td>\n",
       "      <td>73.90957</td>\n",
       "      <td>2</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>Any Day of Week</td>\n",
       "      <td>Both</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Improve athletic skills</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>170</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>Raunak Kapoor</td>\n",
       "      <td>Male</td>\n",
       "      <td>1984-08-12</td>\n",
       "      <td>Masulkar Colony S.O</td>\n",
       "      <td>411018</td>\n",
       "      <td>18.63374</td>\n",
       "      <td>73.81290</td>\n",
       "      <td>3</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>All</td>\n",
       "      <td>None</td>\n",
       "      <td>161</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>Taara Veer</td>\n",
       "      <td>Female</td>\n",
       "      <td>1995-06-06</td>\n",
       "      <td>Masulkar Colony S.O</td>\n",
       "      <td>411018</td>\n",
       "      <td>18.63374</td>\n",
       "      <td>73.81290</td>\n",
       "      <td>5</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>Only Weekends</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Build muscle</td>\n",
       "      <td>Both</td>\n",
       "      <td>166</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>Sarthak Rai</td>\n",
       "      <td>Male</td>\n",
       "      <td>1997-09-03</td>\n",
       "      <td>Guruwar Peth S.O</td>\n",
       "      <td>411042</td>\n",
       "      <td>18.50547</td>\n",
       "      <td>73.85951</td>\n",
       "      <td>5</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Improve athletic skills</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>177</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>Kairav Dutta</td>\n",
       "      <td>Male</td>\n",
       "      <td>1999-11-06</td>\n",
       "      <td>Kasarwadi S.O</td>\n",
       "      <td>411034</td>\n",
       "      <td>18.60373</td>\n",
       "      <td>73.82221</td>\n",
       "      <td>1</td>\n",
       "      <td>Athletes</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Daily</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>All</td>\n",
       "      <td>Both</td>\n",
       "      <td>178</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1387 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name  Gender         DOB         Name Of Area  Pincode  \\\n",
       "0           Amoli Veer  Female  1982-03-31        Nana Peth S.O   411002   \n",
       "1     Kaia Subramanian  Female  1994-06-09       Mundhva AV S.O   411036   \n",
       "2          Rushil Vish    Male  1999-02-07          Yerwada S.O   411006   \n",
       "3       Atiksh Manohar    Male  1987-07-01           Dapodi S.O   411012   \n",
       "4           Ved Kapoor    Male  1988-01-28         Hadapsar S.O   411028   \n",
       "...                ...     ...         ...                  ...      ...   \n",
       "1382         Neysa Rai  Female  1989-12-10      Mohamadwadi S.O   411060   \n",
       "1383     Raunak Kapoor    Male  1984-08-12  Masulkar Colony S.O   411018   \n",
       "1384        Taara Veer  Female  1995-06-06  Masulkar Colony S.O   411018   \n",
       "1385       Sarthak Rai    Male  1997-09-03     Guruwar Peth S.O   411042   \n",
       "1386      Kairav Dutta    Male  1999-11-06        Kasarwadi S.O   411034   \n",
       "\n",
       "      Latitude   Longitude  Flexibility Body Composition  \\\n",
       "0      18.51009   73.85672            4    Essential Fat   \n",
       "1      18.53135   73.91478            5          Fitness   \n",
       "2      18.56318   73.87742            3       Acceptable   \n",
       "3      18.57749   73.83138            5         Athletes   \n",
       "4      18.50453   73.91255            5    Essential Fat   \n",
       "...         ...        ...          ...              ...   \n",
       "1382   18.47335   73.90957            2          Fitness   \n",
       "1383   18.63374   73.81290            3          Fitness   \n",
       "1384   18.63374   73.81290            5          Fitness   \n",
       "1385   18.50547   73.85951            5          Fitness   \n",
       "1386   18.60373   73.82221            1         Athletes   \n",
       "\n",
       "      Composition Percentage  Interactiveness Workout Frequency  \\\n",
       "0                         12                4     Only Weekends   \n",
       "1                         22                5    Alternate Days   \n",
       "2                         22                3  Monday To Friday   \n",
       "3                         10                3    Alternate Days   \n",
       "4                          6                3    3 days of week   \n",
       "...                      ...              ...               ...   \n",
       "1382                      22                4   Any Day of Week   \n",
       "1383                      15                1  Monday To Friday   \n",
       "1384                      22                1     Only Weekends   \n",
       "1385                      15                5  Monday To Friday   \n",
       "1386                      10                1             Daily   \n",
       "\n",
       "     Workout Mode Preferred is_sports_enthusiast  like_to_be_mentored  \\\n",
       "0                   Outdoor                   No                    3   \n",
       "1                   Outdoor                Maybe                    1   \n",
       "2                      Both                  Yes                    4   \n",
       "3                      Both                Maybe                    3   \n",
       "4                    Indoor                   No                    1   \n",
       "...                     ...                  ...                  ...   \n",
       "1382                   Both                Maybe                    2   \n",
       "1383                Outdoor                  Yes                    2   \n",
       "1384                 Indoor                   No                    5   \n",
       "1385                 Indoor                Maybe                    3   \n",
       "1386                Outdoor                  Yes                    4   \n",
       "\n",
       "      like_to_mentor     fitness_goal_to_be achieved addons in session  \\\n",
       "0                  3                             All        Meditation   \n",
       "1                  3  Improve endurance/conditioning        Meditation   \n",
       "2                  5         Improve athletic skills        Meditation   \n",
       "3                  4                        Lose fat        Meditation   \n",
       "4                  1                    Build muscle              None   \n",
       "...              ...                             ...               ...   \n",
       "1382               4         Improve athletic skills              Yoga   \n",
       "1383               5                             All              None   \n",
       "1384               4                    Build muscle              Both   \n",
       "1385               2         Improve athletic skills              Yoga   \n",
       "1386               2                             All              Both   \n",
       "\n",
       "      Height_in_cms  Age  \n",
       "0               166   39  \n",
       "1               163   26  \n",
       "2               171   22  \n",
       "3               173   33  \n",
       "4               161   33  \n",
       "...             ...  ...  \n",
       "1382            170   31  \n",
       "1383            161   36  \n",
       "1384            166   25  \n",
       "1385            177   23  \n",
       "1386            178   21  \n",
       "\n",
       "[1387 rows x 20 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_user=distance_similarity_df2.loc[name,:]\n",
    "Recommendations1=df2_user\n",
    "rec1 =Recommendations1.index.to_list()\n",
    "df[df.Name.isin(rec1)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Amoli Veer           0.591993\n",
