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
• Features
• Installation
• Usage
• How It Works
• Backend Details
• Contributing
• License
• 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:

   git clone https://github.com/LavKalsi/MovieRatingPredictor.git
   cd MovieRatingPredictor

2. Navigate to the frontend directory and install dependencies:

   cd frontend
   npm install

3. Start the React application:

   npm start

Backend Setup

1. Create and activate a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

2. Install the required Python packages:

   pip install -r res/requirements.txt

3. Run the backend server:

   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 file for details.

Contact

LavKalsi - GitHub

Feel free to contact me if you have any questions or suggestions!