This project utilizes Machine Learning to predict the yield of blueberry crops based on various factors such as clone size, density of different bees, temperature range, raining days, and fruitset. The application is built using Python, Flask, CatBoost, and deployed on a local server. It provides a user-friendly web interface for users to input relevant parameters and get yield predictions instantly.
To get a local copy up and running, follow these steps:
- Python 3.7 or newer
- Flask
- Linear Regression, Lasso, Ridge, K-Neighbors Regressor, Decision Tree, Random Forest Regressor XGBRegressor, CatBoosting Regressor, AdaBoost Regressor
- Pandas, Numpy, Sckit-learn
- Clone the repo
gh repo clone amitkedia007/Blueberry-Yield-Prediction
- Install Python packages
pip install -r requirements.txt
- Run the application
python app.py
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Open the application in your web browser. use this link to open the project locally : (http://localhost:5000/predictdata)
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Input the required parameters in the form.
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Click 'Predict' to get the predicted yield.
Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the project
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a pull request
The machine learning model trained on this dataset: https://www.kaggle.com/competitions/playground-series-s3e14/data
Email - amitkedia3000@gmail.com
Project Link: amitkedia007/Blueberry-Yield-Prediction