UseCaseCovid19_DataScience.ipynb

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        "<a href=\"https://colab.research.google.com/github/arianza1210/Data_Analyst/blob/main/UseCaseCovid19_DataScience.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "UseCase Covid 19"
      ],
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        "id": "q0HAVnKUKZJI"
      }
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      "execution_count": 14,
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      "outputs": [
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          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "ATA-6JQhKr25"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "DATA PREPARATION"
      ],
      "metadata": {
        "id": "6dWXYOlsL1pW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('/content/drive/MyDrive/Dataset/owid-covid-data.csv')"
      ],
      "metadata": {
        "id": "rkJpLpgoK9hV"
      },
      "execution_count": 16,
      "outputs": []
    },
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      "source": [
        "df.head()"
      ],
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        "id": "eP6h-qVILELc",
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      "execution_count": 17,
      "outputs": [
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              "  iso_code continent     location        date  total_cases  new_cases  \\\n",
              "0      AFG      Asia  Afghanistan  2020-02-24          1.0        1.0   \n",
              "1      AFG      Asia  Afghanistan  2020-02-25          1.0        0.0   \n",
              "2      AFG      Asia  Afghanistan  2020-02-26          1.0        0.0   \n",
              "3      AFG      Asia  Afghanistan  2020-02-27          1.0        0.0   \n",
              "4      AFG      Asia  Afghanistan  2020-02-28          1.0        0.0   \n",
              "\n",
              "   new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  ...  \\\n",
              "0                 NaN           NaN         NaN                  NaN  ...   \n",
              "1                 NaN           NaN         NaN                  NaN  ...   \n",
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              "3                 NaN           NaN         NaN                  NaN  ...   \n",
              "4                 NaN           NaN         NaN                  NaN  ...   \n",
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              "   gdp_per_capita  extreme_poverty  cardiovasc_death_rate  \\\n",
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              "   diabetes_prevalence  female_smokers  male_smokers  handwashing_facilities  \\\n",
              "0                 9.59             NaN           NaN                  37.746   \n",
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              "3                 9.59             NaN           NaN                  37.746   \n",
              "4                 9.59             NaN           NaN                  37.746   \n",
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              "0                         0.5            64.83                    0.511  \n",
              "1                         0.5            64.83                    0.511  \n",
              "2                         0.5            64.83                    0.511  \n",
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              "      iso_code continent     location        date  total_cases  new_cases  \\\n",
              "0          AFG      Asia  Afghanistan  2020-02-24          1.0        1.0   \n",
              "1          AFG      Asia  Afghanistan  2020-02-25          1.0        0.0   \n",
              "2          AFG      Asia  Afghanistan  2020-02-26          1.0        0.0   \n",
              "3          AFG      Asia  Afghanistan  2020-02-27          1.0        0.0   \n",
              "4          AFG      Asia  Afghanistan  2020-02-28          1.0        0.0   \n",
              "...        ...       ...          ...         ...          ...        ...   \n",
              "73991      ZWE    Africa     Zimbabwe  2021-03-06      36260.0       12.0   \n",
              "73992      ZWE    Africa     Zimbabwe  2021-03-07      36271.0       11.0   \n",
              "73993      ZWE    Africa     Zimbabwe  2021-03-08      36289.0       18.0   \n",
              "73994      ZWE    Africa     Zimbabwe  2021-03-09      36321.0       32.0   \n",
              "73995      ZWE    Africa     Zimbabwe  2021-03-10      36341.0       20.0   \n",
              "\n",
              "       new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  ...  \\\n",
              "0                     NaN           NaN         NaN                  NaN  ...   \n",
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              "...                   ...           ...         ...                  ...  ...   \n",
              "73991              28.857        1485.0         1.0                3.143  ...   \n",
              "73992              26.000        1485.0         0.0                3.143  ...   \n",
              "73993              24.857        1487.0         2.0                2.714  ...   \n",
              "73994              24.714        1489.0         2.0                2.429  ...   \n",
              "73995              23.143        1489.0         0.0                1.571  ...   \n",
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              "       gdp_per_capita  extreme_poverty  cardiovasc_death_rate  \\\n",
              "0            1803.987              NaN                597.029   \n",
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              "...               ...              ...                    ...   \n",
              "73991        1899.775             21.4                307.846   \n",
              "73992        1899.775             21.4                307.846   \n",
              "73993        1899.775             21.4                307.846   \n",
              "73994        1899.775             21.4                307.846   \n",
              "73995        1899.775             21.4                307.846   \n",
              "\n",
              "       diabetes_prevalence  female_smokers  male_smokers  \\\n",
              "0                     9.59             NaN           NaN   \n",
              "1                     9.59             NaN           NaN   \n",
              "2                     9.59             NaN           NaN   \n",
              "3                     9.59             NaN           NaN   \n",
              "4                     9.59             NaN           NaN   \n",
              "...                    ...             ...           ...   \n",
              "73991                 1.82             1.6          30.7   \n",
              "73992                 1.82             1.6          30.7   \n",
              "73993                 1.82             1.6          30.7   \n",
              "73994                 1.82             1.6          30.7   \n",
              "73995                 1.82             1.6          30.7   \n",
              "\n",
              "       handwashing_facilities  hospital_beds_per_thousand  life_expectancy  \\\n",
              "0                      37.746                         0.5            64.83   \n",
              "1                      37.746                         0.5            64.83   \n",
              "2                      37.746                         0.5            64.83   \n",
              "3                      37.746                         0.5            64.83   \n",
              "4                      37.746                         0.5            64.83   \n",
