Septicemia.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.metrics import silhouette_score\n",
    "from scipy.cluster.hierarchy import fcluster\n",
    "from sklearn import preprocessing\n",
    "from sklearn.decomposition import PCA\n",
    "from pandas.plotting import parallel_coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clustering hospital summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sept2015 = pd.read_csv('summary_2015.csv')\n",
    "sept2016 = pd.read_csv('summary_2016.csv')\n",
    "sept2017 = pd.read_csv('summary_2017.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Earl\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "sept = pd.concat([sept2015,sept2016,sept2017])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of Beds</th>\n",
       "      <th>System</th>\n",
       "      <th>apr_drg_code</th>\n",
       "      <th>apr_severity_of_illness</th>\n",
       "      <th>apr_severity_of_illness_description</th>\n",
       "      <th>area_sqmi</th>\n",
       "      <th>facility_name</th>\n",
       "      <th>mean_length_of_stay</th>\n",
       "      <th>mean_total_charges_day</th>\n",
       "      <th>patients</th>\n",
       "      <th>payment_typology_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Major</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7017.275000</td>\n",
       "      <td>2</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>6852.591667</td>\n",
       "      <td>6</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>14.228571</td>\n",
       "      <td>7124.722571</td>\n",
       "      <td>70</td>\n",
       "      <td>Medicare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Major</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>8.659341</td>\n",
       "      <td>6399.484835</td>\n",
       "      <td>91</td>\n",
       "      <td>Medicare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Minor</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6386.240000</td>\n",
       "      <td>4</td>\n",
       "      <td>Medicare</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number of Beds   System  apr_drg_code apr_severity_of_illness  \\\n",
       "0             912  Health+           720                     NaN   \n",
       "1             912  Health+           720                     NaN   \n",
       "2             912  Health+           720                     NaN   \n",
       "3             912  Health+           720                     NaN   \n",
       "4             912  Health+           720                     NaN   \n",
       "\n",
       "  apr_severity_of_illness_description  area_sqmi             facility_name  \\\n",
       "0                               Major       0.77  Bellevue Hospital Center   \n",
       "1                            Moderate       0.77  Bellevue Hospital Center   \n",
       "2                             Extreme       0.77  Bellevue Hospital Center   \n",
       "3                               Major       0.77  Bellevue Hospital Center   \n",
       "4                               Minor       0.77  Bellevue Hospital Center   \n",
       "\n",
       "   mean_length_of_stay  mean_total_charges_day  patients  \\\n",
       "0             8.000000             7017.275000         2   \n",
       "1             5.666667             6852.591667         6   \n",
       "2            14.228571             7124.722571        70   \n",
       "3             8.659341             6399.484835        91   \n",
       "4             5.000000             6386.240000         4   \n",
       "\n",
       "       payment_typology_1  \n",
       "0  Blue Cross/Blue Shield  \n",
       "1  Blue Cross/Blue Shield  \n",
       "2                Medicare  \n",
       "3                Medicare  \n",
       "4                Medicare  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sept.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of Beds</th>\n",
       "      <th>System</th>\n",
       "      <th>apr_drg_code</th>\n",
       "      <th>apr_severity_of_illness</th>\n",
       "      <th>apr_severity_of_illness_description</th>\n",
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       "      <th>0</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Major</td>\n",
       "      <td>0.770</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7017.275000</td>\n",
       "      <td>2</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>912</td>\n",
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       "      <td>Moderate</td>\n",
       "      <td>0.770</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>6852.591667</td>\n",
       "      <td>6</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>0.770</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>14.228571</td>\n",
       "      <td>7124.722571</td>\n",
       "      <td>70</td>\n",
       "      <td>Medicare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Major</td>\n",
       "      <td>0.770</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>8.659341</td>\n",
       "      <td>6399.484835</td>\n",
       "      <td>91</td>\n",
       "      <td>Medicare</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Minor</td>\n",
       "      <td>0.770</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>5.000000</td>\n",
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       "      <td>Medicare</td>\n",
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       "      <th>5</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>0.770</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>6316.883200</td>\n",
       "      <td>50</td>\n",
       "      <td>Medicare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>0.770</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>12.250000</td>\n",
       "      <td>6490.560000</td>\n",
       "      <td>4</td>\n",
       "      <td>Private Health Insurance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Major</td>\n",
       "      <td>0.770</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>7022.595000</td>\n",
       "      <td>4</td>\n",
       "      <td>Private Health Insurance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Minor</td>\n",
       "      <td>0.770</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6437.370000</td>\n",
       "      <td>2</td>\n",
       "      <td>Private Health Insurance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>0.770</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>7000.660000</td>\n",
       "      <td>4</td>\n",
       "      <td>Private Health Insurance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>0.770</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>17.750000</td>\n",
       "      <td>6315.075000</td>\n",
       "      <td>4</td>\n",
       "      <td>Self-Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Major</td>\n",
       "      <td>0.770</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6730.027500</td>\n",
       "      <td>8</td>\n",
       "      <td>Self-Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Minor</td>\n",
       "      <td>0.770</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>6341.304000</td>\n",
       "      <td>5</td>\n",
       "      <td>Self-Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>0.770</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>4.888889</td>\n",
       "      <td>6371.484444</td>\n",
       "      <td>9</td>\n",
       "      <td>Self-Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>415</td>\n",
       "      <td>Mount Sinai</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>2.194</td>\n",
       "      <td>Bronx-Lebanon Hospital Center - Concourse Divi...</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>4150.565000</td>\n",
       "      <td>4</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>415</td>\n",
       "      <td>Mount Sinai</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Major</td>\n",
       "      <td>2.194</td>\n",
       "      <td>Bronx-Lebanon Hospital Center - Concourse Divi...</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4037.055000</td>\n",
       "      <td>2</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>415</td>\n",
       "      <td>Mount Sinai</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>2.194</td>\n",
       "      <td>Bronx-Lebanon Hospital Center - Concourse Divi...</td>\n",
       "      <td>12.077465</td>\n",
       "      <td>4629.844507</td>\n",
       "      <td>142</td>\n",
       "      <td>Medicare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>415</td>\n",
       "      <td>Mount Sinai</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Major</td>\n",
       "      <td>2.194</td>\n",
       "      <td>Bronx-Lebanon Hospital Center - Concourse Divi...</td>\n",
       "      <td>8.339130</td>\n",
       "      <td>3792.219478</td>\n",
       "      <td>115</td>\n",
       "      <td>Medicare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>415</td>\n",
       "      <td>Mount Sinai</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Minor</td>\n",
       "      <td>2.194</td>\n",
       "      <td>Bronx-Lebanon Hospital Center - Concourse Divi...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3416.727500</td>\n",
       "      <td>4</td>\n",
       "      <td>Medicare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>415</td>\n",
       "      <td>Mount Sinai</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>2.194</td>\n",
       "      <td>Bronx-Lebanon Hospital Center - Concourse Divi...</td>\n",
       "      <td>7.150000</td>\n",
       "      <td>3359.234500</td>\n",
       "      <td>20</td>\n",
       "      <td>Medicare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>415</td>\n",
       "      <td>Mount Sinai</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>2.194</td>\n",
       "      <td>Bronx-Lebanon Hospital Center - Concourse Divi...</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>2661.505333</td>\n",
       "      <td>15</td>\n",
       "      <td>Private Health Insurance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>415</td>\n",
       "      <td>Mount Sinai</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Major</td>\n",
       "      <td>2.194</td>\n",
       "      <td>Bronx-Lebanon Hospital Center - Concourse Divi...</td>\n",
       "      <td>7.666667</td>\n",
       "      <td>2998.503333</td>\n",
       "      <td>12</td>\n",
       "      <td>Private Health Insurance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>415</td>\n",
       "      <td>Mount Sinai</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>2.194</td>\n",
       "      <td>Bronx-Lebanon Hospital Center - Concourse Divi...</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>5558.410000</td>\n",
       "      <td>2</td>\n",
       "      <td>Private Health Insurance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>415</td>\n",
       "      <td>Mount Sinai</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Major</td>\n",
       "      <td>2.194</td>\n",
       "      <td>Bronx-Lebanon Hospital Center - Concourse Divi...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4847.450000</td>\n",
       "      <td>1</td>\n",
       "      <td>Self-Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>415</td>\n",
       "      <td>Mount Sinai</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>2.194</td>\n",
       "      <td>Bronx-Lebanon Hospital Center - Concourse Divi...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3536.570000</td>\n",
       "      <td>1</td>\n",
       "      <td>Self-Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>530</td>\n",
       "      <td>Others</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>14.720</td>\n",
       "      <td>Brookdale Hospital Medical Center</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>11492.145000</td>\n",
       "      <td>2</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>530</td>\n",
       "      <td>Others</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Major</td>\n",
       "      <td>14.720</td>\n",
       "      <td>Brookdale Hospital Medical Center</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>5458.825000</td>\n",
       "      <td>2</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>530</td>\n",
       "      <td>Others</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>14.720</td>\n",
       "      <td>Brookdale Hospital Medical Center</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>5925.015000</td>\n",
       "      <td>2</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>530</td>\n",
       "      <td>Others</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>14.720</td>\n",
       "      <td>Brookdale Hospital Medical Center</td>\n",
       "      <td>12.823834</td>\n",
       "      <td>8116.913679</td>\n",
       "      <td>193</td>\n",
       "      <td>Medicare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>530</td>\n",
       "      <td>Others</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Major</td>\n",
       "      <td>14.720</td>\n",
       "      <td>Brookdale Hospital Medical Center</td>\n",
       "      <td>8.448980</td>\n",
       "      <td>5596.228061</td>\n",
       "      <td>98</td>\n",
       "      <td>Medicare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>342</td>\n",
       "      <td>SUNY</td>\n",
