ParetoNBDModel.expected_purchases#
- ParetoNBDModel.expected_purchases(data=None, *, future_t=None)[source]#
Given recency, frequency, and T for an individual customer, this method predicts the expected number of future purchases across future_t time periods.
Adapted from equation (41) In Bruce Hardie’s notes [2], and
lifetimes
package: CamDavidsonPilon/lifetimes- Parameters:
data (pd.DataFrame, optional) –
- Dataframe containing the following columns:
customer_id
: unique customer identifierfrequency
: number of repeat purchasesrecency
: time between the first and the last purchaseT
: time between the first purchase and the end of the observation period.Model assumptions require T >= recency
future_t
: Number of time periods to predict expected purchases.covariates: Purchase and dropout covariate columns if original model had any.
If not provided, the method will use the fit dataset.
future_t (array_like, optional) – Number of time periods to predict expected purchases. Not needed if
data
parameter is provided with afuture_t
column.
- Return type:
DataArray
References