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Machine Learning Based Multi-Variable Happiness Index Prediction Model
(UMT, Lahore, 2024) Anzar Nawaiz
Happiness index is one of the new way to understand the happiness quantitatively. The introduction of the happiness report in 2011 and its availability afterward every year have given the happiness more scope to be studied and to be further understood as well. The key goal of the thesis was to develop and suggest a machine learning based model which can be further utilized, in the prediction of Happiness Index. The datasets used in the thesis were relevant to the happiness report and the Global key indexes including the social, economic and educational factors. The data for 169 countries was considered. The thesis follows a systematic approach and including the steps of the initial data analysis, followed by the data preprocessing and further applying the models. Machine learning models including the regression models (Multiple, Gradient Boosting, SVM), classification models including the random forest and KNN has been utilized. Along with this a basic Deep learning model of ANN has been utilized to make the approach Key visualizations and metrics which were relevant to the output of the model were utilized to compare the techniques. Based on the results from the approaches applied, classification model of the KNN tends to give the most accurate results with the highest model accuracy and better predictions.