Supervised Learning Algorithms of Machine Learning: Prediction of Brand Loyalty
Nagaraju Kolla1, M. Giridhar Kumar2

1Dr. Nagaraju Kolla, Assistant Professor, G. Pullaiah College of Engineering & Technology (Autonomous) Kurnool/ Andhra Pradesh/ India. 
2Dr. M. Giridhar Kumar, Professor and Head of the Department, Department of Management Studies, G. Pullaiah College of Engineering and Technology (Autonomous), Kurnool, A.P. India
Manuscript received on 20 August 2019. | Revised Manuscript received on 09 September 2019. | Manuscript published on 30 September 2019. | PP: 3886-3889 | Volume-8 Issue-11, September 2019. | Retrieval Number: J94980881019/2019©BEIESP | DOI: 10.35940/ijitee.J9498.0981119
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Abstract: The present research explores the loyalty prediction problem of a brand through supervised learning algorithms of classifications: logistic regression, decision tree, support vector machine, bayes algorithm and K-nearest neighbors (KNN) algorithm. 265 customers’ FMCG loyalty sample data were taken and variables of the data set include; loyalty status, gender, family size, age, frequency of purchase, and FMCG purchase. Data have been analyzed with the help of Python packages such as Pandas (Data analysis), Numpy (Numerical calculation), Matplotlib (Visualization), and Sklearn (Modeling). Among the supervised classification algorithms, logistic regression has outperformed than other techniques.
Keywords: Brand loyalty, FMCG, Logistic regression, Decision tree, Support vector machine, Bayes algorithm and K-nearest neighbors (KNN)
Scope of the Article: Artificial Intelligence and Machine Learning