              "...                       ...                         ...              ...   \n",
              "73991                  36.791                         1.7            61.49   \n",
              "73992                  36.791                         1.7            61.49   \n",
              "73993                  36.791                         1.7            61.49   \n",
              "73994                  36.791                         1.7            61.49   \n",
              "73995                  36.791                         1.7            61.49   \n",
              "\n",
              "       human_development_index  \n",
              "0                        0.511  \n",
              "1                        0.511  \n",
              "2                        0.511  \n",
              "3                        0.511  \n",
              "4                        0.511  \n",
              "...                        ...  \n",
              "73991                    0.571  \n",
              "73992                    0.571  \n",
              "73993                    0.571  \n",
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              "73995                    0.571  \n",
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              "      <th>new_cases_smoothed</th>\n",
              "      <th>total_deaths</th>\n",
              "      <th>new_deaths</th>\n",
              "      <th>new_deaths_smoothed</th>\n",
              "      <th>...</th>\n",
              "      <th>gdp_per_capita</th>\n",
              "      <th>extreme_poverty</th>\n",
              "      <th>cardiovasc_death_rate</th>\n",
              "      <th>diabetes_prevalence</th>\n",
              "      <th>female_smokers</th>\n",
              "      <th>male_smokers</th>\n",
              "      <th>handwashing_facilities</th>\n",
              "      <th>hospital_beds_per_thousand</th>\n",
              "      <th>life_expectancy</th>\n",
              "      <th>human_development_index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-02-24</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-02-25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-02-26</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-02-27</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-02-28</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73991</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2021-03-06</td>\n",
              "      <td>36260.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>28.857</td>\n",
              "      <td>1485.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.143</td>\n",
              "      <td>...</td>\n",
              "      <td>1899.775</td>\n",
              "      <td>21.4</td>\n",
              "      <td>307.846</td>\n",
              "      <td>1.82</td>\n",
              "      <td>1.6</td>\n",
              "      <td>30.7</td>\n",
              "      <td>36.791</td>\n",
              "      <td>1.7</td>\n",
              "      <td>61.49</td>\n",
              "      <td>0.571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73992</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2021-03-07</td>\n",
              "      <td>36271.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>26.000</td>\n",
              "      <td>1485.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.143</td>\n",
              "      <td>...</td>\n",
              "      <td>1899.775</td>\n",
              "      <td>21.4</td>\n",
              "      <td>307.846</td>\n",
              "      <td>1.82</td>\n",
              "      <td>1.6</td>\n",
              "      <td>30.7</td>\n",
              "      <td>36.791</td>\n",
              "      <td>1.7</td>\n",
              "      <td>61.49</td>\n",
              "      <td>0.571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73993</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2021-03-08</td>\n",
              "      <td>36289.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>24.857</td>\n",
              "      <td>1487.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.714</td>\n",
              "      <td>...</td>\n",
              "      <td>1899.775</td>\n",
              "      <td>21.4</td>\n",
              "      <td>307.846</td>\n",
              "      <td>1.82</td>\n",
              "      <td>1.6</td>\n",
              "      <td>30.7</td>\n",
              "      <td>36.791</td>\n",
              "      <td>1.7</td>\n",
              "      <td>61.49</td>\n",
              "      <td>0.571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73994</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2021-03-09</td>\n",
              "      <td>36321.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>24.714</td>\n",
              "      <td>1489.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.429</td>\n",
              "      <td>...</td>\n",
              "      <td>1899.775</td>\n",
              "      <td>21.4</td>\n",
              "      <td>307.846</td>\n",
              "      <td>1.82</td>\n",
              "      <td>1.6</td>\n",
              "      <td>30.7</td>\n",
              "      <td>36.791</td>\n",
              "      <td>1.7</td>\n",
              "      <td>61.49</td>\n",
              "      <td>0.571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73995</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2021-03-10</td>\n",
              "      <td>36341.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>23.143</td>\n",
              "      <td>1489.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.571</td>\n",
              "      <td>...</td>\n",
              "      <td>1899.775</td>\n",
              "      <td>21.4</td>\n",
              "      <td>307.846</td>\n",
              "      <td>1.82</td>\n",
              "      <td>1.6</td>\n",
              "      <td>30.7</td>\n",
              "      <td>36.791</td>\n",
              "      <td>1.7</td>\n",
              "      <td>61.49</td>\n",
              "      <td>0.571</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>73996 rows × 59 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3900c185-98a0-4496-bd5f-7f77820f46a3')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3900c185-98a0-4496-bd5f-7f77820f46a3');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PQpFWX7QLvXd",
        "outputId": "77eb8d44-07e3-4bdf-a179-a803198f9f0a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(73996, 59)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['location'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_eTEASZEMJ5E",
        "outputId": "b14d0717-e531-46e9-b84a-59abba569179"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Argentina           435\n",
            "Mexico              435\n",
            "Thailand            432\n",
            "Taiwan              420\n",
            "South Korea         415\n",
            "                   ... \n",
            "Falkland Islands     16\n",
            "Montserrat           16\n",
            "Macao                11\n",
            "Northern Cyprus       9\n",
            "Saint Helena          1\n",
            "Name: location, Length: 215, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "DATA CLEANSING"
      ],
      "metadata": {
        "id": "i9_lIKRtMkUj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_ina=df.loc[df['location']=='Indonesia']"
      ],
      "metadata": {
        "id": "3BnBUkceRFNO"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#reset index\n",
        "df_ina=df_ina.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "O1fZiyahRiQT"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_ina.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "I0g-WnokR350",
