TeamPi_Hackathon_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hackathon!\n",
    "## Restaurant Turnover Prediction\n",
    "The predicted Annual Turnover for each restaurant in the Test dataset will be compared with the actual Annual Turnover to calculate the RMSE value of the entire prediction. The lower the RMSE value, the better the model will be.\n",
    "\n",
    "### Rules\n",
    "* The measure of accuracy will be RMSE (Root mean square error).\n",
    "* Submission File Format: You are to submit a  '.csv' file with exactly 500 entries plus a header row. The file should have exactly two columns.\n",
    "\n",
    "---\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Author Project Submission| Date |\n",
    "| --- | --- |\n",
    "| Rob Barker | July 24, 2024 | "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries to help with reading and manipulating data.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Importing Simple Imputer to treat the null values.\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# Suppress warnings (FutureWarning).\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category= FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-16T13:19:51.066441Z",
     "start_time": "2020-09-16T13:19:50.965370Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 5 rows of hackathon_train_df:\n"
     ]
    },
    {
     "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>Registration Number</th>\n",
       "      <th>Annual Turnover</th>\n",
       "      <th>Cuisine</th>\n",
       "      <th>City</th>\n",
       "      <th>Restaurant Location</th>\n",
       "      <th>Opening Day of Restaurant</th>\n",
       "      <th>Facebook Popularity Quotient</th>\n",
       "      <th>Endorsed By</th>\n",
       "      <th>Instagram Popularity Quotient</th>\n",
       "      <th>Fire Audit</th>\n",
       "      <th>...</th>\n",
       "      <th>Overall Restaurant Rating</th>\n",
       "      <th>Live Music Rating</th>\n",
       "      <th>Comedy Gigs Rating</th>\n",
       "      <th>Value Deals Rating</th>\n",
       "      <th>Live Sports Rating</th>\n",
       "      <th>Ambience</th>\n",
       "      <th>Lively</th>\n",
       "      <th>Service</th>\n",
       "      <th>Comfortablility</th>\n",
       "      <th>Privacy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60001</td>\n",
       "      <td>42000000</td>\n",
       "      <td>indian,irish</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Near Business Hub</td>\n",
       "      <td>14-02-2009</td>\n",
       "      <td>84.3</td>\n",
       "      <td>Not Specific</td>\n",
       "      <td>95.8</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60002</td>\n",
       "      <td>50000000</td>\n",
       "      <td>indian,irish</td>\n",
       "      <td>Indore</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>29-09-2008</td>\n",
       "      <td>85.4</td>\n",
       "      <td>Tier A Celebrity</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60003</td>\n",
       "      <td>32500000</td>\n",
       "      <td>tibetan,italian</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Near Business Hub</td>\n",
       "      <td>30-07-2011</td>\n",
       "      <td>85.0</td>\n",
       "      <td>Tier A Celebrity</td>\n",
       "      <td>68.2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60004</td>\n",
       "      <td>110000000</td>\n",
       "      <td>turkish,nigerian</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>30-11-2008</td>\n",
       "      <td>85.6</td>\n",
       "      <td>Tier A Celebrity</td>\n",
       "      <td>83.6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60005</td>\n",
       "      <td>20000000</td>\n",
       "      <td>irish,belgian</td>\n",
       "      <td>Manesar</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>22-02-2010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier A Celebrity</td>\n",
       "      <td>76.8</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Registration Number  Annual Turnover           Cuisine       City  \\\n",
       "0                60001         42000000      indian,irish  Bangalore   \n",
       "1                60002         50000000      indian,irish     Indore   \n",
       "2                60003         32500000   tibetan,italian    Chennai   \n",
       "3                60004        110000000  turkish,nigerian    Gurgaon   \n",
       "4                60005         20000000     irish,belgian    Manesar   \n",
       "\n",
       "  Restaurant Location Opening Day of Restaurant  Facebook Popularity Quotient  \\\n",
       "0   Near Business Hub                14-02-2009                          84.3   \n",
       "1      Near Party Hub                29-09-2008                          85.4   \n",
       "2   Near Business Hub                30-07-2011                          85.0   \n",
       "3      Near Party Hub                30-11-2008                          85.6   \n",
       "4      Near Party Hub                22-02-2010                           NaN   \n",
       "\n",
       "        Endorsed By  Instagram Popularity Quotient  Fire Audit  ...  \\\n",
       "0      Not Specific                           95.8           1  ...   \n",
       "1  Tier A Celebrity                           85.0           1  ...   \n",
       "2  Tier A Celebrity                           68.2           1  ...   \n",
       "3  Tier A Celebrity                           83.6           0  ...   \n",
       "4  Tier A Celebrity                           76.8           1  ...   \n",
       "\n",
       "   Overall Restaurant Rating  Live Music Rating  Comedy Gigs Rating  \\\n",
       "0                       10.0                4.0                 NaN   \n",
       "1                        9.0                NaN                 4.0   \n",
       "2                        8.0                3.0                 NaN   \n",
       "3                        9.0                6.0                 NaN   \n",
       "4                        6.0                NaN                 2.0   \n",
       "\n",
       "   Value Deals Rating  Live Sports Rating Ambience Lively  Service  \\\n",
       "0                 NaN                 NaN      8.0      8        6   \n",
       "1                 NaN                 NaN      5.0      7        7   \n",
       "2                 NaN                 NaN      7.0     10        5   \n",
       "3                 NaN                 NaN      7.0      7        4   \n",
       "4                 NaN                 NaN      NaN      6        2   \n",
       "\n",
       "   Comfortablility  Privacy  \n",
       "0                6        6  \n",
       "1                3        8  \n",
       "2                2        8  \n",
       "3                3        5  \n",
       "4                4        6  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the training and testing datasets.\n",
    "hackathon_train_org_df = pd.read_csv(\"/Users/barkz/Desktop/github-barkz/Restaurant-Turnover-Prediction/Train_dataset_(1).csv\")\n",
    "hackathon_test_org_df = pd.read_csv(\"/Users/barkz/Desktop/github-barkz/Restaurant-Turnover-Prediction/Test_dataset_(1).csv\")\n",
    "\n",
    "# Create a copy of the original DataFrame to avoid modifying the original data.\n",
    "hackathon_train_df = hackathon_train_org_df.copy()\n",
    "hackathon_test_df = hackathon_test_org_df.copy()\n",
    "\n",
    "# Print the first 5 rows of hackathon_train_df as formatted data.\n",
    "print(\"First 5 rows of hackathon_train_df:\")\n",
    "hackathon_train_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 5 rows of hackathon_test_df:\n"
     ]
    },
    {
     "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>Registration Number</th>\n",
       "      <th>Cuisine</th>\n",
       "      <th>City</th>\n",
       "      <th>Restaurant Location</th>\n",
       "      <th>Opening Day of Restaurant</th>\n",
       "      <th>Facebook Popularity Quotient</th>\n",
       "      <th>Endorsed By</th>\n",
       "      <th>Instagram Popularity Quotient</th>\n",
       "      <th>Fire Audit</th>\n",
       "      <th>Liquor License Obtained</th>\n",
       "      <th>...</th>\n",
       "      <th>Overall Restaurant Rating</th>\n",
       "      <th>Live Music Rating</th>\n",
       "      <th>Comedy Gigs Rating</th>\n",
       "      <th>Value Deals Rating</th>\n",
       "      <th>Live Sports Rating</th>\n",
       "      <th>Ambience</th>\n",
       "      <th>Lively</th>\n",
       "      <th>Service</th>\n",
       "      <th>Comfortablility</th>\n",
