Abstract:
Happiness has become a major concern across many disciplines starting form public policy, economics and psychology because of the effects that come with not being happy. Psychologist would want to know the effects of low levels of happiness, economist would want to know the effects of levels of happiness in to the market place, researchers from health would be concerned with effects of high and low levels of happiness to health status. While predominantly, people had just a philosophical notion about happiness, currently there are numerous scientific studies on happiness. Approaches like cluster analysis have been employed before. This research used neural networks to classify multinomial levels of happiness of Kenyan youths by considering life style aspects of current life such as Internet usage, Physical activeness, Health, Social life, Education, Income, Country’s top leadership, Dining and Sleeping Habits. The research was able to fit a 14-1-4 neural network model to classify levels of happiness in Kenyan youths, an accuracy of 73% was achieved. The data was randomly split in to 70% training set and 30% test set. The training set was balanced using SMOTE approach. This research trained the model by applying gradient descent using error back propagation algorithm with initial weights drawn from uniform distribution and applied softmax activation function. Accuracy metrics were confusion matrix, precision and recall for each level of happiness, and F-Scores. The top leading factor related to happiness positively was physical activeness with youths who were more active being happier. The second factor was relationship type, the married youths were happier than the singles, separated or engaged. Youths who were more satisfied with their relationship, they were happier. Health was also positively related to happiness. On the other hand, the number of hours a youth spent on social media negatively affected their levels of happiness. The more the number of hours the low levels of happiness.