       "      <td>720</td>\n",
       "      <td>Minor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.265</td>\n",
       "      <td>University Hospital of Brooklyn</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4513.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>Private Health Insurance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>342</td>\n",
       "      <td>SUNY</td>\n",
       "      <td>720</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.265</td>\n",
       "      <td>University Hospital of Brooklyn</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>6394.432500</td>\n",
       "      <td>8</td>\n",
       "      <td>Private Health Insurance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>342</td>\n",
       "      <td>SUNY</td>\n",
       "      <td>720</td>\n",
       "      <td>Major</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.265</td>\n",
       "      <td>University Hospital of Brooklyn</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>5851.070000</td>\n",
       "      <td>2</td>\n",
       "      <td>Self-Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>342</td>\n",
       "      <td>SUNY</td>\n",
       "      <td>720</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.265</td>\n",
       "      <td>University Hospital of Brooklyn</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5620.890000</td>\n",
       "      <td>1</td>\n",
       "      <td>Self-Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>364</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.597</td>\n",
       "      <td>Woodhull Medical &amp; Mental Health Center</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8230.385000</td>\n",
       "      <td>2</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>364</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>Minor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.597</td>\n",
       "      <td>Woodhull Medical &amp; Mental Health Center</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6772.130000</td>\n",
       "      <td>1</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>364</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.597</td>\n",
       "      <td>Woodhull Medical &amp; Mental Health Center</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6279.330000</td>\n",
       "      <td>1</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>364</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.597</td>\n",
       "      <td>Woodhull Medical &amp; Mental Health Center</td>\n",
       "      <td>11.841463</td>\n",
       "      <td>7336.135366</td>\n",
       "      <td>82</td>\n",
       "      <td>Medicare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>364</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>Major</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.597</td>\n",
       "      <td>Woodhull Medical &amp; Mental Health Center</td>\n",
       "      <td>9.864865</td>\n",
       "      <td>6456.292703</td>\n",
       "      <td>74</td>\n",
       "      <td>Medicare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>364</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>Minor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.597</td>\n",
       "      <td>Woodhull Medical &amp; Mental Health Center</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5435.720000</td>\n",
       "      <td>1</td>\n",
       "      <td>Medicare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>364</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.597</td>\n",
       "      <td>Woodhull Medical &amp; Mental Health Center</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>6078.439630</td>\n",
       "      <td>27</td>\n",
       "      <td>Medicare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>364</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.597</td>\n",
       "      <td>Woodhull Medical &amp; Mental Health Center</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15302.160000</td>\n",
       "      <td>1</td>\n",
       "      <td>Private Health Insurance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>364</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>Major</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.597</td>\n",
       "      <td>Woodhull Medical &amp; Mental Health Center</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5653.310000</td>\n",
       "      <td>4</td>\n",
       "      <td>Private Health Insurance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>364</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>Minor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.597</td>\n",
       "      <td>Woodhull Medical &amp; Mental Health Center</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8145.320000</td>\n",
       "      <td>1</td>\n",
       "      <td>Private Health Insurance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>364</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.597</td>\n",
       "      <td>Woodhull Medical &amp; Mental Health Center</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>6026.976667</td>\n",
       "      <td>6</td>\n",
       "      <td>Private Health Insurance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>364</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.597</td>\n",
       "      <td>Woodhull Medical &amp; Mental Health Center</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7338.450000</td>\n",
       "      <td>1</td>\n",
       "      <td>Self-Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>364</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.597</td>\n",
       "      <td>Woodhull Medical &amp; Mental Health Center</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6007.810000</td>\n",
       "      <td>1</td>\n",
       "      <td>Self-Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>324</td>\n",
       "      <td>Others</td>\n",
       "      <td>720</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.720</td>\n",
       "      <td>Wyckoff Heights Medical Center</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>13780.320000</td>\n",
       "      <td>4</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>324</td>\n",
       "      <td>Others</td>\n",
       "      <td>720</td>\n",
       "      <td>Major</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.720</td>\n",
       "      <td>Wyckoff Heights Medical Center</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10662.877500</td>\n",
       "      <td>4</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>324</td>\n",
       "      <td>Others</td>\n",
       "      <td>720</td>\n",
       "      <td>Minor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.720</td>\n",
       "      <td>Wyckoff Heights Medical Center</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8315.610000</td>\n",
       "      <td>5</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>324</td>\n",
       "      <td>Others</td>\n",
       "      <td>720</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.720</td>\n",
       "      <td>Wyckoff Heights Medical Center</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>8287.572857</td>\n",
       "      <td>7</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>324</td>\n",
       "      <td>Others</td>\n",
       "      <td>720</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.720</td>\n",
       "      <td>Wyckoff Heights Medical Center</td>\n",
       "      <td>9.820000</td>\n",
       "      <td>9555.842900</td>\n",
       "      <td>100</td>\n",
       "      <td>Medicare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>324</td>\n",
       "      <td>Others</td>\n",
       "      <td>720</td>\n",
       "      <td>Major</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.720</td>\n",
       "      <td>Wyckoff Heights Medical Center</td>\n",
       "      <td>7.277457</td>\n",
       "      <td>7169.070578</td>\n",
       "      <td>173</td>\n",
       "      <td>Medicare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>324</td>\n",
       "      <td>Others</td>\n",
       "      <td>720</td>\n",
       "      <td>Minor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.720</td>\n",
       "      <td>Wyckoff Heights Medical Center</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6014.803333</td>\n",
       "      <td>6</td>\n",
       "      <td>Medicare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>324</td>\n",
       "      <td>Others</td>\n",
       "      <td>720</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.720</td>\n",
       "      <td>Wyckoff Heights Medical Center</td>\n",
       "      <td>4.772727</td>\n",
       "      <td>6523.774091</td>\n",
       "      <td>66</td>\n",
       "      <td>Medicare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>324</td>\n",
       "      <td>Others</td>\n",
       "      <td>720</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.720</td>\n",
       "      <td>Wyckoff Heights Medical Center</td>\n",
       "      <td>6.181818</td>\n",
       "      <td>10111.331818</td>\n",
       "      <td>11</td>\n",
       "      <td>Private Health Insurance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>324</td>\n",
       "      <td>Others</td>\n",
       "      <td>720</td>\n",
       "      <td>Major</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.720</td>\n",
       "      <td>Wyckoff Heights Medical Center</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>10781.743333</td>\n",
       "      <td>6</td>\n",
       "      <td>Private Health Insurance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>324</td>\n",
       "      <td>Others</td>\n",
       "      <td>720</td>\n",
       "      <td>Minor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.720</td>\n",
       "      <td>Wyckoff Heights Medical Center</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5936.610000</td>\n",
       "      <td>2</td>\n",
       "      <td>Private Health Insurance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>324</td>\n",
       "      <td>Others</td>\n",
       "      <td>720</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.720</td>\n",
       "      <td>Wyckoff Heights Medical Center</td>\n",
       "      <td>4.111111</td>\n",
       "      <td>6973.262222</td>\n",
       "      <td>9</td>\n",
       "      <td>Private Health Insurance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>324</td>\n",
       "      <td>Others</td>\n",
       "      <td>720</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.720</td>\n",
       "      <td>Wyckoff Heights Medical Center</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6100.885000</td>\n",
       "      <td>2</td>\n",
       "      <td>Self-Pay</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1724 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Number of Beds       System  apr_drg_code apr_severity_of_illness  \\\n",
       "0               912      Health+           720                     NaN   \n",
       "1               912      Health+           720                     NaN   \n",
       "2               912      Health+           720                     NaN   \n",
       "3               912      Health+           720                     NaN   \n",
       "4               912      Health+           720                     NaN   \n",
       "5               912      Health+           720                     NaN   \n",
       "6               912      Health+           720                     NaN   \n",
       "7               912      Health+           720                     NaN   \n",
       "8               912      Health+           720                     NaN   \n",
       "9               912      Health+           720                     NaN   \n",
       "10              912      Health+           720                     NaN   \n",
       "11              912      Health+           720                     NaN   \n",
       "12              912      Health+           720                     NaN   \n",
       "13              912      Health+           720                     NaN   \n",
       "14              415  Mount Sinai           720                     NaN   \n",
       "15              415  Mount Sinai           720                     NaN   \n",
       "16              415  Mount Sinai           720                     NaN   \n",
       "17              415  Mount Sinai           720                     NaN   \n",
       "18              415  Mount Sinai           720                     NaN   \n",
       "19              415  Mount Sinai           720                     NaN   \n",
       "20              415  Mount Sinai           720                     NaN   \n",
       "21              415  Mount Sinai           720                     NaN   \n",
       "22              415  Mount Sinai           720                     NaN   \n",
       "23              415  Mount Sinai           720                     NaN   \n",
       "24              415  Mount Sinai           720                     NaN   \n",
       "25              530       Others           720                     NaN   \n",
       "26              530       Others           720                     NaN   \n",
       "27              530       Others           720                     NaN   \n",
       "28              530       Others           720                     NaN   \n",
