#### 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

## Problems

Problem 1: A patient is tested for a virus, V. The accuracy of the test is 98%. This virus, V, is currently present in 4 out of 100 people in the region the patient lives in:

a) What is the probability that a patient having the virus, V, if they test positive?

b) What is the probability of a patient still having the virus if the result of the test is negative?

Problem 2: Apart from assessing whether patients are suffering from the virus, V (in Problem 1), by using the test, a doctor usually also checks for other symptoms. According to a doctor, about 85% of patients with symptoms such as fever, nausea, abdominal discomfort, and malaise have the virus, V:

a) What is the probability of a patient having the virus, V, if they have the symptoms mentioned previously and their test result for the virus is positive?

b) How likely is it that the patient has the virus if they have the symptoms mentioned previously, but the result of the test is negative?

Problem 3: On a certain island, one in two...