Welcome to the repository for my internship task submission for Prodigy Infotech! In this project, I developed machine learning models to predict whether clients will subscribe to a term deposit based on demographic and behavioral data from direct marketing campaigns conducted by a Portuguese banking institution.
The main goal of this project is to create predictive models that can assist banking institutions in targeting clients who are more likely to subscribe to term deposits. By accurately identifying potential subscribers, the institution can optimize its marketing efforts and improve campaign effectiveness.
The dataset used in this project is related to direct marketing campaigns (phone calls) of the Portuguese banking institution. It contains various features such as client demographics, previous marketing interactions, and the outcome of the marketing campaign (whether the client subscribed to a term deposit or not).
The project resulted in the development of decision tree classifiers using both the Gini impurity and entropy criteria. The models achieved high accuracy in predicting term deposit subscriptions, with the Gini impurity criterion slightly outperforming the entropy criterion in terms of testing accuracy and recall for the positive class.
This project demonstrates the potential of machine learning models in predicting term deposit subscriptions in direct marketing campaigns. By leveraging predictive analytics, the banking institution can optimize its marketing strategies and enhance customer engagement.
Thank you for reviewing my internship task submission!
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