to apply the mean values insteadwe do the followingprint(data fillna(data mean())the mean value for each column is calculated and inserted in those data areas with the np nan value for the first columncolumn the mean value was obtained by ( )/ the resulting is then stored at data[ ][ similar operation is carried out for columns and feature scaling the columns in data frame are known as its features the rows are known as records or observations now examine the following data matrix this data will be referenced in subsections so please do take note[ ]feature with data has its values lying between and for feature the data lies between and inconsistent results will be produced if we supply this data to any machine learning algorithm ideallywe will need to scale the data to certain range in order to get consistent results once againa closer inspection reveals that each feature (or columnlies around different mean values thereforewhat we would want to do is to align the features around similar means one benefit of feature scaling is that it boosts the learning parts of machine learning the scikit module has considerable number of scaling algorithms that we shall apply to our data