Employee Retention Prediction: Enhancing Workforce Stability

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Employee Retention Prediction
BY
Subhash kumar
Batch :- AND-JUN2024-DSAI-1
Room NO :- TR 2
Place :- Mumbai

Agenda
Introduction
Importance of employee
retention
Project objectives
Understanding the
Dataset
Overview of features
Initial observations
(missing values,
correlations)
Data Preparation
Handling missing values
Encoding categorical
variables
Data
Transformation
Scaling and feature
transformations
5. Model Selection
1. Algorithms evaluated
2. Evaluation metrics
6. Model Optimization
a. Hyperparameter tuning
b. Performance improvements
7. Predicting Attrition
a. Model performance (accuracy, ROC AUC)
8. Conclusion

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Importance of employee retention
• Employee retention is a critical issue for organizations as losing
valuable employees can lead to increased costs, reduced
productivity, and a negative impact on team morale.
Objectives of the project
• This project focuses on using machine learning to predict
employee attrition, helping organizations proactively
address retention challenges.
Introduction

Understanding the Dataset
The dataset used for this project includes key employee details such as demographics,
experience, education, and job-related attributes
Our initial findings revealed two key points:
--Missing values were present in some columns.

There were notable
correlations
between certain
features, which
could indicate
relationships
affecting attrition.

Preparing the Data
To ensure the data was ready for modeling, we applied several preprocessing steps:
We addressed missing values by imputing the mean for numerical features and the mode
for categorical ones
Categorical data, such as job roles or education levels, was transformed into numeric
formats using one-hot encoding.
These steps were critical in standardizing the dataset and making it usable for machine
learning models.

Transforming the Data
• Next, we scaled the numerical features using a
StandardScaler to normalize the data. This step
was especially important for algorithms
sensitive to feature magnitudes.
• Additionally, we transformed specific features
like 'experience levels' into numerical
equivalents, ensuring consistency and
interpretability.

Choosing the Right Model
• Several machine learning models were
tested, including Logistic Regression,
Random Forest, XGBoost, and
LightGBM
• We evaluated these models using
metrics such as accuracy and the ROC
AUC score. As shown in this below graph
—
• LightGBM outperformed the other
models, demonstrating its ability to
handle large datasets with categorical
variables efficiently."

Improving the Model with Hyperparameter
Tuning
• To enhance model performance, we
optimized the LightGBM classifier using
RandomizedSearchCV.
• RandomizedSearchCV is used for
hyperparameter tuning because it is more
efficient and flexible by randomly sampling
hyperparameters, especially for large
parameter spaces.
• This process allowed us to fine-tune
hyperparameters such as the number of
estimators and learning rate. The results
indicate a noticeable improvement in
accuracy and ROC AUC after tuning.

Predicting Employee Attrition
• With an optimized LightGBM model
achieving 82%accuracy, we were able to
predict employees at risk of attrition
effectively.
• This demonstrates the model’s performance and
its ability to classify high-risk employees accurately.

Impact and Future
Directions
• In terms of impact, this project provides
actionable insights for HR teams to
improve retention, reduce recruitment
costs, and enhance workforce stability.

Conclusion
We developed a model to predict if a data scientist is looking for a job
change, achieving 82% accuracy. This helps the firm improve talent
retention, recruitment planning, and workforce management. By
identifying employees at risk of leaving, the company can take steps to
keep them, resulting in a more stable and motivated team..

Questions ?

Thank You!

Employee Retention Prediction: Enhancing Workforce Stability

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