M3 - Logistic Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Logistic Regression\n",
    "- https://harvard-iacs.github.io/2018-CS109A/lectures/lecture-10/demo/\n",
    "- ``` MODEL.get_params() ``` gives the list of parameters in the results of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPain</th>\n",
       "      <th>RestBP</th>\n",
       "      <th>Chol</th>\n",
       "      <th>Fbs</th>\n",
       "      <th>RestECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExAng</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Thal</th>\n",
       "      <th>AHD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>typical</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>fixed</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>160</td>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>asymptomatic</td>\n",
       "      <td>120</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>reversable</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>nonanginal</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>nontypical</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Age  Sex     ChestPain  RestBP  Chol  Fbs  RestECG  MaxHR  \\\n",
       "0           1   63    1       typical     145   233    1        2    150   \n",
       "1           2   67    1  asymptomatic     160   286    0        2    108   \n",
       "2           3   67    1  asymptomatic     120   229    0        2    129   \n",
       "3           4   37    1    nonanginal     130   250    0        0    187   \n",
       "4           5   41    0    nontypical     130   204    0        2    172   \n",
       "\n",
       "   ExAng  Oldpeak  Slope   Ca        Thal  AHD  \n",
       "0      0      2.3      3  0.0       fixed   No  \n",
       "1      1      1.5      2  3.0      normal  Yes  \n",
       "2      1      2.6      2  2.0  reversable  Yes  \n",
       "3      0      3.5      3  0.0      normal   No  \n",
       "4      0      1.4      1  0.0      normal   No  "
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_heart = pd.read_csv('./data/heart.csv')\n",
    "df_heart.shape \n",
    "df_heart.head()      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict AHD from Max HR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'AHD')"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_heart.MaxHR, df_heart.AHD, 'o', alpha = .5)\n",
    "plt.xlabel(\"Max HR\")\n",
    "plt.ylabel(\"AHD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score or Accuracy =  0.7704918032786885\n",
      "Estimated beta1:  [[-0.04008727]]\n",
      "Estimated beta0:  [5.86670118]\n",
      "For one unit increase in maxHD the log(odds) of heart diseases increases by [0.9607056]\n",
