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shraddha mane

mini project report

Education
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MINI PROJECT on Data Mining For STUDENT PERFORMANCE Group Members MONIKA SHINDE. RASIKA SHINDE. DARSHANA RANBAGLE. SAYALI ZENDE. POOJA WAGH. UNDER THE GUIDANCE OF Prof. S.J.Mane Department of Information Technology BHARATI VIDYAPEETH COLLEGE OF ENGINEERING Sector no.7,C.B.D. Navi Mumbai-400614 UNIVERSITY OF MUMBAI 2016-2017
Problem Statement: This project is for Student Performance. It analyses the data of past students records(Roll no., ssc%, Extracurricular, current result, performance) and gives us the result of student performance. This would help college staff to recognize students records based on this analysis. So for this purpose we will be using ASSOCIATION MINING method. Tool Used: XLMiner Dataset: We have considered a dataset of 75 student’s records. Example. Step 1: Upload dataset.
Dataset in XLMiner. Step 2: Select Associate -> Association Rules.
Step 3 : Give Minimum Support and Minimum Confidence. Step 4: Click on Association Rules -> Run1 -> view
 ASSOCIATION RULES :
CONCLUSION : Association rules are created by analyzing data for frequent if/then patterns and using the criteria support and confidence to identify the most important relationships. Association rule mining is a procedure which is meant to find frequent patterns, correlations, associations, or causal structures from data sets found in various kinds of databases such as relational databases, transactional databases, and other forms of data repositories. REFERENCES: https://www.xlminer.com/ http://www.solver.com/association-rules-example https://en.wikipedia.org/wiki/Apriori_algorithm https://www.youtube.com/watch?v=v0FomcP0AGY

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Dmbi project doc

  • 1. MINI PROJECT on Data Mining For STUDENT PERFORMANCE Group Members MONIKA SHINDE. RASIKA SHINDE. DARSHANA RANBAGLE. SAYALI ZENDE. POOJA WAGH. UNDER THE GUIDANCE OF Prof. S.J.Mane Department of Information Technology BHARATI VIDYAPEETH COLLEGE OF ENGINEERING Sector no.7,C.B.D. Navi Mumbai-400614 UNIVERSITY OF MUMBAI 2016-2017
  • 2. Problem Statement: This project is for Student Performance. It analyses the data of past students records(Roll no., ssc%, Extracurricular, current result, performance) and gives us the result of student performance. This would help college staff to recognize students records based on this analysis. So for this purpose we will be using ASSOCIATION MINING method. Tool Used: XLMiner Dataset: We have considered a dataset of 75 student’s records. Example. Step 1: Upload dataset.
  • 3. Dataset in XLMiner. Step 2: Select Associate -> Association Rules.
  • 4. Step 3 : Give Minimum Support and Minimum Confidence. Step 4: Click on Association Rules -> Run1 -> view
  • 6. CONCLUSION : Association rules are created by analyzing data for frequent if/then patterns and using the criteria support and confidence to identify the most important relationships. Association rule mining is a procedure which is meant to find frequent patterns, correlations, associations, or causal structures from data sets found in various kinds of databases such as relational databases, transactional databases, and other forms of data repositories. REFERENCES: https://www.xlminer.com/ http://www.solver.com/association-rules-example https://en.wikipedia.org/wiki/Apriori_algorithm https://www.youtube.com/watch?v=v0FomcP0AGY