HotelBookingInsights 📊

Project Overview

A Power BI dashboard project that focuses on analyzing hotel booking data. The goal of this project is to identify trends related to cancelled bookings, guest types, and the overall booking patterns in two different types of hotels. After performing data cleaning and analysis, a responsive Power BI dashboard was created to showcase the insights in both desktop and mobile layouts.

📂 Table of Contents

• Project Overview
• Objectives
• Data Cleaning & Analysis
• Power BI Dashboard
• Features
• Technologies Used
• Installation & Setup
• Usage
• Contributing

🎯 Objectives

The main goal of this project was to analyze hotel booking data and extract meaningful insights by:

• Determining the total number of cancelled bookings.
• Understanding the types of guests who are more likely to cancel bookings versus those who complete their stays.
• Comparing total bookings and cancelled bookings between two different types of hotels.

🧹 Data Cleaning & Analysis

Before visualizing the data, extensive data cleaning and preparation were done. Key steps included:

• Handling missing values and outliers.
• Filtering relevant data for analysis.
• Aggregating data based on key attributes such as guest types, hotel types, and booking status.

📊 Power BI Dashboard

The final deliverable of this project is an interactive Power BI dashboard. The dashboard provides:

• A comprehensive view of total bookings and cancellations across hotel types.
• Analysis of guest types and their cancellation patterns.
• A responsive layout that adapts to both desktop and mobile devices.

✨ Features

• Visual Insights: Interactive charts showing booking and cancellation trends.
• Guest Type Analysis: Insights into different guest segments and their booking behavior.
• Mobile-Friendly: A customized mobile layout for an optimal viewing experience on smaller screens.

⚙️ Technologies Used

• Power BI: For creating interactive dashboards and visualizations.
• Python (Pandas): For data cleaning and pre-processing.
• Jupyter Notebook: For performing data analysis and exploration.

📥 Installation & Setup

1. Clone the repository to your local machine:

   git clone https://github.com/YourUsername/Hotel-Booking-Analysis.git
   

2. Data Cleaning: Run the Jupyter Notebook scripts to perform the data cleaning and analysis.
3. Power BI: Open the Power BI dashboard file (.pbix) to view and interact with the visualizations.

💡 Usage

1. Desktop View: Use the Power BI desktop application to open and explore the insights in the dashboard.

   

3. Mobile View: Access the mobile layout using Power BI mobile app for an optimized experience.

hotel.-.28.October.2024.mp4

🤝 Contributing

We welcome contributions! If you find any issues or have suggestions for new features, feel free to open an issue or submit a pull request.

Steps to Contribute:

1. Fork this repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit and push your changes (git commit -m "Added feature").
5. Submit a pull request.