Browsing by Author "1711502042"

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    Effectiveness of Online Classes DuringCovid-19 Pandemic Using Machine Learning
    (North South University, 2021) Asrafi Akter; Naziba Nasir; Saheeb Tareque; Mohammad Monirujjaman Khan; 1711502042; 1612618042; 1721809042
    The deadly COVID-19 started its journey in December 2019 in China. For most of the patients, it’s like a mild fever and non-specific gastrointestinal symptoms to a lesser extent. Aged people with previous illnesses like diabetes, heart problems, and high blood pressure suffer the most. Until 26th May 2021, there have been 169,094,726 confirmed cases of COVID-19, including 3,512,510 deaths, reported to the World Health Organization. Since COVID-19, lifestyle has changed for almost everyone. That’s when e-learning had a bigger impact on all the students worldwide. It is no different for the students of Bangladesh, either. To keep the study of the students, the government of Bangladesh made a decision and advised the educational institutions to take classes online. While attending online classes, students are facing many opportunities and obstacles, such as disruption in class, health issues, financial issues, and saving time from traffic. However, being captive, they have utilized their time a lot better, and this has reflected on their performances too. Machine Learning is a sub-region of man-made reasoning, which predicts outcomes depending on the features of a given dataset. In this paper, with the help of a machine learning approach, 6 university students during the pandemic were found. By creating our dataset, the model was trained, and then a prediction was made depending on the different features of the dataset. The collection of data that helped to create a model and gave the highest accuracy over students’ performance. Exploration was done using different algorithms, i.e., Linear Discriminant Analysis (LDA), Logistic Regression, and K-Nearest Neighbor (KNN) for classification. Prediction score Accuracy of 81.05 % by LDA, 86.3% by Logistic Regression, and 84.2% by KNN was achieved. The highest prediction score was achieved by Logistic Regression (86.3%). The accuracy shows the effectiveness of online classes during the pandemic through a Machine Learning approach. It shows how the online classes are effective for a particular student when the input fields are filled. Overall, after performing the three algorithms (K-Nearest Neighbor, Logistic Regression, Linear Discriminant Analysis)with satisfactory results and successfully predicting with a high level of accuracy, while maintaining its objective of being easy to understand.