Overview of this book

Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem
Title Page
Packt Upsell
Contributors
Preface
Free Chapter
Classification Using K-Nearest Neighbors
Time Series Analysis
Python Reference
Statistics
Glossary of Algorithms and Methods in Data Science
Other Books You May Enjoy
Index

Data types

Some of the data types available in Python are as follows:

• Numeric data types`int` and `float`
• Text data types`str`
• Composite data types`tuple``list``set`, and `dictionary`

int

The `int` data type can hold only integer values.

Input:

```# source_code/appendix_c_python/example02_int.py
rectangle_side_a = 10
rectangle_side_b = 5
rectangle_area = rectangle_side_a * rectangle_side_b
rectangle_perimeter = 2*(rectangle_side_a + rectangle_side_b)
print "Let there be a rectangle with the sides of lengths:"
print rectangle_side_a, "and", rectangle_side_b, "cm."
print "Then the area of the rectangle is", rectangle_area, "cm squared."
print "The perimeter of the rectangle is", rectangle_perimeter, "cm."```

Output:

```\$ python example02_int.py
Let there be a rectangle with the sides of lengths: 10 and 5 cm.
Then the area of the rectangle is 50 cm squared.
The perimeter of the rectangle is 30 cm.```

float

The `float` data type can also hold non-integer rational values.

Input:

```# source_code/appendix_c_python/example03_float.py
pi = 3.14159
circle_perimeter = 2 * pi * circle_radius
print "Then the perimeter of the circle is", circle_perimeter, "cm."
print "The area of the circle is", circle_area, "cm squared."```

Output:

```\$ python example03_float.py
Let there be a circle with the radius 10.2 cm.
Then the perimeter of the circle is 64.088436 cm.
The area of the circle is 326.8510236 cm squared.```

String

A string variable can be used to store text.

Input:

```# source_code/appendix_c_python/example04_string.py
first_name = "Satoshi"
last_name = "Nakamoto"
full_name = first_name + " " + last_name
print "The inventor of Bitcoin is", full_name, "."```

Output:

```\$ python example04_string.py
The inventor of Bitcoin is Satoshi Nakamoto .```

Tuple

`tuple` data type is analogous to a vector in mathematics; for example, `tuple = (integer_number, float_number)`.

Input:

```# source_code/appendix_c_python/example05_tuple.py
import math

point_a = (1.2,2.5)
point_b = (5.7,4.8)
#math.sqrt computes the square root of a float number.
#math.pow computes the power of a float number.
segment_length = math.sqrt(
math.pow(point_a[0] - point_b[0], 2) +
math.pow(point_a[1] - point_b[1], 2))
print "Let the point A have the coordinates", point_a, "cm."
print "Let the point B have the coordinates", point_b, "cm."
print "Then the length of the line segment AB is", segment_length, "cm."```

Output:

```\$ python example05_tuple.py
Let the point A have the coordinates (1.2, 2.5) cm.
Let the point B have the coordinates (5.7, 4.8) cm.
Then the length of the line segment AB is 5.0537115074 cm.```

List

A list in Python is an ordered set of values.

Input:

```# source_code/appendix_c_python/example06_list.py
some_primes = [2, 3]
some_primes.append(5)
some_primes.append(7)
print "The primes less than 10 are:", some_primes```

Output:

```\$ python example06_list.py
The primes less than 10 are: [2, 3, 5, 7]```

Set

`Set` in Python is a non-ordered mathematical set of values.

Input:

```# source_code/appendix_c_python/example07_set.py
from sets import Set

girls = Set(['Eva', 'Mary'])
teenagers = Set(['Samuel', 'Benjamin', 'Mary'])
print 'Jane' in girls
print 'Jane' in girls
teenage_girls = teenagers & girls #intersection
mixed = boys | girls #union
non_teenage_girls = girls - teenage_girls #difference
print teenage_girls
print mixed
print non_teenage_girls```

Output:

```\$ python example07_set.py
True
False
True
Set(['Mary'])
Set(['Benjamin', 'Adam', 'Jane', 'Eva', 'Samuel', 'Mary'])
Set(['Jane', 'Eva'])```

Dictionary

`dictionary` is a data structure that can store values by their keys.

Input:

```# source_code/appendix_c_python/example08_dictionary.py
dictionary_names_heights = {}
```\$ python example08_dictionary.py