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            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "988ddbf399ad40748af35744b16cb62e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Sohammhatre10/Predicto/blob/main/ETH.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "_ynE6CLEX_B9"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set_style('darkgrid')\n",
        "plt.style.use('fivethirtyeight')\n",
        "data = ('ETH-INR.csv')\n",
        "df = pd.read_csv(data)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pycaret"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9G6444XwvCJZ",
        "outputId": "fc1ab2fd-b390-46c6-b62e-a744ab000a5c"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: tinycss2 in /usr/local/lib/python3.10/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.2.1)\n",
            "Requirement already satisfied: cffi>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.15.1)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (2.4.1)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.5.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (2.21)\n"
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    {
      "cell_type": "code",
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        "df.info()"
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        "id": "jsqQfmi1eizm",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e3d43dd-cae0-49a6-a982-78493acdc6b8"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2093 entries, 0 to 2092\n",
            "Data columns (total 7 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Date       2093 non-null   object \n",
            " 1   Open       2093 non-null   float64\n",
            " 2   High       2093 non-null   float64\n",
            " 3   Low        2093 non-null   float64\n",
            " 4   Close      2093 non-null   float64\n",
            " 5   Adj Close  2093 non-null   float64\n",
            " 6   Volume     2093 non-null   float64\n",
            "dtypes: float64(6), object(1)\n",
            "memory usage: 114.6+ KB\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "id": "_9KaQliQenMG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "a0817526-7f84-4223-bd33-633458445514"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Open           High            Low          Close  \\\n",
              "count    2093.000000    2093.000000    2093.000000    2093.000000   \n",
              "mean    90320.048456   92978.104388   87370.748286   90364.041999   \n",
              "std     86845.564062   89316.674065   84033.820307   86809.001514   \n",
              "min      6065.104004    6141.604980    5892.192871    6067.162109   \n",
              "25%     16175.736330   16685.199220   15682.493160   16175.250980   \n",
              "50%     47054.152340   48715.312500   44942.582030   47103.835940   \n",
              "75%    148495.515600  151509.671900  144845.921900  148516.546900   \n",
              "max    355554.500000  364228.468800  349691.875000  355703.531300   \n",
              "\n",
              "           Adj Close        Volume  \n",
              "count    2093.000000  2.093000e+03  \n",
              "mean    90364.041999  9.239513e+11  \n",
              "std     86809.001514  7.730981e+11  \n",
              "min      6067.162109  4.041886e+10  \n",
              "25%     16175.250980  3.572450e+11  \n",
              "50%     47103.835940  7.370550e+11  \n",
              "75%    148516.546900  1.302590e+12  \n",
              "max    355703.531300  6.186120e+12  "
            ],
            "text/html": [
              "\n",
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              "  <div id=\"df-35495a37-5292-46b4-b09f-11c3f73c74b2\">\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>2093.000000</td>\n",
              "      <td>2093.000000</td>\n",
              "      <td>2093.000000</td>\n",
              "      <td>2093.000000</td>\n",
              "      <td>2093.000000</td>\n",
              "      <td>2.093000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>90320.048456</td>\n",
              "      <td>92978.104388</td>\n",
              "      <td>87370.748286</td>\n",
              "      <td>90364.041999</td>\n",
              "      <td>90364.041999</td>\n",
              "      <td>9.239513e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>86845.564062</td>\n",
              "      <td>89316.674065</td>\n",
              "      <td>84033.820307</td>\n",
              "      <td>86809.001514</td>\n",
              "      <td>86809.001514</td>\n",
              "      <td>7.730981e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6065.104004</td>\n",
              "      <td>6141.604980</td>\n",
              "      <td>5892.192871</td>\n",
              "      <td>6067.162109</td>\n",
              "      <td>6067.162109</td>\n",
              "      <td>4.041886e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>16175.736330</td>\n",
              "      <td>16685.199220</td>\n",
              "      <td>15682.493160</td>\n",
              "      <td>16175.250980</td>\n",
              "      <td>16175.250980</td>\n",
              "      <td>3.572450e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>47054.152340</td>\n",
              "      <td>48715.312500</td>\n",
              "      <td>44942.582030</td>\n",
              "      <td>47103.835940</td>\n",
              "      <td>47103.835940</td>\n",
              "      <td>7.370550e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>148495.515600</td>\n",
              "      <td>151509.671900</td>\n",
              "      <td>144845.921900</td>\n",
              "      <td>148516.546900</td>\n",
              "      <td>148516.546900</td>\n",
              "      <td>1.302590e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>355554.500000</td>\n",
              "      <td>364228.468800</td>\n",
              "      <td>349691.875000</td>\n",
              "      <td>355703.531300</td>\n",
              "      <td>355703.531300</td>\n",
              "      <td>6.186120e+12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "              title=\"Suggest charts.\"\n",
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              "\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "    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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-46c377f0-85cb-46d9-b7e0-a6b9fe9878ae button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
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              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "\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",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-35495a37-5292-46b4-b09f-11c3f73c74b2 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-35495a37-5292-46b4-b09f-11c3f73c74b2');\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": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install jinja2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lrH0edU3vxhB",
        "outputId": "c54ed35b-37fc-4302-f097-62b8017b7e9d"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (3.1.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2) (2.1.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from pycaret.regression import *"
      ],
      "metadata": {
        "id": "ux4LN62wmhkp"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df\n",
        "asset = df"
      ],
      "metadata": {
        "id": "YUhxdkFSwH1X"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "future_days = 1\n",
        "asset['Future_Price'] = asset[['Close']].shift(-future_days)\n",
        "asset = asset[['Close', 'Future_Price']]\n",
        "asset.drop(2092)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "lB55aU6cwg-v",
        "outputId": "0b0c0477-5ccc-4bef-ac22-9cf314ccb29d"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Close  Future_Price\n",
              "0      20504.61328   20063.28516\n",
              "1      20063.28516   20722.72852\n",
              "2      20722.72852   22081.06836\n",
              "3      22081.06836   21754.87695\n",
              "4      21754.87695   21606.02930\n",
              "...            ...           ...\n",
              "2087  154748.85940  153125.87500\n",
              "2088  153125.87500  152655.15630\n",
              "2089  152655.15630  154051.95310\n",
