Predicting Employee Churn in Python

Predicting Employee Churn in Python

In this tutorial, you are going to cover the following topics: Employee Churn Analysis; Data loading and understanding feature; Exploratory data analysis and data visualization; Cluster analysis; Building a prediction model using a Gradient Boosting Tree; Evaluating model performance; Conclusion

Analyze employee churn, Why employees are leaving the company, and How to predict, who will leave the company?

In the past, most of the focus on the ‘rates’ such as attrition rate and retention rates. HR Managers compute the previous rates try to predict future rates using data warehousing tools. These rates present the aggregate impact of churn but this is the half picture. Another approach can be the focus on individual records in addition to aggregate.

There are lots of case studies on customer churn are available. In customer churn, you can predict who and when a customer will stop buying. Employee churn is similar to customer churn. It mainly focuses on the employee rather than the customer. Here, you can predict who, and when an employee will terminate the service. Employee churn is expensive, and incremental improvements will give big results. It will help us in designing better retention plans and improving employee satisfaction.

In this tutorial, you are going to cover the following topics:

  • Employee Churn Analysis
  • Data loading and understanding feature
  • Exploratory data analysis and data visualization
  • Cluster analysis
  • Building a prediction model using a Gradient Boosting Tree.
  • Evaluating model performance
  • Conclusion

For more such tutorials, projects, and courses visit DataCamp

Employee Churn Analysis

Employee churn can be defined as a leak or departure of an intellectual asset from a company or organization. or in simple words, you can say, when employees leave the organization is known as churn. another definition can be when a member of a population leaves a population, is known as churn.

In Research, it was found that employee churn will be affected by age, tenure, pay, job satisfaction, salary, working conditions, growth potential, and employee’s perceptions of fairness. Some other variables such as age, gender, ethnicity, education, and marital status, were important factors in the prediction of employee churn. In some cases such as the employee with niche, skills are harder to replace. It affects the ongoing work and productivity of existing employees. Acquiring new employees as a replacement has its own costs like hiring costs and training costs. Also, the new employee will take time to learn skills at a similar level of technical or business expertise knowledge of an older employee. Organizations tackle this problem by applying machine learning techniques to predict employee churn, which helps them in taking necessary actions.

The following points help you to understand, employee and customer churn in a better way:

  • The business chooses the employee to hire someone while in marketing you don’t get to choose your customers.
  • Employees will be the face of your company, and collectively, the employees produce everything your company does.
  • Lossing a customer affects revenues and brand image. acquiring new customers is difficult and costly compared to retain the existing customer. Employee churn also painful for companies in organizations. It requires time and effort in finding and training a replacement.

Employee churn has unique dynamics compared to customer churn. It helps us in designing better employee retention plans and improving employee satisfaction. Data science algorithms can predict future churn.

Exploratory Analysis

Exploratory Data Analysis is an initial process of analysis, in which you can summarize characteristics of data such as pattern, trends, outliers, and hypothesis testing using descriptive statistics and visualization.

data-science business-intelligence python machine-learning artificial-intelligence

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

Data Science Projects | Data Science | Machine Learning | Python

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.

Data Science Projects | Data Science | Machine Learning | Python

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.

Data Science Projects | Data Science | Machine Learning | Python

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.

Data Science Projects | Data Science | Machine Learning | Python

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.

Data Science Projects | Data Science | Machine Learning | Python

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.