Market deregulation of agricultural commodities requires in-depth analysis of day-to-day transactions. The analysis helps to draw greater insights into the market dynamics and builds better policies and advisory system for the farming sector. Reformation to the agricultural commodity market impel to deregulation of market transactions and increasing digitalization of transactions for better transparency across markets. These government policies have been taken based on the need of increasing farmer’s income from farming activities. These policy decisions give farmers immense opportunities for better realization of their farm outputs. To achieve the objective, there is an inevitable need for analyzing the market dynamics to draw better insights from the transactions. Also, farmers face massive loss due to these uncertain fluctuations in the price of agricultural commodities. Price prediction models can help farmers in making necessary decisions and help them in reducing the loss caused by price fluctuations. In this paper, we have chosen cotton as a commodity in which the price and volume of cotton over seven years from the Adoni market in Kurnool district of Andhra Pradesh, which is one of the largest cotton markets in the country market in Andhra Pradesh. The data consist of daily prices and volume of transactions of 7 years starting from 2011 to 2017. We have used machine learning techniques such as the ARIMA model for predicting the prices of cotton.