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)

The following command could help you load any of the datasets:

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()

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)

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Ajitesh Kumar

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. 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.

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Ajitesh Kumar

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. 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.