Project Overview

About Employee Salary Prediction System

AI-powered salary estimation system built with Flask, Python, and Machine Learning.

250K+

Training Records

91%

Model Score

6

Input Features

Project Introduction

The Employee Salary Prediction System is an AI-powered web application designed to predict employee salaries based on experience, education, skills, job role, company size, and location.

The system uses Machine Learning to analyze historical salary data and generate estimated salary results. It helps users understand expected salary ranges and supports HR departments in data-driven decision making.

Quick Facts
  • AlgorithmRandom Forest
  • Dataset size~250,000 rows
  • Accuracy (R2)0.91
  • BackendFlask
  • StorageNeon (Postgres)

Objectives

  • Predict employee salaries using Machine Learning.
  • Provide salary estimation through a web application.
  • Allow users to enter employee profile details.
  • Support data-driven hiring decisions.
  • Build a user-friendly Flask-based system.

Technologies Used

  • Python Programming Language
  • Flask Backend Framework
  • HTML, CSS, Bootstrap
  • Pandas and NumPy
  • Scikit-Learn Machine Learning Library
  • Pickle for Model Saving

Machine Learning Model

The system uses a Random Forest Regression model trained on approximately 250,000 salary records.

The model achieved an R2 score of 0.91, which shows strong prediction performance for this project.

Dataset Features

  • Job Title
  • Experience Years
  • Education Level
  • Skills Count
  • Company Size
  • Location

How the System Works

1
User Input

User enters employee details through the prediction form.

2
Model Processing

Flask sends the encoded data to the trained ML model.

3
Salary Output

The system displays the predicted annual salary.