A study on forecasting bigmart sales using optimized machine learning techniques

(1) * N.M Saravana Kumar Mail (Department of Artificial Intelligence and Data Science, M. Kumarasamy College of Engineering, Karur, India)
(2) K Hariprasath Mail (Department of Information Technology, Vivekanandha College of Engineering for Women, Namakkal, India)
(3) N Kaviyavarshini Mail (Department of Information Technology, Vivekanandha College of Engineering for Women, Namakkal, India)
(4) A Kavinya Mail (Department of Information Technology, Vivekanandha College of Engineering for Women, Namakkal, India)
*corresponding author

Abstract


Data mining is an in-depth study of enormous amounts of data present in an organization or institution’s repository. Business experts mostly utilize data analytics approaches to confirm their opinion. It will rapidly boost the global interest of the organization. In this scenario, the information and conclusion are gathered from Data analysis by data analytics. The experts also use it to validate, diagnose, or authenticate speculate layouts suppositions and completion of the analysis. In this paper, the prediction is based on grocery data sets by inspecting and analyzing the big mart sales data set. Among several predictive algorithms, data mining algorithms are considered for prediction. It includes Decision Tree, Naïve Bayes, Adaboost with Particle Swarm optimization, and Random forest. The proposed method of this research is a novel Naïve Bayes with a PSO algorithm. This algorithm optimizes the model iteratively. Exploration of the data must be done before prediction. The root means squared error (RMSE) is used as evaluation metrics for comparing the data mining algorithms.  The proposed algorithm performs well and gives a lower RMSE value. So, the proposed algorithm fits the best model when compared with the existing algorithms. This paper describes the prediction of high-quality data analysis data and determines the efficiency of data mining algorithms.

Keywords


Exploration; Naive bayes with PSO; Predictive modeling; Decision Tree; Data analysis

   

DOI

https://doi.org/10.31763/sitech.v1i2.167
      

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Science in Information Technology Letters
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