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)

Supervised Data Compression using Linear Discriminant Analysis (LDA)

As discussed previously, PCA transforms features into a set of variables to maximize the variance among the features. In PCA, the output labels are not considered when fitting the model. Meanwhile, LDA uses the dependent variable to help compress data into features that best discriminate the classes of the outcome variable. In this section, we will walk through how to use LDA as a supervised data compression technique.

To demonstrate using LDA as supervised dimensionality compression technique, we will:

  • Fit an LDA model with all possible n_components
  • Transform our features to n_components
  • Tune the number of n_components

Exercise 42: Fitting LDA Model

To fit the model as a supervised learner using the default parameters of the LDA algorithm we will be using a slightly different glass data set, glass_w_outcome.csv. ( This dataset...