Open AccessSupervised Learning Algorithms of Machine Learning: Prediction of Brand Loyalty
Author(s) -
Nagaraju Kolla*,
Manoj Kumar
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.j9498.0981119
Subject(s) - naive bayes classifier , machine learning , computer science , decision tree , python (programming language) , artificial intelligence , support vector machine , logistic regression , loyalty , algorithm , brand loyalty , random forest , statistical classification , supervised learning , data mining , artificial neural network , advertising , marketing , business , operating system
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