       "29              530       Others           720                     NaN   \n",
       "..              ...          ...           ...                     ...   \n",
       "544             342         SUNY           720                   Minor   \n",
       "545             342         SUNY           720                Moderate   \n",
       "546             342         SUNY           720                   Major   \n",
       "547             342         SUNY           720                Moderate   \n",
       "548             364      Health+           720                 Extreme   \n",
       "549             364      Health+           720                   Minor   \n",
       "550             364      Health+           720                Moderate   \n",
       "551             364      Health+           720                 Extreme   \n",
       "552             364      Health+           720                   Major   \n",
       "553             364      Health+           720                   Minor   \n",
       "554             364      Health+           720                Moderate   \n",
       "555             364      Health+           720                 Extreme   \n",
       "556             364      Health+           720                   Major   \n",
       "557             364      Health+           720                   Minor   \n",
       "558             364      Health+           720                Moderate   \n",
       "559             364      Health+           720                 Extreme   \n",
       "560             364      Health+           720                Moderate   \n",
       "561             324       Others           720                 Extreme   \n",
       "562             324       Others           720                   Major   \n",
       "563             324       Others           720                   Minor   \n",
       "564             324       Others           720                Moderate   \n",
       "565             324       Others           720                 Extreme   \n",
       "566             324       Others           720                   Major   \n",
       "567             324       Others           720                   Minor   \n",
       "568             324       Others           720                Moderate   \n",
       "569             324       Others           720                 Extreme   \n",
       "570             324       Others           720                   Major   \n",
       "571             324       Others           720                   Minor   \n",
       "572             324       Others           720                Moderate   \n",
       "573             324       Others           720                Moderate   \n",
       "\n",
       "    apr_severity_of_illness_description  area_sqmi  \\\n",
       "0                                 Major      0.770   \n",
       "1                              Moderate      0.770   \n",
       "2                               Extreme      0.770   \n",
       "3                                 Major      0.770   \n",
       "4                                 Minor      0.770   \n",
       "5                              Moderate      0.770   \n",
       "6                               Extreme      0.770   \n",
       "7                                 Major      0.770   \n",
       "8                                 Minor      0.770   \n",
       "9                              Moderate      0.770   \n",
       "10                              Extreme      0.770   \n",
       "11                                Major      0.770   \n",
       "12                                Minor      0.770   \n",
       "13                             Moderate      0.770   \n",
       "14                              Extreme      2.194   \n",
       "15                                Major      2.194   \n",
       "16                              Extreme      2.194   \n",
       "17                                Major      2.194   \n",
       "18                                Minor      2.194   \n",
       "19                             Moderate      2.194   \n",
       "20                              Extreme      2.194   \n",
       "21                                Major      2.194   \n",
       "22                             Moderate      2.194   \n",
       "23                                Major      2.194   \n",
       "24                             Moderate      2.194   \n",
       "25                              Extreme     14.720   \n",
       "26                                Major     14.720   \n",
       "27                             Moderate     14.720   \n",
       "28                              Extreme     14.720   \n",
       "29                                Major     14.720   \n",
       "..                                  ...        ...   \n",
       "544                                 NaN      2.265   \n",
       "545                                 NaN      2.265   \n",
       "546                                 NaN      2.265   \n",
       "547                                 NaN      2.265   \n",
       "548                                 NaN      4.597   \n",
       "549                                 NaN      4.597   \n",
       "550                                 NaN      4.597   \n",
       "551                                 NaN      4.597   \n",
       "552                                 NaN      4.597   \n",
       "553                                 NaN      4.597   \n",
       "554                                 NaN      4.597   \n",
       "555                                 NaN      4.597   \n",
       "556                                 NaN      4.597   \n",
       "557                                 NaN      4.597   \n",
       "558                                 NaN      4.597   \n",
       "559                                 NaN      4.597   \n",
       "560                                 NaN      4.597   \n",
       "561                                 NaN      8.720   \n",
       "562                                 NaN      8.720   \n",
       "563                                 NaN      8.720   \n",
       "564                                 NaN      8.720   \n",
       "565                                 NaN      8.720   \n",
       "566                                 NaN      8.720   \n",
       "567                                 NaN      8.720   \n",
       "568                                 NaN      8.720   \n",
       "569                                 NaN      8.720   \n",
       "570                                 NaN      8.720   \n",
       "571                                 NaN      8.720   \n",
       "572                                 NaN      8.720   \n",
       "573                                 NaN      8.720   \n",
       "\n",
       "                                         facility_name  mean_length_of_stay  \\\n",
       "0                             Bellevue Hospital Center             8.000000   \n",
       "1                             Bellevue Hospital Center             5.666667   \n",
       "2                             Bellevue Hospital Center            14.228571   \n",
       "3                             Bellevue Hospital Center             8.659341   \n",
       "4                             Bellevue Hospital Center             5.000000   \n",
       "5                             Bellevue Hospital Center             6.540000   \n",
       "6                             Bellevue Hospital Center            12.250000   \n",
       "7                             Bellevue Hospital Center             5.750000   \n",
       "8                             Bellevue Hospital Center             7.000000   \n",
       "9                             Bellevue Hospital Center             4.250000   \n",
       "10                            Bellevue Hospital Center            17.750000   \n",
       "11                            Bellevue Hospital Center             7.000000   \n",
       "12                            Bellevue Hospital Center             3.800000   \n",
       "13                            Bellevue Hospital Center             4.888889   \n",
       "14   Bronx-Lebanon Hospital Center - Concourse Divi...            13.000000   \n",
       "15   Bronx-Lebanon Hospital Center - Concourse Divi...             6.000000   \n",
       "16   Bronx-Lebanon Hospital Center - Concourse Divi...            12.077465   \n",
       "17   Bronx-Lebanon Hospital Center - Concourse Divi...             8.339130   \n",
       "18   Bronx-Lebanon Hospital Center - Concourse Divi...             4.000000   \n",
       "19   Bronx-Lebanon Hospital Center - Concourse Divi...             7.150000   \n",
       "20   Bronx-Lebanon Hospital Center - Concourse Divi...            10.200000   \n",
       "21   Bronx-Lebanon Hospital Center - Concourse Divi...             7.666667   \n",
       "22   Bronx-Lebanon Hospital Center - Concourse Divi...             3.500000   \n",
       "23   Bronx-Lebanon Hospital Center - Concourse Divi...             5.000000   \n",
       "24   Bronx-Lebanon Hospital Center - Concourse Divi...             7.000000   \n",
       "25                   Brookdale Hospital Medical Center             8.500000   \n",
       "26                   Brookdale Hospital Medical Center             6.500000   \n",
       "27                   Brookdale Hospital Medical Center             5.500000   \n",
       "28                   Brookdale Hospital Medical Center            12.823834   \n",
       "29                   Brookdale Hospital Medical Center             8.448980   \n",
       "..                                                 ...                  ...   \n",
       "544                    University Hospital of Brooklyn             2.000000   \n",
       "545                    University Hospital of Brooklyn             4.500000   \n",
       "546                    University Hospital of Brooklyn            10.500000   \n",
       "547                    University Hospital of Brooklyn             5.000000   \n",
       "548            Woodhull Medical & Mental Health Center            12.000000   \n",
       "549            Woodhull Medical & Mental Health Center             5.000000   \n",
       "550            Woodhull Medical & Mental Health Center             4.000000   \n",
       "551            Woodhull Medical & Mental Health Center            11.841463   \n",
       "552            Woodhull Medical & Mental Health Center             9.864865   \n",
       "553            Woodhull Medical & Mental Health Center             7.000000   \n",
       "554            Woodhull Medical & Mental Health Center             6.666667   \n",
       "555            Woodhull Medical & Mental Health Center             1.000000   \n",
       "556            Woodhull Medical & Mental Health Center             9.500000   \n",
       "557            Woodhull Medical & Mental Health Center             3.000000   \n",
       "558            Woodhull Medical & Mental Health Center             8.500000   \n",
       "559            Woodhull Medical & Mental Health Center             2.000000   \n",
       "560            Woodhull Medical & Mental Health Center             7.000000   \n",
       "561                     Wyckoff Heights Medical Center             1.250000   \n",
       "562                     Wyckoff Heights Medical Center             4.000000   \n",
       "563                     Wyckoff Heights Medical Center             2.000000   \n",
       "564                     Wyckoff Heights Medical Center             2.285714   \n",
       "565                     Wyckoff Heights Medical Center             9.820000   \n",
       "566                     Wyckoff Heights Medical Center             7.277457   \n",
       "567                     Wyckoff Heights Medical Center             4.000000   \n",
       "568                     Wyckoff Heights Medical Center             4.772727   \n",
       "569                     Wyckoff Heights Medical Center             6.181818   \n",
       "570                     Wyckoff Heights Medical Center             5.333333   \n",
       "571                     Wyckoff Heights Medical Center             2.000000   \n",
       "572                     Wyckoff Heights Medical Center             4.111111   \n",
       "573                     Wyckoff Heights Medical Center             2.000000   \n",
       "\n",
       "     mean_total_charges_day  patients        payment_typology_1  \n",
       "0               7017.275000         2    Blue Cross/Blue Shield  \n",
       "1               6852.591667         6    Blue Cross/Blue Shield  \n",
       "2               7124.722571        70                  Medicare  \n",
       "3               6399.484835        91                  Medicare  \n",
       "4               6386.240000         4                  Medicare  \n",
       "5               6316.883200        50                  Medicare  \n",
       "6               6490.560000         4  Private Health Insurance  \n",
       "7               7022.595000         4  Private Health Insurance  \n",
       "8               6437.370000         2  Private Health Insurance  \n",
       "9               7000.660000         4  Private Health Insurance  \n",
       "10              6315.075000         4                  Self-Pay  \n",
       "11              6730.027500         8                  Self-Pay  \n",
