This project focuses on data cleaning and exploratory data analysis (EDA) for hotel bookings. It aims to derive insights from guest behavior and pricing patterns, which can be pivotal in making strategic decisions for hotel management.
- Initial steps include cleaning the dataset to ensure the accuracy and reliability of the analysis.
- Analyze the origins of guests to identify key geographical markets and target areas.
- Examine how much guests pay per room per night.
- Explore variations in the average daily rate (ADR) throughout the year.
- Determine the busiest months with the highest number of guests to optimize pricing and promotions.
- Investigate the typical duration of stays, offering insights into preferences for short vs. long stays.
- Select important numerical and categorical features to enhance model predictions.
- Apply mean encoding techniques for categorical variables.
- Manage outliers in the dataset to refine the accuracy of statistical models.
- Prepare data for training models.
- Implement logistic regression and other classification algorithms to predict outcomes based on cleaned and engineered features.
This notebook is intended as a detailed guide for data scientists and analysts in the hospitality industry, aiming to understand market dynamics and optimize operational strategies.