Book Image

Data Science with Python

By : Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen
Book Image

Data Science with Python

By: Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen

Overview of this book

Data Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression. As you make your way through the book, you will understand the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, discover how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome. By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.
Table of Contents (10 chapters)

Logistic Regression

Logistic regression uses categorical and continuous variables to predict a categorical outcome. When the dependent variable of choice has two categorical outcomes, the analysis is termed binary logistic regression. However, if the outcome variable consists of more than two levels, the analysis is referred to as multinomial logistic regression. For the purposes of this chapter, we will focus our learning on the former.

When predicting a binary outcome, we do not have a linear relationship between the features and the outcome variable; an assumption of linear regression. Thus, to express a nonlinear relationship in a linear way, we must transform the data using logarithmic transformation. As a result, logistic regression allows us to predict the probability of the binary outcome occurring given the feature(s) in the model.

For logistic regression with 1 predictor, the logistic regression equation is shown here:

Figure 3.12: Logistic regression formula with 1...