              "2090  154051.95310  152154.42190\n",
              "2091  152154.42190  151490.12500\n",
              "\n",
              "[2092 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-ecfa00be-01b1-42e4-a077-f9ac74c68426\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "      <th>Future_Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20504.61328</td>\n",
              "      <td>20063.28516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20063.28516</td>\n",
              "      <td>20722.72852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20722.72852</td>\n",
              "      <td>22081.06836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>22081.06836</td>\n",
              "      <td>21754.87695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>21754.87695</td>\n",
              "      <td>21606.02930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2087</th>\n",
              "      <td>154748.85940</td>\n",
              "      <td>153125.87500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2088</th>\n",
              "      <td>153125.87500</td>\n",
              "      <td>152655.15630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2089</th>\n",
              "      <td>152655.15630</td>\n",
              "      <td>154051.95310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2090</th>\n",
              "      <td>154051.95310</td>\n",
              "      <td>152154.42190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2091</th>\n",
              "      <td>152154.42190</td>\n",
              "      <td>151490.12500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2092 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ecfa00be-01b1-42e4-a077-f9ac74c68426')\"\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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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "      </button>\n",
              "\n",
              "\n",
              "\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fd6fc7d8-8c0e-4214-8a7f-05eeb07649f2')\"\n",
              "              title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-fd6fc7d8-8c0e-4214-8a7f-05eeb07649f2 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ecfa00be-01b1-42e4-a077-f9ac74c68426 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-ecfa00be-01b1-42e4-a077-f9ac74c68426');\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": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "asset_copy = asset.copy()\n",
        "x_1 = np.array(asset_copy[asset_copy.columns])\n",
        "x_1 = x_1[:len(asset) - future_days]\n",
        "y_1 = np.array(asset_copy['Future_Price'])\n",
        "y_1 = y_1[:-future_days]\n",
        "x_1_train, x_1_test, y_1_train, y_1_test = train_test_split(x_1, y_1,test_size=0.25, random_state = 0, shuffle=False)"
      ],
      "metadata": {
        "id": "-x8U9lY_xZLy"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_1_data = pd.DataFrame(x_1_train, columns=asset_copy.columns)"
      ],
      "metadata": {
        "id": "4tVuhRtoybg1"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_1_data = pd.DataFrame(x_1_test, columns=asset_copy.columns)"
      ],
      "metadata": {
        "id": "859eAHJLy_Oe"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "regression_setup = setup(data=train_1_data, target='Future_Price', session_id=123, use_gpu = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "WuBW58pvzUUK",
        "outputId": "a1b5120f-f47e-4794-b59c-cbb2b466e3b6"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7ee221d85630>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_3f46e_row8_col1, #T_3f46e_row15_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_3f46e\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_3f46e_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_3f46e_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_3f46e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_3f46e_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_3f46e_row0_col1\" class=\"data row0 col1\" >123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3f46e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_3f46e_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_3f46e_row1_col1\" class=\"data row1 col1\" >Future_Price</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3f46e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_3f46e_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_3f46e_row2_col1\" class=\"data row2 col1\" >Regression</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3f46e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_3f46e_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
              "      <td id=\"T_3f46e_row3_col1\" class=\"data row3 col1\" >(1569, 2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3f46e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_3f46e_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_3f46e_row4_col1\" class=\"data row4 col1\" >(1569, 2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3f46e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_3f46e_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_3f46e_row5_col1\" class=\"data row5 col1\" >(1098, 2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3f46e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_3f46e_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_3f46e_row6_col1\" class=\"data row6 col1\" >(471, 2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3f46e_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_3f46e_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
              "      <td id=\"T_3f46e_row7_col1\" class=\"data row7 col1\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3f46e_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_3f46e_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
              "      <td id=\"T_3f46e_row8_col1\" class=\"data row8 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3f46e_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_3f46e_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
              "      <td id=\"T_3f46e_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3f46e_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_3f46e_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_3f46e_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3f46e_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_3f46e_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_3f46e_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3f46e_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_3f46e_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_3f46e_row12_col1\" class=\"data row12 col1\" >KFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3f46e_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_3f46e_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
              "      <td id=\"T_3f46e_row13_col1\" class=\"data row13 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3f46e_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_3f46e_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_3f46e_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3f46e_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_3f46e_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
              "      <td id=\"T_3f46e_row15_col1\" class=\"data row15 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3f46e_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_3f46e_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_3f46e_row16_col1\" class=\"data row16 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3f46e_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_3f46e_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_3f46e_row17_col1\" class=\"data row17 col1\" >reg-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3f46e_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_3f46e_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
              "      <td id=\"T_3f46e_row18_col1\" class=\"data row18 col1\" >5427</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_model=compare_models(sort='rmsle')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613,
          "referenced_widgets": [
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        "outputId": "54435b26-e90a-4f64-c8ae-65aa67f6d75a"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": []
