Numerous studies are available in the academic literature that investigates the customer perception under different contexts. In the present research the researcher tries to investigate the customer perception towards the Indian Government-sponsored social programme from the slum dwellers’ prospective. The author believes that the customer perception towards the government-lead liquefied petroleum gas intervention programme is influenced by multiple functional factors. The functional factors include both process or delivery variables and the outcome factors. In order to test the hypothesis, machine learning binary classifiers like logit, support vector machine, linear discriminant analysis, quadratic discriminant analysis and artificial neural network models are adopted. The binary classifier model efficiencies are analysed with multiple performance measurement parameters like accuracy rate, error rate, F-score, precision, kappa coefficient, Matthews correlation coefficient and area under receiver operating characteristic. While evaluating between the degree of accuracy between actual and predicted cases, the model efficiency results indicate a better predictive power of the classifier models. In relative performance of classifier models, artificial neural network outperformed the other models adopted in the empirical research.
HETEROTIC GROUP FORMATION IN PSIDIUM GUAJAVA L. BY ARTIFICIAL NEURAL NETWORK AND DISCRIMINANT ANALYSIS
