Assignment6.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegressionCV, LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix, roc_auc_score, roc_curve, auc\n",
    "import random\n",
    "random.seed(123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>AgeGroup</th>\n",
       "      <th>Race</th>\n",
       "      <th>Shots</th>\n",
       "      <th>Completed</th>\n",
       "      <th>InsuranceType</th>\n",
       "      <th>MedAssist</th>\n",
       "      <th>Location</th>\n",
       "      <th>LocationType</th>\n",
       "      <th>PracticeType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  AgeGroup  Race  Shots  Completed  InsuranceType  MedAssist  Location  \\\n",
       "0   21         1     0      3          1              3          0         1   \n",
       "1   21         1     0      3          1              3          0         1   \n",
       "2   20         1     0      1          0              1          0         1   \n",
       "3   14         0     0      3          1              3          0         1   \n",
       "4   17         0     3      2          0              3          0         1   \n",
       "\n",
       "   LocationType  PracticeType  \n",
       "0             0             1  \n",
       "1             0             1  \n",
       "2             0             1  \n",
       "3             0             0  \n",
       "4             0             1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "vaccine = pd.read_csv('vaccination.csv')\n",
    "vaccine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Race</th>\n",
       "      <th>LocationType</th>\n",
       "      <th>Completed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Race  LocationType  Completed\n",
       "0   21     0             0          1\n",
       "1   21     0             0          1\n",
       "2   20     0             0          0\n",
       "3   14     0             0          1\n",
       "4   17     3             0          0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "vaccine = vaccine[['Age', 'Race', 'LocationType', 'Completed']]\n",
    "vaccine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1413.000000\n",
       "mean       18.547063\n",
       "std         4.234129\n",
       "min        11.000000\n",
       "25%        15.000000\n",
       "50%        18.000000\n",
       "75%        22.000000\n",
       "max        26.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine['Age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3rcy/AodV1TVD254MfAc4gfunlXl2VX106cuWJC3GREKmquadWraqvgu8vH/M1e4euiA6\nfWmqkyQtlWkdk5EkrQCGjCSpGUNGktSMISNJasaQkSQ1Y8hIkpoxZCRJzRgykqRmDBlJUjOGjCSp\nGUNGktSMISNJasaQkSQ1Y8hIkpoxZCRJzUzqpmXaSsm8t+SRpImzJyNJasaezDK194kXjX2fm846\ncuz7lLS82ZORJDVjyEiSmjFkJEnNGDKSpGYMGUlSM4aMJKkZQ0aS1IwhI0lqxpCRJDVjyEiSmjFk\nJEnNGDKSpGYMGUlSM4aMJKkZQ0aS1IwhI0lqxpCRJDVjyEiSmjFkJEnNGDKSpGYMGUlSM4aMJKkZ\nQ0aS1My2ky5AkgYlmXQJWkL2ZCRJzdiTkTRV9j7xorHub9NZR451fw839mQkSc0YMpKkZgwZSVIz\nhowkqRlDRpLUjCEjSWrGkJEkNWPISJKaMWQkSc0YMpKkZgwZSVIzUxsySQ5OUiMetw61W5XknUlu\nTnJHkkuSHDCpuiVJ91sOE2T+AfBPA6/vnvki3ZzgFwL7AC8DbgFOAi5L8riqunGchUqSHmg5hMwX\nquqqWdYdBTwROKSqLgNIciVwA/BquoCSJE3I1B4uW6CjgK/PBAxAVd0GfBQ4emJVSZKA5REy70ty\nT5JvJfmLJGsG1u0PXDtim43AmiQ7j6dESdIo03y47DbgzcAG4NvAzwKvBa5M8rNVdROwG/DlEdtu\n6Z9XAd9pX6okaZSpDZmq+mfgnwcWbUhyBfAZurGWPwQC1IjN57xJeJLjgeMB1qxZM1dTSdJWWA6H\ny+5TVdcA1wNP6BdtoevNDFvVP98yy/ucV1Xrqmrd6tWrl75QSRKwzEKmN9h72Ug3LjNsP+ArVeWh\nMkmaoGUVMknWAT8FfLpfdCGwV5InDbTZFXh6v06SNEFTOyaT5H1017tcA9xKN/B/EvA14K19swuB\nK4H1SV7F/RdjBnjjuGuWJD3Q1IYM3anJz6W7kn8n4JvAh4FTqupmgKq6N8mRwJuAtwM70oXOk6vq\nqxOpWpJ0n6kNmao6EzhzAe22AMf1D0nSFFlWYzKSpOXFkJEkNWPISJKaMWQkSc0YMpKkZgwZSVIz\nhowkqRlDRpLUjCEjSWrGkJEkNWPISJKaMWQkSc0YMpKkZgwZSVIzhowkqRlDRpLUjCEjSWrGkJEk\nNWPISJKaMWQkSc0YMpKkZgwZSVIzhowkqRlDRpLUjCEjSWrGkJEkNWPISJKaMWQkSc0YMpKkZgwZ\nSVIzhowkqRlDRpLUjCEjSWrGkJEkNWPISJKaMWQkSc0YMpKkZgwZSVIzhowkqRlDRpLUjCEjSWrG\nkJEkNWPISJKaMWQkSc0YMpKkZgwZSVIzhowkqRlDRpLUjCEjSWrGkJEkNWPISJKaMWQkSc0YMpKk\nZgwZSVIzhowkqRlDRpLUzIoImSSPSvJXSW5L8u0kH06yZtJ1SdLD3bIPmSQ7AZcCPwMcC/wm8N+A\ny5L88CRrk6SHu20nXcAS+G3g0cBPV9WXAJL8G/BF4HeAt0ywNkl6WFv2PRngKOCqmYABqKobgH8E\njp5YVZKkFREy+wPXjli+EdhvzLVIkgakqiZdw1ZJ8n3gLVX1mqHlpwOvqao5DwmuW7eurr766oey\n30VvI0nTZGv+/if5bFWtm6/dShiTARj1LzVrCiQ5Hji+f/mdJNc1qWrx9gBunnQR85j2Gqe9PrDG\npTDt9cH017hHkq2pb++FNFoJIXMLsNuI5av6dQ9SVecB57Us6qFIcvVCPhlM0rTXOO31gTUuhWmv\nD6a/xnHVtxLGZDbSjcsM2w/4/JhrkSQNWAkhcyFwYJJHzyxIshb4pX6dJGlCVkLI/BnwZeAjSY5O\nchTwEeCrwJ9OsrCHYOoO4Y0w7TVOe31gjUth2uuD6a9xLPUt+7PLAPopZM4GfpVuwP/vgf9ZVV+e\nZF2S9HC3IkJGkjSdVsLhsmUjySOTvDXJlUnuTFL9+NFgm3VJzkvy732bryR5X5J9pqG+Educ1Lf7\nh9b1LbbGJI9J8qEkNyf5bpLrkpwwDfUlWZPkvf3/751Jrk9y+jjm20vyzCQXJNk08O9yZpJdhtqt\nSvLO/t/vjiSXJDlgGupL8pQk65P8R9/mP5Kcm2TP1vUttMYR2/xp//OwfprqS3Jgko8nubX/f/5c\nkt9YqloMmfHaF3g23anVn5qlzW/QnS33x8DhwGuAnwOuTvKoKajvPv3JFicDNzWua9CCakyyDvg0\nsAPwYuBpwJuBH5p0fX2QXAIcBPwv4AjgncArgHc3rg/glcA9wGuBw4BzgZcCn0yyTV9j6E6cOQx4\nGfDrwHZ0E88+ctL1AS8BdgdO79ucST/FVJKdG9e30Brvk+QXgWOAb4+htgXXl+QI4Argm8Dz6Kbi\n+jNgxyWrpKp8jOkBbDPw9YvpLiJdO9Rm9Yjt9gbuBV4/6fqG2n+C7uSKy4F/mKJ/w23oTm3/6yn9\nP35qv/ypQ8vfANwN7NS4xlE/Yy/oazqkf310//rJA21+BNgC/PEU1DeqzUF9m+PG8P88b40Dy7ej\nm/rqJLqTlNZPQ33ALnQfEM9pWYs9mTGqqnsX0GbziGWbgM3AXi3qGtjPvPXNSPI8uh7WSe0qerAF\n1ngw3XVSY5+Be4H1bd8/D3+qvZUuIJvOWTTqZwz4p/555mfsKODrVXXZwHa3AR+l8cSzC6lvgd9D\nM4vc/6voetBvblrUgAXW9yxgNY3rMmSWgSSPAfYEvjDpWqA7Vk93Nt+rq2rLpOsZ4Yn9845Jrkry\ngyQ3JfnjJI+YaGWdS+huRXFWkv2S7JzkEOAE4B1VdccEanpS/zzzMzbXxLNrxnRIatBwfQ+1TUsP\n2n+SnwT+EPjdqvr+RKq633B9T6TrmR7Qj8PcneSrSU5JsmSHlQ2ZKZdkW+AddD2Zd024nBl/BFwP\nnD/hOmbzE/3zB4GL6U5tfyPd4au/mFRRM6rqe3S/4DOH9W6nO+3+IuD3x11Pkr2A1wOXVNXMbLG7\nMXpappkPFavGURvMWt9wm12Ac+j+gP7NuGob2P9sNb4D+PBgj3ASZqnvJ4Cd6H4nzgd+BXgv3Tjh\nm5Zq3yth7rKV7m3ALwJHVNXIudjGKckv0x3b/bnqD+xOoZkPT+ur6nX915f3n87ekGS/qprYlENJ\ndqQLwD3p7uT6FeDngdfRjcm8dIy17Ex38fLdwIsGV7HIiWdbmKO+wTbbAu+nOwz0S1V19/gqnL3G\nJM8HnkB3196JmePfcBu6Af6Tq2rm0PLlSXYHfi/Jqf0h0q1iT2aKJTmTbrbo46rq4knX0/tTuh7V\njUl+NMmP0n