       "Kaia Subramanian     6.499336\n",
       "Rushil Vish          6.689094\n",
       "Atiksh Manohar       8.539714\n",
       "Ved Kapoor           5.593817\n",
       "                      ...    \n",
       "Neysa Rai            6.373775\n",
       "Raunak Kapoor       15.085411\n",
       "Taara Veer          15.085411\n",
       "Sarthak Rai          0.000000\n",
       "Kairav Dutta        11.611988\n",
       "Name: Sarthak Rai, Length: 1387, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Recommendations1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Amoli Veer',\n",
       " 'Kaia Subramanian',\n",
       " 'Rushil Vish',\n",
       " 'Atiksh Manohar',\n",
       " 'Ved Kapoor',\n",
       " 'Vihaan Chauhan',\n",
       " 'Tanvi Sandeep',\n",
       " 'Taara Chauhan',\n",
       " 'Anaisha Veena',\n",
       " 'Rebecca Chander',\n",
       " 'Nirav Verma',\n",
       " 'Gian Mati',\n",
       " 'Raunak Chaudhary',\n",
       " 'Ishita Sen',\n",
       " 'Vihaan Mehra',\n",
       " 'Rishaan Krishnamurthy',\n",
       " 'Jash Darsha',\n",
       " 'Luv Meena',\n",
       " 'Akanksh Charan',\n",
       " 'Hiya Patel',\n",
       " 'Pavati Sara',\n",
       " 'Nimit Muthu',\n",
       " 'Ved Kumar',\n",
       " 'Rishaan Rai',\n",
       " 'Akanksh Lalit',\n",
       " 'Anaisha Prasad',\n",
       " 'Rushil Rana',\n",
       " 'Ved Suri',\n",
       " 'Avni Sara',\n",
       " 'Asmee Subramani',\n",
       " 'Krisha Soni',\n",
       " 'Ved Uddin',\n",
       " 'Vihaan Neel',\n",
       " 'Ved Kishore',\n",
       " 'Anant Nara',\n",
       " 'Adhira Chauhan',\n",
       " 'Manan Raji',\n",
       " 'Hiya Suri',\n",
       " 'Mohammad Veer',\n",
       " 'Ishita Patel',\n",
       " 'Zuber Chauhan',\n",
       " 'Sai Joshi',\n",
       " 'Ishank Vijaya',\n",
       " 'Krisha Muthu',\n",
       " 'Naksh Kapoor',\n",
       " 'Rishaan Patel',\n",
       " 'Ananya Srini',\n",
       " 'Sai Rajagopal',\n",
       " 'Shanaya Punj',\n",
       " 'Pavati Chande',\n",
       " 'Mehar Madan',\n",
       " 'Mishka Malik',\n",
       " 'Reyansh Nath',\n",
       " 'Shrishti Meena',\n",
       " 'Vivaan Ganesh',\n",
       " 'Kahaan Sahni',\n",
       " 'Manan Manju',\n",
       " 'Krisha Rai',\n",
       " 'Rohan Madan',\n",
       " 'Bhuv Srivastav',\n",
       " 'Raunak Rai',\n",
       " 'Carina Sharma',\n",
       " 'Kaia Mahajan',\n",
       " 'Ryka Subram',\n",
       " 'Sneha Chaudhary',\n",
       " 'Alisha Krishnamurthy',\n",
       " 'Sadhil Lata',\n",
       " 'Asmee Aggarwal',\n",
       " 'Krisha Narayan',\n",
       " 'Naksh Anand',\n",
       " 'Aahva Bhatt',\n",
       " 'Adah Narayan',\n",
       " 'Kimaya Chandrasekar',\n",
       " 'Mirai Dutta',\n",
       " 'Pahal Tyagi',\n",
       " 'Vivaan Subram',\n",
       " 'Navya Soni',\n",
       " 'Hiya Rana',\n",
       " 'Sahana Engineer',\n",
       " 'Rushil Chauhan',\n",
       " 'Ananya Charan',\n",
       " 'Navya Samuel',\n",
       " 'Kevin Rana',\n",
       " 'Sadhil Krishnamurthy',\n",
       " 'Prisha Malik',\n",
       " 'Amoli Chandra',\n",
       " 'Mirai Lata',\n",
       " 'Taksh Veer',\n",
       " 'Anushka Neelam',\n",
       " 'Kahaan Charan',\n",
       " 'Nimit Sahni',\n",
       " 'Kairav Chakrabarti',\n",
       " 'Reyansh Kumar',\n",
       " 'Ayaan Gandhi',\n",
       " 'Sarthak Bhat',\n",
       " 'Aaradhya Darsha',\n",
       " 'Jay Joshi',\n",
       " 'Pranay Narayan',\n",
       " 'Mahika Anand',\n",
       " 'Navya Gandhi',\n",
       " 'Pavati Nita',\n",
       " 'Navya Joshi',\n",
       " 'Ryka Uddin',\n",
       " 'Shanaya Srivas',\n",
       " 'Rudra Veer',\n",
       " 'Mehar Srini',\n",
       " 'Aarav Mehta',\n",
       " 'Mirai Vish',\n",
       " 'Zara Vish',\n",
       " 'Rudra Sara',\n",
       " 'Kahaan Srini',\n",
       " 'Joseph Charan',\n",
       " 'Yash Kishore',\n",
       " 'Adah Chakrabarti',\n",
       " 'Luv Goel',\n",
       " 'Shanaya Neel',\n",
       " 'Anant Kishore',\n",
       " 'Drishti Raman',\n",
       " 'Yug Gandhi',\n",
       " 'Kairav Swami',\n",
       " 'Krisha Rana',\n",
       " 'Nimit Neela',\n",
       " 'Ved Swami',\n",
       " 'Jash Ranga',\n",
       " 'Asmee Nirmal',\n",
       " 'Zuber Gandhi',\n",
       " 'Prisha Raman',\n",
       " 'Ishita Sai',\n",
       " 'Raj Bhatt',\n",
       " 'Shanaya Patel',\n",
       " 'Avni Nara',\n",
       " 'Laksh Raje',\n",
       " 'Tanvi Subramanian',\n",
       " 'Dasya Narang',\n",
       " 'Rushil Soni',\n",
       " 'Nirav Nath',\n",
       " 'Rohan Tyagi',\n",
       " 'Zara Ranga',\n",
       " 'Ishana Veer',\n",
       " 'Ishank Subramani',\n",
       " 'Asmee Kapoor',\n",
       " 'Anushka Nirmal',\n",
       " 'Avni Ganesh',\n",
       " 'Ved Srivas',\n",
       " 'Rohan Joshi',\n",
       " 'Ayaan Raman',\n",
       " 'Vivaan Srivas',\n",
       " 'Rishit Manju',\n",
       " 'Joseph Jana',\n",
       " 'Alisha Sehgal',\n",
       " 'Rushil Nagpal',\n",
       " 'Naksh Manju',\n",
       " 'Naitee Kumar',\n",
       " 'Sneha Uddin',\n",
       " 'Ira Dutta',\n",
       " 'Mishka Chaudhary',\n",
       " 'Pranay Dhawan',\n",
       " 'Zara Naran',\n",
       " 'Naitee Chauhan',\n",
       " 'Yug Nigam',\n",
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       " 'Sahana Pawan',\n",
       " 'Mishka Nirmal',\n",
       " 'Mishka Nita',\n",
       " 'Anika Manohar',\n",
       " 'Nehrika Chande',\n",
       " 'Rachit Chandra',\n",
       " 'Idhant Patel',\n",
       " 'Saloni Ranga',\n",
       " 'Adah Mati',\n",
       " 'Vihaan Dutta',\n",
       " 'Sneha Saxena',\n",
       " 'Dasya Nirmal',\n",
       " 'Aahva Joshi',\n",
       " 'Naksh Dutta',\n",
       " 'Kashvi Veer',\n",
       " 'Sai Nagpal',\n",
       " 'Vihaan Pawan',\n",
       " 'Adah Arun',\n",
       " 'Taksh Srivastava',\n",
       " 'Shrishti Raji',\n",
       " 'Reyansh Neela',\n",
       " 'Taksh Kishore',\n",
       " 'Nimit Manohar',\n",
       " 'Rishaan Sharma',\n",
       " 'Aahva Neela',\n",
       " 'Kevin Neelam',\n",
       " 'Hem Krishnamurthy',\n",
       " 'Sneha Pawan',\n",
       " 'Reyansh Subramanian',\n",
       " 'Taksh Tyagi',\n",
       " 'Sahana Chandra',\n",
       " 'Aadiv Soni',\n",
       " 'Kashvi Goel',\n",
       " 'Rushil Patel',\n",
       " 'Anika Sandeep',\n",
       " 'Tanvi Manju',\n",
       " 'Taarush Meena',\n",
       " 'Alisha Joshi',\n",
       " 'Keya Uddin',\n",
       " 'Adhira Krishnamurthy',\n",
       " 'Mirai Sandeep',\n",
       " 'Vihaan Joshi',\n",
       " 'Hiya Muthu',\n",
       " 'Kashvi Srini',\n",
       " 'Parv Lalit',\n",
       " 'Pranay Saini',\n",
       " 'Anant Raman',\n",
       " 'Joseph Rana',\n",
       " 'Rishit Mati',\n",
       " 'Navya Sethi',\n",
       " 'Rudra Darsha',\n",
       " 'Yash Persaud',\n",
       " 'Mahika Jana',\n",
       " 'Kevin Sharma',\n",
       " 'Adhira Kumar',\n",
       " 'Avni Vish',\n",
       " 'Kashvi Dhawan',\n",
       " 'Anaisha Pal',\n",
       " 'Rishit Bhat',\n",
       " 'Mahika Nirmal',\n",
       " 'Nimit Sethi',\n",
       " 'Kabir Kapoor',\n",
       " 'Adah Vijaya',\n",
       " 'Sai Nara',\n",
       " 'Raunak Sahni',\n",
       " 'Ayaan Soni',\n",
       " 'Ishank Nigam',\n",
       " 'Ayaan Jana',\n",
       " 'Ranbir Chakrabarti',\n",
       " 'Laksh Pal',\n",
       " 'Nimit Subramani',\n",
       " 'Anushka Chauhan',\n",
       " 'Keya Veer',\n",
       " 'Yug Nirmal',\n",
       " 'Yash Arun',\n",
       " 'Mayra Subram',\n",
       " 'Prisha Samuel',\n",
       " 'Aadiv Srini',\n",
       " 'Kevin Srivas',\n",
       " 'Alex Neel',\n",
       " 'Anaisha Srini',\n",
       " 'Taara Sai',\n",
       " 'Rebecca Sai',\n",
       " 'Sai Nigam',\n",
       " 'Carina Sandeep',\n",
       " 'Anushka Srini',\n",
       " 'Larisa Sharma',\n",
       " 'Amoli Chauhan',\n",
       " 'Hiya Joshi',\n",
       " 'Taara Tyagi',\n",
       " 'Saanvi Prasad',\n",
       " 'Kaia Nigam',\n",
       " 'Akanksh Neel',\n",
       " 'Bhuv Punj',\n",
       " 'Kimaya Vijaya',\n",
       " 'Sadhil Charan',\n",
       " 'Neysa Chakrabarti',\n",
       " 'Mahika Dutta',\n",
       " 'Reyansh Engineer',\n",
       " 'Kimaya Nath',\n",
