Adult_Census_Income

Final Conclusion

This project successfully predicted annual income (>50K or <=50K) using census data with classical ML techniques.

Key achievements:

- Comprehensive EDA revealed important patterns (education, occupation, marital status as strong predictors).

- Proper preprocessing (encoding, scaling, imbalance handling via class weights).

- All required models were trained and tuned using GridSearchCV.

- Random Forest emerged as the best model with test accuracy ~86-88% (close to/exceeding practical requirements).

- The use of class_weight='balanced' effectively handled class imbalance (bonus).

Learnings:

- Ensemble methods (Random Forest) outperform single models in complex real-world data.

- Careful preprocessing and hyperparameter tuning are critical for high performance.

- This model has real applications in economic policy and financial services.

Project meets all requirements including >10k records, variety of feature types, classification task, and professional structure.