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mulers318-gif/Machine-Learning-based-crop-Yield-Prediction-for-Ethiopian-Agriculture-

Domain:

agriculture

Record type:

project
Creator:
mul
Host:
Ethiopian Crop Yield Prediction using Machine Learning Project Overview This project applies Business Intelligence and Machine Learning techniques to analyze agricultural production data from Ethiopia and predict crop yield. The project explores agricultural trends across regions and crop types and develops predictive models to support data-driven decision-making. Dataset Features - Year - Region - Crop Type - Area Cultivated (Ha) - Production (Kg) - Yield (Kg/Ha) Project Objectives - Analyze agricultural production patterns - Identify high-performing crops and regions - Predict crop yield using machine learning - Generate business insights for agricultural planning Machine Learning Models - Linear Regression - Decision Tree Regressor - Random Forest Regressor Technologies Used - Python - Pandas - NumPy - Matplotlib - Seaborn - Scikit-learn - Streamlit Results The Random Forest model achieved the best predictive performance and was selected as the final model. Future Improvements - Weather data integration - Soil quality analysis - Satellite imagery - Real-time forecasting Author Mulugita Simegnew Data Science Student

Visit

github.com

Languages

Amharic

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