https://github.com/nafisalawalidris/predicting-credit-card-approvals

Explore credit card approval prediction through data analysis and machine learning. Preprocess data, train logistic regression models, and optimize hyperparameters. Learn data preprocessing, feature engineering, model training, and evaluation. Dive into the world of machine learning with Python and popular libraries.
https://github.com/nafisalawalidris/predicting-credit-card-approvals

approval-prediction credit-card data-analysis data-preprocessing feature-engineering hyperparameter-optimization libraries logistic-regression machine-learning model-evaluation model-training python python3

Last synced: 2 months ago
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Explore credit card approval prediction through data analysis and machine learning. Preprocess data, train logistic regression models, and optimize hyperparameters. Learn data preprocessing, feature engineering, model training, and evaluation. Dive into the world of machine learning with Python and popular libraries.

• Host: GitHub
• URL: https://github.com/nafisalawalidris/predicting-credit-card-approvals
• Owner: nafisalawalidris
• Created: 2023-06-23T09:09:14.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-06-23T10:05:41.000Z (over 1 year ago)
• Last Synced: 2023-10-07T00:25:17.284Z (over 1 year ago)
• Topics: approval-prediction, credit-card, data-analysis, data-preprocessing, feature-engineering, hyperparameter-optimization, libraries, logistic-regression, machine-learning, model-evaluation, model-training, python, python3
• Language: Python
• Homepage:
• Size: 33.2 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        


  
Credit Card Approval Prediction

  
This project focuses on predicting the approval status of credit card applications using machine learning techniques. It involves data preprocessing, model building, and evaluation.


  
Tasks

  


    
Importing Libraries

    
Loading the Dataset

    
Inspecting the Dataset

    
Handling Missing Values

    
Imputing Missing Values in Numeric Columns

    
Imputing Missing Values in Non-Numeric Columns

    
Converting Non-Numeric Values to Numeric

    
Rescaling the Features of the Data

    
Fitting a Logistic Regression Classifier

    
Making Predictions and Evaluating Performance

    
Performing Grid Search and Finding the Best Model

  


  
Instructions

  
To replicate the project, follow the instructions provided in each task. Make sure to have the required libraries installed and the dataset file available.


  
File Structure

  


    


credit_card_approval.ipynb: Jupyter Notebook containing the Python code and tasks.

    


credit_card_data.csv: Dataset file in CSV format.

    


README.html: This README file providing an overview of the project.

  


  
Dependencies

  
The project requires the following Python libraries:

  


    
pandas

    
numpy

    
sklearn

  


  
Usage

  


    
Clone the repository or download the project files.

    
Open the credit_card_approval.ipynb file in Jupyter Notebook or any other compatible environment.

    
Install the required dependencies if not already installed.

    
Execute the code cells sequentially, following the instructions provided in each task.

    
Review the results, including accuracy scores and the best model parameters.

  


  
Contributing

  
Contributions to the project are welcome. You can open an issue to report a bug, propose new features, or submit a pull request with improvements.


  
License

  
This project is licensed under the MIT License. See the LICENSE file for more details.


  
Contact

  
If you have any questions or suggestions, feel free to contact me at [[email protected]]