Inspiration
Through the Melissa Dataset, we wanted to explore trends in the Orange County area. We found interesting statistics about bathroom counts and cities. Based on general consensus and internet research, we observed that Santa Ana was not deemed as well off as other cities in Orange County. We wanted to explore these trends and see if there was anything that we could suggest to better the city of Santa Ana to increase the wealth of the people.
What it does
We analyzed data from the Melissa Consumer Data and Property Assessment dataset. We observed trends between statistics in the dataframe. We analyzed bathroom count versus other societal factors, such as house sizes and room counts. We learned that Santa Ana has a lower amount of bathrooms than other cities. Also, there were some correlations between population density and the average number of rooms to bathrooms.
How we built it
We built it using Python using DeepNote notebooks. We cleaned and analyzed the data using packages like pandas, numpy, and matplotlib.
Challenges we ran into
We had to learn how to properly clean and merge the datasets, which was a time consuming task. We also had trouble finding direction for our problem.
Accomplishments that we're proud of
We believe our final merged dataframe was good. We also believe we put our best foot forward after data analysis.
What we learned
We learned the difficulty of data analysis and finding trends in data. We also learned how important it is to clean and manage data.
What's next for Bathrooms of Orange County
We hope to find better forecasting models in order to make better predictions for bathroom count. This can help us figure out what bathroom counts can help us increase net worth and home values in Santa Ana.
Presentation Link
https://docs.google.com/presentation/d/1xigX_seBCTxf4qumosYUuG3bELlQGl2qQ1Lb6y03hOQ/edit?usp=sharing
Log in or sign up for Devpost to join the conversation.