Book Image

Data Science Algorithms in a Week

By : Dávid Natingga

Book Image

Data Science Algorithms in a Week

By: Dávid Natingga

Overview of this book

<p>Machine learning applications are highly automated and self-modifying, and they continue to improve over time with minimal human intervention as they learn with more data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed that solve these problems perfectly. Data science helps you gain new knowledge from existing data through algorithmic and statistical analysis.</p> <p>This book will address the problems related to accurate and efficient data classification and prediction. Over the course of 7 days, you will be introduced to seven algorithms, along with exercises that will help you learn different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. You will then find out how to predict data based on the existing trends in your datasets.</p> <p>This book covers algorithms such as: k-Nearest Neighbors, Naive Bayes, Decision Trees, Random Forest, k-Means, Regression, and Time-series. On completion of the book, you will understand which machine learning algorithm to pick for clustering, classification, or regression and which is best suited for your problem.</p>

Preface

What this book covers

What you need for this book

Who this book is for

Reader feedback

Customer support

Free Chapter

Classification Using K Nearest Neighbors

Classification Using K Nearest Neighbors

Mary and her temperature preferences

Implementation of 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

Naive Bayes

Medical test - basic application of Bayes' theorem

Proof of Bayes' theorem and its extension

Playing chess - independent events

Implementation of naive Bayes classifier

Playing chess - dependent events

Gender classification - Bayes for continuous random variables

Decision Trees

Swim preference - representing data with decision tree

Information theory

ID3 algorithm - decision tree construction

Classifying with a decision tree

Playing chess - analysis with decision tree

Going shopping - dealing with data inconsistency

Random Forest

Overview of random forest algorithm

Swim preference - analysis with random forest

Implementation of random forest algorithm

Playing chess example

Going shopping - overcoming data inconsistency with randomness and measuring the level of confidence

Clustering into K Clusters

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 clusters k in a semantic context

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 from distance

Ballistic flight analysis – non-linear model

Time Series Analysis

Time Series Analysis

Business profit - analysis of the trend

Electronics shop's sales - analysis of seasonality

Statistics

Bayesian Inference

Cross-validation

R Reference

Linear regression

Python Reference

Python Reference

Glossary of Algorithms and Methods in Data Science

Glossary of Algorithms and Methods in Data Science

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House ownership - data rescaling

For each person, we are given their age, yearly income, and whether their is a house or not:

Age	Annual income in USD	House ownership status
23	50,000	Non-owner
37	34,000	Non-owner
48	40,000	Owner
52	30,000	Non-owner
28	95,000	Owner
25	78,000	Non-owner
35	130,000	Owner
32	105,000	Owner
20	100,000	Non-owner
40	60,000	Owner
50	80,000	Peter

The aim is to predict whether Peter, aged 50, with an income of $80k/year, owns a house and could be a potential customer for our insurance company.

Analysis:

In this case, we could try to apply the 1-NN algorithm. However, we should be careful about how we are going to measure the distances between the data points, since the income range is much wider than the age range. Income levels of $115k and $116k are $1,000 apart. These two data points with these incomes would result in a very long distance. However, relative to each other, the difference is not too large. Because we consider both measures (age and yearly income) to be about as important, we would scale both from 0 to 1 according to the formula:

ScaledQuantity = (ActualQuantity-MinQuantity)/(MaxQuantity-MinQuantity)

In our particular case, this reduces to:

ScaledAge = (ActualAge-MinAge)/(MaxAge-MinAge)

ScaledIncome = (ActualIncome- inIncome)/(MaxIncome-inIncome)

After scaling, we get the following data:

Age	Scaled age	Annual income in USD	Scaled annual income	House ownership status
23	0.09375	50,000	0.2	Non-owner
37	0.53125	34,000	0.04	Non-owner
48	0.875	40,000	0.1	Owner
52	1	30,000	0	Non-owner
28	0.25	95,000	0.65	Owner
25	0.15625	78,000	0.48	Non-owner
35	0.46875	130,000	1	Owner
32	0.375	105,000	0.75	Owner
20	0	100,000	0.7	Non-owner
40	0.625	60,000	0.3	Owner
50	0.9375	80,000	0.5	?

Now, if we apply the 1-NN algorithm with the Euclidean metric, we will find out that Peter more than likely owns a house. Note that, without rescaling, the algorithm would yield a different result. Refer to exercise 1.5.