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Rismon Hasiholan
STUDENT ACADEMIC PERFORMANCE ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI
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1252121STUDENT ACADEMIC PERFORMANCE ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUIhttps://www.gandhi.com.mx/student-academic-performance-analysis-and-prediction-using-machine-learning-with-python-gui/phttps://gandhi.vtexassets.com/arquivos/ids/835592/8a2a783f-e5b6-449e-9068-7bebac2b7818.jpg?v=638336415097430000193193MXNBALIGE PUBLISHINGInStock/Ebooks/1241974STUDENT ACADEMIC PERFORMANCE ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI193193https://www.gandhi.com.mx/student-academic-performance-analysis-and-prediction-using-machine-learning-with-python-gui/phttps://gandhi.vtexassets.com/arquivos/ids/835592/8a2a783f-e5b6-449e-9068-7bebac2b7818.jpg?v=638336415097430000InStockMXN99999DIEbook20221230005536061_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1230005536061_pThe 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. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd period grades. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful./ppAttributes in the dataset are as follows: school - students school (binary: GP - Gabriel Pereira or MS - Mousinho da Silveira); sex - students sex (binary: F - female or M - male); age - students age (numeric: from 15 to 22); address - students home address type (binary: U - urban or R - rural); famsize - family size (binary: LE3 - less or equal to 3 or GT3 - greater than 3); Pstatus - parents cohabitation status (binary: T - living together or A - apart); Medu - mothers education (numeric: 0 - none, 1 - primary education (4th grade), 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education); Fedu - fathers education (numeric: 0 - none, 1 - primary education (4th grade), 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education); Mjob - mothers job (nominal: teacher, health care related, civil services (e.g. administrative or police), at_home or other); Fjob - fathers job (nominal: teacher, health care related, civil services (e.g. administrative or police), at_home or other); reason - reason to choose this school (nominal: close to home, school reputation, course preference or other); guardian - students guardian (nominal: mother, father or other); traveltime - home to school travel time (numeric: 1 - 15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - 1 hour); studytime - weekly study time (numeric: 1 - 2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - 10 hours); failures - number of past class failures (numeric: n if 1n3, else 4); schoolsup - extra educational support (binary: yes or no); famsup - family educational support (binary: yes or no); paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no); activities - extra-curricular activities (binary: yes or no); nursery - attended nursery school (binary: yes or no); higher - wants to take higher education (binary: yes or no); internet - Internet access at home (binary: yes or no); romantic - with a romantic relationship (binary: yes or no); famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent); freetime - free time after school (numeric: from 1 - very low to 5 - very high); goout - going out with friends (numeric: from 1 - very low to 5 - very high); Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 - very high); Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high); health - current health status (numeric: from 1 - very bad to 5 - very good); absences - number of school absences (numeric: from 0 to 93); G1 - first period grade (numeric: from 0 to 20); G2 - second period grade (numeric: from 0 to 20); and G3 - final grade (numeric: from 0 to 20, output target)./ppThe 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./ppFinally, 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./p(*_*)1230005536061_<p>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. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd period grades. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful.</p><p>Attributes in the dataset are as follows: school - students school (binary: GP - Gabriel Pereira or MS - Mousinho da Silveira); sex - students sex (binary: F - female or M - male); age - students age (numeric: from 15 to 22); address - students home address type (binary: U - urban or R - rural); famsize - family size (binary: LE3 - less or equal to 3 or GT3 - greater than 3); Pstatus - parents cohabitation status (binary: T - living together or A - apart); Medu - mothers education (numeric: 0 - none, 1 - primary education (4th grade), 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education); Fedu - fathers education (numeric: 0 - none, 1 - primary education (4th grade), 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education); Mjob - mothers job (nominal: teacher, health care related, civil services (e.g. administrative or police), at_home or other); Fjob - fathers job (nominal: teacher, health care related, civil services (e.g. administrative or police), at_home or other); reason - reason to choose this school (nominal: close to home, school reputation, course preference or other); guardian - students guardian (nominal: mother, father or other); traveltime - home to school travel time (numeric: 1 - <15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour); studytime - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours); failures - number of past class failures (numeric: n if 1<n<3, else 4); schoolsup - extra educational support (binary: yes or no); famsup - family educational support (binary: yes or no); paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no); activities - extra-curricular activities (binary: yes or no); nursery - attended nursery school (binary: yes or no); higher - wants to take higher education (binary: yes or no); internet - Internet access at home (binary: yes or no); romantic - with a romantic relationship (binary: yes or no); famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent); freetime - free time after school (numeric: from 1 - very low to 5 - very high); goout - going out with friends (numeric: from 1 - very low to 5 - very high); Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 - very high); Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high); health - current health status (numeric: from 1 - very bad to 5 - very good); absences - number of school absences (numeric: from 0 to 93); G1 - first period grade (numeric: from 0 to 20); G2 - second period grade (numeric: from 0 to 20); and G3 - final grade (numeric: from 0 to 20, output target).</p><p>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.</p><p>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.</p>...1230005536061_BALIGE PUBLISHINGlibro_electonico_7db6674b-3742-3865-b99f-fa34010efae5_1230005536061;1230005536061_1230005536061Rismon HasiholanInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/f2ca928b-958d-498f-bb70-f76c6615d8f6-epub-8e4611ad-a65c-4d39-9948-3cbdab142740.epub2022-04-20T00:00:00+00:00BALIGE PUBLISHING