Hospitality Analysis Project - final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1> HOSPITALITY ANALYSIS </h1></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Problem Statement\n",
    "\n",
    "\n",
    "\n",
    "AtliQ Grands, a prominent hotel chain operating in multiple Indian cities with various hotel types, such as AtliQ Seasons, AtliQ Exotica, AtliQ Bay, and AtliQ Palace, has been facing declining revenue and market share. To address this challenge and improve their financial performance, AtliQ Grands aims to leverage data analytics to make data-informed decisions. The project's goal is to analyze booking data collected from various sources, including the company's website and third-party booking platforms, in order to identify opportunities for revenue enhancement and market competitiveness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets start with importing of necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as mp\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Data Import and Data Exploration</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset:\n",
    "We have 5 csv files\n",
    "\n",
    "dim_date.csv\n",
    "\n",
    "dim_hotels.csv\n",
    "\n",
    "dim_rooms.csv\n",
    "\n",
    "fact_aggregated_bookings.csv\n",
    "\n",
    "fact_bookings.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Read bookings data in a dataframe<b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bookings=pd.read_csv('datasets/fact_bookings.csv')                 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Explore bookings data<b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>May012216558RT11</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10010</td>\n",
       "      <td>10010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May012216558RT12</td>\n",
       "      <td>16558</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>May012216558RT13</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>4/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100000</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>May012216558RT14</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "0  May012216558RT11        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "1  May012216558RT12        16558     30-04-22      1/5/2022      2/5/2022   \n",
       "2  May012216558RT13        16558     28-04-22      1/5/2022      4/5/2022   \n",
       "3  May012216558RT14        16558     28-04-22      1/5/2022      2/5/2022   \n",
       "4  May012216558RT15        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "0       -3.0           RT1    direct online            1.0    Checked Out   \n",
       "1        2.0           RT1           others            NaN      Cancelled   \n",
       "2        2.0           RT1          logtrip            5.0    Checked Out   \n",
       "3       -2.0           RT1           others            NaN      Cancelled   \n",
       "4        4.0           RT1    direct online            5.0    Checked Out   \n",
       "\n",
       "   revenue_generated  revenue_realized  \n",
       "0              10010             10010  \n",
       "1               9100              3640  \n",
       "2            9100000              9100  \n",
       "3               9100              3640  \n",
       "4              10920             10920  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing first 5 rows from the fact_bookings.csv file\n",
    "\n",
    "df_bookings.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134590, 12)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the size of the table (rows & columns)\n",
    "\n",
    "df_bookings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['RT1', 'RT2', 'RT3', 'RT4'], dtype=object)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Types of available rooms\n",
    "\n",
    "df_bookings.room_category.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['direct online', 'others', 'logtrip', 'tripster', 'makeyourtrip',\n",
       "       'journey', 'direct offline'], dtype=object)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Booking platforms used to book a room\n",
    "\n",
    "df_bookings.booking_platform.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "booking_platform\n",
       "others            55066\n",
       "makeyourtrip      26898\n",
       "logtrip           14756\n",
       "direct online     13379\n",
       "tripster           9630\n",
       "journey            8106\n",
       "direct offline     6755\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of Bookings by each Booking Platform\n",
    "\n",
    "df_bookings.booking_platform.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Bar Chart for the booking platform data\n",
    "\n",
    "graph = df_bookings.booking_platform.value_counts()\n",
    "\n",
    "# Calculate the number of bars\n",
    "num_bars = len(graph)\n",
    "\n",
    "# Dynamic figure size\n",
    "fig_height = max(8, num_bars * 0.5)  # Ensures a minimum height of 8\n",
    "fig_width = 12  # Fixed width or adjust based on preferences\n",
    "\n",
    "# Set figure size\n",
    "plt.figure(figsize=(fig_width, fig_height))\n",
    "\n",
    "ax = graph.plot(kind = 'barh', facecolor = '#ff1f2b')\n",
    "\n",
    "plt.xlabel('Number of Bookings')\n",
    "plt.ylabel('Booking Platforms')\n",
    "plt.title('Number of Bookings per Platform')\n",
    "\n",
    "# Adding labels on the bars with dynamic placement\n",
    "max_value = max(graph)\n",
    "for index, value in enumerate(df_bookings.booking_platform.value_counts()):\n",
    "    ax.text(value - (max_value * 0.02), index, str(value), va='center', ha='right', fontsize=14)\n",
    "\n",
    "# Adjust layout to make sure everything fits\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the figure with a transparent background\n",
    "plt.savefig('no of bookings per platforms.png', transparent = True, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>property_id</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>134590.000000</td>\n",
       "      <td>134587.000000</td>\n",
       "      <td>56683.000000</td>\n",
       "      <td>1.345900e+05</td>\n",
       "      <td>134590.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18061.113493</td>\n",
       "      <td>2.036170</td>\n",
       "      <td>3.619004</td>\n",
       "      <td>1.537805e+04</td>\n",
       "      <td>12696.123256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1093.055847</td>\n",
       "      <td>1.034885</td>\n",
       "      <td>1.235009</td>\n",
       "      <td>9.303604e+04</td>\n",
       "      <td>6928.108124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16558.000000</td>\n",
       "      <td>-17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.500000e+03</td>\n",
       "      <td>2600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17558.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.900000e+03</td>\n",
       "      <td>7600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17564.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.350000e+04</td>\n",
       "      <td>11700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18563.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.800000e+04</td>\n",
       "      <td>15300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19563.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.856000e+07</td>\n",
       "      <td>45220.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         property_id      no_guests  ratings_given  revenue_generated  \\\n",
       "count  134590.000000  134587.000000   56683.000000       1.345900e+05   \n",
       "mean    18061.113493       2.036170       3.619004       1.537805e+04   \n",
       "std      1093.055847       1.034885       1.235009       9.303604e+04   \n",
       "min     16558.000000     -17.000000       1.000000       6.500000e+03   \n",
       "25%     17558.000000       1.000000       3.000000       9.900000e+03   \n",
       "50%     17564.000000       2.000000       4.000000       1.350000e+04   \n",
       "75%     18563.000000       2.000000       5.000000       1.800000e+04   \n",
       "max     19563.000000       6.000000       5.000000       2.856000e+07   \n",
       "\n",
       "       revenue_realized  \n",
       "count     134590.000000  \n",
       "mean       12696.123256  \n",
       "std         6928.108124  \n",
       "min         2600.000000  \n",
       "25%         7600.000000  \n",
       "50%        11700.000000  \n",
       "75%        15300.000000  \n",
       "max        45220.000000  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a summary of the numeric columns\n",
    "\n",
    "df_bookings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6500, 28560000)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifying Minimum & Maximum Revenue Generated\n",
    "\n",
    "df_bookings.revenue_generated.min(),df_bookings.revenue_generated.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "‏"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Reading and Exploring remaining data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_date = pd.read_csv('datasets/dim_date.csv')\n",
    "df_hotels = pd.read_csv('datasets/dim_hotels.csv')\n",
    "df_rooms = pd.read_csv('datasets/dim_rooms.csv')\n",
    "df_fact_agg_bookings = pd.read_csv('datasets/fact_aggregated_bookings.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "‏"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 4)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the size of the table (rows & columns)\n",
    "\n",
    "df_hotels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>property_id</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16558</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16559</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16560</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16561</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16562</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id  property_name  category    city\n",
       "0        16558   Atliq Grands    Luxury   Delhi\n",
       "1        16559  Atliq Exotica    Luxury  Mumbai\n",
       "2        16560     Atliq City  Business   Delhi\n",
       "3        16561      Atliq Blu    Luxury   Delhi\n",
       "4        16562      Atliq Bay    Luxury   Delhi"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing first 5 rows from the dim_hotels.csv file\n",
    "\n",
    "df_hotels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category\n",
       "Luxury      16\n",
       "Business     9\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of Hotels in each Category\n",
    "\n",
    "df_hotels.category.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Bar Chart for Number of Hotels per City\n",
    "\n",
    "graph = df_hotels.city.value_counts().sort_values()\n",
    "\n",
    "# Calculate the number of bars\n",
    "num_bars = len(graph)\n",
    "\n",
    "# Dynamic figure size\n",
    "fig_width = max(8, num_bars * 0.5)  # Ensures a minimum width of 8\n",
    "fig_height = 8  # Fixed height or adjust based on preferences\n",
    "\n",
    "# Set figure size\n",
    "plt.figure(figsize=(fig_width, fig_height))\n",
    "\n",
    "graph.plot(kind = 'bar', facecolor = '#ff1f2b')\n",
    "\n",
    "plt.xlabel('City')\n",
    "plt.ylabel('Number of Hotels')\n",
    "plt.title('Number of Hotels per City')\n",
    "\n",
    "# Adding labels on top of the bars\n",
    "for index, value in enumerate(graph):\n",
    "    plt.text(index, value, str(round(value, 2)), ha = 'center', va = 'bottom', fontsize=14)\n",
    "\n",
    "# Adjust layout to make sure everything fits\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the figure with a transparent background\n",
    "plt.savefig('no of hotels per city.png', transparent = True, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "‏‏‎ ‎"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9200, 5)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the size of the table (rows & columns)\n",
    "\n",
    "df_fact_agg_bookings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>30</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity\n",
       "0        16559      1-May-22           RT1                   25      30.0\n",
       "1        19562      1-May-22           RT1                   28      30.0\n",
       "2        19563      1-May-22           RT1                   23      30.0\n",
       "3        17558      1-May-22           RT1                   30      19.0\n",
       "4        16558      1-May-22           RT1                   18      19.0"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing first 5 rows from the fact_aggregated_bookings.csv file\n",
    "\n",
    "df_fact_agg_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16559, 19562, 19563, 17558, 16558, 17560, 19558, 19560, 17561,\n",
       "       16560, 16561, 16562, 16563, 17559, 17562, 17563, 18558, 18559,\n",
       "       18561, 18562, 18563, 19559, 19561, 17564, 18560], dtype=int64)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out unique property ids in aggregate bookings dataset\n",
