{"id":31954,"date":"2026-04-03T18:32:05","date_gmt":"2026-04-03T13:02:05","guid":{"rendered":"https:\/\/icert.org.in\/?page_id=31954"},"modified":"2026-04-05T18:07:41","modified_gmt":"2026-04-05T12:37:41","slug":"comparative-analysis-of-regression-models-for-predicting-student-satisfaction-in-ai-assisted-learning","status":"publish","type":"page","link":"https:\/\/icert.org.in\/index.php\/shodh-sari-2\/shodh-sari-vol5-issue-1\/comparative-analysis-of-regression-models-for-predicting-student-satisfaction-in-ai-assisted-learning\/","title":{"rendered":"Comparative Analysis of Regression Models for Predicting Student Satisfaction in AI-Assisted Learning"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"31954\" class=\"elementor elementor-31954\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-53fbc96 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"53fbc96\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0996878\" data-id=\"0996878\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f42ca47 elementor-widget elementor-widget-heading\" data-id=\"f42ca47\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">Shodh Sari-An International Multidisciplinary Journal<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d5641c8 elementor-widget elementor-widget-heading\" data-id=\"d5641c8\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Vol-05, Issue-02(Apr - Jun 2026)<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-428710b elementor-widget elementor-widget-heading\" data-id=\"428710b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">An International scholarly\/ academic journal, peer-reviewed\/ refereed journal, ISSN : 2959-1376<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1614314 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1614314\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4187337\" data-id=\"4187337\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c1fb12c elementor-widget elementor-widget-heading\" data-id=\"c1fb12c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Comparative Analysis of Regression Models for Predicting Student Satisfaction in AI-Assisted Learning \n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-59cb194 elementor-widget elementor-widget-text-editor\" data-id=\"59cb194\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: center; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Kumar, Gulshan\u00a0<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: center; border-bottom: solid #000000 0.75pt; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 1pt 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Research Scholar (Department of Computer Science), Punjabi University, Patiala<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-23b0c2e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"23b0c2e\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d272031\" data-id=\"d272031\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-141b102 elementor-widget__width-inherit elementor-widget elementor-widget-elementskit-simple-tab\" data-id=\"141b102\" data-element_type=\"widget\" data-widget_type=\"elementskit-simple-tab.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"ekit-wid-con\" >        <div class=\"elementkit-tab-wraper vertical  elementskit-fitcontent-tab \">\n            <ul class=\"nav nav-tabs elementkit-tab-nav \">\n                                    <li class=\"elementkit-nav-item elementor-repeater-item-ca158ed\">\n                        <a class=\"elementkit-nav-link  active show left-pos\" id=\"content-ca158ed69d9032d08668-tab\" data-ekit-handler-id=\"abstract\" data-ekit-toggle=\"tab\" data-target=\"#content-ca158ed69d9032d08668\" href=\"#Content-ca158ed69d9032d08668\"\n                            data-ekit-toggle-trigger=\"click\"\n                            aria-describedby=\"Content-ca158ed69d9032d08668\">\n                            <span class=\"far fa-file-alt elementskit-tab-icon\"><\/span>                            <span class=\"elementskit-tab-title\"> Abstract<\/span>\n                        <\/a>\n                    <\/li>\n                                        <li class=\"elementkit-nav-item elementor-repeater-item-62e3348\">\n                        <a class=\"elementkit-nav-link  left-pos\" id=\"content-62e334869d9032d08668-tab\" data-ekit-handler-id=\"about-author\" data-ekit-toggle=\"tab\" data-target=\"#content-62e334869d9032d08668\" href=\"#Content-62e334869d9032d08668\"\n                            data-ekit-toggle-trigger=\"click\"\n                            aria-describedby=\"Content-62e334869d9032d08668\">\n                            <span class=\"far fa-user elementskit-tab-icon\"><\/span>                            <span class=\"elementskit-tab-title\"> About Author<\/span>\n                        <\/a>\n                    <\/li>\n                                        <li class=\"elementkit-nav-item elementor-repeater-item-e351830\">\n                        <a class=\"elementkit-nav-link  left-pos\" id=\"content-e35183069d9032d08668-tab\" data-ekit-handler-id=\"impact-statement\" data-ekit-toggle=\"tab\" data-target=\"#content-e35183069d9032d08668\" href=\"#Content-e35183069d9032d08668\"\n                            data-ekit-toggle-trigger=\"click\"\n                            aria-describedby=\"Content-e35183069d9032d08668\">\n                            <span class=\"fas fa-file-import elementskit-tab-icon\"><\/span>                            <span class=\"elementskit-tab-title\"> Impact Statement<\/span>\n                        <\/a>\n                    <\/li>\n                                        <li class=\"elementkit-nav-item elementor-repeater-item-bb8dae5\">\n                        <a class=\"elementkit-nav-link  left-pos\" id=\"content-bb8dae569d9032d08668-tab\" data-ekit-handler-id=\"cite-this-article\" data-ekit-toggle=\"tab\" data-target=\"#content-bb8dae569d9032d08668\" href=\"#Content-bb8dae569d9032d08668\"\n                            data-ekit-toggle-trigger=\"click\"\n                            aria-describedby=\"Content-bb8dae569d9032d08668\">\n                            <span class=\"far fa-address-card elementskit-tab-icon\"><\/span>                            <span class=\"elementskit-tab-title\"> Cite this Article<\/span>\n                        <\/a>\n                    <\/li>\n                                <\/ul>\n\n            <div class=\"tab-content elementkit-tab-content\">\n                                    <div class=\"tab-pane elementkit-tab-pane elementor-repeater-item-ca158ed  active show\" id=\"content-ca158ed69d9032d08668\" role=\"tabpanel\"\n                         aria-labelledby=\"content-ca158ed69d9032d08668-tab\">\n                        <div class=\"animated fadeIn\">\n                            <p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: center; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Abstract<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">The effect of growth of technology can be seen through the rapid increase in the use of AI in every sphere of life. AI has become widely integrated into almost every domain of human activity, including the field of education, where its presence has increased significantly. Consequently, evaluating the level of student satisfaction towards AI assisted learning has emerged as an important area of research. For the study of this paper a dataset is used. The title of the dataset is \u201cAI Assistant Usage in Student Life.