Python for Data Science: A Practical Approach to Machine Learning

Python for Data Science: A Practical Approach to Machine Learning

Jarrel E.

First published by Indy Pub 2023

Copyright © 2023 by Jarrel E.

All rights reserved. No part of this publication may be reproduced, stored or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise without written permission from the publisher. It is illegal to copy this book, post it to a website, or distribute it by any other means without permission.

Jarrel E. has no responsibility for the persistence or accuracy of URLs for external or third-party Internet Websites referred to in this publication and does not guarantee that any content on such Websites is, or will remain, accurate or appropriate.

First edition

ISBN: 9798868999765

Contents

Acknowledgment

Forward

Preface

Basics

Python Ecosystem

Methods for Machine Learning

Data Loading for ML Projects

Understanding Data with Statistics

Understanding Data with Visualization

Preparing Data

Data Feature Selection

Classifications

Logistic Regression

Support Vector Machine (SVM)

Decision Tree

Naïve Bayes

Random Forest

Regression

Linear Regression

Clustering

K-means Algorithm

Mean Shift Algorithm

Hierarchical Clustering

Finding Nearest Neighbors

Performance Metrics

Automatic Workflows

Improving Performance of ML Models

References

Acknowledgment

I extend my sincere gratitude to the individuals and resources that have played a pivotal role in the creation of this book. Their expertise, guidance, and support have been invaluable throughout this journey.

Thank you for sharing your deep insights into the intricate world of Python, data science, and machine learning. Your technical expertise has been instrumental in shaping the content of this book.

I appreciate the thorough review and constructive feedback provided by the reviewers. Your meticulous examination has undoubtedly enhanced the quality and accuracy of the material.

Data Science Community: I extend my gratitude to the vibrant and dynamic data science community for its continuous inspiration. The collective knowledge and passion within the community have been a driving force behind the practical insights shared in this book.

Family and Friends: To my family and friends who provided unwavering support, understanding, and encouragement, I am profoundly grateful. Your belief in this project has been a constant motivation.

Readers: Last but not least, to the readers who embark on this learning journey – your curiosity and commitment to mastering Python for data science and machine learning are the fuel that propels the impact of this book.

This collaborative effort reflects the dedication of a community passionate about advancing the field of data science. Thank you to each contributor for being an integral part of Python for Data Science: A Practical Approach to Machine Learning.

Forward

This book is a comprehensive journey into the heart of data manipulation, analysis, and the application of machine learning algorithms using the Python programming language. Its purpose is to equip both beginners and seasoned practitioners with the tools and knowledge needed to navigate the complexities of this dynamic field.

Navigating the Python Landscape: The book begins by laying a solid foundation in Python, ensuring that readers, regardless of their programming background, can confidently navigate the language’s syntax and data structures. With clarity and precision, it demystifies the intricacies of Python, making it an accessible and powerful tool for data analysis.

Unveiling the World of Data Science: From there, the exploration delves into the fundamental principles of data science. Readers will find a comprehensive guide to data cleaning, exploration, and visualization, laying the groundwork for informed decision-making. The book’s practical approach ensures that theoretical concepts are immediately applied, reinforcing learning through hands-on examples and exercises.

Machine Learning in Action: The heart of the book lies in its coverage of machine learning. From the basics of supervised and unsupervised learning to the intricacies of deep learning, each concept is presented in a practical, real-world context. Through clear explanations and illustrative examples, readers will not only understand the theoretical underpinnings but also gain the skills to implement and experiment with machine learning algorithms.

Real-world Applications: In the final sections, the book goes beyond the theoretical realm, providing insights into the practical applications of Python in data science. Case studies and real-world examples illustrate how Python can be employed to solve complex problems, empowering readers to confidently apply their knowledge in diverse scenarios.

A Call to Action: As we traverse the pages of Python for Data Science: A Practical Approach to Machine Learning, my hope is that readers not only acquire a set of skills but also develop a mindset—a mindset that sees challenges as opportunities, that views data as a narrative waiting to be told, and that recognizes Python as the enabler of these narratives.

Preface

In the pulsating realm of data science, Python has emerged not just as a programming language but as a powerful ally, empowering data enthusiasts to unravel the mysteries concealed within vast datasets. As I present Python for Data Science: A Practical Approach to Machine Learning, my aim is to guide you through this dynamic landscape, providing a hands-on and comprehensive exploration of Python’s applications in the realm of data science and machine learning.

This book is designed for both novices venturing into the world of data science and seasoned practitioners seeking a practical guide to harnessing Python’s potential. The journey begins with a solid foundation in Python programming, ensuring that readers of all backgrounds can embark on this expedition with confidence.

We then delve into the core principles of data science, unraveling the intricacies of data manipulation, exploration, and visualization. The emphasis is not just on theory but on immediate application. Through step-by-step examples and exercises, you will gain the skills to wrangle data and extract meaningful insights.

The heart of the book lies in its exploration of machine learning. From understanding the basics to implementing complex algorithms, each chapter is crafted to provide a blend of theoretical understanding and practical application. Whether you are new to machine learning or seeking to deepen your knowledge, this book offers a road map to navigate this fascinating landscape.

Basics

We are living in the ‘age of data’ that is enriched with better computational power and more storage resources,. This data or information is increasing day by day, but the real challenge is to make sense of all the data. Businesses & organizations are trying to deal with it by building intelligent systems using the concepts and methodologies from Data science, Data Mining and Machine learning. Among them, machine learning is the most exciting field of computer science. It would not be wrong if we call machine learning the application and science of algorithms that provides sense to the data.

