Basic_data_science_In_health_care.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/ELVAN13091999/my-first-binder/blob/main/Data_Science_In_Health_Care_Using_Neural_Networks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Spreading Covid19 in Indonesia by Advanced prognostication using Neural Networks   \n",
        "Personal Project By Elvan Vensiando\n",
        "\n",
        "Special thanks to IBM Corporation and MIT for providing free online courses in cognitiveclass.ai\n",
        "\n",
        "Special thanks to Hannah Ritchie, Edouard Mathieu, Lucas Rodés-Guirao, Cameron Appel, Charlie Giattino, Esteban Ortiz-Ospina, Joe Hasell, Bobbie Macdonald, Diana Beltekian and Max Roser (2020) - \"Coronavirus Pandemic (COVID-19)\". Published online at OurWorldInData.org. Retrieved from: 'https://ourworldindata.org/coronavirus' [Online Resource]"
      ]
    },
    {
      "cell_type": "markdown",
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      },
      "source": [
        "-> Import Library, Download Data and Preliminary Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KqqGUTrhyI8G"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
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      "metadata": {
        "id": "cvSXBTskyQDp",
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        "outputId": "883999b1-14be-48b4-93ec-7e73463dd029"
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      "source": [
        "#firstly, upload data to file\n",
        "covid_word = pd.read_csv('owid-covid-data.csv')\n",
        "covid_word"
      ],
      "execution_count": 4,
      "outputs": [
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              "      <td>104.000</td>\n",
              "      <td>4543.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.143</td>\n",
              "      <td>8375.137</td>\n",
              "      <td>8.614</td>\n",
              "      <td>6.891</td>\n",
              "      <td>301.017</td>\n",
              "      <td>0.331</td>\n",
              "      <td>0.473</td>\n",
              "      <td>0.88</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4063.0</td>\n",
              "      <td>1188549.0</td>\n",
              "      <td>78.753</td>\n",
              "      <td>0.269</td>\n",
              "      <td>3654.0</td>\n",
              "      <td>0.242</td>\n",
              "      <td>0.028</td>\n",
              "      <td>35.1</td>\n",
              "      <td>tests performed</td>\n",
              "      <td>4752356.0</td>\n",
              "      <td>2856655.0</td>\n",
              "      <td>1895701.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>41369.0</td>\n",
              "      <td>31.49</td>\n",
              "      <td>18.93</td>\n",
              "      <td>12.56</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2741.0</td>\n",
              "      <td>65.74</td>\n",
              "      <td>15092171.0</td>\n",
              "      <td>42.729</td>\n",
              "      <td>19.6</td>\n",
              "      <td>2.822</td>\n",
              "      <td>1.882</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",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117462</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2021-09-14</td>\n",
              "      <td>126817.0</td>\n",
              "      <td>418.0</td>\n",
              "      <td>145.857</td>\n",
              "      <td>4550.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.714</td>\n",
              "      <td>8402.833</td>\n",
              "      <td>27.696</td>\n",
              "      <td>9.664</td>\n",
              "      <td>301.481</td>\n",
              "      <td>0.464</td>\n",
              "      <td>0.445</td>\n",
              "      <td>0.91</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5427.0</td>\n",
              "      <td>1193976.0</td>\n",
              "      <td>79.112</td>\n",
              "      <td>0.360</td>\n",
              "      <td>3900.0</td>\n",
              "      <td>0.258</td>\n",
              "      <td>0.037</td>\n",
              "      <td>26.7</td>\n",
              "      <td>tests performed</td>\n",
              "      <td>4800761.0</td>\n",
              "      <td>2873593.0</td>\n",
              "      <td>1927168.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>48405.0</td>\n",
              "      <td>43831.0</td>\n",
              "      <td>31.81</td>\n",
              "      <td>19.04</td>\n",
              "      <td>12.77</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2904.0</td>\n",
              "      <td>65.74</td>\n",
              "      <td>15092171.0</td>\n",
              "      <td>42.729</td>\n",
              "      <td>19.6</td>\n",
              "      <td>2.822</td>\n",
              "      <td>1.882</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",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117463</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2021-09-15</td>\n",
              "      <td>127083.0</td>\n",
              "      <td>266.0</td>\n",
              "      <td>164.571</td>\n",
              "      <td>4551.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.857</td>\n",
              "      <td>8420.459</td>\n",
              "      <td>17.625</td>\n",
              "      <td>10.904</td>\n",
              "      <td>301.547</td>\n",
              "      <td>0.066</td>\n",
              "      <td>0.322</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6482.0</td>\n",
              "      <td>1200458.0</td>\n",
              "      <td>79.542</td>\n",
              "      <td>0.429</td>\n",
              "      <td>3899.0</td>\n",
              "      <td>0.258</td>\n",
              "      <td>0.042</td>\n",
              "      <td>23.7</td>\n",
              "      <td>tests performed</td>\n",
              "      <td>4855816.0</td>\n",
              "      <td>2891837.0</td>\n",
              "      <td>1963979.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>55055.0</td>\n",
              "      <td>43249.0</td>\n",
              "      <td>32.17</td>\n",
              "      <td>19.16</td>\n",
              "      <td>13.01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2866.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15092171.0</td>\n",
              "      <td>42.729</td>\n",
              "      <td>19.6</td>\n",
              "      <td>2.822</td>\n",
              "      <td>1.882</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",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117464</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2021-09-16</td>\n",
              "      <td>127368.0</td>\n",
              "      <td>285.0</td>\n",
              "      <td>187.429</td>\n",
              "      <td>4560.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>5.571</td>\n",
              "      <td>8439.342</td>\n",
              "      <td>18.884</td>\n",
              "      <td>12.419</td>\n",
              "      <td>302.143</td>\n",
              "      <td>0.596</td>\n",
              "      <td>0.369</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4964302.0</td>\n",
              "      <td>2930550.0</td>\n",
              "      <td>2033752.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>108486.0</td>\n",
              "      <td>51755.0</td>\n",
              "      <td>32.89</td>\n",
              "      <td>19.42</td>\n",
              "      <td>13.48</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3429.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15092171.0</td>\n",
              "      <td>42.729</td>\n",
              "      <td>19.6</td>\n",
              "      <td>2.822</td>\n",
              "      <td>1.882</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",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117465</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2021-09-17</td>\n",
              "      <td>127632.0</td>\n",
              "      <td>264.0</td>\n",
              "      <td>209.857</td>\n",
              "      <td>4562.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.286</td>\n",
              "      <td>8456.835</td>\n",
              "      <td>17.493</td>\n",
              "      <td>13.905</td>\n",
              "      <td>302.276</td>\n",
              "      <td>0.133</td>\n",
              "      <td>0.284</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15092171.0</td>\n",
              "      <td>42.729</td>\n",
              "      <td>19.6</td>\n",
              "      <td>2.822</td>\n",
              "      <td>1.882</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",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>117466 rows × 62 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       iso_code continent  ... human_development_index excess_mortality\n",
              "0           AFG      Asia  ...                   0.511              NaN\n",
              "1           AFG      Asia  ...                   0.511              NaN\n",
              "2           AFG      Asia  ...                   0.511              NaN\n",
              "3           AFG      Asia  ...                   0.511              NaN\n",
              "4           AFG      Asia  ...                   0.511              NaN\n",
              "...         ...       ...  ...                     ...              ...\n",
              "117461      ZWE    Africa  ...                   0.571              NaN\n",
              "117462      ZWE    Africa  ...                   0.571              NaN\n",
              "117463      ZWE    Africa  ...                   0.571              NaN\n",
              "117464      ZWE    Africa  ...                   0.571              NaN\n",
              "117465      ZWE    Africa  ...                   0.571              NaN\n",
              "\n",
              "[117466 rows x 62 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LjOECbXvCRwr"
      },
      "source": [
        "-> Let's recognized types of data correctly"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zzEa3ZGJCVoE",
        "outputId": "c80dec43-fe14-47b9-912a-c3e1a0b693e6"
      },
      "source": [
        "covid_word.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 117466 entries, 0 to 117465\n",
            "Data columns (total 62 columns):\n",
            " #   Column                                 Non-Null Count   Dtype  \n",
            "---  ------                                 --------------   -----  \n",
            " 0   iso_code                               117466 non-null  object \n",
            " 1   continent                              112095 non-null  object \n",
            " 2   location                               117466 non-null  object \n",
            " 3   date                                   117466 non-null  object \n",
            " 4   total_cases                            111681 non-null  float64\n",
            " 5   new_cases                              111678 non-null  float64\n",
            " 6   new_cases_smoothed                     110663 non-null  float64\n",
            " 7   total_deaths                           101006 non-null  float64\n",
            " 8   new_deaths                             101161 non-null  float64\n",
            " 9   new_deaths_smoothed                    110663 non-null  float64\n",
            " 10  total_cases_per_million                111092 non-null  float64\n",
            " 11  new_cases_per_million                  111089 non-null  float64\n",
            " 12  new_cases_smoothed_per_million         110079 non-null  float64\n",
            " 13  total_deaths_per_million               100430 non-null  float64\n",
            " 14  new_deaths_per_million                 100585 non-null  float64\n",
            " 15  new_deaths_smoothed_per_million        110079 non-null  float64\n",
            " 16  reproduction_rate                      94434 non-null   float64\n",
            " 17  icu_patients                           13330 non-null   float64\n",
            " 18  icu_patients_per_million               13330 non-null   float64\n",
            " 19  hosp_patients                          15306 non-null   float64\n",
            " 20  hosp_patients_per_million              15306 non-null   float64\n",
            " 21  weekly_icu_admissions                  1177 non-null    float64\n",
            " 22  weekly_icu_admissions_per_million      1177 non-null    float64\n",
            " 23  weekly_hosp_admissions                 2143 non-null    float64\n",
            " 24  weekly_hosp_admissions_per_million     2143 non-null    float64\n",
            " 25  new_tests                              51051 non-null   float64\n",
            " 26  total_tests                            50905 non-null   float64\n",
            " 27  total_tests_per_thousand               50905 non-null   float64\n",
            " 28  new_tests_per_thousand                 51051 non-null   float64\n",
            " 29  new_tests_smoothed                     60501 non-null   float64\n",
            " 30  new_tests_smoothed_per_thousand        60501 non-null   float64\n",
            " 31  positive_rate                          57240 non-null   float64\n",
            " 32  tests_per_case                         56614 non-null   float64\n",
            " 33  tests_units                            62379 non-null   object \n",
            " 34  total_vaccinations                     25791 non-null   float64\n",
            " 35  people_vaccinated                      24665 non-null   float64\n",
            " 36  people_fully_vaccinated                21661 non-null   float64\n",
            " 37  total_boosters                         2276 non-null    float64\n",
            " 38  new_vaccinations                       21342 non-null   float64\n",
            " 39  new_vaccinations_smoothed              46003 non-null   float64\n",
            " 40  total_vaccinations_per_hundred         25791 non-null   float64\n",
            " 41  people_vaccinated_per_hundred          24665 non-null   float64\n",
            " 42  people_fully_vaccinated_per_hundred    21661 non-null   float64\n",
            " 43  total_boosters_per_hundred             2276 non-null    float64\n",
            " 44  new_vaccinations_smoothed_per_million  46003 non-null   float64\n",
            " 45  stringency_index                       96941 non-null   float64\n",
            " 46  population                             116663 non-null  float64\n",
            " 47  population_density                     108612 non-null  float64\n",
            " 48  median_age                             103829 non-null  float64\n",
            " 49  aged_65_older                          102673 non-null  float64\n",
            " 50  aged_70_older                          103259 non-null  float64\n",
            " 51  gdp_per_capita                         104415 non-null  float64\n",
            " 52  extreme_poverty                        70120 non-null   float64\n",
            " 53  cardiovasc_death_rate                  104130 non-null  float64\n",
            " 54  diabetes_prevalence                    107277 non-null  float64\n",
            " 55  female_smokers                         81324 non-null   float64\n",
            " 56  male_smokers                           80142 non-null   float64\n",
            " 57  handwashing_facilities                 52306 non-null   float64\n",
            " 58  hospital_beds_per_thousand             94701 non-null   float64\n",
            " 59  life_expectancy                        111428 non-null  float64\n",
            " 60  human_development_index                104219 non-null  float64\n",
            " 61  excess_mortality                       4007 non-null    float64\n",
            "dtypes: float64(57), object(5)\n",
            "memory usage: 55.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3dcjtKqjC5yj"
      },
      "source": [
        "As we can see the first 4th columns and test_units are object, Let's investigate them"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "H0LBC5CbDJTD",
        "outputId": "9cc57830-7290-452f-954b-e18a76b866c7"
      },
      "source": [
        "fields = ['iso_code', 'continent', 'location', 'tests_units']\n",
        "covid_word[fields]"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>iso_code</th>\n",
              "      <th>continent</th>\n",
              "      <th>location</th>\n",
              "      <th>tests_units</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>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117461</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>tests performed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117462</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>tests performed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117463</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>tests performed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117464</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117465</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>117466 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       iso_code continent     location      tests_units\n",
              "0           AFG      Asia  Afghanistan              NaN\n",
              "1           AFG      Asia  Afghanistan              NaN\n",
              "2           AFG      Asia  Afghanistan              NaN\n",
              "3           AFG      Asia  Afghanistan              NaN\n",
              "4           AFG      Asia  Afghanistan              NaN\n",
              "...         ...       ...          ...              ...\n",