       "      <th>Privacy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20001</td>\n",
       "      <td>tibetan,italian</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Near Business Hub</td>\n",
       "      <td>13-07-2010</td>\n",
       "      <td>78.00</td>\n",
       "      <td>Tier A Celebrity</td>\n",
       "      <td>69.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20002</td>\n",
       "      <td>tibetan,italian</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>05-09-2011</td>\n",
       "      <td>89.17</td>\n",
       "      <td>Not Specific</td>\n",
       "      <td>96.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20003</td>\n",
       "      <td>algerian,belgian</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>12-04-2011</td>\n",
       "      <td>84.00</td>\n",
       "      <td>Not Specific</td>\n",
       "      <td>86.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20004</td>\n",
       "      <td>tibetan,greek</td>\n",
       "      <td>-1</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>16-01-2005</td>\n",
       "      <td>79.38</td>\n",
       "      <td>Not Specific</td>\n",
       "      <td>74.40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20005</td>\n",
       "      <td>cuban,british</td>\n",
       "      <td>Pune</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>10-11-2008</td>\n",
       "      <td>84.67</td>\n",
       "      <td>Not Specific</td>\n",
       "      <td>86.46</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Registration Number           Cuisine       City Restaurant Location  \\\n",
       "0                20001   tibetan,italian  Bangalore   Near Business Hub   \n",
       "1                20002   tibetan,italian  Hyderabad      Near Party Hub   \n",
       "2                20003  algerian,belgian  Hyderabad      Near Party Hub   \n",
       "3                20004     tibetan,greek         -1      Near Party Hub   \n",
       "4                20005     cuban,british      Pune       Near Party Hub   \n",
       "\n",
       "  Opening Day of Restaurant  Facebook Popularity Quotient       Endorsed By  \\\n",
       "0                13-07-2010                         78.00  Tier A Celebrity   \n",
       "1                05-09-2011                         89.17      Not Specific   \n",
       "2                12-04-2011                         84.00      Not Specific   \n",
       "3                16-01-2005                         79.38      Not Specific   \n",
       "4                10-11-2008                         84.67      Not Specific   \n",
       "\n",
       "   Instagram Popularity Quotient  Fire Audit  Liquor License Obtained  ...  \\\n",
       "0                          69.00           1                        1  ...   \n",
       "1                          96.00           1                        1  ...   \n",
       "2                          86.00           1                        1  ...   \n",
       "3                          74.40           0                        1  ...   \n",
       "4                          86.46           1                        1  ...   \n",
       "\n",
       "   Overall Restaurant Rating  Live Music Rating  Comedy Gigs Rating  \\\n",
       "0                        6.0                NaN                 2.0   \n",
       "1                        9.0                3.0                 NaN   \n",
       "2                        6.0                3.0                 NaN   \n",
       "3                        9.0                6.0                 NaN   \n",
       "4                        NaN                NaN                 NaN   \n",
       "\n",
       "   Value Deals Rating Live Sports Rating Ambience  Lively  Service  \\\n",
       "0                 NaN                NaN        5       2        2   \n",
       "1                 NaN                6.0        7       8        5   \n",
       "2                 NaN                NaN        9       7        5   \n",
       "3                 NaN                NaN        6       7        6   \n",
       "4                 NaN                NaN        4       4        7   \n",
       "\n",
       "   Comfortablility  Privacy  \n",
       "0                6        0  \n",
       "1                1        8  \n",
       "2                1        5  \n",
       "3                4        7  \n",
       "4                7        3  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the first 5 rows of hackathon_test_df as formatted data.\n",
    "print(\"First 5 rows of hackathon_test_df:\")\n",
    "hackathon_test_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-16T13:19:59.753437Z",
     "start_time": "2020-09-16T13:19:59.739592Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Registration Number                 0\n",
       "Annual Turnover                     0\n",
       "Cuisine                             0\n",
       "City                                0\n",
       "Restaurant Location                 0\n",
       "Opening Day of Restaurant           0\n",
       "Facebook Popularity Quotient       99\n",
       "Endorsed By                         0\n",
       "Instagram Popularity Quotient      56\n",
       "Fire Audit                          0\n",
       "Liquor License Obtained             0\n",
       "Situated in a Multi Complex         0\n",
       "Dedicated Parking                   0\n",
       "Open Sitting Available              0\n",
       "Resturant Tier                     49\n",
       "Restaurant Type                     0\n",
       "Restaurant Theme                    0\n",
       "Restaurant Zomato Rating            0\n",
       "Restaurant City Tier                0\n",
       "Order Wait Time                     0\n",
       "Staff Responsivness                 0\n",
       "Value for Money                     0\n",
       "Hygiene Rating                      0\n",
       "Food Rating                         0\n",
       "Overall Restaurant Rating         212\n",
       "Live Music Rating                 765\n",
       "Comedy Gigs Rating               2483\n",
       "Value Deals Rating               2707\n",
       "Live Sports Rating               3288\n",
       "Ambience                           25\n",
       "Lively                              0\n",
       "Service                             0\n",
       "Comfortablility                     0\n",
       "Privacy                             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for the null values in train dataset.\n",
    "hackathon_train_df.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3493 entries, 0 to 3492\n",
      "Data columns (total 34 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Registration Number            3493 non-null   int64  \n",
      " 1   Annual Turnover                3493 non-null   int64  \n",
      " 2   Cuisine                        3493 non-null   object \n",
      " 3   City                           3493 non-null   object \n",
      " 4   Restaurant Location            3493 non-null   object \n",
      " 5   Opening Day of Restaurant      3493 non-null   object \n",
      " 6   Facebook Popularity Quotient   3394 non-null   float64\n",
      " 7   Endorsed By                    3493 non-null   object \n",
      " 8   Instagram Popularity Quotient  3437 non-null   float64\n",
      " 9   Fire Audit                     3493 non-null   int64  \n",
      " 10  Liquor License Obtained        3493 non-null   int64  \n",
      " 11  Situated in a Multi Complex    3493 non-null   int64  \n",
      " 12  Dedicated Parking              3493 non-null   int64  \n",
      " 13  Open Sitting Available         3493 non-null   int64  \n",
      " 14  Resturant Tier                 3444 non-null   float64\n",
      " 15  Restaurant Type                3493 non-null   object \n",
      " 16  Restaurant Theme               3493 non-null   object \n",
      " 17  Restaurant Zomato Rating       3493 non-null   int64  \n",
      " 18  Restaurant City Tier           3493 non-null   int64  \n",
      " 19  Order Wait Time                3493 non-null   int64  \n",
      " 20  Staff Responsivness            3493 non-null   int64  \n",
      " 21  Value for Money                3493 non-null   int64  \n",
      " 22  Hygiene Rating                 3493 non-null   int64  \n",
      " 23  Food Rating                    3493 non-null   int64  \n",
      " 24  Overall Restaurant Rating      3281 non-null   float64\n",
      " 25  Live Music Rating              2728 non-null   float64\n",
      " 26  Comedy Gigs Rating             1010 non-null   float64\n",
      " 27  Value Deals Rating             786 non-null    float64\n",
