Footprinting, Enumeration, Scanning, Sniffing, Social Engineering Footprinting, Enumeration, Scanning, Sniffing, Social Engineering
- 1. Final Project – Credit Card Fraud
- 2. Data descriptive analysis • Here is a sample data set that captures the credit card transaction details for a few users • Descriptive analysis for fraud credit card detection involves examining key statistical measures and characteristics of the dataset to gain insights into the distribution and patterns of the data. This typically includes calculating summary statistics such as mean, median, standard deviation, and quartiles for relevant variables such as transaction amount, transaction frequency, time of transaction, etc.
- 3. Models and Development • GBM • XGBoost • Gradient Boosting Classifier
- 4. Model Performance • GBM and XGBoost perform with the accuracy of 50% • If we also apply hyper parameter tuning then we can increase the accuracy of an algorithm.
- 5. Model interpretability • Visualizations: Creating visual representations of the model's decision- making process, such as decision trees, heat maps, and partial dependence plots, to aid in interpretation. • Final Recommendation • For credit card fraud detection, I recommend employing a comprehensive approach that combines both traditional rule-based methods and advanced machine learning techniques. Here are some key recommendations:
- 6. For effective credit card fraud detection, start with thorough data preprocessing to handle missing values, outliers, and skewed distributions, while also employing feature engineering techniques like PCA and feature scaling. Utilize ensemble learning methods such as Random Forest, Gradient Boosting, or XGBoost, along with anomaly detection algorithms like Isolation Forest and One-Class SVM, to build robust fraud detection models. Additionally, ensures model evaluation and validation, real- time monitoring, explainability, transparency, and ongoing maintenance to continuously improve the system's effectiveness in detecting and preventing fraud.