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A deep learning model capable of predicting your income based on Age, Sex, Race, Education, Marital-Status, working hours/week, native country, and occupation with an accuracy of almost 85%.
This project focuses on predicting the income of individuals based on a diverse set of demographic and socio-economic features. Using the Adult Income dataset, I used a Random Forest model to address this classification task.
I analyze and explore US Census Bureau Data using Data Visualization techniques to identify salient features useful for predicting an individual's income level. We use those relevant features and multiple classification methods (Decision-Tree, SVM, and K-Nearest Neighbor) to predict the income level for unknown individuals. Our client is a local…
🔍✨ A machine learning project that predicts income based on various demographic factors using Random Forest and Gradient Boosting algorithms. Includes data preprocessing, hyperparameter tuning, and model evaluation with detailed performance metrics. 📊🤖