6.3.3. Bank Telemarketing Upsell Campaign

6.3.3.1. Summary

This dataset is related to a direct marketing campaigns (phone calls) of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be or not subscribed.

Features recorded before the contact event are removed from the original dataset [1] to avoid data leakage. Only clients with a positive balance are considered, since clients in debt are not eligible for term deposits.

Classes	2
Subscribers	4787
Non-subscribers	33144
Samples	37931
Features	10

Other relevant information can be found in [2] and [3].

6.3.3.2. Using the Dataset

The dataset can be loaded through the load_upsell_bank_telemarketing function. This returns a Dataset object with the following attributes:

• data: the feature matrix

• target: the target vector

• cost_matrix: a CostMatrix with default values pre-filled

• instance_costs: a dict of per-instance cost drivers ('balance')

• feature_names: the feature names

• target_names: the target names

• DESCR: the full description of the dataset

import pandas as pd
from empulse.datasets import load_upsell_bank_telemarketing

dataset = load_upsell_bank_telemarketing(backend=pd)

The backend argument selects the dataframe library used for data and target. Pass the module itself — backend=pd for pandas or backend=pl for polars.

The cost matrix is symbolic: only the customer balance varies per instance, while the interest rate, term deposit fraction and contact cost are parameters with defaults. Pass the cost matrix to the model as a Metric loss, and hand it the instance costs at fit time:

import pandas as pd
from empulse.datasets import load_upsell_bank_telemarketing
from empulse.metrics import Metric, Cost
from empulse.models import CSLogitClassifier
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, TargetEncoder

dataset = load_upsell_bank_telemarketing(backend=pd)
X, y = dataset.data, dataset.target

numeric = X.select_dtypes(include=['number']).columns
categorical = X.select_dtypes(exclude=['number']).columns

pipeline = Pipeline([
    ('preprocessor', ColumnTransformer([
        ('num', StandardScaler(), numeric),
        ('cat', TargetEncoder(), categorical),
    ])),
    ('model', CSLogitClassifier(loss=Metric(dataset.cost_matrix, Cost())))
])
pipeline.fit(X, y, model__balance=dataset.instance_costs['balance'])

6.3.3.3. Cost Matrix

	Actual positive \(y_i = 1\)	Actual negative \(y_i = 0\)
Predicted positive \(\hat{y}_i = 1\)	tp_cost \(= c\)	fp_cost \(= c\)
Predicted negative \(\hat{y}_i = 0\)	fn_cost \(= r \cdot d_i \cdot b_i\)	tn_cost \(= 0\)

with

• \(c\) : cost of contacting the client

• \(r\) : interest rate of the term deposit

• \(d_i\) : fraction of the client’s balance that is deposited in the term deposit

• \(b_i\) : client’s balance

Using default parameters, it is assumed that \(c = 1\), \(r = 0.02463333\), \(d_i = 0.25\) for all clients. The default parameters are based on [4].

These assumptions are symbolic parameters of the cost matrix, exposed under the aliases interest_rate, term_deposit_fraction and contact_cost. Override any of them by passing the alias when evaluating the metric:

import numpy as np
import pandas as pd
from empulse.datasets import load_upsell_bank_telemarketing
from empulse.metrics import Metric, Cost

dataset = load_upsell_bank_telemarketing(backend=pd)

# replace with your own model's predicted probabilities
y_score = np.random.default_rng(0).uniform(size=len(dataset.target))

cost = Metric(dataset.cost_matrix, Cost())
score = cost(
    dataset.target,
    y_score,
    interest_rate=0.05,
    term_deposit_fraction=0.30,
    contact_cost=10,
    **dataset.instance_costs,
)

6.3.3.4. Data Description

Variable Name	Description	Type
age	Age of the client	numeric
balance	Average yearly balance	numeric
previous	Number of contacts performed before this campaign and for this client	numeric
job	Type of job (e.g., ‘admin.’, ‘blue-collar’, ‘entrepreneur’, etc.)	categorical
marital	Marital status (‘divorced’, ‘married’, ‘single’)	categorical
education	Education level (‘primary’, ‘secondary’, ‘tertiary’, ‘unknown’)	categorical
has_credit_in_default	Has credit in default? (‘yes’ = 1, ‘no’ = 0)	binary
has_housing_loan	Has housing loan? (‘yes’ = 1, ‘no’ = 0)	binary
has_personal_loan	Has personal loan? (‘yes’ = 1, ‘no’ = 0)	binary
previous_outcome	Outcome of the previous marketing campaign (‘success’, ‘failure’, ‘other’, ‘unknown’)	categorical
subscribed	Has the client subscribed a term deposit? (‘yes’ = 1, ‘no’ = 0)	binary

6.3.3.5. References

[1]

Moro, S., Rita, P., & Cortez, P. (2014). Bank Marketing [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5K306.

[2]

Moro, S., Cortez, P., & Rita, P. (2014). A data-driven approach to predict the success of bank telemarketing. Decision Support Systems, 62, 22-31.

[3]

S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM’2011, pp. 117-121, Guimaraes, Portugal, October, 2011. EUROSIS. [bank.zip]

[4]

A. Correa Bahnsen, A. Stojanovic, D.Aouada, B, Ottersten, “Improving Credit Card Fraud Detection with Calibrated Probabilities”, in Proceedings of the fourteenth SIAM International Conference on Data Mining, 677-685, 2014.