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.
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
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.
Deep Neural Network performed better than other ML models
Balanced class increased Recall and decreased Overfitting