Hotel Booking Data Analysis 📊

Overview

This project explores and analyzes hotel booking data using Microsoft Excel, highlighting key insights into booking patterns, customer preferences, and trends. The dataset contains records from a hotel, including booking cancellations, stay duration, and customer demographics.

Key Objectives:

• Analyze customer booking patterns and cancellations.
• Identify trends in booking sources and customer preferences.
• Draw actionable insights to improve hotel operations.

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Features

• Data Cleaning: Preprocessed raw data for accurate analysis.
• Data Visualization: Created pivot tables, charts, and graphs for better interpretation.
• Insights:
  • Customer behavior patterns.
  • Seasonal trends in bookings.
  • Analysis of cancellations and their causes.

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Tools Used

• Microsoft Excel: For data cleaning, analysis, and visualization.
• Pivot Tables: To organize and summarize data.
• Charts: To visualize insights (e.g., bar charts, line graphs).

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Visualizations

Here’s an example of the insights generated during the analysis:

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Key Findings

1. Peak Booking Seasons: Highlighted the months with the highest bookings.
2. Cancellations: Identified patterns and factors contributing to cancellations.
3. Customer Segments: Analyzed the top customer segments by revenue.

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How to Use

1. Clone the repository:

   git clone [https://github.com/your-username/your-repo-name.git](https://github.com/ansh2608/Data-Analysis.git)