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            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_7525d th {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_7525d_row0_col0, #T_7525d_row1_col0, #T_7525d_row1_col1, #T_7525d_row1_col2, #T_7525d_row1_col3, #T_7525d_row1_col5, #T_7525d_row1_col6, #T_7525d_row2_col0, #T_7525d_row2_col1, #T_7525d_row2_col2, #T_7525d_row2_col3, #T_7525d_row2_col5, #T_7525d_row2_col6, #T_7525d_row3_col0, #T_7525d_row3_col1, #T_7525d_row3_col2, #T_7525d_row3_col3, #T_7525d_row3_col5, #T_7525d_row3_col6, #T_7525d_row4_col0, #T_7525d_row4_col1, #T_7525d_row4_col2, #T_7525d_row4_col3, #T_7525d_row4_col5, #T_7525d_row4_col6, #T_7525d_row5_col0, #T_7525d_row5_col1, #T_7525d_row5_col2, #T_7525d_row5_col3, #T_7525d_row5_col5, #T_7525d_row5_col6, #T_7525d_row6_col0, #T_7525d_row6_col1, #T_7525d_row6_col2, #T_7525d_row6_col3, #T_7525d_row6_col5, #T_7525d_row6_col6, #T_7525d_row7_col0, #T_7525d_row7_col1, #T_7525d_row7_col2, #T_7525d_row7_col3, #T_7525d_row7_col5, #T_7525d_row7_col6, #T_7525d_row8_col0, #T_7525d_row8_col1, #T_7525d_row8_col2, #T_7525d_row8_col3, #T_7525d_row8_col5, #T_7525d_row8_col6, #T_7525d_row9_col0, #T_7525d_row9_col1, #T_7525d_row9_col2, #T_7525d_row9_col3, #T_7525d_row9_col4, #T_7525d_row9_col5, #T_7525d_row9_col6, #T_7525d_row10_col0, #T_7525d_row10_col1, #T_7525d_row10_col2, #T_7525d_row10_col3, #T_7525d_row10_col4, #T_7525d_row10_col5, #T_7525d_row10_col6, #T_7525d_row11_col0, #T_7525d_row11_col1, #T_7525d_row11_col2, #T_7525d_row11_col3, #T_7525d_row11_col4, #T_7525d_row11_col5, #T_7525d_row11_col6, #T_7525d_row12_col0, #T_7525d_row12_col1, #T_7525d_row12_col2, #T_7525d_row12_col3, #T_7525d_row12_col4, #T_7525d_row12_col5, #T_7525d_row12_col6, #T_7525d_row13_col0, #T_7525d_row13_col1, #T_7525d_row13_col2, #T_7525d_row13_col3, #T_7525d_row13_col4, #T_7525d_row13_col5, #T_7525d_row13_col6, #T_7525d_row14_col0, #T_7525d_row14_col1, #T_7525d_row14_col2, #T_7525d_row14_col3, #T_7525d_row14_col4, #T_7525d_row14_col5, #T_7525d_row14_col6, #T_7525d_row15_col0, #T_7525d_row15_col1, #T_7525d_row15_col2, #T_7525d_row15_col3, #T_7525d_row15_col4, #T_7525d_row15_col5, #T_7525d_row15_col6, #T_7525d_row16_col0, #T_7525d_row16_col1, #T_7525d_row16_col2, #T_7525d_row16_col3, #T_7525d_row16_col4, #T_7525d_row16_col5, #T_7525d_row16_col6, #T_7525d_row17_col0, #T_7525d_row17_col1, #T_7525d_row17_col2, #T_7525d_row17_col3, #T_7525d_row17_col4, #T_7525d_row17_col5, #T_7525d_row17_col6 {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_7525d_row0_col1, #T_7525d_row0_col2, #T_7525d_row0_col3, #T_7525d_row0_col4, #T_7525d_row0_col5, #T_7525d_row0_col6, #T_7525d_row1_col4, #T_7525d_row2_col4, #T_7525d_row3_col4, #T_7525d_row4_col4, #T_7525d_row5_col4, #T_7525d_row6_col4, #T_7525d_row7_col4, #T_7525d_row8_col4 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_7525d_row0_col7, #T_7525d_row1_col7, #T_7525d_row2_col7, #T_7525d_row3_col7, #T_7525d_row4_col7, #T_7525d_row5_col7, #T_7525d_row7_col7, #T_7525d_row8_col7, #T_7525d_row9_col7, #T_7525d_row10_col7, #T_7525d_row11_col7, #T_7525d_row12_col7, #T_7525d_row13_col7, #T_7525d_row14_col7, #T_7525d_row16_col7, #T_7525d_row17_col7 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "#T_7525d_row6_col7, #T_7525d_row15_col7 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_7525d\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_7525d_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_7525d_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
              "      <th id=\"T_7525d_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
              "      <th id=\"T_7525d_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
              "      <th id=\"T_7525d_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
              "      <th id=\"T_7525d_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
              "      <th id=\"T_7525d_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
              "      <th id=\"T_7525d_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_7525d_level0_row0\" class=\"row_heading level0 row0\" >huber</th>\n",
              "      <td id=\"T_7525d_row0_col0\" class=\"data row0 col0\" >Huber Regressor</td>\n",
              "      <td id=\"T_7525d_row0_col1\" class=\"data row0 col1\" >2802.7021</td>\n",
              "      <td id=\"T_7525d_row0_col2\" class=\"data row0 col2\" >35286323.0133</td>\n",
              "      <td id=\"T_7525d_row0_col3\" class=\"data row0 col3\" >5779.0083</td>\n",
              "      <td id=\"T_7525d_row0_col4\" class=\"data row0 col4\" >0.9958</td>\n",
              "      <td id=\"T_7525d_row0_col5\" class=\"data row0 col5\" >0.0520</td>\n",
              "      <td id=\"T_7525d_row0_col6\" class=\"data row0 col6\" >0.0361</td>\n",
              "      <td id=\"T_7525d_row0_col7\" class=\"data row0 col7\" >0.0490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7525d_level0_row1\" class=\"row_heading level0 row1\" >lr</th>\n",
              "      <td id=\"T_7525d_row1_col0\" class=\"data row1 col0\" >Linear Regression</td>\n",
              "      <td id=\"T_7525d_row1_col1\" class=\"data row1 col1\" >2834.1423</td>\n",
              "      <td id=\"T_7525d_row1_col2\" class=\"data row1 col2\" >35370227.2000</td>\n",
              "      <td id=\"T_7525d_row1_col3\" class=\"data row1 col3\" >5787.0880</td>\n",
              "      <td id=\"T_7525d_row1_col4\" class=\"data row1 col4\" >0.9958</td>\n",
              "      <td id=\"T_7525d_row1_col5\" class=\"data row1 col5\" >0.0538</td>\n",
              "      <td id=\"T_7525d_row1_col6\" class=\"data row1 col6\" >0.0390</td>\n",
              "      <td id=\"T_7525d_row1_col7\" class=\"data row1 col7\" >0.0430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7525d_level0_row2\" class=\"row_heading level0 row2\" >lasso</th>\n",
              "      <td id=\"T_7525d_row2_col0\" class=\"data row2 col0\" >Lasso Regression</td>\n",
              "      <td id=\"T_7525d_row2_col1\" class=\"data row2 col1\" >2834.1430</td>\n",
              "      <td id=\"T_7525d_row2_col2\" class=\"data row2 col2\" >35370229.0000</td>\n",
              "      <td id=\"T_7525d_row2_col3\" class=\"data row2 col3\" >5787.0882</td>\n",
              "      <td id=\"T_7525d_row2_col4\" class=\"data row2 col4\" >0.9958</td>\n",
              "      <td id=\"T_7525d_row2_col5\" class=\"data row2 col5\" >0.0538</td>\n",
              "      <td id=\"T_7525d_row2_col6\" class=\"data row2 col6\" >0.0390</td>\n",
              "      <td id=\"T_7525d_row2_col7\" class=\"data row2 col7\" >0.0680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7525d_level0_row3\" class=\"row_heading level0 row3\" >ridge</th>\n",
              "      <td id=\"T_7525d_row3_col0\" class=\"data row3 col0\" >Ridge Regression</td>\n",
              "      <td id=\"T_7525d_row3_col1\" class=\"data row3 col1\" >2834.1428</td>\n",
              "      <td id=\"T_7525d_row3_col2\" class=\"data row3 col2\" >35370223.8000</td>\n",
              "      <td id=\"T_7525d_row3_col3\" class=\"data row3 col3\" >5787.0876</td>\n",
              "      <td id=\"T_7525d_row3_col4\" class=\"data row3 col4\" >0.9958</td>\n",
              "      <td id=\"T_7525d_row3_col5\" class=\"data row3 col5\" >0.0538</td>\n",
              "      <td id=\"T_7525d_row3_col6\" class=\"data row3 col6\" >0.0390</td>\n",
              "      <td id=\"T_7525d_row3_col7\" class=\"data row3 col7\" >0.0660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7525d_level0_row4\" class=\"row_heading level0 row4\" >en</th>\n",
              "      <td id=\"T_7525d_row4_col0\" class=\"data row4 col0\" >Elastic Net</td>\n",
              "      <td id=\"T_7525d_row4_col1\" class=\"data row4 col1\" >2834.1430</td>\n",
              "      <td id=\"T_7525d_row4_col2\" class=\"data row4 col2\" >35370229.0000</td>\n",
              "      <td id=\"T_7525d_row4_col3\" class=\"data row4 col3\" >5787.0882</td>\n",
              "      <td id=\"T_7525d_row4_col4\" class=\"data row4 col4\" >0.9958</td>\n",
              "      <td id=\"T_7525d_row4_col5\" class=\"data row4 col5\" >0.0538</td>\n",
              "      <td id=\"T_7525d_row4_col6\" class=\"data row4 col6\" >0.0390</td>\n",
              "      <td id=\"T_7525d_row4_col7\" class=\"data row4 col7\" >0.0730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7525d_level0_row5\" class=\"row_heading level0 row5\" >lar</th>\n",
              "      <td id=\"T_7525d_row5_col0\" class=\"data row5 col0\" >Least Angle Regression</td>\n",
