heart_failure.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "heart2015 = pd.read_csv('summary_2015.csv')\n",
    "heart2016 = pd.read_csv('summary_2016.csv')\n",
    "heart2017 = pd.read_csv('summary_2017.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "heart = pd.concat([heart2015,heart2016,heart2017])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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>194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>13.00</td>\n",
       "      <td>5862.040</td>\n",
       "      <td>1</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>194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Major</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>1.00</td>\n",
       "      <td>9352.740</td>\n",
       "      <td>1</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>194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Minor</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>12.00</td>\n",
       "      <td>5320.110</td>\n",
       "      <td>1</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>3.75</td>\n",
       "      <td>6556.745</td>\n",
       "      <td>4</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>16.50</td>\n",
       "      <td>6252.629</td>\n",
       "      <td>10</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+           194                     NaN   \n",
       "1             912  Health+           194                     NaN   \n",
       "2             912  Health+           194                     NaN   \n",
       "3             912  Health+           194                     NaN   \n",
       "4             912  Health+           194                     NaN   \n",
       "\n",
       "  apr_severity_of_illness_description  area_sqmi             facility_name  \\\n",
       "0                             Extreme       0.77  Bellevue Hospital Center   \n",
       "1                               Major       0.77  Bellevue Hospital Center   \n",
       "2                               Minor       0.77  Bellevue Hospital Center   \n",
       "3                            Moderate       0.77  Bellevue Hospital Center   \n",
       "4                             Extreme       0.77  Bellevue Hospital Center   \n",
       "\n",
       "   mean_length_of_stay  mean_total_charges_day  patients  \\\n",
       "0                13.00                5862.040         1   \n",
       "1                 1.00                9352.740         1   \n",
       "2                12.00                5320.110         1   \n",
       "3                 3.75                6556.745         4   \n",
       "4                16.50                6252.629        10   \n",
       "\n",
       "       payment_typology_1  \n",
       "0  Blue Cross/Blue Shield  \n",
       "1  Blue Cross/Blue Shield  \n",
       "2  Blue Cross/Blue Shield  \n",
       "3  Blue Cross/Blue Shield  \n",
       "4                Medicare  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.head()"
   ]
  },
  {
   "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "heart['apr_severity_of_illness_description'].fillna(heart['apr_severity_of_illness'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "severity = heart['apr_severity_of_illness_description'].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "system = heart['System'].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "payment = heart['payment_typology_1'].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "facility = heart['facility_name'].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([heart, severity, system, payment, facility], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={0:'severity', 1:'system', 2:'payment', 3:'hospital'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>13.00</td>\n",
       "      <td>5862.040</td>\n",
       "      <td>1</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "      <td>0</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>194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Major</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>1.00</td>\n",
       "      <td>9352.740</td>\n",
       "      <td>1</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>2</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Minor</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>12.00</td>\n",
       "      <td>5320.110</td>\n",
       "      <td>1</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>3.75</td>\n",
       "      <td>6556.745</td>\n",
       "      <td>4</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>4</th>\n",
       "      <td>912</td>\n",
       "      <td>Health+</td>\n",
       "      <td>194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>0.77</td>\n",
       "      <td>Bellevue Hospital Center</td>\n",
       "      <td>16.50</td>\n",
       "      <td>6252.629</td>\n",
       "      <td>10</td>\n",
       "      <td>Medicare</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Number of Beds   System  apr_drg_code apr_severity_of_illness  \\\n",
       "0             912  Health+           194                     NaN   \n",
       "1             912  Health+           194                     NaN   \n",
       "2             912  Health+           194                     NaN   \n",
       "3             912  Health+           194                     NaN   \n",
       "4             912  Health+           194                     NaN   \n",
       "\n",
       "  apr_severity_of_illness_description  area_sqmi             facility_name  \\\n",
       "0                             Extreme       0.77  Bellevue Hospital Center   \n",
       "1                               Major       0.77  Bellevue Hospital Center   \n",
       "2                               Minor       0.77  Bellevue Hospital Center   \n",
       "3                            Moderate       0.77  Bellevue Hospital Center   \n",
       "4                             Extreme       0.77  Bellevue Hospital Center   \n",
       "\n",
       "   mean_length_of_stay  mean_total_charges_day  patients  \\\n",
       "0                13.00                5862.040         1   \n",
       "1                 1.00                9352.740         1   \n",
       "2                12.00                5320.110         1   \n",
       "3                 3.75                6556.745         4   \n",
       "4                16.50                6252.629        10   \n",
       "\n",
       "       payment_typology_1  severity  system  payment  hospital  \n",