              "117461      ZWE    Africa     Zimbabwe  tests performed\n",
              "117462      ZWE    Africa     Zimbabwe  tests performed\n",
              "117463      ZWE    Africa     Zimbabwe  tests performed\n",
              "117464      ZWE    Africa     Zimbabwe              NaN\n",
              "117465      ZWE    Africa     Zimbabwe              NaN\n",
              "\n",
              "[117466 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9lAhJLX2D1k5"
      },
      "source": [
        "-> Let's investigate detail of the fields data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "AFvVv-bvEM87",
        "outputId": "4dbe7872-70a1-4876-d79e-fab9575d1b4a"
      },
      "source": [
        "covid_word[fields]"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>iso_code</th>\n",
              "      <th>continent</th>\n",
              "      <th>location</th>\n",
              "      <th>tests_units</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>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117461</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>tests performed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117462</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>tests performed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117463</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>tests performed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117464</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117465</th>\n",
              "      <td>ZWE</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>117466 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       iso_code continent     location      tests_units\n",
              "0           AFG      Asia  Afghanistan              NaN\n",
              "1           AFG      Asia  Afghanistan              NaN\n",
              "2           AFG      Asia  Afghanistan              NaN\n",
              "3           AFG      Asia  Afghanistan              NaN\n",
              "4           AFG      Asia  Afghanistan              NaN\n",
              "...         ...       ...          ...              ...\n",
              "117461      ZWE    Africa     Zimbabwe  tests performed\n",
              "117462      ZWE    Africa     Zimbabwe  tests performed\n",
              "117463      ZWE    Africa     Zimbabwe  tests performed\n",
              "117464      ZWE    Africa     Zimbabwe              NaN\n",
              "117465      ZWE    Africa     Zimbabwe              NaN\n",
              "\n",
              "[117466 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "LvsekCczFV5d",
        "outputId": "2a31439b-bdbc-4203-c409-7051a794c289"
      },
      "source": [
        "fields = ['iso_code', 'continent', 'location', 'tests_units']\n",
        "covid_word[fields] = covid_word[fields].astype('category')\n",
        "covid_word.loc[:, 'date'] = pd.to_datetime(covid_word['date'])\n",
        "covid_word[fields].describe()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>iso_code</th>\n",
              "      <th>continent</th>\n",
              "      <th>location</th>\n",
              "      <th>tests_units</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>117466</td>\n",
              "      <td>112095</td>\n",
              "      <td>117466</td>\n",
              "      <td>62379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>233</td>\n",
              "      <td>6</td>\n",
              "      <td>233</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>PER</td>\n",
              "      <td>Africa</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>tests performed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>626</td>\n",
              "      <td>29839</td>\n",
              "      <td>626</td>\n",
              "      <td>39159</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       iso_code continent   location      tests_units\n",
              "count    117466    112095     117466            62379\n",
              "unique      233         6        233                4\n",
              "top         PER    Africa  Argentina  tests performed\n",
              "freq        626     29839        626            39159"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W0OCUhwQG666"
      },
      "source": [
        "assigned fields to categorical fields because of many repetitions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E8pcNO5uKAe0"
      },
      "source": [
        "-> Let's show a list of countries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qy-hzAJIKD4C",
        "outputId": "5812cd2e-80e3-4595-fc22-97495ddcc696"
      },
      "source": [
        "covid_word['location'].cat.categories"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Afghanistan', 'Africa', 'Albania', 'Algeria', 'Andorra', 'Angola',\n",
              "       'Anguilla', 'Antigua and Barbuda', 'Argentina', 'Armenia',\n",
              "       ...\n",
              "       'Uzbekistan', 'Vanuatu', 'Vatican', 'Venezuela', 'Vietnam',\n",
              "       'Wallis and Futuna', 'World', 'Yemen', 'Zambia', 'Zimbabwe'],\n",
              "      dtype='object', length=233)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kYzh3f8hKpR6"
      },
      "source": [
        "-> Let's investigate the dynamics of new cases of COVID-19 for a separate country. We will use Indonesia for saving models and further forecast. Let's use a pandas filter to do this."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "dfs-CVWMKxo8",
        "outputId": "19d13993-3bcb-4463-cb49-308ec6a11ba8"
      },
      "source": [
        "covid_word.index = covid_word['date']\n",
        "\n",
        "c_covid = covid_word[covid_word['location'] == \"Indonesia\"]\n",
        "c_covid"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>iso_code</th>\n",
              "      <th>continent</th>\n",
              "      <th>location</th>\n",
              "      <th>date</th>\n",
              "      <th>total_cases</th>\n",
              "      <th>new_cases</th>\n",
              "      <th>new_cases_smoothed</th>\n",
              "      <th>total_deaths</th>\n",
              "      <th>new_deaths</th>\n",
              "      <th>new_deaths_smoothed</th>\n",
              "      <th>total_cases_per_million</th>\n",
              "      <th>new_cases_per_million</th>\n",
              "      <th>new_cases_smoothed_per_million</th>\n",
              "      <th>total_deaths_per_million</th>\n",
              "      <th>new_deaths_per_million</th>\n",
              "      <th>new_deaths_smoothed_per_million</th>\n",
              "      <th>reproduction_rate</th>\n",
              "      <th>icu_patients</th>\n",
              "      <th>icu_patients_per_million</th>\n",
              "      <th>hosp_patients</th>\n",
              "      <th>hosp_patients_per_million</th>\n",
              "      <th>weekly_icu_admissions</th>\n",
              "      <th>weekly_icu_admissions_per_million</th>\n",
              "      <th>weekly_hosp_admissions</th>\n",
              "      <th>weekly_hosp_admissions_per_million</th>\n",
              "      <th>new_tests</th>\n",
              "      <th>total_tests</th>\n",
              "      <th>total_tests_per_thousand</th>\n",
              "      <th>new_tests_per_thousand</th>\n",
              "      <th>new_tests_smoothed</th>\n",
              "      <th>new_tests_smoothed_per_thousand</th>\n",
              "      <th>positive_rate</th>\n",
              "      <th>tests_per_case</th>\n",
              "      <th>tests_units</th>\n",
              "      <th>total_vaccinations</th>\n",
              "      <th>people_vaccinated</th>\n",
              "      <th>people_fully_vaccinated</th>\n",
              "      <th>total_boosters</th>\n",
              "      <th>new_vaccinations</th>\n",
              "      <th>new_vaccinations_smoothed</th>\n",
              "      <th>total_vaccinations_per_hundred</th>\n",
              "      <th>people_vaccinated_per_hundred</th>\n",
              "      <th>people_fully_vaccinated_per_hundred</th>\n",
              "      <th>total_boosters_per_hundred</th>\n",
              "      <th>new_vaccinations_smoothed_per_million</th>\n",
              "      <th>stringency_index</th>\n",
              "      <th>population</th>\n",
              "      <th>population_density</th>\n",
              "      <th>median_age</th>\n",
              "      <th>aged_65_older</th>\n",
              "      <th>aged_70_older</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",
              "      <th>excess_mortality</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-03-02</th>\n",
              "      <td>IDN</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Indonesia</td>\n",
              "      <td>2020-03-02</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.007</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28.70</td>\n",
              "      <td>276361788.0</td>\n",
              "      <td>145.725</td>\n",
              "      <td>29.3</td>\n",
              "      <td>5.319</td>\n",
              "      <td>3.053</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",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-03</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",
              "      <td>0.007</td>\n",
              "      <td>0.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.04</td>\n",
              "      <td>276361788.0</td>\n",
              "      <td>145.725</td>\n",
              "      <td>29.3</td>\n",
              "      <td>5.319</td>\n",
              "      <td>3.053</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",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-04</th>\n",
              "      <td>IDN</td>\n",
              "      <td>Asia</td>\n",
              "      <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",
              "      <td>0.007</td>\n",
              "      <td>0.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.04</td>\n",
              "      <td>276361788.0</td>\n",
              "      <td>145.725</td>\n",
              "      <td>29.3</td>\n",
              "      <td>5.319</td>\n",
              "      <td>3.053</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",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-05</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>0.007</td>\n",
              "      <td>0.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.04</td>\n",
              "      <td>276361788.0</td>\n",
              "      <td>145.725</td>\n",
              "      <td>29.3</td>\n",
              "      <td>5.319</td>\n",
              "      <td>3.053</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",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-06</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>0.014</td>\n",
              "      <td>0.007</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.04</td>\n",
              "      <td>276361788.0</td>\n",
              "      <td>145.725</td>\n",
              "      <td>29.3</td>\n",
              "      <td>5.319</td>\n",
              "      <td>3.053</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",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <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>2021-09-13</th>\n",
              "      <td>IDN</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Indonesia</td>\n",
              "      <td>2021-09-13</td>\n",
              "      <td>4170088.0</td>\n",
              "      <td>2577.0</td>\n",
              "      <td>5236.429</td>\n",
              "      <td>139165.0</td>\n",
              "      <td>276.0</td>\n",
              "      <td>384.571</td>\n",
              "      <td>15089.235</td>\n",
              "      <td>9.325</td>\n",
              "      <td>18.948</td>\n",
              "      <td>503.561</td>\n",
              "      <td>0.999</td>\n",
              "      <td>1.392</td>\n",
              "      <td>0.60</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23199268.0</td>\n",
              "      <td>83.945</td>\n",
              "      <td>NaN</td>\n",
              "      <td>141506.0</td>\n",
              "      <td>0.512</td>\n",
              "      <td>0.037</td>\n",
              "      <td>27.0</td>\n",
              "      <td>people tested</td>\n",
              "      <td>116168745.0</td>\n",
              "      <td>73839683.0</td>\n",
              "      <td>42329062.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1667816.0</td>\n",
              "      <td>1355940.0</td>\n",
              "      <td>42.04</td>\n",
              "      <td>26.72</td>\n",
              "      <td>15.32</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4906.0</td>\n",
              "      <td>68.98</td>\n",
              "      <td>276361788.0</td>\n",
              "      <td>145.725</td>\n",
              "      <td>29.3</td>\n",
              "      <td>5.319</td>\n",
              "      <td>3.053</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",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-14</th>\n",
              "      <td>IDN</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Indonesia</td>\n",
              "      <td>2021-09-14</td>\n",
              "      <td>4174216.0</td>\n",
              "      <td>4128.0</td>\n",
              "      <td>4797.429</td>\n",
              "      <td>139415.0</td>\n",
              "      <td>250.0</td>\n",
              "      <td>322.714</td>\n",
              "      <td>15104.172</td>\n",
              "      <td>14.937</td>\n",
              "      <td>17.359</td>\n",
              "      <td>504.466</td>\n",
              "      <td>0.905</td>\n",
              "      <td>1.168</td>\n",
              "      <td>0.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>116822846.0</td>\n",
              "      <td>74257515.0</td>\n",
              "      <td>42565331.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>654101.0</td>\n",
              "      <td>1349754.0</td>\n",
              "      <td>42.27</td>\n",
              "      <td>26.87</td>\n",
              "      <td>15.40</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4884.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>276361788.0</td>\n",
              "      <td>145.725</td>\n",
              "      <td>29.3</td>\n",
              "      <td>5.319</td>\n",
              "      <td>3.053</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",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-15</th>\n",
              "      <td>IDN</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Indonesia</td>\n",
              "      <td>2021-09-15</td>\n",
              "      <td>4178164.0</td>\n",
              "      <td>3948.0</td>\n",
              "      <td>4399.857</td>\n",
              "      <td>139682.0</td>\n",
              "      <td>267.0</td>\n",
              "      <td>271.429</td>\n",
              "      <td>15118.458</td>\n",
              "      <td>14.286</td>\n",
              "      <td>15.921</td>\n",
              "      <td>505.432</td>\n",
              "      <td>0.966</td>\n",
              "      <td>0.982</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>118103364.0</td>\n",
              "      <td>75140724.0</td>\n",
              "      <td>42962640.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1280518.0</td>\n",
              "      <td>1312465.0</td>\n",
              "      <td>42.74</td>\n",
              "      <td>27.19</td>\n",
              "      <td>15.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4749.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>276361788.0</td>\n",
              "      <td>145.725</td>\n",
              "      <td>29.3</td>\n",
              "      <td>5.319</td>\n",
              "      <td>3.053</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",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-16</th>\n",
              "      <td>IDN</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Indonesia</td>\n",
              "      <td>2021-09-16</td>\n",
              "      <td>4181309.0</td>\n",
              "      <td>3145.0</td>\n",
              "      <td>3993.429</td>\n",
              "      <td>139919.0</td>\n",
              "      <td>237.0</td>\n",
              "      <td>257.571</td>\n",
              "      <td>15129.838</td>\n",
              "      <td>11.380</td>\n",
              "      <td>14.450</td>\n",
              "      <td>506.289</td>\n",
              "      <td>0.858</td>\n",
              "      <td>0.932</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>120328133.0</td>\n",
              "      <td>76583981.0</td>\n",
              "      <td>43744152.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2224769.0</td>\n",
              "      <td>1377536.0</td>\n",
              "      <td>43.54</td>\n",
              "      <td>27.71</td>\n",
              "      <td>15.83</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4985.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>276361788.0</td>\n",
              "      <td>145.725</td>\n",
              "      <td>29.3</td>\n",
              "      <td>5.319</td>\n",
              "      <td>3.053</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",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-17</th>\n",
              "      <td>IDN</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Indonesia</td>\n",
              "      <td>2021-09-17</td>\n",
              "      <td>4185144.0</td>\n",
              "      <td>3835.0</td>\n",
              "      <td>3773.286</td>\n",
              "      <td>140138.0</td>\n",
              "      <td>219.0</td>\n",
