Predictive Analytics of a Community Survey Data by Artificial Neural Network - A Subset of Machine Learning

Objective: To evaluate the capacity of artificial neural network modeling in quantification relative contribution various factors towards happiness index university faculty members and to adjudge degree agreement with results descriptive statistical analysis under hard societal situation. Methods: A relational-research is conducted by statistics ANN 93 variables, grouped into 24 major variables. The primary data are obtained through surveying after random convenient sampling self-administered questionnaire based on five-point Likert scale. study 350 members; 273 duly filled questionnaires received. stemming from varying perceptions teachers highly nonlinear. chosen for its capability capture high nonlinearity; a gold standard method comparison learning tools like multiple regression or logistic reveals it superiority; sample size driven predictive uncertainty makes machine unsuitable. Findings: shows that independent variable ‘salary’ has 46% negative weightage attainment whereas, given working condition related record 45-48% weightage. Descriptive corroborate result, showing salary, job satisfaction work environment cause dissatisfaction recording poor ~ 65%. Novelty: novelty lies implementation community survey generated identification significant affecting members. Consideration influencing input examine if prediction corroborates test significance adds credence present approach hitherto unreported. Keywords: Happiness index; Artificial Neural Network; statistics; Training; student’s ttest; Job