commit 40ba9c33487af72037c5789f90466d63208ec2dd
Author: noah <noah@theschricks.com>
Date:   Fri Apr 21 20:36:43 2023 -0500

    Adding table and data

diff --git a/Schrick-Noah_Learning-Practice-10.ipynb b/Schrick-Noah_Learning-Practice-10.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Learning Practice 10 for the University of Tulsa's QM-7063 Data Mining Course\n",
+    "# Evaluating Predictive Performance\n",
+    "# Professor: Dr. Abdulrashid, Spring 2023\n",
+    "# Noah L. Schrick - 1492657"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Problem 5.7 \n",
+    "Table 5.7 shows a small set of predictive model validation results for a classification\n",
+    "model, with both actual values and propensities.\n",
+    "\n",
+    "| Propensity of 1 | Actual          |\n",
+    "|-----------------|-----------------|\n",
+    "| 0.03            | 0               |\n",
+    "| 0.52            | 0               |\n",
+    "| 0.38            | 0               |\n",
+    "| 0.82            | 1               |\n",
+    "| 0.33            | 0               |\n",
+    "| 0.42            | 0               |\n",
+    "| 0.55            | 1               |\n",
+    "| 0.59            | 0               |\n",
+    "| 0.09            | 0               |\n",
+    "| 0.21            | 0               |\n",
+    "| 0.43            | 0               |\n",
+    "| 0.04            | 0               |\n",
+    "| 0.08            | 0               |\n",
+    "| 0.13            | 0               |\n",
+    "| 0.01            | 0               |\n",
+    "| 0.79            | 1               |\n",
+    "| 0.42            | 0               |\n",
+    "| 0.29            | 0               |\n",
+    "| 0.08            | 0               |\n",
+    "| 0.02            | 0               |"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# a.\n",
+    "Calculate error rates, sensitivity, and specificity using cutoffs of 0.25, 0.5, and 0.75."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    Propensity of 1  Actual\n",
+      "0              0.03       0\n",
+      "1              0.52       0\n",
+      "2              0.38       0\n",
+      "3              0.82       1\n",
+      "4              0.33       0\n",
+      "5              0.42       0\n",
+      "6              0.55       1\n",
+      "7              0.59       0\n",
+      "8              0.09       0\n",
+      "9              0.21       0\n",
+      "10             0.43       0\n",
+      "11             0.04       0\n",
+      "12             0.08       0\n",
+      "13             0.13       0\n",
+      "14             0.01       0\n",
+      "15             0.79       1\n",
+      "16             0.42       0\n",
+      "17             0.29       0\n",
+      "18             0.08       0\n",
+      "19             0.02       0\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = [\n",
+    "        [0.03, 0],\n",
+    "        [0.52, 0],\n",
+    "        [0.38, 0], \n",
+    "        [0.82, 1], \n",
+    "        [0.33, 0], \n",
+    "        [0.42, 0], \n",
+    "        [0.55, 1], \n",
+    "        [0.59, 0], \n",
+    "        [0.09, 0], \n",
+    "        [0.21, 0], \n",
+    "        [0.43, 0], \n",
+    "        [0.04, 0], \n",
+    "        [0.08, 0], \n",
+    "        [0.13, 0], \n",
+    "        [0.01, 0], \n",
+    "        [0.79, 1], \n",
+    "        [0.42, 0], \n",
+    "        [0.29, 0], \n",
+    "        [0.08, 0],\n",
+    "        [0.02, 0]\n",
+    "        ]\n",
+    "\n",
+    "table = pd.DataFrame(data, columns = ['Propensity of 1', 'Actual'])\n",
+    "print(table)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# b.\n",
+    "Create a decile lift chart."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
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+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}