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>

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Data Science Algorithms in a Week
Dávid Natingga
Aug, 2017 | 210 pages
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Table of Contents (12 chapters)
Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
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
Summary
Problems
2
Naive Bayes
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
Summary
Problems
3
Decision Trees
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
Summary
Problems
4
Random Forest
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
Summary
Problems
5
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
Summary
Problems
6
Regression
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
Summary
Problems
7
Time Series Analysis
Time Series Analysis
Business profit - analysis of the trend
Electronics shop's sales - analysis of seasonality
Summary
Problems
8
Statistics
Statistics
Basic concepts
Bayesian Inference
Distributions
Cross-validation
A/B Testing
9
R Reference
R Reference
Introduction
Data types
Linear regression
10
Python Reference
Python Reference
Introduction
Data types
Flow control
11
Glossary of Algorithms and Methods in Data Science
Glossary of Algorithms and Methods in Data Science

Implementation of random forest algorithm

We implement a random forest algorithm using a modified decision tree algorithm from the previous chapter. We also add an option to set a verbose mode within the program that can describe the whole process of how the algorithm works on a specific input- how a random forest is constructed with its random decision trees and how this constructed random forest is used to classify other features.

The implementation of a random forest uses the construction of a decision tree from the previous chapter. A reader is encouraged to consult the function decision_tree.construct_general_tree from the previous chapter:

# source_code/4/random_forest.py
import math
import random
import sys
sys.path.append('../common')
import common # noqa
import decision_tree # noqa
from common import printfv # noqa

#Random forest construction
def sample_with_replacement...
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