attachment_Logistic_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img height=\"60\" width=\"120\" src=\"https://shwetkm.github.io/upxlogo.png\"></img>\n",
    "# UpX Academy - Machine Learning Track\n",
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agenda\n",
    "\n",
    "1. Refresh your memory on how to do linear regression in scikit-learn\n",
    "2. Attempt to use linear regression for classification\n",
    "3. Show you why logistic regression is a better alternative for classification\n",
    "4. Brief overview of probability, odds, e, log, and log-odds\n",
    "5. Explain the form of logistic regression\n",
    "6. Explain how to interpret logistic regression coefficients\n",
    "7. Demonstrate how logistic regression works with categorical features\n",
    "8. Logistic regression Advantages/Disadvantages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Predicting a Continuous Response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ri</th>\n",
       "      <th>na</th>\n",
       "      <th>mg</th>\n",
       "      <th>al</th>\n",
       "      <th>si</th>\n",
       "      <th>k</th>\n",
       "      <th>ca</th>\n",
       "      <th>ba</th>\n",
       "      <th>fe</th>\n",
       "      <th>glass_type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.51966</td>\n",
       "      <td>14.77</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.29</td>\n",
       "      <td>72.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>1.51115</td>\n",
       "      <td>17.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.34</td>\n",
       "      <td>75.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1.52213</td>\n",
       "      <td>14.21</td>\n",
       "      <td>3.82</td>\n",
       "      <td>0.47</td>\n",
       "      <td>71.77</td>\n",
       "      <td>0.11</td>\n",
       "      <td>9.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1.52213</td>\n",
       "      <td>14.21</td>\n",
       "      <td>3.82</td>\n",
       "      <td>0.47</td>\n",
       "      <td>71.77</td>\n",
       "      <td>0.11</td>\n",
       "      <td>9.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1.52320</td>\n",
       "      <td>13.72</td>\n",
       "      <td>3.72</td>\n",
       "      <td>0.51</td>\n",
       "      <td>71.75</td>\n",
       "      <td>0.09</td>\n",
       "      <td>10.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ri     na    mg    al     si     k     ca   ba    fe  glass_type\n",
       "id                                                                        \n",
       "22   1.51966  14.77  3.75  0.29  72.02  0.03   9.00  0.0  0.00           1\n",
       "185  1.51115  17.38  0.00  0.34  75.41  0.00   6.65  0.0  0.00           6\n",
       "40   1.52213  14.21  3.82  0.47  71.77  0.11   9.57  0.0  0.00           1\n",
       "39   1.52213  14.21  3.82  0.47  71.77  0.11   9.57  0.0  0.00           1\n",
       "51   1.52320  13.72  3.72  0.51  71.75  0.09  10.06  0.0  0.16           1"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# glass identification dataset\n",
    "import pandas as pd\n",
    "url = 'https://raw.githubusercontent.com/upxacademy/ML_with_Python/master/Datasets/glass.data?token=AH0Y7F-9aM-g_uOfpoUhYRF3xm7oJjrzks5ZFEl1wA%3D%3D'\n",
    "col_names = ['id','ri','na','mg','al','si','k','ca','ba','fe','glass_type']\n",
    "glass = pd.read_csv(url, names=col_names, index_col='id')\n",
    "glass.sort_values(by='al', inplace=True)\n",
    "glass.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** Pretend that we want to predict **ri**, and our only feature is **al**. How could we do it using machine learning?\n",
    "\n",
    "**Answer:** We could frame it as a regression problem, and use a linear regression model with **al** as the only feature and **ri** as the response.\n",
    "\n",
    "**Question:** How would we **visualize** this model?\n",
    "\n",
    "**Answer:** Create a scatter plot with **al** on the x-axis and **ri** on the y-axis, and draw the line of best fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "sns.set(font_scale=1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1521119e7b8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAU4AAAFPCAYAAAAiDyfMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1521119e828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='al', y='ri', data=glass, ci=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** How would we draw this plot without using Seaborn?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x152115e77b8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAg8AAAFqCAYAAACd9LnWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8AAAAEwhPAAAAFMIDwAAwJT/H+jHfXE0Yc5LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1520c4ffcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot using Pandas\n",
    "glass.plot(kind='scatter', x='al', y='ri')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x152115f3630>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAg8AAAFqCAYAAACd9LnWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl8VOWh//HvQAgEBZJgQIK3EdlCKVTBUEEtRSpLWwO2\nRasvESSNVq5KEBEkBay0TZ26kNZeLCnWQrVKLWvL4k+kyLVWUQIXFUsiWzAQQhYWSTJJOL8/JjNZ\nJ5mTzGTmzHzer5evmsmZM8/DqTzfeVabYRiGAAAAvNQh0AUAAADWQngAAACmEB4AAIAphAcAAGAK\n4QEAAJhCeAAAAKZEBLoAgVRVVa2SkouBLobfxMR0pX4WRv2sK5TrJlE/q4uL69bme4R1z0NERMdA\nF8GvqJ+1UT/rCuW6SdQPYR4eAACAeYQHAABgCuEBAACYQngAAACmEB4AAIAphAcAAGAK4QEAAJhC\neAAAAKYQHgAAgCmEBwAAYArhAQAAmEJ4AAAAphAeAACAKYQHAABgCuEBAACYQngAAACmEB4AAIAp\nhAcAAGAK4QEAAJhCeAAAAKYQHgAAgCmEBwAAYArhAQAAmEJ4AAAAphAeAACAKQEPD0uWLNHixYub\nvWbOnDlKTEzUkCFDlJiYqMTERM2aNavJa7dt26bExETl5+f7o7gAAIS9iEB+eGZmptauXatp06Y1\ne11OTo7mz5+vqVOnul+LjIxsdF1hYaGWLl0qm83m87ICAACngISHvLw8paenKzc3V/Hx8c1e63A4\ndPz4cQ0bNkw9e/Zs9tpFixYpMTFRH3zwgS+LCwAA6gjIsEV2drbi4+O1efNm9e3bt9lrDx8+rOrq\navXv37/Z61555RWdOXNGs2fP9mVRAQBAAwHpeUhOTlZycrJX1+bk5CgiIkKZmZnavXu3OnfurEmT\nJmn27NnuoYsjR44oMzNTr7zyis6dO+fPogMAEPYCPmGyJbm5uZKkAQMGaOXKlXr44Yf1xhtvaOnS\npZKk6upqLViwQKmpqRo4cGAgiwoAQFgI6IRJb8ydO1cpKSnq3r27JGngwIGy2WyaN2+eFi5cqDVr\n1qhDhw768Y9/LEkyDCOQxQUAIOQFfXiQ5A4OLoMHD5YknTx5UuvXr1dhYaFGjBghyRkeDMPQd7/7\nXT344IO6//77m713XFw3/xQ6SFA/a6N+1hXKdZOoX7gL+vCQlpamqqoqvfDCC+7XDhw4oMjISCUk\nJOjPf/6zqqqq6v1u3rx5ysrK0qBBg1q8f2Hheb+UOxjExXWjfhZG/awrlOsmUT+r80UwCrrwUFlZ\nqbNnz6pHjx7q1KmTJk6cqHnz5unll1/W+PHj9cknn8hutyslJUVRUVGKioqq9/7Tp0/LMAzFx8c3\n6rEAAABtF/Dw0HBDp+zsbM2YMUOrV69WUlKSJk+eLIfDoVWrVmn58uWKjY3VzJkzmx2OYJMoAAD8\nx2aE+QzDUO+aon7WRf2sK5TrJlE/q/PFsEXQL9UEAADBhfAAAABMITwAAABTCA8AAMAUwgMAADCF\n8AAAAEwhPAAAAFMIDwAAwBTCAwAAMIXwAAAATCE8AAAAUwgPAADAFMIDAAAwhfAAAABMITwAAABT\nCA8AAMAUwgMAADCF8AAAAEwhPAAAAFMIDwAAwBTCAwAAMIXwAAAATCE8AAAAUwgPAADAFMIDAAAw\nhfAAAABMITwAAABTCA8AAMAUwgMAADCF8AAAAEwhPAAAAFMIDwAAwBTCAwAAMIXwAAAATIkIdAEA\ns4qLS7VgwU7l58coPr5YdvstiomJDnSxACBsEB5gOQsW7NTGjdMl2SQZktYoK+v2AJcKAMIHwxaw\nnGPHussZHCTJVvMzAKC9EB5gOQkJZ+XscZAkQwkJ5wJZHAAIOwxbwHLs9lskramZ81Aiu31coIsE\nAGGF8ADLiYmJVlbW7YqL66bCwvOBLg4AhB2GLQAAgCmEBwAAYArhAQAAmEJ4AAAAphAeAACAKYQH\nAABgCuEBAACYQngAAACmEB4AAIAphAcAAGAK4QEAAJhCeAAAAKYQHgAAgCmcqgkgaBUXl2rBgp06\ndqy7EhLOym6/RTEx0YEuFhD2CA8AgtaCBTu1ceN0STbt22dIWqOsrNsDXSwg7DFsASBoHTvWXZKt\n5idbzc8AAi3g4WHJkiVavHhxs9fMmTNHiYmJGjJkiBITE5WYmKhZs2a5f5+bm6sf//jHGjlypMaM\nGaOlS5fqwoUL/i46AD9LSDgryaj5yVBCwrlAFgdAjYAOW2RmZmrt2rWaNm1as9fl5ORo/vz5mjp1\nqvu1yMhISdLFixd133336YYbbtDf/vY3nT17Vunp6Vq0aJF+85vf+LX8APzLbr9F0pqaOQ/nZLeP\nC3SRAChA4SEvL0/p6enKzc1VfHx8s9c6HA4dP35cw4YNU8+ePRv9Pj8/X9dff72WLVumLl26SJLu\nuOMO/fa3v/VL2QG0n5iYaOY4AEEoIMMW2dnZio+P1+bNm9W3b99mrz18+LCqq6vVv3//Jn8/YMAA\nPf/88+7gcOTIEW3cuFE33XSTz8sNAAAC1POQnJys5ORkr67NyclRRESEMjMztXv3bnXu3FmTJk3S\n7Nmz3UMXLlOnTtVnn32mvn376n/+53/8UXQAAMJewCdMtiQ3N1eSs4dh5cqVevjhh/XGG29o6dKl\nja7NyMjQK6+8ori4ON17772qqKho7+ICABDygn6fh7lz5yolJUXduzuXaA0cOFA2m03z5s3TwoUL\n1aNHD/e1Q4YMkST99re/1dixY/XWW2/pu9/9bkDKDQBAqAr68CDJHRxcBg8eLEk6efKkLly4oM8+\n+0zjx493/z4uLk7R0dEqKCho8d5xcd18W9ggQ/2sjfpZVyjXTaJ+4S7ow0NaWpqqqqr0wgsvuF87\ncOCAIiMjlZCQoH/+85967LHHtHv3bsXGxkpyruYoLi7WgAEDWrx/YeF5v5U90OLiulE/C6N+1hXK\ndZOon9X5IhgF3ZyHyspKnTlzRpWVlZKkiRMn6u2339bLL7+svLw8bdu2TXa7XSkpKYqKitK4ceP0\nX//1X3rsscd06NAh7d27V3PmzNGIESP0zW9+M8C1AQAg9AQ8PNhstno/Z2dn6+abb9a+ffskSZMn\nT1ZGRobWrVun2267TXa7XTNnztQjjzwiSerSpYteeuklXXbZZZo+fboefPBBDR06VCtXrmz3ugAA\nEA5shmEYLV8WukK9a4r6WRf1s65QrptE/awuJIctAABAcCM8AAAAUwgPAADAFMIDAAAwhfAAAABM\nITwAAABTCA8AAMAUwgMAADAl6M+2QGAVF5dqwYKdOnasuxISzspuv0UxMdGBLhYAIIAID2jWggU7\ntXHjdEk27dtnSFqjrKzbA10sAEAAMWyBZh071l2S6/wRW83PAIBwRnhAsxISzkpyHX9iKCHhXKNr\niotLlZq6XhMm7FBq6jqVlJS2axkBAO2LYQt4VFxcKofjoqKjn5HUU6NHX5LdPrHRdQxtAEB4ITzA\nowULdmrr1p/IOWxhKDJyTZOTJRnaAIDwwrAFPPI2FHgztAEACB30PMCjhISzNcMQzp4HT6HAbr9F\n0pqa5ZznZLePa89iAgDaGeEBHnkKBU3t/ZCVdbv79Tvv/Ig9IQAghBEe4FFMTHSTEx89TZBk4iQA\nhAfmPMA0T3MhmDgJAOGB8ADTPE2QZOIkAIQHhi1gmqe5EEycdOI8EAChjvAA0zzNhfD0erhh7geA\nUMewBeBjzP0AEOoID4CPMfcDQKhj2ALwMeZ+AAh1hAfAx5j7ASDUMWwBAABMITwAAABTCA8AAMAU\n5jwAaBU2wwLCF+EBQKuwGRYQvhi2ANAqbIYFhC/CA4BWYTMsIHwxbIGwxrh967EZFhC+CA8Ia4zb\ntx6bYQHhi2ELhDXG7QHAPMIDwppVx+2Li0uVmrpeEybsUGrqOpWUlAa6SADCCMMWCGttHbcP1JwJ\nhlsABBLhAWGtreP2gWrEGW4BEEgMWwBtEKhG3KrDLQBCAz0PIaq4uFQPPfR3HToUxRLEVvJmSCIh\n4WxNj4NN7dmIs0wSQCARHkJUMI2JW3UvBW/+DAPViLNMEkAgER5CVDCNiQdTkDHDmz9DGnEA4Yg5\nDyEqmMbEgynImBFMf4YAEEzoeQhRdvst6tz5tZo5D4EdEw/UvIC2Yl4BADSt2fCwd+9eJSYmqmvX\nrtq7d2+LNxsxYoTPCoa2iYmJ1uuv36XCwvOBLoplG2GGJNqXVefGAOGo2fBw9913a+3atRo+fLju\nvvtu2Wy2Jq8zDEM2m00HDx70SyFhHa4G4PPPO6q4+Jh69hyka6750qcNgesz8vNjFB9fTCMTIqw6\nNwYIR82Gh9WrV6t///6SpAEDBmjq1KkaPnx4uxQM5nz++TH94AebVFJylWJi8rR+/RTFxX3Nb5/n\n6VtibQPwmqQnlJ9v04ED3jUE3n7zrNvIOOck0MiEAqvOjQHCUbPhYdSoUe5/z8vL05AhQ+q9huDx\ngx9sUn7+E5JsKiszdPvtGfriC/+FB0/fEmsbgMvlbUPgCg27dhWotPSxRvdsiEYmNFl1bgwQjrye\nMPn1r39dH374oW688UZ/lgetVFJyleo2qM6f/cdTA17bAJyXs1eg5YagNoj8vcl7NuSLRsaf4+uM\n3beOVefGAOHI6/AwdOhQZWVlafv27RoyZIi6du1a7/c2m01PPfWUzwsI78TE5KmsrLZBjYk54dfP\n89SAuxoA55yHjJo5Dxc9NgTFxaXatatK0llJH0v6XqN7NuT6DOech5JWNTL+HF/3571DOZgwQRWw\nDq/Dw/bt29WrVy+Vl5crOzu70e89TaZE+1i/fopuvz2jZs7DCa1fn+zXz/P0LdFMA/D558c0btzr\nKi9PlPSGpCskPSupp6688oTs9plNvs/1GXFx3Vq9msSfQx/+vHda2nZt25YiVzBxOFbpT3+602f3\nBwBveB0e3n77bX+WA23Ur1+C9u17uN0+zxffEn/wg00qL0+S9E1Jv5O0SK5eh/LyZ/z6jdqf4+v+\nvPe//91BdYPJe++xzxuA9scmUWGqvbu/m/o857yMC5J6SOqruo2i1NNvZZH8O77ui3t7PtjsjOrO\nJZGKfFZuAPAW4SFMtfea+qY+zzlPY7acyzpPqm6jOHr0Jb+VRfLv+Lov7u3p+Ywefbm2bv2LpG6S\nzmv06Mt9UGIAMIfwEKbae7ljU5/nnKexQiUlV6l794saNuz3OnPmyppv6xN98rntsWmVP3h6PsuX\n36bIyJ06dqxaCQlVstu/F7AyAghfhIcw1d5r6pv6vPaYp9GWTav8xZshI0/PhxUJAIJBwMPDkiVL\nZBiGli1b5vGaOXPmaPv27bLZbDIM5ymHY8aM0UsvvSRJOn78uJ5++ml99NFHstlsGjVqlBYuXKg+\nffq0Sx2sqL3X1AdqDX9rNq3yN2+GjHx1sFkwL+0sKipVauqmNpUtmOsHhLKAhofMzEytXbtW06ZN\na/a6nJwczZ8/X1OnTnW/FhkZKUkqKyvTrFmzNGjQIK1Zs0ZVVVXKyMjQ/fffr3Xr1qlTp05+rYNV\ntfc32EB9Y27NplX+ULeRO3q0Si0FGV8dbBbM50XMnr21zWUL5voBoSwg4SEvL0/p6enKzc1VfHx8\ns9c6HA4dP35cw4YNU8+ejWfgv/vuuzp16pQ2bdrk3rjKbrfrW9/6lvbv36/rr7/eL3WANZjdtMpf\n6p/H8YraK8gE81beR460vTcomOsHhLKAhIfs7GzFx8fr+eefV1paWrPXHj58WNXV1e4DuhoaPny4\nsrKy6u146dqw6tw59sYPd8EyR6B+I/ddRUc/o6uvHujTIZymuvCD+byIfv3Oa8+etpUtmOsHhLKA\nhIfk5GQlJ3u3A2JOTo4iIiKUmZmp3bt3q3Pnzpo0aZJmz56tyMhI9erVS7169ar3npUrV6pr1670\nOiBo1G/kemjs2N7Kyhrv089oqgs/mM+LWLHiO6qoaFvZgrl+QCgL+ITJluTm5kpyHgk+ffp0HTp0\nSBkZGSooKFBGRkaj61999VW9+uqrWrx4sbp3pwsTwaE9GrmmuvC97XkJxMTD2Ni29woFS88SEG6C\nPjzMnTtXKSkp7iAwcOBA2Ww2zZs3TwsXLlSPHj3c165YsUKZmZn6yU9+orvvvtur+8fFdfNLuYMF\n9fO/oqJSzZ69VUeOXK5+/c5rxYrvKDa2fsMbF9dNGzbca/reZuo3aNDFel34gwaVef3+hx76e71e\ni86dX9Prr99lurxmBcPz85dQrptE/cJd0IcHSY16EAYPHixJOnnypHr06CHDMLR06VL99a9/1eOP\nP65Zs2Z5fe+2zmZvrfb4ptfUwVGhtLStLQdj+VJq6iZ3w7tnj6GKCt/M+Ddbv2XLbnYPA/Tpc0rn\nz0fouus2ePWcDx2KUt1ei0OHovz+Zxssz88fQrluEvWzOl8Eo6APD2lpaaqqqtILL7zgfu3AgQOK\njIxUQkKCJOlnP/uZ1q1bp4yMjHrLOYNZoJaYsbTN94Jlxn/dLvzU1PWmnjMTDwGYEXThobKyUmfP\nnlWPHj3UqVMnTZw4UfPmzdPLL7+s8ePH65NPPpHdbldKSoqioqL0z3/+U6+99poeeugh3XTTTTpz\n5oz7Xt27d3fvBxFsAtXg+ONzQ6E3w9s6WGVFg9nnzMRDAGYEPDy4llW6ZGdna8aMGVq9erWSkpI0\nefJkORwOrVq1SsuXL1dsbKxmzpyp+++/X5K0efNm2Ww2/e53v9Pvfve7evey2+267bbb2q0uZgSq\nwfHH51q9N6O4uFS33LJG+flPqKU6eLuiIdCByuxzNjPxsL3qFug/QwCeBTw8rF69ut7Po0aN0sGD\nB+u9NmXKFE2ZMqXJ9z/77LN69tln/VY+fwnUNz1/fG6wdNt70lIjtGDBTuXnf00N69DU+5qqa82O\n6ZIkh+NLpaVt13vvlai09DH5OlB526D68/9f7RUWrR5KgVAW8PAQrgK1xMwfnxuM3fZ1tdQIOQPB\nBTXc9bH++0q0Z88KXbwY0cJ1r0q6S9Lf5Y9A5W2D6s8lmu0VFoM9lALhjPCANgv28fKWGqErryyU\n9EM5T968TFde+X+y22fozjs/qvO+bTXDGmcl/UXR0eUaOzZCdvu4Btd1q/l3/5yl8fnnHWvKebmk\n8zU/t15rvt23V1gM9lAKhDPCA9os2DfqaakRstmqJG2Vs+E/r+uu66mYmOgG77us5n+jJd2tq6/e\n4N4hsv515+QMDd9Rw5Dh0pax