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Data Science Algorithms in a Week - Second Edition

By : Natingga, David Toth

3.7 (3)

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Data Science Algorithms in a Week

3.7 (3)

By: Natingga, David Toth

Buy this Book

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

Preface

Who this book is for

What this book covers

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Free Chapter

Classification Using K-Nearest Neighbors

Mary and her temperature preferences

Implementation of the k-nearest neighbors algorithm

Map of Italy example – choosing the value of k

House ownership – data rescaling

Text classification – using non-Euclidean distances

Text classification – k-NN in higher dimensions

Summary

Problems

Naive Bayes

Medical tests – basic application of Bayes' theorem

Bayes' theorem and its extension

Playing chess – independent events

Implementation of a Naive Bayes classifier

Playing chess – dependent events

Gender classification – Bayes for continuous random variables

Summary

Problems

Decision Trees

Swim preference – representing data using a decision tree

Information theory

ID3 algorithm – decision tree construction

Classifying with a decision tree

Playing chess – analysis with a decision tree

Going shopping – dealing with data inconsistencies

Summary

Problems

Random Forests

Introduction to the random forest algorithm

Swim preference – analysis involving a random forest

Implementation of the random forest algorithm

Playing chess example

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

Summary

Problems

Clustering into K Clusters

Household incomes – clustering into k clusters

Gender classification – clustering to classify

Implementation of the k-means clustering algorithm

House ownership – choosing the number of clusters

Document clustering – understanding the number of k clusters in a semantic context

Summary

Problems

Regression

Fahrenheit and Celsius conversion – linear regression on perfect data

Weight prediction from height – linear regression on real-world data

Gradient descent algorithm and its implementation

Flight time duration prediction based on distance

Ballistic flight analysis – non-linear model

Summary

Problems

Time Series Analysis

Business profits – analyzing trends

Electronics shop's sales – analyzing seasonality

Summary

Problems

Python Reference

Introduction

Data types

Flow control

Input and output

Statistics

Basic concepts

Bayesian inference

Distributions

Cross-validation

A/B testing

Glossary of Algorithms and Methods in Data Science

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Classification Using K-Nearest Neighbors

A nearest neighbor algorithm classifies a data instance based on its neighbors. The class of a data instance determined by the k-nearest neighbors algorithm is the class with the highest representation among the k-closest neighbors.

In this chapter, we will cover the following topics:

How to implement the basics of the k-NN algorithm using the example of Mary and her temperature preferences
How to choose a correct k value so that the algorithm can perform correctly and with the highest degree of accuracy using the example of a map of Italy
How to rescale values and prepare them for the k-NN algorithm using the example of house preferences
How to choose a good metric to measure distances between data points
How to eliminate irrelevant dimensions in higher-dimensional space to ensure that the algorithm performs accurately using the text classification example