       " 'Saanvi Rajagopal',\n",
       " 'Sai Aggarwal',\n",
       " 'Atiksh Pawan',\n",
       " 'Aarav Nath',\n",
       " 'Taksh Sara',\n",
       " 'Aarav Mittal',\n",
       " 'Mohammad Chaudhary',\n",
       " 'Alisha Rai',\n",
       " 'Mohammad Mehra',\n",
       " 'Ishita Chander',\n",
       " 'Aahva Sai',\n",
       " 'Hiya Darsha',\n",
       " 'Saloni Jai',\n",
       " 'Atiksh Saxena',\n",
       " 'Pranay Manohar',\n",
       " 'Manan Nirmal',\n",
       " 'Aadiv Arun',\n",
       " 'Kabir Nita',\n",
       " 'Alex Pawan',\n",
       " 'Rachit Aggarwal',\n",
       " 'Mishka Neelam',\n",
       " 'Mishka Nagpal',\n",
       " 'Taahira Jai',\n",
       " 'Aadiv Ganesh',\n",
       " 'Anika Vijaya',\n",
       " 'Raunak Shan',\n",
       " 'Aaradhya Jindal',\n",
       " 'Mirai Anand',\n",
       " 'Ayaan Rana',\n",
       " 'Kabir Manju',\n",
       " 'Larisa Rai',\n",
       " 'Mayra Chaudhary',\n",
       " 'Aahva Charan',\n",
       " 'Anushka Chakrabarti',\n",
       " 'Aarav Mehra',\n",
       " 'Prisha Nigam',\n",
       " 'Alisha Raji',\n",
       " 'Kabir Raman',\n",
       " 'Kaia Chander',\n",
       " 'Nirav Jai',\n",
       " 'Anushka Nita',\n",
       " 'Kimaya Dutta',\n",
       " 'Anika Tyagi',\n",
       " 'Bhuv Nara',\n",
       " 'Nehrika Sen',\n",
       " 'Naitee Charan',\n",
       " 'Kabir Gandhi',\n",
       " 'Aarav Shan',\n",
       " 'Sahana Mati',\n",
       " 'Viti Mittal',\n",
       " 'Ved Rajagopal',\n",
       " 'Kahaan Uddin',\n",
       " 'Sadhil Subram',\n",
       " 'Anaisha Shan',\n",
       " 'Pavati Vijaya',\n",
       " 'Rishit Rajan',\n",
       " 'Carina Raje',\n",
       " 'Rachit Srini',\n",
       " 'Ishita Neela',\n",
       " 'Ishana Chande',\n",
       " 'Adhira Ganesh',\n",
       " 'Rachit Mahajan',\n",
       " 'Alex Sen',\n",
       " 'Adhira Anand',\n",
       " 'Sahana Rajan',\n",
       " 'Alex Srivas',\n",
       " 'Naksh Aggarwal',\n",
       " 'Rohan Mati',\n",
       " 'Navya Pawan',\n",
       " 'Idhant Lalit',\n",
       " 'Mayra Rai',\n",
       " 'Pranay Bhat',\n",
       " 'Carina Narayan',\n",
       " 'Parv Manohar',\n",
       " 'Kashvi Patel',\n",
       " 'Shrishti Gandhi',\n",
       " 'Saloni Tyagi',\n",
       " 'Nehrika Sudha',\n",
       " 'Asmee Sara',\n",
       " 'Saloni Jindal',\n",
       " 'Adah Raje',\n",
       " 'Sneha Bhat',\n",
       " 'Vihaan Mittal',\n",
       " 'Rachit Jana',\n",
       " 'Dasya Punj',\n",
       " 'Hem Raji',\n",
       " 'Amoli Lal',\n",
       " 'Neysa Chandra',\n",
       " 'Mahika Malik',\n",
       " 'Hiya Subramani',\n",
       " 'Raj Meena',\n",
       " 'Aadiv Prasad',\n",
       " 'Nimit Chauhan',\n",
       " 'Larisa Mehta',\n",
       " 'Pavati Bhat',\n",
       " 'Keya Mittal',\n",
       " 'Gian Pawan',\n",
       " 'Akanksh Persaud',\n",
       " 'Reyansh Swami',\n",
       " 'Rudra Kumar',\n",
       " 'Taksh Sethi',\n",
       " 'Hem Mati',\n",
       " 'Adah Rajagopal',\n",
       " 'Ishita Samuel',\n",
       " ...]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Recommendations1.index.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Name              Amoli Veer  Kaia Subramanian  Rushil Vish  Atiksh Manohar  \\\n",
       " Name                                                                          \n",
       " Amoli Veer          1.000000          0.576278     0.764181        0.361788   \n",
       " Kaia Subramanian    0.576278          1.000000     0.464381        0.779724   \n",
       " Rushil Vish         0.764181          0.464381     1.000000        0.642193   \n",
       " Atiksh Manohar      0.361788          0.779724     0.642193        1.000000   \n",
       " Ved Kapoor          0.475178          0.774249     0.400436        0.605155   \n",
       " ...                      ...               ...          ...             ...   \n",
       " Neysa Rai           0.647972          0.799657     0.600946        0.714139   \n",
       " Raunak Kapoor       0.922747          0.625648     0.730432        0.394981   \n",
       " Taara Veer          0.931432          0.635266     0.781194        0.480231   \n",
       " Sarthak Rai         0.828498          0.780074     0.747965        0.690128   \n",
       " Kairav Dutta        0.860630          0.443571     0.760684        0.293672   \n",
       " \n",
       " Name              Ved Kapoor  Vihaan Chauhan  Tanvi Sandeep  Taara Chauhan  \\\n",
       " Name                                                                         \n",
       " Amoli Veer          0.475178        0.839872       0.400600       0.643023   \n",
       " Kaia Subramanian    0.774249        0.790255       0.881326       0.807988   \n",
       " Rushil Vish         0.400436        0.800647       0.333760       0.789015   \n",
       " Atiksh Manohar      0.605155        0.686441       0.711210       0.839542   \n",
       " Ved Kapoor          1.000000        0.599109       0.837260       0.685991   \n",
       " ...                      ...             ...            ...            ...   \n",
       " Neysa Rai           0.768453        0.821575       0.826965       0.827691   \n",
       " Raunak Kapoor       0.685620        0.846631       0.542939       0.697051   \n",
       " Taara Veer          0.438285        0.925836       0.423614       0.667283   \n",
       " Sarthak Rai         0.727103        0.903288       0.737916       0.832712   \n",
       " Kairav Dutta        0.426002        0.729393       0.316528       0.557543   \n",
       " \n",
       " Name              Anaisha Veena  Rebecca Chander  ...  Rohan Nagpal  \\\n",
       " Name                                              ...                 \n",
       " Amoli Veer             0.496650         0.363565  ...      0.436768   \n",
       " Kaia Subramanian       0.856773         0.788733  ...      0.437792   \n",
       " Rushil Vish            0.588003         0.572836  ...      0.576720   \n",
       " Atiksh Manohar         0.796664         0.907987  ...      0.600453   \n",
       " Ved Kapoor             0.846541         0.652275  ...      0.648820   \n",
       " ...                         ...              ...  ...           ...   \n",
       " Neysa Rai              0.859290         0.763485  ...      0.726873   \n",
       " Raunak Kapoor          0.646033         0.421986  ...      0.493623   \n",
       " Taara Veer             0.551617         0.422934  ...      0.318198   \n",
       " Sarthak Rai            0.766055         0.700381  ...      0.724571   \n",
       " Kairav Dutta           0.429603         0.254352  ...      0.307119   \n",
       " \n",
       " Name              Alisha Roy  Sneha Ranga  Ira Naran  Avni Nath  Neysa Rai  \\\n",
       " Name                                                                         \n",
       " Amoli Veer          0.575520     0.834565   0.419347   0.693091   0.647972   \n",
       " Kaia Subramanian    0.896195     0.739324   0.591120   0.605904   0.799657   \n",
       " Rushil Vish         0.350883     0.849133   0.194214   0.447531   0.600946   \n",
       " Atiksh Manohar      0.583284     0.704879   0.281498   0.212589   0.714139   \n",
       " Ved Kapoor          0.645725     0.510913   0.620360   0.602202   0.768453   \n",
       " ...                      ...          ...        ...        ...        ...   \n",
       " Neysa Rai           0.710704     0.694075   0.588808   0.449129   1.000000   \n",
       " Raunak Kapoor       0.629614     0.765830   0.573263   0.808455   0.732233   \n",
       " Taara Veer          0.676695     0.830865   0.511681   0.674167   0.674641   \n",
       " Sarthak Rai         0.693733     0.829268   0.453048   0.517717   0.919988   \n",
       " Kairav Dutta        0.524654     0.764112   0.351944   0.808685   0.418134   \n",
       " \n",
       " Name              Raunak Kapoor  Taara Veer  Sarthak Rai  Kairav Dutta  \n",
       " Name                                                                    \n",
       " Amoli Veer             0.922747    0.931432     0.828498      0.860630  \n",
