Forecasting of the option class(Literary or Scientific or Organization/Society/Economy) of the student in the high school at Fianarantsoa-Madagascar city and using API Rest for the deploiement
# Student Option Classification API
This project provides a RESTful API for predicting the academic track (Literary, Scientific, or Organization/Society/Economy) for high school students in Fianarantsoa, Madagascar. The system uses machine learning (PyCaret) to make predictions based on academic performance and other relevant factors.
## 🚀 Key Features
- **Accurate Predictions**: Machine learning model trained on historical student data
- **Easy Integration**: Simple REST API endpoints for easy integration with other systems
- **Detailed Insights**: Returns prediction probabilities for each possible track
- **Scalable**: Built with FastAPI for high performance
- **Comprehensive Logging**: Built-in logging for monitoring and debugging
## 📋 Table of Contents
- 🚀 Key Features
- 📁 Project Structure
- 🛠 Prerequisites
- 🚀 Installation
- 🚦 Usage
- Setting Up the Environment
- Training the Model
- Running the API
- 🌐 API Endpoints
- Health Check
- Make a Prediction
- Get Model Features
- 🧪 Testing
- ⚙️ Configuration
- 🐳 Deployment
- 🤝 Contributing
## ✨ Features
- **Machine Learning Model**: Predicts student academic tracks using PyCaret
- **RESTful API**: Easy-to-use endpoints for predictions
- **Scalable Architecture**: Modular design for easy maintenance and extension
- **Comprehensive Documentation**: Includes API documentation and examples
- **Health Monitoring**: Built-in health check endpoint
- **Input Validation**: Robust validation of input data
- **Logging**: Comprehensive logging for debugging and monitoring
## 📁 Project Structure
```
model_deploiment_using_apiRest/
├── app/ # Application source code
│ ├── api/ # API endpoints and routes
│ │ └── endpoints.py # API route handlers
│ ├── core/ # Core application configuration
│ │ └── config.py # Application settings and configuration
│ ├── models/ # Data models …