              "      <td>243.857</td>\n",
              "      <td>15143.714</td>\n",
              "      <td>13.877</td>\n",
              "      <td>13.653</td>\n",
              "      <td>507.082</td>\n",
              "      <td>0.792</td>\n",
              "      <td>0.882</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>121542203.0</td>\n",
              "      <td>77417488.0</td>\n",
              "      <td>44124715.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1214070.0</td>\n",
              "      <td>1356993.0</td>\n",
              "      <td>43.98</td>\n",
              "      <td>28.01</td>\n",
              "      <td>15.97</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4910.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>276361788.0</td>\n",
              "      <td>145.725</td>\n",
              "      <td>29.3</td>\n",
              "      <td>5.319</td>\n",
              "      <td>3.053</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",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>565 rows × 62 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           iso_code continent  ... human_development_index excess_mortality\n",
              "date                           ...                                         \n",
              "2020-03-02      IDN      Asia  ...                   0.718              NaN\n",
              "2020-03-03      IDN      Asia  ...                   0.718              NaN\n",
              "2020-03-04      IDN      Asia  ...                   0.718              NaN\n",
              "2020-03-05      IDN      Asia  ...                   0.718              NaN\n",
              "2020-03-06      IDN      Asia  ...                   0.718              NaN\n",
              "...             ...       ...  ...                     ...              ...\n",
              "2021-09-13      IDN      Asia  ...                   0.718              NaN\n",
              "2021-09-14      IDN      Asia  ...                   0.718              NaN\n",
              "2021-09-15      IDN      Asia  ...                   0.718              NaN\n",
              "2021-09-16      IDN      Asia  ...                   0.718              NaN\n",
              "2021-09-17      IDN      Asia  ...                   0.718              NaN\n",
              "\n",
              "[565 rows x 62 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KFwV2hbvMBA_"
      },
      "source": [
        "-> \n",
        "Let's choose the fields 'new_cases', 'new_cases_smoothed' for forecasting. First of all, we should visualize this data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "EHPv3JHSMDAx",
        "outputId": "b522c4b0-f729-4226-9eaf-1abaa65327f6"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fields = ['new_cases', 'new_cases_smoothed']\n",
        "c_covid[fields].plot()\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8HijPAA2NSnc"
      },
      "source": [
        "As we can see, there are large fluctuations in the new cases plot. Let's try to make a forecast for these waves. First of all, we should delete missing data using pandas.DataFrame.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "hRimyxbUN_YO",
        "outputId": "453755d8-ebe9-4b95-ab1a-8b09fd9baf3f"
      },
      "source": [
        "c_covid = c_covid[fields].dropna()\n",
        "c_covid"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>new_cases</th>\n",
              "      <th>new_cases_smoothed</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-03-07</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-08</th>\n",
              "      <td>2.0</td>\n",
              "      <td>0.857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-09</th>\n",
              "      <td>13.0</td>\n",
              "      <td>2.429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-10</th>\n",
              "      <td>8.0</td>\n",
              "      <td>3.571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-11</th>\n",
              "      <td>7.0</td>\n",
              "      <td>4.571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-13</th>\n",
              "      <td>2577.0</td>\n",
              "      <td>5236.429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-14</th>\n",
              "      <td>4128.0</td>\n",
              "      <td>4797.429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-15</th>\n",
              "      <td>3948.0</td>\n",
              "      <td>4399.857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-16</th>\n",
              "      <td>3145.0</td>\n",
              "      <td>3993.429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-17</th>\n",
              "      <td>3835.0</td>\n",
              "      <td>3773.286</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>560 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            new_cases  new_cases_smoothed\n",
              "date                                     \n",
              "2020-03-07        0.0               0.571\n",
              "2020-03-08        2.0               0.857\n",
              "2020-03-09       13.0               2.429\n",
              "2020-03-10        8.0               3.571\n",
              "2020-03-11        7.0               4.571\n",
              "...               ...                 ...\n",
              "2021-09-13     2577.0            5236.429\n",
              "2021-09-14     4128.0            4797.429\n",
              "2021-09-15     3948.0            4399.857\n",
              "2021-09-16     3145.0            3993.429\n",
              "2021-09-17     3835.0            3773.286\n",
              "\n",
              "[560 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cr48afiGOkmQ"
      },
      "source": [
        "-> If we want to make a forecast of time series, the only assumption that can be made - the data for today depends on the previous days values. To check for dependencies, it is necessary to perform a correlation analysis between them. This requires:\n",
        " \n",
        "1.   duplicating the time series of data and moving it\n",
        "2.   vertically down for a certain number of days (lag)\n",
        "3.   deleting the missing data at the beginning and end (they are formed by vertical shift (pandas.DataFrame.shift())\n",
        "4.   calculating the correlation coefficient between the obtained series.\n",
        "\n",
        "\n",
        "\n",
        "Since this operation should be performed for different values of the lag, it is convenient to create a separate function:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yn_lLK7xPBxP"
      },
      "source": [
        "def lag_correlation_ts(y, x, lag):\n",
        "    \"\"\"\n",
        "    Lag correlation for 2 DateSeries\n",
        "    :param y: fixed\n",
        "    :param x: shifted\n",
        "    :param lag: lag for shifting\n",
        "    :return: DataFrame of lags correlation coefficients\n",
        "    \"\"\"\n",
        "    r = [0] * (lag + 1)\n",
        "    y = y.copy()\n",
        "    x = x.copy()\n",
        "    y.name = \"y\"\n",
        "    x.name = \"x\"\n",
        "\n",
        "    for i in range(0, lag + 1):\n",
        "        ds = y.copy().to_frame()\n",
        "        ds = ds.join(x.shift(i), how='outer')\n",
        "        r[i] = ds.corr().values[0][1]\n",
        "    r = pd.DataFrame(r)\n",
        "    r.index.names = ['Lag']\n",
        "    r.columns = ['Correlation']\n",
        "    return r"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7JZtzcuWPPpz"
      },
      "source": [
        "-> Let's create a target DataSet."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "81ejNMTLPYrF",
        "outputId": "f0d596c9-6faa-4c71-d9d7-15ecd176b21a"
      },
      "source": [
        "y_dataset = c_covid['new_cases']\n",
        "y_dataset"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date\n",
              "2020-03-07       0.0\n",
              "2020-03-08       2.0\n",
              "2020-03-09      13.0\n",
              "2020-03-10       8.0\n",
              "2020-03-11       7.0\n",
              "               ...  \n",
              "2021-09-13    2577.0\n",
              "2021-09-14    4128.0\n",
              "2021-09-15    3948.0\n",
              "2021-09-16    3145.0\n",
              "2021-09-17    3835.0\n",
              "Name: new_cases, Length: 560, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zy8bNgxPPh8q"
      },
      "source": [
        "-> Let's test a 30-day lag."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "id": "eZBRDXnhPjcm",
        "outputId": "17c141f7-cf8d-4346-80f6-d8d58225d04c"
      },
      "source": [
        "pd.options.display.float_format = '{:,.4f}'.format\n",
        "l = pd.DataFrame(lag_correlation_ts(y_dataset, y_dataset, 30)) #For time series we should use y_Dataset like input and output\n",
        "print(l)\n",
        "l.plot()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Correlation\n",
            "Lag             \n",
            "0         1.0000\n",
            "1         0.9737\n",
            "2         0.9550\n",
            "3         0.9412\n",
            "4         0.9324\n",
            "5         0.9326\n",
            "6         0.9342\n",
            "7         0.9340\n",
            "8         0.9133\n",
            "9         0.8931\n",
            "10        0.8780\n",
            "11        0.8607\n",
            "12        0.8519\n",
            "13        0.8465\n",
            "14        0.8377\n",
            "15        0.8107\n",
            "16        0.7791\n",
            "17        0.7577\n",
            "18        0.7375\n",
            "19        0.7250\n",
            "20        0.7135\n",
            "21        0.6983\n",
            "22        0.6678\n",
            "23        0.6388\n",
            "24        0.6145\n",
            "25        0.5915\n",
            "26        0.5769\n",
            "27        0.5623\n",
            "28        0.5483\n",
            "29        0.5167\n",
            "30        0.4889\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4d8bbc0610>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_v1Dj3rxRPhL"
      },
      "source": [
        "As we can see, there are some waves observed in the lag plot. You can see from the table that there is a peak for every seventh day. (Maximum lags are: 7, 14, 21, etc. days). This is due to the weekly cycle."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mn-QsTcWRmM0"
      },
      "source": [
        "Any forecast model can be shown as a black box of input-target type. The target should be the data of the original time series, and the input should be the values for the previous days."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fT33VzP0SRuv"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LFfedRlxSUz8"
      },
      "source": [
        "-> To automate this process, let's make a universal function for time series transformation to create this DataSet."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tCQpO-EMSeOE"
      },
      "source": [
        "def series_to_supervised(in_data, tar_data, n_in=1, dropnan=True, target_dep=False):\n",
        "    \"\"\"\n",
        "    Transformation into a training sample taking into account the lag\n",
        "     : param in_data: Input fields\n",
        "     : param tar_data: Output field (single)\n",
        "     : param n_in: Lag shift\n",
        "     : param dropnan: Do destroy empty lines\n",
        "     : param target_dep: Whether to take into account the lag of the input field. If taken into account, the input will start with a lag 1\n",
        "     : return: Training sample. The last field is the source\n",
        "    \"\"\"\n",
        "\n",
        "    n_vars = in_data.shape[1]\n",
        "    cols, names = list(), list()\n",
        "\n",
        "    if target_dep:\n",
        "        i_start = 1\n",
        "    else:\n",
        "        i_start = 0\n",
        "    for i in range(i_start, n_in + 1):\n",
        "        cols.append(in_data.shift(i))\n",
        "        names += [('%s(t-%d)' % (in_data.columns[j], i)) for j in range(n_vars)]\n",
        "\n",
        "    if target_dep:\n",
        "        for i in range(n_in, -1, -1):\n",
        "            cols.append(tar_data.shift(i))\n",
        "            names += [('%s(t-%d)' % (tar_data.name, i))]\n",
        "    else:\n",
        "        # put it all together\n",
        "        cols.append(tar_data)\n",
        "        names.append(tar_data.name)\n",
        "    agg = pd.concat(cols, axis=1)\n",
        "    agg.columns = names\n",
        "\n",
        "    # drop rows with NaN values\n",
        "    if dropnan:\n",
        "        agg.dropna(inplace=True)\n",
        "\n",
        "    return agg"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s1ARNmEySocX"
      },
      "source": [
        "As mentioned above, when forecasting time series, the input and output fields are the same, only shifted by the lag."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "5n76XlZtSru2",
        "outputId": "bdc2d5ae-6ac0-404d-9178-e458df1e4ca5"
      },
      "source": [
        "dataset = series_to_supervised(pd.DataFrame(y_dataset), y_dataset, 14) ##target data based previous days\n",
        "dataset"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>new_cases(t-0)</th>\n",
              "      <th>new_cases(t-1)</th>\n",
              "      <th>new_cases(t-2)</th>\n",
              "      <th>new_cases(t-3)</th>\n",
              "      <th>new_cases(t-4)</th>\n",
              "      <th>new_cases(t-5)</th>\n",
              "      <th>new_cases(t-6)</th>\n",
              "      <th>new_cases(t-7)</th>\n",
              "      <th>new_cases(t-8)</th>\n",
              "      <th>new_cases(t-9)</th>\n",
              "      <th>new_cases(t-10)</th>\n",
              "      <th>new_cases(t-11)</th>\n",
              "      <th>new_cases(t-12)</th>\n",
              "      <th>new_cases(t-13)</th>\n",
              "      <th>new_cases(t-14)</th>\n",
              "      <th>new_cases</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-03-21</th>\n",
              "      <td>81.0000</td>\n",
              "      <td>58.0000</td>\n",
              "      <td>84.0000</td>\n",
              "      <td>55.0000</td>\n",
              "      <td>38.0000</td>\n",
              "      <td>17.0000</td>\n",
              "      <td>21.0000</td>\n",
              "      <td>27.0000</td>\n",
              "      <td>35.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>8.0000</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>2.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>81.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-22</th>\n",
              "      <td>64.0000</td>\n",
              "      <td>81.0000</td>\n",
              "      <td>58.0000</td>\n",
              "      <td>84.0000</td>\n",
              "      <td>55.0000</td>\n",
              "      <td>38.0000</td>\n",
              "      <td>17.0000</td>\n",
              "      <td>21.0000</td>\n",
              "      <td>27.0000</td>\n",
              "      <td>35.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>8.0000</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>2.0000</td>\n",
              "      <td>64.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-23</th>\n",
              "      <td>65.0000</td>\n",
              "      <td>64.0000</td>\n",
              "      <td>81.0000</td>\n",
              "      <td>58.0000</td>\n",
              "      <td>84.0000</td>\n",
              "      <td>55.0000</td>\n",
              "      <td>38.0000</td>\n",
              "      <td>17.0000</td>\n",
              "      <td>21.0000</td>\n",
              "      <td>27.0000</td>\n",
              "      <td>35.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>8.0000</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>65.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-24</th>\n",
              "      <td>107.0000</td>\n",
              "      <td>65.0000</td>\n",
              "      <td>64.0000</td>\n",
              "      <td>81.0000</td>\n",
              "      <td>58.0000</td>\n",
              "      <td>84.0000</td>\n",
              "      <td>55.0000</td>\n",
              "      <td>38.0000</td>\n",
              "      <td>17.0000</td>\n",
              "      <td>21.0000</td>\n",
              "      <td>27.0000</td>\n",
              "      <td>35.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>8.0000</td>\n",
              "      <td>107.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-25</th>\n",
              "      <td>104.0000</td>\n",
              "      <td>107.0000</td>\n",
              "      <td>65.0000</td>\n",
              "      <td>64.0000</td>\n",
              "      <td>81.0000</td>\n",
              "      <td>58.0000</td>\n",
              "      <td>84.0000</td>\n",
              "      <td>55.0000</td>\n",
              "      <td>38.0000</td>\n",
              "      <td>17.0000</td>\n",
              "      <td>21.0000</td>\n",
              "      <td>27.0000</td>\n",
              "      <td>35.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>104.0000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-13</th>\n",
              "      <td>2,577.0000</td>\n",
              "      <td>3,779.0000</td>\n",
              "      <td>5,001.0000</td>\n",
              "      <td>5,376.0000</td>\n",
              "      <td>5,990.0000</td>\n",
              "      <td>6,731.0000</td>\n",
              "      <td>7,201.0000</td>\n",
              "      <td>4,413.0000</td>\n",
              "      <td>5,403.0000</td>\n",
