DA5030 Project Rubric

	A	B	C	D	E
1	DA5030 Project Rubric	135	0	Name:	Section/Term:
2	(note that it is impossible to get all the points as many are mutually exclusive and depend on the chosen data set; the project is graded out of 100 points, so it is possible to get more than 100%); be sure to point us to your code where the work has been done
3	Data Acquisition	Points	Points	Code Block/Lines (required)	Comments/Explanations (required/justify your work)
4	acquisition of data (e.g., CSV or flat file)	2
5	other:
6
7	Data Exploration	Points
8	exploratory data plots	5
9	detection of outliers	5
10	correlation/collinearity analysis	5
11	other:
12
13	Data Cleaning & Shaping	Points
14	data imputation	5
15	proper encoding of data for algorithms used	5
16	normalization/standardization of feature values	3
17	feature engineering: dummy codes	2
18	feature engineering: PCA	5
19	feature engineering: new derived features	3
20	other:
21
22	Model Construction & Evaluation	Points
23	creation of training & validation subsets	2
24	construction of at least three related models	15
25	evaluation of fit of models with holdout method	3
26	evaluation with k-fold cross-validation	5
27	tuning of models	5
28	comparison of models	5
29	interpretation of results/prediction with interval	5
30	use of bagging with homogeneous learners	5
31	construction of ensemble model	10
32	other:
33
34	Data Mining Process	Points
35	follows CRISP-DM	5
36	explanation of steps; good journaling & coding	5
37	peer reviews	5
38	video presentation	5
39
40	Model Deployment	Points
41	Interactive Dashboard (e.g., Shiny)	10
42	Other Model Deployment for Actual Use	10
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100