    "\n",
    "df_fact_agg_bookings.property_id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_id\n",
       "16558    3153\n",
       "16559    7338\n",
       "16560    4693\n",
       "16561    4418\n",
       "16562    4820\n",
       "16563    7211\n",
       "17558    5053\n",
       "17559    6142\n",
       "17560    6013\n",
       "17561    5183\n",
       "17562    3424\n",
       "17563    6337\n",
       "17564    3982\n",
       "18558    4475\n",
       "18559    5256\n",
       "18560    6638\n",
       "18561    6458\n",
       "18562    7333\n",
       "18563    4737\n",
       "19558    4400\n",
       "19559    4729\n",
       "19560    6079\n",
       "19561    5736\n",
       "19562    5812\n",
       "19563    5413\n",
       "Name: successful_bookings, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out total bookings per property_id\n",
    "\n",
    "df_fact_agg_bookings.groupby('property_id')['successful_bookings'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
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       "  <tbody>\n",
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       "      <td>39.0</td>\n",
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       "    <tr>\n",
       "      <th>6209</th>\n",
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       "      <td>123</td>\n",
       "      <td>26.0</td>\n",
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       "    <tr>\n",
       "      <th>8522</th>\n",
       "      <td>19559</td>\n",
       "      <td>25-Jul-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>35</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9194</th>\n",
       "      <td>18563</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>20</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity\n",
       "3           17558      1-May-22           RT1                   30      19.0\n",
       "12          16563      1-May-22           RT1                  100      41.0\n",
       "4136        19558     11-Jun-22           RT2                   50      39.0\n",
       "6209        19560      2-Jul-22           RT1                  123      26.0\n",
       "8522        19559     25-Jul-22           RT1                   35      24.0\n",
       "9194        18563     31-Jul-22           RT4                   20      18.0"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out days on which bookings are greater than capacity\n",
    "\n",
    "df_fact_agg_bookings[df_fact_agg_bookings.successful_bookings > df_fact_agg_bookings.capacity]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out properties that have highest capacity\n",
    "\n",
    "df_fact_agg_bookings.capacity.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <td>38</td>\n",
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       "      <td>27</td>\n",
       "      <td>50.0</td>\n",
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       "      <th>229</th>\n",
       "      <td>17558</td>\n",
       "      <td>3-May-22</td>\n",
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       "      <td>27</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>17558</td>\n",
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       "      <td>RT2</td>\n",
       "      <td>29</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>RT2</td>\n",
       "      <td>22</td>\n",
       "      <td>50.0</td>\n",
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       "    <tr>\n",
       "      <th>8828</th>\n",
       "      <td>17558</td>\n",
       "      <td>28-Jul-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>21</td>\n",
       "      <td>50.0</td>\n",
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       "    <tr>\n",
       "      <th>8928</th>\n",
       "      <td>17558</td>\n",
       "      <td>29-Jul-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>23</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9028</th>\n",
       "      <td>17558</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>32</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9128</th>\n",
       "      <td>17558</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>30</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity\n",
       "27          17558      1-May-22           RT2                   38      50.0\n",
       "128         17558      2-May-22           RT2                   27      50.0\n",
       "229         17558      3-May-22           RT2                   26      50.0\n",
       "328         17558      4-May-22           RT2                   27      50.0\n",
       "428         17558      5-May-22           RT2                   29      50.0\n",
       "...           ...           ...           ...                  ...       ...\n",
       "8728        17558     27-Jul-22           RT2                   22      50.0\n",
       "8828        17558     28-Jul-22           RT2                   21      50.0\n",
       "8928        17558     29-Jul-22           RT2                   23      50.0\n",
       "9028        17558     30-Jul-22           RT2                   32      50.0\n",
       "9128        17558     31-Jul-22           RT2                   30      50.0\n",
       "\n",
       "[92 rows x 5 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Properties with highest capacity\n",
    "\n",
    "df_fact_agg_bookings[df_fact_agg_bookings.capacity == df_fact_agg_bookings.capacity.max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "‏‏‎ ‎"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>DATA CLEANING</h3>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
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       "      <th>1</th>\n",
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       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>May012216558RT13</td>\n",
       "      <td>16558</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100000</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>May012216558RT14</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "0  May012216558RT11        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "1  May012216558RT12        16558     30-04-22      1/5/2022      2/5/2022   \n",
       "2  May012216558RT13        16558     28-04-22      1/5/2022      4/5/2022   \n",
       "3  May012216558RT14        16558     28-04-22      1/5/2022      2/5/2022   \n",
       "4  May012216558RT15        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "0       -3.0           RT1    direct online            1.0    Checked Out   \n",
       "1        2.0           RT1           others            NaN      Cancelled   \n",
       "2        2.0           RT1          logtrip            5.0    Checked Out   \n",
       "3       -2.0           RT1           others            NaN      Cancelled   \n",
       "4        4.0           RT1    direct online            5.0    Checked Out   \n",
       "\n",
       "   revenue_generated  revenue_realized  \n",
       "0              10010             10010  \n",
       "1               9100              3640  \n",
       "2            9100000              9100  \n",
       "3               9100              3640  \n",
       "4              10920             10920  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing first 5 rows from the fact_bookings.csv file\n",
    "\n",
    "df_bookings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>(1) Clean invalid guests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17924</th>\n",
       "      <td>May122218559RT44</td>\n",
       "      <td>18559</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>14-05-22</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>direct online</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>20900</td>\n",
       "      <td>20900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18020</th>\n",
       "      <td>May122218561RT22</td>\n",
       "      <td>18561</td>\n",
       "      <td>8/5/2022</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>14-05-22</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>RT2</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9000</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18119</th>\n",
       "      <td>May122218562RT311</td>\n",
       "      <td>18562</td>\n",
       "      <td>5/5/2022</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>17-05-22</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>RT3</td>\n",
       "      <td>direct offline</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>16800</td>\n",
       "      <td>16800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18121</th>\n",
       "      <td>May122218562RT313</td>\n",
       "      <td>18562</td>\n",
       "      <td>10/5/2022</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>17-05-22</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>RT3</td>\n",
       "      <td>direct online</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>14400</td>\n",
       "      <td>5760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56715</th>\n",
       "      <td>Jun082218562RT12</td>\n",
       "      <td>18562</td>\n",
       "      <td>5/6/2022</td>\n",
       "      <td>8/6/2022</td>\n",
       "      <td>13-06-22</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>6500</td>\n",
       "      <td>6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119765</th>\n",
       "      <td>Jul202219560RT220</td>\n",
       "      <td>19560</td>\n",
       "      <td>19-07-22</td>\n",
       "      <td>20-07-22</td>\n",
       "      <td>22-07-22</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>RT2</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>13500</td>\n",
       "      <td>13500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134586</th>\n",
       "      <td>Jul312217564RT47</td>\n",
       "      <td>17564</td>\n",
       "      <td>30-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>38760</td>\n",
       "      <td>38760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\n",
       "0        May012216558RT11        16558     27-04-22      1/5/2022   \n",
       "3        May012216558RT14        16558     28-04-22      1/5/2022   \n",
       "17924    May122218559RT44        18559    12/5/2022     12/5/2022   \n",
       "18020    May122218561RT22        18561     8/5/2022     12/5/2022   \n",
       "18119   May122218562RT311        18562     5/5/2022     12/5/2022   \n",
       "18121   May122218562RT313        18562    10/5/2022     12/5/2022   \n",
       "56715    Jun082218562RT12        18562     5/6/2022      8/6/2022   \n",
       "119765  Jul202219560RT220        19560     19-07-22      20-07-22   \n",
       "134586   Jul312217564RT47        17564     30-07-22      31-07-22   \n",
       "\n",
       "       checkout_date  no_guests room_category booking_platform  ratings_given  \\\n",
       "0           2/5/2022       -3.0           RT1    direct online            1.0   \n",
       "3           2/5/2022       -2.0           RT1           others            NaN   \n",
       "17924       14-05-22      -10.0           RT4    direct online            NaN   \n",
       "18020       14-05-22      -12.0           RT2     makeyourtrip            NaN   \n",
       "18119       17-05-22       -6.0           RT3   direct offline            5.0   \n",
       "18121       17-05-22       -4.0           RT3    direct online            NaN   \n",
       "56715       13-06-22      -17.0           RT1           others            NaN   \n",
       "119765      22-07-22       -1.0           RT2           others            NaN   \n",
       "134586      1/8/2022       -4.0           RT4          logtrip            2.0   \n",
       "\n",
       "       booking_status  revenue_generated  revenue_realized  \n",
       "0         Checked Out              10010             10010  \n",
       "3           Cancelled               9100              3640  \n",
       "17924         No Show              20900             20900  \n",
       "18020       Cancelled               9000              3600  \n",
       "18119     Checked Out              16800             16800  \n",
       "18121       Cancelled              14400              5760  \n",
       "56715     Checked Out               6500              6500  \n",
       "119765    Checked Out              13500             13500  \n",
       "134586    Checked Out              38760             38760  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Entries where number of guests are 0 or less\n",
    "\n",
    "df_bookings[df_bookings.no_guests<=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134590, 12)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the size of the table (rows & columns)\n",
    "\n",
    "df_bookings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see above, number of guests having less than zero value represents data error. We can ignore these records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
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       "      <th>1</th>\n",
