Datavoorbereiding voor aankoopvoorspelling

Machine Learning voor marketing in Python

Karolis Urbonas

Head of Analytics & Science, Amazon

Regressie - continue variabele voorspellen

Regressie - type supervised learning
Doelvariabele - continue of telvariabele
Simpelste variant - lineaire regressie
Teldata (bv. aantal actieve dagen) soms beter met Poisson- of Negatief-Binomiale regressie

Recency, Frequency, Monetary (RFM)-features

RFM - aanpak achter veel feature-engineeringmethoden
Recency - tijd sinds laatste transactie van klant
Frequency - aantal aankopen in de periode
Monetary waarde - totaal besteed in de periode

Verken verkoopverdeling per maand

# Explore monthly distribution of observations
online.groupby(['InvoiceMonth']).size()

InvoiceMonth
2010-12    4893
2011-01    3580
2011-02    3648
2011-03    4764
2011-04    4148
2011-05    5018
2011-06    4669
2011-07    4610
2011-08    4744
2011-09    7189
2011-10    8808
2011-11    9513
dtype: int64

Featuredata scheiden

# Exclude target variable
online_X = online[online['InvoiceMonth']!='2011-11']

# Define snapshot date
NOW = dt.datetime(2011,11,1)

# Build the features
features = online_X.groupby('CustomerID').agg({
                                'InvoiceDate': lambda x: (NOW - x.max()).days,
                                'InvoiceNo': pd.Series.nunique,
                                'TotalSum': np.sum,
                                'Quantity': ['mean', 'sum']
                                }).reset_index()

features.columns = ['CustomerID', 'recency', 'frequency',
                    'monetary', 'quantity_avg', 'quantity_total']

Features controleren

print(features.head())

Features-kop

Doelvariabele berekenen

# Build pivot table with monthly transactions per customer
cust_month_tx = pd.pivot_table(data=online, index=['CustomerID'], 
                               values='InvoiceNo',
                               columns=['InvoiceMonth'],
                               aggfunc=pd.Series.nunique, fill_value=0)
print(cust_month_tx.head())

Maandelijkse draaitabel per klant

Rond datavoorbereiding af en splits train/test

# Store identifier and target variable column names
custid = ['CustomerID']
target = ['2011-11']

# Extract target variable
Y = cust_month_tx[target]

# Extract feature column names
cols = [col for col in features.columns if col not in custid]

# Store features
X = features[cols]

Splits data in training en test

# Randomly split 25% of the data to testing
from sklearn.model_selection import train_test_split
train_X, test_X, train_Y, test_Y = train_test_split(X, Y, 
                                                    test_size=0.25, 
                                                    random_state=99)

# Print shapes of the datasets
print(train_X.shape, train_Y.shape, test_X.shape, test_Y.shape)

(2529, 5) (2529, 1) (843, 5) (843, 1)

Laten we oefenen!

Machine Learning voor marketing in Python