Learning Predictive Analytics with Python

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

Learning Predictive Analytics with Python

Learning
Ashish Kumar

8 customer reviews
Gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python
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RRP $49.99
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Book Details

ISBN 139781783983261
Paperback354 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. 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.

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
The read_csv method
Use cases of the read_csv method
Case 2 – reading a dataset using the open method of Python
Case 3 – reading data from a URL
Case 4 – miscellaneous cases
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

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

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
The read_csv method
Use cases of the read_csv method
Case 2 – reading a dataset using the open method of Python
Case 3 – reading data from a URL
Case 4 – miscellaneous cases
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

Book Details

ISBN 139781783983261
Paperback354 pages
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