cimm-yeoman,Cimmaron ,github

Hi there 🌱

My name is Cimmaron but you can call me Cimm. Sometimes known as the data wrangler 🤠 🐎.

My background is in the environmental sciences. I am particularly interested in toxic marine cyanobacteria, mycology, and environmental justice. I am currently looking for work as a data analyst or database administrator.

➡️ I would like to upgrade my programming skills and pursue a postgraduate education in data science
↪️ I am currently learning Python 🐍

I have enjoyed learning about more advanced computational statistics, especially Bayesian modelling
- Some fun things I have used in projects: Markov Chain Monte Carlo methods, Bayesian estimation, time series models, Generalized Linear Models, experimental design, and statistical learning
I like ArcGIS Pro and ESRI software 🌐
I primarily use R for my projects, working in RStudio with R Markdown 📊
- I dabble in Excel VBA for some super helpful macros, and SQL for ArcGIS Pro

Cimmaron 's Projects

amazon-sale-prediction

Using non-linear Support Vector Machine (SVM) and random forest modelling, this report will attempt to predict the outcome of sale, based on the combinations of the predictor variables for each Amazon.ca browsing session.

cimm-yeoman

Config files for my GitHub profile.

daily-ozone

This report will attempt to predict the daily ozone level, our response, with a multiple regression Bayesian framework. A smoothing spline will be implemented to help make predictions and identify patterns in the data set.

divorce-prediction

The goal of this report was to identify which variable best predicts divorce using decision trees and other ensemble methods. In the data set, Class is the response variable, with 0 = still married and 1 = divorced.

soil-erosion-rainfall

This data set includes 200 observations of rainfall and soil erosion measurements (mm) recorded 48 hours later. This report will assess the relationship between soil erosion and rainfall, and predict soil erosion from with a linear regression.

triceps-and-cancer

Smoothing splines predict tricep skinfold thickness and lasso regression identifies relevant predictor variables in mean cancer mortalities per capita.

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