This lesson requires a premium membership to access.
Premium membership includes unlimited access to all courses, quizzes, downloadable resources, and future content updates.
In the examples we saw earlier, we had good quality data with all values available for all time indexes. However, in real life, the data may contain missing values which will influence our analysis. Depending on the nature of data, we may choose to ignore missing values. However, in some cases it might be more suitable to estimate and fill the missing values. Data scientists use various techniques to estimate missing values. One common technique is to take the mean of the time series and replace NA with the mean value. Depending on the data this may or may not be suitable. For example, if the data is about loan borrowers and there are missing values in the loan interest rate, then the data scientist may decide to use the average interest rates for missing values, or if he clearly sees a pattern such as the interest rates being higher for self-employed individuals compared to salaries individuals, then the data scientist may decide to fill the missing values with means based on the categories based on their employment status.
