Sales forecasting with spreadsheet integration
How deep learning can solve real business problems directly from your spreadsheets
Build a model that predicts daily sales revenue from many parameters.
The platform lets you deploy this model for production, allowing you to directly integrate predictions in your Google Sheets or integrate predictions in your Microsoft Excel spreadsheets.
- Target audience: Beginners
- Tutorial type: Get started tutorial
- Estimated time: Setup - 10 min | Training - 5 min | Deployment - 1 min
- Problem type: Tabular regression (predict a continuous value)
You will learn to
- Build a deep learning model with no code.
- Predict sales numbers from spreadsheet data.
Create a project
Let’s begin! Log in to the Peltarion Platform and click New project. Name the project, e.g. Sales, so you know what kind of project it is.
Import the data
After creating the project, you will be taken to the Datasets view, where you can import data.
Use our data library
Click the Import free datasets button.
Look for the Sales forecasting - tutorial data dataset in the list. Click on it to get more information.
Click Accept and import.
This will import the dataset in your project, and you can now edit it.
The sales forecasting dataset
There are few companies that openly share revenue data. So for this tutorial, we’ve generated a synthetic dataset that looks like the real thing but is free to use.
The dataset contains daily values for many different shops over a couple of years, such as the date, national holidays, type of shop, ongoing advertisement and promotion campaigns. The appropriate feature encoding is selected automatically for each feature and the dataset is now ready to be used in an experiment.
The shop’s daily revenue is also given, which is what the model will learn to predict, your target.
Build the model
In the Datasets view click Use in new experiment.
The Experiment wizard makes it really easy for you to set up an experiment. Let’s take a look and make sure that all presets are correct:
The Sales forecasting dataset is selected.
Input(s) / target tab
In the Inputs column, select everything EXCEPT the Date, Year, and Revenue (since it’s the target).
We won’t use specific time information, like Date and Year, to train the model because we want to make predictions for any future (or long past) date.
Features like the week number provide enough information about the time period, i.e., if an example is from winter or summer, while being general enough to work for any future year.
Select Revenue in the Target column. The target is what the model will learn to predict.
Problem type tab
Given the inputs and target selected, the wizard automatically recommends Tabular regression as Problem type.
Click on Create, and the wizard will create a model that fits your tabular data.
Everything is set in the Modeling view thanks to the wizard. Click Run to start training your model.
As the model trains, you can follow its performance in the Evaluation view.
Evaluate your experiment
The Evaluation view shows you how the model performance improves as the training progresses. The loss and metrics plot gives an overall idea of the training process. You can also check the scatter plot in the Predictions inspection tab to see predictions of specific examples.
Training will stop automatically when the model stops improving thanks to early stopping. The experiment status will change from Running to Early stopped, and you can move on to the next section to deploy your model.
Deploy your experiment
In the Evaluation view click Create deployment.
Select the Experiment that you want to deploy.
An experiment corresponds to a particular model trained with specific settings.
Select the Best checkpoint, since this is when the model had the best performance.
Click Enable to let your deployment go live! You can now send requests to your model from anywhere, anytime.
Congratulations, you have now completed the sales forecasting tutorial! You have:
Built a simple model which
Analyzed a large amount of tabulated data to
Solved a regression problem.
This dataset is very simple, but much more can be done with the use of deep learning.
Deep learning for sales forecasting could for example use combinations of complex data types like images and text. To see how to solve problems using other types of data, check out our other tutorials.
Integrate your model directly inside your spreadsheets
Time to integrate your model directly inside your spreadsheets. When a model is deployed, the Peltarion Platform makes it available to you from anywhere with an HTTP connection. This means that you can use the deployment API to get predictions in your applications.
We’ve created add-ins for Google spreadsheets and Excel to make it super easy for you to leverage the Peltarion Platform from your favourite tool.
You will learn to
Fill your spreadsheet with predictions from your AI model.
Install Peltarion’s Excel add-in or AI for Sheets add-on.
For Google users: Integrate predictions in your Google Sheets
For Excel users: Integrate predictions in your Microsoft Excel spreadsheets.