Na\"ive Bayes and Random Forest for Crop Yield Prediction

arXiv:2404.15392v1 Announce Type: new
Abstract: This study analyzes crop yield prediction in India from 1997 to 2020, focusing on various crops and key environmental factors. It aims to predict agricultural yields by utilizing advanced machine learning techniques like Linear Regression, Decision Tree, KNN, Na\"ive Bayes, K-Mean Clustering, and Random Forest. The models, particularly Na\"ive Bayes and Random Forest, demonstrate high effectiveness, as shown through data visualizations. The research concludes that integrating these analytical methods significantly enhances the accuracy and reliability …

abstract advanced arxiv bayes clustering crops cs.ai cs.lg decision environmental india ive key knn linear linear regression machine machine learning machine learning techniques mean prediction random regression study tree type