Hotel Recommendation System using PySpark

Built an end-to-end Big Data analytics and recommendation system using Apache Spark (PySpark) to process and analyze hotel booking data at scale. The project covers the complete data pipeline, including data cleaning, preprocessing, feature engineering, and exploratory data analysis (EDA) to extract meaningful insights from large datasets.

Implemented an Alternating Least Squares (ALS) collaborative filtering recommendation engine to generate personalized hotel recommendations based on user interactions. The model was evaluated using Root Mean Squared Error (RMSE) to measure recommendation quality.

This project demonstrates practical experience in distributed data processing, scalable machine learning, recommendation systems, and big data technologies. It highlights the ability to build efficient, production-oriented data pipelines capable of handling large-scale datasets while delivering actionable business insights.