Vaccine_Effectiveness.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "warnings.simplefilter('ignore')"
      ],
      "metadata": {
        "id": "pLnfRwVAqeXW"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Dataset"
      ],
      "metadata": {
        "id": "bn5dn5SFAikF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "aV1farN15_-I"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('COVID Data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
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        "outputId": "07cce51c-22b7-4632-8ca4-2f8226186694"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5331, 5)"
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          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Preprocessing"
      ],
      "metadata": {
        "id": "PwQBOipFAnhN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
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        "id": "RwD44Z-y6xdm",
        "outputId": "8460a4e4-f74d-40b2-9609-4d93b8e7bbbb"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date Administered                   0\n",
              "AgeGroupVacc                        0\n",
              "7-day_avg_group_cases_per_100k      0\n",
              "Administered_Dose1_pct_agegroup     0\n",
              "Series_Complete_Pop_pct_agegroup    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
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        "outputId": "7c71eeed-b186-4b3e-88ff-0584b0c2db68"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date Administered   AgeGroupVacc  7-day_avg_group_cases_per_100k  \\\n",
              "0  10/11/2022 12:00:00 AM       <2 Years                        2.091826   \n",
              "1  10/11/2022 12:00:00 AM  12 - 17 Years                        2.389293   \n",
              "2  10/11/2022 12:00:00 AM  25 - 49 Years                        4.375377   \n",
              "3  10/10/2022 12:00:00 AM       <2 Years                        2.888181   \n",
              "4  10/10/2022 12:00:00 AM    2 - 4 Years                        3.362708   \n",
              "\n",
              "   Administered_Dose1_pct_agegroup  Series_Complete_Pop_pct_agegroup  \n",
              "0                            0.052                             0.017  \n",
              "1                            0.711                             0.608  \n",
              "2                            0.840                             0.710  \n",
              "3                            0.052                             0.017  \n",
              "4                            0.080                             0.032  "
            ],
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              "      <th></th>\n",
              "      <th>Date Administered</th>\n",
              "      <th>AgeGroupVacc</th>\n",
              "      <th>7-day_avg_group_cases_per_100k</th>\n",
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              "      <th>0</th>\n",
              "      <td>10/11/2022 12:00:00 AM</td>\n",
              "      <td>&lt;2 Years</td>\n",
              "      <td>2.091826</td>\n",
              "      <td>0.052</td>\n",
              "      <td>0.017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10/11/2022 12:00:00 AM</td>\n",
              "      <td>12 - 17 Years</td>\n",
              "      <td>2.389293</td>\n",
              "      <td>0.711</td>\n",
              "      <td>0.608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10/11/2022 12:00:00 AM</td>\n",
              "      <td>25 - 49 Years</td>\n",
              "      <td>4.375377</td>\n",
              "      <td>0.840</td>\n",
              "      <td>0.710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10/10/2022 12:00:00 AM</td>\n",
              "      <td>&lt;2 Years</td>\n",
              "      <td>2.888181</td>\n",
              "      <td>0.052</td>\n",
              "      <td>0.017</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10/10/2022 12:00:00 AM</td>\n",
              "      <td>2 - 4 Years</td>\n",
              "      <td>3.362708</td>\n",
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      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Average Cases: \", df['7-day_avg_group_cases_per_100k'].mean())\n",
        "print(\"Minimum Cases: \", df['7-day_avg_group_cases_per_100k'].min())\n",
        "print(\"Maximum Cases: \", df['7-day_avg_group_cases_per_100k'].max())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "cb023fc5-ae82-4e43-bdb3-81306a8118fc"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Cases:  30.625570325845523\n",
            "Minimum Cases:  1.62491137761832\n",
            "Maximum Cases:  300.7184049961944\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hist(df['7-day_avg_group_cases_per_100k'], bins=5)\n",
        "plt.xlabel('Values')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Value Distribution in Column ')\n",
        "plt.show()"
      ],
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      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['7-day_avg_group_cases_per_100k']>299].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "4vxfIZQxuzqg",
        "outputId": "93e20b7a-1644-41e6-ea95-a4764c01c481"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date Administered   AgeGroupVacc  7-day_avg_group_cases_per_100k  \\\n",
              "2199  01/09/2022 12:00:00 AM  18 - 24 Years                      300.718405   \n",
              "2207  01/08/2022 12:00:00 AM  18 - 24 Years                      300.609676   \n",
              "\n",
              "      Administered_Dose1_pct_agegroup  Series_Complete_Pop_pct_agegroup  \n",
              "2199                            0.736                             0.609  \n",
              "2207                            0.736                             0.608  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fb102817-34df-4009-9bd1-54c6f979c78d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Date Administered</th>\n",
              "      <th>AgeGroupVacc</th>\n",
              "      <th>7-day_avg_group_cases_per_100k</th>\n",
              "      <th>Administered_Dose1_pct_agegroup</th>\n",
              "      <th>Series_Complete_Pop_pct_agegroup</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2199</th>\n",
              "      <td>01/09/2022 12:00:00 AM</td>\n",
              "      <td>18 - 24 Years</td>\n",
              "      <td>300.718405</td>\n",
              "      <td>0.736</td>\n",
              "      <td>0.609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2207</th>\n",
              "      <td>01/08/2022 12:00:00 AM</td>\n",
              "      <td>18 - 24 Years</td>\n",
              "      <td>300.609676</td>\n",
              "      <td>0.736</td>\n",
              "      <td>0.608</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fb102817-34df-4009-9bd1-54c6f979c78d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fb102817-34df-4009-9bd1-54c6f979c78d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fb102817-34df-4009-9bd1-54c6f979c78d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_filtered = df[(df['Administered_Dose1_pct_agegroup'] == 0) & (df['Series_Complete_Pop_pct_agegroup'] == 0)]\n",
        "\n",
        "df_filtered.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SYORwxl2rqb2",
        "outputId": "e3b02c35-801f-407e-f432-8d7a4b829884"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1299, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "X0sYL7b47Kte",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71fa6295-4126-444a-d92f-de1492fdd468"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1142, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df[df['Series_Complete_Pop_pct_agegroup'] >= 0.7].shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['AgeGroupVacc'].value_counts())\n",
        "print(\"_____________________________\")\n",
