Hotel cancellation prediction

Developed a machine learning model to predict hotel booking cancellations based on customer and reservation data. The project involved building a complete data science pipeline, including data preprocessing, exploratory data analysis, model development, and performance optimization.

I performed data cleaning and preprocessing by handling missing values, encoding categorical variables, and scaling numerical features. Conducted Exploratory Data Analysis (EDA) using Pandas, NumPy, Matplotlib, and Seaborn to identify patterns and relationships between features such as lead time, customer type, booking changes, previous cancellations, and deposit type.

Implemented classification models including Logistic Regression, Decision Trees, and Random Forest to predict the likelihood of booking cancellations. Evaluated model performance using metrics such as accuracy, precision, recall, F1-score, and confusion matrix. Applied cross-validation and hyperparameter tuning to improve model performance and reduce overfitting.

The final model achieved strong predictive performance and provided insights into the key factors influencing customer cancellations, helping support better decision-making in revenue management and booking strategies. This project enhanced my skills in classification modeling, feature engineering, and real-world data analysis in the hospitality domain.