Data Mining and Machine Learning Lab (Summer 2022)

Section outline

• Select section Welcome to Data Mining

  Welcome to Data Mining

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  Instructor: Md. Aynul Hasan Nahid Office : Room # 712, Level 7, Daffodil Tower Cellphone #: 01674834062  Email @: aynul.cse@diu.edu.bd

  
  Course Rationale
  An introduction to data mining; Data preparation, model building, and data mining techniques such as clustering, decisions trees and neural networks; Induction of predictive models from data: classification, regression, and probability estimation; Application case studies; Data-mining software tools review and comparison.

  Course Objectives
  

  • To apply the concept of data mining in solving problems
    
  • To demonstrate applications of data mining using tools
  • To apply knowledge of data mining in project work

  Course Outcomes (CO’s)
  

  • CO1 Able to possess the basic knowledge of Weka and Python concerning data mining and machine learning
  • CO2 Able to implement different data mining and machine learning algorithms like classification, prediction, clustering and association rule mining to solve real-world problems using Weka and Python
  • CO3 Able to compare and evaluate different data mining and machine learning algorithms like classification, prediction, clustering and association rule mining using Weka and/or Python
  • CO4 Able to apply implementation knowledge of data mining and machine learning in developing research ideas

  Grading Scheme

  Attendance: 10% Lab Performance: 25% Project / Lab Report: 25% Final Exam: 40%

  
  

  • Recommended Books

  1. Introduction to Data Mining and Applications
  2. Data Mining Concepts and Techniques
  3. Data Mining Techniques
  4. Data Mining Using Weka
  5. Weka Manual
  6. Data Mining Using Python

  • Global Data Repository for Data Mining and/or Machine Learning

  1. WISDM
     
  2. UCI ML Repository
     
  3. KDD Cup
     
  4. Kaggle
     
  5. KDnuggets
     

  • Standard Templates

  1. IEEE Template
  2. ACM Template

  • Select activity Announcements

    

    Announcements Forum
  • Select activity Announcements

    

    Announcements Forum
  • Select activity OBE Course Outline

    

    OBE Course Outline File

    Completion

    Students must

    

    Mark as done
• Select section Week 1: Introduction to Weka

  Week 1: Introduction to Weka

  Topics of Discussion

  • Introduction to Weka
  • Relationship to data mining
  • Overview of data mining with Weka
    

  
  

  Expected Learning Outcome

  • Appreciation of the needs of data mining with Weka
  • Visualization of the relationship of Weka to data mining
  • Visualization of different data mining tasks with Weka

  • Select activity Working with Weka

    Completion

    Students must

    

    Mark as done

    
    

    • Working with Weka

• Select section Week 2: Data Visualization Using Weka

  Week 2: Data Visualization Using Weka

  Topics of Discussion

  • Review of data mining task and related application examples
  • Data Visualization with Weka
  • Course Project Team and discussion

  
  

  Expected Learning Outcome

  • On-hand acquaintance and practice of data visualization with Weka
  • Team formation for the course project

  
  

  • Project Guideline

  • Select activity Working with Data Visualization in Weka

    Completion

    Students must

    

    Mark as done

    
    

    • Working with Data Visualization in Weka

  • Select activity Project GuidelineRecord Course Project Team

    Completion

    Students must

    

    Mark as done

    • Project Guideline
      
    • Record Course Project Team

• Select section Week 3: Data Preprocessing and Feature Selection Using Weka

  Week 3: Data Preprocessing and Feature Selection Using Weka

  Topics of Discussion

  • Discussion on feature/attribute selection 
  • Project Discussion and execution plan

  Expected Learning Outcome
  

  • On-hand acquaintance and practice of feature/attribute selection with Weka
  • Selection of project topic by team

  • Select activity Working with Weka

    Completion

    Students must

    

    Mark as done

    
    

    • Working with Weka

• Select section Week 4: Classification Using Weka

  Week 4: Classification Using Weka

  Topics of Discussion

  • Classification and prediction with Weka
  • Classification: decision tree

  Expected Learning Outcome

  • Problem solving skill in classification and prediction
  • Skill in using Weka as a data mining tool for classification and prediction

  • Select activity Working with Weka

    Completion

    Students must

    

    Mark as done

    
    

    • Working with Weka

• Select section Week 5: Continuation of Classification Using Weka

  Week 5: Continuation of Classification Using Weka

  Topics of Discussion

  • Classification and prediction with Weka
    
  • Classification: Bayesian, Instance-based

  Expected Learning Outcome

  • Problem solving skill in classification and prediction
  • Skill in using Weka as a data mining tool for classification and prediction

