Prediction of Optimal Crop to Grow Based on Geographical Information

A country’s GDP is affected by the price fluctuations of agricultural products. In particular, India’s economy is largely dependent on agriculture, with farmers being the backbone of the economy. Predicting the price of grain and drawing conclusions to recommend crops help farmers and those in the agriculture industry make better decisions, which will help in minimizing losses, maximizing production, and reducing the risk of price fluctuations. Trends in prices can help farmers make an intelligent decision before farming a specific type of crop or grain. In our paper, we attempt to construct a reliable predictor of grain prices using data mining and machine learning algorithms. The classifier models used in this study are KNN, random forest, decision tree classifier, and gradient boosting of which gradient boosting provides maximum accuracy.