Python: Advanced Predictive Analytics

Gain practical insights by exploiting data in your business to build advanced predictive modeling applications
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Python: Advanced Predictive Analytics

Ashish Kumar, Joseph Babcock

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Gain practical insights by exploiting data in your business to build advanced predictive modeling applications

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Book Details

ISBN 139781788992367
Paperback660 pages

Book Description

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. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications. This book is your guide to getting started with predictive analytics using Python.

You'll balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and NumPy. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates explains how these methods work. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring to life the insights of predictive modeling.

Finally, you will learn best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. The course provides you with highly practical content from the following Packt books:

  1. Learning Predictive Analytics with Python
  2. Mastering Predictive Analytics with Python

Table of Contents

Chapter 1: Getting Started with Predictive Modelling
Introducing predictive modelling
Applications and examples of predictive modelling
Python and its packages – download and installation
Python and its packages for predictive modelling
IDEs for Python
Summary
Chapter 2: Data Cleaning
Reading the data – variations and examples
Various methods of importing data in Python
Basics – summary, dimensions, and structure
Handling missing values
Creating dummy variables
Visualizing a dataset by basic plotting
Summary
Chapter 3: Data Wrangling
Subsetting a dataset
Generating random numbers and their usage
Grouping the data – aggregation, filtering, and transformation
Random sampling – splitting a dataset in training and testing datasets
Concatenating and appending data
Merging/joining datasets
Summary
Chapter 4: Statistical Concepts for Predictive Modelling
Random sampling and the central limit theorem
Hypothesis testing
Chi-square tests
Correlation
Summary
Chapter 5: Linear Regression with Python
Understanding the maths behind linear regression
Making sense of result parameters
Implementing linear regression with Python
Model validation
Handling other issues in linear regression
Summary
Chapter 6: Logistic Regression with Python
Linear regression versus logistic regression
Understanding the math behind logistic regression
Implementing logistic regression with Python
Model validation and evaluation
Model validation
Summary
Chapter 7: Clustering with Python
Introduction to clustering – what, why, and how?
Mathematics behind clustering
Implementing clustering using Python
Fine-tuning the clustering
Summary
Chapter 8: Trees and Random Forests with Python
Introducing decision trees
Understanding the mathematics behind decision trees
Implementing a decision tree with scikit-learn
Understanding and implementing regression trees
Understanding and implementing random forests
Summary
Chapter 9: Best Practices for Predictive Modelling
Best practices for coding
Best practices for data handling
Best practices for algorithms
Best practices for statistics
Best practices for business contexts
Summary
Chapter 10: From Data to Decisions – Getting Started with Analytic Applications
Designing an advanced analytic solution
Case study: sentiment analysis of social media feeds
Case study: targeted e-mail campaigns
Summary
Chapter 11: Exploratory Data Analysis and Visualization in Python
Exploring categorical and numerical data in IPython
Time series analysis
Working with geospatial data
Introduction to PySpark
Summary
Chapter 12: Finding Patterns in the Noise – Clustering and Unsupervised Learning
Similarity and distance metrics
Affinity propagation – automatically choosing cluster numbers
k-medoids
Agglomerative clustering
Streaming clustering in Spark
Summary
Chapter 13: Connecting the Dots with Models – Regression Methods
Linear regression
Tree methods
Scaling out with PySpark – predicting year of song release
Summary
Chapter 14: Putting Data in its Place – Classification Methods and Analysis
Logistic regression
Fitting the model
Evaluating classification models
Separating Nonlinear boundaries with Support vector machines
Comparing classification methods
Case study: fitting classifier models in pyspark
Summary
Chapter 15: Words and Pixels – Working with Unstructured Data
Working with textual data
Principal component analysis
Images
Case Study: Training a Recommender System in PySpark
Summary
Chapter 16: Learning from the Bottom Up – Deep Networks and Unsupervised Features
Learning patterns with neural networks
The TensorFlow library and digit recognition
Summary
Chapter 17: Sharing Models with Prediction Services
The architecture of a prediction service
Clients and making requests
Server – the web traffic controller
Persisting information with database systems
Case study – logistic regression service
Summary
Chapter 18: Reporting and Testing – Iterating on Analytic Systems
Checking the health of models with diagnostics
Iterating on models through A/B testing
Guidelines for communication
Summary

