Hotel Bookings Cancellation Data Analysis & Dashboard

This project is designed to analyze and visualize hotel bookings cancellation data using Excel. The repository contains the raw dataset and a fully interactive dashboard to help users explore and derive insights from the data.

🌟 Project Overview

This project aims to provide a comprehensive analysis of hotel booking trends, guest demographics, and key metrics using a dataset stored in rawdata.csv. The analysis is visualized through an Excel dashboard in Hotel_Bookings_Dashboard.xlsx, offering a user-friendly interface for data exploration.

📁 Repository Contents

• Hotel_Bookings_Dashboard.xlsx

  • Data_Clean: This sheet contains the cleaned version of the raw data. The data has been processed to remove any inconsistencies or missing values, ensuring accurate analysis.
  • Pivot: This sheet includes pivot tables derived from the cleaned data, which serve as the foundation for the dashboard visualizations.
  • Dashboard: The final dashboard, providing a visual representation of key metrics and trends related to hotel booking cancellations. It includes various charts and graphs for easy interpretation.

• rawdata.csv

  • This file contains the original dataset used in the analysis. The dataset includes 119,390 entries with 36 columns, capturing various attributes related to hotel bookings, such as:
    • hotel: Type of hotel (Resort or City Hotel).
    • is_canceled: Indicates if the booking was canceled.
    • lead_time: Number of days between the booking date and the arrival date.
    • arrival_date_year, arrival_date_month, arrival_date_day_of_month: Booking dates.
    • adults, children, babies: Number of guests.
    • meal, country, market_segment, distribution_channel: Customer and booking details.
    • And more, including financial and reservation details.

📈 Dashboard Features

The Hotel_Bookings_Dashboard.xlsx file includes several features designed to enhance the analysis experience:

• Interactive Slicers:

  • Functionality: Filter the data dynamically by various criteria such as hotel type, booking dates, and guest demographics.
  • Customization: Users can easily modify the slicer settings to tailor the dashboard to specific needs.

• Visualizations:

  • Types: Includes area charts, bar charts, pie charts, and more.
  • Purpose: Visualizations help to illustrate trends, distributions, and relationships within the dataset.

• Summary Statistics:

  • Key Metrics: The dashboard highlights important metrics like hotel type, count of cancelled_bookings for each month, and guest type.
  • Interactive: These metrics update automatically based on slicer selections.

Insights & Analysis

The dashboard is designed to provide insights into:

• Booking Trends: Analyze how bookings fluctuate over time, across different hotel types, and during various seasons.
• Guest Demographics: Understand the geographical distribution of guests and booking preferences.