Alternative segmentation with NMF
In this exercise, you will analyze product purchase data and identify meaningful segments using non-negative matrix factorization algorithm (NMF). It works well with sparse customer by product matrices that are typical in the e-commerce or retail space. Finally, you will extract the components that you will then explore in the upcoming exercise.
We have loaded pandas as pd and numpy as np. Also, the raw customer by product purchase dataset has been loaded as wholesale.
Questo esercizio fa parte del corso
Machine Learning for Marketing in Python
Istruzioni dell'esercizio
- Import the non-negative matrix factorization function from
sklearn.decomposition. - Initialize
NMFinstance with 4 components. - Fit the model on the
wholesalesales data. - Extract and store the components as a
pandasDataFrame.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Import the non-negative matrix factorization module
from sklearn.decomposition import ___
# Initialize NMF instance with 4 components
nmf = ___(4)
# Fit the model on the wholesale sales data
nmf.___(wholesale)
# Extract the components
components = pd.DataFrame(data=nmf.___, columns=wholesale.columns)