#### 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: What is the information entropy of the following multisets? a) {1,2}, b) {1,2,3}, c) {1,2,3,4}, d) {1,1,2,2}, e) {1,1,2,3}

Problem 2: What is the information entropy of the probability space induced by the biased coin that shows head with a probability of 10%, and tail with a probability of 90%?

Problem 3: Let's take another example of playing chess fromChapter 2,Naive Bayes:

a) What is the information gain for each of the non-classifying attributes in the table?

b) What is the decision tree constructed from the given table?

c) How would you classify a data sample `(Warm,Strong,Spring,?)` according to the constructed decision tree?

 Temperature Wind Season Play Cold Strong Winter No Warm Strong Autumn No Warm None Summer Yes Hot None Spring No Hot Breeze Autumn Yes Warm Breeze Spring Yes Cold Breeze Winter No Cold None Spring Yes Hot Strong Summer Yes Warm None Autumn Yes Warm Strong Spring ?

Problem 4: Mary and temperature preferences: Let's take the example from Chapter 1, Classification Using K Nearest Neighbors, regarding...