• Manhã
  • Introduction
  • Reporting in R . Rmarkdown . Notebooks . Parameterized reports . Interactive reports . Hands on
    • Predictive analytics . Main tasks . Evaluation metrics
    • Data pre-processing . Unknown values . Feature creation and selection
• Tarde
  • Predictive Analytics (cont. - modeling) . Linear models § Linear discriminants § Linear regression . Support vector machines . Tree-based models . Random forests . Handling imbalanced domains § Problem description/definition § Main approaches § Solutions with package UBL
  • Model tuning, comparison and evaluation . Methods of comparison evaluation . Package performanceEstimation . Hands on
  • A real world case study: stock market forecasting