Credit Card Prediction Model

1. Project Overview

This project involves building and evaluating machine learning models to predict credit card ownership based on various features from a banking dataset. The dataset contains information about bank customers, including their demographics, financial status, and account types.

2. Dataset

The dataset used in this project is Universalbank.csv, which includes the following features:

Feature	Description
ID	Unique identifier for each customer
Age	Age of the customer
Experience	Years of experience in the banking sector
Income	Annual income of the customer
ZIP Code	Customer’s ZIP code
Family	Number of family members
CCAvg	Average credit card spending
Education	Education level of the customer (1: Undergrad, 2: Graduate, 3: Advanced)
Mortgage	Mortgage amount
Personal Loan	Whether the customer has a personal loan (1: Yes, 0: No)
Securities Account	Whether the customer has a securities account (1: Yes, 0: No)
CD Account	Whether the customer has a CD account (1: Yes, 0: No)
Online	Whether the customer uses online banking (1: Yes, 0: No)
CreditCard	Target variable indicating whether the customer has a credit card (1: Yes, 0: No)

3. Objective

The goal of this project is to build and evaluate machine learning models to predict whether a customer owns a credit card. The models are evaluated based on accuracy, precision, recall, f1-score, and ROC AUC score.

4. Methodology

4.1 Data Exploration and Preparation

• Loaded and explored the dataset using pandas.
• Checked for class imbalance in the target variable CreditCard.
• Performed random under-sampling and over-sampling to address class imbalance.

4.2 Model Building

• Split the dataset into training and testing sets.
• Trained a Decision Tree Classifier on the original dataset and on the balanced datasets (under-sampled and over-sampled).

4.3 Model Evaluation

• Evaluated the performance of each model using metrics such as:
  • Confusion matrix
  • Classification report
  • Accuracy
  • Precision
  • Recall
  • F1-score
  • ROC AUC Score

5. Results

Dataset	Accuracy	Precision	Recall	F1-Score	ROC AUC Score
Original Dataset	61.87%	35.23%	40.52%	37.69%	55.35%
Over-Sampled Dataset	79.93%	75.88%	87.48%	81.27%	79.97%
Under-Sampled Dataset	[To be added based on results]	[To be added]	[To be added]	[To be added]	[To be added]

6. Files

• Universalbank.csv: The dataset used for training and evaluation.
• credit_card_prediction.ipynb: Jupyter Notebook containing code for data exploration, model training, and evaluation.

7. Dependencies

• pandas
• numpy
• matplotlib
• scikit-learn

8. Installation

To run the code, make sure to install the required libraries using pip:

pip install pandas numpy matplotlib scikit-learn

9. Usage

1. Place the Universalbank.csv file in the same directory as the Jupyter Notebook.
2. Open credit_card_prediction.ipynb in Jupyter Notebook or any compatible environment.
3. Run the cells in the notebook to perform data analysis, model training, and evaluation.

10. Conclusion

This project demonstrates the application of machine learning techniques to a real-world dataset to address class imbalance and predict credit card ownership. The models trained on the balanced datasets showed improved performance compared to the model trained on the original imbalanced dataset.