Joshua Plunkett Analytics

Turning Data into Action

Improve Employee Retention

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by

Josh Plunkett
in ,

HR Analysis and Random Forest Classifier

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Project Overview: Understanding and Predicting Employee Turnover

Objective

The primary aim of this case study is to uncover the reasons behind a high employee turnover rate for a stakeholder. Employee turnover refers to the rate at which employees leave a company and are replaced by new hires. High turnover can be costly and disruptive, so understanding its causes is crucial.

Approach

To achieve this goal, the project employs two main techniques:

  1. Exploratory Data Analysis (EDA):

    • What is EDA?: EDA is a method used to analyze and summarize the main characteristics of a dataset. It often involves visualizing data through charts and graphs to identify patterns, trends, and anomalies.
    • Purpose in this project: By examining the data related to employee demographics, job roles, performance metrics, and other relevant factors, EDA helps to identify potential reasons why employees might be leaving.

  2. Predictive Classification Model:

    • What is a Predictive Classification Model?: This is a type of machine learning model that categorizes data into predefined classes. In this context, it predicts whether an employee is likely to leave the company or stay.
    • Purpose in this project: The model uses historical data to learn patterns and make predictions about future employee behavior. This helps in identifying employees at risk of leaving and understanding the factors influencing their decisions.

Project Phases

The project is structured into several key phases:

  1. Data Introduction:

    • Collecting Data: Gathering relevant data from various sources such as employee records, surveys, and performance reviews.
    • Data Cleaning: Ensuring the data is accurate and free from errors or inconsistencies.

  2. Exploratory Data Analysis (EDA):

    • Visualizing Data: Creating charts and graphs to explore the data.
    • Identifying Patterns: Looking for trends and correlations that might explain employee turnover.

  3. Model Development:

    • Building the Model: Using machine learning algorithms to create a predictive model.
    • Training the Model: Feeding the model historical data so it can learn to make accurate predictions.

  4. Model Evaluation:

    • Testing the Model: Assessing the model’s accuracy and reliability using new data.
    • Refining the Model: Making adjustments to improve performance.

  5. Recommendations:

    • Providing Insights: Offering actionable recommendations based on the findings.
    • Strategic Planning: Helping the stakeholder develop strategies to reduce turnover and retain valuable employees.

Conclusion

This project showcases a comprehensive approach to tackling a critical business issue. By combining data analysis and machine learning, it not only identifies the root causes of high employee turnover but also provides predictive insights to prevent future occurrences.