        "outputId": "458b9189-af35-4807-b369-e0a41ce38960"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  iso_code continent   location        date  total_cases  new_cases  \\\n",
              "0      IDN      Asia  Indonesia  2020-03-02          2.0        2.0   \n",
              "1      IDN      Asia  Indonesia  2020-03-03          2.0        0.0   \n",
              "2      IDN      Asia  Indonesia  2020-03-04          2.0        0.0   \n",
              "3      IDN      Asia  Indonesia  2020-03-05          2.0        0.0   \n",
              "4      IDN      Asia  Indonesia  2020-03-06          4.0        2.0   \n",
              "\n",
              "   new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  ...  \\\n",
              "0                 NaN           NaN         NaN                  NaN  ...   \n",
              "1                 NaN           NaN         NaN                  NaN  ...   \n",
              "2                 NaN           NaN         NaN                  NaN  ...   \n",
              "3                 NaN           NaN         NaN                  NaN  ...   \n",
              "4                 NaN           NaN         NaN                  NaN  ...   \n",
              "\n",
              "   gdp_per_capita  extreme_poverty  cardiovasc_death_rate  \\\n",
              "0       11188.744              5.7                342.864   \n",
              "1       11188.744              5.7                342.864   \n",
              "2       11188.744              5.7                342.864   \n",
              "3       11188.744              5.7                342.864   \n",
              "4       11188.744              5.7                342.864   \n",
              "\n",
              "   diabetes_prevalence  female_smokers  male_smokers  handwashing_facilities  \\\n",
              "0                 6.32             2.8          76.1                  64.204   \n",
              "1                 6.32             2.8          76.1                  64.204   \n",
              "2                 6.32             2.8          76.1                  64.204   \n",
              "3                 6.32             2.8          76.1                  64.204   \n",
              "4                 6.32             2.8          76.1                  64.204   \n",
              "\n",
              "   hospital_beds_per_thousand  life_expectancy  human_development_index  \n",
              "0                        1.04            71.72                    0.718  \n",
              "1                        1.04            71.72                    0.718  \n",
              "2                        1.04            71.72                    0.718  \n",
              "3                        1.04            71.72                    0.718  \n",
              "4                        1.04            71.72                    0.718  \n",
              "\n",
              "[5 rows x 59 columns]"
            ],
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              "\n",
              "  <div id=\"df-44cbefca-e128-46a6-ba9c-d268e0f586aa\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>iso_code</th>\n",
              "      <th>continent</th>\n",
              "      <th>location</th>\n",
              "      <th>date</th>\n",
              "      <th>total_cases</th>\n",
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              "      <th>new_cases_smoothed</th>\n",
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              "      <th>new_deaths</th>\n",
              "      <th>new_deaths_smoothed</th>\n",
              "      <th>...</th>\n",
              "      <th>gdp_per_capita</th>\n",
              "      <th>extreme_poverty</th>\n",
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              "      <th>diabetes_prevalence</th>\n",
              "      <th>female_smokers</th>\n",
              "      <th>male_smokers</th>\n",
              "      <th>handwashing_facilities</th>\n",
              "      <th>hospital_beds_per_thousand</th>\n",
              "      <th>life_expectancy</th>\n",
              "      <th>human_development_index</th>\n",
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              "      <td>IDN</td>\n",
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              "      <td>6.32</td>\n",
              "      <td>2.8</td>\n",
              "      <td>76.1</td>\n",
              "      <td>64.204</td>\n",
              "      <td>1.04</td>\n",
              "      <td>71.72</td>\n",
              "      <td>0.718</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>IDN</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Indonesia</td>\n",
              "      <td>2020-03-03</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>5.7</td>\n",
              "      <td>342.864</td>\n",
              "      <td>6.32</td>\n",
              "      <td>2.8</td>\n",
              "      <td>76.1</td>\n",
              "      <td>64.204</td>\n",
              "      <td>1.04</td>\n",
              "      <td>71.72</td>\n",
              "      <td>0.718</td>\n",
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              "    <tr>\n",
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              "      <td>IDN</td>\n",
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              "      <td>Indonesia</td>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>11188.744</td>\n",
              "      <td>5.7</td>\n",
              "      <td>342.864</td>\n",
              "      <td>6.32</td>\n",
              "      <td>2.8</td>\n",
              "      <td>76.1</td>\n",
              "      <td>64.204</td>\n",
              "      <td>1.04</td>\n",
              "      <td>71.72</td>\n",
              "      <td>0.718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>IDN</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Indonesia</td>\n",
              "      <td>2020-03-05</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>11188.744</td>\n",
              "      <td>5.7</td>\n",
              "      <td>342.864</td>\n",
              "      <td>6.32</td>\n",
              "      <td>2.8</td>\n",
              "      <td>76.1</td>\n",
              "      <td>64.204</td>\n",
              "      <td>1.04</td>\n",
              "      <td>71.72</td>\n",
              "      <td>0.718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>IDN</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Indonesia</td>\n",
              "      <td>2020-03-06</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
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              "      <td>5.7</td>\n",
              "      <td>342.864</td>\n",
              "      <td>6.32</td>\n",
              "      <td>2.8</td>\n",
              "      <td>76.1</td>\n",
              "      <td>64.204</td>\n",
              "      <td>1.04</td>\n",
              "      <td>71.72</td>\n",
              "      <td>0.718</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 59 columns</p>\n",
              "</div>\n",
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              "\n",
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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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ina.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "CKnRyL0sSCTg",
        "outputId": "60ffcf5c-7600-4d61-962d-a177b918b361"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    iso_code continent   location        date  total_cases  new_cases  \\\n",
              "369      IDN      Asia  Indonesia  2021-03-06    1373836.0     5767.0   \n",
              "370      IDN      Asia  Indonesia  2021-03-07    1379662.0     5826.0   \n",
