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.

本研究分析了 1997 年至 2020 年期间印度的作物产量预测，重点关注各种作物和关键环境因素。利用线性回归、决策树、KNN、朴素贝叶斯、K 均值聚类和随机森林等先进的机器学习技术来预测农业产量。尤其是朴素贝叶斯和随机森林模型通过数据可视化展示了高度的效果。研究得出结论，整合这些分析方法显著提高了作物产量预测的准确性和可靠性，为农业数据科学做出了重要贡献。