Predicting B.Tech student admission decisions by data mining algorithms

  • Authors

    • Ravinder Ahuja
    • Archit Garg Garg
    • Daksh Jain
    • Deepanshu Sachdeva
    2017-12-31
    https://doi.org/10.14419/ijet.v7i1.3.9664
  • Apriori, Eclat, SVM, Naive Bayes, Constraint, Measure.

  • Abstract

    In learning calculations affiliation govern mining is the most intense capacity in information mining. The age of principles includes two stages in which the primary stage finds the arrangements of continuous components and the second stage creates the run the show. Numerous calculations are determined to discover sets of incessant components from successive examples. In our exploration work an imperative perception is made in the information digging calculations for the informational index of the designing understudies. By discovering relationship between qualities, we can discover the potential outcomes for affirmation and anticipate understudy confirmation choices. To discover solid and substantial affiliation rules, distinctive measures are thought about lift, support, cost, confidence and conviction. The gauge is come to with the utilization of the imperative as needs be amid the age of the affiliation rules. As we move towards the objective, to give an examination the affiliation runs, the understudies who pick the branch have utilized the calculations specified to demonstrate the guidelines and the aftereffects of the affiliation in light of the past database of the records of confirmation.

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  • How to Cite

    Ahuja, R., Garg, A. G., Jain, D., & Sachdeva, D. (2017). Predicting B.Tech student admission decisions by data mining algorithms. International Journal of Engineering & Technology, 7(1.3), 90-94. https://doi.org/10.14419/ijet.v7i1.3.9664

    Received date: 2018-02-22

    Accepted date: 2018-02-22

    Published date: 2017-12-31