      " 28  Live Sports Rating             205 non-null    float64\n",
      " 29  Ambience                       3468 non-null   float64\n",
      " 30  Lively                         3493 non-null   int64  \n",
      " 31  Service                        3493 non-null   int64  \n",
      " 32  Comfortablility                3493 non-null   int64  \n",
      " 33  Privacy                        3493 non-null   int64  \n",
      "dtypes: float64(9), int64(18), object(7)\n",
      "memory usage: 928.0+ KB\n",
      "\n",
      " Object Columns: Index(['Cuisine', 'City', 'Restaurant Location', 'Opening Day of Restaurant',\n",
      "       'Endorsed By', 'Restaurant Type', 'Restaurant Theme'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Check datatypes.\n",
    "hackathon_train_df.info()\n",
    "\n",
    "# Get statistics of the numerical columns.\n",
    "hackathon_train_df.describe()\n",
    "\n",
    "# Get columns with object types only.\n",
    "object_columns = hackathon_train_df.select_dtypes(include=['object']).columns\n",
    "print('\\n Object Columns:', object_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Registration Number                0\n",
       "Cuisine                            0\n",
       "City                               0\n",
       "Restaurant Location                0\n",
       "Opening Day of Restaurant          0\n",
       "Facebook Popularity Quotient       0\n",
       "Endorsed By                        0\n",
       "Instagram Popularity Quotient      0\n",
       "Fire Audit                         0\n",
       "Liquor License Obtained            0\n",
       "Situated in a Multi Complex        0\n",
       "Dedicated Parking                  0\n",
       "Open Sitting Available             0\n",
       "Resturant Tier                     0\n",
       "Restaurant Type                    0\n",
       "Restaurant Theme                   0\n",
       "Restaurant Zomato Rating           0\n",
       "Restaurant City Tier               0\n",
       "Order Wait Time                    0\n",
       "Staff Responsivness                0\n",
       "Value for Money                    0\n",
       "Hygiene Rating                     0\n",
       "Food Rating                        0\n",
       "Overall Restaurant Rating         34\n",
       "Live Music Rating                102\n",
       "Comedy Gigs Rating               370\n",
       "Value Deals Rating               385\n",
       "Live Sports Rating               471\n",
       "Ambience                           0\n",
       "Lively                             0\n",
       "Service                            0\n",
       "Comfortablility                    0\n",
       "Privacy                            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for the null values in test dataset.\n",
    "hackathon_test_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column names in hackathon_train_df:\n",
      "Index(['Registration Number', 'Annual Turnover', 'Cuisine', 'City',\n",
      "       'Restaurant Location', 'Opening Day of Restaurant',\n",
      "       'Facebook Popularity Quotient', 'Endorsed By',\n",
      "       'Instagram Popularity Quotient', 'Fire Audit',\n",
      "       'Liquor License Obtained', 'Situated in a Multi Complex',\n",
      "       'Dedicated Parking', 'Open Sitting Available', 'Resturant Tier',\n",
      "       'Restaurant Type', 'Restaurant Theme', 'Restaurant Zomato Rating',\n",
      "       'Restaurant City Tier', 'Order Wait Time', 'Staff Responsivness',\n",
      "       'Value for Money', 'Hygiene Rating', 'Food Rating',\n",
      "       'Overall Restaurant Rating', 'Live Music Rating', 'Comedy Gigs Rating',\n",
      "       'Value Deals Rating', 'Live Sports Rating', 'Ambience', 'Lively',\n",
      "       'Service', 'Comfortablility', 'Privacy'],\n",
      "      dtype='object')\n",
      "\n",
      "Data types in hackathon_train_df:\n",
      "Registration Number                 int64\n",
      "Annual Turnover                     int64\n",
      "Cuisine                          category\n",
      "City                             category\n",
      "Restaurant Location              category\n",
      "Opening Day of Restaurant        category\n",
      "Facebook Popularity Quotient      float64\n",
      "Endorsed By                      category\n",
      "Instagram Popularity Quotient     float64\n",
      "Fire Audit                          int64\n",
      "Liquor License Obtained             int64\n",
      "Situated in a Multi Complex         int64\n",
      "Dedicated Parking                   int64\n",
      "Open Sitting Available              int64\n",
      "Resturant Tier                    float64\n",
      "Restaurant Type                  category\n",
      "Restaurant Theme                 category\n",
      "Restaurant Zomato Rating            int64\n",
      "Restaurant City Tier                int64\n",
      "Order Wait Time                     int64\n",
      "Staff Responsivness                 int64\n",
      "Value for Money                     int64\n",
      "Hygiene Rating                      int64\n",
      "Food Rating                         int64\n",
      "Overall Restaurant Rating         float64\n",
      "Live Music Rating                 float64\n",
      "Comedy Gigs Rating                float64\n",
      "Value Deals Rating                float64\n",
      "Live Sports Rating                float64\n",
      "Ambience                          float64\n",
      "Lively                              int64\n",
      "Service                             int64\n",
      "Comfortablility                     int64\n",
      "Privacy                             int64\n",
      "dtype: object\n",
      "\n",
      "Data types in hackathon_test_df:\n",
      "Registration Number                 int64\n",
      "Cuisine                          category\n",
      "City                             category\n",
      "Restaurant Location              category\n",
      "Opening Day of Restaurant        category\n",
      "Facebook Popularity Quotient      float64\n",
      "Endorsed By                      category\n",
      "Instagram Popularity Quotient     float64\n",
      "Fire Audit                          int64\n",
      "Liquor License Obtained             int64\n",
      "Situated in a Multi Complex         int64\n",
      "Dedicated Parking                   int64\n",
      "Open Sitting Available              int64\n",
      "Resturant Tier                      int64\n",
      "Restaurant Type                  category\n",
      "Restaurant Theme                 category\n",
      "Restaurant Zomato Rating            int64\n",
      "Restaurant City Tier                int64\n",
      "Order Wait Time                     int64\n",
      "Staff Responsivness                 int64\n",
      "Value for Money                     int64\n",
      "Hygiene Rating                      int64\n",
      "Food Rating                         int64\n",
      "Overall Restaurant Rating         float64\n",
      "Live Music Rating                 float64\n",
      "Comedy Gigs Rating                float64\n",
      "Value Deals Rating                float64\n",
      "Live Sports Rating                float64\n",
      "Ambience                            int64\n",
      "Lively                              int64\n",
      "Service                             int64\n",
      "Comfortablility                     int64\n",
      "Privacy                             int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Print column names to verify their exact names.\n",
    "print(\"Column names in hackathon_train_df:\")\n",
    "print(hackathon_train_df.columns)\n",
    "\n",
    "# Strip any leading/trailing spaces from the column names.\n",
    "hackathon_train_df.columns = hackathon_train_df.columns.str.strip()\n",
    "hackathon_test_df.columns = hackathon_test_df.columns.str.strip()\n",
    "\n",
    "# Convert object columns to categorical data types.\n",
    "object_columns = hackathon_train_df.select_dtypes(include=['object']).columns\n",
    "\n",
    "for col in object_columns:\n",
    "    hackathon_train_df[col] = hackathon_train_df[col].astype('category')\n",
    "    hackathon_test_df[col] = hackathon_test_df[col].astype('category')\n",
    "\n",
    "# Verify the data types.\n",
    "print(\"\\nData types in hackathon_train_df:\")\n",
    "print(hackathon_train_df.dtypes)\n",
    "print(\"\\nData types in hackathon_test_df:\")\n",
    "print(hackathon_test_df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>Registration Number</th>\n",
