https://github.com/abhishekmali21/machine-learning-laboratory

ML LAB PROGRAMS FOR SCHEMES +2015 +2017 +2018
https://github.com/abhishekmali21/machine-learning-laboratory

15csl76 17csl76 18csl76 7thsemcse machinelearning mllab vtu vtulab

Last synced: 3 days ago
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ML LAB PROGRAMS FOR SCHEMES +2015 +2017 +2018

• Host: GitHub
• URL: https://github.com/abhishekmali21/machine-learning-laboratory
• Owner: AbhishekMali21
• License: mit
• Created: 2019-03-17T18:47:20.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-01-27T14:00:28.000Z (almost 4 years ago)
• Last Synced: 2023-03-31T11:42:57.035Z (over 1 year ago)
• Topics: 15csl76, 17csl76, 18csl76, 7thsemcse, machinelearning, mllab, vtu, vtulab
• Language: Jupyter Notebook
• Homepage:
• Size: 114 KB
• Stars: 17
• Watchers: 0
• Forks: 16
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Laboratory Experiments:

1. IMPLEMENT AND DEMONSTRATETHE FIND-S ALGORITHM FOR FINDING THE MOST SPECIFIC HYPOTHESIS BASED ON A GIVEN SET OF TRAINING DATA SAMPLES. READ THE TRAINING DATA FROM A .CSV FILE.

2. FOR A GIVEN SET OF TRAINING DATA EXAMPLES STORED IN A .CSV FILE, IMPLEMENT AND DEMONSTRATE THE CANDIDATE-ELIMINATION ALGORITHMTO OUTPUT A DESCRIPTION OF THE SET

OF ALL HYPOTHESES CONSISTENT WITH THE TRAINING EXAMPLES.

3. WRITE A PROGRAM TO DEMONSTRATE THE WORKING OF THE DECISION TREE BASED ID3 ALGORITHM. USE AN APPROPRIATE DATA SET FOR BUILDING THE DECISION TREE AND APPLY THIS KNOWLEDGE TOCLASSIFY A NEW SAMPLE.

4. BUILD AN ARTIFICIAL NEURAL NETWORK BY IMPLEMENTING THE BACKPROPAGATION ALGORITHM AND TEST THE SAME USING APPROPRIATE DATA SETS.

5. WRITE A PROGRAM TO IMPLEMENT THE NAÏVE BAYESIAN CLASSIFIER FOR A SAMPLE TRAINING DATA SET STORED AS A .CSV FILE. COMPUTE THE ACCURACY OF THE CLASSIFIER, CONSIDERING FEW TEST DATA SETS.

6. ASSUMING A SET OF DOCUMENTS THAT NEED TO BE CLASSIFIED, USE THE NAÏVE BAYESIAN CLASSIFIER MODEL TO PERFORM THIS TASK. BUILT-IN JAVA CLASSES/API CAN BE USED TO WRITE THE PROGRAM. CALCULATE THE ACCURACY, PRECISION, AND RECALL FOR YOUR DATA SET.

7. WRITE A PROGRAM TO CONSTRUCT ABAYESIAN NETWORK CONSIDERING MEDICAL DATA. USE THIS MODEL TO DEMONSTRATE THE DIAGNOSIS OF HEART PATIENTS USING STANDARD HEART DISEASE DATA SET. YOU CAN USE JAVA/PYTHON ML LIBRARY CLASSES/API.

8. APPLY EM ALGORITHM TO CLUSTER A SET OF DATA STORED IN A .CSV FILE. USE THE SAME DATA SET FOR CLUSTERING USING K-MEANS ALGORITHM. COMPARE THE RESULTS OF THESE TWO ALGORITHMS AND COMMENT ON THE QUALITY OF CLUSTERING. YOU CAN ADD JAVA/PYTHON ML LIBRARY CLASSES/API IN THE PROGRAM.

9. WRITE A PROGRAM TO IMPLEMENT K-NEAREST NEIGHBOUR ALGORITHM TO CLASSIFY THE IRIS DATA SET. PRINT BOTH CORRECT AND WRONG PREDICTIONS. JAVA/PYTHON ML LIBRARY CLASSES CAN BE USED FOR THIS PROBLEM.

10. IMPLEMENT THE NON-PARAMETRIC LOCALLY WEIGHTED REGRESSIONALGORITHM IN ORDER TO FIT DATA POINTS. SELECT APPROPRIATE DATA SET FOR YOUR EXPERIMENT AND DRAW GRAPHS.