Analyse you YouTube Watch History using Machine Learning, plot graphs, etc.

• Used FastAPI to create backend APIs to interact with MongoDB database.
• Used YouTube Data API v3 to fetch details about videos (you have watched).
• Used Docker to containerize the FastAPI application.

Models

1. Video's Content Type Predictor
   • Multiclass Classification Problem
   • Uses Video's title and tags to predict Content Type
   • Planning to add Video's categoryId and duration for prediction but wants to sure about improvements
2. Channel Recommender System
   • Recommender System
   • Uses channel's videos title and tags to calculate similarity
   • Uses TfidfVectorizer for text to vec convertion
   • Uses user's channel subscriptions data to recommend channel

API

• Used FastAPI to serve model.
• Containerize FastAPI application and models using Docker.

• Uses Streamlit to create multipage web application where users can upload their required data and see analysis.
• Requires YouTube API Key to fetch video details from API for advance analysis.
• Uses httpx library to interact make requests to "Backend APIs" and "ML APIs".
• Uses Polars for data manipulation.

• Wrote docker-compose.yaml script to build and run all three containers in one go.
• Used mongodb docker image as database, see docker-compose.yaml.

Clone this GitHub Repository

git clone https://github.com/arv-anshul/yt-watch-history-v2
cd yt-watch-history-v2

Open Docker Desktop and run below command:

👀 See docker-compose.yaml

docker compose up --build  # First build the container and then run it (for first time)

• 🪠 Create a ETL pipeline to train models
• 📌 Integrate mlflow¹ for ML Model monitoring
• 🛠️ Build the basics from yt-watch-history project
• 🎨 Draw diagrams for references
• ⛓️ How to intergrate pre-trained ML Model
• 🤖 Build Channel Recommender System
• 👷 Better CTT Model pipeline