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 of crop yield predictions, offering vital contributions to agricultural data science.
@article{arxiv.2404.15392,
title = {Na\"ive Bayes and Random Forest for Crop Yield Prediction},
author = {Abbas Maazallahi and Sreehari Thota and Naga Prasad Kondaboina and Vineetha Muktineni and Deepthi Annem and Abhi Stephen Rokkam and Mohammad Hossein Amini and Mohammad Amir Salari and Payam Norouzzadeh and Eli Snir and Bahareh Rahmani},
journal= {arXiv preprint arXiv:2404.15392},
year = {2024}
}