Tesla_(TSLA)_Stock_Price_prediction_using_Facebook_Prophet.ipynb

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      "gpuType": "T4"
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mrG0EKYUXRoY"
      },
      "source": [
        "# Tesla (TSLA) Stock Price prediction using Facebook Prophet "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z-5WsJ9Ef6k_"
      },
      "source": [
        "## Switch to GPU mode for faster Computation (Runtime> Change runtime> GPU)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EJPImZ5za2DY"
      },
      "source": [
        "## Importing all the necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNPlTwMfbBbv"
      },
      "source": [
        "#Necessary libraries = Pandas, fbprophet and plotly\n",
        "\n",
        "#pandas= data Manipulation and analysis\n",
        "#fbprophet = Forecasting\n",
        "#plotly= data visualization\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip --version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wozQexwgpYGX",
        "outputId": "cda10650-2820-411a-f570-1d886d2d28e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pip 23.1.2 from /usr/local/lib/python3.10/dist-packages/pip (python 3.10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pystan"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nQcO2733pkaZ",
        "outputId": "615f8179-3435-4317-fdf5-4d766143285a"
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      "execution_count": null,
      "outputs": [
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          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install Prophet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nQf6SPVCpstp",
        "outputId": "eabf5859-c7bc-40a4-f9a9-c687014658ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: Prophet in /usr/local/lib/python3.10/dist-packages (1.1.3)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tR4fFCxE8fAg"
      },
      "source": [
        "import pandas as pd\n",
        "import plotly.express as px\n",
        "from prophet import Prophet"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yK5iokzObG8b"
      },
      "source": [
        " #Initializing Plotly\n",
        "import plotly.io as pio\n",
        "pio.renderers.default=\"colab\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LQr_B55bbjQ1"
      },
      "source": [
        "## Importing the Dataset & Exploring it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "76a6siA7bma0"
      },
      "source": [
        "df= pd.read_csv('/content/TSLA.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vezMkspZboYX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "8df5a579-688c-409c-a6ab-07690a943119"
      },
      "source": [
        "df#read_csv function from pandas"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "           Date        Open        High         Low       Close   Adj Close  \\\n",
              "0    2020-03-16   93.900002   98.973999   88.433998   89.014000   89.014000   \n",
              "1    2020-03-17   88.001999   94.370003   79.199997   86.040001   86.040001   \n",
              "2    2020-03-18   77.800003   80.972000   70.101997   72.244003   72.244003   \n",
              "3    2020-03-19   74.940002   90.400002   71.692001   85.528000   85.528000   \n",
              "4    2020-03-20   87.639999   95.400002   85.157997   85.505997   85.505997   \n",
              "..          ...         ...         ...         ...         ...         ...   \n",
              "246  2021-03-08  600.549988  620.130005  558.789978  563.000000  563.000000   \n",
              "247  2021-03-09  608.179993  678.090027  595.210022  673.580017  673.580017   \n",
              "248  2021-03-10  700.299988  717.849976  655.059998  668.059998  668.059998   \n",
              "249  2021-03-11  699.400024  702.500000  677.179993  699.599976  699.599976   \n",
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              "      <th>Open</th>\n",
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              "      <td>2020-03-16</td>\n",
              "      <td>93.900002</td>\n",
              "      <td>98.973999</td>\n",
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              "      <td>2020-03-17</td>\n",
              "      <td>88.001999</td>\n",
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              "      <td>79.199997</td>\n",
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              "      <td>2020-03-18</td>\n",
              "      <td>77.800003</td>\n",
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              "      <td>70.101997</td>\n",
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              "      <td>2020-03-20</td>\n",
              "      <td>87.639999</td>\n",
              "      <td>95.400002</td>\n",
              "      <td>85.157997</td>\n",
              "      <td>85.505997</td>\n",
              "      <td>85.505997</td>\n",
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              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>2021-03-08</td>\n",
              "      <td>600.549988</td>\n",
              "      <td>620.130005</td>\n",
              "      <td>558.789978</td>\n",
              "      <td>563.000000</td>\n",
