CareerMap : Segmenting Job Seekers Using Unsupervised Learning

Project Overview

This project applies clustering algorithms to segment job seekers based on their skills, experience, education level, and career goals. The objective is to group individuals into categories that can help recruiters offer better-targeted opportunities and support services.

Objective

To cluster job seekers into meaningful groups using unsupervised learning, enabling data-driven career matching and personalized job recommendations.

Key Skills Applied

Data Preprocessing (Handling categorical data, scaling)
Feature Engineering (Skill encoding, experience binning)
Clustering Techniques (K-Means, Hierarchical Clustering)
Dimensionality Reduction (PCA, t-SNE for visualization)
Model Evaluation (Accuracy, Precision, Recall, F1-Score)

Tools & Libraries Used

Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn

Learning Outcomes

Learned to work with real-world candidate profile data
Applied clustering to identify distinct career paths (e.g., entry-level tech, mid-career management, skill-focused specialists)
Understood how segmentation helps in career guidance and recruitment workflows
Practiced visualizing high-dimensional clusters for interpretation

Result

Segmented job seekers into [e.g., 4 major groups], helping streamline candidate targeting for training, mentorship, or job placement — such as:

CareerMap : Segmenting Job Seekers Using Unsupervised Learning