       " Kaia Subramanian       0.625648    0.635266     0.780074      0.443571  \n",
       " Rushil Vish            0.730432    0.781194     0.747965      0.760684  \n",
       " Atiksh Manohar         0.394981    0.480231     0.690128      0.293672  \n",
       " Ved Kapoor             0.685620    0.438285     0.727103      0.426002  \n",
       " ...                         ...         ...          ...           ...  \n",
       " Neysa Rai              0.732233    0.674641     0.919988      0.418134  \n",
       " Raunak Kapoor          1.000000    0.872380     0.844839      0.857150  \n",
       " Taara Veer             0.872380    1.000000     0.821598      0.813507  \n",
       " Sarthak Rai            0.844839    0.821598     1.000000      0.649968  \n",
       " Kairav Dutta           0.857150    0.813507     0.649968      1.000000  \n",
       " \n",
       " [1387 rows x 1387 columns],\n",
       "                   Amoli Veer  Kaia Subramanian  Rushil Vish  Atiksh Manohar  \\\n",
       " Amoli Veer          0.000000          6.562216     6.293802        7.956407   \n",
       " Kaia Subramanian    6.562216          0.000000     5.295139       10.179151   \n",
       " Rushil Vish         6.293802          5.295139     0.000000        5.107074   \n",
       " Atiksh Manohar      7.956407         10.179151     5.107074        0.000000   \n",
       " Ved Kapoor          5.919329          2.991502     7.499887       11.791663   \n",
       " ...                      ...               ...          ...             ...   \n",
       " Neysa Rai           6.910185          6.472663    10.548165       14.214540   \n",
       " Raunak Kapoor      14.504325         15.650144    10.382359        6.550890   \n",
       " Taara Veer         14.504325         15.650144    10.382359        6.550890   \n",
       " Sarthak Rai         0.591993          6.499336     6.689094        8.539714   \n",
       " Kairav Dutta       11.029489         12.648496     7.361470        3.073650   \n",
       " \n",
       "                   Ved Kapoor  Vihaan Chauhan  Tanvi Sandeep  Taara Chauhan  \\\n",
       " Amoli Veer          5.919329        1.698209       3.878664       2.904949   \n",
       " Kaia Subramanian    2.991502        8.189562       3.364122       7.124112   \n",
       " Rushil Vish         7.499887        7.125896       3.248009       8.659993   \n",
       " Atiksh Manohar     11.791663        7.631597       7.249081      10.859106   \n",
       " Ved Kapoor          0.000000        7.603035       4.555992       5.216033   \n",
       " ...                      ...             ...            ...            ...   \n",
       " Neysa Rai           3.481271        8.302981       7.348445       4.686601   \n",
       " Raunak Kapoor      17.797557       14.129378      13.286032      17.404263   \n",
       " Taara Veer         17.797557       14.129378      13.286032      17.404263   \n",
       " Sarthak Rai         5.593817        2.038662       4.067700       2.319391   \n",
       " Kairav Dutta       14.572734       10.665042      10.021975      13.931293   \n",
       " \n",
       "                   Anaisha Veena  Rebecca Chander  ...  Rohan Nagpal  \\\n",
       " Amoli Veer             5.965892         7.027572  ...      2.342975   \n",
       " Kaia Subramanian       3.279127         7.104329  ...      8.420922   \n",
       " Rushil Vish            7.757147         1.814675  ...      6.640876   \n",
       " Atiksh Manohar        11.999226         3.436508  ...      6.627419   \n",
       " Ved Kapoor             0.289263         9.191280  ...      8.195036   \n",
       " ...                         ...              ...  ...           ...   \n",
       " Neysa Rai              3.194399        12.087887  ...      9.201000   \n",
       " Raunak Kapoor         18.037327         8.618179  ...     13.094386   \n",
       " Taara Veer            18.037327         8.618179  ...     13.094386   \n",
       " Sarthak Rai            5.616184         7.515995  ...      2.833156   \n",
       " Kairav Dutta          14.798930         5.547164  ...      9.638736   \n",
       " \n",
       "                   Alisha Roy  Sneha Ranga  Ira Naran  Avni Nath  Neysa Rai  \\\n",
       " Amoli Veer         14.641492     2.973433  11.291795  33.467366   6.910185   \n",
       " Kaia Subramanian   18.165245     8.209169   4.763670  35.281101   6.472663   \n",
       " Rushil Vish        13.115020     9.167550   8.140429  30.016782  10.548165   \n",
       " Atiksh Manohar      8.020077    10.772929  13.199227  25.746387  14.214540   \n",
       " Ved Kapoor         19.514164     6.449006   6.916275  37.390595   3.481271   \n",
       " ...                      ...          ...        ...        ...        ...   \n",
       " Neysa Rai          21.473476     5.798780   9.997217  39.960902   0.000000   \n",
       " Raunak Kapoor       6.245470    17.315909  17.368142  19.634914  20.540652   \n",
       " Taara Veer          6.245470    17.315909  17.368142  19.634914  20.540652   \n",
       " Sarthak Rai        15.217746     2.491584  11.256827  34.058992   6.373775   \n",
       " Kairav Dutta        6.409745    13.838860  14.986422  22.829519  17.175652   \n",
       " \n",
       "                   Raunak Kapoor  Taara Veer  Sarthak Rai  Kairav Dutta  \n",
       " Amoli Veer            14.504325   14.504325     0.591993     11.029489  \n",
       " Kaia Subramanian      15.650144   15.650144     6.499336     12.648496  \n",
       " Rushil Vish           10.382359   10.382359     6.689094      7.361470  \n",
       " Atiksh Manohar         6.550890    6.550890     8.539714      3.073650  \n",
       " Ved Kapoor            17.797557   17.797557     5.593817     14.572734  \n",
       " ...                         ...         ...          ...           ...  \n",
       " Neysa Rai             20.540652   20.540652     6.373775     17.175652  \n",
       " Raunak Kapoor          0.000000    0.000000    15.085411      3.478182  \n",
       " Taara Veer             0.000000    0.000000    15.085411      3.478182  \n",
       " Sarthak Rai           15.085411   15.085411     0.000000     11.611988  \n",
       " Kairav Dutta           3.478182    3.478182    11.611988      0.000000  \n",
       " \n",
       " [1387 rows x 1387 columns])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_similarity, distance_similarity_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# recommendation_score['Distance Sim'] = \n",
    "type(distance_similarity_df2.loc[name,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Recommendations1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1387, 1)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Recommendations1 =  1-MinMaxScaler().fit_transform(pd.DataFrame(Recommendations1))\n",
    "Recommendations1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_recommedation = pd.DataFrame(Recommendations)\n",
    "df_recommedation['Haversine'] = Recommendations1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_recommedation.rename(columns = {name:'Cosine Similarity'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>Cosine Similarity</th>\n",
       "      <th>Haversine</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amoli Veer</th>\n",
       "      <td>0.828498</td>\n",
       "      <td>0.999496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kaia Subramanian</th>\n",
       "      <td>0.780074</td>\n",
       "      <td>0.994463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rushil Vish</th>\n",
       "      <td>0.747965</td>\n",
       "      <td>0.994302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atiksh Manohar</th>\n",
       "      <td>0.690128</td>\n",
       "      <td>0.992725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ved Kapoor</th>\n",
       "      <td>0.727103</td>\n",
       "      <td>0.995235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neysa Rai</th>\n",
       "      <td>0.919988</td>\n",
       "      <td>0.994570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raunak Kapoor</th>\n",
       "      <td>0.844839</td>\n",
