Maria Carter's repositories
Rats-in-the-Restaurants
A group project that analyzes data on restaurant inspections and violations as well as community health in Los Angeles and uses a machine learning model to identify and assess factors that could affect grades and scores.
School-District-Analysis
Use Python and Pandas library to analyze school district data and showcase trends in school performance.
Bikesharing
Create worksheets, dashboards, stories from New York City bike-sharing data.
Biodiversity
Build an interactive dashboard using Plotly.js to explore data on the biodiversity of belly buttons. Then deploy the dashboard to a cloud server.
Credit-Risk
Predict credit risk with machine learning models and evaluate using Python.
Cryptocurrencies
Use unsupervised machine learning techniques to analyze cryptocurrency data.
Kickstarter-Analysis
Analyze a dataset consisting of 4,000 crowdfunding projects to discover hidden trends.
Mapping-Earthquakes
Use Javascript's Leaflet library along with the Mapbox API to create visualizations of earthquake data from the U.S. Geological Survey.
Mission-to-Mars
Build an app to scrape websites for data pertaining to the Mission to Mars, and then create an HTML page to display findings.
Movies-ETL
Perform ETL on several movie datasets to predict popular films for a streaming service.
Neural-Networks---Deep-Learning
Create a deep learning neural network to analyze and classify the success of charitable donations.
Pewlett-Hackard-Analysis
Create entity relationship diagrams, perform data modeling, and complete analysis on an employee database using SQL techniques.
Plotly-Deploy
A repo to deploy a dashboard via my GitHub Page of my Biodiversity data visualization.
PyBer-Analysis
Analyze and visualize ride-sharing data using Python, Pandas, and Matplotlib.
R-Analysis
Learning how to use R and statistics in order to analyze vehicle data.
Vine-Reviews
Use ETL, Amazon Web Services, and Spark to analyze big data on an Amazon dataset.
World-Weather-Analysis
Plot the relationship between latitude and weather for cities around the world using Python, Pandas, and APIs.