1Y+aH+9Q6TLQ+Ab/XPnxxaPvNv+Lgx1jLKb9GNGz2tqtZX1RVV9Sa6s8tekuSx4yii\nD7sL6e4se2hV3TiweguzTzwLs0w+u5TmqW+mzTZ0n75/BfgfVfVvretaSI39H/a3AGcB3xv4XdkG\n2K5/vd2k6uvN9XuyHaPnhFw0Q2ZKJTmZ7vTlE6rq/026ngGPoTt99JaBxy8BB/Zfj+1T+Bw29s/D\nn8RnPoUv+ASHRg4Abqmq/xha/pn++TGtC+j/wF1A14N6WlV9bqjJXBPPfqWqvjPh+ma8A3gO8BtV\n9fctaxo2T4170A2q/x8e+LvyKO4/xf2ICdYHY/o98XDZFEryB3Tn/59cVW+ddD1Dnjxi2Tl0Z8+8\nDPjSiPXj9nfAXXTXB1w0sPzQ/nnxd6lbWt8EViXZtwZuGw789/75ay133n/6fx/wFLrDsFeNaHYh\n8KIkT6qqDf12uwJPp/G41gLrI8mb6cbZjq2qsY7DLKDGbzL6d+UDwOeAMxh9YsW46oNu7Oo0ut+T\nwVoOBb63VPUZMmOW5Jn9l4/vnw9PshnYXFUb+ittzwE+Dlya5MCBzb/deixhvvqq6vIR29wKbDtq\n3YRq/FZ/qPF/Jfk2cCmwjm7M471Df9jHXh/dIOvLgY8lOYNuTGYd3YDrZ4F/bFkf8Cd0p6+eAdwx\n9DN2Y39I5ULgSmB9klfRffI+ie5T7hsnXV+SE+n+Dd8NfHGozeYRvcSx10h3/dgDJPke8F9j+F2Z\nt76qujbJ+cDr+1C6hu6w44uB05ast9r6oiAfD7ogqmZ5XN6vP3++NpOsb5ZtLmdMF2MutEa6P4Yv\np+tZfR/YRHd2zXZTUt9+wF/SzRb+Xbqz9d4ErBpDfV+eo8ZTB9rtRvdHfAtwJ90ZcI+dhvr6n7nZ\n2pw/DTXOsd04LsZc6P/x9nRHTb7a/55cT3eIfslqcYJMSVIzDvxLkpoxZCRJzRgykqRmDBlJUjOG\njCSpGUNGktSMISONUbrbGVeSsd/rRpoEr5ORxqS/l803gV3p7ki4V415xmBp3OzJSOPzDLqA+Rjd\nNP+HTbYcqT1DRhqfY+nmAHsh3VQyLxhukOS5Sf49yff6uxUeleTyJJcPtdsjyblJvpbkrn6b48fx\nTUiL4QSZ0hgk+Qm6yQfPq6rNSf4G+LUkq6q/GV2SX6WbOfdCunvL7EE3WeqOdHNKzbzXrnSTaD4C\nOBW4gW7m3HOT7FDTN3O3HsYMGWk8fpPuyMGf96/fCzyX7l4o7+iX/W/g88Azqh8sTfI5upmZrx94\nrxOAvYEDquqL/bJL+ptinZLkXMd6NC08XCaNxwuAL1bVlf3rS4Cv98vpbw29DrigBs7Gqapr6Hoq\ngw4DPg3ckGTbmQfwCWB3uhmepalgT0ZqLMkT6P7wn9X3NmZ8GPj9JD8F3EZ3y9ubRrzFfw293hPY\nF/jBLLvcfesqlpaOISO1d2z/fGL/GPYC4BS60NhzxPofo7ux2Yxv0YXRCbPs77qHVqa09LxORmoo\nyfZ0h8W+BLxmRJOz6W4OtpZuMH9XurGWmTGZx9PdLnpDVR3cLzuV7lbXj6mqUT0faWoYMlJDSX4N\nuAB4YVW9d8T6lwDnAofQHVm4GPgIcB7d2WWn0p1F9oWqOqTf5keAq+jGVM+m67n8MPAzwC9X1dFt\nvytp4Rz4l9o6Frgd+NAs699Pd83MsVX1SeAY4DHAX9MdWnsF3SwBt81sUFW3Ab9Id1HniXQD/u8G\njgYua/JdSA+RPRlpiiV5JN2htjOq6rRJ1yMtliEjTYl+brO30J3efDPwaODVdAP/+1fVNyZYnvSQ\neHaZND3uAX4ceBvdach3AJ8CnmXAaLmyJyNJasaBf0lSM4aMJKkZQ0aS1IwhI0lqxpCRJDVjyEiS\nmvn/K7AioF8szvsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fbb3a51d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = vaccine['Age'].plot(kind='hist', figsize=(6, 6), legend=False, fontsize=16, edgecolor = 'black', linewidth=2);\n",
    "plot.set_ylabel('Count', fontsize=16)\n",
    "plot.set_xlabel('Age', fontsize=16)\n",
    "plt.show()\n",
    "plt.rcdefaults()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DQ8nJybJYLBo9erSioqJ06NAhjRkzRikpKY3O5enpqfT0dAUHBysjI0OhoaEaO3aswsLC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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fbb780f5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "race = vaccine.groupby('Race')['Race'].count()\n",
    "race = pd.DataFrame(race)\n",
    "types = ['White', 'Black', 'Hispanic', 'other/unknown']\n",
    "types = pd.DataFrame(types)\n",
    "race['type'] = types\n",
    "race = race.set_index('type')\n",
    "race.columns = ['Count']\n",
    "race.head()\n",
    "\n",
    "plot = race['Count'].plot(kind='bar', figsize=(6, 6), fontsize=16, edgecolor = 'black', linewidth=2);\n",
