Bank Marketing Prediction

Overview

This repository contains the implementation of various predictive models aimed at predicting subscription to term deposits by clients of a bank. The models analyze client data from direct marketing campaigns (phone calls) to predict whether clients will subscribe to term deposits.

Problem Statement

The goal is to develop a predictive model that can accurately predict whether a client will subscribe ('yes') or not ('no') to a term deposit as part of direct marketing campaigns conducted by a Portuguese banking institution.

Dataset

The dataset involves records from direct marketing campaigns of a bank, which are primarily phone calls. There are 45,210 samples with 17 features, including job type, marital status, education level, and previous campaign outcomes.

Features

• Categorical: Job, marital status, education, default, housing, loan, contact, month, poutcome.
• Numerical: Age, balance, day, duration, campaign, pdays, previous.

Target Variable

• Deposit: Whether the client subscribed to a term deposit ('yes' or 'no').

Models Implemented

Linear Models

• Logistic Regression
  • Accuracy: 0.885838
  • Kappa: 0.2219557
• Linear Discriminant Analysis (LDA)
  • Accuracy: 0.883468
  • Kappa: 0.3063123
• Partial Least Squares Discriminant Analysis (PLSDA)
  • Tuning Parameter (ncomp): 1
  • Accuracy: 0.8829942
  • Kappa: 0.0
• Penalized Models
  • Tuning Parameters: alpha = 0.1, lambda = 0.01
  • Accuracy: 0.8861540
  • Kappa: 0.1599455310

Non-linear Models

• Nonlinear Discriminant Analysis (NDLA)
  • Tuning Parameter (subclasses): 5
  • Accuracy: 0.8764211
  • Kappa: 0.3153305
• Neural Networks
  • Tuning Parameters: size = 9, decay = 0
  • Accuracy: 0.8896926
  • Kappa: 0.29577028
• Flexible Discriminant Analysis (FDA)
  • Tuning Parameters: degree = 3, nprune = 15
  • Accuracy: 0.8815093
  • Kappa: 0.3637292
• Support Vector Machines (SVM)
  • Tuning Parameters: sigma = 0.05006803, C = 1
  • Accuracy: 0.8871020
  • Kappa: 0.1921608
• k-Nearest Neighbors (KNN)
  • Tuning Parameter (k): 3
  • Accuracy: 0.8696602
  • Kappa: 0.2586158
• Naïve Bayes
  • Accuracy: 0.8734198
  • Kappa: 0.357957

Results

Models are evaluated using accuracy and Kappa statistics. The Flexible Discriminant Analysis (FDA) model achieved the highest Kappa among all models, indicating superior performance on the imbalanced dataset.

Contributing

Contributions are welcome! Please open an issue first to discuss what you would like to change.

License

This project is licensed under the MIT License - see the LICENSE file for details.