Project Title & Introduction:

Financial Fraud Detection "This project uses machine learning techniques for financial fraud detection. It leverages a comprehensive Kaggle dataset of anonymized financial transactions, customer profiles, and fraudulent patterns to train and evaluate predictive models."

Dataset Description & Source:

Fraud Detection Dataset 🔒 Dataset Description The Financial Fraud Detection Dataset contains data related to financial transactions and fraudulent patterns. It is designed for the purpose of training and evaluating machine learning models for fraud detection.

📁 Dataset Structure The dataset is organized within the "data" folder and consists of several subfolders, each containing CSV files with specific information related to financial transactions, customer profiles, fraudulent patterns, transaction amounts, and merchant information. The dataset structure is as follows:

📂 data

📂 Transaction Data

transaction_records.csv: Contains transaction records with details such as transaction ID, date, amount, and customer ID.

transaction_metadata.csv: Contains additional metadata for each transaction.

📂 Customer Profiles

customer_data.csv: Includes customer profiles with information such as name, age, address, and contact details.

account_activity.csv: Provides details of customer account activity, including account balance, transaction history, and account status.

📂 Fraudulent Patterns

fraud_indicators.csv: Contains indicators of fraudulent patterns and suspicious activities.

suspicious_activity.csv: Provides specific details of transactions flagged as suspicious.

📂 Transaction Amounts

amount_data.csv: Includes transaction amounts for each transaction.

anomaly_scores.csv: Provides anomaly scores for transaction amounts, indicating potential fraudulence.

📂 Merchant Information

merchant_data.csv: Contains information about merchants involved in transactions.

transaction_category_labels.csv: Provides category labels for different transaction types.

📂 src

data.py: Python file containing code to generate the dataset based on real-world data. 💡 Usage This dataset can be used for various purposes, including:

Developing and evaluating machine learning models for financial fraud detection. Conducting research on fraud detection algorithms and techniques. Training data analysts and data scientists on fraud detection methodologies. Example:

Dataset Overview: This dataset provides anonymized financial transactions including transaction records, customer profiles, fraudulent patterns, transaction amounts, and merchant information.

Source: The dataset is sourced from Kaggle and is intended for educational and research purposes in financial fraud detection. link:https://www.kaggle.com/datasets/goyaladi/fraud-detection-dataset