iABn0rma1 / Credit-Card-Fraud-Detection

Credit Card Fraud Detection with Machine Learning

This Jupyter Notebook explores machine learning techniques for credit card fraud detection. The focus is on identifying fraudulent transactions from historical data.

Key Techniques:

• Data Preparation:
  • Feature Selection (dropping irrelevant features)
  • Handling Missing Values
  • Addressing Class Imbalance (undersampling with RandomUnderSampler)
  • Train-Test Splitting
• Machine Learning Models:
  • Multi-Layer Perceptron (MLP) Classifier
  • Autoencoder for Anomaly Detection with Logistic Regression Classification

Notebook Structure:

1. Data Loading and Exploration:

   • Loads the credit card transaction dataset.
   • Explores data characteristics (shape, missing values, etc.)

2. Data Preprocessing:

   • Selects relevant features for fraud prediction.
   • Handles missing values using median imputation.
   • Addresses class imbalance (many more normal transactions than fraudulent ones) using random undersampling.
   • Splits data into training and testing sets.

3. Multi-Layer Perceptron (MLP) Classifier:

   • Implements an MLP classifier with a single hidden layer for fraud detection.
   • Evaluates model performance using accuracy score.

4. Autoencoder for Anomaly Detection:

   • Builds an autoencoder, a neural network that learns compressed representations of data suitable for anomaly detection.
   • Trains the autoencoder on normal transactions, assuming fraudulent transactions will deviate significantly from the learned patterns.
   • Extracts hidden representations from both normal and fraudulent transactions.
   • Trains a Logistic Regression classifier on the hidden representations to distinguish between normal and fraudulent transactions.
   • Evaluates the Logistic Regression model's performance using accuracy score.

5. Conclusion:

   • Briefly summarizes the findings and potential improvements.

Running the Notebook

1. Make sure you have the required libraries installed (pandas, numpy, scikit-learn, tensorflow, imblearn, seaborn, etc.). You can install them using pip install <library_name>.
2. Install NannyML using: pip install -U nannyml or run !python -m pip install git+https://github.com/NannyML/nannyml in a Jupyter cell.
3. Download the credit card transaction dataset: creditcard.csv
4. Open the Jupyter Notebook and run the cells one by one.

Further Exploration

• Try different hyperparameter tuning techniques to improve model performance.
• Implement cost-sensitive learning to emphasize the importance of correctly classifying fraudulent transactions.