Credit Card Fraud Detection – IT and Computer Engineering Guide
1. Project Overview
Objective: Detect fraudulent credit card transactions using
anomaly detection methods.
Scope: Develop a machine learning model to identify anomalies in transaction
data, distinguishing between legitimate and fraudulent activities.
2. Prerequisites
Knowledge: Basics of Python programming, anomaly detection
techniques, and machine learning.
Tools: Python, Scikit-learn, Pandas, NumPy, Matplotlib, and Seaborn.
Dataset: Credit card transaction dataset, such as the Kaggle Credit Card Fraud
Detection dataset.
3. Project Workflow
- Dataset Preparation: Obtain a labeled dataset containing features and fraud indicators.
- Data Preprocessing: Handle missing values, normalize numerical features, and encode categorical features.
- Exploratory Data Analysis (EDA): Understand the distribution of fraud and legitimate transactions.
- Model Development: Use anomaly detection techniques like Isolation Forest, One-Class SVM, or Autoencoders.
- Model Evaluation: Assess performance using metrics like precision, recall, F1-score, and ROC-AUC.
- Deployment: Deploy the model as a monitoring system to detect fraud in real-time.
4. Technical Implementation
Step 1: Import Libraries
import pandas as pd
import numpy as np
from sklearn.ensemble import IsolationForest
from sklearn.metrics import classification_report, roc_auc_score
import matplotlib.pyplot as plt
import seaborn as sns
Step 2: Load and Preprocess the Dataset
# Load dataset
data = pd.read_csv('creditcard.csv')
# Normalize numerical features
from sklearn.preprocessing import StandardScaler
data['Amount'] =
StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))
# Split features and labels
X = data.drop(columns=['Class'])
y = data['Class']
Step 3: Apply Isolation Forest
# Initialize and fit the Isolation Forest model
isolation_forest = IsolationForest(contamination=0.01, random_state=42)
y_pred = isolation_forest.fit_predict(X)
# Map predictions to binary values
y_pred_binary = [1 if pred == -1 else 0 for pred in y_pred]
Step 4: Evaluate the Model
# Evaluate using metrics
print(classification_report(y, y_pred_binary))
roc_auc = roc_auc_score(y, y_pred_binary)
print(f"ROC-AUC Score: {roc_auc}")
5. Results and Insights
Examine the performance of the model, focusing on its ability to correctly identify fraudulent transactions while minimizing false positives.
6. Challenges and Mitigation
Class Imbalance: Use techniques like SMOTE or under-sampling
to balance the dataset.
Overfitting: Validate the model on unseen data to ensure generalization.
7. Future Enhancements
Incorporate additional features such as geolocation or
transaction patterns.
Experiment with advanced techniques like neural network-based Autoencoders for
unsupervised anomaly detection.
8. Conclusion
The Credit Card Fraud Detection project highlights the effectiveness of anomaly detection methods in identifying fraudulent transactions, offering a robust solution for financial security.