ivnaru / date-a-scientist Goto Github PK

Rubric Score

Criteria 1: Valid Python Code

• Score Level: 4/4
• Comment(s): Code included runs without any errors.

Criteria 2: Exploration of Data

• Score Level: 1/4
• Comment(s): Data is explored very briefly or not at all, and the experimental question(s) chosen are not based on the data exploration. Wrong or inconsistent features chosen to answer the question.

Note:

• I did not see the code for your exploratory data analysis. Please add that to your project.

Criteria 3: Machine Learning Techniques used correctly

• Score Level: 3/4
• Comment(s): 75-90% of algorithms are used correctly and the correct conclusions are drawn from the results.

Notes:

• Think about the times it takes to train a model and generate a prediction separately. If a model takes a very long time to train but generates predictions quickly, would you choose it over a model that trains quickly but takes longer to generate predictions?
• Great job reporting precision and recall in addition to accuracy (I recommend checking out sklearn.metrics.classification_report)
• You should report both training and test scores to ensure your models did not overfit.

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

• Score Level: 4/4
• 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.

Note:

• Good job recognizing that the data is imbalanced.

Criteria 5: Code formatting

• Score Level: 4/4
• Comment(s): Code is formatted clearly and readable.

Note:

• Your python script was very easy to follow. I encourage you to try using Jupyter notebooks (ipynb) next time because it allows you to display your thought process and data visualizations alongside your code.

Overall Score: 16/20

Good job on the project. See my notes above for areas of improvement. You demonstrated that you understand the overall ML process, and with more practice, you'll be able to better identify ways to improve your models.