About
The project centers around a movie recommendation system, tailoring suggestions based on a user's preferences. These recommendations are influenced not only by the individual user's choices but also by the collective voting patterns of other users who share similar movie interests. This system incorporates of data analysis and machine learning to recommend movies based on user's likings.
The project's GUI is constructed employing a blend of HTML, CSS, and JavaScript for the user interface. PHP serves as the intermediary API, integrating the output from Python scripts and aggregating data from both CSV files and the database.
Our infrastructure leverages Google Cloud (GCloud) services, utilizing it not only for database storage but also to curate a Data Lake, housing both structured and unstructured data. Python acts as the principal tool for machine learning algorithms and comprehensive data analysis.
For seamless user interaction, we've implemented asynchronous behavior using AJAX, ensuring prompt display of responses post-user interaction, specifically during movie voting processes. This architecture aims to create a responsive, dynamic, and user-centric environment.
