Comparative Analysis of Crop Price Prediction Using Linear Regression, Xgboost, And A Custom Linear Model

The goal of this is to create a machine learning-based agricultural price prediction system that will help farmers make better decisions and lessen market uncertainty. In order to offer precise price forecasts in Indian rupees per quintal, the model makes use of historical data that includes district, crop type, market, and date. The method entails preparing the data and implementing a custom model that is compared to XGBoost and conventional Linear Regression models. The findings show that the customized model provides a competitive alternative to current techniques and performs similarly to linear regression in terms of accuracy. By offering insights into market trends, this technology has the ability to empower the agricultural community and ultimately improve financial planning and market efficiency.