# How to use Sklearn Datasets For Machine Learning

In this post, you wil learn about how to use Sklearn datasets for training machine learning models. Here is a list of different types of datasets which are available as part of sklearn.datasets
• 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


All of the datasets come with the following and are intended for use with supervised learning:
• Data (to be used for training)
• Labels (Target)
• Labels attriibute
• Description of the dataset
The following command can be used for accessing the value of above:
# Let's use IRIS as an example for reading different aspects of data
iris.data
iris.target
iris.target_names
print(iris.DESCR)