Income Classification Model using Machine Learning (Python)

Built a supervised machine learning model to predict income level based on census demographic data.

The project included full data preprocessing pipeline, feature engineering, model training, and evaluation.

Key Steps:

• Data cleaning & handling missing values

• Feature encoding (One-Hot Encoding)

• Feature scaling

• Model training using Logistic Regression & Random Forest

• Model evaluation using Accuracy, Precision, Recall, and F1-score

The final model achieved strong classification performance and demonstrated practical application of ML in socioeconomic prediction.

Tools Used:

Python, Pandas, NumPy, Scikit-Learn, Matplotlib