Credit Card Approval Machine Learning Model

Overview

This Python script automates the process of analyzing credit card applications using machine learning techniques. Commercial banks often receive numerous credit card applications, and manually reviewing them can be time-consuming and error-prone. This script utilizes logistic regression to streamline the approval process.

Requirements

• Python 3.x
• Libraries:
  • scikit-learn (sklearn)
  • pandas
  • numpy

Installation

No additional dependencies are required beyond the specified Python libraries. Simply install the necessary libraries using pip:

pip install scikit-learn pandas numpy

Usage

1. Ensure that Python and the required libraries are installed.
2. Clone this repository to your local machine.
3. Prepare your input data. The script expects input data in a format similar to the provided Credit Card Approval dataset subset from the UCI Machine Learning Repository.
4. Run the script using Python:

python credit_card_approval.py

Input Data

The input data consists of a small subset of the Credit Card Approval dataset from the UCI Machine Learning Repository. It includes information about credit card applications received by a bank.

Data Preparation

Before using the script, ensure that the input data is scaled using the standard scaler. This preprocessing step is crucial for accurate model predictions.

Output

The output of the script is the score of the logistic regression model based on the provided input data. This score indicates the likelihood of approval for each credit card application.

Customization

This script does not currently support customization of parameters or configurations. However, feel free to modify the code according to your specific requirements.

Additional Information

No further usage tips or additional information are provided at this time.

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