              "      <td>6,727.0000</td>\n",
              "      <td>16,752.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>10,337.0000</td>\n",
              "      <td>10,534.0000</td>\n",
              "      <td>5,436.0000</td>\n",
              "      <td>2,577.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-14</th>\n",
              "      <td>4,128.0000</td>\n",
              "      <td>2,577.0000</td>\n",
              "      <td>3,779.0000</td>\n",
              "      <td>5,001.0000</td>\n",
              "      <td>5,376.0000</td>\n",
              "      <td>5,990.0000</td>\n",
              "      <td>6,731.0000</td>\n",
              "      <td>7,201.0000</td>\n",
              "      <td>4,413.0000</td>\n",
              "      <td>5,403.0000</td>\n",
              "      <td>6,727.0000</td>\n",
              "      <td>16,752.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>10,337.0000</td>\n",
              "      <td>10,534.0000</td>\n",
              "      <td>4,128.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-15</th>\n",
              "      <td>3,948.0000</td>\n",
              "      <td>4,128.0000</td>\n",
              "      <td>2,577.0000</td>\n",
              "      <td>3,779.0000</td>\n",
              "      <td>5,001.0000</td>\n",
              "      <td>5,376.0000</td>\n",
              "      <td>5,990.0000</td>\n",
              "      <td>6,731.0000</td>\n",
              "      <td>7,201.0000</td>\n",
              "      <td>4,413.0000</td>\n",
              "      <td>5,403.0000</td>\n",
              "      <td>6,727.0000</td>\n",
              "      <td>16,752.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>10,337.0000</td>\n",
              "      <td>3,948.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-16</th>\n",
              "      <td>3,145.0000</td>\n",
              "      <td>3,948.0000</td>\n",
              "      <td>4,128.0000</td>\n",
              "      <td>2,577.0000</td>\n",
              "      <td>3,779.0000</td>\n",
              "      <td>5,001.0000</td>\n",
              "      <td>5,376.0000</td>\n",
              "      <td>5,990.0000</td>\n",
              "      <td>6,731.0000</td>\n",
              "      <td>7,201.0000</td>\n",
              "      <td>4,413.0000</td>\n",
              "      <td>5,403.0000</td>\n",
              "      <td>6,727.0000</td>\n",
              "      <td>16,752.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>3,145.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-17</th>\n",
              "      <td>3,835.0000</td>\n",
              "      <td>3,145.0000</td>\n",
              "      <td>3,948.0000</td>\n",
              "      <td>4,128.0000</td>\n",
              "      <td>2,577.0000</td>\n",
              "      <td>3,779.0000</td>\n",
              "      <td>5,001.0000</td>\n",
              "      <td>5,376.0000</td>\n",
              "      <td>5,990.0000</td>\n",
              "      <td>6,731.0000</td>\n",
              "      <td>7,201.0000</td>\n",
              "      <td>4,413.0000</td>\n",
              "      <td>5,403.0000</td>\n",
              "      <td>6,727.0000</td>\n",
              "      <td>16,752.0000</td>\n",
              "      <td>3,835.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>546 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            new_cases(t-0)  new_cases(t-1)  ...  new_cases(t-14)  new_cases\n",
              "date                                        ...                            \n",
              "2020-03-21         81.0000         58.0000  ...           0.0000    81.0000\n",
              "2020-03-22         64.0000         81.0000  ...           2.0000    64.0000\n",
              "2020-03-23         65.0000         64.0000  ...          13.0000    65.0000\n",
              "2020-03-24        107.0000         65.0000  ...           8.0000   107.0000\n",
              "2020-03-25        104.0000        107.0000  ...           7.0000   104.0000\n",
              "...                    ...             ...  ...              ...        ...\n",
              "2021-09-13      2,577.0000      3,779.0000  ...       5,436.0000 2,577.0000\n",
              "2021-09-14      4,128.0000      2,577.0000  ...      10,534.0000 4,128.0000\n",
              "2021-09-15      3,948.0000      4,128.0000  ...      10,337.0000 3,948.0000\n",
              "2021-09-16      3,145.0000      3,948.0000  ...           0.0000 3,145.0000\n",
              "2021-09-17      3,835.0000      3,145.0000  ...      16,752.0000 3,835.0000\n",
              "\n",
              "[546 rows x 16 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6T8nlMO1S7o5"
      },
      "source": [
        "-> As we can see, the first and last columns contain the same target data. Now we should create input (X) and output (Y) DataSets for forecasting models."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sUBBcLiYTKe0",
        "outputId": "a10b6979-dd83-4c4d-fbac-c11a7c12385a"
      },
      "source": [
        "col = dataset.columns\n",
        "X, Y = dataset[col[1:-1]], dataset[col[-1]]\n",
        "print(\"Input: \", X.columns)\n",
        "print(\"Target:\", Y.name)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input:  Index(['new_cases(t-1)', 'new_cases(t-2)', 'new_cases(t-3)', 'new_cases(t-4)',\n",
            "       'new_cases(t-5)', 'new_cases(t-6)', 'new_cases(t-7)', 'new_cases(t-8)',\n",
            "       'new_cases(t-9)', 'new_cases(t-10)', 'new_cases(t-11)',\n",
            "       'new_cases(t-12)', 'new_cases(t-13)', 'new_cases(t-14)'],\n",
            "      dtype='object')\n",
            "Target: new_cases\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hjHK0W4aTdnA"
      },
      "source": [
        "-> After that, we should normalize all the data. To do this, the sklearn.preprocessing.MinMaxScaler module should be used. It allows us to normalize fit_transform() and convert back all the data easily: fit_transform()."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51EWI1IFTfJF"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler_x = MinMaxScaler(feature_range=(0, 1))\n",
        "scaler_y = MinMaxScaler(feature_range=(0, 1))\n",
        "\n",
        "scaled_x = scaler_x.fit_transform(X)\n",
        "scaled_y = scaler_y.fit_transform(Y.values.reshape(-1, 1))"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0lpYoR1T61J"
      },
      "source": [
        "After that, we are going to form training and test DataSets using sklearn.model_selection.train_test_split(). We will make them at the ratio of 70/30. Without shuffling. It means, that test samples are located in the end of X and Y DataSets.\n",
        "\n",
        "Input normalized DataSets: X_train, X_test\n",
        "\n",
        "Target normalized DataSets: y_train, y_test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R8vaRqg9T78L"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(scaled_x, scaled_y, test_size=0.3, shuffle=False)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s6wSYCMcUImu"
      },
      "source": [
        "All the data is normalized now. However, in order to compare to results, we need real scale data of the training and test DataSet:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gjItvpB0UJ8s"
      },
      "source": [
        "res_train = scaler_y.inverse_transform(y_train).flatten()\n",
        "res_test = scaler_y.inverse_transform(y_test).flatten()"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Va3VRRP5VTOR"
      },
      "source": [
        "Target real scale DataSets: res_train, res_test."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LYJcjaCmVbc4"
      },
      "source": [
        "# Three type of models. A Linear regression, a Multilayer Neural Network with Backpropagation and a Long Short-Term Memory Neural Network. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S9OYTCA4W3sp"
      },
      "source": [
        "-> Let's create a LinearRegression():"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t_4y2g-SVniW"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "30AdZbHxV6E8"
      },
      "source": [
        "After that, our model should be fitted on the training DataSet. Regardless of the model type, the function fit() is used to do this."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-0qPDi40V7gi",
        "outputId": "d58da00f-b125-4d8a-9360-f13b0c5e0a15"
      },
      "source": [
        "regressor.fit(X_train, y_train)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B4qpXkpkWHJG"
      },
      "source": [
        "-> We can test it on the test DataSet and use it for prognostication."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hRJ_UIQNWMN2"
      },
      "source": [
        "y_pred_test_ln = regressor.predict(X_test)\n",
        "y_pred_test_ln = scaler_y.inverse_transform(y_pred_test_ln).flatten()"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x4_qOtcHWTf-"
      },
      "source": [
        "-> Let's analyze the results accuracy using sklearn.metrics."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B4j9yi8lWWSp",
        "outputId": "ef12cdc0-2291-42fb-d706-4bb53ecf9a67"
      },
      "source": [
        "from sklearn import metrics\n",
        "print(\"Correlation train\", regressor.score(X_train, y_train))\n",
        "print(\"Correlation test\", regressor.score(X_test, y_test))\n",
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred_test_ln))\n",
        "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred_test_ln))\n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred_test_ln)))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlation train 0.9690726751553604\n",
            "Correlation test 0.9268958308903932\n",
            "Mean Absolute Error: 16012.191407307837\n",
            "Mean Squared Error: 451457290.60015607\n",
            "Root Mean Squared Error: 21247.52434050038\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S8ChUUWiW9rO"
      },
      "source": [
        "The modern approach to the establishment of complex functional dependencies is the use of neural networks. A classical neural network is a multilayer neural network with back propagation.\n",
        "\n",
        "To do this, we will use the keras framework. First of all, we should create a Neural Network model as a separate function.\n",
        "\n",
        "A neural network is a sequence of layers. The function Sequential() is used to create a network.\n",
        "\n",
        "Let's create a network that consists of 2 hidden layers. Each of which consists of 100 neurons. keras.layers.Dense().\n",
        "\n",
        "To avoid retraining problems, we will use additional layers keras.layers.Dropout().\n",
        "\n",
        "The output layer will consist of one neuron, since we have only one value at the output.\n",
        "\n",
        "The model should be compiled for fitting and predicting: keras.Model.compile()."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w-KB88XEXNLw"
      },
      "source": [
        "-> Let's make a Multilayer Neural Network with Backpropagation "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qS_HHgOkXQ6N"
      },
      "source": [
        "def BP_model(X):\n",
        "    \"\"\"\n",
        "    Multilayer neural network with back propagation.\n",
        "    :param X: Input DataSet\n",
        "    :return: keras NN model\n",
        "    \"\"\"\n",
        "    # create model\n",
        "    model = Sequential() \n",
        "    model.add(Dense(100, input_dim=X.shape[1], kernel_initializer='normal', activation='relu'))\n",
        "    model.add(Dropout(0.2))\n",
        "    model.add(Dense(100, kernel_initializer='normal', activation='relu'))\n",
        "    model.add(Dropout(0.2))\n",
        "    model.add(Dense(1, kernel_initializer='normal'))\n",
        "    # Compile model\n",
        "    model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "    return model"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P1ICWJ88XiBd"
      },
      "source": [
        "Once the model function is built, it is necessary to create a neural network directly and specify the learning parameters: keras.wrappers.scikit_learn.KerasRegressor(). Also we should define the number of fitting epoch and batch size."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9HXEFs3zXjAI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ecc3adbc-0fb6-4776-bf0c-eb9bcf098c34"
      },
      "source": [
        "from keras.wrappers.scikit_learn import KerasRegressor\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Dropout\n",
        "epochs = 1000\n",
        "\n",
        "batch_size=int(y_train.shape[0]*.1)\n",
        "\n",
        "estimator = KerasRegressor(build_fn=BP_model, X=X_train, epochs=epochs, batch_size=batch_size, verbose=1)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:9: DeprecationWarning: KerasRegressor is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead.\n",
            "  if __name__ == '__main__':\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_J935k1WXpTH"
      },
      "source": [
        "Now, let’s train our model for 1000 epochs. It should be noted that the fitting process is very slow. Therefore, we saved our fitted model to a file. To save time, we will upload the fitted model. If we like, we can leave the parameter fitting on True to refit the model. If you like, we can leave the parameter fitting_save on True to resave the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jefibeogXvDm",
        "outputId": "5fa8f4bb-6ac2-4399-caf8-bc909e97be49"
      },
      "source": [
        "fitting = True\n",
        "fitting_save = True\n",
        "\n",
        "import pickle\n",
        "\n",
        "if fitting:\n",
        "    history=estimator.fit(X_train,y_train, validation_data=(X_test,y_test)) # Fitting model\n",
        "    if fitting_save:\n",
        "        # Save model\n",
        "        estimator.model.save('BP_saved_model.h5')\n",
        "        print(\"Saved model to disk\")\n",
        "        with open('history.pickle', 'wb') as f:\n",
        "            pickle.dump(history.history, f)\n",
        "# load model \n",
        "from keras.models import load_model\n",
        "\n",
        "# Instantiate the model as you please (we are not going to use this)\n",
        "estimator = KerasRegressor(build_fn=BP_model, X=X_train, epochs=epochs, batch_size=batch_size, verbose=1)\n",
        "# This is where you load the actual saved model into a new variable.\n",
        "estimator.model = load_model('BP_saved_model.h5')    \n",
        "with open('history.pickle', 'rb') as f:\n",
        "    history = pickle.load(f)\n",
        "print(\"Loaded model from disk\")"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/1000\n",
            "11/11 [==============================] - 1s 19ms/step - loss: 0.0051 - val_loss: 0.0552\n",
            "Epoch 2/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0014 - val_loss: 0.0214\n",
            "Epoch 3/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 8.7981e-04 - val_loss: 0.0175\n",
            "Epoch 4/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 4.1961e-04 - val_loss: 0.0122\n",
            "Epoch 5/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 4.5170e-04 - val_loss: 0.0124\n",
            "Epoch 6/1000\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 3.8110e-04 - val_loss: 0.0118\n",
            "Epoch 7/1000\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 4.0494e-04 - val_loss: 0.0124\n",
            "Epoch 8/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 3.9698e-04 - val_loss: 0.0117\n",
            "Epoch 9/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 3.9054e-04 - val_loss: 0.0118\n",
            "Epoch 10/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 3.7513e-04 - val_loss: 0.0118\n",
            "Epoch 11/1000\n",
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            "Epoch 134/1000\n",
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            "Epoch 162/1000\n",
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            "Epoch 163/1000\n",
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            "Epoch 164/1000\n",
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            "Epoch 165/1000\n",
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            "Epoch 166/1000\n",
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            "Epoch 167/1000\n",
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            "Epoch 168/1000\n",
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            "Epoch 169/1000\n",
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            "Epoch 170/1000\n",
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            "Epoch 492/1000\n",
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            "Epoch 512/1000\n",
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            "Epoch 559/1000\n",
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            "Epoch 560/1000\n",
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            "Epoch 564/1000\n",
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            "Epoch 565/1000\n",
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            "Epoch 567/1000\n",
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            "Epoch 569/1000\n",
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            "Epoch 571/1000\n",
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            "Epoch 573/1000\n",
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            "Epoch 578/1000\n",
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            "Epoch 579/1000\n",
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            "Epoch 580/1000\n",