/OLiY5KecNeluLjxRmlmPqc13+7bKywGeygFwhnhASGluLhUc+f+\nQ++9d15ST40eXa20tKHasydDJSVXKSYmT4sW1Z8/c/JkH0nfkitAvPfeCZWUlMpuv0UOx+/13nvn\n9eWXFaqsvFnSNkmX6fTpT1RSMlIxMdF64omR+ve/l6qwsIukKF1xxc/Vu/cQXXNNlez2Wxs12G0Z\ny+/Zc5Dy82sb+549B3m81pvPac23+/YKi639HCZaAv5HeEBIWbBgp7Zu7S7pAUk2bd1qaP/+DPdK\nirIyQ7/85RplZSW43+NsQLfIOVfBptLS7+nxx50NbWRkV5WWPiDncMUKST+RtE75+Z01dOgW3Xpr\nB0k2FRQkut9/+rSh0aMbN9SuRu3NNyUz3/brNoZFRf9R3eGQa6652Oia5iZ3NuTNt3urNcZMtAT8\nj/AQZgLVELTX5zobyI6q22iWlFyl5hrRJ54YqX/8Y7eqqmqvOXy4a537nZWzV6KvnD0PUZJmqarK\nGU6io/+o2rkOTX+GVLdRWyXp1Zr3nFOfPs1/269/nPdYxcdnqFevr9Zr7JtqMBMSjBZ7Fbz5dm+1\nxpiJloD/ER7CTKjvbOnsRYiQVCLXEINhHFJzkxczMvaqqqq03jVFRYckTWzQK/EX1c59qG2cpDOS\nOte8/6ykLTp6tFypqevqhaTaRq2TXL0UzvesarZODSdJxsYm6M0365/IWT/kXK5du05p+/bvyBdz\nBqzWGDPREvA/wkOY8bYh8HVPQXs1QM55Cn/XW289r8rKn0myqbz8ZvXuvVQVFd0k9ZTDUa2SklIZ\nhuoMI8RJelpSvKQv1L37Fe777dr1/1RaapNzEuQLkvqpblC4dKmrrrhiv4qL/0+XLnWStEylpTZt\n3Fg/JDlXdRiSYuv9WZw8eUWzdfJmkmRTQy/O4Zm2BzSrNcZMtAT8j/AQZrxtCHzdU9BeDVBMTLT+\n9Kd7NGHCDu3bV9tAFxdLlZXOTZu2bjUUGblGkmrq+BdJX0haKFcgyM09rxkz/ixnL0GxnI1+tKTe\nknLlbMy7Slqsc+dscg5DSM7egaZDknNVx18kHZP0Pbn+LPr0qd1Sva6iolKlpm5SSUmfevdsapJk\n/ZDT+LPbwmqNcbCv/gFCAeEhzHjbEPi6p6A9GqC6vSWnT38iaYqcddiiysrr1XR9zkq6KKmLpJck\nHZf0ZM18hj9JuleuvR0uv7xE5eURqqqqlpQh6U+qHSqokhQjqUjSUTmHGaK1f/8xjR1boUGDynXi\nRJyc+0n8Qc4Q4VwaKlU2WYczZw7qxImFNdc2niRZV0xMtMaO7VjT2+HbgEZjDKAhwkOY8bYh8HVP\nQXs0QJ4mFh49Wq7SUqm2AT6qzz77SOXlknRQzh6HX8nZqzBStSHDNTThXMJ54UK2nEMOveUMB19I\nqjsf4pycQWCVpKckvSbD+KUOHrTp4EFD8fG/rLlnb0m1Z66cPLnBQx061PzvdyS9pqioSk2YII/B\nq7mAZrUVEwCCG+EBTbJaV7XUsLckRr16fVVvvjleqanrtHHjZLl2kOzQ4d8qL58v6XeShta8J1HO\n/xzq7gxZqNpwYJP0H0kpcs6NmC1nD4Kj5nc3ytlzES9pYM1r9YcwYmOvVlLSGu3adUqlpbXDFnWD\nWf06uLbMjpb0I02Y0PzQUd0zNpzvq2V2GCocw0Y41hloLcIDmmTFrmpPvSXOILS5plEo1ZtvDlJZ\n2TZJw1XbQJepdmdIZ8iIjDwthyNOtY35FXIOTVxX89o0Ofd+MCT9S9KTqp3TYKjhFtX9+1crK+t2\nlZSU6vHHmw5m9eswucllmZ4aueYCQm0oKZW0VW++qUarQVxqTxl9UNI27dsXoz17Vmvnznt90pi6\nyp+fH6P4+GKPZWjvhtxqS1KBQCI8IGR46i1pGISuvfY3KisbJmdwcPVISJGR+9Wlyxfq0OEKjR79\npZ58coZuv32j8vNdjbnkDAPVqu0RuEtduixRRUU/GYZriGG1pCWSeqtz58W65pphGjSowmN5PNVh\n0KAyLVs23esdKpubp1IbSrZK+pHKyhqvBql7f+cpo9sk/UiSTfn5t7k3zmqr+kMznsvQ3g251Zak\nAoFEeEDI8La3ZP36KRo79jWVl8+Tay+I+PiPtXPnw40a6p0773X3EvTpUyZplfLyolRcnKGePQep\nqOiQ8vOfUu2kxmhJD2vKFPONXcNv2ytWTFF1deODrxo2cp9/3lGpqet19OgpedrPwhVKtm27qIqK\n5g/Wqj1l1PPKEbNcdfv884767LOyFu8biIbcaktSgUAiPCDs9OuXoP37H9Djj9cOZdjtjb/hS7WB\npG7D3r//Wf3tb87rJ0yIqDlrovGkxua63pv6XcNv2w8++JqeeuqmRmd19Omjeo1ccfExffzxg5L+\nJukXstkMdevW172fRUxMtLse1177W+XnP1DnvRmNyuNcqTJb0otyTuy0SSrR6dOfasIEtWoYobZu\nruDSfCMdiIbcivN8gECxGUb9aVbhprDwfKCL4Ddxcd2on5eaasxdm0g5G9RP6xzJvaXmtMyOcjgc\n2rr1x3I1cnV7HFJT19frnq/7uxkzXqk5g8O5RfX48QX66KNolZbOcpcpKWmz4uNLtHFjhOruSDl5\n8ouKjLzM3cgdPtxVBw5UyjnE8JpcQw0NP1OSxo/frgMHfuj+ediwN7Rjx8QGZS1RfPwKxcYmqLj4\nWJ0elic83relP8/Dhy+r+dy/SPpSzrkXV6h795Pas+eeRkHEOS9kZ72G3EqTF/lvz9rCoX5tRc8D\noKbH2CXXJlJnJeWrdnnmPPcOkpMnv6gpU2q/rT7xxAilpq7XsWPddfRolTx1vTt7Emp7AN55Z6kq\nK3ur7jfyPn1Oa9cumxqem3HiRJx27Jjovldq6jodOBCjplZ4NOzuv+aaL3XgQOM9IzytVHGp7WFx\n3vc//6nQtdf+1n1S6fr1U9SvX+1hYw3/PGuXqR6Vc2ms8/Mvvzyj2R4feMbqEAQS4QFQc2PsNjkn\nGZ6V9KCkv9e77uTJPvUa2frf4F+R5+75nvXuU119taSb5Ny5spOkBG3fflQVFUMk/Z+kqe775Od/\nqNTUi+5GY9GikdqzZ6Py829TwxUeDbv7PXXNtzRM0PD3R48eV3n5U5KcJ5XefnuG9u172OOfp2uZ\n6ptvXqmysuZ3y4R3PE0q9TZUED7QFoQHQI0bxz59Tmn//iI5d6mUnBs72dRc41xcXKpdu+r2NnxX\n0dHP6OqrBzYaQx89ulpbt9Ye3tWp0+eqqOgiqYdc38wrKjZJ+qakX6jujpRFRWrUaLgmdn7+eUf3\nZM5rrrnYaNze0zf6uqGiT59TcjgiNGHCDnej0jB0vPlmf9UNB86TSz3/ebqWqTr33Gh+t0x/ac/G\n0rW1uD8/y1Pg9XalCktT0RaEB0CNv5E7HBHKz58t51DFQUmDVbsPxF9q5jxE1GucFyzYqdJS1+ma\nNkk9NHZsb2VljVdxce0Yfp8+J+VwVKpTp9rDuyoqhstm2yrD6Kva/Rj+T84Ji0NqPte506V0tRqe\noFlSclaSFBFxmZKS+spu/4apxqpuqKjbe1K3UWm83NVVzxIZRk6zYcP159R4Kap3kxJ90fC350ZZ\ns2dv9XvD7Km3yNuVKixNRVsQHgA1/kY+YcIOOTeEukvSeknj5Fop0L37Cb3//l2NGhLnX77fcl8X\nHf2Z7PYfSWrYcL1ac9+6QyDvyTC6y3m2hms/ht5y9jjkqP5Ol6/U+7m09Hv6/vcz3BMazTRWTTWQ\n3jQq69f9UiKaAAATQklEQVRP0e23Z6ik5CoZRo7Ky5/Svn2ew0ZTf85mJqX54luy2cayLZ955Ijv\nlrl60tohKBeWpqItCA9AHa7GtP6eCRflHE64S5KhceNKmvwG6vzLuPa6sWNrr6vfcLkmQNYfAnHu\ncnle0mJJ10i6Us6ehz/UvO7qkSiTc6OqhsMG5hurphrIhASjxUalX78E9xyHhieY+qOh9MW3ZLON\nZVs+s1+/89qzx78NszdDUM0tOWVpKtqC8ADUUduYOk/SjI4u1+jRFZJW6eTJK1r9l3H9huucGg6B\nlJcfVnn5Mrkamy5dFqu83DUE0rXmf109EilqeNJmTExenWEE7xurphrI118f6bEeTWmPb7C++Ayz\njWVbPnPFiu+oosLzHBIzQy5mh0+8XanCiha0BeEBqKO2MY2WdLeuvnqD/vSn8S28y8nbbaf79Dmn\n2jBSJbv9Vv3wh+/rwIHaRrxfv4EaNKhMu3Y9o0uXotS580lVVPxaFy5coUuXGm9KtWjRFP3yl+a/\nRTbVQJptVNrjG6wvPqM96xUb2/IckubU37Tr01YNSQH+RHgA6vDXt+iWGq6G+y8MGmTThg0/bjQn\noHa1QuOTNrOyEhretkWBaJRbIxDfkn31ma0Z/mj6aHbv3w/4G+EBqCNQ48CBGqcOh65rXy7RbM29\nWhNImz6anYmN/ubNia9wIjwAdQSqMWWc2n98uZ9Ba+7VmsDnzdHs8D1vTnyFE+EBQEjz5X4GrblX\nawJf48DR9MFt8C32vvAe4QFASPPlPJb22huBHqbAYO8L7xEeAIQ0X84TYW+E0OZ6vs45DyU832Zw\nJHeIH7tK/ayL+llXKNdNon5W54sjuTv4oBwAACCMMGwBIOhwXDQQ3AgPAIIOx0UDwY1hCwBBhyVz\nQHAjPAAIOgkJZ+XcpEdiyRwQfBi2ABB0WBIJBDfCA4CgwyZJQHAjPAAATCkqKlVq6qaaI+ZPSupU\nc8Q8K2PCBeEBCHIsW0SwmT17a53VMK9KukusjAkvhAcgyLFsEcHmyJHLVbsapptYGRN+WG0BBDmW\nLSLY9Ot3XrWrYc6JlTHhh54HIMhx0h+CzYoV31FFxZqaOQ/nJK2qmfPAyphwQXgAghzLFhFsYmNZ\nDRPuCA9AkGPZIoBgQ3hAWGDFAgD4DuEBYYEVCwDgO6y2QFhgxQIA+A7hAWGBg5YAwHcYtkBYYMUC\nAPgO4QFhgRULAOA7hAcATWKFCgBPCA9BhL+sEUxYoQLAE8JDEOEvawQTVqgA8ITVFkGEv6wRTFih\nAsCTgPc8LFmyRIZhaNmyZR6vmTNnjrZv3y6bzSbDcP5lNmbMGL300kuNrk1NTdXIkSP1k5/8xG9l\n9hcOQEIwYYUKAE8CGh4yMzO1du1aTZs2rdnrcnJyNH/+fE2dOtX9WmRkZL1rKisrtXTpUv3v//6v\nRo4c6Zfy+ht/WSOYsEIFgCcBCQ95eXlKT09Xbm6u4uPjm73W4XDo+PHjGjZsmHr27NnkNZ9++qnS\n09N14cIFde9u3a5+/rIGAFhBQOY8ZGdnKz4+Xps3b1bfvn2bvfbw4cOqrq5W//79PV7z7rvvatSo\nUdqwYYMuu+wyXxcXAADUEZCeh+TkZCUnJ3t1bU5OjiIiIpSZmandu3erc+fOmjRpkmbPnu0eukhN\nTfVncQEAQB0BnzDZktzcXEnSgAEDNH36dB06dEgZGRkqKChQRkZGgEsHAED4CfrwMHfuXKWkpLjn\nMgwcOFA2m03z5s3TwoUL1aNHjwCXEACA8BL04UFSo0mQgwcPliSdPHmyzeEhLq5bm94f7KiftVE/\n6wrluknUL9wFfXhIS0tTVVWVXnjhBfdrBw4cUGRkpBISEtp8/8LC822+R7CKi+tG/SyM+llXKNdN\non5W54tgFHQ7TFZWVurMmTOqrKyUJE2cOFFvv/22Xn75ZeXl5Wnbtm2y2+1KSUlRVFRUgEsLAED4\nCXjPg81mq/dzdna2ZsyYodWrVyspKUmTJ0+Ww+HQqlWrtHz5csXGxmrmzJm6//77vbofAADwLZvh\n2u85TIV61xT1sy7qZ12hXDeJ+lldSA5bAACA4EZ4AAAAphAeAACAKYQHAABgCuEBAACYQngAAACm\nEB4AAIAphAcAAGAK4QEAAJhCeAAAAKYQHgAAgCmEBwAAYArhAQAAmEJ4AAAAphAeAACAKYQHAABg\nCuEBAACYQngAAACmEB4AAIAphAcAAGAK4QEAAJhCeAAAAKYQHgAAgCmEBwAAYArhAQAAmEJ4AAAA\