High school dropout is one of the biggest problems to solve in Mexico and specifically one of the biggest concerns in the field of education in the state of Jalisco. Machine Learning, an area of Artificial Intelligence, is being used by educational institutions around the world to reduce dropout rates, improve curricula, didactic adequacy and update accreditation systems. In Machine Learning the systems have algorithms that review the data and are able to predict future behaviors as well as improve autonomously over time without human intervention. The objective of this project is to develop a system that generates intervention alerts regarding the risk that students have to leave school by implementing the Machine Learning techniques learned in the Saturdays.AI Guadalajara program.
For a more technical documentation about how to use the scripts in this repository, go to the github page for this project
This project requires Python and the following Python libraries installed:
- NumPy
- Pandas
- seaborn
- matplotlib
- scikit-learn
- imbalanced-learn
All this packages can be installed easily if you run the following command after cloning the repo:
pip install -e .
The dataset consists of school data from the College of Scientific and Technological Studies of the state of Jalisco campus Guadalajara downloaded from the school control platform of the periods:
- August 2017- January 2018
- February 2018 - July 2018
- August 2018- January 2019
- February 2019 - July 2019
The personal data was deleted from the dataset and a new identification code was created for the students, the school data correspond to grading and attendance information of the students of all semesters of 4 different professional careers.
This project is licensed under GNU GENERAL PUBLIC LICENSE
- Angel Cruz Lopez
- Griselda Perez Velazquez
- Marcos Ignacio Bolaños Valerio
- Xochitl Marina Arroyo Alcalá