Inspiration
As a high school student myself, I wanted to learn more about factors causing high school students to drop out. Since this issue is not widely researched, I wanted to shed light on it and help to mitigate high dropout rates in certain communities.
What it does
My app allows the user to select an area in the US to get more information about the dropout rates in that area. This will allow the user to determine the extent to which their community has been impacted by high dropout rates. This will also allow educational institutions and governmental organizations to determine where to focus their efforts in implementing socio-economic reforms.
to determine what states have the highest dropout rates, and determine common traits amongst dropouts. This information allowed me to assess which communities would have the highest dropout rates, so that socio-economic reforms can be implemented in these communities to allow students to successfully complete their high school education.
How we built it
My program uses an open source dataset that shows the percentage of dropouts by category, including race, gender, and income, as well as state. I forecasted the data using a machine learning algorithm which implemented the neuralnet package.
Challenges we ran into
Determining which variables were most important was a challenge.
Accomplishments that we're proud of
I am proud that I was able to create a well developed app in a short amount of time. I think I was really able to challenge myself and utilize my data analysis/data science skills to develop an app relating to dropout rates across the country.
What we learned
I learned that high school dropout rates are most common among
What's next for Categorical Analysis of Dropout Data
Now that I have identified factors associated with high school dropout rates, I can present my app to local governments and other organizations that can implement reforms to tackle the issue of high dropout rates in their communities.
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