Keywords
Decision Tree, Income, Classification
Description
This project applies the decision tree methodology to the adult income data to predict an individual’s income and determine the necessary factors that improve an individual’s income.
Abstract
Decision tree is a commonly used data mining methodology for performing classification tasks. It is a tree-based supervised machine learning algorithm that is used to classify or make predictions in a path of how previous questions are answered. Generally, the decision tree algorithm categorizes data into branch-like segments that develop into a tree that contains a root, nodes, and leaves. This project seeks to explore the decision tree methodology and apply it to the Adult Income dataset from the UCI Machine Learning Repository, to determine whether a person makes over 50K per year and determine the necessary factors that improve an individual’s income. The model was evaluated using the classification metrics. The results show a good performance of the model. Also, the feature importance scores were computed to determine the contributing factors that improve an individual’s income.
Semester
Spring 2023
Course Name
STA 6704 Data Mining 2
Instructor Name
Xie, Rui
College
College of Sciences
STARS Citation
Fiagbe, Roland, "Classification of Adult Income Using Decision Tree" (2023). Data Science and Data Mining. 3.
https://stars.library.ucf.edu/data-science-mining/3
Included in
Applied Statistics Commons, Data Science Commons, Statistical Methodology Commons, Statistical Models Commons