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DevClub Summer of Code 2024

AI/ML Development: Week 1

Learning Tasks

Before diving into the implementation tasks, it's essential to understand the foundational concepts and techniques used in fraud detection using machine learning. Here are some key topics to explore:

  • Machine Learning Fundamentals:

    • Supervised Learning: Understand the concepts of supervised learning, where the model learns from labeled data to make predictions or classifications.
    • Classification Algorithms: Familiarize yourself with popular classification algorithms such as Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (SVM).
    • Model Evaluation Metrics: Learn about evaluation metrics used in classification tasks, such as accuracy, precision, recall, and F1-score.

    Resources:

  • Data Preprocessing and Feature Engineering:

    • Data Cleaning: Learn techniques to handle missing values, outliers, and inconsistencies in the transaction data.
    • Feature Selection: Explore methods to identify and select relevant features from the transaction data that can help in fraud detection.
    • Feature Scaling: Understand the importance of scaling features to a consistent range for improved model performance.

    Resources:

  • Imbalanced Data Handling:

    • Class Imbalance: Understand the challenges posed by imbalanced datasets, where the fraudulent transactions are typically a minority class compared to legitimate transactions.
    • Sampling Techniques: Learn about techniques like oversampling (e.g., SMOTE) and undersampling to address class imbalance.
    • Cost-Sensitive Learning: Explore approaches that assign different misclassification costs to different classes to handle imbalanced data.

    Resources:

  • Model Interpretation and Explainability:

    • Feature Importance: Learn techniques to interpret the importance of features in the trained model, such as feature coefficients or permutation importance.
    • SHAP (SHapley Additive exPlanations): Understand how SHAP values can provide local explanations for individual predictions.
    • Lime (Local Interpretable Model-agnostic Explanations): Explore Lime as a tool for generating local explanations for model predictions.

    Resources:

Task 1: Fraud Detection Model Development

  • Task 1A: Data Exploration and Preprocessing

    • Load the provided transaction-level data into a suitable format (e.g., pandas DataFrame).
    • Explore the dataset to gain insights into its structure, features, and target variable (fraudulent or legitimate transactions).
    • Perform necessary data cleaning steps, such as handling missing values and outliers.
    • Apply appropriate feature scaling techniques to ensure all features are on a similar scale.

  • Task 1B: Feature Engineering and Selection

    • Analyze the available features and brainstorm potential new features that could be derived from the existing data.
    • Implement feature engineering techniques to create meaningful features for fraud detection (e.g., transaction amount, transaction frequency, time since last transaction).
    • Perform feature selection using techniques like correlation analysis, univariate feature selection, or recursive feature elimination to identify the most informative features.

  • Task 1C: Model Training and Evaluation

    • Split the dataset into training and testing sets, ensuring a representative distribution of fraudulent and legitimate transactions in both sets.
    • Train multiple classification models (e.g., Logistic Regression, Random Forest, SVM) on the training data.
    • Evaluate the trained models using appropriate evaluation metrics (e.g., accuracy, precision, recall, F1-score) on the testing set.
    • Compare the performance of different models and select the best-performing one for further analysis.

  • Task 1D: Model Interpretation and Explainability

    • Analyze the selected model to understand the importance of different features in detecting fraudulent transactions.
    • Apply techniques like feature importance, SHAP, or Lime to interpret the model's predictions and gain insights into the factors contributing to fraud detection.
    • Visualize the model's decision-making process using tools like decision tree visualization or feature importance plots.

  • Task 1E: Model Tuning and Refinement

    • Experiment with different hyperparameter settings for the selected model to optimize its performance.
    • Utilize techniques like grid search or random search to find the best combination of hyperparameters.
    • Retrain the model with the optimized hyperparameters and evaluate its performance on the testing set.

Bonus Tasks for Week 1

  1. Anomaly Detection: Explore unsupervised learning techniques like Isolation Forest or Local Outlier Factor (LOF) to detect anomalous transactions that deviate from the norm.
  2. Ensemble Methods: Combine multiple trained models using ensemble techniques like voting or stacking to improve the overall fraud detection performance.
  3. Temporal Analysis: Investigate the temporal patterns in fraudulent transactions and incorporate time-based features or time series analysis techniques into the model.
  4. Real-time Fraud Detection: Design a framework or pipeline to enable real-time fraud detection, where the trained model can make predictions on incoming transactions in near real-time.
  5. Fraud Detection Literature Review: Conduct a literature review of recent research papers and articles on fraud detection using machine learning to stay updated with the latest techniques and advancements in the field.

Remember, fraud detection is an iterative process, and the model may need continuous refinement and updates as new patterns and techniques emerge. The goal of this week is to build a solid foundation in fraud detection using machine learning and gain hands-on experience with the key concepts and techniques involved.