Project_2_Less_Features.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Project 2 Less Features.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/harraz/covid-12-predictor/blob/main/Project_2_Less_Features.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0qZZAjnK3OZO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6cccc71-c605-4268-94c7-11048f670ae6"
      },
      "source": [
        "#!pip install tensorflow\n",
        "!pip install seaborn"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (0.11.1)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from seaborn) (1.4.1)\n",
            "Requirement already satisfied: matplotlib>=2.2 in /usr/local/lib/python3.7/dist-packages (from seaborn) (3.2.2)\n",
            "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.7/dist-packages (from seaborn) (1.1.5)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from seaborn) (1.19.5)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (2.8.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->seaborn) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=2.2->seaborn) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J4fdypcMC8GB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f5d6459-9c64-49fe-bf13-7a6ded7454d7"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MvU3ydql3vua",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08c3ae14-ab92-4ee9-8a74-b32fee7122a8"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.5.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RXGqHiag52KQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "d6041b73-2549-403d-a00e-0c2e45d18e23"
      },
      "source": [
        "raw_cdc_conditions_by_state=pd.read_csv(\"/content/drive/MyDrive/CS510/Project/cdc_agg_table_3_20210527.csv\")\n",
        "raw_cdc_conditions_by_state.tail()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>LocationAbbr</th>\n",
              "      <th>LocationDesc</th>\n",
              "      <th>Asthma</th>\n",
              "      <th>Poverty</th>\n",
              "      <th>OPD</th>\n",
              "      <th>Smoking</th>\n",
              "      <th>CVD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>GU</td>\n",
              "      <td>Guam</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>24.100</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>NJ</td>\n",
              "      <td>New Jersey</td>\n",
              "      <td>61.6</td>\n",
              "      <td>10.2</td>\n",
              "      <td>1321.428571</td>\n",
              "      <td>0.000</td>\n",
              "      <td>5271.642857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>PR</td>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>0.0</td>\n",
              "      <td>42.2</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.675</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>US</td>\n",
              "      <td>United States</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>VI</td>\n",
              "      <td>Virgin Islands</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   LocationAbbr    LocationDesc  Asthma  ...          OPD  Smoking          CVD\n",
              "50           GU            Guam     0.0  ...     0.000000   24.100     0.000000\n",
              "51           NJ      New Jersey    61.6  ...  1321.428571    0.000  5271.642857\n",
              "52           PR     Puerto Rico     0.0  ...     0.000000    9.675     0.000000\n",
              "53           US   United States     0.0  ...     0.000000    0.000     0.000000\n",
              "54           VI  Virgin Islands     0.0  ...     0.000000    0.000     0.000000\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jzNuEWqr6qVG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a6f637a-7b9b-4b7b-959a-b3d7134a1f90"
      },
      "source": [
        "cdc_conditions_by_state=raw_cdc_conditions_by_state.copy()\n",
        "cdc_conditions_by_state.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 55 entries, 0 to 54\n",
            "Data columns (total 7 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   LocationAbbr  55 non-null     object \n",
            " 1   LocationDesc  55 non-null     object \n",
            " 2   Asthma        55 non-null     float64\n",
            " 3   Poverty       55 non-null     float64\n",
            " 4   OPD           55 non-null     float64\n",
            " 5   Smoking       55 non-null     float64\n",
            " 6   CVD           55 non-null     float64\n",
            "dtypes: float64(5), object(2)\n",
            "memory usage: 3.1+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pXNLXOdzBdPt"
      },
      "source": [
        "# load each csv file in the training data zip file and append to a raw data frame\n",
        "from zipfile import ZipFile\n",
        "\n",
        "training_files= '/content/drive/MyDrive/CS510/Project/csse_covid_19_daily_reports/training_set.zip'\n",
        "test_files= '/content/drive/MyDrive/CS510/Project/csse_covid_19_daily_reports/test_data.zip'\n",
        "\n",
        "raw_jhu_training_set = pd.concat(\n",
        "    [pd.read_csv(ZipFile(training_files).open(i)) for i in ZipFile(training_files).namelist()],\n",
        "    ignore_index=True\n",
        ")\n",
        "\n",
        "raw_jhu_testing_set = pd.concat(\n",
        "    [pd.read_csv(ZipFile(test_files).open(i)) for i in ZipFile(test_files).namelist()],\n",
        "    ignore_index=True\n",
        ")\n",
        "\n",
        "# import glob\n",
        "# import os\n",
        "\n",
        "# cols_needed=['Province_State','Deaths','Active','Confirmed','Recovered']\n",
        "\n",
        "# path = r'/content/drive/MyDrive/CS510/Project/csse_covid_19_daily_reports/training_set'\n",
        "# all_files = glob.glob(path + \"/*.csv\")\n",
        "\n",
        "# all_files = glob.glob(os.path.join(path, \"*.csv\"))\n",
        "\n",
        "# df_from_each_file = (pd.read_csv(f, usecols=cols_needed) for f in all_files)\n",
        "# raw_jhu_training_set   = pd.concat(df_from_each_file, ignore_index=False)\n",
        "\n",
        "# path = r'/content/drive/MyDrive/CS510/Project/csse_covid_19_daily_reports/test_data'\n",
        "# all_files = glob.glob(path + \"/*.csv\")\n",
        "\n",
        "# all_files = glob.glob(os.path.join(path, \"*.csv\"))\n",
        "\n",
        "# df_from_each_file = (pd.read_csv(f, usecols=cols_needed) for f in all_files)\n",
        "# raw_jhu_testing_set   = pd.concat(df_from_each_file, ignore_index=False)\n",
        "\n",
        "# raw_jhu_training_set = raw_jhu_training_set[raw_jhu_training_set['Province_State']=='Pennsylvania']\n",
        "# raw_jhu_training_set\n",
        "\n",
        "# raw_jhu_testing_set = raw_jhu_testing_set[raw_jhu_testing_set['Province_State']=='Pennsylvania']\n",
        "\n",
        "# tmp=raw_jhu_testing_set = raw_jhu_testing_set[raw_jhu_testing_set['Active'] ==0]\n",
        "# tmp\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cZNZ0kfSHpWS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "9946654a-8e7c-413e-efbd-3e989c7ee4c9"
      },
      "source": [
        "raw_jhu_testing_set.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>FIPS</th>\n",
              "      <th>Admin2</th>\n",
              "      <th>Province_State</th>\n",
              "      <th>Country_Region</th>\n",
              "      <th>Last_Update</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long_</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "      <th>Active</th>\n",
              "      <th>Combined_Key</th>\n",
              "      <th>Incident_Rate</th>\n",
              "      <th>Case_Fatality_Ratio</th>\n",
              "      <th>Incidence_Rate</th>\n",
              "      <th>Case-Fatality_Ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2021-01-07 05:22:03</td>\n",
              "      <td>33.93911</td>\n",
              "      <td>67.709953</td>\n",
              "      <td>53105</td>\n",
              "      <td>2244</td>\n",
              "      <td>42666.0</td>\n",
              "      <td>8195.0</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>136.417321</td>\n",
              "      <td>4.225591</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Albania</td>\n",
              "      <td>2021-01-07 05:22:03</td>\n",
              "      <td>41.15330</td>\n",
              "      <td>20.168300</td>\n",
              "      <td>61008</td>\n",
              "      <td>1217</td>\n",
              "      <td>36102.0</td>\n",
              "      <td>23689.0</td>\n",
              "      <td>Albania</td>\n",
              "      <td>2119.952742</td>\n",
              "      <td>1.994820</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>2021-01-07 05:22:03</td>\n",
              "      <td>28.03390</td>\n",
              "      <td>1.659600</td>\n",
              "      <td>101120</td>\n",
              "      <td>2786</td>\n",
              "      <td>68383.0</td>\n",
              "      <td>29951.0</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>230.598848</td>\n",
              "      <td>2.755142</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Andorra</td>\n",
              "      <td>2021-01-07 05:22:03</td>\n",
              "      <td>42.50630</td>\n",
