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869_course

Resources for MMA, MMAI, and GMMA 869

Datasets

Name Source Instances Features Target
diabetes Kaggle 769 ID: 1
Numeric: 8
diabetes
0: 65%
1: 35%
German Credit UCI 1000 Numeric: 61 Class
Good: 70%
Bad:35%
HR UCI 14999 Numeric: 7
Categorical: 2
left
0: 76%
1: 24%
Adult (1994 USA Census) UCI 32561 Numeric: 6
Categorical: 8
high_salary
0:76%
1: 24%
Portugese Bank Marketing UCI 4521 Numeric: 7
Categorical: 9
y
no: 88%
yes: 12%
European Credit Card Przemyslaw Zientala 142403 Time: 1
Numeric: 29
Class
0: 99.8%
1: 0.2%
Marketing (Synthetic) generate_data.ipynb 500 Numeric: 2 Bought
0: 50%
1: 50%
German Credit (Synthetic) UCI 1000 Numeric: 48
Categorical: 8
BadCredit
0: 70%
1: 30%
ISLR Student Credit Default ISLR 10000 ID: 1
Numeric: 2
Categorical: 1
default
No: 97%
Yes: 3%
Mall Customers Kaggle 200 ID: 1
Numeric: 3
Categorical: 1
N/A
Groceries Machine Learning with R 9835 Binary: 169 N/A
Orange Juice Purchase ISLR 1070 ID: 1
Numeric: 17
Purchase
CH: 61%
MM: 39%
Groceries Unknown 505 Numeric: 4 N/A

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Resources for MMA, MMAI, and GMMA 869

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