              "      <td id=\"T_7525d_row5_col1\" class=\"data row5 col1\" >2834.1428</td>\n",
              "      <td id=\"T_7525d_row5_col2\" class=\"data row5 col2\" >35370227.6000</td>\n",
              "      <td id=\"T_7525d_row5_col3\" class=\"data row5 col3\" >5787.0880</td>\n",
              "      <td id=\"T_7525d_row5_col4\" class=\"data row5 col4\" >0.9958</td>\n",
              "      <td id=\"T_7525d_row5_col5\" class=\"data row5 col5\" >0.0538</td>\n",
              "      <td id=\"T_7525d_row5_col6\" class=\"data row5 col6\" >0.0390</td>\n",
              "      <td id=\"T_7525d_row5_col7\" class=\"data row5 col7\" >0.0680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7525d_level0_row6\" class=\"row_heading level0 row6\" >llar</th>\n",
              "      <td id=\"T_7525d_row6_col0\" class=\"data row6 col0\" >Lasso Least Angle Regression</td>\n",
              "      <td id=\"T_7525d_row6_col1\" class=\"data row6 col1\" >2834.1428</td>\n",
              "      <td id=\"T_7525d_row6_col2\" class=\"data row6 col2\" >35370227.6000</td>\n",
              "      <td id=\"T_7525d_row6_col3\" class=\"data row6 col3\" >5787.0880</td>\n",
              "      <td id=\"T_7525d_row6_col4\" class=\"data row6 col4\" >0.9958</td>\n",
              "      <td id=\"T_7525d_row6_col5\" class=\"data row6 col5\" >0.0538</td>\n",
              "      <td id=\"T_7525d_row6_col6\" class=\"data row6 col6\" >0.0390</td>\n",
              "      <td id=\"T_7525d_row6_col7\" class=\"data row6 col7\" >0.0400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7525d_level0_row7\" class=\"row_heading level0 row7\" >omp</th>\n",
              "      <td id=\"T_7525d_row7_col0\" class=\"data row7 col0\" >Orthogonal Matching Pursuit</td>\n",
              "      <td id=\"T_7525d_row7_col1\" class=\"data row7 col1\" >2834.1428</td>\n",
              "      <td id=\"T_7525d_row7_col2\" class=\"data row7 col2\" >35370223.8000</td>\n",
              "      <td id=\"T_7525d_row7_col3\" class=\"data row7 col3\" >5787.0876</td>\n",
              "      <td id=\"T_7525d_row7_col4\" class=\"data row7 col4\" >0.9958</td>\n",
              "      <td id=\"T_7525d_row7_col5\" class=\"data row7 col5\" >0.0538</td>\n",
              "      <td id=\"T_7525d_row7_col6\" class=\"data row7 col6\" >0.0390</td>\n",
              "      <td id=\"T_7525d_row7_col7\" class=\"data row7 col7\" >0.0410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7525d_level0_row8\" class=\"row_heading level0 row8\" >br</th>\n",
              "      <td id=\"T_7525d_row8_col0\" class=\"data row8 col0\" >Bayesian Ridge</td>\n",
              "      <td id=\"T_7525d_row8_col1\" class=\"data row8 col1\" >2834.2044</td>\n",
              "      <td id=\"T_7525d_row8_col2\" class=\"data row8 col2\" >35370267.6000</td>\n",
              "      <td id=\"T_7525d_row8_col3\" class=\"data row8 col3\" >5787.0963</td>\n",
              "      <td id=\"T_7525d_row8_col4\" class=\"data row8 col4\" >0.9958</td>\n",
              "      <td id=\"T_7525d_row8_col5\" class=\"data row8 col5\" >0.0538</td>\n",
              "      <td id=\"T_7525d_row8_col6\" class=\"data row8 col6\" >0.0391</td>\n",
              "      <td id=\"T_7525d_row8_col7\" class=\"data row8 col7\" >0.0420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7525d_level0_row9\" class=\"row_heading level0 row9\" >gbr</th>\n",
              "      <td id=\"T_7525d_row9_col0\" class=\"data row9 col0\" >Gradient Boosting Regressor</td>\n",
              "      <td id=\"T_7525d_row9_col1\" class=\"data row9 col1\" >3068.6421</td>\n",
              "      <td id=\"T_7525d_row9_col2\" class=\"data row9 col2\" >40109422.0656</td>\n",
              "      <td id=\"T_7525d_row9_col3\" class=\"data row9 col3\" >6244.1598</td>\n",
              "      <td id=\"T_7525d_row9_col4\" class=\"data row9 col4\" >0.9952</td>\n",
              "      <td id=\"T_7525d_row9_col5\" class=\"data row9 col5\" >0.0571</td>\n",
              "      <td id=\"T_7525d_row9_col6\" class=\"data row9 col6\" >0.0415</td>\n",
              "      <td id=\"T_7525d_row9_col7\" class=\"data row9 col7\" >0.1850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7525d_level0_row10\" class=\"row_heading level0 row10\" >lightgbm</th>\n",
              "      <td id=\"T_7525d_row10_col0\" class=\"data row10 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_7525d_row10_col1\" class=\"data row10 col1\" >3118.8844</td>\n",
              "      <td id=\"T_7525d_row10_col2\" class=\"data row10 col2\" >40061703.8100</td>\n",
              "      <td id=\"T_7525d_row10_col3\" class=\"data row10 col3\" >6236.0535</td>\n",
              "      <td id=\"T_7525d_row10_col4\" class=\"data row10 col4\" >0.9952</td>\n",
              "      <td id=\"T_7525d_row10_col5\" class=\"data row10 col5\" >0.0577</td>\n",
              "      <td id=\"T_7525d_row10_col6\" class=\"data row10 col6\" >0.0420</td>\n",
              "      <td id=\"T_7525d_row10_col7\" class=\"data row10 col7\" >0.1090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7525d_level0_row11\" class=\"row_heading level0 row11\" >knn</th>\n",
              "      <td id=\"T_7525d_row11_col0\" class=\"data row11 col0\" >K Neighbors Regressor</td>\n",
              "      <td id=\"T_7525d_row11_col1\" class=\"data row11 col1\" >3133.1903</td>\n",
              "      <td id=\"T_7525d_row11_col2\" class=\"data row11 col2\" >41036297.8000</td>\n",
              "      <td id=\"T_7525d_row11_col3\" class=\"data row11 col3\" >6304.8449</td>\n",
              "      <td id=\"T_7525d_row11_col4\" class=\"data row11 col4\" >0.9952</td>\n",
              "      <td id=\"T_7525d_row11_col5\" class=\"data row11 col5\" >0.0578</td>\n",
              "      <td id=\"T_7525d_row11_col6\" class=\"data row11 col6\" >0.0420</td>\n",
              "      <td id=\"T_7525d_row11_col7\" class=\"data row11 col7\" >0.0580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7525d_level0_row12\" class=\"row_heading level0 row12\" >rf</th>\n",
              "      <td id=\"T_7525d_row12_col0\" class=\"data row12 col0\" >Random Forest Regressor</td>\n",
              "      <td id=\"T_7525d_row12_col1\" class=\"data row12 col1\" >3370.1747</td>\n",
              "      <td id=\"T_7525d_row12_col2\" class=\"data row12 col2\" >47160961.0384</td>\n",
              "      <td id=\"T_7525d_row12_col3\" class=\"data row12 col3\" >6783.7442</td>\n",
              "      <td id=\"T_7525d_row12_col4\" class=\"data row12 col4\" >0.9944</td>\n",
              "      <td id=\"T_7525d_row12_col5\" class=\"data row12 col5\" >0.0648</td>\n",
              "      <td id=\"T_7525d_row12_col6\" class=\"data row12 col6\" >0.0472</td>\n",
              "      <td id=\"T_7525d_row12_col7\" class=\"data row12 col7\" >0.5530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7525d_level0_row13\" class=\"row_heading level0 row13\" >par</th>\n",
              "      <td id=\"T_7525d_row13_col0\" class=\"data row13 col0\" >Passive Aggressive Regressor</td>\n",
              "      <td id=\"T_7525d_row13_col1\" class=\"data row13 col1\" >3840.8491</td>\n",
              "      <td id=\"T_7525d_row13_col2\" class=\"data row13 col2\" >61959855.1508</td>\n",
              "      <td id=\"T_7525d_row13_col3\" class=\"data row13 col3\" >7320.3361</td>\n",
              "      <td id=\"T_7525d_row13_col4\" class=\"data row13 col4\" >0.9924</td>\n",
              "      <td id=\"T_7525d_row13_col5\" class=\"data row13 col5\" >0.0659</td>\n",
              "      <td id=\"T_7525d_row13_col6\" class=\"data row13 col6\" >0.0524</td>\n",
              "      <td id=\"T_7525d_row13_col7\" class=\"data row13 col7\" >0.0410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7525d_level0_row14\" class=\"row_heading level0 row14\" >et</th>\n",
              "      <td id=\"T_7525d_row14_col0\" class=\"data row14 col0\" >Extra Trees Regressor</td>\n",
              "      <td id=\"T_7525d_row14_col1\" class=\"data row14 col1\" >3633.4954</td>\n",
              "      <td id=\"T_7525d_row14_col2\" class=\"data row14 col2\" >53800520.9856</td>\n",
              "      <td id=\"T_7525d_row14_col3\" class=\"data row14 col3\" >7250.8233</td>\n",
              "      <td id=\"T_7525d_row14_col4\" class=\"data row14 col4\" >0.9936</td>\n",
              "      <td id=\"T_7525d_row14_col5\" class=\"data row14 col5\" >0.0692</td>\n",
              "      <td id=\"T_7525d_row14_col6\" class=\"data row14 col6\" >0.0503</td>\n",
