CollabCraft

This project implements User-User and Item-Item Collaborative Filtering algorithms using Python and Pandas.

Overview

The code predicts user ratings for movies based on historical data. It includes functions for both User-User and Item-Item Collaborative Filtering.

Usage

1. Load your rating data into a Pandas DataFrame with columns 'userId', 'movieId', and 'rating'.
2. Ensure the file path in the code points to your data file.
3. Run the code to predict ratings for users or movies.

Dependencies

• Pandas
• NumPy
• SciPy
• sci-kit-learn

Files

• collabcraft.ipynb: Python script containing the implementations.
• README.md: This file provides basic information about the project.

License

This project is licensed under the MIT License.

Dataset

Kaggle link: https://www.kaggle.com/datasets/grouplens/movielens-20m-dataset?select=rating.csv