🏨 AtliQ Hotels Data Analysis Project

📊 Overview

This project is a comprehensive data analysis of AtliQ Hospitality, focusing on understanding business performance across different dimensions like booking platforms, room categories, and guest ratings. The goal of the project is to provide actionable insights to improve the company’s overall operations and revenue generation.

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1️⃣ Problem Statement

The goal of this project was to:

• Identify key factors affecting revenue and customer retention.
• Leverage data-driven strategies to optimize performance.
• Create a roadmap for sustained growth in the competitive luxury hotel sector.

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2️⃣ Data Gathering and Preprocessing

🗂️ Data Sources

The project used the following datasets:

• Bookings.csv: Data about customer bookings.
• Rooms.csv: Information on different room types and categories.
• Hotels.csv: Data about various hotel locations and their characteristics.
• Revenue.csv: Financial data related to the revenue generated by each booking.

Preprocessing Steps

• Data Cleaning:
  • Handled missing values using Pandas.
  • Removed duplicate entries.
  • Standardized column names for consistency.
• Data Transformation:
  • Converted dates and categorical data into usable formats.
  • Derived new metrics (e.g., weekend vs weekday occupancy).
• Feature Engineering:
  • Grouped and aggregated data by room types, cities, and booking platforms.

Tools Used

• Pandas: For data manipulation and cleaning.
• NumPy: For numerical operations.

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3️⃣ Modeling and Analysis

Key Steps in Analysis

• Exploratory Data Analysis (EDA):
  • Analyzed occupancy patterns, revenue trends, and customer ratings using Matplotlib.
• Data Aggregation:
  • Grouped data by categories like room types, days of the week, and cities to identify trends.

Insights Generated

a) Room Type Popularity

• Presidential Rooms: The most preferred, with a 59.28% occupancy rate.
• Premium & Elite Suites: High-performing categories after presidential rooms.

b) Occupancy Trends

• Weekends: 72.34% occupancy, compared to weekdays at 50.88%.

c) Revenue Insights

• Mumbai: Generated ₹668.56M, leading revenue contributions across cities.

d) Customer Ratings

• Delhi: Topped with an average rating of 3.78.

e) Booking Preferences

• Makeyourtrip: The most popular booking platform among customers.

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4️⃣ Insights and Business Recommendations

1. Increase Marketing for Presidential Rooms:
   Leverage the popularity of presidential rooms to promote premium packages.

2. Focus on Weekend Campaigns:
   Introduce weekend-only offers to maximize occupancy.

3. Strengthen Presence in Mumbai:
   Double down on high-performing cities like Mumbai to optimize revenue.

4. Maintain Service Standards in Delhi:
   Use Delhi as a benchmark for customer service to improve ratings in other cities.

5. Collaborate with 'Makeyourtrip':
   Offer exclusive deals and promotions on the most preferred booking platform.

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🛠️ Tools & Technologies

This project was implemented using:

• Python: For data analysis and transformations.
• Pandas: For data manipulation and cleaning.
• Matplotlib & Seaborn: For creating insightful visualizations.
• Jupyter Notebook: As the environment for running the code and showcasing the process.

🚀 Project Outcomes

By the end of this project:

• Gained a deeper understanding of the hospitality industry’s data.
• Provided actionable insights that could help AtliQ Hospitality optimize their operations.
• Strengthened skills in data cleaning, transformation, and visualization.