FlickPick

FlickPick: The Ultimate Movie Recommendation System

Project Objective

To build a machine learning-based movie recommendation system that suggests movies to users based on their viewing history, ratings, and preferences.

Inspiration

we are inspired by the recent advancements in machine learning and artificial intelligence that have enabled the creation of personalized recommendation systems. The idea of building a movie recommendation system came from my interest in movies and the desire to explore how machine learning can be used to improve the movie-watching experience.

What we learned

During the course of this project, we learned several new concepts and technologies. I learned how to preprocess data, extract features, and build a recommendation system using machine learning algorithms such as Collaborative Filtering and Content-based Filtering.

Building Of Project

I started by collecting data from the Movie Lens dataset and preprocessed it by cleaning, transforming, and normalizing it. I then extracted relevant features such as movie genres, directors, actors, and ratings. I used the Scikit-learn machine learning library to train the model and evaluated its performance using accuracy, precision, recall, and F1 score metrics.

Challenges we ran into

The biggest challenge I faced during this project was selecting the most appropriate machine learning algorithm for building the recommendation system. There are several algorithms to choose from, and each has its strengths and weaknesses. I also had to overcome challenges related to data cleaning and preprocessing, as the dataset contained missing data and duplicates.