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Crop Recommendation and Yield Prediction for Indian Farms

Approx. component budget: Rs. 0 - 2,000

Objective

Combine a crop classifier and a yield regressor and test them on district-level data from public sources.

What you will build

A tool that takes soil values, rainfall, temperature and season and suggests suitable crops and an expected yield range.

Results you can expect

• Classification accuracy
• R-squared and MAE for yield
• Feature importance of rainfall vs soil
• Results for 3 states
• And a comparison with a simple baseline

What you will learn

Working with real government data, regression and classification together, missing values, and communicating uncertainty to non-experts.

Tools and notes

Python, pandas, scikit-learn, public crop and weather datasets (data.gov.in, Kaggle), Streamlit. Present results as advice and not as a promise of yield.

Resume points from this project

• Built and tested Crop Recommendation and Yield Prediction for Indian Farms; objective: combine a crop classifier and a yield regressor and test them on district-level data from public sources.
• Measured and analysed classification accuracy and R-squared and MAE for yield; wrote the report and presented the results in a viva. Add your own numbers: [x].

Replace [x] with your own measured results. Write only what you really did.

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