Do you ever wonder what the causes of fetal deaths like low birth weights have in relation to metropolitan areas? Using modern mathematics and computer science, you can better understand these causes.
The United States is the 154th in the world ranking by birth rates. With forecasting techniques we want to highlight areas around the US to which public policy can be reformed to increase birth rates
24hr analysis of a super hard data project, converting the large file to an SQL format and performing regression operations and other data analysis methods using python pandas.
Using the 1969-1986 dataset to build a linear regression prediction model for underweight babies.
Analysis of data from the National Center for Health Statistics with birth and parental information data sets. Done using python and panda to create graphs in order to find correlations in the data.
It sorts data and produces a lot of graphs
As total beginners, we came to Hackathon looking to learn data science from scratch. We began to understand how to use Python to access and explore the data, and this is what we've learned so far.
As newcomers, the Rowdy Datathon was much more than we anticipated. However, as a team, we pushed through as much as we could. While we didn't collect much data, we honed in on learning and improving.
Our team mainly attempted to analyze the NCHS SQL database given to us using SQL and pgAdmin to better understand what amount of newborns will be born underweight in 2030.
Analyze future counts of underweight infants and infant mortality for counties in the state of Texas.
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