Preprocessing.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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <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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       "  <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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ZZn2yVkVUDShljIkOK7cd4M+fruK0Hq0Yl5lWafmxgzuwens+L369gW4pSVxyfPtKlzF2BmKMiTBHjjpNdpvUj+exi/oGfJ/H787uyQldWnL/B9+RlbMnxFFGBksgxpiI8vC071m78yB/u6QfLRrVC3i5uNgYJlw6kNRmDbju1YXk7j0cwigjgyUQY0zEmLFiO6/N28R1J3XixK5V7327SYN4XhiXwdHiEq6emMWhwqIQRBk5LIEYYyLC9v0F3PveMnq3a8ydZ3avfIEKdGnViH9cOpA1O/K54+0llFjLrApZAjHG1HnFJcrtby3haFEJz4wZUONOEn/WLZn7z+7FjBU7ePKzNUGKMvJYKyxjTJ33ry/XMXf9bh67qC+dkhsFZZ2/HJ7Omu35/P2/2XRLSeLn/doGZb2RxM5AjDF12pLN+/jbf9Zwdt82XDwoNWjrFRH+eF5vjk9vxl3vLGVZ7r6grTtSWAIxxtRZBwuLuHXyYlIaJ/Ln8/sEvWv2hLgYnr18EC0b1eOaSVnsOFAQ1PXXdZZAjDF11gMfLmfznsM8NaY/TerHh2QbLRvV48XxGeQXFHHtpCwKjhWHZDt1kSUQY0yd9OGSLby/aAu/PrUrx6c3D+m2erZpzJO/6M/S3P3c+94yomkgPn88TSAiMlJEVotItojcV858EZFn3PnLRGSgz7wcEflORJaIiA0zaEwU2bznML+bspyMtGb8+tQuYdnmiONac/eI7ny4ZCvPzl4Xlm3Wdp61whKRWGACcAaQCywQkamq+r1PsVFAV/cxBHjW/VvqFFW10WCMiSJFxSXcMnkxCDw1pj9xseH7HXzjyZ1ZvT2fx2espmurpB/GF4lWXp6BDAayVXW9qh4FJgOjy5QZDUxSxzygqYi0CXegxpja4+nP17J40z7+fH4fUps1COu2RYTHLupLn3ZNuG3yYlZtPxDW7dc2XiaQdsBmn9e57rRAyyjwHxFZKCLXVrQREblWRLJEJCsvLy8IYRtjvDJv/W7+MSubiwelenZfRmJ8LM9fkUHDenFcPTGL3QcLPYmjNvAygZTX3q5szZS/MsNVdSDOZa6bROSk8jaiqs+raoaqZiQnV71vHGNM7bD/8DFuf2sJ6S0a8uC5x3kaS+smiTw/LoO8/EJueG0RR4tKPI3HK14mkFzAt9P9VGBroGVUtfTvTmAKziUxY0wEUlXue38Zuw4W8syYATSs530nGv3bN+Wxi/oyP2cPD3y4PCpbZnmZQBYAXUWko4gkAGOAqWXKTAXGua2xhgL7VXWbiDQUkSQAEWkInAksD2fwxpjweWvBZj5dvp27zuxOn9QmXofzg9H923HTKZ2ZvGAzr8zJ8TqcsPMsjatqkYjcDMwAYoGXVHWFiFzvzn8OmAacBWQDh4Gr3MVTgCnuXadxwBuqOj3Mu2CMCYPsnQd56KPvOaFLS645sZPX4fzEnWd0Z82Og/zx4+/pnNyIk7pFz6VyiabTroyMDM3KsltGjKkrCouKOX/CHLYfKGD6rSfSqnGi1yGV61BhERc+O4ct+47wwU3D6RykDh1rCxFZqKoZZafbnejGmFrr8emr+X7bAR67sG+tTR4ADevF8cK4DOJjY7hmYhb7Dx/zOqSwsARijKmVvli9kxe/3sC4zDROrwM37LVv3oDnLh/E5r2HufnNRRQVR37LLEsgxphaJy+/kLveWUr3lCR+e1ZPr8MJ2OCOzfnTeb35au0uHp620utwQs77tnDGGONDVbn73aXkFxTx+tVDSYyP9TqkKvnF8R1Yvf0gL32zge4pSYwZ3MHrkELGzkCMMbXKy9/k8MXqPH53dk+6t07yOpxq+e1ZPTipWzK//3A58zfs8TqckLEEYoypNb7feoBHPl3F6T1bcfnQNK/Dqba42Bj+PnYA7Zs14PrXFrJ5z2GvQwoJSyDGmFrhyNFifv3mIpo2iOexi/oFfXTBcGtSP54Xx2dQVFzCNZOyOFhY5HVIQWcJxBhTK/zxk+9Zv+sQT/6iP80bJngdTlB0Sm7EhMsGsnbnQW5/awklJZF1350lEGOM56Yv384b327i2pM6MbxLS6/DCaoTuybz+7N7MvP7HTwxc7XX4QSVtcIyxnhq2/4j3Pf+MvqmNuHOM7p7HU5IjB+Wzuod+UyYtY5uKUmM7l925Iq6yc5AjDGeKS5Rbn9rCUeLSnh6zAAS4iLzK0lEeOjc3gzu2Jy7313Gks37vA4pKCLzv2WMqROem72Oeev38NC5x9GxZUOvwwmphLgYnrt8EK2S6nHtpCy27y/wOqQaswRijPHE4k17+dvMNfy8X1suGpTqdThh0bxhAv8efzyHCou49tUsCo4Vex1SjVgCMcaEXX7BMW6dvITWjRP503m963yT3aro3jqJp8YM4Lst+7n73WV1eiAqSyDGmLB74MMV5O49zNNj+tOkfrzX4YTdGb1SuHtEdz5aupUJs7K9DqfarBWWMSaspizOZcriLdx+ejcy0pt7HY5nbvhZZ9Zsz+ev/1lDl1ZJjOzd2uuQqszTMxARGSkiq0UkW0TuK2e+iMgz7vxlIjKwzPxYEVksIh+HL2pjTHVt2n2Y33+wguPTm3HTKZ29DsdTIsIjF/alX/um3PH2ElZuO+B1SFXmWQIRkVhgAjAK6AWMFZFeZYqNArq6j2uBZ8vMvxWI/D6TjYkAx4pLuGXyYkTgyV/0Jy7WrqAnxsfywhWDaJwYz9UTs9h1sNDrkKrEy//gYCBbVder6lFgMjC6TJnRwCR1zAOaikgbABFJBc4GXgxn0MaY6nn6s7Us2byPv1zQh9RmDbwOp9Zo1TiRF8ZlsPtQITe8tpDCorrTMstvAhGRASJykYiEYkSXdsBmn9e57rRAyzwF3ANE/rBfxtRx89bvZsIX2VySkco5fdt6HU6t0ye1CY9f1I8FOXv5/QfL60zLrAoTiIg8ALwFXAh8IiLXBHnb5bXbK/uulVtGRM4Bdqrqwko3InKtiGSJSFZeXl514jTG1MC+w0e5/a0ldGzRkD/8/Divw6m1ft6vLbec2oW3s3J56Zscr8MJiL8zkF8A/VV1LHA8Th1EMOUC7X1epwJbAywzHDhXRHJwLn2dKiKvlbcRVX1eVTNUNSM5OTlYsRtjAqCq3Pfed+w6WMjTYwbQsJ41/PTnttO7MeK4FB7+5Htmr6n9P3j9JZACVT0MoKq7KylbHQuAriLSUUQSgDHA1DJlpgLj3NZYQ4H9qrpNVX+jqqmqmu4u919VvTzI8RljaujN+ZuZvmI7d4/oTp/UJl6HU+vFxAh/u6Q/3Vs35uY3FpG986DXIfnlLyl0FpGp7uOjMq/LftFXmaoWATcDM3BaUr2tqitE5HoRud4tNg1YD2QDLwA31nS7xpjwyN6Zz/99vIITu7bk6hM6eR1OndGwXhwvjBtEvbgYrp64gH2Hj3odUoWkosoaEfmZvwVVdXZIIgqhjIwMzcrK8joMYyJeYVEx502Yw44DBUy/9URaNU70OqQ6JytnD2NfmMeQji145arjPW32LCILVTWj7HR/EX0P5KnqbN8HsMudZyJUYVEx/1mxnUWb9nodiqmjHv10NSu3HeCvF/e15FFNGenN+fP5ffg6exd/+qR23u7mr0br7/z0xj1wKrLvBy4NSUTGEyUlStbGvUxZvIVPlm3lQEERzRsmMOe+U0mMj/U6PFOHzFq9k5e+2cCVw9I5tUeK1+HUaRdntGf19nxe/HoD3VKSuHRIB69D+hF/CaRPeZepVHWGiDwRwphMGGXvzGfK4i18sHgrW/YdoX58LCN7t6ZbShKPTl/FJ8u2cWGUdLVtai4vv5C731lKj9ZJ3Deqh9fhRITfnNWTtTsP8sCHy+mU3JChnVp4HdIP/CUQf11kRl/3mRFkZ34BHy3dxgeLt/Ddlv3ECJzQNZm7RnTjzF6taVgvDlXlvUW5TJqbYwnEBKSkRLnrnaXkFxTxxjVD7cw1SGJjhL9fOoDzJ3zDDa8t5MObTqBDi9pxJ7+/BLJWRM5S1Wm+E0VkFE7LKFOHHCos4j/fb2fK4q18vTaPEoU+7Zrw+3N68fN+bWiV9OPr1CLC+Mw0fv/hChZv2suADs08itzUFS/PyWH2mjz+eF5vuqUkeR1ORGmcGM+/xx/P6AnfcPWkBbx3wzCSEr3/He8vgdwOfCwilwCld3xnAJnAOaEOzNRcUXEJ36zbzQeLtzBjxXYOHy2mXdP63HByZ87r346ulXzIzx+YyqPTVzNp7kZLIMav5Vv28+inqzijVwqX17Lr9JEivWVD/nnZQMa9NJ/bJi/h+XEZxMZ4OxBXhQlEVdeISB+cyvLe7uTZwHWqWvcH841QqsryLQeYsngLU5duZdfBQhonxjG6fzvOH9COjLRmxAR40DWqF8dFg1J5/duN/PasniQn1Qtx9KYuOny0iFsnL6ZZw3gevbBvVI0uGG7Du7TkDz/vxQMfruDxGas9r2eqrF+BIuAjVX0ZwL1jfLyI3KGqoehg0VTT5j2Hmbp0K1MWbyF750ESYmM4tUcrzhvQjlN6JFMvrnrXo8dlpvHKnBwmz9/Er0/rGuSoTST448crWb/rEK//agjNGyZ4HU7Eu2JoGqu35/Pc7HV0b92I8wd4V0dZYQIRkTHAv4BDIrIWeBB4FacLksvCEp3xa//hY3zynVMZPj9nDwCD3bbjZ/VpTdMGNf8wd0puxEndknn9201cf3Jn4m0MB+Nj+vJtvDl/Ezec3JlhXVp6HU5UEBEePPc41uUd5N73viO9RUPPLjH7uxN9OXCeqma7IwHOBcao6pRwBhhMkXAnemFRMbNW7WTK4i3MWpXH0eISOic35IKBqZzbry3tmwe/dcbnK3fwq4lZTLh0IGf3bRP09Zu6aeu+I4x6+ivSWzTg3RuG2Y+LMNt76CijJ3zDkWPFTL15OG2a1A/Ztiq6E93fJayjqpoNoKqLRGRDXU4edVl5N/m1bFSPy4emccHAdhzXtnFIrzuf3L0V7ZvXZ+LcHEsgBoDiEuX2t5ZQVFzC02MGWPLwQLOGCbw4PoML/jmHayZl8c51w6ifEN6m0/4SSCsRucPndSPf16r6t9CFZaDim/zOG9CO4Z1bhK1vnNgYYdzQdB6etpKV2w7Qs03jsGzX1F7PfpHNtxv28MTF/Uhv2dDrcKJWt5Qknhnbn19NzOKud5fyj7EDwtqIwV8CeQFI8vPahEBFN/ndPaI7Z/RK8Ww8hYszUnli5momzc3hLxf09SQGUzss2rSXJz9by7n92nLBwLKDiJpwO7VHCveN7MFfPl1F95QkbgljYxd/zXgfAhCRlqq6K2wRRaGq3uTnhaYNEjh/QDumLN7CvSN7BKWC3tQ9BwqOcevkxbRpksifzu9tTXZriWtP6sTqHfn8beYaurZqxKg+4bnU7K8V1jnAy8AxESkBLlHVOWGJKgpUdJPfjSd34bwBbenSqvad7I3LTOfN+Zt5JyuXa06y8R2i0QMfLGfrvgLevi6TxrXgTmjjEBH+fH4fNuw6xB1vL6VDiwYc1zb0A3j5ux7yZ+BEVV0lIkOAxwC/Y4QY//zd5HfBwHYM6hD4TX5e6NmmMYM7NufVeRv55QkdPb8L1oTXlMW5fLBkK3ec0Y1BadYzQW2TGB/Lv64YxOh/fMM1E7P48OYTQn7zr78EUqSqqwBU9VsRqX0/ieuIUN3k54Xxmenc9MYivli9k9N6Wlfd0WLj7kP8bspyBqc356ZTungdjqlAq6REXhiXwUXPzeH61xbyxjVDQvr9UpVWWK2C3QpLREYCTwOxwIuq+kiZ+eLOPws4DFzpNilOBL4E6rn78K6q/qGm8QRTRTf5/eWCPpzVuw1NGtTN0/8zj0uhdeNEJs7daAkkShwrLuGWyUuIjRGeHNPfzjxrud7tmvDExf256Y1F3D9lOY9fFLruZTxrhSUiscAE4AwgF1ggIlNV1Xe0w1FAV/cxBGeAqyFAIXCqqh4UkXjgaxH5VFXnBSu+6qjoJr+7R3QP2U1+4RYfG8NlQzrwxMw1rMs7SOfkRl6HZELsyZlrWLp5H/+8bCDtmobuZjUTPGf3bcOaHV15+vO1dE9JClmdZaWtsEJoMJCtqusBRGQyMJofD5c7Gpikzu3y80SkqYi0UdVtwEG3TLz7KP+W+hCr6Ca/KzLTOH9A6G/y88KYwR34+3+zeXXuRh489zivwzEhNGfdLp6dvY4xx7fnrDC17DHBcetpXVm7M5+/fLqSLq0acUqPVkHfhjc3FTjaAZt9XufinF1UVqYdsM09g1kIdAEmqOq35W1ERK4FrgXo0CF43UzXlpv8vJCcVI+z+7bh3YW53DWiO408ujfFhNbeQ0e5462ldGzZkAd+3svrcEwVxcQIf724Hxt3H+aWNxcz5aZhQW/d6eUnv7yf5WXPIioso6rFQH8RaQpMEZHeqrr8J4VVnweeB6cvrJoEXN5NfifWgpv8vDAuM40pi7cwZVEuV2Smex2OCTJV5d73lrH7UCEvjh9Og4ToObYjSYOEOF4Yl8H1ry2k4FhJ0Nfv5VGRC7T3eZ0KbK1qGVXdJyJfACOBnySQYJj5/Q5enbex1t7k54UBHZrRL7UJE+du5PKhaRF3mS7avTF/E//5fge/O7snvduF/n4CEzptm9bnw5uGh+QzWmkCEZF6wIVAum95Vf2/Gm57AdBVRDoCW4AxOINX+ZoK3OzWjwwB9qvqNhFJBo65yaM+cDrwaA3jqdDiTXtZt/Ngrb7JzwvjMtO5852lzFm3m+HWlXfEWLsjnz9+/D0ndm3JL4d39DocEwRetMIq9SGwH6e+oTBYG1bVIhG5GZiB04z3JVVdISLXu/OfA6bhNOHNxmnGe5W7eBtgolsPEgO8raofByu2sn59alfuOrN7rb7Jzwtn923Dw9NWMnFOjiWQCFFwrJhfv7mYhglxPHFJPzvmjV+BJJBUVR0Zio2r6jScJOE77Tmf5wrcVM5yy4ABoYipPOHuIrmuSIyPZezg9jz7xTpy9x4mtVndb6Yc7R6dvopV2/N5+crjo/byrAlcIE2F5rhjoxvzE5cNceo/Xpu3yetQTA3NWrWTl7/J4cph6SFp8mkiT4UJRES+E5FlwAnAIhFZLSLLfKYbQ9um9TmzVwqTF2yi4Fix1+GYatqZX8Bd7yylR+sk7hvVw+twTB3h7xLWOWGLwtRp4zLT+XT5dqYu3colGe0rX8DUKiUlyp1vL+XQ0SImjx1KYrxdsjWBqfAMRFU3qupG4E+lz32nhS9EU9sN7dSc7ilJTJyTg1NtZeqSl77ZwFdrd/H7c3rRNcVaGJrABVIH8qO+KtyWT4NCE46pi0SEccPSWLH1AIs27fU6HFMFy7fs59HpqzizVwqXDg5eTw0mOvirA/mNiOQDfUXkgPvIB3biNO015gfn9W9HUmIcE+ds9DoUE6BjxSXcOnkxLRrW49ELQ9djq4lc/i5h/UVVk4DHVbWx+0hS1Raq+pswxmjqgIb14rgkoz3TvtvGzgMFXodjAjB5/ibW5R3i4fN706yhDVFsqs7fGUhpU4x3RGRg2UeY4jN1yBVD0yhW5Y351qS3tjtUWMTTn69lSMfmnGpNdk01+WuFdQdOL7ZPlDNPgVNDEpGps9JbNuTkbsm8/u0mbjy5CwlxkdsjcV334lcb2HXwKC+M62GXrky1+buEda3795RyHpY8TLnGDUsnL7+Q6Su2ex2KqcDug4U8/+U6Rh7XmgEdbGxzU32V/kQUka9E5GERGWnjopvK/KxrMuktGjBxTo7XoZgK/P2/2RQUlXD3yO5eh2LquECuMYwHVuP0yDtHRLJE5MnQhmXqqpgY4YrMdBZu3MvyLfu9DseUsWn3YV7/diOXZLS34YhNjVWaQNwhZ2cCnwNfAg2AniGOy9RhFw1KpX58LJPm5ngdiinjiZmriY0Rbju9q9ehmAgQyCWsdcAHQArwb6B3qHrnNZGhSf14LhjYjg+XbGXvoaNeh2Ncy7fs58MlW/nl8I6kNLaedk3NBXIJ6xlgEzAWuAUYLyKdQxqVqfPGZaZTWFTCW1mbKy9swuKxGatp2iCe635mH18THIFcwnpaVS/GGfVvIfAgsCbEcZk6rnvrJDI7teDVuRspLrH+sbw2J3sXX67J4+ZTutCkfrzX4ZgIEcglrCdE5FvgW6Af8ABgF1BNpcYPS2PLviN8vnKH16FENVXlkemraNskkcuHpnkdjokggVzCmgecq6rHqerVqjrRrVivMbdp8GoRyRaR+8qZLyLyjDt/Wekd8CLSXkRmichKEVkhIrcGIx4TXKf3TKFtk0QmWmW6p6Z9t51lufu548zu1lW7CapALmG9o6pB/wnp9uo7ARgF9ALGikivMsVG4ZztdMW5K/5Zd3oRcKeq9gSGAjeVs6zxWFxsDJcNTeOb7N1k78z3OpyodKy4hMdnrKJ7ShLnD2jndTgmwnjZ18RgIFtV16vqUWAyMLpMmdHAJHXMA5qKSBtV3aaqiwBUNR9YCdinoxYac3x7EuJimDTXeun1wuQFm8nZfZh7RnYnNsa6LDHB5WUCaQf4NtHJ5adJoNIyIpIODMCpo/kJEbnWvfkxKy8vr6Yxmypq0ageP+/blvcW5pJfcMzrcKLKocIinv5sLYPTrcNEExoBJRARiRWRtiLSofQRhG2X93OobHMdv2VEpBHwHnCbqh4obyOq+ryqZqhqRnJycrWDNdU3flgah44W897CXK9DiSovfb2BXQcLuXeUdZhoQiOQVli/Bnbg3I3+ifv4OAjbzgV8B9BOBbYGWkZE4nGSx+uq+n4Q4jEh0je1KQM6NGXS3I2UWJPesNh9sJB/fbmeEcelMCjNOkw0oRHIGcitQHe3FVYf99E3CNteAHQVkY4ikgCMAaaWKTMVGOe2xhoK7FfVbeL8nPo3sFJV/xaEWEyIjc9MZ/2uQ3ydvcvrUKLChFnrOHy0iLtHWIeJJnQCSSCbgaD3iqeqRcDNwAycSvC3VXWFiFwvIte7xaYB64Fs4AXgRnf6cOAK4FQRWeI+zgp2jCZ4RvVpTctGCdZLbxhs3nOYV+flcElGe7q0sg60Tej4G1Cq1HrgCxH5BCgsnRiMX/6qOg0nSfhOe87nuQI3lbPc15RfP2JqqXpxsVw6uAN/n5XNpt2H6dCigdchRay/zVxDjAi3nd7N61BMhAvkDGQTTv1HApDk8zCmSi4dkkasCK99a016Q+X7rQf4YMkWrhrekdZNrMNEE1qVnoGo6kMA7mBSqqoHQx6ViUitmyQyondr3lqwmdtP70b9BLsrOtgem7GKxonx3GAdJpowCKQVVm8RWQwsB1aIyEIROS70oZlIND4znf1HjvHhki1ehxJx5qzbxRer87jplM40aWAdJprQC+QS1vPAHaqapqppwJ04FdrGVNnx6c3o2aYxr8zJwaniMsGgqjw6fTVtmyQyLjPd63BMlAgkgTRU1VmlL1T1C6BhyCIyEU1EGJ+Zxqrt+SzI2et1OBFj+vLtLN28j9vO6GYdJpqwCSSBrBeR34tIuvv4HbAh1IGZyDW6fzua1I+3Jr1B4nSYuJpuKY24cGCq1+GYKBJIAvklkAy8D0xxn18VyqBMZKufEMsvjm/P9BXb2b6/wOtw6ry3szazftch7hnRwzpMNGEVSHfue1X1FlUdqKoDVPVWVbVrD6ZGLh+SRokqb1iT3ho5fLSIpz5bS0ZaM07raR0mmvCqMIGIyFPu349EZGrZR9giNBGpQ4sGnNajFW/M30RhUbHX4dRZL3+TQ15+IfdZh4nGA/7uA3nV/fvXcARios+4zHQ+WzmfT7/bznk22FGV7Tl0lOe+WMcZvVLISG/udTgmClV4BqKqC92n/VV1tu8D6B+W6ExEO6FLSzolN+QVq0yvlgmzsjl0tIh7rMNE45FAKtHHlzPtyiDHYaJQTIwwbmgaSzbvY+nmfV6HU6fk7j3Mq3M3ctGgVLqmWM9Cxhv+6kDGishHQMcy9R+zgN3hC9FEsgsHpdIwIZaJc3O8DqVO+dvMNYhgHSYaT/mrA5kDbANaAk/4TM8HloUyKBM9khLjuXBQKpPnb+b+s3rSolE9r0Oq9VZuO8CUxVu49qROtG1a3+twTBTzVweyUVW/UNXMMnUgi9yxPIwJinGZaRwtLmHygs1eh1InPDZ9FUn14rjxZ128DsVEuUA6UxwqIgtE5KCIHBWRYhEpd/xxY6qjS6skTujSktfmbaSouMTrcGq1eet3M2t1Hjee0sU6TDSeC6QS/R/AWGAtUB+4Gvh7MDYuIiNFZLWIZIvIfeXMFxF5xp2/TEQG+sx7SUR2isjyYMRivDUuM41t+wv4bOUOr0OptVSVRz5dRevGiVw5LN3rcIwJKIGgqtlArKoWq+rLwCk13bCIxAITgFFAL2CsiPQqU2wU0NV9XAs86zPvFWBkTeMwtcNpPVNo17S+Nen1Y8aK7SzZvI87rMNEU0sEkkAOi0gCsEREHhOR2wlOb7yDgWxVXa+qR4HJwOgyZUYDk9QxD2gqIm0AVPVLYE8Q4jC1QGyMcEVmGvPW72H19nyvw6l1iopLeGzGarq0asQFA+2mS1M7BJJArgBigZuBQ0B74MIgbLsd4FtrmutOq2oZv0TkWhHJEpGsvLy8agVqwuMXGe2pFxdjTXrL8c7CXNbnHeKeEd2Jiw3owoExIRdIZ4obVfWIqh5Q1YdU9Q73klZNlddxT9kRhgIp45eqPq+qGaqakZycXJVFTZg1a5jA6P5tmbJoC/uPHPM6nFrjyNFinpy5hkFpzTijV4rX4RjzA383En7nVlyX+wjCtnNxzmZKpQJbq1HGRJBxmekcOVbMO1nWpLfUS99sYKd1mGhqIX83Ep4T4m0vALqKSEdgCzAGuLRMmanAzSIyGRgC7FfVbSGOy3iod7smZKQ149V5G/nl8I7ERPn4FnsPHeW52es4vWcrjrcOE00tU9mNhBtVtXTAhq7u850EofLavRnxZmAGsBJ4W1VXiMj1InK9W2wasB7IxhmH/cbS5UXkTWAu0F1EckXkVzWNydQO44als3H3YWavtTqrf36RzaHCIu4e0cPrUIz5CX9nIACIyDU4TWibA51xLiM9B5xW042r6jScJOE77Tmf5wrcVMGyY2u6fVM7jTyuNa2S6jFxTg6ndI/eQZK27DvCxDkbuXBgKt1bW4eJpvYJpDnHTcBw4ACAqq4FovdTbUIuIS6GS4d04IvVeeTsOuR1OJ55cuYaELj9DOsw0dROgSSQQvc+DQBEJI4qtoQypqouHdyBuBhh0tzoHPJ21fYDvLcolyuHpVuHiabWCiSBzBaR3wL1ReQM4B3go9CGZaJdq8aJnNWnDe8s3Myhwujru/Px6atpVC+OG0/u7HUoxlQokARyL5AHfAdch1Nn8btQBmUMwPhhaeQXFDFl8RavQwmr+Rv28PmqndxwcmeaNkjwOhxjKuS3El1EYoBlqtobpxWUMWEzsEMzerdrzKS5OVw2pENU3APhdJi4kpTG9bhqWEevwzHGL79nIKpaAiwVkQ5hiseYH4gI4zLTWbPjIPPWR0e3Z//5fgeLNu3j9tO7UT/BOkw0tVsgl7DaACtE5HPfoW1DHZgxAOf2a0uzBvFMjIJeeouKS3hs+io6JzfkokGpXodjTKUqvQ8EeCjkURhTgcT4WH5xfAee/3IdW/YdoV0Et0h6b1Eu6/IO8dzlg6zDRFMn+D1K3TqQCWWGtJ2tqrPDFJ8xXD7UuYL6+rzIbdLrdJi4lgEdmjLiOOsw0dQNVgdiar3UZg04vWcKkxdspuBYsdfhhMQrc3LYfqCA+0Zah4mm7rA6EFMnjB+Wzp5DR/l4WeT1pbnv8FGe/SKbU3u0YkinFl6HY0zArA7E1AnDOregS6tGTJyTw4UD20XUr/Rnv1hHfmER94zs7nUoxlRJIANKzQZWAUnuY6XVgZhwExHGZ6bx3Zb9LNm8z+twgmbrviO8PCeHCwak0qN1Y6/DMaZKKk0gInIJMB+4GLgE+FZELgp1YMaUdcHAVJLqxUVUk94nZ64B4I4zrcNEU/cEUgdyP3C8qo5X1XHAYOD3oQ3LmJ9qWC+OCwel8sl328jLL/Q6nBpbsyOf9xblMm5oWkQ3TzaRK5AEEqOqO31e7w5wOWOCblxmGseKlTfnb/I6lBp7bPpqGibEcdMpXbwOxZhqCSQRTBeRGSJypYhcCXwCfBrasIwpX6fkRpzULZnXv93IseISr8OptgU5e/hs5Q6uP7kzzRpah4mmbgqkEv1u4F9AX6Af8Lyq3hOMjYvISBFZLSLZInJfOfNFRJ5x5y8TkYGBLmsi1/jMNHYcKGTGiu1eh1Itqsqjn66iVVI9fjncOkw0dVeFCUREuojIcABVfV9V71DV24HdIlLjQQpEJBaYAIwCegFjRaRXmWKjgK7u41rg2SosayLUyd1b0aF5AybNqZt3pn+2cidZG/dym3WYaOo4f2cgTwH55Uw/7M6rqcFAtqqud0c8nAyMLlNmNDBJHfOApiLSJsBlTYSKjRGuGJrG/Jw9fL/1gNfhVElph4mdWjbkkgzrMNHUbf4SSLqqLis7UVWzgPQgbLsdsNnnda47LZAygSwLgIhcKyJZIpKVl5dX46BN7XBxRiqJ8TFMmpvjdShV8v6iLazdeZC7R3S3DhNNnefvCE70My8YbQ7Lu5W47FjrFZUJZFlnourzqpqhqhnJyclVDNHUVk0bJHD+gHZ8sGQL+w4f9TqcgBQcK+bJz9bQr31TRvZu7XU4xtSYvwSyQESuKTtRRH4FLAzCtnOB9j6vU4GtAZYJZFkT4cZlplNwrIS3szZXXrgWmDgnh237C/jNKOsw0UQGfwnkNuAqEflCRJ5wH7OBq4Fbg7DtBUBXEekoIgnAGKBsJ41TgXFua6yhwH5V3RbgsibC9WzTmMEdm/PqvI0Ul5R7Alpr7D98jAmzsjmlezJDrcNEEyEqTCCqukNVh+F0ppjjPh5S1UxVrXH7SVUtAm4GZgArgbdVdYWIXC8i17vFpgHrgWycMdlv9LdsTWMydc/4zHQ27znCrFU7Ky/soWdnl3aY2MPrUIwJmkp741XVWcCsUGxcVafhJAnfac/5PFfgpkCXNdHnzONSaN04kYlzczi9V+0ciGnb/iO8/M0Gzu/fjp5trMNEEzmsGYip0+JjY7hsSAe+WruLdXkHvQ6nXE/NXIsq3H6GdZhoIoslEFPnjRncgYTYGF6dW/tuLFy7I593Fm7m8qFptG/ewOtwjAkqSyCmzktOqsfZfdvw7sJcDhYWeR3Ojzw+YzUNEuK4+VTrMNFEHksgJiKMy0zjYGER7y/K9TqUHyzcuIf/fL+D63/WiebWYaKJQJZATEQY0KEZ/VKbMHFODk7bC2+pKo98uorkpHr88gTrMNFEJksgJmKMy0xnXd4hvsne7XUo/HfVThbk7OXW07rSIKHSxo7G1EmWQEzEOLtvG1o0TGCix/1jFZcoj05fRceWDfnF8e0rX8CYOsoSiIkYifGxjBncns9X7mDznsOexfH+olzW7HA6TIy3DhNNBLOj20SUy4akISK89q03TXoLjhXz5Mw19EttwijrMNFEOEsgJqK0bVqfM3ul8NaCzRQcKw779l+du5Gt+wu41zpMNFHAEoiJOOMy09l3+BhTl4S3g+b9R47xj1nZ/KxbMsM6twzrto3xgiUQE3GGdmpO95QkXglzk97nZq/jQMEx7rUOE02UsARiIo6IMG5YGt9vO8DCjXvDss3t+wt4+ZsNjO7Xll5trcNEEx0sgZiIdP6AdiQlxjExTP1jPf35GopLlDvP7B6W7RlTG1gCMRGpQUIcl2S059PvtrHjQEFIt5W98yBvLbAOE030sQRiItYVQ9MoVuWNbzeFdDuPz1jldJh4inWYaKKLJwlERJqLyEwRWev+bVZBuZEislpEskXkPp/pF4vIChEpEZGM8EVu6pL0lg05uVsyb8zfxNGikpBsY9GmvcxYsYNrT+pEi0b1QrINY2orr85A7gM+V9WuwOfu6x8RkVhgAjAK6AWMFZFe7uzlwAXAl+EJ19RV44alk5dfyKfLtwV93arKI9NW0bJRPX5lHSaaKORVAhkNTHSfTwTOK6fMYCBbVder6lFgsrscqrpSVVeHI1BTt/2sazLpLRowKQSV6bNW72R+zh5uPb0rDetZh4km+niVQFJUdRuA+7dVOWXaAZt9Xue604wJWEyMcEVmOgs37mX5lv1BW29xifLY9NWkt2jAGOsw0USpkCUQEflMRJaX8xgd6CrKmVblu8JE5FoRyRKRrLy8vKoubiLARYNSaZAQy8Q5OUFb5weLt7Bqez53WYeJJoqF7MhX1dNVtXc5jw+BHSLSBsD9u7OcVeQCvj/tUoEq902hqs+raoaqZiQnJ1dnV0wd16R+POcPaMeHS7ey59DRGq+v4Fgxf5u5hr6pTTird5sgRGhM3eTVT6epwHj3+Xjgw3LKLAC6ikhHEUkAxrjLGVNl4zLTOVpUwlsLNldeuBKvzdvIln1HuHdkD2JirMNEE728SiCPAGeIyFrgDPc1ItJWRKYBqGoRcDMwA1gJvK2qK9xy54tILpAJfCIiMzzYB1OHdG+dRGanFrw2byPFJdXvH+tAgdNh4oldWzK8i3WYaKKbJwlEVXer6mmq2tX9u8edvlVVz/IpN01Vu6lqZ1V92Gf6FFVNVdV6qpqiqiO82A9Tt4wflsaWfUf4bOWOaq/jX7PXse+wdZhoDNid6CaKnN4zhbZNEplUzSFvdxwo4N9fb2B0/7b0btckuMEZUwdZAjFRIy42hsuGpvFN9m7W7siv8vJPf77W6TDxDOsw0RiwBGKizJjj25MQF1PlGwvX5TkdJl42JI0OLazDRGPAEoiJMi0a1ePnfdvy3qJcDhQcC3i5v85YTWJcDDefah0mGlPKEoiJOuOHpXH4aDHvLcwNqPziTXv5dPl2rjmpEy2tw0RjfmAJxESdvqlNGdChKZPmbqSkkia9qsojn66iZaMErj6xU5giNKZusARiotL4zHQ27DrEV9m7/Jb7Yk0e327Ywy2ndaWRdZhozI9YAjFR6aw+bWjZqB6T/PSPVVKiPPrpKtJaNGDM8R3CF5wxdYQlEBOVEuJiuHRwe/67eiebdh8ut8yHS50OE+88szsJcfZRMaYs+1SYqHXpkDRiRXh1Xs5P5hUWFfPXGWvo3a4x5/SxDhONKY8lEBO1WjdJZETv1ry1YDOHjxb9aN5r8zaxZd8R7hvZ0zpMNKYClkBMVBufmc6BgiI+XPK/kQIOFBzjH/9dywldWnJCV+sw0ZiKWAIxUe349Gb0bNOYiXNyUHWa9L7w5Xr2WoeJxlTKEoiJaiLC+Mw0Vm3PZ/6GPew8UMCLX23g5/3a0ifVOkw0xh9LICbqje7fjib145k0dyNPf76WY8Ul3HVmN6/DMqbWszujTNSrnxDLL45vz7+/3gDAZUM6kNaiocdRGVP72RmIMcDlQ9IoUaVeXAy/PrWr1+EYUyd4kkBEpLmIzBSRte7fZhWUGykiq0UkW0Tu85n+uIisEpFlIjJFRJqGLXgTkTq0aMDtp3fjoXOPIznJOkw0JhBenYHcB3yuql2Bz93XPyIiscAEYBTQCxgrIr3c2TOB3qraF1gD/CYsUZuIdstpXbk4o73XYRhTZ3iVQEYDE93nE4HzyikzGMhW1fWqehSY7C6Hqv5HVUvv/JoHpIY2XGOMMWV5lUBSVHUbgPu3VTll2gGbfV7nutPK+iXwaUUbEpFrRSRLRLLy8vJqELIxxhhfIWuFJSKfAa3LmXV/oKsoZ9qPBm8QkfuBIuD1ilaiqs8DzwNkZGT4H/zBGGNMwEKWQFT19IrmicgOEWmjqttEpA2ws5xiuYDvBelU4If+JkRkPHAOcJqW3kJsjDEmbLy6hDUVGO8+Hw98WE6ZBUBXEekoIgnAGHc5RGQkcC9wrqqW3xe3McaYkPIqgTwCnCEia4Ez3NeISFsRmQbgVpLfDMwAVgJvq+oKd/l/AEnATBFZIiLPhXsHjDEm2nlyJ7qq7gZOK2f6VuAsn9fTgGnllOsS0gCNMcZUyu5EN8YYUy0STfXPIpIHbKzm4i2BXUEMpy6wfY4Ots/RoSb7nKaqyWUnRlUCqQkRyVLVDK/jCCfb5+hg+xwdQrHPdgnLGGNMtVgCMcYYUy2WQAL3vNcBeMD2OTrYPkeHoO+z1YEYY4ypFjsDMcYYUy2WQIwxxlSLJRBjjDHVEpUJpKKhcquxnldE5KJgxhZqIvKSiOwUkeU1XE+t2ffK9klEThaR/W6/aUtE5IEabu9gTZYPBRFpLyKzRGSliKwQkVtrsK5a87/1R0QSRWS+iCx19/mhGqyrTuxzKRGJFZHFIvJxDdZR432OugRSyVC50eAVYKTXQQTZK1S+T1+pan/38X9hiCncioA7VbUnMBS4KQqO60LgVFXtB/QHRorIUG9DCptbcTqZ9VTUJRD8DJXrS0RaichC93k/EVER6eC+XiciDcqU/6Ob0WNE5BER+V5ElonIX8OwTwFT1S+BPf7K1GTfQxa4H4HsU6BE5B4RucV9/qSI/Nd9fpqIvFambEsRmSsiZ4tIGxH50j3DWS4iJwYjnkCp6jZVXeQ+z8f5cvnJCJ6RdFyro/RsMN59/KRZaSTtM4CIpAJnAy/6KROWffakN16PlTdU7pCyhVR1p3uK3Bg4EcgCThSRr4GdqnpYxBk0UUQeA5oAVwHNgPOBHqqqItI0lDsTCtXd91o+sFemiCzFGZTsLp+hAcr6ErgTeAbIAOqJSDxwAvBVaSERScEZn+Z3qjpTRO4EZqjqw+5ZboOfrjo8RCQdGAB8W3ZepB3X7nu9EOgCTFDViN9n4CngHpwhLcoVrn2OxjOQSofK9TEHGA6cBPzZ/XsiPl8kwO+Bpqp6nfsFegAoAF4UkQuAujrgVXX2vbZahNMZXD/g78AHfsouBAaJSBLOJZK5OInEd9/jgc+Be1R1pjttAXCViDwI9HHPAsJORBoB7wG3qeqBCopFzHGtqsWq2h9nxNLBItK7gqIRsc8icg5OAlgYQPGQ73M0JhC/Q+WW8RXOG56GM2piP5xfol/6lFmA84XTHH4YCGswzof4PGB6EGMPpyrve22lqgdKL3W4Y8zEi0jLCsoeA3Jwfo3NwXkfTgE6879rzkU4iWaEz3Jf4nxAtwCvisi4kOyMH+6Z0nvA66r6vp+iEXdcq+o+4AsqrguLlH0eDpwrIjk4l99PLXtp1Ufo91lVo+qBc9luPdARSACWAsdVUDYd2AS85r6e5r5u5r5+BbgIuBjnl2oS0Aho5c5vDuzxep8r2K/lAZSp0r7X1n0CWvO/XhcGu/shftb1oFvmdCDFfT7FZ/5BIBZ4H7jPnZYGxLnPbwOeCvP+CzApkO1GynENJOP8egaoj/OFeU4k73OZfToZ+NjL/3PU1YGoapGIlA6VGwu8pBVcD1fVHPc6YWnG/hpIVdW9Zcq9417ymApcCnwoIok4H+rbQ7Mn1SMib+IceC1FJBf4g6r+u2y56uy7iJylqkdCugPlKG+fcC4zoarP4XxIbhCRIuAIMEbdT0gFvgLuB+aq6iERKeDHp/2oarGIjAE+EpEDwCHgbhE5hpNgwn0GMhy4AvhORJa4036rzhnXj0TQcd0GmOjWg8TgDHtdbrPWCNrngIVjn60vLGOMMdUSjXUgxhhjgiDqLmGVR0Qm4FwC8PW0qr7sRTzhFK37LiItcFpSlXWaqu4OdzyhEI3/W9vnH4Rln+0SljHGmGqxS1jGGGOqxRKIMcaYarEEYkw1SAh65BWR35Z5PSfY2zAmmKwOxJhqEJGDqtqotq/TmFCyMxBjgkREfi4i34ozTsNnboeLiEiyiMwUkUUi8i8R2Vi2KxUReQSoL05vvq+70w66f08Wkdki8raIrHF7S71MnLEwvhORzj7beU9EFriPsi1zjAkqOwMxphrKO1sQkWbAPlVVEbka6Kmqd4rIP4AtqvoXERkJfAokq+ouf+ssfS0iJ+N0ANkTp9v69cCLqvoHcQaO6qiqt4nIG8A/VfVrcbrunqHO+CDGhITdB2JM8KQCb4lIG5x+1ja400/A6SIbVZ0uInsrWN6fBaq6DZzxHID/uNO/w+nsEZy+u3qVdtMNNBaRJPWoZ2AT+SyBGBM8fwf+pqpT3bOGB93p5Q0hUFWFPs9LfF6X8L/PcQyQ6UV/ZCY6WR2IMcHTBKc7d4DxPtO/Bi4BEJEzcQbtKc8xt0v26voPcHPpCxHpX4N1GVMpSyDGVE8DEcn1edyBc8bxjoh8BfjWbzwEnCkii4BRwDagvMtKzwPLSivRq+EWIEOcoUi/B66v5nqMCYhVohsTYiJSDyh2hxLIBJ5VZxQ9Y+o0qwMxJvQ6AG+LSAxwFLjG43iMCQo7AzHGGFMtVgdijDGmWiyBGGOMqRZLIMYYY6rFEogxxphqsQRijDGmWv4fCYeayqueUqMAAAAASUVORK5CYII=\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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\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": 61,
   "id": "677156ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "map_col('total_cases', new_df, 'Total COVID19 cases per state')"
   ]
  },
  {
   "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": 65,
   "id": "e1526fe9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display.Image(\"Data/png/MA_weekly_rolling_average_cases.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "af3df5aa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display.Image(\"Data/png/ME_weekly_rolling_average_cases.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "91d94503",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display.Image(\"Data/png/VT_weekly_rolling_average_cases.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f382d91f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display.Image(\"Data/png/CT_weekly_rolling_average_cases.png\")"