       "0  Blue Cross/Blue Shield         0       0        0         0  \n",
       "1  Blue Cross/Blue Shield         1       0        0         0  \n",
       "2  Blue Cross/Blue Shield         2       0        0         0  \n",
       "3  Blue Cross/Blue Shield         3       0        0         0  \n",
       "4                Medicare         0       0        1         0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "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, 'Hospital for Special Surgery'),\n",
       " (11, 'Interfaith Medical Center'),\n",
       " (12, 'Jacobi Medical Center'),\n",
       " (13, 'Jamaica Hospital Medical Center'),\n",
       " (14, 'Kings County Hospital Center'),\n",
       " (15, 'Kingsbrook Jewish Medical Center'),\n",
       " (16, 'Lenox Hill Hospital'),\n",
       " (17, 'Lincoln Medical & Mental Health Center'),\n",
       " (18, 'Long Island Jewish Forest Hills'),\n",
       " (19, 'Long Island Jewish Medical Center'),\n",
       " (20, 'Maimonides Medical Center'),\n",
       " (21, 'Memorial Hospital for Cancer and Allied Diseases'),\n",
       " (22, 'Metropolitan Hospital Center'),\n",
       " (23, 'Montefiore Med Center - Jack D Weiler Hosp of A Einstein College Div'),\n",
       " (24, 'Montefiore Medical Center - Henry & Lucy Moses Div'),\n",
       " (25, 'Montefiore Medical Center-Wakefield Hospital'),\n",
       " (26, 'Mount Sinai Beth Israel'),\n",
       " (27, 'Mount Sinai Brooklyn'),\n",
       " (28, 'Mount Sinai Hospital'),\n",
       " (29, 'Mount Sinai Hospital - Mount Sinai Hospital of Queens'),\n",
       " (30, \"Mount Sinai St. Luke's\"),\n",
       " (31, 'Mount Sinai West'),\n",
       " (32, 'NYU Hospitals Center'),\n",
       " (33, 'NYU Langone Hospital-Brooklyn'),\n",
       " (34, 'NYU Langone Hospitals'),\n",
       " (35, 'NYU Langone Orthopedic Hospital'),\n",
       " (36, 'NYU Lutheran Medical Center'),\n",
       " (37, 'New York Community Hospital of Brooklyn, Inc'),\n",
       " (38, 'New York-Presbyterian/Lower Manhattan Hospital'),\n",
       " (39, 'New York-Presbyterian/Queens'),\n",
       " (40, 'North Central Bronx Hospital'),\n",
       " (41, 'Queens Hospital Center'),\n",
       " (42, 'Richmond University Medical Center'),\n",
       " (43, 'SBH Health System'),\n",
       " (44, 'Staten Island University Hosp-North'),\n",
       " (45, 'Staten Island University Hosp-South'),\n",
       " (46, 'University Hospital of Brooklyn'),\n",
       " (47, 'Woodhull Medical & Mental Health Center'),\n",
       " (48, 'Wyckoff Heights Medical Center')]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(enumerate(df['facility_name'].astype('category').cat.categories))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[var_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.88505451, -0.9103731 ,  1.69997853, ..., -1.14043411,\n",
       "        -1.34940634, -1.63115961],\n",
       "       [ 1.88505451, -0.9103731 , -1.13082825, ..., -1.14043411,\n",
       "        -1.34940634, -1.63115961],\n",
       "       [ 1.88505451, -0.9103731 ,  1.46407797, ..., -1.14043411,\n",
       "        -1.34940634, -1.63115961],\n",
       "       ...,\n",
       "       [-0.62301583,  0.44640685, -0.89492768, ...,  1.30301612,\n",
       "         0.62109488,  1.72110004],\n",
       "       [-0.62301583,  0.44640685, -0.82752752, ...,  1.30301612,\n",
       "         0.62109488,  1.72110004],\n",
       "       [-0.62301583,  0.44640685, -0.30517627, ...,  1.30301612,\n",
       "         1.60634548,  1.72110004]])"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "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": 25,
   "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>1.885055</td>\n",
       "      <td>-0.910373</td>\n",
       "      <td>1.699979</td>\n",
       "      <td>-0.819303</td>\n",
       "      <td>-0.464811</td>\n",
       "      <td>-1.482302</td>\n",
       "      <td>-1.140434</td>\n",
       "      <td>-1.349406</td>\n",
       "      <td>-1.63116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.885055</td>\n",
       "      <td>-0.910373</td>\n",
       "      <td>-1.130828</td>\n",
       "      <td>0.137534</td>\n",
       "      <td>-0.464811</td>\n",
       "      <td>-0.571695</td>\n",
       "      <td>-1.140434</td>\n",
       "      <td>-1.349406</td>\n",
       "      <td>-1.63116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.885055</td>\n",
       "      <td>-0.910373</td>\n",
       "      <td>1.464078</td>\n",
       "      <td>-0.967852</td>\n",
       "      <td>-0.464811</td>\n",
       "      <td>0.338912</td>\n",
       "      <td>-1.140434</td>\n",
       "      <td>-1.349406</td>\n",
       "      <td>-1.63116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.885055</td>\n",
       "      <td>-0.910373</td>\n",
       "      <td>-0.482102</td>\n",
       "      <td>-0.628877</td>\n",
       "      <td>-0.415181</td>\n",
       "      <td>1.249519</td>\n",
       "      <td>-1.140434</td>\n",
       "      <td>-1.349406</td>\n",
       "      <td>-1.63116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.885055</td>\n",
       "      <td>-0.910373</td>\n",
       "      <td>2.525631</td>\n",
       "      <td>-0.712238</td>\n",
       "      <td>-0.315921</td>\n",
       "      <td>-1.482302</td>\n",
       "      <td>-1.140434</td>\n",
       "      <td>-0.364156</td>\n",
       "      <td>-1.63116</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        1.885055  -0.910373             1.699979               -0.819303   \n",
       "1        1.885055  -0.910373            -1.130828                0.137534   \n",
       "2        1.885055  -0.910373             1.464078               -0.967852   \n",
       "3        1.885055  -0.910373            -0.482102               -0.628877   \n",
       "4        1.885055  -0.910373             2.525631               -0.712238   \n",
       "\n",
       "   patients  severity    system   payment  hospital  \n",
       "0 -0.464811 -1.482302 -1.140434 -1.349406  -1.63116  \n",
       "1 -0.464811 -0.571695 -1.140434 -1.349406  -1.63116  \n",
       "2 -0.464811  0.338912 -1.140434 -1.349406  -1.63116  \n",
       "3 -0.415181  1.249519 -1.140434 -1.349406  -1.63116  \n",
       "4 -0.315921 -1.482302 -1.140434 -0.364156  -1.63116  "
      ]
     },
     "execution_count": 25,
     "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": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_kmeans = X_scaled[var_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2, the average silhouette_score is : 0.13575929523391306\n",
