A Statistical Approach to Adult Census Income Level Prediction

TL;DR

This paper applies a statistical machine learning approach, specifically gradient boosting, to predict adult income levels using the UCI dataset, achieving high accuracy and addressing economic inequality.

Contribution

It introduces the use of gradient boosting classifier for income prediction, surpassing existing benchmark accuracies in this domain.

Findings

Gradient Boosting achieved 88.16% accuracy.

The approach outperforms previous models.

The study demonstrates machine learning's potential in economic inequality analysis.

Abstract

The prominent inequality of wealth and income is a huge concern especially in the United States. The likelihood of diminishing poverty is one valid reason to reduce the world's surging level of economic inequality. The principle of universal moral equality ensures sustainable development and improve the economic stability of a nation. Governments in different countries have been trying their best to address this problem and provide an optimal solution. This study aims to show the usage of machine learning and data mining techniques in providing a solution to the income equality problem. The UCI Adult Dataset has been used for the purpose. Classification has been done to predict whether a person's yearly income in US falls in the income category of either greater than 50K Dollars or less equal to 50K Dollars category based on a certain set of attributes. The Gradient Boosting Classifier Model was deployed which clocked the highest accuracy of 88.16%, eventually breaking the benchmark accuracy of existing works.