              "      <td>563.000000</td>\n",
              "      <td>51787000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>247</th>\n",
              "      <td>2021-03-09</td>\n",
              "      <td>608.179993</td>\n",
              "      <td>678.090027</td>\n",
              "      <td>595.210022</td>\n",
              "      <td>673.580017</td>\n",
              "      <td>673.580017</td>\n",
              "      <td>67523300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>2021-03-10</td>\n",
              "      <td>700.299988</td>\n",
              "      <td>717.849976</td>\n",
              "      <td>655.059998</td>\n",
              "      <td>668.059998</td>\n",
              "      <td>668.059998</td>\n",
              "      <td>60605700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>2021-03-11</td>\n",
              "      <td>699.400024</td>\n",
              "      <td>702.500000</td>\n",
              "      <td>677.179993</td>\n",
              "      <td>699.599976</td>\n",
              "      <td>699.599976</td>\n",
              "      <td>36253900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>2021-03-12</td>\n",
              "      <td>670.000000</td>\n",
              "      <td>694.880005</td>\n",
              "      <td>666.140015</td>\n",
              "      <td>693.729980</td>\n",
              "      <td>693.729980</td>\n",
              "      <td>33583800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>251 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e33bd34b-faaf-454a-918f-e2645112e4af')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #1967D2;\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e33bd34b-faaf-454a-918f-e2645112e4af button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e33bd34b-faaf-454a-918f-e2645112e4af');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ovLUGCBzQuRd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb62d122-096b-4a6a-fd02-38fbb95a2b42"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 251 entries, 0 to 250\n",
            "Data columns (total 7 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Date       251 non-null    object \n",
            " 1   Open       251 non-null    float64\n",
            " 2   High       251 non-null    float64\n",
            " 3   Low        251 non-null    float64\n",
            " 4   Close      251 non-null    float64\n",
            " 5   Adj Close  251 non-null    float64\n",
            " 6   Volume     251 non-null    int64  \n",
            "dtypes: float64(5), int64(1), object(1)\n",
            "memory usage: 13.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GQGyTaTB8sUM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "849a8350-ca0e-4e1b-b538-6cb4794b0251"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Open        High         Low       Close   Adj Close  \\\n",
              "count  251.000000  251.000000  251.000000  251.000000  251.000000   \n",
              "mean   414.739109  425.323268  402.764717  415.137952  415.137952   \n",
              "std    234.908560  239.374833  229.101662  234.987820  234.987820   \n",
              "min     74.940002   80.972000   70.101997   72.244003   72.244003   \n",
              "25%    194.901001  199.384003  188.566994  193.368995  193.368995   \n",
              "50%    408.951996  418.700012  401.660004  408.500000  408.500000   \n",
              "75%    606.549988  627.795013  586.750000  607.234985  607.234985   \n",
              "max    891.380005  900.400024  871.599976  883.090027  883.090027   \n",
              "\n",
              "             Volume  \n",
              "count  2.510000e+02  \n",
              "mean   6.178552e+07  \n",
              "std    3.306128e+07  \n",
              "min    1.515770e+07  \n",
              "25%    3.628290e+07  \n",
              "50%    5.561600e+07  \n",
              "75%    8.131825e+07  \n",
              "max    2.221262e+08  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8830b3a2-844e-40cb-b01b-49eade80edad\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>251.000000</td>\n",
              "      <td>251.000000</td>\n",
              "      <td>251.000000</td>\n",
              "      <td>251.000000</td>\n",
              "      <td>251.000000</td>\n",
              "      <td>2.510000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>414.739109</td>\n",
              "      <td>425.323268</td>\n",
              "      <td>402.764717</td>\n",
              "      <td>415.137952</td>\n",
              "      <td>415.137952</td>\n",
              "      <td>6.178552e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>234.908560</td>\n",
              "      <td>239.374833</td>\n",
              "      <td>229.101662</td>\n",
              "      <td>234.987820</td>\n",
              "      <td>234.987820</td>\n",
              "      <td>3.306128e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>74.940002</td>\n",
              "      <td>80.972000</td>\n",
              "      <td>70.101997</td>\n",
              "      <td>72.244003</td>\n",
              "      <td>72.244003</td>\n",
              "      <td>1.515770e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>194.901001</td>\n",
