🎬 Movie Recommendation System 🍿

Front Page

SpotLight Video

Movie Recommendation System

Welcome to our Movie Recommendation System repository! 🎉 This project is a part of the Pattern Recognition and Machine Learning (PRML) course, aiming to dive deep into traditional ML techniques by applying them to the exciting task of movie recommendation. We've explored various methods like Collaborative Filtering, Content-Based Filtering, Hybrid Filtering, Naive Bayes, Logistic Regression, and even Neural Networks to recommend movies based on user preferences and movie attributes.

🚀 Project Overview

Our goal is to build a robust Movie Recommendation System using the MovieLens Small Latest Dataset. We're on a mission to recommend movies based on user preferences or specific movie attributes. By trying out different techniques, we're finding the most effective approach for movie recommendation.

📂 Project Structure

• Code: Explore the implementation of different recommendation techniques.
• Data: Dive into the MovieLens dataset used for training and evaluation.
• Reports: Read through our project report detailing our methodology, results, and discussions.
• Web: Check out the web-based demo of our recommendation system using Streamlit.

⚙️ Instructions for Running the Code

1. Clone this repository to your local machine.
2. Navigate to the Code directory.
3. Run the desired script for the recommendation technique you want to evaluate.

📦 Project Deliverables

• Report: A comprehensive report detailing our journey, findings, and discussions.
• Spotlight Video: Watch a concise five-minute video summarizing our project's highlights.
• Code: Explore well-documented code hosted on GitHub with proper documentation and a README.
• Project Page: Visit our dedicated project page linking all project materials and providing a high-level overview.
• Web Demo: Experience an interactive web demo showcasing the functionality of our recommendation system.

🎥 Usage

To access the web demo of our Movie Recommendation System, visit Demo Link.

💻 Installation

Clone the repository:

$ cd your-project
$ git clone https://github.com/your-username/your-project.git](https://github.com/Advaitgaur004/PRML_Project.git
$ pip install -r requirements.txt

Contributing Fork the project. Create a new branch

$ git checkout -b feature

Commit your changes

$ git commit -am 'Add feature'

Push to the branch

$ git push origin feature

Create a new Pull Request.

⚠️ Local environment changes need to be implemented.

file_path = os.path.join(current_dir, 'App', 'movie.pkl')

to

file_path = os.path.join(current_dir, 'movie.pkl')

change the piece of code from

Acknowledgments

Give credit to the tools, libraries, and people who inspired or helped you in building your project. Feel free to adapt and customize this template further based on your project's specific needs and preferences.

👨‍💻 Contributors

• Aansh Chandrakant Dubey (QuantTitan)
• Aditya Padhy
• Aniket Singh
• Advait Gaur
• Qazi Talha Ali

For any inquiries, please contact b22cs004@iitj.ac.in.

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