      "\n",
      "Confusion Matrix \n",
      " [[27  8]\n",
      " [ 6 20]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Hear Disease')"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3395sWLchWzYfk+PufkKS2Vz17StWte0uax923b36w7Bt3q+2YzbfN++/6r09+mJy/S3fWrKuU45+ZD557d92Unc//Y7ZLx33jOzcfc2q9rtf7eOc7w/UbU/C9gch8B2ABD5GRV9jVPQ1Rkl/Y1T0NdowEbdlAAAAYLQEPgAAgEoJfAAAAJUS+AAAACol8AEAAFRK4AMAAKiUwAcAAFApgQ8AAKBSAh8AAEClBD4AAIBKCXwAAACVEvgAAAAqJfABAABUSuADAAColMAHAABQKYEPAACgUgIfAABApQQ+AACASgl8AAAAlRL4AAAAKiXwAQAAVErgAwAAqJTABwAAUCmBDwAAoFICHwAAQKUEPgAAgEoJfAAAAJUS+AAAACol8AEAAFRK4AMAAKiUwAcAAFApgQ8AAKBSAh8AAEClBD4AAIBKCXwAAACVEvgAAAAqJfABAABUSuADAAColMAHAABQKYEPAACgUgIfAABApQQ+AACASgl8AAAAlRL4AAAAKiXwAQAAVErgAwAAqJTABwAAUCmBDwAAoFICHwAAQKUEPgAAgEoJfAAAAJUS+AAAACol8AEAAFRK4AMAAKiUwAcAAFApgQ8AAKBSAh8AAEClBD4AAIBKCXwAAACVEvgAAAAqJfABAABUSuADAAColMAHAABQKYEPAACgUgIfAABApQQ+AACASgl8AAAAlRL4AAAAKiXwAQAAVErgAwAAqJTABwAAUCmBDwAAoFICHwAAQKUEPgAAgEoJfAAAAJUS+AAAACol8AEAAFRK4AMAAKiUwAcAAFApgQ8AAKBSAh8AAEClBD4AAIBKCXwAAACVEvgAAAAqJfABAABUSuADAAColMAHAABQKYEPAACgUgIfAABApQQ+AACASgl8AAAAlRL4AAAAKiXwAQAAVErgAwAAqJTABwAAUCmBDwAAoFICHwAAQKUEPgAAgEoJfAAAAJUS+AAAACol8AEAAFRK4AMAAKiUwAcAAFApgQ8AAKBSAh8AAEClBD4AAIBKCXwAAACVEvgAAAAqJfABAABUSuADAAColMAHAABQKYEPAACgUgIfAABApQQ+AACASgl8AAAAlRL4AAAAKiXwAQAAVErgAwAAqJTABwAAUCmBDwAAoFICHwAAQKUEPgAAgEoJfAAAAJUS+AAAACol8AEAAFRK4AMAAKiUwAcAAFApgQ8AAKBSAh8AAEClBD4AAIBKCXwAAACVEvgAAAAqJfABAABUSuADAAColMAHAABQKYEPAACgUgIfAABApQQ+AACASgl8AAAAlRL4AAAAKiXwAQAAVErgAwAAqJTABwAAUCmBDwAAoFICHwAAQKUEPgAAgEoJfAAAAJUS+AAAACol8AEAAFRK4AMAAKiUwAcAAFCp9YP8UCllU5L7JdmRZFPTNN/ttCoAAACG1vcdvlLKTye5OslfJTkyyddKKQ/pujAAAACGM8hHOv8wyalJbmia5rokz0jyJ51WBQAAwNAGCXzf1zTNlfsfNE3z1xnwo6AAAACMzyCBb28p5QeSzCZJKaV0WxIAAABtGOSdutck+fsk9yqlXJDktCTbOq0KAACAofUNfE3TXFxK+bckj0wyneRVTdNc1XllAAAADGXQ+/Dta5rm3CS7kjyxlLK5u5IAAABowyC3ZfizJL9dSjkuyXlJ7pvkXV0XBgAAwHAGeYfvAUlekORxSc5vmuaXk9yn06oAAAAY2iCBb13TNPsy9x2+T80/933dlQQAAEAbBgl8Xyml/HXmPsr5d6WUv0iyvduyAAAAGNYgge+Xk7w/ycOaptmb5LL55wAAAJhgfQNf0zQ3J/nrJDOllKOT/E2Sh3ZdGAAAAMPpex++Usqrkrx0/uFtSTYmuTLJj3VYFwAAAEMa5COdz0xydJIPJvnhJM9OckWHNQEAANCCQQLffzRN880kVyX58aZp3hvv7gEAAEy8QQLf3lLKsUmaJCeXUtYnuVu3ZQEAADCsQQLf7yc5L8nFSR6f5Gu54358AAAATKi+F21pmubizIW9lFJOzNz3+NyHDwAAYMINcpXOQ5K8IcmPJnlSkjOTvDjJTd2WBgAAwDAG+Ujnm5N8J8k9k3wvyWGZ+4gnAAAAE2yQwPcTTdO8LMnepmm+m+RpSU7stCoAAACGNkjgm1n0eDrJvg5qAQAAoEWDBL7PlFLekGRTKeVRST6c5NPdlgUAAMCwBgl8v525C7TsTvLazF2h8ze7LAoAAIDhDXJbhr1JXp3k1aWUg5Lcs2ma73VeGQAAAEPp+w5fKeVxpZS3lFIOTdIk+f9LKS/qvjQAAACGMchHOl+audswPCHJ55LcJ8kzuiwKAACA4Q0S+KaapvlyklOTXNI0zY0Dvg4AAIAxGiS47SulPDnJo5JcWkr5ubgtAwAAwMQbJPC9OMm2JC9rmubfk7wsie/wAQAATLhBrtL52cx9nHP/45M6rQgAAIBWLBv4SikXNk3z5FLKl5PMLl7eNM3WTisDAABgKL3e4XvD/N8vHEUhAAAAtGvZwNc0zRfn//nNJD+W5