GitHub | Tableau | LinkedIn | Resume
“We manage what we measure, but frequently we measure what is easy" inspired by this quote I found a Kaggle dataset with units and pick time to evaluate.
- Exploring the data I found that there was minimal variance in many features I expected to be predictive.
- I found using a polynomial features model with order complexity was 65% more accurate in predicting the time needed to pick an order.
Project_Repo | Post_Project_Summary
- Scrape 1K GitHub repository urls related to "environmental" to create a dataset for analysis.
- Use NLP techniques to explore the data and build a model that predicts the programming language of the repository based on the text in the README.
- Best performing model was a Logistic Regression using TF-IDF, final result was an average of 47% accuracy on unseen data.
Project_Repo | Post_Project_Summary
- Use Zillow dataset from Codeup cloud database
- Using machine learning clustering to find groups within the data and build a model that predicts logerror 5 min notebook walk through
- Identified 4 cluster groups within the data using KMeans algorithm. My best model unfortunately performed 3.6% under baseline using a Linear Regression algorithm.
Project_Repo | Post_Project_Summary
- Use Zillow dataset from Codeup cloud database
- Using machine learning build a model that predicts property tax value
- Our best model was a 31% improvement over baseline using a Polynomial Regression algorithm
Presentation | Project_Repo | Post_Project_Summary
- Investigate Telco dataset for drivers of customer churn
- Using machine learning build a model that predicts customer churn
- My model increased customer churn prediction accuracy from 73% baseline to 87% using a Random Forest classification model
Project_Repo | Post_Project_Summary
Tableau_Project | Post_Project_Summary
Jupyter_Notebook | Post_Project_Summary
Jupyter_Notebook | Post_Project_Summary
"Python Crash Course" Eric Matthes
"How Charts Lie" Alberto Cairo
Crochet and Knitting projects completed during Bootcamp
Creatures |
Scarves |
Hats |
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