DFW Occupied Hospital Bed Forecasting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# About this notebook\n",
    "### The machine learning model trained in this notebook forecasts the daily # of occupied hospital beds in the Dallas-Forth Worth region based on past data.\n",
    "\n",
    "The data was obtained from [Combined Hospital Data over Time by Trauma Service Area (TSA)](https://dshs.texas.gov/coronavirus/additionaldata.aspx). **When given a list of future dates as input, the model forecasts the expected # of hospital beds that will be occupied on those days and also provides a plausible range of occupied beds.**\n",
    "\n",
    "### There are 3 parts to this notebook\n",
    "1. **[Part 1:](http://localhost:8888/notebooks/Documents/Competitions/Dallas%20Data%20Challenge/Occupied%20Bed%20Forecasting.ipynb#Part-1)** Visualizing # of occupied hospital beds vs available hospital beds in DFW\n",
    "2. **[Part 2:](http://localhost:8888/notebooks/Documents/Competitions/Dallas%20Data%20Challenge/Occupied%20Bed%20Forecasting.ipynb#Part-2---Creating-a-Model)** Training the model on dates before September, then evaluating its accuracy at forecasting the # of occupied beds available on September dates *(for which data is available)*.\n",
    "3. [**Part 3:**](http://localhost:8888/notebooks/Documents/Competitions/Dallas%20Data%20Challenge/Occupied%20Bed%20Forecasting.ipynb#Part-3---The-Future) Training the model on all available data *(including September)* and **forecasting the # of beds occupied for future dates: the next 30 days.**\n",
    "\n",
    "## Note:\n",
    "The process of training the model for occupied bed prediction is very similar to available bed prediction from the **DFW Available Hospital Bed Forecasting** notebook and that's why there are fewer comments in this notebook. For detailed comments and explanations of the process, please refer to the **DFW Available Hospital Bed Forecasting** Jupyter Notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1\n",
    "## Visualizing occupied vs available hospital beds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "from pandas import DataFrame\n",
    "from pandas import to_datetime\n",
    "from pandas import read_csv\n",
    "from pandas import to_datetime\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "import os\n",
    "# fbprophet is Facebook's open source library for time series prediction\n",
    "from fbprophet import Prophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the data from the occupied_beds.csv and available_beds.csv files\n",
    "occupied = pd.read_csv(os.path.join(\"occupied_beds.csv\"), engine='python')\n",
    "occupied = occupied.transpose() # switch rows and columns\n",
    "available = pd.read_csv(os.path.join(\"available_beds.csv\"), engine='python')\n",
    "available = available.transpose() # switch rows and columns\n",
    "\n",
    "# Given a string, this function returns a number. The reason for this is because when splitting the data\n",
    "#.   by spaces, it contains commas and new-line characters.\n",
    "def getNumberFromString(number):\n",
    "    s = \"\"\n",
    "    for pos in range(0, len(bed_number)):\n",
    "        if(not bed_number[pos].isdigit()):\n",
    "            break\n",
    "        s += bed_number[pos]\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x1: List of # of occupied hospital beds\n",
    "# x2: List of # of available hospital beds\n",
    "x1 = []\n",
    "x2 = []\n",
    "# The \"Dallas/Ft.Worth\" can be switched out with any city in the dataset\n",
    "\n",
    "for bed_number in str(occupied[\"Dallas/Ft. Worth\"]).split(\" \"):\n",
    "    if(not bed_number[:1].isdigit()):\n",
    "        continue\n",
    "    x1.append(int(getNumberFromString(bed_number)))\n",
    "    \n",
    "for bed_number in str(available[\"Dallas/Ft. Worth\"]).split(\" \"):\n",
    "    if(not bed_number[:1].isdigit()):\n",
    "        continue\n",
    "    x2.append(int(getNumberFromString(bed_number)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 21600x10800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1296x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max # of occupied beds: 19753\n"
     ]
    }
   ],
   "source": [
    "figure(num=None, figsize=(18, 9), dpi=1200, facecolor='w', edgecolor='k')\n",
    "plt.figure(figsize = (18,9))\n",
    "import matplotlib.patches as mpatches\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.set_title(\"Occupied vs Available Hospital Beds in DFW\", fontsize = 17)\n",
    "\n",
    "color = 'tab:blue'\n",
    "ax1.set_xlabel('Days since April 13', fontsize=16)\n",
    "ax1.set_ylabel('# of occupied beds', color=color,fontsize=15)\n",
