This project focuses on building a predictive machine learning model to understand and forecast customer booking behavior. The goal is to provide actionable insights that can support strategic business decisions.
Objective Develop a robust classification model to: Predict the likelihood of customer bookings Identify key factors influencing booking behavior
Project Workflow Data Preparation Cleaned and preprocessed the dataset to handle missing values, encode categorical features, and scale numerical data. Performed exploratory data analysis to understand trends and patterns. Model Building Implemented a Random Forest Classifier for prediction. Used Grid Search Cross-Validation to fine-tune hyperparameters for optimal performance.
Results: Achieved a final model accuracy of 85.2% after tuning. Performed feature importance analysis to identify the most influential attributes in predicting successful bookings. Insights & Reporting
Key Technologies Python Scikit-learn Pandas Matplotlib / Seaborn (for visualizations)
Outcome The final model provides a reliable tool for predicting customer booking behavior, which can be integrated into decision-making processes such as marketing strategies, customer targeting, and service optimization.