#18AIL66 ML LABORATORY VTU Program 1:
- 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 and show the output for test cases.
- Develop an interactive program by comparing the result by implementing LIST THEN ELIMINATE algorithm.
Program 2:
- For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithm.
- Output a description of the set of all hypotheses consistent with the training examples.
Program 3:
- Demonstrate preprocessing (Data Cleaning, Integration, and Transformation) activity on suitable data.
- Identify and delete Rows that contain duplicate data by considering an appropriate dataset.
- Identify and delete columns that contain a single value by considering an appropriate dataset.
Program 4:
- Demonstrate the working of the decision tree based ID3 algorithm.
- Use an appropriate data set for building the decision tree.
- Apply this knowledge to classify a new sample.
Program 5:
- Demonstrate the working of the Random Forest algorithm.
- Use an appropriate data set for building the decision tree.
- Apply this knowledge to classify a new sample.
Program 6:
- Implement the naïve Bayesian classifier for a sample training data set stored as a .CSV file.
- Compute the accuracy of the classifier, considering a few test data sets.
Program 7:
- Assuming a set of documents that need to be classified, use the naïve Bayesian Classifier model to perform this task.
- Calculate the accuracy, precision, and recall for your data set.
Program 8:
- Construct a Bayesian network considering medical data.
- Use this model to demonstrate the diagnosis of heart patients using standard Heart Disease Data Set.
Program 9:
- Demonstrate the working of EM algorithm to cluster a set of data stored in .CSV file.
Program 10:
- Demonstrate the working of SVM classifier for a suitable dataset.