    "ax1.plot(x1, color = 'deepskyblue')\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "ax1.legend(loc=\"upper right\")\n",
    "\n",
    "blue_patch = mpatches.Patch(color='deepskyblue', label='Occupied')\n",
    "red_patch = mpatches.Patch(color='red', label='Available')\n",
    "plt.legend(bbox_to_anchor=(1, 1), loc=1, borderaxespad=0, handles=[blue_patch, red_patch])\n",
    "\n",
    "ax2 = ax1.twinx()  \n",
    "color = 'red'\n",
    "ax2.set_ylabel('# of available beds', color='tab:red',fontsize=15)\n",
    "ax2.plot(x2, color = color)\n",
    "ax2.tick_params(axis='y', labelcolor='tab:red')\n",
    "plt.show()\n",
    "# fig.savefig('Occupied vs Available Hospital Beds in DFW.png', dpi=1200)\n",
    "print(\"Max # of occupied beds:\", max(x1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2 - Creating a Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of Dates: 142 \n",
      "From 2020-04-12 to 2020-08-31\n",
      "# of Dates to Forecast: 22 \n",
      "From 2020-09-01 to 2020-09-22\n"
     ]
    }
   ],
   "source": [
    "x = x1\n",
    "\n",
    "end_date = pd.to_datetime(\"2020-04-12\") + pd.DateOffset(days=len(x) - 1)\n",
    "dates = pd.date_range(start=\"2020-04-12\",end=end_date)\n",
    "dates = [str(i)[:10] for i in dates]\n",
    "\n",
    "# This code slices the last 22 days off the training data and saves it for later for the model to forecast\n",
    "# At the time of coding this, the testing data spans from September 1st to 22nd\n",
    "testD = dates[-22:]\n",
    "testX = x[-22:]\n",
    "dates = dates[:-22]\n",
    "x = x[:-22]\n",
    "print(\"# of Dates:\", len(dates), \"\\nFrom\", dates[0], \"to\", dates[len(dates) - 1])\n",
    "print(\"# of Dates to Forecast:\", len(testD), \"\\nFrom\", testD[0], \"to\", testD[len(testD) - 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Dates</th>\n",
       "      <th>Beds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-04-12</td>\n",
       "      <td>7347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-04-13</td>\n",
       "      <td>8219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-04-14</td>\n",
       "      <td>8589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-15</td>\n",
       "      <td>8811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>9014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>2020-08-27</td>\n",
       "      <td>11343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>11636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2020-08-29</td>\n",
       "      <td>11545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>11225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>11250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Dates   Beds\n",
       "0    2020-04-12   7347\n",
       "1    2020-04-13   8219\n",
       "2    2020-04-14   8589\n",
       "3    2020-04-15   8811\n",
       "4    2020-04-16   9014\n",
       "..          ...    ...\n",
       "137  2020-08-27  11343\n",
       "138  2020-08-28  11636\n",
       "139  2020-08-29  11545\n",
       "140  2020-08-30  11225\n",
       "141  2020-08-31  11250\n",
       "\n",
       "[142 rows x 2 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a pandas DataFrame object from the list of dates and hospital beds\n",
    "df = pd.DataFrame({'Dates' : dates, 'Beds' : x})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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": [
    "df.plot()\n",
    "plt.title(\"# of Hospital Beds Occupied in DFW\", fontsize=18)\n",
    "plt.xlabel('Day since April 13',fontsize=15)\n",
    "plt.ylabel('# of Beds Occupied',fontsize=18)\n",
    "# plt.savefig('Hospital Beds Occupied.png', dpi=1200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model\n",
    "##### For a detailed explanation of what's happening, please refer to the **DFW Available Hospital Bed Forecasting** notebook. \n",
    "\n",
    "What I am doing below is training the model on the data (dates and the # of occupied hospital beds on each date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x7fcdcf6ac3d0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prophet requires the column names to be \"ds\" and \"y\"\n",
    "df.columns = ['ds', 'y']\n",
    "# make the model\n",
    "model = Prophet()\n",
    "# train the model on the dataset (in this case dataframe df)\n",
    "model.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate a list of future dates for the model to predict\n",
    "future = DataFrame(testD)\n",
    "future.columns = ['ds']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# forecast contains the predictions for the dates we provided\n",
    "forecast = model.predict(future)\n",
    "\n",
    "# plot forecast\n",
