Enhancing Employee Retention:Unsupervised Learning for Segmentation and Personalized Strategies

My task is to use unsupervised learning techniques to define employee segments and make recommendations to increase retention within each one

In this project, I explored HR data in a company using unsupervised learning. Unsupervised learning, finding the patterns or relationships in data, can enhance my understanding of underlying structures within the data without predefined labels or categories. By applying unsupervised learning techniques in HR data, such as clustering or dimensionality reduction, I can gain valuable insights about Employee Retention.

In business perspective, I should start with a clearly difined scope: Who are my end users or stakeholders? What businsess problems am I trying to help them solve? Is this a supervised or unsupervised learning problem? What data do I need for my analysis?

After knowing these answers of the questions, I need to explore the data first, namely, EDA. Exploratory data analysis (EDA) is all about exploring and understanding the data I am working with before applying models or algorithms.

A popular saying within data science is “garbage in, garbage out”, which means that cleaning data properly is key to producing accurate and reliable results.

In general, the unsupervised learning workflow is: Scoping the project, Gathering data,Cleaning data,Exploring data,Modeling data,Sharing Insights.

My code solution is here, I followed the workflow step to finish the project, and applied clusters and dimensionality reduction in the project. [Code]

   
       
   
CSV dataset
   
       
   
cluster with department column, apparently department features dominate the visualization.
   
       
   
cluster without department column.
   
       
   
PCA with 3 components in 3D plot