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   "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": []
  }
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