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)

Introduction to Linear and Logistic Regression

In regression, a single dependent, or outcome variable is predicted using one or more independent variables. Use cases for regression are included, but are not limited to predicting:

  • The win percentage of a team, given a variety of team statistics
  • The risk of heart disease, given family history and a number of physical and psychological characteristics
  • The likelihood of snowfall, given several climate measurements

Linear and logistic regression are popular choices for predicting such outcomes due to the ease and transparency of interpretability, as well as the ability to extrapolate to values not seen in the training data. The end goal of linear regression is to draw a straight line through the observations that minimizes the absolute distance between the line and observations (that is, the line of best fit). Therefore, in linear regression, it is assumed that the relationship between the feature(s) and the continuous dependent...