       "      <td>May012216558RT12</td>\n",
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       "      <td>9100</td>\n",
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       "      <td>10920</td>\n",
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       "      <td>1/5/2022</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
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       "      <td>3640</td>\n",
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       "      <td>31-07-22</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134585</th>\n",
       "      <td>Jul312217564RT46</td>\n",
       "      <td>17564</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>3/8/2022</td>\n",
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       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134587</th>\n",
       "      <td>Jul312217564RT48</td>\n",
       "      <td>17564</td>\n",
       "      <td>30-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>2/8/2022</td>\n",
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       "      <td>17564</td>\n",
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       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134589</th>\n",
       "      <td>Jul312217564RT410</td>\n",
       "      <td>17564</td>\n",
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       "      <td>31-07-22</td>\n",
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       "      <td>12920</td>\n",
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       "<p>134578 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\n",
       "1        May012216558RT12        16558     30-04-22      1/5/2022   \n",
       "2        May012216558RT13        16558     28-04-22      1/5/2022   \n",
       "4        May012216558RT15        16558     27-04-22      1/5/2022   \n",
       "5        May012216558RT16        16558     1/5/2022      1/5/2022   \n",
       "6        May012216558RT17        16558     28-04-22      1/5/2022   \n",
       "...                   ...          ...          ...           ...   \n",
       "134584   Jul312217564RT45        17564     30-07-22      31-07-22   \n",
       "134585   Jul312217564RT46        17564     29-07-22      31-07-22   \n",
       "134587   Jul312217564RT48        17564     30-07-22      31-07-22   \n",
       "134588   Jul312217564RT49        17564     29-07-22      31-07-22   \n",
       "134589  Jul312217564RT410        17564     31-07-22      31-07-22   \n",
       "\n",
       "       checkout_date  no_guests room_category booking_platform  ratings_given  \\\n",
       "1           2/5/2022        2.0           RT1           others            NaN   \n",
       "2           4/5/2022        2.0           RT1          logtrip            5.0   \n",
       "4           2/5/2022        4.0           RT1    direct online            5.0   \n",
       "5           3/5/2022        2.0           RT1           others            4.0   \n",
       "6           6/5/2022        2.0           RT1           others            NaN   \n",
       "...              ...        ...           ...              ...            ...   \n",
       "134584      1/8/2022        2.0           RT4           others            2.0   \n",
       "134585      3/8/2022        1.0           RT4     makeyourtrip            2.0   \n",
       "134587      2/8/2022        1.0           RT4         tripster            NaN   \n",
       "134588      1/8/2022        2.0           RT4          logtrip            2.0   \n",
       "134589      1/8/2022        2.0           RT4     makeyourtrip            NaN   \n",
       "\n",
       "       booking_status  revenue_generated  revenue_realized  \n",
       "1           Cancelled               9100              3640  \n",
       "2         Checked Out            9100000              9100  \n",
       "4         Checked Out              10920             10920  \n",
       "5         Checked Out               9100              9100  \n",
       "6           Cancelled               9100              3640  \n",
       "...               ...                ...               ...  \n",
       "134584    Checked Out              32300             32300  \n",
       "134585    Checked Out              32300             32300  \n",
       "134587      Cancelled              32300             12920  \n",
       "134588    Checked Out              32300             32300  \n",
       "134589      Cancelled              32300             12920  \n",
       "\n",
       "[134578 rows x 12 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing these data\n",
    "\n",
    "df_bookings = df_bookings[df_bookings.no_guests>0]\n",
    "df_bookings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134578, 12)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the size of the new table (rows & columns)\n",
    "\n",
    "df_bookings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>(2) Outlier removal in revenue generated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6500, 28560000)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the Minimum & Maximum Revenue Generated\n",
    "\n",
    "df_bookings.revenue_generated.min(),df_bookings.revenue_generated.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15378.036937686695, 93040.1549314641)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the average and standard deviation for the 'revenue_generated' column\n",
    "\n",
    "avg,std = df_bookings.revenue_generated.mean(),df_bookings.revenue_generated.std()\n",
    "avg, std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "294498.50173207896"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the higher limit\n",
    "\n",
    "higher_limit = avg + (3 * std)\n",
    "higher_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May012216558RT12</td>\n",
       "      <td>16558</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>May012216558RT16</td>\n",
       "      <td>16558</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>May012216558RT17</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>May012216558RT18</td>\n",
       "      <td>16558</td>\n",
       "      <td>26-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134584</th>\n",
       "      <td>Jul312217564RT45</td>\n",
       "      <td>17564</td>\n",
       "      <td>30-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>others</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134585</th>\n",
       "      <td>Jul312217564RT46</td>\n",
       "      <td>17564</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>3/8/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134587</th>\n",
       "      <td>Jul312217564RT48</td>\n",
       "      <td>17564</td>\n",
       "      <td>30-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>2/8/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>tripster</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>32300</td>\n",
       "      <td>12920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134588</th>\n",
       "      <td>Jul312217564RT49</td>\n",
       "      <td>17564</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134589</th>\n",
       "      <td>Jul312217564RT410</td>\n",
       "      <td>17564</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>32300</td>\n",
       "      <td>12920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>134573 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\n",
       "1        May012216558RT12        16558     30-04-22      1/5/2022   \n",
       "4        May012216558RT15        16558     27-04-22      1/5/2022   \n",
       "5        May012216558RT16        16558     1/5/2022      1/5/2022   \n",
       "6        May012216558RT17        16558     28-04-22      1/5/2022   \n",
       "7        May012216558RT18        16558     26-04-22      1/5/2022   \n",
       "...                   ...          ...          ...           ...   \n",
       "134584   Jul312217564RT45        17564     30-07-22      31-07-22   \n",
       "134585   Jul312217564RT46        17564     29-07-22      31-07-22   \n",
       "134587   Jul312217564RT48        17564     30-07-22      31-07-22   \n",
       "134588   Jul312217564RT49        17564     29-07-22      31-07-22   \n",
       "134589  Jul312217564RT410        17564     31-07-22      31-07-22   \n",
       "\n",
       "       checkout_date  no_guests room_category booking_platform  ratings_given  \\\n",
       "1           2/5/2022        2.0           RT1           others            NaN   \n",
       "4           2/5/2022        4.0           RT1    direct online            5.0   \n",
       "5           3/5/2022        2.0           RT1           others            4.0   \n",
       "6           6/5/2022        2.0           RT1           others            NaN   \n",
       "7           3/5/2022        2.0           RT1          logtrip            NaN   \n",
       "...              ...        ...           ...              ...            ...   \n",
       "134584      1/8/2022        2.0           RT4           others            2.0   \n",
       "134585      3/8/2022        1.0           RT4     makeyourtrip            2.0   \n",
       "134587      2/8/2022        1.0           RT4         tripster            NaN   \n",
       "134588      1/8/2022        2.0           RT4          logtrip            2.0   \n",
       "134589      1/8/2022        2.0           RT4     makeyourtrip            NaN   \n",
       "\n",
       "       booking_status  revenue_generated  revenue_realized  \n",
       "1           Cancelled               9100              3640  \n",
       "4         Checked Out              10920             10920  \n",
       "5         Checked Out               9100              9100  \n",
       "6           Cancelled               9100              3640  \n",
       "7             No Show               9100              9100  \n",
       "...               ...                ...               ...  \n",
       "134584    Checked Out              32300             32300  \n",
       "134585    Checked Out              32300             32300  \n",
       "134587      Cancelled              32300             12920  \n",
       "134588    Checked Out              32300             32300  \n",
       "134589      Cancelled              32300             12920  \n",
       "\n",
       "[134573 rows x 12 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing the outliers in the 'revenue_generated' column\n",
    "\n",
    "df_bookings = df_bookings[df_bookings.revenue_generated < higher_limit]\n",
    "df_bookings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    134573.000000\n",
       "mean      12695.983585\n",
       "std        6927.791692\n",
       "min        2600.000000\n",
       "25%        7600.000000\n",
       "50%       11700.000000\n",
       "75%       15300.000000\n",
       "max       45220.000000\n",
       "Name: revenue_realized, dtype: float64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a summary of the numeric column 'revenue_realized'\n",
    "\n",
    "df_bookings.revenue_realized.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12695.983585117372, 6927.791692242814)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the average and standard deviation for the 'revenue_generated' column\n",
    "\n",
    "avg,std = df_bookings.revenue_realized.mean(),df_bookings.revenue_realized.std()\n",
    "avg, std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33479.358661845814"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the higher limit\n",
    "\n",
    "higher_limit = avg + (3 * std)\n",
    "higher_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
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       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>May012216559RT41</td>\n",
       "      <td>16559</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>7/5/2022</td>\n",
       "      <td>4.0</td>\n",
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       "      <th>139</th>\n",
       "      <td>May012216559RT43</td>\n",
       "      <td>16559</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>1/5/2022</td>\n",
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       "      <td>45220</td>\n",
       "      <td>45220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>May012216559RT47</td>\n",
       "      <td>16559</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>3.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>others</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>35530</td>\n",
       "      <td>35530</td>\n",
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       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>May012216559RT413</td>\n",
       "      <td>16559</td>\n",
       "      <td>24-04-22</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>41990</td>\n",
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       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>May012216560RT45</td>\n",