              "371      IDN      Asia  Indonesia  2021-03-08    1386556.0     6894.0   \n",
              "372      IDN      Asia  Indonesia  2021-03-09    1392945.0     6389.0   \n",
              "373      IDN      Asia  Indonesia  2021-03-10    1398578.0     5633.0   \n",
              "\n",
              "     new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  ...  \\\n",
              "369            6394.571       37154.0       128.0              167.571  ...   \n",
              "370            6432.571       37266.0       112.0              157.143  ...   \n",
              "371            6463.143       37547.0       281.0              174.571  ...   \n",
              "372            6559.857       37757.0       210.0              177.000  ...   \n",
              "373            6392.000       37932.0       175.0              173.000  ...   \n",
              "\n",
              "     gdp_per_capita  extreme_poverty  cardiovasc_death_rate  \\\n",
              "369       11188.744              5.7                342.864   \n",
              "370       11188.744              5.7                342.864   \n",
              "371       11188.744              5.7                342.864   \n",
              "372       11188.744              5.7                342.864   \n",
              "373       11188.744              5.7                342.864   \n",
              "\n",
              "     diabetes_prevalence  female_smokers  male_smokers  \\\n",
              "369                 6.32             2.8          76.1   \n",
              "370                 6.32             2.8          76.1   \n",
              "371                 6.32             2.8          76.1   \n",
              "372                 6.32             2.8          76.1   \n",
              "373                 6.32             2.8          76.1   \n",
              "\n",
              "     handwashing_facilities  hospital_beds_per_thousand  life_expectancy  \\\n",
              "369                  64.204                        1.04            71.72   \n",
              "370                  64.204                        1.04            71.72   \n",
              "371                  64.204                        1.04            71.72   \n",
              "372                  64.204                        1.04            71.72   \n",
              "373                  64.204                        1.04            71.72   \n",
              "\n",
              "     human_development_index  \n",
              "369                    0.718  \n",
              "370                    0.718  \n",
              "371                    0.718  \n",
              "372                    0.718  \n",
              "373                    0.718  \n",
              "\n",
              "[5 rows x 59 columns]"
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              "      <td>2021-03-10</td>\n",
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              "      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-41414cde-e87c-4e53-945e-6f79a266222d 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-41414cde-e87c-4e53-945e-6f79a266222d');\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": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ina.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kw8HXRfuSHfR",
        "outputId": "65405406-354a-4bce-c615-696bf1eb4fdb"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(374, 59)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ina.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vMSeW7yMSUcU",
        "outputId": "d5be4666-49e7-40f8-9420-203197ece852"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['iso_code', 'continent', 'location', 'date', 'total_cases', 'new_cases',\n",
              "       'new_cases_smoothed', 'total_deaths', 'new_deaths',\n",
              "       'new_deaths_smoothed', 'total_cases_per_million',\n",
              "       'new_cases_per_million', 'new_cases_smoothed_per_million',\n",
              "       'total_deaths_per_million', 'new_deaths_per_million',\n",
              "       'new_deaths_smoothed_per_million', 'reproduction_rate', 'icu_patients',\n",
              "       'icu_patients_per_million', 'hosp_patients',\n",
              "       'hosp_patients_per_million', 'weekly_icu_admissions',\n",
              "       'weekly_icu_admissions_per_million', 'weekly_hosp_admissions',\n",
              "       'weekly_hosp_admissions_per_million', 'new_tests', 'total_tests',\n",
              "       'total_tests_per_thousand', 'new_tests_per_thousand',\n",
              "       'new_tests_smoothed', 'new_tests_smoothed_per_thousand',\n",
              "       'positive_rate', 'tests_per_case', 'tests_units', 'total_vaccinations',\n",
              "       'people_vaccinated', 'people_fully_vaccinated', 'new_vaccinations',\n",
              "       'new_vaccinations_smoothed', 'total_vaccinations_per_hundred',\n",
              "       'people_vaccinated_per_hundred', 'people_fully_vaccinated_per_hundred',\n",
              "       'new_vaccinations_smoothed_per_million', 'stringency_index',\n",
              "       'population', 'population_density', 'median_age', 'aged_65_older',\n",
              "       'aged_70_older', 'gdp_per_capita', 'extreme_poverty',\n",
              "       'cardiovasc_death_rate', 'diabetes_prevalence', 'female_smokers',\n",
              "       'male_smokers', 'handwashing_facilities', 'hospital_beds_per_thousand',\n",
              "       'life_expectancy', 'human_development_index'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ina=df_ina[['date','total_cases','new_cases','total_deaths','new_deaths']]"
      ],
      "metadata": {
        "id": "buLBT0x-SdSc"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_ina"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "GGhsmMdAS8IE",
        "outputId": "3dffc900-4919-4074-f363-d755290aabc9"
      },
      "execution_count": 31,
      "outputs": [
        {
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              "           date  total_cases  new_cases  total_deaths  new_deaths\n",
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              "369  2021-03-06    1373836.0     5767.0       37154.0       128.0\n",
              "370  2021-03-07    1379662.0     5826.0       37266.0       112.0\n",
              "371  2021-03-08    1386556.0     6894.0       37547.0       281.0\n",
              "372  2021-03-09    1392945.0     6389.0       37757.0       210.0\n",
              "373  2021-03-10    1398578.0     5633.0       37932.0       175.0\n",
              "\n",
              "[374 rows x 5 columns]"
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              "      <td>2020-03-05</td>\n",
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              "      <th>369</th>\n",
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              "      <td>37757.0</td>\n",
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              "      <td>37932.0</td>\n",
              "      <td>175.0</td>\n",
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              "  </tbody>\n",
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              "<p>374 rows × 5 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a008b632-ad5f-4ca2-b008-b9923c401718')\"\n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ubah data NaN jadi 0\n",
        "df_ina=df_ina.fillna(0)"
      ],
      "metadata": {
        "id": "Jd2Undl0TBIm"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_ina"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "zvFKvjw3TSd4",
        "outputId": "53e3c9da-8e03-4d37-f820-f48ef77f84e7"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           date  total_cases  new_cases  total_deaths  new_deaths\n",
