You will get HR Intelligence & Analytics System
Project details
An end-to-end Machine Learning application built with Python, Scikit-learn, XGBoost, and Streamlit to support Human Resources teams in making smarter hiring and employee retention decisions.
The system provides two intelligent prediction modules:
Employee Attrition Prediction – Predicts whether an employee is likely to leave the company based on demographic and professional attributes. Salary Prediction – Estimates the expected salary of a new candidate using information extracted from their CV, such as education, experience, skills, certifications, company size, industry, work location, and remote work status. 🚀 Project Overview
Employee turnover is one of the most expensive challenges for organizations. This project helps HR departments identify employees who are at risk of leaving before resignation occurs, enabling proactive retention strategies.
In addition, the application predicts an appropriate salary for new candidates, helping recruiters maintain fair and consistent compensation during the hiring process.
The application combines machine learning models with an interactive Streamlit interface to deliver real-time predictions in a user-friendly environment.
The system provides two intelligent prediction modules:
Employee Attrition Prediction – Predicts whether an employee is likely to leave the company based on demographic and professional attributes. Salary Prediction – Estimates the expected salary of a new candidate using information extracted from their CV, such as education, experience, skills, certifications, company size, industry, work location, and remote work status. 🚀 Project Overview
Employee turnover is one of the most expensive challenges for organizations. This project helps HR departments identify employees who are at risk of leaving before resignation occurs, enabling proactive retention strategies.
In addition, the application predicts an appropriate salary for new candidates, helping recruiters maintain fair and consistent compensation during the hiring process.
The application combines machine learning models with an interactive Streamlit interface to deliver real-time predictions in a user-friendly environment.
Data Tool
scikit-learnWhat's included
| Service Tiers |
Starter
$30
|
Standard
$80
|
Advanced
$180
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 5 | Unlimited |
Number of Graphs/Charts | 0 | 2 | 4 |
Number of Scenarios | 1 | 3 | 5 |
Number of Model Variations | 1 | 3 | 6 |
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Model Validation/Testing |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$20 - $40
Additional Revision
+$20
Additional Graph/Chart
(+ 1 Day)
+$20About Abdelrahman
Machine Learning
Suez, Egypt - 7:34 pm local time
I am a Machine Learning & Data Engineer specializing in building end-to-end ML models, statistical data pipelines, and scalable predictive systems that solve real business problems.
Core Expertise:
• Machine Learning: Predictive Modeling (XGBoost, LightGBM, Scikit-Learn), Feature Engineering, Regression & Classification.
• Data Processing & ETL: Data Pipelines, Data Cleaning & Preprocessing, Python (Pandas, Polars, PySpark).
• Testing & Analytics: A/B Testing, Hypothesis Testing, EDA, Time-Series Forecasting.
• Deployment & Infrastructure: Containerization (Docker), Git, MLflow, Experiment Tracking.
Tech Stack: Python | XGBoost | Scikit-Learn | Pandas
I focus on clean, production-grade code, reliable architecture, and clear communication.
Steps for completing your project
After purchasing the project, send requirements so Abdelrahman can start the project.
Delivery time starts when Abdelrahman receives requirements from you.
Abdelrahman works on your project following the steps below.
Revisions may occur after the delivery date.
HR Intelligence & Analytics System
An end-to-end Machine Learning app built with Python, XGBoost, and Streamlit for HR teams. It predicts employee attrition to reduce turnover and estimates fair candidate salaries based on CV data in real time through an interactive interface.