This project explores and analyzes hotel booking data to uncover trends and patterns that influence hotel revenue. Using visualizations and machine learning models, it aims to understand the key drivers of revenue and build predictive models to estimate booking revenue.
The notebook includes:
- Exploratory Data Analysis (EDA)
- Data preprocessing and feature engineering
- Machine learning classification to predict cancellations
- Performance evaluation of different models
- Key insights and conclusions
The dataset used in this project downloaded from Kaggle, it includes hotel booking records with various features like booking dates, length of stay, guest details, pricing, and whether the booking was canceled.
Install the following packages before running the notebook:
pip install pandas numpy matplotlib seaborn scikit-learn xgboost