Pre-process RFM data

We have loaded the dataset with RFM values you calculated previously as datamart_rfm. Since the variables are skewed and are on different scales, you will now un-skew and normalize them.

The pandas library is loaded as pd, and numpy as np. Take some time to explore the datamart_rfm in the console.

This exercise is part of the course

Customer Segmentation in Python

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Exercise instructions

Apply log transformation to unskew the datamart_rfm and store it as datamart_log.
Initialize a StandardScaler() instance as scaler and fit it on the datamart_log data.
Transform the data by scaling and centering it with scaler.
Create a pandas DataFrame from 'datamart_normalized' by adding index and column names from datamart_rfm.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Unskew the data
datamart_log = np.____(____)

# Initialize a standard scaler and fit it
scaler = ____()
scaler.____(____)

# Scale and center the data
datamart_normalized = ____.____(____)

# Create a pandas DataFrame
datamart_normalized = pd.____(data=____, index=____.index, columns=____.columns)

Edit and Run Code