nphAeAACAKYQHAABgCuEBAACYQngAAACmEB4AAIAphAcAAGAK4QEAAJhCeAAAAKYQHgAAgCmEBwAA\nYArhAQAAmEJ4AAAAphAeAACAKYQHAABgCuEBAACYQngAAACmEB4AAIAphAcAAGAK4QEAAJhCeAAA\nAKYQHgAAgCmEBwAAYArhAQAAmEJ4AAAAphAeAACAKYQHAABgCuEBAACYQngAAACmEB4AAIAphAcA\nAGBKwMPDkiVLtHjx4mavmTNnjhITEzVkyBAlJiYqMTFRs2bNcv++vLxcixcv1g033KCkpCQtXrxY\nFy9e9HfRAQAISxGB/PDMzEytXbtW06ZNa/a6nJwczZ8/X1OnTnW/FhkZ6f73xYsX6+DBg1q5cqUq\nKyu1aNEiLV26VL/+9a/9VnYAAMJVQMJDXl6e0tPTlZubq/j4+GavdTgcOn78uIYNG6aePXs2+n1B\nQYH+8Y9/aPXq1Ro+fLgk6ec//7nuvfdezZ8/X7169fJLHQAACFcBGbbIzs5WfHy8Nm/erL59+zZ7\n7eHDh1VdXa3+/fs3+fu9e/eqQ4cOuu6669yvjRgxQh07dtRHH33k03IDAIAA9TwkJycrOTnZq2tz\ncnIUERGhzMxM7d69W507d9akSZM0e/ZsRUZGqqCgQD179lTHjh3d7+nYsaNiY2N16tQpf1UBAICw\nFdA5D97Izc2VJA0YMEDTp0/XoUOHlJGRoYKCAmVkZKisrEydO3du9L7IyEhVVFS0d3EBAAh5QR8e\n5s6dq5SUFHXv3l2SNHDgQNlsNs2bN08LFy5Uly5d5HA4Gr3P4XAoKiqqvYsLAEDIC/rwIMkdHFwG\nDx4sSTp58qSuvPJKFRUVyTAM2Ww2SVJ1dbWKi4vVu3fvFu8dF9fN9wUOItTP2qifdYVy3STqF+4C\nvs9DS9LS0vTQQw/Ve+3AgQOKjIxUQkKCRowYoerqamVnZ7t//+GHH8owDI0YMaK9iwsAQMgLuvBQ\nWVmpM2fOqLKyUpI0ceJEvf3223r55ZeVl5enbdu2yW63KyUlRVFRUerdu7cmTZqk9PR07d27Vx9+\n+KGWLFmiKVOmsEwTAAA/CPiwhWuowSU7O1szZszQ6tWrlZSUpMmTJ8vhcGjVqlVavny5YmNjNXPm\nTN1///3u9/ziF7/QsmXL9MADD6hjx46aNGmSFi1a1N5VAQAgLNgMwzACXQgAAGAdQTdsAQAAghvh\nAQAAmBLS4eHSpUt69tlnddNNN+m6667TI488oqKiIo/XHzhwQHfddZeuvfZaTZw4URs2bGjH0ppj\ntm4tnUwazLw5edVKz64hX5wsG2yKioq0YMEC3XTTTUpKSlJKSopycnI8Xm+l52e2blZ7dgUFBXrk\nkUf0jW98Q0lJSXr00Ud1+vRpj9db6dlJ5utntedX1759+zR06FDt2bPH4zWtfn5GCHv++eeNm2++\n2fjXv/5lfPrpp8Ydd9xh3H333U1eW1RUZIwaNcr4+c9/bhw+fNhYs2aNMXToUOPdd99t51J7x0zd\nDMMwJk+ebPzhD38wzpw54/7n3Llz7Vji1lm+fLkxePBg46c//anHa6z27Orypn6GYa3nd+nSJePO\nO+807rzzTuPAgQNGbm6uMWfOHGPMmDFGaWlpo+ut9PzM1s0wrPXsDMMwkpOTjfvuu8/4z3/+Y3z2\n2WfGPffcY3z/+99v8lorPTsXM/UzDOs9P5eLFy8at956q5GYmGh88MEHTV7TlucXsuHB4XAYI0aM\nMNavX+9+7cSJE8bgwYON7OzsRte/+OKLxre//e16ry1cuNCYNWuW38tqltm6VVRUGEOHDjXef//9\n9ixmmxw/ftyYPn26MXr0aGPcuHHNNq5WenYuZupntef36aefGomJicbhw4fdr1VUVBjXXnutsWHD\nhkbXW+n5ma2b1Z5dYWGh8eijjxpffPGF+7W33nrLSExMbLLBtNKzMwzz9bPa86tr8eLFxr333tts\neGjL8wvZYYuDBw/q4sWLGjVqlPu1vn37qm/fvvrwww8bXf/RRx/p+uuvr/faN77xDe3du9fvZTXL\nbN1aOpk0GJk5edVKz87FlyfLBps+ffroxRdfVL9+/dyvdejg/Kvm3Llzja630vMzWzerPbsrrrhC\nzz77rOLj4yVJp06d0uuvv67hw4erW7fGOy5a6dlJ5utntefnsmvXLr3zzjv66U9/KqOZBZVteX4B\n3+fBXwoKCiSp0RbVvXr1avK0zVOnTumrX/1qo2vLy8tVWlqq6Oho/xXWJLN1a+lk0mBk5uRVKz07\nF1+eLBtsoqOjNXbs2HqvrV69WhUVFbrxxhsbXW+l52e2blZ7dnX993//t3bs2KEePXpo9erVTV5j\npWfXkDf1s+LzKy4uVnp6up5++ulGRzs01JbnF7I9D2VlZerQoUO9o7olz6dtlpeXNzqd0/V/jmA7\nndNs3eqeTLpy5Uo9/PDDeuONN7R06dJ2Ka+/WenZtYbVn9+OHTv03HPP6b777tM111zT6PdWfn4t\n1c3Kzy4tLU1//etfNXLkSN13331NTiq08rPzpn5WfH5PPvmkvv3tbzcZZhtqy/ML2Z6HLl266NKl\nS7p06ZK7W1HyfNpm586dG53O6fq5a9eu/i2sSWbr1tLJpD169Gi3svuDlZ5da1j5+a1bt05LlizR\n9773Pc2fP7/Ja6z6/Lypm5Wf3cCBAyVJzz33nMaOHasNGzbU29lXsu6zk7yrn9We3/r163Xw4EFt\n2rRJkpodspDa9vxCtufhyiuvlCQVFhbWe/306dNNnrbZp0+fJq/t2rVrk2NhgWS2blLzJ5NanZWe\nXWtZ8fmtWLFCixYt0l133aVf/epXHq+z4vPztm6StZ5dUVGRtmzZUu+1Ll266Ctf+Yp7uLQuqz07\ns/WTrPX81q9fr1OnTmnMmDG67rrrNHnyZElSamqqnnzyyUbXt+X5hWx4SExMVNeuXfXBBx+4Xztx\n4oS++OILJSUlNbp+5MiRjdbC/vvf/w7KkznN1q2lk0mtzkrPrjWs+PyysrL0m9/8RmlpaUpPT2/2\nWqs9PzN1s9qz++KLL/Too4/qk08+cb92/vx5HTlyRAMGDGh0vdWendn6We35PfPMM9qyZYs2bdqk\nTZs26Q9/+IMk5/lPjzzySKPr2/T8fLAiJGg988wzxo033mi88847xscff2zccccdxr333msYhnO5\nY2FhoeFwOAzDMIwzZ84YSUlJxpIlS4zc3Fxj9erVxte+9rWgXaJjpm5btmwxhgwZYvzxj380jh8/\nbmzdutUYPXq0kZmZGcgqeO2ee+6pt5TR6s+uoZbqZ7Xnd/DgQeOrX/2qkZ6ebhQWFtb75+LFi5Z+\nfmbrZrVnd+nSJeOee+4xpk6dauzfv9/45JNPjFmzZhkTJkyw/LMzDPP1s9rza+jUqVPG4MGD3Us1\nffn8Qjo8VFVVGb/61a+MG264wbj++uuNRx991CgpKTEMwzDef//9Rutf9+/fb0ybNs0YPny4MWnS\nJGPLli2BKnqLzNZtw4YNxm233WZ8/etfN8aNG2f8/ve/D1TRTZs+fXq9xtXqz64hb+pnpef33HPP\nGYmJiU3+s2LFCks/v9bUzUrPzjAMo6SkxFi4cKExZswYY+TIkUZaWppRUFBgGEZo/Ldntn5We351\nnTp1ql59fPn8OFUTAACYErJzHgAAgH8QHgAAgCmEBwAAYArhAQAAmEJ4AAAAphAeAACAKYQHAABg\nCuEBQMBNnz5ds2bNCnQxAHiJ8AAAAEwhPAAAAFMiAl0AAKGvrKxML7zwgt566y3l5+crMjJS1157\nrRYsWKBBgwYFungATCI8APC7+fPna9++fXrsscd01VVX6ejRo8rMzNS8efO0efPmQBcPgEmEBwB+\n5XA4VFFRoaVLl+rWW2+VJF1//fW6cOGCnn76aZWUlAS4hADMIjwA8KvIyEhlZWVJkgoKCnT06FEd\nPXpUO3fulCRVVlYGsngAWoHwAMDvdu/erYyMDB0+fFiXX365EhMTFRUVJUkyDCPApQNgFqstAPhV\nXl6eHnroIX3ta1/Tjh079OGHH+rPf/6zxo0bF+iiAWglwgMAv/r444/lcDj0wAMPqG/fvu7X33nn\nHUn0PABWxLAFAL8aOnSoOnbsKLvdrpkzZ6qiokLr1q3Trl27JElffvllgEsIwCx6HgD41Ve+8hU9\n99xzys/P14MPPqilS5fKZrNpzZo1stls2rt3r2w2W6CLCcAEm0GfIQAAMIGeBwAAYArhAQAAmEJ4\nAAAAphAeAACAKYQHAABgCuEBAACYQngAAACmEB4AAIAphAcAAGDK/wdA8jHq1V48GQAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15211670748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# equivalent scatter plot using Matplotlib\n",
    "plt.scatter(glass.al, glass.ri)\n",
    "plt.xlabel('al')\n",
    "plt.ylabel('ri')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit a linear regression model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "linreg = LinearRegression()\n",
    "feature_cols = ['al']\n",
    "X = glass[feature_cols]\n",
    "y = glass.ri\n",
    "linreg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ri</th>\n",
       "      <th>na</th>\n",
       "      <th>mg</th>\n",
       "      <th>al</th>\n",
       "      <th>si</th>\n",
       "      <th>k</th>\n",
       "      <th>ca</th>\n",
       "      <th>ba</th>\n",
       "      <th>fe</th>\n",
       "      <th>glass_type</th>\n",
       "      <th>ri_pred</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.51966</td>\n",
       "      <td>14.77</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.29</td>\n",
       "      <td>72.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.521227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>1.51115</td>\n",
       "      <td>17.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.34</td>\n",
       "      <td>75.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6</td>\n",
       "      <td>1.521103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1.52213</td>\n",
       "      <td>14.21</td>\n",
       "      <td>3.82</td>\n",
       "      <td>0.47</td>\n",
       "      <td>71.77</td>\n",
       "      <td>0.11</td>\n",
       "      <td>9.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.520781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1.52213</td>\n",
