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README.md

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-# Credit Card Fraud Detection with DuckDB and Medallion Architecture
-
-This project demonstrates an end-to-end pipeline for credit card fraud detection. It uses DuckDB to process data in a Medallion Architecture (Bronze, Silver, Gold) and trains a Random Forest model to identify fraudulent transactions.
-
-## Project Structure
-
-- `data/`: Contains the raw CSV datasets (`fraudTrain.csv`, `fraudTest.csv`).
-- `src/`: Contains the Python scripts for the data pipeline and model training.
-  - `bronze.py`: Ingests raw data into the bronze layer.
-  - `silver.py`: Cleans and transforms data for the silver layer.
-  - `gold.py`: Creates aggregated features for the gold (analytics) layer.
-  - `train.py`: Trains a `RandomForestClassifier` on the gold data and saves the model.
-- `models/`: Directory where the trained model is saved.
-- `requirements.txt`: Lists the required Python packages.
-
-## How to Run
-
-1.  **Install dependencies:**
-    ```bash
-    pip install -r requirements.txt
-    ```
-
-2.  **Run the training pipeline:**
-    This command executes the entire data pipeline (Bronze, Silver, Gold) and trains the model.
-    ```bash
-    python src/train.py
-    ```
-
-## Medallion Architecture
-
--   **Bronze Layer**: Raw, unfiltered data ingested directly from the source CSVs.
--   **Silver Layer**: Cleaned and transformed data. Timestamps are corrected, and new features like cardholder `age` are derived.
--   **Gold Layer**: Analytics-ready data with aggregated features (e.g., `avg_merch_spend`) suitable for machine learning.