    "model.plot(forecast)\n",
    "plt.title(\"Forecasted # of Occupied Hospital Beds in DFW for September\", fontsize=17)\n",
    "plt.xlabel('Dates',fontsize=17)\n",
    "plt.ylabel('# of Beds Occupied',fontsize=23)\n",
    "# plt.savefig('September Occupied Forecast.png', dpi=1200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the Model\n",
    "After having trained the model and having it predict the daily # of occupied beds from September 1st to 22nd, in this step I will evaluate the model's performance.\n",
    "\n",
    "Among the useful information the model provides is the **expected # of occupied beds** and **an expected range of occupied beds** *(interval)*.\n",
    "\n",
    "Below are some of the metrics I am examining\n",
    "\n",
    "- Percent Error\n",
    "- Mean Absolute Error (MAE)\n",
    "- Percentage of Dates within predicted *interval*\n",
    "\n",
    "### 1. Visualizing Forecasts vs Actual # of Occupied Beds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       # of Beds Forecasted vs Actual\n"
     ]
    },
    {
     "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>Dates</th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Difference</th>\n",
       "      <th>Intervals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>10809</td>\n",
       "      <td>10779</td>\n",
       "      <td>30</td>\n",
       "      <td>9426 - 12196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>11431</td>\n",
       "      <td>11358</td>\n",
       "      <td>73</td>\n",
       "      <td>9925 - 12756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>11790</td>\n",
       "      <td>12017</td>\n",
       "      <td>-227</td>\n",
       "      <td>10596 - 13481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-09-04</td>\n",
       "      <td>11869</td>\n",
       "      <td>11774</td>\n",
       "      <td>95</td>\n",
       "      <td>10395 - 13189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-09-05</td>\n",
       "      <td>11645</td>\n",
       "      <td>11552</td>\n",
       "      <td>93</td>\n",
       "      <td>10231 - 12935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-09-06</td>\n",
       "      <td>11308</td>\n",
       "      <td>10411</td>\n",
       "      <td>897</td>\n",
       "      <td>8984 - 11742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-09-07</td>\n",
       "      <td>10760</td>\n",
       "      <td>10363</td>\n",
       "      <td>397</td>\n",
       "      <td>8977 - 11787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-09-08</td>\n",
       "      <td>10705</td>\n",
       "      <td>10738</td>\n",
       "      <td>-33</td>\n",
       "      <td>9343 - 12291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-09-09</td>\n",
       "      <td>10723</td>\n",
       "      <td>11318</td>\n",
       "      <td>-595</td>\n",
       "      <td>9878 - 12823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-09-10</td>\n",
       "      <td>11249</td>\n",
       "      <td>11976</td>\n",
       "      <td>-727</td>\n",
       "      <td>10546 - 13340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-09-11</td>\n",
       "      <td>11669</td>\n",
       "      <td>11734</td>\n",
       "      <td>-65</td>\n",
       "      <td>10230 - 13223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-09-12</td>\n",
       "      <td>11376</td>\n",
       "      <td>11512</td>\n",
       "      <td>-136</td>\n",
       "      <td>10060 - 12972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-09-13</td>\n",
       "      <td>11207</td>\n",
       "      <td>10371</td>\n",
       "      <td>836</td>\n",
       "      <td>8961 - 11809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020-09-14</td>\n",
       "      <td>10994</td>\n",
       "      <td>10323</td>\n",
       "      <td>671</td>\n",
       "      <td>8811 - 11717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-09-15</td>\n",
       "      <td>10927</td>\n",
       "      <td>10698</td>\n",
       "      <td>229</td>\n",
       "      <td>9348 - 12097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020-09-16</td>\n",
       "      <td>11637</td>\n",
       "      <td>11278</td>\n",
       "      <td>359</td>\n",
       "      <td>9903 - 12777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020-09-17</td>\n",
       "      <td>11939</td>\n",
       "      <td>11936</td>\n",
       "      <td>3</td>\n",
       "      <td>10492 - 13520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020-09-18</td>\n",
       "      <td>12200</td>\n",
       "      <td>11693</td>\n",
       "      <td>507</td>\n",
       "      <td>10245 - 13111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-09-19</td>\n",