\u201d This dataset has the record of 10,000 students which is showing the AI interaction session with various attributes. The objective of this paper is to predict Student satisfaction by using machine learning regression techniques. The dataset was processed using Python in Jupyter notebook. The dataset was divided into two parts i.e. training and testing sets in 75:25 ratio. On the dataset four regression algorithms were applied. This research paper shows that the regression-based machine learning models can analyze and predict student satisfaction effectively. The overall study supports the growing role of AI in education because the result shows that most of the students are satisfied after using AI assistants. This paper also tries to provide comparative study of all the regression models applied on the dataset. For this purpose, a confusion matrix has been generated which is divided into two categories labeled as satisfied and not satisfied. This study helps in making decisions about the use of AI in education.<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Keywords<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">: Artificial Intelligence, Machine Learning, Student Satisfaction, Regression Analysis, Confusion Matrix.<\/span><\/p>                        <\/div>\n                    <\/div>\n                                    <div class=\"tab-pane elementkit-tab-pane elementor-repeater-item-62e3348 \" id=\"content-62e334869d9032d08668\" role=\"tabpanel\"\n                         aria-labelledby=\"content-62e334869d9032d08668-tab\">\n                        <div class=\"animated fadeIn\">\n                            <p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: center; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Author\u2019s Profile<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Mr. Gulshan Kumar completed Bachelor of Computer Applications from Panjab University Chandigarh, in the year 2012 and Master of Computer Applications from Panjab University in the year 2015. He worked as Assistant Professor in Department of Computer Applications in the Swami Premanand Mahavidayalaya (S.P.N. College) Mukerian affiliated to Panjab University Chandigarh from July,2016 to September,2022 then worked as Assistant Professor in Department of Computer Science and Applications from September, 2022 to April 2024 in the DAV University Jalandhar. He has more than Eight years of teaching experience. Currently, joined Punjabi University, Patiala as Research Scholar in Department of Computer Science.<\/span><\/p>                        <\/div>\n                    <\/div>\n                                    <div class=\"tab-pane elementkit-tab-pane elementor-repeater-item-e351830 \" id=\"content-e35183069d9032d08668\" role=\"tabpanel\"\n                         aria-labelledby=\"content-e35183069d9032d08668-tab\">\n                        <div class=\"animated fadeIn\">\n                            <p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: center; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Impact Statement<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">This research provides a critical framework for optimizing AI-assisted learning environments by identifying the most accurate predictive models for student satisfaction. By moving beyond a &#8220;one-size-fits-all&#8221; approach to educational technology, this study demonstrates how specific regression techniques can pinpoint the exact factors\u2014such as interface usability, feedback speed, and personalization\u2014that drive learner engagement. The findings empower educators and software developers to move from reactive troubleshooting to proactive design, ensuring AI tools are not just functional, but genuinely supportive of student needs. Ultimately, this work bridges the gap between complex algorithmic performance and the human element of education. By establishing a data-driven standard for measuring satisfaction, the study facilitates the creation of more intuitive, effective, and inclusive digital classrooms, ensuring that the integration of Artificial Intelligence in schools prioritizes the student experience as much as the technological innovation itself.<\/span><\/p>                        <\/div>\n                    <\/div>\n                                    <div class=\"tab-pane elementkit-tab-pane elementor-repeater-item-bb8dae5 \" id=\"content-bb8dae569d9032d08668\" role=\"tabpanel\"\n                         aria-labelledby=\"content-bb8dae569d9032d08668-tab\">\n                        <div class=\"animated fadeIn\">\n                            <p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: center; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Cite This Article<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">APA (7th Edition): Kumar, G. (2026). Comparative analysis of regression models for predicting student satisfaction in AI-assisted learning. <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Shodh Sari-An International Multidisciplinary Journal<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">, <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">5<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">(2), 145\u2013154. <\/span><a style=\"text-decoration: none;\" href=\"https:\/\/doi.org\/10.59231\/SARI7919\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #0000ff; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: underline; -webkit-text-decoration-skip: none; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;\">https:\/\/doi.org\/10.59231\/SARI7919<\/span><\/a><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">MLA (9th Edition): Kumar, Gulshan. &#8220;Comparative Analysis of Regression Models for Predicting Student Satisfaction in AI-Assisted Learning.&#8221; <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Shodh Sari-An International Multidisciplinary Journal<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">, vol. 5, no. 2, 2026, pp. 145\u2013154, doi:10.59231\/SARI7919.<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: center; margin-top: 0pt; margin-bottom: 0pt;\">\u00a0<\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Chicago (17th Edition): Kumar, Gulshan. &#8220;Comparative Analysis of Regression Models for Predicting Student Satisfaction in AI-Assisted Learning.&#8221; <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Shodh Sari-An International Multidisciplinary Journal<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> 5, no. 2 (2026): 145\u2013154. <\/span><a style=\"text-decoration: none;\" href=\"https:\/\/doi.org\/10.59231\/SARI7919\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #0000ff; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: underline; -webkit-text-decoration-skip: none; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;\">https:\/\/doi.org\/10.59231\/SARI7919<\/span><\/a><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">.<\/span><\/p>                        <\/div>\n                    <\/div>\n                                \n            <\/div>\n                    <\/div>\n    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elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-59d0ca1 elementor-align-justify elementor-mobile-align-left elementor-widget-tablet__width-initial elementor-widget elementor-widget-button\" data-id=\"59d0ca1\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/archive.org\/download\/375.-comparative-analysis-of-regression-models-for-predicting-student-satisfaction\/375.