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       "      <td>30-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>5.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>others</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>34580</td>\n",
       "      <td>34580</td>\n",
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       "      <th>...</th>\n",
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       "      <td>Jul312219560RT49</td>\n",
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       "      <td>31-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>2/8/2022</td>\n",
       "      <td>6.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>39900</td>\n",
       "      <td>39900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134331</th>\n",
       "      <td>Jul312219560RT412</td>\n",
       "      <td>19560</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>31-07-22</td>\n",
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       "    <tr>\n",
       "      <th>134467</th>\n",
       "      <td>Jul312219562RT45</td>\n",
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       "      <td>Jul312219562RT412</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>RT4</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>37050</td>\n",
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       "      <th>134581</th>\n",
       "      <td>Jul312217564RT42</td>\n",
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       "      <td>RT4</td>\n",
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       "      <td>38760</td>\n",
       "      <td>38760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1299 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\n",
       "137      May012216559RT41        16559     27-04-22      1/5/2022   \n",
       "139      May012216559RT43        16559     1/5/2022      1/5/2022   \n",
       "143      May012216559RT47        16559     28-04-22      1/5/2022   \n",
       "149     May012216559RT413        16559     24-04-22      1/5/2022   \n",
       "222      May012216560RT45        16560     30-04-22      1/5/2022   \n",
       "...                   ...          ...          ...           ...   \n",
       "134328   Jul312219560RT49        19560     31-07-22      31-07-22   \n",
       "134331  Jul312219560RT412        19560     31-07-22      31-07-22   \n",
       "134467   Jul312219562RT45        19562     28-07-22      31-07-22   \n",
       "134474  Jul312219562RT412        19562     25-07-22      31-07-22   \n",
       "134581   Jul312217564RT42        17564     31-07-22      31-07-22   \n",
       "\n",
       "       checkout_date  no_guests room_category booking_platform  ratings_given  \\\n",
       "137         7/5/2022        4.0           RT4           others            NaN   \n",
       "139         2/5/2022        6.0           RT4         tripster            3.0   \n",
       "143         3/5/2022        3.0           RT4           others            5.0   \n",
       "149         7/5/2022        5.0           RT4          logtrip            NaN   \n",
       "222         3/5/2022        5.0           RT4           others            3.0   \n",
       "...              ...        ...           ...              ...            ...   \n",
       "134328      2/8/2022        6.0           RT4    direct online            5.0   \n",
       "134331      1/8/2022        6.0           RT4           others            2.0   \n",
       "134467      1/8/2022        6.0           RT4     makeyourtrip            4.0   \n",
       "134474      6/8/2022        5.0           RT4   direct offline            5.0   \n",
       "134581      1/8/2022        4.0           RT4     makeyourtrip            4.0   \n",
       "\n",
       "       booking_status  revenue_generated  revenue_realized  \n",
       "137       Checked Out              38760             38760  \n",
       "139       Checked Out              45220             45220  \n",
       "143       Checked Out              35530             35530  \n",
       "149       Checked Out              41990             41990  \n",
       "222       Checked Out              34580             34580  \n",
       "...               ...                ...               ...  \n",
       "134328    Checked Out              39900             39900  \n",
       "134331    Checked Out              39900             39900  \n",
       "134467    Checked Out              39900             39900  \n",
       "134474    Checked Out              37050             37050  \n",
       "134581    Checked Out              38760             38760  \n",
       "\n",
       "[1299 rows x 12 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.revenue_realized > higher_limit]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>room_id</th>\n",
       "      <th>room_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RT2</td>\n",
       "      <td>Elite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RT3</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  room_id    room_class\n",
       "0     RT1      Standard\n",
       "1     RT2         Elite\n",
       "2     RT3       Premium\n",
       "3     RT4  Presidential"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rooms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One observation we can have in above dataframe is that all rooms are RT4 which means presidential suit. Now since RT4 is a luxurious room it is likely their rent will be higher. To make a fair analysis, we need to do data analysis only on RT4 room types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    16071.000000\n",
       "mean     23439.308444\n",
       "std       9048.599076\n",
       "min       7600.000000\n",
       "25%      19000.000000\n",
       "50%      26600.000000\n",
       "75%      32300.000000\n",
       "max      45220.000000\n",
       "Name: revenue_realized, dtype: float64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a summary of the numeric column 'revenue_realized' where 'room_category' == 'RT4'\n",
    "\n",
    "df_bookings[df_bookings.room_category=='RT4'].revenue_realized.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50585.105672000005"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "23439.308444 + (3 * 9048.599076)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here higher limit comes to be 50585  and in our dataframe above we can see that max value for revenue realized is 45220. Hence we can conclude that there is no outlier and we don't need to do any data cleaning on this particular column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "booking_id               0\n",
       "property_id              0\n",
       "booking_date             0\n",
       "check_in_date            0\n",
       "checkout_date            0\n",
       "no_guests                0\n",
       "room_category            0\n",
       "booking_platform         0\n",
       "ratings_given        77897\n",
       "booking_status           0\n",
       "revenue_generated        0\n",
       "revenue_realized         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total values in our dataframe is 134576. Out of that 77899 rows has null rating. Since there are many rows with null rating, we should not filter these values. Also we should not replace this rating with a median or mean rating etc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>(3) In aggregate bookings find columns that have null values. Fill these null values with whatever you think is the appropriate subtitute (possible ways is to use mean or median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>30</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity\n",
       "0        16559      1-May-22           RT1                   25      30.0\n",
       "1        19562      1-May-22           RT1                   28      30.0\n",
       "2        19563      1-May-22           RT1                   23      30.0\n",
       "3        17558      1-May-22           RT1                   30      19.0\n",
       "4        16558      1-May-22           RT1                   18      19.0"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing first 5 rows from the fact_aggregated_bookings.csv file\n",
    "\n",
    "df_fact_agg_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_id            0\n",
       "check_in_date          0\n",
       "room_category          0\n",
       "successful_bookings    0\n",
       "capacity               2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for NULL values\n",
    "\n",
    "df_fact_agg_bookings.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9198.000000\n",
       "mean       25.280496\n",
       "std        11.442080\n",
       "min         3.000000\n",
       "25%        18.000000\n",
       "50%        25.000000\n",
       "75%        34.000000\n",
       "max        50.000000\n",
       "Name: capacity, dtype: float64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a summary of the numeric column 'capacity'\n",
    "\n",
    "df_fact_agg_bookings.capacity.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.0"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the median\n",
    "\n",
    "df_fact_agg_bookings.capacity.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
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       "      <th>8</th>\n",
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       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "    property_id check_in_date room_category  successful_bookings  capacity\n",
       "8         17561      1-May-22           RT1                   22       NaN\n",
       "14        17562      1-May-22           RT1                   12       NaN"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the entries containing NULL values\n",
    "\n",
    "df_fact_agg_bookings[df_fact_agg_bookings.capacity.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing NULL values with median\n",
    "\n",
    "df_fact_agg_bookings.capacity.fillna(df_fact_agg_bookings.capacity.median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "    property_id check_in_date room_category  successful_bookings  capacity\n",
       "8         17561      1-May-22           RT1                   22      25.0\n",
       "14        17562      1-May-22           RT1                   12      25.0"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the entries with replaced values\n",
    "\n",
    "df_fact_agg_bookings.loc[[8,14]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>(4) In aggregate bookings find out records that have successful_bookings value greater than capacity. Filter those records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8522</th>\n",
       "      <td>19559</td>\n",
       "      <td>25-Jul-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>35</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9194</th>\n",
       "      <td>18563</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>20</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity\n",
       "3           17558      1-May-22           RT1                   30      19.0\n",
       "12          16563      1-May-22           RT1                  100      41.0\n",
       "4136        19558     11-Jun-22           RT2                   50      39.0\n",
       "6209        19560      2-Jul-22           RT1                  123      26.0\n",
       "8522        19559     25-Jul-22           RT1                   35      24.0\n",
       "9194        18563     31-Jul-22           RT4                   20      18.0"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for entries that have successful bookings more than capacity\n",
    "\n",
    "df_fact_agg_bookings[df_fact_agg_bookings.successful_bookings > df_fact_agg_bookings.capacity]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9200, 5)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the size of the table (rows & columns)\n",
    "\n",
    "df_fact_agg_bookings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17560</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9195</th>\n",
       "      <td>16563</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>13</td>\n",
       "      <td>18.0</td>\n",
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       "    <tr>\n",
       "      <th>9196</th>\n",
       "      <td>16559</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>13</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9197</th>\n",
       "      <td>17558</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9198</th>\n",
       "      <td>19563</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9199</th>\n",
       "      <td>17561</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9194 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity\n",
       "0           16559      1-May-22           RT1                   25      30.0\n",
       "1           19562      1-May-22           RT1                   28      30.0\n",
       "2           19563      1-May-22           RT1                   23      30.0\n",
       "4           16558      1-May-22           RT1                   18      19.0\n",
