Summary - Githubissues

Rubric Score

Criteria 1: Valid Python Code

Score Level: 4 (Exceeds Expectations)
Comment(s): Code included runs without any errors.

Criteria 2: Exploration of Data

Score Level: 4 (Exceeds Expectations)
Comment(s): Data is explored significantly, and the experimental question(s) chosen are logical and based on the data exploration. Features chosen to answer the question make sense. It is obvious by your many commented out lines of code that you took a look at nearly all the features in the dataset and the variables you create from the dataset, like ['is_outgoing'] or ['is_music'] are thoughtfully prepared.

Criteria 3: Machine Learning Techniques used correctly

Score Level: 4 (Exceeds Expectations)
Comment(s): Algorithms are used correctly and the correct conclusions are drawn from the results. I appreciate that you are willing to admit when your algorithms did not perform as anticipated, sometimes learning what didn't work is just as valuable as creating a predictive model.

Criteria 4: Report: Are conclusions clear and supported by data?

Score Level: 4 (Exceeds Expectations)
Comment(s): Question(s) are stated clearly. The results of 2 regression algorithms and 2 classification algorithms are shown. Conclusions are clearly stated and based on evidence. I appreciate the curiosity you brought to these questions, which were very unique. My one suggestion that maybe the 'job' column, which has an "artistic / musical / writer" response, may have also been an efficient proxy for your 'is_music' label.

Criteria 5: Code formatting

Score Level: 4 (Exceeds Expectations)
Comment(s): Code is formatted clearly and readable.

Overall Score: 20/20

Great work! Your curiosity really shined through on this project. I appreciate how thorough you were in exploring the dataset and the unique questions you asked, which clearly resonated with you. You went above and beyond the requirements of this project by not only comparing 2 classification methods (can we predict who is likely to be a musician?) and 2 regression methods (can we predict level of income?), you also explored an additional classification problem to see if you could determine who was an outgoing or introverted. I also appreciate the format of your analysis in Jupyter notebooks that used Markdown to provide context to your code and interpretation to your results.

You shared some really interesting insights in your final thoughts/conclusions notebook. The comparison to the early days of personal computing is fascinating. I'll be sure to let Albert know that you appreciated his help.

Again, great work on this assignment, you demonstrated that you have a machine learning engineer's greatest asset: curiosity.

steverae1 / codecademy-capstone

Summary #1