| Team Name |
CapGemini Spain for ENEL |
| Track |
Energy |
| Use Case |
Develop predictives models based on industrial KPI and historical economical results. |
| Technology |
Data cleaning
Anomaly Detection
Machine Learning |
| Region |
EMEA |
| Team lead |
Paula |
| Team members |
Adrian, Paula, Joan, Alejandro, José Ignacio, Wilder, Gonzalo |
Description
Predict the results for Enel group based on industrial KPI and historical economical results. Cleaning the data is the first step for the team (SAS Data Studio). Through data cleaning techniques we’ll tidy our dataset so that the team can work with them. Next step is looking for errors and anomalies in the dataset to increase the accuracy of the models (SAS Analytics). Once the data is in a good state, the team will start working with different predictive algorithms and comparing its accuracy in the hope of finding a predictive model good enough (SAS Model Studio).
