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Credit Card Fraud Detection Using Machine Learning
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Project Overview
Description, tech stack, and what is included
Full source
Frontend + backend
Database
.sql file
Setup guide
README included
Fraud Detection model based on anonymized credit card transaction. It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. The datasets contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.
Technical snapshot
- Project
- Credit Card Fraud Detection Using Machine Learning
- Stack
- MACHINE-LEARNING
- Includes
- Code, DB, README
- License
- Academic submission
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Admin Features
Modules and controls available to administrators
-
Dataset Description:
- The dataset consists of credit card transactions made by European cardholders in September 2013.
- It spans transactions over two days, comprising 284,807 transactions.
- Within this dataset, there are 492 instances of fraudulent transactions, making up 0.172% of the total transactions.
- The dataset is highly unbalanced, with frauds being the minority class.
-
Objective:
- Develop a machine learning model to identify fraudulent credit card transactions.
- Ensure that customers are not wrongly charged for purchases they did not make.
-
Data Features:
- The dataset primarily contains numerical input variables.
- Principal Component Analysis (PCA) has been applied to most features, resulting in features labeled as V1 to V28.
- The 'Time' feature represents the seconds elapsed between each transaction and the first transaction in the dataset.
- The 'Amount' feature indicates the transaction amount.
- The response variable 'Class' indicates whether a transaction is fraudulent (1) or not (0).
-
Model Evaluation:
- Due to the class imbalance, traditional metrics like accuracy are not suitable for evaluation.
- The recommended metric for evaluation is the Area Under the Precision-Recall Curve (AUPRC).
- AUPRC provides a comprehensive assessment of the model's performance, particularly in the context of imbalanced datasets.
-
Challenges:
- Dealing with the highly imbalanced nature of the dataset poses a significant challenge.
- Ensuring that the model does not overfit to the majority class and can accurately identify fraudulent transactions despite their rarity.
-
Inspiration:
- The primary motivation is to protect customers from fraudulent charges and maintain the integrity of credit card transactions.
- Detecting fraudulent transactions efficiently is crucial for financial institutions to maintain trust and security in their services.
User Features
What end users can do in this application
-
Dataset Description:
- The dataset consists of credit card transactions made by European cardholders in September 2013.
- It spans transactions over two days, comprising 284,807 transactions.
- Within this dataset, there are 492 instances of fraudulent transactions, making up 0.172% of the total transactions.
- The dataset is highly unbalanced, with frauds being the minority class.
-
Objective:
- Develop a machine learning model to identify fraudulent credit card transactions.
- Ensure that customers are not wrongly charged for purchases they did not make.
-
Data Features:
- The dataset primarily contains numerical input variables.
- Principal Component Analysis (PCA) has been applied to most features, resulting in features labeled as V1 to V28.
- The 'Time' feature represents the seconds elapsed between each transaction and the first transaction in the dataset.
- The 'Amount' feature indicates the transaction amount.
- The response variable 'Class' indicates whether a transaction is fraudulent (1) or not (0).
-
Model Evaluation:
- Due to the class imbalance, traditional metrics like accuracy are not suitable for evaluation.
- The recommended metric for evaluation is the Area Under the Precision-Recall Curve (AUPRC).
- AUPRC provides a comprehensive assessment of the model's performance, particularly in the context of imbalanced datasets.
-
Challenges:
- Dealing with the highly imbalanced nature of the dataset poses a significant challenge.
- Ensuring that the model does not overfit to the majority class and can accurately identify fraudulent transactions despite their rarity.
-
Inspiration:
- The primary motivation is to protect customers from fraudulent charges and maintain the integrity of credit card transactions.
- Detecting fraudulent transactions efficiently is crucial for financial institutions to maintain trust and security in their services.
Other Features
Additional capabilities included in the project
-
Dataset Description:
- The dataset consists of credit card transactions made by European cardholders in September 2013.
- It spans transactions over two days, comprising 284,807 transactions.
- Within this dataset, there are 492 instances of fraudulent transactions, making up 0.172% of the total transactions.
- The dataset is highly unbalanced, with frauds being the minority class.
-
Objective:
- Develop a machine learning model to identify fraudulent credit card transactions.
- Ensure that customers are not wrongly charged for purchases they did not make.
-
Data Features:
- The dataset primarily contains numerical input variables.
- Principal Component Analysis (PCA) has been applied to most features, resulting in features labeled as V1 to V28.
- The 'Time' feature represents the seconds elapsed between each transaction and the first transaction in the dataset.
- The 'Amount' feature indicates the transaction amount.
- The response variable 'Class' indicates whether a transaction is fraudulent (1) or not (0).
-
Model Evaluation:
- Due to the class imbalance, traditional metrics like accuracy are not suitable for evaluation.
- The recommended metric for evaluation is the Area Under the Precision-Recall Curve (AUPRC).
- AUPRC provides a comprehensive assessment of the model's performance, particularly in the context of imbalanced datasets.
-
Challenges:
- Dealing with the highly imbalanced nature of the dataset poses a significant challenge.
- Ensuring that the model does not overfit to the majority class and can accurately identify fraudulent transactions despite their rarity.
-
Inspiration:
- The primary motivation is to protect customers from fraudulent charges and maintain the integrity of credit card transactions.
- Detecting fraudulent transactions efficiently is crucial for financial institutions to maintain trust and security in their services.
How to Run
Step-by-step setup on your laptop or PC
-
Download the Dataset:
- Download the dataset from Kaggle (Download Here) (creditcard.csv).
- Unzip the downloaded file if necessary.
-
Organize the Files:
- Place the
creditcard.csvfile inside themainfolder.
-
Install Required Python Packages:
- If you haven't installed the necessary packages, you can install them using pip. Run the following command in your terminal or command prompt:
- pip install pandas numpy scikit-learn matplotlib seaborn
- pip install notebook
-
Run the Code in Jupyter Notebook:
- Open Jupyter Notebook.
- Navigate to the
Credit-Card-Fraud-Detectionfolder. - Create a new Jupyter Notebook file or use an existing one.
- Import the necessary libraries and load the dataset (
creditcard.csv). - Follow the code provided in the notebook to perform data preprocessing, model training, and evaluation.
- Run the code cells one by one to execute the code.
- run this command : python -m notebook
-
Enjoy:
- Once you have run the code, you should see the results of the model training and evaluation.
- Feel free to explore the dataset and experiment with different machine learning models and techniques for fraud detection
Login Credentials
Default demo accounts for testing after setup
| Panel | Username | Password | |
|---|---|---|---|
| Admin | [email protected] | admin | admin@123 |
| User | [email protected] | User | user@123 |
License
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Credit Card Fraud Detection Using Machine Learning Source Code
Final Year MACHINE-LEARNING Project
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credit card fraud detection
machine learning
python
final year students
source code