Data Science with Python: Combine Python with machine learning principles to discover hidden patterns in raw data PDF
Preview Data Science with Python: Combine Python with machine learning principles to discover hidden patterns in raw data
Data Science with Python Copyright © 2019 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. Authors: Rohan Chopra, Aaron England and Mohamed Noordeen Alaudeen Technical Reviewer: Santiago Riviriego Esbert Managing Editor: Aritro Ghosh Acquisitions Editors: Kunal Sawant and Koushik Sen Production Editor: Samita Warang Editorial Board: David Barnes, Mayank Bhardwaj, Ewan Buckingham, Simon Cox, Mahesh Dhyani, Taabish Khan, Manasa Kumar, Alex Mazonowicz, Douglas Paterson, Dominic Pereira, Shiny Poojary, Erol Staveley, Ankita Thakur, and Jonathan Wray First Published: July 2019 Production Reference: 1090719 ISBN: 978-1-83855-286-2 Published by Packt Publishing Ltd. Livery Place, 35 Livery Street Birmingham B3 2PB, UK Table of Contents Preface About the Book Chapter 1: Introduction to Data Science and Data Pre- Processing Introduction Python Libraries Roadmap for Building Machine Learning Models Data Representation Independent and Target Variables Exercise 1: Loading a Sample Dataset and Creating the Feature Matrix and Target Matrix Data Cleaning Exercise 2: Removing Missing Data Exercise 3: Imputing Missing Data Exercise 4: Finding and Removing Outliers in Data Data Integration Exercise 5: Integrating Data Data Transformation Handling Categorical Data Exercise 6: Simple Replacement of Categorical Data with a Number Exercise 7: Converting Categorical Data to Numerical Data Using Label Encoding Exercise 8: Converting Categorical Data to Numerical Data Using One-Hot Encoding Data in Different Scales Exercise 9: Implementing Scaling Using the Standard Scaler Method Exercise 10: Implementing Scaling Using the MinMax Scaler Method Data Discretization Exercise 11: Discretization of Continuous Data Train and Test Data Exercise 12: Splitting Data into Train and Test Sets Activity 1: Pre-Processing Using the Bank Marketing Subscription Dataset Supervised Learning Unsupervised Learning Reinforcement Learning Performance Metrics Summary Chapter 2: Data Visualization Introduction Functional Approach Exercise 13: Functional Approach – Line Plot Exercise 14: Functional Approach – Add a Second Line to the Line Plot Activity 2: Line Plot Exercise 15: Creating a Bar Plot Activity 3: Bar Plot Exercise 16: Functional Approach – Histogram Exercise 17: Functional Approach – Box-and-Whisker plot Exercise 18: Scatterplot Object-Oriented Approach Using Subplots Exercise 19: Single Line Plot using Subplots Exercise 20: Multiple Line Plots Using Subplots Activity 4: Multiple Plot Types Using Subplots Summary Chapter 3: Introduction to Machine Learning via Scikit-Learn Introduction Introduction to Linear and Logistic Regression Simple Linear Regression Exercise 21: Preparing Data for a Linear Regression Model Exercise 22: Fitting a Simple Linear Regression Model and Determining the Intercept and Coefficient Exercise 23: Generating Predictions and Evaluating the Performance of a Simple Linear Regression Model Multiple Linear Regression Exercise 24: Fitting a Multiple Linear Regression Model and Determining the Intercept and Coefficients Activity 5: Generating Predictions and Evaluating the Performance of a Multiple Linear Regression Model Logistic Regression Exercise 25: Fitting a Logistic Regression Model and Determining the Intercept and Coefficients Exercise 26: Generating Predictions and Evaluating the Performance of a Logistic Regression Model Exercise 27: Tuning the Hyperparameters of a Multiple Logistic Regression Model Activity 6: Generating Predictions and Evaluating Performance of a Tuned Logistic Regression Model Max Margin Classification Using SVMs Exercise 28: Preparing Data for the Support Vector Classifier (SVC) Model Exercise 29: Tuning the SVC Model Using Grid Search Activity 7: Generating Predictions and Evaluating the Performance of the SVC Grid Search Model Decision Trees
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