      "For n_clusters = 3, the average silhouette_score is : 0.1301584109384596\n",
      "For n_clusters = 4, the average silhouette_score is : 0.13198049769049178\n",
      "For n_clusters = 5, the average silhouette_score is : 0.1439753449512624\n",
      "For n_clusters = 6, the average silhouette_score is : 0.1641461842796609\n",
      "For n_clusters = 7, the average silhouette_score is : 0.16872482692752092\n",
      "For n_clusters = 8, the average silhouette_score is : 0.17171205807732476\n",
      "For n_clusters = 9, the average silhouette_score is : 0.16608004958438222\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": 28,
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ITfOA4nC5tfZSIMPMksOzm8jtRQ7buu1VPLV0K88u3UpxZS19eyVzweTBXHLMUI4fna2AEYkQj5fRniG41/Ja2AEghSA4ZgOPmNk9BB0ExgFvE5zBjAt7nm0l6ETweXd3M5sHXEbQI+0q4NnOPhhJLDuq6pj9bjFPLy1ixdZdJPUwThuXw3cuPIJzjxhEnxTd5BdpTTyGzQPAA2a2AqgHrgrPclaa2ePAKoIu0d9w9yYAM7sBmEPQ9fkBd18ZvtatwGNm9hNgKXB/5x6KJAJ3Z87K7Ty2aDP/XFdKU7MzZWg6P/jkJD41NZcB/dRNWeRgLPgclxb5+fm+ePHiWJchcWLJpnJ+8twqlm6uYGhGH2Ycncul04YydmC/WJcmElfMbIm757e1Ph7PbERibnNZDXe9WMBzy7cxsF8vfn7ZUfzbtDzdhxE5RAobkQiVNQ38Zt46Zv1rE0k9jG+fM47rThtNaor+VEQOh/6CRAjGKHto4Sb+Z+46Kvc0cPmxedx83gQG9e8d69JEEoLCRrq1lpv/d76wmo1lNZwyNofvXXgEk3L7x7o0kYSisJFua8mmcu56oYC3N+5k3MC+/Pnq4zhjwgDMdF9GpKMpbKRbaW525q0p4Q9vrOftDTvJ6ZvCTy+ZzGfzh2kYGZEoUthIt1Df2Myzy7Yy8431rCupJje9N//1yUl87rhhpPXSn4FItOmvTBJaVW0Dj769mQfmb+SDXbVMHNyPX332aC46aohGXBbpRAobSUjbd9Xy5zc38vDCTVTVNXLSmGzuuuwoThuXo3syIjGgsJGEsrF0N797rZCnl26lqdm5cMoQvnraGKbkpce6NJFuTWEjCaG2oYnfvfY+v3/tfXr0gCumD+fLp4xmeHZqrEsTERQ2kgBeX7uDHzy7gk1lNXx6ai7fv+gIBurLmCJxRWEjXdYHlbX8+B+reG75NkbnpPHQtcdzyricWJclIq1Q2EiX09jUzKwFm7jnpTU0Njs3nzue604fTa9kzSUjEq8UNtKlLNlUzvefWcHqbbs4Y8IA7vj0ZN2XEekCFDbSJVTU1HPXiwU8+vYWBvfvzX1fmMb5kwerG7NIF6GwkbjW1Oz8bckW7npxDZV7GvjKqaO48Zzx9NW3/kW6FP3FStxatHEnt/99JSu27iJ/RCY/vngyRwzRaMwiXZHCRuJOccUe7nyhgNnvFjMkvTe//tzRfHpqri6ZiXRhChuJG3vqm5j5xnrue70Qd/jW2eP42umaJVMkEeivWGLO3Xlu+Tb++/kCtlbs4aIpQ/jOBRMZlqVeZiKJQmEjMbViayV3/H0Vb2/cyRFD+nP3Z6ZywujsWJclIh1MYSMxUVnTwJ0vruaxRVvITE3hZ5dM4bPHDSOph+7LiCQihY10uuVFlVz/8BK2VdZyzUmjuPGccaT36RnrskQkihQ20mncnYfe2syP/76KnL4pPPG1E5k2PDPWZYlIJ1DYSKeormvke08tZ/a7xZwxYQD3fuZoMtNSYl2WiHQShY1E3ZoPqrj+4SVsLN3NLeeN5+tnjKWH7s2IdCsKG4mqJ5cUcdszy+nbqycPffl4ThqjKQBEuiOFjURFbUMTP5q9kscWbWH6qCx+c8UxmtBMpBtT2EiH21i6m68//A6rtu3i+jPGcPO540lO6hHrskQkhhQ20qFeXLGN/3jiPXr0MB64Op+zJg6KdUkiEgcUNtIh3J3fzC3k7pfXMnVYBr/9/DHkZWq4GREJxN21DTM72swWmtkyM1tsZtPDdjOz/zGzQjN7z8ymRexzlZmtCx9XRbQfa2bLw33+xzRscFQ0NjVz2zMruPvltVxyzFAe/+oJChoR2UfchQ3wc+B2dz8a+EH4HOACYFz4uA64D8DMsoAfAscD04EfmlnLNwXvC7dt2e/8TjqGbmNPfRNfe+gdHnlrM9efMYZ7PjOVXslJsS5LROJMPIaNAy0zZKUDxeHyDOBBDywEMsxsCPAJ4GV33+nu5cDLwPnhuv7uvsDdHXgQuLhTjyTBle+u5wt/WsirBdu5/dNHcuv5EzXnjIi0Kh7v2XwbmGNmvyQIw5PC9qHAlojtisK2A7UXtdL+EWZ2HcEZEMOHDz/8I+gGtuys4ao/v01R+R7u+8I0zp88JNYliUgci0nYmNkrwOBWVt0GnA38u7s/aWafAe4HzgFa+yezH0L7RxvdZwIzAfLz81vdRj60Ymsl1/xlEXUNTTz85eM5bmRWrEsSkTgXk7Bx93PaWmdmDwI3hk+fAP4ULhcBwyI2zSO4xFYEnLFf+2the14r28thmL+ulK89tIT+vZN55PqTGDeoX6xLEpEuIB7v2RQDp4fLZwHrwuXZwJVhr7QTgEp33wbMAc4zs8ywY8B5wJxwXZWZnRD2QrsSeLZTjyTBPLN0K1f/+W3yMvvw1NdPVtCISLvF4z2brwC/NrNkoJbwXgrwPHAhUAjUANcAuPtOM/sxsCjc7g533xkuXw/8BegDvBA+5GNyd/7wxnrufKGAE0ZnMfPKfPr31vwzItJ+FnTUkhb5+fm+ePHiWJcRN9ydO/6xij+/uZFPHjWEu9W1WURaYWZL3D2/rfXxeGYjceTRt7fw5zc3cs3JI/mviyZpagAROSTxeM9G4sTmshp+8twqTh6braARkcOisJFWNTU7Nz+xjKQexi8um6qgEZHDosto0qr7569n0cZy7r58KrkZfWJdjoh0cTqzkY9Y80EVv5yzlk8cOYhLp7U66IKIyMeisJF91Dc2c9Pjy+jXO5mfXTJFY52JSIdod9iY2Slmdk24PMDMRkWvLImV/527jpXFu/jvS6eQ3bdXrMsRkQTRrrAxsx8CtwLfDZt6Ag9FqyiJjaWby/nda+9z2bF5nHdka0PXiYgcmvae2VwCfBrYDeDuxYDGKkkge+qbuPnxdxncvzc/+NSkWJcjIgmmvWFTH84J4wBmlha9kiQW7nqxgPWlu/nFZUdpKBoR6XDtDZvHzewPBBOWfQV4Bfhj9MqSzvRmYSl/+ddGrj5pJCeNzYl1OSKSgNr1PRt3/6WZnQvsAiYAP3D3l6NamXSKXbUN/McT7zJ6QBq3nj8x1uWISII6aNiYWRLBkP3nEEy5LAnk9tmr2F5Vx5PXn0SfFA2wKSLRcdDLaO7eBNSYWXon1COdaM7KD3jynSK+ccYYjh6WEetyRCSBtXe4mlpguZm9TNgjDcDdvxWVqiTqSqvr+N5Ty5k8tD83nDUu1uWISIJrb9g8Fz4kAbg7tz29nKq6Rh79zNGkJGsgCRGJrvZ2EJhlZinA+LBpjbs3RK8siaaXV21nzsrtfOeCiYzX1M4i0gnaFTZmdgYwC9gIGDDMzK5y9zeiV5pEQ019I7f/fRUTB/fj2lM04pCIdI72Xka7GzjP3dcAmNl44FHg2GgVJtHxm7mFbK3YwxNfO5GeSbp8JiKdo72fNj1bggbA3dcSjI8mXUhhSRV//Od6Ljs2j+NGZsW6HBHpRtp7ZrPYzO4H/i98/gVgSXRKkmhwd/7rmZWkpiTz3Qv05U0R6VztDZvrgW8A3yK4Z/MG8LtoFSUdb/a7xSxYX8ZPLp6sqQNEpNO1N2ySgV+7+z2wd1QBfWJ1EbtqG/jJc6uZmpfOFdOHx7ocEemG2nvP5lUgciL6PgSDcUoXcM9LaymtruPHF08mqYdm3hSRztfesOnt7tUtT8Ll1OiUJB1pZXElDy7YyBePH8FReRqSRkRio71hs9vMprU8MbN8YE90SpKO0tzsfP+ZFWSmpnDLeRNiXY6IdGPtvWfzbeAJMysmmEAtF/hs1KqSDvH44i0s3VzB3ZdPJT1VPdVFJHYOeGZjZseZ2WB3XwRMBP4KNAIvAhs6oT45RDt313PniwVMH5nFpdOGxrocEenmDnYZ7Q9Afbh8IvA94LdAOTAzinXJYfr5iwVU1Tby44snY6ZOASISWwe7jJbk7jvD5c8CM939SeBJM1sW3dLkUL2zuZzHFm3hutNGM2GwBtoUkdg72JlNkpm1BNLZwNyIde293yOdqLGpme8/vYLB/Xtz49map0ZE4sPBAuNR4HUzKyXoffZPADMbC1RGuTY5BP+3cBOrtu3id1+YRlov/XtAROLDAc9s3P2nwM3AX4BT3N0j9vvmof5SM7vczFaaWXPYjTpy3XfNrNDM1pjZJyLazw/bCs3sOxHto8zsLTNbZ2Z/Defdwcx6hc8Lw/UjD7XerqJkVy33vLSWU8flcMHkwbEuR0Rkr4N+z8bdF7r70+4eOR30Wnd/5zB+7wrgUoIx1vYys0nA54AjgfOB35lZUjg8zm+BC4BJwBXhtgB3Afe6+ziCjgvXhu3XAuXuPha4N9wuof3s+dXUNTZzxwx1ChCR+BKTCU3cfXXklAURZgCPuXudu28ACoHp4aPQ3de7ez3wGDDDgk/Us4C/hfvPAi6OeK1Z4fLfgLMtgT+B391SwTPLivnKaaMYlZMW63JERPYRb7NnDQW2RDwvCtvaas8GKty9cb/2fV4rXF8Zbv8RZnadmS02s8U7duzooEPpPO7OnS8UkJWWwtdOHxPrckREPiJqYWNmr5jZilYeMw60WyttfgjtB3qtjza6z3T3fHfPHzBgwAHKi0+vrd3BgvVlfOussfTrrZECRCT+RK27krufcwi7FQHDIp7nAcXhcmvtpUCGmSWHZy+R27e8VlHYfTsd2EmCaWp27nqhgBHZqXz++BGxLkdEpFXxdhltNvC5sCfZKGAc8DawCBgX9jxLIehEMDvsHTcPuCzc/yrg2YjXuipcvgyYG9GbLmE8vXQrBR9Ucct5E0hJjre3U0QkEJNPJzO7xMyKCIbAec7M5gC4+0rgcWAVwfhr33D3pvCs5QZgDrAaeDzcFuBW4CYzKyS4J3N/2H4/kB223wTs7S6dKGobmrjnpTUclZfORVOGxLocEZE2WQL+Y/+w5Ofn++LFi2NdRrv8/vX3ufOFAh75yvGcNCYn1uWISDdmZkvcPb+t9bru0kVV1NTzu3mFnDlhgIJGROKewqaL+u28QqrqGrn1gomxLkVE5KAUNl1QUXkNs/61iX+blsfEwf1jXY6IyEEpbLqge15aixncdO74WJciItIuCpsuZlXxLp5etpWrTx5JbkafWJcjItIuCpsu5s4XC+jfuydfP31srEsREWk3hU0X8mZhKW+s3cENZ44lPVXD0ohI16Gw6SKam53/fmE1QzP68P9O1LA0ItK1KGy6iL+/V8yKrbu4+bzx9O6ZFOtyREQ+FoVNF1DX2MQvX1rDEUP6c/HRQw++g4hInFHYdAEPL9zMlp17+O4FE+nRI2HnfxORBKawiXO7ahv437nrOGVsDqeN73pz7YiIgMIm7s18fT3lNQ18R8PSiEgXprCJY7vrGpm1YCMXThnM5KHpsS5HROSQKWzi2N+WFFFV28iXTx0d61JERA6LwiZONTc7f35zA8cMz2Da8MxYlyMiclgUNnFqbkEJG8tq+NLJo2JdiojIYVPYxKn7528gN703F0weHOtSREQOm8ImDq0q3sWC9WVcedJIkpP0FolI16dPsjj0wJsb6NMziSuOGx7rUkREOoTCJs7sqKpj9rJiLjs2TyM7i0jCUNjEmYcWbqK+qZlrTh4Z61JERDqMwiaO1DY08fBbmzhr4kBGD+gb63JERDqMwiaOzH63mNLqeq49Rd2dRSSxKGzihLvzwPwNTBjUj5PGZMe6HBGRDqWwiRML1pdR8EEVXzplJGaaRkBEEovCJk48MH8DWWkpzNDkaCKSgBQ2cWBD6W5eLSjhi8cP15TPIpKQFDZx4C9vbiC5h/HFE0fEuhQRkahQ2MRY5Z4GnlhSxKem5jKwX+9YlyMiEhUKmxj766LN1NQ3aXRnEUloMQkbM7vczFaaWbOZ5Ue0n2tmS8xsefjzrIh1x4bthWb2PxZ22TKzLDN72czWhT8zw3YLtys0s/fMbFrnH+mBNTY1M+tfmzh+VJZm4hSRhBarM5sVwKXAG/u1lwKfcvcpwFXA/0Wsuw+4DhgXPs4P278DvOru44BXw+cAF0Rse124f1yZs3I7Wyv26EucIpLwYhI27r7a3de00r7U3YvDpyuB3mbWy8yGAP3dfYG7O/AgcHG43QxgVrg8azpnZqUAAA3GSURBVL/2Bz2wEMgIXyduPPDmBoZnpXL2EYNiXYqISFTF8z2bfwOWunsdMBQoilhXFLYBDHL3bQDhz4Fh+1BgSxv7xNyyLRUs2VTONSePJKmHvsQpIoktOVovbGavAK1NM3mbuz97kH2PBO4CzmtpamUzP1gJ7d3HzK4juNTG8OGdM4fMA/M30K9XMpfnD+uU3yciEktRCxt3P+dQ9jOzPOBp4Ep3fz9sLgLyIjbLA1out203syHuvi28TFYSsc+wNvbZv9aZwEyA/Pz8g4XYYdtWuYfnl2/jqpNG0rdX1N4CEZG4EVeX0cwsA3gO+K67v9nSHl4eqzKzE8JeaFcCLWdHswk6ExD+jGy/MuyVdgJQ2XK5LdYeeWszze5cfdLIWJciItIpYtX1+RIzKwJOBJ4zsznhqhuAscB/mdmy8NFyD+Z64E9AIfA+8ELYfidwrpmtA84NnwM8D6wPt/8j8PUoH1a7zVn5AcePymZYVmqsSxER6RQxuYbj7k8TXCrbv/0nwE/a2GcxMLmV9jLg7FbaHfjGYRfbwYrKa1i7vZrbLtS9GhHpPuLqMlp3MG/NDgDOnDjwIFuKiCQOhU0nm1dQwvCsVMYMSIt1KSIinUZh04lqG5r41/ulnDVxoCZIE5FuRWHTiRa8X0ZtQ7MuoYlIt6Ow6URzC0ro0zOJ40dlxboUEZFOpbDpJO7O3IISTh6bo9k4RaTbUdh0knUl1Wyt2MNZuoQmIt2QwqaTzC0IRtE5c+KAGFciItL5FDadZG5BCUcM6c+Q9D6xLkVEpNMpbDpBZU0DSzaVc5bOakSkm1LYdII31u2gqdl1v0ZEui2FTSeYV1BCZmpPjh6WGetSRERiQmETZU3Nzmtrd3D6+AGakVNEui2FTZS9W1TBzt31GjVARLo1hU2UzSsooYfB6ePVOUBEui+FTZTNLSjh2BGZZKSmxLoUEZGYUdhE0fZdtaws3qVLaCLS7SlsomheOGqAujyLSHensImiuQUl5Kb3ZsKgfrEuRUQkphQ2UVLX2MT8wlLO1ERpIiIKm2h5e8NOauqbdAlNRASFTdTMLSihV3IPThqTE+tSRERiTmETJfMKSjhxTDZ9UjRRmoiIwiYK1u+oZmNZjS6hiYiEFDZRsHeitAkKGxERUNhExbw1JYwb2JdhWamxLkVEJC4obDpYdV0jb2/YqUtoIiIRFDYdbP66HTQ0uYaoERGJoLDpYHMLSujXO5ljR2iiNBGRFgqbDtTc7Mxbs4PTxg+gZ5L+04qItNAnYgdaWbyLHVV1nKVeaCIi+1DYdKC5BSWYwRkTNFGaiEikmISNmV1uZivNrNnM8ltZP9zMqs3sloi2881sjZkVmtl3ItpHmdlbZrbOzP5qZilhe6/weWG4fmS0j2vumhKm5mWQ3bdXtH+ViEiXEqszmxXApcAbbay/F3ih5YmZJQG/BS4AJgFXmNmkcPVdwL3uPg4oB64N268Fyt19bPh6d3X0QUQqra7jvaIKdXkWEWlFTMLG3Ve7+5rW1pnZxcB6YGVE83Sg0N3Xu3s98Bgww4Kx+88C/hZuNwu4OFyeET4nXH+2RXGs/9fW7MBdE6WJiLQmru7ZmFkacCtw+36rhgJbIp4XhW3ZQIW7N+7Xvs8+4frKcPvWfu91ZrbYzBbv2LHjkGpP79OTcycN4sjc/oe0v4hIIkuO1gub2SvA4FZW3ebuz7ax2+0El8Sq9zsJae2MxA/QfqB9PtroPhOYCZCfn9/qNgdz7qRBnDtp0KHsKiKS8KIWNu5+ziHsdjxwmZn9HMgAms2sFlgCDIvYLg8oBkqBDDNLDs9eWtohOMsZBhSZWTKQDuw8pIMREZHDErWwORTufmrLspn9CKh299+EYTHOzEYBW4HPAZ93dzezecBlBPdxrgJazppmh88XhOvnuvshnbWIiMjhiVXX50vMrAg4EXjOzOYcaPvwrOUGYA6wGnjc3Vs6ENwK3GRmhQT3ZO4P2+8HssP2m4DvICIiMWH6x/6+8vPzffHixbEuQ0SkSzGzJe7+ke9Ntoir3mgiIpKYFDYiIhJ1ChsREYk6hY2IiESdOgjsx8x2AJsimnIIvs+TiBL12HRcXU+iHluiHhd89NhGuHubQ94rbA7CzBYfqIdFV5aox6bj6noS9dgS9bjg4x+bLqOJiEjUKWxERCTqFDYHNzPWBURRoh6bjqvrSdRjS9Tjgo95bLpnIyIiUaczGxERiTqFjYiIRJ3C5gDM7HwzW2NmhWaWMKNGm9lGM1tuZsvMrEuPOmpmD5hZiZmtiGjLMrOXzWxd+DMzljUeijaO60dmtjV835aZ2YWxrPFQmNkwM5tnZqvNbKWZ3Ri2J8J71taxden3zcx6m9nbZvZueFy3h+2jzOyt8D37q5mlHPB1dM+mdWaWBKwFziWYiG0RcIW7r4ppYR3AzDYC+e7e5b9sZmanAdXAg+4+OWz7ObDT3e8M/5GQ6e63xrLOj6uN4/oRwRxPv4xlbYfDzIYAQ9z9HTPrRzAx4sXA1XT996ytY/sMXfh9s2Da5LRwBuWewHzgRoKpW55y98fM7PfAu+5+X1uvozObtk0HCt19vbvXE0zONiPGNcl+3P0NPjoD6wxgVrg8i+APvktp47i6PHff5u7vhMtVBPNTDSUx3rO2jq1L80B1+LRn+HDgLOBvYftB3zOFTduGAlsinheRAP/jhBx4ycyWmNl1sS4mCga5+zYIPgCAgTGupyPdYGbvhZfZutylpkhmNhI4BniLBHvP9js26OLvm5klmdkyoAR4GXgfqAgntoR2fD4qbNpmrbQlyjXHk919GnAB8I3wko3Ev/uAMcDRwDbg7tiWc+jMrC/wJPBtd98V63o6UivH1uXfN3dvcvejgTyCqz5HtLbZgV5DYdO2ImBYxPM8oDhGtXQody8Of5YATxP8z5NItofXz1uuo5fEuJ4O4e7bwz/6ZuCPdNH3Lbzu/yTwsLs/FTYnxHvW2rElyvsG4O4VwGvACUCGmSWHqw76+aiwadsiYFzY4yIF+BwwO8Y1HTYzSwtvXmJmacB5wIoD79XlzAauCpevAp6NYS0dpuXDOHQJXfB9C2823w+sdvd7IlZ1+fesrWPr6u+bmQ0ws4xwuQ9wDsH9qHnAZeFmB33P1BvtAMIuir8CkoAH3P2nMS7psJnZaIKzGYBk4JGufFxm9ihwBsFw59uBHwLPAI8Dw4HNwOXu3qVutrdxXGcQXIpxYCPw1Zb7HF2FmZ0C/BNYDjSHzd8juLfR1d+zto7tCrrw+2ZmRxF0AEgiOEF53N3vCD9LHgOygKXAF929rs3XUdiIiEi06TKaiIhEncJGRESiTmEjIiJRp7AREZGoU9iIiEjUKWykWzEzN7O7I57fEg5w2RGv/Rczu+zgWx7277k8HFl4XivrxpvZ8+FI5avN7HEzG2RmZ5jZPw7x933bzFIPv3LpzhQ20t3UAZeaWU6sC4kUjjLeXtcCX3f3M/d7jd7Ac8B97j7W3Y8gGCplwGGW923gY4XNxzwe6QYUNtLdNBLMnf7v+6/Y/8zEzKrDn2eY2evhWcJaM7vTzL4QzvGx3MzGRLzMOWb2z3C7T4b7J5nZL8xsUTgY41cjXneemT1C8EXA/eu5Inz9FWZ2V9j2A+AU4Pdm9ov9dvk8sMDd/97S4O7z3H2fb6xbML/KLRHPV5jZyHB0iecsmLdkhZl91sy+BeQC81rOpMzsPDNbYGbvmNkT4VhgLfMk/cDM5gOXm9m3zGxVeMyPHeR9kQSXfPBNRBLOb4H3LJj3pr2mEgw+uBNYD/zJ3adbMEHWNwn+9Q8wEjidYODFeWY2FrgSqHT348ysF/Cmmb0Ubj8dmOzuGyJ/mZnlAncBxwLlBKN0Xxx+c/ss4BZ333/iu8kEc6gcqvOBYne/KKwh3d0rzewm4Ex3Lw3PCL8PnOPuu83sVoJ5Te4IX6PW3U8J9y8GRrl7XctwJ9J96cxGup1wJN4HgW99jN0WhfOV1BEMr94SFssJAqbF4+7e7O7rCEJpIsH4c1eGQ7S/BWQD48Lt394/aELHAa+5+45wGPeHgWiPzr2c4MzsLjM71d0rW9nmBGASQWAuIxgTa0TE+r9GLL8HPGxmXyQ4o5RuTGEj3dWvCO59pEW0NRL+TYSDKkZOcxs55lNzxPNm9r1CsP/4T04wXcU33f3o8DHK3VvCancb9bU2xcXBrCQ4EzqYvccZ6g3g7mvD/ZcD/x1esmutrpcjjmWSu18bsT7yeC4iOIs8FlgSMUKwdEMKG+mWwkEeHycInBYb+fDDegbBjIQf1+Vm1iO8jzMaWAPMAa4Ph59v6TGWdqAXITgDOt3McsKb7VcArx9kn0eAk8zsopYGMzvfzKbst91GYFq4fhowKlzOBWrc/SHgly3bAFVAv3B5IXByeHkQM0s1s/H7F2JmPYBh7j4P+E8gA+h7kPolgelfGtKd3Q3cEPH8j8CzZvY28Cptn3UcyBqCUBgEfM3da83sTwSX2t4Jz5h2cJApdN19m5l9l2AYdwOed/cDDuHu7nvCTgm/MrNfAQ0El7JuJLh01+JJPrystwhYG7ZPAX5hZs3hvteH7TOBF8xsm7ufaWZXA4+G958guIezln0lAQ+ZWXpY/73hXCjSTWnUZxERiTpdRhMRkahT2IiISNQpbEREJOoUNiIiEnUKGxERiTqFjYiIRJ3CRkREou7/Azr4eUTjSHeqAAAAAElFTkSuQmCC\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": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "km = KMeans(random_state=234, n_clusters = 8)\n",
    "res = km.fit(X_kmeans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({1: 326, 7: 281, 4: 249, 2: 201, 3: 200, 0: 135, 5: 102, 6: 59})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter, defaultdict\n",
    "print(Counter(res.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_scaled[\"cluster\"] = res.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"cluster\"] = res.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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",
    "    pc = parallel_coordinates(cluster_points[7], 'cluster', color=[addAlpha(palette[7],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": 44,
   "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], 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": 46,
   "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, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2, the average silhouette_score is : 0.0558500047065838\n",
      "For n_clusters = 3, the average silhouette_score is : 0.055287263751408036\n",
      "For n_clusters = 4, the average silhouette_score is : 0.044762195199507825\n",
      "For n_clusters = 5, the average silhouette_score is : 0.07043559723190648\n",
      "For n_clusters = 6, the average silhouette_score is : 0.024284450144362646\n",
      "For n_clusters = 7, the average silhouette_score is : 0.03998966758079396\n",
      "For n_clusters = 8, the average silhouette_score is : 0.05731062698111126\n",
