Inspiration

The drive to create a university admission predictor is fueled by the goal to simplify the intricate and often unclear college admissions process. It aims to ease the worries of students and their families navigating this challenging path, filled with doubts about eligibility, rivalry, and the chances of getting into preferred colleges.

What it does

A university admission predictor analyzes academic and extracurricular profiles of students to forecast their chances of being accepted into specific colleges or universities. It typically uses historical admissions data, including grades, standardized test scores, extracurricular activities, and other relevant criteria, to estimate a student's likelihood of admission. By inputting their personal information, students receive personalized assessments, helping them understand where they stand in the competitive landscape and guiding them in making informed decisions about where to apply.

How we built it

We used regression modelling to train the AI model to predict the university chances given a number of parameters related to the student.

Challenges we ran into

Facing the challenge of integrating a Flask app with HTML was notably complex, mainly due to the intricacies of mastering the Flask framework. Learning Flask’s routing and request handling, alongside understanding how to dynamically pass data to HTML templates, presented a steep learning curve.

Accomplishments that we're proud of

The whole learning process and all the new libraries we learnt throughout the project.

What we learned

Flask Regression Modelling Google Cloud Joblib

What's next for University Admission Predictor

We will make the website a bit more user-friendly and add more specific details like the name of a particular university which will help students get a better understanding of their chances of getting in.