Machine Learning Approaches for Crop Yield Prediction in Punjab, India: A Comparative Analysis

This study investigates the implementation of machine learning models on extensive crop data to predict crop yield in Punjab state, India. The primary objective of this research was to determine which machine learning model demonstrates superior performance in providing accurate predictions. Two machine learning models (decision tree and random forest regression) were implemented, and gradient boosting regression was utilized for optimization. The results indicate that gradient boosting regression reduces the yield prediction error by 5%. Furthermore, for the given dataset, random forest regression exhibited superior performance compared to the other models.