Construction projects regularly exceed calculated budgets.
By leveraging historical data it is possible to radically improve the accuracy of price forecasts.
1. Input parameter, such as project size and product type are analyzed and matched with a predefined ruleset.
2. A classification model trained 3.5 on historical data identifies necessary cost items.
3. A bayesian algorithm learns the relation between parameter and cost items while accurately modeling uncertainty
... and many more