Hands-On Data Science for Marketing

Building predictive models

We are almost ready to start building and training machine learning models to predict customer responses or engagements. There are a few things to clean up in our data. Take a look at the following code:

all_features = continuous_features + categorical_featuresresponse = 'Engaged'sample_df = df[all_features + [response]]sample_df.columns = [x.replace(' ', '.') for x in sample_df.columns]all_features = [x.replace(' ', '.') for x in all_features]

As you can see from this code, we are creating a new DataFrame sample_df, which contains all the features, all_features, and the response variable, response. Then, we are cleaning up the column and feature names by replacing all the spaces in the names with dots. After these ...