Missing data in the training data set can reduce the power/fit of a model or can lead to a biased model because we have not analyzed the behavior and relationship with other variables correctly. It can lead to incorrect prediction or classification. Below is a simple example to illustrate this. Name Weight Gender Play Golf or Not AA55MYes BB62FYes CC58FNo DD54 No EE54MNo FF66FYes GG56 Yes HH56MYes Figure 1 Gender # Count # Play Golf % Play Golf F3266.67% M3266.67% Missing/Blank2150% Figure 2 Please note the missing values in the table shown above: in figure1, we have not treated missing values for our analysis in Figure 2. The inference from this data set is that the chances of playing golf by females and males are similar. On the other hand, if you look at Figure. 4, which shows data after treatment of missing values (based on gender), we can see that females have higher chances of playing cricket compared to males. Name Weight Gender Play Golf or Not AA55MYes BB62FYes CC58FNo DD54MNo EE54MNo FF66FYes GG56MYes HH56MYes Figure 3 Gender # Count # Play Golf % Play Golf F3266.67% M5360% Figure 4