%20Comparative%20Analysis%20of%20Regression%20Models%20for%20Predicting%20Student%20Satisfaction.pdf\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-icon\">\n\t\t\t\t<i aria-hidden=\"true\" class=\"fas fa-university\"><\/i>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span 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elementor-widget-shortcode\" data-id=\"635bd5d\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\"><div class=\"sdm_download_count\"><span class=\"sdm_count_number\">13<\/span><span class=\"sdm_count_string\"> Downloads<\/span><\/div><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9653a5a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9653a5a\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b1626d3\" data-id=\"b1626d3\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-8dbe36e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8dbe36e\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-518b836\" data-id=\"518b836\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-53a6c61 elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"53a6c61\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p dir=\"ltr\" style=\"line-height: 1.2; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">DOI: <\/span><a style=\"text-decoration: none;\" href=\"https:\/\/doi.org\/10.59231\/SARI7919\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #0000ff; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: underline; -webkit-text-decoration-skip: none; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;\">https:\/\/doi.org\/10.59231\/SARI7919<\/span><\/a><\/p><p dir=\"ltr\" style=\"line-height: 1.2; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Subject: Computer Science \/ Educational Technology<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.2; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Page Numbers: 145\u2013154<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-b49fdd7\" data-id=\"b49fdd7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-720c05a elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"720c05a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p dir=\"ltr\" style=\"line-height: 1.2; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Received: Feb 20, 2026<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.2; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Accepted: Mar 11, 2026<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.2; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Published: Apr 01, 2026<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-423024c elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"423024c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p dir=\"ltr\" style=\"line-height: 1.2; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Thematic Classification: Machine Learning in Education, Predictive Modeling, AI-Assisted Learning (AIAL), and Student Satisfaction Analysis.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d2926f4 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d2926f4\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-bba6c7a\" data-id=\"bba6c7a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5c76f91 elementor-widget elementor-widget-heading\" data-id=\"5c76f91\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">I. Introduction\n<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cd57741 elementor-widget elementor-widget-text-editor\" data-id=\"cd57741\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">The increasing growth of AI has introduced various possibilities in different fields including education. In this era AI powered assistants for instance visual tutors, chatbots and other automated systems have taken an inseparable place in the life of students. Students are using these AI tools for various tasks such as studying, writing, making assignments and preparing projects [6]. Students are using these tools rapidly because of easy availability of internet and AI tools and due to this it is becoming important part of learning environment.\u00a0<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">There is no doubt that the use of AI has increased greatly, but it is important to understand whether students are actually satisfied with AI assistants. Just using AI does not always mean that students are benefiting from it. Therefore, it becomes necessary to study how reliable and helpful AI tools really are. The study of satisfaction level of students is also important to know because if they really help the students then the ways should be found to use AI tools in better way [8]. But if these tools provide confusion or wrong answers then the use of AI tools should be restricted.\u00a0<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Earlier the student satisfaction was measured through surveys, feedback forms or interviews etc. No doubt these methods provide good results but these are time consuming too. Now a days with the availability of datasets, it has become possible and easy to analyze the behavior of students and their satisfaction level using data driven and machine learning. This study is trying to analyze the satisfaction of students by using dataset named \u201cAI Assistant Usage in Student Life\u201d [7]. The dataset is showing the information about student AI interaction and various details such as discipline, session duration, number of prompts asked, repeat usage and student satisfaction rating etc.<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; border-bottom: solid #000000 0.75pt; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 1pt 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Regression is generally used technique used for predicting continuous value based on multiple independent variables. In relation to this study regression is helping in estimating the strength of each factor [9]. For the accomplishment of this study, multi linear regression, ridge regression, lasso regression and decision tree regression has been used. Every method has different pros and cons. After applying different regression algorithms, a comparative study has been done. Under this study MSE and R<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"><span style=\"font-size: 0.6em; vertical-align: super;\">2<\/span><\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> score are used to find out how closely the predicted values match the actual satisfaction rating. This study helps in decision making in educational institutions in relation to the use of AI tools by knowing whether students are satisfied or not. A confusion matrix is formed to evaluate the efficiency of the models by classifying students into two categories named satisfied and unsatisfied groups. The reason for working on this research paper is the increasing dependence of students on AI based tools for education purpose. Students are using AI tools for various academic activities because of which it becomes essential to know the level of accuracy and satisfaction of users.<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Objectives:\u00a0<\/span><\/p><ol style=\"margin-top: 0; margin-bottom: 0; padding-inline-start: 48px;\"><li dir=\"ltr\" style=\"list-style-type: lower-alpha; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Predicting student satisfaction with AI Assistants using Machine Learning Regression Model<\/span><\/p><\/li><li dir=\"ltr\" style=\"list-style-type: lower-alpha; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Comparison of different Regression Algorithms in terms of reliability<\/span><\/p><\/li><li dir=\"ltr\" style=\"list-style-type: lower-alpha; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">\u00a0Evaluate Regression outcomes with the help of Confusion matrix<\/span><\/p><\/li><\/ol><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Impact of the Study:<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">The impact of this research is significant for educational institutions. Instead of relying only on traditional methods such as surveys, feedback forms, and interviews, institutions can use machine learning techniques to analyze large datasets and measure student satisfaction more efficiently. This approach saves time and provides more objective and scalable evaluation. The findings of this study help institutions understand whether AI tools are improving the learning experience or creating confusion due to inaccurate or incomplete responses. Based on such analysis, administrators can make informed decisions regarding the proper implementation, regulation, or improvement of AI-based systems in academic environments.