Ensemble Learning with Credit Card Application

In this project, the Credit Card Application Dataset from the UCI Machine Learning Repository is used to predict whether a given customer should be approved for a credit application. The goal is to apply different ensemble learning techniques to improve the accuracy of predictions compared to single machine learning models.

Dataset

The dataset used in this project contains data related to credit card applications, including information about customers and their credit approval status. It is a binary classification problem where the target variable indicates whether a customer's credit application is approved or not.

Techniques Applied

This project explores and implements six ensemble learning techniques, each of which is designed to improve the model's performance by combining the predictions of multiple base learners:

1. Averaging: Combining the predictions of multiple models by averaging their outputs.
2. Weighted Averaging: Similar to Averaging, but assigning different weights to the predictions of each model.
3. Max Voting: The final prediction is based on the majority vote of the individual classifiers.
4. Bagging: A technique that trains multiple models on different subsets of the data and combines their predictions.
5. Boosting: A method that combines weak learners sequentially, where each new model attempts to correct the errors made by the previous one.
6. Stacking: Involves training multiple models and using another model to combine their predictions effectively.

Objective

The primary objective of this analysis is to assess how ensemble learning techniques can improve the accuracy and robustness of predictions when determining whether a customer should be approved for a credit card.

Methodology

• The models were trained and evaluated using a set of ensemble learning techniques.
• The Packt course was used as a reference to define and implement the necessary concepts for each ensemble technique.

Libraries and Tools Used

• Scikit-learn: For implementing various machine learning algorithms and ensemble methods.
• Pandas: For data manipulation and analysis.
• NumPy: For numerical operations and data handling.
• Matplotlib/Seaborn: For data visualization and performance evaluation.