       "12              6341.304000         5                  Self-Pay  \n",
       "13              6371.484444         9                  Self-Pay  \n",
       "14              4150.565000         4    Blue Cross/Blue Shield  \n",
       "15              4037.055000         2    Blue Cross/Blue Shield  \n",
       "16              4629.844507       142                  Medicare  \n",
       "17              3792.219478       115                  Medicare  \n",
       "18              3416.727500         4                  Medicare  \n",
       "19              3359.234500        20                  Medicare  \n",
       "20              2661.505333        15  Private Health Insurance  \n",
       "21              2998.503333        12  Private Health Insurance  \n",
       "22              5558.410000         2  Private Health Insurance  \n",
       "23              4847.450000         1                  Self-Pay  \n",
       "24              3536.570000         1                  Self-Pay  \n",
       "25             11492.145000         2    Blue Cross/Blue Shield  \n",
       "26              5458.825000         2    Blue Cross/Blue Shield  \n",
       "27              5925.015000         2    Blue Cross/Blue Shield  \n",
       "28              8116.913679       193                  Medicare  \n",
       "29              5596.228061        98                  Medicare  \n",
       "..                      ...       ...                       ...  \n",
       "544             4513.500000         1  Private Health Insurance  \n",
       "545             6394.432500         8  Private Health Insurance  \n",
       "546             5851.070000         2                  Self-Pay  \n",
       "547             5620.890000         1                  Self-Pay  \n",
       "548             8230.385000         2    Blue Cross/Blue Shield  \n",
       "549             6772.130000         1    Blue Cross/Blue Shield  \n",
       "550             6279.330000         1    Blue Cross/Blue Shield  \n",
       "551             7336.135366        82                  Medicare  \n",
       "552             6456.292703        74                  Medicare  \n",
       "553             5435.720000         1                  Medicare  \n",
       "554             6078.439630        27                  Medicare  \n",
       "555            15302.160000         1  Private Health Insurance  \n",
       "556             5653.310000         4  Private Health Insurance  \n",
       "557             8145.320000         1  Private Health Insurance  \n",
       "558             6026.976667         6  Private Health Insurance  \n",
       "559             7338.450000         1                  Self-Pay  \n",
       "560             6007.810000         1                  Self-Pay  \n",
       "561            13780.320000         4    Blue Cross/Blue Shield  \n",
       "562            10662.877500         4    Blue Cross/Blue Shield  \n",
       "563             8315.610000         5    Blue Cross/Blue Shield  \n",
       "564             8287.572857         7    Blue Cross/Blue Shield  \n",
       "565             9555.842900       100                  Medicare  \n",
       "566             7169.070578       173                  Medicare  \n",
       "567             6014.803333         6                  Medicare  \n",
       "568             6523.774091        66                  Medicare  \n",
       "569            10111.331818        11  Private Health Insurance  \n",
       "570            10781.743333         6  Private Health Insurance  \n",
       "571             5936.610000         2  Private Health Insurance  \n",
       "572             6973.262222         9  Private Health Insurance  \n",
       "573             6100.885000         2                  Self-Pay  \n",
       "\n",
       "[1724 rows x 11 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# severity = pd.get_dummies(sept['apr_severity_of_illness_description'])\n",
    "# system = pd.get_dummies(sept['System'])\n",
    "# payment = pd.get_dummies(sept['payment_typology_1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "sept['apr_severity_of_illness_description'].fillna(sept['apr_severity_of_illness'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "severity = sept['apr_severity_of_illness_description'].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "system = sept['System'].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "payment = sept['payment_typology_1'].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "facility = sept['facility_name'].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([sept, severity, system, payment, facility], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={0:'severity', 1:'system', 2:'payment', 3:'hospital'})"
   ]
  },
  {
   "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>Number of Beds</th>\n",
       "      <th>System</th>\n",
       "      <th>apr_drg_code</th>\n",
       "      <th>apr_severity_of_illness</th>\n",
       "      <th>apr_severity_of_illness_description</th>\n",
       "      <th>area_sqmi</th>\n",
       "      <th>facility_name</th>\n",
       "      <th>mean_length_of_stay</th>\n",
       "      <th>mean_total_charges_day</th>\n",
       "      <th>patients</th>\n",
       "      <th>payment_typology_1</th>\n",
       "      <th>severity</th>\n",
       "      <th>system</th>\n",
       "      <th>payment</th>\n",
       "      <th>hospital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Major</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7017.275000</td>\n",
       "      <td>2</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>6852.591667</td>\n",
       "      <td>6</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>14.228571</td>\n",
       "      <td>7124.722571</td>\n",
       "      <td>70</td>\n",
       "      <td>Medicare</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Major</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>8.659341</td>\n",
       "      <td>6399.484835</td>\n",
       "      <td>91</td>\n",
       "      <td>Medicare</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Minor</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6386.240000</td>\n",
       "      <td>4</td>\n",
       "      <td>Medicare</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number of Beds   System  apr_drg_code apr_severity_of_illness  \\\n",
       "0             912  Health+           720                     NaN   \n",
       "1             912  Health+           720                     NaN   \n",
       "2             912  Health+           720                     NaN   \n",
       "3             912  Health+           720                     NaN   \n",
       "4             912  Health+           720                     NaN   \n",
       "\n",
       "  apr_severity_of_illness_description  area_sqmi             facility_name  \\\n",
       "0                               Major       0.77  Bellevue Hospital Center   \n",
       "1                            Moderate       0.77  Bellevue Hospital Center   \n",
       "2                             Extreme       0.77  Bellevue Hospital Center   \n",
       "3                               Major       0.77  Bellevue Hospital Center   \n",
       "4                               Minor       0.77  Bellevue Hospital Center   \n",
       "\n",
       "   mean_length_of_stay  mean_total_charges_day  patients  \\\n",
       "0             8.000000             7017.275000         2   \n",
       "1             5.666667             6852.591667         6   \n",
       "2            14.228571             7124.722571        70   \n",
       "3             8.659341             6399.484835        91   \n",
       "4             5.000000             6386.240000         4   \n",
       "\n",
       "       payment_typology_1  severity  system  payment  hospital  \n",
       "0  Blue Cross/Blue Shield         1       0        0         0  \n",
       "1  Blue Cross/Blue Shield         3       0        0         0  \n",
       "2                Medicare         0       0        1         0  \n",
       "3                Medicare         1       0        1         0  \n",
       "4                Medicare         2       0        1         0  "
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Number of Beds', 'System', 'apr_drg_code', 'apr_severity_of_illness',\n",
       "       'apr_severity_of_illness_description', 'area_sqmi', 'facility_name',\n",
       "       'mean_length_of_stay', 'mean_total_charges_day', 'patients',\n",
       "       'payment_typology_1', 'severity', 'system', 'payment', 'hospital'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 'Bellevue Hospital Center'),\n",
       " (1, 'Bronx-Lebanon Hospital Center - Concourse Division'),\n",
       " (2, 'Brookdale Hospital Medical Center'),\n",
       " (3, 'Brooklyn Hospital Center - Downtown Campus'),\n",
       " (4, 'Calvary Hospital Inc'),\n",
       " (5, 'Coney Island Hospital'),\n",
       " (6, 'Elmhurst Hospital Center'),\n",
       " (7, 'Flushing Hospital Medical Center'),\n",
       " (8, 'Harlem Hospital Center'),\n",
       " (9, 'Henry J. Carter Specialty Hospital'),\n",
       " (10, 'Interfaith Medical Center'),\n",
       " (11, 'Jacobi Medical Center'),\n",
       " (12, 'Jamaica Hospital Medical Center'),\n",
       " (13, 'Kings County Hospital Center'),\n",
       " (14, 'Kingsbrook Jewish Medical Center'),\n",
       " (15, 'Lenox Hill Hospital'),\n",
       " (16, 'Lincoln Medical & Mental Health Center'),\n",
       " (17, 'Long Island Jewish Forest Hills'),\n",
       " (18, 'Long Island Jewish Medical Center'),\n",
       " (19, 'Maimonides Medical Center'),\n",
       " (20, 'Memorial Hospital for Cancer and Allied Diseases'),\n",
       " (21, 'Metropolitan Hospital Center'),\n",
       " (22, 'Montefiore Med Center - Jack D Weiler Hosp of A Einstein College Div'),\n",
       " (23, 'Montefiore Medical Center - Henry & Lucy Moses Div'),\n",
       " (24, 'Montefiore Medical Center-Wakefield Hospital'),\n",
       " (25, 'Mount Sinai Beth Israel'),\n",
       " (26, 'Mount Sinai Brooklyn'),\n",
       " (27, 'Mount Sinai Hospital'),\n",
       " (28, 'Mount Sinai Hospital - Mount Sinai Hospital of Queens'),\n",
       " (29, \"Mount Sinai St. Luke's\"),\n",
       " (30, 'Mount Sinai West'),\n",
       " (31, 'NYU Hospitals Center'),\n",
       " (32, 'NYU Langone Hospital-Brooklyn'),\n",
       " (33, 'NYU Langone Hospitals'),\n",
       " (34, 'NYU Langone Orthopedic Hospital'),\n",
       " (35, 'NYU Lutheran Medical Center'),\n",
       " (36, 'New York Community Hospital of Brooklyn, Inc'),\n",
       " (37, 'New York-Presbyterian/Lower Manhattan Hospital'),\n",
       " (38, 'New York-Presbyterian/Queens'),\n",
       " (39, 'North Central Bronx Hospital'),\n",
       " (40, 'Queens Hospital Center'),\n",
       " (41, 'Richmond University Medical Center'),\n",
       " (42, 'SBH Health System'),\n",
       " (43, 'Staten Island University Hosp-North'),\n",
       " (44, 'Staten Island University Hosp-South'),\n",
       " (45, 'University Hospital of Brooklyn'),\n",
       " (46, 'Woodhull Medical & Mental Health Center'),\n",
       " (47, 'Wyckoff Heights Medical Center')]"
      ]
     },
     "execution_count": 417,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(enumerate(df['facility_name'].astype('category').cat.categories))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_list =['Number of Beds',\n",
    "          'area_sqmi',\n",
    "          'mean_length_of_stay',\n",
    "          'mean_total_charges_day',\n",
    "          'patients',\n",
    "          'severity',\n",
    "          'system',\n",
    "          'payment',\n",
    "          'hospital']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[var_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.88585736, -0.90283744,  0.16281059, ..., -1.16504711,\n",
       "        -1.33770204, -1.69359119],\n",
       "       [ 1.88585736, -0.90283744, -0.25294872, ..., -1.16504711,\n",
       "        -1.33770204, -1.69359119],\n",
       "       [ 1.88585736, -0.90283744,  1.27263339, ..., -1.16504711,\n",
       "        -0.39601224, -1.69359119],\n",
       "       ...,\n",
       "       [-0.6310457 ,  0.41489889, -0.90628477, ...,  1.32951867,\n",
       "         0.54567756,  1.68966629],\n",
       "       [-0.6310457 ,  0.41489889, -0.53012159, ...,  1.32951867,\n",
       "         0.54567756,  1.68966629],\n",
       "       [-0.6310457 ,  0.41489889, -0.90628477, ...,  1.32951867,\n",
       "         1.48736736,  1.68966629]])"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the sklearn function\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Standardize the data\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "X_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a k-means clustering model\n",
    "kmeans = KMeans(init='random', n_clusters=3, n_init=10)\n",
    "\n",
    "# Fit the data to the model\n",
    "kmeans.fit(X_scaled)\n",
    "\n",
    "# Determine which clusters each data point belongs to:\n",
    "clusters =  kmeans.predict(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "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>Number of Beds</th>\n",
       "      <th>area_sqmi</th>\n",
       "      <th>mean_length_of_stay</th>\n",
       "      <th>mean_total_charges_day</th>\n",
