Supervised learning algorithms of machine learning: Prediction of brand loyalty

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.