What You Will Learn

  • Understand the statistical and mathematical concepts behind predictive analytics algorithms and implement them using Python libraries
  • Get to know various methods for importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and NumPy
  • Master the use of Python notebooks for exploratory data analysis and rapid prototyping
  • Get to grips with applying regression, classification, clustering, and deep learning algorithms
  • Discover advanced methods to analyze structured and unstructured data
  • Visualize the performance of models and the insights they produce
  • Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis

Authors

Table of Contents

Chapter 1: Getting Started with Predictive Modelling
Introducing predictive modelling
Applications and examples of predictive modelling
Python and its packages – download and installation
Python and its packages for predictive modelling
IDEs for Python
Summary
Chapter 2: Data Cleaning
Reading the data – variations and examples
Various methods of importing data in Python
Basics – summary, dimensions, and structure
Handling missing values
Creating dummy variables
Visualizing a dataset by basic plotting
Summary
Chapter 3: Data Wrangling
Subsetting a dataset
Generating random numbers and their usage
Grouping the data – aggregation, filtering, and transformation
Random sampling – splitting a dataset in training and testing datasets
Concatenating and appending data
Merging/joining datasets
Summary
Chapter 4: Statistical Concepts for Predictive Modelling
Random sampling and the central limit theorem
Hypothesis testing
Chi-square tests
Correlation
Summary
Chapter 5: Linear Regression with Python
Understanding the maths behind linear regression
Making sense of result parameters
Implementing linear regression with Python
Model validation
Handling other issues in linear regression
Summary
Chapter 6: Logistic Regression with Python
Linear regression versus logistic regression
Understanding the math behind logistic regression
Implementing logistic regression with Python
Model validation and evaluation
Model validation
Summary
Chapter 7: Clustering with Python
Introduction to clustering – what, why, and how?
Mathematics behind clustering
Implementing clustering using Python
Fine-tuning the clustering
Summary
Chapter 8: Trees and Random Forests with Python
Introducing decision trees
Understanding the mathematics behind decision trees
Implementing a decision tree with scikit-learn
Understanding and implementing regression trees
Understanding and implementing random forests
Summary
Chapter 9: Best Practices for Predictive Modelling
Best practices for coding
Best practices for data handling
Best practices for algorithms
Best practices for statistics
Best practices for business contexts
Summary
Chapter 10: From Data to Decisions – Getting Started with Analytic Applications
Designing an advanced analytic solution
Case study: sentiment analysis of social media feeds
Case study: targeted e-mail campaigns
Summary
Chapter 11: Exploratory Data Analysis and Visualization in Python
Exploring categorical and numerical data in IPython
Time series analysis
Working with geospatial data
Introduction to PySpark
Summary
Chapter 12: Finding Patterns in the Noise – Clustering and Unsupervised Learning
Similarity and distance metrics
Affinity propagation – automatically choosing cluster numbers
k-medoids
Agglomerative clustering
Streaming clustering in Spark
Summary
Chapter 13: Connecting the Dots with Models – Regression Methods
Linear regression
Tree methods
Scaling out with PySpark – predicting year of song release
Summary
Chapter 14: Putting Data in its Place – Classification Methods and Analysis
Logistic regression
Fitting the model
Evaluating classification models
Separating Nonlinear boundaries with Support vector machines
Comparing classification methods
Case study: fitting classifier models in pyspark
Summary
Chapter 15: Words and Pixels – Working with Unstructured Data
Working with textual data
Principal component analysis
Images
Case Study: Training a Recommender System in PySpark
Summary
Chapter 16: Learning from the Bottom Up – Deep Networks and Unsupervised Features
Learning patterns with neural networks
The TensorFlow library and digit recognition
Summary
Chapter 17: Sharing Models with Prediction Services
The architecture of a prediction service
Clients and making requests
Server – the web traffic controller
Persisting information with database systems
Case study – logistic regression service
Summary
Chapter 18: Reporting and Testing – Iterating on Analytic Systems
Checking the health of models with diagnostics
Iterating on models through A/B testing
Guidelines for communication
Summary

Book Details

ISBN 139781788992367
Paperback660 pages
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