DS 640

Final Project

Q1. As a part of Week 4, you were able to build a prediction model using Support Vector Machines on breast cancer dataset along with hyperparameter tuning. (50 Marks)

a.Perform PCA on the dataset as discussed during the classroom exercise (week 6).

b.On reducing the dimension to 2 components, pass on the new form of the dataset to the SVM model.

c.Compare the prediction results with the previous best model built and results obtained.

d.Provide your insights on your observation on comparing the 2 prediction results (svm with and without PCA).

Q2. Please describe KNN algorithm in detail (25 marks)

Feel free to use the format, tool and approach that best fits your requirements for the report.

You can use research papers, code snippets, mathematical functions, and examples to support your content.

Please make sure you provide all references that you have used to complete your report.

Q3. Today onwards, maintain a daily journal of how much you practice empathy, productivity, and how you feel at the end of the day. Rate these variables on a scale of 0 to 3, with zero signifying low/bad and 3 signifying high/ very good. Use correlation and multiple linear regression to see if there is any influence of empathy and productivity practiced during the day on the feeling at the end of day. Please note the empathy and productivity practiced should be rated in accordance with your personal best, and not in comparison with others. (25 marks)