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+{
+ "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
+}