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            "Epoch 586/1000\n",
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            "Epoch 587/1000\n",
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            "Epoch 588/1000\n",
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            "Epoch 590/1000\n",
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            "Epoch 591/1000\n",
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            "Epoch 592/1000\n",
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            "Epoch 593/1000\n",
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            "Epoch 594/1000\n",
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            "Epoch 596/1000\n",
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            "Epoch 597/1000\n",
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            "Epoch 598/1000\n",
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            "Epoch 599/1000\n",
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            "Epoch 600/1000\n",
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            "Epoch 601/1000\n",
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            "Epoch 602/1000\n",
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            "Epoch 603/1000\n",
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            "Epoch 604/1000\n",
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            "Epoch 605/1000\n",
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            "Epoch 606/1000\n",
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            "Epoch 607/1000\n",
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            "Epoch 608/1000\n",
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            "Epoch 609/1000\n",
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            "Epoch 610/1000\n",
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            "Epoch 611/1000\n",
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            "Epoch 612/1000\n",
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            "Epoch 613/1000\n",
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            "Epoch 615/1000\n",
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            "Epoch 616/1000\n",
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            "Epoch 617/1000\n",
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            "Epoch 618/1000\n",
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            "Epoch 619/1000\n",
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            "Epoch 620/1000\n",
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            "Epoch 621/1000\n",
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            "Epoch 622/1000\n",
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            "Epoch 623/1000\n",
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            "Epoch 624/1000\n",
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            "Epoch 625/1000\n",
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            "Epoch 626/1000\n",
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            "Epoch 627/1000\n",
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            "Epoch 628/1000\n",
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            "Epoch 629/1000\n",
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            "Epoch 631/1000\n",
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            "Epoch 632/1000\n",
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            "Epoch 633/1000\n",
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            "Epoch 634/1000\n",
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            "Epoch 635/1000\n",
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            "Epoch 636/1000\n",
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            "Epoch 637/1000\n",
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            "Epoch 638/1000\n",
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            "Epoch 639/1000\n",
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            "Epoch 640/1000\n",
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            "Epoch 641/1000\n",
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            "Epoch 642/1000\n",
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            "Epoch 643/1000\n",
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            "Epoch 644/1000\n",
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            "Epoch 645/1000\n",
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            "Epoch 646/1000\n",
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            "Epoch 647/1000\n",
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            "Epoch 648/1000\n",
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            "Epoch 649/1000\n",
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            "Epoch 650/1000\n",
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            "Epoch 651/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7039e-04 - val_loss: 0.0045\n",
            "Epoch 652/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6335e-04 - val_loss: 0.0043\n",
            "Epoch 653/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 2.0203e-04 - val_loss: 0.0043\n",
            "Epoch 654/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6909e-04 - val_loss: 0.0043\n",
            "Epoch 655/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.6961e-04 - val_loss: 0.0046\n",
            "Epoch 656/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.7812e-04 - val_loss: 0.0046\n",
            "Epoch 657/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6121e-04 - val_loss: 0.0048\n",
            "Epoch 658/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8489e-04 - val_loss: 0.0043\n",
            "Epoch 659/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7705e-04 - val_loss: 0.0043\n",
            "Epoch 660/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7833e-04 - val_loss: 0.0045\n",
            "Epoch 661/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.9268e-04 - val_loss: 0.0043\n",
            "Epoch 662/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6950e-04 - val_loss: 0.0050\n",
            "Epoch 663/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 2.4511e-04 - val_loss: 0.0044\n",
            "Epoch 664/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 2.7582e-04 - val_loss: 0.0046\n",
            "Epoch 665/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7139e-04 - val_loss: 0.0044\n",
            "Epoch 666/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 2.0121e-04 - val_loss: 0.0044\n",
            "Epoch 667/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.5997e-04 - val_loss: 0.0043\n",
            "Epoch 668/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6246e-04 - val_loss: 0.0043\n",
            "Epoch 669/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.7457e-04 - val_loss: 0.0043\n",
            "Epoch 670/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.7665e-04 - val_loss: 0.0043\n",
            "Epoch 671/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.0301e-04 - val_loss: 0.0045\n",
            "Epoch 672/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6962e-04 - val_loss: 0.0051\n",
            "Epoch 673/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8599e-04 - val_loss: 0.0048\n",
            "Epoch 674/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7359e-04 - val_loss: 0.0044\n",
            "Epoch 675/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.8865e-04 - val_loss: 0.0045\n",
            "Epoch 676/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.8095e-04 - val_loss: 0.0044\n",
            "Epoch 677/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.6079e-04 - val_loss: 0.0047\n",
            "Epoch 678/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.7595e-04 - val_loss: 0.0043\n",
            "Epoch 679/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8434e-04 - val_loss: 0.0043\n",
            "Epoch 680/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.5057e-04 - val_loss: 0.0043\n",
            "Epoch 681/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.6891e-04 - val_loss: 0.0043\n",
            "Epoch 682/1000\n",
            "11/11 [==============================] - 0s 10ms/step - loss: 1.9741e-04 - val_loss: 0.0054\n",
            "Epoch 683/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.1938e-04 - val_loss: 0.0045\n",
            "Epoch 684/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.8256e-04 - val_loss: 0.0053\n",
            "Epoch 685/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 2.7020e-04 - val_loss: 0.0053\n",
            "Epoch 686/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.2206e-04 - val_loss: 0.0046\n",
            "Epoch 687/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.1102e-04 - val_loss: 0.0043\n",
            "Epoch 688/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.8742e-04 - val_loss: 0.0048\n",
            "Epoch 689/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 2.1344e-04 - val_loss: 0.0046\n",
            "Epoch 690/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 2.2094e-04 - val_loss: 0.0043\n",
            "Epoch 691/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6487e-04 - val_loss: 0.0043\n",
            "Epoch 692/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.8039e-04 - val_loss: 0.0045\n",
            "Epoch 693/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.9968e-04 - val_loss: 0.0046\n",
            "Epoch 694/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6975e-04 - val_loss: 0.0044\n",
            "Epoch 695/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8437e-04 - val_loss: 0.0043\n",
            "Epoch 696/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.8837e-04 - val_loss: 0.0047\n",
            "Epoch 697/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.4803e-04 - val_loss: 0.0043\n",
            "Epoch 698/1000\n",
            "11/11 [==============================] - 0s 11ms/step - loss: 1.8131e-04 - val_loss: 0.0043\n",
            "Epoch 699/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7398e-04 - val_loss: 0.0043\n",
            "Epoch 700/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.5709e-04 - val_loss: 0.0045\n",
            "Epoch 701/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.8345e-04 - val_loss: 0.0043\n",
            "Epoch 702/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.9005e-04 - val_loss: 0.0043\n",
            "Epoch 703/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6214e-04 - val_loss: 0.0047\n",
            "Epoch 704/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.8495e-04 - val_loss: 0.0043\n",
            "Epoch 705/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.7611e-04 - val_loss: 0.0043\n",
            "Epoch 706/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.8938e-04 - val_loss: 0.0043\n",
            "Epoch 707/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.5102e-04 - val_loss: 0.0043\n",
            "Epoch 708/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.9260e-04 - val_loss: 0.0044\n",
            "Epoch 709/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.5251e-04 - val_loss: 0.0044\n",
            "Epoch 710/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6971e-04 - val_loss: 0.0046\n",
            "Epoch 711/1000\n",
            "11/11 [==============================] - 0s 10ms/step - loss: 2.6240e-04 - val_loss: 0.0043\n",
            "Epoch 712/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.3196e-04 - val_loss: 0.0051\n",
            "Epoch 713/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.9450e-04 - val_loss: 0.0043\n",
            "Epoch 714/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6767e-04 - val_loss: 0.0043\n",
            "Epoch 715/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8657e-04 - val_loss: 0.0043\n",
            "Epoch 716/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.0864e-04 - val_loss: 0.0043\n",
            "Epoch 717/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.7416e-04 - val_loss: 0.0043\n",
            "Epoch 718/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.8188e-04 - val_loss: 0.0048\n",
            "Epoch 719/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.3937e-04 - val_loss: 0.0045\n",
            "Epoch 720/1000\n",
            "11/11 [==============================] - 0s 11ms/step - loss: 1.9356e-04 - val_loss: 0.0049\n",
            "Epoch 721/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.8316e-04 - val_loss: 0.0043\n",
            "Epoch 722/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 2.0637e-04 - val_loss: 0.0047\n",
            "Epoch 723/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.0538e-04 - val_loss: 0.0045\n",
            "Epoch 724/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.8416e-04 - val_loss: 0.0044\n",
            "Epoch 725/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.7202e-04 - val_loss: 0.0044\n",
            "Epoch 726/1000\n",
            "11/11 [==============================] - 0s 11ms/step - loss: 1.7172e-04 - val_loss: 0.0043\n",
            "Epoch 727/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8556e-04 - val_loss: 0.0043\n",
            "Epoch 728/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.6474e-04 - val_loss: 0.0043\n",
            "Epoch 729/1000\n",
            "11/11 [==============================] - 0s 12ms/step - loss: 1.8758e-04 - val_loss: 0.0043\n",
            "Epoch 730/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.4844e-04 - val_loss: 0.0047\n",
            "Epoch 731/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.9403e-04 - val_loss: 0.0044\n",
            "Epoch 732/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.4928e-04 - val_loss: 0.0043\n",
            "Epoch 733/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8369e-04 - val_loss: 0.0043\n",
            "Epoch 734/1000\n",
            "11/11 [==============================] - 0s 11ms/step - loss: 1.6000e-04 - val_loss: 0.0043\n",
            "Epoch 735/1000\n",
            "11/11 [==============================] - 0s 10ms/step - loss: 1.5474e-04 - val_loss: 0.0044\n",
            "Epoch 736/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7462e-04 - val_loss: 0.0045\n",
            "Epoch 737/1000\n",
            "11/11 [==============================] - 0s 10ms/step - loss: 1.9792e-04 - val_loss: 0.0044\n",
            "Epoch 738/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.9137e-04 - val_loss: 0.0046\n",
            "Epoch 739/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 2.0061e-04 - val_loss: 0.0044\n",
            "Epoch 740/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.7862e-04 - val_loss: 0.0043\n",
            "Epoch 741/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.9234e-04 - val_loss: 0.0046\n",
            "Epoch 742/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6985e-04 - val_loss: 0.0043\n",
            "Epoch 743/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.5953e-04 - val_loss: 0.0043\n",
            "Epoch 744/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.6095e-04 - val_loss: 0.0043\n",
            "Epoch 745/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.7550e-04 - val_loss: 0.0043\n",
            "Epoch 746/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.7773e-04 - val_loss: 0.0044\n",
            "Epoch 747/1000\n",
            "11/11 [==============================] - 0s 12ms/step - loss: 1.6497e-04 - val_loss: 0.0045\n",
            "Epoch 748/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.7435e-04 - val_loss: 0.0044\n",
            "Epoch 749/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.8517e-04 - val_loss: 0.0043\n",
            "Epoch 750/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7414e-04 - val_loss: 0.0051\n",
            "Epoch 751/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 2.2689e-04 - val_loss: 0.0046\n",
            "Epoch 752/1000\n",
            "11/11 [==============================] - 0s 10ms/step - loss: 2.0808e-04 - val_loss: 0.0044\n",
            "Epoch 753/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6944e-04 - val_loss: 0.0045\n",
            "Epoch 754/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.7668e-04 - val_loss: 0.0043\n",
            "Epoch 755/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8725e-04 - val_loss: 0.0056\n",
            "Epoch 756/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.3831e-04 - val_loss: 0.0043\n",
            "Epoch 757/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.5591e-04 - val_loss: 0.0044\n",
            "Epoch 758/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.8435e-04 - val_loss: 0.0043\n",
            "Epoch 759/1000\n",
            "11/11 [==============================] - 0s 10ms/step - loss: 1.4330e-04 - val_loss: 0.0051\n",
            "Epoch 760/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.9121e-04 - val_loss: 0.0046\n",
            "Epoch 761/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6534e-04 - val_loss: 0.0045\n",
            "Epoch 762/1000\n",
            "11/11 [==============================] - 0s 10ms/step - loss: 1.7869e-04 - val_loss: 0.0044\n",
            "Epoch 763/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8576e-04 - val_loss: 0.0047\n",
