Welcome to the WeatherPlay project! This web application predicts whether it’s suitable to play a match based on weather conditions. Built with Python Flask, HTML, and CSS, it integrates a pre-trained Gaussian Naive Bayes machine learning model for accurate predictions.
This project demonstrates the integration of machine learning into a web application. The application uses a Gaussian Naive Bayes model to analyze weather conditions and determine match suitability. The interface is user-friendly and built with Flask for seamless interaction.
- 🔍 Machine Learning Integration: Uses a Gaussian Naive Bayes model to predict match suitability.
- 🌐 Multi-Page Interface: Developed with Flask, HTML, and CSS for an intuitive user experience.
- ⚡ Real-Time Predictions: Provides immediate feedback based on user input.
- Python: Server-side scripting and machine learning integration.
- Flask: Web framework for handling HTTP requests and routing.
- HTML/CSS: For structuring and styling the web pages.
- Scikit-learn: For implementing the Gaussian Naive Bayes model.
Follow these steps to set up and run the project on your local machine:
- Python 3.x
- Flask
- Scikit-learn
-
Clone the repository:
git clone https://github.com/Priyamjain1105/WeatherPlay.git cd WeatherPlay
-
Create and activate a virtual environment:
python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`
-
Install the required dependencies:
pip install -r requirements.txt
- Start the Flask server:
flask run
- Open a web browser and navigate to:
http://localhost:5000
- Enter Weather Data
- Go to the prediction page and input the weather conditions such as temperature, humidity, and wind speed.
- Submit and Get Prediction
- Click submit to receive a prediction on whether it's suitable to play a match.
- Explore the Interface
- Use the navigation bar to visit different pages and access various features.
WeatherPlay/
│
├── app.py # Main Flask application
├── templates/ # HTML templates
│ ├── index.html # Home page
│ ├── predict.html # Prediction page
│ └── layout.html # Base template
│
├── static/ # Static files (CSS, JS)
│ ├── styles.css # Stylesheet
│
├── model/ # Pre-trained model
│ └── model.pkl # Serialized model file
│
├── requirements.txt # Project dependencies
└── README.md # Project README file
This project is licensed under the MIT License - see the LICENSE file for details.
Feel free to suggest additional enhancements or provide feedback! Happy coding! 🎉