Matches in SemOpenAlex for { <https://semopenalex.org/work/W3213007518> ?p ?o ?g. }
Showing items 1 to 85 of
85
with 100 items per page.
- W3213007518 endingPage "235" @default.
- W3213007518 startingPage "226" @default.
- W3213007518 abstract "Predicting Student’s Performance System is to find students who may require early intervention before they fail to graduate. It is generally meant for the teaching faculty members to analyze Student's Performance and Results. It stores Student Details in a database and uses Machine Learning Model using i. Python Data Analysis tools like Pandas and ii. Data Visualization tools like Seaborn to analyze the overall Performance of the Class. The proposed system suggests student performance prediction through Machine Learning Algorithms and Data Mining Techniques. The Data Mining technique used here is classification, which classifies the students based on student’s attributes. The Front end of the application is made using React JS Library with Data Visualization Charts and connected to a backend Database where all student’s records are stored in MongoDB and the Machine Learning model is trained and deployed through Flask. In this process, the machine learning algorithm is trained using a dataset to create a model and predict the output on the basis of that model. Three different types of data used in Machine Learning are continuous, categorical and binary. In this study, a brief description and comparative analysis of various classification techniques is done using student performance dataset. The six different machine learning Classification algorithms, which have been compared, are Logistic Regression, Decision Tree, K-Nearest Neighbor, Naïve Bayes, Support Vector Machine and Random Forest. The results of Naïve Bayes classifier are comparatively higher than other techniques in terms of metrics such as precision, recall and F1 score. The values of precision, recall and F1 score are 0.93, 0.92 and 0.92 respectively." @default.
- W3213007518 created "2021-11-22" @default.
- W3213007518 creator A5001281165 @default.
- W3213007518 creator A5029693326 @default.
- W3213007518 creator A5048435904 @default.
- W3213007518 creator A5065682889 @default.
- W3213007518 date "2021-10-27" @default.
- W3213007518 modified "2023-10-01" @default.
- W3213007518 title "Predicting Student Performance for Early Intervention using Classification Algorithms in Machine Learning" @default.
- W3213007518 cites W2096352448 @default.
- W3213007518 cites W2135420268 @default.
- W3213007518 cites W2136132422 @default.
- W3213007518 cites W2699041389 @default.
- W3213007518 cites W2756291343 @default.
- W3213007518 cites W2794075952 @default.
- W3213007518 cites W2800700858 @default.
- W3213007518 cites W2803635848 @default.
- W3213007518 cites W2908926274 @default.
- W3213007518 cites W2938336646 @default.
- W3213007518 cites W2955141036 @default.
- W3213007518 cites W2960262989 @default.
- W3213007518 cites W3010911345 @default.
- W3213007518 cites W3012375008 @default.
- W3213007518 cites W3023981672 @default.
- W3213007518 cites W4242081620 @default.
- W3213007518 doi "https://doi.org/10.52547/jist.9.36.226" @default.
- W3213007518 hasPublicationYear "2021" @default.
- W3213007518 type Work @default.
- W3213007518 sameAs 3213007518 @default.
- W3213007518 citedByCount "0" @default.
- W3213007518 crossrefType "journal-article" @default.
- W3213007518 hasAuthorship W3213007518A5001281165 @default.
- W3213007518 hasAuthorship W3213007518A5029693326 @default.
- W3213007518 hasAuthorship W3213007518A5048435904 @default.
- W3213007518 hasAuthorship W3213007518A5065682889 @default.
- W3213007518 hasBestOaLocation W32130075181 @default.
- W3213007518 hasConcept C110083411 @default.
- W3213007518 hasConcept C111919701 @default.
- W3213007518 hasConcept C11413529 @default.
- W3213007518 hasConcept C119857082 @default.
- W3213007518 hasConcept C12267149 @default.
- W3213007518 hasConcept C124101348 @default.
- W3213007518 hasConcept C154945302 @default.
- W3213007518 hasConcept C169258074 @default.
- W3213007518 hasConcept C41008148 @default.
- W3213007518 hasConcept C519991488 @default.
- W3213007518 hasConcept C52001869 @default.
- W3213007518 hasConcept C5274069 @default.
- W3213007518 hasConcept C81669768 @default.
- W3213007518 hasConcept C84525736 @default.
- W3213007518 hasConceptScore W3213007518C110083411 @default.
- W3213007518 hasConceptScore W3213007518C111919701 @default.
- W3213007518 hasConceptScore W3213007518C11413529 @default.
- W3213007518 hasConceptScore W3213007518C119857082 @default.
- W3213007518 hasConceptScore W3213007518C12267149 @default.
- W3213007518 hasConceptScore W3213007518C124101348 @default.
- W3213007518 hasConceptScore W3213007518C154945302 @default.
- W3213007518 hasConceptScore W3213007518C169258074 @default.
- W3213007518 hasConceptScore W3213007518C41008148 @default.
- W3213007518 hasConceptScore W3213007518C519991488 @default.
- W3213007518 hasConceptScore W3213007518C52001869 @default.
- W3213007518 hasConceptScore W3213007518C5274069 @default.
- W3213007518 hasConceptScore W3213007518C81669768 @default.
- W3213007518 hasConceptScore W3213007518C84525736 @default.
- W3213007518 hasIssue "36" @default.
- W3213007518 hasLocation W32130075181 @default.
- W3213007518 hasOpenAccess W3213007518 @default.
- W3213007518 hasPrimaryLocation W32130075181 @default.
- W3213007518 hasRelatedWork W10715555 @default.
- W3213007518 hasRelatedWork W12634471 @default.
- W3213007518 hasRelatedWork W13188192 @default.
- W3213007518 hasRelatedWork W2005780 @default.
- W3213007518 hasRelatedWork W621929 @default.
- W3213007518 hasRelatedWork W6310906 @default.
- W3213007518 hasRelatedWork W6552940 @default.
- W3213007518 hasRelatedWork W790158 @default.
- W3213007518 hasRelatedWork W8394581 @default.
- W3213007518 hasRelatedWork W9481221 @default.
- W3213007518 hasVolume "9" @default.
- W3213007518 isParatext "false" @default.
- W3213007518 isRetracted "false" @default.
- W3213007518 magId "3213007518" @default.
- W3213007518 workType "article" @default.