LtJrmia5tpRFAYAAMBwen2k85Ak70/y35L8j8x9rPO4UspfJXl20zS3jqZEAAAAVqPXVTpfnuS6JPdqmubBTdP8VJJ7JbkxyWtHURwAAACr1yvwPSbJ2U3TfG//E03TfDfJr2fBVTsBAACYTL0C321N08wsfrJpmluS3OV5AAAAJkuvwHeXWzEsMNV2IQAAALSr120Zvr+U8rgsHe42d1QPAAAALekV+K5N8ms9lgEAADDBet2H7+EjrAMAAICW9foOHwAAAGuYwAcAAFCpvoGvlLJhFIUAAADQrkHe4ftC51UAAADQukEC382llKM6rwQAAIBW9botw34HJ9lZSvlakpv2P9k0zdbOqgIAAGBogwS+F3VeBQAAAK3r+5HOpmn+PsmXk1yTZGfmbrq+seO6AAAAGFLfd/hKKa9K8tL5h7dlLuxdmeTHOqwLAACAIQ1y0ZZnJjk6yQeT/HCSZye5osOaAAAAaMEgge8/mqb5ZpKrkvx40zTvjXf3AAAAJt4ggW9vKeXYJE2Sk0sp65PcrduyAAAAGNYgge/3k5yX5OIkj0/ytSSf6rIoAAAAhtf3oi1N01ycubCXUsqJmfse3/ZuywIAAGBYg1yl85Akb0jyo0melOTMJC/OgpuwAwAAMHkG+Ujnm5N8J8k9k3wvyWGZ+4gnAAAAE2yQwPcTTdO8LMnepmm+m+RpSU7stCoAAACGNkjgm1n0eDrJvg5qAQAAoEWDBL7PlFLekGRTKeVRST6c5NPdlgUAAMCwBgl8v525C7TsTvLazF2h8ze7LAoAAIDhDXJbhr1JXj3/BwAAgDVi2cBXSvlYktnlljdN8wudVAQAAEArer3D98EF/35Vkt/tuBYAAABatGzga5rm/P3/LqX8+sLHAAAATL5BLtqS9PhoJwAAAJNp0MAHAADAGtProi13X/BwupTyA0mm9j/RNM23uywMAACA4fS6aMv1mfso5/6Qd8OCZbNJprsqCgAAgOH1umiLj3sCAACsYUIdAABApQQ+AACASgl8AAAAlRL4AAAAKiXwAQAAVErgAwAAqJTABwAAUCmBDwAAoFICHwAAQKUEPgAAgEoJfAAAAJUS+AAAACol8AEAAFRK4AMAAKiUwAcAAFApgQ8AAKBSAh8AAEClBD4AAIBKCXwAAACVEvgAAAAqJfABAABUSuADAAColMAHAABQKYEPAACgUgIfAABApQQ+AACASgl8AAAAlRL4AAAAKiXwAQAAVErgAwAAqJTABwAAUCmBDwAAoFICHwAAQKUEPgAAgEoJfAAAAJUS+AAAACol8AEAAFRK4AMAAKiUwAcAAFApgQ8AAKBSAh8AAEClBD4AAIBKCXwAAACVEvgAAAAqJfABAABUSuADAAColMAHAABQKYEPAACgUgIfAABApQQ+AACASgl8AAAAlRL4AAAAKiXwAQAAVErgAwAAqJTABwAAUCmBDwAAoFICHwAAQKUEPgAAgEoJfAAAAJUS+AAAACol8AEAAFRK4AMAAKiUwAcAAFApgQ8AAKBSAh8AAEClBD4AAIBKCXwAAACVEvgAAAAqJfABAABUSuADAAColMAHAABQKYEPAACgUgIfAABApQQ+AACASgl8AAAAlRL4AAAAKiXwAQAAVErgAwAAqJTABwAAUCmBDwAAoFICHwAAQKUEPgAAgEoJfAAAAJUS+AAAACol8AEAAFRK4AMAAKiUwAcAAFApgQ8AAKBSAh8AAEClBD4AAIBKCXwAAACVEvgAAAAqJfABAABUSuADAAColMAHAABQKYEPAACgUgIfAABApQQ+AACASgl8AAAAlRL4AAAAKiXwAQAAVErgAwAAqJTABwAAUCmBDwAAoFICHwAAQKUEPgAAgEoJfAAAAJUS+AAAACol8AEAAFRK4AMAAKiUwAcAAFApgQ8AAKBSAh8AAEClBD4AAIBKCXwAAACVEvgAAAAqJfABAABUSuADAAColMAHAABQKYEPAACgUgIfAABApQQ+AACASgl8AAAAlRL4AAAAKiXwAQAAVErgAwAAqJTABwAAUCmBDwAAoFICHwAAQKUEPgAAgEoJfAAAAJUS+AAAACol8AEAAFRK4AMAAKiUwAcAAFApgQ8AAKBSAh8AAEClBD4AAIBKCXwAAACVEvgAAAAqJfABAABUSuADAAColMAHAABQKYEPAACgUgIfAABApQQ+AACASgl8AAAAlRL4AAAAKiXwAQAAVErgAwAAqNT6cRdwoLjyhivynh3vyOVfvyxfvXFX9uzbk43rNuY+h23JSUeenGef8Jzc//Djx13mki6++qK84fOvzTW7r87efXtvf37Dug257+Zj89sPfllOP/aMJV/bb7//21EPz2eu+7tVH5cuj+tabrMureXj0qv2uZqncuUNOyZuv972pT/NG7/4B9l963fusmzzQd+fFz/gt/L8E1/YybYnub371XbfzcfmL67682Xnrqcd98xcs/vqZfrDCUlmc+UNV3Qyd7FyXZ9Phtn2OM9Vw3IehfpNzc7OjruGYW1JsvOGG27Kvn2Tty+7du/MWZ/Ylh03bM+emT2ZmZ25y89MT01n4/TGnHCPrTnnlPOyZfMxQ2/3iCMOzbe+9V9DrePy6y7L0y95Sm7ee1Pfnz1kwyF576M/kJOOOjnJYPvdT6/j0uVxHVebTbq1fFyG7Y/j2q8PNRfmrE9ty77ZfX1/dt3UupzziPPyhPLkVrY9ie29f15rY37p0qSOg7Wq6/PJco444tB84SvbhxoHkziORlHbJO/3JGrjdzZYt24qhx9+SJIck2TXwmUCX4cu+sqH82ufesHAJ6j9k9+bH3Fuzrjf44fa9rCTx8s/+5Kct/2tK37dtq1n5kH3evCK9rufxcely+M6zjabZGv5uKy09l5GuV9PvfiJ+eS1l674daccfVouOP2DQ217Utv7iCMOzTs+d36r80uXJmkcrFVtjt9kZW3y6f+4JM/+yLNXPQ4mdRwl3Y7xSd7vSSXw0QaBbwz2T3i33HbLil+7af2moSe+YSaP1Ya9/dZlOvvS/i9im9ZvyjPu/8t575Xv7uS4jrvNJtVaPi7D1N5L1/u12rC33zChb5Lbe/8v4G23Z9fGPQ7Wqq7Gb9L9+aDLc9Wwuhzjkzx/TDKBjzYIfCO2a/fOPOwDPz3USWrT+k35zC/+U+5z2JZVvX61k8fl112Wx330Mava5lqw3HGdhDabRGv5uLRRey9d7deHmgvzgk8+Z+j1/Nkj35XH/fATV/SaSW7vXbt35uEX/ky+u/e7ra53VGqcH7rU9fhNuj0fDKvLcdTVGJ/k+WPSCXy0oVfgc5XODpz1iW3ZM7NnqHXsmdmTMz/x3JYqGtzTL3nKyLc5Sssd17XcZl1ay8eljdp76Wq/zvrUtlbW84JPrDw0TnJ7n/WJbbn1tltbX++o1Dg/dKnr8Zt0ez4YVpfjqKsxPsnzBxzoOgl8pZQ/LaV8cNFzp5VSrimlHNrFNifFFdfvyI4btg/9XYOZ2ZnsuH57rrzhipYq6++jX/nIQBdoWcuWOq5ruc26tJaPS1u199LFfp3zr28e6AItg9g3uy9v+9KfDvzzk9zeo2jPrtU2P3RpVO3d5flgWJM8jpxHYe3p6h2+lyR5QCnlF5KklHJwknOT/ErTNFW/Z33+Fe/M4//le9n5pmTmFcnONyVP3X7H8qduz7LLFtszsyfn73hn1yXf7g+/8Lqey99ycbL3lcm+V8z9/ZaLB193v/1eyXFZzfoXWnxcz7/inT3/V3KS26xLXR6Xgz50Ye7+k8fnHvfcnLv/5PE56EMXtlh5/9p79eVxtvf/8y9/1HP5x8+fq3n/n4+ff+fli2v/6jtePfC2ux4Hw7T5sLX1m7t6LV/pMT9Q5ocu9WvvNq30fDBKbfeXNvety+NmnED7OrkPX9M0N5VSnpvkXaWUTyZ5VZKPJrm5lPLZJN+X5Pokz2uaZmcp5TeSPCvJviSfb5rmeV3UNQo/cNFf5dUfm83B87d82rI7efvH7lj+9o9lyWUXbL3rumZmZ3L5Ny7rtuAFrtl99bLL3nJxctY/J1Pzj9fPzj1OkrNP773ep27vvd/9lvez0tcvPq6Xf/2yZf9Xcth1r2VdHZeDPnRhDv2NszN1y9z3PKav+1oO/Y2zkyS3PqGdWwr0qr1XX/6Ho8fb3kvdZ2+/j5+fnLbzjrqTuccfPz/52Wct3SZ/8pe3ZOa/XzjQce1yHAzb5sPU1m/u6rX8h29Y+TE/UOaHLvVq77at5Hwwam33lzb3rcvjZpxA+zr7Dl/TNJ9I8jdJ3p3ktCSvSPKOJL/UNM1PJnljkreXUqaTvDTJA5M8IMnGUsqRXdXVtRd97N9vP/nvd/De5HWfnPuz3LLl7Nq9s/0il7HwxsSLPf+Ld/6lJ5l7/Pwv9l9vv/1ezXFZyfqXsvC4fvXGXZ2tey3r6rgc/NpX3v6L/35Tt9ySg1/7ytWWehe9au/Vlye5vRcHj2Tu8Wnzm1+u9kGPa5fjYNg2H6a2fnNXr+WrPeaT0F/Wsl7t3YVBzwfj0GZ/aXvfujxuxgm0q5N3+BZ4cZJrkzw2yQ8lOTbJR0sp+5cf1jTNTCnlH5J8IclFSd7YNM3XV7qh+avSjN2+3Us/f/Qyz/dbtnff3hxxxOq+9rja1y1lepkLoC73/ELL7d/+5/stH3b9S1l4XPfsW/5jKMOuey3r7Lh8/boll09//brWjluv2nv15bXc3svVOOhx7XQcDNnmw9TWb+7qcm5byqT0l0nWq727MOj5YBza7C9t71uXx+1AHCcH2v4yWp0GvqZpbiylfCdzlwbdnOSapmlOTJL5d/buOf+jj03y00keneTjpZSnNU3z9yvZ1qTcluG7m5P7LHGyv3bz3N9beixbyoZ1G1Z1qd62L/E7MzX3Uaelnu/n2s2997vf8mHXv5SFx3Xjuo3LnqyGXfda1tVxufuRR2X6uq/dZfnMkUfl2y0dt1619+rL1x22dtt7uTYZ9Lh2OQ6GbfNhaus3d/Va3i/0HcjzQ5d6tXcXBj0fjEOb/aXtfevyuB1o48RtGWjDgtsy3HXZCOv4tyR3L6WcPP/4V5K8v5RyRJIrk3y5aZrfTXJpkgG+uTWZ/uTn75WbN9z5uZs3JL9zytyf5ZYtZ8vmY9ovchkb1m1YdtnbHpAs/t1ndv75fvrt92qOy0rWv5SFx7XX/X4mvc261NVxufllv5fZTZvutGx206bc/LLfW22pd9Gr9l59eZLb+9Jjlq770vnNL1f7oMe1y3EwbJsPU1u/uavX8tUe80noL2vZqO/BNuj5YBza7C9t71uXx804gXaNLPA1TXNrkicleWMpZXvmLtLyq03TfCvJeUm+UEr5YpK7JXnXqOpq23+e8Zg87+ensmvz3BVodm1Onvvzc1/gv2Dr3L+XWraU6anpnHTvk5de2IH7bj522WVnn56c88Dktqm5