       "      <th>Annual Turnover</th>\n",
       "      <th>Cuisine</th>\n",
       "      <th>City</th>\n",
       "      <th>Restaurant Location</th>\n",
       "      <th>Opening Day of Restaurant</th>\n",
       "      <th>Facebook Popularity Quotient</th>\n",
       "      <th>Endorsed By</th>\n",
       "      <th>Instagram Popularity Quotient</th>\n",
       "      <th>Fire Audit</th>\n",
       "      <th>...</th>\n",
       "      <th>Overall Restaurant Rating</th>\n",
       "      <th>Live Music Rating</th>\n",
       "      <th>Comedy Gigs Rating</th>\n",
       "      <th>Value Deals Rating</th>\n",
       "      <th>Live Sports Rating</th>\n",
       "      <th>Ambience</th>\n",
       "      <th>Lively</th>\n",
       "      <th>Service</th>\n",
       "      <th>Comfortablility</th>\n",
       "      <th>Privacy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60001</td>\n",
       "      <td>42000000</td>\n",
       "      <td>indian,irish</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Near Business Hub</td>\n",
       "      <td>14-02-2009</td>\n",
       "      <td>84.3</td>\n",
       "      <td>Not Specific</td>\n",
       "      <td>95.8</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60002</td>\n",
       "      <td>50000000</td>\n",
       "      <td>indian,irish</td>\n",
       "      <td>Indore</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>29-09-2008</td>\n",
       "      <td>85.4</td>\n",
       "      <td>Tier A Celebrity</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60003</td>\n",
       "      <td>32500000</td>\n",
       "      <td>tibetan,italian</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Near Business Hub</td>\n",
       "      <td>30-07-2011</td>\n",
       "      <td>85.0</td>\n",
       "      <td>Tier A Celebrity</td>\n",
       "      <td>68.2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60004</td>\n",
       "      <td>110000000</td>\n",
       "      <td>turkish,nigerian</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>30-11-2008</td>\n",
       "      <td>85.6</td>\n",
       "      <td>Tier A Celebrity</td>\n",
       "      <td>83.6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60005</td>\n",
       "      <td>20000000</td>\n",
       "      <td>irish,belgian</td>\n",
       "      <td>Manesar</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>22-02-2010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier A Celebrity</td>\n",
       "      <td>76.8</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Registration Number  Annual Turnover           Cuisine       City  \\\n",
       "0                60001         42000000      indian,irish  Bangalore   \n",
       "1                60002         50000000      indian,irish     Indore   \n",
       "2                60003         32500000   tibetan,italian    Chennai   \n",
       "3                60004        110000000  turkish,nigerian    Gurgaon   \n",
       "4                60005         20000000     irish,belgian    Manesar   \n",
       "\n",
       "  Restaurant Location Opening Day of Restaurant  Facebook Popularity Quotient  \\\n",
       "0   Near Business Hub                14-02-2009                          84.3   \n",
       "1      Near Party Hub                29-09-2008                          85.4   \n",
       "2   Near Business Hub                30-07-2011                          85.0   \n",
       "3      Near Party Hub                30-11-2008                          85.6   \n",
       "4      Near Party Hub                22-02-2010                           NaN   \n",
       "\n",
       "        Endorsed By  Instagram Popularity Quotient  Fire Audit  ...  \\\n",
       "0      Not Specific                           95.8           1  ...   \n",
       "1  Tier A Celebrity                           85.0           1  ...   \n",
       "2  Tier A Celebrity                           68.2           1  ...   \n",
       "3  Tier A Celebrity                           83.6           0  ...   \n",
       "4  Tier A Celebrity                           76.8           1  ...   \n",
       "\n",
       "   Overall Restaurant Rating  Live Music Rating  Comedy Gigs Rating  \\\n",
       "0                       10.0                4.0                 NaN   \n",
       "1                        9.0                NaN                 4.0   \n",
       "2                        8.0                3.0                 NaN   \n",
       "3                        9.0                6.0                 NaN   \n",
       "4                        6.0                NaN                 2.0   \n",
       "\n",
       "   Value Deals Rating  Live Sports Rating Ambience Lively  Service  \\\n",
       "0                 NaN                 NaN      8.0      8        6   \n",
       "1                 NaN                 NaN      5.0      7        7   \n",
       "2                 NaN                 NaN      7.0     10        5   \n",
       "3                 NaN                 NaN      7.0      7        4   \n",
       "4                 NaN                 NaN      NaN      6        2   \n",
       "\n",
       "   Comfortablility  Privacy  \n",
       "0                6        6  \n",
       "1                3        8  \n",
       "2                2        8  \n",
       "3                3        5  \n",
       "4                4        6  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the first 5 rows of hackathon_train_df as formatted data.\n",
    "hackathon_train_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>Registration Number</th>\n",
       "      <th>Cuisine</th>\n",
       "      <th>City</th>\n",
       "      <th>Restaurant Location</th>\n",
       "      <th>Opening Day of Restaurant</th>\n",
       "      <th>Facebook Popularity Quotient</th>\n",
       "      <th>Endorsed By</th>\n",
       "      <th>Instagram Popularity Quotient</th>\n",
       "      <th>Fire Audit</th>\n",
       "      <th>Liquor License Obtained</th>\n",
       "      <th>...</th>\n",
       "      <th>Overall Restaurant Rating</th>\n",
       "      <th>Live Music Rating</th>\n",
       "      <th>Comedy Gigs Rating</th>\n",
       "      <th>Value Deals Rating</th>\n",
       "      <th>Live Sports Rating</th>\n",
       "      <th>Ambience</th>\n",
       "      <th>Lively</th>\n",
       "      <th>Service</th>\n",
       "      <th>Comfortablility</th>\n",
       "      <th>Privacy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20001</td>\n",
       "      <td>tibetan,italian</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Near Business Hub</td>\n",
       "      <td>13-07-2010</td>\n",
       "      <td>78.00</td>\n",
       "      <td>Tier A Celebrity</td>\n",
       "      <td>69.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20002</td>\n",
       "      <td>tibetan,italian</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>05-09-2011</td>\n",
       "      <td>89.17</td>\n",
       "      <td>Not Specific</td>\n",
       "      <td>96.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20003</td>\n",
       "      <td>algerian,belgian</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>12-04-2011</td>\n",
       "      <td>84.00</td>\n",
       "      <td>Not Specific</td>\n",
       "      <td>86.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20004</td>\n",
       "      <td>tibetan,greek</td>\n",
       "      <td>-1</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>16-01-2005</td>\n",
       "      <td>79.38</td>\n",
       "      <td>Not Specific</td>\n",
       "      <td>74.40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20005</td>\n",
       "      <td>cuban,british</td>\n",
       "      <td>Pune</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>10-11-2008</td>\n",
       "      <td>84.67</td>\n",
       "      <td>Not Specific</td>\n",
       "      <td>86.46</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Registration Number           Cuisine       City Restaurant Location  \\\n",
       "0                20001   tibetan,italian  Bangalore   Near Business Hub   \n",
       "1                20002   tibetan,italian  Hyderabad      Near Party Hub   \n",
       "2                20003  algerian,belgian  Hyderabad      Near Party Hub   \n",
       "3                20004     tibetan,greek         -1      Near Party Hub   \n",
       "4                20005     cuban,british      Pune       Near Party Hub   \n",
       "\n",
       "  Opening Day of Restaurant  Facebook Popularity Quotient       Endorsed By  \\\n",
       "0                13-07-2010                         78.00  Tier A Celebrity   \n",
