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Name: gtrunz
Type: User
Name: gtrunz
Type: User
This repository contains a single notebook, notebook.ipynb, which analyzes the ability of three machine learning algoithms — Multinomial Naive Bayes, Logistic Regression, and Support Vector Machine — to determine whether customer reviews of the Disneyland amusement park in California are positive or negative.
This analysis seeks to forecast the daily number of car accidents in the county of Denver, Colorado using both traditional time series methods from the statsmodels library, as well as the Prophet forecasting framework as implemented in the prophet library.
The purpose of this project is to promote understanding -- my own and others' -- of fundamental data science and machine learning concepts and tools. It currently consists of one notebook that classifies fruit types based on weight, volume, and image data.
This repository contains the code used to process the data and launch a dashboard for Washington vehicle registrations on a local machine from Jupyter. The app is available at: https://gtrunz.pythonanywhere.com/
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