Employee Salary Prediction – ML Web App

This project is a machine learning web application built using Streamlit to predict whether an employee earns more than 50K annually based on features like age, education, occupation, etc. Overview

• Predicts employee salary category (>50K or ≤50K)

• Includes visual analysis of the dataset (pie charts, count plots, heatmap)

• Compares multiple ML models:

• Logistic Regression

• K-Nearest Neighbors

• MLP Classifier

• Accepts user inputs for prediction through an interactive form

  Files and Structure

├── app.py # Main Streamlit app ├── model_comparison.py # ML model training and evaluation ├── data/ │ └── adult 3.csv # Dataset used ├── assets/ # Visualizations (optional) ├── requirements.txt # Python packages

Dataset

• Name: UCI Adult Income Dataset
• Target: income ( >50K or ≤50K )
• Rows: ~48,000+

⚙️ Tech Stack

• Python, Pandas, Scikit-learn
• Streamlit for UI
• Seaborn, Matplotlib for visualization

🚀 How to Run Locally

pip install -r requirements.txt
streamlit run app.py