Data Analysis in Hospitality Domain Using Python

📋 Project Overview

This project analyzes booking data for AtliQ Grands, a hotel chain operating across four major cities in India: Delhi, Mumbai, Bangalore, and Hyderabad. The objective was to uncover insights into occupancy rates, revenue patterns, and customer behavior to help AtliQ Grands address declining market share and improve revenue.

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📚 Table of Contents

1. Project Overview
2. Problem Statement
3. Key Objectives
4. Tools & Technologies
5. Data Analysis Workflow
6. Key Insights
7. Recommendations
8. Impact of This Analysis
9. Contact

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🏨 Problem Statement

AtliQ Grands, established for over 20 years, operates multiple hotel categories including AtliQ Seasons, AtliQ Exotica, AtliQ Bay, and AtliQ Palace, catering to diverse customer segments. Despite their strong presence, they are losing revenue and market share due to rising competition.

The goal of this project is to leverage data analytics to provide actionable insights and recommendations for business growth.

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🚀 Key Objectives

1. Analyze occupancy rates across cities, room types, and booking platforms.
2. Identify trends in revenue realization.
3. Understand customer behavior based on booking patterns.
4. Provide data-driven recommendations to improve performance.

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🛠️ Tools & Technologies

• Python: Core programming language for data analysis.
• Jupyter Notebook: For interactive data exploration and visualization.
• Pandas: Data manipulation and cleaning.

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📊 Data Analysis Workflow

1. Understanding the Business Problem

   • Identified key questions, such as occupancy rates and revenue trends.

2. Data Collection

   • Worked with booking data stored in CSV files.

3. Exploratory Data Analysis (EDA)

   • Cleaned and explored data to understand structure and patterns.

4. Data Cleaning

   • Addressed issues like missing values and outliers (e.g., negative guest counts).

5. Data Transformation

   • Created key metrics like Occupancy Percentage, which served as the primary KPI.

6. Insights & Visualization

   • Generated actionable insights using visualizations and statistical analysis.

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📈 Key Insights

• Occupancy Trends:

  • Highest occupancy in Presidential rooms (59.28%); lowest in Standard rooms (57.89%).
  • Delhi had the best occupancy rate (61.51%), while Bangalore was the lowest (56.33%).
  • Occupancy was significantly higher on weekends (72.34%) compared to weekdays (50.88%).

• Revenue Realization:

  • Mumbai contributed the most revenue (₹668.57M), while Delhi contributed the least (₹294.40M).

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📑 Recommendations

1. Focus on Delhi and Presidential rooms to sustain high occupancy rates.
2. Increase weekend-focused promotions to leverage higher demand.
3. Target Bangalore with strategic offers to improve occupancy.
4. Diversify revenue streams by partnering with booking platforms.

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✨ Impact of This Analysis

By leveraging Python-based data analysis, this project equips AtliQ Grands with actionable insights to:

• Enhance revenue generation.
• Optimize resource allocation across cities and room types.
• Strengthen decision-making using data-driven strategies.

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📬 Contact

If you have questions or want to discuss this project, feel free to reach out:

Rachana Hadke

• LinkedIn
• Email