        "print(len(df['AgeGroupVacc'].unique()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_CDK4-c1DJEw",
        "outputId": "b524e4ac-08f3-4310-81e4-d01c0fb24778"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<2 Years         667\n",
            "12 - 17 Years    667\n",
            "25 - 49 Years    667\n",
            "2 - 4 Years      666\n",
            "5 - 11 Years     666\n",
            "18 - 24 Years    666\n",
            "50 - 64 Years    666\n",
            "65+ Years        666\n",
            "Name: AgeGroupVacc, dtype: int64\n",
            "_____________________________\n",
            "8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Otv2o15Q8pYH",
        "outputId": "7394e770-1eaa-4070-e718-51659a2c74ca"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date Administered   AgeGroupVacc  7-day_avg_group_cases_per_100k  \\\n",
              "0  10/11/2022 12:00:00 AM       <2 Years                        2.091826   \n",
              "1  10/11/2022 12:00:00 AM  12 - 17 Years                        2.389293   \n",
              "2  10/11/2022 12:00:00 AM  25 - 49 Years                        4.375377   \n",
              "3  10/10/2022 12:00:00 AM       <2 Years                        2.888181   \n",
              "4  10/10/2022 12:00:00 AM    2 - 4 Years                        3.362708   \n",
              "\n",
              "   Administered_Dose1_pct_agegroup  Series_Complete_Pop_pct_agegroup  \n",
              "0                            0.052                             0.017  \n",
              "1                            0.711                             0.608  \n",
              "2                            0.840                             0.710  \n",
              "3                            0.052                             0.017  \n",
              "4                            0.080                             0.032  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-94bc064c-9d78-4821-8300-63056ec8d372\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Date Administered</th>\n",
              "      <th>AgeGroupVacc</th>\n",
              "      <th>7-day_avg_group_cases_per_100k</th>\n",
              "      <th>Administered_Dose1_pct_agegroup</th>\n",
              "      <th>Series_Complete_Pop_pct_agegroup</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10/11/2022 12:00:00 AM</td>\n",
              "      <td>&lt;2 Years</td>\n",
              "      <td>2.091826</td>\n",
              "      <td>0.052</td>\n",
              "      <td>0.017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10/11/2022 12:00:00 AM</td>\n",
              "      <td>12 - 17 Years</td>\n",
              "      <td>2.389293</td>\n",
              "      <td>0.711</td>\n",
              "      <td>0.608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10/11/2022 12:00:00 AM</td>\n",
              "      <td>25 - 49 Years</td>\n",
              "      <td>4.375377</td>\n",
              "      <td>0.840</td>\n",
              "      <td>0.710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10/10/2022 12:00:00 AM</td>\n",
              "      <td>&lt;2 Years</td>\n",
              "      <td>2.888181</td>\n",
              "      <td>0.052</td>\n",
              "      <td>0.017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10/10/2022 12:00:00 AM</td>\n",
              "      <td>2 - 4 Years</td>\n",
              "      <td>3.362708</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.032</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-94bc064c-9d78-4821-8300-63056ec8d372')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-94bc064c-9d78-4821-8300-63056ec8d372 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-94bc064c-9d78-4821-8300-63056ec8d372');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ojX8nuq48qg-",
        "outputId": "28331742-b847-4b89-b3df-a29f54bef844"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date Administered                   0\n",
              "AgeGroupVacc                        0\n",
              "7-day_avg_group_cases_per_100k      0\n",
              "Administered_Dose1_pct_agegroup     0\n",
              "Series_Complete_Pop_pct_agegroup    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Feature Selection"
      ],
      "metadata": {
        "id": "EBSWzSK6eoDc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Correlation Matrix"
      ],
      "metadata": {
        "id": "Ku027E5tBb-8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corr_matrix = df.corr()\n",
        "\n",
        "print(corr_matrix)"
      ],
      "metadata": {
        "id": "VOvJh9rpFqia",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da1e971d-1629-481d-9e53-3a290abbfcf5"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                  7-day_avg_group_cases_per_100k  \\\n",
            "7-day_avg_group_cases_per_100k                          1.000000   \n",
            "Administered_Dose1_pct_agegroup                         0.068510   \n",
            "Series_Complete_Pop_pct_agegroup                        0.062031   \n",
            "\n",
            "                                  Administered_Dose1_pct_agegroup  \\\n",
            "7-day_avg_group_cases_per_100k                           0.068510   \n",
            "Administered_Dose1_pct_agegroup                          1.000000   \n",
            "Series_Complete_Pop_pct_agegroup                         0.992843   \n",
            "\n",
            "                                  Series_Complete_Pop_pct_agegroup  \n",
            "7-day_avg_group_cases_per_100k                            0.062031  \n",
            "Administered_Dose1_pct_agegroup                           0.992843  \n",
            "Series_Complete_Pop_pct_agegroup                          1.000000  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.heatmap(df.corr(),annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "bIrJvaPLA9au",
        "outputId": "20d34712-4292-486e-aa0a-6f32b3855e6b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Importance"
      ],
      "metadata": {
        "id": "dmaKGBE8Betd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "# Create an instance of the model\n",
        "rf = RandomForestRegressor()\n",
        "\n",
        "# Fit the model on the data\n",
        "rf.fit(df[['Series_Complete_Pop_pct_agegroup']], df['7-day_avg_group_cases_per_100k'])\n",
        "\n",
        "# Get the feature importance that column\n",
        "importance = rf.feature_importances_\n",
        "\n",
        "print(\"Importance of series column: \", importance)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J9siJZhbBAss",
        "outputId": "e63c8ea5-6420-43fe-f22e-aa62cc42bbd5"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Importance of series column:  [1.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##Target Variable"
      ],
      "metadata": {
        "id": "Z-AozLBwy7ny"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing unvaccinated cases\n",
        "df = df[df['Series_Complete_Pop_pct_agegroup'] != 0]\n",
        "\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ZbEtdRz-p6R",
        "outputId": "e6118bb3-3f3c-41c3-9f9c-34666af9152e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3844, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Immunity'] = (df[\"7-day_avg_group_cases_per_100k\"] < 100) & (df['Series_Complete_Pop_pct_agegroup'] > 0.65)\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "A5J2J5wd6k2N",
        "outputId": "e8c61fde-5b44-4652-801a-8c6bb2c12805"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date Administered   AgeGroupVacc  7-day_avg_group_cases_per_100k  \\\n",
              "0  10/11/2022 12:00:00 AM       <2 Years                        2.091826   \n",
              "1  10/11/2022 12:00:00 AM  12 - 17 Years                        2.389293   \n",