              "      <td>1.521800</td>\n",
              "      <td>8348</td>\n",
              "      <td>84</td>\n",
              "      <td>7615.0</td>\n",
              "      <td>649.0</td>\n",
              "      <td>Andorra</td>\n",
              "      <td>10804.374555</td>\n",
              "      <td>1.006229</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Angola</td>\n",
              "      <td>2021-01-07 05:22:03</td>\n",
              "      <td>-11.20270</td>\n",
              "      <td>17.873900</td>\n",
              "      <td>17864</td>\n",
              "      <td>413</td>\n",
              "      <td>11477.0</td>\n",
              "      <td>5974.0</td>\n",
              "      <td>Angola</td>\n",
              "      <td>54.353600</td>\n",
              "      <td>2.311912</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   FIPS Admin2  ... Incidence_Rate Case-Fatality_Ratio\n",
              "0   NaN    NaN  ...            NaN                 NaN\n",
              "1   NaN    NaN  ...            NaN                 NaN\n",
              "2   NaN    NaN  ...            NaN                 NaN\n",
              "3   NaN    NaN  ...            NaN                 NaN\n",
              "4   NaN    NaN  ...            NaN                 NaN\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oLQAnE3-RwFH"
      },
      "source": [
        "states_to_delete=['American Samoa', 'District of Columbia', 'Guam','Marshall Islands', 'Northern Mariana Island','Puerto Rico']\n",
        "raw_jhu_testing_set = raw_jhu_testing_set[~raw_jhu_testing_set.Province_State.isin(states_to_delete)]\n",
        "raw_jhu_training_set = raw_jhu_training_set[~raw_jhu_training_set.Province_State.isin(states_to_delete)]\n",
        "\n",
        "us_idx=raw_jhu_testing_set[raw_jhu_testing_set['Country_Region'] !='US'].index\n",
        "raw_jhu_testing_set.drop(us_idx,inplace=True)\n",
        "\n",
        "us_idx=raw_jhu_training_set[ raw_jhu_training_set['Country_Region'] !='US'].index\n",
        "raw_jhu_training_set.drop(us_idx,inplace=True)\n",
        "\n",
        "idx=raw_jhu_training_set.query('Confirmed <= 0').index\n",
        "idx2=(raw_jhu_training_set.query('Active <= 0').index)\n",
        "idx.append(idx2)\n",
        "raw_jhu_training_set.drop(idx,inplace=True)\n",
        "raw_jhu_training_set\n",
        "\n",
        "idx=raw_jhu_testing_set.query('Confirmed <= 0', engine='python').index\n",
        "idx2=(raw_jhu_testing_set.query('Active <= 0').index)\n",
        "idx.append(idx2)\n",
        "raw_jhu_testing_set.drop(idx,inplace=True)\n",
        "\n",
        "raw_jhu_training_set.fillna(0,inplace=True)\n",
        "raw_jhu_testing_set.fillna(0,inplace=True)\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rSjkYXMrIk5X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "outputId": "68fe2484-4985-4282-c17c-ddac4152c3b2"
      },
      "source": [
        "sns.pairplot(raw_jhu_training_set[['Confirmed','Active', 'Deaths', 'Recovered']], diag_kind='kde')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f77f6e4ad90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mKXa9x0zRv_B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "5ff5001e-b1c3-4bff-9a81-0001bff75e0b"
      },
      "source": [
        "# join JHU training data with cdc conditions data using state\n",
        "cdc_jhu_training = pd.merge(raw_jhu_training_set, cdc_conditions_by_state, left_on='Province_State', right_on='LocationDesc', how=\"inner\")\n",
        "cdc_jhu_training.drop(['LocationDesc', 'LocationAbbr'], axis=1)\n",
        "cdc_jhu_training = cdc_jhu_training.get(['Province_State','Deaths','Active','Confirmed','Poverty'])\n",
        "\n",
        "\n",
        "cdc_jhu_testing = pd.merge(raw_jhu_testing_set, cdc_conditions_by_state, left_on='Province_State', right_on='LocationDesc', how=\"inner\")\n",
        "cdc_jhu_testing.drop(['LocationDesc', 'LocationAbbr'], axis=1)\n",
        "cdc_jhu_testing = cdc_jhu_testing.get(['Province_State','Deaths','Active','Confirmed','Poverty'])\n",
        "\n",
        "raw_jhu_testing_set.dropna(inplace=True)\n",
        "raw_jhu_training_set.dropna(inplace=True)\n",
        "\n",
        "cdc_jhu_training\n",
        "# cdc_jhu_training.to_csv('cdc_jhu_training.csv')\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Province_State</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Active</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Poverty</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>50.0</td>\n",
              "      <td>4189.0</td>\n",
              "      <td>4239</td>\n",
              "      <td>18.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>169.0</td>\n",
              "      <td>13654.0</td>\n",
              "      <td>13823</td>\n",
              "      <td>18.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>33.0</td>\n",
              "      <td>1484.0</td>\n",
              "      <td>1517</td>\n",
              "      <td>18.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>46.0</td>\n",
              "      <td>1808.0</td>\n",
              "      <td>1854</td>\n",
              "      <td>18.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>63.0</td>\n",
              "      <td>4630.0</td>\n",
              "      <td>4693</td>\n",
              "      <td>18.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1052729</th>\n",
              "      <td>Wyoming</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2950.0</td>\n",
              "      <td>2966</td>\n",
              "      <td>12.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1052730</th>\n",
              "      <td>Wyoming</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2134.0</td>\n",
              "      <td>2138</td>\n",
              "      <td>12.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1052731</th>\n",
              "      <td>Wyoming</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1551.0</td>\n",
              "      <td>1558</td>\n",
              "      <td>12.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1052732</th>\n",
              "      <td>Wyoming</td>\n",
              "      <td>19.0</td>\n",
              "      <td>762.0</td>\n",
              "      <td>781</td>\n",
              "      <td>12.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1052733</th>\n",
              "      <td>Wyoming</td>\n",
              "      <td>2.0</td>\n",
              "      <td>474.0</td>\n",
              "      <td>476</td>\n",
              "      <td>12.66</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1052734 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Province_State  Deaths   Active  Confirmed  Poverty\n",
              "0              Alabama    50.0   4189.0       4239    18.60\n",
              "1              Alabama   169.0  13654.0      13823    18.60\n",
              "2              Alabama    33.0   1484.0       1517    18.60\n",
              "3              Alabama    46.0   1808.0       1854    18.60\n",
              "4              Alabama    63.0   4630.0       4693    18.60\n",
              "...                ...     ...      ...        ...      ...\n",
              "1052729        Wyoming    16.0   2950.0       2966    12.66\n",
              "1052730        Wyoming     4.0   2134.0       2138    12.66\n",
              "1052731        Wyoming     7.0   1551.0       1558    12.66\n",
              "1052732        Wyoming    19.0    762.0        781    12.66\n",
              "1052733        Wyoming     2.0    474.0        476    12.66\n",
              "\n",
              "[1052734 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vmze4ajsU7mh"
      },
      "source": [
        "# one hot encoding\n",
        "cdc_jhu_training['AL']= (cdc_jhu_training.Province_State == 'Alabama') * 1.0\n",
        "cdc_jhu_training['AK']= (cdc_jhu_training.Province_State == 'Alaska') * 1.0\n",
        "cdc_jhu_training['AZ']= (cdc_jhu_training.Province_State == 'Arizona') * 1.0\n",
        "cdc_jhu_training['AR']= (cdc_jhu_training.Province_State == 'Arkansas') * 1.0\n",
        "cdc_jhu_training['CA']= (cdc_jhu_training.Province_State == 'California') * 1.0\n",
        "cdc_jhu_training['CO']= (cdc_jhu_training.Province_State == 'Colorado') * 1.0\n",
        "cdc_jhu_training['CT']= (cdc_jhu_training.Province_State == 'Connecticut') * 1.0\n",
        "cdc_jhu_training['DE']= (cdc_jhu_training.Province_State == 'Delaware') * 1.0\n",
        "cdc_jhu_training['FL']= (cdc_jhu_training.Province_State == 'Florida') * 1.0\n",
        "cdc_jhu_training['GA']= (cdc_jhu_training.Province_State == 'Georgia') * 1.0\n",
        "cdc_jhu_training['HI']= (cdc_jhu_training.Province_State == 'Hawaii') * 1.0\n",
        "cdc_jhu_training['ID']= (cdc_jhu_training.Province_State == 'Idaho') * 1.0\n",
        "cdc_jhu_training['IL']= (cdc_jhu_training.Province_State == 'Illinois') * 1.0\n",
        "cdc_jhu_training['IN']= (cdc_jhu_training.Province_State == 'Indiana') * 1.0\n",
        "cdc_jhu_training['IA']= (cdc_jhu_training.Province_State == 'Iowa') * 1.0\n",
        "cdc_jhu_training['KS']= (cdc_jhu_training.Province_State == 'Kansas') * 1.0\n",
        "cdc_jhu_training['KY']= (cdc_jhu_training.Province_State == 'Kentucky') * 1.0\n",
