Income Prediction Model

In an era of data-driven decision-making, predicting individual income levels plays a pivotal role in various socio-economic applications, such as targeted marketing, social welfare distribution, and financial planning. In this project, we employ the power of Random Forest, a robust and versatile machine learning algorithm, to develop a predictive model for estimating an individual's income level.

Our model showcases an impressive 86% accuracy rate, achieved through rigorous hyperparameter tuning and careful feature selection. With 92 thoughtfully curated features, excluding the target variable income, our model demonstrates a remarkable capability to discern the underlying patterns that influence earnings. The project's significance lies not only in its predictive accuracy but also in the identification of key factors that drive income differentiation. Notably, age, capital gain, education level, hours worked per week, and marital status emerge as influential determinants in predicting income levels.

This project serves as a valuable resource for stakeholders across a spectrum of domains, enabling them to make informed decisions based on income predictions that are not only highly accurate but also underpinned by a comprehensive understanding of the contributing factors. In the following sections, we delve deeper into our methodology, feature engineering, and model evaluation, providing insights into the inner workings of our Random Forest model, while also highlighting its practical implications in real-world scenarios.