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

1. Importing Libraries
2. Loading the Dataset
3. Inspecting the Dataset
4. Handling Missing Values
5. Imputing Missing Values in Numeric Columns
6. Imputing Missing Values in Non-Numeric Columns
7. Converting Non-Numeric Values to Numeric
8. Rescaling the Features of the Data
9. Fitting a Logistic Regression Classifier
10. Making Predictions and Evaluating Performance
11. 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

1. Clone the repository or download the project files.
2. Open the credit_card_approval.ipynb file in Jupyter Notebook or any other compatible environment.
3. Install the required dependencies if not already installed.
4. Execute the code cells sequentially, following the instructions provided in each task.
5. 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]]