  
  

  • Select activity Working with Weka

    Completion

    Students must

    

    Mark as done

    
    

    • Working with Weka

• Select section Week 6: Cluster Analysis Using Weka

  Week 6: Cluster Analysis Using Weka

  Topics of Discussion

  • Cluster Analysis with Weka
  • Cluster Analysis: partitional (K-means), hierarchical, density-based

  
  

  Expected Learning Outcome

  • Problem solving skill in classification and prediction
    
  • Skill in using Weka as a data mining tool for cluster analysis

  • Select activity Working with Weka

    Completion

    Students must

    

    Mark as done

    
    

    • Working with Weka

• Select section Week 7: Mid Exam

  Week 7: Mid Exam

  Midterm Examination Week

  
  

  
  

  
  
• Select section Week 8: Presentation of Project # 1 (Using Weka)

  Week 8: Presentation of Project # 1 (Using Weka)

  Project # 1 (with Weka) Presentation

  
  

  
  
• Select section Week 9: Introduction to Python

  Week 9: Introduction to Python

  Topics of Discussion

  • Introduction to Python
  • Relationship to machine learning
  • Overview of machine learning with Python

  Expected Learning Outcome

  • Appreciation of the needs of machine learning with Python
  • Visualization of the relationship of Python to machine learning
  • Visualization of different machine learning tasks with Python

  • Select activity  Working with Python BasicsWorking with Pytho...

    Completion

    Students must

    

    Mark as done

     

    • Working with Python Basics
    • Working with Python for Machine Learning

    
    

    • Working with Python and NumPy
    • Working with NumPy Part 2
    • Download Data File
    • Working with Data File

    
    

    • Working with Python and Panda
    • Download CSV File
    • Working with Python and Panda-Part 1
    • Download Data File-Part 1
    • Working with Python and Panda-Part 2
    • Download Data File-Part 2

    
    

• Select section Week 10: Classification Using Python

  Week 10: Classification Using Python

  
  

  Topics of Discussion

  • Classification and prediction with Python
  • Classification: decision tree

  Expected Learning Outcome

  • Problem solving skill in classification and prediction
  • Skill in using Weka as a data mining tool for classification and prediction

  • Select activity Working with Python and ClassificationDownload Dat...

    Completion

    Students must

    

    Mark as done

    
    

    • Working with Python and Classification
    • Download Data File
    • Working with Python and Classification-Part 2
    • Download Data File for Part 2

• Select section Week 11: Classification Using Python (Continued)

  Week 11: Classification Using Python (Continued)

  
  

  Topics of Discussion

  • Classification and prediction with Python
  • Classification: decision tree

  
  

  Expected Learning Outcome

  • Problem solving skill in classification and prediction
  • Skill in using Weka as a data mining tool for classification and prediction

  • Select activity Working with Python and Nearest NeighborDownload D...

    Completion

    Students must

    

    Mark as done

    
    

    • Working with Python and Nearest Neighbor
    • Download Data File

    
    

• Select section Week 12: Cluster Analysis Using Python

  Week 12: Cluster Analysis Using Python

  
  

  Topics of Discussion

  • Cluster Analysis with Python
  • Cluster Analysis: partitional (K-means), hierarchical, density-based

  
  

  Expected Learning Outcome

  • Problem solving skill in classification and prediction
  • Skill in using Weka as a data mining tool for cluster analysis
  • Ability to apply data mining knowledge in development project
    

  
  

  • Select activity  Working with Python and Clustering

    Completion

    Students must

    

    Mark as done

     

    • Working with Python and Clustering

• Select section Week 13: Presentation of Project # 1 (Using Python)

  Week 13: Presentation of Project # 1 (Using Python)

  Project # 2 (with Python) Presentation

  
  

  
  
  

  • Select activity Project Report

    

    Project Report Assignment

    Opened: Sunday, 21 August 2022, 12:00 AM

    Due: Monday, 22 August 2022, 11:59 PM

    Completion

    Students must

    

    View

    

    Make a submission

    
    

• Select section Week 14: Final Examination

  Week 14: Final Examination

  Semester Final Examination Week

  

  
  

  Topics to be included in final exam:

  • Classification (with Weka and Python)
  • Cluster Analysis (with Weka and Python)

  • Select activity Semester Final Examination

    

    Semester Final Examination Assignment

    Opened: Saturday, 13 August 2022, 2:00 PM

    Due: Saturday, 13 August 2022, 6:00 PM

    Completion

    Students must

    

    View

    

    Make a submission