| 1A |
Design a simple machine learning model to train the training instances and test the same using Python. |
| 1B |
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. |
| 2A |
Perform Data Loading, Feature selection(Principal Component analysis) and Feature Scoring and Ranking. |
| 2B |
For a given set of training data examples stored in .CSV file, implement and demonstrate the Candidate- |
|
Elimination algorithm to output a description of the set of all hypotheses consistent with the training examples. |
| 3 |
Write a program to implement Decision Tree and Random forest with Prediction, Test Score and Confusion Matrix. |
| 4A |
For a given set of training data examples stored in a .CSV file implement Least Square Regression |
|
algorithm. (Use Univariate dataset ) |
| 4B |
For a given set of training data examples stored ina .CSV file implement Logistic Regression algorithm. |
|
(Use Multivariate dataset ) |
| 5A |
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 to classify a new sample. |
| 5B |
Write a program to implement k-Nearest Neighbor algorithm to classify the iris data set. |
| 6A |
Implement the different Distance methods (Euclidean, Manhattan Distance, Minkowski Distance) with Prediction, |
|
Test Score and Confusion Matrix. |
| 6B |
Implement the classification model using clustering for the following techniques with K means clustering with |
|
Prediction, Test Score and Confusion Matrix. |
| 7A |
Implement the classification model using clustering for the following techniques with hierarchical clustering |
|
with Prediction, Test Score and Confusion Matrix |
| 7B |
Implement the Rule based method and test the same. |
| 8A |
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. |
| 8B |
Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Select appropriate |
|
data set for your experiment and draw graphs. |