Graduate Application Pool Simulator

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  Ideation and weights

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  Welcome page

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  Information form

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  Saved data on the database with the score in the last field.

Inspiration

Saves Money For graduate applicants in lower income areas. The graduate application costs can be hefty on international students. Let's them make wise choices while selecting schools.

What it does

Calculates a score for every applicant based on their application information and ranks them against other applicants applying to a particular graduate school. Then it tells them if they will be selected according to their rank among the pool.

How I built it

Using python and Django. I gave a weightage to each parameter in the information section and calculated a weighted average. Since letters of recommendations are regarded as the most important factors in deciding the admission chance, the application takes into account the reputation of the recommender by scraping their total citations from the google scholars site.

Challenges I ran into

I ran into challenges initially while planning out the project which used up a lot of my time, which could have gone into development.

Accomplishments that I'm proud of

The web scraper which fetches the citations, and the weighted average methodology.

What I learned

How to use modules like Beautiful soup to scrape html pages.

What's next for Graduate Application Pool Simulator

It can have an additional functionality of calculating chances of an applicant by running their data into a machine learning model based on the data scraped by Debhargya Das from grad cafe ( https://github.com/deedy/gradcafe_data ). Also this can be improved by taking into account the statement of purpose and using Natural Language Processing to weigh the quality.

Built With

• django
• webscrpaing