Sure! Here's a DEV.to style blog post for your project Crop Yield Prediction:
π Predicting Crop Yield using Machine Learning πΎ
An ML project to empower smarter agriculture decisions
GitHub Repo β
π Introduction
Agriculture remains the backbone of the Indian economy, yet farmers still face unpredictable yields due to varying environmental and input conditions. To tackle this issue, I built a machine learning model that predicts crop yield based on historical and input-based features.
This project is simple, beginner-friendly, and practical.
π§ Problem Statement
"How can we accurately predict crop yield based on inputs like rainfall, fertilizer, and pesticide use?"
Farmers often rely on experience or guesswork. This model helps bring data-driven decision-making to the field.
π Dataset Overview
The dataset includes:
- Area
- Production
- Crop Year
- Rainfall
- Fertilizer
- Pesticide
π Categorical features are label-encoded for model compatibility.
π οΈ Tech Stack
- Python π
- Pandas & NumPy
- Scikit-learn
- Matplotlib (optional for plots)
π§ͺ Models Used
I trained and evaluated three regression models:
- Linear Regression
- Random Forest Regressor
- Gradient Boosting Regressor β Best performer
Evaluation Metrics:
- RΒ² Score
- Adjusted RΒ²
- RMSE
Gradient Boosting gave the highest RΒ² score on the test set and was chosen as the final model.
π₯οΈ Interactive Prediction Interface
The script allows users to input values for the features and get instant yield predictions.
β
Handles feature consistency
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Uses the trained model and encoders
π Try it Out!
Clone the repo and run the script locally:
git clone https://github.com/h4ck3r0/crop-yielding-prediction
cd crop-yielding-prediction
python main.py
π± Future Improvements
- Add UI with Streamlit or Flask
- Integrate with real-time weather APIs
- Visual analytics for predictions
β Final Thoughts
This is a practical application of ML in solving a real-world agricultural problem.
Feel free to fork, contribute, or reach out for collaboration!
π GitHub: h4ck3r0/crop-yielding-prediction
Let me know what you think! Would love to hear feedback or ideas for improvements.
#MachineLearning #Python #DataScience #AI #Agriculture #OpenSource #DEVCommunity
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