              "      <td>199.384003</td>\n",
              "      <td>188.566994</td>\n",
              "      <td>193.368995</td>\n",
              "      <td>193.368995</td>\n",
              "      <td>3.628290e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>408.951996</td>\n",
              "      <td>418.700012</td>\n",
              "      <td>401.660004</td>\n",
              "      <td>408.500000</td>\n",
              "      <td>408.500000</td>\n",
              "      <td>5.561600e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>606.549988</td>\n",
              "      <td>627.795013</td>\n",
              "      <td>586.750000</td>\n",
              "      <td>607.234985</td>\n",
              "      <td>607.234985</td>\n",
              "      <td>8.131825e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.380005</td>\n",
              "      <td>900.400024</td>\n",
              "      <td>871.599976</td>\n",
              "      <td>883.090027</td>\n",
              "      <td>883.090027</td>\n",
              "      <td>2.221262e+08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8830b3a2-844e-40cb-b01b-49eade80edad')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
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              "\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8830b3a2-844e-40cb-b01b-49eade80edad button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8830b3a2-844e-40cb-b01b-49eade80edad');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xAifThcH8xUH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "766c8f1d-7880-40fc-d147-c876ba203838"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(251, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v3X8Z1fFb6or"
      },
      "source": [
        "# Data Visualization using plotly express- Visualizing the historical performance of Tesla"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2PKVDQq2ih4S"
      },
      "source": [
        "#Line graph, Area graph , box plot (Analyzing price and volume)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sBuNquI5Qpe3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "6a9b65f9-e689-4c0e-c0dd-5160c2f448ae"
      },
      "source": [
        "px.area(df,x=\"Date\",y=\"Close\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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a9lTXNaae2t37qeq6ba6eR1Hh1ryXXfs2peJrbUFuEdXs4naVWG0k842rj/61cvbf8k2vf8AsIp/6BT9M8TaToeoLdaboz7jkO09xuYKey8YHbk5rKXWETw7caQtucS3QnEhf7oAxjGOfrRKpHltfv3627io4KuqrkoWTdN/ZXwt30j5Nd2dJ4r17UtNudOt7C5a2T7FDIxi4LtjHzHvwBxWZ46CtrkFwFVXubSKaTaMZYjk/pWbrurjWbm3mEBi8m2SDG7dnb36CjXdXGs3NvMIDF5NskGN27O3v0FRUqqSkr9rHVgsDKjKjLks0pKW3W1r9zb8BLAz62t0zJbnTpBKy9QnGSPfGauXr23irwy0WjxPavpbFhYhs+bF/fx3Yf1PqK5nRtZGkxakhgMv2y0e2zuxs3d+nP0qvpGqXGjanDfWzYeM8qejDup9jRGrFQUHt1FWy+rPEVMRDSSs466NpWd157X6bo3/AAVcNa2+u3CqrPFYl1DjI3A5Bx9cVPpGrX2taH4gt9SuXuo0tDOnm87HB4I9Kzj4jtI7rV5LXTjDHqNuYvL83IjY9SOOntVHSdXGmW2pQmAyfbLYwZ3Y2Z79OacaijaN9NSauDnWdSq6dpNwa2urWvr5a+pu6nf3OgeHNFtdLla2+1QfaZ5YjteRieAT14q0NVc6XoHiW6IN7BeG3llAw0seOc+pAyPxrDtdfs5dKg03WLBruK2JNvLFLskjB6r0ORVfWdcGpQW1nbWq2lhag+VCrbjk9WY9yaftUldPorLz0IWAnOSpzp680nKWmsXfTe+t0rNWVvJHZ6VbQ+Gdf1e7mRfKkvIrOHPTZK28/kuKxbizPhzw9raEES3V59iiJ6+Wh3Ej2PArO1nxPJq+kadYmDymtR+8kDZ81goUN04OBSeJvE0niJrTdAIRAhBAbO9zjc3QdcCnKrTs1Hpt89/uM6GBxftIyqr4rc2v8luX/wACav8APU61bK3vPG+mG5VGjg0mOYK4ypKg4z9M5/CobLUh/aLSar4rs72ymBWe1ZXKspH8IxgY46Vzc3iuf+3LHVLWERSWtukBRm3BwAQc9OCDU8XiLRrG4e+0/QjHfEHZ5k++KInqQuOfoapVoX0fXz/r7zGWW4jlSnFv3Ulbl0d3fV7bp3j+iLMc39g+Dri80qUrNc6g1v8AaV+8IlBIAPbPWnTapdar8OpzeSGWWK+RPOb7zDbxk98c81j6Zr0dvZ3On6la/bbG4k80qH2Okn95T61Pf+IrOXw++jWOmfZoPOWVXMu5iQOS3HJP6YqFUXLvpa1jqlg6ntVeneXOpc2m2nnfTa1vM0/FGs6hoOtppWl3D2tpYxxrGkfAclQSzf3sk962ZILfVPHGkzXUMYjXSUufKK/KCNxAx6Anp7VmXf2lxYrqnhv+1LoQp5F3byPslX+HdgfMR+FR+Ktcn03xnaXduYvtNpbRxyovKbsEsv0+bFauXLeUnpdadjz4UXVUKVGKU+Sack17z0V9G3q+rSabL1lqQ/tJpNU8WWd5ZTZWe1ZXKMp7AEYGPavP7pIo7uZIH3wrIwRvVc8H8q6OLxFo1lcNfafoRjviDs8yffFESOoXHP0rmXdpJGdzlmJJPqa5601JJXv9/wCp7OW4aVKcpOLimkteVbX6R0+d9fkdPL/yS+H/ALCp/wDRZq94n17UdNfSrewuXtl+wQyOY+C7Yxye4AA4rnX1kN4WTRfIOVu/tPm7uvylcYx79c03W9WGrz2sghMXkW0dvjduztHXpTdVKPuvWyM4YGUqydWF4803rZ72sdpJcSx/EPRbiJzE9/awvcbOA5IOc/8AfI/Kq+gu91qHiDVru8QXVmvlwXFzlxDuZgGxz0xx9awJvE5fW9K1KO1Cmwgji2M+d+3OTnHGc1Inia2tNYuLmy00LZXcZS6tJZSwkySSQccdeK09tHmvfr+m/wB5xvLq/suRQ1cEr6dJXa+cdF07m3FdW0mn39rrHie21GGaFjErBy0co+6ykjj6VQ1G/utA8NaLbaZM1t9qiNzPLEdrSMTwCevA4qk2v6bZWV1Do+lNBNdIY5Jp5vMKIeqqMcfWr1obj/hGLFdQ0X+1rEs5tngkZZIDn5lYgHgntRz82iett9e/3j+ruk1OpH3XL4XyK/uvVJWj971tfdItahfzanovhS6ucGZrtw7AY3kOo3fXjmoPFOtalP4nutIh+e0+0Kgs1UBZDkHBxzktz+NS+J737LpnhtJbNLWaBnnNmpIMabhtBzzkgdT3zXOXWuSTeJ21uGIRv54mWNjuAIxwTxnpSqzt7t+1/uHgML7S1VU1Ze05drJ891b9GvkzshcavD4ghkvfEVlZYlRf7OgkaQIMgbNijHtTNPmlsfGXie3tZGhhW2nlCIcAOMEHHqMmsKXxHpA1M6tBojfb2k80+bcFolfOSwUAE889aZF4qjj8UXurGxLQXkbRy25l52sBnDY9R6VXtoprXr5mKy+vKEl7O14Wt7q1TTsrN+dm3/wZop5bnwBqs88jSSvfxlnY5JOO9WfFWrX2g6rHpGlXElpaWcSBRCdvmMQCWb1znvWNNrdsNHvtMtbJoobi5WZN0u4oAMY6c/WrTeIdN1GC3OtaU9zdQIIxPDP5ZkUdA4wfzFZ865bKVnprr5nZ9VqKr7SdLmheT5dOqik7N20s1v1OktFSfxl4Y1Ty1jnvrZpJwowGcIwLY96oeGvEGo6h4wSwuJ99hOZImtcDy1QK2FC9BjArJj8Wyf8ACT2mrSWq+TaJ5UNrG21UTaQADj361n6Lq40nxBDqhgMojZm8vdjOQR1x71XtkpKz66/h/wAEy/sypKlUU4Jv2do7aO82kvS8Vf8AEzplCTSIOisQPzq6dc1E6ANCNx/xLRcfaRDsX/WYxu3Y3dO2cVRkfzJXfGNzE4rSN/pp8NCwGlgakLnzTf8AmnJjxjy9nTrzmuM+jV7amdD/AK+P/eH869h+JWreD7Xx3fw6t4Zub29VYt86X7Rhv3a4+UDjAwPwrx1G2SK2M4INbnjPxGPFnim61lbU2onCDyi+/btQL1wPT0oGdJ4Ht9D1j4jtc2ulGLTrW2kuorGaQy5dE6Enr83NS+EfHfiDWPG9pZ6revf2GpzC3ubOb5oSj8cL0XGc8elcX4e1288Na5batYlfPgYna4yrqRgqfYgmuqt/G3hzSL19W0TwqbbV2DeU012ZIbdiOWRNoz1OATxQBu2F5L4O8G+NBpThZrTWRbW0xAYxgFl3DPfbkZ96n8OXdzP4E1PxPc+ILew1vUb9bN9UuUJZI1jB2LtB2k4646D1Arz6PxO48Janos0DSzX94l01yZOhXOQRjnOeuan8N+LItJ0u80XVNOXUtGvGEjweYY3jkHR0YA4OMfXFAHV67e6feeAL6z1fxbZ63qlvKkunSqHMq5OJELMoJBHPXqPYUzx34g1Xwhqun+H9AvpdPsbG0hYC2O3zpGGWd/72ffiud1bxXpp8OyaD4f0Y2FnPKstzNPN500xX7ozgBQPQf45tf8Jlour2VivifQJb+9sYlgjube6MJmjX7qyDBzj1HNAGroWr65rvi/Uta03R9Osr57DM15cHy4rXIwbgE8Bjj0Pf3re05rm58K+LLLVfGFr4hK6ZJOII2eYROvIcSMAOuOB/SuTi+JbT6rqrappUc+lajbJaNZQyGPyY0zsCNg9Mk9OSe1MsfHGiaLYarpuk+HHjtdRs5Leaae73zksMKQdu0KMn5QOeOeKANrw/q9pqHgrS9I0rxavhjUrR5DOkmY47pmbIcyj0HGD+XAq5azeJbL4xaBJq/wBlinu4o4WurBj5d9EP42OfmJIGeB0HHSuPs/Fei3egWWleI9DkvDYBltrq1uPJk2E52sMEN9aLrx9I/iXRNRs9OjtrLRQqWln5hb5Aedznkk+uP/rgHSaFqN5qXiH4gyXtzLcPHol/EjSMWKoG4UewqG58QajoPwf8LHS5za3E812rXEYAkVBISVDdQCcE4/uishvG+lW2ra7eaZocltHq+mTWckTXRfbLIcmQEr06fL7daxdR8Ri/8H6LoItSh02SZzNvz5nmNu6Y4x9TQB3l4n/CX6P8ObnWX8+5vL2W0uJzw8sYlAAJ78cZ9z61Q1Tx5r1r8R57SK4ZdNtr42S6aFHkGFX2bCnTkDr71zg8Ry3+h+GtBtkFpcabdO8d48uF3SSBgSMfKFPfmvQCL2Xx1b/bPh6H8RC4TzNQSWQ2rEEfvtmNvTnOcZ96AOE+J3/JSdc/67j/ANBWtbQLuXw18KdQ1/TCItUu9TXT/tKgF4YhHvO09iTx+XtWF8Q7yC/+IGt3FtIskTXJVXU5B2gKce2RR4b8VQ6Vpt7o2qacNR0e9ZXkg8wxvHIOjowzg/zxQBPL4q1jxHpFlpWoWZ1eaG9VoLmbc0vzYHk7hyQx989PQV6A954jtfFdtNqnjPS9H2yxoNGtJnlEQ4AiMaLt56cnvXDz+O4NPtbGx8M6X/Z9na3iXzmeUyyXEq/d3nAwox0FWLrxr4cbXG8Q23hZ/wC2Hl88i4vC8CS5yXCBQTzzgnGaAOt0/wArSPiX8Q5be3iK2+l3FwsTIChYbH5HTGe1YvhTxJq/ijRvGFhrl9Lf266NPdos+G2SpgqV9OT0HpWNL4/ifxH4n1VNMdV1vT5LPyjPnyS6qC2dvzcrnGB161jeGfEY8PR6yptTP/aWmzWIw+3y9+Pm6HOMdOPrQBg16p468X63o9r4XsNLvZrGMaLazO8B2tKSpABI5KjHTpya8rrv7zxz4f1u00u31vwzJN/Z1nFbRTQXhjd9owQ3y/dPBA6jnnmgDsLTSbDxJ448H6zqNvCZb7TGu7yMqAs0sY4cj3ODjuFqHTteP9vm61n4h6Zf6ZOSt1p7RymJozwVVCuBjt9K4O6+IGpSeL7LX7OGG0GnosNnaJzHFCARs98gnJ469uMaMHjXwxp+otrOm+ETFqx3NGJbwvbxOerBNoz1OBnA7dKAON1aK0h1m+isJfNs0uJFgk/vxhjtP4jFehS6/qOg/Bzw62mTm2uJ7q5Q3EYAkVA+Sqt1AJ2k4/uivNZ55Lm4lnmbdLK5d2x1JOSa2r/xGL3wbpOgC1KGwmllM+/O/ec4244x9aAO68XeL9btPCPhS/tbxrfUL+0f7XeRALNMI2wgLDnAyT7k1Jrwvb/x74L1G00uDUtUvdHtrqWGUbUll2tl29McH/gIrgdb8RjWNB0HTBamI6VDJEZN+7zdzA5xjjp71uQ/EeW117w/qcGnLjStMj06SJ5cidVBBOcfLnPvjHegDvvCc+rv43ig1rx3p2o/azIkulW8r3CP8jErjbsTGM/hiuP0u5l8LfDLVda0k+RqN1rH9n/aEHzwwhN+FPbJ4z9PQVW0fxv4c8Ma1Fqeh+F5RMGO5ru9LlEPBWPC8EjjccnGfWs3SPGFvZpqunajpn27RNRmMzWxl2SQvn5XRwPvAYB45xQB0H/CRajr/wAGdaGpztczW17bqtxJy7KTkKzdTg56+tdH4iiOhJpejaT40s/D9ta2kbmACRXlkYZaRyq/Nn0ziuE1Hxppb+D7vwzpGhGytJpkmEz3HmSMwPJfjnOAABjGO9O/4TLRtY06xg8UaFLe3VjEIIru2uvKeSMdFcYOcevWgBfiVc6Tf3+m31hf2l7fS2oXUZbVCqPMvG/BA5YH9Kn+FN62nah4kvViSVrfQLmUI4yrENGcEdx61zvinxI3iS/hkSyhsbO1hWC1tYuRGg6ZP8R96n8GeKx4R1C+uzYJe/arKS08qRsL8zKctxyPlxjjr1oA6vwP4h1Xxfe6toOv3sl/ZXljNL/pB3eRIoyrp/dx6DitKxgXRvhnoQ0/xJa+H7jUzLcXVy4cSz7WwqqygkBQeR7+5rk28ZaPpmmX0PhnQH0+71CIwT3M90ZjHGfvJHwMZ9TzUGk+L7H/AIRyPw/4i0ltRsLeRpLWSKbyprctywBwQVJ7H1+mADf8X6xanw5pF7F4lttR8T2NyYxe2gdJGgKkgsxAyVIAz7+5rofEGu6rF+0BZ2EeoXK2i3FvGIRIdoV1jLjHoSBmvNvEfii21TTbLR9K0tNO0qzYukZk8ySSQ9Xd8DJ9u2fpjV1nx/aan4k0rxJDonkavbSxy3Tm5LJcbAAAFx8vT/8AXQB1PhuR9R8aeMNb1K/jN1pEcsdlcXuZEtsyOFbHPC84HvRY6jZy2ep2Xibx7Y6xYXdu4WKQSs0U3VHQsvykHsOK5Q+PbOz8WXOr6Toghs7+F4tRsLicyJcb2JfnHy9RjHTHvikHjDQdJsr5fDfh2S0vb2Iwtc3V153ko3UIMDn3PPFAG3/wkupeG/g74dk0qf7Ncz3dwpnVQWCBySoJHAJ25/3a8yubma8uprm4kMk8zmSRz1Zick/nWzf+Ixe+DdJ0AWpQ2E0spn3537znG3HGPrWDQB6n418Xa1o2leEdP0u9msk/sK0neSA7WkJUgAkclRg8dOTVq+11NL8R+CfGFwiRzanabNTCqAJVzsZyPcHP/ARXP3PjjQNYsNJs9c8NSTrpllFbRTQXhjkbYuDu+XBU4Bx1HPJzWJr2s3/jXWrdLTTmRIYRb2VhaKZPKiUE7QAMscZJOP5UAdnpuhf8IL4j8W6zIuI9FjaPTyf4pZ+IiPXCnn615WSWYsxJJOST3r0z4kazeReFfDfh29UR6itslzqC/wAW4LsiD/7QXJIPqK8yoA9aJ8S22h6MkviLTfCNlFaqYrVbl1mmH/PVkUEkt7n8OtbV7pdjqvxY8LXN15d8JdFS9lcRbBdyKrkOVI74U4PYYrhtQ8aaBry2l5rnhya51S3gWBnhvTHFOF6Fl2kj8D+NR6t8R7u+8RaHr1nZRWV7pluINqNmJwCeAvVVIYjGTx3oA3fh/wCP/EWqfEezi1C+kura9lZXt35SP5SQUH8ODjp2zVewu5vDngDxBr2mt5WqXOsnT/tKj54ogu87T2yTjP09BUGm+PvDWi66muab4RaO/ZiXDXpMce4fN5Y28E5PXoDwKxNE8YRWEeqWGpaaL/R9Sk82W280o8bg5Do2OCPpzigDpIvEepa98G/ESanO1zNbXNsq3EnMjKXyFZupwQcZ/vVtRf8AJQvhl/2A7X/0B642/wDGulf8IffeGdI0FrO1uJI5RNJc+ZIWVsktxzkAAAYxjvmhfHyr4i8L6r/ZxxodjFaGPzv9dsDDdnb8ud3TmgDpPC/jPW7/AOKselzXZOlTXMtqbAKPIWIBgFC4xxgc/nVKwkbwp4A1/WdIPk6hLrJ01Z1GXhhVd3ynsT0z9K5DRPEY0fxrD4hNqZRHcPP5G/bndnjdj39K6bwrdaldaT4huV0aLWtEnuFe80wSsJkZiSsiFRkY5GR6e1AFu18R6nr3we8Upqk73UtrNaBLiXmRlaXO0t1IBBIz/eNeYV63qVzFafCDV4R4fOhW13dwJaQzOzTTsrBndiwBIwoA4AGDXklABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUVvR+HBJ4Em8S/aiDHfiz+z7OuU3bt2fwxisGgAooooAKKKKACiprOKKe+t4Z5hBDJIqySkZ8tScFsd8DmptVtrWz1W5trK8F5bRyFYrgJtEi+uO1AFOiiigAore8WeHB4ZvrK2F0bj7TZRXe4pt27wfl6nOMdawaACiiigAooooAKKK3vFnhweGb6ythdG4+02UV3uKbdu8H5epzjHWgDBooooAKK3oPDgm8DXXiP7UQYL1bTyNn3sru3bs/pisGgC7b6xqdpB5FtqN3DD/cjmZV/IGqbMWYsxJJOST3pKKbbe5EacItuKs2FFFFIsKKKKACiiigAq1Z6nf6eGFne3FuG+8IpCoP1xVWimm1qiZwjNcsldEk08txK0s8ryyNyzuxYn6k1HV/RNIuNe1q00q1aNZ7qQRo0hwoPqcVZtNJsWn1eC/1RbSSyikMA8st9olU4CDnjPrSGkkrIx6KKKBhRRRQAUV0d54Yis/DXhzWZL7CavJMjqY+IBHJsznPzevQVYsvCVnqV74jjstX8610myku4p1h4uAuOMZ+XOevNAHKUUUUAFFFb3hvw4NfttamN0YP7NsJLwAJu8zbj5eox9eaAMGiiigAoqzp9r9u1O1sy+zz5ki3Yzt3EDOPxre1DwzYaX4h17SrzWBEdOidrd2h/4+ZBjCYz8ucnnnpQBzFFFbupeHhYeE9E1wXJc6m06mHZjy/LYL1zznPoKAMKiiigAooooAKKK3PCnhuTxRrBsluUtYYoXuLi4dSwiiX7zYHU8jj3oAw61R4n18WH2Ea3qP2Tbt8j7U+zb6YzjHtWxrfhTToPDSeIdB1aS/sFufss6zweVJG+Mg4ycgj+f1xyVABRRRQAUUUUAFFa1h4futQ8PavrUcka22mCLzVYncxkcKoA/Mn6UkugXMPheDX3kj+zT3TWyICd25VySe2KAMqiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKns7y60+7jurO4lt7iM5SWJyrL24I5qCigCW5uri9uZLm6nknnkOXllcszH1JPJqKiigAooooAKKKKACiiigAq1YalfaXcfaNPvLi0mxjzIJSjY9Mg1VooAuahquo6vMJtSv7m8lUYD3ErOQPQZPFU6KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvQNFki8KfDgeJLe0tp9Xvr42sE1xEJBbxquSVU8bieM+lef11+geIdIk8MT+GPEcd0LFrgXVtdWoDSW8uNpypIypHb6/gAdDqOuHxH8HJpZLS2gvRrMaTvbxiNZmMZw5A4DY4OPSjxZr7eAdcHhnQLHT0gsoohcyz2qSvduyBiXLDOPmHAxjmsjV/EXhuLwG/hnQ4r8sL5Lo3NyqgzHaQSQD8vYAc9M1Y1LXvCPi+W31PxA2q2WrJEkd39jjSRLnaMBhkjaxA78dKAOo0nQtJk+KPha/t9Pgista017uSy2ho45DE+4KD2zgiqPg3xFbeKdT1LRNQ0XTToiWcs1vbRW4RotmCuHHzZx1JOc1mWPxFsI/iLpWsyWU1vo2l2ps7W2iw8ixhGUE5IBJLc8/niud8D+ILPw5rF1d3qTNHLZTW6iJQTucYHUjigDqNJ10+MPDPiex1LTtOSKy09ruz+zWqxm3ZSMBSOcc96PC8fiAeE7R/C/h22t33ubvV74QkS88BDLwFHIIGeR9a5Xwtr1potl4ghuVlZtQ017WHy1BAckEbskYHHvW1Pr/AIY8Q+F9FsdZn1SxudJhMIW1hWWOZc9RlhhuOSaAN7xbpsJ1HwFqFxb6b9vvrgx3r2G0wzFJYwD8vBOGIPvx2qWzt9NtvGfxEluNMtrmCytpJY7d4xsyrAgcdBkc47ZrntW8ZaDd23hGDT7O8totCuWZo5Nrl4y6Nu3AjLHZkjAGTTT410w6z41u/LufL1u2kithsGVLHI388D6ZoA0NL1p/GvhTxNbavY6fu0+y+12ktvarE0JU/dG0dD0rzGum8K+ILPRNN8RW90kzPqOntawmNQQHJ/iyRgfnXM0Ae9a34atWa28VXkMepHT9Dt5LfSlcbpSo5kcf8813A4HXB+h47wNfQanN4v1fVtPtLxotOa4EDRBY9ysCFAHReAOO1UtQ+IAi8Y6NrukLMBZWENrNFMAolCgh14J+Ug/17UReJvDem3viv+y4b1LLVrBobaJ4lBhkY5KnDfdB6H07UAael62/jXwp4mttXsdPLafY/a7SWC1WJoSp+6Co6GoLvUB4C8JeH/7JtLQ6pqluby5vZ4FlYKT8qJuBAGOtc74V8QWeiab4it7pJmfUdPa1hMaggOT/ABZIwPzrRg8QeHte8NadpHiX7da3OmBo7a9tEWQNETnY6kjp2I//AFgHQx3emzWvhXxzPp1nbytqJsNSjjiAimBB/ebOgO3JPv8ASrnhPw7pvhzxl4kfV7WKWzivItMtklUFT58gIPPQhMH6GuG8UeJNOvNH07w/oVvPFpNgzSeZckebPK3V2A4HsPetLxd49t9e8NaNZ2kU0V9E6T6hMwAEkyRrGjKcnPAPXFAD10iPwt4Q8WT3cKPdPfDSLVpFBKlSWkYZ9VA5FdVc6BaeIvidoNvfoJLSDQILmWMttEgVThSewyRn2zXH/EXxvZ+LRpyafbyW8UStNcq6hd9w+NxGCcjCjB9zU9z8Q4IPGmka5YW0ksFrp0VlcwTgL5oCkOBgnjng+3SgDsLC21PVNSNl4kg8LDw/MGQw201ujWowdrRsp3ZBx6965axig8FeCb3XLaC1vNVm1V9PtriaNZFhjQEl1ByMkjr6VUt734d6Veyapb22qai+GMGmXkSCJWI4Dtk7lGew9Kp6F4k0mbw9eeG/EUNwthNc/bLeezUFraXGD8p4KkcY7c+vABv32ut4h+Dl7NNaW0N6urRJM9vEI1mO3hio4Ddjj0FHizXj4D1lPC+j6dpxtbKGMXbXFqsrXjsgZi5YZxzjAxj+WVqniLwzD4Bn8M6JFftI14lybq5RR5xAwTgH5QOABzWj4hvdDv7qym8ZaVrVtry20YkFm0bR3qgYRyScqT0JGaAN678O6N4h+J3h+IWMVvpZ0KK+a1TCBgNxCk/iuSewqewt9T1PUzZeI7fwqPD8+5DBbzW6tajB2tGy/NkHHrWX408UHwz8TdH1CGzQC20qGG4sSeArBsx59gwx9BXP2978OtLvZNVt7bVNQfDGDTLyJBErEcB2ydyjPHGelAHFahbLZ6ldWqSrKsMzxiRTwwBIyPrivWtJ0O+0TwNo114fj0ddU1FGuLm81B4t6pn5EjEnGMdeOteQSyebK8m1V3MW2oMAZ7AdhXaWviHw/rfhaw0TxML23n00sLS+tEV8xsclHUkdOxH+OQDX8bWqWGmaJ4jK6RDryXBju4bJ45IpcfMkhRcjkAg+ua2J7Hw/baW3xOj04NHPCFh0tocxJeElGY8Y8sEZHqT2OBXD6nqXhGebSdMsbG6h0u1ctd3zRobu5ycngHAHYc9/atkfEyGTxPMk9m58Jy2/2D+zVx8luPusBnG8HnOfbPQ0AO8O6nBY/DfW9dn0+zu9RGqJ5Dzwqyxuy/e24xgAsQOmcccVFJdf8Jt8O9a1LUbe2XVtFlgdbuGFYjNHIxXYwUAHGCRx6e+b/h5tAi+GPiJL1b2bSX1WNI5UVVnjG0bX25wSO4z3Nc9qmv6HpvhS48OeGheTJezJLfXt2qoZAnKIqgnAB5ye+aAPQdT0PV/C32TSfDUfh+GKGBDdzXskBmuZCMtu38hfQDFUrfQtJh+MHhl7eDT/AC7+3aW7srd1lhimEb7guMjbnBArmL/X/Cfi+OzvPELanY6tBCsNxJaRpIlyF4DckFWx+FN07xhoGn/EbS9Ys9Jey0fT4zEscSKZpR5bLvfkAsSwzz270AbfhTxzc3/xA0jTLbTtMtNPS5aGFYrRd6xdhvOTn5RyMHOar6VdHXNU8eXF9b2ryQaRcrFtgRQu1uGAA+979a4nw1rUWheLbDWJYnkit7gSMi43Fe+PfBrpk8S+GdL1LxM+mHUpLbV9Mmgj8+NAyTSMTg4P3Bxz1oA3fC8Nzc+DNMTwXPoi6uDJ/aMF2kZuZW3fLt3gjbj0x+ear2Xh1PEHxXtrXWfDi6MI7X7ReWiOBHOyA5ZcYCqxxkAnoec1zlpe+DdU0KxttWhvNL1GzBRrmxgR1uVzkFwSDuHTP+RpXXxJjt/FWi3umW082n6Ta/Ywt4wMtzGRhi5GQCew5xj8KAOtsbfVNS1T7H4it/Cv/CPTbkaC3mt1a2Ug4aNlO7IOP1rxfUbVbHVLu0SVZVgmeISKchwrEZH1xXawXvw60y+fVbe21W/bDNBpd3EgiViOA75O5R9M9OtcJLJ5szybUTexbagwoz2A7CgD0W+VX8AfDZWAZTd3QIIyCPtAro9L0sXfxC+Iel2ggtxNp80MecJGmdoycdAM1wdx4nsZfDHhDTVSfztHnmkuCVG1g8ocbeeeB3xWk3jfSz4i8Z34juvJ1qylt7YbF3KzYxu54HHbNACeOoLfwnYWvhnTLWNoJ4UuptVZVZr3cMjY3O2MegP+J7LUtD1bwsLPSfDMfh+GKKBDdzXskBmuZCMtu8zkL6AYrzpPE1jqHgJ9A1lJ2u7F/M0q5jUNsB+9G+SDtPbGf0Fad9r/AIT8XxWd34ibU7HVreBYJ5bSNJEuVXgNyQVb9KAKvxL0rT7DV7G6sBZxNe2qy3NtZyq8cM/RwuDwvcCr3wqnhtR4quLi3S4ii0WZ3hf7sgBB2n2PSuX8U6ppeqalGdF0xbCwt4VhjUgeZJj+OQjqxq14S8QWeh2niCK6SZm1DTJbSHy1BAdsYLZIwPzoA6rRtcfxr4c8T2er2GnYsdOe8tHt7VImgZOgUqOlVp75fAng3QJNKtLRtV1aJ7q4vZ4FlZEzhUQMCAMdfpXPeEvEFnodp4giukmZtQ0yW0h8tQQHbGC2SMD860LXxBoGt+F9P0XxL9ut59MLi0vrRFkzGxyUdSR07Ef45ANm5S11/RvDPi5bK3tNQ/tlLC8FugRJjw6vtHAOBzjrmr11bwT+PfiSZoY5DHpVy6b1B2sNmCM9D71zOpeLdJji0PR9Ft7pNG0y7F3JJcbfOuJMjLEDgYGQBnvU7+NdMbxN4x1ER3Pk6zYTW1sNg3Kz7cbueBwemaALM9+fAngvQH0u1tDqerRvdXN3PAsrBM4VF3AgDHWpPGWo22r+B/BN5/Z6QJJPdedbWg2KW3pu2Dtu5OOxNZVpr/h/WvC1hoviX7dbzaYXFpe2iK+Y2OSjqSOh6Ef/AK7ep+NdEih8JxaHYXQi0G6eUpdbcygurAkj+IlSTxxnjNAHX2y+KU1i1VdB0PRfDryIq2N+LdS0ZIBDZ+csefxrGs9K0x/Hvi/wZHbQpFfpILDcozDOg3oFJ6Dr+QFZl/rvgmTxS/igNrV5dPcC6FhKiIqyZzhpMn5QegA6ACsjWfEQv/iY/iDQo5yz3cc1vG6AOWG35SAT1II980AWtLtItE+G2s6td26G81GcabZiVASgHzSsM9OPlz2Ndzbw6jPYabJ4Ei0C/wBJS2j+06c6RG4kcD5/N3jOT65H+PKfGHVIJvE8ej2SCK104OWjU5AnlbzJD+ZA+oNVpNR8B6pNa6lOuqaNeRRoJ7bToU8t2UfejORtJ9//AK5AOY8SKE8R34GlPpQ80n7CzZMP+znA49OOhFdX8NoG0ZrnxneXn2XSbA/Z5EWPe12zD/UqvToQcnpx7kc74z8R/wDCV+KbvVxB5CS7VjjJyQqqFGT3PGTWn4Y8S6TF4bv/AAx4hguW026mFzFPa4MkEwAGcHgggAfn68AHZ+JPD134r0zSbfwbHZ2/hacSXRUZQQzhSX848844Hbt0wa8bPWvU9G8deE/C1jJoWnWmp3+mX5ZdRuLlxG+0qV/dopwOD1JBOMemPMrz7N9tn+xeb9l8xvJ83G/ZnjdjjOKAIa9T8VeJm8LQeHrfRdP0+C5m0i2murh7VJGlyvC/MDgcEkjk568V5ZXrnjNfCc1v4Wi119StbpNCtXE1miuJIyCNhDEYIIJB/wBrnpQBn6xp+lt4n8K63DoJuYNYthcTaVaDaHmGQwUdlzg49AfWuu8PWXiS/wDEEem+KdM8P2+j3QdP7OIt1kQbSQIwnzZGB16DJri7X4kWdr4503UYtPlj0XTrT7Db24YGVYsEF89N/Of0z3o8O694I8I+JoNYtX1nU5QzYM0aRiFWBBb7x3tgkdhzmgC74T1u70r4ZeMVt1tm/s+a2EPm26PnfMQd2R83tnp2qG/8RXx+C2mrstMS3klm3+jJ/qxGACOOG/2uvvWT4b8RaBbWPibRtYF+NO1d43jmtVUyJ5chYZBOOcj16fjUdvrvh64+H03h/UP7Qjura6lurKWFFKyFkwofJyBnrigDvr/Q9W8LwWGmeGY9AgVLdHvJ72SAzXErDLAiTkL6AVSTQ9Ki+LHhaaGDTwL+Ive2Vs6ywxzBG3BQMgKeCBXNXmv+FfF1tY3HiJtSsdWtYFt5ZrSNZEuUXocEgq36f0jsvF2gWHxB0jVbDSpLPSNOTywqIpnm+RhvfkAsSR37UAbfh/xGniL4j6PozaTpkOlWt3KIIo7YbtgRwAzHO71Pqeak8LeJU1nxwvhWfRtLGgXEktutqtqu5AFba2/7xb5Rkk9z0rifCWvWmheOrPWrpZWtYZnkZY1BfBVgMAkDuO9O8LeILPRfH9trl0kzWsU8kjLGoL4YMBwSB3HegDqfA1vbR6XrFppV1pMHihLzbBJqaqQ0AGCI9wI3E5zx0/Osz4hw6xFBpw17w5DYaiu8PqFtsEd2O2Qg27h65zz0FUtM1XwteWGoafrtncQtNdm5ttRtIlaZM5GxwSMr3wD1p/iTxJpT+ErLwtorX1xaW9ybp7q9AVmfaVCooJ2rgn8aAONooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACup0/4i+KtMsYbO21T9zANsPmwRyNEPRWZSR+fFctRQBPeXlzqF5Ld3k8k9xK26SSRssx9zUFFFABRRRQAUUUUAXo9Yv4dGm0iO4IsJ5Vmkh2j5nHAOcZ/WqNFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVr6B4n1fwvcTXGj3f2aWZPLdvLV8jOf4gcH3rIooAknnlubiSeeRpJpWLu7nJ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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ASIJnLjScv9B"
      },
      "source": [
        "## Data Preperation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8VU3NvfDczau",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "fc92cb45-ff31-4a84-f753-e8d9df5400e1"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date        Open        High         Low       Close   Adj Close  \\\n",
              "0    2020-03-16   93.900002   98.973999   88.433998   89.014000   89.014000   \n",
              "1    2020-03-17   88.001999   94.370003   79.199997   86.040001   86.040001   \n",
              "2    2020-03-18   77.800003   80.972000   70.101997   72.244003   72.244003   \n",
              "3    2020-03-19   74.940002   90.400002   71.692001   85.528000   85.528000   \n",
              "4    2020-03-20   87.639999   95.400002   85.157997   85.505997   85.505997   \n",
              "..          ...         ...         ...         ...         ...         ...   \n",
              "246  2021-03-08  600.549988  620.130005  558.789978  563.000000  563.000000   \n",
              "247  2021-03-09  608.179993  678.090027  595.210022  673.580017  673.580017   \n",
              "248  2021-03-10  700.299988  717.849976  655.059998  668.059998  668.059998   \n",
              "249  2021-03-11  699.400024  702.500000  677.179993  699.599976  699.599976   \n",
              "250  2021-03-12  670.000000  694.880005  666.140015  693.729980  693.729980   \n",
              "\n",
              "        Volume  \n",
              "0    102447500  \n",
              "1    119973000  \n",
              "2    118931000  \n",
              "3    150977500  \n",
              "4    141427500  \n",
              "..         ...  \n",
              "246   51787000  \n",
              "247   67523300  \n",
              "248   60605700  \n",
              "249   36253900  \n",
              "250   33583800  \n",
              "\n",
              "[251 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c4d9d3b1-baf2-4636-b431-9066347c58c5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-03-16</td>\n",
              "      <td>93.900002</td>\n",
              "      <td>98.973999</td>\n",
              "      <td>88.433998</td>\n",
              "      <td>89.014000</td>\n",
              "      <td>89.014000</td>\n",
              "      <td>102447500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>88.001999</td>\n",
              "      <td>94.370003</td>\n",
              "      <td>79.199997</td>\n",
              "      <td>86.040001</td>\n",
              "      <td>86.040001</td>\n",
              "      <td>119973000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-03-18</td>\n",
              "      <td>77.800003</td>\n",
              "      <td>80.972000</td>\n",
              "      <td>70.101997</td>\n",
              "      <td>72.244003</td>\n",
              "      <td>72.244003</td>\n",
              "      <td>118931000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-03-19</td>\n",
              "      <td>74.940002</td>\n",
              "      <td>90.400002</td>\n",
              "      <td>71.692001</td>\n",
              "      <td>85.528000</td>\n",
              "      <td>85.528000</td>\n",
              "      <td>150977500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-03-20</td>\n",
              "      <td>87.639999</td>\n",
              "      <td>95.400002</td>\n",
              "      <td>85.157997</td>\n",
              "      <td>85.505997</td>\n",
              "      <td>85.505997</td>\n",
              "      <td>141427500</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>2021-03-08</td>\n",
              "      <td>600.549988</td>\n",
              "      <td>620.130005</td>\n",
              "      <td>558.789978</td>\n",
