#### 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

## Implementation of the k-means clustering algorithm

We will now implement the k-means clustering algorithm. It takes a CSV file as input with one data item per line. A data item is converted into a point. The algorithms classify these points into the specified number of clusters. In the end, the clusters are visualized on a graph using the `matplotlib` library:

```# source_code/5/k-means_clustering.py
import math
import imp
import sys
import matplotlib.pyplot as plt
import matplotlib
import sys
sys.path.append('../common')
import common # noqa
matplotlib.style.use('ggplot')

# Returns k initial centroids for the given points.
def choose_init_centroids(points, k):
centroids = []
centroids.append(points[0])
while len(centroids) < k:
# Find the centroid that with the greatest possible distance
# to the closest already chosen centroid.
candidate = points[0]
candidate_dist = min_dist(points[0], centroids)
for point in points:
dist =...```