100+ SQL Scripts - PostgreSQL, MySQL, Oracle, Google BigQuery, MariaDB, AWS Athena. DBA, Analytics, DevOps, performance engineering. Google BigQuery ML machine learning classification.

Fraudfinder: A comprehensive lab series on how to build a real-time fraud detection system on Google Cloud

Package for dbt that allows users to train, audit and use BigQuery ML models.

Looker extension designed to give business users access to BigQuery and Vertex AI's machine learning capabilities.

Biq Query + langchain REST API written in python

An example of machine pipeline on Bigquery ML using Dataform

Google BigQuery Tutorial

CloudSkillBoost: Data Analyst Path

Solutions to the Google Cloud challenge lab for the "Create ML Models with BigQuery ML" quest

Using BigQuery ML; create a machine learning model to recognize the US accent from within six other accents

The primary objective of this project is to predict the likelihood of a visitor making a purchase during a subsequent visit to the Google Merchandise Store.

SmartStockAI uses AI to predict inventory trends, minimize deadstock risks, and provide actionable insights through advanced models and interactive visualizations.

bigquery sql - k means model - tableau

Google Capstone Project

This Vertex AI Pipeline orchestrates the selection, deployment, and real-time monitoring of the highest-performing machine learning model from a pool of candidates. Designed to support a dynamic and collaborative model development environment, it ensures that only the most accurate and relevant models are deployed for fraud detection tasks.

Forecasting COVID-19 using BigQueryML with ARIMA+ method

Iowa Liquor Sales Forecast Model

Loan Prediction project made at BigQuery

This repository showcases two machine learning projects using Google Cloud's BigQuery ML and Natural Language API to predict visitor purchases and perform entity and sentiment analysis on textual data.

This project uses BigQuery to explore the Google Analytics dataset, and build a Machine Learning Model to predict whether a visitor on the website will make a purchase or not.