-Hotel-Booking-Data-Analysis-Power-BI-

📊 Hotel Booking Data Analysis | Power BI

🚀 Project Overview

This project focuses on analyzing hotel booking data using Power BI to extract insights related to booking trends, customer preferences, and revenue patterns. The interactive dashboard provides a comprehensive view of hotel performance.

🔥 Features

✅ Booking Trends – Analyze seasonal and monthly booking patterns.

✅ Customer Segmentation – Identify customer types (business vs. leisure travelers).

✅ Revenue Analysis – Track total revenue, ADR (Average Daily Rate), and occupancy rates.

✅ Cancellation Analysis – Understand booking cancellations and reasons.

✅ Geographical Insights – Visualize bookings based on customer locations.

✅ Interactive Filters – Drill down into specific time periods, customer types, and hotel categories.

🛠 Tech Stack

Data Processing: Excel / SQL

Data Visualization: Power BI

Database: SQL (PostgreSQL / MySQL)

Dashboard Development: Power BI

📂 Dataset

Source: Public Hotel Booking Dataset / Company Hotel Data

Format: CSV / Excel / SQL Database

Key Fields: Booking ID, Check-in Date, Room Type, Customer Type, Revenue, Country, Cancellation Status

🔍 Additional Insights & Techniques

🏨 Setting Up the Power BI Report

A hotel reservation dashboard is created, illustrating step-by-step setup of metrics, slicers, and background for the report.

💡 Exploring Data Preparation Techniques

Learn about data cleaning and preparation, including the calculation of relevant columns such as booking and stay dates.

📊 Visualization Techniques

Key visuals such as trend lines, slicers, heatmaps, and tables are used to display booking trends, customer segments, and insights.

🔧 Adding Aesthetic Features

Background customization, theme setting, and color scheme creation in Power BI are demonstrated to enhance report visuals.

📈 Advanced Data Analysis

Insights on weekday vs. weekend bookings, loyalty levels, booking channels, and customer behavior are included.

📑 DAX Calculation Guidance

Overview of using DAX functions like SUM, COUNT, AVERAGE for calculating metrics and deriving insights.

📌 Installation & Usage

Clone the Repository

git clone https://github.com/your-username/hotel-booking-analysis.git

Navigate to Project Folder

cd hotel-booking-analysis

Open Power BI and Load the Dataset

Use SQL Queries (if applicable) to preprocess data before visualization.

📸 Dashboard Previews

🎯 Key Insights

Identify peak booking periods and optimize pricing strategies.

Reduce cancellations by analyzing common cancellation reasons.

Improve customer retention with personalized marketing strategies.

Optimize room allocation based on booking trends.

🚀 Future Enhancements

Automating data updates with scheduled reports.

Incorporating predictive analytics for demand forecasting.

Enhancing visualizations with advanced Power BI features.

🤝 Contributing

Contributions are welcome! Feel free to fork, submit issues, or create pull requests.

📞 Contact

💼 LinkedIn: Your LinkedIn📧 Email: muk.786422@gmail.com

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