       "      <td>14.21</td>\n",
       "      <td>3.82</td>\n",
       "      <td>0.47</td>\n",
       "      <td>71.77</td>\n",
       "      <td>0.11</td>\n",
       "      <td>9.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.520781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1.52320</td>\n",
       "      <td>13.72</td>\n",
       "      <td>3.72</td>\n",
       "      <td>0.51</td>\n",
       "      <td>71.75</td>\n",
       "      <td>0.09</td>\n",
       "      <td>10.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1</td>\n",
       "      <td>1.520682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ri     na    mg    al     si     k     ca   ba    fe  glass_type  \\\n",
       "id                                                                           \n",
       "22   1.51966  14.77  3.75  0.29  72.02  0.03   9.00  0.0  0.00           1   \n",
       "185  1.51115  17.38  0.00  0.34  75.41  0.00   6.65  0.0  0.00           6   \n",
       "40   1.52213  14.21  3.82  0.47  71.77  0.11   9.57  0.0  0.00           1   \n",
       "39   1.52213  14.21  3.82  0.47  71.77  0.11   9.57  0.0  0.00           1   \n",
       "51   1.52320  13.72  3.72  0.51  71.75  0.09  10.06  0.0  0.16           1   \n",
       "\n",
       "      ri_pred  \n",
       "id             \n",
       "22   1.521227  \n",
       "185  1.521103  \n",
       "40   1.520781  \n",
       "39   1.520781  \n",
       "51   1.520682  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make predictions for all values of X\n",
    "glass['ri_pred'] = linreg.predict(X)\n",
    "glass.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x15211add7f0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhcAAAF4CAYAAADnk9taAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15211acdcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot those predictions connected by a line\n",
    "plt.plot(glass.al, glass.ri_pred, color='red')\n",
    "plt.xlabel('al')\n",
    "plt.ylabel('Predicted ri')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x23615f2eeb8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAAFhCAYAAAAP07LiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niAjA/wMbvYjuYjoxygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2361644bef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# put the plots together\n",
    "plt.scatter(glass.al, glass.ri)\n",
    "plt.plot(glass.al, glass.ri_pred, color='red')\n",
    "plt.xlabel('al')\n",
    "plt.ylabel('ri')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Refresher: interpreting linear regression coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear regression equation: $y = \\beta_0 + \\beta_1x$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.51699012])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute prediction for al=2 using the equation\n",
    "linreg.intercept_ + linreg.coef_ * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.51699012])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute prediction for al=2 using the predict method\n",
    "linreg.predict(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zip at 0x236164368c8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine coefficient for al\n",
    "zip(feature_cols, linreg.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** A 1 unit increase in 'al' is associated with a 0.0025 unit decrease in 'ri'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5145125136125304"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# increasing al by 1 (so that al=3) decreases ri by 0.0025\n",
    "1.51699012 - 0.0024776063874696243"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.51451251])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute prediction for al=3 using the predict method\n",
    "linreg.predict(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: Predicting a Categorical Response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    70\n",
       "2    76\n",
       "3    17\n",
       "5    13\n",
       "6     9\n",
       "7    29\n",
       "Name: glass_type, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine glass_type\n",
    "glass.glass_type.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ri</th>\n",
       "      <th>na</th>\n",
       "      <th>mg</th>\n",
       "      <th>al</th>\n",
       "      <th>si</th>\n",
       "      <th>k</th>\n",
       "      <th>ca</th>\n",
       "      <th>ba</th>\n",
       "      <th>fe</th>\n",
       "      <th>glass_type</th>\n",
       "      <th>ri_pred</th>\n",
       "      <th>household</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.51966</td>\n",
       "      <td>14.77</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.29</td>\n",
       "      <td>72.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.521227</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>1.51115</td>\n",
       "      <td>17.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.34</td>\n",
       "      <td>75.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6</td>\n",
       "      <td>1.521103</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1.52213</td>\n",
       "      <td>14.21</td>\n",
       "      <td>3.82</td>\n",
       "      <td>0.47</td>\n",
       "      <td>71.77</td>\n",
       "      <td>0.11</td>\n",
       "      <td>9.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.520781</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1.52213</td>\n",
       "      <td>14.21</td>\n",
       "      <td>3.82</td>\n",
       "      <td>0.47</td>\n",
       "      <td>71.77</td>\n",
       "      <td>0.11</td>\n",
       "      <td>9.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.520781</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1.52320</td>\n",
       "      <td>13.72</td>\n",
       "      <td>3.72</td>\n",
       "      <td>0.51</td>\n",
       "      <td>71.75</td>\n",
       "      <td>0.09</td>\n",
       "      <td>10.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1</td>\n",
       "      <td>1.520682</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ri     na    mg    al     si     k     ca   ba    fe  glass_type  \\\n",
