Data Visualization Quiz
Objective: Visualization Analysis from the dataset used in class
1. Question 1: Explain in 300 words or more about the steps from the beginning to
the end of this project and explain your learning from it.
Answer: From the start the primary thing we do is run the Jupyter document in Machine
Learning Azure Studio. We just run the given scripts and were not normal to write in python
for the lab action or schoolwork. When you go to the site, you "join" by signing in with your
email account. You click on free workspace and once you get to the Azure ML Studio Setup,
you click "+New''. Wherever you are brought to another page you click on "from
neighborhood record". When the screen of "transfer another scratch pad" comes up, you
select "python 3". We at that point stacked the jupytr journal to Azure ML. Studio and ran
every one of the cells in the scratch pad. I took in a considerable amount from this
movement, particularly about information representation. Firstly, I figured out how to arrange
azure studio account and got to gradually comprehend jupyter notebook organization and
climate. I also had the opportunity to figure out how to decipher information outline and
perceptions. I even figured out how to scientifically respond to questions dependent on
perceptions. Moreover, I discovered that information representation helps one in arriving at
keen resolutions among the information because of the entwined connections among
information that must be acknowledged once it's dissected and additionally coordinated.
2. Question 2: Why do you think the result from 'df.describe().T' is important? What
does .T do? Why would we use .T at all?
Answer: .T is used to transpose the result of df.describe(). Whenever you run df.describe(),
all the columns are represented as a row. .T transposes the x and y values of the function
that we call. That is why we use it.
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