       "      <th>patients</th>\n",
       "      <th>severity</th>\n",
       "      <th>system</th>\n",
       "      <th>payment</th>\n",
       "      <th>hospital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.885857</td>\n",
       "      <td>-0.902837</td>\n",
       "      <td>0.162811</td>\n",
       "      <td>-0.511051</td>\n",
       "      <td>-0.441399</td>\n",
       "      <td>-0.418705</td>\n",
       "      <td>-1.165047</td>\n",
       "      <td>-1.337702</td>\n",
       "      <td>-1.693591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.885857</td>\n",
       "      <td>-0.902837</td>\n",
       "      <td>-0.252949</td>\n",
       "      <td>-0.550330</td>\n",
       "      <td>-0.391154</td>\n",
       "      <td>1.324889</td>\n",
       "      <td>-1.165047</td>\n",
       "      <td>-1.337702</td>\n",
       "      <td>-1.693591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.885857</td>\n",
       "      <td>-0.902837</td>\n",
       "      <td>1.272633</td>\n",
       "      <td>-0.485424</td>\n",
       "      <td>0.412779</td>\n",
       "      <td>-1.290503</td>\n",
       "      <td>-1.165047</td>\n",
       "      <td>-0.396012</td>\n",
       "      <td>-1.693591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.885857</td>\n",
       "      <td>-0.902837</td>\n",
       "      <td>0.280294</td>\n",
       "      <td>-0.658400</td>\n",
       "      <td>0.676570</td>\n",
       "      <td>-0.418705</td>\n",
       "      <td>-1.165047</td>\n",
       "      <td>-0.396012</td>\n",
       "      <td>-1.693591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.885857</td>\n",
       "      <td>-0.902837</td>\n",
       "      <td>-0.371737</td>\n",
       "      <td>-0.661559</td>\n",
       "      <td>-0.416277</td>\n",
       "      <td>0.453092</td>\n",
       "      <td>-1.165047</td>\n",
       "      <td>-0.396012</td>\n",
       "      <td>-1.693591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number of Beds  area_sqmi  mean_length_of_stay  mean_total_charges_day  \\\n",
       "0        1.885857  -0.902837             0.162811               -0.511051   \n",
       "1        1.885857  -0.902837            -0.252949               -0.550330   \n",
       "2        1.885857  -0.902837             1.272633               -0.485424   \n",
       "3        1.885857  -0.902837             0.280294               -0.658400   \n",
       "4        1.885857  -0.902837            -0.371737               -0.661559   \n",
       "\n",
       "   patients  severity    system   payment  hospital  \n",
       "0 -0.441399 -0.418705 -1.165047 -1.337702 -1.693591  \n",
       "1 -0.391154  1.324889 -1.165047 -1.337702 -1.693591  \n",
       "2  0.412779 -1.290503 -1.165047 -0.396012 -1.693591  \n",
       "3  0.676570 -0.418705 -1.165047 -0.396012 -1.693591  \n",
       "4 -0.416277  0.453092 -1.165047 -0.396012 -1.693591  "
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add cluster number to the original data\n",
    "X_scaled = pd.DataFrame(X_scaled, columns=X.columns, index=X.index)\n",
    "#X_scaled['hospital_name'] = sept['facility_name']\n",
    "\n",
    "X_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_kmeans = X_scaled[var_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2, the average silhouette_score is : 0.13810407216726672\n",
      "For n_clusters = 3, the average silhouette_score is : 0.12306318059482062\n",
      "For n_clusters = 4, the average silhouette_score is : 0.14231436901408265\n",
      "For n_clusters = 5, the average silhouette_score is : 0.17239712621790307\n",
      "For n_clusters = 6, the average silhouette_score is : 0.15864722826140612\n",
      "For n_clusters = 7, the average silhouette_score is : 0.16487975300276658\n",
      "For n_clusters = 8, the average silhouette_score is : 0.16907495764616948\n",
      "For n_clusters = 9, the average silhouette_score is : 0.163416635122614\n"
     ]
    }
   ],
   "source": [
    "range_n_clusters = range(2,10)\n",
    "sil = []\n",
    "\n",
    "for n_clusters in range_n_clusters:\n",
    "    km = KMeans(random_state = 100, n_clusters = n_clusters)\n",
    "    res = km.fit_predict(X_kmeans)\n",
    "    silhouette_avg = silhouette_score(X_kmeans, res)\n",
    "    sil.append(silhouette_avg)\n",
    "    print(\"For n_clusters = {},\".format(n_clusters)+\" the average silhouette_score is : {}\".format(silhouette_avg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "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": [
    "plt.plot(range_n_clusters, sil)\n",
    "plt.title('Silhouette Score for Optimal Clusters')\n",
    "plt.xlabel('# of Clusters')\n",
    "plt.ylabel('Silhouette Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "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": [
    "# Run a number of tests, for 1, 2, ... num_clusters\n",
    "num_clusters = 30\n",
    "kmeans_tests = [KMeans(n_clusters=i, init='random', n_init=10) for i in range(1, num_clusters)]\n",
    "score = [kmeans_tests[i].fit(X_kmeans).score(X_kmeans) for i in range(len(kmeans_tests))]\n",
    "\n",
    "# Plot the curve\n",
    "plt.plot(range(1, num_clusters),score)\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Score')\n",
    "plt.title('Elbow Curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "km = KMeans(random_state=234, n_clusters = 5)\n",
    "res = km.fit(X_kmeans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({3: 559, 1: 407, 4: 407, 0: 255, 2: 96})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter, defaultdict\n",
    "print(Counter(res.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_scaled[\"cluster\"] = res.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"cluster\"] = res.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "palette = sns.color_palette(\"bright\", 10)\n",
    "\n",
    "def addAlpha(colour, alpha):\n",
    "    '''Add an alpha to the RGB colour'''\n",
    "    \n",
    "    return (colour[0],colour[1],colour[2],alpha)\n",
    "\n",
    "def display_parallel_coordinates(df, num_clusters):\n",
    "    '''Display a parallel coordinates plot for the clusters in df'''\n",
    "\n",
    "    # Select data points for individual clusters\n",
    "    cluster_points = []\n",
    "    for i in range(num_clusters):\n",
    "        cluster_points.append(df[df.cluster==i])\n",
    "    \n",
    "    # Create the plot\n",
    "    fig = plt.figure(figsize=(12, 5))\n",
    "    title = fig.suptitle(\"Parallel Coordinates Plot for the Clusters\", fontsize=18)\n",
    "    fig.subplots_adjust(top=0.9, wspace=0)\n",
    "\n",
    "    # Display one plot for each cluster, with the lines for the main cluster appearing over the lines for the other clusters\n",
    "#     for i in range(num_clusters):    \n",
    "#         plt.subplot(num_clusters, 1, i+1)\n",
    "#         for j,c in enumerate(cluster_points): \n",
    "#             if i!= j:\n",
    "#                 pc = parallel_coordinates(c, 'cluster', color=[addAlpha(palette[j],0.5)])\n",
    "    pc = parallel_coordinates(cluster_points[0], 'cluster', color=[addAlpha(palette[0],0.5)])\n",
    "    pc = parallel_coordinates(cluster_points[1], 'cluster', color=[addAlpha(palette[1],0.5)])\n",
    "    pc = parallel_coordinates(cluster_points[2], 'cluster', color=[addAlpha(palette[2],0.5)])\n",
    "    pc = parallel_coordinates(cluster_points[3], 'cluster', color=[addAlpha(palette[3],0.5)])\n",
    "    pc = parallel_coordinates(cluster_points[4], 'cluster', color=[addAlpha(palette[4],0.5)])\n",
    "#     pc = parallel_coordinates(cluster_points[5], 'cluster', color=[addAlpha(palette[5],0.5)])\n",
    "#     pc = parallel_coordinates(cluster_points[6], 'cluster', color=[addAlpha(palette[6],0.5)])\n",
    "\n",
    "# Stagger the axes\n",
    "    ax=plt.gca()\n",
    "    for tick in ax.xaxis.get_major_ticks()[1::2]:\n",
    "        tick.set_pad(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_attributes = ['Number of Beds',\n",
    "          'area_sqmi',\n",
    "          'mean_length_of_stay',\n",
    "          'mean_total_charges_day',\n",
    "          'patients',\n",
    "          'severity',\n",
    "          'system',\n",
    "          'payment',\n",
    "          'hospital',\n",
    "          'cluster']\n",
    "# Display parallel coordinates plots, one for each cluster\n",
    "display_parallel_coordinates(X_scaled[cluster_attributes], 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_parallel_coordinates_centroids(df, num_clusters):\n",
    "    '''Display a parallel coordinates plot for the centroids in df'''\n",
    "\n",
    "    # Create the plot\n",
    "    fig = plt.figure(figsize=(12, 5))\n",
    "    title = fig.suptitle(\"Parallel Coordinates plot for the Centroids\", fontsize=18)\n",
    "    fig.subplots_adjust(top=0.9, wspace=0)\n",
    "\n",
    "    # Draw the chart\n",
    "    parallel_coordinates(df, 'cluster', color=palette)\n",
    "\n",
    "    # Stagger the axes\n",
    "    ax=plt.gca()\n",
    "    for tick in ax.xaxis.get_major_ticks()[1::2]:\n",
    "        tick.set_pad(20) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "attributes_1 = ['Number of Beds',\n",
    "          'area_sqmi',\n",
    "          'mean_length_of_stay',\n",
    "          'mean_total_charges_day',\n",
    "          'patients',\n",
    "          'severity',\n",
    "          'system',\n",
    "          'hospital',\n",
    "          'payment']\n",
    "centroids = pd.DataFrame(km.cluster_centers_, columns = X_scaled[attributes_1].columns)\n",
    "centroids['cluster'] = centroids.index\n",
    "\n",
    "display_parallel_coordinates_centroids(centroids, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "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": [
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "pca = PCA(n_components = 2)\n",
    "# defaults number of PCs to number of columns in imported data (ie number of\n",
    "# features), but can be set to any integer less than or equal to that value\n",
    "\n",
    "dat = X_scaled[attributes_1]\n",
    "pca.fit(dat)\n",
    "\n",
    "\n",
    "\n",
    "## project data into PC space\n",
    "\n",
    "# 0,1 denote PC1 and PC2; change values for other PCs\n",
    "xvector = pca.components_[0] # see 'prcomp(my_data)$rotation' in R\n",
    "yvector = pca.components_[1]\n",
    "\n",
    "xs = pca.transform(dat)[:,0] # see 'prcomp(my_data)$x' in R\n",
    "ys = pca.transform(dat)[:,1]\n",
    "\n",
    "## visualize projections\n",
    "    \n",
    "## Note: scale values for arrows and text are a bit inelegant as of now,\n",
    "##       so feel free to play around with them\n",
    "\n",
    "for i in range(len(xvector)):\n",
    "# arrows project features (ie columns from csv) as vectors onto PC axes\n",
    "    plt.arrow(0, 0, xvector[i]*max(xs), yvector[i]*max(ys),\n",
    "              color='r', width=0.0005, head_width=0.0025)\n",
    "    plt.text(xvector[i]*max(xs)*1.2, yvector[i]*max(ys)*1.2,\n",
    "             list(dat.columns.values)[i], color='r')\n",
    "\n",
    "for i in range(len(xs)):\n",
    "# circles project documents (ie rows from csv) as points onto PC axes\n",
    "    \n",
    "    plt.plot(xs[i], ys[i], 'bo')\n",
    "    plt.text(xs[i]*1.2, ys[i]*1.2, list(dat.index)[i], color='b')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2, the average silhouette_score is : 0.05818871496199499\n",
      "For n_clusters = 3, the average silhouette_score is : 0.070452284495084\n",
      "For n_clusters = 4, the average silhouette_score is : 0.05206711651054036\n",
      "For n_clusters = 5, the average silhouette_score is : 0.05397824770068897\n",
      "For n_clusters = 6, the average silhouette_score is : 0.05945145079359464\n",
      "For n_clusters = 7, the average silhouette_score is : 0.04457753445470443\n",
      "For n_clusters = 8, the average silhouette_score is : 0.06888420292287194\n",
      "For n_clusters = 9, the average silhouette_score is : 0.04370196403235283\n"
     ]
    }
   ],
   "source": [
    "# Scaled Gaussian mixture models\n",
    "\n",
    "from sklearn.mixture import GaussianMixture\n",
    "\n",
    "range_n_clusters = range(2,10)\n",
    "\n",
    "for n_clusters in range_n_clusters:\n",
    "    gmm = GaussianMixture(n_components = n_clusters, random_state = 100)\n",
    "    res = gmm.fit_predict(X_scaled[var_list])\n",
    "    silhouette_avg = silhouette_score(X_scaled[var_list], res)\n",
    "    print(\"For n_clusters = {},\".format(n_clusters)+\" the average silhouette_score is : {}\".format(silhouette_avg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2, the average silhouette_score is : 0.0959755450457266\n",
      "For n_clusters = 3, the average silhouette_score is : -0.012562173243408543\n",
      "For n_clusters = 4, the average silhouette_score is : -0.011386256486551031\n",
      "For n_clusters = 5, the average silhouette_score is : -0.14038947307007477\n",
      "For n_clusters = 6, the average silhouette_score is : -0.1243451521930132\n",
      "For n_clusters = 7, the average silhouette_score is : -0.18162893730831361\n",
      "For n_clusters = 8, the average silhouette_score is : -0.09235478758131903\n",
      "For n_clusters = 9, the average silhouette_score is : -0.15079398585994164\n"
     ]
    }
   ],
   "source": [
    "# Unscaled Gaussian mixture models\n",
    "\n",
    "from sklearn.mixture import GaussianMixture\n",
    "\n",
