kaumaron / musclehub_submission Goto Github PK
View Code? Open in Web Editor NEWCapstone submission for Codecademy IDA
Capstone submission for Codecademy IDA
musclehub_submission/musclehub.py
Line 168 in dafbc96
Good use of the "magic line" to generate graphs within the notebook. Another way of executing this is: %matplotlib inline
musclehub_submission/musclehub.py
Lines 316 to 321 in dafbc96
I appreciate that you generate your input values and contingency tables directly from the app_pivot
data rather than inputting manually. Plus, bonus points for including the additional binomial test.
musclehub_submission/musclehub.py
Lines 203 to 242 in dafbc96
Very advanced! You clearly know how to make customized charts beyond what our course teaches. Great work!
musclehub_submission/musclehub.py
Lines 113 to 114 in dafbc96
You're pulling in additional columns that we did not specify, wondering what you're going to use them for! Excited to see your analysis!
musclehub_submission/musclehub.py
Lines 120 to 152 in dafbc96
Very nice SQL query! You make good use of subqueries and aliases here.
musclehub_submission/musclehub.py
Lines 154 to 155 in dafbc96
Did we ever cover string replacers with .format()
? Anyway good work! This is the current, up-to-date method for printing passing data into strings and print to the terminal. You are clearly bringing in outside knowledge or did some good research!
musclehub_submission/musclehub.py
Lines 531 to 647 in dafbc96
Just wow
musclehub_submission/musclehub.py
Lines 324 to 328 in dafbc96
Smart use of if/else logic to interpret the results of your statistical tests.
musclehub_submission/musclehub.py
Lines 454 to 474 in dafbc96
Great work customizing your graphs. You clearly have gone through the documentation and are familiar with all the options available to alter your graphs. My favorites in here are the removal of the right and top spines, the color of your bars and adjust font size.
musclehub_submission/musclehub.py
Line 37 in dafbc96
Good job printing to the terminal as a sanity check and to make sure you understand the data!
AMAZING WORK!! Your code is impeccable and your presentation (plus the video!) communicates a firm understanding of the data and how it translates to this real-world problem.
All dataframes, graphs and statistical significance tests were constructed and ran correctly in your code. Well done! You went above and beyond when customization of your graphs. The final line graph and Sankey graph are brilliant ways of visualizing the funnel (I agree much better than bar graphs). Plus the inclusion of binomial tests and logic to interpret your p-val results demonstrates a thorough understanding of the statistical concepts.
In the slides and video, I appreciate the thorough descriptions of the problem and steps you took to solve it. You really translated and condensed the process in your own words, demonstrating your complete understanding of the project. Your inclusion of actual individuals from the dataset under qualitative examples is a great touch. Including additional statistics like lift make your argument more convincing and would be much appreciated by a client.
All in all, fantastic job! To be honest one of the best and most thorough projects I have reviewed. I like your passion! You have a great analytical future ahead of you!
musclehub_submission/musclehub.py
Line 177 in dafbc96
It's good practice when working with None
data types to use the operators
is
or pd.isnull()
instead of ==
and
is not
or pd.notnull()
instead of !=
.
The reason for this is that by definition , None
is not a value and something cannot be equal to it. However, the people you write python know that people often make the same assumpution as you and made a workaround where ==None
will raise is None
. You can read up on it here. Your code executes correctly as written, so no worries here.
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