AGRICULTURE COMMODITY PRICE PREDICTIONS

AGRICULTURE COMMODITY PRICE PREDICTIONS

Hitesh Badgujar, Saurabh Pardeshi, Vaibhav Thombre, Chhagan Gavit, Nirmiti Tamore, NARENDRA JOSHI

School Of Computer Science & Engineering SANDIP UNIVERSITY, NASHIK

Abstract

The Agriculture Commodities Price Prediction project aims to leverage machine learning, specifically the Support Vector Machines (SVM) algorithm, to forecast crop prices based on a comprehensive government dataset. The objective is to provide farmers, traders, and policymakers with accurate and timely information to make informed decisions in the volatile agricultural commodities market. The project involves preprocessing and analyzing diverse data points such as historical prices, climate conditions, and economic indicators. Through the implementation of SVM, a powerful algorithm for classification and regression tasks, our model strives to capture complex relationships within the dataset to enhance prediction accuracy. The proposed system aims to contribute to the sustainability of the agricultural sector by assisting stakeholders in mitigating risks and optimizing resource allocation. The utilization of government datasets ensures the reliability and authenticity of the information, making the model a valuable tool for stakeholders involved in agriculture and related industries..

Keywords: Agriculture Commodities Price Prediction Machine Learning Support Vector Machines (SVM) Government Dataset Forecasting Crop Prices Data Preprocessing Historical Prices Climate Conditions Economic Indicators