How it works...

In this recipe, we have learned about the following concepts:

• Imputing missing values: We have learned that one of the ways to impute the missing values of a variable is by replacing the missing values with the median of the corresponding variable. Other ways to deal with the missing values is by replacing them with the mean value, and also by replacing the missing value with the mean of the variable's value in the rows that are most similar to the row that contains a missing value (this technique is called identifying the K-Nearest Neighbours).
• Capping the outlier values: We have also learned that one way to cap the outliers is by replacing values that are above the 95th percentile value with the 95th percentile value. The reason we performed this exercise is to ensure that the input variable does not have all the values clustered around a small value (when the variable is scaled by the maximum value, which is an outlier).
• Scaling dataset: Finally, we scaled the dataset so that it can then be passed to a neural network.