    "range_n_clusters = range(2,10)\n",
    "\n",
    "for n_clusters in range_n_clusters:\n",
    "    gmm = GaussianMixture(n_components = n_clusters, random_state = 100)\n",
    "    res = gmm.fit_predict(X[var_list])\n",
    "    silhouette_avg = silhouette_score(X[var_list], res)\n",
    "    print(\"For n_clusters = {},\".format(n_clusters)+\" the average silhouette_score is : {}\".format(silhouette_avg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Number of Beds</th>\n",
       "      <th>area_sqmi</th>\n",
       "      <th>mean_length_of_stay</th>\n",
       "      <th>mean_total_charges_day</th>\n",
       "      <th>patients</th>\n",
       "      <th>severity</th>\n",
       "      <th>system</th>\n",
       "      <th>payment</th>\n",
       "      <th>hospital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Number of Beds</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.202828</td>\n",
       "      <td>-0.033305</td>\n",
       "      <td>0.263765</td>\n",
       "      <td>0.181902</td>\n",
       "      <td>0.001238</td>\n",
       "      <td>-0.125800</td>\n",
       "      <td>0.032496</td>\n",
       "      <td>-0.217734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_sqmi</th>\n",
       "      <td>-0.202828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001262</td>\n",
       "      <td>-0.221408</td>\n",
       "      <td>0.032581</td>\n",
       "      <td>0.010858</td>\n",
       "      <td>0.237002</td>\n",
       "      <td>-0.014321</td>\n",
       "      <td>0.125988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_length_of_stay</th>\n",
       "      <td>-0.033305</td>\n",
       "      <td>-0.001262</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.077959</td>\n",
       "      <td>0.105472</td>\n",
       "      <td>-0.480258</td>\n",
       "      <td>-0.047700</td>\n",
       "      <td>-0.051408</td>\n",
       "      <td>-0.101401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_total_charges_day</th>\n",
       "      <td>0.263765</td>\n",
       "      <td>-0.221408</td>\n",
       "      <td>-0.077959</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.114197</td>\n",
       "      <td>-0.227832</td>\n",
       "      <td>0.040933</td>\n",
       "      <td>0.024556</td>\n",
       "      <td>0.128648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patients</th>\n",
       "      <td>0.181902</td>\n",
       "      <td>0.032581</td>\n",
       "      <td>0.105472</td>\n",
       "      <td>0.114197</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.181431</td>\n",
       "      <td>0.031764</td>\n",
       "      <td>-0.149831</td>\n",
       "      <td>0.012306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>severity</th>\n",
       "      <td>0.001238</td>\n",
       "      <td>0.010858</td>\n",
       "      <td>-0.480258</td>\n",
       "      <td>-0.227832</td>\n",
       "      <td>-0.181431</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001214</td>\n",
       "      <td>0.004190</td>\n",
       "      <td>0.023931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>system</th>\n",
       "      <td>-0.125800</td>\n",
       "      <td>0.237002</td>\n",
       "      <td>-0.047700</td>\n",
       "      <td>0.040933</td>\n",
       "      <td>0.031764</td>\n",
       "      <td>0.001214</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.045101</td>\n",
       "      <td>0.101864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment</th>\n",
       "      <td>0.032496</td>\n",
       "      <td>-0.014321</td>\n",
       "      <td>-0.051408</td>\n",
       "      <td>0.024556</td>\n",
       "      <td>-0.149831</td>\n",
       "      <td>0.004190</td>\n",
       "      <td>-0.045101</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.036617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hospital</th>\n",
       "      <td>-0.217734</td>\n",
       "      <td>0.125988</td>\n",
       "      <td>-0.101401</td>\n",
       "      <td>0.128648</td>\n",
       "      <td>0.012306</td>\n",
       "      <td>0.023931</td>\n",
       "      <td>0.101864</td>\n",
       "      <td>-0.036617</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Number of Beds  area_sqmi  mean_length_of_stay  \\\n",
       "Number of Beds                1.000000  -0.202828            -0.033305   \n",
       "area_sqmi                    -0.202828   1.000000            -0.001262   \n",
       "mean_length_of_stay          -0.033305  -0.001262             1.000000   \n",
       "mean_total_charges_day        0.263765  -0.221408            -0.077959   \n",
       "patients                      0.181902   0.032581             0.105472   \n",
       "severity                      0.001238   0.010858            -0.480258   \n",
       "system                       -0.125800   0.237002            -0.047700   \n",
       "payment                       0.032496  -0.014321            -0.051408   \n",
       "hospital                     -0.217734   0.125988            -0.101401   \n",
       "\n",
       "                        mean_total_charges_day  patients  severity    system  \\\n",
       "Number of Beds                        0.263765  0.181902  0.001238 -0.125800   \n",
       "area_sqmi                            -0.221408  0.032581  0.010858  0.237002   \n",
       "mean_length_of_stay                  -0.077959  0.105472 -0.480258 -0.047700   \n",
       "mean_total_charges_day                1.000000  0.114197 -0.227832  0.040933   \n",
       "patients                              0.114197  1.000000 -0.181431  0.031764   \n",
       "severity                             -0.227832 -0.181431  1.000000  0.001214   \n",
       "system                                0.040933  0.031764  0.001214  1.000000   \n",
       "payment                               0.024556 -0.149831  0.004190 -0.045101   \n",
       "hospital                              0.128648  0.012306  0.023931  0.101864   \n",
       "\n",
       "                         payment  hospital  \n",
       "Number of Beds          0.032496 -0.217734  \n",
       "area_sqmi              -0.014321  0.125988  \n",
       "mean_length_of_stay    -0.051408 -0.101401  \n",
       "mean_total_charges_day  0.024556  0.128648  \n",
       "patients               -0.149831  0.012306  \n",
       "severity                0.004190  0.023931  \n",
       "system                 -0.045101  0.101864  \n",
       "payment                 1.000000 -0.036617  \n",
       "hospital               -0.036617  1.000000  "
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[var_list].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of Beds</th>\n",
       "      <th>area_sqmi</th>\n",
       "      <th>mean_length_of_stay</th>\n",
       "      <th>mean_total_charges_day</th>\n",
       "      <th>patients</th>\n",
       "      <th>severity</th>\n",
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       "      <th>hospital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Number of Beds</th>\n",
       "      <td>1.000000</td>\n",
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       "      <th>area_sqmi</th>\n",
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       "      <td>0.125988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_length_of_stay</th>\n",
       "      <td>-0.033305</td>\n",
       "      <td>-0.001262</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.077959</td>\n",
       "      <td>0.105472</td>\n",
       "      <td>-0.480258</td>\n",
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       "      <td>-0.051408</td>\n",
       "      <td>-0.101401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_total_charges_day</th>\n",
       "      <td>0.263765</td>\n",
       "      <td>-0.221408</td>\n",
       "      <td>-0.077959</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.114197</td>\n",
       "      <td>-0.227832</td>\n",
       "      <td>0.040933</td>\n",
       "      <td>0.024556</td>\n",
       "      <td>0.128648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patients</th>\n",
       "      <td>0.181902</td>\n",
       "      <td>0.032581</td>\n",
       "      <td>0.105472</td>\n",
       "      <td>0.114197</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.181431</td>\n",
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       "      <td>-0.149831</td>\n",
       "      <td>0.012306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>severity</th>\n",
       "      <td>0.001238</td>\n",
       "      <td>0.010858</td>\n",
       "      <td>-0.480258</td>\n",
       "      <td>-0.227832</td>\n",
       "      <td>-0.181431</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001214</td>\n",
       "      <td>0.004190</td>\n",
       "      <td>0.023931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>system</th>\n",
       "      <td>-0.125800</td>\n",
       "      <td>0.237002</td>\n",
       "      <td>-0.047700</td>\n",
       "      <td>0.040933</td>\n",
       "      <td>0.031764</td>\n",
       "      <td>0.001214</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.045101</td>\n",
       "      <td>0.101864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment</th>\n",
       "      <td>0.032496</td>\n",
       "      <td>-0.014321</td>\n",
       "      <td>-0.051408</td>\n",
       "      <td>0.024556</td>\n",
       "      <td>-0.149831</td>\n",
       "      <td>0.004190</td>\n",
       "      <td>-0.045101</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.036617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hospital</th>\n",
       "      <td>-0.217734</td>\n",
       "      <td>0.125988</td>\n",
       "      <td>-0.101401</td>\n",
       "      <td>0.128648</td>\n",
       "      <td>0.012306</td>\n",
       "      <td>0.023931</td>\n",
       "      <td>0.101864</td>\n",
       "      <td>-0.036617</td>\n",
       "      <td>1.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Number of Beds  area_sqmi  mean_length_of_stay  \\\n",
       "Number of Beds                1.000000  -0.202828            -0.033305   \n",
       "area_sqmi                    -0.202828   1.000000            -0.001262   \n",
       "mean_length_of_stay          -0.033305  -0.001262             1.000000   \n",
       "mean_total_charges_day        0.263765  -0.221408            -0.077959   \n",
       "patients                      0.181902   0.032581             0.105472   \n",
       "severity                      0.001238   0.010858            -0.480258   \n",
       "system                       -0.125800   0.237002            -0.047700   \n",
       "payment                       0.032496  -0.014321            -0.051408   \n",
       "hospital                     -0.217734   0.125988            -0.101401   \n",
       "\n",
       "                        mean_total_charges_day  patients  severity    system  \\\n",
       "Number of Beds                        0.263765  0.181902  0.001238 -0.125800   \n",
       "area_sqmi                            -0.221408  0.032581  0.010858  0.237002   \n",
       "mean_length_of_stay                  -0.077959  0.105472 -0.480258 -0.047700   \n",
       "mean_total_charges_day                1.000000  0.114197 -0.227832  0.040933   \n",
       "patients                              0.114197  1.000000 -0.181431  0.031764   \n",
       "severity                             -0.227832 -0.181431  1.000000  0.001214   \n",
       "system                                0.040933  0.031764  0.001214  1.000000   \n",
       "payment                               0.024556 -0.149831  0.004190 -0.045101   \n",
       "hospital                              0.128648  0.012306  0.023931  0.101864   \n",
       "\n",
       "                         payment  hospital  \n",
       "Number of Beds          0.032496 -0.217734  \n",
       "area_sqmi              -0.014321  0.125988  \n",
       "mean_length_of_stay    -0.051408 -0.101401  \n",
       "mean_total_charges_day  0.024556  0.128648  \n",
       "patients               -0.149831  0.012306  \n",
       "severity                0.004190  0.023931  \n",
       "system                 -0.045101  0.101864  \n",
       "payment                 1.000000 -0.036617  \n",
       "hospital               -0.036617  1.000000  "
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled[var_list].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "# With PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_scaled_pca = X_scaled[var_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=9, random_state=None,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the PCA function from sklearn\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Select the number of principal components we will return\n",
    "num_components = 9\n",
    "\n",
    "# Create the PCA model\n",
    "pca = PCA(n_components=num_components)\n",
    "\n",
    "# Fit the model with the standardised data\n",
    "pca.fit(X_scaled_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.18845054, 0.16663103, 0.14149699, 0.1209317 , 0.10821218,\n",
       "       0.09242006, 0.07289552, 0.06414235, 0.04481964])"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.18845054, 0.35508157, 0.49657856, 0.61751025, 0.72572243,\n",
       "       0.81814249, 0.89103801, 0.95518036, 1.        ])"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "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": [
