Lecture Notes by Michael K

Wednesday, February 2, 2022

Data Mining using Python LAB_IV_2021-22_R20_IT_LBRCE

 Syllabus

Lesson Plan - A Section

Lesson Plan - B Section

Experiment - 1 

  1. Download Dataset from Kaggle
  2. Loading the Dataset
  3. Dealing with missing data
Experiment - 2 
  • Dealing with Categorical Data, Feature Scaling, Splitting dataset into Training and Testing Sets
Experiment - 3
  • Similarity and Dissimilarity Measures using python 
    • Pearson’s Correlation, Cosine Similarity, Jaccard Similarity, Euclidean Distance, Manhattan Distance

Experiment - 4 - Build a model using a linear regression algorithm on any dataset.
 
Experiment - 5 - Decision Tree

Experiment - 6 - Naive Bayes Classifier

Experiment - 7 - Apriori Algorithm

Experiment - 8,9,10 - Clustering - K - means, Hierarchical, DBSCAN

D2D A-Section   B-Section

Michael Kona at 2:03 AM No comments:
Share

DWDM_Theory_IV_2021-22_R20_IT_LBRCE

  • Academic Calendar
  • Syllabus
  • Time Tables
    • IV - A 
    • IV - B
  • Lesson Plan
  • Lecture Notes
    • Unit - I ppt (click here to view)
    • Unit - II
      • Datamining Introduction, Motivating Challenges
      • Datamining Origin & Tasks, Types of Data
      • Data Quality
      • Data Preprocessing
      • Similarity and Dissimilarity measures (by Dr.Bhagath, CSE, LBRCE)
      • Measures of Similarity and Dissimilarity
    • Unit - III
      • Classification - Decision Tree
      • Model Overfitting
        • Bayes Theorem, Naive Bayes Classifier
  • Descriptive Questions (Cycle - I)
  • Assignment Questions (Cycle - I)
  • Assignment Marks
  • Mid Objective Question Paper
  • Key for Mid Objective Question Paper
    • Unit - IV - Association Analysis: 
        • Basic Concepts and Algorithms: Problem Definition, Frequent Item Set Generation, Apriori Principle, Apriori Algorithm
        • Rule Generation, Compact Representation of Frequent Itemsets, 
        • FPGrowthAlgorithm
    • Unit - V - Clustering:
        • ppt
        • DBSCAN solved problem
        • DBSCAN ASSIGNMENT LINK 
        •  K-Means Solved Problem-1
        • K-means Solved Problem - 2
        • K -means Solved Problem - 3
        • Agglomerative Hierarchical Clustering - solved problem
  • Cycle - II
    • Descriptive Questions
    • Objective Questions
    • Assignment Questions
    • ICT Tools used
  • Mid-II Question Papers
    • DESCRIPTIVE - final answers - Scheme
      • solution for 2(a) - Apriori
      • solution for 3(a) - K means
      • solution for 3(d) - Dendrogram - Hierarchical - Single link - Euclidean
    • OBJECTIVE - Answers
  • Mid Marks
    • A Section
    • B Section
  • Consolidated Internal Marks
    • A Section
    • B Section
  • External Question Paper

Michael Kona at 2:02 AM No comments:
Share
‹
›
Home
View web version

About Me

My photo
Michael Kona
Vijayawada, Andhra Pradesh, India
Working as Assistant Professor in the Department of CSE, PVP Siddhartha Institute of Technology.
View my complete profile
Powered by Blogger.