       "5           17560      1-May-22           RT1                   28      40.0\n",
       "...           ...           ...           ...                  ...       ...\n",
       "9195        16563     31-Jul-22           RT4                   13      18.0\n",
       "9196        16559     31-Jul-22           RT4                   13      18.0\n",
       "9197        17558     31-Jul-22           RT4                    3       6.0\n",
       "9198        19563     31-Jul-22           RT4                    3       6.0\n",
       "9199        17561     31-Jul-22           RT4                    3       4.0\n",
       "\n",
       "[9194 rows x 5 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing entries where successful bookings were more than capacity\n",
    "\n",
    "df_fact_agg_bookings = df_fact_agg_bookings[df_fact_agg_bookings.successful_bookings <= df_fact_agg_bookings.capacity]\n",
    "df_fact_agg_bookings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9194, 5)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the size of the table (rows & columns)\n",
    "\n",
    "df_fact_agg_bookings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>DATA TRANSFORMATION</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Create occupancy percentage column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1-May-22</td>\n",
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      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity\n",
       "0        16559      1-May-22           RT1                   25      30.0\n",
       "1        19562      1-May-22           RT1                   28      30.0\n",
       "2        19563      1-May-22           RT1                   23      30.0\n",
       "4        16558      1-May-22           RT1                   18      19.0\n",
       "5        17560      1-May-22           RT1                   28      40.0"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing first 5 rows from the fact_aggregated_bookings.csv file\n",
    "\n",
    "df_fact_agg_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.750000</td>\n",
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      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0           16559      1-May-22           RT1                   25      30.0   \n",
       "1           19562      1-May-22           RT1                   28      30.0   \n",
       "2           19563      1-May-22           RT1                   23      30.0   \n",
       "4           16558      1-May-22           RT1                   18      19.0   \n",
       "5           17560      1-May-22           RT1                   28      40.0   \n",
       "...           ...           ...           ...                  ...       ...   \n",
       "9195        16563     31-Jul-22           RT4                   13      18.0   \n",
       "9196        16559     31-Jul-22           RT4                   13      18.0   \n",
       "9197        17558     31-Jul-22           RT4                    3       6.0   \n",
       "9198        19563     31-Jul-22           RT4                    3       6.0   \n",
       "9199        17561     31-Jul-22           RT4                    3       4.0   \n",
       "\n",
       "       occ_pct  \n",
       "0     0.833333  \n",
       "1     0.933333  \n",
       "2     0.766667  \n",
       "4     0.947368  \n",
       "5     0.700000  \n",
       "...        ...  \n",
       "9195  0.722222  \n",
       "9196  0.722222  \n",
       "9197  0.500000  \n",
       "9198  0.500000  \n",
       "9199  0.750000  \n",
       "\n",
       "[9194 rows x 6 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating the 'occ_pct' column\n",
    "\n",
    "df_fact_agg_bookings['occ_pct'] = (df_fact_agg_bookings['successful_bookings'] / df_fact_agg_bookings['capacity'])\n",
    "df_fact_agg_bookings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert it to a percentage value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fact_agg_bookings['occ_pct'] = df_fact_agg_bookings['occ_pct'].apply(lambda x: round(x*100, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559      1-May-22           RT1                   25      30.0   \n",
       "1        19562      1-May-22           RT1                   28      30.0   \n",
       "2        19563      1-May-22           RT1                   23      30.0   \n",
       "4        16558      1-May-22           RT1                   18      19.0   \n",
       "5        17560      1-May-22           RT1                   28      40.0   \n",
       "\n",
       "   occ_pct  \n",
       "0    83.33  \n",
       "1    93.33  \n",
       "2    76.67  \n",
       "4    94.74  \n",
       "5    70.00  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing first 5 rows from the fact_aggregated_bookings.csv file\n",
    "\n",
    "df_fact_agg_bookings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>INSIGHTS GENERATION</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>1. What is an average occupancy rate in each of the room categories?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_category\n",
       "RT1    57.89\n",
       "RT2    58.01\n",
       "RT3    58.03\n",
       "RT4    59.28\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fact_agg_bookings.groupby('room_category')['occ_pct'].mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>room_id</th>\n",
       "      <th>room_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RT2</td>\n",
       "      <td>Elite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RT3</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RT4</td>\n",
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      ],
      "text/plain": [
       "  room_id    room_class\n",
       "0     RT1      Standard\n",
       "1     RT2         Elite\n",
       "2     RT3       Premium\n",
       "3     RT4  Presidential"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rooms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print room categories such as Standard, Premium, Elite etc along with average occupancy percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0           16559      1-May-22           RT1                   25      30.0   \n",
       "1           19562      1-May-22           RT1                   28      30.0   \n",
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       "4           17560      1-May-22           RT1                   28      40.0   \n",
       "...           ...           ...           ...                  ...       ...   \n",
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       "9190        16559     31-Jul-22           RT4                   13      18.0   \n",
       "9191        17558     31-Jul-22           RT4                    3       6.0   \n",
       "9192        19563     31-Jul-22           RT4                    3       6.0   \n",
       "9193        17561     31-Jul-22           RT4                    3       4.0   \n",
       "\n",
       "      occ_pct room_id    room_class  \n",
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       "...       ...     ...           ...  \n",
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       "9191    50.00     RT4  Presidential  \n",
       "9192    50.00     RT4  Presidential  \n",
       "9193    75.00     RT4  Presidential  \n",
       "\n",
       "[9194 rows x 8 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
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   "source": [
    "df = pd.merge(df_fact_agg_bookings, df_rooms, left_on = 'room_category', right_on = 'room_id')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
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    {
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      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559      1-May-22           RT1                   25      30.0   \n",
       "1        19562      1-May-22           RT1                   28      30.0   \n",
       "2        19563      1-May-22           RT1                   23      30.0   \n",
       "3        16558      1-May-22           RT1                   18      19.0   \n",
       "4        17560      1-May-22           RT1                   28      40.0   \n",
       "\n",
       "   occ_pct room_class  \n",
       "0    83.33   Standard  \n",
       "1    93.33   Standard  \n",
       "2    76.67   Standard  \n",
       "3    94.74   Standard  \n",
       "4    70.00   Standard  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('room_id',axis=1,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_class\n",
       "Elite           58.01\n",
       "Premium         58.03\n",
       "Presidential    59.28\n",
       "Standard        57.89\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('room_class')['occ_pct'].mean().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>2. What is the average occupancy rate per city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   property_id  property_name  category    city\n",
       "0        16558   Atliq Grands    Luxury   Delhi\n",
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     },
     "execution_count": 149,
     "metadata": {},
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   ],
   "source": [
    "df_hotels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>9189</th>\n",
       "      <td>16563</td>\n",
       "      <td>27-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>10</td>\n",
       "      <td>18.0</td>\n",
       "      <td>55.56</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9190</th>\n",
       "      <td>16563</td>\n",
       "      <td>28-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9191</th>\n",
       "      <td>16563</td>\n",
       "      <td>29-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9192</th>\n",
       "      <td>16563</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>11</td>\n",
       "      <td>18.0</td>\n",
       "      <td>61.11</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9193</th>\n",
       "      <td>16563</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>13</td>\n",
       "      <td>18.0</td>\n",
       "      <td>72.22</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9194 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0           16559      1-May-22           RT1                   25      30.0   \n",
       "1           16559      2-May-22           RT1                   20      30.0   \n",
       "2           16559      3-May-22           RT1                   17      30.0   \n",
       "3           16559      4-May-22           RT1                   21      30.0   \n",
       "4           16559      5-May-22           RT1                   16      30.0   \n",
       "...           ...           ...           ...                  ...       ...   \n",
       "9189        16563     27-Jul-22           RT4                   10      18.0   \n",
       "9190        16563     28-Jul-22           RT4                    9      18.0   \n",
       "9191        16563     29-Jul-22           RT4                    9      18.0   \n",
       "9192        16563     30-Jul-22           RT4                   11      18.0   \n",
       "9193        16563     31-Jul-22           RT4                   13      18.0   \n",
       "\n",
       "      occ_pct    room_class  property_name  category    city  \n",
       "0       83.33      Standard  Atliq Exotica    Luxury  Mumbai  \n",
       "1       66.67      Standard  Atliq Exotica    Luxury  Mumbai  \n",
       "2       56.67      Standard  Atliq Exotica    Luxury  Mumbai  \n",
       "3       70.00      Standard  Atliq Exotica    Luxury  Mumbai  \n",
       "4       53.33      Standard  Atliq Exotica    Luxury  Mumbai  \n",
       "...       ...           ...            ...       ...     ...  \n",
       "9189    55.56  Presidential   Atliq Palace  Business   Delhi  \n",
       "9190    50.00  Presidential   Atliq Palace  Business   Delhi  \n",
       "9191    50.00  Presidential   Atliq Palace  Business   Delhi  \n",
       "9192    61.11  Presidential   Atliq Palace  Business   Delhi  \n",
       "9193    72.22  Presidential   Atliq Palace  Business   Delhi  \n",
       "\n",
       "[9194 rows x 10 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df, df_hotels, on = 'property_id')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bangalore    56.33\n",
       "Mumbai       57.91\n",
       "Hyderabad    58.12\n",
       "Delhi        61.51\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('city')['occ_pct'].mean().round(2).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph = df.groupby('city')['occ_pct'].mean().sort_values()\n",
    "\n",
    "# Calculate the number of bars\n",
    "num_bars = len(graph)\n",
    "\n",
    "# Dynamic figure size\n",
    "fig_width = max(8, num_bars * 0.5)  # Ensures a minimum width of 8\n",
    "fig_height = 8  # Fixed height or adjust based on preferences\n",
    "\n",
    "# Set figure size\n",
    "plt.figure(figsize=(fig_width, fig_height))\n",
    "\n",
    "graph.plot(kind = 'bar', facecolor = '#ff1f2b')\n",
