Learning Predictive Analytics with Python(Paperback)

Ashish Kumar

  • 出版商: Packt Publishing
  • 出版日期: 2016-02-11
  • 售價: $1,730
  • 貴賓價: 9.5$1,644
  • 語言: 英文
  • 頁數: 354
  • 裝訂: Paperback
  • ISBN: 1783983264
  • ISBN-13: 9781783983261
  • 相關分類: Python程式語言Machine Learning
  • 立即出貨 (庫存=1)

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商品描述

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

About This Book

  • A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices
  • Get to grips with the basics of Predictive Analytics with Python
  • Learn how to use the popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering

Who This Book Is For

If you wish to learn how to implement Predictive Analytics algorithms using Python libraries, then this is the book for you. If you are familiar with coding in Python (or some other programming/statistical/scripting language) but have never used or read about Predictive Analytics algorithms, this book will also help you. The book will be beneficial to and can be read by any Data Science enthusiasts. Some familiarity with Python will be useful to get the most out of this book, but it is certainly not a prerequisite.

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

In Detail

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.

Style and approach

All the concepts in this book been explained and illustrated using a dataset, and in a step-by-step manner. The Python code snippet to implement a method or concept is followed by the output, such as charts, dataset heads, pictures, and so on. The statistical concepts are explained in detail wherever required.

商品描述(中文翻譯)

這本書將帶領讀者使用Python在公開數據集上實施預測建模,以獲得實用的預測建模見解。

關於本書:
- 提供逐步指南,包括許多技巧、技巧和最佳實踐
- 掌握Python的預測分析基礎知識
- 學習如何使用常用的預測建模算法,如線性回歸、決策樹、邏輯回歸和聚類

本書適合對使用Python庫實施預測分析算法感興趣的讀者。如果您熟悉Python編程(或其他編程/統計/腳本語言),但從未使用過或閱讀過預測分析算法,本書也將對您有所幫助。本書對於任何數據科學愛好者都有益處。對Python的一些熟悉將有助於更好地理解本書的內容,但這絕對不是必要條件。

您將學到什麼:
- 理解預測分析算法背後的統計和數學概念,並使用Python庫實施預測分析算法
- 分析實施預測分析算法產生的結果參數
- 從頭開始編寫Python模塊/函數,以執行這些算法的部分或全部
- 識別和解決與實施預測分析算法相關的各種偶發事件和問題
- 了解使用pandas和numpy導入、清理、子集、合併、連接、拼接、探索、分組和繪圖數據的各種方法
- 使用Python的numpy和pandas庫創建虛擬數據集和簡單的數學模擬
- 理解在Python中處理數據集並創建預測模型時的最佳實踐

詳細內容:
社交媒體和物聯網帶來了大量的數據。數據具有強大的力量,但在其原始形式下並不有效-它需要被處理和建模,而Python是其中最強大的工具之一。它擁有一系列用於預測建模的包和一套可供選擇的IDE。在這個數據時代,學習使用Python來預測誰會贏、輸、購買、說謊或死亡是一項不可或缺的技能。

本書將指導您如何使用Python開始進行預測分析。您將了解如何處理數據並從中建立預測模型。我們將平衡統計和數學概念,並使用pandas、scikit-learn和numpy等庫在Python中實施它們。

您將從了解預測建模的基礎知識開始,然後學習如何清理數據並為預測建模做好準備。您還將更深入地了解線性回歸、決策樹和邏輯回歸等最佳預測建模算法。最後,您將了解預測建模的最佳實踐,以及預測建模在現代世界中的不同應用。

風格和方法:
本書中的所有概念都使用數據集進行解釋和示範,並以逐步方式呈現。實施方法或概念的Python代碼片段後面跟著輸出,例如圖表、數據集頭部、圖片等。在需要的地方詳細解釋統計概念。