      "For n_clusters = 9, the average silhouette_score is : 0.04375012572083702\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": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2, the average silhouette_score is : -0.026286252190227427\n",
      "For n_clusters = 3, the average silhouette_score is : -0.04949478721758533\n",
      "For n_clusters = 4, the average silhouette_score is : -0.12763890407045783\n",
      "For n_clusters = 5, the average silhouette_score is : -0.18063491857724043\n",
      "For n_clusters = 6, the average silhouette_score is : -0.31816986855061813\n",
      "For n_clusters = 7, the average silhouette_score is : -0.15809124299702323\n",
      "For n_clusters = 8, the average silhouette_score is : -0.2516462651972893\n",
      "For n_clusters = 9, the average silhouette_score is : -0.17578602325355303\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": 50,
   "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>Number of Beds</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.178806</td>\n",
       "      <td>-0.010990</td>\n",
       "      <td>0.342551</td>\n",
       "      <td>0.132515</td>\n",
       "      <td>-0.026679</td>\n",
       "      <td>-0.134049</td>\n",
       "      <td>0.011389</td>\n",
       "      <td>-0.183384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_sqmi</th>\n",
       "      <td>-0.178806</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009763</td>\n",
       "      <td>-0.200966</td>\n",
       "      <td>0.016894</td>\n",
       "      <td>0.006828</td>\n",
       "      <td>0.225432</td>\n",
       "      <td>0.019218</td>\n",
       "      <td>0.112433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_length_of_stay</th>\n",
       "      <td>-0.010990</td>\n",
       "      <td>-0.009763</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.166569</td>\n",
       "      <td>0.026310</td>\n",
       "      <td>-0.545751</td>\n",
       "      <td>0.019789</td>\n",
       "      <td>-0.069834</td>\n",
       "      <td>-0.091388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_total_charges_day</th>\n",
       "      <td>0.342551</td>\n",
       "      <td>-0.200966</td>\n",
       "      <td>-0.166569</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044907</td>\n",
       "      <td>-0.032213</td>\n",
       "      <td>-0.036067</td>\n",
       "      <td>-0.040837</td>\n",
       "      <td>0.171513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patients</th>\n",
       "      <td>0.132515</td>\n",
       "      <td>0.016894</td>\n",
       "      <td>0.026310</td>\n",
       "      <td>0.044907</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.057569</td>\n",
       "      <td>0.022402</td>\n",
       "      <td>-0.143225</td>\n",
       "      <td>0.013222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>severity</th>\n",
       "      <td>-0.026679</td>\n",
       "      <td>0.006828</td>\n",
       "      <td>-0.545751</td>\n",
       "      <td>-0.032213</td>\n",
       "      <td>0.057569</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.037917</td>\n",
       "      <td>0.009821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>system</th>\n",
       "      <td>-0.134049</td>\n",
       "      <td>0.225432</td>\n",
       "      <td>0.019789</td>\n",
       "      <td>-0.036067</td>\n",
       "      <td>0.022402</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.062116</td>\n",
       "      <td>0.080478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment</th>\n",
       "      <td>0.011389</td>\n",
       "      <td>0.019218</td>\n",
       "      <td>-0.069834</td>\n",
       "      <td>-0.040837</td>\n",
       "      <td>-0.143225</td>\n",
       "      <td>0.037917</td>\n",
       "      <td>-0.062116</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.039406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hospital</th>\n",
       "      <td>-0.183384</td>\n",
       "      <td>0.112433</td>\n",
       "      <td>-0.091388</td>\n",
       "      <td>0.171513</td>\n",
       "      <td>0.013222</td>\n",
       "      <td>0.009821</td>\n",
       "      <td>0.080478</td>\n",
       "      <td>-0.039406</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  -0.178806            -0.010990   \n",
       "area_sqmi                    -0.178806   1.000000            -0.009763   \n",
       "mean_length_of_stay          -0.010990  -0.009763             1.000000   \n",
       "mean_total_charges_day        0.342551  -0.200966            -0.166569   \n",
       "patients                      0.132515   0.016894             0.026310   \n",
       "severity                     -0.026679   0.006828            -0.545751   \n",
       "system                       -0.134049   0.225432             0.019789   \n",
       "payment                       0.011389   0.019218            -0.069834   \n",
       "hospital                     -0.183384   0.112433            -0.091388   \n",
       "\n",
       "                        mean_total_charges_day  patients  severity    system  \\\n",
       "Number of Beds                        0.342551  0.132515 -0.026679 -0.134049   \n",
       "area_sqmi                            -0.200966  0.016894  0.006828  0.225432   \n",
       "mean_length_of_stay                  -0.166569  0.026310 -0.545751  0.019789   \n",
       "mean_total_charges_day                1.000000  0.044907 -0.032213 -0.036067   \n",
       "patients                              0.044907  1.000000  0.057569  0.022402   \n",
       "severity                             -0.032213  0.057569  1.000000  0.000149   \n",
       "system                               -0.036067  0.022402  0.000149  1.000000   \n",
       "payment                              -0.040837 -0.143225  0.037917 -0.062116   \n",
       "hospital                              0.171513  0.013222  0.009821  0.080478   \n",
       "\n",
       "                         payment  hospital  \n",
       "Number of Beds          0.011389 -0.183384  \n",
       "area_sqmi               0.019218  0.112433  \n",
       "mean_length_of_stay    -0.069834 -0.091388  \n",
       "mean_total_charges_day -0.040837  0.171513  \n",
       "patients               -0.143225  0.013222  \n",
       "severity                0.037917  0.009821  \n",
       "system                 -0.062116  0.080478  \n",
       "payment                 1.000000 -0.039406  \n",
       "hospital               -0.039406  1.000000  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[var_list].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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",
