Abstract

India's agricultural sector has been grappling with the adverse effects of climate change over the last two decades, resulting in the diminished performance of various crops. Predicting crop yields well in advance of harvest holds immense significance, aiding policymakers and farmers in making informed decisions regarding marketing and storage. This project endeavors to provide farmers with a tool that offers predictive insights into crop yields before they embark on the cultivation process. The proposed solution involves the development of a credit card firm's need to be on the lookout for a fraudulent prototype for an interactive prediction system, featuring a user-friendly web-based graphical interface and employing machine learning algorithms. The prediction results are readily accessible to farmers, enabling them to plan and strategize effectively. In the face of challenges posed by climatic variables such as weather, temperature, humidity, rainfall, and moisture, this project harnesses various data analytics techniques and algorithms to enhance crop yield predictions. The Random Forest algorithm, a robust and widely-used supervised machine learning method capable of both classification and regression tasks, is leveraged for this purpose.