       "      <td>12155</td>\n",
       "      <td>11471</td>\n",
       "      <td>684</td>\n",
       "      <td>9993 - 12899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020-09-20</td>\n",
       "      <td>11906</td>\n",
       "      <td>10330</td>\n",
       "      <td>1576</td>\n",
       "      <td>8790 - 11740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020-09-21</td>\n",
       "      <td>11480</td>\n",
       "      <td>10283</td>\n",
       "      <td>1197</td>\n",
       "      <td>8823 - 11585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020-09-22</td>\n",
       "      <td>11652</td>\n",
       "      <td>10658</td>\n",
       "      <td>994</td>\n",
       "      <td>9206 - 12145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Dates  Actual  Predicted  Difference      Intervals\n",
       "0   2020-09-01   10809      10779          30   9426 - 12196\n",
       "1   2020-09-02   11431      11358          73   9925 - 12756\n",
       "2   2020-09-03   11790      12017        -227  10596 - 13481\n",
       "3   2020-09-04   11869      11774          95  10395 - 13189\n",
       "4   2020-09-05   11645      11552          93  10231 - 12935\n",
       "5   2020-09-06   11308      10411         897   8984 - 11742\n",
       "6   2020-09-07   10760      10363         397   8977 - 11787\n",
       "7   2020-09-08   10705      10738         -33   9343 - 12291\n",
       "8   2020-09-09   10723      11318        -595   9878 - 12823\n",
       "9   2020-09-10   11249      11976        -727  10546 - 13340\n",
       "10  2020-09-11   11669      11734         -65  10230 - 13223\n",
       "11  2020-09-12   11376      11512        -136  10060 - 12972\n",
       "12  2020-09-13   11207      10371         836   8961 - 11809\n",
       "13  2020-09-14   10994      10323         671   8811 - 11717\n",
       "14  2020-09-15   10927      10698         229   9348 - 12097\n",
       "15  2020-09-16   11637      11278         359   9903 - 12777\n",
       "16  2020-09-17   11939      11936           3  10492 - 13520\n",
       "17  2020-09-18   12200      11693         507  10245 - 13111\n",
       "18  2020-09-19   12155      11471         684   9993 - 12899\n",
       "19  2020-09-20   11906      10330        1576   8790 - 11740\n",
       "20  2020-09-21   11480      10283        1197   8823 - 11585\n",
       "21  2020-09-22   11652      10658         994   9206 - 12145"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intervals = [] # Intervals of bed predictions\n",
    "predicted = [] # Predicted # of occupied beds\n",
    "differences = [] # Difference (actual - predicted)\n",
    "\n",
    "# For each date that the model had to forecast, I get the predicted number of beds, and the confidence interval (or range of beds for that day)\n",
    "for count, (pred, lower, upper) in enumerate(zip(forecast['yhat'], forecast['yhat_lower'], forecast['yhat_upper'])):\n",
    "    intervals.append(\"{:.0f} - {:.0f}\".format(lower, upper))\n",
    "    differences.append(testX[count] - int(round(pred)))\n",
    "    predicted.append(int(round(pred)))\n",
    "\n",
    "# data frame of the summary between actual # and forecasted # of occupied beds\n",
    "summary = pd.DataFrame({'Dates' : testD, 'Actual': testX, 'Predicted' : predicted, \n",
    "                       'Difference' : differences, 'Intervals' : intervals})\n",
    "# Displaying the dataframe\n",
    "print(\"       # of Beds Forecasted vs Actual\")\n",
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------\n",
      "AVERAGE PERCENT ERROR: 2.7 %\n",
      "MEAN ABSOLUTE ERROR: 474 beds\n",
      "\n",
      "On 95% of the days (21 out of 22 days), the model's forecast interval of the # of occupied beds contained the actual # of beds available on that day.\n",
      "---------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Dates</th>\n",
       "      <th>Percent Error</th>\n",
       "      <th>Accurate Forecast Interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>0.3%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>0.6%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-09-03</td>\n",
       "      <td>-1.9%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-09-04</td>\n",
       "      <td>0.8%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-09-05</td>\n",
       "      <td>0.8%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-09-06</td>\n",
       "      <td>7.9%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-09-07</td>\n",
       "      <td>3.7%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-09-08</td>\n",
       "      <td>-0.3%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-09-09</td>\n",
