https://github.com/mayankghatawal/movie-recommendation-system-ml
Movie Recommendation System Using Machine Learning
https://github.com/mayankghatawal/movie-recommendation-system-ml
ai artificial-intelligence machine-learning ml movie-recommendation-app python python3 streamlit
Last synced: about 2 months ago
JSON representation
Movie Recommendation System Using Machine Learning
- Host: GitHub
- URL: https://github.com/mayankghatawal/movie-recommendation-system-ml
- Owner: MayankGhatawal
- Created: 2024-09-18T15:36:42.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-11T17:21:12.000Z (11 months ago)
- Last Synced: 2025-04-05T16:37:56.254Z (7 months ago)
- Topics: ai, artificial-intelligence, machine-learning, ml, movie-recommendation-app, python, python3, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 3.44 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Movie Recommendation System 🎥
## Overview
This **Movie Recommendation System** is a web application that suggests movies based on user input. The system uses a **content-based recommendation algorithm** to recommend movies similar to those the user likes. The frontend is built using **Streamlit**, and the backend processes are powered by **machine learning**.
## Features
- Users can search for a movie and get a list of similar movies.
- Movie recommendations are based on features such as genre, plot, and user ratings.
- The system uses a **content-based filtering** approach.
- Built with a user-friendly interface using **Streamlit**.
- Data is processed with **Pandas** and **Scikit-learn**.
## Project Architecture
- **Frontend**: Streamlit for the user interface.
- **Backend**: Python-based Machine Learning model using `sklearn`.
- **Database**: MongoDB (optional for storing user ratings or movie metadata).
- **Model**: Content-based recommendation using cosine similarity.
## Requirements
To run this project locally, ensure you have the following installed:
- **Python 3.x**
- **Streamlit**
- **Pandas**
- **Scikit-learn**
- **MongoDB** (Optional, if you are integrating with a database)
### Install the dependencies
You can install the required libraries using the following command:
```bash
pip install -r requirements.txt
```
Example `requirements.txt` file:
```text
streamlit
scikit-learn
pandas
numpy
```
## How It Works
1. **Data Preprocessing**: The movie dataset is preprocessed to extract relevant features such as title, genre, and plot.
2. **Model Training**: The system uses **cosine similarity** to compute similarity between movies based on their features.
3. **Recommendation Engine**: When a user inputs a movie, the system finds movies similar to the selected one by computing their cosine similarity score.
4. **Web Interface**: The **Streamlit** interface allows the user to interact with the system easily.
## Getting Started
### Clone the repository
First, clone this repository:
```bash
git clone https://github.com/yourusername/movie-recommendation-system.git
cd movie-recommendation-system
```
### Running the Application
Once you have cloned the repository and installed the necessary dependencies, run the application using Streamlit:
```bash
streamlit run app.py
```
This will launch the web app locally, and you can access it at `http://localhost:8501`.
## File Structure
```plaintext
├── app.py # Main Streamlit application
├── model.py # Contains the recommendation model logic
├── similarity.pkl # Precomputed similarity matrix
├── movies.csv # Dataset containing movie information
├── requirements.txt # Dependencies
└── README.md # Project Documentation
```
## Dataset
The dataset used in this project contains metadata of movies such as:
- Movie title
- Genre
- Plot description
- Ratings
This data can be fetched from popular datasets such as **IMDb**, **TMDB**, or **MovieLens**.
## Example Usage
1. Launch the Streamlit application.
2. Enter the name of a movie you like in the search bar.
3. The app will suggest a list of movies similar to the one you searched for.
## Screenshots
## Future Enhancements
- **Collaborative Filtering**: Integrating collaborative filtering to recommend movies based on user preferences and ratings.
- **User Authentication**: Adding login functionality for users to save their favorite movie recommendations.
- **Improved UI**: Enhance the UI/UX of the web application with more styling and interactive features.
## Contributing
Feel free to fork this repository and submit pull requests. For major changes, please open an issue to discuss the changes you wish to make.
## License
This project is licensed under the **MIT License**.
+----------------------------------+
| User Selected Movie |
+----------------------------------+
|
v
+----------------------------+
| Feature Extraction |
| (Genres, Rating, Director, |
| Cast, Release Year) |
+----------------------------+
|
v
+--------------------------------------+
| Feature Representation |
| (Vectorization, TF-IDF, Embeddings) |
+--------------------------------------+
|
v
+-----------------------------+
| Similarity Calculation |
| (Cosine, Euclidean) |
+-----------------------------+
|
v
+--------------------------------------+
| Recommendation Engine |
| (Content-Based Filtering) |
+--------------------------------------+
|
v
+------------------------------+
| MongoDB Database |
| (Movie Data with Feature |
| Vectors) |
+------------------------------+
|
v
+--------------------------------------+
| Rank and Recommend |
+--------------------------------------+
|
v
+--------------------------------------+
| Recommended Movies Display |
+--------------------------------------+