https://github.com/lavkalsi/movieratingpredictor
Movie Rating Predictor is a React web app that predicts movie ratings (from 0 to 10) based on details like duration, actors' names, and director's name. It uses a Python machine learning model for predictions, with Flask facilitating communication between the backend and frontend. Backend files are located in the res folder.
https://github.com/lavkalsi/movieratingpredictor
datascience flask machine-learning nodejs python reactjs
Last synced: 3 months ago
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Movie Rating Predictor is a React web app that predicts movie ratings (from 0 to 10) based on details like duration, actors' names, and director's name. It uses a Python machine learning model for predictions, with Flask facilitating communication between the backend and frontend. Backend files are located in the res folder.
- Host: GitHub
- URL: https://github.com/lavkalsi/movieratingpredictor
- Owner: LavKalsi
- Created: 2024-06-21T16:18:57.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-03T14:12:07.000Z (about 2 years ago)
- Last Synced: 2025-01-20T15:33:19.383Z (over 1 year ago)
- Topics: datascience, flask, machine-learning, nodejs, python, reactjs
- Language: Jupyter Notebook
- Homepage: https://lavkalsi.github.io/MovieRatingPredictor
- Size: 519 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Movie Rating Predictor
A React web application that predicts movie ratings based on various details about the movie using a Python machine learning model. Flask is used for communication between the Python backend and the React app.
## Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [How It Works](#how-it-works)
- [Backend Details](#backend-details)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
## Introduction
Movie Rating Predictor is a web application built with React for the frontend and a Python machine learning model for the backend. It predicts the rating of a movie (from 0 to 10) based on details such as duration, actors' names, and director's name.
## Features
- **User-friendly Interface:** Simple interface to input movie details and predict ratings.
- **Real-time Predictions:** Quickly processes input to provide movie rating predictions.
- **Machine Learning:** Utilizes a trained machine learning model for accurate predictions.
## Installation
### Prerequisites
- Node.js
- Python 3.x
- pip (Python package installer)
### Frontend Setup
1. Clone the repository:
```sh
git clone https://github.com/LavKalsi/MovieRatingPredictor.git
cd MovieRatingPredictor
```
2. Navigate to the `frontend` directory and install dependencies:
```sh
cd frontend
npm install
```
3. Start the React application:
```sh
npm start
```
### Backend Setup
1. Create and activate a virtual environment (optional but recommended):
```sh
python -m venv venv
source venv/bin/activate # On Windows, use `venv\Scripts\activate`
```
2. Install the required Python packages:
```sh
pip install -r res/requirements.txt
```
3. Run the backend server:
```sh
python res/Server.py
```
## Usage
1. Ensure both the frontend and backend servers are running.
2. Open your browser and navigate to `http://localhost:3000`.
3. Enter the movie details (such as duration, actors' names, and director's name).
4. Click the "Predict" button to receive the rating prediction.
## How It Works
The Movie Rating Predictor web app allows users to predict the rating of a movie. Here's how you can use it:
1. **Input Details:** Users can input movie details such as duration, actors' names, and director's name into the provided fields on the web app.
2. **Submit for Prediction:** After entering the details, users click the "Predict" button to submit the information for analysis.
3. **Backend Processing:** The frontend sends the movie details to the backend Python server, where the machine learning model processes them.
4. **Receive Results:** The backend returns the prediction result (movie rating from 0 to 10) to the frontend, which is then displayed to the user.
## Backend Details
The backend is a Python Flask application that serves a machine learning model trained to predict movie ratings. The backend files, including the model and Flask app, are located in the `res` folder.
### Files in `res` Folder
- `Server.py`: The Flask application that handles HTTP requests from the frontend.
- `model.jb`: The trained machine learning model.
- `requirements.txt`: The dependencies required for the Python backend.
## Contributing
Contributions are welcome! Please open an issue or submit a pull request if you have any improvements or suggestions.
1. Fork the repository.
2. Create your feature branch (`git checkout -b feature/your-feature`).
3. Commit your changes (`git commit -am 'Add your feature'`).
4. Push to the branch (`git push origin feature/your-feature`).
5. Open a pull request.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Contact
LavKalsi - [GitHub](https://github.com/LavKalsi)
Feel free to contact me if you have any questions or suggestions!