       "      <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>-0.178806</td>\n",
       "      <td>-0.010990</td>\n",
       "      <td>0.342551</td>\n",
       "      <td>0.132515</td>\n",
       "      <td>-0.026679</td>\n",
       "      <td>-0.134049</td>\n",
       "      <td>0.011389</td>\n",
       "      <td>-0.183384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_sqmi</th>\n",
       "      <td>-0.178806</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009763</td>\n",
       "      <td>-0.200966</td>\n",
       "      <td>0.016894</td>\n",
       "      <td>0.006828</td>\n",
       "      <td>0.225432</td>\n",
       "      <td>0.019218</td>\n",
       "      <td>0.112433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_length_of_stay</th>\n",
       "      <td>-0.010990</td>\n",
       "      <td>-0.009763</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.166569</td>\n",
       "      <td>0.026310</td>\n",
       "      <td>-0.545751</td>\n",
       "      <td>0.019789</td>\n",
       "      <td>-0.069834</td>\n",
       "      <td>-0.091388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_total_charges_day</th>\n",
       "      <td>0.342551</td>\n",
       "      <td>-0.200966</td>\n",
       "      <td>-0.166569</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044907</td>\n",
       "      <td>-0.032213</td>\n",
       "      <td>-0.036067</td>\n",
       "      <td>-0.040837</td>\n",
       "      <td>0.171513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patients</th>\n",
       "      <td>0.132515</td>\n",
       "      <td>0.016894</td>\n",
       "      <td>0.026310</td>\n",
       "      <td>0.044907</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.057569</td>\n",
       "      <td>0.022402</td>\n",
       "      <td>-0.143225</td>\n",
       "      <td>0.013222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>severity</th>\n",
       "      <td>-0.026679</td>\n",
       "      <td>0.006828</td>\n",
       "      <td>-0.545751</td>\n",
       "      <td>-0.032213</td>\n",
       "      <td>0.057569</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.037917</td>\n",
       "      <td>0.009821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>system</th>\n",
       "      <td>-0.134049</td>\n",
       "      <td>0.225432</td>\n",
       "      <td>0.019789</td>\n",
       "      <td>-0.036067</td>\n",
       "      <td>0.022402</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.062116</td>\n",
       "      <td>0.080478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment</th>\n",
       "      <td>0.011389</td>\n",
       "      <td>0.019218</td>\n",
       "      <td>-0.069834</td>\n",
       "      <td>-0.040837</td>\n",
       "      <td>-0.143225</td>\n",
       "      <td>0.037917</td>\n",
       "      <td>-0.062116</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.039406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hospital</th>\n",
       "      <td>-0.183384</td>\n",
       "      <td>0.112433</td>\n",
       "      <td>-0.091388</td>\n",
       "      <td>0.171513</td>\n",
       "      <td>0.013222</td>\n",
       "      <td>0.009821</td>\n",
       "      <td>0.080478</td>\n",
       "      <td>-0.039406</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  -0.178806            -0.010990   \n",
       "area_sqmi                    -0.178806   1.000000            -0.009763   \n",
       "mean_length_of_stay          -0.010990  -0.009763             1.000000   \n",
       "mean_total_charges_day        0.342551  -0.200966            -0.166569   \n",
       "patients                      0.132515   0.016894             0.026310   \n",
       "severity                     -0.026679   0.006828            -0.545751   \n",
       "system                       -0.134049   0.225432             0.019789   \n",
       "payment                       0.011389   0.019218            -0.069834   \n",
       "hospital                     -0.183384   0.112433            -0.091388   \n",
       "\n",
       "                        mean_total_charges_day  patients  severity    system  \\\n",
       "Number of Beds                        0.342551  0.132515 -0.026679 -0.134049   \n",
       "area_sqmi                            -0.200966  0.016894  0.006828  0.225432   \n",
       "mean_length_of_stay                  -0.166569  0.026310 -0.545751  0.019789   \n",
       "mean_total_charges_day                1.000000  0.044907 -0.032213 -0.036067   \n",
       "patients                              0.044907  1.000000  0.057569  0.022402   \n",
       "severity                             -0.032213  0.057569  1.000000  0.000149   \n",
       "system                               -0.036067  0.022402  0.000149  1.000000   \n",
       "payment                              -0.040837 -0.143225  0.037917 -0.062116   \n",
       "hospital                              0.171513  0.013222  0.009821  0.080478   \n",
       "\n",
       "                         payment  hospital  \n",
       "Number of Beds          0.011389 -0.183384  \n",
       "area_sqmi               0.019218  0.112433  \n",
       "mean_length_of_stay    -0.069834 -0.091388  \n",
       "mean_total_charges_day -0.040837  0.171513  \n",
       "patients               -0.143225  0.013222  \n",
       "severity                0.037917  0.009821  \n",
       "system                 -0.062116  0.080478  \n",
       "payment                 1.000000 -0.039406  \n",
       "hospital               -0.039406  1.000000  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled[var_list].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# With PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_scaled_pca = X_scaled[var_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": 54,
     "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": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.18067536, 0.17394925, 0.13903149, 0.12209589, 0.10574664,\n",
       "       0.09557177, 0.08138173, 0.05720924, 0.04433864])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.18067536, 0.35462461, 0.4936561 , 0.61575199, 0.72149862,\n",
       "       0.81707039, 0.89845212, 0.95566136, 1.        ])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": 58,
   "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": 59,
   "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": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for max_depth = 1: 0.49356223175965663\n",
