MakeMyTrip Dataset Analysis

Overview

This project analyzes the MakeMyTrip dataset, uncovering insights about travel trends, customer experiences, and ratings across different locations.

Key Challenges

1. Identify the Top 5 cities, areas, and states with the highest activity.
2. Determine the Top 10 busiest months for queries.
3. Analyze the average food rating across the top 10 cities.
4. Discover the average value-for-money rating across the top 10 cities.
5. Evaluate the average visitor experience across the top 10 cities.
6. Explore the distribution of special added values.
7. Analyze the average approval rating based on area, city, and state.

Dataset

The dataset used in this project contains information on various hotels, customer ratings, and booking trends. It is loaded and processed using pandas.

Technologies Used

• Python (Data processing and analysis)
• Pandas & NumPy (Data manipulation)
• Matplotlib & Seaborn (Visualization)
• Plotly (Interactive charts)

Installation

1. Clone this repository:

   git clone https://github.com/your-username/makemytrip-analysis.git
   cd makemytrip-analysis

2. Install dependencies:

   pip install pandas numpy matplotlib seaborn plotly

3. Run the Jupyter Notebook to explore the analysis.

Results & Insights

• Top 5 cities & states: Identified key travel destinations.
• Busiest months: Peak booking periods discovered.
• Ratings analysis: Trends in food, value-for-money, and visitor experience.
• Special added values: Common amenities offered.

Contributing

Contributions are welcome! Feel free to submit pull requests or open issues for improvements.

License

This project is licensed under the MIT License.