              "      <td id=\"T_7525d_row14_col7\" class=\"data row14 col7\" >0.4960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7525d_level0_row15\" class=\"row_heading level0 row15\" >dt</th>\n",
              "      <td id=\"T_7525d_row15_col0\" class=\"data row15 col0\" >Decision Tree Regressor</td>\n",
              "      <td id=\"T_7525d_row15_col1\" class=\"data row15 col1\" >3941.3056</td>\n",
              "      <td id=\"T_7525d_row15_col2\" class=\"data row15 col2\" >60387688.7945</td>\n",
              "      <td id=\"T_7525d_row15_col3\" class=\"data row15 col3\" >7674.7440</td>\n",
              "      <td id=\"T_7525d_row15_col4\" class=\"data row15 col4\" >0.9928</td>\n",
              "      <td id=\"T_7525d_row15_col5\" class=\"data row15 col5\" >0.0760</td>\n",
              "      <td id=\"T_7525d_row15_col6\" class=\"data row15 col6\" >0.0551</td>\n",
              "      <td id=\"T_7525d_row15_col7\" class=\"data row15 col7\" >0.0400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7525d_level0_row16\" class=\"row_heading level0 row16\" >ada</th>\n",
              "      <td id=\"T_7525d_row16_col0\" class=\"data row16 col0\" >AdaBoost Regressor</td>\n",
              "      <td id=\"T_7525d_row16_col1\" class=\"data row16 col1\" >6193.6925</td>\n",
              "      <td id=\"T_7525d_row16_col2\" class=\"data row16 col2\" >82288061.9032</td>\n",
              "      <td id=\"T_7525d_row16_col3\" class=\"data row16 col3\" >8995.9505</td>\n",
              "      <td id=\"T_7525d_row16_col4\" class=\"data row16 col4\" >0.9902</td>\n",
              "      <td id=\"T_7525d_row16_col5\" class=\"data row16 col5\" >0.2649</td>\n",
              "      <td id=\"T_7525d_row16_col6\" class=\"data row16 col6\" >0.2192</td>\n",
              "      <td id=\"T_7525d_row16_col7\" class=\"data row16 col7\" >0.0910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7525d_level0_row17\" class=\"row_heading level0 row17\" >dummy</th>\n",
              "      <td id=\"T_7525d_row17_col0\" class=\"data row17 col0\" >Dummy Regressor</td>\n",
              "      <td id=\"T_7525d_row17_col1\" class=\"data row17 col1\" >73791.7117</td>\n",
              "      <td id=\"T_7525d_row17_col2\" class=\"data row17 col2\" >8479538944.0000</td>\n",
              "      <td id=\"T_7525d_row17_col3\" class=\"data row17 col3\" >91878.1742</td>\n",
              "      <td id=\"T_7525d_row17_col4\" class=\"data row17 col4\" >-0.0085</td>\n",
              "      <td id=\"T_7525d_row17_col5\" class=\"data row17 col5\" >1.3692</td>\n",
              "      <td id=\"T_7525d_row17_col6\" class=\"data row17 col6\" >2.7164</td>\n",
              "      <td id=\"T_7525d_row17_col7\" class=\"data row17 col7\" >0.0580</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/81 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1 = create_model(best_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457,
          "referenced_widgets": [
            "eb934e36c4094d5091ade22e9106f8fb",
            "2ab24e778efb4b62bd826200dc8e4c87",
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            "828e4a98ca8e469c865c1381fabcc884",
            "d139bd1ff8674cfbb28119f3b73dde64",
            "09e15bc5deb74ae9b4d66b8a574c6380",
            "1589ff72f0434fc39b5bcde3b9750c7b",
            "2e002773b5524f3e81dabb509f2ddb07",
            "8a86830c44504e16af42fc3a64ae1555",
            "f44d28f8e8a84fbeaa4de730933da5d3",
            "988ddbf399ad40748af35744b16cb62e"
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        },
        "id": "2pw6JjjYz-rM",
        "outputId": "6abfac6f-14e2-4b91-ee60-8bd142d372b1"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7ee218236e30>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_da64b_row10_col0, #T_da64b_row10_col1, #T_da64b_row10_col2, #T_da64b_row10_col3, #T_da64b_row10_col4, #T_da64b_row10_col5 {\n",
              "  background: yellow;\n",
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              "</style>\n",
              "<table id=\"T_da64b\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_da64b_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
              "      <th id=\"T_da64b_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
              "      <th id=\"T_da64b_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
              "      <th id=\"T_da64b_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
              "      <th id=\"T_da64b_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
              "      <th id=\"T_da64b_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Fold</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_da64b_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_da64b_row0_col0\" class=\"data row0 col0\" >2860.3668</td>\n",
              "      <td id=\"T_da64b_row0_col1\" class=\"data row0 col1\" >37987044.3555</td>\n",
              "      <td id=\"T_da64b_row0_col2\" class=\"data row0 col2\" >6163.3631</td>\n",
              "      <td id=\"T_da64b_row0_col3\" class=\"data row0 col3\" >0.9956</td>\n",
              "      <td id=\"T_da64b_row0_col4\" class=\"data row0 col4\" >0.0561</td>\n",
              "      <td id=\"T_da64b_row0_col5\" class=\"data row0 col5\" >0.0396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_da64b_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_da64b_row1_col0\" class=\"data row1 col0\" >2104.5130</td>\n",
              "      <td id=\"T_da64b_row1_col1\" class=\"data row1 col1\" >15417536.7958</td>\n",
              "      <td id=\"T_da64b_row1_col2\" class=\"data row1 col2\" >3926.5171</td>\n",
              "      <td id=\"T_da64b_row1_col3\" class=\"data row1 col3\" >0.9973</td>\n",
              "      <td id=\"T_da64b_row1_col4\" class=\"data row1 col4\" >0.0502</td>\n",
              "      <td id=\"T_da64b_row1_col5\" class=\"data row1 col5\" >0.0363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_da64b_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_da64b_row2_col0\" class=\"data row2 col0\" >2755.3576</td>\n",
              "      <td id=\"T_da64b_row2_col1\" class=\"data row2 col1\" >40464739.4032</td>\n",
              "      <td id=\"T_da64b_row2_col2\" class=\"data row2 col2\" >6361.1901</td>\n",
              "      <td id=\"T_da64b_row2_col3\" class=\"data row2 col3\" >0.9949</td>\n",
              "      <td id=\"T_da64b_row2_col4\" class=\"data row2 col4\" >0.0471</td>\n",
              "      <td id=\"T_da64b_row2_col5\" class=\"data row2 col5\" >0.0323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_da64b_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_da64b_row3_col0\" class=\"data row3 col0\" >3271.0115</td>\n",
              "      <td id=\"T_da64b_row3_col1\" class=\"data row3 col1\" >42931203.2975</td>\n",
              "      <td id=\"T_da64b_row3_col2\" class=\"data row3 col2\" >6552.1907</td>\n",
              "      <td id=\"T_da64b_row3_col3\" class=\"data row3 col3\" >0.9957</td>\n",
              "      <td id=\"T_da64b_row3_col4\" class=\"data row3 col4\" >0.0535</td>\n",
              "      <td id=\"T_da64b_row3_col5\" class=\"data row3 col5\" >0.0381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_da64b_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_da64b_row4_col0\" class=\"data row4 col0\" >2930.3682</td>\n",
              "      <td id=\"T_da64b_row4_col1\" class=\"data row4 col1\" >35248640.7459</td>\n",
              "      <td id=\"T_da64b_row4_col2\" class=\"data row4 col2\" >5937.0566</td>\n",
              "      <td id=\"T_da64b_row4_col3\" class=\"data row4 col3\" >0.9967</td>\n",
              "      <td id=\"T_da64b_row4_col4\" class=\"data row4 col4\" >0.0387</td>\n",
              "      <td id=\"T_da64b_row4_col5\" class=\"data row4 col5\" >0.0276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_da64b_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_da64b_row5_col0\" class=\"data row5 col0\" >3075.6385</td>\n",
              "      <td id=\"T_da64b_row5_col1\" class=\"data row5 col1\" >35518380.1455</td>\n",
              "      <td id=\"T_da64b_row5_col2\" class=\"data row5 col2\" >5959.7299</td>\n",
              "      <td id=\"T_da64b_row5_col3\" class=\"data row5 col3\" >0.9961</td>\n",
              "      <td id=\"T_da64b_row5_col4\" class=\"data row5 col4\" >0.0498</td>\n",
