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Recommendation Model for Students

In this presentation, Shanmugam introduces Analytics and devices an innovative model that gives out recommendations to students regarding choosing the right engineering streams. Shanmugam employs data analytics to achieve this.

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Recommendation Model for Students

  1. 1. Name: Shanmugam Marimuthu E-mail: shanmugam.m212@gmail.com Twitter ID: @shanmugam_m212 University: Sri Manakula Vinayagar Engineering College, Pondicherry University. Year/Semester: III yr / VI sem Course: B.Tech Branch: Information Technology ANALYTICS
  2. 2. Introduction OAnalytics is the computational analysis of data or statistics OIt is the implementation of practical knowledge in mathematics via data and its statistics OThe techniques and models are based on the study which is used to sustain business in today’s competitive world
  3. 3. Trends OAnalytics is more essential for business magnets to increase their market value OThe recent trends in analytics are Predictive analytics, Enterprise decision Management, Retail Analytics, Marketing mix modeling, etc., OIn my perspective PREDICTIVE ANALYTICS will be having a huge impact in analytics for next five years OIt is because using this, the performance of any product can be increased by overcoming the negatives in the product
  4. 4. Interest Areas OMy Interests are Predictive analytics, Retail Analytics, Risk analytics OI will utilize my practical knowledge to explore innovative things and bring out new opportunities
  5. 5. Identified Problem Area OThe intakes of IT Streams in engineering admissions are eradicating now-a-days OMost of the students and parents are preferring Non IT Streams than IT Streams OAt the end of their graduation, they are opting IT jobs ODue to this issue, upcoming students are struggling to choose the streams
  6. 6. Proposed Model OThe focus made here is to list out the factors which mislead the students about the Professional Courses, especially the IT Streams OHere primary and secondary data sources are collected OFor primary data sources, data are collected through feedback from students, parents, managements, the recruiting industries and agencies
  7. 7. OHistorical data serves as secondary data sources OOur analytics mechanism operates on the data sources to generate the facts OThis serves the student and parents community to choose the right career with the help of Business Intelligence Model Contd.,
  8. 8. Model Diagram
  9. 9. Advantages OAnalytics will help the students and parents community to choose their right courses in professional streams OThis model has predictive data analytics Outcomes OGraphical representation forecasts the engineering admissions OThis representation will increase the intakes of IT streams in engineering admissions
  10. 10. Thank you

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