Abstract:
As a result of technological enhancement, freelancing has become a
significant business field all over the world. During the COVID-19 pandemic,
millions of people worldwide lost their jobs, and some countries are facing
financial crises in different ways because of the low foreign exchange
reserves. Therefore, freelancing is a proper solution for those kinds of
situations and it is become important to find the freelancers’ job satisfaction.
The main objective of this study is to create a model to predict the job
satisfaction of freelancers in Sri Lanka using machine learning algorithms. It’s
potential to do this study since no previous research is directly relevant to this
study. Primary data is gathered through social media platforms like Facebook,
WhatsApp, and LinkedIn from freelancers in Sri Lanka using a Google form.
Initially, the collected data is pre-processed and the model is created by
analyzing the data set using five supervised machine learning algorithms such
as Naïve Bayes, Support Vector Machine (SVM), Decision tree (J48), Random
Forest, and Multilayer Perception (MLP). In this study, the cross-validation
test option is used, and 10 folds showed a better output. The decision tree
shows the best results among those algorithms shown as 92.5% accuracy rate
as the highest accuracy including the highest precision, recall, and f-measure.
Root Mean Square Error (RMSE) and Mean Absolute Error is the lowest in
the decision tree algorithm. The result will help to predict the job satisfaction
of freelancers and make relevant arrangements at the earliest for the
freelancers’ issues.