      "for max_depth = 2: 0.6051502145922747\n",
      "for max_depth = 3: 0.7124463519313304\n",
      "for max_depth = 4: 0.6909871244635193\n",
      "for max_depth = 5: 0.7381974248927039\n",
      "for max_depth = 6: 0.7467811158798283\n",
      "for max_depth = 7: 0.7532188841201717\n",
      "for max_depth = 8: 0.7682403433476395\n",
      "for max_depth = 9: 0.7381974248927039\n",
      "for max_depth = 10: 0.721030042918455\n",
      "for max_depth = 11: 0.7167381974248928\n",
      "for max_depth = 12: 0.7103004291845494\n",
      "for max_depth = 13: 0.7124463519313304\n",
      "for max_depth = 14: 0.7060085836909872\n",
      "for max_depth = 15: 0.7103004291845494\n",
      "for max_depth = 16: 0.6974248927038627\n",
      "for max_depth = 17: 0.7167381974248928\n",
      "for max_depth = 18: 0.7081545064377682\n",
      "for max_depth = 19: 0.6974248927038627\n",
      "for max_depth = 20: 0.7188841201716738\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": 61,
   "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": 62,
   "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': 7}\n",
      "0.7489270386266095\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": 63,
   "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 = 1087, value = [351, 368, 368], class = low\", fillcolor=\"#ffffff\"] ;\n",
      "1 [label=\"system <= 1.0, samples = 320, value = [17, 205, 98], class = low\", fillcolor=\"#a0f2c2\"] ;\n",
      "0 -> 1 [labeldistance=2.5, labelangle=45, headlabel=\"True\"] ;\n",
      "2 [label=\"samples = 139, value = [1, 59, 79], class = medium\", fillcolor=\"#e0cef8\"] ;\n",
      "1 -> 2 ;\n",
      "3 [label=\"samples = 181, value = [16, 146, 19], class = low\", fillcolor=\"#64eb9c\"] ;\n",
      "1 -> 3 ;\n",
      "4 [label=\"hospital <= 38.5, samples = 767, value = [334, 163, 270], class = high\", fillcolor=\"#fcefe6\"] ;\n",
      "0 -> 4 [labeldistance=2.5, labelangle=-45, headlabel=\"False\"] ;\n",
      "5 [label=\"samples = 547, value = [310, 66, 171], class = high\", fillcolor=\"#f5d0b6\"] ;\n",
      "4 -> 5 ;\n",
      "6 [label=\"samples = 220, value = [24, 97, 99], class = medium\", fillcolor=\"#fdfcff\"] ;\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": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 64,
     "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": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7510729613733905\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": 66,
   "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': 18}\n",
      "0.7660944206008584\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": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.21454014, 0.25557256, 0.0504924 , 0.0113852 , 0.00560315,\n",
       "       0.20579801, 0.00283667, 0.25377188])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_pred.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "severity = pd.get_dummies(heart['apr_severity_of_illness_description'])\n",
    "system = pd.get_dummies(heart['System'])\n",
    "payment = pd.get_dummies(heart['payment_typology_1'])\n",
    "hospital_facility = pd.get_dummies(heart['facility_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reg = pd.concat([heart, severity, system, payment, hospital_facility], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1553, 76)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_list = list(df_reg.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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": 76,
   "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": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reg.columns = df_reg.columns.str.replace(' ', '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reg.columns = df_reg.columns.str.replace(\"'\", '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reg.columns = df_reg.columns.str.replace(\"-\", '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reg.columns = df_reg.columns.str.replace(\".\", '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reg.columns = df_reg.columns.str.replace(\"/\", '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reg.columns = df_reg.columns.str.replace(\",\", '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reg.columns = df_reg.columns.str.replace(\"&\", '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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', 'Hospital_for_Special_Surgery',\n",
       "       'Interfaith_Medical_Center', 'Jacobi_Medical_Center',\n",
       "       'Jamaica_Hospital_Medical_Center', 'Kings_County_Hospital_Center',\n",
       "       'Kingsbrook_Jewish_Medical_Center', 'Lenox_Hill_Hospital',\n",
       "       '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": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reg.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_str = (listToString(reg_var)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_str = reg_str[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_str = reg_str.replace(\" \", \"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_str = reg_str.replace(\"'\", \"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_str = reg_str.replace(\"-\", \"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_str = reg_str.replace(\".\", \"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_str = reg_str.replace(\"/\", \"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_str = reg_str.replace(\",\", \"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_str = reg_str.replace(\"&\", \"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('results.csv', 'w') as fh:\n",
