forecast_Covid_19_Vaccines_Prophet_FIX.ipynb

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    "colab": {
      "name": "forecasting_Covid-19_Vaccines-Prophet-FIX.ipynb",
      "provenance": []
    },
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      "name": "python3",
      "display_name": "Python 3"
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      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FycO6_-V8cO7",
        "outputId": "6a825ced-2b5d-4b7b-8fd4-1be74266c0f1"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PaRpWfm579jD"
      },
      "source": [
        "import numpy as np # numpy arrays / linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import plotly.express as px # map plot\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore') \n",
        "\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt \n",
        "import matplotlib.dates as md\n",
        "%matplotlib inline\n",
        "from scipy.optimize import curve_fit # predictive modeling\n",
        "import seaborn as sns\n",
        "import statsmodels.api as sm\n",
        "\n",
        "import datetime\n",
        "from datetime import date"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "T6DupBdv8guo",
        "outputId": "7136f6fd-615a-402b-8b19-d4fcbf08ceda"
      },
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/dataset/country_vaccinations.csv', index_col='date', parse_dates=True, engine='python', header=0)\n",
        "df = df[df['vaccines']=='Sinopharm/Beijing, Sinopharm/Wuhan, Sinovac']['total_vaccinations']\n",
        "df.dropna(inplace=True)\n",
        "df = df.reset_index()\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>total_vaccinations</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-12-15</td>\n",
              "      <td>1500000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>4500000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-01-09</td>\n",
              "      <td>9000000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-01-13</td>\n",
              "      <td>10000000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-01-20</td>\n",
              "      <td>15000000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  total_vaccinations\n",
              "0 2020-12-15           1500000.0\n",
              "1 2020-12-31           4500000.0\n",
              "2 2021-01-09           9000000.0\n",
              "3 2021-01-13          10000000.0\n",
              "4 2021-01-20          15000000.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "jOdfl6T55hsZ",
        "outputId": "1c6ba8e1-96ed-45b8-ca1f-b6bef5d8d251"
      },
      "source": [
        "df=df.rename(columns={'date':'ds', 'total_vaccinations':'y'})\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "        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>ds</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-12-15</td>\n",
              "      <td>1500000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>4500000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-01-09</td>\n",
              "      <td>9000000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-01-13</td>\n",
              "      <td>10000000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-01-20</td>\n",
              "      <td>15000000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          ds           y\n",
              "0 2020-12-15   1500000.0\n",
              "1 2020-12-31   4500000.0\n",
              "2 2021-01-09   9000000.0\n",
              "3 2021-01-13  10000000.0\n",
              "4 2021-01-20  15000000.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FWHJnoq36rcJ",
        "outputId": "46e55cef-71a8-4e8a-ed65-9aa6162d1b8a"
      },
      "source": [
        "from fbprophet import Prophet\n",
        "model = Prophet(daily_seasonality=True)\n",
        "model.fit(df);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nFjCXg676uM5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "9bb3d409-90da-462c-a581-3050b229fd50"
      },
      "source": [
        "future = model.make_future_dataframe(periods=60, freq = 'd')\n",
        "future.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>2021-07-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>2021-07-02</td>\n",
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              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>2021-07-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>2021-07-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>2021-07-05</td>\n",
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            ],
            "text/plain": [
              "            ds\n",
              "113 2021-07-01\n",
              "114 2021-07-02\n",
              "115 2021-07-03\n",
              "116 2021-07-04\n",
              "117 2021-07-05"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1D65si26wU6"
      },
      "source": [
        "forecast = model.predict(future)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WmNp-S5N60Jh",
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          "height": 305
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        "outputId": "0faf0525-ed2c-40b6-e099-93d7aa7a55d3"
      },
      "source": [
        "forecast.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>ds</th>\n",
              "      <th>trend</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
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              "      <th>daily_upper</th>\n",
              "      <th>weekly</th>\n",
              "      <th>weekly_lower</th>\n",
              "      <th>weekly_upper</th>\n",
              "      <th>multiplicative_terms</th>\n",
              "      <th>multiplicative_terms_lower</th>\n",
              "      <th>multiplicative_terms_upper</th>\n",
              "      <th>yhat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>2021-07-01</td>\n",
