Preprocessing-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7fc3b147",
   "metadata": {},
   "source": [
    "# Pre-processing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9bd136d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import geopandas as gpd\n",
    "from datetime import datetime\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact\n",
    "import pycaret\n",
    "from pycaret.regression import *\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from sklearn.dummy import DummyRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from IPython import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "107bbb45",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load dataframe\n",
    "#parse datetime column\n",
    "df=pd.read_csv('~/Test/Springboard/Capstone_COVID19_Weather/Data/state_cov_weather2.csv', parse_dates=[0])\n",
    "df.set_index('date', inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49e31a00",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VT_Avg_Temp(F)</th>\n",
       "      <th>VT_PRCP(mm)</th>\n",
       "      <th>ME_Avg_Temp(F)</th>\n",
       "      <th>ME_PRCP(mm)</th>\n",
       "      <th>CT_Avg_Temp(F)</th>\n",
       "      <th>CT_PRCP(mm)</th>\n",
       "      <th>MA_Avg_Temp(F)</th>\n",
       "      <th>MA_PRCP(mm)</th>\n",
       "      <th>CT_Conf_Cases</th>\n",
       "      <th>VT_Conf_Cases</th>\n",
       "      <th>ME_Conf_Cases</th>\n",
       "      <th>MA_Conf_Cases</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-08</th>\n",
       "      <td>72.86</td>\n",
       "      <td>24.1</td>\n",
       "      <td>63.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>935.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>1475.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-09</th>\n",
       "      <td>67.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.42</td>\n",
       "      <td>35.3</td>\n",
       "      <td>71.42</td>\n",
       "      <td>3.8</td>\n",
       "      <td>70.88</td>\n",
       "      <td>8.1</td>\n",
       "      <td>626.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>2407.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-10</th>\n",
       "      <td>59.90</td>\n",
       "      <td>0.5</td>\n",
       "      <td>58.46</td>\n",
       "      <td>13.0</td>\n",
       "      <td>66.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.54</td>\n",
       "      <td>7.6</td>\n",
       "      <td>625.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>1873.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-11</th>\n",
       "      <td>63.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.99</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-12</th>\n",
       "      <td>69.44</td>\n",
       "      <td>0.3</td>\n",
       "      <td>63.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            VT_Avg_Temp(F)  VT_PRCP(mm)  ME_Avg_Temp(F)  ME_PRCP(mm)  \\\n",
       "date                                                                   \n",
       "2021-09-08           72.86         24.1           63.50          0.0   \n",
       "2021-09-09           67.46          0.0           62.42         35.3   \n",
       "2021-09-10           59.90          0.5           58.46         13.0   \n",
       "2021-09-11           63.86          0.0           59.36          0.0   \n",
       "2021-09-12           69.44          0.3           63.50          0.0   \n",
       "\n",
       "            CT_Avg_Temp(F)  CT_PRCP(mm)  MA_Avg_Temp(F)  MA_PRCP(mm)  \\\n",
       "date                                                                   \n",
       "2021-09-08           70.16          0.0           71.96          0.0   \n",
       "2021-09-09           71.42          3.8           70.88          8.1   \n",
       "2021-09-10           66.92          0.0           68.54          7.6   \n",
       "2021-09-11           63.14          0.0           59.99          0.3   \n",
       "2021-09-12           69.44          0.0           71.42          0.0   \n",
       "\n",
       "            CT_Conf_Cases  VT_Conf_Cases  ME_Conf_Cases  MA_Conf_Cases  \\\n",
       "date                                                                     \n",
       "2021-09-08          935.0          111.0          244.0         1475.0   \n",
       "2021-09-09          626.0          152.0          620.0         2407.0   \n",
       "2021-09-10          625.0          147.0          506.0         1873.0   \n",
       "2021-09-11            0.0          199.0          584.0            0.0   \n",
       "2021-09-12            0.0          180.0            0.0            0.0   \n",
       "\n",
       "            weekday  \n",
       "date                 \n",
       "2021-09-08        2  \n",
       "2021-09-09        3  \n",
       "2021-09-10        4  \n",
       "2021-09-11        5  \n",
       "2021-09-12        6  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "98e0b0e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vt=df.drop(columns=['ME_Avg_Temp(F)', 'ME_PRCP(mm)', 'ME_Conf_Cases', 'CT_Avg_Temp(F)', 'CT_PRCP(mm)', 'CT_Conf_Cases', 'MA_Avg_Temp(F)', 'MA_PRCP(mm)', 'MA_Conf_Cases','weekday'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1682c0b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_me=df.drop(columns=['VT_Avg_Temp(F)', 'VT_PRCP(mm)', 'VT_Conf_Cases', 'CT_Avg_Temp(F)', 'CT_PRCP(mm)', 'CT_Conf_Cases', 'MA_Avg_Temp(F)', 'MA_PRCP(mm)', 'MA_Conf_Cases','weekday'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dc32d41c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ma=df.drop(columns=['ME_Avg_Temp(F)', 'ME_PRCP(mm)', 'ME_Conf_Cases', 'CT_Avg_Temp(F)', 'CT_PRCP(mm)', 'CT_Conf_Cases', 'VT_Avg_Temp(F)', 'VT_PRCP(mm)', 'VT_Conf_Cases','weekday'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fcf38d54",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ct= df.drop(columns=['ME_Avg_Temp(F)', 'ME_PRCP(mm)', 'ME_Conf_Cases', 'VT_Avg_Temp(F)', 'VT_PRCP(mm)', 'VT_Conf_Cases', 'MA_Avg_Temp(F)', 'MA_PRCP(mm)', 'MA_Conf_Cases','weekday'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d1c4229",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3ad8a934",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make duplicates of datasets for processing with Pycaret:\n",
    "df_vt3 = df_vt.reset_index()\n",
    "df_ct3 = df_ct.reset_index()\n",
    "df_me3 = df_me.reset_index()\n",
    "df_ma3 = df_ma.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0174d50b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_list = [df_ct3, df_ma3, df_me3, df_vt3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "40f3fd63",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add 'day_of_week' and 'day_of_year' columns to each dataframe\n",
    "for df in df_list:\n",
    "    df['day_of_week'] = [i.dayofweek for i in df['date']]\n",
    "    df['day_of_year'] = [i.dayofyear for i in df['date']]\n",
    "    df['Year'] =[i.year for i in df['date']]\n",
    "    df['Month'] = [i.month for i in df['date']]\n",
    "    df['Day'] = [i.day for i in df['date']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b33037b",
   "metadata": {},
   "source": [
    "### US Census Bureau state populations\n",
    "* Massachusetts: 7,029,917 \n",
    "* Vermont: 643,077\t\n",
    "* Connecticut: 3,605,944\t\n",
    "* Maine: 1,362,359\n",
    "\n",
    "Source: [U.S. Census Bureau](https://www.census.gov/quickfacts/fact/table/VT,CT,MA,ME/PST045219) April 1, 2020 Census.\n",
    "\n",
    "Because some states are very densely populated, while others are much more sparsely populated, I'll calculate confirmed Covid cases as a percentage of overall population. \n",
    "\n",
    "In order to do this, I'll divide by the appropriate state's total population (as per the April 1, 2020 U.S. Census Bureau), and multiply by 100,000 for ten thousandths of a percent. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "11e1dd85",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ma3['new_case_percent_pop*'] = (df_ma3['MA_Conf_Cases'] / 70.29917)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a006fad3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_ct3['new_case_percent_pop*'] = (df_ct3['CT_Conf_Cases'] / 36.05944)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "31dd7ef4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_me3['new_case_percent_pop*'] = (df_me3['ME_Conf_Cases'] / 13.62359)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "50974aa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vt3['new_case_percent_pop*'] = (df_vt3['VT_Conf_Cases'] / 6.43077)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "177aeedf",
   "metadata": {},
   "source": [
    "Try the model using new cases as ten thousandths of a percent of population, rather than count."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3298fda2",
   "metadata": {},
   "source": [
    "## Using the mean as a baseline prediction model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14a3acbd",
   "metadata": {},
   "source": [
    "#### Massachusetts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a839de5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R2 score of using the mean to predict Massachusetts' COVID19 cases is: -0.053284810131488225\n"
     ]
    }
   ],
   "source": [
    "# Create features\n",
    "X_ma, y_ma = df_ma3.drop('MA_Conf_Cases', axis =1), df_ma3.MA_Conf_Cases\n",
    "# Make test and training split\n",
    "X_train_ma, X_test_ma, y_train_ma, y_test_ma = train_test_split(X_ma, y_ma, test_size = 0.2, random_state=42)\n",
    "dummy_mean_ma = DummyRegressor(strategy='mean')\n",
    "# \"Train\" dummy regressor\n",
    "dummy_mean_ma.fit(X_train_ma, y_train_ma)\n",
    "# Get R-squared score\n",
    "score_mean_ma = dummy_mean_ma.score(X_test_ma, y_test_ma) \n",
    "print(\"The R2 score of using the mean to predict Massachusetts' COVID19 cases is:\", score_mean_ma)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e0052b",
   "metadata": {},
   "source": [
    "#### Connecticut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c63811ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R2 score of using the mean to predict Connecticut's COVID19 cases is: -0.006702217631290974\n"
     ]
    }
   ],
   "source": [
    "# Create features\n",
    "X_ct, y_ct = df_ct3.drop('CT_Conf_Cases', axis =1), df_ct3.CT_Conf_Cases\n",
    "# Make test and training split\n",
    "X_train_ct, X_test_ct, y_train_ct, y_test_ct = train_test_split(X_ct, y_ct, test_size=0.2, random_state=42)\n",
    "# Create a dummy regressor\n",
    "dummy_mean_ct = DummyRegressor(strategy='mean')\n",
    "# \"Train\" dummy regressor\n",
    "dummy_mean_ct.fit(X_train_ct, y_train_ct)\n",
    "# Get R-squared score\n",
    "score_mean_ct = dummy_mean_ct.score(X_test_ct, y_test_ct) \n",
    "print(\"The R2 score of using the mean to predict Connecticut's COVID19 cases is:\", score_mean_ct)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c122447",
   "metadata": {},
   "source": [
    "#### Vermont"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2ab27e6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R2 score of using the mean to predict Vermont's COVID19 cases is: -0.013915984644440993\n"
     ]
    }
   ],
   "source": [
    "# Create features\n",
    "X_vt, y_vt = df_vt3.drop('VT_Conf_Cases', axis = 1), df_vt3.VT_Conf_Cases\n",
    "# Make test and training split\n",
    "X_train_vt, X_test_vt, y_train_vt, y_test_vt = train_test_split(X_vt, y_vt, test_size = 0.2,random_state=42)\n",
    "# Create a dummy regressor\n",
    "dummy_mean_vt = DummyRegressor(strategy='mean')\n",
    "# \"Train\" dummy regressor\n",
    "dummy_mean_vt.fit(X_train_vt, y_train_vt)\n",
    "# Get R-squared score\n",
    "score_mean_vt = dummy_mean_vt.score(X_test_vt, y_test_vt) \n",
    "print(\"The R2 score of using the mean to predict Vermont's COVID19 cases is:\", score_mean_vt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c07e736",
   "metadata": {},
   "source": [
    "#### Maine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2ba6fa46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R2 score of using the mean to predict Maine's COVID19 cases is: -0.10540068459291052\n"
     ]
    }
   ],
   "source": [
    "# Create features\n",
    "X_me, y_me = df_me3.drop('ME_Conf_Cases', axis =1), df_me3.ME_Conf_Cases\n",
    "# Make test and training split\n",
    "X_train_me, X_test_me, y_train_me, y_test_me = train_test_split(X_me, y_me, test_size=0.2,random_state=42)\n",
    "# Create a dummy regressor\n",
    "dummy_mean_me = DummyRegressor(strategy='mean')\n",
    "# \"Train\" dummy regressor\n",
    "dummy_mean_me.fit(X_train_me, y_train_me)\n",
    "# Get R-squared score\n",
    "score_mean_me = dummy_mean_me.score(X_test_me, y_test_me) \n",
    "print(\"The R2 score of using the mean to predict Maine's COVID19 cases is:\", score_mean_me)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b3864a1",
   "metadata": {},
   "source": [
    "## Correlation between COVID cases and lagged *tempurature* values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f46ae593",
   "metadata": {},
   "source": [
    "We know that there is a period of incubation between the moment of COVID-19 infection and the manifestation of symptoms, so it might be reasonable to expect a higher correlation between average temperatures and some lagged value of COVID-19 cases. If COVID-19 infection rates truly are correlated with temperature, cases that got tested *because* they manifested symptoms, would likely demonstrate some lag with the temperature on the day they were infected. On the other hand, those cases that get tested regularly (for work, medical reasons, etc.), may not demonstrate much of a lag, if any at all, with the temperature on the day they were infected. It would be worthwhile to explore lagged values of COVID cases to determine if there isn't a higher correlation with a lagged value of cases than with the current value of COVID-19 cases. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e62af3d2",
   "metadata": {},
   "source": [
    "#### Massachusetts temperature lag cross-correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b92fcb06",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ma4=df_ma3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1ca702ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MA_Avg_Temp(F)</th>\n",
       "      <th>MA_Conf_Cases</th>\n",
       "      <th>new_case_percent_pop*</th>\n",
       "      <th>1_wk</th>\n",
       "      <th>1.5_wks</th>\n",
       "      <th>2_wks</th>\n",
       "      <th>3_wks</th>\n",
       "      <th>4_wks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MA_Avg_Temp(F)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.628138</td>\n",
       "      <td>-0.628138</td>\n",
       "      <td>-0.617129</td>\n",
       "      <td>-0.604865</td>\n",
       "      <td>-0.591901</td>\n",
       "      <td>-0.546742</td>\n",
       "      <td>-0.497032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA_Conf_Cases</th>\n",
       "      <td>-0.628138</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.920939</td>\n",
       "      <td>0.795222</td>\n",
       "      <td>0.839697</td>\n",
       "      <td>0.747557</td>\n",
       "      <td>0.638274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_case_percent_pop*</th>\n",
       "      <td>-0.628138</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.920939</td>\n",
       "      <td>0.795222</td>\n",
       "      <td>0.839697</td>\n",
       "      <td>0.747557</td>\n",
       "      <td>0.638274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1_wk</th>\n",
       "      <td>-0.617129</td>\n",
       "      <td>0.920939</td>\n",
       "      <td>0.920939</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.833874</td>\n",
       "      <td>0.920121</td>\n",
       "      <td>0.838120</td>\n",
       "      <td>0.745091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5_wks</th>\n",
       "      <td>-0.604865</td>\n",
       "      <td>0.795222</td>\n",
       "      <td>0.795222</td>\n",
       "      <td>0.833874</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.844313</td>\n",
       "      <td>0.791362</td>\n",
       "      <td>0.714971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2_wks</th>\n",
       "      <td>-0.591901</td>\n",
       "      <td>0.839697</td>\n",
       "      <td>0.839697</td>\n",
       "      <td>0.920121</td>\n",
       "      <td>0.844313</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.919334</td>\n",
       "      <td>0.835688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3_wks</th>\n",
       "      <td>-0.546742</td>\n",
       "      <td>0.747557</td>\n",
       "      <td>0.747557</td>\n",
       "      <td>0.838120</td>\n",
       "      <td>0.791362</td>\n",
       "      <td>0.919334</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.916362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4_wks</th>\n",
       "      <td>-0.497032</td>\n",
       "      <td>0.638274</td>\n",
       "      <td>0.638274</td>\n",
       "      <td>0.745091</td>\n",
       "      <td>0.714971</td>\n",
       "      <td>0.835688</td>\n",
       "      <td>0.916362</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       MA_Avg_Temp(F)  MA_Conf_Cases  new_case_percent_pop*  \\\n",
       "MA_Avg_Temp(F)               1.000000      -0.628138              -0.628138   \n",
       "MA_Conf_Cases               -0.628138       1.000000               1.000000   \n",
       "new_case_percent_pop*       -0.628138       1.000000               1.000000   \n",
       "1_wk                        -0.617129       0.920939               0.920939   \n",
       "1.5_wks                     -0.604865       0.795222               0.795222   \n",
       "2_wks                       -0.591901       0.839697               0.839697   \n",
       "3_wks                       -0.546742       0.747557               0.747557   \n",
       "4_wks                       -0.497032       0.638274               0.638274   \n",
       "\n",
       "                           1_wk   1.5_wks     2_wks     3_wks     4_wks  \n",
       "MA_Avg_Temp(F)        -0.617129 -0.604865 -0.591901 -0.546742 -0.497032  \n",
       "MA_Conf_Cases          0.920939  0.795222  0.839697  0.747557  0.638274  \n",
       "new_case_percent_pop*  0.920939  0.795222  0.839697  0.747557  0.638274  \n",
       "1_wk                   1.000000  0.833874  0.920121  0.838120  0.745091  \n",
       "1.5_wks                0.833874  1.000000  0.844313  0.791362  0.714971  \n",
       "2_wks                  0.920121  0.844313  1.000000  0.919334  0.835688  \n",
       "3_wks                  0.838120  0.791362  0.919334  1.000000  0.916362  \n",
       "4_wks                  0.745091  0.714971  0.835688  0.916362  1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_cov_lags = {'1_wk': -7, '1.5_wks': -10, '2_wks': -14, '3_wks': -21, '4_wks': -28}\n",
    "for key, value in temp_cov_lags.items():\n",
    "    df_ma4[key] = df_ma4['MA_Conf_Cases'].shift(value, axis=0)\n",
    "df_ma4 = df_ma4.drop(['MA_PRCP(mm)', 'day_of_week', 'day_of_year', 'Year', 'Month', 'Day'], axis =1).dropna()\n",
    "ma_corr = pd.DataFrame(df_ma4.corr())\n",
    "ma_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f8a72802",
   "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": [
    "ma_corr.drop(columns=['MA_Avg_Temp(F)', 'new_case_percent_pop*'], inplace=True)\n",
    "ma_corr.rename(columns={'MA_Conf_Cases': '0_wks'}, inplace=True)\n",
    "ma_corr.loc['MA_Avg_Temp(F)', :].plot()\n",
    "plt.title('Correlation of temp vs lagged COV cases: MA')\n",
    "plt.xlabel('Lag time')\n",
    "plt.ylabel('Correlation with average temperature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0175022f",
   "metadata": {},
   "source": [
    "#### Connecticut temperature lag cross-correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ce9d126d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ct4=df_ct3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "695e9325",
   "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>CT_Avg_Temp(F)</th>\n",
