  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.
