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.

本研究旨在探索使用机器学习和数据挖掘技术解决收入不平等问题。使用 UCI 成年人数据集进行分类，并采用 Gradient Boosting 分类器模型，最终达到了 88.16％的准确率，打破了现有工作的基准准确率。