<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Overall, the impact of this research lies in offering a reliable, data-driven framework for evaluating student satisfaction with AI assistants. It supports educational institutions, AI developers, and researchers in improving the quality, reliability, and effectiveness of AI-powered learning systems while ensuring that student learning remains the central focus.<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">II. Literature Review<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">As more people are using AI in different fields, the research interest in this issue is also increasing. The researchers are trying to explore that how AI tools are influencing the student performance, behavior and level of satisfaction. This segment of this paper is showing the important work done in relation to the use of AI and Machine Learning in education.<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Early studies initially were focusing on the AI based tutoring system in improving the understanding of students. Those studies showed that AI provides instant help to students, which was helping in providing instant solution to the problems [1], [2]. The studies reported that students using AI were more engaged in task.<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">After those researchers started analyzing the evaluation of student\u2019s behavior in digital learning environment by using data driven approaches. The findings of those studies highlighted the importance of usage of behavior in understanding student\u2019s experience [3], [4].<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Then due to the immense growth of AI and ML, various Machine Learning methods have been widely adopted for predicting academic performance and student engagement. Regression models, classification algorithms and other ML techniques are used to analyze the datasets. Many researchers argued that ML models outperform traditional statistical methods in predictive accuracy when large datasets are available [9]. The previous studies examined the effectiveness of AI tools. Those studies found that AI assistants are helpful for various tasks for instance answering the questions, providing help in writing and making assignments etc. In today\u2019s era the emphasis is shifting to evaluate the level of satisfaction of students using AI tools [5], [6].<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">III. Methodology<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">This experiment was conducted on a dataset which was acquired from Kaggle and uploaded the dataset file on Jupyter Notebook, which provides Python environment. For this purpose, required Python libraries such as numpy, pandas, scikit learn, matplotlib were imported. The dataset titled \u201cAI Assistant Usage in student life\u201d containing 10000 records [7]. The dataset contained both categorical and numerical variables which require appropriate transformation to make it suitable for regression-based prediction task.\u00a0<\/span><\/p><ul style=\"margin-top: 0; margin-bottom: 0; padding-inline-start: 48px;\"><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Dataset Description<\/span><\/p><\/li><\/ul><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Key Attributes:<\/span><\/p><ul style=\"margin-top: 0; margin-bottom: 0; padding-inline-start: 48px;\"><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Student Level<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> (Undergraduate, Postgraduate, etc.)<\/span><\/p><\/li><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Discipline<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> (Engineering, Psychology, Arts, etc.)<\/span><\/p><\/li><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Session Length Min<\/span><\/p><\/li><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Total Prompts<\/span><\/p><\/li><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Task Type<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> (Coding, Writing, Studying, etc.)<\/span><\/p><\/li><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">AI Assistance Level<\/span><\/p><\/li><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Final Outcome<\/span><\/p><\/li><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Used Again<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> (Boolean)<\/span><\/p><\/li><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Target Variable: <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Satisfaction Rating<\/span><\/p><\/li><\/ul><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">The aim is to predict <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">student satisfaction<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> via numerous regression models.<\/span><\/p><ul style=\"margin-top: 0; margin-bottom: 0; padding-inline-start: 48px;\"><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Data Pre-processing<\/span><\/p><\/li><\/ul><ul style=\"margin-top: 0; margin-bottom: 0; padding-inline-start: 48px;\"><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Feature\u2013Target Separation:<\/span><\/p><ol style=\"margin-top: 0; margin-bottom: 0; padding-inline-start: 48px;\"><li dir=\"ltr\" style=\"list-style-type: lower-alpha; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -54pt;\" aria-level=\"2\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Independent variables: All attributes except <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Satisfaction Rating<\/span><\/p><\/li><li dir=\"ltr\" style=\"list-style-type: lower-alpha; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -54pt;\" aria-level=\"2\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Dependent variable: <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Satisfaction Rating<\/span><\/p><\/li><\/ol><\/li><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Encoding of Categorical Attributes:<\/span><\/p><ol style=\"margin-top: 0; margin-bottom: 0; padding-inline-start: 48px;\"><li dir=\"ltr\" style=\"list-style-type: lower-alpha; font-size: 12pt; font-family: 'Times New Roman',serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -54pt;\" aria-level=\"2\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Categorical variables were transformed using <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">One-Hot Encoding<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> to convert them into numerical format suitable for regression algorithms.<\/span><\/p><\/li><\/ol><\/li><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Train\u2013Test Split:<\/span><\/p><\/li><\/ul><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">The dataset was divided into:<\/span><\/p><ul style=\"margin-top: 0; margin-bottom: 0; padding-inline-start: 48px;\"><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"2\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">75% Training Set<\/span><\/p><\/li><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"2\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">25% Testing Set<\/span><\/p><\/li><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Regression Models Implemented<\/span><\/p><\/li><\/ul><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Following regression algorithms were applied:<\/span><\/p><ol style=\"margin-top: 0; margin-bottom: 0; padding-inline-start: 48px;\"><li dir=\"ltr\" style=\"list-style-type: decimal; font-size: 12pt; font-family: 'Times New Roman',serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Multiple Linear Regression (MLR): <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">MLR Model predicts a dependent variable using two or more independent variables,<\/span><\/p><\/li><\/ol><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Algorithm:\u00a0<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Input: Dataset D with features X and target y<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Output: Predicted satisfaction values