QM-7063-Learning-Practice-6/Schrick-Noah_Learning-Practice-6.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Learning Practice 6 for the University of Tulsa's QM-7063 Data Mining Course\n",
    "# Logistic Regression for Classification\n",
    "# # Professor: Dr. Abdulrashid, Spring 2023\n",
    "# Noah L. Schrick - 1492657\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV\n",
    "from sklearn.linear_model import LinearRegression, Lasso, Ridge, LassoCV, BayesianRidge\n",
    "from dmba import stepwise_selection\n",
    "from dmba import regressionSummary\n",
    "from sklearn.model_selection import train_test_split\n",
    "import statsmodels.api as sm\n",
    "from pandas.plotting import scatter_matrix\n",
    "import seaborn as sns\n",
    "from dmba.metric import AIC_score"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 10.3\n",
    "\n",
    "A company that manufactures riding mowers wants to identify the best sales prospects for an intensive sales campaign. In particular, the manufacturer is interested in classifying households as prospective owners or nonowners on the basis of Income (in $1000s) and Lot Size (in 1000 ft2). The marketing expert looked at a random sample of 24 households, given in the file RidingMowers.csv. \n",
    "\n",
    "Use all the data to fit a logistic regression of ownership on the two predictors.\n",
    "\n",
    "a. What percentage of households in the study were owners of a riding mower?  \n",
    "b. Create a scatter plot of Income vs. Lot Size using color or symbol to distinguish owners from nonowners. From the scatter plot, which class seems to have a higher average income, owners or nonowners?  \n",
    "c. Among nonowners, what is the percentage of households classified correctly?  \n",
    "d. To increase the percentage of correctly classified nonowners, should the cutoff probability be increased or decreased?  \n",
    "e. What are the odds that a household with a $60K income and a lot size of 20,000ft2 is an owner?  \n",
    "f. What is the classification of a household with a $60K income and a lot size of 20,000 ft2? Use cutoff = 0.5.  \n",
    "g. What is the minimum income that a household with 16,000 ft2 lot size should have before it is classified as an owner?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Owner       50.0\n",
      "Nonowner    50.0\n",
      "Name: Ownership, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "mowers_df = pd.read_csv('RidingMowers.csv')\n",
    "\n",
    "# a\n",
    "owner_pctg = mowers_df['Ownership'].value_counts(normalize=True) * 100\n",
    "print(owner_pctg)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ownership\n",
      "Nonowner    57.400\n",
      "Owner       79.475\n",
      "Name: Income, dtype: float64\n"
     ]
    },
    {
     "data": {
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vNQxDTzzxhPr27auwsDBlZGSotLTUxKoBoHPxQFug43w6AL3wwgt65ZVX9KMf/UiHDh3SCy+8oKVLlyo/P999zNKlS7VixQqtXr1ae/bsUXh4uDIzM3Xu3DkTKweAzsMDbYGO8+kxQO+8844mT56se+65R5KUlJSkTZs2ae/evZI+7f156aWX9P3vf1+TJ0+WJK1fv16xsbHaunWr7r//ftNqB4DOwhoyQMf5dA/QmDFjVFBQoA8//FCS9N577+ntt9/WxIkTJUnl5eWqqqpSRkaG+zUOh0OjRo1ScXGxKTUDQGdjDRmg43y6B2jhwoVyuVxKTU1VUFCQWlpa9Oyzz2r69OmSpKqqKklSbGysx+tiY2Pd+9rS1NSkpqYm988ul6sTqgeAzpM/LU25mw54zAJjDRng2vl0AHrttdf0y1/+Uhs3btSwYcNUUlKiuXPnKj4+XjNmzGj3eZcsWaLFixd7sVIA6FqsIQN0jM0wLppG4EMSEhK0cOFC5eTkuNueeeYZbdiwQR988IGOHj2qAQMG6MCBA7r55pvdx9xxxx26+eab9fLLL7d53rZ6gBISEuR0OmW32zvt7wEAAN7jcrnkcDja9f3t02OAGhsb1a2bZ4lBQUFqbW2VJCUnJysuLk4FBQXu/S6XS3v27FF6evplzxsaGiq73e6xAQAA6/DpW2CTJk3Ss88+q8TERA0bNkwHDhzQsmXL9NBDD0mSbDab5s6dq2eeeUYpKSlKTk7WokWLFB8frylTpphbPAAA8Fk+HYDy8/O1aNEiffvb39apU6cUHx+v//qv/9ITTzzhPubxxx9XQ0ODZs2apbq6Oo0bN07bt29Xjx49TKwcAAD4Mp8eA9RVOnIPEQAAmCNgxwABAAB0BgIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwHAIQAACwnA4FoPPnz+vw4cP65JNPvFUPAABAp2tXAGpsbFR2drZ69uypYcOG6cSJE5Kk3NxcPf/8814tEAAAwNvaFYDy8vL03nvvaefOnR7P3MrIyNCrr77qteIAAAA6Q7sehrp161a9+uqrGj16tGw2m7t92LBhOnLkiNeKAwAA6AztCkA1NTXq06fPJe0NDQ0egQgAcKmjNfU6fqZRSVHhSo4ON7scwJLaFYBuvfVW/e53v1Nubq4kuUPPz372M6Wnp3uvOgAIIHWN5zVnU4mKSmvcbeNTYpQ/LU2OnsEmVgZYT7sC0HPPPaeJEyfq/fff1yeffKKXX35Z77//vt555x0VFhZ6u0YACAhzNpVoV1mtR9uuslrlbjqg9dm3m1QVYE3tGgQ9btw4lZSU6JNPPtHw4cP1xz/+UX369FFxcbFGjhzp7RoBwO8dralXUWmNWgzDo73FMFRUWqPy2gaTKgOsqV09QJI0YMAA/fSnP/VmLQAQsI6fabzi/mOnGxgPBHShdgcgSTp16pROnTql1tZWj/YRI0Z0qCgACDT9I3tecX9SFOEH6ErtCkD79+/XjBkzdOjQIRkXdefabDa1tLR4pTgACBQ3xkRofEqMdpXVetwGC7LZNHZgNL0/QBdrVwB66KGHNGjQIK1Zs0axsbFMfQeAa5A/LU25mw54zAIbOzBa+dPSTKwKsCabcXEXzjXo1auXDhw4oIEDB3ZGTV3O5XLJ4XDI6XTKbrebXQ6AAFde26BjpxtYBwjooI58f7erB+iLX/yi3nvvvYAJQADQlZKjCT6A2doVgH72s59pxowZOnjwoD7/+c8rONhzAa97773XK8UBAAB0hnYFoOLiYu3atUt/+MMfLtnHIGgAAODr2rUQYm5urr7xjW+osrJSra2tHhvhBwAA+Lp2BaDTp09r3rx5io2N9XY9AAAAna5dAWjq1KnasWOHt2sBAADoEu0aAzRo0CDl5eXp7bff1vDhwy8ZBD1nzhyvFAcAANAZ2rUOUHJy8uVPaLPp6NGjHSqqq7EOEAAA/qfL1wEqLy9vz8sAAAB8QrvGAH2WYRiXPA8MAADAl7U7AK1fv17Dhw9XWFiYwsLCNGLECP3iF7/wZm0AAACdol23wJYtW6ZFixZp9uzZGjt2rCTp7bff1iOPPKLa2lrNmzfPq0UCAAB4U7t6gPLz8/XKK6/ohRde0L333qt7771XS5cu1apVq7RixQqvFpiUlCSbzXbJlpOTI0k6d+6ccnJyFBUVpYiICGVlZam6utqrNQAAgMDSrgBUWVmpMWPGXNI+ZswYVVZWdrioz9q3b58qKyvd21tvvSVJ+trXviZJmjdvnrZt26bNmzersLBQFRUVmjp1qldrAAAAgaVdAWjgwIF67bXXLml/9dVXlZKS0uGiPismJkZxcXHu7be//a0GDBigO+64Q06nU2vWrNGyZct01113aeTIkVq7dq3eeecd7d6926t1AACAwNGuMUCLFy/Wfffdp6KiIvcYoF27dqmgoKDNYOQt58+f14YNGzR//nzZbDbt379fzc3NysjIcB+TmpqqxMREFRcXa/To0Z1WCwAA8F/tCkBZWVnas2ePli9frq1bt0qShgwZor179yotLc2b9XnYunWr6urq9M1vflOSVFVVpZCQEPXu3dvjuNjYWFVVVV32PE1NTWpqanL/7HK5OqNcAADgo9oVgCRp5MiR2rBhgzdruao1a9Zo4sSJio+P79B5lixZosWLF3upKgAwz9Gaeh0/06ikqHAlR4ebXQ7gN9oVgH7/+98rKChImZmZHu1vvvmmWltbNXHiRK8U91nHjx/Xn/70J/3mN79xt8XFxen8+fOqq6vz6AWqrq5WXFzcZc+Vl5en+fPnu392uVxKSEjwes0A0FnqGs9rzqYSFZXWuNvGp8Qof1qaHD2Dr/BKAFI7B0EvXLhQLS0tl7QbhqGFCxd2uKi2rF27Vn369NE999zjbhs5cqSCg4NVUFDgbjt8+LBOnDih9PT0y54rNDRUdrvdYwMAfzJnU4l2ldV6tO0qq1XupgMmVQT4l3b1AJWWlmro0KGXtKempqqsrKzDRV2stbVVa9eu1YwZM9S9+79Ldjgcys7O1vz58xUZGSm73a7c3Fylp6czABpAwDpaU+/R83NBi2GoqLRG5bUN3A4DrqJdAcjhcOjo0aNKSkryaC8rK1N4uPffdH/605904sQJPfTQQ5fsW758ubp166asrCw1NTUpMzNTq1at8noNAOArjp9pvOL+Y6cJQMDVtOsW2OTJkzV37lwdOXLE3VZWVqYFCxbo3nvv9VpxF0yYMEGGYWjQoEGX7OvRo4dWrlypM2fOqKGhQb/5zW+uOP4HAPxd/8ieV9yfFEX4Aa6mXQFo6dKlCg8PV2pqqpKTk5WcnKwhQ4YoKipKL774ordrBAB8xo0xERqfEqMgm82jPchm0/iUGHp/gGtgMwzDaM8LDcPQW2+9pffee8/9NPjx48d7u74u4XK55HA45HQ6GRANwC84G5uVu+kAs8BgaR35/m53AAokBCAA/qq8tkHHTjewDhAsqSPf3+1eCLGgoEAFBQU6deqUWltbPfb9/Oc/b+9pAQDXITma4AO0R7ufBfaDH/xAt956q/r27SvbRfehAQAAfFm7AtDq1au1bt06PfDAA96uBwAAoNO1axbY+fPnNWbMGG/XAgAA0CXaFYAefvhhbdy40du1AICkT1c63nH4lMprG8wuBUCAatctsHPnzuknP/mJ/vSnP2nEiBEKDvaccrls2TKvFAfAWnjAJ4Cu0q4A9Le//U0333yzJOngwYPerAeAhV3pAZ/rs283qSoAgahdAWjHjh3ergOAxfGATwBd6boC0NSpU696jM1m069//et2FwTAmnjAJ4CudF0ByOFwdFYdAK7D0Zp6HT/TGFCr//KAT3S2QHzfoP2uKwCtXbu2s+oAcA0CeZDwhQd87iqrVctnntATZLNp7MBovrDQboH8vkH7tWsaPABzXGmQcCDIn5amsQOjPdrGDoxW/rQ0kypCIAj09w3ap93PAgPQtawwSNjRM1jrs2/nAZ/wGiu8b9A+9AABfuJaBgkHiuTocN05uA9fTOgwK71vcH0IQICfYJAwcP143+ByCECAn7gwSDjIZvNoD7LZND4lht4SoA28b3A5BCDAjzBIGLh+vG/QFpthfGa+qUW5XC45HA45nU7Z7Xazy+l0rIXh/xgkDFw/3jeBpyPf38wCsxDWwggcydF8gAPXi/cNPotbYBbCWhgAAHyKAGQRF9bCaLnojudn18IAAMAqCEAWwVoYAAD8GwHIIlgLAwCAfyMAWQRrYQAA8G8EIAthLQwAXeVoTb12HD7F+EL4LKbBWwgPmgTQ2VhuA/6CHiAL4kGTADoLy23AXxCAAABewXIb8CcEIACAV7DcBvwJAQgA4BUstwF/4vMB6OOPP9Y3vvENRUVFKSwsTMOHD9e7777r3m8Yhp544gn17dtXYWFhysjIUGlpqYkVA+goZhD5J5bbgD/x6QD0z3/+U2PHjlVwcLD+8Ic/6P3339cPf/hDfe5zn3Mfs3TpUq1YsUKrV6/Wnj17FB4erszMTJ07d87EygG0R13jeT24Zq/u+mGhZq7dpztf3KkH1+yVs7HZ7NJwjVhuA/7CZhgXjVbzIQsXLtSuXbv0l7/8pc39hmEoPj5eCxYs0KOPPipJcjqdio2N1bp163T//fdf0+9xuVxyOBxyOp2y2+1eqx/A9XlwzV7tKqv1GEQbZLNp7MBorc++3cTKcL1YbgNdoSPf3z7dA/TGG2/o1ltv1de+9jX16dNHaWlp+ulPf+reX15erqqqKmVkZLjbHA6HRo0apeLi4suet6mpSS6Xy2ND4OD2iX9iBlFgYbkN+DqfXgjx6NGjeuWVVzR//nx997vf1b59+zRnzhyFhIRoxowZqqqqkiTFxsZ6vC42Nta9ry1LlizR4sWLO7V2dD0WYPNv1zKDiC9TAN7i0z1Ara2tuuWWW/Tcc88pLS1Ns2bN0re+9S2tXr26Q+fNy8uT0+l0bydPnvRSxTATC7D5N2YQAehKPh2A+vbtq6FDh3q0DRkyRCdOnJAkxcXFSZKqq6s9jqmurnbva0toaKjsdrvHBv/G7RP/xwwiAF3JpwPQ2LFjdfjwYY+2Dz/8UP3795ckJScnKy4uTgUFBe79LpdLe/bsUXp6epfWCnOxAFtgYAYRgK7i02OA5s2bpzFjxui5557T17/+de3du1c/+clP9JOf/ESSZLPZNHfuXD3zzDNKSUlRcnKyFi1apPj4eE2ZMsXc4tGluH0SGHhgL4Cu4tMB6LbbbtOWLVuUl5enH/zgB0pOTtZLL72k6dOnu495/PHH1dDQoFmzZqmurk7jxo3T9u3b1aNHDxMrR1e7cPvkclOo+RL1L8nRBB8Ancun1wHqKqwDFBicjc3K3XSAWWAAYBEd+f726R4g4Hpw+wQAcK0IQAg43D4BAFyNT88CAwAA6AwEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDndzS4AABB4jtbU6/iZRiVFhSs5OtzscoBLEIAAAF5T13heczaVqKi0xt02PiVG+dPS5OgZbGJlgCdugQEAvGbOphLtKqv1aNtVVqvcTQdMqghoGwEIAOAVR2vqVVRaoxbD8GhvMQwVldaovLbBpMqASxGAAABecfxM4xX3HztNAILvIAABALyif2TPK+5PimIwNHwHAQgA4BU3xkRofEqMgmw2j/Ygm03jU2KYDQafQgACAHhN/rQ0jR0Y7dE2dmC08qelmVQR0DamwXci1sEAYDWOnsFan327ymsbdOx0Q8B+/vH57v8IQJ2AdTAAWF1ydGAGAz7fAwe3wDoB62AAQGDi8z1wEIC8jHUwACAw8fkeWHw+AD311FOy2WweW2pqqnv/uXPnlJOTo6ioKEVERCgrK0vV1dWm1cs6GAAQmPh8Dyw+H4AkadiwYaqsrHRvb7/9tnvfvHnztG3bNm3evFmFhYWqqKjQ1KlTTauVdTAAIDDx+R5Y/GIQdPfu3RUXF3dJu9Pp1Jo1a7Rx40bdddddkqS1a9dqyJAh2r17t0aPHt3VpbrXwdhVVuvRTRpks2nswOiAHBQIAFbA53tg8YseoNLSUsXHx+vGG2/U9OnTdeLECUnS/v371dzcrIyMDPexqampSkxMVHFxsVnlsg4GAAQoPt8Dh8/3AI0aNUrr1q3T4MGDVVlZqcWLF+s//uM/dPDgQVVVVSkkJES9e/f2eE1sbKyqqqoue86mpiY1NTW5f3a5XF6t2SrrYMD/sZYJcH34fA8cPh+AJk6c6P7vESNGaNSoUerfv79ee+01hYWFteucS5Ys0eLFi71V4mUF6joY8H+sZQJ0DJ/v/s8vboF9Vu/evTVo0CCVlZUpLi5O58+fV11dnccx1dXVbY4ZuiAvL09Op9O9nTx5spOrBnwLa5kAsDq/C0D19fU6cuSI+vbtq5EjRyo4OFgFBQXu/YcPH9aJEyeUnp5+2XOEhobKbrd7bIBVsJYJAPjBLbBHH31UkyZNUv/+/VVRUaEnn3xSQUFBmjZtmhwOh7KzszV//nxFRkbKbrcrNzdX6enppswAA/zBtaxlQtc+gEDn8wHoo48+0rRp03T69GnFxMRo3Lhx2r17t2JiYiRJy5cvV7du3ZSVlaWmpiZlZmZq1apVJlcN+C7WMgEAyWYYF/WDW5DL5ZLD4ZDT6eR2GCzhwTV7L7uWyfrs202sDACuXUe+v/1uDBCAjmMtEwBW5/O3wAAzBeo6OaxlAsDqCEBAG6yyTg5rmQCwKm6BAW1gnRwACGwEIOAirJMDAIGPAARc5FrWyQEA+DcCEHAR1skBgMBHAAIucmNMhManxCjIZvNoD7LZND4lhkHDABAACEBAG1gnBwACG9PggTawTg4ABDYCEHAFrJMDAIGJW2AAAMByCEAAAMByCEAAAMByCEAAAMByCEAAAMByCEAAAMByCEAAAMByCEAAAMByWAgRACBJOlpTr+NnGln5HJZAAAIAi6trPK85m0pUVFrjbhufEqP8aWly9Aw2sTKg83ALDAAsbs6mEu0qq/Vo21VWq9xNB0yqCOh8BCAAsLCjNfUqKq1Ri2F4tLcYhopKa1Re22BSZUDnIgABgIUdP9N4xf3HThOAEJgIQABgYf0je15xf1IUg6ERmAhAAGBhN8ZEaHxKjIJsNo/2IJtN41NimA2GgEUAAgCLy5+WprEDoz3axg6MVv60NJMqAjof0+ABwOIcPYO1Pvt2ldc26NjpBtYBgiUQgAAAkqTkaIIPrINbYAAAwHIIQAAAwHIIQAAAwHIIQAAAwHL8KgA9//zzstlsmjt3rrvt3LlzysnJUVRUlCIiIpSVlaXq6mrzigQAAD7PbwLQvn379OMf/1gjRozwaJ83b562bdumzZs3q7CwUBUVFZo6dapJVQIAAH/gFwGovr5e06dP109/+lN97nOfc7c7nU6tWbNGy5Yt01133aWRI0dq7dq1euedd7R7924TKwYAAL7MLwJQTk6O7rnnHmVkZHi079+/X83NzR7tqampSkxMVHFx8WXP19TUJJfL5bEBAADr8PmFEH/1q1/pr3/9q/bt23fJvqqqKoWEhKh3794e7bGxsaqqqrrsOZcsWaLFixd7u1QAAOAnfLoH6OTJk/qf//kf/fKXv1SPHj28dt68vDw5nU73dvLkSa+dGwAA+D6fDkD79+/XqVOndMstt6h79+7q3r27CgsLtWLFCnXv3l2xsbE6f/686urqPF5XXV2tuLi4y543NDRUdrvdYwMAANbh07fAvvjFL+rvf/+7R9vMmTOVmpqq73znO0pISFBwcLAKCgqUlZUlSTp8+LBOnDih9PR0M0oGAAB+wKcDUK9evfT5z3/eoy08PFxRUVHu9uzsbM2fP1+RkZGy2+3Kzc1Venq6Ro8ebUbJAADAD/h0ALoWy5cvV7du3ZSVlaWmpiZlZmZq1apVZpcFAAB8mM0wDMPsIszmcrnkcDjkdDoZDwQAgJ/oyPe3Tw+CBgAA6AwEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDndzS4AADrT0Zp6HT/TqKSocCVHh5tdDgAfQQACEJDqGs9rzqYSFZXWuNvGp8Qof1qaHD2DTawMgC/gFhiAgDRnU4l2ldV6tO0qq1XupgMmVQTAlxCAAAScozX1KiqtUYtheLS3GIaKSmtUXttgUmUAfAUBCEDAOX6m8Yr7j50mAAFWRwACEHD6R/a84v6kKAZDA1ZHAAIQcG6MidD4lBgF2Wwe7UE2m8anxDAbDAABCEBgyp+WprEDoz3axg6MVv60NJMqAuBLmAYPICA5egZrffbtKq9t0LHTDawDBMADAQhAQEuOJvgAuBS3wAAAgOUQgAAAgOX4fAB65ZVXNGLECNntdtntdqWnp+sPf/iDe/+5c+eUk5OjqKgoRUREKCsrS9XV1SZWDAAAfJ3PB6B+/frp+eef1/79+/Xuu+/qrrvu0uTJk/WPf/xDkjRv3jxt27ZNmzdvVmFhoSoqKjR16lSTqwYAAL7MZhgXrRXvByIjI/W///u/+upXv6qYmBht3LhRX/3qVyVJH3zwgYYMGaLi4mKNHj36ms7ncrnkcDjkdDplt9s7s3QAAOAlHfn+9vkeoM9qaWnRr371KzU0NCg9PV379+9Xc3OzMjIy3MekpqYqMTFRxcXFJlYKAAB8mV9Mg//73/+u9PR0nTt3ThEREdqyZYuGDh2qkpIShYSEqHfv3h7Hx8bGqqqq6rLna2pqUlNTk/tnl8vVWaUDAAAf5Bc9QIMHD1ZJSYn27Nmj//7v/9aMGTP0/vvvt/t8S5YskcPhcG8JCQlerBYAAPg6vwhAISEhGjhwoEaOHKklS5bopptu0ssvv6y4uDidP39edXV1HsdXV1crLi7usufLy8uT0+l0bydPnuzkvwAAAPgSvwhAF2ttbVVTU5NGjhyp4OBgFRQUuPcdPnxYJ06cUHp6+mVfHxoa6p5Wf2EDAADW4fNjgPLy8jRx4kQlJibq7Nmz2rhxo3bu3Kk333xTDodD2dnZmj9/viIjI2W325Wbm6v09PRrngEmSRcmwjEWCAAA/3Hhe7s9E9p9PgCdOnVKDz74oCorK+VwODRixAi9+eab+tKXviRJWr58ubp166asrCw1NTUpMzNTq1atuq7fcfbsWUliLBAAAH7o7Nmzcjgc1/Uav1wHyNtaW1tVUVGhXr16yWazee28LpdLCQkJOnnyJLfZuhDX3Rxcd3Nw3c3BdTfHxdfdMAydPXtW8fHx6tbt+kb1+HwPUFfo1q2b+vXr12nnZ5yRObju5uC6m4Prbg6uuzk+e92vt+fnAr8cBA0AANARBCAAAGA5BKBOFBoaqieffFKhoaFml2IpXHdzcN3NwXU3B9fdHN687gyCBgAAlkMPEAAAsBwCEAAAsBwCEAAAsBwCkBcUFRVp0qRJio+Pl81m09atWy977COPPCKbzaaXXnqpy+oLVNdy3Q8dOqR7771XDodD4eHhuu2223TixImuLzaAXO2619fXa/bs2erXr5/CwsI0dOhQrV692pxiA8SSJUt02223qVevXurTp4+mTJmiw4cPexxz7tw55eTkKCoqShEREcrKylJ1dbVJFQeGq133M2fOKDc3V4MHD1ZYWJgSExM1Z84cOZ1OE6v2f9fy//sFhmFo4sSJV/3ubQsByAsaGhp00003aeXKlVc8bsuWLdq9e7fi4+O7qLLAdrXrfuTIEY0bN06pqanauXOn/va3v2nRokXq0aNHF1caWK523efPn6/t27drw4YNOnTokObOnavZs2frjTfe6OJKA0dhYaFycnK0e/duvfXWW2pubtaECRPU0NDgPmbevHnatm2bNm/erMLCQlVUVGjq1KkmVu3/rnbdKyoqVFFRoRdffFEHDx7UunXrtH37dmVnZ5tcuX+7lv/fL3jppZfa/wQHA14lydiyZcsl7R999JFxww03GAcPHjT69+9vLF++vMtrC2RtXff77rvP+MY3vmFOQRbR1nUfNmyY8YMf/MCj7ZZbbjG+973vdWFlge3UqVOGJKOwsNAwDMOoq6szgoODjc2bN7uPOXTokCHJKC4uNqvMgHPxdW/La6+9ZoSEhBjNzc1dWFlgu9x1P3DggHHDDTcYlZWVl/3uvRJ6gLpAa2urHnjgAT322GMaNmyY2eVYQmtrq373u99p0KBByszMVJ8+fTRq1Kjr7iLF9RszZozeeOMNffzxxzIMQzt27NCHH36oCRMmmF1awLhwiyUyMlKStH//fjU3NysjI8N9TGpqqhITE1VcXGxKjYHo4ut+uWPsdru6d+dJU97S1nVvbGzUf/7nf2rlypWKi4tr13kJQF3ghRdeUPfu3TVnzhyzS7GMU6dOqb6+Xs8//7zuvvtu/fGPf9RXvvIVTZ06VYWFhWaXF9Dy8/M1dOhQ9evXTyEhIbr77ru1cuVKjR8/3uzSAkJra6vmzp2rsWPH6vOf/7wkqaqqSiEhIerdu7fHsbGxsaqqqjKhysDT1nW/WG1trZ5++mnNmjWri6sLXJe77vPmzdOYMWM0efLkdp+biNrJ9u/fr5dffll//etfvfqkeVxZa2urJGny5MmaN2+eJOnmm2/WO++8o9WrV+uOO+4ws7yAlp+fr927d+uNN95Q//79VVRUpJycHMXHx3v0UKB9cnJydPDgQb399ttml2IpV7vuLpdL99xzj4YOHaqnnnqqa4sLYG1d9zfeeEN//vOfdeDAgQ6dmx6gTvaXv/xFp06dUmJiorp3767u3bvr+PHjWrBggZKSkswuL2BFR0ere/fuGjp0qEf7kCFDmAXWif71r3/pu9/9rpYtW6ZJkyZpxIgRmj17tu677z69+OKLZpfn92bPnq3f/va32rFjh/r16+duj4uL0/nz51VXV+dxfHV1dbtvD+DfLnfdLzh79qzuvvtu9erVS1u2bFFwcLAJVQaey133P//5zzpy5Ih69+7t/l6VpKysLH3hC1+45vPTA9TJHnjggUv+1ZuZmakHHnhAM2fONKmqwBcSEqLbbrvtkqmTH374ofr3729SVYGvublZzc3N6tbN899WQUFB7l45XD/DMJSbm6stW7Zo586dSk5O9tg/cuRIBQcHq6CgQFlZWZKkw4cP68SJE0pPTzej5IBwtesufdrzk5mZqdDQUL3xxhvMMvWCq133hQsX6uGHH/ZoGz58uJYvX65JkyZd8+8hAHlBfX29ysrK3D+Xl5erpKREkZGRSkxMVFRUlMfxwcHBiouL0+DBg7u61IBytev+2GOP6b777tP48eN15513avv27dq2bZt27txpXtEB4GrX/Y477tBjjz2msLAw9e/fX4WFhVq/fr2WLVtmYtX+LScnRxs3btTrr7+uXr16ucf1OBwOhYWFyeFwKDs7W/Pnz1dkZKTsdrtyc3OVnp6u0aNHm1y9/7radXe5XJowYYIaGxu1YcMGuVwuuVwuSVJMTIyCgoLMLN9vXe26x8XFtdmzmZiY2GZIvSwvzVKztB07dhiSLtlmzJjR5vFMg/eOa7nua9asMQYOHGj06NHDuOmmm4ytW7eaV3CAuNp1r6ysNL75zW8a8fHxRo8ePYzBgwcbP/zhD43W1lZzC/djbV1vScbatWvdx/zrX/8yvv3tbxuf+9znjJ49expf+cpXjMrKSvOKDgBXu+6Xey9IMsrLy02t3Z9dy//vbb3meqfB8zR4AABgOQyCBgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgBJTz31lG6++WazywDQRQhAAEzxzW9+U1OmTGnXa7/whS9o7ty51/WaLVu2aPTo0XI4HOrVq5eGDRvmcY5HH31UBQUF7aoHgP/hYagAAl5BQYHuu+8+Pfvss7r33ntls9n0/vvv66233nIfExERoYiICBOrBNCV6AEC4HMKCwt1++23KzQ0VH379tXChQv1ySefSPq056iwsFAvv/yybDabbDabjh07dsXzbdu2TWPHjtVjjz2mwYMHa9CgQZoyZYpWrlzpPubiW2AXzv3ZLSkpyb3/4MGDmjhxoiIiIhQbG6sHHnhAtbW13rwMADoRAQiAT/n444/15S9/Wbfddpvee+89vfLKK1qzZo2eeeYZSdLLL7+s9PR0fetb31JlZaUqKyuVkJBwxXPGxcXpH//4hw4ePHjNdVw4d2VlpcrKyjRw4ECNHz9eklRXV6e77rpLaWlpevfdd7V9+3ZVV1fr61//evv/cABdiltgAHzKqlWrlJCQoB/96Eey2WxKTU1VRUWFvvOd7+iJJ56Qw+FQSEiIevbsqbi4uGs6Z25urv7yl79o+PDh6t+/v0aPHq0JEyZo+vTpCg0NbfM1F85tGIaysrLkcDj04x//WJL0ox/9SGlpaXruuefcx//85z9XQkKCPvzwQw0aNKiDVwFAZ6MHCIBPOXTokNLT02Wz2dxtY8eOVX19vT766KN2nTM8PFy/+93vVFZWpu9///uKiIjQggULdPvtt6uxsfGKr/3ud7+r4uJivf766woLC5Mkvffee9qxY4d73FBERIRSU1MlSUeOHGlXjQC6Fj1AACxjwIABGjBggB5++GF973vf06BBg/Tqq69q5syZbR6/YcMGLV++XDt37tQNN9zgbq+vr9ekSZP0wgsvXPKavn37dlr9ALyHAATApwwZMkS//vWvZRiGuxdo165d6tWrl/r16ydJCgkJUUtLS4d+T1JSknr27KmGhoY29xcXF+vhhx/Wj3/8Y40ePdpj3y233KJf//rXSkpKUvfufIwC/ohbYABM43Q6VVJS4rHNmjVLJ0+eVG5urj744AO9/vrrevLJJzV//nx16/bpR1ZSUpL27NmjY8eOqba2Vq2trVf8PU899ZQef/xx7dy5U+Xl5Tpw4IAeeughNTc360tf+tIlx1dVVekrX/mK7r//fmVmZqqqqkpVVVWqqamRJOXk5OjMmTOaNm2a9u3bpyNHjujNN9/UzJkzOxzMAHQNAhAA0+zcuVNpaWke29NPP63f//732rt3r2666SY98sgjys7O1ve//3336x599FEFBQVp6NChiomJ0YkTJ674e+644w4dPXpUDz74oFJTUzVx4kRVVVXpj3/8owYPHnzJ8R988IGqq6v1f//3f+rbt697u+222yRJ8fHx2rVrl1paWjRhwgQNHz5cc+fOVe/evd0hDYBvsxmGYZhdBAAAQFfinyoAAMByCEAA/N4jjzziMSX9s9sjjzxidnkAfBC3wAD4vVOnTsnlcrW5z263q0+fPl1cEQBfRwACAACWwy0wAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOf8P02QXJki4evgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# b \n",
    "mowers_df.plot.scatter(x='Lot_Size', y='Income', legend=True)\n",
    "owner_inc = mowers_df.groupby('Ownership')['Income'].mean()\n",
    "print(owner_inc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classified Correctly: 80.0 %\n",
      "      actual      p(0)      p(1) predicted\n",
      "13  Nonowner  0.418583  0.581417     Owner\n",
      "14  Nonowner  0.838644  0.161356  Nonowner\n",
      "17  Nonowner  0.936463  0.063537  Nonowner\n",
      "18  Nonowner  0.958456  0.041544  Nonowner\n",
      "20  Nonowner  0.979416  0.020584  Nonowner\n"
     ]
    }
   ],
   "source": [
    "# c\n",
    "predictors = ['Lot_Size', 'Income']\n",
    "outcome = 'Ownership'\n",
    "\n",
    "X = pd.get_dummies(mowers_df[predictors], drop_first=True)\n",