        "cdc_jhu_training['LA']= (cdc_jhu_training.Province_State == 'Louisiana') * 1.0\n",
        "cdc_jhu_training['ME']= (cdc_jhu_training.Province_State == 'Maine') * 1.0\n",
        "cdc_jhu_training['MD']= (cdc_jhu_training.Province_State == 'Maryland') * 1.0\n",
        "cdc_jhu_training['MA']= (cdc_jhu_training.Province_State == 'Massachusetts') * 1.0\n",
        "cdc_jhu_training['MI']= (cdc_jhu_training.Province_State == 'Michigan') * 1.0\n",
        "cdc_jhu_training['MN']= (cdc_jhu_training.Province_State == 'Minnesota') * 1.0\n",
        "cdc_jhu_training['MS']= (cdc_jhu_training.Province_State == 'Mississippi') * 1.0\n",
        "cdc_jhu_training['MO']= (cdc_jhu_training.Province_State == 'Missouri') * 1.0\n",
        "cdc_jhu_training['MT']= (cdc_jhu_training.Province_State == 'Montana') * 1.0\n",
        "cdc_jhu_training['NE']= (cdc_jhu_training.Province_State == 'Nebraska') * 1.0\n",
        "cdc_jhu_training['NV']= (cdc_jhu_training.Province_State == 'Nevada') * 1.0\n",
        "cdc_jhu_training['NH']= (cdc_jhu_training.Province_State == 'New Hampshire') * 1.0\n",
        "cdc_jhu_training['NJ']= (cdc_jhu_training.Province_State == 'New Jersey') * 1.0\n",
        "cdc_jhu_training['NM']= (cdc_jhu_training.Province_State == 'New Mexico') * 1.0\n",
        "cdc_jhu_training['NY']= (cdc_jhu_training.Province_State == 'New York') * 1.0\n",
        "cdc_jhu_training['NC']= (cdc_jhu_training.Province_State == 'North Carolina') * 1.0\n",
        "cdc_jhu_training['ND']= (cdc_jhu_training.Province_State == 'North Dakota') * 1.0\n",
        "cdc_jhu_training['OH']= (cdc_jhu_training.Province_State == 'Ohio') * 1.0\n",
        "cdc_jhu_training['OK']= (cdc_jhu_training.Province_State == 'Oklahoma') * 1.0\n",
        "cdc_jhu_training['OR']= (cdc_jhu_training.Province_State == 'Oregon') * 1.0\n",
        "cdc_jhu_training['PA']= (cdc_jhu_training.Province_State == 'Pennsylvania') * 1.0\n",
        "cdc_jhu_training['RI']= (cdc_jhu_training.Province_State == 'Rhode Island') * 1.0\n",
        "cdc_jhu_training['SC']= (cdc_jhu_training.Province_State == 'South Carolina') * 1.0\n",
        "cdc_jhu_training['SD']= (cdc_jhu_training.Province_State == 'South Dakota') * 1.0\n",
        "cdc_jhu_training['TN']= (cdc_jhu_training.Province_State == 'Tennessee') * 1.0\n",
        "cdc_jhu_training['TX']= (cdc_jhu_training.Province_State == 'Texas') * 1.0\n",
        "cdc_jhu_training['UT']= (cdc_jhu_training.Province_State == 'Utah') * 1.0\n",
        "cdc_jhu_training['VT']= (cdc_jhu_training.Province_State == 'Vermont') * 1.0\n",
        "cdc_jhu_training['VA']= (cdc_jhu_training.Province_State == 'Virginia') * 1.0\n",
        "cdc_jhu_training['WA']= (cdc_jhu_training.Province_State == 'Washington') * 1.0\n",
        "cdc_jhu_training['WV']= (cdc_jhu_training.Province_State == 'West Virginia') * 1.0\n",
        "cdc_jhu_training['WI']= (cdc_jhu_training.Province_State == 'Wisconsin') * 1.0\n",
        "cdc_jhu_training['WY']= (cdc_jhu_training.Province_State == 'Wyoming') * 1.0\n",
        "# cdc_jhu_training['AS']= (cdc_jhu_training.Province_State == 'American Samoa') * 1.0\n",
        "# cdc_jhu_training['DC']= (cdc_jhu_training.Province_State == 'District of Columbia') * 1.0\n",
        "# cdc_jhu_training['GU']= (cdc_jhu_training.Province_State == 'Guam') * 1.0\n",
        "# cdc_jhu_training['MH']= (cdc_jhu_training.Province_State == 'Marshall Islands') * 1.0\n",
        "# cdc_jhu_training['MP']= (cdc_jhu_training.Province_State == 'Northern Mariana Island') * 1.0\n",
        "# cdc_jhu_training['PR']= (cdc_jhu_training.Province_State == 'Puerto Rico') * 1.0\n",
        "\n",
        "# do the same for test data set\n",
        "cdc_jhu_testing['AL']= (cdc_jhu_testing.Province_State == 'Alabama') * 1.0\n",
        "cdc_jhu_testing['AK']= (cdc_jhu_testing.Province_State == 'Alaska') * 1.0\n",
        "cdc_jhu_testing['AZ']= (cdc_jhu_testing.Province_State == 'Arizona') * 1.0\n",
        "cdc_jhu_testing['AR']= (cdc_jhu_testing.Province_State == 'Arkansas') * 1.0\n",
        "cdc_jhu_testing['CA']= (cdc_jhu_testing.Province_State == 'California') * 1.0\n",
        "cdc_jhu_testing['CO']= (cdc_jhu_testing.Province_State == 'Colorado') * 1.0\n",
        "cdc_jhu_testing['CT']= (cdc_jhu_testing.Province_State == 'Connecticut') * 1.0\n",
        "cdc_jhu_testing['DE']= (cdc_jhu_testing.Province_State == 'Delaware') * 1.0\n",
        "cdc_jhu_testing['FL']= (cdc_jhu_testing.Province_State == 'Florida') * 1.0\n",
        "cdc_jhu_testing['GA']= (cdc_jhu_testing.Province_State == 'Georgia') * 1.0\n",
        "cdc_jhu_testing['HI']= (cdc_jhu_testing.Province_State == 'Hawaii') * 1.0\n",
        "cdc_jhu_testing['ID']= (cdc_jhu_testing.Province_State == 'Idaho') * 1.0\n",
        "cdc_jhu_testing['IL']= (cdc_jhu_testing.Province_State == 'Illinois') * 1.0\n",
        "cdc_jhu_testing['IN']= (cdc_jhu_testing.Province_State == 'Indiana') * 1.0\n",
        "cdc_jhu_testing['IA']= (cdc_jhu_testing.Province_State == 'Iowa') * 1.0\n",
        "cdc_jhu_testing['KS']= (cdc_jhu_testing.Province_State == 'Kansas') * 1.0\n",
        "cdc_jhu_testing['KY']= (cdc_jhu_testing.Province_State == 'Kentucky') * 1.0\n",
        "cdc_jhu_testing['LA']= (cdc_jhu_testing.Province_State == 'Louisiana') * 1.0\n",
        "cdc_jhu_testing['ME']= (cdc_jhu_testing.Province_State == 'Maine') * 1.0\n",
        "cdc_jhu_testing['MD']= (cdc_jhu_testing.Province_State == 'Maryland') * 1.0\n",
        "cdc_jhu_testing['MA']= (cdc_jhu_testing.Province_State == 'Massachusetts') * 1.0\n",
        "cdc_jhu_testing['MI']= (cdc_jhu_testing.Province_State == 'Michigan') * 1.0\n",
        "cdc_jhu_testing['MN']= (cdc_jhu_testing.Province_State == 'Minnesota') * 1.0\n",
        "cdc_jhu_testing['MS']= (cdc_jhu_testing.Province_State == 'Mississippi') * 1.0\n",
        "cdc_jhu_testing['MO']= (cdc_jhu_testing.Province_State == 'Missouri') * 1.0\n",
        "cdc_jhu_testing['MT']= (cdc_jhu_testing.Province_State == 'Montana') * 1.0\n",
        "cdc_jhu_testing['NE']= (cdc_jhu_testing.Province_State == 'Nebraska') * 1.0\n",
        "cdc_jhu_testing['NV']= (cdc_jhu_testing.Province_State == 'Nevada') * 1.0\n",
        "cdc_jhu_testing['NH']= (cdc_jhu_testing.Province_State == 'New Hampshire') * 1.0\n",
        "cdc_jhu_testing['NJ']= (cdc_jhu_testing.Province_State == 'New Jersey') * 1.0\n",
        "cdc_jhu_testing['NM']= (cdc_jhu_testing.Province_State == 'New Mexico') * 1.0\n",
        "cdc_jhu_testing['NY']= (cdc_jhu_testing.Province_State == 'New York') * 1.0\n",
        "cdc_jhu_testing['NC']= (cdc_jhu_testing.Province_State == 'North Carolina') * 1.0\n",
        "cdc_jhu_testing['ND']= (cdc_jhu_testing.Province_State == 'North Dakota') * 1.0\n",
        "cdc_jhu_testing['OH']= (cdc_jhu_testing.Province_State == 'Ohio') * 1.0\n",
        "cdc_jhu_testing['OK']= (cdc_jhu_testing.Province_State == 'Oklahoma') * 1.0\n",
        "cdc_jhu_testing['OR']= (cdc_jhu_testing.Province_State == 'Oregon') * 1.0\n",
        "cdc_jhu_testing['PA']= (cdc_jhu_testing.Province_State == 'Pennsylvania') * 1.0\n",
        "cdc_jhu_testing['RI']= (cdc_jhu_testing.Province_State == 'Rhode Island') * 1.0\n",
        "cdc_jhu_testing['SC']= (cdc_jhu_testing.Province_State == 'South Carolina') * 1.0\n",
        "cdc_jhu_testing['SD']= (cdc_jhu_testing.Province_State == 'South Dakota') * 1.0\n",
        "cdc_jhu_testing['TN']= (cdc_jhu_testing.Province_State == 'Tennessee') * 1.0\n",
        "cdc_jhu_testing['TX']= (cdc_jhu_testing.Province_State == 'Texas') * 1.0\n",
        "cdc_jhu_testing['UT']= (cdc_jhu_testing.Province_State == 'Utah') * 1.0\n",
        "cdc_jhu_testing['VT']= (cdc_jhu_testing.Province_State == 'Vermont') * 1.0\n",
        "cdc_jhu_testing['VA']= (cdc_jhu_testing.Province_State == 'Virginia') * 1.0\n",
        "cdc_jhu_testing['WA']= (cdc_jhu_testing.Province_State == 'Washington') * 1.0\n",
        "cdc_jhu_testing['WV']= (cdc_jhu_testing.Province_State == 'West Virginia') * 1.0\n",
        "cdc_jhu_testing['WI']= (cdc_jhu_testing.Province_State == 'Wisconsin') * 1.0\n",
        "cdc_jhu_testing['WY']= (cdc_jhu_testing.Province_State == 'Wyoming') * 1.0\n",
        "# cdc_jhu_testing['AS']= (cdc_jhu_testing.Province_State == 'American Samoa') * 1.0\n",
        "# cdc_jhu_testing['DC']= (cdc_jhu_testing.Province_State == 'District of Columbia') * 1.0\n",
        "# cdc_jhu_testing['GU']= (cdc_jhu_testing.Province_State == 'Guam') * 1.0\n",
        "# cdc_jhu_testing['MH']= (cdc_jhu_testing.Province_State == 'Marshall Islands') * 1.0\n",
        "# cdc_jhu_testing['MP']= (cdc_jhu_testing.Province_State == 'Northern Mariana Island') * 1.0\n",
        "# cdc_jhu_testing['PR']= (cdc_jhu_testing.Province_State == 'Puerto Rico') * 1.0\n"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hfzFVbYXw0Qs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "0530b79e-7c34-4f3f-9b6a-883f8ba88a15"
      },
      "source": [
        "cdc_jhu_training"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Province_State</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Active</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Poverty</th>\n",
              "      <th>AL</th>\n",
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              "      <th>KY</th>\n",
              "      <th>LA</th>\n",
              "      <th>ME</th>\n",
              "      <th>MD</th>\n",
              "      <th>MA</th>\n",
              "      <th>MI</th>\n",
              "      <th>MN</th>\n",
              "      <th>MS</th>\n",
              "      <th>MO</th>\n",
              "      <th>MT</th>\n",
              "      <th>NE</th>\n",
              "      <th>NV</th>\n",
              "      <th>NH</th>\n",
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              "      <th>NM</th>\n",
              "      <th>NY</th>\n",
              "      <th>NC</th>\n",
              "      <th>ND</th>\n",
              "      <th>OH</th>\n",
              "      <th>OK</th>\n",
              "      <th>OR</th>\n",
              "      <th>PA</th>\n",
              "      <th>RI</th>\n",
              "      <th>SC</th>\n",
              "      <th>SD</th>\n",
              "      <th>TN</th>\n",
              "      <th>TX</th>\n",
              "      <th>UT</th>\n",
              "      <th>VT</th>\n",
              "      <th>VA</th>\n",
              "      <th>WA</th>\n",
              "      <th>WV</th>\n",
              "      <th>WI</th>\n",
              "      <th>WY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>50.0</td>\n",
              "      <td>4189.0</td>\n",
              "      <td>4239</td>\n",
              "      <td>18.60</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>Alabama</td>\n",
              "      <td>169.0</td>\n",
              "      <td>13654.0</td>\n",
              "      <td>13823</td>\n",
              "      <td>18.60</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>33.0</td>\n",
              "      <td>1484.0</td>\n",
              "      <td>1517</td>\n",
              "      <td>18.60</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>46.0</td>\n",
              "      <td>1808.0</td>\n",
              "      <td>1854</td>\n",
              "      <td>18.60</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>63.0</td>\n",
              "      <td>4630.0</td>\n",
              "      <td>4693</td>\n",
              "      <td>18.60</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1052729</th>\n",
              "      <td>Wyoming</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2950.0</td>\n",
              "      <td>2966</td>\n",
              "      <td>12.66</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1052730</th>\n",
              "      <td>Wyoming</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2134.0</td>\n",
              "      <td>2138</td>\n",
              "      <td>12.66</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1052731</th>\n",
              "      <td>Wyoming</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1551.0</td>\n",
              "      <td>1558</td>\n",
              "      <td>12.66</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1052732</th>\n",
              "      <td>Wyoming</td>\n",
              "      <td>19.0</td>\n",
              "      <td>762.0</td>\n",
              "      <td>781</td>\n",
              "      <td>12.66</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1052733</th>\n",
              "      <td>Wyoming</td>\n",
              "      <td>2.0</td>\n",
              "      <td>474.0</td>\n",
              "      <td>476</td>\n",
              "      <td>12.66</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1052734 rows × 55 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Province_State  Deaths   Active  Confirmed  ...   WA   WV   WI   WY\n",
              "0              Alabama    50.0   4189.0       4239  ...  0.0  0.0  0.0  0.0\n",
              "1              Alabama   169.0  13654.0      13823  ...  0.0  0.0  0.0  0.0\n",
              "2              Alabama    33.0   1484.0       1517  ...  0.0  0.0  0.0  0.0\n",
              "3              Alabama    46.0   1808.0       1854  ...  0.0  0.0  0.0  0.0\n",
              "4              Alabama    63.0   4630.0       4693  ...  0.0  0.0  0.0  0.0\n",
              "...                ...     ...      ...        ...  ...  ...  ...  ...  ...\n",
              "1052729        Wyoming    16.0   2950.0       2966  ...  0.0  0.0  0.0  1.0\n",
              "1052730        Wyoming     4.0   2134.0       2138  ...  0.0  0.0  0.0  1.0\n",
              "1052731        Wyoming     7.0   1551.0       1558  ...  0.0  0.0  0.0  1.0\n",
              "1052732        Wyoming    19.0    762.0        781  ...  0.0  0.0  0.0  1.0\n",
              "1052733        Wyoming     2.0    474.0        476  ...  0.0  0.0  0.0  1.0\n",
              "\n",
              "[1052734 rows x 55 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rCeh1WGuV7Ys",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "91cc0dc8-a3e5-49b0-987b-d376d0ae84d3"
      },
      "source": [
        "# drop some state columns \n",
        "# cdc_jhu_training.drop(cdc_jhu_training.columns[7:],axis=1,inplace=True)\n",
        "\n",
        "# cdc_jhu_testing.drop(cdc_jhu_testing.columns[7:],axis=1,inplace=True)\n",
        "\n",
        "cdc_jhu_training.pop('Province_State')\n",
        "cdc_jhu_testing.pop('Province_State')\n",
        "cdc_jhu_training.describe()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Deaths</th>\n",
              "      <th>Active</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Poverty</th>\n",
              "      <th>AL</th>\n",
              "      <th>AK</th>\n",
              "      <th>AZ</th>\n",
              "      <th>AR</th>\n",
              "      <th>CA</th>\n",
              "      <th>CO</th>\n",
              "      <th>CT</th>\n",
              "      <th>DE</th>\n",
              "      <th>FL</th>\n",
              "      <th>GA</th>\n",
              "      <th>HI</th>\n",
              "      <th>ID</th>\n",
              "      <th>IL</th>\n",
              "      <th>IN</th>\n",
              "      <th>IA</th>\n",
              "      <th>KS</th>\n",
              "      <th>KY</th>\n",
              "      <th>LA</th>\n",
              "      <th>ME</th>\n",
              "      <th>MD</th>\n",
              "      <th>MA</th>\n",
              "      <th>MI</th>\n",
              "      <th>MN</th>\n",
              "      <th>MS</th>\n",
              "      <th>MO</th>\n",
              "      <th>MT</th>\n",
              "      <th>NE</th>\n",
              "      <th>NV</th>\n",
              "      <th>NH</th>\n",
              "      <th>NJ</th>\n",
              "      <th>NM</th>\n",
              "      <th>NY</th>\n",
              "      <th>NC</th>\n",
              "      <th>ND</th>\n",
              "      <th>OH</th>\n",
              "      <th>OK</th>\n",
              "      <th>OR</th>\n",
              "      <th>PA</th>\n",
              "      <th>RI</th>\n",
              "      <th>SC</th>\n",
              "      <th>SD</th>\n",
              "      <th>TN</th>\n",
              "      <th>TX</th>\n",
              "      <th>UT</th>\n",
              "      <th>VT</th>\n",
              "      <th>VA</th>\n",
              "      <th>WA</th>\n",
              "      <th>WV</th>\n",
              "      <th>WI</th>\n",
              "      <th>WY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "      <td>1.052734e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9.204196e+01</td>\n",
              "      <td>2.527087e+03</td>\n",
              "      <td>4.555723e+03</td>\n",
              "      <td>1.420277e+01</td>\n",
              "      <td>2.178708e-02</td>\n",
              "      <td>8.154957e-03</td>\n",
              "      <td>4.963267e-03</td>\n",
              "      <td>2.442782e-02</td>\n",
              "      <td>1.873788e-02</td>\n",
              "      <td>2.061109e-02</td>\n",
              "      <td>2.950413e-03</td>\n",
              "      <td>1.294724e-03</td>\n",
              "      <td>2.212145e-02</td>\n",
              "      <td>5.219647e-02</td>\n",
              "      <td>1.678487e-03</td>\n",
              "      <td>1.352668e-02</td>\n",
              "      <td>3.290955e-02</td>\n",
              "      <td>2.991259e-02</td>\n",
              "      <td>3.169937e-02</td>\n",
              "      <td>3.186750e-02</td>\n",
              "      <td>3.815684e-02</td>\n",
              "      <td>2.121144e-02</td>\n",
              "      <td>5.469568e-03</td>\n",
              "      <td>7.863335e-03</td>\n",
              "      <td>4.602302e-03</td>\n",
              "      <td>2.791303e-02</td>\n",
              "      <td>2.788359e-02</td>\n",
              "      <td>2.671425e-02</td>\n",
              "      <td>3.653535e-02</td>\n",
              "      <td>1.583781e-02</td>\n",
              "      <td>2.749507e-02</td>\n",
              "      <td>5.137100e-03</td>\n",
              "      <td>3.568803e-03</td>\n",
              "      <td>7.220247e-03</td>\n",
              "      <td>1.061237e-02</td>\n",
              "      <td>1.993476e-02</td>\n",
              "      <td>3.245834e-02</td>\n",
              "      <td>1.598409e-02</td>\n",
              "      <td>2.854377e-02</td>\n",
              "      <td>2.462255e-02</td>\n",
              "      <td>1.135425e-02</td>\n",
              "      <td>2.181938e-02</td>\n",
              "      <td>1.972008e-03</td>\n",
