Learn to predict Airbnb listing prices in New York City using tidymodels in this 41-minute screencast. Explore data visualization techniques, build and evaluate a custom model, and combine tabular and unstructured text data. Master the creation of a custom metric for model evaluation, specifically focusing on Root Mean Squared Logarithmic Error (RMSLE). Discover how to visualize predicted prices, implement transparent point scaling, and interpret model results. Compare your findings with the SLICED competition outcomes and gain practical insights into advanced data analysis techniques for real-world pricing scenarios.
Overview
Syllabus
Introduction
Data Visualization
New York City
Build a model
Run the model
Add a dollar
Transparent
Points
Scale
Predicted price
RMSEvec function
Truth and estimate
Generic RMSLE
Metric summarizer
Metric name
Data truth
Using lang
Creating a metric set
Changing the price
Real price scale
Average percent error
Moment of truth
Sliced results
Summary
Taught by
Julia Silge