              "      <td>563.000000</td>\n",
              "      <td>563.000000</td>\n",
              "      <td>51787000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>247</th>\n",
              "      <td>2021-03-09</td>\n",
              "      <td>608.179993</td>\n",
              "      <td>678.090027</td>\n",
              "      <td>595.210022</td>\n",
              "      <td>673.580017</td>\n",
              "      <td>673.580017</td>\n",
              "      <td>67523300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>2021-03-10</td>\n",
              "      <td>700.299988</td>\n",
              "      <td>717.849976</td>\n",
              "      <td>655.059998</td>\n",
              "      <td>668.059998</td>\n",
              "      <td>668.059998</td>\n",
              "      <td>60605700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>2021-03-11</td>\n",
              "      <td>699.400024</td>\n",
              "      <td>702.500000</td>\n",
              "      <td>677.179993</td>\n",
              "      <td>699.599976</td>\n",
              "      <td>699.599976</td>\n",
              "      <td>36253900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>2021-03-12</td>\n",
              "      <td>670.000000</td>\n",
              "      <td>694.880005</td>\n",
              "      <td>666.140015</td>\n",
              "      <td>693.729980</td>\n",
              "      <td>693.729980</td>\n",
              "      <td>33583800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>251 rows × 7 columns</p>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-c4d9d3b1-baf2-4636-b431-9066347c58c5');\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sO8g9RCI95Xe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f391b060-6598-4cbf-c3d8-ed74c896c46e"
      },
      "source": [
        "columns=['Date',\"Close\"] \n",
        "ndf=pd.DataFrame(df, columns=columns)\n",
        "print(ndf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           Date       Close\n",
            "0    2020-03-16   89.014000\n",
            "1    2020-03-17   86.040001\n",
            "2    2020-03-18   72.244003\n",
            "3    2020-03-19   85.528000\n",
            "4    2020-03-20   85.505997\n",
            "..          ...         ...\n",
            "246  2021-03-08  563.000000\n",
            "247  2021-03-09  673.580017\n",
            "248  2021-03-10  668.059998\n",
            "249  2021-03-11  699.599976\n",
            "250  2021-03-12  693.729980\n",
            "\n",
            "[251 rows x 2 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prophet_df=ndf.rename(columns={\"Date\":'ds', \"Close\":'y'})\n",
        "print(prophet_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ELlmr8RCq55J",
        "outputId": "fd8c43f3-19d3-4089-c88a-a2207203fff4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             ds           y\n",
            "0    2020-03-16   89.014000\n",
            "1    2020-03-17   86.040001\n",
            "2    2020-03-18   72.244003\n",
            "3    2020-03-19   85.528000\n",
            "4    2020-03-20   85.505997\n",
            "..          ...         ...\n",
            "246  2021-03-08  563.000000\n",
            "247  2021-03-09  673.580017\n",
            "248  2021-03-10  668.059998\n",
            "249  2021-03-11  699.599976\n",
            "250  2021-03-12  693.729980\n",
            "\n",
            "[251 rows x 2 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j57-ly4Jc5va"
      },
      "source": [
        "## Creating Facebook Prophet Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GdFXAfTFc7iw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfeecd78-85cf-4806-e07a-07ae4d9d2eb3"
      },
      "source": [
        "m=Prophet()\n",
        "m.fit(prophet_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpl1sbo24h/t04g2nl0.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpl1sbo24h/kqg8ltcb.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=93867', 'data', 'file=/tmp/tmpl1sbo24h/t04g2nl0.json', 'init=/tmp/tmpl1sbo24h/kqg8ltcb.json', 'output', 'file=/tmp/tmpl1sbo24h/prophet_modelxpdf22s7/prophet_model-20230531115754.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']\n",
            "11:57:54 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "11:57:54 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<prophet.forecaster.Prophet at 0x7feb0190beb0>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T-0eY9xOdA4S"
      },
      "source": [
        "##Forecasting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qGPpV0kmdCjh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0fbadef0-cfc7-4d42-ca16-64c87816b015"
      },
      "source": [
        "future= m.make_future_dataframe(periods=30)\n",
        "forecast=m.predict(future)\n",
        "print(forecast)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            ds       trend  yhat_lower   yhat_upper  trend_lower  trend_upper  \\\n",
            "0   2020-03-16   65.599460  -25.274520   138.571925    65.599460    65.599460   \n",
            "1   2020-03-17   67.394950  -18.436697   150.580988    67.394950    67.394950   \n",
            "2   2020-03-18   69.190441  -17.028033   149.815619    69.190441    69.190441   \n",
