- Iris (Iris plant datasets used – Classification)
- Boston (Boston house prices – Regression)
- Wine (Wine recognition set – Classification)
- Breast Cancer (Breast cancer wisconsin diagnostic – Classification)
- Digits (Optical recognition of handwritten digits dataset – Classification)
- Linnerud (Linnerrud dataset – Classification)
- Diabetes (Diabetes – Regression)
from sklearn import datasets
iris = datasets.load_iris()
boston = datasets.load_boston()
breast_cancer = datasets.load_breast_cancer()
diabetes = datasets.load_diabetes()
wine = datasets.load_wine()
datasets.load_linnerud()
digits = datasets.load_digits()
- Data (to be used for training)
- Labels (Target)
- Labels attriibute
- Description of the dataset
# Let's use IRIS as an example for reading different aspects of data
iris.data
iris.target
iris.target_names
print(iris.DESCR)
I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking. Check out my other blog, Revive-n-Thrive.com
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking. Check out my other blog, Revive-n-Thrive.com
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