#### 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

## Summary

In this chapter, we learned about Naive Bayes and how it can be applied in different ways for different purposes.

Bayes' theorem states the following:

Here, P(A|B) is the conditional probability of A being true, given that B is true. It is used to update the value of the probability that A is true given the new observations about other probabilistic events. This theorem can be extended to a statement with multiple random variables:

The random variables B1,...,Bn have to be conditionally independent given A. The random variables can be discrete or continuous and follow a probability distribution, for example, normal (Gaussian) distribution.

We also studied the discrete random variable. We learned that it is best to ensure that you have a data item for each value of a discrete random variable given any of the conditions (with a value of A) by collecting sufficient data.

The more independent random variables we have, the more accurately we can determine the posterior probability. However...