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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