Predicting and Investigating Causes for Employee Attrition Rates

Bokyung Ko, Lorena Burrell, Ruqayyah Muse

CSC/DSCI 4810 – Spring 2023

This repository contains the data preprocessing and model implementation steps for an HR attrition dataset. The objective is to prepare the dataset for developing an AI-driven model that can then be used to predict employee attrition and help organizations implement effective retention strategies.

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Table of Contents

1. Introduction
2. Data Processing and Analysis
3. Implementing the Models
4. Usage
5. Conclusion

Introduction

Employee attrition is a significant concern for companies as it results in high recruitment and training costs, reduced productivity, and loss of valuable knowledge. The primary objective of this project is to preprocess and explore the HR attrition dataset to make it suitable for future machine learning model development.

Data Processing and Analysis

The data preprocessing steps are as follows:

1. Data Import and Exploration: Import necessary libraries, read data files, and display initial entries. The dataset comprises employee demographic information, performance metrics, and attrition status.
2. Dataset Merging and Cleaning: Join tables on employee_id, rename columns, and build a features dataframe. The dataset is cleaned by handling missing values and removing duplicate entries, if any.
3. Data Quality Reports: Create Data Quality Reports for continuous and categorical features. This step allows for a thorough understanding of the data distribution, unique values, missing values, and other essential characteristics.
4. Data Visualization: Visualize the dataset using Seaborn to understand the distribution and identify trends. This process highlights patterns and relationships between different features and the target variable, employee attrition.
5. Outlier Handling: Use the inter-quartile range method to handle outliers in continuous features, such as age, years of experience, and salary. Outliers can significantly impact model performance, and addressing them improves the overall quality of the data.
6. Data Normalization: Apply range normalization for continuous features to bring them within a similar scale. Normalizing the data helps ensure that the model does not prioritize features with larger magnitudes and thus improves model performance.
7. Export Processed Data: Export the final processed and encoded data to a .csv file.

Implementing the Models

In this section, we detail the reasoning behind our model selection, evaluation metrics, and hyperparameter optimization.

1. Model Selection: We chose three machine learning models - Decision Trees, Logistic Regression, and Naive Bayes - to address the employee attrition prediction problem. We selected these models because they represent different types of algorithms: Decision Trees are a classification model that test specific attributes, Logistic Regression is a simple yet powerful linear model, and Naive Bayes is a probabilistic classifier based on the Bayes Theorem. By choosing diverse models, we can compare their performances and understand the underlying data's complexity.
2. Evaluation Metrics: Accuracy, precision, recall, and F1-score are the evaluation metrics we used to assess our models' performances. These metrics provide a comprehensive picture of our models' ability to correctly predict employee attrition. We chose these metrics as they cater to the class imbalance in the dataset and provide a better understanding of the models' strengths and weaknesses. Furthermore, we used a confusion matrix to visualize the model's performance and identify areas for improvement.
3. Hyperparameter Optimization: Hyperparameter optimization is a crucial step to fine-tune our models and enhance their performance. We used Grid Search to optimize the hyper-parameters of our models. Hyperparameter optimization allows us to identify the best combination of parameters that leads to the highest performance on the test set. This process ensures that the model generalizes well to unseen data and improves its overall predictive capabilities.

Usage

To preprocess your own HR attrition dataset, follow these steps:

1. Clone this repository to your local machine.
2. Replace the existing dataset with your own dataset, ensuring the files have the same format and structure.
3. Run the data preprocessing script, adjusting any necessary parameters to match your dataset.
4. Verify that the output .csv file contains the processed data, ready for machine learning model development.

Conclusion

In this repository, we provide a comprehensive approach to preprocess an HR attrition dataset and show some basic implementation methods. The preprocessed dataset can now be used for the development of AI-driven attrition prediction models, ultimately helping organizations retain their workforce and reduce employee turnover costs.