⛳ The Golfable Oracle: Personalized Playability Forecasts 🚀 Pandas Plotly
Inspiration:
Golfers know that a great game hinges on the surrounding weather conditions. Perhaps golfing will be pleasant now, but it'll rain in 2hr at the 10th tee box. Or maybe instead of playing today on a windy day, I should wait till Friday for ideal conditions? A useful tool might provide days and times that meet my ideal golfing weather conditions.
Project:
To solve this problem, a dynamic web application was developed that enables users to input their city location, the min weather temperature, max average daily wind speed and max acceptable chance of precipitation as their golfable parameters. Through intigration with the Tomorrow.io API, today's high temp is displayed along with the difference in degrees from your minimum, wind speed along with its difference from your max, chance of precipitation and difference. A graph with these weather metrics is created highlighting the hours that you would be comfortable golfing during the day. The sum of these hours are displayed. Under this are today and the next 4 days with numbers displaying total golfable hours in each day given your parameters. Click on a day's button to see the graph and metric breakdown for that day.
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💊 Alcohol Treament: Study Design Explorer with Statistical Insights 🚀 Pandas Matplotlib Numpy Scipy
Inspiration:
I wanted to create a complete dashboard of a scientific study to share and display the study design, data generated, data analysis, and its findings.
Project:
To solve this problem, a dynamic, user-friendly, interactive web application was developed with the Python library Streamlit. The web app enabled the breakdown of the study design with a schematic visual, and then the dashboard steps through the data analysis guiding the viewer through group comparisons and statistical analysis. The final graph with key results of the treatment is then easily understood. All graphs are enhanced by being interactive and able to blow-up for closer inspection. The groups are also color coded for tracking group manipulations and comparisons throughout the study/analysis.
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📈 The Stock Stack Up: Visualizing Risk and Return Across Stocks 🚀 Yfinance Numpy Pandas Plotly
Inspiration:
Common finance websites allow users to see individual stock performance, yet do a poor job displaying multiple stocks performance against each other on identical timelines. Furthermore, a stock's price can present an incomplete picture of its change in value as there are multiple factors to consider such as percent change and variability over time.
Project:
To solve this problem, a dynamic web application was developed that enables users to choose up to ten stock ticker symbols and the timeframe to compare them. The web application then outputs three graphs displaying 1-Price change, 2-percent price change, and 3-each of their risk vs return over the timespan selected. All these measures function to better inform the user of their selected stocks historical performance.
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🦾 Of Reps & Results: Visualizing Gym Activity Data 🚀 Pandas Numpy Plotly
Inspiration:
Public datasets have become increasingly available, however, with the increase in access, there is a need for the data to be easily interpreted if it is to become meaningful to the general public. Here, I found an interesting dataset collected about gym goers that I wanted to analyze and share some interesting patterns and trends discovered.
Project:
To solve this problem, a dynamic web application was developed that enables users to follow all the steps I used for exploratory data analysis, data cleaning, and the interpretation of each graph with its accompanying statistical calculation. Key conclusions drawn from the data are therefore more accessible as well as justifiable.
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🚴 From Rows to Results: Visualizing Big Data with Streamlit 🚀 Pandas Plotly
Inspiration:
While working on a BigQuery | SQL project, I sought to highlight the versatility and power of SQL not only to data professionals but also to those less familiar with data analysis. The challenge was to make the analysis process accessible and intuitive for a broader audience. To address this, I explored tools that could simplify complex datasets while maintaining their analytical depth.
Project:
This project involved analyzing multiple CSV files, which together comprised 144,873 rows and 13 columns of data points. SQL proved invaluable for structuring and performing meaningful analysis on such a large dataset, unlocking actionable insights with remarkable efficiency. To showcase this analysis, I developed an interactive dashboard using Streamlit, providing a user-friendly interface that brings SQL’s capabilities to life in a visual and intuitive way.
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