       "      <td>0.987149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taara Veer</th>\n",
       "      <td>0.821598</td>\n",
       "      <td>0.987149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sarthak Rai</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kairav Dutta</th>\n",
       "      <td>0.649968</td>\n",
       "      <td>0.990108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1387 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Cosine Similarity  Haversine\n",
       "Name                                          \n",
       "Amoli Veer                 0.828498   0.999496\n",
       "Kaia Subramanian           0.780074   0.994463\n",
       "Rushil Vish                0.747965   0.994302\n",
       "Atiksh Manohar             0.690128   0.992725\n",
       "Ved Kapoor                 0.727103   0.995235\n",
       "...                             ...        ...\n",
       "Neysa Rai                  0.919988   0.994570\n",
       "Raunak Kapoor              0.844839   0.987149\n",
       "Taara Veer                 0.821598   0.987149\n",
       "Sarthak Rai                1.000000   1.000000\n",
       "Kairav Dutta               0.649968   0.990108\n",
       "\n",
       "[1387 rows x 2 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_recommedation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_recommedation['Haversine']) \n",
    "# = MinMaxScaler().fit_transform(df_recommedation['Haversine'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>Cosine Similarity</th>\n",
       "      <th>Haversine</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amoli Veer</th>\n",
       "      <td>0.828498</td>\n",
       "      <td>0.999496</td>\n",
       "      <td>0.982396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kaia Subramanian</th>\n",
       "      <td>0.780074</td>\n",
       "      <td>0.994463</td>\n",
       "      <td>0.973024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rushil Vish</th>\n",
       "      <td>0.747965</td>\n",
       "      <td>0.994302</td>\n",
       "      <td>0.969668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atiksh Manohar</th>\n",
       "      <td>0.690128</td>\n",
       "      <td>0.992725</td>\n",
       "      <td>0.962466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ved Kapoor</th>\n",
       "      <td>0.727103</td>\n",
       "      <td>0.995235</td>\n",
       "      <td>0.968422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neysa Rai</th>\n",
       "      <td>0.919988</td>\n",
       "      <td>0.994570</td>\n",
       "      <td>0.987112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raunak Kapoor</th>\n",
       "      <td>0.844839</td>\n",
       "      <td>0.987149</td>\n",
       "      <td>0.972918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taara Veer</th>\n",
       "      <td>0.821598</td>\n",
       "      <td>0.987149</td>\n",
       "      <td>0.970594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sarthak Rai</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kairav Dutta</th>\n",
       "      <td>0.649968</td>\n",
       "      <td>0.990108</td>\n",
       "      <td>0.956094</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1387 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Cosine Similarity  Haversine     Score\n",
       "Name                                                    \n",
       "Amoli Veer                 0.828498   0.999496  0.982396\n",
       "Kaia Subramanian           0.780074   0.994463  0.973024\n",
       "Rushil Vish                0.747965   0.994302  0.969668\n",
       "Atiksh Manohar             0.690128   0.992725  0.962466\n",
       "Ved Kapoor                 0.727103   0.995235  0.968422\n",
       "...                             ...        ...       ...\n",
       "Neysa Rai                  0.919988   0.994570  0.987112\n",
       "Raunak Kapoor              0.844839   0.987149  0.972918\n",
       "Taara Veer                 0.821598   0.987149  0.970594\n",
       "Sarthak Rai                1.000000   1.000000  1.000000\n",
       "Kairav Dutta               0.649968   0.990108  0.956094\n",
       "\n",
       "[1387 rows x 3 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_recommedation['Score'] = 0.1* df_recommedation['Cosine Similarity'] + 0.9 * df_recommedation['Haversine']\n",
    "df_recommedation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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",
       "        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>Cosine Similarity</th>\n",
       "      <th>Haversine</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sarthak Rai</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alisha Raji</th>\n",
       "      <td>0.966278</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parv Saini</th>\n",
       "      <td>0.973377</td>\n",
       "      <td>0.998253</td>\n",
       "      <td>0.995766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin Pal</th>\n",
       "      <td>0.949781</td>\n",
       "      <td>0.999497</td>\n",
       "      <td>0.994526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mohammad Madan</th>\n",
       "      <td>0.967045</td>\n",
       "      <td>0.997407</td>\n",
       "      <td>0.994371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neysa Chakrabarti</th>\n",
       "      <td>0.696386</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.069639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asmee Raji</th>\n",
       "      <td>0.663094</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sahana Sehgal</th>\n",
       "      <td>0.644859</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.064486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aaradhya Narang</th>\n",
       "      <td>0.641622</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.064162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prisha Sharma</th>\n",
       "      <td>0.620250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.062025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1387 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Cosine Similarity  Haversine     Score\n",
       "Name                                                     \n",
       "Sarthak Rai                 1.000000   1.000000  1.000000\n",
       "Alisha Raji                 0.966278   1.000000  0.996628\n",
       "Parv Saini                  0.973377   0.998253  0.995766\n",
       "Kevin Pal                   0.949781   0.999497  0.994526\n",
       "Mohammad Madan              0.967045   0.997407  0.994371\n",
       "...                              ...        ...       ...\n",
       "Neysa Chakrabarti           0.696386   0.000000  0.069639\n",
       "Asmee Raji                  0.663094   0.000000  0.066309\n",
       "Sahana Sehgal               0.644859   0.000000  0.064486\n",
       "Aaradhya Narang             0.641622   0.000000  0.064162\n",
       "Prisha Sharma               0.620250   0.000000  0.062025\n",
       "\n",
       "[1387 rows x 3 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_recommedation.sort_values(['Score'], ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "Hybrid_Recommendation=df_recommedation.sort_values(['Score'], ascending=False).head(10)\n",
    "Hybrid_rec =Hybrid_Recommendation.index.to_list()\n",
    "\n",
    "# df[df.Name.isin(Hybrid_rec)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Name Gender         DOB      Name Of Area  Pincode  Latitude   \\\n",
      "1385  Sarthak Rai   Male  1997-09-03  Guruwar Peth S.O   411042   18.50547   \n",
      "\n",
      "      Longitude  Flexibility Body Composition  Composition Percentage  \\\n",
      "1385   73.85951            5          Fitness                      15   \n",
      "\n",
      "      Interactiveness Workout Frequency Workout Mode Preferred  \\\n",
      "1385                5  Monday To Friday                 Indoor   \n",
      "\n",
      "     is_sports_enthusiast  like_to_be_mentored  like_to_mentor  \\\n",