    "plot.set_ylabel('Count', fontsize=16)\n",
    "plot.set_xlabel('Race', fontsize=16)\n",
    "plt.show()\n",
    "plt.rcdefaults()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SJC8vL61YsUI+Pj764IMPGrM7AAAAp2tUOMrNzVX//v3l6+tb7zxfX1/1799f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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fbb78d8438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loc = vaccine.groupby('LocationType')['LocationType'].count()\n",
    "loc = pd.DataFrame(loc)\n",
    "types = ['suburban', 'urban']\n",
    "types = pd.DataFrame(types)\n",
    "loc['type'] = types\n",
    "loc = loc.set_index('type')\n",
    "loc.columns = ['Count']\n",
    "loc.head()\n",
    "\n",
    "plot = loc['Count'].plot(kind='bar', figsize=(6, 6), fontsize=16, edgecolor = 'black', linewidth=2);\n",
    "plot.set_ylabel('Count', fontsize=16)\n",
    "plot.set_xlabel('Location Type', fontsize=16)\n",
    "plt.show()\n",
    "plt.rcdefaults()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "vaccineC = vaccine.copy()\n",
    "\n",
    "for i, loc in enumerate(vaccine['LocationType']):\n",
    "    if loc == 0:\n",
    "        vaccineC['LocationType'][i] = \"suburban\"\n",
    "    else:\n",
    "        vaccineC['LocationType'][i] = \"urban\"\n",
    "\n",
    "for i, loc in enumerate(vaccine['Race']):\n",
    "    if loc == 0:\n",
    "        vaccineC['Race'][i] = \"White\"\n",
    "    elif loc == 1:\n",
    "        vaccineC['Race'][i] = \"Black\"\n",
    "    elif loc == 2:\n",
    "        vaccineC['Race'][i] = \"Hispanic\"\n",
    "    else:\n",
    "        vaccineC['Race'][i] = \"other/unknown\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LocationType</th>\n",
       "      <th>Race</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">suburban</th>\n",
       "      <th>Black</th>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other/unknown</th>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">urban</th>\n",
       "      <th>Black</th>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other/unknown</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Count\n",
       "LocationType Race                \n",
       "suburban     Black            245\n",
       "             Hispanic          21\n",
       "             White            534\n",
       "             other/unknown    163\n",
       "urban        Black            198\n",
       "             Hispanic          31\n",
       "             White            198\n",
       "             other/unknown     23"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups = vaccineC.groupby(['LocationType', 'Race'])['Age'].count()\n",
    "groups = pd.DataFrame(groups)\n",
    "groups.columns = ['Count']\n",
