Category: Machine Learning Basics
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Basic Data Ingestion and Real Time Processing in GCP
Say you need to deploy sensor devices (e.g. Air quality measurement devices) across different cities globally. The data to be collected from these devices should be ingested, processed and analyzed on real time basis. The basic pipeline starts with Pub Sub. Ingest using Pub Sub, Dump into DataFlow for pre processing and then from DataFlow,…
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Linear Regression for Property Price Prediction
Example – Build a predictive model for property prices in your city Algorithm Choices – Neural Network, XGBoost and SVR all have poor interpretability. Logistic Regression is suitable mostly for Classification models. Linear Regression is the best choice – it provides accuracy and interpretability. Need a hands-on Data Architect, AI, ML or GCP Consultant? Need…
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Classification Models vs. Regression Models
A simple example – say you have Gender (M,F) and Height and Weight data for a group of individuals. Say you want to predict the ‘Gender’ of the next individual. This would be a classification model problem (aka Logistic Regression). Say you want to predict the height of the next individual. This would be a Linear…