              "      <td>1.504369e-02</td>\n",
              "      <td>1.991101e-02</td>\n",
              "      <td>3.138400e-02</td>\n",
              "      <td>7.892687e-02</td>\n",
              "      <td>4.438918e-03</td>\n",
              "      <td>4.892024e-03</td>\n",
              "      <td>4.263755e-02</td>\n",
              "      <td>1.299189e-02</td>\n",
              "      <td>1.730827e-02</td>\n",
              "      <td>2.304096e-02</td>\n",
              "      <td>7.346585e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.862084e+02</td>\n",
              "      <td>1.443556e+04</td>\n",
              "      <td>2.176896e+04</td>\n",
              "      <td>2.446131e+00</td>\n",
              "      <td>1.459878e-01</td>\n",
              "      <td>8.993587e-02</td>\n",
              "      <td>7.027544e-02</td>\n",
              "      <td>1.543733e-01</td>\n",
              "      <td>1.355979e-01</td>\n",
              "      <td>1.420785e-01</td>\n",
              "      <td>5.423754e-02</td>\n",
              "      <td>3.595899e-02</td>\n",
              "      <td>1.470786e-01</td>\n",
              "      <td>2.224231e-01</td>\n",
              "      <td>4.093496e-02</td>\n",
              "      <td>1.155150e-01</td>\n",
              "      <td>1.783999e-01</td>\n",
              "      <td>1.703463e-01</td>\n",
              "      <td>1.751986e-01</td>\n",
              "      <td>1.756473e-01</td>\n",
              "      <td>1.915749e-01</td>\n",
              "      <td>1.440886e-01</td>\n",
              "      <td>7.375403e-02</td>\n",
              "      <td>8.832616e-02</td>\n",
              "      <td>6.768401e-02</td>\n",
              "      <td>1.647238e-01</td>\n",
              "      <td>1.646394e-01</td>\n",
              "      <td>1.612471e-01</td>\n",
              "      <td>1.876181e-01</td>\n",
              "      <td>1.248479e-01</td>\n",
              "      <td>1.635210e-01</td>\n",
              "      <td>7.148927e-02</td>\n",
              "      <td>5.963279e-02</td>\n",
              "      <td>8.466476e-02</td>\n",
              "      <td>1.024683e-01</td>\n",
              "      <td>1.397762e-01</td>\n",
              "      <td>1.772141e-01</td>\n",
              "      <td>1.254138e-01</td>\n",
              "      <td>1.665204e-01</td>\n",
              "      <td>1.549720e-01</td>\n",
              "      <td>1.059497e-01</td>\n",
              "      <td>1.460935e-01</td>\n",
              "      <td>4.436351e-02</td>\n",
              "      <td>1.217267e-01</td>\n",
              "      <td>1.396946e-01</td>\n",
              "      <td>1.743533e-01</td>\n",
              "      <td>2.696247e-01</td>\n",
              "      <td>6.647720e-02</td>\n",
              "      <td>6.977175e-02</td>\n",
              "      <td>2.020387e-01</td>\n",
              "      <td>1.132392e-01</td>\n",
              "      <td>1.304174e-01</td>\n",
              "      <td>1.500336e-01</td>\n",
              "      <td>8.539684e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>-2.087000e+03</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.000000e+02</td>\n",
              "      <td>1.264000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.200000e+01</td>\n",
              "      <td>1.670000e+02</td>\n",
              "      <td>6.670000e+02</td>\n",
              "      <td>1.422000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.900000e+01</td>\n",
              "      <td>1.167000e+03</td>\n",
              "      <td>2.564000e+03</td>\n",
              "      <td>1.532000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.420100e+04</td>\n",
              "      <td>1.179853e+06</td>\n",
              "      <td>1.239127e+06</td>\n",
              "      <td>1.900000e+01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Deaths        Active  ...            WI            WY\n",
              "count  1.052734e+06  1.052734e+06  ...  1.052734e+06  1.052734e+06\n",
              "mean   9.204196e+01  2.527087e+03  ...  2.304096e-02  7.346585e-03\n",
              "std    4.862084e+02  1.443556e+04  ...  1.500336e-01  8.539684e-02\n",
              "min    0.000000e+00 -2.087000e+03  ...  0.000000e+00  0.000000e+00\n",
              "25%    1.000000e+00  2.000000e+00  ...  0.000000e+00  0.000000e+00\n",
              "50%    1.200000e+01  1.670000e+02  ...  0.000000e+00  0.000000e+00\n",
              "75%    4.900000e+01  1.167000e+03  ...  0.000000e+00  0.000000e+00\n",
              "max    2.420100e+04  1.179853e+06  ...  1.000000e+00  1.000000e+00\n",
              "\n",
              "[8 rows x 54 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7dla4Zu1b28S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 744
        },
        "outputId": "2a2b13a8-844a-488c-af45-d0313c598158"
      },
      "source": [
        "sns.pairplot(cdc_jhu_training[['Deaths','Confirmed','Active','Poverty']], diag_kind='kde')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f77f8a13750>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gTiRc_JXBqmS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "01762266-a6b9-43b1-e28d-2f970e3680b8"
      },
      "source": [
        "train_stats = cdc_jhu_training.describe()\n",
        "train_stats.pop('Deaths')\n",
        "train_stats = train_stats.transpose()\n",
        "train_stats"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Active</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>2527.087164</td>\n",
              "      <td>14435.561659</td>\n",
              "      <td>-2087.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>167.00</td>\n",
              "      <td>1167.00</td>\n",
              "      <td>1179853.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Confirmed</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>4555.722925</td>\n",
              "      <td>21768.958895</td>\n",
              "      <td>1.0</td>\n",
              "      <td>100.00</td>\n",
              "      <td>667.00</td>\n",
              "      <td>2564.00</td>\n",
              "      <td>1239127.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Poverty</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>14.202770</td>\n",
              "      <td>2.446131</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.64</td>\n",
              "      <td>14.22</td>\n",
              "      <td>15.32</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AL</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.021787</td>\n",
              "      <td>0.145988</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AK</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.008155</td>\n",
              "      <td>0.089936</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AZ</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.004963</td>\n",
              "      <td>0.070275</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AR</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.024428</td>\n",
              "      <td>0.154373</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CA</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.018738</td>\n",
              "      <td>0.135598</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CO</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.020611</td>\n",
              "      <td>0.142078</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CT</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.002950</td>\n",
              "      <td>0.054238</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DE</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.001295</td>\n",
              "      <td>0.035959</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FL</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.022121</td>\n",
              "      <td>0.147079</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GA</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.052196</td>\n",
              "      <td>0.222423</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HI</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.001678</td>\n",
              "      <td>0.040935</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.013527</td>\n",
              "      <td>0.115515</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IL</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.032910</td>\n",
              "      <td>0.178400</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IN</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.029913</td>\n",
              "      <td>0.170346</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IA</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.031699</td>\n",
              "      <td>0.175199</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KS</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.031867</td>\n",
              "      <td>0.175647</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KY</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.038157</td>\n",
              "      <td>0.191575</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LA</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.021211</td>\n",
