AForbis

Amazon_Vine_Analysis

ETL in the cloud - PySpark, AWS, postgres

bikesharing

Analyzed and visualized NYC bike sharing data with Tableau.

Bootcamp_Final_Project

All-NBA Team Machine Learning Model

Cryptocurrencies

Create an analysis for your clients who are preparing to get into the cryptocurrency market. Specifically, create a report that includes what cryptocurrencies are on the trading market and how they could be grouped to create a classification system for a potential new investment. The data will need to be processed to fit the machine learning models, and since there is no known output for what the client is looking for we have to use unsupervised learning. To group the cryptocurrencies, we decided on a clustering algorithm.

Election_Analysis

Complete an audit of a congressional election with Python script.

Movies-ETL

Refactoring code from the module to create one function that takes in the three files—Wikipedia data, Kaggle metadata, and the MovieLens rating data—and performs the ETL process by adding the data to a PostgreSQL database.

Pewlett-Hackard-Analysis

Using postgreSQL, determine the number of retirement-age employees and identify those who are eligible to participate in the mentorship program.

PyBer_Analysis

Create a summary DataFrame of ride-sharing by city type and then visualize the data with matplotlib.

School_District_Analysis

Replace the corrupted values in the school district analysis with NaNs, repeat the analysis, present the new summary DataFrame, and compare and contrast results.

World_Weather_Analysis

Generate a larger random set of latitudes and longitudes, retrieve the nearest city, and perform another API call with the OpenWeatherMap with additional data points. Then create a custom travel destinations map and travel itinerary map with the Google Maps Places and Google Directions APIs.

AForbis/Amazon_Vine_Analysis

ETL in the cloud - PySpark, AWS, postgres

AForbis/bikesharing

Analyzed and visualized NYC bike sharing data with Tableau.

AForbis/Bootcamp_Final_Project

All-NBA Team Machine Learning Model

AForbis/Cryptocurrencies

Create an analysis for your clients who are preparing to get into the cryptocurrency market. Specifically, create a report that includes what cryptocurrencies are on the trading market and how they could be grouped to create a classification system for a potential new investment. The data will need to be processed to fit the machine learning models, and since there is no known output for what the client is looking for we have to use unsupervised learning. To group the cryptocurrencies, we decided on a clustering algorithm.

AForbis/Election_Analysis

Complete an audit of a congressional election with Python script.

AForbis/Mapping-Earthquakes

AForbis/MechaCar_Statistical_Analysis

Using R, perform an array of statistical tests on the MechaCar dataset to answer various questions about the MechaCar prototypes.

AForbis/Movies-ETL

Refactoring code from the module to create one function that takes in the three files—Wikipedia data, Kaggle metadata, and the MovieLens rating data—and performs the ETL process by adding the data to a PostgreSQL database.

AForbis/Pewlett-Hackard-Analysis

Using postgreSQL, determine the number of retirement-age employees and identify those who are eligible to participate in the mentorship program.

AForbis/PyBer_Analysis

Create a summary DataFrame of ride-sharing by city type and then visualize the data with matplotlib.

AForbis/School_District_Analysis

Replace the corrupted values in the school district analysis with NaNs, repeat the analysis, present the new summary DataFrame, and compare and contrast results.

AForbis/World_Weather_Analysis

Generate a larger random set of latitudes and longitudes, retrieve the nearest city, and perform another API call with the OpenWeatherMap with additional data points. Then create a custom travel destinations map and travel itinerary map with the Google Maps Places and Google Directions APIs.

AForbis/AForbis.github.io

AForbis/Bacteria-Dashboard

AForbis/github-slideshow

A robot powered training repository :robot:

AForbis/kickstarter-analysis

Analyze the trends in Kickstarter data with Excel descriptive stats, pivot tables, and more.

AForbis/Neural_Network_Charity_Analysis

AForbis/plotlydeploy

AForbis/surfs_up

AForbis/UFOs