Welcome to the Crop Yield Prediction System project! This project aims to revolutionize agriculture by utilizing data-driven insights to predict crop yield. By forecasting both hg/ha yield and area yield, it endeavors to empower farmers, policymakers, and stakeholders with invaluable information for informed decision-making, ultimately contributing to enhanced food security and sustainable agricultural practices.
This project employs Support Vector Regression (SVR), Random Forest Regression, Neural Network Regression, and K-Nearest Neighbors (KNN) algorithms to predict crop yield. It encompasses various stages such as data cleaning, exploratory data analysis, and model training to create an effective crop yield prediction system.
- Python 3.x
- Jupyter Notebook
- Required libraries (see requirements.txt)
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Clone the repository:
git clone https://github.com/hzaheer48/crop-yield-prediction.git cd crop-yield-prediction -
Install dependencies:
pip install -r requirements.txt
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Open the Jupyter Notebook:
jupyter notebook Yield.ipynb
The project begins with importing crop yield-related data and cleaning it to ensure the dataset's integrity. Columns with missing values are handled, and unnecessary columns are dropped.
Visualizations provide insights into crop yield distribution across different items, years, and geographic areas. Explore the visualizations to better understand the dataset.
Various machine learning models, including SVR, Random Forest, Neural Network, and KNN, are employed for training and evaluating the crop yield prediction system.
- Open the Jupyter Notebook in your local environment.
- Execute each cell sequentially to run the entire project.
- Analyze the visualizations and model evaluations to understand the predictive performance.
Contributions are welcome! If you have suggestions or improvements, please open an issue or create a pull request.