       "      <td>-5.5%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-09-10</td>\n",
       "      <td>-6.5%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-09-11</td>\n",
       "      <td>-0.6%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-09-12</td>\n",
       "      <td>-1.2%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-09-13</td>\n",
       "      <td>7.5%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020-09-14</td>\n",
       "      <td>6.1%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-09-15</td>\n",
       "      <td>2.1%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020-09-16</td>\n",
       "      <td>3.1%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020-09-17</td>\n",
       "      <td>0.0%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020-09-18</td>\n",
       "      <td>4.2%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-09-19</td>\n",
       "      <td>5.6%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020-09-20</td>\n",
       "      <td>13.2%</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020-09-21</td>\n",
       "      <td>10.4%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020-09-22</td>\n",
       "      <td>8.5%</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Dates Percent Error  Accurate Forecast Interval\n",
       "0   2020-09-01          0.3%                        True\n",
       "1   2020-09-02          0.6%                        True\n",
       "2   2020-09-03         -1.9%                        True\n",
       "3   2020-09-04          0.8%                        True\n",
       "4   2020-09-05          0.8%                        True\n",
       "5   2020-09-06          7.9%                        True\n",
       "6   2020-09-07          3.7%                        True\n",
       "7   2020-09-08         -0.3%                        True\n",
       "8   2020-09-09         -5.5%                        True\n",
       "9   2020-09-10         -6.5%                        True\n",
       "10  2020-09-11         -0.6%                        True\n",
       "11  2020-09-12         -1.2%                        True\n",
       "12  2020-09-13          7.5%                        True\n",
       "13  2020-09-14          6.1%                        True\n",
       "14  2020-09-15          2.1%                        True\n",
       "15  2020-09-16          3.1%                        True\n",
       "16  2020-09-17          0.0%                        True\n",
       "17  2020-09-18          4.2%                        True\n",
       "18  2020-09-19          5.6%                        True\n",
       "19  2020-09-20         13.2%                       False\n",
       "20  2020-09-21         10.4%                        True\n",
       "21  2020-09-22          8.5%                        True"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error = []\n",
    "absolute_error = 0 # Mean Absolute Error\n",
    "contains = [] # True or False based on whether a forecasted interval contains the actual # of occupied beds\n",
    "average = 0\n",
    "for i in range (0, len(predicted)):\n",
    "    # Calculate percent error for each date.  testX is a list of the actual # of beds\n",
    "    average += (testX[i] - predicted[i])/testX[i] * 100\n",
    "    error.append(round(((testX[i] - predicted[i])/testX[i] * 100), 1))\n",
    "    absolute_error += abs(testX[i] - predicted[i])\n",
    "    \n",
    "    # Determining whether forecast interval contains actual # of beds\n",
    "    lower_bound = forecast['yhat_lower'][i]\n",
    "    upper_bound = forecast['yhat_upper'][i]\n",
    "    contains.append((lower_bound <= testX[i] and testX[i] <= upper_bound))\n",
    "\n",
    "# calculate average percent error\n",
    "average = round(average/len(error),1)\n",
    "# calculate mean absolute error\n",
    "absolute_error = round(absolute_error / len(error))\n",
    "\n",
    "# Count # of forecasted intervals that contained the actual # of occupied beds\n",
    "numberCorrect = sum(interval for interval in contains)\n",
    "percentCorrect = int((numberCorrect * 100)/len(contains))\n",
    "print(\"---------------------------------\")\n",
    "print(\"AVERAGE PERCENT ERROR:\", average, \"%\")\n",
    "print(\"MEAN ABSOLUTE ERROR:\", absolute_error, \"beds\\n\")\n",
    "print(\"On {0}% of the days ({1} out of 22 days), the model's forecast interval of the # of occupied beds contained the actual # of beds available on that day.\".format(percentCorrect, numberCorrect))\n",
    "print(\"---------------------------------\")\n",
    "\n",
    "results = pd.DataFrame({'Dates' : testD, 'Percent Error': [str(e) + \"%\" for e in error],\n",
