TalentPulse transforms raw job postings into real time insights. Here’s the roadmap of the system step by step from data to decision.
Aggregate job postings via APIs, scraping or datasets. Normalize fields like title, company, location, salary and skills.
Clean and enrich data with feature engineering using Pandas/Numpy: extract skills, compute salary ranges, encode locations and skills.
Train regression models to predict Time to Hire. Version models and store predictions alongside each job posting.
Django REST Framework exposes endpoints for jobs, skills, analytics (top skills, jobs by location, salary averages) and exports.
A live dashboard powered by Chart.js Users filter by Location and Skill, exploring demand, salaries and predicted outcomes.
APIs, scraping, datasets normalized into structured jobs.
Cleaning, feature engineering, encoding features.
Predicting time to hire with regression models.
Endpoints for analytics and export.
Interactive charts with filters and insights.
Recruitment is most effective when it is equitable, consistent and guided by datadriven insights.
TalentPulse was built to make recruitment smarter:
highlighting in demand skills, providing salary transparency and forecasting hiring timelines.
This project is a fusion of Backend Engineering,
Machine Learning and
Data Visualization
demonstrating how system understanding translates into real business value.
A real time analytics dashboard showing skills demand, salary insights, and predicted time to hire.
| Title | Company | Location | Salary | Prediction (days) |
|---|