Project Summary: Understanding Employee Segments for Enhanced Retention

Goal: This project focuses on analyzing the company’s employees to identify distinct segments and provide strategic recommendations to improve employee retention. By leveraging data analysis and clustering techniques, the project aims to uncover key behavioral and performance patterns across different employee groups.

Tools Used: The project will utilize several key tools and libraries for data manipulation, visualization, and clustering:

• Pandas and NumPy for data preparation and analysis.
• Matplotlib and Seaborn for data visualization.
• KMeans Clustering and PCA (Principal Component Analysis) from scikit-learn for employee segmentation and dimensionality reduction.
• Custom functions will also be built to streamline various processes throughout the project.

Scope:

1. Data Preparation & Exploratory Data Analysis (EDA): The first step involved cleaning and preparing the dataset to ensure its readiness for further analysis. EDA was conducted to explore the features of the employee dataset, identifying any patterns, correlations, or anomalies that could influence the modeling process.
2. K-Means Clustering (Round 1): Initial segmentation was performed using K-Means clustering. This unsupervised learning technique was applied to group employees into clusters based on their features, such as performance, job level, and income. These initial segments provided an overview of the workforce’s diverse characteristics.
3. Principal Component Analysis (PCA) for Visualization (Round 1): To better understand and visualize the employee segments, PCA was used to reduce the dimensionality of the data. This allowed for easier interpretation of the clustering results by projecting the complex data into a simpler visual format, highlighting the relationships between clusters.
4. Refining K-Means Clustering (Round 2): Insights gained from the initial clustering and visualization were used to refine the clustering process. By adjusting parameters and re-evaluating the data, more accurate and meaningful employee segments were identified.
5. PCA for Visualization (Round 2): Following the refined clustering, PCA was applied again to visualize the improved clusters. This second round of visualization helped in validating the new segmentation and provided clearer insights into the structure and relationships between employee groups.
6. Exploratory Data Analysis on Clusters: A deeper analysis was conducted on the final clusters to explore the specific characteristics of each employee segment. This phase focused on understanding behavior, job satisfaction, performance ratings, and other key metrics within each group.
7. Recommendations: Based on the insights derived from the clustering analysis, tailored recommendations were made to improve employee retention. These suggestions address the unique needs and challenges of each employee segment, aiming to enhance satisfaction, performance, and long-term engagement across the company.

The final outcome of this project provides the company with a clearer understanding of its employee segments, enabling data-driven strategies to improve retention and foster a more supportive work environment.