Credit Card Approval Prediction System

Overview

The Credit Card Approval Prediction System is a comprehensive project that utilizes advanced techniques in regression modeling, AutoML (Automated Machine Learning), SHAP (SHapley Additive exPlanations) analysis, feature selection, and sophisticated data visualization methods. The primary goal is to enhance accuracy in predicting credit card approvals, offering a unique blend of modeling, automation, and interpretability.

Table of Contents

• Features
• How to Use
• License
• References

Features

• Regression Models: The system incorporates state-of-the-art regression models for credit card approval predictions, ensuring high accuracy and reliability.

• AutoML: Leveraging Automated Machine Learning streamlines the model selection and tuning process, making it efficient and effective.

• SHAP Analysis: The inclusion of SHAP analysis provides insights into feature importance, enhancing the interpretability of the models.

• Feature Selection: The project implements feature selection techniques to identify and utilize the most relevant features for prediction, improving model efficiency.

• Data Visualization: Advanced data visualization techniques are employed to present the results in an intuitive and understandable manner.

How to Use

1. Open the Colab Notebook:

   • Navigate to the Colab Notebook.

2. Make a Copy:

   • Click on "File" -> "Save a copy in Drive" to create your copy of the notebook.

3. Run the Notebook:

   • Execute the cells in sequence to run the entire project.

4. Install Dependencies (if required):

   • If there are external dependencies not present in Colab, install them using the following commands:

     !pip install package-name

License

MIT License

Copyright (c) [2023] [Muskan Raisinghani]

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS," WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

References

• H20.ai- https://docs.h2o.ai/
• Kaggle Dataset- https://www.kaggle.com/code/rikdifos/credit-card-approval-prediction-using-ml/
• Kaggle notebook - https://www.kaggle.com/code/rikdifos/credit-card-approval-prediction-using-ml/notebook
• AutoML Notebook- https://github.com/aiskunks/YouTube/blob/main/A_Crash_Course_in_Statistical_Learning/AutoML/CC_Kaggle_AutoML_Regression_Melbourne_Housing.ipynb