Excited to launch the ‘Contoso Machine Learning Generator’ 🥳🥳🥳 Effectively, this tool offers data professionals a script to easily procure models to demo their abilities. Initially designed for Data Scientists, all data data-genres can benefit! – Data Engineers can load data into bronze data lake layers and use the script for ELT processing into silver […]
Announcement: Launching Contoso Machine Learning Generator Read More »
“Practical Time Series Forecasting” by Galit Shmueli is a renowned book that skillfully guides readers through time series forecasting using R. What about those Python enthusiasts who are keen to dive into the same material? Well, we’ve got you covered! With great pride, Analytical Ants presents our first open-source translation published on Github! We have meticulously translated
Practical Time Series Forecasting – Python Version! Read More »
KNN as a Feature Engine can aid in ensemble learning by quantifying anecdotal knowledge through supervised machine learning.
A good example would be targeting a minority group of customers who are known to have a desirable trait (e.g., similar features/patterns in customer behavior indicative of higher ‘value’ buyers, etc.).
Part two of this two-part series incorporates the KNN Feature Engine from part one into different ensemble models and reviews the findings.
KNN as a Feature Engine with Imbalanced Data (Part 2/2) Read More »
KNN as a Feature Engine can aid in ensemble learning by quantifying anecdotal knowledge through supervised machine learning.
Part one of this two-part series reviews model transformations, metric evaluation, and practices for implementing KNN as a Feature Engine whilst giving users a self-containing script to follow.
KNN as a Feature Engine with Imbalanced Data (Part 1) Read More »