       "1                05-09-2011                         89.17      Not Specific   \n",
       "2                12-04-2011                         84.00      Not Specific   \n",
       "3                16-01-2005                         79.38      Not Specific   \n",
       "4                10-11-2008                         84.67      Not Specific   \n",
       "\n",
       "   Instagram Popularity Quotient  Fire Audit  Liquor License Obtained  ...  \\\n",
       "0                          69.00           1                        1  ...   \n",
       "1                          96.00           1                        1  ...   \n",
       "2                          86.00           1                        1  ...   \n",
       "3                          74.40           0                        1  ...   \n",
       "4                          86.46           1                        1  ...   \n",
       "\n",
       "   Overall Restaurant Rating  Live Music Rating  Comedy Gigs Rating  \\\n",
       "0                        6.0                NaN                 2.0   \n",
       "1                        9.0                3.0                 NaN   \n",
       "2                        6.0                3.0                 NaN   \n",
       "3                        9.0                6.0                 NaN   \n",
       "4                        NaN                NaN                 NaN   \n",
       "\n",
       "   Value Deals Rating Live Sports Rating Ambience  Lively  Service  \\\n",
       "0                 NaN                NaN        5       2        2   \n",
       "1                 NaN                6.0        7       8        5   \n",
       "2                 NaN                NaN        9       7        5   \n",
       "3                 NaN                NaN        6       7        6   \n",
       "4                 NaN                NaN        4       4        7   \n",
       "\n",
       "   Comfortablility  Privacy  \n",
       "0                6        0  \n",
       "1                1        8  \n",
       "2                1        5  \n",
       "3                4        7  \n",
       "4                7        3  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the first 5 rows of hackathon_test_df as formatted data.\n",
    "hackathon_test_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-16T13:13:45.659151Z",
     "start_time": "2020-09-16T13:13:45.653193Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 5 rows of hackathon_train_df after imputation:\n"
     ]
    },
    {
     "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>Registration Number</th>\n",
       "      <th>Annual Turnover</th>\n",
       "      <th>Cuisine</th>\n",
       "      <th>City</th>\n",
       "      <th>Restaurant Location</th>\n",
       "      <th>Opening Day of Restaurant</th>\n",
       "      <th>Facebook Popularity Quotient</th>\n",
       "      <th>Endorsed By</th>\n",
       "      <th>Instagram Popularity Quotient</th>\n",
       "      <th>Fire Audit</th>\n",
       "      <th>...</th>\n",
       "      <th>Overall Restaurant Rating</th>\n",
       "      <th>Live Music Rating</th>\n",
       "      <th>Comedy Gigs Rating</th>\n",
       "      <th>Value Deals Rating</th>\n",
       "      <th>Live Sports Rating</th>\n",
       "      <th>Ambience</th>\n",
       "      <th>Lively</th>\n",
       "      <th>Service</th>\n",
       "      <th>Comfortablility</th>\n",
       "      <th>Privacy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60001.0</td>\n",
       "      <td>42000000.0</td>\n",
       "      <td>indian,irish</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Near Business Hub</td>\n",
       "      <td>14-02-2009</td>\n",
       "      <td>84.300000</td>\n",
       "      <td>Not Specific</td>\n",
       "      <td>95.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>2.932673</td>\n",
       "      <td>3.655216</td>\n",
       "      <td>3.590244</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60002.0</td>\n",
       "      <td>50000000.0</td>\n",
       "      <td>indian,irish</td>\n",
       "      <td>Indore</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>29-09-2008</td>\n",
       "      <td>85.400000</td>\n",
       "      <td>Tier A Celebrity</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.01283</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.655216</td>\n",
       "      <td>3.590244</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60003.0</td>\n",
       "      <td>32500000.0</td>\n",
       "      <td>tibetan,italian</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Near Business Hub</td>\n",
       "      <td>30-07-2011</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>Tier A Celebrity</td>\n",
       "      <td>68.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>2.932673</td>\n",
       "      <td>3.655216</td>\n",
       "      <td>3.590244</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60004.0</td>\n",
       "      <td>110000000.0</td>\n",
       "      <td>turkish,nigerian</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>30-11-2008</td>\n",
       "      <td>85.600000</td>\n",
       "      <td>Tier A Celebrity</td>\n",
       "      <td>83.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>2.932673</td>\n",
       "      <td>3.655216</td>\n",
       "      <td>3.590244</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60005.0</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>irish,belgian</td>\n",
       "      <td>Manesar</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>22-02-2010</td>\n",
       "      <td>77.938715</td>\n",
       "      <td>Tier A Celebrity</td>\n",
       "      <td>76.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.01283</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.655216</td>\n",
       "      <td>3.590244</td>\n",
       "      <td>6.42301</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Registration Number  Annual Turnover           Cuisine       City  \\\n",
       "0              60001.0       42000000.0      indian,irish  Bangalore   \n",
       "1              60002.0       50000000.0      indian,irish     Indore   \n",
       "2              60003.0       32500000.0   tibetan,italian    Chennai   \n",
       "3              60004.0      110000000.0  turkish,nigerian    Gurgaon   \n",
       "4              60005.0       20000000.0     irish,belgian    Manesar   \n",
       "\n",
       "  Restaurant Location Opening Day of Restaurant  Facebook Popularity Quotient  \\\n",
       "0   Near Business Hub                14-02-2009                     84.300000   \n",
       "1      Near Party Hub                29-09-2008                     85.400000   \n",
       "2   Near Business Hub                30-07-2011                     85.000000   \n",
       "3      Near Party Hub                30-11-2008                     85.600000   \n",
       "4      Near Party Hub                22-02-2010                     77.938715   \n",
       "\n",
       "        Endorsed By  Instagram Popularity Quotient  Fire Audit  ...  \\\n",
       "0      Not Specific                           95.8         1.0  ...   \n",
       "1  Tier A Celebrity                           85.0         1.0  ...   \n",
       "2  Tier A Celebrity                           68.2         1.0  ...   \n",
       "3  Tier A Celebrity                           83.6         0.0  ...   \n",
       "4  Tier A Celebrity                           76.8         1.0  ...   \n",
       "\n",
       "   Overall Restaurant Rating  Live Music Rating  Comedy Gigs Rating  \\\n",
       "0                       10.0            4.00000            2.932673   \n",
       "1                        9.0            4.01283            4.000000   \n",
       "2                        8.0            3.00000            2.932673   \n",
       "3                        9.0            6.00000            2.932673   \n",
       "4                        6.0            4.01283            2.000000   \n",
       "\n",
       "   Value Deals Rating  Live Sports Rating Ambience Lively  Service  \\\n",
       "0            3.655216            3.590244  8.00000    8.0      6.0   \n",
       "1            3.655216            3.590244  5.00000    7.0      7.0   \n",
       "2            3.655216            3.590244  7.00000   10.0      5.0   \n",
       "3            3.655216            3.590244  7.00000    7.0      4.0   \n",
       "4            3.655216            3.590244  6.42301    6.0      2.0   \n",
       "\n",
       "   Comfortablility  Privacy  \n",