    "                        'Accurate Forecast Interval' : contains})\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results/Conclusion\n",
    "- The model had an **average percent of error of about 2.7%.** Taking into consideration the 2 outliers on July 23rd, and 26th, this is a good result.\n",
    "- Additionally, when predicting the # of beds occupied for the first 22 days in September, the model provided an expected range of occupied beds. For **21 out of the 22 days (95%)**, this expected range accurately captured the correct # of occupied beds, meaning that the model can forecast the ballpark of number of beds occupied.\n",
    "- **The model's performance will improve as more time passes by and more data is available.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 3 - The Future\n",
    "### Forecasting future dates - the next 31 days\n",
    "In this part, I will train a model to forecast the daily # of occupied hospital beds in Dallas/Fort Worth for dates that have not yet arrived *(late September and October)*. \n",
    "\n",
    "This time, I will include data from September when training the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of Dates: 164 \n",
      "From 2020-04-12 to 2020-09-22\n"
     ]
    }
   ],
   "source": [
    "x = []\n",
    "\n",
    "# Reading & storing the # of available beds in DFW\n",
    "for bed_number in str(occupied[\"Dallas/Ft. Worth\"]).split(\" \"):\n",
    "    if(not bed_number[:1].isdigit()):\n",
    "        continue\n",
    "    \n",
    "    x.append(int(getNumberFromString(bed_number)))\n",
    "        \n",
    "# Generate dates from April 12th to last date in the dataset\n",
    "end_date = pd.to_datetime(\"2020-04-12\") + pd.DateOffset(days=len(x) - 1)\n",
    "dates = pd.date_range(start=\"2020-04-12\",end=end_date)\n",
    "dates = [str(i)[:10] for i in dates]\n",
    "\n",
    "print(\"# of Dates:\", len(dates), \"\\nFrom\", dates[0], \"to\", dates[len(dates) - 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x7fcd46c00f90>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Dates' : dates, 'Beds' : x})\n",
    "df.columns = ['ds', 'y']\n",
    "model = Prophet()\n",
    "model.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generating a list of future dates for the model to forecast\n",
    "future_dates = pd.date_range(start=\"2020-09-23\",end=\"2020-10-23\")\n",
    "future = DataFrame(future_dates)\n",
    "future.columns = ['ds']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# forecast contains the predictions for the dates we provided\n",
    "forecast = model.predict(future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alternate = forecast[:]\n",
    "\n",
    "model.plot(alternate)\n",
    "plt.title(\"Forecasted # of Occupied Hospital Beds in DFW\", fontsize=17)\n",
    "plt.xlabel('Dates',fontsize=17)\n",
    "plt.ylabel('# of Beds Occupied',fontsize=23)\n",
    "# plt.savefig('Future Occupied Forecast.png', dpi=1200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zoomed-in graph\n",
    "The code below creates a graph that zooms in on the graph above. **It shows the last 31 days where the # of occupied beds was known next to the forecasted # of beds in Dallas from September 23rd to October 23rd that the model has predicted.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "toplot = []\n",
    "before = 31\n",
    "for i in x[-before:]:\n",
    "    toplot.append(i)\n",
    "for i in alternate['yhat']:\n",
    "    toplot.append(int(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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": [
    "# total occupied beds\n",
    "import matplotlib.patches as mpatches\n",
    "from matplotlib.pyplot import figure\n",
    "alternate\n",
    "\n",
    "plt.title(\"Forecasted # of Occupied Hospital Beds in DFW\", fontsize=17)\n",
    "plt.xlabel('Days since September 3',fontsize=17)\n",
    "plt.ylabel('# of Beds Occupied',fontsize=18)\n",
    "\n",
    "# Generate the before and after lists for x values\n",
    "x1 = [i for i in range(0,before)]\n",
    "x2 = [i for i in range(before, len(alternate) + before)]\n",
    "\n",
    "# Create the legend\n",
    "blue_patch = mpatches.Patch(color='blue', label='Last known # of beds')\n",
    "red_patch = mpatches.Patch(color='red', label='Forecasted # of beds')\n",
    "plt.legend(handles=[blue_patch, red_patch])\n",
    "\n",
    "plt.plot(x1, toplot[:before], color='blue')\n",
    "plt.plot(x2, toplot[before:], color='red')\n",
    "\n",
    "# plt.savefig('Future Occupied Forecast Zoomed.png', dpi=1200)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# End of Notebook   "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}