            "3   2020-03-19   70.985932  -17.524307   154.606497    70.985932    70.985932   \n",
            "4   2020-03-20   72.781423  -26.571142   147.722768    72.781423    72.781423   \n",
            "..         ...         ...         ...          ...          ...          ...   \n",
            "276 2021-04-07  900.291503  810.746376   984.496561   899.090296   901.228731   \n",
            "277 2021-04-08  902.748588  814.073253   986.823798   901.465147   903.735724   \n",
            "278 2021-04-09  905.205673  815.199899   981.717111   903.844092   906.243249   \n",
            "279 2021-04-10  907.662758  831.524692  1005.892951   906.204795   908.756296   \n",
            "280 2021-04-11  910.119844  837.153158  1010.677653   908.601714   911.282429   \n",
            "\n",
            "     additive_terms  additive_terms_lower  additive_terms_upper     weekly  \\\n",
            "0         -5.864685             -5.864685             -5.864685  -5.864685   \n",
            "1         -2.383380             -2.383380             -2.383380  -2.383380   \n",
            "2         -3.494360             -3.494360             -3.494360  -3.494360   \n",
            "3         -3.843579             -3.843579             -3.843579  -3.843579   \n",
            "4         -7.762547             -7.762547             -7.762547  -7.762547   \n",
            "..              ...                   ...                   ...        ...   \n",
            "276       -3.494360             -3.494360             -3.494360  -3.494360   \n",
            "277       -3.843579             -3.843579             -3.843579  -3.843579   \n",
            "278       -7.762547             -7.762547             -7.762547  -7.762547   \n",
            "279       11.674274             11.674274             11.674274  11.674274   \n",
            "280       11.674275             11.674275             11.674275  11.674275   \n",
            "\n",
            "     weekly_lower  weekly_upper  multiplicative_terms  \\\n",
            "0       -5.864685     -5.864685                   0.0   \n",
            "1       -2.383380     -2.383380                   0.0   \n",
            "2       -3.494360     -3.494360                   0.0   \n",
            "3       -3.843579     -3.843579                   0.0   \n",
            "4       -7.762547     -7.762547                   0.0   \n",
            "..            ...           ...                   ...   \n",
            "276     -3.494360     -3.494360                   0.0   \n",
            "277     -3.843579     -3.843579                   0.0   \n",
            "278     -7.762547     -7.762547                   0.0   \n",
            "279     11.674274     11.674274                   0.0   \n",
            "280     11.674275     11.674275                   0.0   \n",
            "\n",
            "     multiplicative_terms_lower  multiplicative_terms_upper        yhat  \n",
            "0                           0.0                         0.0   59.734775  \n",
            "1                           0.0                         0.0   65.011571  \n",
            "2                           0.0                         0.0   65.696081  \n",
            "3                           0.0                         0.0   67.142353  \n",
            "4                           0.0                         0.0   65.018876  \n",
            "..                          ...                         ...         ...  \n",
            "276                         0.0                         0.0  896.797143  \n",
            "277                         0.0                         0.0  898.905009  \n",
            "278                         0.0                         0.0  897.443127  \n",
            "279                         0.0                         0.0  919.337033  \n",
            "280                         0.0                         0.0  921.794119  \n",
            "\n",
            "[281 rows x 16 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JzPozfvMDUUX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "8e941a21-81a4-4ddc-a86b-af4fba558f58"
      },
      "source": [
        "px.line(forecast ,x=\"ds\",y=\"yhat\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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    {
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      "metadata": {
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "figure2=m.plot_components(forecast)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "6Nq5QFvjrPrd",
        "outputId": "33499e92-a700-4569-f886-014747813e72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6csAGFe7DTij"
      },
      "source": [
        "##Downloading the Forecast data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FFchZPzWDY9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "126a6e29-9c60-4fa2-a8c8-74ab487112c4"
      },
      "source": [
        "from google.colab import files\n",
        "forecast.to_csv(\"forecast.csv\")\n",
        "files.download(\"forecast.csv\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_1bd791d4-b27c-4c77-ac99-ea8e8bc94984\", \"forecast.csv\", 70915)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}