Employee absenteeism is a massive problem for organizations of all sizes. According to the Bureau of Labor Statistics, the average worker has about a 3% absence rate. Nationwide, this translates to a loss of around $226 billion each year, or $1,685 per employee.
Predicting employee absenteeism is essential to avoid these losses. Akkio makes this easy with our no-code machine learning algorithms. With Akkio, you can build a machine learning model to predict employee absences with just a few clicks.
This is important because predicting absences is difficult. Human behavior is complex, and there are many factors that can contribute to an absence. With Akkio, you can build a model that takes all of these factors into account and predicts absences with greater accuracy than any human could.
With Akkio, you can also easily deploy your model in any setting. You can use our API to deploy your model in a web application, or you can use our automation tools to deploy your model in any other setting.
Overall, Akkio makes it easy to predict employee absences and avoid the losses in productivity that come with them.
We’ll use a Kaggle absenteeism dataset, which includes the following columns:
The dataset is synthetic, but we can use it to demonstrate the process of building and deploying an absenteeism prediction model. With a model to predict absent hours, businesses could identify which shift patterns have the most potential for absences, and could also manage staffing levels more effectively.
Each of the dataset’s columns is potentially indicative of absenteeism hours, and each will be given a corresponding weight in the model. For example, perhaps employees at certain departments and certain stores have higher absent hours, but only if they have a low service length.
These sorts of relationships in the data are automatically discovered by the model. Crucially, the file includes our desired target variable, or the number of absent hours. We can use machine learning to predict absent hours, and then allocate resources or reach out to workers accordingly.
There are many factors that could affect absent hours. With Akkio, you can build custom models to predict and minimize absenteeism. And with our drag-and-drop interface, you can build any predictive model you want. First, we’ll just download the dataset as a CSV, and upload it to Akkio in a new model flow. You'll get a preview of the dataset, including modifiable data types, columns, and row count.
Now, we can click on the second step in the AI flow, which is “Predict.” Under “predict fields” you can select the column to predict, named “AbsentHours.”
You can add or remove as many columns as you’d like, and we'll build a model based on the selected columns. Then just hit “Create Predictive Model,” and you’re done.
You can select a longer or shorter training time—ranging from 10 seconds to 5 minutes—for potentially more accurate models. Keep in mind that longer training times will not always necessarily perform better.
Also note that you don’t pay for model training time, unlike with many typical automated machine learning tools, so feel free to build as many models as you’d like.
After a model is created, you get a simple overview, which highlights the top fields, model prediction quality, sample predictions, data segments, and more.
For example, segment 1 highlights an employee group that has particularly high absent hours. We can see that these employees are more likely to be at stores like Prince George and Cranbrook, and very likely to be cashiers.
In contrast, segment 3 highlights employees that have the lowest absent hours. These employees are more likely to be at stores like Fort St John and Surrey.
There’s also the option to “See Model Report,” which lets us easily collaborate with others and share these model details with anyone, even if they don’t have an Akkio account. In short, we’ve discovered that it’s very easy to build an accurate model to optimize inventory.
Now that we’ve built a model, it’s time to deploy it in the real world.
With Akkio, it becomes trivial to deploy complex machine learning models. Deployment via web is an easy way to instantly serve predictions. It’s also possible to deploy through Salesforce, Google Sheets, Snowflake, and the Akkio API, with many more methods coming soon.
Further, you could deploy the model in practically any setting with Zapier, a no-code automation tool that connects tools with “Zaps.” Zapier allows you to link up thousands of different tools, so no matter where your data is coming from, it’s easy to connect it to Akkio to get a prediction, even if you don’t have any technical expertise.
If you’re looking for more technical power, you can also use Akkio’s API, which is formatted as a Curl command, allowing you to send a GET request that contains your flow key, API key, and input data, and get a prediction back.
We've explored how to use Akkio to predict employee absenteeism in this article. We started by importing a dataset of historical data, and then we chose a column to predict (absent hours). Finally, we built a model with Akkio's easy-to-use interface, and we deployed the model in a web application.
With Akkio, predicting employee absenteeism is easy and accurate. You can build custom models that take into account any number of factors, and you can deploy your models in any setting. Akkio enables many other use-cases, such as predicting credit card fraud, scoring leads, predicting churn, and more. So don't wait, sign up to Akkio for free today!