              "      <td>0.144089</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ME</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.005470</td>\n",
              "      <td>0.073754</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MD</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.007863</td>\n",
              "      <td>0.088326</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MA</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.004602</td>\n",
              "      <td>0.067684</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MI</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.027913</td>\n",
              "      <td>0.164724</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MN</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.027884</td>\n",
              "      <td>0.164639</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MS</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.026714</td>\n",
              "      <td>0.161247</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MO</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.036535</td>\n",
              "      <td>0.187618</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MT</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.015838</td>\n",
              "      <td>0.124848</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NE</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.027495</td>\n",
              "      <td>0.163521</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NV</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.005137</td>\n",
              "      <td>0.071489</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NH</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.003569</td>\n",
              "      <td>0.059633</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NJ</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.007220</td>\n",
              "      <td>0.084665</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NM</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.010612</td>\n",
              "      <td>0.102468</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NY</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.019935</td>\n",
              "      <td>0.139776</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NC</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.032458</td>\n",
              "      <td>0.177214</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ND</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.015984</td>\n",
              "      <td>0.125414</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OH</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.028544</td>\n",
              "      <td>0.166520</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OK</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.024623</td>\n",
              "      <td>0.154972</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OR</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.011354</td>\n",
              "      <td>0.105950</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PA</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.021819</td>\n",
              "      <td>0.146094</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RI</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.001972</td>\n",
              "      <td>0.044364</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SC</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.015044</td>\n",
              "      <td>0.121727</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.019911</td>\n",
              "      <td>0.139695</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TN</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.031384</td>\n",
              "      <td>0.174353</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TX</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.078927</td>\n",
              "      <td>0.269625</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UT</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.004439</td>\n",
              "      <td>0.066477</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VT</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.004892</td>\n",
              "      <td>0.069772</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VA</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.042638</td>\n",
              "      <td>0.202039</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WA</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.012992</td>\n",
              "      <td>0.113239</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WV</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.017308</td>\n",
              "      <td>0.130417</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WI</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.023041</td>\n",
              "      <td>0.150034</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WY</th>\n",
              "      <td>1052734.0</td>\n",
              "      <td>0.007347</td>\n",
              "      <td>0.085397</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               count         mean           std  ...     50%      75%        max\n",
              "Active     1052734.0  2527.087164  14435.561659  ...  167.00  1167.00  1179853.0\n",
              "Confirmed  1052734.0  4555.722925  21768.958895  ...  667.00  2564.00  1239127.0\n",
              "Poverty    1052734.0    14.202770      2.446131  ...   14.22    15.32       19.0\n",
              "AL         1052734.0     0.021787      0.145988  ...    0.00     0.00        1.0\n",
              "AK         1052734.0     0.008155      0.089936  ...    0.00     0.00        1.0\n",
              "AZ         1052734.0     0.004963      0.070275  ...    0.00     0.00        1.0\n",
              "AR         1052734.0     0.024428      0.154373  ...    0.00     0.00        1.0\n",
              "CA         1052734.0     0.018738      0.135598  ...    0.00     0.00        1.0\n",
              "CO         1052734.0     0.020611      0.142078  ...    0.00     0.00        1.0\n",
              "CT         1052734.0     0.002950      0.054238  ...    0.00     0.00        1.0\n",
              "DE         1052734.0     0.001295      0.035959  ...    0.00     0.00        1.0\n",
              "FL         1052734.0     0.022121      0.147079  ...    0.00     0.00        1.0\n",
              "GA         1052734.0     0.052196      0.222423  ...    0.00     0.00        1.0\n",
              "HI         1052734.0     0.001678      0.040935  ...    0.00     0.00        1.0\n",
              "ID         1052734.0     0.013527      0.115515  ...    0.00     0.00        1.0\n",
              "IL         1052734.0     0.032910      0.178400  ...    0.00     0.00        1.0\n",
              "IN         1052734.0     0.029913      0.170346  ...    0.00     0.00        1.0\n",
              "IA         1052734.0     0.031699      0.175199  ...    0.00     0.00        1.0\n",
              "KS         1052734.0     0.031867      0.175647  ...    0.00     0.00        1.0\n",
              "KY         1052734.0     0.038157      0.191575  ...    0.00     0.00        1.0\n",
              "LA         1052734.0     0.021211      0.144089  ...    0.00     0.00        1.0\n",
              "ME         1052734.0     0.005470      0.073754  ...    0.00     0.00        1.0\n",
              "MD         1052734.0     0.007863      0.088326  ...    0.00     0.00        1.0\n",
              "MA         1052734.0     0.004602      0.067684  ...    0.00     0.00        1.0\n",
              "MI         1052734.0     0.027913      0.164724  ...    0.00     0.00        1.0\n",
              "MN         1052734.0     0.027884      0.164639  ...    0.00     0.00        1.0\n",
              "MS         1052734.0     0.026714      0.161247  ...    0.00     0.00        1.0\n",
              "MO         1052734.0     0.036535      0.187618  ...    0.00     0.00        1.0\n",
              "MT         1052734.0     0.015838      0.124848  ...    0.00     0.00        1.0\n",
              "NE         1052734.0     0.027495      0.163521  ...    0.00     0.00        1.0\n",
              "NV         1052734.0     0.005137      0.071489  ...    0.00     0.00        1.0\n",
              "NH         1052734.0     0.003569      0.059633  ...    0.00     0.00        1.0\n",
              "NJ         1052734.0     0.007220      0.084665  ...    0.00     0.00        1.0\n",
              "NM         1052734.0     0.010612      0.102468  ...    0.00     0.00        1.0\n",
              "NY         1052734.0     0.019935      0.139776  ...    0.00     0.00        1.0\n",
              "NC         1052734.0     0.032458      0.177214  ...    0.00     0.00        1.0\n",
              "ND         1052734.0     0.015984      0.125414  ...    0.00     0.00        1.0\n",
              "OH         1052734.0     0.028544      0.166520  ...    0.00     0.00        1.0\n",
              "OK         1052734.0     0.024623      0.154972  ...    0.00     0.00        1.0\n",
              "OR         1052734.0     0.011354      0.105950  ...    0.00     0.00        1.0\n",
              "PA         1052734.0     0.021819      0.146094  ...    0.00     0.00        1.0\n",
