Skip to content

Fake News Detection Project

Welcome to our Fake News Detection Project, where we embark on a journey to build a robust system that can determine the authenticity of news articles. In a world inundated with information, the ability to distinguish between real and fake news is crucial.

Real Project from the ground up

We'll begin by creating a solid development environment, leverage cutting-edge Natural Language Processing (NLP) techniques to convert news articles into meaningful embeddings, and train a machine learning model capable of making accurate predictions.

project_schema

Throughout this project, we will embrace best practices in software development, version control, and containerization to ensure reproducibility and ease of deployment. Our goal is to not only develop a powerful tool but also to learn and grow as we build a project from the ground up. Let's get started on this exciting journey towards a more informed and discerning world!

Project Overview

  1. Create a virtual environment
  2. Start a new project using Poetry
  3. Manage the repository using GIT
  4. Download dataset from Kaggle
  5. Docker-Compose: Create environment with MLFlow
  6. Use a sentence transformers model to transform news to vectors.
  7. Retrieve the embeddings and Train a single model on those embeddings and register to MLFlow
  8. Docker-Compose: Add a service that takes the latest model from MLFlow and crates an API
  9. Create a Streamlit App that calls the API and returns a response
  10. Dockerize Streamlit App and add it to the docker compose