Foundations of Machine Learning Assignment 2

Description

• Markings will be based on the correctness and soundness of the outputs.

• Marks will be deducted in case of plagiarism.

• Proper indentation and appropriate comments (if necessary) are mandatory.

• Use of frameworks like scikit-learn etc is allowed.

• All benchmarks(accuracy etc), answers to questions and supporting examples should be added in a separate file with the name ‘report’.

• All code needs to be submitted in ‘.py’ format. Even if you code it in ‘.ipynb’ format, download it in ‘.py’ format and then submit

• You should zip all the required files and name the zip file as:

• • <roll_no>_assignment_<#>.zip, eg. 1501cs11_assignment_01.zip.

• Upload your assignment ( the zip file ) in the following link:

• • https://www.dropbox.com/request/Y7uWRDGgJj2uKkiOYDGi

Problem Statement:

• The assignment targets to implement DBScan algorithms to cluster the 3 datasets with blob, moon and circle structures. Each csv file consists of two columns in the datasets.

  • https://www.dropbox.com/scl/fo/9jbpw1ah58jcvva5bzwdl/h?dl=0&rlkey=4a

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Implementation Steps:

• Design and implement DBSCAN algorithm to cluster the 3 datasets mentioned. Assume hyperparameters wherever necessary.

• Use Silhouette index to evaluate the clustering quality.

• Run dataset for the K-means algorithm implemented in assignment 1. Set the num_clusters for K-means to the no. of clusters from DBSCAN. Compare it to DBSCAN in terms of cluster visualization and Silhouette index score.

Documents to submit:

• Model code

• Silhouette index

• Visualization of clusters for DBSCAN and K-means clustering