              "RI         1052734.0     0.001972      0.044364  ...    0.00     0.00        1.0\n",
              "SC         1052734.0     0.015044      0.121727  ...    0.00     0.00        1.0\n",
              "SD         1052734.0     0.019911      0.139695  ...    0.00     0.00        1.0\n",
              "TN         1052734.0     0.031384      0.174353  ...    0.00     0.00        1.0\n",
              "TX         1052734.0     0.078927      0.269625  ...    0.00     0.00        1.0\n",
              "UT         1052734.0     0.004439      0.066477  ...    0.00     0.00        1.0\n",
              "VT         1052734.0     0.004892      0.069772  ...    0.00     0.00        1.0\n",
              "VA         1052734.0     0.042638      0.202039  ...    0.00     0.00        1.0\n",
              "WA         1052734.0     0.012992      0.113239  ...    0.00     0.00        1.0\n",
              "WV         1052734.0     0.017308      0.130417  ...    0.00     0.00        1.0\n",
              "WI         1052734.0     0.023041      0.150034  ...    0.00     0.00        1.0\n",
              "WY         1052734.0     0.007347      0.085397  ...    0.00     0.00        1.0\n",
              "\n",
              "[53 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7tWIln_4G0G7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe457881-1c19-4557-895a-1eb06cd22535"
      },
      "source": [
        "train_labels = cdc_jhu_training.pop('Deaths')\n",
        "test_labels = cdc_jhu_testing.pop('Deaths')\n",
        "\n",
        "train_labels"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0           50.0\n",
              "1          169.0\n",
              "2           33.0\n",
              "3           46.0\n",
              "4           63.0\n",
              "           ...  \n",
              "1052729     16.0\n",
              "1052730      4.0\n",
              "1052731      7.0\n",
              "1052732     19.0\n",
              "1052733      2.0\n",
              "Name: Deaths, Length: 1052734, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qCz6vTTu-S95"
      },
      "source": [
        "def norm(x):\n",
        "  return (x - train_stats['mean']) / train_stats['std']\n",
        "normed_train_data = norm(cdc_jhu_training)\n",
        "normed_test_data = norm(cdc_jhu_testing)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0nqw7It_IOgr"
      },
      "source": [
        "def build_model():\n",
        "  model = keras.Sequential([\n",
        "    layers.Dense(64, activation=tf.nn.sigmoid, input_shape=[len(normed_train_data.keys())]),\n",
        "    layers.Dense(64, activation=tf.nn.sigmoid),\n",
        "    layers.Dense(1)\n",
        "  ])\n",
        "\n",
        "  optimizer = tf.keras.optimizers.RMSprop(0.05)\n",
        "\n",
        "  # optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)\n",
        "\n",
        "  model.compile(loss='mse',\n",
        "                optimizer=optimizer,\n",
        "                metrics=['mse','mae'])\n",
        "  return model"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xWe6DcbjKRYk"
      },
      "source": [
        "model=build_model()"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MDCzxTQsKZyF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79fdf28c-15ad-414f-ed0f-95ea7791bd7a"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_13 (Dense)             (None, 64)                3456      \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 7,681\n",
            "Trainable params: 7,681\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5d5oHOKwKuO7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f0673fe-055d-4fec-c823-41b8780c8efc"
      },
      "source": [
        "example_batch = normed_train_data[:10]\n",
        "example_batch\n",
        "\n",
        "example_result = model.predict(example_batch)\n",
        "example_result\n"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f77fa0677a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.71402806],\n",
              "       [-0.7174918 ],\n",
              "       [-0.71293026],\n",
              "       [-0.71306175],\n",
              "       [-0.71420026],\n",
              "       [-0.7126748 ],\n",
              "       [-0.7129247 ],\n",
              "       [-0.71600556],\n",
              "       [-0.7132618 ],\n",
              "       [-0.7129008 ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7LeJ1IdEQN9u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b517c2a-b7cf-42ca-8a42-4ea4670ea567"
      },
      "source": [
        "# display training progress\n",
        "class PrintDot(keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs):\n",
        "    if epoch % 100 == 0: print('')\n",
        "    print('.', end='')\n",
        "\n",
        "EPOCHS = 100\n",
        "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n",
        "history = model.fit(\n",
        "    x=normed_train_data, y=train_labels, batch_size=None, epochs=EPOCHS, verbose='auto',\n",
        "    callbacks=[early_stop], validation_split=0.2, validation_data=None, shuffle=True,\n",
        "    class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None,\n",
        "    validation_steps=None, validation_batch_size=None, validation_freq=1,\n",
        "    max_queue_size=10, workers=1, use_multiprocessing=True\n",
        ")\n",
        "\n",
        "# history = model.fit(\n",
        "#     normed_train_data, train_labels, \n",
        "#     epochs=EPOCHS, validation_split = 0.2, verbose=0,\n",
        "#     callbacks=[PrintDot()])"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "26319/26319 [==============================] - 72s 3ms/step - loss: 106074.0703 - mse: 106074.0703 - mae: 41.0968 - val_loss: 9981506.0000 - val_mse: 9981506.0000 - val_mae: 1761.8188\n",
            "Epoch 2/100\n",
            "26319/26319 [==============================] - 67s 3ms/step - loss: 63957.3047 - mse: 63957.3047 - mae: 38.0201 - val_loss: 5717927.0000 - val_mse: 5717927.0000 - val_mae: 1192.0780\n",
            "Epoch 3/100\n",
            "26319/26319 [==============================] - 71s 3ms/step - loss: 54745.6992 - mse: 54745.6992 - mae: 37.5342 - val_loss: 4313323.5000 - val_mse: 4313323.5000 - val_mae: 975.6387\n",
            "Epoch 4/100\n",
            "26319/26319 [==============================] - 71s 3ms/step - loss: 51301.1641 - mse: 51301.1641 - mae: 37.8443 - val_loss: 2320275.2500 - val_mse: 2320275.2500 - val_mae: 715.3485\n",
            "Epoch 5/100\n",
            "26319/26319 [==============================] - 67s 3ms/step - loss: 44284.2852 - mse: 44284.2852 - mae: 37.6133 - val_loss: 2333132.5000 - val_mse: 2333132.5000 - val_mae: 729.7546\n",
            "Epoch 6/100\n",
            "26319/26319 [==============================] - 71s 3ms/step - loss: 41860.2500 - mse: 41860.2500 - mae: 37.5638 - val_loss: 1848649.6250 - val_mse: 1848649.6250 - val_mae: 665.4617\n",
            "Epoch 7/100\n",
            "26319/26319 [==============================] - 71s 3ms/step - loss: 37651.5820 - mse: 37651.5820 - mae: 37.6010 - val_loss: 1388039.1250 - val_mse: 1388039.1250 - val_mae: 563.5272\n",
            "Epoch 8/100\n",
            "26319/26319 [==============================] - 68s 3ms/step - loss: 34696.7734 - mse: 34696.7734 - mae: 37.6942 - val_loss: 845773.0625 - val_mse: 845773.0625 - val_mae: 433.7283\n",
            "Epoch 9/100\n",
            "26319/26319 [==============================] - 67s 3ms/step - loss: 31938.4883 - mse: 31938.4883 - mae: 37.7587 - val_loss: 969880.6875 - val_mse: 969880.6875 - val_mae: 702.1015\n",
            "Epoch 10/100\n",
            "26319/26319 [==============================] - 67s 3ms/step - loss: 30539.1602 - mse: 30539.1602 - mae: 37.7516 - val_loss: 966543.1875 - val_mse: 966543.1875 - val_mae: 699.8184\n",
            "Epoch 11/100\n",
            "26319/26319 [==============================] - 67s 3ms/step - loss: 29945.4160 - mse: 29945.4160 - mae: 37.8197 - val_loss: 909833.1250 - val_mse: 909833.1250 - val_mae: 670.0778\n",
            "Epoch 12/100\n",
            "26319/26319 [==============================] - 67s 3ms/step - loss: 30221.9629 - mse: 30221.9629 - mae: 38.1539 - val_loss: 1094043.8750 - val_mse: 1094043.8750 - val_mae: 811.2004\n",
            "Epoch 13/100\n",
            "26319/26319 [==============================] - 67s 3ms/step - loss: 28308.7246 - mse: 28308.7246 - mae: 38.1891 - val_loss: 784771.9375 - val_mse: 784771.9375 - val_mae: 597.7773\n",
            "Epoch 14/100\n",
            "26319/26319 [==============================] - 70s 3ms/step - loss: 28908.0000 - mse: 28908.0000 - mae: 38.0333 - val_loss: 1035623.2500 - val_mse: 1035623.2500 - val_mae: 773.5607\n",
            "Epoch 15/100\n",
            "26319/26319 [==============================] - 70s 3ms/step - loss: 25360.8340 - mse: 25360.8340 - mae: 39.0124 - val_loss: 869784.6875 - val_mse: 869784.6875 - val_mae: 781.1679\n",
            "Epoch 16/100\n",
            "26319/26319 [==============================] - 67s 3ms/step - loss: 24668.1387 - mse: 24668.1387 - mae: 38.7211 - val_loss: 981461.8750 - val_mse: 981461.8750 - val_mae: 799.1730\n",
            "Epoch 17/100\n",
