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🧠 Adult Income Classification Using Machine Learning

This project is focused on building and evaluating multiple machine learning models to predict whether an individual's annual income exceeds $50K, based on demographic and employment-related attributes from the Adult Census Income Dataset.

📌 Project Highlights

  • ✅ 91.39% Accuracy Achieved using Gradient Boosting
  • ✅ Compared 4 algorithms: Logistic Regression, Random Forest, Gradient Boosting, XGBoost
  • ✅ Used Optuna for hyperparameter optimization
  • ✅ Applied SMOTE for class balancing
  • ✅ Performed detailed data cleaning, encoding, and scaling
  • ✅ Real-world dataset: Adult Census Income Dataset (large, noisy & imbalanced)

📂 Dataset Info

  • Source: Kaggle (UCI Adult Dataset based, Pakistan-based version)
  • Target Column: income (<=50K or >50K)

Example Row:

age,workclass,education,marital.status,occupation,relationship,race,sex,capital.gain,hours.per.week,native.country,income
41,Private,Bachelors,Married,Exec-managerial,Husband,White,Male,0,40,United-States,>50K