Developing an Agricultural Product Price Prediction Model using HADT Algorithm
S. Rajeswari, K. Suthendran
1S. Rajeswari, Department of Computer Applications, Kalasalingam Academy of Research and Education College, Krishnan Koil (Tamil Nadu), India.
2K. Suthendran, Department of Information Technology, Kalasalingam Academy of Research and Education College, Krishnan Koil (Tamil Nadu), India.
Manuscript received on 24 November 2019 | Revised Manuscript received on 18 December 2019 | Manuscript Published on 30 December 2019 | PP: 569-575 | Volume-9 Issue-1S4 December 2019 | Retrieval Number: A11261291S419/19©BEIESP | DOI: 10.35940/ijeat.A1126.1291S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Crop Price, Big Data, Data Mining, Classification, Association Rule, Decision Tree.
Scope of the Article: Regression and Prediction