Open AccessDeveloping an Agricultural Product Price Prediction Model using HADT Algorithm
Author(s) -
S. Rȧjeswari,
K. Suthendran
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1126.1291s419
Subject(s) - agriculture , decision tree , computer science , predictive analytics , profit (economics) , decision tree learning , predictive modelling , big data , product (mathematics) , field (mathematics) , data mining , tree (set theory) , analytics , econometrics , machine learning , economics , mathematics , geography , mathematical analysis , geometry , archaeology , pure mathematics , microeconomics
Big Data Predictive Analytics and Data mining are emerging recent research field to analyse the agricultural crop price. The applications and techniques of data mining as well as Big Data using agriculture data is considered in this paper. In particular, the farmers are more concern about estimating that how much profit they are about to expect for the chosen crop. As with many other sectors the amount of agriculture data are increasing on a daily source. In this work, agriculture crop price dataset of Virudhunagar District, Tamilnadu, India is considered and for the price prediction model based on data mining decision tree techniques. The main goal is to establish the new predictive model based on Hybrid Association rule-based Decision Tree algorithm (HADT). The outcome for the suggested HADT forecast model is heartening and precise to predict agricultural product prices than other current decision tree models.