    "\n",
    "plt.xlabel('City')\n",
    "plt.ylabel('Average Occupancy Rate (%)')\n",
    "plt.title('Average Occupancy Rate by City')\n",
    "\n",
    "# Adding labels on top of the bars\n",
    "for index, value in enumerate(graph):\n",
    "    plt.text(index, value, str(round(value, 2)), ha = 'center', va = 'bottom', fontsize = 14)\n",
    "\n",
    "# Adjust layout to make sure everything fits\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the figure with a transparent background\n",
    "plt.savefig('average occupancy rate per city.png', transparent = True, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Insight:\n",
    "\n",
    "Delhi has the highest average occupancy rate among cities (60.40%), while Bangalore has the lowest (55.29%)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>3. When was the occupancy better? Weekday or Weekend?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "        date  mmm yy week no  day_type\n",
       "0  01-May-22  May 22    W 19   weekend\n",
       "1  02-May-22  May 22    W 19  weekeday\n",
       "2  03-May-22  May 22    W 19  weekeday\n",
       "3  04-May-22  May 22    W 19  weekeday\n",
       "4  05-May-22  May 22    W 19  weekeday"
      ]
     },
     "execution_count": 153,
     "metadata": {},
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    }
   ],
   "source": [
    "df_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>room_category</th>\n",
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       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
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       "      <td>16559</td>\n",
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       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559      1-May-22           RT1                   25      30.0   \n",
       "1        16559      2-May-22           RT1                   20      30.0   \n",
       "2        16559      3-May-22           RT1                   17      30.0   \n",
       "3        16559      4-May-22           RT1                   21      30.0   \n",
       "4        16559      5-May-22           RT1                   16      30.0   \n",
       "\n",
       "   occ_pct room_class  property_name category    city  \n",
       "0    83.33   Standard  Atliq Exotica   Luxury  Mumbai  \n",
       "1    66.67   Standard  Atliq Exotica   Luxury  Mumbai  \n",
       "2    56.67   Standard  Atliq Exotica   Luxury  Mumbai  \n",
       "3    70.00   Standard  Atliq Exotica   Luxury  Mumbai  \n",
       "4    53.33   Standard  Atliq Exotica   Luxury  Mumbai  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Standard</td>\n",
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       "      <td>May 22</td>\n",
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       "      <td>13</td>\n",
       "      <td>18.0</td>\n",
       "      <td>72.22</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19562</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559     10-May-22           RT1                   18      30.0   \n",
       "1        16559     10-May-22           RT2                   25      41.0   \n",
       "2        16559     10-May-22           RT3                   20      32.0   \n",
       "3        16559     10-May-22           RT4                   13      18.0   \n",
       "4        19562     10-May-22           RT1                   18      30.0   \n",
       "\n",
       "   occ_pct    room_class  property_name category       city       date  \\\n",
       "0    60.00      Standard  Atliq Exotica   Luxury     Mumbai  10-May-22   \n",
       "1    60.98         Elite  Atliq Exotica   Luxury     Mumbai  10-May-22   \n",
       "2    62.50       Premium  Atliq Exotica   Luxury     Mumbai  10-May-22   \n",
       "3    72.22  Presidential  Atliq Exotica   Luxury     Mumbai  10-May-22   \n",
       "4    60.00      Standard      Atliq Bay   Luxury  Bangalore  10-May-22   \n",
       "\n",
       "   mmm yy week no  day_type  \n",
       "0  May 22    W 20  weekeday  \n",
       "1  May 22    W 20  weekeday  \n",
       "2  May 22    W 20  weekeday  \n",
       "3  May 22    W 20  weekeday  \n",
       "4  May 22    W 20  weekeday  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df, df_date, right_on = 'date', left_on = 'check_in_date')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day_type\n",
       "weekeday    50.88\n",
       "weekend     72.34\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('day_type')['occ_pct'].mean().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Insight:\n",
    "\n",
    "Weekends show significantly higher occupancy rates compared to weekdays, indicating a potential opportunity for targeted marketing or pricing adjustments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>4: In the month of June, what is the occupancy for different cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['May 22', 'Jun 22', 'Jul 22'], dtype=object)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mmm yy'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>20</td>\n",
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       "      <td>62.50</td>\n",
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       "      <td>Luxury</td>\n",
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       "      <td>10-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
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       "      <th>2203</th>\n",
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       "      <td>10-Jun-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>11</td>\n",
       "      <td>18.0</td>\n",
       "      <td>61.11</td>\n",
       "      <td>Presidential</td>\n",
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       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 24</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2204</th>\n",
       "      <td>19562</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>19</td>\n",
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       "      <td>63.33</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 24</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "2200        16559     10-Jun-22           RT1                   20      30.0   \n",
       "2201        16559     10-Jun-22           RT2                   26      41.0   \n",
       "2202        16559     10-Jun-22           RT3                   20      32.0   \n",
       "2203        16559     10-Jun-22           RT4                   11      18.0   \n",
       "2204        19562     10-Jun-22           RT1                   19      30.0   \n",
       "\n",
       "      occ_pct    room_class  property_name category       city       date  \\\n",
       "2200    66.67      Standard  Atliq Exotica   Luxury     Mumbai  10-Jun-22   \n",
       "2201    63.41         Elite  Atliq Exotica   Luxury     Mumbai  10-Jun-22   \n",
       "2202    62.50       Premium  Atliq Exotica   Luxury     Mumbai  10-Jun-22   \n",
       "2203    61.11  Presidential  Atliq Exotica   Luxury     Mumbai  10-Jun-22   \n",
       "2204    63.33      Standard      Atliq Bay   Luxury  Bangalore  10-Jun-22   \n",
       "\n",
       "      mmm yy week no  day_type  \n",
       "2200  Jun 22    W 24  weekeday  \n",
       "2201  Jun 22    W 24  weekeday  \n",
       "2202  Jun 22    W 24  weekeday  \n",
       "2203  Jun 22    W 24  weekeday  \n",
       "2204  Jun 22    W 24  weekeday  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_june = df[df['mmm yy'] == 'Jun 22']\n",
    "df_june.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Delhi        62.47\n",
       "Hyderabad    58.46\n",
       "Mumbai       58.38\n",
       "Bangalore    56.44\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_june.groupby('city')['occ_pct'].mean().round(2).sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph = df_june.groupby('city')['occ_pct'].mean().round(2).sort_values(ascending = False)\n",
    "\n",
    "# Calculate the number of bars\n",
    "num_bars = len(graph)\n",
    "\n",
    "# Dynamic figure size\n",
    "fig_width = max(8, num_bars * 0.5)  # Ensures a minimum width of 8\n",
    "fig_height = 8  # Fixed height or adjust based on preferences\n",
    "\n",
    "# Set figure size\n",
    "plt.figure(figsize=(fig_width, fig_height))\n",
    "\n",
    "graph.plot(kind = 'bar', facecolor = '#ff1f2b')\n",
    "\n",
    "plt.xlabel('City')\n",
    "plt.ylabel('Average Occupancy Rate (%)')\n",
    "plt.title('Average Occupancy Rate in June by City')\n",
    "\n",
    "# Adding labels on top of the bars\n",
    "for index, value in enumerate(graph):\n",
    "    plt.text(index, value, str(value), ha = 'center', va = 'bottom', fontsize = 14)\n",
    "\n",
    "# Adjust layout to make sure everything fits\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the figure with a transparent background\n",
    "plt.savefig('average occupancy rate in june by city.png', transparent = True, bbox_inches = 'tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Insight:\n",
    "\n",
    "In the month of June, Delhi had the highest occupancy rate (62.47%), followed closely by Hyderabad (58.46%)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>5: We got new data for the month of august. Append that to existing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "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>property_id</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "      <th>room_category</th>\n",
       "      <th>room_class</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
       "      <th>day_type</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>76.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19558</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>75.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19560</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>20</td>\n",
       "      <td>26</td>\n",
       "      <td>76.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id  property_name  category       city room_category room_class  \\\n",
       "0        16559  Atliq Exotica    Luxury     Mumbai           RT1   Standard   \n",
       "1        19562      Atliq Bay    Luxury  Bangalore           RT1   Standard   \n",
       "2        19563   Atliq Palace  Business  Bangalore           RT1   Standard   \n",
       "3        19558   Atliq Grands    Luxury  Bangalore           RT1   Standard   \n",
       "4        19560     Atliq City  Business  Bangalore           RT1   Standard   \n",
       "\n",
       "  check_in_date  mmm yy week no  day_type  successful_bookings  capacity  \\\n",
       "0     01-Aug-22  Aug-22    W 32  weekeday                   30        30   \n",
       "1     01-Aug-22  Aug-22    W 32  weekeday                   21        30   \n",
       "2     01-Aug-22  Aug-22    W 32  weekeday                   23        30   \n",
       "3     01-Aug-22  Aug-22    W 32  weekeday                   30        40   \n",
       "4     01-Aug-22  Aug-22    W 32  weekeday                   20        26   \n",
       "\n",
       "     occ%  \n",
       "0  100.00  \n",
       "1   70.00  \n",
       "2   76.67  \n",
       "3   75.00  \n",
       "4   76.92  "
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_august=pd.read_csv('datasets/new_data_august.csv')\n",
    "df_august.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['property_id', 'property_name', 'category', 'city', 'room_category',\n",
       "       'room_class', 'check_in_date', 'mmm yy', 'week no', 'day_type',\n",
       "       'successful_bookings', 'capacity', 'occ%'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_august.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['property_id', 'check_in_date', 'room_category', 'successful_bookings',\n",
       "       'capacity', 'occ_pct', 'room_class', 'property_name', 'category',\n",
       "       'city', 'date', 'mmm yy', 'week no', 'day_type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 13)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_august.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6497, 14)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "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>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "      <th>room_class</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
       "      <th>day_type</th>\n",
       "      <th>occ%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6499</th>\n",
       "      <td>19563</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>76.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6500</th>\n",
       "      <td>19558</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>30</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>75.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6501</th>\n",
       "      <td>19560</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>20</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>76.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6502</th>\n",
       "      <td>17561</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>69.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6503</th>\n",
