Income Prediction Using Machine Learning
DOI:
https://doi.org/10.22105/scfa.v1i3.46Keywords:
Income prediction, Machine learning, Data preprocessing, KMeans clustering, Random Forest, Predictive analytics, Model evaluationAbstract
This initiative utilizes machine learning techniques to forecast personal income levels based on demographic and employment information. The research improves predictive precision by grouping individuals with similar traits using KMeans and applying algorithms such as Random Forest and XGBoost. Important data preprocessing procedures—Like managing missing values and encoding categorical variables—were crucial in enhancing model effectiveness. Of all the models assessed, Random Forest achieved the best accuracy.
This research highlights the importance of predicting income in areas such as finance, policymaking, and marketing, where insights based on data facilitate targeted decision-making. The study demonstrates how machine learning can offer accurate income predictions, allowing for well-informed decisions across various industries.
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