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This event is going to focus in depth on how the process for developing a machine learning model is done. There will be a lot of concepts explained. We will be sharing the development process of our Account Receivable (A/R) solution, based on a XGBoost classification. A senior panel will discuss the process and the implications for data professionals. Concretely, topics will include:

• Define adequately our problem (objective, desired outputs) 
• Gather data
• Choose a measure of success
• Set an evaluation protocol 
• Prepare the data (dealing with missing values, with categorical values) 
• Split correctly the data
• Differentiate between over and underfitting, defining what they are and explaining the best ways to avoid them
• An overview of how a model learns
• Choose an adequate model and tune it to get the best performance possible