This project recommends movies similar to the user's choice. It uses stemming and vectorization of relevant features of a movie, such as plot, actors, director, etc. to find similar movies. The dataset used contains almost 5000 movies and the movie posters and metadata is collected from OMDB.

This project aims to predict the stock price of a variety of companies. It uses a Long Short Term Memory Model to predict the future price of a company. The model is trained on data from 2000 to till date and is able to predict the future price of most companies with a high accuracy.

This project aims to predict the academic performance of a student based on their previous academic record. It contains machine learning pipelines created using Python and uses models from the Scikit-Learn library.

This project was a college project during which a team of four students analysed the performance of different classification models on the MNIST dataset.