Aspiring Data Professional skilled in Python, SQL, Excel, and PowerBI/Tableau
Built by Noah Brannon.
LinkedIn : Link
Google Cloud Award Page: Link
Hello! I'm Noah Brannon, and I appreciate you taking the time to explore my portfolio. I graduated from the University of Michigan - Dearborn in December 2022, earning a degree in Economics and a minor in History. Currently, I reside in Trenton, Michigan, which is located just 20 minutes south of Detroit. Ideally, I am looking for opportunities in Metro Detroit, but I am open to relocating within the United States. Last updated 2/22/2025.
In this project, I analyzed an Auto Insurance dataset to predict Customer Churn and estimate Customer Lifetime Value (CLV). For churn prediction, I assigned weighted scores to key variables such as account age, open complaints, premium cost, and more. Customers crossing certain thresholds received points, with higher scores indicating a greater likelihood of churn. Some factors, like open complaints, carried more weight than others. Using these churn scores, I then estimated customer lifespans, alongside other variables such as monthly premiums, and profit margins to calculate CLV.
Python Code - GitHub Link
In this project, I used the yfinance (Yahoo finance) python library to download 73 different companies' stock data dating back to January 2015. I then created 5 pandas dataframes for each variable of data (Closing price, low price, etc), and then used the pandas_gbq Python library to ingest the data into a BigQuery database. The script can be enabled to run weekdays during after-market hours to retrieve new data.
Project Folder - GitHub Link
Developed an interactive world map visualizing life expectancy using Flask (backend framework) to connect to a local instance SQLite database and to retrieve data. Utilized Pandas for data processing and manipulation. Streamlined development through Folium a Python library to create Leaflet maps (JavaScript, HTML, CSS) and JSON data manipulation, to create the web application and tooltips
WebApp - Link Code - GitHub Link
In this project, I ingested over 150 million rows of raw NYC Taxi data into BigQuery using the pandas-gbq Python library. I then connected my BigQuery database to dbt (Data build tool), to unify and transform the data and create models for more efficient querying in BigQuery. Python Code - GitHub Link
Established a fully functional PostgreSQL database on a local Docker container and successfully ingested and processed a dataset (CSV via API) with over a million data points using Python scripts (Pandas and SQLAlchemy libraries) within the Docker environment
Project Folder - GitHub Link
In this project, I connected to the Bureau of Economic Analysis API to scrape the GDP data for each state. I collected GDP information from the years 1998, 2002, 2007, 2012, 2017, and 2022. Using this data, I developed a model to predict the GDP for each state in 2027. It is worth noting that the model's projections may seem exaggerated since the selected data did not account for any recessions or economic downturns. As each year used was before or after any of the three recessions that occurred in the United States during the timeframe. Consequently, the model assumed a continuous upward trend in GDP. However, while the results may appear ambitious, they are not entirely unrealistic, and detailed explanations justifying these projections can be found in the README file.
Python Code - GitHub Link
This was an in-depth analysis of a dataset comprising Airbnb listings in New York City for the years 2019-2020. Through this analysis, I computed average prices and occupancy days for each neighborhood and created two metrics - price-to-occupancy and price-to-review ratios. Furthermore, I conducted a comprehensive examination of overall metrics for NYC Airbnbs, identifying high-ratio listings and exploring borough averages. I then created three interactive Tableau dashboards for the price, price-to-occupancy, and price-to-review metrics
SQL Syntax - GitHub Link Tableau - Tableau Link
Web Scrapped the top 50 worldwide manufacturers by revenue in 2021 and top 100 for 2020 off of Wikipedia using the BeatifulSoup library. Extracted the data into pandas dataframes and Then, the data was transformed into CSV files to be uploaded to the SQL Server using SQLAlchemy. Create tables and database schema within Python using SQLAlchemy as well.
Python Code - GitHub Link
: In this project, I conducted an extensive data analysis of baseball statistics from 1945 to 2015, utilizing Python and Microsoft Excel. Using Python, I performed a comprehensive statistical analysis to explore the relationship between the number of home runs and strikeouts per season, revealing a positive correlation between these variables. To gain further insights, I implemented an ARIMA model to forecast three seasons' worth of wins for each of the 30 MLB teams. Based on these predictions, I simulated playoff-like scenarios using a head-and-tails model that assigned higher weights to top-seeded teams. The season standings and playoffs were visualized and presented using Microsoft Excel
Project Folder - GitHub Link
Here are some more SQL projects that I have done.
Repository - GitHub Link
Here are some more Python projects I have done.
Repository - GitHub Link
Here are some Microsoft Excel projects I have done
Repository - GitHub LinkPlease feel free to contact me if you have any questions about anything. Here is the link to the entire repo if needed GitHub