Machine learning helps to create programs and “predictors” in a situation where the experts themselves cannot set down the relevant rules with sufficient accuracy. In this case, the rules will be automatically derived through the data analysis.
The possibility to administer and analyse the information that hide in the increasing data volumes is ever more crucial for making effective and right business choices. Our data scientists can create models that act as helping tools for adopt the most useful choice.
In order to find new connections and make predictions with the machine learning, we first train it with the data. The more data available, the more effective is the trained model and more future values we can predict.
As an example, a partner may send us the list of its customers’ orders. After we have received the data we will create the characteristics describing the problem. Once that is done, we train the model with the created characteristics. It is followed by the iterative process: we specify characteristics and complement the model. We use the acquired model to predict new occurrences. For example we could predict loyalty of the business’s new customers in the future.
Experts in STACC have been involved with the machine learning for more than 10 years.