adejumok/credit-card-approval

This project aims to analyze credit card approval data using Python and various data analysis techniques. The goal is to gain insights into the factors influencing credit card approval decisions and build a predictive model to predict credit card approval outcomes.

Credit-Card-Approval

This project aims to analyze credit card approval data using Python and various data analysis techniques. The goal is to gain insights into the factors influencing credit card approval decisions and build a predictive model to predict credit card approval outcomes.

Table of Contents

• Project Overview
• Data
• Installation
• Usage
• Conclusion
• Contributing

Project Overview

The project involves the following steps:

1. Data Collection: Obtain the credit card approval dataset for analysis. The dataset should include information about various attributes such as income, age, employment status, and credit history, along with the approval status.

2. Data Preprocessing: Clean the dataset by handling missing values, removing duplicates, and addressing any inconsistencies or outliers. Perform necessary data transformations and feature engineering.

3. Exploratory Data Analysis (EDA): Conduct exploratory analysis to understand the distribution of variables, identify patterns, and explore relationships between different attributes. Visualize the data using appropriate graphs and charts.

4. Feature Selection: Identify the most relevant features that strongly influence credit card approval decisions. Apply feature selection techniques such as correlation analysis, chi-square test, or recursive feature elimination.

5. Model Building: Develop a predictive model to predict credit card approval outcomes. Use suitable machine learning algorithms such as logistic regression, decision trees, or random forests. Train the model on a portion of the dataset and evaluate its performance using appropriate metrics.

6. Model Evaluation: Assess the model's performance using evaluation metrics such as accuracy, precision, recall, and F1 score. Fine-tune the model by adjusting hyperparameters or trying different algorithms to improve its performance.

7. Documentation: Document the analysis process, key findings, and insights obtained from the data. Explain the methodology, assumptions made, and limitations of the analysis.

Data

• datasets folder: Contains the dataset files
  • cc_approvals.data
  • notebook.ipynb
• README.md: Project overview and instructions.

Installation

Clone the repository:

git clone https://github.com/your-username/credit-card-approval.git

Install the required dependencies:

!pip install pandas numpy matplotlib seaborn

Usage

Navigate to the project directory:

cd credit-card-approval

• Launch Jupyter Notebook: jupyter notebook
• Open the desired notebook file (e.g., notebook.ipynb)
• Run the notebook cells to execute the code and generate the analysis outputs

Conclusion

While building this credit card predictor, we tackled some of the most widely-known preprocessing steps such as scaling, label encoding, and missing value imputation. We finished with some machine learning to predict if a person's application for a credit card would get approved or not given some information about that person.

Contributing

Contributions are welcome! If you find any issues or want to enhance the project, feel free to open a pull request or submit an issue describing your suggestion.