Academic Project - Learn and implement different classification techniques learned in the class
Data: Multiple in built datasets.
Language: Python 3
Goal: This was a project designed by the professor to test our understanding and application of different classifcation techniques learned in the class. This module included topics such as Decision Tree, Naive Bayes, SVM and many Classifer performance measures.
Approach: Professor provides us with dataset and then asks students to implement certain operations and then asks us explain the results of those operations i.e. why an alogorithm behaved the way it behaved on a given dataset. A sample practise module is also provided to familiarize with test.
Citation/Acknowledgement:
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This analysis was done as a part of requirement for CS 6052 - Intelligent Data Analysis course instructed by Dr. Gowtham Atluri at the University of Cincinnati. Some of the code used is from the practise module provided before the excercise.
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This is due to the project was done using the OSC's cloud (https://www.osc.edu/resources/online_portals/ondemand) and a PDF was downloaded from their to submit as final work.
Score: 10/10