       "      <th>CT_Conf_Cases</th>\n",
       "      <th>new_case_percent_pop*</th>\n",
       "      <th>1_wk</th>\n",
       "      <th>1.5_wks</th>\n",
       "      <th>2_wks</th>\n",
       "      <th>3_wks</th>\n",
       "      <th>4_wks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CT_Avg_Temp(F)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.589089</td>\n",
       "      <td>-0.589089</td>\n",
       "      <td>-0.583183</td>\n",
       "      <td>-0.570396</td>\n",
       "      <td>-0.552986</td>\n",
       "      <td>-0.528611</td>\n",
       "      <td>-0.486397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_Conf_Cases</th>\n",
       "      <td>-0.589089</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.777177</td>\n",
       "      <td>0.650712</td>\n",
       "      <td>0.727060</td>\n",
       "      <td>0.643448</td>\n",
       "      <td>0.568160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_case_percent_pop*</th>\n",
       "      <td>-0.589089</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.777177</td>\n",
       "      <td>0.650712</td>\n",
       "      <td>0.727060</td>\n",
       "      <td>0.643448</td>\n",
       "      <td>0.568160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1_wk</th>\n",
       "      <td>-0.583183</td>\n",
       "      <td>0.777177</td>\n",
       "      <td>0.777177</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666420</td>\n",
       "      <td>0.774834</td>\n",
       "      <td>0.724540</td>\n",
       "      <td>0.640356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5_wks</th>\n",
       "      <td>-0.570396</td>\n",
       "      <td>0.650712</td>\n",
       "      <td>0.650712</td>\n",
       "      <td>0.666420</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.696944</td>\n",
       "      <td>0.663887</td>\n",
       "      <td>0.583644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2_wks</th>\n",
       "      <td>-0.552986</td>\n",
       "      <td>0.727060</td>\n",
       "      <td>0.727060</td>\n",
       "      <td>0.774834</td>\n",
       "      <td>0.696944</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.772775</td>\n",
       "      <td>0.721319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3_wks</th>\n",
       "      <td>-0.528611</td>\n",
       "      <td>0.643448</td>\n",
       "      <td>0.643448</td>\n",
       "      <td>0.724540</td>\n",
       "      <td>0.663887</td>\n",
       "      <td>0.772775</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.769964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4_wks</th>\n",
       "      <td>-0.486397</td>\n",
       "      <td>0.568160</td>\n",
       "      <td>0.568160</td>\n",
       "      <td>0.640356</td>\n",
       "      <td>0.583644</td>\n",
       "      <td>0.721319</td>\n",
       "      <td>0.769964</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       CT_Avg_Temp(F)  CT_Conf_Cases  new_case_percent_pop*  \\\n",
       "CT_Avg_Temp(F)               1.000000      -0.589089              -0.589089   \n",
       "CT_Conf_Cases               -0.589089       1.000000               1.000000   \n",
       "new_case_percent_pop*       -0.589089       1.000000               1.000000   \n",
       "1_wk                        -0.583183       0.777177               0.777177   \n",
       "1.5_wks                     -0.570396       0.650712               0.650712   \n",
       "2_wks                       -0.552986       0.727060               0.727060   \n",
       "3_wks                       -0.528611       0.643448               0.643448   \n",
       "4_wks                       -0.486397       0.568160               0.568160   \n",
       "\n",
       "                           1_wk   1.5_wks     2_wks     3_wks     4_wks  \n",
       "CT_Avg_Temp(F)        -0.583183 -0.570396 -0.552986 -0.528611 -0.486397  \n",
       "CT_Conf_Cases          0.777177  0.650712  0.727060  0.643448  0.568160  \n",
       "new_case_percent_pop*  0.777177  0.650712  0.727060  0.643448  0.568160  \n",
       "1_wk                   1.000000  0.666420  0.774834  0.724540  0.640356  \n",
       "1.5_wks                0.666420  1.000000  0.696944  0.663887  0.583644  \n",
       "2_wks                  0.774834  0.696944  1.000000  0.772775  0.721319  \n",
       "3_wks                  0.724540  0.663887  0.772775  1.000000  0.769964  \n",
       "4_wks                  0.640356  0.583644  0.721319  0.769964  1.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for key, value in temp_cov_lags.items():\n",
    "    df_ct4[key] = df_ct4['CT_Conf_Cases'].shift(value, axis=0)\n",
    "df_ct4 = df_ct4.drop(['CT_PRCP(mm)', 'day_of_week', 'day_of_year', 'Year', 'Month', 'Day'], axis =1).dropna()\n",
    "ct_corr = pd.DataFrame(df_ct4.corr())\n",
    "ct_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aeee0901",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ct_corr.drop(columns=['CT_Avg_Temp(F)', 'new_case_percent_pop*'], inplace=True)\n",
    "ct_corr.rename(columns={'CT_Conf_Cases': '0_wks'}, inplace=True)\n",
    "ct_corr.loc['CT_Avg_Temp(F)', :].plot()\n",
    "plt.title('Correlation of temp vs lagged COV cases: CT')\n",
    "plt.xlabel('Lag time')\n",
    "plt.ylabel('Correlation with average temperature')\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f9b2689",
   "metadata": {},
   "source": [
    "#### Vermont temperature lag cross-correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2aecd09e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vt4=df_vt3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c5e96d7b",
   "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>VT_Avg_Temp(F)</th>\n",
       "      <th>VT_Conf_Cases</th>\n",
       "      <th>new_case_percent_pop*</th>\n",
       "      <th>1_wk</th>\n",
       "      <th>1.5_wks</th>\n",
       "      <th>2_wks</th>\n",
       "      <th>3_wks</th>\n",
       "      <th>4_wks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VT_Avg_Temp(F)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.571510</td>\n",
       "      <td>-0.571510</td>\n",
       "      <td>-0.553044</td>\n",
       "      <td>-0.545564</td>\n",
       "      <td>-0.566260</td>\n",
       "      <td>-0.518298</td>\n",
       "      <td>-0.482696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VT_Conf_Cases</th>\n",
       "      <td>-0.571510</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.844868</td>\n",
       "      <td>0.734993</td>\n",
       "      <td>0.781832</td>\n",
       "      <td>0.709235</td>\n",
       "      <td>0.644609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_case_percent_pop*</th>\n",
       "      <td>-0.571510</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.844868</td>\n",
       "      <td>0.734993</td>\n",
       "      <td>0.781832</td>\n",
       "      <td>0.709235</td>\n",
       "      <td>0.644609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1_wk</th>\n",
       "      <td>-0.553044</td>\n",
       "      <td>0.844868</td>\n",
       "      <td>0.844868</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.791469</td>\n",
       "      <td>0.842056</td>\n",
       "      <td>0.782368</td>\n",
       "      <td>0.713320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5_wks</th>\n",
       "      <td>-0.545564</td>\n",
       "      <td>0.734993</td>\n",
       "      <td>0.734993</td>\n",
       "      <td>0.791469</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.761727</td>\n",
       "      <td>0.725684</td>\n",
       "      <td>0.663498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2_wks</th>\n",
       "      <td>-0.566260</td>\n",
       "      <td>0.781832</td>\n",
       "      <td>0.781832</td>\n",
       "      <td>0.842056</td>\n",
       "      <td>0.761727</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.839935</td>\n",
       "      <td>0.779342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3_wks</th>\n",
       "      <td>-0.518298</td>\n",
       "      <td>0.709235</td>\n",
       "      <td>0.709235</td>\n",
       "      <td>0.782368</td>\n",
       "      <td>0.725684</td>\n",
       "      <td>0.839935</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.840092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4_wks</th>\n",
       "      <td>-0.482696</td>\n",
       "      <td>0.644609</td>\n",
       "      <td>0.644609</td>\n",
       "      <td>0.713320</td>\n",
       "      <td>0.663498</td>\n",
       "      <td>0.779342</td>\n",
       "      <td>0.840092</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       VT_Avg_Temp(F)  VT_Conf_Cases  new_case_percent_pop*  \\\n",
       "VT_Avg_Temp(F)               1.000000      -0.571510              -0.571510   \n",
       "VT_Conf_Cases               -0.571510       1.000000               1.000000   \n",
       "new_case_percent_pop*       -0.571510       1.000000               1.000000   \n",
       "1_wk                        -0.553044       0.844868               0.844868   \n",
       "1.5_wks                     -0.545564       0.734993               0.734993   \n",
       "2_wks                       -0.566260       0.781832               0.781832   \n",
       "3_wks                       -0.518298       0.709235               0.709235   \n",
       "4_wks                       -0.482696       0.644609               0.644609   \n",
       "\n",
       "                           1_wk   1.5_wks     2_wks     3_wks     4_wks  \n",
       "VT_Avg_Temp(F)        -0.553044 -0.545564 -0.566260 -0.518298 -0.482696  \n",
       "VT_Conf_Cases          0.844868  0.734993  0.781832  0.709235  0.644609  \n",
       "new_case_percent_pop*  0.844868  0.734993  0.781832  0.709235  0.644609  \n",
       "1_wk                   1.000000  0.791469  0.842056  0.782368  0.713320  \n",
       "1.5_wks                0.791469  1.000000  0.761727  0.725684  0.663498  \n",
       "2_wks                  0.842056  0.761727  1.000000  0.839935  0.779342  \n",
       "3_wks                  0.782368  0.725684  0.839935  1.000000  0.840092  \n",
       "4_wks                  0.713320  0.663498  0.779342  0.840092  1.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for key, value in temp_cov_lags.items():\n",
    "    df_vt4[key] = df_vt4['VT_Conf_Cases'].shift(value, axis=0)\n",
    "df_vt4 = df_vt4.drop(['VT_PRCP(mm)', 'day_of_week', 'day_of_year', 'Year', 'Month', 'Day'], axis =1).dropna()\n",
    "vt_corr = pd.DataFrame(df_vt4.corr())\n",
    "vt_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c9f22782",
   "metadata": {
    "scrolled": true
   },
   "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": [
    "vt_corr.drop(columns=['VT_Avg_Temp(F)', 'new_case_percent_pop*'], inplace=True)\n",
    "vt_corr.rename(columns={'VT_Conf_Cases': '0_wks'}, inplace=True)\n",
    "vt_corr.loc['VT_Avg_Temp(F)', :].plot()\n",
    "plt.title('Correlation of temp vs lagged COV cases: VT')\n",
    "plt.xlabel('Lag time')\n",
    "plt.ylabel('Correlation with average temperature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf407fce",
   "metadata": {},
   "source": [
    "#### Maine temperature lag cross-correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c3f930a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_me4=df_me3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "76f132af",
   "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>ME_Avg_Temp(F)</th>\n",
       "      <th>ME_Conf_Cases</th>\n",
       "      <th>new_case_percent_pop*</th>\n",
       "      <th>1_wk</th>\n",
       "      <th>1.5_wks</th>\n",
       "      <th>2_wks</th>\n",
       "      <th>3_wks</th>\n",
       "      <th>4_wks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ME_Avg_Temp(F)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.494485</td>\n",
       "      <td>-0.494485</td>\n",
       "      <td>-0.492485</td>\n",
       "      <td>-0.495851</td>\n",
       "      <td>-0.489141</td>\n",
       "      <td>-0.444906</td>\n",
       "      <td>-0.421420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME_Conf_Cases</th>\n",
       "      <td>-0.494485</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.895255</td>\n",
       "      <td>0.758257</td>\n",
       "      <td>0.800753</td>\n",
       "      <td>0.681238</td>\n",
       "      <td>0.547517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_case_percent_pop*</th>\n",
       "      <td>-0.494485</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.895255</td>\n",
       "      <td>0.758257</td>\n",
       "      <td>0.800753</td>\n",
       "      <td>0.681238</td>\n",
       "      <td>0.547517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1_wk</th>\n",
       "      <td>-0.492485</td>\n",
       "      <td>0.895255</td>\n",
       "      <td>0.895255</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.803828</td>\n",
       "      <td>0.891650</td>\n",
       "      <td>0.786226</td>\n",
       "      <td>0.664028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5_wks</th>\n",
       "      <td>-0.495851</td>\n",
       "      <td>0.758257</td>\n",
       "      <td>0.758257</td>\n",
       "      <td>0.803828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.807657</td>\n",
       "      <td>0.744874</td>\n",
       "      <td>0.659837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2_wks</th>\n",
       "      <td>-0.489141</td>\n",
       "      <td>0.800753</td>\n",
       "      <td>0.800753</td>\n",
       "      <td>0.891650</td>\n",
       "      <td>0.807657</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.886078</td>\n",
       "      <td>0.770015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3_wks</th>\n",
       "      <td>-0.444906</td>\n",
       "      <td>0.681238</td>\n",
       "      <td>0.681238</td>\n",
       "      <td>0.786226</td>\n",
       "      <td>0.744874</td>\n",
       "      <td>0.886078</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.872222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4_wks</th>\n",
       "      <td>-0.421420</td>\n",
       "      <td>0.547517</td>\n",
       "      <td>0.547517</td>\n",
       "      <td>0.664028</td>\n",
       "      <td>0.659837</td>\n",
       "      <td>0.770015</td>\n",
       "      <td>0.872222</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       ME_Avg_Temp(F)  ME_Conf_Cases  new_case_percent_pop*  \\\n",
       "ME_Avg_Temp(F)               1.000000      -0.494485              -0.494485   \n",
       "ME_Conf_Cases               -0.494485       1.000000               1.000000   \n",
       "new_case_percent_pop*       -0.494485       1.000000               1.000000   \n",
       "1_wk                        -0.492485       0.895255               0.895255   \n",
       "1.5_wks                     -0.495851       0.758257               0.758257   \n",
       "2_wks                       -0.489141       0.800753               0.800753   \n",
       "3_wks                       -0.444906       0.681238               0.681238   \n",
       "4_wks                       -0.421420       0.547517               0.547517   \n",
       "\n",
       "                           1_wk   1.5_wks     2_wks     3_wks     4_wks  \n",
       "ME_Avg_Temp(F)        -0.492485 -0.495851 -0.489141 -0.444906 -0.421420  \n",
       "ME_Conf_Cases          0.895255  0.758257  0.800753  0.681238  0.547517  \n",
       "new_case_percent_pop*  0.895255  0.758257  0.800753  0.681238  0.547517  \n",
       "1_wk                   1.000000  0.803828  0.891650  0.786226  0.664028  \n",
       "1.5_wks                0.803828  1.000000  0.807657  0.744874  0.659837  \n",
       "2_wks                  0.891650  0.807657  1.000000  0.886078  0.770015  \n",
       "3_wks                  0.786226  0.744874  0.886078  1.000000  0.872222  \n",
       "4_wks                  0.664028  0.659837  0.770015  0.872222  1.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for key, value in temp_cov_lags.items():\n",
    "    df_me4[key] = df_me4['ME_Conf_Cases'].shift(value, axis=0)\n",
    "df_me4 = df_me4.drop(['ME_PRCP(mm)', 'day_of_week', 'day_of_year', 'Year', 'Month', 'Day'], axis =1).dropna()\n",
    "me_corr = pd.DataFrame(df_me4.corr())\n",
    "me_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "20bbd54b",
   "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": [
    "me_corr.drop(columns=['ME_Avg_Temp(F)', 'new_case_percent_pop*'], inplace=True)\n",
    "me_corr.rename(columns={'ME_Conf_Cases': '0_wks'}, inplace=True)\n",
    "plt.title('Correlation of temp vs lagged COV cases: ME')\n",
    "me_corr.loc['ME_Avg_Temp(F)', :].plot()\n",
    "plt.xlabel('Lag time')\n",
    "plt.ylabel('Correlation with average temperature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20db89eb",
   "metadata": {},
   "source": [
    "Maine seems to have the strongest negative correlation between temperature and a 1.5 week lag of COVID-19 cases. Vermont seems to have the strongest negative correlations with current cases, as well as those lagged by two weeks. However, Massachusetts and Connecticut both very clearly have the strongest correlation between temperature and lag 0 (or, current) case values. While it seems that for some states there may be a strong correlation with lagged COVID case values, since 50-75% of the data don't show any increased correlation using lags (and the remaining 25-50% of data disagree on an optimal lagged value), let's stick with our original lag 0 COVID-19 case values.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "15cb3a19",
   "metadata": {},
   "outputs": [],
   "source": [
    "lag_cols = ['1_wk', '1.5_wks', '2_wks', '3_wks', '4_wks']\n",
    "df_list = [df_ma3, df_ct3, df_me3, df_vt3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bbb88710",
   "metadata": {},
   "outputs": [],
   "source": [
    "for df in df_list:\n",
    "    df.drop(columns=lag_cols, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3442afdb",
   "metadata": {},
   "source": [
    "## Correlation between COVID cases and lagged *precipitation* values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5dd64fb",
   "metadata": {},
   "source": [
    "#### Massachusetts PRCP lag cross-correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2c4ee3be",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ma4=df_ma3.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7c11ee39",
   "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>MA_PRCP(mm)</th>\n",
       "      <th>MA_Conf_Cases</th>\n",
       "      <th>new_case_percent_pop*</th>\n",
       "      <th>1_wk</th>\n",
       "      <th>1.5_wks</th>\n",
       "      <th>2_wks</th>\n",
       "      <th>3_wks</th>\n",
       "      <th>4_wks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MA_PRCP(mm)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009234</td>\n",
       "      <td>0.009234</td>\n",
       "      <td>0.020513</td>\n",
       "      <td>0.007562</td>\n",
       "      <td>0.032721</td>\n",
       "      <td>0.022007</td>\n",
       "      <td>0.034374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA_Conf_Cases</th>\n",
       "      <td>0.009234</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.920939</td>\n",
       "      <td>0.795222</td>\n",
       "      <td>0.839697</td>\n",
       "      <td>0.747557</td>\n",
       "      <td>0.638274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_case_percent_pop*</th>\n",
       "      <td>0.009234</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.920939</td>\n",
       "      <td>0.795222</td>\n",
       "      <td>0.839697</td>\n",
       "      <td>0.747557</td>\n",
       "      <td>0.638274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1_wk</th>\n",
       "      <td>0.020513</td>\n",
       "      <td>0.920939</td>\n",
       "      <td>0.920939</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.833874</td>\n",
       "      <td>0.920121</td>\n",
       "      <td>0.838120</td>\n",
       "      <td>0.745091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5_wks</th>\n",
       "      <td>0.007562</td>\n",
       "      <td>0.795222</td>\n",
       "      <td>0.795222</td>\n",