y_pred<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">1. Load dataset D<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">2. Separate features X and target variable y<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">3. Identify categorical and numerical attributes<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">4. Apply One-Hot Encoding to categorical attributes<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">5. Split data into training set (75%) and testing set (25%)<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">6. Initialize Linear Regression model<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">7. Train model using training data (X_train, y_train)<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">8. Predict output using test data: y_pred = model.predict(X_test)<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">9. Compute MSE and R2 Score<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">10. Return evaluation results<\/span><b style=\"font-weight: normal;\"><\/b><\/p><ol style=\"margin-top: 0; margin-bottom: 0; padding-inline-start: 48px;\" start=\"2\"><li dir=\"ltr\" style=\"list-style-type: decimal; font-size: 12pt; font-family: 'Times New Roman',serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Ridge Regression: <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Ridge Regression Model is a kind of regularized linear regression that adds an L2 penalty (sum of squared coefficients) to the loss function, efficiently lessening coefficients to avoid overfitting as well as reduce multicollinearity.<\/span><\/p><\/li><\/ol><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Algorithm:\u00a0<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.150000000000002pt; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">1. Load dataset D<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.150000000000002pt; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">2. Preprocess data (encoding + train-test split)<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.150000000000002pt; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">3. Initialize Ridge model with regularization parameter alpha<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.150000000000002pt; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">4. Train model using X_train and y_train<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.150000000000002pt; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">5. Predict: y_pred = model.predict(X_test)<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.150000000000002pt; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">6. Compute MSE and R2 Score<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.150000000000002pt; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">7. Return results<\/span><\/p><ol style=\"margin-top: 0; margin-bottom: 0; padding-inline-start: 48px;\" start=\"3\"><li dir=\"ltr\" style=\"list-style-type: decimal; font-size: 12pt; font-family: 'Times New Roman',serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Lasso Regression: <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Lasso Regression Model stands for Least Absolute Shrinkage and Selection Operator is a machine learning technique that enhances linear regression by adding an L1 penalty, shrinking less important feature coefficients towards zero, which performs automatic feature selection and regularization to prevent overfitting and simplify models, making them more interpretable, especially with high-dimensional data.\u00a0<\/span><\/p><\/li><\/ol><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Algorithm:\u00a0<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">1. Load dataset D<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">2. Apply preprocessing and encoding<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">3. Initialize Lasso model with alpha parameter<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">4. Train model on training data<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">5. Generate predictions on test data<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">6. Evaluate using MSE and R2 Score<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">7. Identify reduced features (coefficients shrunk to zero)<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">8. Return results<\/span><\/p><ol style=\"margin-top: 0; margin-bottom: 0; padding-inline-start: 48px;\" start=\"4\"><li dir=\"ltr\" style=\"list-style-type: decimal; font-size: 12pt; font-family: 'Times New Roman',serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Decision Tree Regression: <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">A non-linear model that partitions the feature space into decision rules for predicting satisfaction.<\/span><\/p><\/li><\/ol><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Algorithm:\u00a0<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.150000000000002pt; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">1. Load dataset D<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.150000000000002pt; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">2. Perform preprocessing and train-test split<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.150000000000002pt; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">3. Initialize Decision Tree Regressor<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.150000000000002pt; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">4. Train decision tree on X_train and y_train<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.150000000000002pt; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">5. Predict satisfaction for X_test<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.150000000000002pt; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">6. Compute MSE and R2 Score<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.150000000000002pt; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">7. Return evaluation results<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.150000000000002pt; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Each model was trained using the same training dataset and tested using the same test dataset for fair comparison.<\/span><\/p><ul style=\"margin-top: 0; margin-bottom: 0; padding-inline-start: 48px;\"><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Model Evaluation Metrics<\/span><\/p><\/li><\/ul><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">To evaluate the performance of regression models, the following metrics were used:<\/span><\/p><ol style=\"margin-top: 0; margin-bottom: 0; padding-inline-start: 48px;\"><li dir=\"ltr\" style=\"list-style-type: decimal; font-size: 12pt; font-family: 'Times New Roman',serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Mean Squared Error (MSE): <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Measures the average squared difference between predicted and actual satisfaction ratings.<\/span><\/p><\/li><li dir=\"ltr\" style=\"list-style-type: decimal; font-size: 12pt; font-family: 'Times New Roman',serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Coefficient of Determination (R\u00b2 Score): <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Indicates the proportion of variance in satisfaction explained by the model.