#### Overview of this book

Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain. The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.
Preface
Basic Packages, Functions, and Concepts
Free Chapter
Mathematical Plotting with Matplotlib
Working with Randomness and Probability
Geometric Problems
Finding Optimal Solutions
Miscellaneous Topics
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# Classifying using logarithmic regression

```        Logarithmic regression solves a different problem to ordinary linear regression. It is commonly used for classification problems where, typically, we wish to classify data into two distinct groups, according to a number of predictor variables. Underlying this technique is a transformation that's performed using logarithms. The original classification problem is transformed into a problem of constructing a model for the log-odds. This model can be completed with simple linear regression. We apply the inverse transformation to the linear model, which leaves us with a model of the probability that the desired outcome will occur, given the predictor data. The transform we apply here is called the logistic function, which gives its name to the method. The probability we obtain can then be used in the classification problem we originally aimed to solve.
In this recipe, we will learn how to perform logistic regression and use this...```