X3hum5p73O+CLUn//V7pcVnp+hdbfFxPOvLkTE9Nd7Lutayr43LrE56c//rjt2TmqB/K7NRUZo76ofzXH7+ltQu29Ku9V18ed3tvPuj7l132s8+6I4Ds/3PpMXPPJ0vX/qLHbRr4uHY5DoZt82Fq6zd39Vq+mmN+oMwPXerV3m1byflg1NruL23uW5fHzTiB9k3Nzo7/Y5BD2pJk56R8pPOK63fk5z58Sm657Zb+P9zHpvWbcskTPpX7H378il+7mo8HfPQrH8lzLn3mire11iw+rpPSZpNmLR+XNmvvpe39Oudf35xXfu7lrawrSV71kNfl+Se+cKCfneT2HlV7dq2m+aFLo2zvLs8Hw5rkceQ82i4f6aQNCz7SeUzmvk53x7JxFFSz4+9xQk44fOvQ/9M1PTWdE+6xdaQT3i/c77E5eMNkXPymK0sd17XcZl1ay8elrdp76WK/zvqJX8u6qXam5XVT6wYOe8lkt/co2rNrtc0PXRpVe3d5PhjWJI8j51FYewS+Dpxz6nnZOL1xqHVsnN6Yt5769pYqGtz7Hv2BkW9zlJY7rmu5zbq0lo9LG7X30tV+nfOI81pZz7mnvmPl257g9j7n1PNy0PqDWl/vqNQ4P3Sp6/GbdHs+GFaX46irMT7J8wcc6AS+DmzZfEze/Ihzs2n9pv4/vIRN6zflzY84dyxfHj/pqJOzbeuZQ61jXbr5n9FN6zdl29YzOzmua7nNurSWj8uwtffS5X49oTw5pxx92lDrOOXo0/K4H37iil83ye29ZfMxefcZ7+6kPbtW6/zQpS7Hb9L9+aCrc9Wwuhzjkzx/wIFO4OvIGfd7/O0T36AfcZiemr59wjvjfo/vuMLlveahr1916Nu29cz82WnvXNF+97PwuLzmoa/v7Liu5Tbr0lo+LqupvZdR7dcFp39w1aHvlKNPywWnf3DV257k9n7y8U9utT27NinjYK1qe/wmKzsfvOex71n1OOjyXDWsLsf4JM8fcCBz0ZaO7dq9M2d9clt2XL89e2b2ZGZ25i4/Mz01nY3TG3PCPbbmrae+vZX/3WrjC8CXX3dZnnHJU3LT3pv6/uwhGw7J+x5zYR5y74cmGWy/++l1XLo8ruNqs0m3lo/LsP1xXPv1oebCnPWpbdk3u6/vz66bWpdzT33Hqt7ZW8oktvf+ea2N+aVLkzoO1qquzyfLOeKIQ/OFr2wfahxM4jgaRW2TvN+TyEVbaEOvi7YIfCNy5Q1X5Pwd78zl37gsu3bvzJ59e7Jx3cZs2XxMTrr3yXnWCb/a6peU25w8Lr76ovzB51+Xa3Zffacbq25ctzH33XxsfuvBv5PTjz1jydf22++Tj3pYLrvu71d9XLo8rqNus7Vi8XHZu29vNqzbsCaOS682Pe7uxyeZylXf3jFx7f22L/1p/viLf5Dv3Pqduyz7/oO+P7/xgN9a0QVaVmKSxsHiea1fbcdsvm/ef9V7l527fum4Z2Tn7muW6Q8nJJnNVd++opO5i5Xr+nyy2ML+Nuw4mKRxNMraJnm/J4nARxsEvgOQyYNR0dcYFX2NUdLfGBV9jTa4LQMAAMABSOADAAColMAHAABQKYEPAACgUgIfAABApQQ+AACASq0fdwEtmE7mLkXKnTkmjIq+xqjoa4yS/sao6GsMa0Efml68rIb78D00yWXjLgIAAGDMTk7y2YVP1BD4DkryoCTfTDIz5loAAABGbTrJDyb5QpJbFy6oIfABAACwBBdtAQAAqJTABwAAUCmBDwAAoFICHwAAQKUEPgAAgEoJfAAAAJUS+AAAACol8AEAAFRq/bgLYPVKKYcl+YckpzdNs6uUcmqSP06yKckHmqZ5+fzPnZjkHUkOS/KZJM9vmua28VTNWrREX9uW5NeSzCb55yTPa5pmj77GsBb3tQXPvzDJE5umefj84xOjrzGkJea2n0nypiSHJtme5FnmNtqwRF87LckfJplO8i9JnqOv0RXv8K1RpZSfSvLZJD8y/3hTknclOSPJcUkeVEp59PyPvy/JC5um+ZEkU0meO/qKWauW6Gs/kuQ3kzwkydbMzSNnzf+4vsaqLe5rC56/f5KXLPpxfY2hLDG3HZbkw0m2NU1z/PyP/er83/obq7bM3PbOJL/YNM0JSb4vyTPnn9fXaJ3At3Y9N3O/ZH9j/vGDk/zPpml2zv9P0PuSPKmUcp8km5qm+cf5n3tPkieNuljWtMV97dYkZzZNc2PTNLNJvpzkaH2NFizuaymlHJTkz5L87oLn9DXasLi/PTLJ55qm2T7/+Owkf6m/0YK7zG2Ze2fvsFLKdJK7JblFX6MrPtK5RjVN85wkKaXsf+reSb654Ee+meSoHs/DQBb3taZpvprkq/PPHZHkhUmeHX2NIS0xryXJ72fu0ws7FzynrzG0Jfrb/ZLcVEr5f5P8aJLLk7w4yU9Ef2MIy8xtZyb5uyQ3Zm5++2CSn4y+Rge8w1ePdZn7PtV+U0n29XgehlJKOTLJJ5O8s2mav4u+RstKKY9McnTTNO9etEhfowvrkzwqyUuTPCDJwZn7KLH+RqtKKfdK8vokJyT5wST/mLlrMOhrdELgq8d1mZs09rtX5j46sNzzsGqllB/N3JfPz2+a5tXzT+trtO2pSY4vpXwpcxcxeGAp5QPR1+jGvyf5x/mvRswkuTBzX5fQ32jbyUl2NE1zddM0+5K8PcnDo6/REYGvHv+UpJRS7jf/efBfSnLJ/MfvvldKOWn+556R5JJxFcnaV0o5NMmlSV7eNM0b9z+vr9G2pml+pWma45qmOTHJc5L8c9M0T9HX6MilSR5QSvmh+cenJ/mi/kYHdiR5cCnlnvOPz0jyBX2Nrgh8lWia5nuZ+x7Vh5JcmeTfMvd58CR5WpI3lVL+LckhSd48jhqpxnOS3DPJi0spX5r/86r5Zfoao6Kv0aqmab6W5HlJPjbfr+6eue+QJvobLWqa5qok/3eST5dStid5YJL/a36xvkbrpmZnZ/v/FAAAAGuOd/gAAAAqJfABAABUSuADAAColMAHAABQKYEPAACgUuvHXQAAjEopZUuSnUk+0zTNwxYte0+SZyU5omma61vY1uzidZVSnp3kiU3TnD7/7z+ZrydJppIcluSyJNvmb7cDAEPxDh8AB5rvJSmllPvsf6KUcnCSk5Z/SWcua5rmxPk/P57kuCT3z1zwBICheYcPgAPNTJIPZO4Gx6+bf+7xSS5K8uIkKaWsS/KmJD+d5NDMvfv2nCSfS/K3Sb7YNM1vlVJOTfKeJA9omuZ/tVDb4Uk2J/l2C+sCAIEPgAPSnyd5X+4IfM9K8uuZD3xJfirJvZP8TNM0+0opL0nykqZpfr6U8vQk/1pKuTzJOUl+qUfY+3QpZWbB47sn2b7g8cmllC8luVvmwt7/SPJHTdP8f8PuIAAkPtIJwAGoaZovJpkppTyglPJDSQ5tmmbHguWfS/LyJM8rpfxRkicmOWR+2TeTPDfJXyb5s6ZpPtNjU/99wUc2T0zyu4uWXzb//P2TvCVzoU/YA6A1Ah8AB6r3Jnl6kmfM//t2pZTHJPmr+YcXJXlb5j7Wud/xSf5X5t4JHFrTNPuapnlVkl2Z+4goALRC4APgQPW+JE9K8pQk71+07JFJPtY0zblJ/jnJY5NMJ0kp5cFJXpTkgUk2l1Je1GJNZyU5rZRyRovrBOAAJvABcEBqmubrSa5K8j+bpll8kZS3JXl4KeXLSf4lydVJjimlbE5yQZKz51//7CS/W0r5iZZqujrJG5K8qZRytzbWCcCBbWp2dnbcNQAAANAB7/ABAABUSuADAAColMAHAABQKYEPAACgUgIfAABApQQ+AACASgl8AAAAlfrf0WMWb3aX8/UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(22)\n",
    "# Split into train and test sets\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(df_heart['MaxHR'].values.reshape(-1,1),\n",
    "                                                df_heart['AHD'], \n",
    "                                                test_size = 0.2)\n",
    "\n",
    "# create model\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "# fit model\n",
    "logreg.fit(Xtrain, ytrain);\n",
    "\n",
    "# test model\n",
    "score = logreg.score(Xtest, ytest)\n",
    "print(\"Score or Accuracy = \", score)\n",
    "\n",
    "# Coefficients\n",
    "print('Estimated beta1: ', logreg.coef_)\n",
    "print('Estimated beta0: ', logreg.intercept_)\n",
    "print('For one unit increase in maxHD the log(odds) of heart diseases increases by', np.exp(logreg.coef_[0]))\n",
    "\n",
    "# predict y values\n",
    "pred_y = logreg.predict(Xtest)\n",
    "conf_matrix = confusion_matrix(ytest, pred_y)\n",
    "print('\\nConfusion Matrix \\n', conf_matrix)\n",
    "\n",
    "# plot results\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.plot(np.sort(Xtest), ytest, 'o', alpha = 1, color = 'green', markersize = 20)\n",
    "plt.plot(np.sort(Xtest), pred_y, 'o', alpha = 1, color = 'red')\n",
    "plt.xlabel(\"Max HR\")\n",
    "plt.ylabel(\"Hear Disease\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score or Accuracy =  0.7540983606557377\n",
      "Estimated beta1:  [[-0.04047751  1.16013526]]\n",
      "Estimated beta0:  [5.13227087]\n",
