This project is a movie recommendation system developed as part of the ALX program. The goal was to utilize various machine learning techniques to recommend movies based on user preferences and historical data.

In this project, I developed a movie recommendation system using datasets from Kaggle. The system includes data preprocessing, exploratory data analysis (EDA), and the implementation of multiple recommendation algorithms.

This project provided hands-on experience with data preprocessing, exploratory data analysis, and the implementation of recommendation algorithms. The final model offers personalized movie recommendations based on user preferences and historical data.