YoVDO

Predict Housing Prices in Austin TX with Tidymodels and XGBoost

Offered By: Julia Silge via YouTube

Tags

Machine Learning Courses Data Science Courses Predictive Modeling Courses Linear Models Courses Data Exploration Courses Feature Engineering Courses XGBoost Courses Model Tuning Courses tidymodels Courses

Course Description

Overview

Explore feature engineering techniques to incorporate text information as indicator variables for boosted tree models in this 52-minute screencast. Learn how to predict housing prices in Austin, TX using tidymodels and xgboost, based on data from the #SLICED semifinals. Follow along with exploratory visualization, tokenization, linear modeling, and advanced feature engineering. Dive into the process of running and tuning the model to achieve optimal results. Gain practical insights into data science workflows and machine learning applications in real estate analysis.

Syllabus

Introduction
Data
Exploratory visualization
Plot Austin
Tokenization
Top Words
Linear models
Model tidy
Model words
Model shape
Data set
Filter by scale
Modeling
Feature Engineering
Running the model
Tuning the model
Tuning the model again
Results


Taught by

Julia Silge

Related Courses

Data Science at Scale - Capstone Project
University of Washington via Coursera
Feature Engineering for Improving Learning Environments
University of Texas Arlington via edX
How to Win a Data Science Competition: Learn from Top Kagglers
Higher School of Economics via Coursera
Advanced Machine Learning
The Open University via FutureLearn
Feature Engineering
Google Cloud via Coursera