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How to Build a Simple Credit Card Fraud Detection Model Using Logistic Regression in Python?

Credit Card Fraud Detection Screenshot

Condition for Building a Simple Credit Card Fraud Detection Model Using Logistic Regression in Python

  • Description:
    This project demonstrates a simple credit card fraud detection using logistic regression. It includes steps for data preprocessing, model training, and evaluation using Python.
Why Should We Use Logistic Regression?
  • Simplicity: Easy to implement and interpret.
  • Efficiency: Works well with large datasets.
  • Linear Model: Effective for classification tasks with a linear decision boundary.
  • Probability Output: Provides probability scores for prediction.
Step by Step Process
  • Data Loading: Load the credit card fraud dataset.
  • Preprocessing: Handle missing values and split data into training/testing sets.
  • Model Training: Train the logistic regression model.
  • Evaluation: Use metrics like accuracy, confusion matrix, and classification report.
Sample Source Code
  • # Importing libraries
    import pandas as pd
    from sklearn.model_selection import train_test_split
    from sklearn.linear_model import LogisticRegression
    from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

    # Load dataset
    data = pd.read_csv('creditcard.csv')
    X = data.drop('Class', axis=1)
    y = data['Class']

    # Split data
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

    # Train logistic regression
    model = LogisticRegression()
    model.fit(X_train, y_train)

    # Predictions
    y_pred = model.predict(X_test)

    # Evaluate
    print("Accuracy:", accuracy_score(y_test, y_pred))
    print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
    print("Classification Report:\n", classification_report(y_test, y_pred))
Screenshot
  • Feature Importance