   ]
  },
  {
   "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",
       "            background-color:  yellow;\n",
       "        }#T_4c407_row0_col7,#T_4c407_row1_col7,#T_4c407_row2_col7,#T_4c407_row3_col7,#T_4c407_row4_col7,#T_4c407_row5_col7,#T_4c407_row8_col7,#T_4c407_row10_col7,#T_4c407_row11_col7,#T_4c407_row12_col7,#T_4c407_row15_col7,#T_4c407_row16_col7,#T_4c407_row18_col7{\n",
       "            text-align:  left;\n",
       "            text-align:  left;\n",
       "            background-color:  lightgrey;\n",
       "        }#T_4c407_row6_col7,#T_4c407_row7_col7,#T_4c407_row9_col7,#T_4c407_row13_col7,#T_4c407_row14_col7,#T_4c407_row17_col7{\n",
       "            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": {
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       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "      <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": [
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       "<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",
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       "      <td>2020-03-01</td>\n",
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       "      <td>62</td>\n",
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       "      <td>65</td>\n",
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       "      <td>70.16</td>\n",
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      ],
      "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",
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       "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
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       "        }#T_58318_row0_col7,#T_58318_row1_col7,#T_58318_row2_col7,#T_58318_row3_col7,#T_58318_row4_col7,#T_58318_row5_col7,#T_58318_row6_col7,#T_58318_row7_col7,#T_58318_row9_col7,#T_58318_row10_col7,#T_58318_row11_col7,#T_58318_row12_col7,#T_58318_row13_col7,#T_58318_row14_col7,#T_58318_row18_col7{\n",
       "            text-align:  left;\n",
       "            text-align:  left;\n",
       "            background-color:  lightgrey;\n",
       "        }#T_58318_row8_col7,#T_58318_row15_col7,#T_58318_row16_col7,#T_58318_row17_col7{\n",
       "            text-align:  left;\n",
       "            text-align:  left;\n",
       "            background-color:  yellow;\n",
       "            background-color:  lightgrey;\n",
       "        }</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": [
    {
     "data": {
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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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