       "id                                                                           \n",
       "22   1.51966  14.77  3.75  0.29  72.02  0.03   9.00  0.0  0.00           1   \n",
       "185  1.51115  17.38  0.00  0.34  75.41  0.00   6.65  0.0  0.00           6   \n",
       "40   1.52213  14.21  3.82  0.47  71.77  0.11   9.57  0.0  0.00           1   \n",
       "39   1.52213  14.21  3.82  0.47  71.77  0.11   9.57  0.0  0.00           1   \n",
       "51   1.52320  13.72  3.72  0.51  71.75  0.09  10.06  0.0  0.16           1   \n",
       "\n",
       "      ri_pred  household  \n",
       "id                        \n",
       "22   1.521227          0  \n",
       "185  1.521103          1  \n",
       "40   1.520781          0  \n",
       "39   1.520781          0  \n",
       "51   1.520682          0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# types 1, 2, 3 are window glass\n",
    "# types 5, 6, 7 are household glass\n",
    "glass['household'] = glass.glass_type.map({1:0, 2:0, 3:0, 5:1, 6:1, 7:1})\n",
    "glass.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's change our task, so that we're predicting **household** using **al**. Let's visualize the relationship to figure out how to do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x236164ba080>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAFhCAYAAAB3dgP3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23616493898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(glass.al, glass.household)\n",
    "plt.xlabel('al')\n",
    "plt.ylabel('household')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's draw a **regression line**, like we did before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# fit a linear regression model and store the predictions\n",
    "feature_cols = ['al']\n",
    "X = glass[feature_cols]\n",
    "y = glass.household\n",
    "linreg.fit(X, y)\n",
    "glass['household_pred'] = linreg.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2361652e668>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAAFhCAYAAAAP07LiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23616538a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot that includes the regression line\n",
    "plt.scatter(glass.al, glass.household)\n",
    "plt.plot(glass.al, glass.household_pred, color='red')\n",
    "plt.xlabel('al')\n",
    "plt.ylabel('household')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If **al=3**, what class do we predict for household? **1**\n",
    "\n",
    "If **al=1.5**, what class do we predict for household? **0**\n",
    "\n",
    "We predict the 0 class for **lower** values of al, and the 1 class for **higher** values of al. What's our cutoff value? Around **al=2**, because that's where the linear regression line crosses the midpoint between predicting class 0 and class 1.\n",
    "\n",
    "Therefore, we'll say that if **household_pred >= 0.5**, we predict a class of **1**, else we predict a class of **0**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['small', 'big', 'small'], \n",
       "      dtype='<U5')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# understanding np.where\n",
    "import numpy as np\n",
    "nums = np.array([5, 15, 8])\n",
    "\n",
    "# np.where returns the first value if the condition is True, and the second value if the condition is False\n",
    "np.where(nums > 10, 'big', 'small')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ri</th>\n",
       "      <th>na</th>\n",
       "      <th>mg</th>\n",
       "      <th>al</th>\n",
       "      <th>si</th>\n",
       "      <th>k</th>\n",
       "      <th>ca</th>\n",
       "      <th>ba</th>\n",
       "      <th>fe</th>\n",
       "      <th>glass_type</th>\n",
       "      <th>ri_pred</th>\n",
       "      <th>household</th>\n",
       "      <th>household_pred</th>\n",
       "      <th>household_pred_class</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.51966</td>\n",
       "      <td>14.77</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.29</td>\n",
       "      <td>72.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.521227</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.340495</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>1.51115</td>\n",
       "      <td>17.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.34</td>\n",
       "      <td>75.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6</td>\n",
       "      <td>1.521103</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.315436</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1.52213</td>\n",
       "      <td>14.21</td>\n",
       "      <td>3.82</td>\n",
       "      <td>0.47</td>\n",
       "      <td>71.77</td>\n",
       "      <td>0.11</td>\n",
       "      <td>9.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.520781</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.250283</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1.52213</td>\n",
       "      <td>14.21</td>\n",
       "      <td>3.82</td>\n",
       "      <td>0.47</td>\n",
       "      <td>71.77</td>\n",
       "      <td>0.11</td>\n",
       "      <td>9.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.520781</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.250283</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1.52320</td>\n",
       "      <td>13.72</td>\n",
       "      <td>3.72</td>\n",
       "      <td>0.51</td>\n",
       "      <td>71.75</td>\n",
       "      <td>0.09</td>\n",
       "      <td>10.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1</td>\n",
       "      <td>1.520682</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.230236</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ri     na    mg    al     si     k     ca   ba    fe  glass_type  \\\n",
       "id                                                                           \n",
       "22   1.51966  14.77  3.75  0.29  72.02  0.03   9.00  0.0  0.00           1   \n",
       "185  1.51115  17.38  0.00  0.34  75.41  0.00   6.65  0.0  0.00           6   \n",
       "40   1.52213  14.21  3.82  0.47  71.77  0.11   9.57  0.0  0.00           1   \n",
