A Machine Learning project that predicts whether a customer is likely to default on their credit card payment using historical customer information and financial attributes.
This project applies supervised machine learning techniques to classify customers as either likely to default or not default on their credit card payments.
The workflow includes:
- Data Loading
- Data Cleaning
- Exploratory Data Analysis (EDA)
- Data Visualization
- Feature Selection
- Model Training
- Model Evaluation
- Prediction on Test Data
The goal of this project is to build a binary classification model that can accurately predict credit card default risk using customer demographic and financial information.
- Python
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
Credit-Card-Default-Prediction/
│
├── Credit_Card_Default_Prediction.ipynb
├── README.md
├── requirements.txt
└── LICENSE
- Import libraries
- Load dataset
- Explore the dataset
- Handle missing values (if any)
- Visualize important features
- Split data into training and testing sets
- Train Logistic Regression model
- Evaluate model using:
- Accuracy Score
- Confusion Matrix
- Classification Report
- Predict outcomes on unseen data
The model performance is evaluated using:
- Accuracy
- Precision
- Recall
- F1-Score
- Confusion Matrix
- Clone this repository
git clone https://github.com/akhtarsaaeem-sys/Credit-Card-Default-Prediction.git- Install the required libraries
pip install -r requirements.txt- Open the notebook
jupyter notebook Credit_Card_Default_Prediction.ipynbRun all cells to reproduce the results.
The project uses a Credit Card Default dataset for binary classification.
Saaeem Akhtar
GitHub: https://github.com/akhtarsaaeem-sys
This project is licensed under the MIT License.