<\/span><\/p><\/li><\/ol><ul style=\"margin-top: 0; margin-bottom: 0; padding-inline-start: 48px;\"><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Confusion Matrix\u00a0<\/span><\/p><\/li><\/ul><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Since regression models produce continuous outputs, a confusion matrix cannot be directly applied. Therefore, the regression output was converted into a binary classification problem:<\/span><\/p><ul style=\"margin-top: 0; margin-bottom: 0; padding-inline-start: 48px;\"><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: 'Noto Sans Symbols',sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Satisfied (1):<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> Satisfaction Rating \u2265 3.0<\/span><\/p><\/li><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: 'Noto Sans Symbols',sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Not Satisfied (0):<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> Satisfaction Rating &lt; 3.0<\/span><\/p><\/li><\/ul><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Using this threshold-based discretization:<\/span><\/p><ul style=\"margin-top: 0; margin-bottom: 0; padding-inline-start: 48px;\"><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: 'Noto Sans Symbols',sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">A <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">confusion matrix<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> was generated.<\/span><\/p><\/li><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: 'Noto Sans Symbols',sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre; margin-left: -18pt;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Classification accuracy<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> was computed to evaluate predictive consistency in categorical terms.<\/span><\/p><\/li><\/ul><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">This approach enables both numerical and categorical interpretation of model performance.<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Algorithm:\u00a0<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Input: Actual values y_test, Predicted values y_pred, Threshold T<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: -14.2pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.2pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Output: Confusion Matrix CM, Accuracy<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.200000000000001pt; text-indent: -14.15pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.15pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">1. Convert y_test into binary classes:<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.200000000000001pt; text-indent: -14.15pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.15pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0if y_test &gt;= T \u2192 class = 1 (Satisfied)<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.200000000000001pt; text-indent: -14.15pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.15pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0else \u2192 class = 0 (Not Satisfied)<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.200000000000001pt; text-indent: -14.15pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.15pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">2. Convert y_pred into binary classes using same threshold T<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.200000000000001pt; text-indent: -14.15pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.15pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">3. Compute confusion matrix CM between actual and predicted classes<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.200000000000001pt; text-indent: -14.15pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.15pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">4. Calculate classification accuracy<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; margin-left: 14.200000000000001pt; text-indent: -14.15pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 0pt 14.15pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">5. Return CM and accuracy<\/span><b style=\"font-weight: normal;\"><\/b><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">IV. Results and Discussion<\/span><\/p><ul style=\"margin-top: 0; margin-bottom: 0; padding-inline-start: 48px;\"><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Regression performance analysis<\/span><\/p><\/li><\/ul><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: 28.35pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">On the dataset, four regression models were applied, named Multiple Linear Regression, Ridge Regression, Lasso Regression and Decision Tree Regression. The performance was evaluated by using MSE and R<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"><span style=\"font-size: 0.6em; vertical-align: super;\">2 <\/span><\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">score. The outcome shows that Multiple Linear Regression and Ridge Regression achieved stable performance with low prediction error and good R<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"><span style=\"font-size: 0.6em; vertical-align: super;\">2<\/span><\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> values. This clearly defines that student satisfaction has strong relationship with the key attributes of dataset.<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-indent: 28.35pt; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Lasso Regression produces slightly higher error. But by reducing the weights of less important variables to zero, Lasso Regression simplified the model. This makes the Lasso more useful for identifying important attributes and provides better results. Decision Tree Regression caught non-linear patterns. While it showed good performance on training data but its performance on test data was less consistent and over-fitted. Comparative study shows that decision tree Regression is less reliable than linear regression models.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a23099e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a23099e\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-06e7142\" data-id=\"06e7142\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-3bda21e\" data-id=\"3bda21e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5435cfd elementor-widget elementor-widget-image\" data-id=\"5435cfd\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"637\" height=\"340\" src=\"https:\/\/icert.org.in\/wp-content\/uploads\/2026\/04\/Screenshot-80.png\" class=\"attachment-large size-large wp-image-32199\" alt=\"\" srcset=\"https:\/\/icert.org.in\/wp-content\/uploads\/2026\/04\/Screenshot-80.png 637w, https:\/\/icert.org.in\/wp-content\/uploads\/2026\/04\/Screenshot-80-300x160.png 300w, https:\/\/icert.org.in\/wp-content\/uploads\/2026\/04\/Screenshot-80-600x320.png 600w\" sizes=\"(max-width: 637px) 100vw, 637px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-0fd180e\" data-id=\"0fd180e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4373a61 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4373a61\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-bf195bc\" data-id=\"bf195bc\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c4cf077 elementor-widget elementor-widget-text-editor\" data-id=\"c4cf077\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul style=\"margin-top: 0; margin-bottom: 0; padding-inline-start: 48px;\"><li dir=\"ltr\" style=\"list-style-type: disc; font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre;\" aria-level=\"1\"><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\" role=\"presentation\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Confusion Matrix<\/span><\/p><\/li><\/ul><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">In order to warrant practical interpretation, the continuous satisfaction rating was converted into two categories: Satisfied and Not Satisfied. A confusion matrix was generated. It showed that the model correctly classified satisfied students which indicate that regression model is effectual in identifying user experience. The dual evaluation approach provides both numerical accuracy and practical insights, making the model more useful for education.