🏨 Hospitality Data Analysis Using Python

Project Type: Python
Domain: Hospitality Analytics / Data Analysis

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📌 Project Overview

This project focuses on analyzing hotel booking and performance data using Python to generate actionable insights for the hospitality industry.
The analysis covers occupancy trends, revenue patterns, booking behavior, cancellations, and room category performance across multiple cities.

Using structured datasets and exploratory data analysis (EDA), this project demonstrates how raw hospitality data can be transformed into business-ready insights that support revenue optimization and occupancy management.

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🎯 Business Objectives

The primary objectives of this project are to:

• Analyze city-wise occupancy performance
• Identify revenue contribution by booking platforms
• Understand booking behavior and customer trends
• Evaluate room category demand
• Study cancellations and no-show patterns
• Highlight seasonality and demand fluctuations

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🛠 Tools & Technologies Used

• Python
• Pandas & NumPy – Data cleaning, transformation, and aggregation
• Matplotlib & Seaborn – Data visualization and comparative analysis
• Jupyter Notebook – Analysis and reporting

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📂 Datasets Used

The analysis is performed using multiple structured datasets:

• dim_date.csv – Date and time dimensions
• dim_hotels.csv – Hotel-level details
• dim_rooms.csv – Room categories and attributes
• fact_bookings.csv – Booking-level transactional data
• fact_aggregated_bookings.csv – Aggregated booking metrics
• new_data_august.csv – Additional booking data for extended analysis

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📊 Key Analysis & Visual Insights

1️⃣ City-wise Occupancy Analysis

• Compared average occupancy percentages across cities
• Delhi recorded the highest occupancy, followed by Hyderabad, Mumbai, and Bangalore
• Helps identify high-demand vs underperforming locations

2️⃣ Revenue Distribution by Booking Platform

• Analyzed revenue contribution across booking channels
• OTA platforms (e.g., MakeYourTrip) contribute the majority of revenue
• Direct offline bookings show the lowest contribution
• Useful for channel strategy and marketing optimization

3️⃣ Booking Behavior & Trends

• Identified seasonal patterns and demand fluctuations
• Observed variations between weekday and weekend bookings

4️⃣ Room Category Performance

• Analyzed occupancy and demand across room types
• Revealed significant differences in room-level utilization

5️⃣ Cancellations & No-Shows

• Studied cancellation patterns to understand booking reliability
• Helps hotels improve forecasting and operational planning

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🔍 Key Insights Generated

• High occupancy cities can be leveraged for premium pricing strategies
• OTA platforms are the primary revenue drivers
• Certain cities and room categories have untapped growth potential
• Clear seasonality impacts booking volume and occupancy
• Direct booking channels require strategic improvement

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🚀 Business Impact

This analysis helps hospitality stakeholders to:

• Improve occupancy planning and pricing decisions
• Optimize booking channel strategies
• Understand customer behavior and demand patterns
• Support data-driven revenue optimization

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▶️ Project Demo & Links

• 📽 Project Video:
  https://www.youtube.com/embed/RONngToL4H4?si=q3BWFlvUmfR7eDk8

• 💼 LinkedIn Post:
  https://www.linkedin.com/posts/krishnatanwars_python-dataanalytics-pandas-activity-7395832208319639552-WBie

• 📂 GitHub Repository:
  https://github.com/KrishnaTanwars/Hospitality-Data-Analysis-Python

• 📊 Project Presentation (Canva):
  https://www.canva.com/design/DAG7yWgYzxg/5FlJE-HpclxRm0XjyvCdUg/view

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▶️ How to Run the Project

# Clone the repository
git clone https://github.com/KrishnaTanwars/Hospitality-Data-Analysis-Python

# Open Jupyter Notebook
jupyter notebook

# Explore the analysis notebooks
hotels_analysis.ipynb
exercise_solution.ipynb

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📌 Final Note

This project demonstrates my ability to apply Python for data cleaning, exploratory data analysis, visualization, and insight generation in a real-world hospitality analytics context, aligning with Business Intelligence and Data Analyst roles.

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