    "groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iYmLk7e1tUxYdHa3AwECbu95yM2fOHIWFhVnD0KhRo7R7927NmTNHHTt2tGsc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\nRWLIISIiIkViyCEiIiJF+n+ESRPGlmfKjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fbb78ea5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groups = groups.unstack(\"LocationType\")\n",
    "plot = groups.plot(kind='bar', figsize=(6, 6), fontsize=16, edgecolor = 'black', linewidth=2);\n",
    "plot.set_ylabel('Count', fontsize=16)\n",
    "plot.set_xlabel('Race', fontsize=16)\n",
    "plt.show()\n",
    "plt.rcdefaults()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3319179051663128"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete = vaccine['Completed']\n",
    "complete.sum() / len(complete)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vaccine = vaccine.ix[:, vaccine.columns != 'Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dummy_ranks = pd.get_dummies(vaccine['Race'], prefix='Race')\n",
    "dummy_ranks2 = pd.get_dummies(vaccine['LocationType'], prefix='LocationType')\n",
    "vaccine = vaccine[['Completed']].join(dummy_ranks.ix[:,'Race_1':])\n",
    "vaccine = vaccine[list(vaccine)].join(dummy_ranks2.ix[:,'LocationType_1':])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "msk = np.random.rand(len(vaccine)) < 0.7\n",
    "vaccineTrain = vaccine[msk]\n",
    "vaccineTest = vaccine[~msk]\n",
    "\n",
    "vaccineTrain_features = np.array(vaccineTrain.ix[:, vaccineTrain.columns != 'Completed'])\n",
    "vaccineTrain_lables = np.array(vaccineTrain[['Completed']])\n",
    "vaccineTrain_lables = vaccineTrain_lables.ravel()\n",
    "\n",
    "vaccineTest_features = np.array(vaccineTest.ix[:, vaccineTest.columns != 'Completed'])\n",
    "vaccineTest_lables = np.array(vaccineTest[['Completed']])\n",
    "vaccineTest_lables = vaccineTest_lables.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "L1cv = LogisticRegressionCV(Cs=list(np.power(10.0, np.arange(-10, 10))), random_state=333, scoring='neg_log_loss', cv=10, penalty='l1', solver='liblinear')\n",
    "L1cv.fit(vaccineTrain_features, vaccineTrain_lables)\n",
    "predsL1 = L1cv.predict(vaccineTest_features)\n",
    "\n",
    "L2cv = LogisticRegressionCV(Cs=list(np.power(10.0, np.arange(-10, 10))),random_state=333, scoring='neg_log_loss', cv=10, penalty='l2', solver='liblinear')\n",
    "L2cv.fit(vaccineTrain_features, vaccineTrain_lables)\n",
    "predsL2 = L2cv.predict(vaccineTest_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted 0</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0          289            0\n",
       "Actual 1          134            0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# L1 regularized logistic regression confusion matrix\n",
    "l1v1 = pd.DataFrame(confusion_matrix(vaccineTest_lables, predsL1))\n",
    "l1v1.columns = ['Predicted 0', 'Predicted 1']\n",
    "l1v1.index = ['Actual 0', 'Actual 1']\n",
    "l1v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted 0</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0          289            0\n",
