Exploring COVID-19 data provided by Our World In Data to demonstrate joins, CTE's, temp tables, windows functions, aggregate functions, creating views, and converting data types in SQL with visualizations in Tableau.
Cross tabulated data from UCI's Machine Learning Repository with MySQL to explore factors that significantly contribute to hospital readmission rates for diabetes. Generated plots, statistics, and correlational analysis in Python using Pandas, Matplotlib, Seaborn, Numpy, Pymysql, and Statsmodels.
Utilized data from Inside Airbnb to provide metrics and visualizations on the relationships between number of listings, price, reviews, and location. Dashboards with combinations of heat maps, bar charts, histograms, and scatter plots of the Airbnb data were implemented in Tableau.
Created a Descriptive Customer Analysis Dashboard portraying the relationships of revenue to geographic and demographic data. Dashboard including a bar chart, butterfly chart, and scatter plot of the data was implemented in Tableau.
Trained a Naive Bayes classifier to predict sentiment from thousands of Twitter tweets in Python. This process can be done automatically without having humans manually review thousands of tweets and customer reviews.
Python project pulling in stock data using Pandas, cleaning the data, and creating a number of visualizations with libraries such as Matplotlib and Seaborn in order to display the findings of my EDA.
In this project we look at what variables affect the gross revenue from movies.