Python has become one of any data scientist's favorite tools for doing Predictive Analytics. In this hands-on course, you will learn how to build predictive models with Python.
During the course, we will talk about the most important theoretical concepts that are essential when building predictive models for real-world problems. The main tool used in this course is scikit -learn, which is recognized as a great tool: it has a great variety of models, many useful routines, and a consistent interface that makes it easy to use. All the topics are taught using practical examples and throughout the course, we build many models using real-world datasets.
By the end of this course, you will learn the various techniques in making predictions about bankruptcy and identifying spam text messages and then use our knowledge to create a credit card using a linear model for classification along with logistic regression.
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.
- Understand Regression and Classification
- Refresh your visualization skills
The course is designed for Data analysts or data scientists interested in learning how to use Python to perform Predictive Analytics as well as Business analysts/business Intelligence experts who would like to go from descriptive analysis to predictive analysis. Software engineers and developers interested in producing predictions via Python will also benefit from the course.
Knowledge of the Python programming language is assumed. Basic familiarity with Python's Data Science Stack would be useful, although a brief review is given. Familiarity with basic mathematics and statistical concepts is also advantageous to take full advantage of this course.
About The Author
Alvaro Fuentes: Alvaro Fuentes is a senior data scientist with a background in applied mathematics and economics. He has more than 14 years of experience in various analytical roles and is an analytics consultant at one of the ‘Big Three’ global management consulting firms, leading advanced analytics projects in different industries like banking, technology, and consumer goods. Alvaro is also an author and trainer in analytics and data science and has published courses and books, such as 'Become a Python Data Analyst' and 'Hands-On Predictive Analytics with Python'. He has also taught data science and related topics to thousands of students both on-site and online through different platforms such as Springboard, Simplilearn, Udemy, and BSG Institute, among others.
Table of contents
- Chapter 1 : The Tools for Doing Predictive Analytics with Python
- Chapter 2 : Visualization Refresher
- Chapter 3 : Concepts in Predictive Analytics
- Chapter 4 : Regression: Concepts and Models
- Chapter 5 : Regression: Predicting Crime, Stock Prices, and Post Popularity
- Chapter 6 : Classification: Concepts and Models
- Chapter 7 : Classification: Predicting Bankruptcy, Credit Default, and Spam Text Messages
- Title: Making Predictions with Data and Python
- Release date: August 2017
- Publisher(s): Packt Publishing
- ISBN: 9781788297448
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