Agriculture Product Marketing Data Analysis using Machine Learning

Agricultural marketing plays a very important role in the Indian economy. Farmers and their families depend on agriculture. Because unaware of market information and their location to sell their commodities to third party vendors at low prices and also not getting the proper prices for the crops, and sometimes lose a huge amount of the products. To overcome these challenges it is necessary to analyze the customer's purchase data from different regions/locations of the state/country. In this proposed work, it is intended to understand the demands of the customers and helps in direct communication between the farmers and customers, which eliminates the middle man profit and facilities the win-win situation for both customers and farmers. In this proposed project K-means algorithm is used categories the commodities based on prices of the agricultural products, which are grown in different parts of the region. The proposed model is simulated in anaconda navigator to analyze the customers purchase data for this scheme. To test effectiveness of proposed algorithm clusters are analyzed. © 2021 IEEE.