<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Outcome of Confusion Matrix:<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Accuracy: 0.7932<\/span><\/p><div><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">\u00a0<\/span><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-34178dc elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"34178dc\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-afb2331\" data-id=\"afb2331\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-f07bbb4\" data-id=\"f07bbb4\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b392fc0 elementor-widget elementor-widget-image\" data-id=\"b392fc0\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"557\" height=\"388\" src=\"https:\/\/icert.org.in\/wp-content\/uploads\/2026\/04\/Screenshot-81.png\" class=\"attachment-large size-large wp-image-32200\" alt=\"\" srcset=\"https:\/\/icert.org.in\/wp-content\/uploads\/2026\/04\/Screenshot-81.png 557w, https:\/\/icert.org.in\/wp-content\/uploads\/2026\/04\/Screenshot-81-300x209.png 300w\" sizes=\"(max-width: 557px) 100vw, 557px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-2b4d575\" data-id=\"2b4d575\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-53884d7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"53884d7\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b90cc1a\" data-id=\"b90cc1a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d66e1ce elementor-widget elementor-widget-text-editor\" data-id=\"d66e1ce\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Conclusion<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">This study demonstrates that Machine Learning regression model can predict student satisfaction using AI Assistants. Linear model give transparency. Educational stakeholders can use Machine Learning Regression techniques for decision making. The findings of this paper support the mounting role of AI in education and also describe the importance of data driven evaluation because it can help the educators, administrators etc. in improving AI tools to better support the students which can help in improving learning experience.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-75fdb9d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"75fdb9d\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ddf8f3b\" data-id=\"ddf8f3b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-47e362b elementor-widget elementor-widget-heading\" data-id=\"47e362b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">Statements &amp; Declarations<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-83558ca elementor-widget elementor-widget-text-editor\" data-id=\"83558ca\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"><strong>Peer-Review Method:<\/strong> This article has been published following a double-blind peer-review process by two external reviewers with expertise in Data Science and Machine Learning Applications in Education.<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"><strong>Competing Interests:<\/strong> The author, Gulshan Kumar, declares that there are no financial or personal interests that could have inappropriately influenced the research, data analysis, or conclusions presented in this manuscript.<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"><strong>Funding:<\/strong> This research was conducted independently as part of a doctoral study at Punjabi University, Patiala. No specific grants or financial support were received from any commercial or non-profit funding agencies.<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"><strong>Data Availability:<\/strong> The datasets and regression models developed for this study are detailed within the paper. Specific code implementations or supplementary data used for the comparative analysis are available from the author upon reasonable request.<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"><strong>Ethical Approval:<\/strong> The research involved the use of student feedback data collected through standard educational assessment protocols. All data were anonymized to protect participant identity in accordance with the ethical research guidelines of Punjabi University, Patiala.<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"><strong>License:<\/strong> &#8220;Comparative Analysis of Regression Models for Predicting Student Satisfaction in AI-Assisted Learning&#8221; \u00a9 2026 by Gulshan Kumar is licensed under CC BY 4.0. This work is published by the International Council for Education Research and Training (ICERT).<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3d429e4 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3d429e4\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d11c1c0\" data-id=\"d11c1c0\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-cd89d65 elementor-widget elementor-widget-heading\" data-id=\"cd89d65\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h6 class=\"elementor-heading-title elementor-size-default\">References<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ce3f382 elementor-widget elementor-widget-text-editor\" data-id=\"ce3f382\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">[1] Alina-Elena, O. (2025, June 1). <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">The use of AI in education: Positive aspects and potential risk<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> [Paper presentation]. International Conference Knowledge-Based Organization.<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">[2] Lampou, R. (2023, August 18). The integration of artificial intelligence in education: Opportunities and challenges. <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Review of Artificial Intelligence in Education<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">, <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">4<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">. <\/span><a style=\"text-decoration: none;\" href=\"https:\/\/doi.org\/10.37497\/rev.artif.intell.educ.v4i00.15\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #0000ff; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: underline; -webkit-text-decoration-skip: none; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;\">https:\/\/doi.org\/10.37497\/rev.artif.intell.educ.v4i00.15<\/span><\/a><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">[3] Anggraeni, A., Toruan, J. B. G. L., &amp; Karsen, M. (2025, August 28). <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">The use of AI in education: A systematic literature review<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> [Paper presentation]. International Conference on [Conference Name Missing].<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">[4] Dziubanovska, N., &amp; Maslii, V. (2025). The impact of AI integration on the formation of students\u2019 critical thinking in the modern educational process. <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">West Ukrainian National University<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">.<\/span><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">[5] Roser, M. (2022). <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">The brief history of artificial intelligence: The world has changed fast \u2013 what might be next?