# Machine Learning for Beginners in Python: Loading scikit-learn’s Boston Housing Dataset

## Preliminaries

from sklearn import datasets
import matplotlib.pyplot as plt

The Boston housing dataset is a famous dataset from the 1970s. It contains 506 observations on housing prices around Boston. It is often used in regression examples and contains 15 features.

# Create feature matrix
X = boston.data

# Create target vector
y = boston.target

# View the first observation's feature values
X[0]
array([  6.32000000e-03,   1.80000000e+01,   2.31000000e+00,
0.00000000e+00,   5.38000000e-01,   6.57500000e+00,
6.52000000e+01,   4.09000000e+00,   1.00000000e+00,
2.96000000e+02,   1.53000000e+01,   3.96900000e+02,
4.98000000e+00])

As you can see, the features are not standardized. This is more easily seen if we display the values as decimals:

# Display each feature value of the first observation as floats
['{:f}'.format(x) for x in X[0]]
['0.006320',
'18.000000',
'2.310000',
'0.000000',
'0.538000',
'6.575000',
'65.200000',
'4.090000',
'1.000000',
'296.000000',
'15.300000',
'396.900000',
'4.980000']

Therefore, it is often beneficial and/or required to standardize the value of the features.

# Special 95% discount

## Two Machine Learning Fields

There are two sides to machine learning:

• Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
• Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes

Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!

Latest end-to-end Learn by Coding Recipes in Project-Based Learning:

Applied Statistics with R for Beginners and Business Professionals

Data Science and Machine Learning Projects in Python: Tabular Data Analytics

Data Science and Machine Learning Projects in R: Tabular Data Analytics

Python Machine Learning & Data Science Recipes: Learn by Coding

R Machine Learning & Data Science Recipes: Learn by Coding

Comparing Different Machine Learning Algorithms in Python for Classification (FREE)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.