       "39   1.52213  14.21  3.82  0.47  71.77  0.11   9.57  0.0  0.00           1   \n",
       "51   1.52320  13.72  3.72  0.51  71.75  0.09  10.06  0.0  0.16           1   \n",
       "\n",
       "      ri_pred  household  household_pred  household_pred_class  \n",
       "id                                                              \n",
       "22   1.521227          0       -0.340495                     0  \n",
       "185  1.521103          1       -0.315436                     0  \n",
       "40   1.520781          0       -0.250283                     0  \n",
       "39   1.520781          0       -0.250283                     0  \n",
       "51   1.520682          0       -0.230236                     0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transform household_pred to 1 or 0\n",
    "glass['household_pred_class'] = np.where(glass.household_pred >= 0.5, 1, 0)\n",
    "glass.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x23616529c50>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAFhCAYAAAB3dgP3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23616571518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the class predictions\n",
    "plt.scatter(glass.al, glass.household)\n",
    "plt.plot(glass.al, glass.household_pred_class, color='red')\n",
    "plt.xlabel('al')\n",
    "plt.ylabel('household')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3: Using Logistic Regression Instead\n",
    "\n",
    "Logistic regression can do what we just did:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# fit a logistic regression model and store the class predictions\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "logreg = LogisticRegression(C=1e9)\n",
    "feature_cols = ['al']\n",
    "X = glass[feature_cols]\n",
    "y = glass.household\n",
    "logreg.fit(X, y)\n",
    "glass['household_pred_class'] = logreg.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x236166a3438>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAFhCAYAAAB3dgP3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiIKcP8fPVm7mJ1zjGgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2361667f518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the class predictions\n",
    "plt.scatter(glass.al, glass.household)\n",
    "plt.plot(glass.al, glass.household_pred_class, color='red')\n",
    "plt.xlabel('al')\n",
    "plt.ylabel('household')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we wanted the **predicted probabilities** instead of just the **class predictions**, to understand how confident we are in a given prediction?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# store the predicted probabilites of class 1\n",
    "glass['household_pred_prob'] = logreg.predict_proba(X)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x23616736b00>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAFhCAYAAAB3dgP3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x236166bf7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the predicted probabilities\n",
    "plt.scatter(glass.al, glass.household)\n",
    "plt.plot(glass.al, glass.household_pred_prob, color='red')\n",
    "plt.xlabel('al')\n",
    "plt.ylabel('household')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-27-1fafdcdb2b32>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-27-1fafdcdb2b32>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print logreg.predict_proba(1)\u001b[0m\n\u001b[0m               ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "# examine some example predictions\n",
    "print logreg.predict_proba(1)\n",
    "print logreg.predict_proba(2)\n",
    "print logreg.predict_proba(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first column indicates the predicted probability of **class 0**, and the second column indicates the predicted probability of **class 1**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 4: Probability, odds, e, log, log-odds\n",
    "\n",
    "$$probability = \\frac {one\\ outcome} {all\\ outcomes}$$\n",
    "\n",
    "$$odds = \\frac {one\\ outcome} {all\\ other\\ outcomes}$$\n",
    "\n",
    "Examples:\n",
    "\n",
    "- Dice roll of 1: probability = 1/6, odds = 1/5\n",
    "- Even dice roll: probability = 3/6, odds = 3/3 = 1\n",
    "- Dice roll less than 5: probability = 4/6, odds = 4/2 = 2\n",
    "\n",
    "$$odds = \\frac {probability} {1 - probability}$$\n",
    "\n",
    "$$probability = \\frac {odds} {1 + odds}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# create a table of probability versus odds\n",
    "table = pd.DataFrame({'probability':[0.1, 0.2, 0.25, 0.5, 0.6, 0.8, 0.9]})\n",
    "table['odds'] = table.probability/(1 - table.probability)\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is **e**? It is the base rate of growth shared by all continually growing processes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# exponential function: e^1\n",
    "np.exp(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is a **(natural) log**? It gives you the time needed to reach a certain level of growth:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# time needed to grow 1 unit to 2.718 units\n",
    "np.log(2.718)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also the **inverse** of the exponential function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.log(np.exp(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# add log-odds to the table\n",
    "table['logodds'] = np.log(table.odds)\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 5: What is Logistic Regression?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Linear regression:** continuous response is modeled as a linear combination of the features:\n",
    "\n",
    "$$y = \\beta_0 + \\beta_1x$$\n",
    "\n",
    "**Logistic regression:** log-odds of a categorical response being \"true\" (1) is modeled as a linear combination of the features:\n",
    "\n",
    "$$\\log \\left({p\\over 1-p}\\right) = \\beta_0 + \\beta_1x$$\n",
    "\n",
    "This is called the **logit function**.\n",
    "\n",
    "Probability is sometimes written as pi:\n",
    "\n",
    "$$\\log \\left({\\pi\\over 1-\\pi}\\right) = \\beta_0 + \\beta_1x$$\n",
    "\n",
