Learning Predictive Analytics with Python

Book Description

Gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python

Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form – It needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Learning to predict who would win, lose, buy, lie, or die with Python is an indispensable skill set to have in this data age.

This book is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. We balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and numpy.

You’ll start by getting an understanding of the basics of predictive modeling, then you will see how to cleanse your data of impurities and get it ready it for predictive modeling. You will also learn more about the best predictive modeling algorithms such as Linear Regression, Decision Trees, and Logistic Regression. Finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world.

What You Will Learn

• Understand the statistical and mathematical concepts behind Predictive Analytics algorithms and implement Predictive Analytics algorithms using Python libraries
• Analyze the result parameters arising from the implementation of Predictive Analytics algorithms
• Write Python modules/functions from scratch to execute segments or the whole of these algorithms
• Recognize and mitigate various contingencies and issues related to the implementation of Predictive Analytics algorithms
• Get to know various methods of importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and numpy
• Create dummy datasets and simple mathematical simulations using the Python numpy and pandas libraries
• Understand the best practices while handling datasets in Python and creating predictive models out of them

Table of Contents

Chapter 1. Getting Started with Predictive Modelling
Chapter 2. Data Cleaning
Chapter 3. Data Wrangling
Chapter 4. Statistical Concepts for Predictive Modelling
Chapter 5. Linear Regression with Python
Chapter 6. Logistic Regression with Python
Chapter 7. Clustering with Python
Chapter 8. Trees and Random Forests with Python
Chapter 9. Best Practices for Predictive Modelling

中文：

书名：Learning Predictive Analytics with Python

通过使用Python在公共数据集上实施预测分析算法，获得对预测建模的实际见解

社交媒体和物联网导致了数据的雪崩。数据很强大，但不是原始形式，需要处理和建模，而Python是实现这一点的最健壮的工具之一。它有一系列用于预测建模的软件包和一套IDE可供选择。在这个数据时代，学习预测谁会赢，谁会输，谁会买，谁会撒谎，谁会死，是一项不可或缺的技能。

这本书是您开始使用Python进行预测分析的指南。您将了解如何处理数据并根据数据建立预测模型。我们平衡了统计和数学概念，并使用PANAS、SCRICIT-LEARN和NumPy等库在Python中实现它们。

您将从了解预测建模的基本知识开始，然后了解如何清除数据中的杂质并使其为预测建模做好准备。您还将学习更多关于最佳预测建模算法的知识，例如线性回归、决策树和Logistic回归。最后，您将看到预测建模的最佳实践，以及预测建模在现代世界中的不同应用。

你将学到什么

• 了解预测分析算法背后的统计和数学概念，并使用Python库实现预测分析算法
• Analyze the result parameters arising from the implementation of Predictive Analytics algorithms
• 从头开始编写Python模块/函数以执行这些算法的部分或全部
• 认识和减轻与实施预测分析算法有关的各种意外情况和问题
• 了解导入、清理、子设置、合并、连接、连接、探索、分组以及使用PANDA和NumPy绘制数据的各种方法
• 使用PythonNumpy和Pandas库创建虚拟数据集和简单的数学模拟
• 了解在Python中处理数据集和基于数据集创建预测模型时的最佳实践

目录表

第1章：预测建模入门
第2章.数据清理
第三章：数据角力
第4章：预测性建模的统计概念
Chapter 5. Linear Regression with Python
第6章.使用Python进行Logistic回归
第7章：使用Python进行集群
第8章：树和随机森林与Python
第9章：预测性建模的最佳实践