This project aims to predict whether an individual's annual income exceeds $50K based on various demographic and employment-related features using machine learning. It includes data preprocessing, model training, evaluation, and a user-friendly interface built with Streamlit.
Given a dataset with attributes like age, education, occupation, hours per week, etc., the goal is to build a model that predicts whether a person earns more than $50K or less than or equal to $50K per year.
- Cleaned and preprocessed real-world dataset (UCI Adult dataset)
- Visual Exploratory Data Analysis (EDA)
- Training of classification models (Logistic Regression, Decision Tree, Random Forest)
- Model evaluation with accuracy, confusion matrix, and classification report
- Streamlit web app for interactive salary prediction
- Python 3
- Pandas and NumPy for data manipulation
- Matplotlib and Seaborn for visualization
- Scikit-learn for machine learning models
- Streamlit for frontend deployment