            "26319/26319 [==============================] - 66s 3ms/step - loss: 23906.7207 - mse: 23906.7207 - mae: 39.1622 - val_loss: 810772.8750 - val_mse: 810772.8750 - val_mae: 742.0230\n",
            "Epoch 18/100\n",
            "26319/26319 [==============================] - 70s 3ms/step - loss: 26397.4473 - mse: 26397.4473 - mae: 39.9908 - val_loss: 680052.5000 - val_mse: 680052.5000 - val_mae: 621.8522\n",
            "Epoch 19/100\n",
            "26319/26319 [==============================] - 70s 3ms/step - loss: 27011.9727 - mse: 27011.9727 - mae: 39.4517 - val_loss: 1082859.1250 - val_mse: 1082859.1250 - val_mae: 531.1502\n",
            "Epoch 20/100\n",
            "26319/26319 [==============================] - 71s 3ms/step - loss: 27120.6445 - mse: 27120.6445 - mae: 38.7938 - val_loss: 636287.9375 - val_mse: 636287.9375 - val_mae: 535.6155\n",
            "Epoch 21/100\n",
            "26319/26319 [==============================] - 67s 3ms/step - loss: 25430.1035 - mse: 25430.1035 - mae: 39.9725 - val_loss: 825064.6250 - val_mse: 825064.6250 - val_mae: 600.7662\n",
            "Epoch 22/100\n",
            "26319/26319 [==============================] - 66s 3ms/step - loss: 26568.8848 - mse: 26568.8848 - mae: 40.1298 - val_loss: 873979.3125 - val_mse: 873979.3125 - val_mae: 528.3834\n",
            "Epoch 23/100\n",
            "26319/26319 [==============================] - 70s 3ms/step - loss: 25419.6172 - mse: 25419.6172 - mae: 39.4474 - val_loss: 951662.7500 - val_mse: 951662.7500 - val_mae: 485.1075\n",
            "Epoch 24/100\n",
            "26319/26319 [==============================] - 68s 3ms/step - loss: 25295.8125 - mse: 25295.8125 - mae: 39.2163 - val_loss: 835839.0000 - val_mse: 835839.0000 - val_mae: 522.3398\n",
            "Epoch 25/100\n",
            "26319/26319 [==============================] - 67s 3ms/step - loss: 27073.6680 - mse: 27073.6680 - mae: 38.4095 - val_loss: 1239322.0000 - val_mse: 1239322.0000 - val_mae: 690.4455\n",
            "Epoch 26/100\n",
            "26319/26319 [==============================] - 70s 3ms/step - loss: 28645.5977 - mse: 28645.5977 - mae: 38.3888 - val_loss: 1306210.8750 - val_mse: 1306210.8750 - val_mae: 724.3997\n",
            "Epoch 27/100\n",
            "26319/26319 [==============================] - 67s 3ms/step - loss: 26596.8418 - mse: 26596.8418 - mae: 39.4024 - val_loss: 1154387.5000 - val_mse: 1154387.5000 - val_mae: 708.5190\n",
            "Epoch 28/100\n",
            "26319/26319 [==============================] - 71s 3ms/step - loss: 26202.7148 - mse: 26202.7148 - mae: 39.1845 - val_loss: 1082516.5000 - val_mse: 1082516.5000 - val_mae: 770.4886\n",
            "Epoch 29/100\n",
            "26319/26319 [==============================] - 67s 3ms/step - loss: 25577.0215 - mse: 25577.0215 - mae: 39.1202 - val_loss: 1254046.6250 - val_mse: 1254046.6250 - val_mae: 791.0488\n",
            "Epoch 30/100\n",
            "26319/26319 [==============================] - 71s 3ms/step - loss: 26473.1367 - mse: 26473.1367 - mae: 38.9473 - val_loss: 1107109.7500 - val_mse: 1107109.7500 - val_mae: 670.1199\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ccWZJ1AqaeB1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "1ce49c48-dfcd-4917-dea6-b88c80f3a401"
      },
      "source": [
        "hist = pd.DataFrame(history.history)\n",
        "hist['epoch'] = history.epoch\n",
        "hist.tail()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>loss</th>\n",
              "      <th>mse</th>\n",
              "      <th>mae</th>\n",
              "      <th>val_loss</th>\n",
              "      <th>val_mse</th>\n",
              "      <th>val_mae</th>\n",
              "      <th>epoch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>28645.597656</td>\n",
              "      <td>28645.597656</td>\n",
              "      <td>38.388763</td>\n",
              "      <td>1306210.875</td>\n",
              "      <td>1306210.875</td>\n",
              "      <td>724.399719</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>26596.841797</td>\n",
              "      <td>26596.841797</td>\n",
              "      <td>39.402382</td>\n",
              "      <td>1154387.500</td>\n",
              "      <td>1154387.500</td>\n",
              "      <td>708.519043</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>26202.714844</td>\n",
              "      <td>26202.714844</td>\n",
              "      <td>39.184490</td>\n",
              "      <td>1082516.500</td>\n",
              "      <td>1082516.500</td>\n",
              "      <td>770.488586</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>25577.021484</td>\n",
              "      <td>25577.021484</td>\n",
              "      <td>39.120205</td>\n",
              "      <td>1254046.625</td>\n",
              "      <td>1254046.625</td>\n",
              "      <td>791.048767</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>26473.136719</td>\n",
              "      <td>26473.136719</td>\n",
              "      <td>38.947350</td>\n",
              "      <td>1107109.750</td>\n",
              "      <td>1107109.750</td>\n",
              "      <td>670.119873</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            loss           mse        mae  ...      val_mse     val_mae  epoch\n",
              "25  28645.597656  28645.597656  38.388763  ...  1306210.875  724.399719     25\n",
              "26  26596.841797  26596.841797  39.402382  ...  1154387.500  708.519043     26\n",
              "27  26202.714844  26202.714844  39.184490  ...  1082516.500  770.488586     27\n",
              "28  25577.021484  25577.021484  39.120205  ...  1254046.625  791.048767     28\n",
              "29  26473.136719  26473.136719  38.947350  ...  1107109.750  670.119873     29\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1zRfCzebgQwy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0469d2f-bf5d-45ec-d275-98e703262072"
      },
      "source": [
        "history.history.keys()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'mse', 'mae', 'val_loss', 'val_mse', 'val_mae'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tsWjJHg-eOTa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "bb2be0b0-c8b9-41d8-deb3-b9ca31482211"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_history(history):\n",
        "  hist = pd.DataFrame(history.history)\n",
        "  hist['epoch'] = history.epoch\n",
        "\n",
        "  plt.figure()\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Mean Absolute Error [Deaths]') \n",
        "  plt.plot(hist['epoch'], hist['mae'], label ='Train Error')\n",
        "  plt.plot(hist['epoch'], hist['val_mae'], label = 'Validation Error')\n",
        "  plt.gca()\n",
        "  plt.yscale('log')\n",
        "  plt.legend()\n",
        "  plt.ylim([10,3000])\n",
        "\n",
        "  plt.figure()\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Mean Square Error [$Deaths^2$]') \n",
        "  plt.plot(hist['epoch'], hist['mse'], label ='Train Error')\n",
        "  # plt.plot(hist['epoch'], hist['val_mse'], label = 'Validation Mean Squared Error')\n",
        "  # plt.yscale('log')\n",
        "  plt.legend()\n",
        "  plt.ylim([20000,250000])\n",
        "plot_history(history)\n"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CacCZtOaB6D3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15eb894d-f3e1-4802-ae67-44b65580dd69"
      },
      "source": [
        "loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=0)\n",
        "print(\"Testing set mean Absolute Error: {:5.7f} Deaths\".format(mae))\n",
        "\n",
        "#Testing set mean Absolute Error: 2393219.0000000 Deaths\n",
        "#Testing set mean Absolute Error: 1033921.6250000 Deaths (add layer)\n",
        "#Testing set mean Absolute Error: 1637497.3750000 Deaths (remove layer and use sigmoid)\n",
        "#Testing set mean Absolute Error: 582547.6250000 Deaths\n",
        "#Testing set mean Absolute Error: 1449834.2500000 Deaths\n",
        "#Testing set mean Absolute Error: 1420743.2500000 Deaths (added layer and patience to 20)\n",
        "# Testing set mean Absolute Error: 991202.8750000 Deaths (changed optimizer to )\n"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing set mean Absolute Error: 252535.6406250 Deaths\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kzhZRUITD_8C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "d6b9e7c7-a0fc-4d3f-feac-6a09b970f4e5"
      },
      "source": [
        "test_predictions = model.predict(normed_test_data).flatten()\n",
        "\n",
        "plt.scatter(test_labels, test_predictions)\n",
        "plt.xlabel(\"True Values [Deaths]\")\n",
        "plt.ylabel(\"Predictions [Deaths]\")\n",
        "plt.axis('equal')\n",
        "plt.axis('square')\n",
        "plt.xlim([0,plt.xlim()[1]])\n",
        "plt.ylim([0,plt.ylim()[1]])\n",
        "_ = plt.plot([-10,10],[-10,10])"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XkefYeuVFoJJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "fb1bc251-e1a4-4539-e734-1979ef03afa1"
      },
      "source": [
        "err = test_predictions - test_labels\n",
        "plt.hist(err, bins=10)\n",
        "plt.xlabel(\"Predictin Error [Deaths]\")\n",
        "_ = plt.ylabel(\"count\")"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}