CropYieldPredictor

Overview

CropYieldPredictor is a machine learning project designed to predict crop yields based on various agricultural parameters such as soil quality, weather conditions, and farming practices.

Features

• Predicts crop yield using historical data
• Supports multiple machine learning models
• Data preprocessing and visualization tools
• User-friendly interface for inputting parameters

Installation

1. Clone the repository:

   git clone https://github.com/Nandukumar-koribilli/CropYieldPredictor.git

2. Navigate to the project directory:

   cd CropYieldPredictor

3. Install dependencies:

   pip install -r requirements.txt

Usage

1. Prepare your dataset in CSV format with relevant features (e.g., soil nutrients, rainfall, temperature).
2. Run the main script:

   python main.py

3. Follow the prompts to input data or use the default dataset.
4. View predictions and visualizations in the output folder.

Requirements

• Python 3.8+
• Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn

Project Structure

• data/: Contains sample datasets
• models/: Trained machine learning models
• src/: Source code for preprocessing, training, and prediction
• main.py: Entry point for running the application

Contributing

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -m "Add feature").
4. Push to the branch (git push origin feature-branch).
5. Open a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.