🌟 Adult Income Classification – Machine Learning Flask Web App

Predict whether a person earns > 50K or ≤ 50K using a machine-learning model trained on the Adult Census Income Dataset (UCI).

This project includes a fully functional Flask web application with a clean UI for interacting with the prediction model.

📌 Project Highlights

• 🚀 Flask Web App with clean UI
• 🤖 Machine Learning Model (model.pkl)
• 🧠 Feature engineering with One-Hot Encoding + Education Mapping
• 📊 Handles both categorical & numerical inputs
• ✔ Automatic feature alignment with trained model
• 🌍 Ready for deployment (Heroku / Render / PythonAnywhere / AWS)

📸 Screenshots

Home Page	Prediction Result

📁 Project Structure

│── app.py │── model.pkl │── templates/ │ └── index.html │── static/ │ └── style.css │── README.md

🔹 Input Features Captured:

• Age
• Workclass
• Education
• Marital Status
• Occupation
• Relationship
• Sex
• Capital Gain
• Capital Loss
• Hours per Week
• Country

🔹 Education Level Mapping

Education values are converted into numerical levels before prediction, e.g.:

Preschool → 1 Doctorate → 16

🔹 One-Hot Encoding Applied To:

Workclass Occupation Marital_status Relationship

🔹 Automatic Feature Alignment

Ensures clean matching with the model’s training columns:

📦 Model Training Overview

The ML model was trained using:

Pandas & Scikit-Learn

One-hot encoding for categorical features

Numerical encoding for education

Feature alignment with column consistency

Exported using pickle (model.pkl)

🛠 Technologies Used

Python

Flask

Pandas

Scikit-Learn

HTML / CSS

⭐ Contributing

Feel free to fork this repository and submit improvements or suggestions!

👍 Support the Project

If you find this project useful, please star the repository ⭐