task5: README.md updated

README.md

@@ -1,60 +1,127 @@
-
 # Fraud Detection for E-commerce and Banking
 
-This project utilizes machine learning to detect fraudulent activity in e-commerce and banking transactions. The model facilitates data-driven decisions for enhanced security and risk management.
+This project leverages machine learning to detect fraudulent transactions in e-commerce and banking, aiding in proactive security and risk management. The goal is to provide a robust fraud detection pipeline with explainability, deployment, and dashboard visualization for actionable insights.
+
+---
+
+## Project Overview
+
+### Key Features
+- **Data Analysis & Preprocessing**: Handling missing values, data cleaning, and feature engineering for fraud detection.
+- **Model Building & Training**: Comparison of multiple models, including deep learning architectures (CNN, RNN, LSTM).
+- **Explainability**: Interpretation using SHAP and LIME for feature influence insights.
+- **Deployment**: API service for real-time fraud predictions via Flask, Dockerized for scalability.
+- **Dashboard**: Interactive visualization of fraud insights using Dash.
+
+---
 
 ## Project Directory Structure
 
-The repository is well-organized for efficient development:
+The repository is organized as follows:
 
-* **`.github/workflows/`**: Automates tasks like testing through GitHub Actions.
-* **`.vscode/`**: Enhances the development experience with configurations for Visual Studio Code.
-* **`app/`**: Contains the API implementation for interacting with the machine learning model via RESTful endpoints.
-* **`notebooks/`**: Jupyter notebooks are used for exploring data, feature engineering, and initial model exploration.
-* **`scripts/`**: Python scripts handle data preprocessing, feature extraction, visualization, and model implementation.
-* **`tests/`**: Unit tests ensure the model and data processing logic function correctly.
+- **`.github/workflows/`**: Contains GitHub Actions for CI/CD and automated testing.
+- **`.vscode/`**: Development configuration for Visual Studio Code.
+- **`fraud-detection-api/`**: REST API implementation for serving fraud detection models.
+- **`fraud-dashboard/`**: Dash application for real-time fraud data visualization.
+- **`notebooks/`**: Jupyter notebooks for data exploration, feature engineering, and model prototyping.
+- **`scripts/`**: Scripts for data preprocessing, visualization, and model building.
+- **`tests/`**: Unit tests for model integrity and data processing functions.
+
+---
 
 ## Installation
 
-- To run the project locally, follow these steps:
+Follow these steps to set up and run the project locally:
 
-1. **Clone the Repository:**
+1. **Clone the Repository**
 
    ```bash
    git clone https://github.com/epythonlab/fraud-detection.git
    cd fraud-detection
    ```
 
-2. **Set Up the Virtual Environment**
+2. **Set Up a Virtual Environment**
 
-    - Create a virtual environment to manage the project's dependencies:
+   **For Linux/MacOS:**
+   ```bash
+   python3 -m venv .venv
+   source .venv/bin/activate
+   ```
 
-    **For Linux/MacOS:**
+   **For Windows:**
+   ```bash
+   python -m venv .venv
+   .venv\Scripts\activate
+   ```
 
-    ```bash
-    python3 -m venv .venv
-    source .venv/bin/activate
-    ```
+3. **Install Required Packages**
 
-    **For Windows:**
+   ```bash
+   pip install -r requirements.txt
+   ```
 
+---
+
+## Project Tasks and Workflow
+
+### Task 1 - Data Analysis and Preprocessing
+- **Handling Missing Values**: Imputation or removal of missing data.
+- **Data Cleaning**: Removing duplicates and correcting data types.
+- **Exploratory Data Analysis (EDA)**:
+  - Univariate and bivariate analysis.
+- **Geolocation Analysis**: 
+  - Convert IP addresses to integers.
+  - Merge `Fraud_Data.csv` with `IpAddress_to_Country.csv`.
+- **Feature Engineering**:
+  - Transaction frequency and velocity.
+  - Time-based features (hour of day, day of week).
+- **Normalization and Scaling**
+- **Encoding Categorical Features**
+
+### Task 2 - Model Building and Training
+- **Data Preparation**: Feature and target separation, and train-test split.
+- **Model Selection**:
+  - Classical models: Logistic Regression, Decision Tree, Random Forest.
+  - Advanced models: Gradient Boosting, MLP, CNN, RNN, LSTM.
+- **Model Training and Evaluation**:
+  - Train on both `creditcard` and `Fraud_Data` datasets.
+- **MLOps**: 
+  - Use MLflow for versioning, experiment tracking, and model comparison.
+
+### Task 3 - Model Explainability
+- **SHAP (SHapley Additive exPlanations)**:
+  - Explain feature importance using SHAP summary, force, and dependence plots.
+- **LIME (Local Interpretable Model-agnostic Explanations)**:
+  - Generate feature importance plots for individual predictions.
+
+### Task 4 - Model Deployment and API Development
+- **Setting Up the Flask API**:
+  - Serve models via Flask in `serve_model.py`.
+- **Dockerization**:
+  - Create a Docker container for the API with a `Dockerfile`.
+  - Run the container with:
     ```bash
-    python -m venv .venv
-    .venv\Scripts\activate
+    docker build -t fraud-detection-model .
+    docker run -p 5000:5000 fraud-detection-model
     ```
+- **Logging**:
+  - Use Flask-Logging to monitor requests and track predictions.
 
-3. **Install Dependencies**
+### Task 5 - Dashboard Development with Flask and Dash
+- **Interactive Dashboard**:
+  - Visualize fraud insights (transaction count, fraud cases, geographic data).
+  - Use Dash to create charts (line, bar) and summary boxes for fraud trends.
+  - Set up a Flask endpoint to serve fraud data for the Dash frontend.
 
-    - Install the required Python packages by running:
+---
 
-    ```bash
-    pip install -r requirements.txt
-    ```
-
 ## Contributing
 
-We welcome contributions to improve the project. Please follow the steps below to contribute:
+We welcome contributions to enhance the project:
+
+1. Fork the repository and create a new branch.
+2. Make changes with clear, descriptive commit messages.
+3. Submit a pull request with a detailed explanation.
+
+---
 
-- Fork the repository.
-- Create a new branch for your feature or bug fix.
-- Submit a pull request with a detailed explanation of your changes.