An Interactive Approach to Understanding Unsupervised Learning Algorithms
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Updated
Jan 21, 2021 - Jupyter Notebook
An Interactive Approach to Understanding Unsupervised Learning Algorithms
A software quality analysis tool based on hotspot prioritization and commits
Analysis of when and where New York City (NYC) vehicle collisions occur with a focus on collisions involving pedestrians and cyclists.
We will visualize the results of hotspot analysis and use kernel density estimation, which is the most popular algorithm for building distributions using a collection of observations. By the end of the course, you should be able to leverage Python libraries to build multi-dimensional density estimation models and work with geo-spatial data.
Contains Weekly Activites for the (ISTE 740) Geographic Information Sciences and Technologies Course @ RIT
Spatial Hotspot Analysis on Geo-Spatial Data using Apache Spark and Scala
Hotspot analysis on Big Data of a major taxi company using Apache Spark and Scala
Spatiotemporal analysis of the course of the COVID-19 pandemic in Germany
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