Hotel Reservation Analysis with SQL is a project focused on analyzing a hotel reservation dataset using SQL queries. It provides insights into guest preferences, booking trends, and key operational factors.
This project instruction is provided by Mentroness Community where I am taking a one-month data analyst internship. I derived these answers independently.
- Clone the repository to your local machine.
- Install the required dependencies using
pip install -r requirements.txt. - Set up your SQLite database by running the provided SQL script.
- Open the project in your preferred SQL editor or Jupyter Notebook environment.
- Execute SQL queries to analyze the hotel reservation data.
- Use the provided Python scripts for additional data processing or visualization.
- Refer to the project documentation for detailed instructions on using specific features.
- Booking_ID: Unique identifier for each reservation.
- no_of_adults: Number of adults.
- no_of_children: Number of children.
- no_of_weekend_nights: Weekend nights.
- no_of_week_nights: Weekday nights.
- type_of_meal_plan: Meal plan chosen.
- room_type_reserved: Room type reserved.
- lead_time: Days between booking and arrival.
- arrival_date: Date of arrival.
- market_segment_type: Market segment of the reservation.
- avg_price_per_room: Average price per room.
- booking_status: Status of the booking.
If you'd like to contribute to this project, please follow these steps:
- Fork the repository.
- Create a new branch for your feature or bug fix.
- Make your changes and commit them with descriptive messages.
- Push your changes to your fork.
- Submit a pull request detailing your changes and any relevant information.
This project is licensed under the MIT License.
Developed by Yoon Thiri Ko.