              "0    2020-03-02          2.0        2.0           0.0         0.0\n",
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              "..          ...          ...        ...           ...         ...\n",
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              "371  2021-03-08    1386556.0     6894.0       37547.0       281.0\n",
              "372  2021-03-09    1392945.0     6389.0       37757.0       210.0\n",
              "373  2021-03-10    1398578.0     5633.0       37932.0       175.0\n",
              "\n",
              "[374 rows x 5 columns]"
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              "      <td>5633.0</td>\n",
              "      <td>37932.0</td>\n",
              "      <td>175.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>374 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-16050cf7-fa7f-47fc-a8d5-4835d234c260')\"\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-16050cf7-fa7f-47fc-a8d5-4835d234c260 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-16050cf7-fa7f-47fc-a8d5-4835d234c260');\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": "markdown",
      "source": [
        "**Exploration Data Analyzing (EDA)**\n",
        "Tahapan memahami isi data:\n",
        "\n",
        "\n",
        "1.   Univariat Analysis\n",
        "2.   Bivariat Analysis\n",
        "\n",
        "1.   Multivariat Analysis\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "KZakpczaT9J4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_ina['new_cases'].plot(label='Kasus Baru')\n",
        "df_ina['new_deaths'].plot(label='Kasus Kematian')\n",
        "plt.xlabel('Hari ke-')\n",
        "plt.ylabel('Kasus per hari')\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "ukkexmfiThIo",
        "outputId": "b7d183de-99de-47db-e906-75f9ac78e25c"
      },
      "execution_count": 34,
      "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": [
        "for i in range(len(df_ina)):\n",
        "  if df_ina['new_cases'].iloc[i]==df_ina['new_cases'].max():\n",
        "    print('tanggal kasus positif tertinggi {}'.format(df_ina['date'].iloc[i]))\n",
        "    print('Dengan Jumlah = {} '.format(df_ina['new_cases'].iloc[i]))\n",
        "    break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bdeEMujKVcnp",
        "outputId": "437ee37a-299e-4962-dfc6-de5f59ae16d8"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tanggal kasus positif tertinggi 2021-01-30\n",
            "Dengan Jumlah = 14518.0 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ina['total_cases'].plot(label='Positif')\n",
        "df_ina['total_deaths'].plot(label='Kematian')\n",
        "plt.xlabel('Hari Ke-')\n",
        "plt.ylabel('Jumlah Kasus Kumulatif per Hari')\n",
        "plt.legend(loc='best')\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "20nNxDOJXe6g",
        "outputId": "fdde8548-411b-45cb-c5db-638e84fc5973"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(*args, **kw)>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "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": "markdown",
      "source": [
        "**Model Prediksi**"
      ],
      "metadata": {
        "id": "D5qg1VypYTsn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def kurva_sigmoid(t,a,t0,c):\n",
        "   return c/(1+np.exp(-(t-t0)/(a)))"
      ],
      "metadata": {
        "id": "YNezaCW-cT97"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=list(df_ina.index)\n",
        "y=list(df_ina['total_cases'])"
      ],
      "metadata": {
        "id": "WYvzO1iHYOEw"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.optimize import curve_fit, fsolve"
      ],
      "metadata": {
        "id": "a6nUswBOasF6"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fit=curve_fit(kurva_sigmoid,x,y,method='trf')\n",
        "varA,varB=fit"
      ],
      "metadata": {
        "id": "V6oTQvDibJ3j"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "varA"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sJIFUsmactjn",
        "outputId": "3efba6fc-191f-4b37-c19a-7aa72838eb20"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6.91350214e+01, 3.71790857e+02, 2.84763171e+06])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "varB"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2YhEfHAxcvAv",
        "outputId": "99a30ec7-3607-4685-cbba-ef0d0c057796"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.78360266e-01, 2.65323681e+00, 5.86834658e+04],\n",
              "       [2.65323681e+00, 1.35936193e+01, 3.06836336e+05],\n",
              "       [5.86834658e+04, 3.06836336e+05, 6.98107781e+09]])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "std_er=np.zeros(len(varA))\n",
        "for i in range(len(varA)):\n",
        "  std_er[i]=np.sqrt(varB[i][i])"
      ],
      "metadata": {
        "id": "pSM-CX01c7_U"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "std_er"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DZsJRzGhdKBi",
        "outputId": "29a250ba-0b81-46a8-f334-bd1c32b8b600"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7.60500011e-01, 3.68695257e+00, 8.35528444e+04])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a=varA[0]+std_er[0]\n",
        "t0=varA[1]+std_er[1]\n",
        "c=varA[2]+std_er[2]"
      ],
      "metadata": {
        "id": "KMDnwb0SdfQ-"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def puncak(x):\n",
        "  return kurva_sigmoid(x,a,t0,c)-int(c)"
      ],
      "metadata": {
        "id": "xzHw0sLAdrYO"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_puncak=int(fsolve(puncak,t0))"
      ],
      "metadata": {
        "id": "qpw0dSq7eIJR"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Hari puncak Wabah covid-19 adalah hari Ke-'+str(n_puncak))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5dNyXJgSeTq2",
        "outputId": "942ed52f-3024-4b7f-e935-ce888ca88637"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hari puncak Wabah covid-19 adalah hari Ke-1458\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analisis Data"
      ],
      "metadata": {
        "id": "uIw4o6WCe22C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_0=max(x)+1\n",
        "pred_x=list(range(n_0,n_puncak))"
      ],
      "metadata": {
        "id": "1SZp7S7oe6eY"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('kita memiliki {} hari menuju puncak wabah'.format(str(len(pred_x))))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NyHBt4yxfbbx",
        "outputId": "1c8684e2-5ea4-47d9-9268-f8c389a5d782"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "kita memiliki 1084 hari menuju puncak wabah\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_y=np.zeros(len(x+pred_x))\n",
        "for i in range(n_puncak):\n",
        "  pred_y[i]=kurva_sigmoid(i,a,t0,c)"
      ],
      "metadata": {
        "id": "1Y48jkC3fwx7"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Prediksi Jumlah Puncak {} orang'.format(int(pred_y[-1])))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GMSQjnO2gKMn",
        "outputId": "2908e2d3-fe63-4481-fbf9-0a796b76c282"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediksi Jumlah Puncak 2931183 orang\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(x+pred_x,pred_y,label=\"Prediksi\")\n",
        "plt.scatter(x,y,label='Data Asli', s=10,color='red')\n",
        "plt.xlabel('Hari Ke-')\n",
        "plt.ylabel=('Jumlah Kumulatif kasus positif')\n",
        "plt.grid()\n",
        "plt.legend(loc='best')\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "i_Ab0NLGgVFw",
        "outputId": "e77cf34b-5a62-47bc-e07e-068003ddf28b"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(*args, **kw)>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "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": "markdown",