            "Epoch 764/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.8029e-04 - val_loss: 0.0044\n",
            "Epoch 765/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.6999e-04 - val_loss: 0.0044\n",
            "Epoch 766/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.9014e-04 - val_loss: 0.0045\n",
            "Epoch 767/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.5590e-04 - val_loss: 0.0051\n",
            "Epoch 768/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.9183e-04 - val_loss: 0.0044\n",
            "Epoch 769/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.0806e-04 - val_loss: 0.0045\n",
            "Epoch 770/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 2.5020e-04 - val_loss: 0.0054\n",
            "Epoch 771/1000\n",
            "11/11 [==============================] - 0s 10ms/step - loss: 1.7546e-04 - val_loss: 0.0044\n",
            "Epoch 772/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6933e-04 - val_loss: 0.0043\n",
            "Epoch 773/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.8992e-04 - val_loss: 0.0044\n",
            "Epoch 774/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.9774e-04 - val_loss: 0.0046\n",
            "Epoch 775/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.9548e-04 - val_loss: 0.0043\n",
            "Epoch 776/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.5138e-04 - val_loss: 0.0047\n",
            "Epoch 777/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.9624e-04 - val_loss: 0.0045\n",
            "Epoch 778/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6526e-04 - val_loss: 0.0044\n",
            "Epoch 779/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6355e-04 - val_loss: 0.0043\n",
            "Epoch 780/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6531e-04 - val_loss: 0.0044\n",
            "Epoch 781/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.5563e-04 - val_loss: 0.0044\n",
            "Epoch 782/1000\n",
            "11/11 [==============================] - 0s 10ms/step - loss: 1.8057e-04 - val_loss: 0.0043\n",
            "Epoch 783/1000\n",
            "11/11 [==============================] - 0s 10ms/step - loss: 1.5852e-04 - val_loss: 0.0046\n",
            "Epoch 784/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 2.0171e-04 - val_loss: 0.0045\n",
            "Epoch 785/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.6983e-04 - val_loss: 0.0044\n",
            "Epoch 786/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.3400e-04 - val_loss: 0.0045\n",
            "Epoch 787/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8315e-04 - val_loss: 0.0044\n",
            "Epoch 788/1000\n",
            "11/11 [==============================] - 0s 11ms/step - loss: 1.8714e-04 - val_loss: 0.0044\n",
            "Epoch 789/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.5388e-04 - val_loss: 0.0043\n",
            "Epoch 790/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.6903e-04 - val_loss: 0.0044\n",
            "Epoch 791/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.8705e-04 - val_loss: 0.0045\n",
            "Epoch 792/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6510e-04 - val_loss: 0.0043\n",
            "Epoch 793/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 2.0876e-04 - val_loss: 0.0043\n",
            "Epoch 794/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.7497e-04 - val_loss: 0.0043\n",
            "Epoch 795/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.7436e-04 - val_loss: 0.0043\n",
            "Epoch 796/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.5703e-04 - val_loss: 0.0043\n",
            "Epoch 797/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.3798e-04 - val_loss: 0.0043\n",
            "Epoch 798/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7679e-04 - val_loss: 0.0043\n",
            "Epoch 799/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.7127e-04 - val_loss: 0.0046\n",
            "Epoch 800/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.7222e-04 - val_loss: 0.0045\n",
            "Epoch 801/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6162e-04 - val_loss: 0.0043\n",
            "Epoch 802/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.5204e-04 - val_loss: 0.0046\n",
            "Epoch 803/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7060e-04 - val_loss: 0.0044\n",
            "Epoch 804/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.8714e-04 - val_loss: 0.0044\n",
            "Epoch 805/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.7189e-04 - val_loss: 0.0043\n",
            "Epoch 806/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6065e-04 - val_loss: 0.0044\n",
            "Epoch 807/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8227e-04 - val_loss: 0.0045\n",
            "Epoch 808/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.5175e-04 - val_loss: 0.0045\n",
            "Epoch 809/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.5663e-04 - val_loss: 0.0043\n",
            "Epoch 810/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6619e-04 - val_loss: 0.0043\n",
            "Epoch 811/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.5964e-04 - val_loss: 0.0045\n",
            "Epoch 812/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.5962e-04 - val_loss: 0.0044\n",
            "Epoch 813/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8555e-04 - val_loss: 0.0044\n",
            "Epoch 814/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.5476e-04 - val_loss: 0.0044\n",
            "Epoch 815/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8599e-04 - val_loss: 0.0043\n",
            "Epoch 816/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.7124e-04 - val_loss: 0.0043\n",
            "Epoch 817/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.9745e-04 - val_loss: 0.0045\n",
            "Epoch 818/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.5779e-04 - val_loss: 0.0046\n",
            "Epoch 819/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6725e-04 - val_loss: 0.0047\n",
            "Epoch 820/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 2.1106e-04 - val_loss: 0.0047\n",
            "Epoch 821/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 2.7392e-04 - val_loss: 0.0046\n",
            "Epoch 822/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.1657e-04 - val_loss: 0.0045\n",
            "Epoch 823/1000\n",
            "11/11 [==============================] - 0s 11ms/step - loss: 1.9797e-04 - val_loss: 0.0045\n",
            "Epoch 824/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 2.2928e-04 - val_loss: 0.0044\n",
            "Epoch 825/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.8351e-04 - val_loss: 0.0046\n",
            "Epoch 826/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.8218e-04 - val_loss: 0.0046\n",
            "Epoch 827/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6914e-04 - val_loss: 0.0044\n",
            "Epoch 828/1000\n",
            "11/11 [==============================] - 0s 10ms/step - loss: 2.0954e-04 - val_loss: 0.0047\n",
            "Epoch 829/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.7724e-04 - val_loss: 0.0045\n",
            "Epoch 830/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8731e-04 - val_loss: 0.0046\n",
            "Epoch 831/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.9055e-04 - val_loss: 0.0044\n",
            "Epoch 832/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.5887e-04 - val_loss: 0.0044\n",
            "Epoch 833/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.5733e-04 - val_loss: 0.0044\n",
            "Epoch 834/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.6440e-04 - val_loss: 0.0043\n",
            "Epoch 835/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.9058e-04 - val_loss: 0.0044\n",
            "Epoch 836/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.6654e-04 - val_loss: 0.0043\n",
            "Epoch 837/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7111e-04 - val_loss: 0.0044\n",
            "Epoch 838/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.0382e-04 - val_loss: 0.0043\n",
            "Epoch 839/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.0081e-04 - val_loss: 0.0043\n",
            "Epoch 840/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 2.8854e-04 - val_loss: 0.0056\n",
            "Epoch 841/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 2.5289e-04 - val_loss: 0.0051\n",
            "Epoch 842/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 2.0686e-04 - val_loss: 0.0047\n",
            "Epoch 843/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8893e-04 - val_loss: 0.0047\n",
            "Epoch 844/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 2.1660e-04 - val_loss: 0.0043\n",
            "Epoch 845/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 2.0748e-04 - val_loss: 0.0045\n",
            "Epoch 846/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.9082e-04 - val_loss: 0.0043\n",
            "Epoch 847/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.7057e-04 - val_loss: 0.0051\n",
            "Epoch 848/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.8663e-04 - val_loss: 0.0043\n",
            "Epoch 849/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.7038e-04 - val_loss: 0.0044\n",
            "Epoch 850/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 2.2108e-04 - val_loss: 0.0055\n",
            "Epoch 851/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 2.6527e-04 - val_loss: 0.0053\n",
            "Epoch 852/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8240e-04 - val_loss: 0.0044\n",
            "Epoch 853/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.7692e-04 - val_loss: 0.0045\n",
            "Epoch 854/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.9800e-04 - val_loss: 0.0043\n",
            "Epoch 855/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.9256e-04 - val_loss: 0.0049\n",
            "Epoch 856/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.9249e-04 - val_loss: 0.0043\n",
            "Epoch 857/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.0606e-04 - val_loss: 0.0044\n",
            "Epoch 858/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 2.1018e-04 - val_loss: 0.0044\n",
            "Epoch 859/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.7219e-04 - val_loss: 0.0046\n",
            "Epoch 860/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6872e-04 - val_loss: 0.0043\n",
            "Epoch 861/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.8397e-04 - val_loss: 0.0042\n",
            "Epoch 862/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7179e-04 - val_loss: 0.0044\n",
            "Epoch 863/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.7568e-04 - val_loss: 0.0043\n",
            "Epoch 864/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7001e-04 - val_loss: 0.0043\n",
            "Epoch 865/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.4763e-04 - val_loss: 0.0043\n",
            "Epoch 866/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.8936e-04 - val_loss: 0.0044\n",
            "Epoch 867/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.5217e-04 - val_loss: 0.0043\n",
            "Epoch 868/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8512e-04 - val_loss: 0.0045\n",
            "Epoch 869/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.8164e-04 - val_loss: 0.0044\n",
            "Epoch 870/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 2.0082e-04 - val_loss: 0.0047\n",
            "Epoch 871/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8744e-04 - val_loss: 0.0043\n",
            "Epoch 872/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8387e-04 - val_loss: 0.0045\n",
            "Epoch 873/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.4972e-04 - val_loss: 0.0043\n",
            "Epoch 874/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6525e-04 - val_loss: 0.0043\n",
            "Epoch 875/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.7991e-04 - val_loss: 0.0044\n",
            "Epoch 876/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6872e-04 - val_loss: 0.0043\n",
            "Epoch 877/1000\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 1.8184e-04 - val_loss: 0.0043\n",
            "Epoch 878/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6896e-04 - val_loss: 0.0043\n",
            "Epoch 879/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.7001e-04 - val_loss: 0.0046\n",
            "Epoch 880/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6309e-04 - val_loss: 0.0043\n",
            "Epoch 881/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.7987e-04 - val_loss: 0.0043\n",
            "Epoch 882/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8481e-04 - val_loss: 0.0044\n",
            "Epoch 883/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8843e-04 - val_loss: 0.0046\n",
            "Epoch 884/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8507e-04 - val_loss: 0.0044\n",
            "Epoch 885/1000\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 1.8078e-04 - val_loss: 0.0043\n",
            "Epoch 886/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.8447e-04 - val_loss: 0.0046\n",
            "Epoch 887/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6590e-04 - val_loss: 0.0043\n",
            "Epoch 888/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.8201e-04 - val_loss: 0.0044\n",
            "Epoch 889/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.7656e-04 - val_loss: 0.0043\n",
            "Epoch 890/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6727e-04 - val_loss: 0.0043\n",
            "Epoch 891/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.3016e-04 - val_loss: 0.0043\n",
            "Epoch 892/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7708e-04 - val_loss: 0.0043\n",
            "Epoch 893/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.7490e-04 - val_loss: 0.0046\n",
            "Epoch 894/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.8162e-04 - val_loss: 0.0043\n",
            "Epoch 895/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6927e-04 - val_loss: 0.0045\n",
            "Epoch 896/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7133e-04 - val_loss: 0.0043\n",
            "Epoch 897/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7847e-04 - val_loss: 0.0043\n",
            "Epoch 898/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6171e-04 - val_loss: 0.0043\n",
            "Epoch 899/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.8520e-04 - val_loss: 0.0043\n",
            "Epoch 900/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.5092e-04 - val_loss: 0.0044\n",
            "Epoch 901/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 2.0083e-04 - val_loss: 0.0055\n",
            "Epoch 902/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 2.2216e-04 - val_loss: 0.0050\n",
            "Epoch 903/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8954e-04 - val_loss: 0.0046\n",
            "Epoch 904/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.5139e-04 - val_loss: 0.0045\n",
            "Epoch 905/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.5864e-04 - val_loss: 0.0043\n",
            "Epoch 906/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6296e-04 - val_loss: 0.0043\n",
            "Epoch 907/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.4316e-04 - val_loss: 0.0043\n",
            "Epoch 908/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.7230e-04 - val_loss: 0.0044\n",
            "Epoch 909/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.7678e-04 - val_loss: 0.0058\n",
            "Epoch 910/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.4454e-04 - val_loss: 0.0044\n",
            "Epoch 911/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 2.1990e-04 - val_loss: 0.0043\n",
            "Epoch 912/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8158e-04 - val_loss: 0.0043\n",
            "Epoch 913/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6403e-04 - val_loss: 0.0043\n",
            "Epoch 914/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.5738e-04 - val_loss: 0.0044\n",
            "Epoch 915/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.5701e-04 - val_loss: 0.0043\n",
            "Epoch 916/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6282e-04 - val_loss: 0.0043\n",
            "Epoch 917/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.9028e-04 - val_loss: 0.0054\n",
            "Epoch 918/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.2318e-04 - val_loss: 0.0043\n",
            "Epoch 919/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.9659e-04 - val_loss: 0.0043\n",
            "Epoch 920/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.5920e-04 - val_loss: 0.0044\n",
            "Epoch 921/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.5895e-04 - val_loss: 0.0043\n",
            "Epoch 922/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6826e-04 - val_loss: 0.0042\n",
            "Epoch 923/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.9296e-04 - val_loss: 0.0043\n",
            "Epoch 924/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.9031e-04 - val_loss: 0.0047\n",
            "Epoch 925/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.7207e-04 - val_loss: 0.0043\n",
            "Epoch 926/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.7814e-04 - val_loss: 0.0043\n",