    "#plot explained variance over the number of compinents\n",
    "n = 9\n",
    "plt.bar(np.arange(n)+1, pca.explained_variance_ratio_.cumsum())\n",
    "plt.xlabel(\"Number of components\")\n",
    "plt.ylabel(\"Explained Variance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Earl\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "labels = ['low', 'medium', 'high']\n",
    "\n",
    "X['price_category'] = pd.qcut((X['mean_total_charges_day']),3, labels=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "var_list_1 =['Number of Beds',\n",
    "             'area_sqmi',\n",
    "             'mean_length_of_stay',\n",
    "             'patients',\n",
    "             'severity',\n",
    "             'system',\n",
    "             'payment',\n",
    "             'hospital']\n",
    "\n",
    "x = X[var_list_1]\n",
    "y = X['price_category']\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(x, y, test_size=0.3, random_state=999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for max_depth = 1: 0.49034749034749037\n",
      "for max_depth = 2: 0.5868725868725869\n",
      "for max_depth = 3: 0.6428571428571429\n",
      "for max_depth = 4: 0.6718146718146718\n",
      "for max_depth = 5: 0.6776061776061776\n",
      "for max_depth = 6: 0.7084942084942085\n",
      "for max_depth = 7: 0.7355212355212355\n",
      "for max_depth = 8: 0.7239382239382239\n",
      "for max_depth = 9: 0.7027027027027027\n",
      "for max_depth = 10: 0.7104247104247104\n",
      "for max_depth = 11: 0.7065637065637066\n",
      "for max_depth = 12: 0.7104247104247104\n",
      "for max_depth = 13: 0.7046332046332047\n",
      "for max_depth = 14: 0.7046332046332047\n",
      "for max_depth = 15: 0.7123552123552124\n",
      "for max_depth = 16: 0.7084942084942085\n",
      "for max_depth = 17: 0.696911196911197\n",
      "for max_depth = 18: 0.693050193050193\n",
      "for max_depth = 19: 0.7065637065637066\n",
      "for max_depth = 20: 0.693050193050193\n"
     ]
    }
   ],
   "source": [
    "max_depth_list = list(range(1,21))\n",
    "accuracy_test = []\n",
    "\n",
    "for i in max_depth_list:\n",
    "    dt = DecisionTreeClassifier(max_depth = i)\n",
    "    dt.fit(X_train, y_train)\n",
    "    accuracy_test.append(dt.score(X_test, y_test))\n",
    "    print(\"for max_depth = {}:\".format(i), accuracy_test[i-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "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": [
    "import matplotlib.pylab as plt\n",
    "\n",
    "# your code here\n",
    "plt.plot(max_depth_list, accuracy_test)\n",
    "plt.xlabel('Maximum Tree Depth')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Accuracy vs Simplicity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Earl\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 9}\n",
      "0.7046332046332047\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# your code here\n",
    "dt = DecisionTreeClassifier(random_state = 999)\n",
    "param_grid = {'max_depth':range(1,20)}\n",
    "gr = GridSearchCV(dt, param_grid = param_grid, scoring='accuracy')\n",
    "rs = gr.fit(X_train,y_train)\n",
    "\n",
    "print(rs.best_params_)\n",
    "print(rs.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "digraph Tree {\n",
      "node [shape=box, style=\"filled, rounded\", color=\"black\", fontname=helvetica] ;\n",
      "edge [fontname=helvetica] ;\n",
      "0 [label=\"hospital <= 13.5, samples = 1206, value = [409, 393, 404], class = high\", fillcolor=\"#fffefe\"] ;\n",
      "1 [label=\"severity <= 0.5, samples = 345, value = [23, 214, 108], class = low\", fillcolor=\"#a6f3c7\"] ;\n",
      "0 -> 1 [labeldistance=2.5, labelangle=45, headlabel=\"True\"] ;\n",
      "2 [label=\"samples = 92, value = [13, 33, 46], class = medium\", fillcolor=\"#e3d3f9\"] ;\n",
      "1 -> 2 ;\n",
      "3 [label=\"samples = 253, value = [10, 181, 62], class = low\", fillcolor=\"#84efb0\"] ;\n",
      "1 -> 3 ;\n",
      "4 [label=\"hospital <= 37.5, samples = 861, value = [386, 179, 296], class = high\", fillcolor=\"#fbebdf\"] ;\n",
      "0 -> 4 [labeldistance=2.5, labelangle=-45, headlabel=\"False\"] ;\n",
      "5 [label=\"samples = 594, value = [367, 74, 153], class = high\", fillcolor=\"#f2c29f\"] ;\n",
      "4 -> 5 ;\n",
      "6 [label=\"samples = 267, value = [19, 105, 143], class = medium\", fillcolor=\"#e1d1f9\"] ;\n",
      "4 -> 6 ;\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "\n",
    "# your code here\n",
    "dt = DecisionTreeClassifier(max_depth = 2, random_state = 999)\n",
    "dt.fit(X_train, y_train)\n",
    "thestring = tree.export_graphviz(dt,out_file=None,\n",
    "                         feature_names=X_train.columns.values,  \n",
    "                         class_names = dt.classes_,  \n",
    "                         filled=True, rounded=True,  \n",
    "                         special_characters=True,impurity=False).replace(\"<br/>\",\", \").replace(\"&le;\",\"<=\").replace(\"=<\",\"=\\\"\").replace(\">,\",\"\\\",\")\n",
    "print(thestring)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image  \n",
    "import pydotplus\n",
    "import os     \n",
    "from sklearn import tree\n",
    "\n",
    "os.environ[\"PATH\"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'\n",
    "dt = DecisionTreeClassifier(max_depth=10, random_state = 999) # just to keep it simple for visualization\n",
    "dt.fit(X_train,y_train)\n",
    "graph = pydotplus.graph_from_dot_data(thestring)  \n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.693050193050193\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# your code here\n",
    "rf = RandomForestClassifier(n_estimators=30, n_jobs=-1,max_leaf_nodes=10, random_state = 50)\n",
    "rf_pred = rf.fit(X_train, y_train)\n",
    "pred = rf_pred.score(X_test,y_test)\n",
    "print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Earl\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_leaf_nodes': 19}\n",
      "0.7316602316602316\n"
     ]
    }
   ],
   "source": [
    "# hyperparameter tuning\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=30, n_jobs=-1,max_leaf_nodes=10, random_state = 50)\n",
    "\n",
    "# alternatively, rf can be written as\n",
    "# rf = RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "param_grid = {'max_leaf_nodes':range(2,20)}\n",
    "gr_rf = GridSearchCV(rf, param_grid = param_grid, scoring='accuracy')\n",
    "rs_rf = gr_rf.fit(X_train,y_train)\n",
    "\n",
    "print(rs_rf.best_params_)\n",
    "print(rs_rf.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.15258554, 0.32150553, 0.03706097, 0.04290686, 0.039     ,\n",
       "       0.17817363, 0.00134627, 0.22742121])"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_pred.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_list_1 =['Number of Beds',\n",
    "             'area_sqmi',\n",
    "             'mean_length_of_stay',\n",
    "             'patients',\n",
    "             'severity',\n",
    "             'system',\n",
    "             'payment',\n",
    "             'hospital']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "severity = pd.get_dummies(sept['apr_severity_of_illness_description'])\n",
    "system = pd.get_dummies(sept['System'])\n",
    "payment = pd.get_dummies(sept['payment_typology_1'])\n",
    "hospital_facility = pd.get_dummies(sept['facility_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reg = pd.concat([sept, severity, system, payment, hospital_facility], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1724, 75)"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_list = list(df_reg.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_var = ['Number of Beds',\n",
    " 'area_sqmi',\n",
    " 'mean_length_of_stay',\n",
    " 'patients',\n",
    " 'Mount Sinai',\n",
    " 'NYU Langone',\n",
    " 'NewYork-Presbyterian',\n",
    " 'Northwell',\n",
    " 'Others',\n",
    " 'SUNY',\n",
    " 'Blue Cross/Blue Shield',\n",
    " 'Medicare',\n",
    " 'Private Health Insurance',\n",
    " 'Self-Pay',\n",
    " 'Bellevue Hospital Center',\n",
    " 'Bronx-Lebanon Hospital Center - Concourse Division',\n",
    " 'Brookdale Hospital Medical Center',\n",
    " 'Brooklyn Hospital Center - Downtown Campus',\n",
    " 'Calvary Hospital Inc',\n",
    " 'Coney Island Hospital',\n",
    " 'Elmhurst Hospital Center',\n",
    " 'Flushing Hospital Medical Center',\n",
    " 'Harlem Hospital Center',\n",
    " 'Henry J. Carter Specialty Hospital',\n",
    " 'Interfaith Medical Center',\n",
    " 'Jacobi Medical Center',\n",
    " 'Jamaica Hospital Medical Center',\n",
    " 'Kings County Hospital Center',\n",
    " 'Kingsbrook Jewish Medical Center',\n",
    " 'Lenox Hill Hospital',\n",
    " 'Lincoln Medical & Mental Health Center',\n",
    " 'Long Island Jewish Forest Hills',\n",
    " 'Long Island Jewish Medical Center',\n",
    " 'Maimonides Medical Center',\n",
    " 'Memorial Hospital for Cancer and Allied Diseases',\n",
    " 'Metropolitan Hospital Center',\n",
    " 'Montefiore Med Center - Jack D Weiler Hosp of A Einstein College Div',\n",
    " 'Montefiore Medical Center - Henry & Lucy Moses Div',\n",
    " 'Montefiore Medical Center-Wakefield Hospital',\n",
    " 'Mount Sinai Beth Israel',\n",
    " 'Mount Sinai Brooklyn',\n",
    " 'Mount Sinai Hospital',\n",
    " 'Mount Sinai Hospital - Mount Sinai Hospital of Queens',\n",
    " \"Mount Sinai St. Luke's\",\n",
    " 'Mount Sinai West',\n",
    " 'NYU Hospitals Center',\n",
    " 'NYU Langone Hospital-Brooklyn',\n",
    " 'NYU Langone Hospitals',\n",
    " 'NYU Langone Orthopedic Hospital',\n",
    " 'NYU Lutheran Medical Center',\n",
    " 'New York Community Hospital of Brooklyn, Inc',\n",
    " 'New York-Presbyterian/Lower Manhattan Hospital',\n",
    " 'New York-Presbyterian/Queens',\n",
    " 'North Central Bronx Hospital',\n",
    " 'Queens Hospital Center',\n",
    " 'Richmond University Medical Center',\n",
    " 'SBH Health System',\n",
    " 'Staten Island University Hosp-North',\n",
    " 'Staten Island University Hosp-South',\n",
    " 'University Hospital of Brooklyn',\n",
    " 'Woodhull Medical & Mental Health Center',\n",
    " 'Wyckoff Heights Medical Center']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "def listToString(s):  \n",
    "    \n",
    "    # initialize an empty string \n",
    "    str1 = \"\"  \n",
    "    \n",
    "    # traverse in the string   \n",
    "    for ele in s:  \n",
    "        str1 += ele +'+'  \n",
    "    \n",
    "    # return string   \n",
    "    return str1  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reg.columns = df_reg.columns.str.replace(' ', '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reg.columns = df_reg.columns.str.replace(\"'\", '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reg.columns = df_reg.columns.str.replace(\"-\", '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reg.columns = df_reg.columns.str.replace(\".\", '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reg.columns = df_reg.columns.str.replace(\"/\", '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reg.columns = df_reg.columns.str.replace(\",\", '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reg.columns = df_reg.columns.str.replace(\"&\", '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Number_of_Beds', 'System', 'apr_drg_code', 'apr_severity_of_illness',\n",
       "       'apr_severity_of_illness_description', 'area_sqmi', 'facility_name',\n",
       "       'mean_length_of_stay', 'mean_total_charges_day', 'patients',\n",
       "       'payment_typology_1', 'Extreme', 'Major', 'Minor', 'Moderate',\n",
       "       'Health+', 'Montefiore', 'Mount_Sinai', 'NYU_Langone',\n",
       "       'NewYork_Presbyterian', 'Northwell', 'Others', 'SUNY',\n",
       "       'Blue_Cross_Blue_Shield', 'Medicare', 'Private_Health_Insurance',\n",
       "       'Self_Pay', 'Bellevue_Hospital_Center',\n",
       "       'Bronx_Lebanon_Hospital_Center___Concourse_Division',\n",
       "       'Brookdale_Hospital_Medical_Center',\n",
       "       'Brooklyn_Hospital_Center___Downtown_Campus', 'Calvary_Hospital_Inc',\n",
       "       'Coney_Island_Hospital', 'Elmhurst_Hospital_Center',\n",
       "       'Flushing_Hospital_Medical_Center', 'Harlem_Hospital_Center',\n",
       "       'Henry_J__Carter_Specialty_Hospital', 'Interfaith_Medical_Center',\n",
       "       'Jacobi_Medical_Center', 'Jamaica_Hospital_Medical_Center',\n",
       "       'Kings_County_Hospital_Center', 'Kingsbrook_Jewish_Medical_Center',\n",
       "       'Lenox_Hill_Hospital', 'Lincoln_Medical___Mental_Health_Center',\n",
       "       'Long_Island_Jewish_Forest_Hills', 'Long_Island_Jewish_Medical_Center',\n",
       "       'Maimonides_Medical_Center',\n",
       "       'Memorial_Hospital_for_Cancer_and_Allied_Diseases',\n",
       "       'Metropolitan_Hospital_Center',\n",
       "       'Montefiore_Med_Center___Jack_D_Weiler_Hosp_of_A_Einstein_College_Div',\n",
       "       'Montefiore_Medical_Center___Henry___Lucy_Moses_Div',\n",
       "       'Montefiore_Medical_Center_Wakefield_Hospital',\n",
       "       'Mount_Sinai_Beth_Israel', 'Mount_Sinai_Brooklyn',\n",
       "       'Mount_Sinai_Hospital',\n",
       "       'Mount_Sinai_Hospital___Mount_Sinai_Hospital_of_Queens',\n",
       "       'Mount_Sinai_St__Luke_s', 'Mount_Sinai_West', 'NYU_Hospitals_Center',\n",
       "       'NYU_Langone_Hospital_Brooklyn', 'NYU_Langone_Hospitals',\n",
