dsc-3-34-09-principle-component-analysis-in-scikitlearn-lab's Introduction

Performing Principal Component Analysis (PCA) - Lab

Introduction

Now that you have high level overview of PCA as well as some of the details in the algorithm itself, its time to practice implementing PCA on your own using the NumPy package.

Objectives

You will be able to:

Implement PCA from scratch using NumPy

Import the Data

To start, import the data stored in the file 'foodusa.csv'.

#Your code here

Normalize the Data

Next, normalize your data by subtracting the feature mean from each of the columns

#Your code here

Calculate the Covariance Matrix

The next step for PCA is to calculate to covariance matrix for your normalized data. Do so here.

#Your code here

Calculate the Eigenvectors

Next, calculate the eigenvectors for your covariance matrix.

#Your code here

Sorting the Eigenvectors to Determine Primary Components

Great! Now that you have the eigenvectors and their associated eigenvalues, sort the eigenvectors based on their eigenvalues!

#Your code here

Reprojecting the Data

Finally, reproject the dataset using your eigenvectors. Reproject the dataset down to 2 dimensions.

#Your code here

Summary

Well done! You've now coded PCA on your own using NumPy! With that, it's time to look at further application of PCA.

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dsc-3-34-09-principle-component-analysis-in-scikitlearn-lab's Issues

Erroneous Tip given

Load Iris dataset into a pandas data frame from the source "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data". (You can use read_scv() to load it directly from the server.

The tip gives the incorrect method. The solution uses read_csv.

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