Look around the office. Chances are half of your coworkers are looking for a new job, according to the latest Gallup report.
High attrition numbers could be frightening for a human resource manager. However, it is possible to predict which employees have their eye on the door long before they hand in their two-week notice. Besides, knowing who might be at risk of leaving may even help HR retain the best talent.
In this blog, we are going to focus on how to predict attrition using binary classification algorithms and show how to use those inbuilt algorithms for addressing a real-life, common question for any organization. In this case: how can we identify which employees are likely to quit?
Our latest release of Oracle Data Visualization has built-in machine learning features. This means users can now build their own models from training data and use these trained models for prediction and classification. The platform comes equipped with a host of machine learning algorithms that can perform numeric prediction, multi and binary classification, and clustering. In addition, you can build your own custom model scripts for training and scoring.
Before we venture any further let us try to understand briefly what we mean by binary classification. Binary classification is a technique of classifying records/elements of a given dataset into two groups on the basis of classification rules for ex: Employee Attrition Prediction whether the employee is expected to Leave or Not Leave. Leave and Not Leave are the two different groups.
These classification rules are generated when we train a model using training dataset which contains information about the employees and whether the employee has left the company or not. Oracle Data Visualization ships with multiple algorithms that can perform Binary classification. Here is a snapshot showing list of inbuilt algorithms in Oracle Data Visualization that can perform binary classification:
Users can also upload their own Python/R scripts (with appropriate tags) which can perform Binary classification and these custom algorithms will show up in the list and can be used for prediction.
Now let us see how one of these inbuilt algorithms can be used to predict Employee Attrition prediction. Will this set of employees leave or not? Yes or No?
The recording below explains the process of model creation as well as prediction process (i.e. scoring using created a model).
Of course, seeing is believing. If you like what you see and you want to try it for yourself, visit www.oracle.com/goto/datavisualization to learn more about Oracle Data Visualization and get your free trial.