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

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Data Science Algorithms in a Week - Second Edition
David Natingga
Oct, 2018 | 214 pages
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Table of Contents (16 chapters)
Title Page
Title Page
Packt Upsell
Packt Upsell
Contributors
Contributors
Preface
Preface
Free Chapter
1
Classification Using K-Nearest Neighbors
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
2
Naive Bayes
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
3
Decision Trees
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
4
Random Forests
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
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 k clusters 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 based on distance
Ballistic flight analysis – non-linear model
Summary
Problems
7
Time Series Analysis
Time Series Analysis
Business profits – analyzing trends
Electronics shop's sales – analyzing seasonality
Summary
Problems
Python Reference
Python Reference
Introduction
Data types
Flow control
Input and output
Statistics
Statistics
Basic concepts
Bayesian inference
Distributions
Cross-validation
A/B testing
Glossary of Algorithms and Methods in Data Science
Glossary of Algorithms and Methods in Data Science
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Other Books You May Enjoy
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Index
Index

Implementation of the 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.

You are encouraged to consult the decision_tree.construct_general_tree  function 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(population, size):
    sample = []
    for i in range(0, size):
        sample.append(population[random.randint(0, len(population) - 1)])
    return sample

def construct_random_forest(verbose, heading, complete_data...
Previous Section
End of Section 4
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