    "    fh.write(lm.summary().as_csv())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>   0.369</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                      <td>OLS</td>          <th>  Adj. R-squared:    </th> <td>   0.364</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>Least Squares</td>     <th>  F-statistic:       </th> <td>   69.21</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                <td>Sat, 02 May 2020</td>    <th>  Prob (F-statistic):</th> <td>1.60e-143</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                    <td>15:24:14</td>        <th>  Log-Likelihood:    </th> <td> -14584.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>         <td>  1553</td>         <th>  AIC:               </th> <td>2.920e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>             <td>  1539</td>         <th>  BIC:               </th> <td>2.927e+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> 7063.5144</td> <td>  206.162</td> <td>   34.262</td> <td> 0.000</td> <td> 6659.126</td> <td> 7467.903</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number_of_Beds</th>           <td>    2.8742</td> <td>    0.364</td> <td>    7.895</td> <td> 0.000</td> <td>    2.160</td> <td>    3.588</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>area_sqmi</th>                <td> -130.5799</td> <td>   15.210</td> <td>   -8.585</td> <td> 0.000</td> <td> -160.415</td> <td> -100.744</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mean_length_of_stay</th>      <td> -119.7644</td> <td>   17.833</td> <td>   -6.716</td> <td> 0.000</td> <td> -154.745</td> <td>  -84.784</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>patients</th>                 <td>    1.1461</td> <td>    1.524</td> <td>    0.752</td> <td> 0.452</td> <td>   -1.843</td> <td>    4.135</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mount_Sinai</th>              <td> -517.0916</td> <td>  212.449</td> <td>   -2.434</td> <td> 0.015</td> <td> -933.811</td> <td> -100.372</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>NYU_Langone</th>              <td> 2990.7577</td> <td>  347.903</td> <td>    8.597</td> <td> 0.000</td> <td> 2308.345</td> <td> 3673.171</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>NewYork_Presbyterian</th>     <td>  411.3398</td> <td>  374.067</td> <td>    1.100</td> <td> 0.272</td> <td> -322.395</td> <td> 1145.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Northwell</th>                <td> 3980.4759</td> <td>  290.556</td> <td>   13.700</td> <td> 0.000</td> <td> 3410.549</td> <td> 4550.403</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Others</th>                   <td>-2037.6185</td> <td>  228.990</td> <td>   -8.898</td> <td> 0.000</td> <td>-2486.784</td> <td>-1588.453</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SUNY</th>                     <td> -187.7535</td> <td>  323.439</td> <td>   -0.580</td> <td> 0.562</td> <td> -822.182</td> <td>  446.675</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Blue_Cross_Blue_Shield</th>   <td> 2129.5674</td> <td>  145.376</td> <td>   14.649</td> <td> 0.000</td> <td> 1844.412</td> <td> 2414.723</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Medicare</th>                 <td> 1460.9696</td> <td>  158.329</td> <td>    9.227</td> <td> 0.000</td> <td> 1150.406</td> <td> 1771.533</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Private_Health_Insurance</th> <td> 1838.3585</td> <td>  136.314</td> <td>   13.486</td> <td> 0.000</td> <td> 1570.979</td> <td> 2105.738</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Self_Pay</th>                 <td> 1634.6190</td> <td>  164.414</td> <td>    9.942</td> <td> 0.000</td> <td> 1312.121</td> <td> 1957.117</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>301.398</td> <th>  Durbin-Watson:     </th> <td>   0.752</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 688.657</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.077</td>  <th>  Prob(JB):          </th> <td>2.88e-150</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.450</td>  <th>  Cond. No.          </th> <td>6.07e+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 1.17e-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:                       0.369\n",
       "Model:                                OLS   Adj. R-squared:                  0.364\n",
       "Method:                     Least Squares   F-statistic:                     69.21\n",
       "Date:                    Sat, 02 May 2020   Prob (F-statistic):          1.60e-143\n",
       "Time:                            15:24:14   Log-Likelihood:                -14584.\n",
       "No. Observations:                    1553   AIC:                         2.920e+04\n",
       "Df Residuals:                        1539   BIC:                         2.927e+04\n",
       "Df Model:                              13                                         \n",
       "Covariance Type:                nonrobust                                         \n",
       "============================================================================================\n",
       "                               coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------\n",
       "Intercept                 7063.5144    206.162     34.262      0.000    6659.126    7467.903\n",
       "Number_of_Beds               2.8742      0.364      7.895      0.000       2.160       3.588\n",
       "area_sqmi                 -130.5799     15.210     -8.585      0.000    -160.415    -100.744\n",
       "mean_length_of_stay       -119.7644     17.833     -6.716      0.000    -154.745     -84.784\n",
       "patients                     1.1461      1.524      0.752      0.452      -1.843       4.135\n",
       "Mount_Sinai               -517.0916    212.449     -2.434      0.015    -933.811    -100.372\n",
       "NYU_Langone               2990.7577    347.903      8.597      0.000    2308.345    3673.171\n",
       "NewYork_Presbyterian       411.3398    374.067      1.100      0.272    -322.395    1145.074\n",
       "Northwell                 3980.4759    290.556     13.700      0.000    3410.549    4550.403\n",
       "Others                   -2037.6185    228.990     -8.898      0.000   -2486.784   -1588.453\n",
       "SUNY                      -187.7535    323.439     -0.580      0.562    -822.182     446.675\n",
       "Blue_Cross_Blue_Shield    2129.5674    145.376     14.649      0.000    1844.412    2414.723\n",
       "Medicare                  1460.9696    158.329      9.227      0.000    1150.406    1771.533\n",
       "Private_Health_Insurance  1838.3585    136.314     13.486      0.000    1570.979    2105.738\n",
       "Self_Pay                  1634.6190    164.414      9.942      0.000    1312.121    1957.117\n",
       "==============================================================================\n",
       "Omnibus:                      301.398   Durbin-Watson:                   0.752\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              688.657\n",
       "Skew:                           1.077   Prob(JB):                    2.88e-150\n",
       "Kurtosis:                       5.450   Cond. No.                     6.07e+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 1.17e-29. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 98,
     "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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