CreditCardFraudDetection

Hackathon_Project

Fraud transaction detection using machine learning kaggle dataset file link is here: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud

Project Aims

This project aims to convey several key points:

1. Practical Application of Machine Learning: Demonstrates a real-world application of machine learning in financial security, specifically in detecting credit card fraud.

2. Handling Imbalanced Datasets: Showcases how to deal with imbalanced datasets, a common challenge in fraud detection. The code uses undersampling of the majority class (legitimate transactions) to balance the dataset.

3. Basic Machine Learning Workflow: Illustrates the fundamental steps in a machine learning project:

   • Data loading and preprocessing
   • Splitting data into features and target
   • Dividing data into training and testing sets
   • Model selection, training, and evaluation

4. Use of Popular Data Science Libraries: Demonstrates the use of common Python libraries for data science and machine learning:

   • pandas for data manipulation
   • scikit-learn for machine learning tasks
   • numpy for numerical operations

5. Simple Model Implementation: Uses Logistic Regression, a straightforward and interpretable model, as a starting point for fraud detection.

6. Model Evaluation: Shows how to evaluate a model's performance using accuracy scores on both training and test data.

7. Reproducibility: Includes a link to the dataset and provides the code, emphasizing reproducibility in data science.

8. Importance of Fraud Detection: Highlights the significance of fraud detection in the financial sector, addressing a real-world problem that affects many people and businesses.

Data Preprocessing

1. Data Loading:

   • The dataset is loaded from 'creditcard.csv' using pandas:

     data = pd.read_csv("creditcard.csv")

2. Class Separation:

   • Legitimate and fraudulent transactions are separated:

     legit = data[data.Class == 0]
     fraud = data[data['Class'] == 1]

   • This separation allows for analysis of class imbalance.

3. Feature and Target Separation:

   • Features (X) and target variable (y) are split:

     x = data.drop('Class', axis=1)
     y = data['Class']

4. Handling Class Imbalance:

   • Undersampling of the majority class (legitimate transactions) is performed:

     legit_s = legit.sample(n=len(fraud), random_state=2)
     data = pd.concat([legit_s, fraud], axis=0)

   • This creates a balanced dataset for training.

Model Training

1. Train-Test Split:

   • Data is split into training and testing sets:

     x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, stratify=y, random_state=2)

   • 20% of data is reserved for testing.
   • Stratification ensures that the class distribution is maintained in both sets.

2. Model Selection:

   • Logistic Regression is chosen as the classification algorithm:

     model = LogisticRegression()

   • This is a good baseline model for binary classification tasks.

3. Model Training:

   • The model is trained on the training data:

     model.fit(x_train, y_train)

Model Evaluation

1. Accuracy Calculation:
   • The model's performance is evaluated using accuracy scores:

     train_acc = accuracy_score(model.predict(x_train), y_train)
     test_acc = accuracy_score(model.predict(x_test), y_test)

   • Both training and testing accuracies are calculated to assess overfitting.

Conclusion

This implementation provides a solid foundation for credit card fraud detection. The use of undersampling to balance the dataset and Logistic Regression as the classification algorithm offers a good starting point. The separate calculation of training and testing accuracies allows for basic assessment of model generalization.This project serves as an introductory example of applying machine learning to a critical financial problem, demonstrating how relatively simple techniques can be used to approach complex real-world issues. It provides a starting point for understanding and implementing fraud detection systems.

Thank You !