9781789134544_

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

Key Features

Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects
Explore advanced predictive modeling algorithms with an emphasis on theory with intuitive explanations
Learn to deploy a predictive models results as an interactive application

Book Description

The books step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model.

Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Pythons data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics.

By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming.

What you will learn

Get to grips with the main concepts and principles of predictive analytics
Learn about the stages involved in producing complete predictive analytics solutions
Understand how to define a problem, propose a solution, and prepare a dataset
Use visualizations to explore relationships and gain insights into the dataset
Learn to build regression and classification models using scikit-learn
Use Keras to build powerful neural network models that produce accurate predictions
Learn to serve a models predictions as a web application

Who this book is for

This book is for data analysts, data scientists, data engineers, and Python developers who want to learn about predictive modeling and would like to implement predictive analytics solutions using Pythons data stack. People from other backgrounds who would like to enter this exciting field will greatly benefit from reading this book. All you need is to be proficient in Python programming and have a basic understanding of statistics and college-level algebra.

(*_*)9781789134544_

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

Key Features

Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects
Explore advanced predictive modeling algorithms with an emphasis on theory with intuitive explanations
Learn to deploy a predictive models results as an interactive application

Book Description

By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming.

What you will learn

Get to grips with the main concepts and principles of predictive analytics
Learn about the stages involved in producing complete predictive analytics solutions
Understand how to define a problem, propose a solution, and prepare a dataset
Use visualizations to explore relationships and gain insights into the dataset
Learn to build regression and classification models using scikit-learn
Use Keras to build powerful neural network models that produce accurate predictions
Learn to serve a models predictions as a web application

Who this book is for

...(*_*)9781789134544_

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

Key Features

Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects
Explore advanced predictive modeling algorithms with an emphasis on theory with intuitive explanations
Learn to deploy a predictive model's results as an interactive application

Book Description

The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model.

Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics.

By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming.

What you will learn

Get to grips with the main concepts and principles of predictive analytics
Learn about the stages involved in producing complete predictive analytics solutions
Understand how to define a problem, propose a solution, and prepare a dataset
Use visualizations to explore relationships and gain insights into the dataset
Learn to build regression and classification models using scikit-learn
Use Keras to build powerful neural network models that produce accurate predictions
Learn to serve a model's predictions as a web application

Who this book is for

This book is for data analysts, data scientists, data engineers, and Python developers who want to learn about predictive modeling and would like to implement predictive analytics solutions using Python's data stack. People from other backgrounds who would like to enter this exciting field will greatly benefit from reading this book. All you need is to be proficient in Python programming and have a basic understanding of statistics and college-level algebra.

n(*_*)9781789134544_

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

Key Features

Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects
Explore advanced predictive modeling algorithms with an emphasis on theory with intuitive explanations
Learn to deploy a predictive model's results as an interactive application

Book Description

Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming.

What you will learn

Get to grips with the main concepts and principles of predictive analytics
Learn about the stages involved in producing complete predictive analytics solutions
Understand how to define a problem, propose a solution, and prepare a dataset
Use visualizations to explore relationships and gain insights into the dataset
Learn to build regression and classification models using scikit-learn
Use Keras to build powerful neural network models that produce accurate predictions
Learn to serve a model s predictions as a web application

Who this book is for

This book is for data analysts, data scientists, data engineers, and Python developers who want to learn about predictive modeling and would like to implement predictive analytics solutions using Python's data stack. People from other backgrounds who would like to enter this exciting field will greatly benefit from reading this book. All you need is to be proficient in Python programming and have a basic understanding of statistics and college-level algebra.

Hands-On Predictive Analytics with Python

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