              "      <td>6.705261e+08</td>\n",
              "      <td>6.141475e+08</td>\n",
              "      <td>7.220045e+08</td>\n",
              "      <td>6.139656e+08</td>\n",
              "      <td>7.207100e+08</td>\n",
              "      <td>6.864466e+05</td>\n",
              "      <td>6.864466e+05</td>\n",
              "      <td>6.864466e+05</td>\n",
              "      <td>699347.246323</td>\n",
              "      <td>699347.246323</td>\n",
              "      <td>699347.246323</td>\n",
              "      <td>-1.290068e+04</td>\n",
              "      <td>-1.290068e+04</td>\n",
              "      <td>-1.290068e+04</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.712125e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>2021-07-02</td>\n",
              "      <td>6.771686e+08</td>\n",
              "      <td>6.216502e+08</td>\n",
              "      <td>7.318220e+08</td>\n",
              "      <td>6.186238e+08</td>\n",
              "      <td>7.290194e+08</td>\n",
              "      <td>3.004306e+06</td>\n",
              "      <td>3.004306e+06</td>\n",
              "      <td>3.004306e+06</td>\n",
              "      <td>699347.246323</td>\n",
              "      <td>699347.246323</td>\n",
              "      <td>699347.246323</td>\n",
              "      <td>2.304959e+06</td>\n",
              "      <td>2.304959e+06</td>\n",
              "      <td>2.304959e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.801729e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>2021-07-03</td>\n",
              "      <td>6.838111e+08</td>\n",
              "      <td>6.258757e+08</td>\n",
              "      <td>7.395016e+08</td>\n",
              "      <td>6.234797e+08</td>\n",
              "      <td>7.369773e+08</td>\n",
              "      <td>2.189580e+06</td>\n",
              "      <td>2.189580e+06</td>\n",
              "      <td>2.189580e+06</td>\n",
              "      <td>699347.246323</td>\n",
              "      <td>699347.246323</td>\n",
              "      <td>699347.246323</td>\n",
              "      <td>1.490232e+06</td>\n",
              "      <td>1.490232e+06</td>\n",
              "      <td>1.490232e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.860007e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>2021-07-04</td>\n",
              "      <td>6.904536e+08</td>\n",
              "      <td>6.280171e+08</td>\n",
              "      <td>7.446853e+08</td>\n",
              "      <td>6.283538e+08</td>\n",
              "      <td>7.453083e+08</td>\n",
              "      <td>-1.324883e+05</td>\n",
              "      <td>-1.324883e+05</td>\n",
              "      <td>-1.324883e+05</td>\n",
              "      <td>699347.246323</td>\n",
              "      <td>699347.246323</td>\n",
              "      <td>699347.246323</td>\n",
              "      <td>-8.318356e+05</td>\n",
              "      <td>-8.318356e+05</td>\n",
              "      <td>-8.318356e+05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.903211e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>2021-07-05</td>\n",
              "      <td>6.970961e+08</td>\n",
              "      <td>6.332544e+08</td>\n",
              "      <td>7.535284e+08</td>\n",
              "      <td>6.332285e+08</td>\n",
              "      <td>7.531826e+08</td>\n",
              "      <td>-7.153827e+03</td>\n",
              "      <td>-7.153827e+03</td>\n",
              "      <td>-7.153827e+03</td>\n",
              "      <td>699347.246323</td>\n",
              "      <td>699347.246323</td>\n",
              "      <td>699347.246323</td>\n",
              "      <td>-7.065011e+05</td>\n",
              "      <td>-7.065011e+05</td>\n",
              "      <td>-7.065011e+05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.970889e+08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            ds         trend  ...  multiplicative_terms_upper          yhat\n",
              "113 2021-07-01  6.705261e+08  ...                         0.0  6.712125e+08\n",
              "114 2021-07-02  6.771686e+08  ...                         0.0  6.801729e+08\n",
              "115 2021-07-03  6.838111e+08  ...                         0.0  6.860007e+08\n",
              "116 2021-07-04  6.904536e+08  ...                         0.0  6.903211e+08\n",
              "117 2021-07-05  6.970961e+08  ...                         0.0  6.970889e+08\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wvgR58Gh623x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "outputId": "ed1d807b-3b14-4c02-e9a4-f87e045a9a21"
      },
      "source": [
        "forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>yhat</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>2021-06-26</td>\n",
              "      <td>6.395032e+08</td>\n",
              "      <td>5.914318e+08</td>\n",
              "      <td>6.841666e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>2021-06-27</td>\n",
              "      <td>6.438236e+08</td>\n",
              "      <td>5.935001e+08</td>\n",
              "      <td>6.893528e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>2021-06-28</td>\n",
              "      <td>6.505915e+08</td>\n",
              "      <td>5.977907e+08</td>\n",
              "      <td>6.980254e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>2021-06-29</td>\n",
              "      <td>6.573154e+08</td>\n",
              "      <td>6.036709e+08</td>\n",
              "      <td>7.055478e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>2021-06-30</td>\n",
              "      <td>6.629641e+08</td>\n",
              "      <td>6.076408e+08</td>\n",
              "      <td>7.121722e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>2021-07-01</td>\n",
              "      <td>6.712125e+08</td>\n",
              "      <td>6.141475e+08</td>\n",
