Using LDA for classification

Linear discriminant analysis (LDA) attempts to fit a linear combination of features to predict an outcome variable. LDA is often used as a pre-processing step. We'll walk through both methods in this recipe.

Getting ready

In this recipe, we will do the following:

1. Grab stock data from Google.
2. Rearrange it in a shape we're comfortable with.
3. Create an LDA object to fit and predict the class labels.
4. Give an example of how to use LDA for dimensionality reduction.

Before starting on step 1 and grabbing stock data from Google, install a version of pandas that supports the latest stock reader. Do so at an Anaconda command line by typing this:

conda install -c anaconda pandas-datareader

Note that your pandas version will be updated. If this is a problem, create a new environment for this pandas version. Now open a notebook and check whether the pandas-datareader imports correctly:

from pandas-datareader import data

If it is imported correctly, no errors will show up.

How to do it...