       "0              6.0      6.0  \n",
       "1              3.0      8.0  \n",
       "2              2.0      8.0  \n",
       "3              3.0      5.0  \n",
       "4              4.0      6.0  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an instance of SimpleImputer with strategy='mean'\n",
    "SI = SimpleImputer(strategy='mean')\n",
    "\n",
    "# Select numeric columns to apply imputation\n",
    "numeric_columns = hackathon_train_df.select_dtypes(include=['int64', 'float64']).columns\n",
    "\n",
    "# Fit the imputer on the numeric columns and transform the data\n",
    "hackathon_train_df[numeric_columns] = SI.fit_transform(hackathon_train_df[numeric_columns])\n",
    "\n",
    "# Print the first 5 rows of hackathon_train_df after imputation\n",
    "print(\"First 5 rows of hackathon_train_df after imputation:\")\n",
    "hackathon_train_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Data types in hackathon_train_df:\n",
      "Registration Number               float64\n",
      "Annual Turnover                   float64\n",
      "Cuisine                          category\n",
      "City                             category\n",
      "Restaurant Location              category\n",
      "Opening Day of Restaurant        category\n",
      "Facebook Popularity Quotient      float64\n",
      "Endorsed By                      category\n",
      "Instagram Popularity Quotient     float64\n",
      "Fire Audit                        float64\n",
      "Liquor License Obtained           float64\n",
      "Situated in a Multi Complex       float64\n",
      "Dedicated Parking                 float64\n",
      "Open Sitting Available            float64\n",
      "Resturant Tier                    float64\n",
      "Restaurant Type                  category\n",
      "Restaurant Theme                 category\n",
      "Restaurant Zomato Rating          float64\n",
      "Restaurant City Tier              float64\n",
      "Order Wait Time                   float64\n",
      "Staff Responsivness               float64\n",
      "Value for Money                   float64\n",
      "Hygiene Rating                    float64\n",
      "Food Rating                       float64\n",
      "Overall Restaurant Rating         float64\n",
      "Live Music Rating                 float64\n",
      "Comedy Gigs Rating                float64\n",
      "Value Deals Rating                float64\n",
      "Live Sports Rating                float64\n",
      "Ambience                          float64\n",
      "Lively                            float64\n",
      "Service                           float64\n",
      "Comfortablility                   float64\n",
      "Privacy                           float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Verify the data types.\n",
    "print(\"\\nData types in hackathon_train_df:\")\n",
    "print(hackathon_train_df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 5 rows of hackathon_train_df after imputation:\n"
     ]
    },
    {
     "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>Registration Number</th>\n",
       "      <th>Annual Turnover</th>\n",
       "      <th>Cuisine</th>\n",
       "      <th>City</th>\n",
       "      <th>Restaurant Location</th>\n",
       "      <th>Opening Day of Restaurant</th>\n",
       "      <th>Facebook Popularity Quotient</th>\n",
       "      <th>Endorsed By</th>\n",
       "      <th>Instagram Popularity Quotient</th>\n",
       "      <th>Fire Audit</th>\n",
       "      <th>...</th>\n",
       "      <th>Overall Restaurant Rating</th>\n",
       "      <th>Live Music Rating</th>\n",
       "      <th>Comedy Gigs Rating</th>\n",
       "      <th>Value Deals Rating</th>\n",
       "      <th>Live Sports Rating</th>\n",
       "      <th>Ambience</th>\n",
       "      <th>Lively</th>\n",
       "      <th>Service</th>\n",
       "      <th>Comfortablility</th>\n",
       "      <th>Privacy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60001.0</td>\n",
       "      <td>42000000.0</td>\n",
       "      <td>indian,irish</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Near Business Hub</td>\n",
       "      <td>14-02-2009</td>\n",
       "      <td>84.300000</td>\n",
       "      <td>Not Specific</td>\n",
       "      <td>95.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>2.932673</td>\n",
       "      <td>3.655216</td>\n",
       "      <td>3.590244</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60002.0</td>\n",
       "      <td>50000000.0</td>\n",
       "      <td>indian,irish</td>\n",
       "      <td>Indore</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>29-09-2008</td>\n",
       "      <td>85.400000</td>\n",
       "      <td>Tier A Celebrity</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.01283</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.655216</td>\n",
       "      <td>3.590244</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60003.0</td>\n",
       "      <td>32500000.0</td>\n",
       "      <td>tibetan,italian</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Near Business Hub</td>\n",
       "      <td>30-07-2011</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>Tier A Celebrity</td>\n",
       "      <td>68.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>2.932673</td>\n",
       "      <td>3.655216</td>\n",
       "      <td>3.590244</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60004.0</td>\n",
       "      <td>110000000.0</td>\n",
       "      <td>turkish,nigerian</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>30-11-2008</td>\n",
       "      <td>85.600000</td>\n",
       "      <td>Tier A Celebrity</td>\n",
       "      <td>83.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>2.932673</td>\n",
       "      <td>3.655216</td>\n",
       "      <td>3.590244</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60005.0</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>irish,belgian</td>\n",
       "      <td>Manesar</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>22-02-2010</td>\n",
       "      <td>77.938715</td>\n",
       "      <td>Tier A Celebrity</td>\n",
       "      <td>76.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.01283</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.655216</td>\n",
       "      <td>3.590244</td>\n",
       "      <td>6.42301</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Registration Number  Annual Turnover           Cuisine       City  \\\n",
       "0              60001.0       42000000.0      indian,irish  Bangalore   \n",
       "1              60002.0       50000000.0      indian,irish     Indore   \n",
       "2              60003.0       32500000.0   tibetan,italian    Chennai   \n",
       "3              60004.0      110000000.0  turkish,nigerian    Gurgaon   \n",
       "4              60005.0       20000000.0     irish,belgian    Manesar   \n",
       "\n",
       "  Restaurant Location Opening Day of Restaurant  Facebook Popularity Quotient  \\\n",
       "0   Near Business Hub                14-02-2009                     84.300000   \n",
       "1      Near Party Hub                29-09-2008                     85.400000   \n",
       "2   Near Business Hub                30-07-2011                     85.000000   \n",
       "3      Near Party Hub                30-11-2008                     85.600000   \n",
       "4      Near Party Hub                22-02-2010                     77.938715   \n",
       "\n",
       "        Endorsed By  Instagram Popularity Quotient  Fire Audit  ...  \\\n",
       "0      Not Specific                           95.8         1.0  ...   \n",
       "1  Tier A Celebrity                           85.0         1.0  ...   \n",
       "2  Tier A Celebrity                           68.2         1.0  ...   \n",
       "3  Tier A Celebrity                           83.6         0.0  ...   \n",
       "4  Tier A Celebrity                           76.8         1.0  ...   \n",
       "\n",
       "   Overall Restaurant Rating  Live Music Rating  Comedy Gigs Rating  \\\n",
       "0                       10.0            4.00000            2.932673   \n",
       "1                        9.0            4.01283            4.000000   \n",
       "2                        8.0            3.00000            2.932673   \n",
       "3                        9.0            6.00000            2.932673   \n",
       "4                        6.0            4.01283            2.000000   \n",
       "\n",
       "   Value Deals Rating  Live Sports Rating Ambience Lively  Service  \\\n",
