Geospatial Data Science in Python
Syllabus
Schedule
Section 401
Section 402
Content
Assignments
Overview
Section 401
Section 402
Resources
GitHub
Canvas
Ed Discussion
12. Predictive Modeling with Scikit-Learn, Part 1
Weekly Course Content
1. Exploratory Data Science in Python
2. Data Visualization Fundamentals
3. More Interactive Data Viz, Intro to Vector Data & GeoPandas
4. Geospatial Analysis & Mapping
5. More Geospatial Analysis: Street Networks and Raster Data
6. Web Scraping
7. Working with APIs
8. Analyzing and Visualizing Large Datasets
9. From Notebooks to the Web: Part 1
10. From Notebooks to the Web: Part 2
11. Clustering Analysis in Python
12. Predictive Modeling with Scikit-Learn, Part 1
13. Predictive Modeling with Scikit-Learn, Part 2
14. Advanced Raster Analysis
On this page
Reference
Week 12: Predictive Modeling with Scikit-Learn
Content for lectures 12A and 12B
View materials:
MUSA-550-Fall-2023/week-12
HTML slides:
Lecture 12A
Lecture 12B
Executable slides:
Lecture 12A
Lecture 12B
Reference
scikit-learn
User guide
RandomForestRegressor
Column transformer example
Decision trees
Machine learning models in the real estate industry
Airbnb recommends pricing to hosts
Trulia converts house photos to house features
Zillow’s Zestimate
The missingno package
Presentation on the Office of Property Assessment’s methodology
City Controller analysis of property assessments
Algorithmic fairness with case study on modeling Philadelphia’s home values
11. Clustering Analysis in Python
13. Predictive Modeling with Scikit-Learn, Part 2