The data comes from direct marketing efforts of a European banking institution. The marketing campaign involves making a phone call to a customer, often multiple times to ensure a product subscription, in this case a term deposit. Term deposits are usually short-term deposits with maturities ranging from one month to a few years. The customer must understand when buying a term deposit that they can withdraw their funds only after the term ends. All customer information that might reveal personal information is removed due to privacy concerns.
age : age of customer (numeric)
job : type of job (categorical)
marital : marital status (categorical)
education (categorical)
default: has credit in default? (binary)
balance: average yearly balance, in euros (numeric)
housing: has a housing loan? (binary)
loan: has personal loan? (binary)
contact: contact communication type (categorical)
day: last contact day of the month (numeric)
month: last contact month of year (categorical)
duration: last contact duration, in seconds (numeric)
campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)
y - has the client subscribed to a term deposit? (binary)
Predict if the customer will subscribe (yes/no) to a term deposit (variable y)
Logistic Regression -> The accuracy of the model: 0.934125
Decision Tree Classifier -> The accuracy of the model: 0.919375
K-Nearest Neighbors -> The accuracy of the model: 0.9295
Support Vector Classification -> The accuracy of the model: 0.929125
Random Forest Classifier -> The accuracy of the model: 0.937875
AdaBoost Classifier -> The accuracy of the model: 0.933375
LightGBM Classifier -> The accuracy of the model: 0.93925
LightGBM Classifier -> The accuracy of the model: 0.9445
Random Forest Classifier -> The accuracy of the model: 0.9385