       "      <td>0.833874</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.844313</td>\n",
       "      <td>0.791362</td>\n",
       "      <td>0.714971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2_wks</th>\n",
       "      <td>0.032721</td>\n",
       "      <td>0.839697</td>\n",
       "      <td>0.839697</td>\n",
       "      <td>0.920121</td>\n",
       "      <td>0.844313</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.919334</td>\n",
       "      <td>0.835688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3_wks</th>\n",
       "      <td>0.022007</td>\n",
       "      <td>0.747557</td>\n",
       "      <td>0.747557</td>\n",
       "      <td>0.838120</td>\n",
       "      <td>0.791362</td>\n",
       "      <td>0.919334</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.916362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4_wks</th>\n",
       "      <td>0.034374</td>\n",
       "      <td>0.638274</td>\n",
       "      <td>0.638274</td>\n",
       "      <td>0.745091</td>\n",
       "      <td>0.714971</td>\n",
       "      <td>0.835688</td>\n",
       "      <td>0.916362</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       MA_PRCP(mm)  MA_Conf_Cases  new_case_percent_pop*  \\\n",
       "MA_PRCP(mm)               1.000000       0.009234               0.009234   \n",
       "MA_Conf_Cases             0.009234       1.000000               1.000000   \n",
       "new_case_percent_pop*     0.009234       1.000000               1.000000   \n",
       "1_wk                      0.020513       0.920939               0.920939   \n",
       "1.5_wks                   0.007562       0.795222               0.795222   \n",
       "2_wks                     0.032721       0.839697               0.839697   \n",
       "3_wks                     0.022007       0.747557               0.747557   \n",
       "4_wks                     0.034374       0.638274               0.638274   \n",
       "\n",
       "                           1_wk   1.5_wks     2_wks     3_wks     4_wks  \n",
       "MA_PRCP(mm)            0.020513  0.007562  0.032721  0.022007  0.034374  \n",
       "MA_Conf_Cases          0.920939  0.795222  0.839697  0.747557  0.638274  \n",
       "new_case_percent_pop*  0.920939  0.795222  0.839697  0.747557  0.638274  \n",
       "1_wk                   1.000000  0.833874  0.920121  0.838120  0.745091  \n",
       "1.5_wks                0.833874  1.000000  0.844313  0.791362  0.714971  \n",
       "2_wks                  0.920121  0.844313  1.000000  0.919334  0.835688  \n",
       "3_wks                  0.838120  0.791362  0.919334  1.000000  0.916362  \n",
       "4_wks                  0.745091  0.714971  0.835688  0.916362  1.000000  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prcp_cov_lags = {'1_wk': -7, '1.5_wks': -10, '2_wks': -14, '3_wks': -21, '4_wks': -28}\n",
    "for key, value in prcp_cov_lags.items():\n",
    "    df_ma4[key] = df_ma4['MA_Conf_Cases'].shift(value, axis=0)\n",
    "df_ma4 = df_ma4.drop(['MA_Avg_Temp(F)', 'day_of_week', 'day_of_year', 'Year', 'Month', 'Day'], axis =1).dropna()\n",
    "ma_corr = pd.DataFrame(df_ma4.corr())\n",
    "ma_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "827e46c4",
   "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": [
    "ma_corr.drop(columns=['MA_PRCP(mm)', 'new_case_percent_pop*'], inplace=True)\n",
    "ma_corr.rename(columns={'MA_Conf_Cases': '0_wks'}, inplace=True)\n",
    "ma_corr.loc['MA_PRCP(mm)', :].plot()\n",
    "plt.title('Correlation of PRCP vs lagged COV cases: MA')\n",
    "plt.xlabel('Lag time')\n",
    "plt.ylabel('Correlation with PRCP')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fc90a0f",
   "metadata": {},
   "source": [
    "#### Connecticut PRCP lag cross-correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "253128a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ct4=df_ct3.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2d8c24dc",
   "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>CT_PRCP(mm)</th>\n",
       "      <th>CT_Conf_Cases</th>\n",
       "      <th>new_case_percent_pop*</th>\n",
       "      <th>1_wk</th>\n",
       "      <th>1.5_wks</th>\n",
       "      <th>2_wks</th>\n",
       "      <th>3_wks</th>\n",
       "      <th>4_wks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CT_PRCP(mm)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028828</td>\n",
       "      <td>0.028828</td>\n",
       "      <td>0.037386</td>\n",
       "      <td>-0.018751</td>\n",
       "      <td>0.025447</td>\n",
       "      <td>0.058460</td>\n",
       "      <td>0.029693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_Conf_Cases</th>\n",
       "      <td>0.028828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.777177</td>\n",
       "      <td>0.650712</td>\n",
       "      <td>0.727060</td>\n",
       "      <td>0.643448</td>\n",
       "      <td>0.568160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_case_percent_pop*</th>\n",
       "      <td>0.028828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.777177</td>\n",
       "      <td>0.650712</td>\n",
       "      <td>0.727060</td>\n",
       "      <td>0.643448</td>\n",
       "      <td>0.568160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1_wk</th>\n",
       "      <td>0.037386</td>\n",
       "      <td>0.777177</td>\n",
       "      <td>0.777177</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666420</td>\n",
       "      <td>0.774834</td>\n",
       "      <td>0.724540</td>\n",
       "      <td>0.640356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5_wks</th>\n",
       "      <td>-0.018751</td>\n",
       "      <td>0.650712</td>\n",
       "      <td>0.650712</td>\n",
       "      <td>0.666420</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.696944</td>\n",
       "      <td>0.663887</td>\n",
       "      <td>0.583644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2_wks</th>\n",
       "      <td>0.025447</td>\n",
       "      <td>0.727060</td>\n",
       "      <td>0.727060</td>\n",
       "      <td>0.774834</td>\n",
       "      <td>0.696944</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.772775</td>\n",
       "      <td>0.721319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3_wks</th>\n",
       "      <td>0.058460</td>\n",
       "      <td>0.643448</td>\n",
       "      <td>0.643448</td>\n",
       "      <td>0.724540</td>\n",
       "      <td>0.663887</td>\n",
       "      <td>0.772775</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.769964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4_wks</th>\n",
       "      <td>0.029693</td>\n",
       "      <td>0.568160</td>\n",
       "      <td>0.568160</td>\n",
       "      <td>0.640356</td>\n",
       "      <td>0.583644</td>\n",
       "      <td>0.721319</td>\n",
       "      <td>0.769964</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       CT_PRCP(mm)  CT_Conf_Cases  new_case_percent_pop*  \\\n",
       "CT_PRCP(mm)               1.000000       0.028828               0.028828   \n",
       "CT_Conf_Cases             0.028828       1.000000               1.000000   \n",
       "new_case_percent_pop*     0.028828       1.000000               1.000000   \n",
       "1_wk                      0.037386       0.777177               0.777177   \n",
       "1.5_wks                  -0.018751       0.650712               0.650712   \n",
       "2_wks                     0.025447       0.727060               0.727060   \n",
       "3_wks                     0.058460       0.643448               0.643448   \n",
       "4_wks                     0.029693       0.568160               0.568160   \n",
       "\n",
       "                           1_wk   1.5_wks     2_wks     3_wks     4_wks  \n",
       "CT_PRCP(mm)            0.037386 -0.018751  0.025447  0.058460  0.029693  \n",
       "CT_Conf_Cases          0.777177  0.650712  0.727060  0.643448  0.568160  \n",
       "new_case_percent_pop*  0.777177  0.650712  0.727060  0.643448  0.568160  \n",
       "1_wk                   1.000000  0.666420  0.774834  0.724540  0.640356  \n",
       "1.5_wks                0.666420  1.000000  0.696944  0.663887  0.583644  \n",
       "2_wks                  0.774834  0.696944  1.000000  0.772775  0.721319  \n",
       "3_wks                  0.724540  0.663887  0.772775  1.000000  0.769964  \n",
       "4_wks                  0.640356  0.583644  0.721319  0.769964  1.000000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for key, value in prcp_cov_lags.items():\n",
    "    df_ct4[key] = df_ct4['CT_Conf_Cases'].shift(value, axis=0)\n",
    "df_ct4 = df_ct4.drop(['CT_Avg_Temp(F)', 'day_of_week', 'day_of_year', 'Year', 'Month', 'Day'], axis =1).dropna()\n",
    "ct_corr = pd.DataFrame(df_ct4.corr())\n",
    "ct_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c87a96a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ct_corr.drop(columns=['CT_PRCP(mm)', 'new_case_percent_pop*'], inplace=True)\n",
    "ct_corr.rename(columns={'CT_Conf_Cases': '0_wks'}, inplace=True)\n",
    "ct_corr.loc['CT_PRCP(mm)', :].plot()\n",
    "plt.title('Correlation of PRCP vs lagged COV cases: CT')\n",
    "plt.xlabel('Lag time')\n",
    "plt.ylabel('Correlation with PRCP')\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c995672",
   "metadata": {},
   "source": [
    "#### Vermont PRCP lag cross-correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "aa997fed",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vt4=df_vt3.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b0ae44e1",
   "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>VT_PRCP(mm)</th>\n",
       "      <th>VT_Conf_Cases</th>\n",
       "      <th>new_case_percent_pop*</th>\n",
       "      <th>1_wk</th>\n",
       "      <th>1.5_wks</th>\n",
       "      <th>2_wks</th>\n",
       "      <th>3_wks</th>\n",
       "      <th>4_wks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VT_PRCP(mm)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.043127</td>\n",
       "      <td>-0.043127</td>\n",
       "      <td>-0.065671</td>\n",
       "      <td>-0.064759</td>\n",
       "      <td>-0.042046</td>\n",
       "      <td>-0.053783</td>\n",
       "      <td>-0.032076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VT_Conf_Cases</th>\n",
       "      <td>-0.043127</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.844868</td>\n",
       "      <td>0.734993</td>\n",
       "      <td>0.781832</td>\n",
       "      <td>0.709235</td>\n",
       "      <td>0.644609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_case_percent_pop*</th>\n",
       "      <td>-0.043127</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.844868</td>\n",
       "      <td>0.734993</td>\n",
       "      <td>0.781832</td>\n",
       "      <td>0.709235</td>\n",
       "      <td>0.644609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1_wk</th>\n",
       "      <td>-0.065671</td>\n",
       "      <td>0.844868</td>\n",
       "      <td>0.844868</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.791469</td>\n",
       "      <td>0.842056</td>\n",
       "      <td>0.782368</td>\n",
       "      <td>0.713320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5_wks</th>\n",
       "      <td>-0.064759</td>\n",
       "      <td>0.734993</td>\n",
       "      <td>0.734993</td>\n",
       "      <td>0.791469</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.761727</td>\n",
       "      <td>0.725684</td>\n",
       "      <td>0.663498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2_wks</th>\n",
       "      <td>-0.042046</td>\n",
       "      <td>0.781832</td>\n",
       "      <td>0.781832</td>\n",
       "      <td>0.842056</td>\n",
       "      <td>0.761727</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.839935</td>\n",
       "      <td>0.779342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3_wks</th>\n",
       "      <td>-0.053783</td>\n",
       "      <td>0.709235</td>\n",
       "      <td>0.709235</td>\n",
       "      <td>0.782368</td>\n",
       "      <td>0.725684</td>\n",
       "      <td>0.839935</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.840092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4_wks</th>\n",
       "      <td>-0.032076</td>\n",
       "      <td>0.644609</td>\n",
       "      <td>0.644609</td>\n",
       "      <td>0.713320</td>\n",
       "      <td>0.663498</td>\n",
       "      <td>0.779342</td>\n",
       "      <td>0.840092</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       VT_PRCP(mm)  VT_Conf_Cases  new_case_percent_pop*  \\\n",
       "VT_PRCP(mm)               1.000000      -0.043127              -0.043127   \n",
       "VT_Conf_Cases            -0.043127       1.000000               1.000000   \n",
       "new_case_percent_pop*    -0.043127       1.000000               1.000000   \n",
       "1_wk                     -0.065671       0.844868               0.844868   \n",
       "1.5_wks                  -0.064759       0.734993               0.734993   \n",
       "2_wks                    -0.042046       0.781832               0.781832   \n",
       "3_wks                    -0.053783       0.709235               0.709235   \n",
       "4_wks                    -0.032076       0.644609               0.644609   \n",
       "\n",
       "                           1_wk   1.5_wks     2_wks     3_wks     4_wks  \n",
       "VT_PRCP(mm)           -0.065671 -0.064759 -0.042046 -0.053783 -0.032076  \n",
       "VT_Conf_Cases          0.844868  0.734993  0.781832  0.709235  0.644609  \n",
       "new_case_percent_pop*  0.844868  0.734993  0.781832  0.709235  0.644609  \n",
       "1_wk                   1.000000  0.791469  0.842056  0.782368  0.713320  \n",
       "1.5_wks                0.791469  1.000000  0.761727  0.725684  0.663498  \n",
       "2_wks                  0.842056  0.761727  1.000000  0.839935  0.779342  \n",
       "3_wks                  0.782368  0.725684  0.839935  1.000000  0.840092  \n",
       "4_wks                  0.713320  0.663498  0.779342  0.840092  1.000000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for key, value in prcp_cov_lags.items():\n",
    "    df_vt4[key] = df_vt4['VT_Conf_Cases'].shift(value, axis=0)\n",
    "df_vt4 = df_vt4.drop(['VT_Avg_Temp(F)', 'day_of_week', 'day_of_year', 'Year', 'Month', 'Day'], axis =1).dropna()\n",
    "vt_corr = pd.DataFrame(df_vt4.corr())\n",
    "vt_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e205ac59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VvQB4CHhBRFrXEt90EckUkcyDBw8GvmHGGBMBZmfkECMwbXSaJ+uPC9WCVfWK2saJyH4R6aqqhSLSFTjgZ7I8wPe6uR5AgZ/pXgDeAn6pqqVAqbv+tSKSDZwDZPqJbzYwGyA9Pd2a1YwxUePAiRJeytzLTRf2oEubRE9i8KpZbDEw2X09GXjdzzRrgH4ikioi8cBEdz5EpJ/PdNcBn7rDO7oXAiAiaUA/IDQPiDbGmDD11IpcKiqrmDEmNI8wDkTIjlzq8TiwSETuBvYANwO4lxTPVdVrVLVCRB4E3gFigXmqurV6fhE5F6gCdgPVV4WNAf5HRCqASuBeVT3caFtljDEeO3aqnOc+2s21g7uRktzSszg8SS6qWgR8xc/wAuAan/4lwBI/091Yy3JfAV4JXqTGGBNZFny0i5Nlldw3zrujFrA79I0xJmqcLK1g3oe5XNG/E/27+r2WqdFYcjHGmCjx4uo9HD1Vzv0hfoRxICy5GGNMFCitqGTO8hwuSevAsF5+K2o1KksuxhgTBV5dl8/+46U8EAZHLWDJxRhjIl5FZRVPLstmSI82jOzbwetwAEsuxhgT8d7aXMjuolPcf1lfREL/CONAWHIxxpgIVlWlzFyaTb9Orbiyf2evw/mMJRdjjIlg//30ANv3n+D+y/oQExMeRy1gycUYYyKWqvL3pVn0aNecrw/u5nU4X2DJxRhjItRHOUVs2HuUe8f2IS42vHbn4RWNMcaYgM1cmk3HpARuurCH16F8iSUXY4yJQBv2HmVF1iGmjU4lsVms1+F8iSUXY4yJQDOXZtGmeTNuvbi316H4ZcnFGGMizI79J/jPJ/uZcmkKrRK8enJK3Sy5GGNMhPnHB9m0iI9lyqUpXodSK0suxhgTQfYUnWLxxgJuu7gX7VrGex1OrSy5GGNMBJmVkU2sCPeMTvM6lDpZcjHGmAhx4HgJL2fmcVN6Dzq3TvQ6nDpZcjHGmAgxd0UuFVVV3DvG20cYB8KSizHGRICjp8p4btVurhvSjV4dWngdTr0suRhjTAR4euUuTpVVct+48HgYWH08SS4i0l5E3hWRne5fv8/kFJHxIrJdRLJE5BE/478vIioiyT7DfuxOv11ErgrldhhjTGMoLq1g/oe7uHJAZ87tkuR1OAHx6sjlEeB9Ve0HvO/2f4GIxAJPAFcDA4BJIjLAZ3xP4Epgj8+wAcBEYCAwHpjpLscYYyLWix/v4djpcu4fF/7nWqp5lVwmAAvc1wuA6/1MMxzIUtUcVS0DFrrzVfsT8ENAayx3oaqWqmoukOUuxxhjIlJJeSVzlucwsm8HLujlt5EnLNWZXETkAhG5SUT6B3m9nVW1EMD928nPNN2BvT79ee4wROQ6IF9VNwY6T00iMl1EMkUk8+DBg2e3FcYYE2KvrMvjwIlSHoiQcy3Vak0uIvIL4CXgRuAtEZl2JgsWkfdEZIufbkL9czuL8DNMRaQF8FPgF4HO42/hqjpbVdNVNb1jx44BhmSMMY2norKKJ5dlM7RnWy7p08HrcM5IXRXPvgkMVdVTItIBeBuYE+iCVfWK2saJyH4R6aqqhSLSFTjgZ7I8oKdPfw+gAOgDpAIbRaR6+DoRGV7HPMYYE3He3FTI3sOn+cW1A3H3dxGjrmaxElU9BaCqRfVMe6YWA5Pd15OB1/1MswboJyKpIhKPc6J+sapuVtVOqpqiqik4CWWYqu5zlztRRBJEJBXoB6wOYtzGGNMoqqqUmR9kcW7nJL5ynr8zB+GtriOXPiKy2H0tNfpR1esasN7HgUUicjfO1V43A4hIN2Cuql6jqhUi8iDwDhALzFPVrXUtVFW3isgi4BOgAnhAVSsbEKcxxnjivW372bG/mL9MHEpMTGQdtQCIqt9TEojI2LpmVNVlIYnIA+np6ZqZmel1GMYYA4Cqcv3MlRw5WcZ/Hx5LXGx43u8uImtVNd3fuLqOXD4BOqrqJzUWNhD/50iMMcYEwcrsIjbuPcr/fmNQ2CaW+tQV9d8Af5dR9QD+EppwjDHGPLE0i05JCdx4od87KSJCXcllkL+mL1V9BxgcupCMMabpWrfnCCuzi5g+Jo2EuMgtMFJXcml2luOMMcacpZlLs2nbohmThvfyOpQGqSu57BSRa2oOFJGrgZzQhWSMMU3Tp/uO8962/Uy9NJWWCXWdEg9/dUX/PeBNEbkFWOsOSwcuAa4NdWDGGO8s/fQAK7MP8aPx50XsCeVI9I8PsmkZH8vkS3t7HUqD1ZpcVHWHiAwCbgXOdwcvA2aoakljBGeMaXzllVX87LUt5B89TXFpBf/7jUERd3d4JNpddJI3NhYwbXQabVvEex1Og9V33FUBvKGq8wHcO+Uni8hDqhrsYpbGmDCwZHMh+UdPM6pvMi+u3kv3ts158PJ+XocV9Z5clkNcbAx3j0r1OpSgqKtw5UTgMLBJRJaJyGU451quAW5rpPiMMY1IVZmdkUOfji1ZcNdwbrigO7//zw5eWZvndWhRbd+xEl5Zm8ct6T3o1DrR63CCoq4jl58BF6pqlogMAz4CJqrqvxonNGNMY/sou4itBcd5/IZBxMYIj984mP0nSvjRK5vo1DqB0f2sgngozF2eQ6UqM8ZEzsPA6lPXmboyVc0CUNV1QK4lFmOi26yMHJJbJXD9Bc7Ne/FxMfzj9gvp26kV9z23jk8KjnscYfQ5crKM5z/ew4Qh3ejZvoXX4QRNXcmlk4g8VN0BrWr0N3knSsp5akUuJ0srvA7FmAb7dN9xlu04yNSRKSQ2+/zmvdaJzZg/9SKSEuOY+vRqCo6e9jDK6DN/5S5Ol1dyXwQ9wjgQdSWXOUCST1ezv8nbsb+YX735CYsy99Y/sTFhbk5GLi3iY7nt4i/fvNe1TXPmT72IU6WVTJm/mmOnyz2IMPoUl1bw9Ie5XDWwM/06R9duta5LkR8DEJFkVT3UeCFFjgt7tyO9dzvmLs/ljhG97X4AE7H2HSth8cZ8bru4d62XwZ7XpTWz7riQyfNXM+PZTBbcNTyiy5OEg+dX7eZ4SQX3R9gjjANR19Vi14rIQZyrxfJE5NJGjCtizBjbh/yjp3lrc6HXoRhz1uavzKWySuu9DPbSvsn8/uYhrMo5zA9e3kRVlf9Hdpj6lZRXMmd5LqP7JTOkZ1uvwwm6un5q/y8wWlW7ATcCv22ckCLLV87rRJ+OLZmdkUNtz8YxJpydKCnnhVV7uGZQ14BOKE8Y2p0fjj+XxRsL+N072xshwuj08to8DhWXRuVRC9SdXCpU9VMAVf0YO8/iV0yMMH1MGlsLjvNhVpHX4Rhzxhau3suJ0gqmj0kLeJ77xvbh9hG9eHJZNs98tCt0wUWp8soqZi3LZlivtoxIa+91OCFR130unWpcFfaFflX9Y+jCiizXuzeazcrIZlS/ZK/DMSZg5ZVVzPswlxFp7Rnco23A84kIj113PvuOlfLo4q10aZ3IVwd2CV2gUeaNjQXkHTnNY9cNjNrSOna1WBAkxMUydWQKy3ceYmvBMa/DMSZgb24qoPBYyVndvBcbI/xt0gUM6tGWby9cz7o9R0IQYfSpqlJmfpDNeV2SuPy8Tl6HEzK1JhdVfayurjGDjAS3XdyblvGxzMmwpxGYyOCUesmlX6dWjD3n7O68bx4fy1OT0+ncOpF7FmSSe+hkkKOMPv/5ZD9ZB4q5/7K+UXvUAnUfuYSMiLQXkXdFZKf7t10t040Xke0ikiUij/gZ/30RURFJdvtTROS0iGxwuydDvS3V2jR3Hu7zxqZC8o6caqzVGnPWVmQdYlvhcaaNSSMm5ux3csmtElgwdTgAU+av5lBxabBCjDqqyswPskjp0IKvDerqdTgh5dWNGY8A76tqP+B9t/8LRCQWeAK4GhgATBKRAT7jewJXAntqzJqtqkPd7t5QbYA/d41KRYB5K3Y15mqNOSuzM3LolJTAhKHdGryslOSWzJ2czv7jJdy9IJPTZZVBiDD6rMg6xKa8Y9w7tg+xDUjokcCr5DIBWOC+XgBc72ea4UCWquaoahmw0J2v2p+AHwJhc/1vt7bNuW5INxau2cOxU3YHswlfnxQcZ/nOQ0wZmRK0GyGH9WrHXydewOa8o3zrxXVUVFYFZbnR5ImlWXRpncg3hnX3OpSQqze5iEiCiNwqIj8RkV9Udw1cb2dVLQRw//o7q9Ud8K2rkucOQ0SuA/JVdaOf+VJFZL37mIDRtQUgItNFJFNEMg8ePHjWG1LTtDFpnCqr5LmPdwdtmcYE25zlObSMj+W2i4P7xMOvDuzCo9cN5L1tB3j0ja1275ePtbsPsyrnMNPGpDWJygaBPKT5deAYzqOOA25MFZH3AH/XJv400EX4GaYi0sJdxlf9jC8EeqlqkYhcCLwmIgNV9UulXFV1NjAbID09PWjfgP5dWzP2nI7M/3AXd49K/UIBQGPCQcHR07yxsYA7L0mhTfNmQV/+nZekkH/0NLOW5dC9bYuoK8h4tmYuzaZdi2ZMGt7T61AaRSDJpYeqjj/TBavqFbWNE5H9ItJVVQtFpCtwwM9keYDvf6EHUAD0AVKBje6VFj2AdSIyXFX34SZAVV0rItnAOUDmmcbfEDPGpHHr3I/51/p8Jg3/chFAY7w0/8NcFLhrVErI1vGjq86j8GgJ//f2p3Rtk/hZCf+m6pOC47z/6QEevvIcWsQHstuNfIGcc1kpIoOCvN7FwGT39WSco6Oa1gD9RCTVfbzyRGCxqm5W1U6qmqKqKThJaJiq7hORju6FAIhIGtAP5+mZjeqSPh0Y1L0NczJyrPaSCSvHS8p5cfVevjaoKz3ahe7ZITExwv+7eTAj0trzg39uZGVW0659+49l2bRKiOPOS1K8DqXR1FW4crOIbAJG4RwZbBeRTT7DG+Jx4EoR2Ylzxdfj7jq7icgSAFWtAB4E3gG2AYtUdWs9yx2DU2hzI/BP4F5VPdzAWM+YiFMSJufQSd7dtr+xV29MrV78eA/FZ1jq5WwlxMUy6450UpNbMuPZtXy6r2k+aCz30Ene2lTA7SN606ZF8Jshw5XUdsJNROo806eqUXPGOj09XTMzg9tyVlFZxWV/+ICOrRJ49f6RQV22MWejrKKKMb9bSp9OLXn+nhGNtt78o6e5YeaHxIjw6v2X0rVN80Zbdzh45JVN/Gt9Pit+dDkdkxK8DieoRGStqqb7G1fXHfq73QTy6+rXvsNCFWy0iIuN4Z5Raazbc5TMXY1+8GTMl7yxsYB9x0uYNjr0Ry2+urdtzvwpwzlRUsHU+Ws4XtJ0LtMvPHaaV9bl8c2LekZdYqlPIOdcBvr2uOc0LgxNONHl5vQetGvRjCeXWUkY4y1VZc7yHM7tnHTWpV4aYkC31vzj9mFkHSjmvufWUlbRNO6BmZORiyqN0gwZbuo65/JjETkBDBaR4253AufKLn8n4E0NLeLjuOOSFN7b5tQSMsYry3Yc5NN9J5g2Js2zelaj+3Xk/24czIdZRTzyyqaovwemqLiUF1fvYcLQ7iG9eCJc1dUs9ltVTQL+n6q2drskVe2gqj9uxBgj2uRLepMQF2MFLY2n5izPoUvrRK4b0vBSLw1x44U9ePjKc3h1fT5/+M8OT2MJtadX7qKkopL7xjW9oxao+8jlPPflyyIyrGbXSPFFvA6tErg5vQf/Wp/PgeMlXodjmqAt+cf4MKuIqSNTiI/zquLT5x68