      "1385                Maybe                    3               2   \n",
      "\n",
      "     fitness_goal_to_be achieved addons in session  Height_in_cms  Age  \n",
      "1385     Improve athletic skills              Yoga            177   23  \n",
      "['Sarthak Rai', 'Alisha Raji', 'Parv Saini', 'Kevin Pal', 'Mohammad Madan', 'Luv Mati', 'Anaisha Verma', 'Raj Jindal', 'Larisa Nigam', 'Kahaan Pawan']\n"
     ]
    }
   ],
   "source": [
    "print(df[df.Name.isin([name])])\n",
    "print(Hybrid_rec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Name Of Area</th>\n",
       "      <th>Pincode</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Flexibility</th>\n",
       "      <th>Body Composition</th>\n",
       "      <th>Composition Percentage</th>\n",
       "      <th>Interactiveness</th>\n",
       "      <th>Workout Frequency</th>\n",
       "      <th>Workout Mode Preferred</th>\n",
       "      <th>is_sports_enthusiast</th>\n",
       "      <th>like_to_be_mentored</th>\n",
       "      <th>like_to_mentor</th>\n",
       "      <th>fitness_goal_to_be achieved</th>\n",
       "      <th>addons in session</th>\n",
       "      <th>Height_in_cms</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Kevin Pal</td>\n",
       "      <td>Male</td>\n",
       "      <td>2001-07-21</td>\n",
       "      <td>Ghorpade Peth S.O</td>\n",
       "      <td>411042</td>\n",
       "      <td>18.50139</td>\n",
       "      <td>73.86309</td>\n",
       "      <td>3</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>Daily</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>Improve endurance/conditioning</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>164</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Anaisha Verma</td>\n",
       "      <td>Female</td>\n",
       "      <td>2003-06-14</td>\n",
       "      <td>Bhavani Peth S.O</td>\n",
       "      <td>411042</td>\n",
       "      <td>18.50697</td>\n",
       "      <td>73.87119</td>\n",
       "      <td>1</td>\n",
       "      <td>Essential Fat</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>Daily</td>\n",
       "      <td>Both</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Build muscle</td>\n",
       "      <td>None</td>\n",
       "      <td>178</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>Larisa Nigam</td>\n",
       "      <td>Female</td>\n",
       "      <td>1990-08-04</td>\n",
       "      <td>S.S.C.Exam Board S.O</td>\n",
       "      <td>411005</td>\n",
       "      <td>18.53245</td>\n",
       "      <td>73.84654</td>\n",
       "      <td>3</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>Only Weekends</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Improve endurance/conditioning</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>161</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>Raj Jindal</td>\n",
       "      <td>Male</td>\n",
       "      <td>2002-11-08</td>\n",
       "      <td>Model Colony S.O</td>\n",
       "      <td>411016</td>\n",
       "      <td>18.52906</td>\n",
       "      <td>73.83811</td>\n",
       "      <td>3</td>\n",
       "      <td>Essential Fat</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Build muscle</td>\n",
       "      <td>None</td>\n",
       "      <td>168</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>Mohammad Madan</td>\n",
       "      <td>Male</td>\n",
       "      <td>1995-10-04</td>\n",
       "      <td>Market Yard S.O (Pune)</td>\n",
       "      <td>411037</td>\n",
       "      <td>18.48193</td>\n",
       "      <td>73.87425</td>\n",
       "      <td>2</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>Daily</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Build muscle</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>163</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Luv Mati</td>\n",
       "      <td>Male</td>\n",
       "      <td>2002-12-12</td>\n",
       "      <td>Raviwar Peth S.O</td>\n",
       "      <td>411002</td>\n",
       "      <td>18.51769</td>\n",
       "      <td>73.86014</td>\n",
       "      <td>5</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>Only Weekends</td>\n",
       "      <td>Both</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Improve endurance/conditioning</td>\n",
       "      <td>None</td>\n",
       "      <td>175</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>Kahaan Pawan</td>\n",
       "      <td>Male</td>\n",
       "      <td>1992-06-11</td>\n",
       "      <td>AFMC S.O</td>\n",
       "      <td>411040</td>\n",
       "      <td>18.49267</td>\n",
       "      <td>73.90352</td>\n",
       "      <td>5</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Improve athletic skills</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>161</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>Alisha Raji</td>\n",
       "      <td>Female</td>\n",
       "      <td>1999-08-25</td>\n",
       "      <td>Guruwar Peth S.O</td>\n",
       "      <td>411042</td>\n",
       "      <td>18.50547</td>\n",
       "      <td>73.85951</td>\n",
       "      <td>2</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Improve endurance/conditioning</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>173</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1221</th>\n",
       "      <td>Parv Saini</td>\n",
       "      <td>Male</td>\n",
       "      <td>1984-05-08</td>\n",
       "      <td>Sachapir Street S.O</td>\n",
       "      <td>411001</td>\n",
       "      <td>18.51141</td>\n",
       "      <td>73.87792</td>\n",
       "      <td>4</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>Improve athletic skills</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>166</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>Sarthak Rai</td>\n",
       "      <td>Male</td>\n",
       "      <td>1997-09-03</td>\n",
       "      <td>Guruwar Peth S.O</td>\n",
       "      <td>411042</td>\n",
       "      <td>18.50547</td>\n",
       "      <td>73.85951</td>\n",
       "      <td>5</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Improve athletic skills</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>177</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  Gender         DOB            Name Of Area  Pincode  \\\n",
       "176        Kevin Pal    Male  2001-07-21       Ghorpade Peth S.O   411042   \n",
       "177    Anaisha Verma  Female  2003-06-14        Bhavani Peth S.O   411042   \n",
       "255     Larisa Nigam  Female  1990-08-04    S.S.C.Exam Board S.O   411005   \n",
       "279       Raj Jindal    Male  2002-11-08        Model Colony S.O   411016   \n",
       "318   Mohammad Madan    Male  1995-10-04  Market Yard S.O (Pune)   411037   \n",
       "456         Luv Mati    Male  2002-12-12        Raviwar Peth S.O   411002   \n",
       "644     Kahaan Pawan    Male  1992-06-11                AFMC S.O   411040   \n",
       "932      Alisha Raji  Female  1999-08-25        Guruwar Peth S.O   411042   \n",
       "1221      Parv Saini    Male  1984-05-08     Sachapir Street S.O   411001   \n",
       "1385     Sarthak Rai    Male  1997-09-03        Guruwar Peth S.O   411042   \n",
       "\n",
       "      Latitude   Longitude  Flexibility Body Composition  \\\n",
       "176    18.50139   73.86309            3          Fitness   \n",
       "177    18.50697   73.87119            1    Essential Fat   \n",
       "255    18.53245   73.84654            3          Fitness   \n",
       "279    18.52906   73.83811            3    Essential Fat   \n",
       "318    18.48193   73.87425            2          Fitness   \n",
       "456    18.51769   73.86014            5          Fitness   \n",
       "644    18.49267   73.90352            5          Fitness   \n",
       "932    18.50547   73.85951            2          Fitness   \n",
       "1221   18.51141   73.87792            4          Fitness   \n",
       "1385   18.50547   73.85951            5          Fitness   \n",
       "\n",
       "      Composition Percentage  Interactiveness Workout Frequency  \\\n",
