https://github.com/prakharjadaun/applications-of-ml-in-ind-upes

Repository contains lab experiments of subject Application of machine learning in industries
https://github.com/prakharjadaun/applications-of-ml-in-ind-upes

lab-experiments upes

Last synced: about 2 months ago
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Repository contains lab experiments of subject Application of machine learning in industries

• Host: GitHub
• URL: https://github.com/prakharjadaun/applications-of-ml-in-ind-upes
• Owner: prakharjadaun
• License: mit
• Created: 2023-04-22T10:53:51.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2023-04-22T11:06:15.000Z (about 2 years ago)
• Last Synced: 2025-01-18T16:47:38.209Z (3 months ago)
• Topics: lab-experiments, upes
• Language: Jupyter Notebook
• Homepage:
• Size: 186 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# **Applications-of-ML-in-Ind-UPES**

## Experiments 





Implement and demonstrate the 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.

For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithm to output a description of the set of all hypotheses consistent with the training examples.

Write a program to demonstrate the working of the decision tre e based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample.

Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets.

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.

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.

Write a program to construct a Bayesian 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.

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.

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.

Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Select appropriate data set for your experiment and draw graphs.