Developed a movie recommendation system using machine learning techniques. The goal of the project was to provide personalized movie recommendations to users based on their viewing history and preferences. The project was implemented using Python programming language and utilized libraries such as Pandas, Numpy, and Scikit-learn. I used a collaborative filtering algorithm to build the recommendation model and fine-tuned it using various techniques such as matrix factorization and cosine similarity. I collected movie data from the IMDb database and performed extensive data pre-processing and cleaning to ensure the quality and consistency of the data. This was followed by data analysis to uncover patterns and relationships in the data that could be used to make recommendations. The user interface for the recommendation system was designed to be user-friendly and intuitive, allowing users to easily browse through recommended movies and select their favorites.
Here, as you can see the website is showing up movie recommendations for Iron Man Movie.
Here, you can see for The Dark Knight movie
Like this you can see recommendations of around 4500 movies.