vC+Thvfk70uzeD5KK1mcKCnn6ZW7GD+wC307Nc0nlNR1N89DwHTgD37GKXB5SCKKQveMSuP5j/cwf+UufjT+vPpnMCaI5izPoVVCHJMuDo8bekWEX004n33HSvj5a1vo0jqRr/Tv7HVYQfXcqj2cKKmI2kcYB6KuZrHp7t/L/HSWWM5ASnJLrj6/C8+t2k1xaYXX4ZgmJO/IKd7cVMik4T1pnRg+91jExcbw91uHMbBbGx58YT0b9x71OqSgKSmv5KkVOYw5pyODerTxOhzPBFK4crmI/MZ9tkrTPL4Lgulj+nCipIKFq2s+IcCY0Jm3YhcCTB2Z6nUoX9IyIY55Uy4iOSmeu55ew+6i6HjQ2KLMvRwqLuOBJl5TLZAG2MnAduBGnFIwmSLyp9CGFX2G9mzLxanteWpFLuVWitw0gmOnylm4Zg9fH9KNbm3D88bFjkkJPD11OJWqTJm/hsMny7wOqUHKK6uYtSyH9N7tGJ7a3utwPFVvclHVHOBdnId6ZQAtgP4hjisqzRibRuGxEt7YWOB1KKYJeH71bk6VVTb6TZNnqk/HVsy9M538o6e5Z8EaSsoj90Fjr28oIP/oaR6I8kcYByKQZrFs4DWgM/AUcP7ZVEk2MO6cTpzTuRWzM3Ki/hp/463Sikqe/nAXo/slM6Bba6/DqVd6Snv+8s2hrN97lO8sXE9lBBZ8raxyHmHcv2trxp3b+DeqhptAmsX+ivMo4UnAt4HJItK0GxPPUkyMMG10Gp/uO8GyHcF7QJkxNb2+oYADJ0oj6s7wqwd15edfG8A7W/fzqzc/ibgfYP/Zuo+cgyd54LI+Tf6oBQJrFvuLqt4MXIHzwLBHgei++ymEJgztTufWCcy2mypNiFRVKXMycujftTWj+iZ7Hc4ZuWtUKveMSuXplbuYuzzX63ACpqo88UEWqcktufr8rl6HExYCaRb7g4h8DHwMDAF+gfOcFHMW4uNiuGtkKiuzi9icd8zrcEwUWrbjIDsPFDN9TGpE/oL+yTX9+drgrvxmyTYWR8j5yYydh9iSf5z7xvYhNiby3vNQCKRZbBVwnaoOVNV7VHWBe5LfnKVJF/ciKSGOWRnZXodiotCsjGy6tknk2sHelno5WzExwh9uHsLwlPZ8f9FGVuUUeR1SvZ5YmmVP3KwhkGaxl1XVnngVRK0Tm3Hrxb1YsrmQvYdPeR2OiSKb8o6yKucwd41MpVms96VezlZis1hm33khPds3Z/ozmezcf8LrkGq1ZtdhVuceZvqYtLAorxMu7J3wyNSRqcTGCHOX20GgCZ7ZGTkkJcQxcXhPr0NpsLYt4nl66nASmsUyZf4a9odpbb6ZS7No3zKeiReFR3mdcGHJxSNd2iQyYWh3XsrcG/E3jpnwsPfwKZZsLuTWi3uRFEalXhqiZ/sWzJ9yEUdOlTF1/pqwK5+0teAYS7cf5O5RqTSPj/U6nLASUHIRkVj3+fa9qrtQB9YUTB+TRkl5Fc9+FJ2VYU3jempFLrExEpalXhri/O5tmHnbMLbvP8F9z60NqwoXMz/IJikhjttH1PlU+CYpkKvFvgXsx7lL/y23ezPEcTUJ53RO4vLzOrHgo10RfVey8d7RU2W8tGYv1w3pTpc2iV6HE3Tjzu3Eb78xiOU7D/HjVzeHxT0wOQeLWbK5kDsv7U2b5tFxpBhMgRy5fAc4171abJDbDQ51YE3FjDFpHD5Zxstr87wOxUSw5z/ew+nySqaNia6jFl+3XNST73ylH/9cm8ef3tvpdTg8uSybhLiYqDtSDJZAksteIKg3ZIhIexF5V0R2un/b1TLdeBHZLiJZIvKIz/BHRSRfRDa43TU+437sTr9dRK4KZtyhMDy1PUN6tmXu8pyILHlhvFdSXsn8D3cx9pyOnNcl/Eu9NMR3r+jHzRf24K/v7+SlNd5VGM8/eppX1+Uz8aJeJLdK8CyOcBZIcskBPnB32g9Vdw1c7yPA+6raD6cg5iM1JxCRWOAJ4GpgADBJRAb4TPInVR3qdkvceQYAE4GBwHhgprucsCUi3Dsmjd1Fp3hn6z6vwzER6LX1+RwqjqxSL2dLRPjfGwYx5pyO/ORfW1i6/YAncVQ/trwpvOdnK5DksgfnfEs8kOTTNcQEYIH7egFwvZ9phgNZqpqjqmXAQne++pa7UFVLVTUXyHKXE9a+OrALKR1aMGtZdli0JZvIUVWlzFmew8Burbm0Twevw2kUzWJjmHnbMM7rksQDz69jS37jVro4VFzKwjV7uGFY97B9lEE4COQmysdU9THgj8AffPoborOqFrrLLwQ6+ZmmO06TXLU8d1i1B0Vkk4jM82lWq2+ez4jIdPfZNJkHD3pbRDI2RrhndBob847xce5hT2MxkeW/nx4g++BJpo9Ji8hSL2erVUIc86dcRLsW8UyZv6ZRb0ae/2EupRVV3DvW6vfWJZCrxc4XkfXAFmCriKwVkYEBzPeeiGzx09V39PHZIvwMq/5Z/w+gDzAUKAT+EMA8XxyoOltV01U1vWNH78tj33RhDzq0jLeCluaMzF6eQ/e2zblmUNMrltipdSIL7rqIsopKJs9fzdFTob9f7HhJOc+s3M01g7qS1rFVyNcXyQJpFpsNPKSqvVW1N/AwMKe+mVT1ClU930/3OrBfRLoCuH/9NZzmAb63GfcACtxl71fVSlWtcmMZXt884S6xWSyTL03hv58eYEcYl7ow4WPD3qOszj3MXaMiu9RLQ/TtlMTcyReRd/g0057JDPkl/c9+tJsTpRXc38QfYRyIQD6RLVV1aXWPqn4AtGzgehfjPD4Z9+/rfqZZA/QTkVQRicc5Ub8YPktI1b6Bc1RVvdyJIpIgIqk41ZtXNzDWRnPHiN40bxZrRy8mILMzsklKjOObF0V+qZeGGJ7anj9+cwhrdh3hoUUbqArRVZenyyqZtyKXy87tyMBubUKyjmgS0NViIvJzEUlxu58BDX3QwuPAlSKyE7jS7cetArAEQFUrgAeBd4BtwCJV3erO/zsR2Swim4DLgO+582wFFgGfAG8DD6hqxNyd2K5lPN+8qCevb8hn37HwrKNkwsPuopO8vWUft4/oTauEOK/D8dy1g7vx02v6s2TzPn6zZFtI1vHSmj0UnSzjgcv6hmT50SaQT+VdwGPAqzjnNDKAqQ1ZqaoWAV/xM7wAuManfwmwxM90d9Sx7N8Av2lIfF66e1Qqz3y0i/kf5vLja/p7HY4JU5+Verk0xetQwsY9o1PJP3qap1bk0q1tc+4eFbybG8sqqpidkcPw1Pakp7QP2nKjWb3JRVWP4Dze2DSCnu1b8LXB3Xj+4z08cHlfWkdJAUITPEdOlrEocy/XD+1Op9bRV+rlbIkIP792APuOlfDrtz6ha5vEoF3o8NqGfAqOlfDbG604SaBqbRYTkT+7f98QkcU1u0aLsAmaMSaN4tIKXvzYuzuQTfh6dtVuSsqrmGY38H1JbIzw54lDGdarHd99aQNrdjX80v7KKuXJD7I5v3trxvSLrMdGe6mucy7Pun9/j3Opb83OhMj53dtwaZ8OzPswl7KK8KkAa7xXUl7JgpW7uOzcjpzTuaH3MkenxGaxzLkznR5tmzPtmUyyDhQ3aHlvb9lHzqGTPDCub5O6l6ihak0uqrrWfTlUVZf5djj3l5gQmjG2D/uPl/L6hnyvQzFh5NV1+RSdLGP6GLsUti7tWzoPGouLEabMX82BE2d3gYyq8sTSLPp0bMlVA7sEOcroFsjVYpP9DJsS5DhMDWP6JXNelyRmZ+SE7NJKE1mqqpS5y3MY3KMNI9LspHJ9enVowbwpF1FUXMZdT6/h5Fk8aOyDHQf5pPA4943rS0yMHbWcibrOuUwSkTeA1BrnW5YCRY0XYtMkIswYm8bOA8V8sMOb4nwmvLy3bT85h04ybXTTKvXSEIN7tOWJ2y7gk4LjPPDCOirO8EFjM5dm0b1tcyYM7RaiCKNXXUcuK3HOrXzKF8+1PIxTcdiE2LWDu9GtTSJPLrObKg3MzsihR7vmXH2+Nc+cicvP68yvrx/EB9sP8rPXtgRcHHZ17mHW7DrCjLFpTbYCQkPUeimyqu4GdgOXNF44xlez2BjuGpXKr9/axvo9R7igl9/H3pgmYO3uI2TuPsIvvz6AONvRnbFbL+5FwdHT/H1pFt3aNufbX+lX7zxPLM0iuVU8t6Q37QoIZyuQwpUjRGSNiBSLSJmIVIrI8cYIzsDE4b1ISoyzkjBN3JyMHNo0b2Y7ugZ4+KvncMOw7vzx3R28nLm3zmm35B9j2Y6D3D0qjcRmYf1IqLAVyE+gvwOTgJ1Ac+Ae4G+hDMp8rlVCHHeM6M3bW/ex69BJr8MxHsg9dJJ3PtnHHSN609JKvZw1EeHxGwYzqm8yP351Mxk7an/UxswPskhKjOP2Eb0aMcLoEtDxtapmAbFuJeL5OPW8TCOZcmkKzWJimLPcjl6aoqdW5NAsJoY7L+3tdSgRLz4uhn/cPoy+nVpx33Nr2Vrw5QeNZR0o5t9b9jHl0hSSrELGWQskuZxyqxJvEJHficj3aHhVZHMGOrVO5IZh3fnn2jwOFZd6HY5pREXFpbycmccNw7rTKclKvQRDUmIznp46nNbNmzF1/hryj57+wvgnl2WTGBfL1JHBq03WFAWSXO4AYnEqFJ/EeV7KjaEMynzZPaPTKK2o4pmVu7wOxTSiZz7aTWlFFfeMth1dMHVpk8jTU4dzurySKfNWc+xUOQB5R07x2vp8Jg3vRfuW8R5HGdkCeczxblU9rarH3UccP+Q2k5lG1LdTK64c0JlnVu3mVNmZ3wxmIs/pskqeXbWbK/p3om8nK/USbOd2SWLWHReyq+gk05/NpLSikjkZOYjAtDGWzBuqrpsoN7vPqPfbNWaQxjFjTBpHT5WzaE3dV7qY6PDPdXkctlIvIXVpn2R+f/MQPs49zAPPr2fhmr3cOKwHXds09zq0iFfXpSfXNloUJiDpKe25sHc75q7I5fYRve1+hyhWWaU8tTyHIT3bclGK3d8UShOGdqfgaAn/9/anxIhT1880XH03UQIgIr2Bfqr6nog0r2s+E1rTx6Qx49m1LNmyj+uGWEmKaPXuJ/vYVXSKmePPs1IvjeDesWmflYZJTbbrlYKh3iQhItOA6UB7oA/QA3gSP0+SNKF3Zf/OpCW3ZHZGNl8f3NV2PFFIVZmVkUOv9i2sEm8jERG+FcBd+yZwgbSrPACMBI4DqOpOoFMogzK1i4kRpo1JY0v+cVZmW/3QaLR29xHW7znKPaNTibVKvCZCBZJcSlW1rLpHROIAqwHvoW9c0J3kVgnMspIwUWlWRg7tWjTj5gut1IuJXIEkl2Ui8hOguYhcCbwMvNGQlYpIexF5V0R2un/9nrEUkfEisl1EskTkEZ/hj4pIvohscLtr3OEpInLaZ/iTDYkzXCU2i2XqyBQydhzkkwIr8xZNsg8W8962/dwxojfN462mlYlcgSSXHwEHgc3ADGAJ8LMGrvcR4H1V7Qe87/Z/gYjEAk8AVwMDgEkiMsBnkj+p6lC3W+IzPNtn+L0NjDNs3X5xb1rEx1pJmCgzd3kuzWJjuPPSFK9DMaZB6kwuIhIDbFbVOap6s6re5L5uaLPYBGCB+3oBcL2faYYDWaqa4zbLLXTnM0CbFs2YeFEvFm8s+FL5ChOZDp4o5ZV1edw4rAfJrRK8DseYBqkzuahqFbBRRIJdGrSzqha66yjE/wUC3QHfuwXz3GHVHnRv6JxXo1ktVUTWi8gyERldWwAiMl1EMkUk8+DB2qujhrO73ZIg81bkehyJCYZnP9pFeaWVejHRIZBmsa7AVhF53/dxx/XNJCLvicgWP12gRx/+LpOpPmL6B85l0UOBQpwnZOK+7qWqFwAPAS+ISGt/C1fV2aqarqrpHTt2DDCk8NK9bXO+PrgrC1fv+aw2kolMp8oqeGbVbq7o35k+HVt5HY4xDRbIzZCPnc2CVfWK2saJyH4R6aqqhSLSFfD3kPg8nCKZ1XoABe6y9/ssaw7wpju8FCh1X68VkWzgHCDzbLYhEkwf04fXNhTw3Me7eeCyvl6HY87SP9fmcfRUOTPGpHkdijFBEcg5lydUdVnNroHrXQxMdl9PBl73M80aoJ+IpLol/ye68+EmpGrfALa4wzu6FwIgImlAPyCqz3gP6Naa0f2SeXrlLkrKK70Ox5yFyipl7vJchvVqS3pKe6/DMSYovDrn8jhwpYjsBK50+xGRbiKyxF13BU6Z/3eAbcAiVd3qzv+76sKaOA8u+547fAywSUQ2Av8E7lXVw0GOPezcO7YPB0+U8tr6fK9DMWfh7S372HP4FNPtqMVEEanvwi8R+S9wEbAa53kuAKjqdaENrfGkp6drZmbktpypKtf+bQWnyyt573tjibG7uiOGqnL9Ex9y7HQ57z88zu7INxFFRNaqarq/cSE752Iaj4gwfUwa31m4gfe27eerVo8qYqzOPczGvGP86vrzLbGYqBLIw8KWAZ8CSW63LQjnXEyQfW1QV7q3bc5sKwkTUeYsz6F9y3huvrCH16EYE1T1JhcRuQWnSexm4BbgYxG5KdSBmTMTFxvDPaNTydx9hLW7o/40U1TIOnCC97Yd4M5LepPYzEq9mOgSyH0uPwUuUtXJqnonzp3zPw9tWOZsfPOinrRt0YxZy+zoJRLMycglIS6GO0b09joUY4IukOQSo6q+96EUBTifaWQt4uO4Y0Rv3t22n+yDxV6HY+pw4EQJ/1qfz83pPehgpV5MFAokSbwtIu+IyBQRmQK8Bfw7tGGZszX50hSaxcYw1wpahrUFK3dRXlXF3aPs8mMTnQI5of8DYBYwGBgCzFbVH4Y6MHN2klslcNOFPXhlbT4HTpR4HY7x42RpBc+t2sNVA7rYI3VN1Ko1uYhIXxEZCaCqr6rqQ6r6PaBIRPo0WoTmjE0bnUZ5VRULVu7yOhTjx6LMvRw7Xc70sXbUYqJXXUcufwZO+Bl+yh1nwlRqckuuGtCFZz/aTXFphdfhGB8VlVU8tSKX9N7tGNbL7zPyjIkKdSWXFFXdVHOgqmYCKSGLyATFjLFpHC+p4KU1e+uf2DSaf2/ZR96R01bqxUS9upJLYh3jmgc7EBNcF/Rqx/CU9jy1PIfyyiqvwzE4pV5mZ+SQltySK/p39jocY0KqruSyRkSm1RwoIncDa0MXkgmWGWPTKDhWwlubCr0OxQCrcg6zOf8Y94xOs/pvJurVVVvsu8C/ROQ2Pk8m6UA8Tpl7E+YuO7cTfTu14sll2UwY2g0R26F5aXZGNsmt4rlhWPf6JzYmwtV65KKq+1X1UpzClbvc7jFVvURV9zVOeKYhYmKcgpaf7jvB8p2HvA6nSdux/wRLtx/kzktSrNSLaRICuc9lqar+ze3+2xhBmeCZMLQbnZISmJWR7XUoTdqcjBwSm1mpF9N0WBmXKJcQF8tdo1L5MKuILfnHvA6nSdp/vITXNuRzS3pP2rWM9zocYxqFJZcm4NaLe9EqIY5ZVo7fE0+v3EVllXKPlXoxTYgllyagdWIzbr24F0s2F7L38Cmvw2lSiksreG7Vbq4+vyu9OrTwOhxjGo0llyZi6sgUBHhqRa7XoTQpC1fv4URJBdPspknTxFhyaSK6tmnOhKHdeWnNXo6cLPM6nCahvLKK+R/uYnhqe4b2bOt1OMY0Kk+Si4i0F5F3RWSn+9dvkSURGS8i20UkS0QeqTHuW+64rSLyO5/hP3an3y4iV4V6WyLJ9DFpnC6v5NlVu70OpUlYsrmQ/KOnmT7ajlpM0+PVkcsjwPuq2g943+3/AhGJBZ4ArgYGAJNEZIA77jJgAjBYVQcCv3eHDwAmAgOB8cBMdzkGOLdLEped25EFK3dRUl7pdThRrbrUS5+OLbn8vE5eh2NMo/MquUwAFrivFwDX+5lmOJClqjmqWgYsdOcDuA94XFVLAXyelDkBWKiqpaqaC2S5yzGu6WP6UHSyjH+uzfM6lKi2MruIrQXHmT7GSr2Ypsmr5NJZVQsB3L/+ftp1B3xL+ua5wwDOAUaLyMciskxELgpgHgOMSGvPkB5tmLs8h8oq9TqcqDUrI4fkVglMGGofP9M0hSy5iMh7IrLFTzeh/rmdRfgZVr03jAPaASOAHwCLxCmcVdc8NeObLiKZIpJ58ODBAEOKfCLC9DF92FV0iv9stSo+obCt8DgZOw4ydaSVejFNV12FKxtEVa+obZyI7BeRrqpaKCJdgQN+JssDevr09wAKfMa9qqoKrBaRKiC5nnlqxjcbmA2Qnp7epH7Cjz+/C73at+DJjBzGn9/FCloG2ZzlObSIj+W2i3t5HYoxnvGqWWwxMNl9PRl43c80a4B+IpIqIvE4J+oXu+NeAy4HEJFzcCo1H3LHTxSRBBFJBfoBq0O1EZEqNkaYNjqVjXuPsjr3sNfhRJXCY6dZvKGAW9J70raFlXoxTZdXyeVx4EoR2Qlc6fYjIt1EZAmAqlYADwLvANuARaq61Z1/HpAmIltwTvRPVsdWYBHwCfA28ICq2mVRftx0YU/at4xntpWECaqnP9xFlSp3j0r1OhRjPBWyZrG6qGoR8BU/wwuAa3z6lwBL/ExXBtxey7J/A/wmaMFGqebxsdx5SW/+/N5Odu4/Qb/OSV6HFPFOlJTzwsd7uGZQV3q2t1IvpmmzO/SbMOfZIjF29BIkC1fv5URpBdOt1IsxllyasvYt47klvSevbchn37ESr8OJaOWVVcz7MJcRae0Z3KOt1+EY4zlLLk3cPaPSqKxS5q+0gpYN8eamAgqPlTBjTB+vQzEmLFhyaeJ6dWjB1YO68sKqPZwoKfc6nIikqsxalkO/Tq0Yd25Hr8MxJixYcjHMGJPGidIKXly9x+tQItLynYf4dN8Jpo1Js3uGjHFZcjEM7tGWS9I6MG/FLsoqqrwOJ+LMWZ5Dp6QEJgzt5nUoxoQNSy4GgOlj09h3vITFG/0WNDC12FpwjOU7DzFlZAoJcVbqxZhqllwMAOPO6ci5nZOYnZGNU1XHBGLu8lxaxsdy28W9vQ7FmLBiycUA1QUt09ixv5gPtjedQp4NUXD0NG9sLGDi8F60ad7M63CMCSuWXMxnvj6kG13bJDIrI9vrUCLCvBW5KDB1ZIrXoRgTdiy5mM/Ex8Vw18hUVuUcZuPeo16HE9aOnS7nxdV7uHZwV3q0s1IvxtRkycV8wcThPUlKjLOSMPV4cfUeTpZVMm20lXoxxh9LLuYLkhKbcdvFvfn3lkJ2F530OpywVFZRxfwPcxnZtwPnd2/jdTjGhCVLLuZLpo5MIS4mhrnLrSSMP4s3FrD/eCnTrdSLMbWy5GK+pHPrRK6/oBuLMvdSVFzqdThhRVWZk5HDeV2SGNMv2etwjAlbllyMX9PHpFFaUcUzH+32OpSwsmzHQbbvP8G00VbqxZi6WHIxfvXtlMQV/TvxzEe7OF1mD/OsNjsjhy6tE/n6ECv1YkxdLLmYWs0Y24cjp8p5ee1er0MJC1vyj7Eyu4ipI1OIj7OvjjF1sW+IqVV673Zc0Kstc5bnUFFpBS1nZ+TQKiGOSRf38joUY8KeJRdTKxFhxpg+7D18mre37vM6HE/lHTnFW5sLmTS8J60TrdSLMfWx5GLqdOWAzqQmt2TWspwmXdBy3opdCDB1ZKrXoRgTETxJLiLSXkTeFZGd7t92tUw3XkS2i0iWiDxSY9y33HFbReR37rAUETktIhvc7snG2J5oFhsjTBudxub8Y3yUU+R1OJ44VFzKwjV7+PqQbnRr29zrcIyJCHEerfcR4H1VfdxNGo8AP/KdQERigSeAK4E8YI2ILFbVT0TkMmACMFhVS0Wkk8+s2ao6tFG2oom4YVh3/vjudmYty+HSPpF/b4eqcux0OYeKSzlUXMah4lKK3L/+hp1yr5azUi/GBM6r5DIBGOe+XgB8QI3kAgwHslQ1B0BEFrrzfQLcBzyuqqUAqnog9CE3XYnNYpl8SQp/eHcH2wqP079ra69D+pLyyioOnyzj4IlSik6WcehEKUUnP08Uh4o/H1ZUXEZF1Zeb+GIE2reMJ7lVAh1axdOrV1s6tEwgOSmeoT3aMqBb+G23MeHKq+TSWVULAVS1sMaRR7XugO81sHnAxe7rc4DRIvIboAT4vqquccelish64DjwM1Vd7i8AEZkOTAfo1cuu/qnPHZf0ZuYH2czJyOGP3xzaKOs8WVpBUXEZB4tLKXITRFH10YWbQA4VO8nk6Klyv8uIj4uhY6sEklvF06VNIud3b02HVgkku8OS3dcdWsXTrkU8sTF2Y6QxwRCy5CIi7wFd/Iz6aaCL8DOs+udmHNAOGAFcBCwSkTSgEOilqkUiciHwmogMVNXjX1qQ6mxgNkB6enrTPVMdoLYt4vnmRT15btVuvn/VuWd17qGqSjl6upyi4lI3YdRskvq8aaqouIzT5f5v3kxKjHMTRgLndE76LDn4JowO7utWCXF2J70xHghZclHVK2obJyL7RaSre9TSFfDXrJUH9PTp7wEU+Ix7VZ3Ll1aLSBWQrKoHgeqmsrUiko1zlJPZ8C0yd49K5dlVu5m3IpefXTsAcCoEHz7pJIUvJowvNkkVFZdy+GRdzVGfJ4aUDi2+kCB8jy46tIq3Z9UbEwG8ahZbDEwGHnf/vu5nmjVAPxFJBfKBicCt7rjXgMuBD0TkHCAeOCQiHYHDqlrpHsn0A+zBJEHSs30LvjaoK8+u2s1/tx+gqLiMY6f9N0clxMU4SSEpgW5tEhncvc3nRxdJCSS3jCc5KYEOLZ3mqBhrjjImqniVXB7Hacq6G9gD3AwgIt2Auap6japWiMiDwDtALDBPVbe6888D5onIFqAMmKyqKiJjgP8RkQqgErhXVQ837qZFt+9c0Y8jp8pISoxzjibcE94dWibQMenzJqmW8bHWHGVMEyZN+ca4aunp6ZqZaS1nxhhzJkRkraqm+xtnd+gbY4wJOksuxhhjgs6SizHGmKCz5GKMMSboLLkYY4wJOksuxhhjgs6SizHGmKCz5GKMMSbo7CZKQEQOArsbsIhk4FCQwokETW17wba5qbBtPjO9VbWjvxGWXIJARDJru0s1GjW17QXb5qbCtjl4rFnMGGNM0FlyMcYYE3SWXIJjttcBNLKmtr1g29xU2DYHiZ1zMcYYE3R25GKMMSboLLkYY4wJOksuxhhjgs6Siw8RGS8i20UkS0QeacBynhaRm4IZW6iJyDwROeA+Orohywmbba9vm0RknIgcE5ENbveLBq6vuCHzh4qI9BSRpSKyTUS2ish3GrCssPn/1kVEEkVktYhsdLf5sQYsKyK2GUBEYkVkvYi82YBlBGV7Lbm4RCQWeAK4GhgATBKRAd5G1aieBsZ7HUSQPU3927RcVYe63f80QkxeqAAeVtX+wAjggSbw2S4FLlfVIcBQYLyIjPA2pEbxHWCb10GAJRdfw4EsVc1R1TJgITCh5kQi0klE1rqvh4iIikgvtz9bRFrUmP5X7i+BGBF5XEQ+EZFNIvL7RtimgKlqBnC4rmkasu0hC7wOgWxToETkhyLybff1n0Tkv+7rr4jIczWmTRaRj0TkayLSVUQy3COjLSIyOhjxnAlVLVTVde7rEzg7n+41p4umz7Y6qo8km7ndly6NjaZtFpEewNeAuXVM02jbG9ewzYkq3YG9Pv15wMU1J1LVA+4hd2tgNJAJjBaRFcABVT0lIgCIyO+ANsBUoB3wDeA8VVURaRvKjQmFs912De/r3S8RkY1AAfB9Vd1ay3QZwMPAX4F0IEFEmgGjgOXVE4lIZ2Ax8DNVfVdEHgbeUdXfuEfHLb686MYjIinABcDHNcdF22fbfb/XAn2BJ1Q12rf5z8APgaTaJmjM7bUjl8+Jn2G17RRXAiOBMcD/un9H47OTAX4OtFXVGe7O9ThQAswVkRuAU8EKvJGdzbaHq3U4hfeGAH8DXqtj2rXAhSKShNPk8hFOkvHd9mbA+8APVfVdd9gaYKqIPAoMco8cPCEirYBXgO+q6vFaJouaz7aqVqrqUKAHMFxEzq9l0ojfZhG5Fic5rA1g8kbZXksun8sDevr098D5NevPcpx/Rm/gdWAIzi/YDJ9p1uDsjNoDqGoFTtPbK8D1wNtBjL0xnfG2hytVPV7ddKKqS4BmIpJcy7TlwC6cX3Ercd6Hy4A+fN7GXYGThK7ymS8D58ubDzwrIneGZGPq4R5lvQI8r6qv1jFp1H22VfUo8AG1n3+Lhm0eCVwnIrtwmvQvr9lc66NxtldVrXN+XMcBOUAqEA9sBAbWMm0KsAd4zu1f4va3c/ufBm4Cbsb5hZsEtAI6uePbA4e93uZatmtLANOc0baH6zYBXfi8SsVwdzukjmU96k5zBdDZff0vn/HFQCzwKvCIO6w3EOe+/i7wZw/eAwGeCWTd0fLZBjri/PIGaI6zQ702mrfZZ3vGAW96/T+2cy4uVa0QkQeBd3B2EPO0lvZ3Vd3ltktWZ/oVQA9VPVJjupfdZpTFwK3A6yKSiPNl/15otuTsiMiLOB/KZBHJA36pqk/VnO5stl1ErlHV0yHdAD/8bRNO0xWq+iTOF+g+EakATgMT1f321GI58FPgI1U9KSIlfLEpAVWtFJGJwBsichw4CfxARMpxko8XRy4jgTuAzSKywR32E3WO1r4gij7bXYEF7nmXGGCRqvq9PDeKtjkgjbW9VlvMGGNM0Nk5F2OMMUFnzWJ1EJEncJoUfP1FVed7EU9jaqrbLiIdcK74qukrqlrU2PGESlP8/za1bfZ6e61ZzBhjTNBZs5gxxpigs+RijDEm6Cy5GBNEEoLKyCLykxr9K4O9DmOCzc65GBNEIlKsqq3CfZnGhJoduRgTYiLydRH5WJznbLznFrdERDqKyLsisk5EZonI7prlZ0TkcaC5OFWVn3eHFbt/x4nIMhFZJCI73Kq1t4nzHJPNItLHZz2viMgat6t5BZExQWdHLsYEkb+jDBFpBxxVVRWRe4D+qvqwiPwdyFfV34rIeODfQEdVPVTXMqv7RWQcTrHN/jiPFsgB5qrqL8V5IFiqqn5XRF4AZqrqCnHKq7+jzrNdjAkZu8/FmNDrAbwkIl1x6tblusNH4ZQxR1XfFpEjtcxflzWqWgjO8ziA/7jDN+MU1gSnFtqA6lLqQGsRSVIPKzSb6GfJxZjQ+xvwR1Vd7B5tPOoO9/eYhzNV6vO6yqe/is+/3zHAJV7UdzNNl51zMSb02uCU3AeY7DN8BXALgIh8FeeBTP6UuyXzz9Z/gAere0RkaAOWZUxALLkYE1wtRCTPp3sI50jlZRFZDvieT3kM+KqIrAOuBgoBf01Vs4FN1Sf0z8K3gXRxHk/7CXDvWS7HmIDZCX1jPCIiCUCl+7iHS4B/qPPkRGMinp1zMcY7vYBFIhIDlAHTPI7HmKCxIxdjjDFBZ+dcjDHGBJ0lF2OMMUFnycUYY0zQWXIxxhgTdJZcjDHGBN3/BzuLQ/sUjDqkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vt_corr.drop(columns=['VT_PRCP(mm)', 'new_case_percent_pop*'], inplace=True)\n",
    "vt_corr.rename(columns={'VT_Conf_Cases': '0_wks'}, inplace=True)\n",
    "vt_corr.loc['VT_PRCP(mm)', :].plot()\n",
    "plt.title('Correlation of PRCP vs lagged COV cases: VT')\n",
    "plt.xlabel('Lag time')\n",
    "plt.ylabel('Correlation with PRCP')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e16fcd4",
   "metadata": {},
   "source": [
    "#### Maine PRCP lag cross-correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "20389f0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_me4=df_me3.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0f5c2f00",
   "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>ME_PRCP(mm)</th>\n",
       "      <th>ME_Conf_Cases</th>\n",
       "      <th>new_case_percent_pop*</th>\n",
       "      <th>1_wk</th>\n",
       "      <th>1.5_wks</th>\n",
       "      <th>2_wks</th>\n",
       "      <th>3_wks</th>\n",
       "      <th>4_wks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ME_PRCP(mm)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002177</td>\n",
       "      <td>0.002177</td>\n",
       "      <td>0.024677</td>\n",
       "      <td>0.092199</td>\n",
       "      <td>0.027075</td>\n",
       "      <td>0.075240</td>\n",
       "      <td>0.118860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME_Conf_Cases</th>\n",
       "      <td>0.002177</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.895255</td>\n",
       "      <td>0.758257</td>\n",
       "      <td>0.800753</td>\n",
       "      <td>0.681238</td>\n",
       "      <td>0.547517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_case_percent_pop*</th>\n",
       "      <td>0.002177</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.895255</td>\n",
       "      <td>0.758257</td>\n",
       "      <td>0.800753</td>\n",
       "      <td>0.681238</td>\n",
       "      <td>0.547517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1_wk</th>\n",
       "      <td>0.024677</td>\n",
       "      <td>0.895255</td>\n",
       "      <td>0.895255</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.803828</td>\n",
       "      <td>0.891650</td>\n",
       "      <td>0.786226</td>\n",
       "      <td>0.664028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5_wks</th>\n",
       "      <td>0.092199</td>\n",
       "      <td>0.758257</td>\n",
       "      <td>0.758257</td>\n",
       "      <td>0.803828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.807657</td>\n",
       "      <td>0.744874</td>\n",
       "      <td>0.659837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2_wks</th>\n",
       "      <td>0.027075</td>\n",
       "      <td>0.800753</td>\n",
       "      <td>0.800753</td>\n",
       "      <td>0.891650</td>\n",
       "      <td>0.807657</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.886078</td>\n",
       "      <td>0.770015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3_wks</th>\n",
       "      <td>0.075240</td>\n",
       "      <td>0.681238</td>\n",
       "      <td>0.681238</td>\n",
       "      <td>0.786226</td>\n",
       "      <td>0.744874</td>\n",
       "      <td>0.886078</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.872222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4_wks</th>\n",
       "      <td>0.118860</td>\n",
       "      <td>0.547517</td>\n",
       "      <td>0.547517</td>\n",
       "      <td>0.664028</td>\n",
       "      <td>0.659837</td>\n",
       "      <td>0.770015</td>\n",
       "      <td>0.872222</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       ME_PRCP(mm)  ME_Conf_Cases  new_case_percent_pop*  \\\n",
       "ME_PRCP(mm)               1.000000       0.002177               0.002177   \n",
       "ME_Conf_Cases             0.002177       1.000000               1.000000   \n",
       "new_case_percent_pop*     0.002177       1.000000               1.000000   \n",
       "1_wk                      0.024677       0.895255               0.895255   \n",
       "1.5_wks                   0.092199       0.758257               0.758257   \n",
       "2_wks                     0.027075       0.800753               0.800753   \n",
       "3_wks                     0.075240       0.681238               0.681238   \n",
       "4_wks                     0.118860       0.547517               0.547517   \n",
       "\n",
       "                           1_wk   1.5_wks     2_wks     3_wks     4_wks  \n",
       "ME_PRCP(mm)            0.024677  0.092199  0.027075  0.075240  0.118860  \n",
       "ME_Conf_Cases          0.895255  0.758257  0.800753  0.681238  0.547517  \n",
       "new_case_percent_pop*  0.895255  0.758257  0.800753  0.681238  0.547517  \n",
       "1_wk                   1.000000  0.803828  0.891650  0.786226  0.664028  \n",
       "1.5_wks                0.803828  1.000000  0.807657  0.744874  0.659837  \n",
       "2_wks                  0.891650  0.807657  1.000000  0.886078  0.770015  \n",
       "3_wks                  0.786226  0.744874  0.886078  1.000000  0.872222  \n",
       "4_wks                  0.664028  0.659837  0.770015  0.872222  1.000000  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for key, value in prcp_cov_lags.items():\n",
    "    df_me4[key] = df_me4['ME_Conf_Cases'].shift(value, axis=0)\n",
    "df_me4 = df_me4.drop(['ME_Avg_Temp(F)', 'day_of_week', 'day_of_year', 'Year', 'Month', 'Day'], axis =1).dropna()\n",
    "me_corr = pd.DataFrame(df_me4.corr())\n",
    "me_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ae7e3586",
   "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": [
    "me_corr.drop(columns=['ME_PRCP(mm)', 'new_case_percent_pop*'], inplace=True)\n",
    "me_corr.rename(columns={'ME_Conf_Cases': '0_wks'}, inplace=True)\n",
    "plt.title('Correlation of PRCP vs lagged COV cases: ME')\n",
    "me_corr.loc['ME_PRCP(mm)', :].plot()\n",
    "plt.xlabel('Lag time')\n",
    "plt.ylabel('Correlation with PRCP')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dbad23c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "lag_cols = ['1_wk', '1.5_wks', '2_wks', '3_wks', '4_wks']\n",
    "df_list = [df_ma4, df_ct4, df_me4, df_vt4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "dfb0c077",
   "metadata": {},
   "outputs": [],
   "source": [
    "for df in df_list:\n",
    "    df.drop(columns=lag_cols, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4086f731",
   "metadata": {},
   "source": [
    "## Merge state dataframes back into one dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74695ad5",
   "metadata": {},
   "source": [
