Predicting The Occupation Progress of A Person Using Decision Tree-Based Analysis

Abstract

Many data accessed from different information systems are commonly not clear on how these interpret in a meaningful way to produce concrete information and how to embrace this innovation that could be used for a decision-making approach. Hence, this study involved predicting the occupation progress of a person using decision tree-based analysis. Using the Information System for Sex-disaggregated Data (ISSDD) platform, the datasets of residents' information were collected and cleaned for confidentiality. This involves the recording condition of inhabitants that could give an action by the government to improve the quality of life in their communities. Hence, the prediction made in this study analyzed the occupation progress of a resident based on their age, civil status, and highest educational attainment. The data mining approach was used to develop new knowledge for the in-depth interpretation of data such as identification of the source of information, collection of data points to be analyzed, extraction of relevant data content, classification of key values from the extracted data set, and evaluation and recording of results. The percent accuracy of the confusion matrix is 77.69% where 90.48% of single residents have a chance to be a success in their occupation over the 71.59% for the married.