14. Limitations of predictive analytics.

The Limitations of the Data in Predictive Analytics. The data could be incomplete. Missing values, even the lack of a section or a substantial part of the data, could limit its usability. If you're using data from surveys, keep in mind that people don't always provide accurate information.

To determine the limitations of your data, be sure to:

• Verify all the variables you’ll use in your model.
• Assess the scope of the data, especially over time, so your model can avoid the seasonality trap.
• Check for missing values, identify them, and assess their impact on the overall analysis.
• Watch out for extreme values (outliers) and decide on whether to include them in the analysis.
• Confirm that the pool of training and test data is large enough.
• Make sure data type (integers, decimal values, or characters, and so forth) is correct and set the upper and lower bounds of possible values.
• Pay extra attention to data integration when your data comes from multiple sources.
  • Choose a relevant dataset that is representative of the whole population.
  • Choose the right parameters for your analysis.
  • Any values missing from the data.
  • Any inconsistencies and/or errors existing in the data.
  • Any duplicates or outliers in the data.
  • Any normalization or other transformation of the data.
  • Any derived data needed for the analysis.
    
    
    dummies. (2019). The Limitations of the Data in Predictive Analytics - dummies. [online] Available at: https://www.dummies.com/programming/big-data/data-science/the-limitations-of-the-data-in-predictive-analytics/ [Accessed 18 Jan. 2019].