       "       'NYU_Langone_Orthopedic_Hospital', 'NYU_Lutheran_Medical_Center',\n",
       "       'New_York_Community_Hospital_of_Brooklyn__Inc',\n",
       "       'New_York_Presbyterian_Lower_Manhattan_Hospital',\n",
       "       'New_York_Presbyterian_Queens', 'North_Central_Bronx_Hospital',\n",
       "       'Queens_Hospital_Center', 'Richmond_University_Medical_Center',\n",
       "       'SBH_Health_System', 'Staten_Island_University_Hosp_North',\n",
       "       'Staten_Island_University_Hosp_South',\n",
       "       'University_Hospital_of_Brooklyn',\n",
       "       'Woodhull_Medical___Mental_Health_Center',\n",
       "       'Wyckoff_Heights_Medical_Center'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reg.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_str = (listToString(reg_var)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_str = reg_str[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_str = reg_str.replace(\" \", \"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_str = reg_str.replace(\"'\", \"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_str = reg_str.replace(\"-\", \"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_str = reg_str.replace(\".\", \"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_str = reg_str.replace(\"/\", \"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_str = reg_str.replace(\",\", \"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_str = reg_str.replace(\"&\", \"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Number_of_Beds+area_sqmi+mean_length_of_stay+patients+Mount_Sinai+NYU_Langone+NewYork_Presbyterian+Northwell+Others+SUNY+Blue_Cross_Blue_Shield+Medicare+Private_Health_Insurance+Self_Pay+Bellevue_Hospital_Center+Bronx_Lebanon_Hospital_Center___Concourse_Division+Brookdale_Hospital_Medical_Center+Brooklyn_Hospital_Center___Downtown_Campus+Calvary_Hospital_Inc+Coney_Island_Hospital+Elmhurst_Hospital_Center+Flushing_Hospital_Medical_Center+Harlem_Hospital_Center+Henry_J__Carter_Specialty_Hospital+Interfaith_Medical_Center+Jacobi_Medical_Center+Jamaica_Hospital_Medical_Center+Kings_County_Hospital_Center+Kingsbrook_Jewish_Medical_Center+Lenox_Hill_Hospital+Lincoln_Medical___Mental_Health_Center+Long_Island_Jewish_Forest_Hills+Long_Island_Jewish_Medical_Center+Maimonides_Medical_Center+Memorial_Hospital_for_Cancer_and_Allied_Diseases+Metropolitan_Hospital_Center+Montefiore_Med_Center___Jack_D_Weiler_Hosp_of_A_Einstein_College_Div+Montefiore_Medical_Center___Henry___Lucy_Moses_Div+Montefiore_Medical_Center_Wakefield_Hospital+Mount_Sinai_Beth_Israel+Mount_Sinai_Brooklyn+Mount_Sinai_Hospital+Mount_Sinai_Hospital___Mount_Sinai_Hospital_of_Queens+Mount_Sinai_St__Luke_s+Mount_Sinai_West+NYU_Hospitals_Center+NYU_Langone_Hospital_Brooklyn+NYU_Langone_Hospitals+NYU_Langone_Orthopedic_Hospital+NYU_Lutheran_Medical_Center+New_York_Community_Hospital_of_Brooklyn__Inc+New_York_Presbyterian_Lower_Manhattan_Hospital+New_York_Presbyterian_Queens+North_Central_Bronx_Hospital+Queens_Hospital_Center+Richmond_University_Medical_Center+SBH_Health_System+Staten_Island_University_Hosp_North+Staten_Island_University_Hosp_South+University_Hospital_of_Brooklyn+Woodhull_Medical___Mental_Health_Center+Wyckoff_Heights_Medical_Center'"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "lm = smf.ols(formula='mean_total_charges_day ~ Number_of_Beds+area_sqmi+mean_length_of_stay+patients+Mount_Sinai+NYU_Langone+NewYork_Presbyterian+Northwell+Others+SUNY+Blue_Cross_Blue_Shield+Medicare+Private_Health_Insurance+Self_Pay+Bellevue_Hospital_Center+Bronx_Lebanon_Hospital_Center___Concourse_Division+Brookdale_Hospital_Medical_Center+Brooklyn_Hospital_Center___Downtown_Campus+Calvary_Hospital_Inc+Coney_Island_Hospital+Elmhurst_Hospital_Center+Flushing_Hospital_Medical_Center+Harlem_Hospital_Center+Henry_J__Carter_Specialty_Hospital+Interfaith_Medical_Center+Jacobi_Medical_Center+Jamaica_Hospital_Medical_Center+Kings_County_Hospital_Center+Kingsbrook_Jewish_Medical_Center+Lenox_Hill_Hospital+Lincoln_Medical___Mental_Health_Center+Long_Island_Jewish_Forest_Hills+Long_Island_Jewish_Medical_Center+Maimonides_Medical_Center+Memorial_Hospital_for_Cancer_and_Allied_Diseases+Metropolitan_Hospital_Center+Montefiore_Med_Center___Jack_D_Weiler_Hosp_of_A_Einstein_College_Div+Montefiore_Medical_Center___Henry___Lucy_Moses_Div+Montefiore_Medical_Center_Wakefield_Hospital+Mount_Sinai_Beth_Israel+Mount_Sinai_Brooklyn+Mount_Sinai_Hospital+Mount_Sinai_Hospital___Mount_Sinai_Hospital_of_Queens+Mount_Sinai_St__Luke_s+Mount_Sinai_West+NYU_Hospitals_Center+NYU_Langone_Hospital_Brooklyn+NYU_Langone_Hospitals+NYU_Langone_Orthopedic_Hospital+NYU_Lutheran_Medical_Center+New_York_Community_Hospital_of_Brooklyn__Inc+New_York_Presbyterian_Lower_Manhattan_Hospital+New_York_Presbyterian_Queens+North_Central_Bronx_Hospital+Queens_Hospital_Center+Richmond_University_Medical_Center+SBH_Health_System+Staten_Island_University_Hosp_North+Staten_Island_University_Hosp_South+University_Hospital_of_Brooklyn+Woodhull_Medical___Mental_Health_Center+Wyckoff_Heights_Medical_Center', data = df_reg).fit()\n",
    "results = lm.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('results.csv', 'w') as fh:\n",
    "    fh.write(lm.summary().as_csv())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>mean_total_charges_day</td> <th>  R-squared:         </th> <td>   0.275</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                      <td>OLS</td>          <th>  Adj. R-squared:    </th> <td>   0.269</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>Least Squares</td>     <th>  F-statistic:       </th> <td>   49.84</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                <td>Thu, 30 Apr 2020</td>    <th>  Prob (F-statistic):</th> <td>1.99e-109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                    <td>23:40:07</td>        <th>  Log-Likelihood:    </th> <td> -16549.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>         <td>  1724</td>         <th>  AIC:               </th> <td>3.313e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>             <td>  1710</td>         <th>  BIC:               </th> <td>3.320e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                 <td>    13</td>         <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>         <td>nonrobust</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "              <td></td>                <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                <td> 6983.0992</td> <td>  245.075</td> <td>   28.494</td> <td> 0.000</td> <td> 6502.421</td> <td> 7463.777</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number_of_Beds</th>           <td>    1.6111</td> <td>    0.427</td> <td>    3.769</td> <td> 0.000</td> <td>    0.773</td> <td>    2.449</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>area_sqmi</th>                <td> -186.6484</td> <td>   17.704</td> <td>  -10.543</td> <td> 0.000</td> <td> -221.372</td> <td> -151.925</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mean_length_of_stay</th>      <td>  -34.5182</td> <td>   15.653</td> <td>   -2.205</td> <td> 0.028</td> <td>  -65.220</td> <td>   -3.816</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>patients</th>                 <td>    7.2072</td> <td>    1.372</td> <td>    5.252</td> <td> 0.000</td> <td>    4.516</td> <td>    9.899</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mount_Sinai</th>              <td>  694.4559</td> <td>  243.506</td> <td>    2.852</td> <td> 0.004</td> <td>  216.854</td> <td> 1172.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>NYU_Langone</th>              <td> 4744.6260</td> <td>  394.739</td> <td>   12.020</td> <td> 0.000</td> <td> 3970.404</td> <td> 5518.848</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>NewYork_Presbyterian</th>     <td> 1167.1052</td> <td>  433.960</td> <td>    2.689</td> <td> 0.007</td> <td>  315.958</td> <td> 2018.253</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Northwell</th>                <td> 4172.9979</td> <td>  334.829</td> <td>   12.463</td> <td> 0.000</td> <td> 3516.281</td> <td> 4829.715</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Others</th>                   <td> -488.6851</td> <td>  273.308</td> <td>   -1.788</td> <td> 0.074</td> <td>-1024.738</td> <td>   47.368</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SUNY</th>                     <td>   57.5053</td> <td>  392.627</td> <td>    0.146</td> <td> 0.884</td> <td> -712.575</td> <td>  827.586</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Blue_Cross_Blue_Shield</th>   <td> 2122.5881</td> <td>  167.738</td> <td>   12.654</td> <td> 0.000</td> <td> 1793.594</td> <td> 2451.582</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Medicare</th>                 <td>  654.1803</td> <td>  185.034</td> <td>    3.535</td> <td> 0.000</td> <td>  291.263</td> <td> 1017.098</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Private_Health_Insurance</th> <td> 1899.7506</td> <td>  159.336</td> <td>   11.923</td> <td> 0.000</td> <td> 1587.237</td> <td> 2212.265</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Self_Pay</th>                 <td> 2306.5800</td> <td>  182.035</td> <td>   12.671</td> <td> 0.000</td> <td> 1949.546</td> <td> 2663.614</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1389.947</td> <th>  Durbin-Watson:     </th> <td>   1.278</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>78410.462</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 3.327</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>35.362</td>  <th>  Cond. No.          </th> <td>4.28e+18</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 2.63e-29. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              OLS Regression Results                              \n",
       "==================================================================================\n",
       "Dep. Variable:     mean_total_charges_day   R-squared:                       0.275\n",
       "Model:                                OLS   Adj. R-squared:                  0.269\n",
       "Method:                     Least Squares   F-statistic:                     49.84\n",
       "Date:                    Thu, 30 Apr 2020   Prob (F-statistic):          1.99e-109\n",
       "Time:                            23:40:07   Log-Likelihood:                -16549.\n",
       "No. Observations:                    1724   AIC:                         3.313e+04\n",
       "Df Residuals:                        1710   BIC:                         3.320e+04\n",
       "Df Model:                              13                                         \n",
       "Covariance Type:                nonrobust                                         \n",
       "============================================================================================\n",
       "                               coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------\n",
       "Intercept                 6983.0992    245.075     28.494      0.000    6502.421    7463.777\n",
       "Number_of_Beds               1.6111      0.427      3.769      0.000       0.773       2.449\n",
       "area_sqmi                 -186.6484     17.704    -10.543      0.000    -221.372    -151.925\n",
       "mean_length_of_stay        -34.5182     15.653     -2.205      0.028     -65.220      -3.816\n",
       "patients                     7.2072      1.372      5.252      0.000       4.516       9.899\n",
       "Mount_Sinai                694.4559    243.506      2.852      0.004     216.854    1172.057\n",
       "NYU_Langone               4744.6260    394.739     12.020      0.000    3970.404    5518.848\n",
       "NewYork_Presbyterian      1167.1052    433.960      2.689      0.007     315.958    2018.253\n",
       "Northwell                 4172.9979    334.829     12.463      0.000    3516.281    4829.715\n",
       "Others                    -488.6851    273.308     -1.788      0.074   -1024.738      47.368\n",
       "SUNY                        57.5053    392.627      0.146      0.884    -712.575     827.586\n",
       "Blue_Cross_Blue_Shield    2122.5881    167.738     12.654      0.000    1793.594    2451.582\n",
       "Medicare                   654.1803    185.034      3.535      0.000     291.263    1017.098\n",
       "Private_Health_Insurance  1899.7506    159.336     11.923      0.000    1587.237    2212.265\n",
       "Self_Pay                  2306.5800    182.035     12.671      0.000    1949.546    2663.614\n",
       "==============================================================================\n",
       "Omnibus:                     1389.947   Durbin-Watson:                   1.278\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            78410.462\n",
       "Skew:                           3.327   Prob(JB):                         0.00\n",
       "Kurtosis:                      35.362   Cond. No.                     4.28e+18\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 2.63e-29. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_1 = smf.ols(formula='mean_total_charges_day ~ Number_of_Beds+area_sqmi+mean_length_of_stay+patients+Mount_Sinai+NYU_Langone+NewYork_Presbyterian+Northwell+Others+SUNY+Blue_Cross_Blue_Shield+Medicare+Private_Health_Insurance+Self_Pay', data = df_reg).fit()\n",
    "lm_1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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