Add README for Sentiment Analysis MLOps project

This README provides an overview of the Sentiment Analysis project, detailing its objectives, tech stack, architecture, CI/CD pipeline, installation instructions, and future improvements.

README.md

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+📊 End-to-End MLOps Pipeline for Sentiment Analysis
+🚀 Project Overview
+This repository hosts a production-ready Sentiment Analysis System designed to monitor online brand reputation. Beyond simple model training, this project implements a robust MLOps pipeline that automates the testing, integration, and deployment of Machine Learning models.
+
+The goal is to solve the business challenge of manual reputation tracking by providing an automated, scalable solution that classifies social media feedback (Positive, Neutral, Negative) in real-time.
+
+Key Objectives
+Scalability: Moving from experimental notebooks to modular, production-grade code.
+
+Automation: Implementing CI/CD pipelines to ensure code quality and seamless deployment.
+
+Observability: Setting up monitoring strategies to detect data drift and ensure model reliability over time.
+
+🛠️ Tech Stack & Tools
+Machine Learning: Python, Scikit-learn / PyTorch, Transformers (Hugging Face).
+
+Model Architecture: [Insert Model Name, e.g., FastText / RoBERTa-base].
+
+MLOps & CI/CD: GitHub Actions.
+
+Deployment: Hugging Face Spaces / Docker.
+
+Version Control: Git & DVC (Data Version Control).
+
+⚙️ Architecture & MLOps Workflow
+This project follows MLOps best practices to ensure the lifecycle of the model is managed efficiently.
+
+1. Data & Modeling
+Utilized public datasets for sentiment classification.
+
+Implemented a pre-trained [FastText / RoBERTa] model fine-tuned for social media contexts.
+
+Code is modularized for easy retraining and scalability.
+
+2. CI/CD Pipeline (GitHub Actions)
+Every push to the main branch triggers an automated pipeline:
+
+Linting & Formatting: Ensures code consistency.
+
+Unit & Integration Tests: Verifies that the model inference logic works as expected before deployment.
+
+Build: Packages the application.
+
+3. Continuous Deployment
+Upon passing the CI checks, the application is automatically deployed to Hugging Face Spaces.
+
+This enables real-time interaction with the model via a web interface or API.
+
+4. Continuous Monitoring & Retraining strategy
+The system is designed to support feedback loops.
+
+Future Work: Implementation of drift detection to trigger automatic retraining when model performance degrades due to changing language trends.
+
+📂 Repository Structure
+Bash
+
+├── .github/workflows   # CI/CD configurations (GitHub Actions)
+├── app/                # Application code for deployment (Streamlit/Gradio/FastAPI)
+├── src/                # Source code for model training and inference
+│   ├── model.py        # Model architecture
+│   ├── preprocess.py   # Data cleaning pipelines
+│   └── predict.py      # Inference logic
+├── tests/              # Unit and integration tests
+├── notebooks/          # Exploratory Data Analysis (EDA) and prototyping
+├── requirements.txt    # Project dependencies
+└── README.md           # Documentation
+💻 Installation & Usage
+To run this project locally:
+
+Clone the repository:
+
+Bash
+
+git clone https://github.com/your-username/your-repo-name.git
+cd your-repo-name
+Install dependencies:
+
+Bash
+
+pip install -r requirements.txt
+Run the application:
+
+Bash
+
+python app/main.py
+# OR if using Streamlit/Gradio
+streamlit run app/app.py
+Run Tests:
+
+Bash
+
+pytest tests/
+📈 Results and Performance
+Model Accuracy: [Insert Accuracy, e.g., 85%]
+
+F1-Score: [Insert F1 Score]
+
+Inference Speed: [Optional: e.g., <50ms per tweet]
+
+Note: Detailed analysis of the model's performance and the confusion matrix can be found in the notebooks directory.
+
+🔮 Future Improvements
+Drift Detection: Implementing tools like Evidently AI to visualize data drift.
+
+Containerization: Fully Dockerizing the application for cloud-agnostic deployment (AWS/GCP).
+
+API Expansion: Creating a REST API using FastAPI for integration with external dashboards.
+
+🤝 Contributing
+Contributions, issues, and feature requests are welcome! Feel free to check the issues page.
+
+📝 License
+Distributed under the MIT License. See LICENSE for more information.
+
+💡 Note for the Reviewer
+This project was developed as a comprehensive exercise to demonstrate Full-Stack Data Science capabilities, bridging the gap between model development and production engineering.