Henryle-hd/BongoScamDetection

BongoScam - Ni tumie kwa namba hii SMS Detection with Machine Learning & a dataset of 1,508 Tanzania Swahili-based SMS examples, showcasing various scam patterns. The dataset is available on Kaggle: swahili-sms-detection, and this project also includes a basic machine learning model to detect and predict such fraudulent messages. # BongoScam - Ni tumie kwa namba hii SMS Detection with Machine Learning In Tanzania, scammers often use SMS to steal money by pretending to be people you trust, such as close friends or relatives, or by continuing fake conversations about money transfers. These scams are commonly recognized with phrases like ``"NI TUMIE KWA NAMBA HII"``, or they claim to be agents like Freemasons, landlords, or employers offering fake jobs. To address this problem, I created a dataset of 1,508 Tanzania Swahili-based SMS examples, showcasing various scam patterns. The dataset is available on Kaggle: swahili-sms-detection, and this project also includes a basic machine learning model to detect and predict such fraudulent messages. ## Features - Real-time SMS scam detection using machine learning - Clean and modern UI built with Next.js and Tailwind CSS - Flask backend API with scikit-learn model - Supports Swahili language messages - 98.7% accuracy on test data ## Tech Stack ### Frontend - Next.js 15+ with App Router - TypeScript - Tailwind CSS - Shadcn UI Components ### Backend - Python - Flask - Scikit-learn - Pandas - Joblib ## Getting Started 1. Clone the repository ```bash git clone github.com ``` ```bash cd bongoscam ``` 2. Install frontend dependencies ```bash cd frontend npm install ``` 3. Install backend dependencies ```bash cd backend pip install -r requirements.txt ``` 4. Start the backend server ```bash cd backend python main.py ``` 5. Start the frontend development server ```bash cd frontend npm run dev ``` 6. Open ```localhost``` in your browser ## Model Training The SMS scam detection model was trained on a custom dataset of Swahili messages labeled as either scam or trust. The model uses: - CountVectorizer for text feature extraction - Multinomial Naive Bayes classifier - Achieved 98.7% accuracy on test set - Trained on 10000 messages - Dataset available on kaggle: swahili-sms-detection ## …