This project applies clustering techniques to IBM HR analytics data to identify patterns and insights that can guide decision-making in human resources management. The analysis leverages various machine learning methods to uncover employee trends and segments.
- Introduction
- Objectives
- Data Overview
- Methods
- Results and Insights
- Model Performance
- How to Use
- Contributors
Human resource analytics is crucial for understanding workforce dynamics and improving organizational outcomes. This project employs clustering algorithms to explore patterns in employee data, focusing on areas like attrition, performance, and engagement.
- Identify key employee clusters based on demographic and behavioral attributes.
- Provide actionable insights to improve employee retention and satisfaction.
- Assess and visualize the performance of various clustering models.
The dataset used in this project includes anonymized HR metrics, such as:
- Demographic Information
- Job Role and Satisfaction
- Attrition Rate
- Performance Scores
The following techniques were employed:
- Data Preprocessing: Handling missing values, scaling features, and encoding categorical variables.
- Clustering Algorithms:
- K-Means
- Hierarchical Clustering
- DBSCAN
- Evaluation Metrics: Silhouette Score, Davies-Bouldin Index, and more.
- Visualization: PCA and t-SNE for dimensionality reduction.
Key findings include:
- [Add high-level insights here, e.g., "Employees with higher engagement scores were less likely to leave the company."]
- Visual representations of clusters revealed distinct employee groups.
Visualization of Results
- Add performance graphs and tables here, such as:
- A bar chart comparing silhouette scores across models.
- A scatter plot showing clustered data in reduced dimensions.
- A table summarizing evaluation metrics for each algorithm.
- Clone the repository: