Employee Churn Prediction Using Machine Learning

Overview

This project focuses on predicting employee churn using machine learning techniques. By analyzing employee data, it aims to identify key factors contributing to churn and generate actionable insights for workforce management.

Features

• Data Analysis: Exploratory analysis to uncover patterns in employee demographics, salaries, and performance.
• Machine Learning Models: Implemented and evaluated predictive models to classify employee churn.
• Data Visualization: Utilized Tableau dashboards and Python libraries (e.g., Matplotlib, Seaborn) for insightful visualizations.
• Actionable Insights: Delivered insights on attrition factors, experience-salary correlations, and workforce dynamics.

Tools and Technologies

• Programming Languages: Python
• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
• Visualization Tools: Tableau
• Algorithms: Logistic Regression, Decision Trees, Random Forest, Gradient Boosting

Project Workflow

1. Data Preprocessing: Cleaning and preparing data for model training.
2. Feature Engineering: Extracting meaningful features to improve model accuracy.
3. Model Building: Training and testing multiple machine learning models.
4. Evaluation: Comparing models using performance metrics like accuracy, precision, recall, and F1-score.
5. Visualization: Generating dashboards to visualize findings and communicate results effectively.

Results

• Achieved high accuracy in predicting employee churn.
• Identified critical factors influencing employee retention, such as salary, job satisfaction, and work-life balance.
• Delivered actionable insights to improve workforce strategies.