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

Going shopping – overcoming data inconsistencies with randomness and measuring the level of confidence


We take the problem from the previous chapter. We have the following data relating to the shopping preferences of our friend, Jane:

Temperature

Rain

Shopping

Cold

None

Yes

Warm

None

No

Cold

Strong

Yes

Cold

None

No

Warm

Strong

No

Warm

None

Yes

Cold

None

?

 

In the previous chapter, decision trees were not able to classify the feature (Cold, None). So, this time, we would like to establish whether Jane would go shopping if the temperature was cold and there was no rain using the random forest algorithm.

 

Analysis

To perform an analysis using the random forest algorithm, we use the program implemented.

Input:

We insert the data from the table into the following CSV file:

# source_code/4/shopping.csv  
Temperature,Rain,Shopping  
Cold,None,Yes  
Warm,None,No  
Cold,Strong,Yes  
Cold,None,No  
Warm,Strong,No  
Warm,None,Yes 
Cold,None,? 

Output:

We want to use a slightly higher number of trees than we used in the previous examples...