Name: Steve Tuttle
Type: User
Bio: Data Analyst with extensive knowledge in production manufacturing & controls environments. Experienced in optimizing processes to operate more efficiently.
Location: Winston-Salem, NC, United States
Steve Tuttle's Projects
Build an interactive dashboard about microbes in human navels using JavaScript and HTML to read a JSON from an external URL. The link to this dashboard can be found in the Project README.md.
Develop machine learning models to predict loan statuses based on various financial features then produce a report to review the findings.
Explore cryptocurrency market trends with Python using unsupervised learning techniques. Using Jupyter Notebooks to implement K-means clustering and Principal Component Analysis (PCA) to analyze and predict price trends of cryptocurrencies over 24-hour and 7-day periods.
Develop a tool in Google Colab using machine learning and neural networks to select applicants for funding with the best chance of success based on the source data provided by the organization.
Perform data modeling, data engineering, and data analysis. Design tables to hold data from CSV files, import the CSV files into an SQL database, and then answer questions about the data.
Project using SQLAlchemy in Jupyter Notebook to analyze weather in Hawaii from different stations over one year. Then design and create a Flask app based on the queries from the Jupyter Notebook.
Use SparkSQL to determine key metrics of the data. Use Spark to create temporary views, partition the data, cache and uncache a temporary table, and verify that the table has been uncached.
Perform web-scraping and data analysis first to scrape titles and preview text from Mars news articles then to scrape and analyze Mars weather data, which exists in a table from Mars data websites.
Pharmaceutical testing data conducted by Pymaceuticals Inc. containing Python code for data preprocessing, statistical analysis, and visualization of the effects of various drug regimens on tumor growth in laboratory mice.
This repository contains code and documentation for analyzing UK food hygiene ratings data. The analysis is divided into setting up the database, updating it with modifications, and conducting exploratory analysis using Python libraries like PyMongo and Pandas.
Visualize USGS earthquake data, looking at previous 7-day seismic activity in terms of magnitude and depth over three interactive map types.
Create two Jupyter Notebooks: use API calls to determine weather conditions for 500 cities around the equator; use Geoapify API based on the weather analysis to plan future vacations.