              "2  10/11/2022 12:00:00 AM  25 - 49 Years                        4.375377   \n",
              "3  10/10/2022 12:00:00 AM       <2 Years                        2.888181   \n",
              "4  10/10/2022 12:00:00 AM    2 - 4 Years                        3.362708   \n",
              "\n",
              "   Administered_Dose1_pct_agegroup  Series_Complete_Pop_pct_agegroup  Immunity  \n",
              "0                            0.052                             0.017     False  \n",
              "1                            0.711                             0.608     False  \n",
              "2                            0.840                             0.710      True  \n",
              "3                            0.052                             0.017     False  \n",
              "4                            0.080                             0.032     False  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8c1876b6-89ce-424d-aa2a-c98a9f336c2d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date Administered</th>\n",
              "      <th>AgeGroupVacc</th>\n",
              "      <th>7-day_avg_group_cases_per_100k</th>\n",
              "      <th>Administered_Dose1_pct_agegroup</th>\n",
              "      <th>Series_Complete_Pop_pct_agegroup</th>\n",
              "      <th>Immunity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10/11/2022 12:00:00 AM</td>\n",
              "      <td>&lt;2 Years</td>\n",
              "      <td>2.091826</td>\n",
              "      <td>0.052</td>\n",
              "      <td>0.017</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10/11/2022 12:00:00 AM</td>\n",
              "      <td>12 - 17 Years</td>\n",
              "      <td>2.389293</td>\n",
              "      <td>0.711</td>\n",
              "      <td>0.608</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10/11/2022 12:00:00 AM</td>\n",
              "      <td>25 - 49 Years</td>\n",
              "      <td>4.375377</td>\n",
              "      <td>0.840</td>\n",
              "      <td>0.710</td>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10/10/2022 12:00:00 AM</td>\n",
              "      <td>&lt;2 Years</td>\n",
              "      <td>2.888181</td>\n",
              "      <td>0.052</td>\n",
              "      <td>0.017</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10/10/2022 12:00:00 AM</td>\n",
              "      <td>2 - 4 Years</td>\n",
              "      <td>3.362708</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.032</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-8c1876b6-89ce-424d-aa2a-c98a9f336c2d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
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              "          const element = document.querySelector('#df-8c1876b6-89ce-424d-aa2a-c98a9f336c2d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Immunity'].value_counts()\n",
        "\n",
        "one = (df[\"Immunity\"].value_counts()[1].sum()/df[\"Immunity\"].value_counts()[0].sum()) * 100\n",
        "\n",
        "print(f\"Percentage of values which are one: {round(one, 2)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kugmlqVZ6oUH",
        "outputId": "0bf154d3-42be-45cc-fb08-779fbd2ae869"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of values which are one: 53.76\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "q1goupkH4Klx",
        "outputId": "13e8d54e-a184-493d-bb6b-8ace1e330525"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date Administered   AgeGroupVacc  7-day_avg_group_cases_per_100k  \\\n",
              "0  10/11/2022 12:00:00 AM       <2 Years                        2.091826   \n",
              "1  10/11/2022 12:00:00 AM  12 - 17 Years                        2.389293   \n",
              "2  10/11/2022 12:00:00 AM  25 - 49 Years                        4.375377   \n",
              "3  10/10/2022 12:00:00 AM       <2 Years                        2.888181   \n",
              "4  10/10/2022 12:00:00 AM    2 - 4 Years                        3.362708   \n",
              "\n",
              "   Administered_Dose1_pct_agegroup  Series_Complete_Pop_pct_agegroup  Immunity  \n",
              "0                            0.052                             0.017     False  \n",
              "1                            0.711                             0.608     False  \n",
              "2                            0.840                             0.710      True  \n",
              "3                            0.052                             0.017     False  \n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    {
      "cell_type": "code",
      "source": [
        "f = lambda x: 1 if x==True else 0\n",
        "\n",
        "df['Immunity'] = df[['Immunity']].applymap(f)\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "qUob3q6t5zWN",
        "outputId": "84041cc1-a6a5-47ed-e897-99de993265f6"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date Administered   AgeGroupVacc  7-day_avg_group_cases_per_100k  \\\n",
              "0  10/11/2022 12:00:00 AM       <2 Years                        2.091826   \n",
              "1  10/11/2022 12:00:00 AM  12 - 17 Years                        2.389293   \n",
              "2  10/11/2022 12:00:00 AM  25 - 49 Years                        4.375377   \n",
              "3  10/10/2022 12:00:00 AM       <2 Years                        2.888181   \n",
              "4  10/10/2022 12:00:00 AM    2 - 4 Years                        3.362708   \n",
              "\n",
              "   Administered_Dose1_pct_agegroup  Series_Complete_Pop_pct_agegroup  Immunity  \n",
              "0                            0.052                             0.017         0  \n",
              "1                            0.711                             0.608         0  \n",
              "2                            0.840                             0.710         1  \n",
              "3                            0.052                             0.017         0  \n",
              "4                            0.080                             0.032         0  "
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              "\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "one = df[df['Immunity'] == 1]\n",
        "\n",
        "zero = df[df['Immunity'] == 0]"
      ],
      "metadata": {
        "id": "MFBA0mDOtbtF"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "zero.shape"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h1Yepa8Itdsb",
        "outputId": "03b9b8d4-6437-4948-8d3b-7f6453a61133"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2500, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "one.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xGSQMjKFtj2v",
        "outputId": "196a2834-39f9-42cd-80e0-0abae72b15ca"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1344, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "one = df[df['Immunity'] == 1]\n",
        "\n",
        "zero = df[df['Immunity'] == 0]\n",
        "\n",
        "zero = zero.iloc[800:]\n",
        "\n",
        "final_df = pd.concat([one, zero], ignore_index=True)\n",
        "\n",
        "df  = final_df.drop(\"Date Administered\", axis=1)"
      ],
      "metadata": {
        "id": "IUDK26MuzDSG"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Immunity Charts"
      ],
      "metadata": {
        "id": "ZCRwN4CETzQ8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Immunity = df['Immunity'].value_counts()\n",
        "\n",
        "Immunity.plot(kind='pie')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "2Z2EWpBYzRhV",
        "outputId": "a9df4787-24a0-43a3-afbc-af42a065e221"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:ylabel='Immunity'>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['Immunity']==1].shape[0] + df[df['Immunity']==0].shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aTYzauLhcdG0",