      "source": [
        "**Akurasi**"
      ],
      "metadata": {
        "id": "dC4V3lc_hp1s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def akurasi_r2(y_asli,y_prediksi,x):\n",
        "  atas=sum((y_asli-y_prediksi[0:len(x)]))**2\n",
        "  bawah=sum((y_asli-np.mean(y))**2)\n",
        "  r=1-(atas/bawah)\n",
        "  return r"
      ],
      "metadata": {
        "id": "HrpBeHx0hOWq"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "akur=akurasi_r2(y,pred_y,x)"
      ],
      "metadata": {
        "id": "dFnPN2RZiqhb"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "akur*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T4DbsMQSiwVt",
        "outputId": "a535a381-c634-4876-f2ac-58c944ea3132"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "94.58612643674313"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import datetime, timedelta"
      ],
      "metadata": {
        "id": "YD222GzFiztv"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "sttXVN4RjBDI",
        "outputId": "d09d15eb-d623-4235-fb26-0619553ddf3c"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      iso_code continent     location        date  total_cases  new_cases  \\\n",
              "0          AFG      Asia  Afghanistan  2020-02-24          1.0        1.0   \n",
              "1          AFG      Asia  Afghanistan  2020-02-25          1.0        0.0   \n",
              "2          AFG      Asia  Afghanistan  2020-02-26          1.0        0.0   \n",
              "3          AFG      Asia  Afghanistan  2020-02-27          1.0        0.0   \n",
              "4          AFG      Asia  Afghanistan  2020-02-28          1.0        0.0   \n",
              "...        ...       ...          ...         ...          ...        ...   \n",
              "73991      ZWE    Africa     Zimbabwe  2021-03-06      36260.0       12.0   \n",
              "73992      ZWE    Africa     Zimbabwe  2021-03-07      36271.0       11.0   \n",
              "73993      ZWE    Africa     Zimbabwe  2021-03-08      36289.0       18.0   \n",
              "73994      ZWE    Africa     Zimbabwe  2021-03-09      36321.0       32.0   \n",
              "73995      ZWE    Africa     Zimbabwe  2021-03-10      36341.0       20.0   \n",
              "\n",
              "       new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  ...  \\\n",
              "0                     NaN           NaN         NaN                  NaN  ...   \n",
              "1                     NaN           NaN         NaN                  NaN  ...   \n",
              "2                     NaN           NaN         NaN                  NaN  ...   \n",
              "3                     NaN           NaN         NaN                  NaN  ...   \n",
              "4                     NaN           NaN         NaN                  NaN  ...   \n",
              "...                   ...           ...         ...                  ...  ...   \n",
              "73991              28.857        1485.0         1.0                3.143  ...   \n",
              "73992              26.000        1485.0         0.0                3.143  ...   \n",
              "73993              24.857        1487.0         2.0                2.714  ...   \n",
              "73994              24.714        1489.0         2.0                2.429  ...   \n",
              "73995              23.143        1489.0         0.0                1.571  ...   \n",
              "\n",
              "       gdp_per_capita  extreme_poverty  cardiovasc_death_rate  \\\n",
              "0            1803.987              NaN                597.029   \n",
              "1            1803.987              NaN                597.029   \n",
              "2            1803.987              NaN                597.029   \n",
              "3            1803.987              NaN                597.029   \n",
              "4            1803.987              NaN                597.029   \n",
              "...               ...              ...                    ...   \n",
              "73991        1899.775             21.4                307.846   \n",
              "73992        1899.775             21.4                307.846   \n",
              "73993        1899.775             21.4                307.846   \n",
              "73994        1899.775             21.4                307.846   \n",
              "73995        1899.775             21.4                307.846   \n",
              "\n",
              "       diabetes_prevalence  female_smokers  male_smokers  \\\n",
              "0                     9.59             NaN           NaN   \n",
              "1                     9.59             NaN           NaN   \n",
              "2                     9.59             NaN           NaN   \n",
              "3                     9.59             NaN           NaN   \n",
              "4                     9.59             NaN           NaN   \n",
              "...                    ...             ...           ...   \n",
              "73991                 1.82             1.6          30.7   \n",
              "73992                 1.82             1.6          30.7   \n",
              "73993                 1.82             1.6          30.7   \n",
              "73994                 1.82             1.6          30.7   \n",
              "73995                 1.82             1.6          30.7   \n",
              "\n",
              "       handwashing_facilities  hospital_beds_per_thousand  life_expectancy  \\\n",
              "0                      37.746                         0.5            64.83   \n",
              "1                      37.746                         0.5            64.83   \n",
              "2                      37.746                         0.5            64.83   \n",
              "3                      37.746                         0.5            64.83   \n",
              "4                      37.746                         0.5            64.83   \n",
              "...                       ...                         ...              ...   \n",
              "73991                  36.791                         1.7            61.49   \n",
              "73992                  36.791                         1.7            61.49   \n",
              "73993                  36.791                         1.7            61.49   \n",
              "73994                  36.791                         1.7            61.49   \n",
              "73995                  36.791                         1.7            61.49   \n",
              "\n",
              "       human_development_index  \n",
              "0                        0.511  \n",
              "1                        0.511  \n",
              "2                        0.511  \n",
              "3                        0.511  \n",
              "4                        0.511  \n",
              "...                        ...  \n",
              "73991                    0.571  \n",
              "73992                    0.571  \n",
              "73993                    0.571  \n",
              "73994                    0.571  \n",
              "73995                    0.571  \n",
              "\n",
              "[73996 rows x 59 columns]"
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              "      <th>iso_code</th>\n",
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              "      <td>NaN</td>\n",
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              "          const element = document.querySelector('#df-f7ce0340-6774-4695-abd2-1decaf8ab94c');\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": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ina"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "GIEiDAj1jF0r",
