1. Collage Score Card & Software defect prediction
Prepared by:
Meetkumar Patel
Srivats Srinivasan
GUIDANCE BY:
Prof. Meiliu Lu

2. Agenda Data Warehousing Project Data Mining Project
Background
Introduction
Technologies Explored
Implementation Steps
Future scope
Data Mining Project
Objective
Algorithm Applied
Demo
Learning Experience
References

3. Background
Source website : ,
Two datasets :
Collage Scorecard
Software Defect Prediction dataset
Collage Scorecard dataset :
Data from
17 attributes,37835 entries
Software Defect Prediction dataset:
22 attributes,1100 entries

4. Introduction
The primary objective of our project is to design data mart.
We have used Star schema to generate it.
This data mart answers questions related to US universities.
The primary users of the Data Mart would be High School Students.

5. Technologies Explored
Data Preprocessing
Microsoft Excel Spreadsheet
MySQL Server
Data Mart
MsSQL Server
Java
OLAP Operations
SQL Server Queries

6. Implementation Steps Data Cleaning and Preprocessing Data Mart
Querying Tool

7. Data Cleaning and Preprocessing
Original data had 80,000 rows and 1700 columns, we trimmed data to rows and 17 related columns.
Add missing values using SQL Script.
Since 5 years data are there we added year column for segregation.

8. Data Mart Data mart is implemented on star schema base
Data Mart provided following information to user
University details on basis of below attributes
University ID
Programs
Type of Degree
SAT & AWT scores
Region
State

9. Highest Degree
Degree_ID
Degree_Name
State
State_ID
State_Name
Fact Table
University_ID
State_ID
Degree_ID
PDegree_ID
Region_ID
Program_ID
Scores
University
University_ID
University_name
Zip
Website
Predominant Degree
PDegree_ID
PDegreee_Name
Region
Region_ID
Region_Name
Program
Program_ID
Program_Name
Star Schema

11. Future Scope Privileged user can insert new records in future
Integrate Google Maps for location and directions
Develop Web based and Mobile based environment.

12. Objective Mining data to extract knowledge from available data.
Analyze the behavior of different data mining tools.
This project focus on the high-performance fault/error predictors based on data mining technique such as Random Forests and the algorithms based on a new computational intelligence approach.

13. Data Mining Tools Used Classification Algorithm Weka Rapid Miner J48
Random Tree
Logistic

14. Data Mining
We will use attributes like cyclomatic complexity, essential complexity, design complexity, total number of operators, total no. of operands, volume, program length, difficulty, intelligence , effort , line count etc.
Mining these attributes to study how they affect the quality of software to be produced.
The final result using these attributes is to predict if its a defect or not. {true, false}.

15. Data Mining Pre- processing data –
The collected data were noisy, missing useful info and inconsistent. First step was the Data preparation processes that consist of checking the data distribution and outliers, dealing with empty or missing values, enriching data, and transforming data into analyzable formats should be employed to improve data quality and to thus enable effective data mining.

16. Data Mining Algorithm Implementation
Firstly, the algorithm is implemented in WEKA to gain the
“Root Mean square error” and then used the Rapid Miner to obtain the graphical output.
The lesser the “Root Mean square error” the efficient the algorithm is with the particular data set.

17. Data Mining (WEKA)
J48

18. Data Mining (WEKA)
Naïve Bayesian

19. Data Mining (WEKA)
Random Tree

20. Data Mining (Rapid Miner)

21. Data Mining

22. Learning Experience Analytical processing
Learned different data mining tools like Weka, rapid Miner
Learned about real time application for different data mining algorithms

23. DEMO

24. Conclusion
Weka predicted the “Root Mean Square error” on basis of which few algorithms were shortlisted. But, Weka wasn’t able to show the graphical representation sound and clear.
So, Rapid Miner came into consideration through which we were able to simplify the graphs and able predict the probability of defect with ease.

25. References