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https://github.com/md-emon-hasan/ml-project-movie-recommender-system

ML project on movie recommendation systems, showcasing collaborative filtering and content-based approaches using Python relevant libraries.
https://github.com/md-emon-hasan/ml-project-movie-recommender-system

collaborative-filtering data-science deployment recommendation-system recommender-system streamlit-webapp tmdb-api

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ML project on movie recommendation systems, showcasing collaborative filtering and content-based approaches using Python relevant libraries.

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README 

        
# Machine Learning Project: Movie Recommender System



Welcome to the **Movie Recommender System** machine learning project repository! This project focuses on recommending movies to content based on their preferences using collaborative filtering techniques.



![movie](https://github.com/user-attachments/assets/12f7311d-31a7-42bf-a2c6-fab7523483a7)



## 📋 Contents



- [Introduction](#introduction)

- [Why This Project](#why-this-project)

- [Dataset](#dataset)

- [Features](#features)

- [Models Implemented](#models-implemented)

- [Evaluation Metrics](#evaluation-metrics)

- [Setup and Installation](#setup-and-installation)

- [Demo](#demo)

- [Contributing](#contributing)

- [Challenges Faced](#challenges-faced)

- [Lessons Learned](#lessons-learned)

- [License](#license)

- [Contact](#contact)



---



## 📖 Introduction



This repository contains a machine learning project focused on recommending movies to content based on their historical categories and preferences. It includes data preprocessing, model development, evaluation, and deployment aspects of the project.



---



## 🎯 Why This Project



The primary motivation behind creating this project is to enhance user experience on movie platforms by providing personalized recommendations. This not only improves user satisfaction but also increases engagement with the platform.



---



## 📊 Dataset



The dataset used for this project contains user ratings for various movies. It includes information such as user IDs, movie IDs, ratings, and timestamps. This data is essential for training the recommendation models.



---



## 🌟 Features



- **Data Preprocessing:** Cleaned and transformed dataset for model compatibility.

- **Model Development:** Implemented collaborative filtering models for movie recommendations.

- **Model Evaluation:** Evaluated models using metrics such as accuracy, precision, and recall.

- **Deployment:** Integrated a simple web-based application for demonstrating movie recommendations.



---



## 🧠 Models Implemented



Several collaborative filtering models were implemented and evaluated:



- Content based Collaborative Filtering

- Matrix Factorization (e.g., Singular Value Decomposition, Alternating Least Squares)

- Hybrid Approaches (combining collaborative and content-based filtering)



Each model's performance was assessed based on metrics reflecting the recommendations' quality.



---



## 📊 Evaluation Metrics



The models were evaluated using metrics such as:



- **Accuracy:** Measures the correctness of predicted ratings.

- **Precision:** Measures the relevance of recommended items.

- **Recall:** Measures the coverage of recommended items.



---



## 🚀 Setup and Installation



To run this project locally, follow these steps:



1. Clone the repository:



   ```bash

   git clone https://github.com/Md-Emon-Hasan/ML-Project-Movie-Recommender-System.git

   ```



2. Navigate to the project directory:



   ```bash

   cd ML-Project-Movie-Recommender-System

   ```



3. Install the required dependencies:



   ```bash

   pip install -r requirements.txt

   ```



4. Run the notebooks or scripts as per your requirements.



---



## 🌐 Demo



Explore the live demo of the project [here](https://em-moive.onrender.com)



---



## 🤝 Contributing



Contributions to enhance or expand the project are welcome! Here's how you can contribute:



1. **Fork the repository.**

2. **Create a new branch:**



   ```bash

   git checkout -b feature/new-feature

   ```



3. **Make your changes:**



   - Implement new features, improve model performance, or enhance documentation.



4. **Commit your changes:**



   ```bash

   git commit -am 'Add a new feature or update'

   ```



5. **Push to the branch:**



   ```bash

   git push origin feature/new-feature

   ```



6. **Submit a pull request.**



---



## 🛠️ Challenges Faced



During the development of this project, the following challenges were encountered:



- Handling sparse and large-scale data for collaborative filtering.

- Optimizing recommendation quality while balancing computational resources.

- Integrating real-time updates and scalability for growing user bases.



---



## 📚 Lessons Learned



Key lessons learned from this project include:



- Understanding the trade-offs between different collaborative filtering techniques.

- Importance of data quality and preprocessing in recommendation systems.

- Deployment considerations for scalable and user-friendly applications.



---



## 📄 License



This project is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for more details.



---



## 📬 Contact



- **Email:** [[email protected]](mailto:[email protected])

- **WhatsApp:** [+8801834363533](https://wa.me/8801834363533)

- **GitHub:** [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)

- **LinkedIn:** [Md Emon Hasan](https://www.linkedin.com/in/md-emon-hasan)

- **Facebook:** [Md Emon Hasan](https://www.facebook.com/mdemon.hasan2001/)



Feel free to reach out if you have any questions or feedback about the project!



---



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