SergioEstevao11 / FEUP-IART-SupervisedLearning

:student: Supervised Learning problem to predict students' academic success

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Students' dropout and Academic Success: Supervised Learning

Description - Supervised Learning problem based on a dataset created to predict, in higher education institutions, the students' dropout and academic success.

Course - Artificial Intelligence

Disclaimer - This repository was used for educational purposes and I do not take any responsibility for anything related to its content. You are free to use any code or algorithm you find but do so at your own risk.

Requirements

To execute this project, these libraries are needed:

  • NumPy
  • pandas
  • matplotlib
  • sklearn
  • seaborn
  • imbalanced-learn

Install requirements using pip install -r requirements.txt on the /src/ folder.

How To Execute

The project source code is in the jupyter notebook file AcademicSuccess.ipynb, where the code used for the project is present and documented.

Group Members (Group 24_1D )

  • Sofia Germer, up201907461
  • Pedro Jesus, up201907523
  • Sérgio Estêvão, up201905680

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:student: Supervised Learning problem to predict students' academic success


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Language:Jupyter Notebook 100.0%