Report skeleton

Schrick-Noah_Learning-Practice-2.ipynb

@@ -0,0 +1,50 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Learning Practice 2 for the University of Tulsa's QM-7063 Data Mining Course\n",
+    "# Dimension Reduction\n",
+    "# Professor: Dr. Abdulrashid, Spring 2023\n",
+    "# Noah L. Schrick - 1492657\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.decomposition import PCA\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Read in Breakfast Cereal data\n",
+    "cereals_df = pd.read_csv('Cereals.csv')\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10.9 (main, Dec 19 2022, 17:35:49) [GCC 12.2.0]"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "767d51c1340bd893661ea55ea3124f6de3c7a262a8b4abca0554b478b1e2ff90"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Schrick-Noah_Lecture-Work.ipynb

@@ -6,7 +6,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Learning Practice 2 for the University of Tulsa's QM-7063 Data Mining Course\n",
+    "# Lecture 3 for the University of Tulsa's QM-7063 Data Mining Course\n",
     "# Dimension Reduction\n",
     "# Professor: Dr. Abdulrashid, Spring 2023\n",
     "# Noah L. Schrick - 1492657\n",
@@ -890,6 +890,27 @@
     "   len(pcsSummary_df.columns) + 1)]\n",
     "pcsSummary_df.round(4)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 379.63089542, -188.68156228],\n",
+       "       [-188.68156228,  197.32632105]])"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.cov(cereals_df.calories, cereals_df.rating)"
+   ]
   }
  ],
  "metadata": {
@@ -898,18 +919,6 @@
    "language": "python",
    "name": "python3"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  },
   "orig_nbformat": 4
  },
  "nbformat": 4,