README.md

# 📊 End-to-End MLOps Pipeline for Sentiment Analysis regarding Online Reputation

![Build Status](https://img.shields.io/badge/build-passing-brightgreen)
![Python](https://img.shields.io/badge/python-3.9%2B-blue)
![Deployment](https://img.shields.io/badge/deployed%20on-HuggingFace-orange)
![License](https://img.shields.io/badge/license-MIT-green)

## 🚀 Project Overview

**MachineInnovators Inc.** focuses on scalable, production-ready machine learning applications. This project is a comprehensive **MLOps solution** designed to monitor online company reputation through automated sentiment analysis.

Unlike standard data science experiments, this repository demonstrates a **full-cycle ML workflow**, moving from model training to automated deployment. It addresses the business need for real-time reputation tracking by classifying social media feedback (Positive, Neutral, Negative) using an automated pipeline.

### Key Features
* **Production-First Approach:** Focus on scalability, modularity, and code quality.
* **CI/CD Automation:** Integrated pipeline for automated testing and deployment using GitHub Actions.
* **Continuous Deployment:** Automatic deployment to Hugging Face Spaces upon successful builds.
* **Reproducibility:** Code and environment are strictly versioned to ensure consistent results.

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## 🛠️ Tech Stack & Tools

* **Core:** Python 3.9+
* **Machine Learning:** [FastText / Transformers (RoBERTa)] **
* **MLOps & CI/CD:** GitHub Actions
* **Deployment:** Hugging Face Spaces
* **Version Control:** Git
* **Development:** Google Colab (Prototyping) -> VS Code (Production)

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## ⚙️ Architecture & MLOps Workflow

The project follows a rigorous MLOps pipeline to ensure reliability and speed of delivery:

1.  **Data Ingestion & Preprocessing:**
    * Cleaning and tokenization of social media data using industry-standard libraries.
    * Usage of public datasets labeled for sentiment analysis.

2.  **Model Development:**
    * Implementation of a robust sentiment classification model.
    * Optimization for inference speed and accuracy.

3.  **CI/CD Pipeline (GitHub Actions):**
    * **Linting:** Enforces code style (PEP8) to maintain high readability.
    * **Testing:** Unit tests ensure that data processing and prediction logic function correctly before any merge.
    * **Delivery:** Upon passing all checks on the `main` branch, the application is packaged and deployed.

4.  **Deployment:**
    * The model is served via a web interface hosted on **Hugging Face Spaces**, allowing for immediate user interaction and testing.

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## 📂 Repository Structure

```bash
├── .github/workflows/  # CI/CD configurations (GitHub Actions)
├── app/                # Application code (Inference & UI)
├── src/                # Source code for training and processing
│   ├── model.py        # Model architecture and training logic
│   ├── preprocess.py   # Data cleaning pipeline
│   └── utils.py        # Utility functions
├── tests/              # Unit and integration tests
├── notebooks/          # Exploratory Data Analysis (EDA) and prototyping
├── requirements.txt    # Project dependencies
└── README.md           # Project documentation

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.