ADEYEMIBolaji/african-foodstore-anomaly

# 🛒 African Foodstore Anomaly Detection App 🇬🇧🌍 This project builds a real-time anomaly detection system for a typical African Food Store's e-commerce data in the UK. It covers everything from data generation, model training, to a live Streamlit web app where users can upload their transactions and detect anomalies. --- ## 🚀 Project Overview **Features:** - Generate synthetic African food e-commerce transactions - Train and compare three anomaly detection models: - Isolation Forest - AutoEncoder (Deep Learning) - One-Class SVM - Select and save the best performing model - Deploy a Streamlit app for real-time anomaly detection - Allow users to upload CSV files and download detected results --- ## 🌐 Live Demo You can try the deployed app here: 👉 African Foodstore Anomaly Detection App --- ## 🏗 Project Structure ```bash african-foodstore-anomaly/ ├── app.py # Streamlit frontend ├── data/ # Synthetic dataset and test data │ ├── ecommerce_data.csv │ ├── test_data.csv │ └── generate_data.py ├── models/ # Model training and saved models │ ├── train_models.py │ ├── best_model.pkl or best_model.h5 │ ├── scaler.pkl ├── notebooks/ # EDA and preprocessing notebooks │ ├── eda_preprocessing.ipynb │ └── model_training.ipynb ├── requirements.txt # Project dependencies ├── README.md # Project documentation └── .gitignore ``` --- ## 📦 Tech Stack - **Python 3.12** - **Streamlit** - **Scikit-learn** - **Tensorflow / Keras** - **Pandas / Numpy** - **Matplotlib / Seaborn** - **Joblib** --- ## 🛠 How to Run the Project Locally 1. **Clone the repository:** ```bash git clone github.com cd african-foodstore-anomaly ``` 2. **Create a virtual environment (optional but recommended):** ```bash python -m venv venv source venv/bin/activate # On Mac/Linux venv\Scripts\activate # On Windows ``` 3. **Install dependencies …