Student-s-Mental-State-Recognition-Using-Machine-Learning-Algorithm

after acquiring the EEG signal, beta band wave was used. Statistical tools were used to extract statistical features. For classification Random Forest, logistic, voting, linear and gradient boosting regression were used to classify the student mental state

Dataset link

https://data.mendeley.com/datasets/8c26dn6c7w/1

dataset

dataset/Emotic 30s EDF/

train the model and get pickle file

Train the model/main.ipynb

simulate the result in confusion_matrix

simulate result/main.ipynb

Flask interface

interface/init.sh