       "Actual 1          134            0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# L2 regularized logistic regression confusion matrix\n",
    "l2v1 = pd.DataFrame(confusion_matrix(vaccineTest_lables, predsL2))\n",
    "l2v1.columns = ['Predicted 0', 'Predicted 1']\n",
    "l2v1.index = ['Actual 0', 'Actual 1']\n",
    "l2v1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both of the models predict a label of 0 for all of the test observations! This implies that the model is no better than a random guess, as the true positive rate equals the false positive rate.\n",
    "\n",
    "\n",
    "The true positive rate is given as: true positive / (true positive + false negative)\n",
    "\n",
    "The false positive rate is given as: false positive / (false positive + true negative)\n",
    "\n",
    "Therefore, the true positive rate for the two cases above is equal to: 0 / (0 + 128) = 0\n",
    "\n",
    "and the false positive rate is equal to: 0 / (0 + 328) = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To (try to) improve the predictive power of this model we will add additional features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InsuranceType</th>\n",
       "      <th>Location</th>\n",
       "      <th>MedAssist</th>\n",
       "      <th>Race</th>\n",
       "      <th>LocationType</th>\n",
       "      <th>Completed</th>\n",
       "      <th>PracticeType</th>\n",
       "      <th>AgeGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   InsuranceType  Location  MedAssist  Race  LocationType  Completed  \\\n",
       "0              3         1          0     0             0          1   \n",
       "1              3         1          0     0             0          1   \n",
       "2              1         1          0     0             0          0   \n",
       "3              3         1          0     0             0          1   \n",
       "4              3         1          0     3             0          0   \n",
       "\n",
       "   PracticeType  AgeGroup  \n",
       "0             1         1  \n",
       "1             1         1  \n",
       "2             1         1  \n",
       "3             0         0  \n",
       "4             1         0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccine = pd.read_csv('vaccination.csv')\n",
    "vaccine = vaccine[['InsuranceType', 'Location', 'MedAssist', 'Race', 'LocationType','Completed', 'PracticeType', 'AgeGroup']]\n",
    "vaccine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dummy_ranks = pd.get_dummies(vaccine['Race'], prefix='Race')\n",
    "dummy_ranks2 = pd.get_dummies(vaccine['LocationType'], prefix='LocationType')\n",
    "dummy_ranks3 = pd.get_dummies(vaccine['InsuranceType'], prefix='InsuranceType')\n",
    "dummy_ranks4 = pd.get_dummies(vaccine['Location'], prefix='Location')\n",
    "dummy_ranks5 = pd.get_dummies(vaccine['PracticeType'], prefix='PracticeType')\n",
    "\n",
    "vaccine = vaccine[['Completed', 'MedAssist']].join(dummy_ranks.ix[:,'Race_1':])\n",
