the output is generated as depicted in figure - and figure - figure - shows the output of dummy encoding with the map(approach we keep the number of features in checkyet have to be careful about the caveats mentioned in this section figure - dataframe with user_type attribute dummy encoded the second imagefigure - showcases the output of one hot encoding the user_type attribute we discuss more these approaches in detail in when we discuss feature engineering figure - dataframe with user_type attribute one hot encoded normalizing values attribute normalization is the process of standardizing the range of values of attributes machine learning algorithms in many cases utilize distance metricsattributes or features of different scales/ranges which might adversely affect the calculations or bias the outcomes normalization is also called feature scaling there are various ways of scaling/normalizing featuressome of them are rescalingstandardization (or zero-mean unit variance)unit scaling and many more we may choose normalization technique based upon the featurealgorithm and use case at hand this will be clearer when we work on use cases we also cover feature scaling strategies in detail in feature engineering and selection the following snippet showcases quick example of using min-max scaleravailable from the preprocessing module of sklearnwhich rescales attributes to the desired given range df_normalized df dropna(copy(min_max_scaler preprocessing minmaxscaler(np_scaled min_max_scaler fit_transform(df_normalized['price'reshape(- , )df_normalized['normalized_price'np_scaled reshape(- , figure - showcases the unscaled price values and the normalized price values that have been scaled to range of [