<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> Our World in Data. <\/span><a style=\"text-decoration: none;\" href=\"https:\/\/ourworldindata.org\/brief-history-of-ai\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #0000ff; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: underline; -webkit-text-decoration-skip: none; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;\">https:\/\/ourworldindata.org\/brief-history-of-ai<\/span><\/a><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">[6] Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., &amp; Du, Z. (2024). Artificial intelligence in education: A systematic literature review. <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Expert Systems with Applications<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">, <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">252<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">, Article 124167. <\/span><a style=\"text-decoration: none;\" href=\"https:\/\/doi.org\/10.1016\/j.eswa.2024.124167\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #0000ff; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: underline; -webkit-text-decoration-skip: none; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;\">https:\/\/doi.org\/10.1016\/j.eswa.2024.124167<\/span><\/a><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">[7] Ayeshasal89. (2024). <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">AI assistant usage in student life &#8211; synthetic<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"> [Data set]. Kaggle. <\/span><a style=\"text-decoration: none;\" href=\"https:\/\/www.kaggle.com\/datasets\/ayeshasal89\/ai-assistant-usage-in-student-life-synthetic\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #0000ff; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: underline; -webkit-text-decoration-skip: none; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;\">https:\/\/www.kaggle.com\/datasets\/ayeshasal89\/ai-assistant-usage-in-student-life-synthetic<\/span><\/a><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">[8] Compilatio. (2025, May 10). <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Artificial intelligence in education: Opportunities and challenges in 2025<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">. <\/span><a style=\"text-decoration: none;\" href=\"https:\/\/www.compilatio.net\/en\/blog\/ai-in-education\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #0000ff; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: underline; -webkit-text-decoration-skip: none; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;\">https:\/\/www.compilatio.net\/en\/blog\/ai-in-education<\/span><\/a><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; margin-top: 0pt; margin-bottom: 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">[9] Verma, B. K., Srivastava, N., &amp; Bharti, A. K. (2025, February 27). <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Towards smart education systems: A hybrid data mining and machine learning approach to predicting academic outcomes<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">. IEEE InTech. <\/span><a style=\"text-decoration: none;\" href=\"https:\/\/www.google.com\/search?q=https:\/\/doi.org\/10.1109\/InTech64186.2025.11198559&amp;authuser=1\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #0000ff; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: underline; -webkit-text-decoration-skip: none; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;\">https:\/\/doi.org\/10.1109\/InTech64186.2025.11198559<\/span><\/a><\/p><p dir=\"ltr\" style=\"line-height: 1.7999999999999998; text-align: justify; border-bottom: solid #000000 0.75pt; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 1pt 0pt;\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">[10] Rani, B. T. (2024). Artificial intelligence tools in learning English language and teaching: How can AI be used for language learning. <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">Edumania-An International Multidisciplinary Journal<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">, <\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">2<\/span><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\">(4), 230\u2013234. <\/span><a style=\"text-decoration: none;\" href=\"https:\/\/doi.org\/10.59231\/edumania\/9085\"><span style=\"font-size: 12pt; font-family: Arial,sans-serif; color: #0000ff; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: underline; -webkit-text-decoration-skip: none; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;\">https:\/\/doi.org\/10.59231\/edumania\/9085<\/span><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e4fec3a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e4fec3a\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-278911c\" data-id=\"278911c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-f8cbbb8\" data-id=\"f8cbbb8\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-37a6c73 elementor-widget elementor-widget-elementskit-social-share\" data-id=\"37a6c73\" data-element_type=\"widget\" data-widget_type=\"elementskit-social-share.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"ekit-wid-con\" >\t\t<ul class=\"ekit_socialshare\">\n                            <li class=\"elementor-repeater-item-db288b3\" data-social=\"facebook\">\n                    <div class=\"facebook\">\n                        \n                        <i aria-hidden=\"true\" class=\"icon icon-facebook\"><\/i>                        \n                                                                                            <\/div>\n                <\/li>\n                                            <li class=\"elementor-repeater-item-8c1acc4\" data-social=\"whatsapp\">\n                    <div class=\"whatsapp\">\n                        \n                        <i aria-hidden=\"true\" class=\"icon icon-whatsapp-1\"><\/i>                        \n                                                                                            <\/div>\n                <\/li>\n                                            <li class=\"elementor-repeater-item-a0e5dae\" data-social=\"linkedin\">\n                    <div class=\"linkedin\">\n                        \n                        <i aria-hidden=\"true\" class=\"icon icon-linkedin\"><\/i>                        \n                                                                                            <\/div>\n                <\/li>\n                                    <\/ul>\n        <\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-10a5381\" data-id=\"10a5381\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-60f3b4f elementor-align-right elementor-widget elementor-widget-button\" data-id=\"60f3b4f\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/icert.org.in\/index.php\/shodh-sari-2\/shodh-sari-vol5-issue-1\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Back<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Shodh Sari-An International Multidisciplinary Journal Vol-05, Issue-02(Apr &#8211; Jun 2026) An International scholarly\/ academic journal, peer-reviewed\/ refereed journal, ISSN : 2959-1376 Comparative Analysis of Regression Models for Predicting Student Satisfaction in AI-Assisted Learning Kumar, Gulshan\u00a0 Research Scholar (Department of Computer Science), Punjabi University, Patiala Abstract About Author Impact Statement Cite this Article Abstract The effect [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":30837,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"postBodyCss":"","postBodyMargin":[],"postBodyPadding":[],"postBodyBackground":{"backgroundType":"classic","gradient":""},"site-sidebar-layout":"no-sidebar","site-content-layout":"page-builder","ast-site-content-layout":"full-width-container","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-31954","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/icert.org.in\/index.php\/wp-json\/wp\/v2\/pages\/31954","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/icert.org.in\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/icert.org.in\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/icert.org.in\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/icert.org.in\/index.php\/wp-json\/wp\/v2\/comments?post=31954"}],"version-history":[{"count":22,"href":"https:\/\/icert.org.in\/index.php\/wp-json\/wp\/v2\/pages\/31954\/revisions"}],"predecessor-version":[{"id":32203,"href":"https:\/\/icert.org.in\/index.php\/wp-json\/wp\/v2\/pages\/31954\/revisions\/32203"}],"up":[{"embeddable":true,"href":"https:\/\/icert.org.in\/index.php\/wp-json\/wp\/v2\/pages\/30837"}],"wp:attachment":[{"href":"https:\/\/icert.org.in\/index.php\/wp-json\/wp\/v2\/media?parent=31954"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}