This project implements a sentiment analysis system using machine learning techniques. It's designed as a practical application to demonstrate core concepts in AI development, combining data handling, machine learning, and API development.

• Python: Primary programming language
• Pandas: Data manipulation and analysis
• Scikit-learn: Machine learning implementation
• FastAPI: API framework
• Uvicorn: ASGI server implementation
• Podman: Container engine

• Data processing from CSV files
• Sentiment analysis using machine learning models
• RESTful API endpoints for real-time analysis
• Scalable architecture for handling multiple requests
• Containerized deployment

sentiment_analysis_api/
├── data/            # CSV and data files
├── main.py          # Main application
|-- model.py         # Contains actual model functions
├── requirements.txt # for all the required packages
|-- Containerfile    # Contains required podman configurations to create and run this app in a container
└── README.md

1. Clone the repository
2. Install dependencies:

pip install -r requirements.txt

1. Run the application:

uvicorn main:app --reload

1. Build the container:

podman build -t sentiment-analysis .

1. Run the container:

podman run -p 8000:8000 sentiment-analysis

The API will provide endpoints for:

• Sentiment analysis of text input
• Model training status
• Analysis results retrieval

• Implement robust data preprocessing
• Build and train ML models
• Create RESTful API endpoints