This is a repository made for InfoAid Internship's second task, in which a movies recommendation system has to be created that recommends movies to users based on their ratings and the ratings of similar users.
The code provided in this repository focuses on building an interactive movie recommendation system using text-based similarity and collaborative filtering, and it doesn't involve training a predictive model on the training data. This code is more focused on the user interface and interactivity.
The content-based recommendation part of this code focuses on using text similarity and TF-IDF to recommend movies based on their titles. The collaborative filtering part calculates scores based on user interactions with movies and recommends movies accordingly. This approach doesn't involve predictive modeling.
The datasets used in this code are - movie.csv and rating.csv. You can find the datasets from kaggle - https://www.kaggle.com/datasets/grouplens/movielens-20m-dataset
Dataset Name: MovieLens 20M Dataset
movie.csv - https://www.kaggle.com/datasets/grouplens/movielens-20m-dataset?select=movie.csv
rating.csv - https://www.kaggle.com/datasets/grouplens/movielens-20m-dataset?select=rating.csv