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Machine Learning Lab Internal Questions

 Internal Lab Programs

1. Read a CSV File, apply preprocessing, apply a few data visualizations, and train SVM model for a dataset and calculate its accuracy. Download DATASET

2. Read the CSV File, apply preprocessing, and train the data decision tree-based ID3 algorithm. Calculate each attribute value and display 

3. Create a data and save it CSV, apply preprocessing by using that file demonstrate the FIND-S algorithm for finding the most specific hypothesis based on a given set of training data samples.

4. Read the super Market CSV file apply relevant visualizations (Box plot, Scatter plot, Bargraphs), linear regression and display performance metrics

5. 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.

6. Write a program for implementing Density based clustering algorithm. 

7. Write a program to implement k-Nearest Neighbour algorithm to classify the iris data set. Print both correct and wrong predictions.

8. Write a program to implement the Naive Bayesian classifier for a sample training data set stored as  .CSV file. Compute the accuracy of the classifier, considering few test data sets.

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

10. Write a program to implement the Naive Bayesian classifier for a sample training data set stored as  .CSV file. Compute the accuracy of the classifier, considering few test data sets.








Super Market -Regression (rating)


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