From 809b79e208f651e325b77c8d3b65e13b438ae403 Mon Sep 17 00:00:00 2001
From: Jake Walker <jacob.walker@qinetiq.com>
Date: Thu, 22 Feb 2024 16:40:46 +0000
Subject: [PATCH] Add initial PCA notebook

---
 PCA Clustering.ipynb | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)
 create mode 100644 PCA Clustering.ipynb

diff --git a/PCA Clustering.ipynb b/PCA Clustering.ipynb
new file mode 100644
index 0000000..78e4816
--- /dev/null
+++ b/PCA Clustering.ipynb	
@@ -0,0 +1,22 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Principal Component Analysis (PCA)\n",
+    "\n",
+    "Principal Component Analysis (PCA) is a statistical method used to simplify and reduce the dimensionality of large data sets. It works by transforming a large set of variables into a smaller set of uncorrelated variables called principal components. These new variables capture the most important information from the original data, allowing for easier exploration, visualization, and analysis. In simple terms, PCA helps to identify the main features that can distinguish between different classes in a dataset, making it easier to work with the data and visualize it in a more understandable way.\n",
+    "\n",
+    "From: <https://www.datacamp.com/tutorial/principal-component-analysis-in-python>"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}