{Provide a description of the data set that you are analyzing. Include the link of where you obtained the data.}
This data set I found and downloaded contains the median house prices accross the U.S. in the past 16 years. It is organized in columns by date, then organized in rows by location. It is specific enough to where most big cities in the nation are in the data set
The purpose of this software was to learn how to maniuplate data and be able to use other libraries in Python to learn how to manipulate this data. This program is also here to help epople who are trying to learn where they can live based on the prices of houses.
Question 1: What are the top 10 cheapest cities to live in this U.S.?
answer:
Helena, AR: $46,609.69
Clarksdale, MS: $49,711.60
Greenville, MS: $64,396.47
Forrest City, AR: $73,950.08
Selma, AL: $75,788.56
Middlesborough, KY: $81,603.09
Danville, IL: $82,300.26
Kennett, MO: $83,957.30
Bennettsville, SC: $84,129.92
Vernon, TX: $87,762.11
Question 2: How much more expensive are housing prices in the U.S. compared to 10 years ago?
answer: based on the data given to me through Zillow, and the graph of the median housing prices changes, it looks to have doubled in price in the past 10 years.
I developed this software using Python, Pandaspy, numpy, and CustomTKinter which is a GUI library in python.
{Make a list of things that you need to fix, improve, and add in the future.}
- Add more customization and UI design to the GUI
- I will add a way to compare the housing prices to weather patterns in a certain area.