Movie Recommender System

• Tech Stack:
• Language: Python
• Libraries: Pytorch,Fastai,Scikit-Learn
• Notebook: Google Colab
• Github URL: Project Link

This repository contains the code for my content-based movie recommender system, which provides personalized movie recommendations using features like genres, actors, directors, and plot keywords from a dataset (e.g., IMDb). I preprocessed the data with one-hot and label encoding, then used cosine similarity to rank movies based on feature vector similarity, implemented in Python with Pandas, Scikit-learn, and NumPy. The repository includes a Jupyter Notebook showcasing the workflow from data preprocessing to generating top-N recommendations.