            "Epoch 927/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.5442e-04 - val_loss: 0.0046\n",
            "Epoch 928/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6816e-04 - val_loss: 0.0043\n",
            "Epoch 929/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.9563e-04 - val_loss: 0.0042\n",
            "Epoch 930/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.5063e-04 - val_loss: 0.0043\n",
            "Epoch 931/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.5190e-04 - val_loss: 0.0043\n",
            "Epoch 932/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6579e-04 - val_loss: 0.0049\n",
            "Epoch 933/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 2.0024e-04 - val_loss: 0.0045\n",
            "Epoch 934/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 2.2722e-04 - val_loss: 0.0047\n",
            "Epoch 935/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.7535e-04 - val_loss: 0.0049\n",
            "Epoch 936/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.9341e-04 - val_loss: 0.0046\n",
            "Epoch 937/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.9330e-04 - val_loss: 0.0043\n",
            "Epoch 938/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.9400e-04 - val_loss: 0.0043\n",
            "Epoch 939/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.5169e-04 - val_loss: 0.0043\n",
            "Epoch 940/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.3700e-04 - val_loss: 0.0043\n",
            "Epoch 941/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.9905e-04 - val_loss: 0.0047\n",
            "Epoch 942/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.1020e-04 - val_loss: 0.0045\n",
            "Epoch 943/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7891e-04 - val_loss: 0.0043\n",
            "Epoch 944/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.7079e-04 - val_loss: 0.0045\n",
            "Epoch 945/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.9785e-04 - val_loss: 0.0043\n",
            "Epoch 946/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.7224e-04 - val_loss: 0.0042\n",
            "Epoch 947/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.0842e-04 - val_loss: 0.0047\n",
            "Epoch 948/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 2.0921e-04 - val_loss: 0.0050\n",
            "Epoch 949/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 2.8029e-04 - val_loss: 0.0044\n",
            "Epoch 950/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.4130e-04 - val_loss: 0.0043\n",
            "Epoch 951/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.6145e-04 - val_loss: 0.0048\n",
            "Epoch 952/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.7029e-04 - val_loss: 0.0043\n",
            "Epoch 953/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7180e-04 - val_loss: 0.0042\n",
            "Epoch 954/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.6327e-04 - val_loss: 0.0042\n",
            "Epoch 955/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7071e-04 - val_loss: 0.0042\n",
            "Epoch 956/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6538e-04 - val_loss: 0.0043\n",
            "Epoch 957/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.8751e-04 - val_loss: 0.0045\n",
            "Epoch 958/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.9459e-04 - val_loss: 0.0043\n",
            "Epoch 959/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7469e-04 - val_loss: 0.0050\n",
            "Epoch 960/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.7454e-04 - val_loss: 0.0044\n",
            "Epoch 961/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.8223e-04 - val_loss: 0.0048\n",
            "Epoch 962/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.4218e-04 - val_loss: 0.0044\n",
            "Epoch 963/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.7410e-04 - val_loss: 0.0043\n",
            "Epoch 964/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 2.2181e-04 - val_loss: 0.0052\n",
            "Epoch 965/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.9309e-04 - val_loss: 0.0043\n",
            "Epoch 966/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.7309e-04 - val_loss: 0.0043\n",
            "Epoch 967/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.7096e-04 - val_loss: 0.0045\n",
            "Epoch 968/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.5348e-04 - val_loss: 0.0045\n",
            "Epoch 969/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8624e-04 - val_loss: 0.0044\n",
            "Epoch 970/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.3681e-04 - val_loss: 0.0044\n",
            "Epoch 971/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.8464e-04 - val_loss: 0.0047\n",
            "Epoch 972/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 2.2096e-04 - val_loss: 0.0046\n",
            "Epoch 973/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6572e-04 - val_loss: 0.0044\n",
            "Epoch 974/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6650e-04 - val_loss: 0.0043\n",
            "Epoch 975/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.7607e-04 - val_loss: 0.0043\n",
            "Epoch 976/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 2.1752e-04 - val_loss: 0.0047\n",
            "Epoch 977/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.7762e-04 - val_loss: 0.0043\n",
            "Epoch 978/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.9080e-04 - val_loss: 0.0044\n",
            "Epoch 979/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 2.0492e-04 - val_loss: 0.0043\n",
            "Epoch 980/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.5563e-04 - val_loss: 0.0044\n",
            "Epoch 981/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.6190e-04 - val_loss: 0.0043\n",
            "Epoch 982/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.4844e-04 - val_loss: 0.0045\n",
            "Epoch 983/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.8701e-04 - val_loss: 0.0045\n",
            "Epoch 984/1000\n",
            "11/11 [==============================] - 0s 10ms/step - loss: 2.9053e-04 - val_loss: 0.0044\n",
            "Epoch 985/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.0790e-04 - val_loss: 0.0057\n",
            "Epoch 986/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.9112e-04 - val_loss: 0.0045\n",
            "Epoch 987/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.8772e-04 - val_loss: 0.0043\n",
            "Epoch 988/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.6385e-04 - val_loss: 0.0046\n",
            "Epoch 989/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.5741e-04 - val_loss: 0.0043\n",
            "Epoch 990/1000\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 1.4387e-04 - val_loss: 0.0043\n",
            "Epoch 991/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.4763e-04 - val_loss: 0.0043\n",
            "Epoch 992/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.6656e-04 - val_loss: 0.0043\n",
            "Epoch 993/1000\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 1.5973e-04 - val_loss: 0.0043\n",
            "Epoch 994/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.5585e-04 - val_loss: 0.0044\n",
            "Epoch 995/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.8669e-04 - val_loss: 0.0045\n",
            "Epoch 996/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 2.2021e-04 - val_loss: 0.0048\n",
            "Epoch 997/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 2.1370e-04 - val_loss: 0.0044\n",
            "Epoch 998/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.7243e-04 - val_loss: 0.0044\n",
            "Epoch 999/1000\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 1.5559e-04 - val_loss: 0.0043\n",
            "Epoch 1000/1000\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 1.7275e-04 - val_loss: 0.0050\n",
            "Saved model to disk\n",
            "Loaded model from disk\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:18: DeprecationWarning: KerasRegressor is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ws9-1GCyYNR3"
      },
      "source": [
        "-> Let's show the loss and validation loss dynamics."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "ywxpgq5iYP0X",
        "outputId": "a2ed0234-cb83-449c-f1c3-e8e602b68274"
      },
      "source": [
        "plt.figure()\n",
        "plt.plot(history['loss'], label='train')\n",
        "plt.plot(history['val_loss'], label='test')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "#plt.plot(history.history['acc'], label='acc')\n",
        "#plt.plot(history.history['val_acc'], label='acc test')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4hoPWRWDYnAO"
      },
      "source": [
        "As we can see, a Neural Network is well-fitted and no overfitting is observed. Let's calculate a prediction of the training (res_train_ANN) an test (res_test_ANN) sets.\n",
        "\n",
        "-> Let's calculate a forecast and make an inverse normalization to real scale."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cV26ceu3YrUK",
        "outputId": "b818ab1d-dbd6-43dc-ea13-1ffc9e4b987e"
      },
      "source": [
        "res_tr=estimator.predict(X_train)\n",
        "res_ts=estimator.predict(X_test)\n",
        "res_train_ANN=scaler_y.inverse_transform(res_tr.reshape(-1, 1)).flatten()\n",
        "res_test_ANN=scaler_y.inverse_transform(res_ts.reshape(-1, 1)).flatten()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "11/11 [==============================] - 0s 2ms/step\n",
            "5/5 [==============================] - 0s 3ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ecLK5QIKYw4_"
      },
      "source": [
        "Let's compare the accuracy of a Linear Regression and a Neural Network."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JwHjh5ckY07P",
        "outputId": "f9a0d268-ca6c-4458-b878-b73d0af95b2e"
      },
      "source": [
        "print(\"Correlation train\", np.corrcoef(res_train, res_train_ANN)[0,1])\n",
        "print(\"Correlation test\", np.corrcoef(res_test, res_test_ANN)[0,1])\n",
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, res_test_ANN))\n",
        "print('Mean Squared Error:', metrics.mean_squared_error(y_test, res_test_ANN))\n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, res_test_ANN)))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlation train 0.9852830089355795\n",
            "Correlation test 0.9660568277513011\n",
            "Mean Absolute Error: 17212.014040537095\n",
            "Mean Squared Error: 520371602.5964304\n",
            "Root Mean Squared Error: 22811.65497276404\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_64E63YbZlaz"
      },
      "source": [
        "We got slightly better results for a Neural Network than for a Linear Regression."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "csFDJAFJZ6cM"
      },
      "source": [
        "Unlike standard feedforward neural networks, LSTM has feedback connections. It can process not only single data points, but also entire sequences of data (such as speech, video or time series).\n",
        "\n",
        "In case of time series, the neural network has one input and one output. However, there should be a vector of time series values for the previous time period on the input."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_3UUq9toaUkF"
      },
      "source": [
        "![LSTM.PNG](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "llRPFZPwam8c"
      },
      "source": [
        "-> Let's Long Short-Term Memory Neural Network.\n",
        "To do this, we should transform input DataSets into 3D shape."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CaRV5R7za2z-"
      },
      "source": [
        "train_x_LSTM = X_train.reshape((X_train.shape[0], 1, 14))\n",
        "test_x_LSTM = X_test.reshape((X_test.shape[0], 1, 14))"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NS8jApL8a_QF"
      },
      "source": [
        "-> Let's create an LSTM Neural Network consisting of one LSTM layer and one BP layer like in the previous case. As you can see, in this case our NN will consist of 7 LSTM and 7 BP neurons only."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e37caUMKbG-1"
      },
      "source": [
        "from keras.layers import LSTM\n",
        "\n",
        "batch_size=int(y_train.shape[0]*.1)\n",
        "model = Sequential()\n",
        "model.add(LSTM(7, input_shape=(train_x_LSTM.shape[1], train_x_LSTM.shape[2])))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(7, kernel_initializer='normal', activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(y_train.shape[1])) #activation='sigmoid'\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "#model.compile(loss='mae', optimizer='adam')\n"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HBx1bkF1bSW5"
      },
      "source": [
        "-> All subsequent steps of learning, storing, reading and predicting are similar to the ones we used with the previous neural network. We can see that only 400 epochs are enough for an LSTM."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8cySqRsTbZ3Z",
        "outputId": "94afd277-bece-4d60-c5d5-22a09808ce4a"
      },
      "source": [
        "fitting = True\n",
        "fitting_save = True\n",
        "epochs = 400\n",
        "\n",
        "if fitting:\n",
        "    history = model.fit(train_x_LSTM, y_train, epochs=epochs, batch_size=batch_size, validation_data=(test_x_LSTM, y_test), verbose=1, shuffle=False)    \n",
        "    if fitting_save:\n",
        "    # serialize model to JSON\n",
        "        model_json = model.to_json()\n",
        "        with open(\"LSTM_model.json\", \"w\") as json_file:\n",
        "            json_file.write(model_json)\n",
        "        # serialize weights to HDF5\n",
        "        model.save_weights(\"LSTM_model.h5\")\n",
        "        print(\"Saved model to disk\")\n",
        "        with open('history_LSTM.pickle', 'wb') as f:\n",
        "            pickle.dump(history.history, f)\n",
        "# load model  \n",
        "from keras.models import model_from_json\n",
        "json_file = open('LSTM_model.json', 'r')\n",
        "loaded_model_json = json_file.read()\n",
        "json_file.close()\n",
        "model = model_from_json(loaded_model_json)\n",
        "# load weights into new model\n",
        "model.load_weights(\"LSTM_model.h5\")        \n",
        "with open('history_LSTM.pickle', 'rb') as f:\n",
        "    history = pickle.load(f)\n",
        "print(\"Loaded model from disk\")"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/400\n",
            "11/11 [==============================] - 3s 61ms/step - loss: 0.0063 - val_loss: 0.1298\n",
            "Epoch 2/400\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0042 - val_loss: 0.1151\n",
            "Epoch 3/400\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0033 - val_loss: 0.1048\n",
            "Epoch 4/400\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0029 - val_loss: 0.0977\n",
            "Epoch 5/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0029 - val_loss: 0.0929\n",
            "Epoch 6/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.0029 - val_loss: 0.0896\n",
            "Epoch 7/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.0028 - val_loss: 0.0868\n",
            "Epoch 8/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0027 - val_loss: 0.0842\n",
            "Epoch 9/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0028 - val_loss: 0.0814\n",
            "Epoch 10/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0027 - val_loss: 0.0777\n",
            "Epoch 11/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0026 - val_loss: 0.0739\n",
            "Epoch 12/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0022 - val_loss: 0.0702\n",
            "Epoch 13/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0021 - val_loss: 0.0665\n",
            "Epoch 14/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.0020 - val_loss: 0.0628\n",
            "Epoch 15/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0020 - val_loss: 0.0593\n",
            "Epoch 16/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0020 - val_loss: 0.0556\n",
            "Epoch 17/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0018 - val_loss: 0.0521\n",
            "Epoch 18/400\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0015 - val_loss: 0.0483\n",
            "Epoch 19/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0016 - val_loss: 0.0453\n",
            "Epoch 20/400\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0016 - val_loss: 0.0417\n",
            "Epoch 21/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0015 - val_loss: 0.0395\n",
            "Epoch 22/400\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0014 - val_loss: 0.0362\n",
            "Epoch 23/400\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0014 - val_loss: 0.0329\n",
            "Epoch 24/400\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0011 - val_loss: 0.0305\n",
            "Epoch 25/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 0.0291\n",
            "Epoch 26/400\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0012 - val_loss: 0.0286\n",
            "Epoch 27/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.0011 - val_loss: 0.0273\n",
            "Epoch 28/400\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 9.7996e-04 - val_loss: 0.0256\n",
            "Epoch 29/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 9.1116e-04 - val_loss: 0.0246\n",