    "In order to do this, first we'll make a separate column in each state's dataframe indicating state_id. With this new identifier column, we'll no longer need state-specific identifiers in the column names themselves, and we can update each state's column names to mirror each other in preparation for the merge. Before we do this, we can go ahead and drop the PRCP columns for each state, as precipitation has proved to have little correlation with (and even less predictive power of) COVID19 cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a6681372",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create identifying state_id columns for each state df\n",
    "df_ma3['state_id'] = 'MA'\n",
    "df_ct3['state_id'] = 'CT'\n",
    "df_vt3['state_id'] = 'VT'\n",
    "df_me3['state_id'] = 'ME'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bfb31abb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare df_ma3 for merge by updating column names\n",
    "df_ma3.drop('MA_PRCP(mm)', axis=1, inplace=True)\n",
    "ma_cols = {'MA_Avg_Temp(F)': 'Avg_Temp(F)',\n",
    "           'MA_Conf_Cases' : 'Conf_Cases'}\n",
    "df_ma3.rename(columns=ma_cols, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7b30ff82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare df_ct3 for merge by updating column names\n",
    "df_ct3.drop('CT_PRCP(mm)', axis=1, inplace=True)\n",
    "ct_cols = {'CT_Avg_Temp(F)': 'Avg_Temp(F)',\n",
    "           'CT_Conf_Cases' : 'Conf_Cases'}\n",
    "df_ct3.rename(columns=ct_cols, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4d0d81fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare df_vt3 for merge by updating column names\n",
    "df_vt3.drop('VT_PRCP(mm)', axis=1, inplace=True)\n",
    "vt_cols = {'VT_Avg_Temp(F)': 'Avg_Temp(F)',\n",
    "           'VT_Conf_Cases' : 'Conf_Cases'}\n",
    "df_vt3.rename(columns=vt_cols, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "90ac9de1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare df_me3 for merge by updating column names\n",
    "df_me3.drop('ME_PRCP(mm)', axis=1, inplace=True)\n",
    "me_cols = {'ME_Avg_Temp(F)': 'Avg_Temp(F)',\n",
    "           'ME_Conf_Cases' : 'Conf_Cases'}\n",
    "df_me3.rename(columns=me_cols, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a837e61b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df_ma3, df_me3, df_vt3, df_ct3], axis=0).set_index('date').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3c8f0f71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the data to a new csv file\n",
    "df.to_csv('COVID19_modeling.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0746c25",
   "metadata": {},
   "source": [
    "## Mapping with geopandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a669224e",
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = '~/Test/Springboard/Capstone_COVID19_Weather/Data/geo_export/geo_export_13390475-3a1c-4c1a-b57d-965976a165df.shp'\n",
    "states = (gpd.read_file(fp)).to_crs(\"EPSG:3395\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9e0d614d",
   "metadata": {},
   "outputs": [],
   "source": [
    "states2= states.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4b4bc0b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "states2.drop(columns=['drawseq'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "02a4f93e",
   "metadata": {},
   "outputs": [],
   "source": [
    "cov_states = states2[states2['state_abbr'].isin(['MA', 'ME', 'CT', 'VT'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "bab8616b",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = pd.DataFrame({'state_abbr':['MA', 'ME', 'CT', 'VT'],\n",
    "        'total_cases':[df_ma4['MA_Conf_Cases'].sum(), df_me4['ME_Conf_Cases'].sum(), \n",
    "                       df_ct4['CT_Conf_Cases'].sum(), df_vt4['VT_Conf_Cases'].sum()]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6fe36f0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = pd.merge(cov_states, new_df, on='state_abbr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0e2871c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_col(col, df, title):\n",
    "    # create figure and axes for Matplotlib\n",
    "    fig, ax = plt.subplots(1, figsize=(22, 14))\n",
    "    # create map\n",
    "    df.plot(column=col, cmap='flare', legend=True, linewidth=0.8, ax=ax, edgecolor='0.8')\n",
    "    # remove the axis\n",
    "    ax.axis('off')\n",
    "    # add a title & annotate\n",
    "    ax.set_title(title, fontdict={'fontsize': '14', 'fontweight' : '3'})\n",
    "    df.apply(lambda x: ax.annotate(s=x.state_name + \"\\n\" + str(round(x.total_cases)), \n",
    "                                   xy=x.geometry.centroid.coords[0], \n",
    "                                   ha='center', \n",
    "                                   fontsize=14, \n",
    "                                   color='#ffe6cc'),axis=1)\n",
    "    plt.savefig(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "310784bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "start_date = datetime(2020, 3, 1)\n",
    "end_date = datetime(2021, 9, 12)\n",
    "\n",
    "dates = pd.date_range(start_date, end_date, freq='D')\n",
    "\n",
    "options = [(date.strftime('%d/%m/%Y'), date) for date in dates]\n",
    "index = (0, len(options)-1)\n",
    "\n",
    "selection_range_slider = widgets.SelectionRangeSlider(\n",
    "    options=options,\n",
    "    index=index,\n",
    "    description='Date',\n",
    "    orientation='horizontal',\n",
    "    layout={'width': '600px'}\n",
    ")\n",
    "\n",
    "#selection_range_slider"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "be97b406",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b60eaf259fee439c86a11b9a9800f74e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(SelectionRangeSlider(description='Date', index=(0, 560), layout=Layout(width='600px'), o…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.cov_plot(date)>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df = pd.DataFrame()\n",
    "for i in df.date.unique():\n",
    "    mergetemp =cov_states.merge(df[df.date==i],left_on='state_abbr' ,right_on='state_id',how='left')\n",
    "    merged_df = merged_df.append(mergetemp)\n",
    "def cov_plot(date):\n",
    "    merged_df[merged_df.date==date[0]].plot(column='new_case_percent_pop*',figsize=(20,9), legend = True)\n",
    "\n",
    "interact(cov_plot,date=selection_range_slider)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8b3eda3",
   "metadata": {},
   "source": [
    "## Moving average plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0789d6d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfor i in df[\\'state_id\\'].unique():\\n    subset = df[df[\\'state_id\\'] == i]\\n    subset.loc[:,\\'moving_average\\'] = subset[\\'new_case_percent_pop*\\'].rolling(7).mean()\\n    fig = px.line(subset, x=\"date\", y=[\"new_case_percent_pop*\",\"moving_average\"], title = (i + \\' weekly rolling average\\'), template = \\'plotly_dark\\')\\n    fig.show()\\n    #plt.savefig(i)\\n    #plt.show()\\n'"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "for i in df['state_id'].unique():\n",
    "    subset = df[df['state_id'] == i]\n",
    "    subset.loc[:,'moving_average'] = subset['new_case_percent_pop*'].rolling(7).mean()\n",
    "    fig = px.line(subset, x=\"date\", y=[\"new_case_percent_pop*\",\"moving_average\"], title = (i + ' weekly rolling average'), template = 'plotly_dark')\n",
    "    fig.show()\n",
    "    #plt.savefig(i)\n",
    "    #plt.show()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "eb8e26ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfor i in df[\\'state_id\\'].unique():\\n    subset = df[df[\\'state_id\\'] == i]\\n    subset.loc[:,\\'moving_average\\'] = subset[\\'new_case_percent_pop*\\'].rolling(14).mean()\\n    fig = px.line(subset, x=\"date\", y=[\"new_case_percent_pop*\",\"moving_average\"], title = (i + \\' weekly rolling average\\'), template = \\'plotly_dark\\')\\n    fig.show()\\n'"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "for i in df['state_id'].unique():\n",
    "    subset = df[df['state_id'] == i]\n",
    "    subset.loc[:,'moving_average'] = subset['new_case_percent_pop*'].rolling(14).mean()\n",
    "    fig = px.line(subset, x=\"date\", y=[\"new_case_percent_pop*\",\"moving_average\"], title = (i + ' weekly rolling average'), template = 'plotly_dark')\n",
    "    fig.show()\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83fd1a8f",
   "metadata": {},
   "source": [
    "## Determining optimal models with pycaret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "4c24e98c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                     datetime64[ns]\n",
       "Avg_Temp(F)                     float64\n",
       "Conf_Cases                      float64\n",
       "day_of_week                       int64\n",
       "day_of_year                       int64\n",
       "Year                              int64\n",
       "Month                             int64\n",
       "Day                               int64\n",
       "new_case_percent_pop*           float64\n",
       "state_id                         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c5dcc846",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "            text-align:  left;\n",
       "            text-align:  left;\n",
       "            background-color:  yellow;\n",
       "            background-color:  lightgrey;\n",
       "        }</style><table id=\"T_4c407_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>        <th class=\"col_heading level0 col7\" >TT (Sec)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_4c407_level0_row0\" class=\"row_heading level0 row0\" >catboost</th>\n",
       "                        <td id=\"T_4c407_row0_col0\" class=\"data row0 col0\" >CatBoost Regressor</td>\n",
       "                        <td id=\"T_4c407_row0_col1\" class=\"data row0 col1\" >5.4085</td>\n",
       "                        <td id=\"T_4c407_row0_col2\" class=\"data row0 col2\" >82.3826</td>\n",
       "                        <td id=\"T_4c407_row0_col3\" class=\"data row0 col3\" >8.9722</td>\n",
       "                        <td id=\"T_4c407_row0_col4\" class=\"data row0 col4\" >0.7989</td>\n",
       "                        <td id=\"T_4c407_row0_col5\" class=\"data row0 col5\" >0.5617</td>\n",
       "                        <td id=\"T_4c407_row0_col6\" class=\"data row0 col6\" >1.2713</td>\n",
       "                        <td id=\"T_4c407_row0_col7\" class=\"data row0 col7\" >0.3000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4c407_level0_row1\" class=\"row_heading level0 row1\" >rf</th>\n",
       "                        <td id=\"T_4c407_row1_col0\" class=\"data row1 col0\" >Random Forest Regressor</td>\n",
       "                        <td id=\"T_4c407_row1_col1\" class=\"data row1 col1\" >5.4007</td>\n",
       "                        <td id=\"T_4c407_row1_col2\" class=\"data row1 col2\" >88.5067</td>\n",
       "                        <td id=\"T_4c407_row1_col3\" class=\"data row1 col3\" >9.2734</td>\n",
       "                        <td id=\"T_4c407_row1_col4\" class=\"data row1 col4\" >0.7850</td>\n",
       "                        <td id=\"T_4c407_row1_col5\" class=\"data row1 col5\" >0.5154</td>\n",
       "                        <td id=\"T_4c407_row1_col6\" class=\"data row1 col6\" >0.8268</td>\n",
       "                        <td id=\"T_4c407_row1_col7\" class=\"data row1 col7\" >0.0833</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4c407_level0_row2\" class=\"row_heading level0 row2\" >gbr</th>\n",
       "                        <td id=\"T_4c407_row2_col0\" class=\"data row2 col0\" >Gradient Boosting Regressor</td>\n",
       "                        <td id=\"T_4c407_row2_col1\" class=\"data row2 col1\" >6.1456</td>\n",
       "                        <td id=\"T_4c407_row2_col2\" class=\"data row2 col2\" >94.8521</td>\n",
       "                        <td id=\"T_4c407_row2_col3\" class=\"data row2 col3\" >9.6102</td>\n",
       "                        <td id=\"T_4c407_row2_col4\" class=\"data row2 col4\" >0.7697</td>\n",
       "                        <td id=\"T_4c407_row2_col5\" class=\"data row2 col5\" >0.6479</td>\n",
       "                        <td id=\"T_4c407_row2_col6\" class=\"data row2 col6\" >1.8508</td>\n",
       "                        <td id=\"T_4c407_row2_col7\" class=\"data row2 col7\" >0.0233</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4c407_level0_row3\" class=\"row_heading level0 row3\" >et</th>\n",
       "                        <td id=\"T_4c407_row3_col0\" class=\"data row3 col0\" >Extra Trees Regressor</td>\n",
       "                        <td id=\"T_4c407_row3_col1\" class=\"data row3 col1\" >5.6182</td>\n",
       "                        <td id=\"T_4c407_row3_col2\" class=\"data row3 col2\" >104.7353</td>\n",
       "                        <td id=\"T_4c407_row3_col3\" class=\"data row3 col3\" >10.1806</td>\n",
       "                        <td id=\"T_4c407_row3_col4\" class=\"data row3 col4\" >0.7377</td>\n",
       "                        <td id=\"T_4c407_row3_col5\" class=\"data row3 col5\" >0.4865</td>\n",
       "                        <td id=\"T_4c407_row3_col6\" class=\"data row3 col6\" >0.7529</td>\n",
       "                        <td id=\"T_4c407_row3_col7\" class=\"data row3 col7\" >0.0767</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4c407_level0_row4\" class=\"row_heading level0 row4\" >lightgbm</th>\n",
       "                        <td id=\"T_4c407_row4_col0\" class=\"data row4 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_4c407_row4_col1\" class=\"data row4 col1\" >6.5687</td>\n",
       "                        <td id=\"T_4c407_row4_col2\" class=\"data row4 col2\" >117.5741</td>\n",
       "                        <td id=\"T_4c407_row4_col3\" class=\"data row4 col3\" >10.7080</td>\n",
       "                        <td id=\"T_4c407_row4_col4\" class=\"data row4 col4\" >0.7138</td>\n",
       "                        <td id=\"T_4c407_row4_col5\" class=\"data row4 col5\" >0.6299</td>\n",
       "                        <td id=\"T_4c407_row4_col6\" class=\"data row4 col6\" >1.4978</td>\n",
       "                        <td id=\"T_4c407_row4_col7\" class=\"data row4 col7\" >0.0800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4c407_level0_row5\" class=\"row_heading level0 row5\" >xgboost</th>\n",
       "                        <td id=\"T_4c407_row5_col0\" class=\"data row5 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_4c407_row5_col1\" class=\"data row5 col1\" >6.1255</td>\n",
       "                        <td id=\"T_4c407_row5_col2\" class=\"data row5 col2\" >115.8177</td>\n",
       "                        <td id=\"T_4c407_row5_col3\" class=\"data row5 col3\" >10.7480</td>\n",
       "                        <td id=\"T_4c407_row5_col4\" class=\"data row5 col4\" >0.7035</td>\n",
       "                        <td id=\"T_4c407_row5_col5\" class=\"data row5 col5\" >0.5549</td>\n",
       "                        <td id=\"T_4c407_row5_col6\" class=\"data row5 col6\" >1.5577</td>\n",
       "                        <td id=\"T_4c407_row5_col7\" class=\"data row5 col7\" >0.1800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4c407_level0_row6\" class=\"row_heading level0 row6\" >dt</th>\n",
       "                        <td id=\"T_4c407_row6_col0\" class=\"data row6 col0\" >Decision Tree Regressor</td>\n",
       "                        <td id=\"T_4c407_row6_col1\" class=\"data row6 col1\" >6.3990</td>\n",
       "                        <td id=\"T_4c407_row6_col2\" class=\"data row6 col2\" >138.5668</td>\n",
       "                        <td id=\"T_4c407_row6_col3\" class=\"data row6 col3\" >11.6107</td>\n",
       "                        <td id=\"T_4c407_row6_col4\" class=\"data row6 col4\" >0.6635</td>\n",
       "                        <td id=\"T_4c407_row6_col5\" class=\"data row6 col5\" >0.5451</td>\n",
       "                        <td id=\"T_4c407_row6_col6\" class=\"data row6 col6\" >0.6479</td>\n",
       "                        <td id=\"T_4c407_row6_col7\" class=\"data row6 col7\" >0.0067</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4c407_level0_row7\" class=\"row_heading level0 row7\" >lr</th>\n",
       "                        <td id=\"T_4c407_row7_col0\" class=\"data row7 col0\" >Linear Regression</td>\n",
       "                        <td id=\"T_4c407_row7_col1\" class=\"data row7 col1\" >8.1727</td>\n",
       "                        <td id=\"T_4c407_row7_col2\" class=\"data row7 col2\" >138.6516</td>\n",
       "                        <td id=\"T_4c407_row7_col3\" class=\"data row7 col3\" >11.6809</td>\n",
       "                        <td id=\"T_4c407_row7_col4\" class=\"data row7 col4\" >0.6586</td>\n",
       "                        <td id=\"T_4c407_row7_col5\" class=\"data row7 col5\" >0.8615</td>\n",
       "                        <td id=\"T_4c407_row7_col6\" class=\"data row7 col6\" >3.3170</td>\n",
       "                        <td id=\"T_4c407_row7_col7\" class=\"data row7 col7\" >0.0067</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4c407_level0_row8\" class=\"row_heading level0 row8\" >br</th>\n",
       "                        <td id=\"T_4c407_row8_col0\" class=\"data row8 col0\" >Bayesian Ridge</td>\n",
       "                        <td id=\"T_4c407_row8_col1\" class=\"data row8 col1\" >8.1854</td>\n",
       "                        <td id=\"T_4c407_row8_col2\" class=\"data row8 col2\" >141.1920</td>\n",
       "                        <td id=\"T_4c407_row8_col3\" class=\"data row8 col3\" >11.7737</td>\n",
       "                        <td id=\"T_4c407_row8_col4\" class=\"data row8 col4\" >0.6537</td>\n",
       "                        <td id=\"T_4c407_row8_col5\" class=\"data row8 col5\" >0.8714</td>\n",
       "                        <td id=\"T_4c407_row8_col6\" class=\"data row8 col6\" >3.2830</td>\n",
       "                        <td id=\"T_4c407_row8_col7\" class=\"data row8 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4c407_level0_row9\" class=\"row_heading level0 row9\" >ridge</th>\n",
       "                        <td id=\"T_4c407_row9_col0\" class=\"data row9 col0\" >Ridge Regression</td>\n",
       "                        <td id=\"T_4c407_row9_col1\" class=\"data row9 col1\" >8.2038</td>\n",
       "                        <td id=\"T_4c407_row9_col2\" class=\"data row9 col2\" >142.5635</td>\n",
       "                        <td id=\"T_4c407_row9_col3\" class=\"data row9 col3\" >11.8263</td>\n",
       "                        <td id=\"T_4c407_row9_col4\" class=\"data row9 col4\" >0.6507</td>\n",
       "                        <td id=\"T_4c407_row9_col5\" class=\"data row9 col5\" >0.8718</td>\n",
       "                        <td id=\"T_4c407_row9_col6\" class=\"data row9 col6\" >3.2807</td>\n",
       "                        <td id=\"T_4c407_row9_col7\" class=\"data row9 col7\" >0.0067</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4c407_level0_row10\" class=\"row_heading level0 row10\" >knn</th>\n",
       "                        <td id=\"T_4c407_row10_col0\" class=\"data row10 col0\" >K Neighbors Regressor</td>\n",
       "                        <td id=\"T_4c407_row10_col1\" class=\"data row10 col1\" >7.6550</td>\n",
       "                        <td id=\"T_4c407_row10_col2\" class=\"data row10 col2\" >147.9495</td>\n",
       "                        <td id=\"T_4c407_row10_col3\" class=\"data row10 col3\" >12.0517</td>\n",
       "                        <td id=\"T_4c407_row10_col4\" class=\"data row10 col4\" >0.6364</td>\n",
       "                        <td id=\"T_4c407_row10_col5\" class=\"data row10 col5\" >0.7750</td>\n",
       "                        <td id=\"T_4c407_row10_col6\" class=\"data row10 col6\" >3.1631</td>\n",
       "                        <td id=\"T_4c407_row10_col7\" class=\"data row10 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4c407_level0_row11\" class=\"row_heading level0 row11\" >ada</th>\n",
       "                        <td id=\"T_4c407_row11_col0\" class=\"data row11 col0\" >AdaBoost Regressor</td>\n",
       "                        <td id=\"T_4c407_row11_col1\" class=\"data row11 col1\" >9.6647</td>\n",
       "                        <td id=\"T_4c407_row11_col2\" class=\"data row11 col2\" >157.7885</td>\n",
