Geospatial Data Science in Python
Syllabus
Schedule
Section 401
Section 402
Content
Assignments
Overview
Section 401
Section 402
Resources
GitHub
Canvas
Ed Discussion
13. Predictive Modeling with Scikit-Learn, Part 2
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 13: Predictive Modeling Continued
Content for lectures 13A and 13B
View materials:
MUSA-550-Fall-2023/week-13
HTML slides:
Lecture 13A
Lecture 13B
Executable slides:
Lecture 13A
Lecture 13B
Reference
Bikeshares and subsidies
MUSA Practicum analysis on bikeshare demand
Indego data
Useful scikit-learn references:
Imputation of missing values
Ensemble methods
Metrics and scoring
Other bike share data:
Boston
Chicago
Washington D.C
12. Predictive Modeling with Scikit-Learn, Part 1
14. Advanced Raster Analysis