Analysis of data to improve system of an educational organization
Loading...
Date
2020
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
In this thesis, there was done a set of computational experiments on the datasets of educational organizations. In the first part of the work, there were executed lots of experiments with decision tree ID3 algorithm on the educational camp dataset (”Educon”) that automatically predicts a participant’s feedback using Scikit-learn library and extrapolatory data analysis of that was done using Pandas library and Python programming language. The experimental results showed that the most optimal maximum depth (which is the number of edges starting from the root till the leaf) for the decision tree is 3 and the most optimal minimum number of splits (which is the minimum amount of samples of the dataset that are required to split an internal node of the decision tree) is 192. Based on that, there was achieved optimum results of precision, recall, and f1 score machine learning metrics that vary between 75 to 98 depending on the change of tunable variables of the ID3 algorithm. In the subsequent parts of the thesis, the information extraction system was built based on an educational camp dataset and recommendations for hackathon improvement were derived. The datasets are not open-source and were collected manually through the use of surveys.
Description
Keywords
computational experiments, datasets, educational organizations, Python programming language