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Bank Marketing Prediction

This work has been done as a partial fulfillment of the course Machine Learning (ML) Applications for Business during Fall 2022 at IIT Jodhpur.

Dataset

I have used the Bank Marketing Dataset from UCI ML Repository link. The details about the dataset can be found in the given link.

Notebook

Contains snippets of EDA and classification algorithms. The EDA plots can be found in Plots folder.

The classification algorithms have been trained using different settings of hyperparameters and the best models for each of the classifiers have been retained. Classification algorithms used -

  • Logistic Regression
  • Support Vector Machine
  • Decision Tree
  • Random Forest

Finally, for each of the classifiers, Importance of Features have been shown. This may vary for every classifier as they have different ways of interpreting them.

Reference

  • S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014
  • 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.