Wikipedia Natural Language Processing Dashboard
SUMMARY
This project utilizes Natural language Processing techniques to explore Wikipedia as a search engine. Written in python and using plotly dash, this application allows users to explore Wikipedia with a Wikipedia API. Features include page suggestions, summary of page, page address, term frequency Analysis, and entity recognition analysis with visualization.
TAGS: Python, NLP, Wikipedia, plotly, dash, entity recognition, text mining, API, search engine, term frequency analysis.
Marketing Study Dashboard
SUMMARY
This Tableau dashboard was created to demonstrate the trending of a website after a new advertisement campaign was launched. The data was created from scratch in python, uploaded to AWS s3, and then connected to Tableau through AWS Athena. The dashboard demonstrates a real-world example of how a company may track marketing success with the use of testing and visuals for engagement variables like number of Users in a day, time spent engaging, ads viewed, pages viewed, and number of clicks. Initially the test results were calculated with an R script, but Tableau Public is not script friendly.
TAGS: Python, R, AWS S3, AWS Athena, TABLEAU, USER ENGAGEMENT, Trending, Linear Regression, and Hypothesis testing.
Budget Forecasting
SUMMARY
For this project I wanted to do A complete end-to-end process. I created the data from scratch in python, Uploaded to AWS s3, and then connected to Tableau through AWS Athena after querying a useable database. The dashboard demonstrates a real-world example of how a company may track and Forecast spending Given previous data. The Process can be viewed below. Even though the data I use is an extract, live data could be implemented in the same process for up to date information.
Building the Data
Uploading the Data to AWS S3
Using AWS Athena to create a database to store the data.
Adding all of the data from our created data frame using SQL to the table.
Creating a view of the table.
Checking to verify the data is entered correctly.
Connect Tableau to AWS Athena Data.
TAGS: Python, AWS S3, AWS Athena, TABLEAU, Budget Tracking, finance, Forecasting, Trending, Linear Regression.
Hospital Patient FLOW DASHBOARD
SUMMARY
As in any industry, some aspects of production need to be rounded out for better product flow. For a hospital a lot of revenue comes from patient turnaround and the ability to see as many patients as possible. For example, you need to get a patient out of the ER and into a inpatient room. if the rooms are not available then the ER becomes backed up and ultimately reduces the amount of patients seen by a physician.
The issue we faced was that too many patients were leaving at the same time and there was not enough staff to clean the sudden abundance of dirty rooms. The goal of this project was to reduce wait times and to better understand patient flow. I charted the areas and times of patients departure to help reduce these times.
The Tableau dashboard is based off of a similar visual I used at the start of the project. It highlights hours and floors that experienced an unusually high amount of discharges at once. This helped the nurse supervisor coordinate and plan patient discharges better. The Result was reduced strain on staff, wait times in the ER, and increased Revenue to the hospital.
TAGS: Microsoft EXCEL, Tableau, and production flow.
out-of-state Moving Tool
SUMMARY
I wanted to build the perfect tool for examining states as a whole and get the most insight as to which areas are a good fit for me. I could not find the perfect data set that fit my needs for this project, so I decided to build it myself from scratch using several sources via web scraping and API. You can explore the map to adjust variables and set ranges you feel comfortable with, resulting in the highlighted states that are the best fit for you. Project Features Tableau dashboard, shiny application, and a write-up of cleaning and analyzing the data. The project write-up can be viewed below.
TAGS: R, HTML, markdown, shiny, Tableau, dashboard, API, web scraping, data cleaning.
John Hopkin's Dashboard Replication
SUMMARY
As a group project, the class had to replicate the John hopkin's Covid-19 tableau dashboard. We split up and coordinated the workload into priorities through Atlassian Jira. The workload consisted of collecting and storing the data from github to connect it to Tableau. Most of the work I did included the initial data extraction and building the layout and some of the functions of the Dashboard. The John Hopkins Dashboard can be viewed below.
U.S. Energy Overview Dashboard
SUMMARY
In this Dashboard I wanted to do A end-to-end project with a scheduled script to scrape data. I created a script in Python that actively scrapes Energy data from www.eia.gov every hour, Uploads to AWS s3, and then connects to Tableau through AWS Athena after querying a useable database. The dashboard demonstrates a real-time energy consumption in the U.S. states and implements linear regression for trend lines. You can view this project, but it will not be live because Tableau Public does not allow live feed. So it will be utilizing extracted data from time of publishing.
TAGS: Python, AWS S3, AWS Athena, TABLEAU, Web scraping, Query language, Trending, Linear Regression, forecasting, and live-data feed.