Enhancing Crop Yield Prediction in India: A Comparative Analysis of Machine Learning Models

Farming is very important for India's economy. Lots of people in India depend on farming to make a living. But farming has become harder because the weather, climate, and environment keep changing. These changes make it tough to grow crops well. We proposed a mechanism called crop yield prediction. It helps us predict how much crops will grow and helps farmers decide what to plant. We use things like where the farm is, how much rain it gets, how hot or cold it is, and what kind of soil is there to make these predictions. These things help us figure out how much crop we can expect. In this proposal we tried out some ma-chine learning models like the Gradient Boosting Regressor, Random Forest Regressor, Support Vector Regressor, and Decision Tree Regressor. We wanted to see which one is the best at predicting crop yields. It turns out the Decision Tree Regressor did the best job and was really accurate with the da-ta we had. This method helps farmers because it means we can use this model to make better predictions about how much food we can grow, which helps the farming community in India grow more food in a good way.