India is an agriculturally dependent nation, and as such, agriculture both totally and partially determines the nation's economic situation. Seasonal and economic factors have an impact on agriculture. It is difficult to anticipate crop yields since one must use the data that are currently available. This study focuses on applying machine learning techniques to predict crop yield because they are a critical decision-supporting tool. Before planting their crop, farmers will find this document useful in determining crop output. Various supervised machine learning approaches that can be used for prediction. In this study, regression methods including Decision Tree Regression, Multiple Linear Regression, and Random Forest Regression are used.

Decision Tree (DT), Machine Learning (ML), Multiple Linear Regression (MLR), Random Forest Regression (RFR)