Code-str8/customer-frauds-detection

fraud detection challenge for STEG (Tunisian Company of Electricity and Gas) focused on identifying fraudulent meter manipulation through billing history data # Customer Frauds Detection 🔍⚡ This project addresses a fraud detection challenge for STEG (Tunisian Company of Electricity and Gas), focusing on identifying fraudulent meter manipulation through billing history data. ## 📋 Table of Contents - Introduction - Dataset - Installation - Usage - Streamlit App - API Usage - Model Training - API Documentation - Methodology - Results - Challenges & Trade-offs - Future Work - Contributing - License ## 🚀 Introduction The goal of this project is to develop a machine learning model that can accurately detect fraudulent activities in electricity and gas consumption. By analyzing historical billing data, the model aims to help STEG reduce losses due to fraud. ## 💾 Dataset The dataset consists of historical billing data, including features such as client ID, invoice date, consumption levels, and counter types. The target variable indicates whether a client is fraudulent or not. ## 🛠️ Installation To run this project, you need to have Python installed along with the required libraries. You can install the dependencies using the following command: ```bash pip install -r requirements.txt ``` ## 🚀 Usage ### 💫 Streamlit App We've developed an interactive Streamlit application that provides a user-friendly interface for fraud detection: 1. Start the Streamlit app: ```bash streamlit run 1_Welcome.py ``` 2. Login credentials: - Username: admin - Password: Admin01 The app includes several features: #### 🏠 Welcome Page A welcoming interface introducing the fraud detection system. #### 📚 Data Explorer Explore and analyze the dataset with interactive visualizations. #### 🔮 Prediction Interface Easy-to-use form for making fraud predictions: - Input transaction details - Choose between models - Get instant predictions Detailed prediction results with confidence scores. #### ⏳ History Tracking Track and analyze prediction history: - View all past predictions - Analyze trends - Export results ### 🔄 API Usage 1. …