        "outputId": "dcb9f0e4-c3b3-4116-fa84-87465db3e939"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           date  total_cases  new_cases  total_deaths  new_deaths\n",
              "0    2020-03-02          2.0        2.0           0.0         0.0\n",
              "1    2020-03-03          2.0        0.0           0.0         0.0\n",
              "2    2020-03-04          2.0        0.0           0.0         0.0\n",
              "3    2020-03-05          2.0        0.0           0.0         0.0\n",
              "4    2020-03-06          4.0        2.0           0.0         0.0\n",
              "..          ...          ...        ...           ...         ...\n",
              "369  2021-03-06    1373836.0     5767.0       37154.0       128.0\n",
              "370  2021-03-07    1379662.0     5826.0       37266.0       112.0\n",
              "371  2021-03-08    1386556.0     6894.0       37547.0       281.0\n",
              "372  2021-03-09    1392945.0     6389.0       37757.0       210.0\n",
              "373  2021-03-10    1398578.0     5633.0       37932.0       175.0\n",
              "\n",
              "[374 rows x 5 columns]"
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              "\n",
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              "      <td>37757.0</td>\n",
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              "      <td>1398578.0</td>\n",
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              "      <td>37932.0</td>\n",
              "      <td>175.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>374 rows × 5 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5b382e8d-0b38-4b42-9727-286c35651c3e')\"\n",
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              "\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-5b382e8d-0b38-4b42-9727-286c35651c3e 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-5b382e8d-0b38-4b42-9727-286c35651c3e');\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": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "frmt='%Y-%m-%d'\n",
        "n1=df_ina['date'][0]\n",
        "tgl_0=datetime.strptime(n1,frmt)\n",
        "print(tgl_0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qh0LtzfYjKqO",
        "outputId": "da41dcc9-235c-48dd-d209-4dbc28d1f578"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2020-03-02 00:00:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tgl_puncak=tgl_0+timedelta(days=n_puncak)\n",
        "print(tgl_puncak)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7YsoBMROkAIj",
        "outputId": "2f7cee9d-3b1a-4bc1-8e5f-10c3c9bba041"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2024-02-28 00:00:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "h_puncak=tgl_puncak.strftime(frmt)"
      ],
      "metadata": {
        "id": "gaT_ht3dkZ02"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "h_puncak"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "tMJbsHntkj_e",
        "outputId": "a45c55d9-dbb1-45d2-cff1-d4631d0ef51a"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2024-02-28'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ina"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "eUidbkshkmSk",
        "outputId": "0d1b25ec-0638-443d-c330-6f0e8fddc016"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           date  total_cases  new_cases  total_deaths  new_deaths\n",
              "0    2020-03-02          2.0        2.0           0.0         0.0\n",
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              "4    2020-03-06          4.0        2.0           0.0         0.0\n",
              "..          ...          ...        ...           ...         ...\n",
              "369  2021-03-06    1373836.0     5767.0       37154.0       128.0\n",
              "370  2021-03-07    1379662.0     5826.0       37266.0       112.0\n",
              "371  2021-03-08    1386556.0     6894.0       37547.0       281.0\n",
              "372  2021-03-09    1392945.0     6389.0       37757.0       210.0\n",
              "373  2021-03-10    1398578.0     5633.0       37932.0       175.0\n",
              "\n",
              "[374 rows x 5 columns]"
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
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        "df_ina['total_pred']=pred_y[0:len(x)].astype(int)\n",
        "df_ina['selisih']=abs(df_ina['total_pred']-df_ina['total_cases'])\n"
      ],
      "metadata": {
        "id": "QbN-_2GOkr_N"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_ina"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
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        "id": "inFr9nDZlKK5",
        "outputId": "16d6fb0e-ab1f-40f5-b96b-2d96d13b4a80"
      },
      "execution_count": 78,
      "outputs": [
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              "           date  total_cases  new_cases  total_deaths  new_deaths  total_pred  \\\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df_ina=df_ina[['date','new_cases','total_deaths','new_deaths','total_cases','total_pred','selisih']]"
      ],
      "metadata": {
        "id": "_uXpw8lLlRx6"
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      "execution_count": 80,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "df_ina"
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          "height": 424
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        "id": "G3NAkO2zlzD3",
        "outputId": "cc6596b1-26c6-4192-cf72-dc5234faf0a9"
      },
      "execution_count": 81,
      "outputs": [
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            "text/plain": [
              "           date  new_cases  total_deaths  new_deaths  total_cases  total_pred  \\\n",
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              "      <td>1418662</td>\n",
              "      <td>32106.0</td>\n",
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              "    <tr>\n",
              "      <th>372</th>\n",
              "      <td>2021-03-09</td>\n",
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              "      <td>175.0</td>\n",
              "      <td>1398578.0</td>\n",
              "      <td>1439617</td>\n",
              "      <td>41039.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>374 rows × 7 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6f24cbe1-aabc-4b46-9e0f-f9571a492c7b')\"\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6f24cbe1-aabc-4b46-9e0f-f9571a492c7b');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ina.tail(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "T84tKZPJl3DG",
        "outputId": "69f0d80e-c074-42b8-f240-c502c6cf052e"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "           date  new_cases  total_deaths  new_deaths  total_cases  total_pred  \\\n",
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              "            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": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualisasi data"
      ],
      "metadata": {
        "id": "EeihKWikmJEe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "label=['Real','Prediksi']\n",
        "value=[df_ina['total_cases'].iloc[-1],\n",
        "       df_ina['total_pred'].iloc[-1]]\n",
        "plt.bar(label,value)\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "7oraQz14mINM",
        "outputId": "206fb2c7-cef3-4064-ad20-26d002c8efe9"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(*args, **kw)>"
            ]
          },
          "metadata": {},
          "execution_count": 84
        },
        {
          "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": [
        "label=np.array(df_ina['date'].iloc[-10:-1],dtype=str)\n",
