Prediction of Crop Yield using Decision Tree and Random Forest Technique

India’s economy is considered to be agrarian, with agriculture, either in its entirety or in its component portions, largely determining the country’s economic standing. Economic and seasonal factors can have an impact on agriculture. Because one must rely on the data that is already accessible, this makes forecasting crop results challenging. Since machine learning is a crucial tool in crop management decision-making, this study aims to address the phenomenon of crop yield prediction using this technique. Before planting their crop, growers will find this material useful in estimating crop productivity. There are various supervised machine learning methods that can be used to make predictions. Multiple Linear Regression, Decision Tree Regression, Random Forest Regression were used in this study.