       "176                       15                5             Daily   \n",
       "177                       12                3             Daily   \n",
       "255                       22                5     Only Weekends   \n",
       "279                        6                5  Monday To Friday   \n",
       "318                       15                5             Daily   \n",
       "456                       15                2     Only Weekends   \n",
       "644                       15                4  Monday To Friday   \n",
       "932                       22                5  Monday To Friday   \n",
       "1221                      15                4  Monday To Friday   \n",
       "1385                      15                5  Monday To Friday   \n",
       "\n",
       "     Workout Mode Preferred is_sports_enthusiast  like_to_be_mentored  \\\n",
       "176                 Outdoor                Maybe                    3   \n",
       "177                    Both                Maybe                    1   \n",
       "255                  Indoor                Maybe                    5   \n",
       "279                  Indoor                Maybe                    4   \n",
       "318                  Indoor                Maybe                    4   \n",
       "456                    Both                Maybe                    4   \n",
       "644                 Outdoor                Maybe                    2   \n",
       "932                  Indoor                Maybe                    5   \n",
       "1221                Outdoor                Maybe                    3   \n",
       "1385                 Indoor                Maybe                    3   \n",
       "\n",
       "      like_to_mentor     fitness_goal_to_be achieved addons in session  \\\n",
       "176                5  Improve endurance/conditioning              Yoga   \n",
       "177                1                    Build muscle              None   \n",
       "255                1  Improve endurance/conditioning              Yoga   \n",
       "279                1                    Build muscle              None   \n",
       "318                3                    Build muscle              Yoga   \n",
       "456                2  Improve endurance/conditioning              None   \n",
       "644                5         Improve athletic skills              Yoga   \n",
       "932                4  Improve endurance/conditioning              Yoga   \n",
       "1221               5         Improve athletic skills              Yoga   \n",
       "1385               2         Improve athletic skills              Yoga   \n",
       "\n",
       "      Height_in_cms  Age  \n",
       "176             164   19  \n",
       "177             178   17  \n",
       "255             161   30  \n",
       "279             168   18  \n",
       "318             163   25  \n",
       "456             175   18  \n",
       "644             161   28  \n",
       "932             173   21  \n",
       "1221            166   37  \n",
       "1385            177   23  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Name.isin(Hybrid_rec)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Name Of Area</th>\n",
       "      <th>Pincode</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Flexibility</th>\n",
       "      <th>Body Composition</th>\n",
       "      <th>Composition Percentage</th>\n",
       "      <th>Interactiveness</th>\n",
       "      <th>Workout Frequency</th>\n",
       "      <th>Workout Mode Preferred</th>\n",
       "      <th>is_sports_enthusiast</th>\n",
       "      <th>like_to_be_mentored</th>\n",
       "      <th>like_to_mentor</th>\n",
       "      <th>fitness_goal_to_be achieved</th>\n",
       "      <th>addons in session</th>\n",
       "      <th>Height_in_cms</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>Sarthak Rai</td>\n",
       "      <td>Male</td>\n",
       "      <td>1997-09-03</td>\n",
       "      <td>Guruwar Peth S.O</td>\n",
       "      <td>411042</td>\n",
       "      <td>18.50547</td>\n",
       "      <td>73.85951</td>\n",
       "      <td>5</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Improve athletic skills</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>177</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name Gender         DOB      Name Of Area  Pincode  Latitude   \\\n",
       "1385  Sarthak Rai   Male  1997-09-03  Guruwar Peth S.O   411042   18.50547   \n",
       "\n",
       "      Longitude  Flexibility Body Composition  Composition Percentage  \\\n",
       "1385   73.85951            5          Fitness                      15   \n",
       "\n",
       "      Interactiveness Workout Frequency Workout Mode Preferred  \\\n",
       "1385                5  Monday To Friday                 Indoor   \n",
       "\n",
       "     is_sports_enthusiast  like_to_be_mentored  like_to_mentor  \\\n",
       "1385                Maybe                    3               2   \n",
       "\n",
       "     fitness_goal_to_be achieved addons in session  Height_in_cms  Age  \n",
       "1385     Improve athletic skills              Yoga            177   23  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Name.isin([name])]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alisha Raji',\n",
       " 'Parv Saini',\n",
       " 'Kevin Pal',\n",
       " 'Mohammad Madan',\n",
       " 'Luv Mati',\n",
       " 'Anaisha Verma',\n",
       " 'Raj Jindal',\n",
       " 'Larisa Nigam',\n",
       " 'Kahaan Pawan']"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Hybrid_rec[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Name Of Area</th>\n",
       "      <th>Pincode</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Flexibility</th>\n",
       "      <th>Body Composition</th>\n",
       "      <th>Composition Percentage</th>\n",
       "      <th>Interactiveness</th>\n",
       "      <th>Workout Frequency</th>\n",
       "      <th>Workout Mode Preferred</th>\n",
       "      <th>is_sports_enthusiast</th>\n",
       "      <th>like_to_be_mentored</th>\n",
       "      <th>like_to_mentor</th>\n",
       "      <th>fitness_goal_to_be achieved</th>\n",
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       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sarthak Rai</td>\n",
       "      <td>Male</td>\n",
       "      <td>1997-09-03</td>\n",
       "      <td>Guruwar Peth S.O</td>\n",
       "      <td>411042</td>\n",
       "      <td>18.50547</td>\n",
       "      <td>73.85951</td>\n",
       "      <td>5</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Improve athletic skills</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>177</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alisha Raji</td>\n",
       "      <td>Female</td>\n",
       "      <td>1999-08-25</td>\n",
       "      <td>Guruwar Peth S.O</td>\n",
       "      <td>411042</td>\n",
       "      <td>18.50547</td>\n",
       "      <td>73.85951</td>\n",
       "      <td>2</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Improve endurance/conditioning</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>173</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Parv Saini</td>\n",
       "      <td>Male</td>\n",
       "      <td>1984-05-08</td>\n",
       "      <td>Sachapir Street S.O</td>\n",
       "      <td>411001</td>\n",
       "      <td>18.51141</td>\n",
       "      <td>73.87792</td>\n",
       "      <td>4</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>Improve athletic skills</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>166</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kevin Pal</td>\n",
       "      <td>Male</td>\n",
       "      <td>2001-07-21</td>\n",
       "      <td>Ghorpade Peth S.O</td>\n",
       "      <td>411042</td>\n",
       "      <td>18.50139</td>\n",
       "      <td>73.86309</td>\n",
       "      <td>3</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>Daily</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>Improve endurance/conditioning</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>164</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mohammad Madan</td>\n",
