Exploit the power of data in your business by predicting probabilities and trends and creating advanced analytic solutions with PythonAbout This Book* Master the use of open source Python tools to build sophisticated predictive models* Learn to identify the right machine learning algorithm for your problem with this forward-thinking guide* Grasp the major methods of predictive modeling and move beyond black box thinking to a deeper level of understandingWho This Book Is ForThis book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python. You're expected to have basic development experience with Python.What You Will Learn* Gain an insight into components and design decisions for an analytical application* Master the use Python notebooks for exploratory data analysis and rapid prototyping* Get to grips with applying regression, classification, clustering, and deep learning algorithms* Discover the advanced methods to analyze structured and unstructured data* Find out how to deploy a machine learning model in a production environment* Visualize the performance of models and the insights they produce* Scale your solutions as your data grows using Python* Ensure the robustness of your analytic applications by mastering the best practices of predictive analysisIn DetailThe volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations.In Mastering Predictive Analytics with Python, you will work through a step-by-step process to turn raw data into powerful insights. Power-packed with case studies and code examples using popular open-source Python libraries, this volume illustrates the complete development process for analytic applications. The detailed examples illustrate robust and scalable applications for common use cases. You will learn to quickly apply these methods to your own data.Covering a wide range of algorithms for classification, regression, clustering, and cutting-edge techniques such as deep learning, you will learn not only how these methods work, but how to implement them in practice. You will also gain the skill to choose the right approach for your problem. This guide also explains how to develop engaging visualizations from these algorithms, to bring the insights of predictive modeling to life for the analyst and their stakeholders.
Author(s): Joseph Babcock
Year: 2016
Language: English
Pages: 231
Contents
Preface
From Data to Decisions
Designing an advanced analytic solution
Case study: sentiment analysis of social media feeds
Case study: targeted e-mail campaigns
Summary
Exploratory Data Analysis & Visualization in Python
Exploring categorical & numerical data in IPython
Time series analysis
Working with geospatial data
Introduction to PySpark
Summary
Clustering & Unsupervised Learning
Similarity and distance metrics
Affinity propagation – automatically choosing cluster numbers
k-medoids
Agglomerative clustering
Streaming clustering in Spark
Summary
Regression Methods
Linear regression
Tree methods
Scaling out with PySpark
Summary
Classification Methods & Analysis
Logistic regression
Fitting the model
Evaluating classification model
Separating Nonlinear boundaries with SVMs
Comparing classification method
Case study: fitting classifier model in pyspark
Summary
Unstructured Data
Working with textual data
Principal component analysis
Images
Case Study: Training Recommender System in PySpark
Summary
Deep Networks & Unsupervised Features
Learning patterns with neural networks
The TensorFlow library & digit recognition
Summary
Sharing Models with Prediction Services
Architecture of prediction service
Clients and making requests
Server – the web traffic controlle
Persisting information with database systems
Case study – logistic regression service
Summary
Iterating on Analytic Systems
Checking the health of models with diagnostics
Iterating on models through A/B testing
Guidelines for communication
Summary
Index
