Welcome to the Income Prediction Project! In this project, the goal is to create a machine learning model to predict whether an individual earns above or below a certain income threshold. The dataset includes information on approximately 200,000 individuals, with key variables such as age, gender, education, and employment details.
The dataset comprises both training and test sets, totaling around 300,000 individuals. The target variable is whether an individual earns above 50,000 of a specific currency. Key features include demographic information, employment details, and financial indicators.
The objective is to build a machine learning model that aids in predicting income levels, offering insights for policy-making, financial planning, and resource allocation. The model aims to contribute to understanding and addressing income inequality issues.
The F1 score is used as the primary evaluation metric, combining precision and recall. The closer the score is to 1, the better the model. The F1 score reflects the model's ability to balance precision and recall in predicting income levels.
This project draws inspiration from the challenge of predicting income levels, akin to similar challenges in the field of text classification. The dataset's diverse variables provide an opportunity to explore and implement machine learning techniques for valuable predictions. Happy coding and predicting income levels! 🚀