    "y = mowers_df[outcome]\n",
    "classes = ['Owner', 'Nonowner']\n",
    "\n",
    "# split into training and validation\n",
    "train_X, valid_X, train_y, valid_y = train_test_split(X, y, test_size=0.25, \n",
    "                                                      random_state=1)\n",
    "\n",
    "logit_full = LogisticRegression(penalty=\"l2\", C=1e42, solver='liblinear')\n",
    "logit_full.fit(train_X, train_y)\n",
    "\n",
    "logit_reg_pred = logit_full.predict_proba(valid_X)\n",
    "full_result = pd.DataFrame({'actual': valid_y, \n",
    "                            'p(0)': [p[0] for p in logit_reg_pred],\n",
    "                            'p(1)': [p[1] for p in logit_reg_pred],\n",
    "                            'predicted': logit_full.predict(valid_X)})\n",
    "full_result = full_result.sort_values(by=['p(1)'], ascending=False)\n",
    "\n",
    "subset_df = full_result.loc[full_result['actual'] == 'Nonowner']\n",
    "\n",
    "num_corr = 0\n",
    "total = 0\n",
    "for index, row in subset_df.iterrows():    \n",
    "    if (row['actual'] == row['predicted']):\n",
    "        num_corr += 1\n",
    "        total += 1\n",
    "    else:\n",
    "        total += 1\n",
    "\n",
    "print(\"Classified Correctly:\", num_corr/total*100.00, \"%\")\n",
    "print(subset_df)\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# d\n",
    "Cutoff percentage should be decreased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Odds of event: 1.7719334017055501\n"
     ]
    }
   ],
   "source": [
    "# e\n",
    "data = [[20, 60]]\n",
    "pred = pd.DataFrame(data, columns=['Lot_Size', 'Income'])\n",
    "\n",
    "logit_reg_pred_s = logit_full.predict_proba(pred)\n",
    "p0 = [p[0] for p in logit_reg_pred_s]\n",
    "p1 = [p[1] for p in logit_reg_pred_s]\n",
    "full_result = pd.DataFrame({'p(0)': p0,\n",
    "                            'p(1)': p1,\n",
    "                            'predicted': logit_full.predict(pred)})\n",
    "print(\"Odds of event:\", np.exp(p1[0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nonowner\n"
     ]
    }
   ],
   "source": [
    "# f\n",
    "print(full_result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94.9000000000068\n"
     ]
    }
   ],
   "source": [
    "# g. What is the minimum income that a household with 16,000 ft2 lot size should have before it is classified as an owner? \n",
    "init = 60\n",
    "while(True):\n",
    "    data = [[16, init]]\n",
    "    pred = pd.DataFrame(data, columns=['Lot_Size', 'Income'])\n",
    "\n",
    "    logit_reg_pred_s = logit_full.predict_proba(pred)\n",
    "    p0 = [p[0] for p in logit_reg_pred_s]\n",
    "    p1 = [p[1] for p in logit_reg_pred_s]\n",
    "    full_result = pd.DataFrame({'p(0)': p0,\n",
    "                                'p(1)': p1,\n",
    "                                'predicted': logit_full.predict(pred)})\n",
    "    if(full_result['predicted'][0] == 'Nonowner'):\n",
    "        init = init + 0.025\n",
    "    else:\n",
    "        print(init)\n",
    "        break\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 10.4\n",
    "\n",
    "The file eBayAuctions.csv contains information on 1972 auctions transacted on eBay.com during May–June 2004. The goal is to use these data to build a model that will distinguish competitive auctions from non-competitive ones. A competitive auction is defined as an auction with at least two bids placed on the item being auctioned. The data include variables that describe the item (auction category), the seller (his or her eBay rating), and the auction terms that the seller selected (auction duration, opening price, currency, day of week of auction close). In addition, we have the price at which the auction closed. The goal is to predict whether or not an auction of interest will be competitive.\n",
    "\n",
    "Data preprocessing. Create dummy variables for the categorical predictors.\n",
    "These include Category (18 categories), Currency (USD, GBP, Euro), EndDay\n",
    "(Monday–Sunday), and Duration (1, 3, 5, 7, or 10 days).\n",
    "\n",
    "a. Create pivot tables for the mean of the binary outcome (Competitive?) as a function of the various categorical variables (use the original variables, not the dummies). Use the information in the tables to reduce the number of dummies that will be used in the model. For example, categories that appear most similar with respect to the distribution of competitive auctions could be combined.  \n",
    "b. Split the data into training (60%) and validation (40%) datasets. Run a logistic model with all predictors with a cutoff of 0.5.  \n",
    "c. If we want to predict at the start of an auction whether it will be competitive, we cannot use the information on the closing price. Run a logistic model with all predictors as above, excluding price. How does this model compare to the full model with respect to predictive accuracy?  \n",
    "d. Interpret the meaning of the coefficient for closing price. Does closing price have a practical significance? Is it statistically significant for predicting competitiveness of auctions? (Use a 10% significance level.)  \n",
    "e. Use stepwise regression as described in Section 6.4 to find the model with the best fit to the training data (highest accuracy). Which predictors are used?  \n",
    "f. Use stepwise regression to find the model with the highest accuracy on the validation data. Which predictors are used?  \n",
    "g. What is the danger of using the best predictive model that you found?  \n",
    "h. Explain how and why the best-fitting model and the best predictive models are the same or different.  \n",