      "log(odds) of heart diseases for male is 3.1903647705885345 more than that for female\n",
      "\n",
      "Confusion Matrix \n",
      " [[27  8]\n",
      " [ 7 19]]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(22)\n",
    "# Split into train and test sets\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(df_heart[['MaxHR', 'Sex']],\n",
    "                                                df_heart['AHD'], \n",
    "                                                test_size = 0.2)\n",
    "\n",
    "# create model\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "# fit model\n",
    "logreg.fit(Xtrain, ytrain);\n",
    "\n",
    "# test model\n",
    "score = logreg.score(Xtest, ytest)\n",
    "print(\"Score or Accuracy = \", score)\n",
    "\n",
    "# Coefficients\n",
    "print('Estimated beta1: ', logreg.coef_)\n",
    "print('Estimated beta0: ', logreg.intercept_)\n",
    "print('log(odds) of heart diseases for male is', np.exp(logreg.coef_.item(1)), \"more than that for female\")\n",
    "\n",
    "\n",
    "# predict y values\n",
    "pred_y = logreg.predict(Xtest)\n",
    "conf_matrix = confusion_matrix(ytest, pred_y)\n",
    "print('\\nConfusion Matrix \\n', conf_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Convert AHD into 1s and 0s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% women =  0.3201320132013201\n",
      "% men =  0.6798679867986799\n",
      "\n",
      "\n",
      "Estimated beta1:  [[1.18597443]]\n",
      "Estimated beta0:  [-0.99483151]\n"
     ]
    }
   ],
   "source": [
    "data_x = df_heart.Sex.values\n",
    "data_y = df_heart.AHD.map(lambda x:0 if x == 'No' else 1).values\n",
    "\n",
    "print(\"% women = \", sum(df_heart['Sex'] == 0)/df_heart['Sex'].shape[0])\n",
    "print(\"% men = \", sum(df_heart['Sex'] == 1)/df_heart['Sex'].shape[0])\n",
    "print(\"\\n\")\n",
    "\n",
    "logreg.fit(data_x.reshape(-1,1), data_y);\n",
    "\n",
    "print('Estimated beta1: ', logreg.coef_)\n",
    "print('Estimated beta0: ', logreg.intercept_)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With more than 2 categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>politicalparty</th>\n",
       "      <th>income</th>\n",
       "      <th>educ</th>\n",
       "      <th>abortion</th>\n",
       "      <th>republican</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Republican</td>\n",
       "      <td>2152</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Republican</td>\n",
       "      <td>906</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Democrat</td>\n",
       "      <td>1373</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Democrat</td>\n",
       "      <td>1941</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Democrat</td>\n",