       "      <td>17564</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>10</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>62.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "6499        19563     01-Aug-22           RT1                   23      30.0   \n",
       "6500        19558     01-Aug-22           RT1                   30      40.0   \n",
       "6501        19560     01-Aug-22           RT1                   20      26.0   \n",
       "6502        17561     01-Aug-22           RT1                   18      26.0   \n",
       "6503        17564     01-Aug-22           RT1                   10      16.0   \n",
       "\n",
       "      occ_pct room_class  property_name  category       city date  mmm yy  \\\n",
       "6499      NaN   Standard   Atliq Palace  Business  Bangalore  NaN  Aug-22   \n",
       "6500      NaN   Standard   Atliq Grands    Luxury  Bangalore  NaN  Aug-22   \n",
       "6501      NaN   Standard     Atliq City  Business  Bangalore  NaN  Aug-22   \n",
       "6502      NaN   Standard      Atliq Blu    Luxury     Mumbai  NaN  Aug-22   \n",
       "6503      NaN   Standard  Atliq Seasons  Business     Mumbai  NaN  Aug-22   \n",
       "\n",
       "     week no  day_type   occ%  \n",
       "6499    W 32  weekeday  76.67  \n",
       "6500    W 32  weekeday  75.00  \n",
       "6501    W 32  weekeday  76.92  \n",
       "6502    W 32  weekeday  69.23  \n",
       "6503    W 32  weekeday  62.50  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_df=pd.concat([df, df_august], ignore_index = True, axis = 0)\n",
    "latest_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6504, 15)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>6. What is the revenue realized per city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "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>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May012216558RT12</td>\n",
       "      <td>16558</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>May012216558RT16</td>\n",
       "      <td>16558</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>May012216558RT17</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>May012216558RT18</td>\n",
       "      <td>16558</td>\n",
       "      <td>26-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "1  May012216558RT12        16558     30-04-22      1/5/2022      2/5/2022   \n",
       "4  May012216558RT15        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "5  May012216558RT16        16558     1/5/2022      1/5/2022      3/5/2022   \n",
       "6  May012216558RT17        16558     28-04-22      1/5/2022      6/5/2022   \n",
       "7  May012216558RT18        16558     26-04-22      1/5/2022      3/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "1        2.0           RT1           others            NaN      Cancelled   \n",
       "4        4.0           RT1    direct online            5.0    Checked Out   \n",
       "5        2.0           RT1           others            4.0    Checked Out   \n",
       "6        2.0           RT1           others            NaN      Cancelled   \n",
       "7        2.0           RT1          logtrip            NaN        No Show   \n",
       "\n",
       "   revenue_generated  revenue_realized  \n",
       "1               9100              3640  \n",
       "4              10920             10920  \n",
       "5               9100              9100  \n",
       "6               9100              3640  \n",
       "7               9100              9100  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16558</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16559</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16560</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16561</td>\n",
       "      <td>Atliq Blu</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16562</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id  property_name  category    city\n",
       "0        16558   Atliq Grands    Luxury   Delhi\n",
       "1        16559  Atliq Exotica    Luxury  Mumbai\n",
       "2        16560     Atliq City  Business   Delhi\n",
       "3        16561      Atliq Blu    Luxury   Delhi\n",
       "4        16562      Atliq Bay    Luxury   Delhi"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
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       "      <th>revenue_generated</th>\n",
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       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>May012216558RT12</td>\n",
       "      <td>16558</td>\n",
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       "      <td>Delhi</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May012216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
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       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
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       "      <td>May012216558RT16</td>\n",
       "      <td>16558</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>1/5/2022</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
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       "      <td>Checked Out</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
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       "      <th>3</th>\n",
       "      <td>May012216558RT17</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT18</td>\n",
       "      <td>16558</td>\n",
       "      <td>26-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "0  May012216558RT12        16558     30-04-22      1/5/2022      2/5/2022   \n",
       "1  May012216558RT15        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "2  May012216558RT16        16558     1/5/2022      1/5/2022      3/5/2022   \n",
       "3  May012216558RT17        16558     28-04-22      1/5/2022      6/5/2022   \n",
       "4  May012216558RT18        16558     26-04-22      1/5/2022      3/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "0        2.0           RT1           others            NaN      Cancelled   \n",
       "1        4.0           RT1    direct online            5.0    Checked Out   \n",
       "2        2.0           RT1           others            4.0    Checked Out   \n",
       "3        2.0           RT1           others            NaN      Cancelled   \n",
       "4        2.0           RT1          logtrip            NaN        No Show   \n",
       "\n",
       "   revenue_generated  revenue_realized property_name category   city  \n",
       "0               9100              3640  Atliq Grands   Luxury  Delhi  \n",
       "1              10920             10920  Atliq Grands   Luxury  Delhi  \n",
       "2               9100              9100  Atliq Grands   Luxury  Delhi  \n",
       "3               9100              3640  Atliq Grands   Luxury  Delhi  \n",
       "4               9100              9100  Atliq Grands   Luxury  Delhi  "
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all = pd.merge(df_bookings, df_hotels, on = 'property_id')\n",
    "df_bookings_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bangalore    420383550\n",
       "Delhi        294404488\n",
       "Hyderabad    325179310\n",
       "Mumbai       668569251\n",
       "Name: revenue_realized, dtype: int64"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all.groupby('city')['revenue_realized'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Insight:\n",
    "\n",
    "Mumbai generates the highest revenue among the cities, totaling 668,569,251 units."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>7. Print month by month revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
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       "      <th>revenue_realized</th>\n",
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       "      <th>category</th>\n",
       "      <th>city</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>May012216558RT12</td>\n",
       "      <td>16558</td>\n",
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       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May012216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>May012216558RT16</td>\n",
       "      <td>16558</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>May012216558RT17</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT18</td>\n",
       "      <td>16558</td>\n",
       "      <td>26-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "0  May012216558RT12        16558     30-04-22      1/5/2022      2/5/2022   \n",
       "1  May012216558RT15        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "2  May012216558RT16        16558     1/5/2022      1/5/2022      3/5/2022   \n",
       "3  May012216558RT17        16558     28-04-22      1/5/2022      6/5/2022   \n",
       "4  May012216558RT18        16558     26-04-22      1/5/2022      3/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "0        2.0           RT1           others            NaN      Cancelled   \n",
       "1        4.0           RT1    direct online            5.0    Checked Out   \n",
       "2        2.0           RT1           others            4.0    Checked Out   \n",
       "3        2.0           RT1           others            NaN      Cancelled   \n",
       "4        2.0           RT1          logtrip            NaN        No Show   \n",
       "\n",
       "   revenue_generated  revenue_realized property_name category   city  \n",
       "0               9100              3640  Atliq Grands   Luxury  Delhi  \n",
       "1              10920             10920  Atliq Grands   Luxury  Delhi  \n",
       "2               9100              9100  Atliq Grands   Luxury  Delhi  \n",
       "3               9100              3640  Atliq Grands   Luxury  Delhi  \n",
       "4               9100              9100  Atliq Grands   Luxury  Delhi  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 134573 entries, 0 to 134572\n",
      "Data columns (total 15 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   booking_id         134573 non-null  object \n",
      " 1   property_id        134573 non-null  int64  \n",
      " 2   booking_date       134573 non-null  object \n",
      " 3   check_in_date      134573 non-null  object \n",
      " 4   checkout_date      134573 non-null  object \n",
      " 5   no_guests          134573 non-null  float64\n",
      " 6   room_category      134573 non-null  object \n",
      " 7   booking_platform   134573 non-null  object \n",
      " 8   ratings_given      56676 non-null   float64\n",
      " 9   booking_status     134573 non-null  object \n",
      " 10  revenue_generated  134573 non-null  int64  \n",
      " 11  revenue_realized   134573 non-null  int64  \n",
      " 12  property_name      134573 non-null  object \n",
      " 13  category           134573 non-null  object \n",
      " 14  city               134573 non-null  object \n",
      "dtypes: float64(2), int64(3), object(10)\n",
      "memory usage: 15.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df_bookings_all.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 92 entries, 0 to 91\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   date      92 non-null     object\n",
      " 1   mmm yy    92 non-null     object\n",
      " 2   week no   92 non-null     object\n",
      " 3   day_type  92 non-null     object\n",
      "dtypes: object(4)\n",
      "memory usage: 3.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_date.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Pravesh\\AppData\\Local\\Temp\\ipykernel_27064\\1849468159.py:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  df_date[\"date\"] = pd.to_datetime(df_date[\"date\"])\n"
     ]
    },
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       "        date  mmm yy week no  day_type\n",
       "0 2022-05-01  May 22    W 19   weekend\n",
       "1 2022-05-02  May 22    W 19  weekeday\n",
       "2 2022-05-03  May 22    W 19  weekeday\n",
       "3 2022-05-04  May 22    W 19  weekeday\n",
       "4 2022-05-05  May 22    W 19  weekeday"
      ]
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     "execution_count": 175,
     "metadata": {},
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   ],
   "source": [
    "df_date['date'] = pd.to_datetime(df_date['date'])\n",
    "df_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 92 entries, 0 to 91\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype         \n",
      "---  ------    --------------  -----         \n",
      " 0   date      92 non-null     datetime64[ns]\n",
      " 1   mmm yy    92 non-null     object        \n",
      " 2   week no   92 non-null     object        \n",
      " 3   day_type  92 non-null     object        \n",
      "dtypes: datetime64[ns](1), object(3)\n",
      "memory usage: 3.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_date.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "0  May012216558RT12        16558     30-04-22      1/5/2022      2/5/2022   \n",