    "i. Use regularized logistic regression with L1 penalty on the training data. Compare its selected predictors and classification performance to the best-fitting and best predictive models.  \n",
    "j. If the major objective is accurate classification, what cutoff value should be used?  \n",
    "k. Based on these data, what auction settings set by the seller (duration, opening price, ending day, currency) would you recommend as being most likely to lead to a competitive auction.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_27606/691198861.py:10: FutureWarning: The `inplace` parameter in pandas.Categorical.rename_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.\n",
      "  auction_df.currency.cat.rename_categories(new_categories, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# Pre-processing\n",
    "orig_auction_df = pd.read_csv('eBayAuctions.csv')\n",
    "auction_df = pd.read_csv('eBayAuctions.csv')\n",
    "auction_df.columns = [c.replace(' ', '_') for c in auction_df.columns]\n",
    "\n",
    "auction_df['Duration'] = auction_df['Duration'].astype('category')\n",
    "\n",
    "auction_df['currency'] = auction_df['currency'].astype('category')\n",
    "new_categories = {1: 'USD', 2: 'GBP', 3: 'Euro'}\n",
    "auction_df.currency.cat.rename_categories(new_categories, inplace=True)\n",
    "auction_df = pd.get_dummies(auction_df, prefix_sep='_', drop_first=True)\n",
    "\n",
    "category_cols = [col for col in auction_df.columns if 'Category_' in col]\n",
    "endDay_cols = [col for col in auction_df.columns if 'endDay_' in col]\n",
    "\n",
    "for col in category_cols:\n",
    "    auction_df[col] = auction_df[col].astype('category')\n",
    "\n",
    "for col in endDay_cols:\n",
    "    auction_df[col] = auction_df[col].astype('category')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Competitive?\n",
      "Duration              \n",
      "1             0.521739\n",
      "3             0.450704\n",
      "5             0.686695\n",
      "7             0.489142\n",
      "10            0.544554\n",
      "          Competitive?\n",
      "currency              \n",
      "EUR           0.551595\n",
      "GBP           0.687075\n",
      "US            0.519350\n",
      "            Competitive?\n",
      "endDay_Mon              \n",
      "0               0.489466\n",
      "1               0.673358\n",
      "            Competitive?\n",
      "endDay_Sat              \n",
      "0               0.565083\n",
      "1               0.427350\n",
      "            Competitive?\n",
      "endDay_Sun              \n",
      "0               0.552020\n",
      "1               0.485207\n",
      "            Competitive?\n",
      "endDay_Thu              \n",
      "0               0.533333\n",
      "1               0.603960\n",
      "            Competitive?\n",
      "endDay_Tue              \n",
      "0               0.541366\n",
      "1               0.532164\n",
      "            Competitive?\n",
      "endDay_Wed              \n",
      "0               0.542963\n",
      "1               0.480000\n",
      "                     Competitive?\n",
      "Category_Automotive              \n",
      "0                        0.559086\n",
      "1                        0.353933\n",
      "                Competitive?\n",
      "Category_Books              \n",
      "0                    0.54171\n",
      "1                    0.50000\n",
      "                              Competitive?\n",
      "Category_Business/Industrial              \n",
      "0                                 0.539406\n",
      "1                                 0.666667\n",
      "                               Competitive?\n",
      "Category_Clothing/Accessories              \n",
      "0                                  0.542903\n",
      "1                                  0.504202\n",
      "                       Competitive?\n",
      "Category_Coins/Stamps              \n",
      "0                          0.545220\n",
      "1                          0.297297\n",
      "                       Competitive?\n",
      "Category_Collectibles              \n",
      "0                          0.535488\n",
      "1                          0.577406\n",
      "                   Competitive?\n",
      "Category_Computer              \n",
      "0                      0.538223\n",
      "1                      0.666667\n",
      "                      Competitive?\n",
      "Category_Electronics              \n",
      "0                         0.533125\n",
      "1                         0.800000\n",
      "                         Competitive?\n",
      "Category_EverythingElse              \n",
      "0                            0.543223\n",
      "1                            0.235294\n",
      "                        Competitive?\n",
      "Category_Health/Beauty              \n",
      "0                           0.552935\n",
      "1                           0.171875\n",
      "                      Competitive?\n",
      "Category_Home/Garden              \n",
      "0                         0.534225\n",
      "1                         0.656863\n",
      "                  Competitive?\n",
      "Category_Jewelry              \n",
      "0                     0.548148\n",
      "1                     0.365854\n",
      "                           Competitive?\n",
      "Category_Music/Movie/Game              \n",
      "0                              0.524538\n",
      "1                              0.602978\n",
      "                      Competitive?\n",
      "Category_Photography              \n",
      "0                         0.538540\n",