       "0            3.655216            3.590244  8.00000    8.0      6.0   \n",
       "1            3.655216            3.590244  5.00000    7.0      7.0   \n",
       "2            3.655216            3.590244  7.00000   10.0      5.0   \n",
       "3            3.655216            3.590244  7.00000    7.0      4.0   \n",
       "4            3.655216            3.590244  6.42301    6.0      2.0   \n",
       "\n",
       "   Comfortablility  Privacy  \n",
       "0              6.0      6.0  \n",
       "1              3.0      8.0  \n",
       "2              2.0      8.0  \n",
       "3              3.0      5.0  \n",
       "4              4.0      6.0  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an instance of SimpleImputer with strategy='mean'.\n",
    "SI = SimpleImputer(strategy='mean')\n",
    "\n",
    "# Fit the imputer on the 'Instagram Popularity Quotient' column.\n",
    "#SI.fit(hackathon_train_df[['Instagram Popularity Quotient']])\n",
    "\n",
    "# Fit the imputer on the 'Instagram Popularity Quotient' column.\n",
    "SI.fit(hackathon_train_df[['Instagram Popularity Quotient']])\n",
    "\n",
    "# Transform the column to replace missing values with the mean.\n",
    "#hackathon_train_df['Instagram Popularity Quotient'] = SI.transform(hackathon_train_df[['Instagram Popularity Quotient']])\n",
    "\n",
    "# Fit the imputer on the 'Instagram Popularity Quotient' column.\n",
    "hackathon_train_df['Instagram Popularity Quotient'] = SI.transform(hackathon_train_df[['Instagram Popularity Quotient']])\n",
    "\n",
    "# Print the first 5 rows of hackathon_train_df after imputation.\n",
    "print(\"First 5 rows of hackathon_train_df after imputation:\")\n",
    "hackathon_train_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Data types in hackathon_train_df:\n",
      "Registration Number               float64\n",
      "Annual Turnover                   float64\n",
      "Cuisine                          category\n",
      "City                             category\n",
      "Restaurant Location              category\n",
      "Opening Day of Restaurant        category\n",
      "Facebook Popularity Quotient      float64\n",
      "Endorsed By                      category\n",
      "Instagram Popularity Quotient     float64\n",
      "Fire Audit                        float64\n",
      "Liquor License Obtained           float64\n",
      "Situated in a Multi Complex       float64\n",
      "Dedicated Parking                 float64\n",
      "Open Sitting Available            float64\n",
      "Resturant Tier                    float64\n",
      "Restaurant Type                  category\n",
      "Restaurant Theme                 category\n",
      "Restaurant Zomato Rating          float64\n",
      "Restaurant City Tier              float64\n",
      "Order Wait Time                   float64\n",
      "Staff Responsivness               float64\n",
      "Value for Money                   float64\n",
      "Hygiene Rating                    float64\n",
      "Food Rating                       float64\n",
      "Overall Restaurant Rating         float64\n",
      "Live Music Rating                 float64\n",
      "Comedy Gigs Rating                float64\n",
      "Value Deals Rating                float64\n",
      "Live Sports Rating                float64\n",
      "Ambience                          float64\n",
      "Lively                            float64\n",
      "Service                           float64\n",
      "Comfortablility                   float64\n",
      "Privacy                           float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Verify the data types.\n",
    "print(\"\\nData types in hackathon_train_df:\")\n",
    "print(hackathon_train_df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformed 'Instagram Popularity Quotient' column:\n",
      "[[95.8]\n",
      " [85. ]\n",
      " [68.2]\n",
      " ...\n",
      " [86.8]\n",
      " [86. ]\n",
      " [67. ]]\n"
     ]
    }
   ],
   "source": [
    "# Create an instance of SimpleImputer with strategy='mean'.\n",
    "SI = SimpleImputer(strategy='mean')\n",
    "\n",
    "# Fit the imputer on the 'Instagram Popularity Quotient' column.\n",
    "SI.fit(hackathon_train_df[['Instagram Popularity Quotient']])\n",
    "\n",
    "# Transform the column to replace missing values with the mean.\n",
    "IPQ = SI.transform(hackathon_train_df[['Instagram Popularity Quotient']])\n",
    "\n",
    "# Print the transformed column.\n",
    "print(\"Transformed 'Instagram Popularity Quotient' column:\")\n",
    "print(IPQ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression model: LinearRegression()\n",
      "Model score: 0.026482712263984687\n"
     ]
    }
   ],
   "source": [
    "# Declaring the Linear Regression function.\n",
    "LR = LinearRegression()\n",
    "\n",
    "# Fitting the Linear Regression function.\n",
    "model = LR.fit(IPQ, hackathon_train_df['Annual Turnover'])\n",
    "\n",
    "# Print the model to confirm.\n",
    "print(\"Linear Regression model:\", model)\n",
    "\n",
    "# Checking the score of the function on the training data.\n",
    "print('Model score: ' + str(model.score(IPQ, hackathon_train_df['Annual Turnover'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the Test Data and using the Test Data to Predict the target Variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** In the test dataset the spelling of column \"Endorsed By\" was \"Endoresed By\". Before using the dataset the column name was updated. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 5 rows of hackathon_test_df:\n"
     ]
    },
    {
     "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>Registration Number</th>\n",
       "      <th>Cuisine</th>\n",
       "      <th>City</th>\n",
       "      <th>Restaurant Location</th>\n",
       "      <th>Opening Day of Restaurant</th>\n",
       "      <th>Facebook Popularity Quotient</th>\n",
       "      <th>Endorsed By</th>\n",
       "      <th>Instagram Popularity Quotient</th>\n",
       "      <th>Fire Audit</th>\n",
       "      <th>Liquor License Obtained</th>\n",
       "      <th>...</th>\n",
       "      <th>Overall Restaurant Rating</th>\n",
       "      <th>Live Music Rating</th>\n",
       "      <th>Comedy Gigs Rating</th>\n",
       "      <th>Value Deals Rating</th>\n",
       "      <th>Live Sports Rating</th>\n",
       "      <th>Ambience</th>\n",
       "      <th>Lively</th>\n",
       "      <th>Service</th>\n",
       "      <th>Comfortablility</th>\n",
       "      <th>Privacy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20001</td>\n",
       "      <td>tibetan,italian</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Near Business Hub</td>\n",
       "      <td>13-07-2010</td>\n",
       "      <td>78.00</td>\n",
       "      <td>Tier A Celebrity</td>\n",
       "      <td>69.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20002</td>\n",
       "      <td>tibetan,italian</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>05-09-2011</td>\n",
       "      <td>89.17</td>\n",
       "      <td>Not Specific</td>\n",
       "      <td>96.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20003</td>\n",
       "      <td>algerian,belgian</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>12-04-2011</td>\n",
       "      <td>84.00</td>\n",
       "      <td>Not Specific</td>\n",
       "      <td>86.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20004</td>\n",
       "      <td>tibetan,greek</td>\n",
       "      <td>-1</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>16-01-2005</td>\n",
       "      <td>79.38</td>\n",
       "      <td>Not Specific</td>\n",
       "      <td>74.40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20005</td>\n",
       "      <td>cuban,british</td>\n",
       "      <td>Pune</td>\n",
       "      <td>Near Party Hub</td>\n",
       "      <td>10-11-2008</td>\n",
       "      <td>84.67</td>\n",
       "      <td>Not Specific</td>\n",
       "      <td>86.46</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Registration Number           Cuisine       City Restaurant Location  \\\n",
       "0                20001   tibetan,italian  Bangalore   Near Business Hub   \n",
       "1                20002   tibetan,italian  Hyderabad      Near Party Hub   \n",
       "2                20003  algerian,belgian  Hyderabad      Near Party Hub   \n",
       "3                20004     tibetan,greek         -1      Near Party Hub   \n",
       "4                20005     cuban,british      Pune       Near Party Hub   \n",
       "\n",