              "      <td id=\"T_da64b_row5_col5\" class=\"data row5 col5\" >0.0355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_da64b_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_da64b_row6_col0\" class=\"data row6 col0\" >2733.7446</td>\n",
              "      <td id=\"T_da64b_row6_col1\" class=\"data row6 col1\" >29640477.6881</td>\n",
              "      <td id=\"T_da64b_row6_col2\" class=\"data row6 col2\" >5444.3069</td>\n",
              "      <td id=\"T_da64b_row6_col3\" class=\"data row6 col3\" >0.9966</td>\n",
              "      <td id=\"T_da64b_row6_col4\" class=\"data row6 col4\" >0.0765</td>\n",
              "      <td id=\"T_da64b_row6_col5\" class=\"data row6 col5\" >0.0461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_da64b_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_da64b_row7_col0\" class=\"data row7 col0\" >1867.1453</td>\n",
              "      <td id=\"T_da64b_row7_col1\" class=\"data row7 col1\" >17558861.6353</td>\n",
              "      <td id=\"T_da64b_row7_col2\" class=\"data row7 col2\" >4190.3295</td>\n",
              "      <td id=\"T_da64b_row7_col3\" class=\"data row7 col3\" >0.9976</td>\n",
              "      <td id=\"T_da64b_row7_col4\" class=\"data row7 col4\" >0.0436</td>\n",
              "      <td id=\"T_da64b_row7_col5\" class=\"data row7 col5\" >0.0304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_da64b_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_da64b_row8_col0\" class=\"data row8 col0\" >3138.0421</td>\n",
              "      <td id=\"T_da64b_row8_col1\" class=\"data row8 col1\" >38222753.3142</td>\n",
              "      <td id=\"T_da64b_row8_col2\" class=\"data row8 col2\" >6182.4553</td>\n",
              "      <td id=\"T_da64b_row8_col3\" class=\"data row8 col3\" >0.9955</td>\n",
              "      <td id=\"T_da64b_row8_col4\" class=\"data row8 col4\" >0.0540</td>\n",
              "      <td id=\"T_da64b_row8_col5\" class=\"data row8 col5\" >0.0412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_da64b_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_da64b_row9_col0\" class=\"data row9 col0\" >3062.3059</td>\n",
              "      <td id=\"T_da64b_row9_col1\" class=\"data row9 col1\" >40864491.0526</td>\n",
              "      <td id=\"T_da64b_row9_col2\" class=\"data row9 col2\" >6392.5340</td>\n",
              "      <td id=\"T_da64b_row9_col3\" class=\"data row9 col3\" >0.9949</td>\n",
              "      <td id=\"T_da64b_row9_col4\" class=\"data row9 col4\" >0.0451</td>\n",
              "      <td id=\"T_da64b_row9_col5\" class=\"data row9 col5\" >0.0321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_da64b_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_da64b_row10_col0\" class=\"data row10 col0\" >2779.8494</td>\n",
              "      <td id=\"T_da64b_row10_col1\" class=\"data row10 col1\" >33385412.8433</td>\n",
              "      <td id=\"T_da64b_row10_col2\" class=\"data row10 col2\" >5710.9673</td>\n",
              "      <td id=\"T_da64b_row10_col3\" class=\"data row10 col3\" >0.9961</td>\n",
              "      <td id=\"T_da64b_row10_col4\" class=\"data row10 col4\" >0.0515</td>\n",
              "      <td id=\"T_da64b_row10_col5\" class=\"data row10 col5\" >0.0359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_da64b_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_da64b_row11_col0\" class=\"data row11 col0\" >431.0206</td>\n",
              "      <td id=\"T_da64b_row11_col1\" class=\"data row11 col1\" >9149428.7584</td>\n",
              "      <td id=\"T_da64b_row11_col2\" class=\"data row11 col2\" >877.6475</td>\n",
              "      <td id=\"T_da64b_row11_col3\" class=\"data row11 col3\" >0.0009</td>\n",
              "      <td id=\"T_da64b_row11_col4\" class=\"data row11 col4\" >0.0097</td>\n",
              "      <td id=\"T_da64b_row11_col5\" class=\"data row11 col5\" >0.0053</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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    },
    {
      "cell_type": "code",
      "source": [
        "unseen_predictions = predict_model(model_1, data=test_1_data)\n",
        "unseen_predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "kas6TnAX0LMb",
        "outputId": "62995b1e-6f28-4e7d-ea98-dc635163a849"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7ee2236d0820>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_64004\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_64004_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_64004_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
              "      <th id=\"T_64004_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
              "      <th id=\"T_64004_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
              "      <th id=\"T_64004_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
              "      <th id=\"T_64004_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
              "      <th id=\"T_64004_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_64004_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_64004_row0_col0\" class=\"data row0 col0\" >Huber Regressor</td>\n",
              "      <td id=\"T_64004_row0_col1\" class=\"data row0 col1\" >3661.9753</td>\n",
              "      <td id=\"T_64004_row0_col2\" class=\"data row0 col2\" >27929588.7342</td>\n",
              "      <td id=\"T_64004_row0_col3\" class=\"data row0 col3\" >5284.8452</td>\n",
              "      <td id=\"T_64004_row0_col4\" class=\"data row0 col4\" >0.9821</td>\n",
              "      <td id=\"T_64004_row0_col5\" class=\"data row0 col5\" >0.0385</td>\n",
              "      <td id=\"T_64004_row0_col6\" class=\"data row0 col6\" >0.0263</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Close   Future_Price  prediction_label\n",
              "0    196813.171875  219772.203125     197058.643647\n",
              "1    219772.203125  225318.671875     220050.747819\n",
              "2    225318.671875  222983.531250     225605.206363\n",
              "3    222983.531250  215099.484375     223266.701923\n",
              "4    215099.484375  199984.218750     215371.297928\n",
              "..             ...            ...               ...\n",
              "518  154748.859375  153125.875000     154933.736703\n",
              "519  153125.875000  152655.156250     153308.414388\n",
              "520  152655.156250  154051.953125     152837.017558\n",
              "521  154051.953125  152154.421875     154235.826546\n",
              "522  152154.421875  151490.125000     152335.561866\n",
              "\n",
              "[523 rows x 3 columns]"
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              "\n",
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              "  <div id=\"df-88de1058-5778-41bf-a98d-aba8b2cbcc96\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        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>Close</th>\n",
              "      <th>Future_Price</th>\n",
              "      <th>prediction_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>196813.171875</td>\n",
              "      <td>219772.203125</td>\n",
              "      <td>197058.643647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>219772.203125</td>\n",
              "      <td>225318.671875</td>\n",
              "      <td>220050.747819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>225318.671875</td>\n",
              "      <td>222983.531250</td>\n",
              "      <td>225605.206363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>222983.531250</td>\n",
              "      <td>215099.484375</td>\n",
              "      <td>223266.701923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>215099.484375</td>\n",
              "      <td>199984.218750</td>\n",
              "      <td>215371.297928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>518</th>\n",
              "      <td>154748.859375</td>\n",
              "      <td>153125.875000</td>\n",
              "      <td>154933.736703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>519</th>\n",
              "      <td>153125.875000</td>\n",
              "      <td>152655.156250</td>\n",
              "      <td>153308.414388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>520</th>\n",
              "      <td>152655.156250</td>\n",
              "      <td>154051.953125</td>\n",
              "      <td>152837.017558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>521</th>\n",
