It’s an odd time for the United States real estate market. On one hand, you have all-time high (or nearly all-time high) housing prices and “Finding the best locations in North Carolina’s Research Triangle to buy a home using Zillow data”
Tag: Data Cleaning
Creating a data strategy for sustainable investing via an interactive dashboard using Plotly and Dash
Sustainable investing is a fairly recent concept. With rising global temperatures, disappearing rainforests and an increase in carbon emissions, the past decade or so has “Creating a data strategy for sustainable investing via an interactive dashboard using Plotly and Dash”
Three overlooked but powerful Python functions to replace the ‘replace’ function for hacking dataframes
If you are a frequent Python user, you are probably aware of the powerful Pandas library, the bread and butter of data analysis in this “Three overlooked but powerful Python functions to replace the ‘replace’ function for hacking dataframes”
Adding new metrics to the Python finance tracking dashboard and making it accessible via a Streamlit App
To provide some background and a recap, the idea behind the dashboard was to track financial indexes, economic indicators and COVID statistics on a consolidated “Adding new metrics to the Python finance tracking dashboard and making it accessible via a Streamlit App”
Understanding and predicting compliance for property maintenance fines in Detroit
Back when I first tackled this project I didn’t really understand, rather pay attention to, the actual business problem being addressed. Like a lot of “Understanding and predicting compliance for property maintenance fines in Detroit”
Creating a dashboard using Python to track critical financial and economic indicators
If you like to dabble in stocks (like me), you are likely conscious about events of high volatility in the market which could lead to “Creating a dashboard using Python to track critical financial and economic indicators”
Picking the right movie to watch: Part II (using the simplest decision tree model)
Following up from the my previous attempt to model how best to pick a movie, this new iteration takes a very different approach. In the “Picking the right movie to watch: Part II (using the simplest decision tree model)”
Analyzing collision events in Seattle and predicting severe occurrences
Why do we care about severe collisions? The safety of drivers, passengers, pedestrians and property is an ever-growing concern when it comes to the operation “Analyzing collision events in Seattle and predicting severe occurrences”