      "1                         0.846154\n",
      "                        Competitive?\n",
      "Category_Pottery/Glass              \n",
      "0                            0.54252\n",
      "1                            0.35000\n",
      "                        Competitive?\n",
      "Category_SportingGoods              \n",
      "0                           0.528139\n",
      "1                           0.725806\n",
      "                       Competitive?\n",
      "Category_Toys/Hobbies              \n",
      "0                          0.542002\n",
      "1                          0.529915\n"
     ]
    }
   ],
   "source": [
    "# a\n",
    "dur_pivot = orig_auction_df.pivot_table(index =['Duration'],\n",
    "                                    values =['Competitive?'],\n",
    "                                    aggfunc ='mean')\n",
    "print(dur_pivot)\n",
    "\n",
    "cur_pivot = orig_auction_df.pivot_table(index =['currency'],\n",
    "                                    values =['Competitive?'],\n",
    "                                    aggfunc ='mean')\n",
    "print(cur_pivot)\n",
    "\n",
    "for col in endDay_cols:\n",
    "    date_pivot = auction_df.pivot_table(index = [col],\n",
    "                                        values =['Competitive?'],\n",
    "                                        aggfunc ='mean')\n",
    "    print(date_pivot)\n",
    "\n",
    "for col in category_cols:\n",
    "    cat_pivot = auction_df.pivot_table(index = [col],\n",
    "                                        values =['Competitive?'],\n",
    "                                        aggfunc ='mean')\n",
    "    print(cat_pivot)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x700 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt=scatter_matrix(orig_auction_df,diagonal='kde',figsize=(12,7))\n",
    "\n",
    "# Combine open and close price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      actual      p(0)      p(1)  predicted\n",
      "480        1  0.000000  1.000000          1\n",
      "512        1  0.000000  1.000000          1\n",
      "1664       1  0.000000  1.000000          1\n",
      "1704       1  0.000000  1.000000          1\n",
      "1963       1  0.000000  1.000000          1\n",
      "...      ...       ...       ...        ...\n",
      "1863       0  0.963370  0.036630          0\n",
      "1960       0  0.979843  0.020157          0\n",
      "1955       0  0.995957  0.004043          0\n",
      "1952       0  0.996900  0.003100          0\n",
      "1967       0  0.998912  0.001088          0\n",
      "\n",
      "[789 rows x 4 columns]\n",
      "Classified Correctly: 76.1723700887199 %\n"
     ]
    }
   ],
   "source": [
    "# b\n",
    "outcome = auction_df['Competitive?']\n",
    "predictors = auction_df.drop('Competitive?',axis=1)\n",
    "X = auction_df.drop(columns=['Competitive?'])\n",
    "\n",
    "\n",
    "df_dummies=pd.get_dummies(predictors,drop_first=True)\n",
    "#df_dummies.insert(0,'Intercept',[1]*len(df_dummies))\n",
    "\n",
    "train_X,valid_X,train_y,valid_y=train_test_split(df_dummies,outcome,test_size=0.40, random_state=1)\n",
    "train_X_p = train_X\n",
    "valid_X_p = valid_X\n",
    "\n",
    "\n",
    "logit_full_p = LogisticRegression(penalty=\"l2\", C=1e42, solver='liblinear')\n",
    "logit_full_p.fit(train_X, train_y)\n",
    "\n",
    "logit_reg_pred_p = logit_full_p.predict_proba(valid_X)\n",
    "full_result_p = pd.DataFrame({'actual': valid_y, \n",
    "                            'p(0)': [p[0] for p in logit_reg_pred_p],\n",
    "                            'p(1)': [p[1] for p in logit_reg_pred_p],\n",
    "                            'predicted': logit_full_p.predict(valid_X)})\n",
    "full_result_p = full_result_p.sort_values(by=['p(1)'], ascending=False)\n",
    "print(full_result_p)\n",
    "\n",
    "num_corr = 0\n",
    "total = 0\n",
    "for index, row in full_result_p.iterrows():    \n",
    "    if (row['actual'] == row['predicted']):\n",
    "        num_corr += 1\n",
    "        total += 1\n",
    "    else:\n",
    "        total += 1\n",
    "\n",
    "inc_price_pctg = num_corr/total*100.00\n",
    "\n",
    "print(\"Classified Correctly:\", inc_price_pctg, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      actual      p(0)      p(1)  predicted\n",
      "1772       0  0.030589  0.969411          1\n",
      "852        1  0.083026  0.916974          1\n",
      "955        1  0.096801  0.903199          1\n",
      "1836       1  0.097049  0.902951          1\n",
      "1622       1  0.099306  0.900694          1\n",
      "...      ...       ...       ...        ...\n",
      "1081       1  0.910252  0.089748          0\n",
      "348        0  0.910385  0.089615          0\n",
      "1237       1  0.910617  0.089383          0\n",
      "1955       0  0.940586  0.059414          0\n",
      "1952       0  0.963785  0.036215          0\n",
      "\n",
      "[789 rows x 4 columns]\n",
      "Classified Correctly: 63.37135614702155 %\n",
      "When not including close price, the model is 1.202 times worse\n"
     ]
    }
   ],
   "source": [
    "# c\n",
    "new_predictors = predictors.drop('ClosePrice',axis=1)\n",
    "\n",
    "df_dummies=pd.get_dummies(new_predictors,drop_first=True)\n",
    "df_dummies.insert(0,'Intercept',[1]*len(df_dummies))\n",
    "\n",
    "train_X,valid_X,train_y,valid_y=train_test_split(df_dummies,outcome,test_size=0.40, random_state=1)\n",
    "\n",
    "logit_full = LogisticRegression(penalty=\"l2\", C=1e42, solver='liblinear')\n",
    "logit_full.fit(train_X, train_y)\n",
    "\n",