       "1  May012216558RT15        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "2  May012216558RT16        16558     1/5/2022      1/5/2022      3/5/2022   \n",
       "3  May012216558RT17        16558     28-04-22      1/5/2022      6/5/2022   \n",
       "4  May012216558RT18        16558     26-04-22      1/5/2022      3/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "0        2.0           RT1           others            NaN      Cancelled   \n",
       "1        4.0           RT1    direct online            5.0    Checked Out   \n",
       "2        2.0           RT1           others            4.0    Checked Out   \n",
       "3        2.0           RT1           others            NaN      Cancelled   \n",
       "4        2.0           RT1          logtrip            NaN        No Show   \n",
       "\n",
       "   revenue_generated  revenue_realized property_name category   city  \n",
       "0               9100              3640  Atliq Grands   Luxury  Delhi  \n",
       "1              10920             10920  Atliq Grands   Luxury  Delhi  \n",
       "2               9100              9100  Atliq Grands   Luxury  Delhi  \n",
       "3               9100              3640  Atliq Grands   Luxury  Delhi  \n",
       "4               9100              9100  Atliq Grands   Luxury  Delhi  "
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_bookings_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\n",
       "0        May012216558RT12        16558     30-04-22    2022-01-05   \n",
       "1        May012216558RT15        16558     27-04-22    2022-01-05   \n",
       "2        May012216558RT16        16558     1/5/2022    2022-01-05   \n",
       "3        May012216558RT17        16558     28-04-22    2022-01-05   \n",
       "4        May012216558RT18        16558     26-04-22    2022-01-05   \n",
       "...                   ...          ...          ...           ...   \n",
       "134568   Jul312217564RT45        17564     30-07-22    2022-07-31   \n",
       "134569   Jul312217564RT46        17564     29-07-22    2022-07-31   \n",
       "134570   Jul312217564RT48        17564     30-07-22    2022-07-31   \n",
       "134571   Jul312217564RT49        17564     29-07-22    2022-07-31   \n",
       "134572  Jul312217564RT410        17564     31-07-22    2022-07-31   \n",
       "\n",
       "       checkout_date  no_guests room_category booking_platform  ratings_given  \\\n",
       "0           2/5/2022        2.0           RT1           others            NaN   \n",
       "1           2/5/2022        4.0           RT1    direct online            5.0   \n",
       "2           3/5/2022        2.0           RT1           others            4.0   \n",
       "3           6/5/2022        2.0           RT1           others            NaN   \n",
       "4           3/5/2022        2.0           RT1          logtrip            NaN   \n",
       "...              ...        ...           ...              ...            ...   \n",
       "134568      1/8/2022        2.0           RT4           others            2.0   \n",
       "134569      3/8/2022        1.0           RT4     makeyourtrip            2.0   \n",
       "134570      2/8/2022        1.0           RT4         tripster            NaN   \n",
       "134571      1/8/2022        2.0           RT4          logtrip            2.0   \n",
       "134572      1/8/2022        2.0           RT4     makeyourtrip            NaN   \n",
       "\n",
       "       booking_status  revenue_generated  revenue_realized  property_name  \\\n",
       "0           Cancelled               9100              3640   Atliq Grands   \n",
       "1         Checked Out              10920             10920   Atliq Grands   \n",
       "2         Checked Out               9100              9100   Atliq Grands   \n",
       "3           Cancelled               9100              3640   Atliq Grands   \n",
       "4             No Show               9100              9100   Atliq Grands   \n",
       "...               ...                ...               ...            ...   \n",
       "134568    Checked Out              32300             32300  Atliq Seasons   \n",
       "134569    Checked Out              32300             32300  Atliq Seasons   \n",
       "134570      Cancelled              32300             12920  Atliq Seasons   \n",
       "134571    Checked Out              32300             32300  Atliq Seasons   \n",
       "134572      Cancelled              32300             12920  Atliq Seasons   \n",
       "\n",
       "        category    city  \n",
       "0         Luxury   Delhi  \n",
       "1         Luxury   Delhi  \n",
       "2         Luxury   Delhi  \n",
       "3         Luxury   Delhi  \n",
       "4         Luxury   Delhi  \n",
       "...          ...     ...  \n",
       "134568  Business  Mumbai  \n",
       "134569  Business  Mumbai  \n",
       "134570  Business  Mumbai  \n",
       "134571  Business  Mumbai  \n",
       "134572  Business  Mumbai  \n",
       "\n",
       "[134573 rows x 15 columns]"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all['check_in_date'] = pd.to_datetime(df_bookings_all['check_in_date'],format = 'mixed')\n",
    "df_bookings_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 134573 entries, 0 to 134572\n",
      "Data columns (total 15 columns):\n",
      " #   Column             Non-Null Count   Dtype         \n",
      "---  ------             --------------   -----         \n",
      " 0   booking_id         134573 non-null  object        \n",
      " 1   property_id        134573 non-null  int64         \n",
      " 2   booking_date       134573 non-null  object        \n",
      " 3   check_in_date      134573 non-null  datetime64[ns]\n",
      " 4   checkout_date      134573 non-null  object        \n",
      " 5   no_guests          134573 non-null  float64       \n",
      " 6   room_category      134573 non-null  object        \n",
      " 7   booking_platform   134573 non-null  object        \n",
      " 8   ratings_given      56676 non-null   float64       \n",
      " 9   booking_status     134573 non-null  object        \n",
      " 10  revenue_generated  134573 non-null  int64         \n",
      " 11  revenue_realized   134573 non-null  int64         \n",
      " 12  property_name      134573 non-null  object        \n",
      " 13  category           134573 non-null  object        \n",
      " 14  city               134573 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(3), object(9)\n",
      "memory usage: 15.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df_bookings_all.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
       "      <th>day_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>May052216558RT11</td>\n",
       "      <td>16558</td>\n",
       "      <td>15-04-22</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>7/5/2022</td>\n",
       "      <td>3.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>tripster</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10010</td>\n",
       "      <td>10010</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May052216558RT12</td>\n",
       "      <td>16558</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>7/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>May052216558RT13</td>\n",
       "      <td>16558</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>3.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct offline</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10010</td>\n",
       "      <td>10010</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>May052216558RT14</td>\n",
       "      <td>16558</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>tripster</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May052216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>10/5/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "0  May052216558RT11        16558     15-04-22    2022-05-05      7/5/2022   \n",
       "1  May052216558RT12        16558     30-04-22    2022-05-05      7/5/2022   \n",
       "2  May052216558RT13        16558     1/5/2022    2022-05-05      6/5/2022   \n",
       "3  May052216558RT14        16558     3/5/2022    2022-05-05      6/5/2022   \n",
       "4  May052216558RT15        16558     30-04-22    2022-05-05     10/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "0        3.0           RT1         tripster            5.0    Checked Out   \n",
       "1        2.0           RT1           others            NaN      Cancelled   \n",
       "2        3.0           RT1   direct offline            5.0    Checked Out   \n",
       "3        2.0           RT1         tripster            3.0    Checked Out   \n",
       "4        4.0           RT1           others            4.0    Checked Out   \n",
       "\n",
       "   revenue_generated  revenue_realized property_name category   city  \\\n",
       "0              10010             10010  Atliq Grands   Luxury  Delhi   \n",
       "1               9100              3640  Atliq Grands   Luxury  Delhi   \n",
       "2              10010             10010  Atliq Grands   Luxury  Delhi   \n",
       "3               9100              9100  Atliq Grands   Luxury  Delhi   \n",
       "4              10920             10920  Atliq Grands   Luxury  Delhi   \n",
       "\n",
       "        date  mmm yy week no  day_type  \n",
       "0 2022-05-05  May 22    W 19  weekeday  \n",
       "1 2022-05-05  May 22    W 19  weekeday  \n",
       "2 2022-05-05  May 22    W 19  weekeday  \n",
       "3 2022-05-05  May 22    W 19  weekeday  \n",
       "4 2022-05-05  May 22    W 19  weekeday  "
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all = pd.merge(df_bookings_all, df_date, left_on = 'check_in_date', right_on = 'date')\n",
    "df_bookings_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mmm yy\n",
       "Jul 22    389940912\n",
       "Jun 22    377191229\n",
       "May 22    408375641\n",
       "Name: revenue_realized, dtype: int64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all.groupby('mmm yy')['revenue_realized'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Insight:\n",
    "\n",
    "The hotel's revenue fluctuates month by month, with July 2022 generating 389,940,912 units and May 2022 generating 408,375,641 units."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category\n",
       "Business    451950715\n",
       "Luxury      723557067\n",
       "Name: revenue_realized, dtype: int64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print revenue realized per hotel type\n",
    "df_bookings_all.groupby('category')['revenue_realized'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_name\n",
       "Atliq Seasons     45920757\n",
       "Atliq Grands     145860641\n",
       "Atliq Blu        179203544\n",
       "Atliq Bay        179416721\n",
       "Atliq City       196555383\n",
       "Atliq Palace     209474575\n",
       "Atliq Exotica    219076161\n",
       "Name: revenue_realized, dtype: int64"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print revenue realized per hotel\n",
    "\n",
    "df_bookings_all.groupby('property_name')['revenue_realized'].sum().round(2).sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bangalore    3.40\n",
       "Delhi        3.78\n",
       "Hyderabad    3.66\n",
       "Mumbai       3.64\n",
       "Name: ratings_given, dtype: float64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print average rating per city\n",
    "df_bookings_all.groupby('city')['ratings_given'].mean().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ‎"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1150x1150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print a pie chart of revenue realized per booking platform\n",
    "\n",
    "graph = df_bookings_all.groupby('booking_platform')['revenue_realized'].sum()\n",
    "\n",
    "# Calculate dynamic figure size based on the number of categories\n",
    "num_categories = len(graph)\n",
    "fig_width = 8 + (num_categories * 0.5)  # Increase width based on number of categories\n",
    "fig_height = 8 + (num_categories * 0.5)  # Increase height based on number of categories\n",
    "\n",
    "# Set figure size\n",
    "plt.figure(figsize=(fig_width, fig_height))\n",
    "\n",
    "ax = graph.plot(kind = 'pie', autopct = '%1.1f%%', startangle = 90, textprops = {'fontsize': 14})\n",
    "\n",
    "# Hide the y-axis label\n",
    "ax.set_ylabel('')\n",
    "\n",
    "# Move title to the bottom\n",
    "plt.figtext(0.5, 0.05, 'Revenue Realized per Booking Platform', ha = 'center', va = 'center', fontsize = 16)\n",
    "\n",
    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "# Save the figure with a transparent background\n",
    "plt.savefig('revenue realized per booking platform.png', transparent = True, bbox_inches = 'tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Insight:\n",
    "\n",
    "The revenue realized per booking platform varies, with 'others' being the highest contributor (480,698,244 units), followed by 'makeyourtrip' (233,132,708 units)."
   ]
  }
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