Develop an end-to-end data engineering project for real-time stock market anomaly detection using machine learning models. Learn to architect a complete anomaly detection pipeline, fetch stock market data via FTP, and set up a stock data producer with Quix Streams. Create and deploy an anomaly detection system using Isolation Forests, perform real-time data transformation and processing, and integrate Docker for streamlined project deployment. Gain hands-on experience with state-of-the-art tools like Quix Streams, Redpanda, and Docker while building a complete data pipeline. Master the process of collecting stock market data, deploying advanced anomaly detection models, and troubleshooting using Quix documentation resources. Perfect for those looking to enhance their skills in modern data engineering and machine learning.
Realtime Stock Market Anomaly Detection Using Machine Learning - End-to-End Data Engineering Project
CodeWithYu via YouTube
Overview
Syllabus
Introduction
System Architecture
Getting Stock Market Data via FTP
Setting Up a Fresh Project
Creating a Stock Market Data Producer with Quix Starter Source
Creating an Anomaly Detector using Quix Transformation Source
Building an Isolation Forest Model for Anomaly Detection
Testing and Review of Results
Quix Documentation and Help Resources
Outro
Taught by
CodeWithYu
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