       "      <td>355</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  politicalparty  income  educ  abortion  republican\n",
       "0     Republican    2152     9         0           1\n",
       "1     Republican     906     6         0           1\n",
       "2       Democrat    1373     6         0           0\n",
       "3       Democrat    1941     4         0           0\n",
       "4       Democrat     355     7         0           0"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "party_df = pd.read_csv('./data/gsspartyid.csv')\n",
    "party_df.head()\n",
    "#print(party_df['politicalparty'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated beta1:  [[-0.02351597 -0.01273136 -1.00384789]]\n",
      "Estimated beta0:  [0.30520363]\n"
     ]
    }
   ],
   "source": [
    "party_df['logincome'] = np.log(party_df['income'])\n",
    "train_x = party_df[['logincome', 'educ', 'abortion']]\n",
    "train_y = party_df['republican']\n",
    "\n",
    "logitm = LogisticRegression(C = 10000000)\n",
    "\n",
    "logitm.fit(train_x, train_y);\n",
    "\n",
    "print('Estimated beta1: ', logitm.coef_)\n",
    "print('Estimated beta0: ', logitm.intercept_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.50253905, 0.51716683, 0.51472534, ..., 0.28452455, 0.27767379,\n",
       "       0.27906997])"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict probabilities and build confusion matrix\n",
    "\n",
    "yhat = logitm.predict_proba(train_x) # gives prob of repub = 1 or 0\n",
    "yhat = yhat[:,1]  # get only the second col that gives prob of republican\n",
    "yhat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[478 297]\n",
      " [207 325]]\n",
      "[[430 345]\n",
      " [166 366]]\n",
      "[[775   0]\n",
      " [532   0]]\n"
     ]
    }
   ],
   "source": [
    "import sklearn.metrics as met\n",
    "\n",
    "# confusion_matrix(actual, pred)\n",
    "# Columns are for predicted (0,1) and rows are observed (0,1)\n",
    "\n",
    "print(met.confusion_matrix(train_y, yhat > 0.5))\n",
    "print(met.confusion_matrix(train_y, yhat > 0.4)) # increases # classified as Rs\n",
    "print(met.confusion_matrix(train_y, yhat > 0.7)) # decreases # classified as R\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curve\n",
    "\n",
    "fpr, tpr, threshold = met.roc_curve(train_y, yhat)\n",
    "\n",
    "x = np.arange(0,1,0.01)\n",
    "\n",
    "plt.plot(x , x, '--', color = 'gray')\n",
    "plt.plot(fpr, tpr)\n",
    "plt.title(\"ROC curve for party ID using Logistic Reg\")\n",
    "plt.xlabel(\"False Positive Rate (FPR)\")\n",
    "plt.ylabel(\"True Positive Rate (TPR)\")\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}