              "      <td>154051.953125</td>\n",
              "      <td>152154.421875</td>\n",
              "      <td>154235.826546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>522</th>\n",
              "      <td>152154.421875</td>\n",
              "      <td>151490.125000</td>\n",
              "      <td>152335.561866</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>523 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-88de1058-5778-41bf-a98d-aba8b2cbcc96')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-da467604-1136-47aa-b42c-c47ca227ac81\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-da467604-1136-47aa-b42c-c47ca227ac81')\"\n",
              "              title=\"Suggest charts.\"\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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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-da467604-1136-47aa-b42c-c47ca227ac81 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-88de1058-5778-41bf-a98d-aba8b2cbcc96 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-88de1058-5778-41bf-a98d-aba8b2cbcc96');\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": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "unseen_predictions[r'Future_Price'].plot()\n",
        "unseen_predictions[r'prediction_label'].plot()\n",
        "unseen_predictions[r'Close'].plot()\n",
        "plt.ylabel(None)\n",
        "plt.xlabel(None)\n",
        "plt.legend('Future_Price', 'prediction_label')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "EbNfh179tY45",
        "outputId": "e4bb7b8f-0c24-4b74-8ba2-22e1178093b9"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "plt.plot(df[r'Open'], linewidth=1,color='green')\n",
        "plt.plot(df[r'Close'], linewidth=1, color='red')\n",
        "plt.ylabel(None)\n",
        "plt.xlabel(None)\n",
        "plt.title('Opening & close price of price')\n",
        "plt.legend(['Open Price','Close Price'])\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "id": "yLpTPF08erwC",
        "outputId": "b93054d0-e682-48d8-bf96-f838c32c704e"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s1 = pd.Series([None, None])\n",
        "for i in range(0,len(train_1_data)):\n",
        "  unseen_predictions = unseen_predictions.append(s1,ignore_index=True)\n",
        "unseen_predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Dd-EnOrzSzmP",
        "outputId": "b0acf7a8-6955-47af-da1f-e06484f60af2"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Close   Future_Price  prediction_label    0    1\n",
              "0     196813.171875  219772.203125     197058.643647  NaN  NaN\n",
              "1     219772.203125  225318.671875     220050.747819  NaN  NaN\n",
              "2     225318.671875  222983.531250     225605.206363  NaN  NaN\n",
              "3     222983.531250  215099.484375     223266.701923  NaN  NaN\n",
              "4     215099.484375  199984.218750     215371.297928  NaN  NaN\n",
              "...             ...            ...               ...  ...  ...\n",
              "3656            NaN            NaN               NaN  NaN  NaN\n",
              "3657            NaN            NaN               NaN  NaN  NaN\n",
              "3658            NaN            NaN               NaN  NaN  NaN\n",
              "3659            NaN            NaN               NaN  NaN  NaN\n",
              "3660            NaN            NaN               NaN  NaN  NaN\n",
              "\n",
              "[3661 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-f88a6dd1-610e-4e41-b5b7-e9462fde765f\">\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>Close</th>\n",
              "      <th>Future_Price</th>\n",
              "      <th>prediction_label</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>196813.171875</td>\n",
              "      <td>219772.203125</td>\n",
              "      <td>197058.643647</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>219772.203125</td>\n",
              "      <td>225318.671875</td>\n",
              "      <td>220050.747819</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>225318.671875</td>\n",
              "      <td>222983.531250</td>\n",
              "      <td>225605.206363</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>222983.531250</td>\n",
              "      <td>215099.484375</td>\n",
              "      <td>223266.701923</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>215099.484375</td>\n",
              "      <td>199984.218750</td>\n",
              "      <td>215371.297928</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3656</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3657</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3658</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3659</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3660</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3661 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f88a6dd1-610e-4e41-b5b7-e9462fde765f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-281bbe5a-ff7e-4779-a30b-3d6bdbc02963\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-281bbe5a-ff7e-4779-a30b-3d6bdbc02963')\"\n",
              "              title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-281bbe5a-ff7e-4779-a30b-3d6bdbc02963 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f88a6dd1-610e-4e41-b5b7-e9462fde765f 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-f88a6dd1-610e-4e41-b5b7-e9462fde765f');\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": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "df[r'High'].plot()\n",
        "df[r'Low'].plot()\n",
        "plt.ylabel(None)\n",
        "plt.xlabel(None)\n",
        "plt.title('High & Low price of Ethereum')\n",
        "plt.legend(['High Price','Low Price'])\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "_ii4RGNnh6Mg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "outputId": "6cf591f9-f163-4c4a-b971-952269952fbd"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_cols = 1\n",
        "dataset = df[r'Close']\n",
        "dataset = pd.DataFrame(dataset)\n",
        "data = dataset.values\n",
        "\n",
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-mM9qbFpi8Qm",
        "outputId": "1196a12e-e8ab-40fa-9172-9841a84d23fd"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2093, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "scaled_data = scaler.fit_transform(np.array(data))\n",
        "train_size = int(len(data) * (0.75))\n",
        "test_size = len(data) - train_size\n",
        "print(train_size, test_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4mkHsHRCjOUG",
        "outputId": "08230669-7961-4f2a-8f53-a8759146246b"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1569 524\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = scaled_data[0:train_size, :]\n",
        "train_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6JedEEgFkCfH",
        "outputId": "df677698-aad7-4d3f-ca31-a9e940def245"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1569, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = []\n",
        "y_train = []\n",
        "time_steps = 60\n",
        "n_cols = 1\n",
        "for i in range(time_steps, len(train_data)):\n",
        "  x_train.append(train_data[i-time_steps:i, :n_cols])\n",
        "  y_train.append(train_data[i, :n_cols])\n",
        "  if i<= time_steps:\n",
        "    print(x_train)\n",
        "    print(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QLDku115kLMU",
        "outputId": "cba2a91c-59dd-41f3-f3d4-7e62e321326b"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[array([[0.04129276],\n",
            "       [0.04003051],\n",
            "       [0.0419166 ],\n",
            "       [0.0458016 ],\n",
            "       [0.04486866],\n",
            "       [0.04444294],\n",
            "       [0.04445125],\n",
            "       [0.04728082],\n",
            "       [0.04854035],\n",
            "       [0.0509247 ],\n",
            "       [0.04951445],\n",
            "       [0.05327173],\n",
            "       [0.05837809],\n",
            "       [0.07048847],\n",
            "       [0.06889131],\n",
            "       [0.06982611],\n",
            "       [0.07122407],\n",
            "       [0.06975169],\n",
            "       [0.06133213],\n",
            "       [0.06510407],\n",
            "       [0.06872664],\n",
            "       [0.06815634],\n",