    "vaccine = vaccine[list(vaccine)].join(dummy_ranks2.ix[:,'LocationType_1':])\n",
    "vaccine = vaccine[list(vaccine)].join(dummy_ranks3.ix[:,'InsuranceType_1':])\n",
    "vaccine = vaccine[list(vaccine)].join(dummy_ranks4.ix[:,'Location_1':])\n",
    "vaccine = vaccine[list(vaccine)].join(dummy_ranks5.ix[:,'PracticeType_1':])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "msk = np.random.rand(len(vaccine)) < 0.7\n",
    "vaccineTrain = vaccine[msk]\n",
    "vaccineTest = vaccine[~msk]\n",
    "\n",
    "vaccineTrain_features = np.array(vaccineTrain.ix[:, vaccineTrain.columns != 'Completed'])\n",
    "vaccineTrain_lables = np.array(vaccineTrain[['Completed']])\n",
    "vaccineTrain_lables = vaccineTrain_lables.ravel()\n",
    "\n",
    "vaccineTest_features = np.array(vaccineTest.ix[:, vaccineTest.columns != 'Completed'])\n",
    "vaccineTest_lables = np.array(vaccineTest[['Completed']])\n",
    "vaccineTest_lables = vaccineTest_lables.ravel()\n",
    "\n",
    "L1cv = LogisticRegressionCV(Cs=list(np.power(10.0, np.arange(-10, 10))),  random_state=333, scoring='neg_log_loss', penalty='l1', solver='liblinear', max_iter=10000)\n",
    "L1cv.fit(vaccineTrain_features, vaccineTrain_lables)\n",
    "predsL1 = L1cv.predict(vaccineTest_features)\n",
    "\n",
    "L2cv = LogisticRegressionCV(Cs=list(np.power(10.0, np.arange(-10, 10))), random_state=333, scoring='neg_log_loss', cv=10, penalty='l2', solver='liblinear', max_iter=10000)\n",
    "L2cv = LogisticRegression()\n",
    "L2cv.fit(vaccineTrain_features, vaccineTrain_lables)\n",
    "predsL2 = L2cv.predict(vaccineTest_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted 0</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
       "      <td>306</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>152</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0          306            0\n",
       "Actual 1          152            0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# L1 regularized logistic regression confusion matrix\n",
    "l1v2 = pd.DataFrame(confusion_matrix(vaccineTest_lables, predsL1))\n",
    "l1v2.columns = ['Predicted 0', 'Predicted 1']\n",
    "l1v2.index = ['Actual 0', 'Actual 1']\n",
    "l1v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "\n",
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      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0          289           17\n",
       "Actual 1          133           19"
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# L2 regularized logistic regression confusion matrix\n",
    "l2v2 = pd.DataFrame(confusion_matrix(vaccineTest_lables, predsL2))\n",
    "l2v2.columns = ['Predicted 0', 'Predicted 1']\n",
    "l2v2.index = ['Actual 0', 'Actual 1']\n",
    "l2v2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By adding new features to the model we have improved the predictive power of the L2 regularized model."
   ]
  }
 ],
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