Running faster than 5 min per km.. I haven’t done this since 2014 at Zurich City Run.. but recently on a running track of a nearby school, after a warmup round, I’ve reached 4’12” and 4’16”: my new highs since ACL surgery. Stocked!Continue reading “I only compete vs myself of yesterday”
This report is for the final course of the IBM Data Science Specialization hosted on Coursera platform. The project allows learners to be as creative as they want and come up with an idea to leverage the location data available via FourSquare API to compare neighborhoods of a city of choice, come up with a problem which can be solved using that data.
In our problem statement, we have a group of athletes who are planning to live in Seattle for several weeks. They would need to find several flats, so it’s desirable that they are located nearby to make the collective work-outs easier. Additional preferences include presence of a park nearby and low criminality in that district because they are planning to be outside very often (jogging in the evenings, etc). Also, the apartments should be affordable, but the factor of low criminality is valued higher by our clients.
The target audience for this report are:
- potential buyers, who can roughly estimate which neighborhoods are more desired (and the models used for analysis should be easily adjustable),
- real estate builders and planners who can decide what kind of neighborhoods are more attractive on the market to maximize selling price of newly built flats,
- and of course, to this course’s instructors and learners who will grade my project,
- anyone who is curious how Python can be applied to easily crawl web pages; parse CSV or JSON files; create powerful visualizations of data as scatter plots, heat maps, density plots using matplotlib, seaborn and map visualizations using Folium; process data using lists, dictionaries, pandas DataFrames.
All the code with data analysis is available on my GitHub page.Continue reading “Affordable & Safe Housing in Seattle, WA”