Tasks
1. Find a dataset from any source – the dataset should have at least 500 rows and at least
two categorical attributes and at least two numerical attributes. You can search on the
web for datasets. You should not use any of the datasets that we have already used in
the course materials.
2. Run the dataset by me as soon as possible so that I can see that it is appropriate for the
project and provides enough scope for analysis.
3. Identify the categorical and numerical attributes in the dataset (categories and
measures).
4. Perform basic analysis on the dataset – like overall summaries, analysis of individual
columns, appropriate plots of individual columns depending on the nature of the
column, etc. For each analysis, you should show the R code and also include your
interpretation of the results. You should include at least five basic analyses and make
sure that each one says something significant about the dataset. Just listing the number
of rows and columns will not count towards the five, although you can include that in
your analysis.
5. Perform more advances analysis – reports and plots that relate multiple attributes. In
this section, you will first pose several (at least five) interesting questions that you
would like to answer and then provide your answers via reports and charts. You can use
the EDA framework that we studied to generate the questions as applicable to your
dataset. Generate as many questions as you can and list all questions you generated.
Then select those that you think are the interesting ones with reasons for selection.
Here are some general themes for generating questions:
a. two-category counts
b. distribution of measures with breakup by category
c. relationships between numeric variables
d. contribution to measures by individual categories
e. contribution to measures by two-categories
f. comparison of variation of measure by category
g. time series of measures, etc.
6. For each of the above you should show your R code, plots and provide your
interpretation. You should make good use of dplyr, pipes and ggplot. Use aesthetics to
bring multiple attributes into play and use facets where needed.
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