    "logit_reg_pred = logit_full.predict_proba(valid_X)\n",
    "full_result = pd.DataFrame({'actual': valid_y, \n",
    "                            'p(0)': [p[0] for p in logit_reg_pred],\n",
    "                            'p(1)': [p[1] for p in logit_reg_pred],\n",
    "                            'predicted': logit_full.predict(valid_X)})\n",
    "full_result = full_result.sort_values(by=['p(1)'], ascending=False)\n",
    "print(full_result)\n",
    "\n",
    "num_corr = 0\n",
    "total = 0\n",
    "for index, row in full_result.iterrows():    \n",
    "    if (row['actual'] == row['predicted']):\n",
    "        num_corr += 1\n",
    "        total += 1\n",
    "    else:\n",
    "        total += 1\n",
    "\n",
    "not_inc_price_pctg = num_corr/total*100.00\n",
    "print(\"Classified Correctly:\", not_inc_price_pctg, \"%\")\n",
    "   \n",
    "\n",
    "print(\"When not including close price, the model is\", inc_price_pctg/not_inc_price_pctg, \"times worse\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intercept  -0.36315695612599685\n",
      "       sellerRating  ClosePrice  OpenPrice  Category_Automotive  \\\n",
      "coeff     -0.000046    0.088855  -0.105865             1.758587   \n",
      "\n",
      "       Category_Books  Category_Business/Industrial  \\\n",
      "coeff        0.557255                      -0.08761   \n",
      "\n",
      "       Category_Clothing/Accessories  Category_Coins/Stamps  \\\n",
      "coeff                       0.323714              -0.033867   \n",
      "\n",
      "       Category_Collectibles  Category_Computer  ...  Duration_3  Duration_5  \\\n",
      "coeff               0.171399          -0.609743  ...    1.256207   -0.108202   \n",
      "\n",
      "       Duration_7  Duration_10  endDay_Mon  endDay_Sat  endDay_Sun  \\\n",
      "coeff   -0.186949     0.315695    0.280735   -0.612956   -0.468657   \n",
      "\n",
      "       endDay_Thu  endDay_Tue  endDay_Wed  \n",
      "coeff    -0.56343   -0.198906   -0.712514  \n",
      "\n",
      "[1 rows x 32 columns]\n"
     ]
    }
   ],
   "source": [
    "# d\n",
    "print('intercept ', logit_full_p.intercept_[0])\n",
    "print(pd.DataFrame({'coeff': logit_full_p.coef_[0]}, index=X.columns).transpose())\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Closing Price\n",
    "The coefficient of closing price indicates that it has a positive effect on competitiveness. The coefficient is 0.089, which is considered statistically significant when using a p-value of 0.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variables: sellerRating, ClosePrice, OpenPrice, currency_GBP, currency_US, Duration_3, Duration_5, Duration_7, Duration_10, Category_Automotive_1, Category_Books_1, Category_Business/Industrial_1, Category_Clothing/Accessories_1, Category_Coins/Stamps_1, Category_Collectibles_1, Category_Computer_1, Category_Electronics_1, Category_EverythingElse_1, Category_Health/Beauty_1, Category_Home/Garden_1, Category_Jewelry_1, Category_Music/Movie/Game_1, Category_Photography_1, Category_Pottery/Glass_1, Category_SportingGoods_1, Category_Toys/Hobbies_1, endDay_Mon_1, endDay_Sat_1, endDay_Sun_1, endDay_Thu_1, endDay_Tue_1, endDay_Wed_1\n",
      "Start: score=1716.20, constant\n",
      "Step: score=1676.05, add endDay_Mon_1\n",
      "Step: score=1645.10, add ClosePrice\n",
      "Step: score=1599.18, add OpenPrice\n",
      "Step: score=1571.92, add Category_Health/Beauty_1\n",
      "Step: score=1551.14, add currency_GBP\n",
      "Step: score=1536.20, add Category_Coins/Stamps_1\n",
      "Step: score=1524.50, add Category_Automotive_1\n",
      "Step: score=1519.89, add Duration_5\n",
      "Step: score=1515.38, add sellerRating\n",
      "Step: score=1511.82, add Category_Clothing/Accessories_1\n",
      "Step: score=1507.95, add Category_EverythingElse_1\n",
      "Step: score=1505.33, add Category_Jewelry_1\n",
      "Step: score=1503.52, add Category_Business/Industrial_1\n",
      "Step: score=1501.89, add Category_SportingGoods_1\n",
      "Step: score=1500.47, add Category_Pottery/Glass_1\n",
      "Step: score=1500.47, unchanged None\n",
      "['endDay_Mon_1', 'ClosePrice', 'OpenPrice', 'Category_Health/Beauty_1', 'currency_GBP', 'Category_Coins/Stamps_1', 'Category_Automotive_1', 'Duration_5', 'sellerRating', 'Category_Clothing/Accessories_1', 'Category_EverythingElse_1', 'Category_Jewelry_1', 'Category_Business/Industrial_1', 'Category_SportingGoods_1', 'Category_Pottery/Glass_1']\n",
      "LinearRegression()\n"
     ]
    }
   ],
   "source": [
    "# e\n",
    "def train_model(variables):\n",
    "    if len(variables) == 0:\n",
    "        return None\n",
    "    model = LinearRegression()\n",
    "    model.fit(train_X[variables], train_y)\n",
    "    return model\n",
    "\n",
    "def score_model(model, variables):\n",
    "    if len(variables) == 0:\n",
    "        return AIC_score(train_y, [train_y.mean()] * len(train_y), model, df=1)\n",
    "    return AIC_score(train_y, model.predict(train_X[variables]), model)\n",
    "\n",
    "best_step_model, best_step_variables = stepwise_selection(train_X_p.columns, train_model, score_model, verbose=True)\n",
    "print(best_step_variables)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LASSO\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "               Mean Error (ME) : 0.0219\n",
      "Root Mean Squared Error (RMSE) : 0.4804\n",
      "     Mean Absolute Error (MAE) : 0.4766\n",
      "\n",
      "\n",
      "LASSO CV\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "               Mean Error (ME) : 0.0218\n",
      "Root Mean Squared Error (RMSE) : 0.4813\n",
      "     Mean Absolute Error (MAE) : 0.4776\n",
