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.
Cet exercice fait partie du cours
Customer Segmentation in Python
Instructions
- Apply log transformation to unskew the
datamart_rfmand store it asdatamart_log. - Initialize a
StandardScaler()instance asscalerand fit it on thedatamart_logdata. - Transform the
databy scaling and centering it withscaler. - Create a pandas DataFrame from 'datamart_normalized' by adding index and column names from
datamart_rfm.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de 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)