代做program、代写Python设计程序

日期：2024-11-02 11:31

Clustering

1 Clustering

In this assignment, we cluster stocks in the stock market by using the k-means

algorithm. In particular, you are provided with a dataset (available on the

moodle website) which specifies for each of 30 stocks the percentage change in

price of that stock in each given week, for a total of 25 weeks. In our dataset,

some stocks might deal with technology, some other with oil, etc. We will

try to group together stocks with similar price trends in the stock market.

In other words, in a same cluster we would like to have stocks whose price

changes by similar amounts every week. This can be used for coming up with

successful investment policies. We will see that stocks related to the same

market (e.g. technology) have often “similar” price trends. For this assignment,

we recommend k = 8.

Input File Format. The first line of the file specifies the weeks considered in

our dataset, while the rest of the lines specifies the data. In each line, the first

element specifies the name of the stock. We use ’,’ as a separator. For this task,

you should consider all continuous-ordinal attributes and ignore the rest of the

attributes.

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Write your answers in the Jupyter Notebook. Make sure to explain your

answers.

Questions.

1. You should run the k-means algorithm on the stock data, while using

init=’random’ and the default values for the other parameters. Compute

the sum of squared errors (SSE) for the clustering you obtained and include

it in your report.

2. You should then try to decrease the SSE as much as possible (while keeping

k = 8) by changing some of the parameters accordingly. To this end, select

two parameters (numeric or not) that you think should impact the results

the most. For each parameter explain : a) how you expect that changing

that parameter would affect the results (e.g. if numeric, increasing its

value means better or worse results?) b) whether changing the value of

the parameter should always improve the results or not necessarily.

3. Then look at the clustering you obtained and try to label each cluster

with a topic. For example: cluster of technology stocks, oil stocks, etc.

Don’t expect your clustering to be perfect. In particular, you might have

different kinds of stocks in a given cluster, while you might not be able

to label all clusters. We expect that you should be able to label at least

three clusters with a topic. It is fine to describe a cluster as a technology

cluster if most of the stocks deal with technology, for example. Explain

your answers.

What to submit. You should send us your Jupyter notebook with the code

in Python, as well as the answers to your questions.

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