What is Machine Learning?

Machine Learning (ML)is that field of computer science with the help of which computer systems can provide sense to data in much the same way as human beings do.

In simple words, ML is a type of artificial intelligence that extract patterns out of raw data by using an algorithm or method. The main focus of ML is to allow computer systems learn from experience without being explicitly programmed or human intervention.

Need for Machine Learning

Human beings, at this moment, are the most intelligent and advanced species on earth because they can think, evaluate and solve complex problems. On the other side, AI is still in it’s initial stage and haven’t surpassed human intelligence in many aspects. Then the question is that what is the need to make machine learn? The most suitable reason for doing this is, to make decisions, based on data, with efficiency and scale.

Lately, organizations are investing heavily in newer technologies like Artificial Intelligence, Machine Learning and Deep Learning to get the key information from data to perform several real-world tasks and solve problems. We can call it data-driven decisions taken by machines, particularly to automate the process. These data-driven decisions can be used, instead of using programming logic, in the problems that cannot be programmed inherently. The fact is that we can’t do without human intelligence, but other aspect is that we all need to solve real-world problems with efficiency at a huge scale. That is why the need for machine learning arises.

Why & When to Make Machines Learn?

We have already discussed the need for machine learning, but another question arises that in what scenarios we must make the machine learn? There can be several circumstances where we need machines to take data-driven decisions with efficiency and at a huge scale. The followings are some of such circumstances where making machines learn would be more effective:

Lack of human expertise

The very first scenario in which we want a machine to learn and take data-driven decisions, can be the domain where there is a lack of human expertise. The examples can be navigation in unknown territories or spatial planets.

Dynamic scenarios

There are some scenarios which are dynamic in nature i.e. they keep changing over time. In case of these scenarios and behaviors, we want a machine to learn and take data-driven decisions. Some of the examples can be network connectivity and availability of infrastructure in an organization.

Difficulty in translating expertise into computational tasks

There can be various domains in which humans have their expertise,; however, they are unable to translate this expertise into computational tasks. In such circumstances we want machine learning. The examples can be the domains of speech recognition, cognitive tasks etc.

Machine Learning Model

Before discussing the machine learning model, we must need to understand the following formal definition of ML given by professor Mitchell:

"A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."

The above definition is basically focusing on three parameters, also the main components of any learning algorithm, namely Task(T), Performance(P) and experience (E). In this context, we can simplify this definition as:

ML is a field of AI consisting of learning algorithms that:

Improve their performance (P)

At executing some task (T)

Over time with experience (E)

Based on the above, the following diagram represents a Machine Learning Model:

Let us discuss them more in detail now:

Task(T)

From the perspective of problem, we may define the task T as the real-world problem to be solved. The problem can be anything like finding best house price in a specific location or to find best marketing strategy etc. On the other hand, if we talk about machine learning, the definition of task is different because it is difficult to solve ML based tasks by conventional programming approach.

A task T is said to be a ML based task when it is based on the process and the system must follow for operating on data points. The examples of ML based tasks are Classification, Regression, Structured annotation, Clustering, Transcription etc.

Experience (E)

As name suggests, it is the knowledge gained from data points provided to the algorithm or model. Once provided with the dataset, the model will run iteratively and will learn some inherent pattern. The learning thus acquired is called experience(E). Making an analogy with human learning, we can think of this situation as in which a human being is learning or gaining some experience from various attributes like situation, relationships etc. Supervised, unsupervised and reinforcement learning are some ways to learn or gain experience. The experience gained by out ML model or algorithm will be used to solve the task T.

Performance (P)

An ML algorithm is supposed to perform task and gain experience with the passage of time. The measure which tells whether ML algorithm is performing as per expectation or not is its performance (P). P is basically a quantitative metric that tells how a model is performing the task, T, using its experience, E. There are many metrics that help to understand the ML performance, such as accuracy score, F1 score, confusion matrix, precision, recall, sensitivity etc.

Challenges in Machines Learning

While Machine Learning is rapidly evolving, making significant strides with cyber security and autonomous cars, this segment of AI as whole still has a long way to go. The reason behind is that ML has not been able to overcome number of challenges. The challenges that ML is facing currently are:

Quality of data: Having good-quality data for ML algorithms is one of the biggest challenges. Use of low-quality data leads to the problems related to data preprocessing and feature extraction.

Time-Consuming task: Another challenge faced by ML models is the consumption of time especially for data acquisition, feature extraction and retrieval.

Lack of specialist persons: As ML technology is still in its infancy stage, availability of expert resources is a tough job.

No clear objective for formulating business problems: Having no clear objective and well-defined goal for business problems is another key challenge for ML because this technology is not that mature yet.

Issue of overfitting & underfitting: If the model is overfitting or underfitting, it cannot be represented well for the problem.

Curse of dimensionality: Another challenge ML model faces is too many features of data points. This can be a real hindrance.

Difficulty in deployment: Complexity of the ML model makes it quite difficult to be deployed in real life.

Applications of Machines Learning

Machine Learning is the most rapidly growing technology and according to researchers we are in the golden year of AI and ML. It is used to solve many real-world complex problems which cannot be solved with traditional approach. Following are some real-world applications of ML:

Emotion analysis

Sentiment analysis

Error detection and prevention

Weather forecasting and prediction

Stock market analysis and forecasting

Speech synthesis

Speech recognition

Customer segmentation

Object