       "                        <td id=\"T_4c407_row11_col3\" class=\"data row11 col3\" >12.5484</td>\n",
       "                        <td id=\"T_4c407_row11_col4\" class=\"data row11 col4\" >0.5898</td>\n",
       "                        <td id=\"T_4c407_row11_col5\" class=\"data row11 col5\" >1.0459</td>\n",
       "                        <td id=\"T_4c407_row11_col6\" class=\"data row11 col6\" >4.9789</td>\n",
       "                        <td id=\"T_4c407_row11_col7\" class=\"data row11 col7\" >0.0333</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4c407_level0_row12\" class=\"row_heading level0 row12\" >huber</th>\n",
       "                        <td id=\"T_4c407_row12_col0\" class=\"data row12 col0\" >Huber Regressor</td>\n",
       "                        <td id=\"T_4c407_row12_col1\" class=\"data row12 col1\" >9.5109</td>\n",
       "                        <td id=\"T_4c407_row12_col2\" class=\"data row12 col2\" >207.9681</td>\n",
       "                        <td id=\"T_4c407_row12_col3\" class=\"data row12 col3\" >14.2964</td>\n",
       "                        <td id=\"T_4c407_row12_col4\" class=\"data row12 col4\" >0.4879</td>\n",
       "                        <td id=\"T_4c407_row12_col5\" class=\"data row12 col5\" >0.9014</td>\n",
       "                        <td id=\"T_4c407_row12_col6\" class=\"data row12 col6\" >3.2273</td>\n",
       "                        <td id=\"T_4c407_row12_col7\" class=\"data row12 col7\" >0.0200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4c407_level0_row13\" class=\"row_heading level0 row13\" >lasso</th>\n",
       "                        <td id=\"T_4c407_row13_col0\" class=\"data row13 col0\" >Lasso Regression</td>\n",
       "                        <td id=\"T_4c407_row13_col1\" class=\"data row13 col1\" >10.7041</td>\n",
       "                        <td id=\"T_4c407_row13_col2\" class=\"data row13 col2\" >247.8038</td>\n",
       "                        <td id=\"T_4c407_row13_col3\" class=\"data row13 col3\" >15.5575</td>\n",
       "                        <td id=\"T_4c407_row13_col4\" class=\"data row13 col4\" >0.3949</td>\n",
       "                        <td id=\"T_4c407_row13_col5\" class=\"data row13 col5\" >0.9988</td>\n",
       "                        <td id=\"T_4c407_row13_col6\" class=\"data row13 col6\" >4.4554</td>\n",
       "                        <td id=\"T_4c407_row13_col7\" class=\"data row13 col7\" >0.0067</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4c407_level0_row14\" class=\"row_heading level0 row14\" >en</th>\n",
       "                        <td id=\"T_4c407_row14_col0\" class=\"data row14 col0\" >Elastic Net</td>\n",
       "                        <td id=\"T_4c407_row14_col1\" class=\"data row14 col1\" >10.8159</td>\n",
       "                        <td id=\"T_4c407_row14_col2\" class=\"data row14 col2\" >252.3086</td>\n",
       "                        <td id=\"T_4c407_row14_col3\" class=\"data row14 col3\" >15.7256</td>\n",
       "                        <td id=\"T_4c407_row14_col4\" class=\"data row14 col4\" >0.3817</td>\n",
       "                        <td id=\"T_4c407_row14_col5\" class=\"data row14 col5\" >1.0017</td>\n",
       "                        <td id=\"T_4c407_row14_col6\" class=\"data row14 col6\" >4.3434</td>\n",
       "                        <td id=\"T_4c407_row14_col7\" class=\"data row14 col7\" >0.0067</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4c407_level0_row15\" class=\"row_heading level0 row15\" >omp</th>\n",
       "                        <td id=\"T_4c407_row15_col0\" class=\"data row15 col0\" >Orthogonal Matching Pursuit</td>\n",
       "                        <td id=\"T_4c407_row15_col1\" class=\"data row15 col1\" >10.8344</td>\n",
       "                        <td id=\"T_4c407_row15_col2\" class=\"data row15 col2\" >273.5033</td>\n",
       "                        <td id=\"T_4c407_row15_col3\" class=\"data row15 col3\" >16.3063</td>\n",
       "                        <td id=\"T_4c407_row15_col4\" class=\"data row15 col4\" >0.3381</td>\n",
       "                        <td id=\"T_4c407_row15_col5\" class=\"data row15 col5\" >0.9991</td>\n",
       "                        <td id=\"T_4c407_row15_col6\" class=\"data row15 col6\" >4.7757</td>\n",
       "                        <td id=\"T_4c407_row15_col7\" class=\"data row15 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4c407_level0_row16\" class=\"row_heading level0 row16\" >par</th>\n",
       "                        <td id=\"T_4c407_row16_col0\" class=\"data row16 col0\" >Passive Aggressive Regressor</td>\n",
       "                        <td id=\"T_4c407_row16_col1\" class=\"data row16 col1\" >14.8517</td>\n",
       "                        <td id=\"T_4c407_row16_col2\" class=\"data row16 col2\" >382.2646</td>\n",
       "                        <td id=\"T_4c407_row16_col3\" class=\"data row16 col3\" >19.4257</td>\n",
       "                        <td id=\"T_4c407_row16_col4\" class=\"data row16 col4\" >0.0533</td>\n",
       "                        <td id=\"T_4c407_row16_col5\" class=\"data row16 col5\" >1.3004</td>\n",
       "                        <td id=\"T_4c407_row16_col6\" class=\"data row16 col6\" >6.7961</td>\n",
       "                        <td id=\"T_4c407_row16_col7\" class=\"data row16 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4c407_level0_row17\" class=\"row_heading level0 row17\" >llar</th>\n",
       "                        <td id=\"T_4c407_row17_col0\" class=\"data row17 col0\" >Lasso Least Angle Regression</td>\n",
       "                        <td id=\"T_4c407_row17_col1\" class=\"data row17 col1\" >14.8262</td>\n",
       "                        <td id=\"T_4c407_row17_col2\" class=\"data row17 col2\" >411.7114</td>\n",
       "                        <td id=\"T_4c407_row17_col3\" class=\"data row17 col3\" >20.2073</td>\n",
       "                        <td id=\"T_4c407_row17_col4\" class=\"data row17 col4\" >-0.0291</td>\n",
       "                        <td id=\"T_4c407_row17_col5\" class=\"data row17 col5\" >1.2859</td>\n",
       "                        <td id=\"T_4c407_row17_col6\" class=\"data row17 col6\" >6.6089</td>\n",
       "                        <td id=\"T_4c407_row17_col7\" class=\"data row17 col7\" >0.0067</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4c407_level0_row18\" class=\"row_heading level0 row18\" >lar</th>\n",
       "                        <td id=\"T_4c407_row18_col0\" class=\"data row18 col0\" >Least Angle Regression</td>\n",
       "                        <td id=\"T_4c407_row18_col1\" class=\"data row18 col1\" >30.6074</td>\n",
       "                        <td id=\"T_4c407_row18_col2\" class=\"data row18 col2\" >2404.7110</td>\n",
       "                        <td id=\"T_4c407_row18_col3\" class=\"data row18 col3\" >39.4153</td>\n",
       "                        <td id=\"T_4c407_row18_col4\" class=\"data row18 col4\" >-6.9233</td>\n",
       "                        <td id=\"T_4c407_row18_col5\" class=\"data row18 col5\" >1.4216</td>\n",
       "                        <td id=\"T_4c407_row18_col6\" class=\"data row18 col6\" >13.6277</td>\n",
       "                        <td id=\"T_4c407_row18_col7\" class=\"data row18 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fe65b794e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x7fe65b80c5b0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ts = df['state_id'].unique()\n",
    "\n",
    "all_results = []\n",
    "#final_model = {}\n",
    "\n",
    "for i in all_ts:\n",
    "    df_subset = df[df['state_id'] == i]\n",
    "    \n",
    "    # initialize setup from pycaret.regression\n",
    "    s = setup(df_subset, target ='new_case_percent_pop*' , train_size = 0.8,\n",
    "              data_split_shuffle = True, fold = 3,\n",
    "              ignore_features = ['date', 'Conf_Cases'],\n",
    "              numeric_features = ['day_of_year', 'Year', 'Avg_Temp(F)'],\n",
    "              categorical_features = ['Month', 'day_of_week'],\n",
    "              silent = True, verbose = False, session_id = 123, normalize=False)\n",
    "    # compare all models and select best one based on MAE\n",
    "    best_model = compare_models(sort = 'R2', verbose=False)\n",
    "    \n",
    "    # capture the compare result grid and store best model in list\n",
    "    p = pull().iloc[0:1]\n",
    "    p['time_series'] = str(i)\n",
    "    all_results.append(p)\n",
    "best = compare_models(sort = 'R2')\n",
    "best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "7f61f9e4",
   "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>Model</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "      <th>TT (Sec)</th>\n",
       "      <th>time_series</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>catboost</th>\n",
       "      <td>CatBoost Regressor</td>\n",
       "      <td>4.2306</td>\n",
       "      <td>48.0835</td>\n",
       "      <td>6.8310</td>\n",
       "      <td>0.8989</td>\n",
       "      <td>0.4429</td>\n",
       "      <td>2.5155</td>\n",
       "      <td>0.2800</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>catboost</th>\n",
       "      <td>CatBoost Regressor</td>\n",
       "      <td>3.1247</td>\n",
       "      <td>30.0530</td>\n",
       "      <td>5.4149</td>\n",
       "      <td>0.7938</td>\n",
       "      <td>0.4447</td>\n",
       "      <td>0.9482</td>\n",
       "      <td>0.3033</td>\n",
       "      <td>ME</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf</th>\n",
       "      <td>Random Forest Regressor</td>\n",
       "      <td>2.4798</td>\n",
       "      <td>17.0064</td>\n",
       "      <td>4.1136</td>\n",
       "      <td>0.8164</td>\n",
       "      <td>0.3687</td>\n",
       "      <td>0.7144</td>\n",
       "      <td>0.1067</td>\n",
       "      <td>VT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>catboost</th>\n",
       "      <td>CatBoost Regressor</td>\n",
       "      <td>5.4085</td>\n",
       "      <td>82.3826</td>\n",
       "      <td>8.9722</td>\n",
       "      <td>0.7989</td>\n",
       "      <td>0.5617</td>\n",
       "      <td>1.2713</td>\n",
       "      <td>0.2667</td>\n",
       "      <td>CT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model     MAE      MSE    RMSE      R2   RMSLE  \\\n",
       "catboost       CatBoost Regressor  4.2306  48.0835  6.8310  0.8989  0.4429   \n",
       "catboost       CatBoost Regressor  3.1247  30.0530  5.4149  0.7938  0.4447   \n",
       "rf        Random Forest Regressor  2.4798  17.0064  4.1136  0.8164  0.3687   \n",
       "catboost       CatBoost Regressor  5.4085  82.3826  8.9722  0.7989  0.5617   \n",
       "\n",
       "            MAPE  TT (Sec) time_series  \n",
       "catboost  2.5155    0.2800          MA  \n",
       "catboost  0.9482    0.3033          ME  \n",
       "rf        0.7144    0.1067          VT  \n",
       "catboost  1.2713    0.2667          CT  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat_results = pd.concat(all_results,axis=0)\n",
    "concat_results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6058a601",
   "metadata": {},
   "source": [
    "Without any hyperparameter tuning, pycaret has determined that a CatBoost Regressor produced the best model metrics for Massachusetts, Maine, and Connecticut, and a Random Forest Regressor produced the best model metrics for Vermont. The top three performing models (using $R^2$ as the evaluation metric) on the entire dataset (including all four states) were: CatBoost Regressor, Random Forest Regressor, and Gradient Boosting Regressor (in that order).\n",
    "\n",
    "Let's try one-hot-encoding some, and then all, of the categorical columns in the dataset and see if that helps or hurts model performance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28dffa31",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23f87bfa",
   "metadata": {},
   "source": [
    "## pycaret: One-hot-encoding categorical column: 'state_id'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8f62fc4",
   "metadata": {},
   "source": [
    "While a CatBoost Regressor would certainly be able to accomodate our categorical columns, most other ML models can't handle category objects. Let's try one-hot-encoding the `state_id` column and compare pycaret's top performing models and metrics. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "32c0825a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#one-hot-encode data\n",
    "one_hot_encoded_data = pd.get_dummies(df, columns = ['state_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "83b9254f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\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>date</th>\n",
       "      <th>Avg_Temp(F)</th>\n",
       "      <th>Conf_Cases</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>new_case_percent_pop*</th>\n",
       "      <th>state_id_CT</th>\n",
       "      <th>state_id_MA</th>\n",
       "      <th>state_id_ME</th>\n",
       "      <th>state_id_VT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>26.42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>61</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>36.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.014225</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>55.94</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.014225</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>46.94</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>64</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.028450</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>42.98</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0.113799</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2219</th>\n",
       "      <td>2021-09-08</td>\n",
       "      <td>70.16</td>\n",
       "      <td>935.0</td>\n",
       "      <td>2</td>\n",
       "      <td>251</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>25.929410</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2220</th>\n",
       "      <td>2021-09-09</td>\n",
       "      <td>71.42</td>\n",
       "      <td>626.0</td>\n",
       "      <td>3</td>\n",
       "      <td>252</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>17.360225</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2221</th>\n",
       "      <td>2021-09-10</td>\n",
       "      <td>66.92</td>\n",
       "      <td>625.0</td>\n",
       "      <td>4</td>\n",
       "      <td>253</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>17.332493</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2222</th>\n",
       "      <td>2021-09-11</td>\n",
       "      <td>63.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>254</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2223</th>\n",
       "      <td>2021-09-12</td>\n",
       "      <td>69.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>255</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2224 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  Avg_Temp(F)  Conf_Cases  day_of_week  day_of_year  Year  \\\n",
       "0    2020-03-01        26.42         1.0            6           61  2020   \n",
       "1    2020-03-02        36.50         1.0            0           62  2020   \n",
       "2    2020-03-03        55.94         1.0            1           63  2020   \n",
       "3    2020-03-04        46.94         2.0            2           64  2020   \n",
       "4    2020-03-05        42.98         8.0            3           65  2020   \n",
       "...         ...          ...         ...          ...          ...   ...   \n",
       "2219 2021-09-08        70.16       935.0            2          251  2021   \n",
       "2220 2021-09-09        71.42       626.0            3          252  2021   \n",
       "2221 2021-09-10        66.92       625.0            4          253  2021   \n",
       "2222 2021-09-11        63.14         0.0            5          254  2021   \n",
       "2223 2021-09-12        69.44         0.0            6          255  2021   \n",
       "\n",
       "      Month  Day  new_case_percent_pop*  state_id_CT  state_id_MA  \\\n",
       "0         3    1               0.014225            0            1   \n",
       "1         3    2               0.014225            0            1   \n",
       "2         3    3               0.014225            0            1   \n",
       "3         3    4               0.028450            0            1   \n",
       "4         3    5               0.113799            0            1   \n",
       "...     ...  ...                    ...          ...          ...   \n",
       "2219      9    8              25.929410            1            0   \n",
       "2220      9    9              17.360225            1            0   \n",
       "2221      9   10              17.332493            1            0   \n",
       "2222      9   11               0.000000            1            0   \n",
       "2223      9   12               0.000000            1            0   \n",
       "\n",
       "      state_id_ME  state_id_VT  \n",
       "0               0            0  \n",
       "1               0            0  \n",
       "2               0            0  \n",
       "3               0            0  \n",
       "4               0            0  \n",
       "...           ...          ...  \n",
       "2219            0            0  \n",
       "2220            0            0  \n",
       "2221            0            0  \n",
       "2222            0            0  \n",
       "2223            0            0  \n",
       "\n",
       "[2224 rows x 13 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_encoded_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "56a1e6dd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "        }</style><table id=\"T_58318_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>        <th class=\"col_heading level0 col7\" >TT (Sec)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_58318_level0_row0\" class=\"row_heading level0 row0\" >catboost</th>\n",
       "                        <td id=\"T_58318_row0_col0\" class=\"data row0 col0\" >CatBoost Regressor</td>\n",
       "                        <td id=\"T_58318_row0_col1\" class=\"data row0 col1\" >4.0767</td>\n",
       "                        <td id=\"T_58318_row0_col2\" class=\"data row0 col2\" >51.4359</td>\n",
       "                        <td id=\"T_58318_row0_col3\" class=\"data row0 col3\" >7.1543</td>\n",
       "                        <td id=\"T_58318_row0_col4\" class=\"data row0 col4\" >0.8280</td>\n",
       "                        <td id=\"T_58318_row0_col5\" class=\"data row0 col5\" >0.5081</td>\n",
       "                        <td id=\"T_58318_row0_col6\" class=\"data row0 col6\" >1.4200</td>\n",
       "                        <td id=\"T_58318_row0_col7\" class=\"data row0 col7\" >0.3667</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_58318_level0_row1\" class=\"row_heading level0 row1\" >rf</th>\n",
       "                        <td id=\"T_58318_row1_col0\" class=\"data row1 col0\" >Random Forest Regressor</td>\n",
       "                        <td id=\"T_58318_row1_col1\" class=\"data row1 col1\" >3.9441</td>\n",
       "                        <td id=\"T_58318_row1_col2\" class=\"data row1 col2\" >52.8238</td>\n",
       "                        <td id=\"T_58318_row1_col3\" class=\"data row1 col3\" >7.2373</td>\n",
       "                        <td id=\"T_58318_row1_col4\" class=\"data row1 col4\" >0.8233</td>\n",
       "                        <td id=\"T_58318_row1_col5\" class=\"data row1 col5\" >0.4268</td>\n",
       "                        <td id=\"T_58318_row1_col6\" class=\"data row1 col6\" >1.0654</td>\n",
       "                        <td id=\"T_58318_row1_col7\" class=\"data row1 col7\" >0.1200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_58318_level0_row2\" class=\"row_heading level0 row2\" >et</th>\n",
       "                        <td id=\"T_58318_row2_col0\" class=\"data row2 col0\" >Extra Trees Regressor</td>\n",
       "                        <td id=\"T_58318_row2_col1\" class=\"data row2 col1\" >3.8059</td>\n",
