https://github.com/shaadclt/hotel-booking-analysis-python
This project provides an analysis of hotel booking cancellations using Python. It aims to uncover patterns and insights related to hotel booking cancellations and understand the factors that contribute to cancellations in the hospitality industry.
https://github.com/shaadclt/hotel-booking-analysis-python
matplotlib pandas python seaborn
Last synced: 6 months ago
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This project provides an analysis of hotel booking cancellations using Python. It aims to uncover patterns and insights related to hotel booking cancellations and understand the factors that contribute to cancellations in the hospitality industry.
- Host: GitHub
- URL: https://github.com/shaadclt/hotel-booking-analysis-python
- Owner: shaadclt
- Created: 2023-03-23T18:18:46.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-06-28T13:09:17.000Z (over 2 years ago)
- Last Synced: 2025-02-02T09:41:23.454Z (8 months ago)
- Topics: matplotlib, pandas, python, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 2.73 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Hotel Booking Cancellation Analysis using Python
This project provides an analysis of hotel booking cancellations using Python. It aims to uncover patterns and insights related to hotel booking cancellations and understand the factors that contribute to cancellations in the hospitality industry.
## Table of Contents
- [Introduction](#introduction)
- [Dataset](#dataset)
- [Setup](#setup)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
## Introduction
The Hotel Booking Cancellation Analysis project uses Python to explore and analyze a dataset containing information about hotel bookings and cancellations. By examining various features such as booking dates, customer details, and hotel characteristics, this project aims to gain insights into the reasons behind cancellations and identify potential trends.
## Dataset
The project utilizes a dataset that includes information about hotel bookings, including customer demographics, booking dates, hotel type, and whether the booking was eventually canceled. The dataset can be found in the file `hotel_bookings.csv` and will be used for the analysis.
## Setup
To use this project locally, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/shaadclt/Hotel-Booking-Analysis-Python.git
```
2. Install the required dependencies by running:
```bash
pip install -r requirements.txt
```
3. Download the dataset file `hotel_bookings.csv` and place it in the project directory.
## Usage
To perform the hotel booking cancellation analysis, follow these steps:
1. Import the necessary Python libraries and modules in your script.
2. Load the dataset using a library such as pandas.
3. Preprocess the dataset, which may include handling missing values, data cleaning, and feature engineering.
4. Conduct exploratory data analysis (EDA) to gain initial insights into the dataset. This may involve creating visualizations, calculating summary statistics, or identifying patterns and correlations.
5. Perform more in-depth analysis to investigate factors related to hotel booking cancellations. This could include examining cancellation rates based on customer demographics, booking channels, hotel characteristics, or other relevant features.
6. Generate visualizations or statistical analysis results to present the findings of the analysis.
7. Interpret the results and draw conclusions about the factors influencing hotel booking cancellations.
Feel free to customize the analysis according to your specific requirements and explore additional research questions related to hotel bookings and cancellations.
## Contributing
Contributions to this project are welcome. If you find any issues or have suggestions for improvement, please open an issue or submit a pull request on the project's GitHub repository.
## License
This project is licensed under the [MIT License](LICENSE). You are free to modify and use the code for both personal and commercial purposes.