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Reddy Ranjith Kumar - Project

This document presents a project on employee salary prediction using machine learning, focusing on estimating salaries based on demographics and job-related factors. The system utilizes the LightGBM algorithm for classification and features an interactive frontend developed with Streamlit. The project aims to enhance hiring strategies and reduce bias in HR decisions, demonstrating successful implementation and high accuracy in predictions.

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hritiks221
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6 views13 pages

Reddy Ranjith Kumar - Project

This document presents a project on employee salary prediction using machine learning, focusing on estimating salaries based on demographics and job-related factors. The system utilizes the LightGBM algorithm for classification and features an interactive frontend developed with Streamlit. The project aims to enhance hiring strategies and reduce bias in HR decisions, demonstrating successful implementation and high accuracy in predictions.

Uploaded by

hritiks221
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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EMPLOYEE SALARY PREDICTION USING

MACHINE LEARNING

Presented By:
1. Reddy Ranjith Kumar-Raghu Engineering College-ECE
OUTLINE
 Problem Statement
 System Development Approach
 Algorithm & Deployment (Step by Step Procedure)

 Result
 Conclusion

 Future Scope
 References
PROBLEM STATEMENT
▪ In today’s competitive job market, estimating a fair salary based
on an employee’s education, experience, job type, and other
demographics is essential for HR professionals and job seekers
alike.
▪ This project aims to build a predictive system using machine
learning that accurately estimates whether a person’s salary is
above or below 50k per year, based on census income data. The
main goal is to improve hiring strategies, reduce bias, and assist
in data-driven HR decisions.
SYSTEM APPROACH
 System requirements
Python
Jupyter Notebook/ VS Code
Streamlit for Web App development
 Library required to build the model
pandas, numpy
scikit- learn
lightgbm
matplotlib, seaborn
streamlit
ALGORITHM & DEPLOYMENT
 Load dataset and clean missing values.
 Chose LightGBM, a gradient boosting framework known for
performance and speed.
 Train the LightGBM classifier. Evaluate using accuracy, pressision, and
confusion matrix.
 Developed an interactive frontend using Streamlit where users can
input their profiles to predict salary class.
 Hosted locally and optionally on platform like Streamlit.
RESULT
RESULT
RESULT
RESULT
RESULT

 Git hub link: https://github.com/508522-


tech/salary_predector_app.git
CONCLUSION
 The project successfully demonstrates the use of machine learning to
predict whether an individual's income exceeds $50,000 based on
demographic and occupational features. The LightGBM model
provided high accuracy and fast training performance. Challenges
included handling imbalanced classes and encoding categorical
variables properly. The Streamlit app offers an intuitive interface for
non-technical users to use the model effectively.
.
REFERENCES

 UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/adult

 LightGBM Documentation: https://lightgbm.readthedocs.io

 Scikit-learn Documentation: https://scikit-learn.org


 Streamlit Docs: https://docs.streamlit.io
THANK YOU

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