       "                        <td id=\"T_58318_row2_col2\" class=\"data row2 col2\" >54.6242</td>\n",
       "                        <td id=\"T_58318_row2_col3\" class=\"data row2 col3\" >7.3398</td>\n",
       "                        <td id=\"T_58318_row2_col4\" class=\"data row2 col4\" >0.8205</td>\n",
       "                        <td id=\"T_58318_row2_col5\" class=\"data row2 col5\" >0.3953</td>\n",
       "                        <td id=\"T_58318_row2_col6\" class=\"data row2 col6\" >0.7701</td>\n",
       "                        <td id=\"T_58318_row2_col7\" class=\"data row2 col7\" >0.1067</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_58318_level0_row3\" class=\"row_heading level0 row3\" >lightgbm</th>\n",
       "                        <td id=\"T_58318_row3_col0\" class=\"data row3 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_58318_row3_col1\" class=\"data row3 col1\" >4.2811</td>\n",
       "                        <td id=\"T_58318_row3_col2\" class=\"data row3 col2\" >55.2234</td>\n",
       "                        <td id=\"T_58318_row3_col3\" class=\"data row3 col3\" >7.4053</td>\n",
       "                        <td id=\"T_58318_row3_col4\" class=\"data row3 col4\" >0.8149</td>\n",
       "                        <td id=\"T_58318_row3_col5\" class=\"data row3 col5\" >0.5077</td>\n",
       "                        <td id=\"T_58318_row3_col6\" class=\"data row3 col6\" >1.5072</td>\n",
       "                        <td id=\"T_58318_row3_col7\" class=\"data row3 col7\" >0.2067</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_58318_level0_row4\" class=\"row_heading level0 row4\" >xgboost</th>\n",
       "                        <td id=\"T_58318_row4_col0\" class=\"data row4 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_58318_row4_col1\" class=\"data row4 col1\" >4.5415</td>\n",
       "                        <td id=\"T_58318_row4_col2\" class=\"data row4 col2\" >63.3190</td>\n",
       "                        <td id=\"T_58318_row4_col3\" class=\"data row4 col3\" >7.9373</td>\n",
       "                        <td id=\"T_58318_row4_col4\" class=\"data row4 col4\" >0.7888</td>\n",
       "                        <td id=\"T_58318_row4_col5\" class=\"data row4 col5\" >0.5242</td>\n",
       "                        <td id=\"T_58318_row4_col6\" class=\"data row4 col6\" >1.3935</td>\n",
       "                        <td id=\"T_58318_row4_col7\" class=\"data row4 col7\" >0.2167</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_58318_level0_row5\" class=\"row_heading level0 row5\" >gbr</th>\n",
       "                        <td id=\"T_58318_row5_col0\" class=\"data row5 col0\" >Gradient Boosting Regressor</td>\n",
       "                        <td id=\"T_58318_row5_col1\" class=\"data row5 col1\" >4.9521</td>\n",
       "                        <td id=\"T_58318_row5_col2\" class=\"data row5 col2\" >67.3713</td>\n",
       "                        <td id=\"T_58318_row5_col3\" class=\"data row5 col3\" >8.1814</td>\n",
       "                        <td id=\"T_58318_row5_col4\" class=\"data row5 col4\" >0.7760</td>\n",
       "                        <td id=\"T_58318_row5_col5\" class=\"data row5 col5\" >0.6091</td>\n",
       "                        <td id=\"T_58318_row5_col6\" class=\"data row5 col6\" >2.4909</td>\n",
       "                        <td id=\"T_58318_row5_col7\" class=\"data row5 col7\" >0.0433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_58318_level0_row6\" class=\"row_heading level0 row6\" >dt</th>\n",
       "                        <td id=\"T_58318_row6_col0\" class=\"data row6 col0\" >Decision Tree Regressor</td>\n",
       "                        <td id=\"T_58318_row6_col1\" class=\"data row6 col1\" >5.0958</td>\n",
       "                        <td id=\"T_58318_row6_col2\" class=\"data row6 col2\" >98.2415</td>\n",
       "                        <td id=\"T_58318_row6_col3\" class=\"data row6 col3\" >9.8295</td>\n",
       "                        <td id=\"T_58318_row6_col4\" class=\"data row6 col4\" >0.6763</td>\n",
       "                        <td id=\"T_58318_row6_col5\" class=\"data row6 col5\" >0.5202</td>\n",
       "                        <td id=\"T_58318_row6_col6\" class=\"data row6 col6\" >0.7874</td>\n",
       "                        <td id=\"T_58318_row6_col7\" class=\"data row6 col7\" >0.0133</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_58318_level0_row7\" class=\"row_heading level0 row7\" >ridge</th>\n",
       "                        <td id=\"T_58318_row7_col0\" class=\"data row7 col0\" >Ridge Regression</td>\n",
       "                        <td id=\"T_58318_row7_col1\" class=\"data row7 col1\" >7.4452</td>\n",
       "                        <td id=\"T_58318_row7_col2\" class=\"data row7 col2\" >116.5455</td>\n",
       "                        <td id=\"T_58318_row7_col3\" class=\"data row7 col3\" >10.7790</td>\n",
       "                        <td id=\"T_58318_row7_col4\" class=\"data row7 col4\" >0.6101</td>\n",
       "                        <td id=\"T_58318_row7_col5\" class=\"data row7 col5\" >0.8441</td>\n",
       "                        <td id=\"T_58318_row7_col6\" class=\"data row7 col6\" >4.6870</td>\n",
       "                        <td id=\"T_58318_row7_col7\" class=\"data row7 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_58318_level0_row8\" class=\"row_heading level0 row8\" >br</th>\n",
       "                        <td id=\"T_58318_row8_col0\" class=\"data row8 col0\" >Bayesian Ridge</td>\n",
       "                        <td id=\"T_58318_row8_col1\" class=\"data row8 col1\" >7.4453</td>\n",
       "                        <td id=\"T_58318_row8_col2\" class=\"data row8 col2\" >116.5529</td>\n",
       "                        <td id=\"T_58318_row8_col3\" class=\"data row8 col3\" >10.7793</td>\n",
       "                        <td id=\"T_58318_row8_col4\" class=\"data row8 col4\" >0.6101</td>\n",
       "                        <td id=\"T_58318_row8_col5\" class=\"data row8 col5\" >0.8439</td>\n",
       "                        <td id=\"T_58318_row8_col6\" class=\"data row8 col6\" >4.6872</td>\n",
       "                        <td id=\"T_58318_row8_col7\" class=\"data row8 col7\" >0.0067</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_58318_level0_row9\" class=\"row_heading level0 row9\" >lr</th>\n",
       "                        <td id=\"T_58318_row9_col0\" class=\"data row9 col0\" >Linear Regression</td>\n",
       "                        <td id=\"T_58318_row9_col1\" class=\"data row9 col1\" >7.4607</td>\n",
       "                        <td id=\"T_58318_row9_col2\" class=\"data row9 col2\" >116.5554</td>\n",
       "                        <td id=\"T_58318_row9_col3\" class=\"data row9 col3\" >10.7801</td>\n",
       "                        <td id=\"T_58318_row9_col4\" class=\"data row9 col4\" >0.6099</td>\n",
       "                        <td id=\"T_58318_row9_col5\" class=\"data row9 col5\" >0.8448</td>\n",
       "                        <td id=\"T_58318_row9_col6\" class=\"data row9 col6\" >4.7224</td>\n",
       "                        <td id=\"T_58318_row9_col7\" class=\"data row9 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_58318_level0_row10\" class=\"row_heading level0 row10\" >lar</th>\n",
       "                        <td id=\"T_58318_row10_col0\" class=\"data row10 col0\" >Least Angle Regression</td>\n",
       "                        <td id=\"T_58318_row10_col1\" class=\"data row10 col1\" >7.7560</td>\n",
       "                        <td id=\"T_58318_row10_col2\" class=\"data row10 col2\" >121.0936</td>\n",
       "                        <td id=\"T_58318_row10_col3\" class=\"data row10 col3\" >10.9997</td>\n",
       "                        <td id=\"T_58318_row10_col4\" class=\"data row10 col4\" >0.5921</td>\n",
       "                        <td id=\"T_58318_row10_col5\" class=\"data row10 col5\" >0.8772</td>\n",
       "                        <td id=\"T_58318_row10_col6\" class=\"data row10 col6\" >5.1790</td>\n",
       "                        <td id=\"T_58318_row10_col7\" class=\"data row10 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_58318_level0_row11\" class=\"row_heading level0 row11\" >knn</th>\n",
       "                        <td id=\"T_58318_row11_col0\" class=\"data row11 col0\" >K Neighbors Regressor</td>\n",
       "                        <td id=\"T_58318_row11_col1\" class=\"data row11 col1\" >7.7186</td>\n",
       "                        <td id=\"T_58318_row11_col2\" class=\"data row11 col2\" >138.9834</td>\n",
       "                        <td id=\"T_58318_row11_col3\" class=\"data row11 col3\" >11.7840</td>\n",
       "                        <td id=\"T_58318_row11_col4\" class=\"data row11 col4\" >0.5322</td>\n",
       "                        <td id=\"T_58318_row11_col5\" class=\"data row11 col5\" >0.8742</td>\n",
       "                        <td id=\"T_58318_row11_col6\" class=\"data row11 col6\" >4.7626</td>\n",
       "                        <td id=\"T_58318_row11_col7\" class=\"data row11 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_58318_level0_row12\" class=\"row_heading level0 row12\" >huber</th>\n",
       "                        <td id=\"T_58318_row12_col0\" class=\"data row12 col0\" >Huber Regressor</td>\n",
       "                        <td id=\"T_58318_row12_col1\" class=\"data row12 col1\" >8.7172</td>\n",
       "                        <td id=\"T_58318_row12_col2\" class=\"data row12 col2\" >183.7950</td>\n",
       "                        <td id=\"T_58318_row12_col3\" class=\"data row12 col3\" >13.4795</td>\n",
       "                        <td id=\"T_58318_row12_col4\" class=\"data row12 col4\" >0.3947</td>\n",
       "                        <td id=\"T_58318_row12_col5\" class=\"data row12 col5\" >0.9728</td>\n",
       "                        <td id=\"T_58318_row12_col6\" class=\"data row12 col6\" >5.4826</td>\n",
       "                        <td id=\"T_58318_row12_col7\" class=\"data row12 col7\" >0.0200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_58318_level0_row13\" class=\"row_heading level0 row13\" >ada</th>\n",
       "                        <td id=\"T_58318_row13_col0\" class=\"data row13 col0\" >AdaBoost Regressor</td>\n",
       "                        <td id=\"T_58318_row13_col1\" class=\"data row13 col1\" >11.8163</td>\n",
       "                        <td id=\"T_58318_row13_col2\" class=\"data row13 col2\" >188.1072</td>\n",
       "                        <td id=\"T_58318_row13_col3\" class=\"data row13 col3\" >13.7097</td>\n",
       "                        <td id=\"T_58318_row13_col4\" class=\"data row13 col4\" >0.3663</td>\n",
       "                        <td id=\"T_58318_row13_col5\" class=\"data row13 col5\" >1.3496</td>\n",
       "                        <td id=\"T_58318_row13_col6\" class=\"data row13 col6\" >10.3931</td>\n",
       "                        <td id=\"T_58318_row13_col7\" class=\"data row13 col7\" >0.0400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_58318_level0_row14\" class=\"row_heading level0 row14\" >lasso</th>\n",
       "                        <td id=\"T_58318_row14_col0\" class=\"data row14 col0\" >Lasso Regression</td>\n",
       "                        <td id=\"T_58318_row14_col1\" class=\"data row14 col1\" >9.6383</td>\n",
       "                        <td id=\"T_58318_row14_col2\" class=\"data row14 col2\" >206.1311</td>\n",
       "                        <td id=\"T_58318_row14_col3\" class=\"data row14 col3\" >14.3087</td>\n",
       "                        <td id=\"T_58318_row14_col4\" class=\"data row14 col4\" >0.3163</td>\n",
       "                        <td id=\"T_58318_row14_col5\" class=\"data row14 col5\" >1.0332</td>\n",
       "                        <td id=\"T_58318_row14_col6\" class=\"data row14 col6\" >6.2943</td>\n",
       "                        <td id=\"T_58318_row14_col7\" class=\"data row14 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_58318_level0_row15\" class=\"row_heading level0 row15\" >omp</th>\n",
       "                        <td id=\"T_58318_row15_col0\" class=\"data row15 col0\" >Orthogonal Matching Pursuit</td>\n",
       "                        <td id=\"T_58318_row15_col1\" class=\"data row15 col1\" >10.2208</td>\n",
       "                        <td id=\"T_58318_row15_col2\" class=\"data row15 col2\" >209.4312</td>\n",
       "                        <td id=\"T_58318_row15_col3\" class=\"data row15 col3\" >14.4449</td>\n",
       "                        <td id=\"T_58318_row15_col4\" class=\"data row15 col4\" >0.3006</td>\n",
       "                        <td id=\"T_58318_row15_col5\" class=\"data row15 col5\" >1.0967</td>\n",
       "                        <td id=\"T_58318_row15_col6\" class=\"data row15 col6\" >6.7180</td>\n",
       "                        <td id=\"T_58318_row15_col7\" class=\"data row15 col7\" >0.0067</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_58318_level0_row16\" class=\"row_heading level0 row16\" >en</th>\n",
       "                        <td id=\"T_58318_row16_col0\" class=\"data row16 col0\" >Elastic Net</td>\n",
       "                        <td id=\"T_58318_row16_col1\" class=\"data row16 col1\" >9.9184</td>\n",
       "                        <td id=\"T_58318_row16_col2\" class=\"data row16 col2\" >216.6040</td>\n",
       "                        <td id=\"T_58318_row16_col3\" class=\"data row16 col3\" >14.6817</td>\n",
       "                        <td id=\"T_58318_row16_col4\" class=\"data row16 col4\" >0.2790</td>\n",
       "                        <td id=\"T_58318_row16_col5\" class=\"data row16 col5\" >1.0613</td>\n",
       "                        <td id=\"T_58318_row16_col6\" class=\"data row16 col6\" >6.6383</td>\n",
       "                        <td id=\"T_58318_row16_col7\" class=\"data row16 col7\" >0.0067</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_58318_level0_row17\" class=\"row_heading level0 row17\" >llar</th>\n",
       "                        <td id=\"T_58318_row17_col0\" class=\"data row17 col0\" >Lasso Least Angle Regression</td>\n",
       "                        <td id=\"T_58318_row17_col1\" class=\"data row17 col1\" >12.4393</td>\n",
       "                        <td id=\"T_58318_row17_col2\" class=\"data row17 col2\" >302.5482</td>\n",
       "                        <td id=\"T_58318_row17_col3\" class=\"data row17 col3\" >17.3508</td>\n",
       "                        <td id=\"T_58318_row17_col4\" class=\"data row17 col4\" >-0.0067</td>\n",
       "                        <td id=\"T_58318_row17_col5\" class=\"data row17 col5\" >1.2973</td>\n",
       "                        <td id=\"T_58318_row17_col6\" class=\"data row17 col6\" >8.5023</td>\n",
       "                        <td id=\"T_58318_row17_col7\" class=\"data row17 col7\" >0.0067</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_58318_level0_row18\" class=\"row_heading level0 row18\" >par</th>\n",
       "                        <td id=\"T_58318_row18_col0\" class=\"data row18 col0\" >Passive Aggressive Regressor</td>\n",
       "                        <td id=\"T_58318_row18_col1\" class=\"data row18 col1\" >18.0307</td>\n",
       "                        <td id=\"T_58318_row18_col2\" class=\"data row18 col2\" >444.3401</td>\n",
       "                        <td id=\"T_58318_row18_col3\" class=\"data row18 col3\" >20.5263</td>\n",
       "                        <td id=\"T_58318_row18_col4\" class=\"data row18 col4\" >-0.4205</td>\n",
       "                        <td id=\"T_58318_row18_col5\" class=\"data row18 col5\" >1.5862</td>\n",
       "                        <td id=\"T_58318_row18_col6\" class=\"data row18 col6\" >15.5280</td>\n",
       "                        <td id=\"T_58318_row18_col7\" class=\"data row18 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fe638dc1d00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x7fe65bbc7a00>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = setup(one_hot_encoded_data, target ='new_case_percent_pop*', train_size = 0.8,\n",
    "            data_split_shuffle = True, fold = 3,\n",
    "            ignore_features = ['date', 'Conf_Cases'],\n",
    "            numeric_features = ['day_of_year', 'Year', 'Avg_Temp(F)'],\n",
    "            categorical_features = ['Month', 'day_of_week', 'state_id_MA', 'state_id_CT', 'state_id_VT', 'state_id_ME'],\n",
    "            silent = True, verbose = False, session_id = 123, normalize=False)\n",
    "    # compare all models and select best one based on MAE\n",
    "best_model = compare_models(sort = 'R2', verbose=False)\n",
    "    \n",
    "    # capture the compare result grid and store best model in list\n",
    "p = pull().iloc[0:1]\n",
    "p['time_series'] = str(i)\n",
    "all_results.append(p)\n",
    "best = compare_models(sort = 'R2')\n",
    "best"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced4f791",
   "metadata": {},
   "source": [
    "Interestingly, we see a somewhat significant improvement in $R^2$ when we one-hot-encode the `state_id` column. It also appears that the Extra Trees Regressor now outperforms the Gradient Boosting Regressor. \n",
    "\n",
    "Let's check and see if one-hot-encoding the other categorical columns continues to improve model performance!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a1a745aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the data to a new csv file\n",
    "#one_hot_encoded_data.to_csv('ohe_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6861ffe7",
   "metadata": {},
   "source": [
    "## pycaret: One-hot-encoding all categorical data: state_id, Month, day_of_week"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2353fe2",
   "metadata": {},
   "source": [
    "Let's now one-hot-encode each of our categorical columns (`state_id`, `Month`, and `day_of_week`) and re-run pycaret to see if we can further improve the model metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "abe75db0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#one-hot-encode data\n",
    "one_hot_encoded_data3 = pd.get_dummies(df, columns = ['state_id', 'Month', 'day_of_week'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4f4d8142",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'Avg_Temp(F)', 'Conf_Cases', 'day_of_year', 'Year', 'Day',\n",
       "       'new_case_percent_pop*', 'state_id_CT', 'state_id_MA', 'state_id_ME',\n",
       "       'state_id_VT', 'Month_1', 'Month_2', 'Month_3', 'Month_4', 'Month_5',\n",
       "       'Month_6', 'Month_7', 'Month_8', 'Month_9', 'Month_10', 'Month_11',\n",
       "       'Month_12', 'day_of_week_0', 'day_of_week_1', 'day_of_week_2',\n",
       "       'day_of_week_3', 'day_of_week_4', 'day_of_week_5', 'day_of_week_6'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_encoded_data3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "48539fd4",
   "metadata": {},
   "outputs": [
    {
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       "        }</style><table id=\"T_79615_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>        <th class=\"col_heading level0 col7\" >TT (Sec)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_79615_level0_row0\" class=\"row_heading level0 row0\" >catboost</th>\n",
       "                        <td id=\"T_79615_row0_col0\" class=\"data row0 col0\" >CatBoost Regressor</td>\n",
       "                        <td id=\"T_79615_row0_col1\" class=\"data row0 col1\" >4.0272</td>\n",
       "                        <td id=\"T_79615_row0_col2\" class=\"data row0 col2\" >49.6088</td>\n",
       "                        <td id=\"T_79615_row0_col3\" class=\"data row0 col3\" >7.0267</td>\n",
       "                        <td id=\"T_79615_row0_col4\" class=\"data row0 col4\" >0.8340</td>\n",
       "                        <td id=\"T_79615_row0_col5\" class=\"data row0 col5\" >0.5039</td>\n",
       "                        <td id=\"T_79615_row0_col6\" class=\"data row0 col6\" >1.4677</td>\n",
       "                        <td id=\"T_79615_row0_col7\" class=\"data row0 col7\" >0.4667</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_79615_level0_row1\" class=\"row_heading level0 row1\" >et</th>\n",