        "outputId": "8515b0b0-d768-47ce-e94b-aeba46174322"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3044"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# data\n",
        "names = ['1', '0']\n",
        "values = [df[df['Immunity']==1].shape[0], df[df['Immunity']==0].shape[0]]\n",
        "\n",
        "# create bar chart\n",
        "fig, ax = plt.subplots()\n",
        "ax.bar(names, values, color=['blue', 'red'], width=0.24)\n",
        "\n",
        "# set chart title and axis labels\n",
        "ax.set_title('Immunity in vaccinated patients')\n",
        "ax.set_ylabel('Cases', fontsize = 16)\n",
        "\n",
        "# Set Margins\n",
        "plt.margins(x=0.2, y=0.2)\n",
        "\n",
        "\n",
        "# Hide grid lines\n",
        "# plt.grid(False)\n",
        "\n",
        "# Hide axes ticks\n",
        "plt.yticks([0, 500, 1000, 1500, 2000])\n",
        "\n",
        "\n",
        "# show chart\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "TA3ogXPsdCis",
        "outputId": "30a1e2a5-3d09-482b-9b78-6beb6ebc88f2"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# data\n",
        "labels = ['1', '0']\n",
        "sizes = [df[df['Immunity']==1].shape[0], df[df['Immunity']==0].shape[0]]\n",
        "\n",
        "# create donut chart\n",
        "fig, ax = plt.subplots()\n",
        "ax.pie(sizes, labels=labels, autopct='%1.0f%%', startangle=90, pctdistance=0.85)\n",
        "\n",
        "# create white circle in the center to make a donut chart\n",
        "circle = plt.Circle((0,0), 0.7, color='white')\n",
        "fig.gca().add_artist(circle)\n",
        "\n",
        "# set chart title\n",
        "ax.set_title('COVID_19 Vaccine Immunity')\n",
        "\n",
        "# show chart\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "QAapMhImlhlu",
        "outputId": "0d164204-1ce2-46a6-8ad9-322036b35917"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['AgeGroupVacc'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "27sRjfQZUxFp",
        "outputId": "fcf5d626-65e6-402c-bf65-3316f877d1b1"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['25 - 49 Years', '18 - 24 Years', '50 - 64 Years', '65+ Years',\n",
              "       '5 - 11 Years', '12 - 17 Years'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Group by age_group and binary_value columns and count\n",
        "counts = df.groupby(['AgeGroupVacc', 'Immunity']).size().unstack()\n",
        "\n",
        "# desired order of the rows\n",
        "desired_order = [\"5 - 11 Years\", \"12 - 17 Years\", \"18 - 24 Years\", \"25 - 49 Years\", \"50 - 64 Years\", \"65+ Years\"]\n",
        "\n",
        "# reindex the dataframe with the desired order of rows\n",
        "counts = counts.reindex(index=desired_order)"
      ],
      "metadata": {
        "id": "g5H_h45kTuHz"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "P_kUrTsUUNCE",
        "outputId": "200bebd7-0180-4b08-e071-de72aefe8a86"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Immunity           0      1\n",
              "AgeGroupVacc               \n",
              "5 - 11 Years   264.0    NaN\n",
              "12 - 17 Years  378.0    NaN\n",
              "18 - 24 Years  413.0   56.0\n",
              "25 - 49 Years  353.0  292.0\n",
              "50 - 64 Years  188.0  457.0\n",
              "65+ Years      104.0  539.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-22f2826e-1215-4c81-9172-30e2a636d658\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Immunity</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AgeGroupVacc</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5 - 11 Years</th>\n",
              "      <td>264.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12 - 17 Years</th>\n",
              "      <td>378.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18 - 24 Years</th>\n",
              "      <td>413.0</td>\n",
              "      <td>56.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25 - 49 Years</th>\n",
              "      <td>353.0</td>\n",
              "      <td>292.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50 - 64 Years</th>\n",
              "      <td>188.0</td>\n",
              "      <td>457.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65+ Years</th>\n",
              "      <td>104.0</td>\n",
              "      <td>539.0</td>\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-22f2826e-1215-4c81-9172-30e2a636d658 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-22f2826e-1215-4c81-9172-30e2a636d658');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot bar chart\n",
        "counts.plot(kind='bar', stacked=True, color=['red', 'green'])\n",
        "\n",
        "# Add title and axis labels\n",
        "plt.title('Vaccine Effectiveness by age group')\n",
        "plt.xlabel('Age group')\n",
        "plt.ylabel('Immunity')\n",
        "\n",
        "# Show plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "ezUMO5fPWzV5",
        "outputId": "f4e09556-f28c-401c-be4c-970f2dc87e9b"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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J/Pa3v2X27NlcfvnlzJ49u2t1MDOro0YmhRagJSL+ksavIUsSz7Y2C6W/z6XyhcCoiuVHpmmlMWPGDMaMGcNmm23GmmuuySGHHMINN9xQdFhmZrmGJYWIWAQskLRlmrQXMBuYChyZph0JtH4rTgWOSL2QdgFermhmKoWFCxcyatTbeW/kyJEsXFiqvGdmTa7RVzR/AbhU0prAk8DRZInoKknHAvOBg9K8NwETgLnAq2leMzPrRg1NChExC6h2f429qswbwImNjKdoI0aMYMGCt8+lt7S0MGLEKufSzcwK4yuau9GOO+7IE088wVNPPcXrr7/OFVdcwcSJE4sOy8ws5xvidaO+ffty7rnn8vGPf5w333yTY445hq233rrosMzMcr07KRRw2/AJEyYwYcKEbt+umVkt3HxkZmY5JwUzM8s5KZiZWc5JwczMck4KZmaWc1IwM7Ock0I3O+aYY9hwww153/veV3QoZmar6NXXKeiM1d/uujPi9NVf93DUUUfx+c9/niOOOKKu2zYzqwcfKXSzD3/4wwwePLjoMMzMqnJSMDOznJOCmZnlnBTMzCznpGBmZjknhW526KGHsuuuu/L4448zcuRILrzwwqJDMjPL9eouqbV0Ia23yy+/vNu3aWZWKx8pmJlZzknBzMxyTgpmZpYrZVKIAh6z2R3KWi8z6zlKlxQGDBjAkiVLSvcFGhEsWbKEAQMGFB2KmZVYQ3sfSZoHLAXeBFZExHhJg4ErgdHAPOCgiHhRkoBzgAnAq8BREXF/Z7c5cuRIWlpaWLx4cX0q0YMMGDCAkSNHFh2GmZVYd3RJ3SMinq8YPxW4LSImSTo1jX8N2AcYm147A+elv53Sr18/Nt10065HbWbWCxXRfLQfMDkNTwb2r5g+JTL3AIMkDS8gPjOzXqvRSSGA30u6T9IJadqwiHgmDS8ChqXhEcCCimVb0jQzM+smjW4+2i0iFkraELhF0mOVhRERkjp1RjgllxMANt544/pFamZmjT1SiIiF6e9zwHXATsCzrc1C6e9zafaFwKiKxUemaW3XeUFEjI+I8UOHDm1k+GZmvU7DkoKktSUNbB0G9gYeAaYCR6bZjgRuSMNTgSOU2QV4uaKZyczMukEjm4+GAddlPU3pC1wWETdLuhe4StKxwHzgoDT/TWTdUeeSdUk9uoGxmZlZFQ1LChHxJLBNlelLgL2qTA/gxEbFY2Zmq1e6K5rNzOydc1IwM7Ock4KZmeWcFMzMLNerH8dpzUlnqFu3V8RjW82K4iMFMzPLOSmYmVnOScHMzHJOCmZmlnNSMDOznJOCmZnlnBTMzCznpGBmZjknBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8v5yWvWdOJb3bzB07t5e2YFaviRgqQ1JD0gaVoa31TSXyTNlXSlpDXT9P5pfG4qH93o2MzMbGXd0Xx0MjCnYvwHwNkRMQZ4ETg2TT8WeDFNPzvNZ2Zm3aimpCBpyDtZuaSRwL8Av0zjAvYErkmzTAb2T8P7pXFS+V5pfjMz6ya1HincI+lqSRM6+UX9v8BXgbfS+BDgpYhYkcZbgBFpeASwACCVv5zmNzOzblJrUtgCuAA4HHhC0vckbdHRApL2BZ6LiPu6GGPb9Z4gaaakmYsXL67nqs3Mer2akkJkbomIQ4HjgSOBGZLulLRrO4t9EJgoaR5wBVmz0TnAIEmtvZ5GAgvT8EJgFEAqXw9YUiWWCyJifESMHzp0aC3hm5lZjWo+pyDpZEkzga8AXwA2AL4MXFZtmYg4LSJGRsRo4BDgDxHxWeB24IA025HADWl4ahonlf8hIqLzVTIzs3eq1usU7gYuAfaPiJaK6TMlnd/JbX4NuELSd4AHgAvT9AuBSyTNBV4gSyRmZtaNak0K34iIqyonSDowIq6OiNV2HY2IO4A70vCTwE5V5lkOHFhjPGZm1gC1nmg+tcq00+oZiJmZFa/DIwVJ+wATgBGSflxRtC6wovpSZmbWrFbXfPQ0MBOYCFR2LV0KnNKooKyLuvuaP/cHMCuNDpNCRDwIPCjp0ooLzszMrKRW13x0VUQcBDwgaZWfgxHx/oZFZmZm3W51zUcnp7/7NjoQMzMr3uqaj55Jf+d3TzhmZlakWq9o/pSkJyS9LOkVSUslvdLo4MzMrHvVevHaWcAnI2LOauc0M7OmVevFa886IZiZlV+tRwozJV0JXA/8s3ViRPymEUGZ9VZ+/rQVrdaksC7wKrB3xbQAnBTMzEqkpqQQEUc3OhAzMyteTUlB0q/IjgxWEhHH1D0iMzMrTK3NR9MqhgcA/0p2XyQzMyuRWpuPrq0cl3Q5ML0hEZmZWWFq7ZLa1lhgw3oGYmZmxav1nMJSsnMKSn8XkT1W08zMSqTW5qOBjQ7EzMyKV+uJZiS9HxhduYwvXjMzK5dam48uAt4PPAq8lSb74jUzs5Kp9Uhhl4jYqqGRmJlZ4WrtfXS3JCcFM7OSqzUpTCFLDI9LekjSw5Ie6mgBSQMkzZD0oKRHJZ2Rpm8q6S+S5kq6UtKaaXr/ND43lY/uUs3MzKzTam0+uhA4HHiYt88prM4/gT0jYpmkfsB0Sb8FvgScHRFXSDofOBY4L/19MSLGSDoE+AFwcCfqYmZmXVTrkcLiiJgaEU9FxPzWV0cLRGZZGu2XXgHsCVyTpk8G9k/D+6VxUvleklRjfGZmVge1Hik8IOky4EY68TwFSWsA9wFjgJ8CfwNeiogVaZYWYEQaHgEsSOtdIellYAjwfI0xmplZF9WaFNYiSwadep5CRLwJbCtpEHAd8J53EONKJJ0AnACw8cYbd3V1ZmZWoVuepxARL0m6HdgVGCSpbzpaGAksTLMtBEYBLZL6AusBS6qs6wLgAoDx48evcjtvMzN752o6p5B6DP1I0m8kTW19rWaZoekIAUlrAR8D5gC3Awek2Y4EbkjDU9M4qfwPEeEvfTOzblRr89H1ZD2QbqT23kfDgcnpvEIf4KqImCZpNnCFpO8AD6T1kv5eImku8AJwSI3bMTOzOqk1KSyPiB93ZsUR8RCwXZXpTwI7VZm+HDiwM9swM7P6qjUpnCPpdOD3rNz76P6GRGVmZoWoNSmMI7t4bU9WviHeno0IyszMilFrUjgQ2CwiXm9kMGZmVqxar2h+BBjUwDjMzKwHqPVIYRDwmKR7WfmcwsRGBGVmZsWoNSmc3tAozMysR6j1iuY7Gx1It+ru++z5GjwzaxIdJgVJS8l6Ga1SRHYj1HUbEpWZmRWiw6QQEQO7KxAzMyterb2PzMysF3BSMDOznJOCmZnlnBTMzCznpGBmZjknBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s1LClIGiXpdkmzJT0q6eQ0fbCkWyQ9kf6un6ZL0o8lzZX0kKTtGxWbmZlV18gjhRXAlyNiK2AX4ERJWwGnArdFxFjgtjQOsA8wNr1OAM5rYGxmZlZFw5JCRDwTEfen4aXAHGAEsB8wOc02Gdg/De8HTInMPcAgScMbFZ+Zma2qW84pSBoNbAf8BRgWEc+kokXAsDQ8AlhQsVhLmmZmZt2k4UlB0jrAtcAXI+KVyrKICKo/A7qj9Z0gaaakmYsXL65jpGZm1tCkIKkfWUK4NCJ+kyY/29oslP4+l6YvBEZVLD4yTVtJRFwQEeMjYvzQoUMbF7yZWS/UyN5HAi4E5kTEjyqKpgJHpuEjgRsqph+ReiHtArxc0cxkZmbdoG8D1/1B4HDgYUmz0rSvA5OAqyQdC8wHDkplNwETgLnAq8DRDYzNzMyqaFhSiIjpgNop3qvK/AGc2Kh4zKx48a1u3uDp3by9EvAVzWZmlnNSMDOznJOCmZnlnBTMzCznpGBmZjknBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWc5JwczMck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmOScFMzPLOSmYmVnOScHMzHINSwqSLpL0nKRHKqYNlnSLpCfS3/XTdEn6saS5kh6StH2j4jIzs/Y18kjhYuATbaadCtwWEWOB29I4wD7A2PQ6ATivgXGZmVk7+jZqxRHxR0mj20zeD9g9DU8G7gC+lqZPiYgA7pE0SNLwiHimUfGZmdVbfKubN3h6/VfZ3ecUhlV80S8ChqXhEcCCivla0rRVSDpB0kxJMxcvXty4SM3MeqHCTjSno4J4B8tdEBHjI2L80KFDGxCZmVnv1d1J4VlJwwHS3+fS9IXAqIr5RqZpZmbWjbo7KUwFjkzDRwI3VEw/IvVC2gV42ecTzMy6X8NONEu6nOyk8gaSWshOiUwCrpJ0LDAfOCjNfhMwAZgLvAoc3ai4zMysfY3sfXRoO0V7VZk3gBMbFYuZmdXGVzSbmVnOScHMzHJOCmZmlnNSMDOznJOCmZnlnBTMzCznpGBmZjknBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWc5JwczMck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmuR6VFCR9QtLjkuZKOrXoeMzMepsekxQkrQH8FNgH2Ao4VNJWxUZlZta79JikAOwEzI2IJyPideAKYL+CYzIz61X6Fh1AhRHAgorxFmDntjNJOgE4IY0uk/R4N8TWagPg+U4vJdU/ksZw/appjvqVuW7g+lX3zuu3SXsFPSkp1CQiLgAuKGLbkmZGxPgitt0dXL/mVea6gevXnXpS89FCYFTF+Mg0zczMuklPSgr3AmMlbSppTeAQYGrBMZmZ9So9pvkoIlZI+jzwO2AN4KKIeLTgsNoqpNmqG7l+zavMdQPXr9soIoqOwczMeoie1HxkZmYFc1IwM7Ock4IBIGl9Se8vOg7rPEl9JK1bdBxWDk4KHZB0sqR1lblQ0v2S9i46rnqRdEeq32DgfuAXkn5UdFz1IumsVL9+km6TtFjSYUXHVQ+SLkt1Wxt4BJgt6T+LjqteyrzvoGfXz0mhY8dExCvA3sD6wOHApGJDqqv1Uv0+BUyJiJ2BjxYcUz3tneq3LzAPGAOU5Ytzq1S3/YHfApuSfT7Losz7Dnpw/ZwUOtZ6DfkE4JLURbZprpuvQV9Jw4GDgGlFB9MA/dLffwGujoiXiwymzvpJ6keWFKZGxBtAmboSlnnfQQ+un5NCx+6T9HuypPA7SQOBtwqOqZ7OILsuZG5E3CtpM+CJgmOqp6mSHgN2AG6TNBRYXnBM9XI+2S/MtYE/StoEeKXQiOqrzPsOenD9fJ1COySJ7FYbQ4EnI+IlSUOAERHxULHRdV26VflJEXF20bE0gqQ+wC7AY8DLEfFman8fGBGLio2ua1LdDoiIqyqmCVgjIlYUF1l9lHnfQc+vn5NCByQ9HBHjio6jUSTNiIidio6jUSQ9EBHbFR1HI/SkG6g1Qpn3HfTs+rn5qGP3S9qx6CAa6E+SzpX0IUnbt76KDqqObpP06fQrumxulfQVSaMkDW59FR1UHZV530EPrp+PFDqQ2vzGAPOBf5CdZI6IKEV/fkm3V5kcEbFntwfTAJKWkrW5ryBrr23df03fp1/SU1UmR0Rs1u3BNECZ9x307Po5KXQgnbxbRUTM7+5YzMy6Q4+5S2pP1PrlL2lDYEDB4TSEpH8BtqaifhFxZnER1Zek9YGxrFy/PxYXUf1Ieh/Z88wr6zaluIjqq8z7Dnpu/ZwUOiBpIvBDYCPgObJH2M0h+xJtepLOB94F7AH8EjgAmFFoUHUk6TjgZLJeZLPIenzcDTR985ik04HdyZLCTcA+wHSgFEmhzPsOenb9fKK5Y98m21l/jYhNgb2Ae4oNqa4+EBFHAC9GxBnArsAWBcdUTycDOwLzI2IPYDvgpUIjqp8DyD6PiyLiaGAbYL1iQ6qrMu876MH1c1Lo2BsRsQToI6lPRNwOlKkb4Gvp76uSNgLeAIYXGE+9LY+I5QCS+kfEY8CWBcdUL69FxFvAinQzvOdY+XG2za7M+w56cP3cfNSxlyStA9wFXCrpObJeSGUxTdIg4L/JbogXZM1IZdGS6nc9cIukF8l6kpXBzFS3XwD3AcvImh/Kosz7Dnpw/dz7qAPpKsPXyI6oPkt2eH5pOnooFUn9gQE96R4s9STpI2T77+aIeL3oeOpJ0mhg3TJcaV9Nmfcd9Lz6OSmsRuqWOjYibpX0LrJbCSwtOq56SPX5MrBxRBwvaSywZUSU5uZ4knYj23+/SveXWSciqvXxbyrpoqfPAptFxJmSNgbeHRFl6ihQyn3XqqfWz+cUOiDpeOAa4Odp0giyw72y+BXwT7ITzAALge8UF059pR46XwNOS5P6Ab8uLqK6+hnZfjs0jS8FflpcOPVV8n3Xo+vnpNCxE4EPku4+GRFPABsWGlF9bR4RZ5GdYCYiXqVctwb/V2Ai6TxQRDwNDCw0ovrZOSJOJN1ZMyJeBNYsNqS6KvO+gx5cPyeFjv2zso1PUl/Kdc/61yWtRaqTpM3JjhzK4vXI2kdb67d2wfHU0xvpTretdRtKuW7rXuZ9Bz24fk4KVaR/NoA7JX0dWEvSx4CrgRuLi6w+JO2SBr8F3AyMknQpcBvw1aLiqhe9/bziqyT9HBiUmgJvJeut07TSSWWAHwPXARtK+i7ZhWvfKyqueinzvoPmqJ9PNFch6UHgc8BfgGPJHscpsgfS/DKa/E2TdD9wL1mb5hpkF+gJuCcini8ytnqQ9DfgvyLiipTM8/0XEbcUG13XSJpL1m34f8hu1rgXWd1ui4g5RcZWD2Xed9Ac9XNSqELSzsBPgAeBr6b22tJID/k4CfgP4NsRcUnBIdVV6jH2v8A6wH+kc0GloOzpf2eS3Q7h8xFxV8Eh1VWZ9x00R/2cFNqRuvx9DvgK2YPR8/baiDipqLjqSdJWZBc89SFr2+wxt++tB0n7ABeTHRVV7r+JRcVUL5J2IGvuayGrW9lu617afQc9u36+orl9g8nuTbKY7IrRMp3EQ9KxwKnAfwE/bfYmsbYkbUmW0O8i66pZmv0naU/gHLJmpFLVDcq976Dn189JoQpJnwP+k+z2D8eW8Avzz2QPff9QT3gmbL1JmgTsB5wSETcXHU89SbqC7M6an4mIh4uOp97KvO+gOernpFDdbsCuEfFc0YE0yDcj4taig2igFcB2rTccK5lbI6JM96dqq8z7Dpqgfj6nYGZmOV+nYGZmOScFMzPLOSl0Unq+QlOTdJKkMj2QZbUkNf3Vvq0krSPpAEmnpH35iXTtSSmVad+1lZ7T3KP4nEInSfp7RGxcdBxdIellshtx/Q24HLg6IhYXG1X9SPpx20nA4aTnFzfzdSaSDiLrzvgQ2bO1/0z2424c8Nlm75FU5n1XjaT7I2L7ouOo5N5HVUj6UntFZFciNrsngR2AjwIHA2dIuo8sQfymBM+L+FfgTuD3vH3X10PIrjdpdt8AdomIVyVtQPbQp49Lej/ZLd4/UGx4XVbmfVdNj7srsY8UqpC0nOwahRVVik+JiEHdG1F9tf11IqkfsA/Zvfk/GhFDCwuuDtKtIL5Ndpvzr0TE05KejIjNCg6tyyQ9DLw/IiLd4fbPEbFdKnskIt5XbIRdU+Z910rSEa2DwBnAN1vLImJKIUFV8JFCdfcD10fEKr9OJB1XQDz1ttKvk4h4A5gKTE1PY2tq6Ujni+lWEJdK+j/Kc/7sJuBmSX8EPkF2514kDaYH/ursrJLvu1abVgz3B0aTblNSSDRt+EihinQZ+pJqdwyVNCwini0grLqRtEVE/LXoOLpDuofVf5BdjHhY0fHUg6QJwFbAg6131kwnmvtFRGmeh1HGfddWTzyn4KRgK5G0TkQsKzoOq52kiRExteg4GiE9f2As8GTZ7lYMIOmB1ua/nqJsh2UNJ+mComNosNlFB9BVkkZJukLSXZK+ns6ZtJZdX2BoXSbpU21enwYuaB0vOr6ukvTrdAIdSR8HHgF+AMySdGChwTXG4UUH0JbPKVSR2merFgETujOWRugFvasuAq4F7iF7SNKdkj4ZEUuATQqNrOuuJHvYU2UX4rWBT5K1Sf+miKDqaJuKZtvTgQ9HxLyUKG4jnUMpi4h4pOgY2nJSqG4xMJ+VT9y1Pm9gw0Iiqq/v0X7vqjIcPQ6NiPPT8BckHQb8UdJEesjJvC74ADAJmBER5wFI2j0iji42rLrpI2ndiHiF7JbSfweIiOeVPSO9qUn6ROvdUSWtB/yI7Bb9j5D1bCz8fGXTv8kN8iSwV0T8vW2BpAUFxFNvZe9d1U/SgNY7UUbEryUtIvuF3WMekP5ORMS96TGOX5B0O9kjVZs90VU6A7hd0k+BPwFXS5pKdqFej7zVdCd9j7fr8UPgGbKjvE+RXWeyfzFhvc0nmquQdCIwPSIerFL2hYj4SQFh1U0v6F11CnB/RNzZZvp2wFkR8bFiIqsvSRuRPdpxfMn68Y8Bjge2IPvh2kL2I+Z3hQZWB5W9jSTNiohtK8pWGi+Kk4KZWTeR1ELWZCTgRGDz1od4SXqoJzxOtQztx2ZmzeIXwECyDh2TgdaeVu8GZhUX1tt8pGBmZjkfKZiZdRNJO6cL8pC0lqQzJN0o6QepN1LhnBRqJOlbRcfQSJLuLzqGRpI0regYGsX7rqlcBLyahs8B1iO7OO9V4FdFBVXJXVJrNxH4VtFBNFDT30xtNUYUHUADed81jz4R0Xp90PiK+x5NlzSroJhW4iOF2pX9H+//ig6gwR4oOoAG8r5rHo9Iar3Q8EFJ4yG7SSXwRnFhvc0nmmskqU9EvFV0HGbWvNJ5g3OADwHPA9sDC9LrpGrXRnU3JwUzs26WTjZvSro4ryddMOqkYGZmOZ9TMDOznJNCDSTtJulLkvYuOpZ6kLSupO9LukTSZ9qU/ayouOpF0nhJt6d784+SdIuklyXdm+5/1LQkfaJieD1JF0p6SNJlkoYVGVs9pDpNkvSYpBckLZE0J00bVHR8vYGTQhWSZlQMHw+cS3Zp+umSTi0ssPr5FVlvqmuBQyRdK6l/KtuluLDq5mfAWWS9cv4M/Dwi1gNOTWXN7HsVw5V32byX7C6bze4q4EVg94gYHBFDyO6Q+mIqswbzOYUqKh+RJ+leYEJELJa0NnBPRIwrNsKuqXJ3xv8ie3jQROCWnvbM2M5qs//+HhEbVytrRs1wl82ukPR4RGzZ2TKrH1+8Vl0fSeuTHUkpIhYDRMQ/JFV7ME2z6V/ZxTYivitpIfBHyvHkteWpqW89ICTtHxHXS/oI8GbBsXXVhunJeQLWlaR4+5ddGY7850v6KjC5tUdOahY7iqzbpjVYGT5EjbAecB8wExgsaThkD7WnHBex3QjsWTkhIi4Gvgy8XkRAdfY5srocA3wc2EPSS2RNRycXGFc99Pi7bHbRwcAQskeoviDpBeAOYDBwUJGB9RZuPuoESe8ChkXEU0XHYmbWCD5S6ISIeLXsCaHiEvxSKkP9JL1H0l7pyLVy+ifaW6ZZla3nXzNwUrC2zig6gAZr6vpJOgm4AfgC2X109qso/l71pZpHL+j51+P5RHMvJOmh9oqAMvR1L3P9jgd2iIhlkkYD10gaHRHnUI7zXf0qhk8APpZ6/v0PcA8wqZiweg8nhd5pGNkJ2BfbTBdZv/5mV+b69YmIZQARMU/S7mSJYRPKkRTK3vOvx3NS6J2mAetExKy2BZLu6PZo6q/M9XtW0ratdUtHDPuSPbylqa+fSVp7/omsO/HwiHimRD3/ejz3PjJrIpJGAisiYlGVsg9GxJ8KCKvh3POv+zgpmJlZzr2PzMws56RgZmY5JwXrtSTtLykkvafoWMx6CicF680OBaanv91Gknv9WY/lpGC9UuriuBtwLHBIxfQ+kn6WHvJyi6SbJB2QynaQdKek+yT9rvVGiW3Wu7mkeyQ9LOk7kpal6btLukvSVGC2pAGSfpXme0DSHmm+oySdW7G+aelaBCQtk3S2pEcl3SZpaOPeIeutnBSst9oPuDki/goskbRDmv4pYDSwFXA4sCuApH7AT4ADImIHsusCvltlvecA56RnbrS0KdseODkitgBOBCLNdygwWdKA1cS8NjAzIrYG7gRO70R9zWripGC91aHAFWn4Ct5uQtoNuDoi3krXAtyepm8JvA+4RdIs4BvAyCrr3RW4Og1f1qZsRkU/+92AXwNExGPAfGCL1cT8FnBlGv51WodZXblt03odSYPJnicxTlIAa5BdPfufHS0GPBoRu3Zh0/+oYZ4VrPxjraOjB19kZHXnIwXrjQ4ALomITSJidESMAp4CPgT8Cfh0OrcwDNg9LfM4MFRS3pwkaesq674H+HQaPqRKeau7gM+mdW0BbJy2MQ/YNm1/FLBTxTJ9UuwAnyE7SW5WV04K1hsdClzXZtq1afq1ZOcCZpM10dwPvBwRr5N9If9A0oNkTzn7QJV1fxH4UrpT6xjg5XZi+BnZzd8eJmsSOioi/kmWlJ5K2/9x2n6rfwA7SXqE7EjnzNqrbFYb3+bCrA1J66QbzQ0BZgAfrHavoXaWfRfwWkSEpEOAQyNiv9UtV+O6l0VEGZ6hbT2YzymYrWqapEHAmsC3a00IyQ7AuZIEvET2nGizpuEjBTMzy/mcgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWc5JwczMcv8f6aSyDy2sRg4AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the color palette\n",
        "colors = ['#FF4136', '#2ECC40']\n",
