Unsupervised Learning Models

This section aims to illustrate K-Means Clustering and PCA in unsupervised learning models.

This shows the precision, recall and accuracy of the models.
It also shows prediction results for each model.

As we have learned in our chapter on unsupervised learning, we can use PCA to reduce the dimensionality of our data and K-Means Clustering to group similar data points together.

For this playground we will use a seeds dataset.

The dataset contains 210 samples of wheat seeds. There are 7 features for each sample.

The examined group comprised kernels belonging to three different varieties of wheat: Kama, Rosa and Canadian, 70 elements each, randomly selected for the experiment.

We created some models using KMeans only and KMeans with PCA. We created a jupyter notebook with explicit explanations throughout the codebase to help you understand and try out (hands-on) creating unsupervised learning models with KMeans and PCA. You can access the jupyter notebook pdf below.

Unsupervised learning models