              "      <td>7.220045e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>2021-07-02</td>\n",
              "      <td>6.801729e+08</td>\n",
              "      <td>6.216502e+08</td>\n",
              "      <td>7.318220e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>2021-07-03</td>\n",
              "      <td>6.860007e+08</td>\n",
              "      <td>6.258757e+08</td>\n",
              "      <td>7.395016e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>2021-07-04</td>\n",
              "      <td>6.903211e+08</td>\n",
              "      <td>6.280171e+08</td>\n",
              "      <td>7.446853e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>2021-07-05</td>\n",
              "      <td>6.970889e+08</td>\n",
              "      <td>6.332544e+08</td>\n",
              "      <td>7.535284e+08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            ds          yhat    yhat_lower    yhat_upper\n",
              "108 2021-06-26  6.395032e+08  5.914318e+08  6.841666e+08\n",
              "109 2021-06-27  6.438236e+08  5.935001e+08  6.893528e+08\n",
              "110 2021-06-28  6.505915e+08  5.977907e+08  6.980254e+08\n",
              "111 2021-06-29  6.573154e+08  6.036709e+08  7.055478e+08\n",
              "112 2021-06-30  6.629641e+08  6.076408e+08  7.121722e+08\n",
              "113 2021-07-01  6.712125e+08  6.141475e+08  7.220045e+08\n",
              "114 2021-07-02  6.801729e+08  6.216502e+08  7.318220e+08\n",
              "115 2021-07-03  6.860007e+08  6.258757e+08  7.395016e+08\n",
              "116 2021-07-04  6.903211e+08  6.280171e+08  7.446853e+08\n",
              "117 2021-07-05  6.970889e+08  6.332544e+08  7.535284e+08"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lHKJ_cWl66n5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "c4f1dcca-824d-4f50-9c83-a68155bfdc91"
      },
      "source": [
        "model.plot(forecast);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BRTc50BO6-zh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        },
        "outputId": "2e60bb70-d116-4841-ed55-496cf205c7ad"
      },
      "source": [
        "model.plot_components(forecast);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ItPLD7dw7CM5"
      },
      "source": [
        "metric_df = forecast.set_index('ds')[['yhat']].join(df.set_index('ds').y).reset_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cFvaBNSY7EfQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "9cefe6e9-77cf-4489-d16e-b21f4b12673e"
      },
      "source": [
        "metric_df.dropna(inplace=True)\n",
        "metric_df.tail()"
      ],
      "execution_count": null,
      "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>ds</th>\n",
              "      <th>yhat</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>2021-05-02</td>\n",
              "      <td>2.718441e+08</td>\n",
              "      <td>275338000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>2021-05-03</td>\n",
              "      <td>2.786120e+08</td>\n",
              "      <td>279905000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>2021-05-04</td>\n",
              "      <td>2.853359e+08</td>\n",
              "      <td>284595000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>2021-05-05</td>\n",
              "      <td>2.909845e+08</td>\n",
              "      <td>289627000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>2021-05-06</td>\n",
              "      <td>2.992330e+08</td>\n",
              "      <td>297734000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ds          yhat            y\n",
              "53 2021-05-02  2.718441e+08  275338000.0\n",
              "54 2021-05-03  2.786120e+08  279905000.0\n",
              "55 2021-05-04  2.853359e+08  284595000.0\n",
              "56 2021-05-05  2.909845e+08  289627000.0\n",
              "57 2021-05-06  2.992330e+08  297734000.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SSHR1API7MP4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d46a8522-6a51-44c9-ea9d-e3f968c70cf7"
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_score(metric_df.y, metric_df.yhat)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.999591853579626"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fdyYk8Ur7O9I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93465476-cbce-4017-d8fb-10ad46ed5c05"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "mean_squared_error(metric_df.y, metric_df.yhat)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2797862243366.1426"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Q0AVvFj9qj5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9de36fa-c081-42be-8978-1ce70d6fc74e"
      },
      "source": [
        "metric_df.yhat.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "149002598.2124216"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1-kLjEIi-J2h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de81f914-b5ef-47fb-e59f-20cbe1643ab4"
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "print(mean_absolute_error(metric_df.y, metric_df.yhat))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1353209.451508771\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ruk1mWnh-RCP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "841b0fa2-ca03-4560-fdb0-022dabbb449f"
      },
      "source": [
        "from sklearn.metrics import median_absolute_error\n",
        "print(median_absolute_error(metric_df.y, metric_df.yhat))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1212807.897076413\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOhBHFDB-iig",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b00cc3e-0255-49c3-f167-6aca7137e903"
      },
      "source": [
        "from math import sqrt\n",
        "rmse = sqrt(mean_squared_error(metric_df.y, metric_df.yhat))\n",
        "print(rmse)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1672681.154125359\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}