Book Image

Data Science Algorithms in a Week. - Second Edition

By : David Natingga
Book Image

Data Science Algorithms in a Week. - Second Edition

By: David Natingga

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
Table of Contents (16 chapters)
Title Page
Packt Upsell
Contributors
Preface
Glossary of Algorithms and Methods in Data Science
Index

Problems


Problem1: Let's take the example of playing chess from Chapter 2, Naive Bayes. How would you classify a (Warm,Strong,Spring,?)data sample according to the random forest algorithm?

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 2: Would it be a good idea to use only one tree and a random forest? Justify your answer.

Problem 3: Can cross-validation improve the results of the classification by the random forest? Justify your answer.

Analysis

Problem 1: We run the program to construct the random forest and classify the feature (Warm, Strong, Spring).

Input:

source_code/4/chess_with_seasons.csv  
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...