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.

翻译：本研究分析了1997年至2020年印度农作物产量预测情况，重点关注多种农作物及关键环境因素。通过运用线性回归、决策树、K近邻、朴素贝叶斯、K均值聚类和随机森林等先进机器学习技术，旨在实现农业产量的精准预测。数据可视化结果表明，朴素贝叶斯和随机森林模型展现出显著的高效性。研究最终得出结论：整合上述分析方法可大幅提升农作物产量预测的准确性与可靠性，为农业数据科学领域提供了重要贡献。