Simple Movie Recommender System

This repository contains code for a simple movie recommender system built using the Python library Surprise. The project demonstrates how to implement and evaluate recommender systems using different algorithms on the MovieLens 100K dataset.

Installation

To run the project, you need Python installed on your system. Then, you'll need to install the Surprise library. You can install it using pip:

pip install scikit-surprise

Dataset

The project uses the MovieLens 100K dataset which is directly loaded using Surprise's built-in dataset loader.

Structure

The project includes:

• Loading the dataset.
• Splitting the data into training and testing sets.
• Implementing a K-Nearest Neighbors (KNN) recommender.
• Implementing a Singular Value Decomposition (SVD) recommender.
• Evaluating both models using Root Mean Square Error (RMSE).
• Generating recommendations for a specific user.

Usage

To run the recommender system, simply execute the Python script:

python recommender_system.py

This will train the KNN and SVD models on the MovieLens data, evaluate them, and make some sample predictions.

Code Overview

• Data Loading: The MovieLens 100K dataset is loaded.
• Train-Test Split: The dataset is split into training and testing data.
• Training the Recommender Systems:
  • A KNN-based recommender is trained.
  • An SVD-based recommender is trained.
• Evaluating the Systems: The performance of both recommenders is evaluated using RMSE.
• Making Recommendations: Recommendations are made for a user who has already rated some movies.

Contributing

Contributions to the project are welcome. Please ensure to update tests as appropriate.

License

Distributed under the MIT License. See LICENSE for more information.