      "Lasso-CV chosen regularization:  1.242215531068193\n"
     ]
    }
   ],
   "source": [
    "print(\"LASSO\")\n",
    "lasso = Lasso(alpha=1)\n",
    "lasso.fit(train_X, train_y)\n",
    "regressionSummary(valid_y, lasso.predict(valid_X))\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"LASSO CV\")\n",
    "lasso_cv = LassoCV(cv=5)\n",
    "lasso_cv.fit(train_X, train_y)\n",
    "regressionSummary(valid_y, lasso_cv.predict(valid_X))\n",
    "print('Lasso-CV chosen regularization: ', lasso_cv.alpha_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RIDGE\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "               Mean Error (ME) : 0.0172\n",
      "Root Mean Squared Error (RMSE) : 0.4623\n",
      "     Mean Absolute Error (MAE) : 0.4303\n",
      "\n",
      "\n",
      "BAYESIAN RIDGE\n",
      "\n",
      "Regression statistics\n",
      "\n",
      "               Mean Error (ME) : 0.0179\n",
      "Root Mean Squared Error (RMSE) : 0.4607\n",
      "     Mean Absolute Error (MAE) : 0.4367\n",
      "Bayesian ridge chosen regularization:  16.53562606806346\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# f\n",
    "print(\"RIDGE\")\n",
    "ridge = Ridge(alpha=1)\n",
    "ridge.fit(train_X, train_y)\n",
    "regressionSummary(valid_y, ridge.predict(valid_X))\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"BAYESIAN RIDGE\")\n",
    "bayesianRidge = BayesianRidge()\n",
    "bayesianRidge.fit(train_X, train_y)\n",
    "regressionSummary(valid_y, bayesianRidge.predict(valid_X))\n",
    "print('Bayesian ridge chosen regularization: ', bayesianRidge.lambda_ / bayesianRidge.alpha_)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Best Model\n",
    "RIDGE: Lowest ME (0.0172), lowest MAE (0.4303), second lowest RMSE (0.4623)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# g\n",
    "The biggest concern with using Bayesian Ridge Regression is that the underlying model assumes a linear relationship. This linear relationship is not able to capture the logistic regression fit and accurately map all outcomes, as indicated by the high MAE and RMSE."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# h\n",
    "The best-fitting models and the best predictive models can often differ due to many factors. A model that fits very well to the training data may be overfitted, leading to poor results when predicting future, unknown data. The best predictive model on the test data set may be too simplistic, and fail to properly represent data with abnormal or unique behavior unseen from the model found in the training set. Various errors are a good indicator of where a best-fit model may differ from the best predictive model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      actual      p(0)      p(1)  predicted\n",
      "480        1  0.000000  1.000000          1\n",
      "1661       1  0.000000  1.000000          1\n",
      "1962       1  0.000000  1.000000          1\n",
      "1704       1  0.000000  1.000000          1\n",
      "1664       1  0.000000  1.000000          1\n",
      "...      ...       ...       ...        ...\n",
      "1863       0  0.962442  0.037558          0\n",
      "1960       0  0.978774  0.021226          0\n",
      "1955       0  0.995925  0.004075          0\n",
      "1952       0  0.996922  0.003078          0\n",
      "1967       0  0.998845  0.001155          0\n",
      "\n",
      "[789 rows x 4 columns]\n",
      "Classified Correctly: 75.66539923954373 %\n"
     ]
    }
   ],
   "source": [
    "# i\n",
    "logit_full_1 = LogisticRegression(penalty=\"l1\", C=1e42, solver='liblinear')\n",
    "logit_full_1.fit(train_X, train_y)\n",
    "\n",
    "logit_reg_pred_1 = logit_full_1.predict_proba(valid_X)\n",
    "full_result_1 = pd.DataFrame({'actual': valid_y, \n",
    "                            'p(0)': [p[0] for p in logit_reg_pred_1],\n",
    "                            'p(1)': [p[1] for p in logit_reg_pred_1],\n",
    "                            'predicted': logit_full_1.predict(valid_X)})\n",
    "full_result_1 = full_result_1.sort_values(by=['p(1)'], ascending=False)\n",
    "print(full_result_1)\n",
    "\n",
    "num_corr = 0\n",
    "total = 0\n",
    "for index, row in full_result_1.iterrows():    \n",
    "    if (row['actual'] == row['predicted']):\n",
    "        num_corr += 1\n",
    "        total += 1\n",
    "    else:\n",
    "        total += 1\n",
    "\n",
    "pctg_1 = num_corr/total*100.00\n",
    "print(\"Classified Correctly:\", pctg_1, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='ClosePrice', ylabel='Competitive?'>"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# j\n",
    "sns.regplot(x='ClosePrice', y='Competitive?', data=auction_df, logistic=True)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# j\n",
    "This plot alone does not give much insight into a good cutoff value. The logistic regression model is multi-variate, and many variables have differing coefficients. Using PCA, plotting more variables, and varying cutoff values to obtain error rates are necessary to experimentally find a good cutoff value. Using the default of 0.5 suffices for this problem, since the error rates are not abnormally high. Adjusting the cutoff value will alter both the true negative and false positive error rates."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k\n",
    "An auction that lasts 10 days contributes most strongly to a competitive auction. The ending day has multiple candidates that all negatively contribute to a competitive auction."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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,
 "nbformat_minor": 2
}