YoVDO

How to Handle High Cardinality Predictors for Data on Museums in the UK

Offered By: Julia Silge via YouTube

Tags

Data Analysis Courses Predictive Modeling Courses Feature Engineering Courses tidymodels Courses

Course Description

Overview

Explore techniques for handling high cardinality predictors in data analysis using tidymodels, focusing on effect and likelihood encodings. Learn through a practical demonstration using #TidyTuesday data on museums in the UK. Follow along as the screencast covers reading the data, setting up the model, implementing feature engineering techniques, and building the final model. Gain insights into effectively managing complex categorical variables in your data science projects. Access the accompanying code on Julia Silge's blog for further study and implementation.

Syllabus

Introduction
Reading the data
Setting up the model
Feature engineering
The model
Summary


Taught by

Julia Silge

Related Courses

Big Data Analytics in Healthcare
Georgia Institute of Technology via Udacity
Model Building and Validation
AT&T via Udacity
Maths for Humans: Linear, Quadratic & Inverse Relations
University of New South Wales via FutureLearn
Regression Modeling in Practice
Wesleyan University via Coursera
Data Science at Scale - Capstone Project
University of Washington via Coursera