Hands-On Predictive Analytics with Python: A practical guide to building high performance predictive analytics solutions with Python

Step-by-step guide to build high performing predictive applications

Key Features

• Master how to use Python and its data analytics ecosystem to implement end-to-end Predictive Analytics projects
• Explore advanced predictive modeling algorithms such as support vector machine, apriori, and neural networks
• Find the right balance between theory, intuition and practice to be a useful resource for practitioners

Book Description

Predictive Analytics involves meticulous usage of data, statistical algorithms and techniques of machine learning in establishing the future outcomes based on historical data.

This book provides practical coverage in understanding important concepts of predictive analytics, how to process massive data sets, building predictive models along with usage of cutting-edge python tools and packages. The book walks you step-by-step right from defining the problem statement, identifying relevant data, performing data treatment, synthesizing analytics model and optimizing them with effective Python codes. You will understand each and every phase of predictive analytics process with the help of successful demonstrated applications. You will work with predictive algorithms such as SVM, k-NN, Apriori algorithm, NLP process and neural networks. You will learn to code with various python libraries such as TensorFlow, NumPy, SciPy, pandas and build machine learning models, intuitive visualizations, performing complex calculations and achieving actionable predictive results.

With this book, you will be all set to build the predictive analytic application with practical examples and best practices using robust tools of python programming.

What you will learn

• Understand the main concepts and principles of Predictive Analytics and how to use them when building real-world predictive models.
• Properly use scikit-learn, the main Python library for Predictive Analytics and Machine Learning.
• Learn the types of Predictive Analytics problem and how to apply the main models and algorithms to solve real world problems.
• Build, evaluate, and interpret classification and regression models on real-world datasets.
• Prepare a dataset for modelling and extract information using Exploratory Data Analysis
• Explore how to implement a predictive model as a web application

Who This Book Is For

This book is for Python programmers who wants to learn predictive modeling and aspire to enter data science and machine learning areas. All you need is basic familiarity with linear algebra and statistical knowledge.

逐步指南：建立高效能預測應用程式

主要特點：
- 掌握如何使用Python及其資料分析生態系統來實施端到端的預測分析專案
- 探索高級預測建模演算法，如支持向量機、Apriori和神經網絡
- 在理論、直覺和實踐之間找到適當的平衡，成為實踐者的有用資源

書籍描述：
預測分析涉及對歷史數據進行細緻使用，結合統計演算法和機器學習技術，以建立未來結果。

本書提供實用的內容，幫助理解預測分析的重要概念，如何處理大型數據集，以及如何使用尖端的Python工具和套件建立預測模型。本書從定義問題陳述、識別相關數據、進行數據處理、合成分析模型以及使用有效的Python程式碼進行優化的步驟逐步引導讀者。通過成功演示的應用案例，您將了解預測分析過程的每個階段。您將使用支持向量機、k-NN、Apriori演算法、NLP過程和神經網絡等預測演算法。您將學習使用各種Python庫，如TensorFlow、NumPy、SciPy、pandas，建立機器學習模型、直觀的視覺化、執行複雜計算並實現可行的預測結果。

通過本書，您將準備好使用堅固的Python編程工具，通過實際示例和最佳實踐來建立預測分析應用程式。

您將學到什麼：
- 瞭解預測分析的主要概念和原則，以及在構建實際預測模型時如何應用它們。
- 正確使用scikit-learn，這是主要的Python庫，用於預測分析和機器學習。
- 瞭解預測分析問題的類型，以及如何應用主要模型和演算法來解決實際問題。
- 在實際數據集上建立、評估和解釋分類和回歸模型。
- 為建模準備數據集，並使用探索性數據分析提取信息。
- 探索如何將預測模型實現為Web應用程式。

本書適合對象：
本書適合想學習預測建模並希望進入數據科學和機器學習領域的Python程式設計師。您只需要基本熟悉線性代數和統計知識即可。