        "\n",
        "# Calculate percentages\n",
        "total_counts = counts.sum(axis=1)\n",
        "percentages = counts.divide(total_counts, axis=0) * 100\n",
        "\n",
        "# Plot bar chart\n",
        "ax = percentages.plot(kind='bar', stacked=True, color=colors, figsize=(12, 8))\n",
        "\n",
        "# Add percentages as text labels\n",
        "for i in ax.containers:\n",
        "    ax.bar_label(i, label_type='edge', labels=[f'{int(j)}%' for j in i.datavalues])\n",
        "\n",
        "# Set the title and axis labels\n",
        "plt.title('Vaccine Effectiveness by age group', fontsize=20)\n",
        "plt.xlabel('Age group', fontsize=16)\n",
        "plt.ylabel('Immunity Count', fontsize=16)\n",
        "\n",
        "# Set Margins\n",
        "plt.margins(x=0.2, y=0.2)\n",
        "\n",
        "# Set the legend\n",
        "ax.legend([\"Vaccine wasn't effective\", 'Vaccine was effective'])\n",
        "\n",
        "# Show plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "kcMcjEVqT9Ih",
        "outputId": "3b0c5b34-1ef5-46a7-f538-67ed54fdf054"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Machine Learning Techniques"
      ],
      "metadata": {
        "id": "Ptz6Sqy0D-Nc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Data Preprocessing for Algorithms"
      ],
      "metadata": {
        "id": "u_lGbFGLQLuO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['AgeGroupVacc'].value_counts())\n",
        "print(\"_____________________________\")\n",
        "print(len(df['AgeGroupVacc'].unique()))"
      ],
      "metadata": {
        "id": "PS0e9htbRoJz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "51566845-26cc-40fc-fb6d-82a1f19828a6"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "25 - 49 Years    645\n",
            "50 - 64 Years    645\n",
            "65+ Years        643\n",
            "18 - 24 Years    469\n",
            "12 - 17 Years    378\n",
            "5 - 11 Years     264\n",
            "Name: AgeGroupVacc, dtype: int64\n",
            "_____________________________\n",
            "6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "\n",
        "le = preprocessing.LabelEncoder()\n",
        "df['AgeGroupVacc'] = le.fit_transform(df.AgeGroupVacc.values)"
      ],
      "metadata": {
        "id": "05SKVjjcRqHK"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "id": "AWDept0KRseq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "a7f4a361-3d28-47bb-abf8-a02951484436"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   AgeGroupVacc  7-day_avg_group_cases_per_100k  \\\n",
              "0             2                        4.375377   \n",
              "1             1                        4.940567   \n",
              "2             2                        5.533741   \n",
              "3             4                        4.938932   \n",
              "4             5                        6.052993   \n",
              "\n",
              "   Administered_Dose1_pct_agegroup  Series_Complete_Pop_pct_agegroup  Immunity  \n",
              "0                            0.840                             0.710         1  \n",
              "1                            0.805                             0.655         1  \n",
              "2                            0.840                             0.710         1  \n",
              "3                            0.946                             0.827         1  \n",
              "4                            0.950                             0.928         1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2abb7354-9a24-43da-ae22-64b5d8434bd1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>AgeGroupVacc</th>\n",
              "      <th>7-day_avg_group_cases_per_100k</th>\n",
              "      <th>Administered_Dose1_pct_agegroup</th>\n",
              "      <th>Series_Complete_Pop_pct_agegroup</th>\n",
              "      <th>Immunity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>4.375377</td>\n",
              "      <td>0.840</td>\n",
              "      <td>0.710</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>4.940567</td>\n",
              "      <td>0.805</td>\n",
              "      <td>0.655</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>5.533741</td>\n",
              "      <td>0.840</td>\n",
              "      <td>0.710</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.938932</td>\n",
              "      <td>0.946</td>\n",
              "      <td>0.827</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>6.052993</td>\n",
              "      <td>0.950</td>\n",
              "      <td>0.928</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2abb7354-9a24-43da-ae22-64b5d8434bd1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2abb7354-9a24-43da-ae22-64b5d8434bd1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2abb7354-9a24-43da-ae22-64b5d8434bd1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Support Vector machine"
      ],
      "metadata": {
        "id": "O8XS0Y1oS7GP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Seperating features and labels\n",
        "X = df.iloc[:, :4].values\n",
        "y = df.iloc[:, -1].values\n",
        "\n",
        "\n",
        "# split the data using Scikit-Learn's train_test_split\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y)"
      ],
      "metadata": {
        "id": "05AxbbuaO5Ad"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Import svm model\n",
        "from sklearn import svm\n",
        "\n",
        "#Create a svm Classifier\n",
        "clf = svm.SVC(kernel='linear') # Linear Kernel\n",
        "# clf = svm.SVC(kernel='rbf', C=10, gamma=0.001)\n",
        "\n",
        "#Train the model using the training sets\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "#Predict the response for test dataset\n",
        "y_pred = clf.predict(X_test)"
      ],
      "metadata": {
        "id": "KQ_M_08gTap3"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Import scikit-learn metrics module for accuracy calculation\n",
        "from sklearn import metrics\n",
        "\n",
        "SVM_acc = metrics.accuracy_score(y_test, y_pred)*100\n",
        "# Model Accuracy: how often is the classifier correct?\n",
        "print(\"Accuracy:\", SVM_acc)"
      ],
      "metadata": {
        "id": "Da0AZX0wTgOU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24ee21df-d60f-4c3f-b225-53ba9582fe5d"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 96.97766097240473\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "id": "SY6neu1zDbn1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a922f819-845a-4f39-83c6-302754256a6e"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.96      0.97       433\n",
            "           1       0.95      0.98      0.97       328\n",
            "\n",
            "    accuracy                           0.97       761\n",
            "   macro avg       0.97      0.97      0.97       761\n",
            "weighted avg       0.97      0.97      0.97       761\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "SVM_f1 = f1_score(y_test, y_pred)*100\n",
        "\n",
        "SVM_f1"
      ],
      "metadata": {
        "id": "-mOsBQhUjsYT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da864291-0fe2-4c76-bc74-8ae7925e6dfa"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "96.54135338345866"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Logistics Regression/Linear Classification"
      ],
      "metadata": {
        "id": "7hFAT8mNTj3W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Seperating features and labels\n",
        "X = df.iloc[:, :4].values\n",
        "y = df.iloc[:, -1].values\n",
        "\n",
        "\n",
        "# split the data using Scikit-Learn's train_test_split\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y)"
      ],
      "metadata": {