        "value=np.array(df_ina['total_cases'].iloc[-10:-1],dtype=int)\n",
        "plt.barh(label,value)\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "qjyWwi7Gmrsb",
        "outputId": "77745c8c-b440-4eb4-a943-51adf78b7519"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(*args, **kw)>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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mSSciIlqTpBMREa3Jnk6Dge27mLvqkckOIyLigDgQfxi6L5npREREa5J0IiKiNUk6ERHRmiSdiIhoTZJORES0pjHplJM9H5f0vKQtkq4o5UdIekzSy+Xx8FJ+vqRnJQ1I2ihpQa2vtZJ2lvNs9jXmEkkvShqUtKpWvkbS5tL/A+UI7dHan1DGH5R0S+dIakm/LWl77aTR07v7NkVExEToZqYzDFxtuw9YDFwuqQ9YBWyw3QtsKK8BXgVOtj0PuAFYXevrLmDJvgaT1APcCpwG9AHnlvEArrK9wPZ84HVg5Rjd3A5cBPSWr/qYv2d7Yfn6k8ZPHxERE6Yx6djeYfvp8vxdYCswC1gKrCvV1gFnljobbb9TyjcBs2t9PQG83TDkicCg7Vdsvw/cV8bC9m7Ye4T2FOAjJ9BJmglMtb2pnAp6dye2iIiYXOPa05E0FzgeeBKYYXtHeesNYMYoTVbwwVHR3ZoFbKu9HiplnRjuLOMdB3xzjPZDY7UHVpblubWdJcGRJF0sqV9S/573do0z/IiIGEvXSafsnzwIXNmZcXSUGYVH1D+VKulcMwFx1se6EDiKasZ19jib3w4cCywEdgC/O8YYq20vsr2o57Bp+xNuRETUdJV0JB1ClXDusb2+FL9ZlrI6S1o7a/XnA3cAS22/1dD3nNrG/iXAdmBOrcrsUraX7T1Uy25nSeqptb++1J09Wnvbb9reY/t7wB9QLeVFRERLGu+9VvZP1gBbbd9Ue+thYDlwY3l8qNQ/GlgPLLP9UlP/trdRzTw64x0M9Eo6hipZnAOcV+I41vZgeX4G8EJJQAvrfUraLWkx1TLgBZRlOEkza0uCvwjs8yq6iIiYWN3c8PMkYBkwIOmZUvY1qmRzv6QVwGvAV8p71wJHAreVK5WHbS8CkHQvcAowXdIQcJ3tNfXBbA9LWgk8CvQAa21vkXQQsE7SVEDAZuDSMWK+jOpKuSlUe0qdfaWvS1pItRT4HeBfdPH5IyJigqjajomxHDqz1zOX3zzZYUREHBAH6i7Tkp7qTDjqckeCiIhoTZJORES0JkknIiJak5NDG8ybNY3+lk/Wi4j4fpWZTkREtCZJJyIiWpOkExERrUnSiYiI1uRCggYD23cxd9Ujkx1GRMSEO1B/GLovmelERERrknQiIqI1SToREdGaJJ2IiGhNY9Iph6w9Lul5SVskXVHKj5D0mKSXy+Phpfz8chz0gKSNkhbU+loraaekfZ5jI2mJpBclDUpaVStfI2lz6f+BcprpaO1PKOMPSrqlnL9Tf/9qSZY0venzR0TExOlmpjMMXG27D1gMXC6pD1gFbLDdC2worwFeBU62PQ+4AVhd6+suYMm+BpPUA9wKnAb0AeeW8QCusr3A9nzgdWDlGN3cDlwE9JavvWNKmgP8QmkfEREtakw6tnfYfro8fxfYCswClgLrSrV1wJmlzkbb75TyTdSOjrb9BPB2w5AnAoO2X7H9PtWx1EtL+92w9zTTKVSHsX1IOTp7qu1Nrg4LursTW/F7wG+O1jYiIg6sce3pSJoLHE91DPSM2tHPbwAzRmmygg9O7ezWLGBb7fVQKevEcGcZ7zjKMdSjtB8arb2kpcB225vHGVNEREyArpNO2T95ELiyM+PoKDMKj6h/KlXSuWYC4qyPdSFwFNWM6+xu20k6jOqY7Wu7qHuxpH5J/Xve2/WxY42IiA/rKulIOoQq4dxje30pfrMsZXWWtHbW6s8H7gCW2n6roe85kp4pX5cA24E5tSqzS9letvdQLbudJamn1v76Unf2KO2PBY4BNkv6Til/WtKPjYzJ9mrbi2wv6jls2r6/ORER0bXG2+CU/ZM1wFbbN9XeehhYDtxYHh8q9Y8G1gPLbL/U1L/tbcDC2ngHA72SjqFKFucA55U4jrU9WJ6fAbxQEtDCep+SdktaTLUMeAHwTdsDwOdqdb4DLLL93aYYIyJiYnRz77WTgGXAgKRnStnXqJLN/ZJWAK8BXynvXQscCdxWrlQetr0IQNK9wCnAdElDwHW219QHsz0saSXwKNADrLW9RdJBwDpJUwEBm4FLx4j5Mqor5aZQ7SmNd18pIiIOAFXbMTGWQ2f2eubymyc7jIiICXcgb/gp6anOhKMudySIiIjWJOlERERrknQiIqI1SToREdGanBzaYN6safRPwul6ERHfjzLTiYiI1iTpREREa5J0IiKiNdnTaTCwfRdzVz0y2WFEREyYA/lHoU0y04mIiNYk6URERGuSdCIiojVJOhER0ZoknYiIaE1j0iknez4u6XlJWyRdUcqPkPSYpJfL4+Gl/HxJz0oakLRR0oJaX2sl7ZT0XMOYSyS9KGlQ0qpa+RpJm0v/D5QjtEdrf0IZf1DSLeXQNyTdUNo+I+nPJB3V3bcpIiImQjcznWHgatt9wGLgckl9wCpgg+1eYEN5DfAqcLLtecANwOpaX3cBS/Y1mKQe4FbgNKAPOLeMB3CV7QW25wOvAyvH6OZ24CKgt3x1xvyG7fm2FwL/jerAuYiIaElj0rG9w/bT5fm7wFZgFrAUWFeqrQPOLHU22n6nlG8CZtf6egJ4u2HIE4FB26/Yfh+4r4yF7d2w9wjtKcBHTqCTNBOYanuTqxPq7q7FtrtW9R+M1j4iIg6cce3pSJoLHA88CcywvaO89QYwY5QmKxj/UdGzgG2110OlrBPDnWW844BvjtF+aB/t/52kbcD5jDHTkXSxpH5J/Xve2zXO8CMiYixdJ52yf/IgcOWIGQNlRuER9U+lSjrXTECc9bEuBI6imnGd/THa/5btOcA9jLE8Z3u17UW2F/UcNm2/4o2IiA90lXQkHUKVcO6xvb4Uv1mWsjpLWjtr9ecDdwBLbb/V0PecsrH/jKRLgO3AnFqV2aVsL9t7qJbdzpLUU2t/fak7e1/ti3uAs5o+e0RETJzGe6+V/ZM1wFbbN9XeehhYDtxYHh8q9Y8G1gPLbL/U1L/tbcDC2ngHA72SjqFKFucA55U4jrU9WJ6fAbxQEtDCep+SdktaTLUMeAFlGU5Sr+2XS7WlwAtN8UVExMTp5oafJwHLgAFJz5Syr1Elm/slrQBeA75S3rsWOBK4rVypPGx7EYCke4FTgOmShoDrbK+pD2Z7WNJK4FGgB1hre4ukg4B1kqYCAjYDl44R82VUV8pNodpT6uwr3SjpC8D3SsyXdPH5IyJigqjajomxHDqz1zOX3zzZYURETJg27jIt6anOhKMudySIiIjWJOlERERrknQiIqI1OTm0wbxZ0+ifxFP2IiK+n2SmExERrUnSiYiI1iTpREREa7Kn02Bg+y7mrnpkssOIiNhvbfx9TpPMdCIiojVJOhER0ZoknYiIaE2STkREtCZJJyIiWtOYdMoha49Lel7SFklXlPIjJD0m6eXyeHgpP1/Ss5IGJG2UtKDW11pJOyU91zDmEkkvShqUtKpWvkbS5tL/A+U009Han1DGH5R0Szl/B0nfkPRCaf9Hkn6ku29TRERMhG5mOsPA1bb7gMXA5ZL6gFXABtu9wIbyGuBV4GTb84AbgNW1vu4CluxrMEk9wK3AaUAfcG4ZD+Aq2wtszwdeZ4zjpoHbgYuA3vLVGfMx4Iul/UvAv27++BERMVEak47tHbafLs/fBbYCs6hO3lxXqq0Dzix1Ntp+p5RvonZ0tO0ngLcbhjwRGLT9iu33qY6lXlra74a9p5lOAT5yGFA5Onuq7U2uDgu6uxbbn9keHi22iIg48Ma1pyNpLnA81THQM2zvKG+9AcwYpckKPji1s1uzgG2110OlrBPDnWW84yjHUI/Sfmis9jW/PlZski6W1C+pf897u8YXfUREjKnrpFP2Tx4EruzMODrKjMIj6p9KlXSumYA462NdCBxFNeM6++P0Iem3qJYN7xljjNW2F9le1HPYtI8da0REfFhXSUfSIVQJ5x7b60vxm2Upq7OktbNWfz5wB7DU9lsNfc+R9Ez5ugTYDsypVZldyvayvYdq2e0sST219teXurPHai/p14B/DpzvnNUdEdGqxnuvlf2TNcBW2zfV3noYWA7cWB4fKvWPBtYDy2y/1NS/7W3Awtp4BwO9ko6hShbnAOeVOI61PVienwG8UBLQwnqfknZLWky1DHgBZRlO0hLgN6kudHivKbaIiJhY3dzw8yRgGTAg6ZlS9jWqZHO/pBXAa8BXynvXAkcCt5UrlYdtLwKQdC9wCjBd0hBwne019cFsD0taCTwK9ABrbW+RdBCwTtJUQMBm4NIxYr6M6kq5KVT7Np29m/8IHAo8VmLbZPuSLr4HERExAZQVpn07dGavZy6/ebLDiIjYb23eZVrSU50JR13uSBAREa1J0omIiNYk6URERGtycmiDebOm0f8JOG0vIuL7QWY6ERHRmiSdiIhoTZJORES0JkknIiJakwsJGgxs38XcVY9MdhgREfulzT8M3ZfMdCIiojVJOhER0ZoknYiIaE2STkREtCZJJyIiWtOYdMrJno9Lel7SFklXlPIjJD0m6eXyeHgpP1/Ss5IGJG2UtKDW11pJOyU91zDmEkkvShqUtKpWvkbS5tL/A+UI7dHan1DGH5R0Szn0DUm/Uj7D9yR95JbbERFxYHUz0xkGrrbdBywGLpfUB6wCNtjuBTaU1wCvUp3MOQ+4AVhd6+suYMm+BpPUA9wKnAb0AeeW8QCusr3A9nzgdWDlGN3cDlwE9JavzpjPAb8EPNHF546IiAnWmHRs77D9dHn+LrAVmAUsBdaVauuAM0udjbbfKeWbgNm1vp4A3m4Y8kRg0PYrtt8H7itjYXs37D1CewrwkRPoJM0Eptre5OqEurtrsW21/WLTZ46IiANjXHs6kuYCxwNPAjNs7yhvvQHMGKXJCj44Krpbs4BttddDpawTw51lvOOAb47Rfmis9t2QdLGkfkn9e97bNZ6mERGxD10nnbJ/8iBwZWfG0VFmFB5R/1SqpHPNBMRZH+tC4CiqGdfZE9l3bYzVthfZXtRz2LQDMURExGdSV0lH0iFUCece2+tL8ZtlKauzpLWzVn8+cAew1PZbDX3PkfRM+boE2A7MqVWZXcr2sr2HatntLEk9tfbXl7qz99U+IiImR+O918r+yRpgq+2bam89DCwHbiyPD5X6RwPrgWW2X2rq3/Y2YGFtvIOBXknHUCWLc4DzShzH2h4sz88AXigJaGG9T0m7JS2mWga8gNGX4SIiomXdzHROApYBX6rNKE6nSjY/L+ll4MvlNcC1wJHAbaVuf6cjSfcC3wa+IGlI0oqRg9keproq7VGqJbT7bW8BBKyTNAAMADOB68eI+TKqmdYg8NeUfSVJvyhpCPgp4BFJj3bx+SMiYoKo2o6JsRw6s9czl9882WFEROyXtu8yLekp2x/5e8jckSAiIlqTpBMREa1J0omIiNbk5NAG82ZNo/8TcuJeRMSnXWY6ERHRmiSdiIhoTZJORES0JkknIiJak6QTERGtSdKJiIjWJOlERERrknQiIqI1SToREdGa3GW6gaR3gRcnO46PYTrw3ckO4mP4NMb9aYwZEnfbPo1x70/MP277R0cW5jY4zV4c7fbcn3SS+hN3Oz6NMUPibtunMe4DEXOW1yIiojVJOhER0ZoknWarJzuAjylxt+fTGDMk7rZ9GuOe8JhzIUFERLQmM52IiGhNkk5ERLQmSaeQtETSi5IGJa0a5f1DJX2rvP+kpLntR/mRmJpi/peSnpf0rKQNkn58MuIcqSnuWr2zJFnSJ+Iy027ilvSV8j3fIum/tB3jaLr4OTla0uOS/qr8rJw+GXGOiGmtpJ2SnhvjfUm6pXymZyX9RNsxjqaLuM8v8Q5I2ihpQdsxjqYp7lq9n5Q0LOmXP/Zgtj/zX0AP8NfAPwR+ANgM9I2ocxnw++X5OcC3PgUxnwocVp5fOtkxdxt3qffDwBPAJmDRpyFuoBf4K+Dw8vpzn5K4VwOXlud9wHc+AXH/LPATwHNjvH868KeAgMXAk5Mdc5dx/3Tt5+O0T0vctZ+lPwf+BPjljztWZjqVE4FB26/Yfh+4D1g6os5SYF15/gDwc5LUYowjNcZs+3Hb75WXm4DZLcc4mm6+1wA3AP8e+Ls2g9uHbuK+CLjV9jsAtne2HONouonbwJGKNqoAAAOzSURBVNTyfBrwNy3GNyrbTwBv76PKUuBuVzYBPyJpZjvRja0pbtsbOz8ffHL+TXbz/Qb4KvAgsF8/10k6lVnAttrroVI2ah3bw8Au4MhWohtdNzHXraD6zXCyNcZdlkrm2H6kzcAadPP9/jzweUn/S9ImSUtai25s3cT928CvShqi+i32q+2Etl/G+/P/SfRJ+TfZSNIs4BeB2/e3r9wG5zNA0q8Ci4CTJzuWJpIOAm4Cfm2SQ/k4DqZaYjuF6jfYJyTNs/23kxpVs3OBu2z/rqSfAv6zpC/a/t5kB/b9StKpVEnnn052LF26GbjG9vf2d4EnSaeyHZhTez27lI1WZ0jSwVTLEG+1E96ouokZSV8Gfgs42fbftxTbvjTF/cPAF4G/KD/cPwY8LOkM2/2tRflR3Xy/h6jW6P8f8Kqkl6iS0F+2E+Kouol7BbAEwPa3Jf0g1Y0ePwnLg2Pp6uf/k0jSfOAO4DTbk/l/yHgsAu4r/yanA6dLGrb9x+PtKMtrlb8EeiUdI+kHqC4UeHhEnYeB5eX5LwN/7rK7NkkaY5Z0PPCfgDM+IfsL0BC37V22p9uea3su1br3ZCcc6O5n5I+pZjlImk613PZKm0GOopu4Xwd+DkDSPwJ+EPg/rUY5fg8DF5Sr2BYDu2zvmOygmkg6GlgPLLP90mTH0y3bx9T+TT4AXPZxEg5kpgNUezSSVgKPUl2hsdb2FknXA/22HwbWUC07DFJtuJ0zeRF3HfM3gB8C/rD8hvK67TMmLWi6jvsTp8u4HwV+QdLzwB7gNyb7N9ku474a+ANJV1FdVPBrk/wLFZLupUrg08te03XAIQC2f59q7+l0YBB4D7hwciL9sC7ivpZqL/i28m9y2J+AO093EffEjTXJP1sREfEZkuW1iIhoTZJORES0JkknIiJak6QTERGtSdKJiIi9ur35Z63+uG5ym6vXIiJiL0k/C/xfqnvbfbGhbi9wP/Al2+9I+lzT3wRmphMREXuNdvNPScdK+u+SnpL0PyUdV94a901uk3QiIqLJauCrtk8A/hVwWykf901uc0eCiIgYk6QfojoHqHNnE4BDy+O4b3KbpBMREftyEPC3theO8t64b3Kb5bWIiBiT7d1UCeVXYO9R4Z1jtsd9k9sknYiI2Kvc/PPbwBckDUlaAZwPrJC0GdjCB6fPPgq8VW5y+zhd3OQ2l0xHRERrMtOJiIjWJOlERERrknQiIqI1SToREdGaJJ2IiGhNkk5ERLQmSSciIlrz/wEVhD0etKj+lQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}