IoT data is notoriously messy (just in case the message has not been driven home yet) and missing values are a common occurrence. There are some options to deal with this problem in order to enhance the quality of your ML models. This is where the art comes into play and judgment is important.
The following are some methods for handling missing values:
- Remove data rows with missing values: This is crude, but if only a small percentage is lost, and this percentage appears to be random, then it will have minimal effect on the results. Use a tool such as Tableau to analyze the data with missing values and compare it to the data without missing values to judge the impact of removing the rows. R and Python work well ...