        "id": "-7Io0q8lTiTh"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# log_Regressor = LogisticRegression()\n",
        "# log_Regressor.fit(X_train , y_train)"
      ],
      "metadata": {
        "id": "LmPxxY31T3VJ"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.linear_model import LogisticRegression\n",
        "# from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# # Define the hyperparameters to tune\n",
        "# hyperparameters = {\n",
        "#     'penalty': ['l1', 'l2'],\n",
        "#     'C': [0.01, 0.1, 1, 10, 100]\n",
        "# }\n",
        "\n",
        "# # Create a logistic regression model\n",
        "# log_Regressor = LogisticRegression()\n",
        "\n",
        "# # Use GridSearchCV to tune the hyperparameters\n",
        "# grid_search = GridSearchCV(log_Regressor, hyperparameters, cv=5, n_jobs=-1)\n",
        "# grid_search.fit(X_train, y_train)\n",
        "\n",
        "# # Print the best hyperparameters\n",
        "# print('Best Penalty:', grid_search.best_estimator_.get_params()['penalty'])\n",
        "# print('Best C:', grid_search.best_estimator_.get_params()['C'])\n",
        "\n",
        "# # Use the best hyperparameters to create a new logistic regression model\n",
        "# log_Regressor = LogisticRegression(penalty=grid_search.best_estimator_.get_params()['penalty'],\n",
        "#                                     C=grid_search.best_estimator_.get_params()['C'])\n",
        "# log_Regressor.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "h-BmAxXZ7-2d"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Evaluate the accuracy of the model\n",
        "# LR_acc = log_Regressor.score(X_test, y_test)*100\n",
        "# print('Accuracy:', LR_acc)\n",
        "\n",
        "#Import scikit-learn metrics module for accuracy calculation\n",
        "from sklearn import metrics\n",
        "\n",
        "LR_acc = metrics.accuracy_score(y_test, y_pred)*100\n",
        "# Model Accuracy: how often is the classifier correct?\n",
        "print(\"Accuracy:\", LR_acc)"
      ],
      "metadata": {
        "id": "pIe1I0dlUBoa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6160c7ff-78a6-4238-c99e-55d252c41f8b"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 51.64257555847569\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "id": "7KD3qrb-DgEg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16818722-6b8d-4393-d6de-4ccbb5e4b539"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.59      0.56      0.58       442\n",
            "           1       0.43      0.45      0.44       319\n",
            "\n",
            "    accuracy                           0.52       761\n",
            "   macro avg       0.51      0.51      0.51       761\n",
            "weighted avg       0.52      0.52      0.52       761\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "LR_f1 = f1_score(y_test, y_pred)*100\n",
        "\n",
        "LR_f1"
      ],
      "metadata": {
        "id": "PhCNxxI6kmK8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0b4d3f0-77ca-484d-bdce-9a17f9c0063c"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "43.90243902439025"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Gaussian Naive Bayes"
      ],
      "metadata": {
        "id": "Zj1XJjyAUQhm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Seperating features and labels\n",
        "X = df.iloc[:, :1].values\n",
        "y = df.iloc[:, -1].values\n",
        "\n",
        "\n",
        "# split the data using Scikit-Learn's train_test_split\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y)"
      ],
      "metadata": {
        "id": "LHjJya5Ciizl"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# training the model on training set\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "gnb = GaussianNB()\n",
        "gnb.fit(X_train, y_train)\n",
        " \n",
        "# making predictions on the testing set\n",
        "gnb_y_pred = gnb.predict(X_test)\n",
        "\n",
        "NB_acc = metrics.accuracy_score(y_test, gnb_y_pred)*100\n",
        "# Accuracy of the model\n",
        "print(\"ACCURACY OF THE MODEL: \", NB_acc)"
      ],
      "metadata": {
        "id": "6gzswgaCULgU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f5db3a7-800e-4e9f-dd97-24782e8bc7f2"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ACCURACY OF THE MODEL:  79.5006570302234\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(y_test, gnb_y_pred))"
      ],
      "metadata": {
        "id": "VB5UWM-NDhkf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83dc74ea-3713-45a6-8c02-19422707e469"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.85      0.82       423\n",
            "           1       0.79      0.73      0.76       338\n",
            "\n",
            "    accuracy                           0.80       761\n",
            "   macro avg       0.79      0.79      0.79       761\n",
            "weighted avg       0.79      0.80      0.79       761\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "GNB_f1 = f1_score(y_test, gnb_y_pred)*100\n",
        "\n",
        "GNB_f1"
      ],
      "metadata": {
        "id": "TeagvDQ9kxqV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ec39baf-4fb0-41cb-b311-85b0bf9d0af3"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "76.0"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##XG Boost"
      ],
      "metadata": {
        "id": "2YKvk0V3Vpoj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import datasets\n",
        "from sklearn import metrics\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.style.use(\"ggplot\")\n",
        "\n",
        "import xgboost as xgb"
      ],
      "metadata": {
        "id": "dzk2QoaVWX-n"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)"
      ],
      "metadata": {
        "id": "_b9R25lVVnId"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# model = xgb.XGBClassifier(learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.5, colsample_bytree=0.5, random_state=42)\n",
        "model = xgb.XGBClassifier()\n",
        "model.fit(X_train, y_train)\n",
        "print(); print(model)"
      ],
      "metadata": {
        "id": "cA0XKr5sWIzt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2271826c-5786-48da-bba3-83d92d4716f1"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
            "              colsample_bylevel=None, colsample_bynode=None,\n",
            "              colsample_bytree=None, early_stopping_rounds=None,\n",
            "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
            "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
            "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
            "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
            "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
            "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
            "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
            "              predictor=None, random_state=None, ...)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "expected_y  = y_test\n",
        "predicted_y = model.predict(X_test)\n",
        "\n",
        "print(metrics.classification_report(expected_y, predicted_y))"
      ],
      "metadata": {
        "id": "BEUZqio0Wu-u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d03ba9e-7855-4477-a072-e9e7ae0bc333"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.84      0.83       423\n",
            "           1       0.79      0.75      0.77       338\n",
            "\n",
            "    accuracy                           0.80       761\n",
            "   macro avg       0.80      0.80      0.80       761\n",
            "weighted avg       0.80      0.80      0.80       761\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "XG_acc = metrics.accuracy_score(expected_y, predicted_y)*100\n",
        "\n",
        "print(\"ACCURACY OF THE MODEL: \", XG_acc)"
      ],
      "metadata": {