    "The equation can be rearranged into the **logistic function**:\n",
    "\n",
    "$$\\pi = \\frac{e^{\\beta_0 + \\beta_1x}} {1 + e^{\\beta_0 + \\beta_1x}}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In other words:\n",
    "\n",
    "- Logistic regression outputs the **probabilities of a specific class**\n",
    "- Those probabilities can be converted into **class predictions**\n",
    "\n",
    "The **logistic function** has some nice properties:\n",
    "\n",
    "- Takes on an \"s\" shape\n",
    "- Output is bounded by 0 and 1\n",
    "\n",
    "We have covered how this works for **binary classification problems** (two response classes). But what about **multi-class classification problems** (more than two response classes)?\n",
    "\n",
    "- Most common solution for classification models is **\"one-vs-all\"** (also known as **\"one-vs-rest\"**): decompose the problem into multiple binary classification problems\n",
    "- **Multinomial logistic regression** can solve this as a single problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 6: Interpreting Logistic Regression Coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# plot the predicted probabilities again\n",
    "plt.scatter(glass.al, glass.household)\n",
    "plt.plot(glass.al, glass.household_pred_prob, color='red')\n",
    "plt.xlabel('al')\n",
    "plt.ylabel('household')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# compute predicted log-odds for al=2 using the equation\n",
    "logodds = logreg.intercept_ + logreg.coef_[0] * 2\n",
    "logodds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# convert log-odds to odds\n",
    "odds = np.exp(logodds)\n",
    "odds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# convert odds to probability\n",
    "prob = odds/(1 + odds)\n",
    "prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# compute predicted probability for al=2 using the predict_proba method\n",
    "logreg.predict_proba(2)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# examine the coefficient for al\n",
    "zip(feature_cols, logreg.coef_[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** A 1 unit increase in 'al' is associated with a 4.18 unit increase in the log-odds of 'household'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# increasing al by 1 (so that al=3) increases the log-odds by 4.18\n",
    "logodds = 0.64722323 + 4.1804038614510901\n",
    "odds = np.exp(logodds)\n",
    "prob = odds/(1 + odds)\n",
    "prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# compute predicted probability for al=3 using the predict_proba method\n",
    "logreg.predict_proba(3)[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Bottom line:** Positive coefficients increase the log-odds of the response (and thus increase the probability), and negative coefficients decrease the log-odds of the response (and thus decrease the probability)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# examine the intercept\n",
    "logreg.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** For an 'al' value of 0, the log-odds of 'household' is -7.71."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# convert log-odds to probability\n",
    "logodds = logreg.intercept_\n",
    "odds = np.exp(logodds)\n",
    "prob = odds/(1 + odds)\n",
    "prob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That makes sense from the plot above, because the probability of household=1 should be very low for such a low 'al' value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/upxacademy/ML_with_Python/master/images/logistic_betas.png?token=AH0Y7MGArRDNHepr5kTRYw2tiEVTCCcbks5ZFEqWwA%3D%3D\"></img>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Changing the $\\beta_0$ value shifts the curve **horizontally**, whereas changing the $\\beta_1$ value changes the **slope** of the curve."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 7: Using Logistic Regression with Categorical Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic regression can still be used with **categorical features**. Let's see what that looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a categorical feature\n",
    "glass['high_ba'] = np.where(glass.ba > 0.5, 1, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use Seaborn to draw the logistic curve:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# original (continuous) feature\n",
    "sns.lmplot(x='ba', y='household', data=glass, ci=None, logistic=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# categorical feature\n",
    "sns.lmplot(x='high_ba', y='household', data=glass, ci=None, logistic=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# categorical feature, with jitter added\n",
    "sns.lmplot(x='high_ba', y='household', data=glass, ci=None, logistic=True, x_jitter=0.05, y_jitter=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# fit a logistic regression model\n",
    "feature_cols = ['high_ba']\n",
    "X = glass[feature_cols]\n",
    "y = glass.household\n",
    "logreg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# examine the coefficient for high_ba\n",
    "zip(feature_cols, logreg.coef_[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** Having a high 'ba' value is associated with a 4.43 unit increase in the log-odds of 'household' (as compared to a low 'ba' value)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 8: Advantages and Disadvantages\n",
    "\n",
    "Advantages of logistic regression:\n",
    "\n",
    "- Highly interpretable (if you remember how)\n",
    "- Model training and prediction are fast\n",
    "- No tuning is required (excluding regularization)\n",
    "- Features don't need scaling\n",
    "- Can perform well with a small number of observations\n",
    "- Outputs well-calibrated predicted probabilities\n",
    "\n",
    "Disadvantages of logistic regression:\n",
    "\n",
    "- Presumes a linear relationship between the features and the log-odds of the response\n",
    "- Performance is (generally) not competitive with the best supervised learning methods\n",
    "- Can't automatically learn feature interactions"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}