       "  Opening Day of Restaurant  Facebook Popularity Quotient       Endorsed By  \\\n",
       "0                13-07-2010                         78.00  Tier A Celebrity   \n",
       "1                05-09-2011                         89.17      Not Specific   \n",
       "2                12-04-2011                         84.00      Not Specific   \n",
       "3                16-01-2005                         79.38      Not Specific   \n",
       "4                10-11-2008                         84.67      Not Specific   \n",
       "\n",
       "   Instagram Popularity Quotient  Fire Audit  Liquor License Obtained  ...  \\\n",
       "0                          69.00           1                        1  ...   \n",
       "1                          96.00           1                        1  ...   \n",
       "2                          86.00           1                        1  ...   \n",
       "3                          74.40           0                        1  ...   \n",
       "4                          86.46           1                        1  ...   \n",
       "\n",
       "   Overall Restaurant Rating  Live Music Rating  Comedy Gigs Rating  \\\n",
       "0                        6.0                NaN                 2.0   \n",
       "1                        9.0                3.0                 NaN   \n",
       "2                        6.0                3.0                 NaN   \n",
       "3                        9.0                6.0                 NaN   \n",
       "4                        NaN                NaN                 NaN   \n",
       "\n",
       "   Value Deals Rating Live Sports Rating Ambience  Lively  Service  \\\n",
       "0                 NaN                NaN        5       2        2   \n",
       "1                 NaN                6.0        7       8        5   \n",
       "2                 NaN                NaN        9       7        5   \n",
       "3                 NaN                NaN        6       7        6   \n",
       "4                 NaN                NaN        4       4        7   \n",
       "\n",
       "   Comfortablility  Privacy  \n",
       "0                6        0  \n",
       "1                1        8  \n",
       "2                1        5  \n",
       "3                4        7  \n",
       "4                7        3  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Testing dataset loaded previously at start of notebook.\n",
    "#hackathon_test_org_df = pd.read_csv(\"/Users/robbarker/Desktop/Restaurant-Turnover-Prediction/Test_dataset_(1).csv\")\n",
    "\n",
    "# Create a copy of the original DataFrame to avoid modifying the original data.\n",
    "hackathon_test_df = hackathon_test_org_df.copy()\n",
    "\n",
    "# Print the first 5 rows of hackathon_test_df as formatted data.\n",
    "print(\"First 5 rows of hackathon_test_df:\")\n",
    "hackathon_test_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-16T13:27:49.517810Z",
     "start_time": "2020-09-16T13:27:49.503853Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Registration Number                0\n",
       "Cuisine                            0\n",
       "City                               0\n",
       "Restaurant Location                0\n",
       "Opening Day of Restaurant          0\n",
       "Facebook Popularity Quotient       0\n",
       "Endorsed By                        0\n",
       "Instagram Popularity Quotient      0\n",
       "Fire Audit                         0\n",
       "Liquor License Obtained            0\n",
       "Situated in a Multi Complex        0\n",
       "Dedicated Parking                  0\n",
       "Open Sitting Available             0\n",
       "Resturant Tier                     0\n",
       "Restaurant Type                    0\n",
       "Restaurant Theme                   0\n",
       "Restaurant Zomato Rating           0\n",
       "Restaurant City Tier               0\n",
       "Order Wait Time                    0\n",
       "Staff Responsivness                0\n",
       "Value for Money                    0\n",
       "Hygiene Rating                     0\n",
       "Food Rating                        0\n",
       "Overall Restaurant Rating         34\n",
       "Live Music Rating                102\n",
       "Comedy Gigs Rating               370\n",
       "Value Deals Rating               385\n",
       "Live Sports Rating               471\n",
       "Ambience                           0\n",
       "Lively                             0\n",
       "Service                            0\n",
       "Comfortablility                    0\n",
       "Privacy                            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for the null values in test dataset.\n",
    "hackathon_test_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions on the test set:\n",
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      " 33256626.66163973 29165763.71513521 27802142.73296705 32477414.67182934\n",
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      " 23954783.53327828 29750172.707493   37704628.43680733 28711223.38774583]\n"
     ]
    }
   ],
   "source": [
    "# Create an instance of SimpleImputer with strategy='mean'.\n",
    "SI = SimpleImputer(strategy='mean')\n",
    "\n",
    "# Fit the imputer on the 'Instagram Popularity Quotient' column of the test set.\n",
    "SI.fit(hackathon_test_df[['Instagram Popularity Quotient']])\n",
    "\n",
    "# Transform the column to replace missing values with the mean.\n",
    "IPQ_test = SI.transform(hackathon_test_df[['Instagram Popularity Quotient']])\n",
    "\n",
    "# Using the model built on the Training set to predict on the Test Set.\n",
    "prediction = model.predict(IPQ_test)\n",
    "\n",
    "# Print the predictions.\n",
    "print(\"Predictions on the test set:\")\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving the output in a data frame and then exporting it to a '.csv' file with the appropriate 'Registration Number'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-16T13:38:44.072923Z",
     "start_time": "2020-09-16T13:38:44.055736Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "     Registration Number\n",
       "0                  20001\n",
       "1                  20002\n",
       "2                  20003\n",
       "3                  20004\n",
       "4                  20005\n",
       "..                   ...\n",
       "495                20496\n",
       "496                20497\n",
       "497                20498\n",
       "498                20499\n",
       "499                20500\n",
       "\n",
       "[500 rows x 1 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solution_df = pd.DataFrame(hackathon_test_df['Registration Number'])\n",
    "solution_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-16T13:40:01.957550Z",
     "start_time": "2020-09-16T13:40:01.935844Z"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "     Registration Number  Annual Turnover\n",
       "0                  20001     2.897096e+07\n",
       "1                  20002     3.773710e+07\n",
       "2                  20003     3.449038e+07\n",
       "3                  20004     3.072419e+07\n",
       "4                  20005     3.463973e+07\n",
       "..                   ...              ...\n",
       "495                20496     2.994498e+07\n",
       "496                20497     2.395478e+07\n",
       "497                20498     2.975017e+07\n",
       "498                20499     3.770463e+07\n",
       "499                20500     2.871122e+07\n",
       "\n",
       "[500 rows x 2 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding the predictions to the solution dataframe.\n",
    "solution_df['Annual Turnover'] = prediction  # type: ignore\n",
    "solution_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-16T14:08:38.203042Z",
     "start_time": "2020-09-16T14:08:38.196084Z"
    }
   },
   "outputs": [],
   "source": [
    "# Setting the directory to export the file as a '.csv' file.\n",
    "import os\n",
    "location = '/Users/barkz/Desktop/github-barkz/Restaurant-Turnover-Prediction'\n",
    "os.chdir(location)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-16T14:07:06.368811Z",
     "start_time": "2020-09-16T14:07:06.345683Z"
    }
   },
   "outputs": [],
   "source": [
    "# Exporting the data frame to a '.csv' file and setting the index = False as we do want the index.\n",
    "\n",
    "solution_df.to_csv('TeamPi_Hackathon_Project_7.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# END"
   ]
  }
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