        "id": "ZFXFy0wmfyqf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bda44fe4-6437-4421-960d-f11cea033c38"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ACCURACY OF THE MODEL:  80.15768725361366\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "XG_f1 = f1_score(expected_y, predicted_y)*100\n",
        "\n",
        "XG_f1"
      ],
      "metadata": {
        "id": "L9kJmmxKlA1p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e09f65ca-68a1-4b51-fc4e-93385ccb39a3"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "77.01674277016744"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Random Forest"
      ],
      "metadata": {
        "id": "D-H1FNjDA_p0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Seperating features and labels\n",
        "X = df.iloc[:, :4].values\n",
        "y = df.iloc[:, -1].values\n",
        "\n",
        "# split the data using Scikit-Learn's train_test_split\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y)"
      ],
      "metadata": {
        "id": "WfTfNPUqBD9x"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# importing random forest classifier from assemble module\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "\n",
        "# creating a RF classifier\n",
        "# clf = RandomForestClassifier(n_estimators = 1, min_samples_split=850, min_samples_leaf=700, max_depth=1) \n",
        "clf = RandomForestClassifier() \n",
        "\n",
        "# Training the model on the training dataset\n",
        "clf.fit(X_train, y_train)\n",
        " \n",
        "# performing predictions on the test dataset\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "# metrics are used to find accuracy or error\n",
        "from sklearn import metrics \n",
        "print()\n",
        "\n",
        "RF_acc = metrics.accuracy_score(y_test, y_pred)*100\n",
        "# using metrics module for accuracy calculation\n",
        "print(\"ACCURACY OF THE MODEL: \", RF_acc)"
      ],
      "metadata": {
        "id": "Bv5ZtEvTBD3y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8efea4ca-feb7-4882-a31d-36f5c130444a"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "ACCURACY OF THE MODEL:  100.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "id": "yQXF8EXpBO4Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81003a75-d27f-4a05-f454-a70d1fd3c860"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       427\n",
            "           1       1.00      1.00      1.00       334\n",
            "\n",
            "    accuracy                           1.00       761\n",
            "   macro avg       1.00      1.00      1.00       761\n",
            "weighted avg       1.00      1.00      1.00       761\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "RF_f1 = f1_score(expected_y, predicted_y)*100\n",
        "\n",
        "RF_f1"
      ],
      "metadata": {
        "id": "CyID_xtQlPtv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3900abb-b6c6-4b30-a934-cb5d3fcdf88b"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "77.01674277016744"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Plotting Results of all algorithms"
      ],
      "metadata": {
        "id": "-JVk-CyIfaeJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from matplotlib.pyplot import figure\n",
        "\n",
        "figure(figsize=(10,6), dpi=80)\n",
        "\n",
        "# Create an array of 5 values\n",
        "data = np.round(np.array([RF_acc, LR_acc, NB_acc, SVM_acc, XG_acc]))\n",
        "\n",
        "# Specify the x-axis labels\n",
        "labels = ['RF', 'LR', 'GNB', 'SVM', 'XGBoost']\n",
        "\n",
        "# Colors\n",
        "colors = ['#eab676']\n",
        "\n",
        "# Create the histogram plot\n",
        "plt.bar(labels, data, color=colors, width=0.34)\n",
        "\n",
        "# Add values to each bar\n",
        "for i, v in enumerate(data):\n",
        "    plt.text(i, v, str(v), color='black', ha='center')\n",
        "\n",
        "# Add title and labels\n",
        "plt.title(\"Accuracies of all 5 models\", size=16, fontweight=\"bold\")\n",
        "\n",
        "# Set Margins\n",
        "plt.margins(x=0.1, y=0.1)\n",
        "\n",
        "# Hide grid lines\n",
        "# plt.grid(False)\n",
        "\n",
        "# Hide axes ticks\n",
        "# plt.xticks([])\n",
        "plt.yticks([0, 20, 40, 60, 80, 100])\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "o_mGhhCSjk0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "outputId": "29442abd-f521-494d-cc84-bc50e8ab13de"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a list of model names and their F1 scores\n",
        "model_names = ['RF', 'LR', 'GNB', 'SVM', 'XGBoost']\n",
        "f1_scores = [RF_f1, LR_f1, GNB_f1, SVM_f1, XG_f1]\n",
        "\n",
        "# Create a horizontal bar plot\n",
        "fig, ax = plt.subplots(figsize=(10, 8))\n",
        "\n",
        "# Define gradient color\n",
        "colors = plt.cm.Reds(np.linspace(0.3, 1, len(model_names)))\n",
        "\n",
        "# Add f1 score labels on bars\n",
        "for i, v in enumerate(f1_scores):\n",
        "    ax.text(v + 0.01, i, str(round(v, 2)), color='black')\n",
        "\n",
        "# Plot Horizontal bar\n",
        "ax.barh(model_names, f1_scores, align='center', color=colors, height=0.5)\n",
        "\n",
        "# Add axis labels and title\n",
        "ax.set_xlabel('F1 Scores')\n",
        "ax.set_ylabel('Models')\n",
        "ax.set_title('F1 Score Comparison of all 5 Models', size=16)\n",
        "\n",
        "# Set Margins\n",
        "ax.margins(x=0.12, y=0.12)\n",
        "\n",
        "# Set x-axis limits and tick labels\n",
        "ax.tick_params(axis='x', labelsize=10)\n",
        "\n",
        "# Hide top and right spines\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['right'].set_visible(False)\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "TlLrZsJjldjA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "outputId": "91070897-a474-4b98-d64a-f5db7f91462d"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "figure(figsize=(8,6), dpi=80)\n",
        "ax = sns.distplot(df['Series_Complete_Pop_pct_agegroup'][df.Immunity == 1], color = 'darkturquoise', rug=True)\n",
        "sns.distplot(df['Series_Complete_Pop_pct_agegroup'][df.Immunity == 0], color = 'lightcoral', rug=True)\n",
        "plt.legend(['Bad Immunity without Vaccination', \"Good Immunity with Vaccination\"])"
      ],
      "metadata": {
        "id": "6WgiKOZ4s3v1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "38712b4b-e9ab-475a-d451-a056092ed026"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fb43e7cb580>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "figure(figsize=(10,6), dpi=80)\n",
        "\n",
        "model_names = ['RF', 'LR', 'GNB', 'SVM', 'XGBoost']\n",
        "f1_scores = [RF_f1, LR_f1, GNB_f1, SVM_f1, XG_f1]\n",
        "\n",
        "# Create a dictionary to store the data\n",
        "data = {'Model': model_names, 'F1 Score': f1_scores}\n",
        "\n",
        "# Convert data to pandas dataframe\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Create a bar plot using seaborn\n",
        "sns.set(style=\"whitegrid\")\n",
        "sns.barplot(x=\"Model\", y=\"F1 Score\", data=df, color=\"salmon\", palette=\"Blues_d\", saturation=0.7)\n",
        "\n",
        "# Add title and axis labels\n",
        "plt.title('F1 Scores of 5 ML Models')\n",
        "plt.xlabel('Model')\n",
        "plt.ylabel('F1 Score')\n",
        "\n",
        "# Show plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "7Y4ylvDswNqU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "outputId": "3574f1a1-0d05-4e63-d4da-16b056086a30"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NNGqrXDavWLl"
      },
      "execution_count": 68,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "O8XS0Y1oS7GP",
        "7hFAT8mNTj3W",
        "Zj1XJjyAUQhm",
        "2YKvk0V3Vpoj"
      ]
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}