Movie Recommendation System with Autoencoder

Welcome to the Movie Recommendation System project! This system leverages autoencoder neural networks to provide personalized movie recommendations to users. By analyzing user preferences and movie ratings, the system generates recommendations that enhance the user's movie-watching experience.

Project Overview

• Objective: Build a movie recommendation system using autoencoders.
• Dataset: MovieLens dataset with user ratings.
• Key Libraries: Python, TensorFlow, Pandas, NumPy, Matplotlib.
• Model: Autoencoder neural network.
• Evaluation Metric: Training and Validation Loss

Getting Started

Follow these steps to get started with the project:

1. Clone the Repository: Clone this GitHub repository to your local machine.

2. Install Dependencies: Ensure you have Python and the required libraries installed. You can install them using pip:

3. Data Preparation: Load and preprocess the MovieLens dataset inside "data/ratings.csv"

4. Autoencoder Training: Train the autoencoder neural network on the movie ratings data.

5. Recommendations: Generate movie recommendations for a given user ID.

6. Evaluation: Observe the training and validation loss after training.

Project Structure

• data/: Contains the MovieLens dataset used for training.
• autoencoder.ipynb: Jupyter notebooks for data analysis and model development.
• README.md: This file, providing an overview of the project.

Contributing

Contributions are welcome! If you'd like to enhance the project or report issues, please open an issue or create a pull request.