            "Epoch 30/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.0010 - val_loss: 0.0242\n",
            "Epoch 31/400\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 9.1333e-04 - val_loss: 0.0235\n",
            "Epoch 32/400\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0011 - val_loss: 0.0230\n",
            "Epoch 33/400\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 9.7129e-04 - val_loss: 0.0224\n",
            "Epoch 34/400\n",
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            "Epoch 98/400\n",
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            "Epoch 99/400\n",
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            "Epoch 100/400\n",
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            "Epoch 105/400\n",
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            "Epoch 109/400\n",
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            "Epoch 110/400\n",
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            "Epoch 111/400\n",
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            "Epoch 112/400\n",
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            "Epoch 113/400\n",
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            "Epoch 115/400\n",
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            "Epoch 117/400\n",
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            "Epoch 118/400\n",
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            "Epoch 119/400\n",
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            "Epoch 120/400\n",
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            "Epoch 121/400\n",
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            "Epoch 126/400\n",
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            "Epoch 127/400\n",
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            "Epoch 128/400\n",
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            "Epoch 129/400\n",
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            "Epoch 130/400\n",
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            "Epoch 131/400\n",
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            "Epoch 132/400\n",
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            "Epoch 133/400\n",
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            "Epoch 134/400\n",
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            "Epoch 135/400\n",
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            "Epoch 136/400\n",
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            "Epoch 137/400\n",
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            "Epoch 138/400\n",
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            "Epoch 139/400\n",
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            "Epoch 140/400\n",
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            "Epoch 141/400\n",
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            "Epoch 142/400\n",
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            "Epoch 143/400\n",
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            "Epoch 144/400\n",
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            "Epoch 145/400\n",
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            "Epoch 146/400\n",
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            "Epoch 147/400\n",
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            "Epoch 148/400\n",
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            "Epoch 149/400\n",
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            "Epoch 150/400\n",
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            "Epoch 151/400\n",
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            "Epoch 152/400\n",
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            "Epoch 153/400\n",
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            "Epoch 154/400\n",
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            "Epoch 155/400\n",
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            "Epoch 156/400\n",
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            "Epoch 157/400\n",
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            "Epoch 158/400\n",
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            "Epoch 159/400\n",
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            "Epoch 160/400\n",
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            "Epoch 161/400\n",
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            "Epoch 162/400\n",
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            "Epoch 163/400\n",
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            "Epoch 164/400\n",
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            "Epoch 165/400\n",
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            "Epoch 166/400\n",
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            "Epoch 167/400\n",
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            "Epoch 168/400\n",
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            "Epoch 169/400\n",
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            "Epoch 170/400\n",
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            "Epoch 171/400\n",
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            "Epoch 172/400\n",
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            "Epoch 173/400\n",
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            "Epoch 174/400\n",
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            "Epoch 175/400\n",
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            "Epoch 176/400\n",
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            "Epoch 177/400\n",
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            "Epoch 178/400\n",
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            "Epoch 179/400\n",
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            "Epoch 180/400\n",
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            "Epoch 181/400\n",
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            "Epoch 182/400\n",
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            "Epoch 183/400\n",
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            "Epoch 184/400\n",
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            "Epoch 185/400\n",
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            "Epoch 186/400\n",
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            "Epoch 187/400\n",
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            "Epoch 188/400\n",
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            "Epoch 189/400\n",
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            "Epoch 190/400\n",
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            "Epoch 191/400\n",
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            "Epoch 192/400\n",
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            "Epoch 193/400\n",
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            "Epoch 194/400\n",
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            "Epoch 255/400\n",
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            "Epoch 300/400\n",
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            "Epoch 312/400\n",
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            "Epoch 339/400\n",
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            "Epoch 340/400\n",
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            "Epoch 341/400\n",
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            "Epoch 342/400\n",
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            "Epoch 344/400\n",
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            "Epoch 345/400\n",
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            "Epoch 346/400\n",
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            "Epoch 347/400\n",
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            "Epoch 348/400\n",
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            "Epoch 349/400\n",
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            "Epoch 350/400\n",
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            "Epoch 351/400\n",
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            "Epoch 352/400\n",
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            "Epoch 353/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 6.2251e-04 - val_loss: 0.0058\n",
            "Epoch 354/400\n",
            "11/11 [==============================] - 0s 10ms/step - loss: 5.7151e-04 - val_loss: 0.0057\n",
            "Epoch 355/400\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 6.1510e-04 - val_loss: 0.0059\n",
            "Epoch 356/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 6.1432e-04 - val_loss: 0.0057\n",
            "Epoch 357/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 5.6444e-04 - val_loss: 0.0055\n",
            "Epoch 358/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 5.7223e-04 - val_loss: 0.0056\n",
            "Epoch 359/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 6.0960e-04 - val_loss: 0.0056\n",
            "Epoch 360/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 6.0653e-04 - val_loss: 0.0055\n",
            "Epoch 361/400\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 5.4422e-04 - val_loss: 0.0058\n",
            "Epoch 362/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 7.0148e-04 - val_loss: 0.0060\n",
            "Epoch 363/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 6.0541e-04 - val_loss: 0.0054\n",
            "Epoch 364/400\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 7.3372e-04 - val_loss: 0.0054\n",
            "Epoch 365/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 7.1321e-04 - val_loss: 0.0057\n",
            "Epoch 366/400\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 6.8631e-04 - val_loss: 0.0055\n",
            "Epoch 367/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 6.0691e-04 - val_loss: 0.0056\n",
            "Epoch 368/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 4.3298e-04 - val_loss: 0.0058\n",
            "Epoch 369/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 6.0110e-04 - val_loss: 0.0054\n",
            "Epoch 370/400\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 6.8988e-04 - val_loss: 0.0053\n",
            "Epoch 371/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 5.4460e-04 - val_loss: 0.0057\n",
            "Epoch 372/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 6.0047e-04 - val_loss: 0.0054\n",
            "Epoch 373/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 7.0196e-04 - val_loss: 0.0055\n",
            "Epoch 374/400\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 5.1698e-04 - val_loss: 0.0057\n",
            "Epoch 375/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 6.5279e-04 - val_loss: 0.0057\n",
            "Epoch 376/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 6.1891e-04 - val_loss: 0.0059\n",
            "Epoch 377/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 6.1474e-04 - val_loss: 0.0057\n",
            "Epoch 378/400\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 5.4322e-04 - val_loss: 0.0055\n",
            "Epoch 379/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 5.3168e-04 - val_loss: 0.0056\n",
            "Epoch 380/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 5.8271e-04 - val_loss: 0.0057\n",
            "Epoch 381/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 5.8444e-04 - val_loss: 0.0056\n",
            "Epoch 382/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 6.5105e-04 - val_loss: 0.0055\n",
            "Epoch 383/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 6.0314e-04 - val_loss: 0.0054\n",
            "Epoch 384/400\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 6.5916e-04 - val_loss: 0.0056\n",
            "Epoch 385/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 5.1497e-04 - val_loss: 0.0055\n",
            "Epoch 386/400\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 6.6450e-04 - val_loss: 0.0055\n",
            "Epoch 387/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 6.0028e-04 - val_loss: 0.0060\n",
            "Epoch 388/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 6.2356e-04 - val_loss: 0.0056\n",
            "Epoch 389/400\n",
            "11/11 [==============================] - 0s 10ms/step - loss: 7.0234e-04 - val_loss: 0.0051\n",
            "Epoch 390/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 5.5950e-04 - val_loss: 0.0052\n",
            "Epoch 391/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 7.0169e-04 - val_loss: 0.0054\n",
            "Epoch 392/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 5.5176e-04 - val_loss: 0.0054\n",
            "Epoch 393/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 5.6391e-04 - val_loss: 0.0052\n",
            "Epoch 394/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 4.8378e-04 - val_loss: 0.0054\n",
            "Epoch 395/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 5.3106e-04 - val_loss: 0.0053\n",
            "Epoch 396/400\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 5.9707e-04 - val_loss: 0.0053\n",
            "Epoch 397/400\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 5.5715e-04 - val_loss: 0.0055\n",
            "Epoch 398/400\n",
            "11/11 [==============================] - 0s 12ms/step - loss: 6.2347e-04 - val_loss: 0.0055\n",
            "Epoch 399/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 6.8864e-04 - val_loss: 0.0055\n",
            "Epoch 400/400\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 6.3519e-04 - val_loss: 0.0063\n",
            "Saved model to disk\n",
            "Loaded model from disk\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2TJlSQCAboB3"
      },
      "source": [
        "-> Let's plot the dynamic of loss and val loss like in the previous case."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "4wBVGzNfbrOm",
        "outputId": "83d8f653-7655-4308-b421-b98afd17794b"
      },
      "source": [
        "plt.figure()\n",
        "plt.plot(history['loss'], label='train')\n",
        "plt.plot(history['val_loss'], label='test')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "\n",
        "plt.legend()\n",
        "plt.show() "
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r9lgmADGb3mH"
      },
      "source": [
        "-> Let's calculate our forecast."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EEeGWwK1b47C"
      },
      "source": [
        "res_tr_LSTM = model.predict(train_x_LSTM)\n",
        "res_ts_LSTM = model.predict(test_x_LSTM)\n",
        "res_train_LSTM=scaler_y.inverse_transform(res_tr_LSTM).flatten()\n",
        "res_test_LSTM=scaler_y.inverse_transform(res_ts_LSTM).flatten()"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5j3o5kapcA7z"
      },
      "source": [
        "-> Let's calculate accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XuTy95VPcE1e",
        "outputId": "d7227f45-59a9-49a0-c92c-46204b7e0381"
      },
      "source": [
        "print(\"Correlation train\", np.corrcoef(res_train, res_train_LSTM)[0,1])\n",
        "print(\"Correlation train\", np.corrcoef(res_test, res_test_LSTM)[0,1])\n",
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, res_test_LSTM))\n",
        "print('Mean Squared Error:', metrics.mean_squared_error(y_test, res_test_LSTM))\n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, res_test_LSTM)))\n"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlation train 0.9785436510628981\n",
            "Correlation train 0.9610595598954806\n",
            "Mean Absolute Error: 14437.889083708296\n",
            "Mean Squared Error: 363920945.54806477\n",
            "Root Mean Squared Error: 19076.712126256578\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "flw_9ynQclFX"
      },
      "source": [
        "-> As we can see, the forecast results of the test data set are much better than ones of the previous models. Let's visualize these 3 results:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "CAGLVLZXcpxG",
        "outputId": "3a7fb02c-e804-46bd-b0d4-cc97fce1fb02"
      },
      "source": [
        "res_pred_test_ln = pd.Series(y_pred_test_ln, name = 'Predicted test Linear Model')\n",
        "res_pred_test_ANN = pd.Series(res_test_ANN, name = 'Predicted test ANN')\n",
        "res_pred_test_LSTM = pd.Series(res_test_LSTM, name = 'Predicted test LSTM')\n",
        "\n",
        "df_2 = pd.DataFrame({'Actual test': res_test, 'Linear Model': res_pred_test_ln, 'ANN Model': res_pred_test_ANN,  'LSTM Model': res_pred_test_LSTM,})\n",
        "df_2.index = dataset.index[len(dataset)-len(res_test):]\n",
        "df_2.plot()\n",
        "plt.show()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WDTKexAIdGVG"
      },
      "source": [
        "Conclusion \n",
        "As we can see, an LSTM model makes a perfect forecast. A Linear Regression is the fastest forecasting model.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FMeXNORodTum"
      },
      "source": [
        ""
      ],
      "execution_count": 38,
      "outputs": []
    }
  ]
}