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STUDENT ACADEMIC PERFORMANCE ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI

STUDENT ACADEMIC PERFORMANCE ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI

Vivian Siahaan
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The dataset used in this project consists of student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school-related features) and it was collected by using school reports and questionnaires.

Attributes in the dataset are as school - student's school ; sex - student's sex ; age - student's age ; address - student's home address type ; famsize - family size ; Pstatus - parent's cohabitation status ; Medu - mother's education ( 0 - none, 1 - primary education , 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education); Fedu - father's education ( 0 - none, 1 - primary education , 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education); Mjob - mother's job ( 'teacher', 'health' care related, civil 'services' , 'at_home' or 'other'); Fjob - father's job ( 'teacher', 'health' care related, civil 'services' , 'at_home' or 'other'); reason - reason to choose this school ; guardian - student's guardian ; traveltime - home to school travel time ; studytime - weekly study time ; failures - number of past class failures ; schoolsup - extra educational support ; famsup - family educational support ; paid - extra paid classes within the course subject ; activities - extra-curricular activities ; nursery - attended nursery school ; higher - wants to take higher education ; internet - Internet access at home ; romantic - with a romantic relationship ; famrel - quality of family relationships ; freetime - free time after school ; goout - going out with friends ; Dalc - workday alcohol consumption ; Walc - weekend alcohol consumption ; health - current health status ; absences - number of school absences ; G1 - first period grade ; G2 - second period grade ; and G3 - final grade .

The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, and XGB classifier. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler.

Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy.
Language
English
Pages
238
Format
Paperback
Release
March 20, 2022
ISBN 13
9798436271781

STUDENT ACADEMIC PERFORMANCE ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI

Vivian Siahaan
0/5 ( ratings)
Read Download
The dataset used in this project consists of student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school-related features) and it was collected by using school reports and questionnaires.

Attributes in the dataset are as school - student's school ; sex - student's sex ; age - student's age ; address - student's home address type ; famsize - family size ; Pstatus - parent's cohabitation status ; Medu - mother's education ( 0 - none, 1 - primary education , 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education); Fedu - father's education ( 0 - none, 1 - primary education , 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education); Mjob - mother's job ( 'teacher', 'health' care related, civil 'services' , 'at_home' or 'other'); Fjob - father's job ( 'teacher', 'health' care related, civil 'services' , 'at_home' or 'other'); reason - reason to choose this school ; guardian - student's guardian ; traveltime - home to school travel time ; studytime - weekly study time ; failures - number of past class failures ; schoolsup - extra educational support ; famsup - family educational support ; paid - extra paid classes within the course subject ; activities - extra-curricular activities ; nursery - attended nursery school ; higher - wants to take higher education ; internet - Internet access at home ; romantic - with a romantic relationship ; famrel - quality of family relationships ; freetime - free time after school ; goout - going out with friends ; Dalc - workday alcohol consumption ; Walc - weekend alcohol consumption ; health - current health status ; absences - number of school absences ; G1 - first period grade ; G2 - second period grade ; and G3 - final grade .

The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, and XGB classifier. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler.

Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy.
Language
English
Pages
238
Format
Paperback
Release
March 20, 2022
ISBN 13
9798436271781

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