Predicting-Credit-Card-Approvals

Description

This project from DataCamp taught me to build an automatic credit card approval predictor using machine learning techniques, just like real banks do.

Commercial banks receive a lot of applications for credit cards. Many of them get rejected for many reasons, like high loan balances, low income levels, or too many inquiries on an individual's credit report, for example. Manually analyzing these applications is mundane, error-prone, and time-consuming (and time is money!).

The data used is a small subset of the Credit Card Approval dataset from the UCI Machine Learning Repository showing the credit card applications a bank receives.

Installation Instructions

1. Clone the repository: git clone https://github.com/A-n-i-e/Predicting-Credit-Card-Approvals.git

2. Navigate to the project directory: cd Predicting-Credit-Card-Approvals

3. Install the required dependencies: pip install -r requirements.txt

4. Launch the Jupyter notebook: jupyter notebook notebooks/transaction_analysis.ipynb

Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Jupyter Notebook

Results

Model trained with Logistic Regression

Accuracy: 84.78296478296479% and Parameters used: {'C': 0.01, 'penalty': 'l2'}