       "                        <td id=\"T_79615_row1_col0\" class=\"data row1 col0\" >Extra Trees Regressor</td>\n",
       "                        <td id=\"T_79615_row1_col1\" class=\"data row1 col1\" >3.7804</td>\n",
       "                        <td id=\"T_79615_row1_col2\" class=\"data row1 col2\" >53.9427</td>\n",
       "                        <td id=\"T_79615_row1_col3\" class=\"data row1 col3\" >7.2986</td>\n",
       "                        <td id=\"T_79615_row1_col4\" class=\"data row1 col4\" >0.8225</td>\n",
       "                        <td id=\"T_79615_row1_col5\" class=\"data row1 col5\" >0.3934</td>\n",
       "                        <td id=\"T_79615_row1_col6\" class=\"data row1 col6\" >0.7279</td>\n",
       "                        <td id=\"T_79615_row1_col7\" class=\"data row1 col7\" >0.1067</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_79615_level0_row2\" class=\"row_heading level0 row2\" >rf</th>\n",
       "                        <td id=\"T_79615_row2_col0\" class=\"data row2 col0\" >Random Forest Regressor</td>\n",
       "                        <td id=\"T_79615_row2_col1\" class=\"data row2 col1\" >3.9468</td>\n",
       "                        <td id=\"T_79615_row2_col2\" class=\"data row2 col2\" >53.1076</td>\n",
       "                        <td id=\"T_79615_row2_col3\" class=\"data row2 col3\" >7.2567</td>\n",
       "                        <td id=\"T_79615_row2_col4\" class=\"data row2 col4\" >0.8225</td>\n",
       "                        <td id=\"T_79615_row2_col5\" class=\"data row2 col5\" >0.4255</td>\n",
       "                        <td id=\"T_79615_row2_col6\" class=\"data row2 col6\" >1.0604</td>\n",
       "                        <td id=\"T_79615_row2_col7\" class=\"data row2 col7\" >0.1200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_79615_level0_row3\" class=\"row_heading level0 row3\" >lightgbm</th>\n",
       "                        <td id=\"T_79615_row3_col0\" class=\"data row3 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_79615_row3_col1\" class=\"data row3 col1\" >4.2811</td>\n",
       "                        <td id=\"T_79615_row3_col2\" class=\"data row3 col2\" >55.2234</td>\n",
       "                        <td id=\"T_79615_row3_col3\" class=\"data row3 col3\" >7.4053</td>\n",
       "                        <td id=\"T_79615_row3_col4\" class=\"data row3 col4\" >0.8149</td>\n",
       "                        <td id=\"T_79615_row3_col5\" class=\"data row3 col5\" >0.5077</td>\n",
       "                        <td id=\"T_79615_row3_col6\" class=\"data row3 col6\" >1.5072</td>\n",
       "                        <td id=\"T_79615_row3_col7\" class=\"data row3 col7\" >0.2800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_79615_level0_row4\" class=\"row_heading level0 row4\" >xgboost</th>\n",
       "                        <td id=\"T_79615_row4_col0\" class=\"data row4 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_79615_row4_col1\" class=\"data row4 col1\" >4.5464</td>\n",
       "                        <td id=\"T_79615_row4_col2\" class=\"data row4 col2\" >63.4020</td>\n",
       "                        <td id=\"T_79615_row4_col3\" class=\"data row4 col3\" >7.9425</td>\n",
       "                        <td id=\"T_79615_row4_col4\" class=\"data row4 col4\" >0.7885</td>\n",
       "                        <td id=\"T_79615_row4_col5\" class=\"data row4 col5\" >0.5245</td>\n",
       "                        <td id=\"T_79615_row4_col6\" class=\"data row4 col6\" >1.3973</td>\n",
       "                        <td id=\"T_79615_row4_col7\" class=\"data row4 col7\" >0.2400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_79615_level0_row5\" class=\"row_heading level0 row5\" >gbr</th>\n",
       "                        <td id=\"T_79615_row5_col0\" class=\"data row5 col0\" >Gradient Boosting Regressor</td>\n",
       "                        <td id=\"T_79615_row5_col1\" class=\"data row5 col1\" >4.9590</td>\n",
       "                        <td id=\"T_79615_row5_col2\" class=\"data row5 col2\" >67.6630</td>\n",
       "                        <td id=\"T_79615_row5_col3\" class=\"data row5 col3\" >8.1984</td>\n",
       "                        <td id=\"T_79615_row5_col4\" class=\"data row5 col4\" >0.7751</td>\n",
       "                        <td id=\"T_79615_row5_col5\" class=\"data row5 col5\" >0.6093</td>\n",
       "                        <td id=\"T_79615_row5_col6\" class=\"data row5 col6\" >2.4912</td>\n",
       "                        <td id=\"T_79615_row5_col7\" class=\"data row5 col7\" >0.0500</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_79615_level0_row6\" class=\"row_heading level0 row6\" >dt</th>\n",
       "                        <td id=\"T_79615_row6_col0\" class=\"data row6 col0\" >Decision Tree Regressor</td>\n",
       "                        <td id=\"T_79615_row6_col1\" class=\"data row6 col1\" >5.1577</td>\n",
       "                        <td id=\"T_79615_row6_col2\" class=\"data row6 col2\" >99.8945</td>\n",
       "                        <td id=\"T_79615_row6_col3\" class=\"data row6 col3\" >9.9046</td>\n",
       "                        <td id=\"T_79615_row6_col4\" class=\"data row6 col4\" >0.6710</td>\n",
       "                        <td id=\"T_79615_row6_col5\" class=\"data row6 col5\" >0.5210</td>\n",
       "                        <td id=\"T_79615_row6_col6\" class=\"data row6 col6\" >0.8183</td>\n",
       "                        <td id=\"T_79615_row6_col7\" class=\"data row6 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_79615_level0_row7\" class=\"row_heading level0 row7\" >ridge</th>\n",
       "                        <td id=\"T_79615_row7_col0\" class=\"data row7 col0\" >Ridge Regression</td>\n",
       "                        <td id=\"T_79615_row7_col1\" class=\"data row7 col1\" >7.4453</td>\n",
       "                        <td id=\"T_79615_row7_col2\" class=\"data row7 col2\" >116.5473</td>\n",
       "                        <td id=\"T_79615_row7_col3\" class=\"data row7 col3\" >10.7790</td>\n",
       "                        <td id=\"T_79615_row7_col4\" class=\"data row7 col4\" >0.6101</td>\n",
       "                        <td id=\"T_79615_row7_col5\" class=\"data row7 col5\" >0.8441</td>\n",
       "                        <td id=\"T_79615_row7_col6\" class=\"data row7 col6\" >4.6873</td>\n",
       "                        <td id=\"T_79615_row7_col7\" class=\"data row7 col7\" >0.0067</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_79615_level0_row8\" class=\"row_heading level0 row8\" >br</th>\n",
       "                        <td id=\"T_79615_row8_col0\" class=\"data row8 col0\" >Bayesian Ridge</td>\n",
       "                        <td id=\"T_79615_row8_col1\" class=\"data row8 col1\" >7.4453</td>\n",
       "                        <td id=\"T_79615_row8_col2\" class=\"data row8 col2\" >116.5529</td>\n",
       "                        <td id=\"T_79615_row8_col3\" class=\"data row8 col3\" >10.7793</td>\n",
       "                        <td id=\"T_79615_row8_col4\" class=\"data row8 col4\" >0.6101</td>\n",
       "                        <td id=\"T_79615_row8_col5\" class=\"data row8 col5\" >0.8439</td>\n",
       "                        <td id=\"T_79615_row8_col6\" class=\"data row8 col6\" >4.6872</td>\n",
       "                        <td id=\"T_79615_row8_col7\" class=\"data row8 col7\" >0.0067</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_79615_level0_row9\" class=\"row_heading level0 row9\" >lr</th>\n",
       "                        <td id=\"T_79615_row9_col0\" class=\"data row9 col0\" >Linear Regression</td>\n",
       "                        <td id=\"T_79615_row9_col1\" class=\"data row9 col1\" >7.4606</td>\n",
       "                        <td id=\"T_79615_row9_col2\" class=\"data row9 col2\" >116.5535</td>\n",
       "                        <td id=\"T_79615_row9_col3\" class=\"data row9 col3\" >10.7800</td>\n",
       "                        <td id=\"T_79615_row9_col4\" class=\"data row9 col4\" >0.6099</td>\n",
       "                        <td id=\"T_79615_row9_col5\" class=\"data row9 col5\" >0.8448</td>\n",
       "                        <td id=\"T_79615_row9_col6\" class=\"data row9 col6\" >4.7223</td>\n",
       "                        <td id=\"T_79615_row9_col7\" class=\"data row9 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_79615_level0_row10\" class=\"row_heading level0 row10\" >lar</th>\n",
       "                        <td id=\"T_79615_row10_col0\" class=\"data row10 col0\" >Least Angle Regression</td>\n",
       "                        <td id=\"T_79615_row10_col1\" class=\"data row10 col1\" >7.7588</td>\n",
       "                        <td id=\"T_79615_row10_col2\" class=\"data row10 col2\" >121.1444</td>\n",
       "                        <td id=\"T_79615_row10_col3\" class=\"data row10 col3\" >11.0017</td>\n",
       "                        <td id=\"T_79615_row10_col4\" class=\"data row10 col4\" >0.5920</td>\n",
       "                        <td id=\"T_79615_row10_col5\" class=\"data row10 col5\" >0.8811</td>\n",
       "                        <td id=\"T_79615_row10_col6\" class=\"data row10 col6\" >5.2576</td>\n",
       "                        <td id=\"T_79615_row10_col7\" class=\"data row10 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_79615_level0_row11\" class=\"row_heading level0 row11\" >knn</th>\n",
       "                        <td id=\"T_79615_row11_col0\" class=\"data row11 col0\" >K Neighbors Regressor</td>\n",
       "                        <td id=\"T_79615_row11_col1\" class=\"data row11 col1\" >7.7186</td>\n",
       "                        <td id=\"T_79615_row11_col2\" class=\"data row11 col2\" >138.9834</td>\n",
       "                        <td id=\"T_79615_row11_col3\" class=\"data row11 col3\" >11.7840</td>\n",
       "                        <td id=\"T_79615_row11_col4\" class=\"data row11 col4\" >0.5322</td>\n",
       "                        <td id=\"T_79615_row11_col5\" class=\"data row11 col5\" >0.8742</td>\n",
       "                        <td id=\"T_79615_row11_col6\" class=\"data row11 col6\" >4.7626</td>\n",
       "                        <td id=\"T_79615_row11_col7\" class=\"data row11 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_79615_level0_row12\" class=\"row_heading level0 row12\" >ada</th>\n",
       "                        <td id=\"T_79615_row12_col0\" class=\"data row12 col0\" >AdaBoost Regressor</td>\n",
       "                        <td id=\"T_79615_row12_col1\" class=\"data row12 col1\" >11.7135</td>\n",
       "                        <td id=\"T_79615_row12_col2\" class=\"data row12 col2\" >185.0008</td>\n",
       "                        <td id=\"T_79615_row12_col3\" class=\"data row12 col3\" >13.5885</td>\n",
       "                        <td id=\"T_79615_row12_col4\" class=\"data row12 col4\" >0.3794</td>\n",
       "                        <td id=\"T_79615_row12_col5\" class=\"data row12 col5\" >1.3494</td>\n",
       "                        <td id=\"T_79615_row12_col6\" class=\"data row12 col6\" >10.2978</td>\n",
       "                        <td id=\"T_79615_row12_col7\" class=\"data row12 col7\" >0.0433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_79615_level0_row13\" class=\"row_heading level0 row13\" >huber</th>\n",
       "                        <td id=\"T_79615_row13_col0\" class=\"data row13 col0\" >Huber Regressor</td>\n",
       "                        <td id=\"T_79615_row13_col1\" class=\"data row13 col1\" >8.6942</td>\n",
       "                        <td id=\"T_79615_row13_col2\" class=\"data row13 col2\" >189.6384</td>\n",
       "                        <td id=\"T_79615_row13_col3\" class=\"data row13 col3\" >13.7104</td>\n",
       "                        <td id=\"T_79615_row13_col4\" class=\"data row13 col4\" >0.3731</td>\n",
       "                        <td id=\"T_79615_row13_col5\" class=\"data row13 col5\" >0.9562</td>\n",
       "                        <td id=\"T_79615_row13_col6\" class=\"data row13 col6\" >5.6530</td>\n",
       "                        <td id=\"T_79615_row13_col7\" class=\"data row13 col7\" >0.0233</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_79615_level0_row14\" class=\"row_heading level0 row14\" >lasso</th>\n",
       "                        <td id=\"T_79615_row14_col0\" class=\"data row14 col0\" >Lasso Regression</td>\n",
       "                        <td id=\"T_79615_row14_col1\" class=\"data row14 col1\" >9.6384</td>\n",
       "                        <td id=\"T_79615_row14_col2\" class=\"data row14 col2\" >206.1315</td>\n",
       "                        <td id=\"T_79615_row14_col3\" class=\"data row14 col3\" >14.3087</td>\n",
       "                        <td id=\"T_79615_row14_col4\" class=\"data row14 col4\" >0.3163</td>\n",
       "                        <td id=\"T_79615_row14_col5\" class=\"data row14 col5\" >1.0332</td>\n",
       "                        <td id=\"T_79615_row14_col6\" class=\"data row14 col6\" >6.2944</td>\n",
       "                        <td id=\"T_79615_row14_col7\" class=\"data row14 col7\" >0.0067</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_79615_level0_row15\" class=\"row_heading level0 row15\" >omp</th>\n",
       "                        <td id=\"T_79615_row15_col0\" class=\"data row15 col0\" >Orthogonal Matching Pursuit</td>\n",
       "                        <td id=\"T_79615_row15_col1\" class=\"data row15 col1\" >10.2208</td>\n",
       "                        <td id=\"T_79615_row15_col2\" class=\"data row15 col2\" >209.4312</td>\n",
       "                        <td id=\"T_79615_row15_col3\" class=\"data row15 col3\" >14.4449</td>\n",
       "                        <td id=\"T_79615_row15_col4\" class=\"data row15 col4\" >0.3006</td>\n",
       "                        <td id=\"T_79615_row15_col5\" class=\"data row15 col5\" >1.0967</td>\n",
       "                        <td id=\"T_79615_row15_col6\" class=\"data row15 col6\" >6.7180</td>\n",
       "                        <td id=\"T_79615_row15_col7\" class=\"data row15 col7\" >0.0067</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_79615_level0_row16\" class=\"row_heading level0 row16\" >en</th>\n",
       "                        <td id=\"T_79615_row16_col0\" class=\"data row16 col0\" >Elastic Net</td>\n",
       "                        <td id=\"T_79615_row16_col1\" class=\"data row16 col1\" >9.9184</td>\n",
       "                        <td id=\"T_79615_row16_col2\" class=\"data row16 col2\" >216.6042</td>\n",
       "                        <td id=\"T_79615_row16_col3\" class=\"data row16 col3\" >14.6817</td>\n",
       "                        <td id=\"T_79615_row16_col4\" class=\"data row16 col4\" >0.2790</td>\n",
       "                        <td id=\"T_79615_row16_col5\" class=\"data row16 col5\" >1.0613</td>\n",
       "                        <td id=\"T_79615_row16_col6\" class=\"data row16 col6\" >6.6383</td>\n",
       "                        <td id=\"T_79615_row16_col7\" class=\"data row16 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_79615_level0_row17\" class=\"row_heading level0 row17\" >llar</th>\n",
       "                        <td id=\"T_79615_row17_col0\" class=\"data row17 col0\" >Lasso Least Angle Regression</td>\n",
       "                        <td id=\"T_79615_row17_col1\" class=\"data row17 col1\" >12.4393</td>\n",
       "                        <td id=\"T_79615_row17_col2\" class=\"data row17 col2\" >302.5482</td>\n",
       "                        <td id=\"T_79615_row17_col3\" class=\"data row17 col3\" >17.3508</td>\n",
       "                        <td id=\"T_79615_row17_col4\" class=\"data row17 col4\" >-0.0067</td>\n",
       "                        <td id=\"T_79615_row17_col5\" class=\"data row17 col5\" >1.2973</td>\n",
       "                        <td id=\"T_79615_row17_col6\" class=\"data row17 col6\" >8.5023</td>\n",
       "                        <td id=\"T_79615_row17_col7\" class=\"data row17 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_79615_level0_row18\" class=\"row_heading level0 row18\" >par</th>\n",
       "                        <td id=\"T_79615_row18_col0\" class=\"data row18 col0\" >Passive Aggressive Regressor</td>\n",
       "                        <td id=\"T_79615_row18_col1\" class=\"data row18 col1\" >18.0307</td>\n",
       "                        <td id=\"T_79615_row18_col2\" class=\"data row18 col2\" >444.3401</td>\n",
       "                        <td id=\"T_79615_row18_col3\" class=\"data row18 col3\" >20.5263</td>\n",
       "                        <td id=\"T_79615_row18_col4\" class=\"data row18 col4\" >-0.4205</td>\n",
       "                        <td id=\"T_79615_row18_col5\" class=\"data row18 col5\" >1.5862</td>\n",
       "                        <td id=\"T_79615_row18_col6\" class=\"data row18 col6\" >15.5280</td>\n",
       "                        <td id=\"T_79615_row18_col7\" class=\"data row18 col7\" >0.0067</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fe65b7943a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x7fe638c35040>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = setup(one_hot_encoded_data3, target ='new_case_percent_pop*', train_size = 0.8,\n",
    "            data_split_shuffle = True, fold = 3,\n",
    "            ignore_features = ['date', 'Conf_Cases'],\n",
    "            numeric_features = ['day_of_year', 'Year', 'Avg_Temp(F)'],\n",
    "            categorical_features = ['state_id_CT', 'state_id_MA', 'state_id_ME',\n",
    "                                       'state_id_VT', 'Month_1', 'Month_2', 'Month_3', 'Month_4', 'Month_5',\n",
    "                                       'Month_6', 'Month_7', 'Month_8', 'Month_9', 'Month_10', 'Month_11',\n",
    "                                       'Month_12', 'day_of_week_0', 'day_of_week_1', 'day_of_week_2',\n",
    "                                       'day_of_week_3', 'day_of_week_4', 'day_of_week_5', 'day_of_week_6'],\n",
    "            silent = True, verbose = False, session_id = 123, normalize=False)\n",
    "    # compare all models and select best one based on MAE\n",
    "best_model = compare_models(sort = 'R2', verbose=False)\n",
    "    \n",
    "    # capture the compare result grid and store best model in list\n",
    "p = pull().iloc[0:1]\n",
    "p['time_series'] = str(i)\n",
    "all_results.append(p)\n",
    "best = compare_models(sort = 'R2')\n",
    "best"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f796356d",
   "metadata": {},
   "source": [
    "Even better! One-hot-encoding all three categorical columns produced the best model metrics yet! Let's save the dataset with these columns one-hot-encoded and begin tuning the hyperparameters of a CatBoost Regressor, Extra Trees Regressor, and Random Forest Regressor to feed into a Voting Regressor in the next steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6d279f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the data to a new csv file\n",
    "#one_hot_encoded_data3.to_csv('ohe_data2.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa8897be",
   "metadata": {},
   "source": [
    "*****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbdf9e2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "s = setup(one_hot_encoded_data, target ='new_case_percent_pop*', train_size = 0.8,\n",
    "            data_split_shuffle = True, fold = 3,\n",
    "            ignore_features = ['date', 'Conf_Cases'],\n",
    "            numeric_features = ['day_of_year', 'Year', 'Avg_Temp(F)'],\n",
    "            categorical_features = ['Month', 'day_of_week', 'state_id_MA', 'state_id_CT', 'state_id_VT', 'state_id_ME'],\n",
    "            silent = True, verbose = False, session_id = 123, normalize=True)\n",
    "    # compare all models and select best one based on MAE\n",
    "best_model = compare_models(sort = 'R2', verbose=False)\n",
    "    \n",
    "    # capture the compare result grid and store best model in list\n",
    "p = pull().iloc[0:1]\n",
    "p['time_series'] = str(i)\n",
    "all_results.append(p)\n",
    "best = compare_models(sort = 'R2')\n",
    "best\n",
    "'''"
   ]
  }
 ],
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