bank-marketing-campaign's Introduction

Bank Marketing Campaign

In today's cutthroat marketing environment, banks provide a variety of packages to entice clients. It would be beneficial for the banks to create a package based on a certain demography, allowing them to target those consumer groups particularly or adjust their packages as necessary. This categorization model aims to forecast whether a consumer will purchase their term deposit or not. Whereas a term deposit is a type of deposit offered by a financial institution with a fixed rate of interest that is high and an established maturity date.

Data

Dataset is from the UCI Machine Learning Repository. Features of this dataset includes:

Age
Job type
Marital status
Education
Credit default status
Average yearly balance
Mortgage
Personal loan
Contact type
Day of the month
Month of the year
Call duration
Number of contacts
Days since last contact
Number of contacts
Previous campaign outcome
Output: Opened Term Deposit

Model Workflow

we have built five models:

K-Nearest Neighbor (KNN)
Support Vector Machine (SVC)
Random Forest (RFC)
Gradient Boosting (GBC)
Deep Neural Network (DNN)

The data sets that we have used are imbalanced, with 88% no and 12% yes. So, Balanced data set is created for training and comparing to the imbalanced data set.

Comparison of Model's Performance:

For unbalanced classes

For balanced classes

Conclusion:

Deep Neural Network performed better than other ML models

Balanced class increased Recall and decreased Overfitting

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