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Projects

This application is an interactive tool developed using Plotly Dash to visualize gun violence trends and patterns in Philadelphia from 2015 through Present. By transforming raw data into insightful visualizations, the application seeks to provide a more nuanced understanding of gun violence trends and patterns in Philadelphia.

Tools:
Python, Plotly Dash

Web App Link

GitHub Link

Hi there! My name is Matt Snell, and I'm a Data Analyst on the Actuary and Advanced Analytics team at Farmers Insurance. I love digging into complex datasets and discovering hidden patterns that can help drive business growth.  With my expertise in data analysis and visualization tools, including Python, SQL, Alteryx, Tableau, Power BI & Microsoft Excel, I've helped Farmers Insurance make data-driven decisions that have fueled growth and innovation.

This interactive map showcases the motor vehicle crashes that have occurred in Philadelphia from 2010 to 2021. The map is interactive and allows users to filter the data by total collisions, total injuries, total fatalities, motorcycle fatalities, and pedestrian fatalities. The map is rendered using the PyDeck library and the data is sourced from Pennsylvania's Department of Transportation.

Tools:
Python, Streamlit, PyDeck

Web App Link

GitHub Link

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Case Study Analysis of Philadelphia's Motor Vehicle Collisions

Conducted data analysis of motor vehicle accidents in Philadelphia, Pennsylvania between January 2015 and December 2020, leveraging Python and SQL, and used Tableau to create an intuitive dashboard that summarized my findings.

Tools:
Python, SQL, Tableau

Tableau Dashboard

GitHub Link

Explore Notebook

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Compounding Capital: A Dynamic Tool to Project Your Investment's Future

Understanding compound interest is crucial for any investor. Through compoundingcapital.streamlit.app, users can effortlessly input their parameters and watch their investments multiply over time. Harnessing the efficiency of Streamlit, this tool makes financial forecasting both engaging and educative.

Tools:
Python, Streamlit

Web App Link 

GitHub Link

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