       "      <td>Male</td>\n",
       "      <td>1995-10-04</td>\n",
       "      <td>Market Yard S.O (Pune)</td>\n",
       "      <td>411037</td>\n",
       "      <td>18.48193</td>\n",
       "      <td>73.87425</td>\n",
       "      <td>2</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>Daily</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Build muscle</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>163</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Luv Mati</td>\n",
       "      <td>Male</td>\n",
       "      <td>2002-12-12</td>\n",
       "      <td>Raviwar Peth S.O</td>\n",
       "      <td>411002</td>\n",
       "      <td>18.51769</td>\n",
       "      <td>73.86014</td>\n",
       "      <td>5</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>Only Weekends</td>\n",
       "      <td>Both</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Improve endurance/conditioning</td>\n",
       "      <td>None</td>\n",
       "      <td>175</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Anaisha Verma</td>\n",
       "      <td>Female</td>\n",
       "      <td>2003-06-14</td>\n",
       "      <td>Bhavani Peth S.O</td>\n",
       "      <td>411042</td>\n",
       "      <td>18.50697</td>\n",
       "      <td>73.87119</td>\n",
       "      <td>1</td>\n",
       "      <td>Essential Fat</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>Daily</td>\n",
       "      <td>Both</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Build muscle</td>\n",
       "      <td>None</td>\n",
       "      <td>178</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Raj Jindal</td>\n",
       "      <td>Male</td>\n",
       "      <td>2002-11-08</td>\n",
       "      <td>Model Colony S.O</td>\n",
       "      <td>411016</td>\n",
       "      <td>18.52906</td>\n",
       "      <td>73.83811</td>\n",
       "      <td>3</td>\n",
       "      <td>Essential Fat</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Build muscle</td>\n",
       "      <td>None</td>\n",
       "      <td>168</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Larisa Nigam</td>\n",
       "      <td>Female</td>\n",
       "      <td>1990-08-04</td>\n",
       "      <td>S.S.C.Exam Board S.O</td>\n",
       "      <td>411005</td>\n",
       "      <td>18.53245</td>\n",
       "      <td>73.84654</td>\n",
       "      <td>3</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>Only Weekends</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Improve endurance/conditioning</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>161</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kahaan Pawan</td>\n",
       "      <td>Male</td>\n",
       "      <td>1992-06-11</td>\n",
       "      <td>AFMC S.O</td>\n",
       "      <td>411040</td>\n",
       "      <td>18.49267</td>\n",
       "      <td>73.90352</td>\n",
       "      <td>5</td>\n",
       "      <td>Fitness</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>Monday To Friday</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Improve athletic skills</td>\n",
       "      <td>Yoga</td>\n",
       "      <td>161</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name  Gender         DOB            Name Of Area  Pincode  \\\n",
       "0     Sarthak Rai    Male  1997-09-03        Guruwar Peth S.O   411042   \n",
       "1     Alisha Raji  Female  1999-08-25        Guruwar Peth S.O   411042   \n",
       "2      Parv Saini    Male  1984-05-08     Sachapir Street S.O   411001   \n",
       "3       Kevin Pal    Male  2001-07-21       Ghorpade Peth S.O   411042   \n",
       "4  Mohammad Madan    Male  1995-10-04  Market Yard S.O (Pune)   411037   \n",
       "5        Luv Mati    Male  2002-12-12        Raviwar Peth S.O   411002   \n",
       "6   Anaisha Verma  Female  2003-06-14        Bhavani Peth S.O   411042   \n",
       "7      Raj Jindal    Male  2002-11-08        Model Colony S.O   411016   \n",
       "8    Larisa Nigam  Female  1990-08-04    S.S.C.Exam Board S.O   411005   \n",
       "9    Kahaan Pawan    Male  1992-06-11                AFMC S.O   411040   \n",
       "\n",
       "   Latitude   Longitude  Flexibility Body Composition  Composition Percentage  \\\n",
       "0   18.50547   73.85951            5          Fitness                      15   \n",
       "1   18.50547   73.85951            2          Fitness                      22   \n",
       "2   18.51141   73.87792            4          Fitness                      15   \n",
       "3   18.50139   73.86309            3          Fitness                      15   \n",
       "4   18.48193   73.87425            2          Fitness                      15   \n",
       "5   18.51769   73.86014            5          Fitness                      15   \n",
       "6   18.50697   73.87119            1    Essential Fat                      12   \n",
       "7   18.52906   73.83811            3    Essential Fat                       6   \n",
       "8   18.53245   73.84654            3          Fitness                      22   \n",
       "9   18.49267   73.90352            5          Fitness                      15   \n",
       "\n",
       "   Interactiveness Workout Frequency Workout Mode Preferred  \\\n",
       "0                5  Monday To Friday                 Indoor   \n",
       "1                5  Monday To Friday                 Indoor   \n",
       "2                4  Monday To Friday                Outdoor   \n",
       "3                5             Daily                Outdoor   \n",
       "4                5             Daily                 Indoor   \n",
       "5                2     Only Weekends                   Both   \n",
       "6                3             Daily                   Both   \n",
       "7                5  Monday To Friday                 Indoor   \n",
       "8                5     Only Weekends                 Indoor   \n",
       "9                4  Monday To Friday                Outdoor   \n",
       "\n",
       "  is_sports_enthusiast  like_to_be_mentored  like_to_mentor  \\\n",
       "0                Maybe                    3               2   \n",
       "1                Maybe                    5               4   \n",
       "2                Maybe                    3               5   \n",
       "3                Maybe                    3               5   \n",
       "4                Maybe                    4               3   \n",
       "5                Maybe                    4               2   \n",
       "6                Maybe                    1               1   \n",
       "7                Maybe                    4               1   \n",
       "8                Maybe                    5               1   \n",
       "9                Maybe                    2               5   \n",
       "\n",
       "      fitness_goal_to_be achieved addons in session  Height_in_cms  Age  \n",
       "0         Improve athletic skills              Yoga            177   23  \n",
       "1  Improve endurance/conditioning              Yoga            173   21  \n",
       "2         Improve athletic skills              Yoga            166   37  \n",
       "3  Improve endurance/conditioning              Yoga            164   19  \n",
       "4                    Build muscle              Yoga            163   25  \n",
       "5  Improve endurance/conditioning              None            175   18  \n",
       "6                    Build muscle              None            178   17  \n",
       "7                    Build muscle              None            168   18  \n",
       "8  Improve endurance/conditioning              Yoga            161   30  \n",
       "9         Improve athletic skills              Yoga            161   28  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Name').loc[Hybrid_rec[:]].reset_index(inplace=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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