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YouTube Watch History

                                     

Overview

A streamlit app where you can upload your YouTube Watch History Data to see insights on your viewing pattern. Your data will go through a ML Model which predicts the ContentType of each video your have watch. The app fetches more details of each video through YouTube API. There is also a Channel Recommender System in the app which recommend you similar channels on the basis of channel's video titles and tags they had used.

Explanation

  1. Project Introduction: Hello, I am Anshul Raj Verma and I am exited to share my end-to-end Machine Learning project where I've used FastAPI, Streamlit, MongoDB and Docker as tech stack. Also this is version 2 of the project because the version 1 got very complicated and its hard to modify and refactor the codes there that's why created new version 2 where I am trying keep better attention on project architecture.

  2. Project Overview: This project consists a streamlit app where you can upload your YouTube Watch History Data to see insights on your viewing pattern. Your data will go through a ML Model which predicts the ContentType of each uploaded video. The app fetches more details of each video through YouTube API. There is also a Channel Recommender System in project which recommend you similar channels on the basis of channel's videos title and tags they had used.

  3. Components of Project: The project is divided into three major components Backend API, ML & Frontend.

    • Backend API: This is a FastAPI app which interacts with MongoDB database where YouTube videos details were stored and it also fetches YouTube videos details from official YouTube Data API (for this you requires the API_KEY).
    • ML: Here the code for ML Model were present through they will get trained and do predcitions on user's uploaded data after some preprocessing. The ML Models get served as API through a FastAPI app.
    • Frontend: Here all the above components meets and work together to show awesome insights on user's uploaded data. This is a streamlit web app where users can upload their watch history data and see insights. Here above API services were called to fetch videos details from official YouTube API, to store data in database, to make predcitions using ML Models, to recommend channels and etc.

  4. Project Architecture: I have created some diagrams to showcase the project's architecture and for that created a dedicated page.

  5. Containerization with Docker: All the three components (Backend API, ML & Frontend) of project were containerized using docker and used docker compose to wrap all three images in a container. uv is used to install packages in docker images. mongodb image is used as database for the project.

  6. More in Project: As I am learning MLOps concepts I am trying to implement them in this project because planning to add DVC and MLFlow into the project.