Description
Overview
Collaboration: Do your work and report individually. You can collaborate on the right tools to
use and setting up your programming environment.
Hand in: One report per person, via the LEARN dropbox. Also submit the code / scripts needed
to reproduce your work. Report as a PDF or a python notebook.
General Objective: To study how to apply some of the methods discussed in class on two
datasets. The emphasis is on analysis and presentation of results not on code implemented
or used.
Specific Objectives: • Establish your software stack to carry out data analysis homework,
assignments and the project for the rest of the course.
• Load a simple dataset and perform some basic data preprocessing.
Tools: You can use libraries available in python, R or any other programs available to you. You
need to mention which libraries you are using, any blogs or papers you used to figure out how
to set carry out your calculations.
Data sets
For this homework you will use the Wine Quality Data Set:
• http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/
• Download from Data Folder link, read data set description.
Tasks
In the class we talked about how to normalize data and introduced basic distance metrics. In the
wine quality dataset select the first 10 rows and perform the following computations and report
them in a table:
• min-max normalized values, z-score normalized values, mean subtracted normalized values
• for each of the first 10 data points report the nearest and farthest out of the other first 10
points using the following distance metrics (so in the end you will have 10 rows and two
columns, nearest and fasthest):
– manhatten distance
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– euclidean distance
– cosine distance
• For fun (on your own): Plot all the datapoints along each pair of two dimensions using
each type of normalization.
Resources
Blog on running doing some of these calculations in python using SciKitLearn for a similar dataset :
http://sebastianraschka.com/Articles/2014_about_feature_scaling.html#about-standardization
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