            "       [0.06859989],\n",
            "       [0.06921439],\n",
            "       [0.06791333],\n",
            "       [0.06173653],\n",
            "       [0.06286021],\n",
            "       [0.0667355 ],\n",
            "       [0.06995701],\n",
            "       [0.06409686],\n",
            "       [0.07745715],\n",
            "       [0.1026352 ],\n",
            "       [0.11219134],\n",
            "       [0.11067121],\n",
            "       [0.10807062],\n",
            "       [0.1102258 ],\n",
            "       [0.11458086],\n",
            "       [0.12855942],\n",
            "       [0.13411319],\n",
            "       [0.13283641],\n",
            "       [0.13296409],\n",
            "       [0.10619784],\n",
            "       [0.11434967],\n",
            "       [0.10972901],\n",
            "       [0.12285299],\n",
            "       [0.12438436],\n",
            "       [0.12252341],\n",
            "       [0.11770486],\n",
            "       [0.12022381],\n",
            "       [0.11359045],\n",
            "       [0.12079723],\n",
            "       [0.12335888],\n",
            "       [0.14315103],\n",
            "       [0.15749379],\n",
            "       [0.16050501],\n",
            "       [0.16336528],\n",
            "       [0.17132783],\n",
            "       [0.19152211],\n",
            "       [0.19114156],\n",
            "       [0.21931581]])]\n",
            "[array([0.21115682])]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, y_train = np.array(x_train), np.array(y_train)"
      ],
      "metadata": {
        "id": "0Hf94OnukLvs"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], n_cols))\n",
        "x_train.shape, y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CfjQHSDakL8U",
        "outputId": "6cda734a-b7c1-42c8-d278-c9dfdcc5e862"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1509, 60, 1), (1509, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential([\n",
        "    LSTM(50,return_sequences = True, input_shape = (x_train.shape[1], n_cols)),\n",
        "    LSTM(64, return_sequences= False),\n",
        "    Dense(32),\n",
        "    Dense(16),\n",
        "    Dense(n_cols)\n",
        "])\n",
        "\n",
        "model.compile(optimizer = 'adam', loss = 'mse', metrics = 'mean_absolute_error')"
      ],
      "metadata": {
        "id": "DUIiVLyombKD"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x9xy_4lhmbSJ",
        "outputId": "7f72e37c-0d2c-4f75-f36b-4235461e494d"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_2 (LSTM)               (None, 60, 50)            10400     \n",
            "                                                                 \n",
            " lstm_3 (LSTM)               (None, 64)                29440     \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 32)                2080      \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 16)                528       \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 17        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 42,465\n",
            "Trainable params: 42,465\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(x_train, y_train, epochs = 100, batch_size = 32)"
      ],
      "metadata": {
        "id": "UvtQgnDdn1CP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81ee5328-8600-4f3b-a270-e769749d5c67"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "48/48 [==============================] - 9s 77ms/step - loss: 0.0162 - mean_absolute_error: 0.0653\n",
            "Epoch 2/100\n",
            "48/48 [==============================] - 4s 82ms/step - loss: 0.0014 - mean_absolute_error: 0.0195\n",
            "Epoch 3/100\n",
            "48/48 [==============================] - 3s 64ms/step - loss: 0.0015 - mean_absolute_error: 0.0210\n",
            "Epoch 4/100\n",
            "48/48 [==============================] - 5s 97ms/step - loss: 0.0012 - mean_absolute_error: 0.0179\n",
            "Epoch 5/100\n",
            "48/48 [==============================] - 3s 71ms/step - loss: 0.0010 - mean_absolute_error: 0.0168\n",
            "Epoch 6/100\n",
            "48/48 [==============================] - 3s 64ms/step - loss: 0.0010 - mean_absolute_error: 0.0184\n",
            "Epoch 7/100\n",
            "48/48 [==============================] - 3s 64ms/step - loss: 9.0865e-04 - mean_absolute_error: 0.0165\n",
            "Epoch 8/100\n",
            "48/48 [==============================] - 4s 85ms/step - loss: 9.9310e-04 - mean_absolute_error: 0.0180\n",
            "Epoch 9/100\n",
            "48/48 [==============================] - 4s 78ms/step - loss: 8.7320e-04 - mean_absolute_error: 0.0168\n",
            "Epoch 10/100\n",
            "48/48 [==============================] - 3s 62ms/step - loss: 7.6165e-04 - mean_absolute_error: 0.0158\n",
            "Epoch 11/100\n",
            "48/48 [==============================] - 3s 64ms/step - loss: 7.2589e-04 - mean_absolute_error: 0.0162\n",
            "Epoch 12/100\n",
            "48/48 [==============================] - 4s 78ms/step - loss: 7.5414e-04 - mean_absolute_error: 0.0157\n",
            "Epoch 13/100\n",
            "48/48 [==============================] - 4s 89ms/step - loss: 6.5692e-04 - mean_absolute_error: 0.0144\n",
            "Epoch 14/100\n",
            "48/48 [==============================] - 3s 64ms/step - loss: 6.0631e-04 - mean_absolute_error: 0.0135\n",
            "Epoch 15/100\n",
            "48/48 [==============================] - 3s 60ms/step - loss: 6.4501e-04 - mean_absolute_error: 0.0145\n",
            "Epoch 16/100\n",
            "48/48 [==============================] - 3s 69ms/step - loss: 5.4412e-04 - mean_absolute_error: 0.0131\n",
            "Epoch 17/100\n",
            "48/48 [==============================] - 5s 96ms/step - loss: 5.5031e-04 - mean_absolute_error: 0.0129\n",
            "Epoch 18/100\n",
            "48/48 [==============================] - 3s 63ms/step - loss: 5.2323e-04 - mean_absolute_error: 0.0129\n",
            "Epoch 19/100\n",
            "48/48 [==============================] - 3s 64ms/step - loss: 5.6794e-04 - mean_absolute_error: 0.0132\n",
            "Epoch 20/100\n",
            "48/48 [==============================] - 3s 63ms/step - loss: 5.1714e-04 - mean_absolute_error: 0.0130\n",
            "Epoch 21/100\n",
            "48/48 [==============================] - 5s 102ms/step - loss: 4.8310e-04 - mean_absolute_error: 0.0126\n",
            "Epoch 22/100\n",
            "48/48 [==============================] - 3s 64ms/step - loss: 5.1877e-04 - mean_absolute_error: 0.0129\n",
            "Epoch 23/100\n",
            "48/48 [==============================] - 4s 83ms/step - loss: 4.6009e-04 - mean_absolute_error: 0.0122\n",
            "Epoch 24/100\n",
            "48/48 [==============================] - 4s 93ms/step - loss: 6.2426e-04 - mean_absolute_error: 0.0146\n",
            "Epoch 25/100\n",
            "48/48 [==============================] - 4s 91ms/step - loss: 4.6738e-04 - mean_absolute_error: 0.0127\n",
            "Epoch 26/100\n",
            "48/48 [==============================] - 3s 63ms/step - loss: 5.8461e-04 - mean_absolute_error: 0.0141\n",
            "Epoch 27/100\n",
            "48/48 [==============================] - 3s 64ms/step - loss: 4.3730e-04 - mean_absolute_error: 0.0119\n",
            "Epoch 28/100\n",
            "48/48 [==============================] - 3s 68ms/step - loss: 4.4534e-04 - mean_absolute_error: 0.0121\n",
            "Epoch 29/100\n",
            "48/48 [==============================] - 5s 99ms/step - loss: 4.1492e-04 - mean_absolute_error: 0.0119\n",
            "Epoch 30/100\n",
            "48/48 [==============================] - 3s 63ms/step - loss: 4.1446e-04 - mean_absolute_error: 0.0113\n",
            "Epoch 31/100\n",
            "48/48 [==============================] - 3s 63ms/step - loss: 3.6592e-04 - mean_absolute_error: 0.0102\n",
            "Epoch 32/100\n",
            "48/48 [==============================] - 3s 63ms/step - loss: 5.1618e-04 - mean_absolute_error: 0.0140\n",
            "Epoch 33/100\n",
            "48/48 [==============================] - 5s 104ms/step - loss: 3.7723e-04 - mean_absolute_error: 0.0107\n",
            "Epoch 34/100\n",
            "48/48 [==============================] - 3s 65ms/step - loss: 4.9441e-04 - mean_absolute_error: 0.0138\n",
            "Epoch 35/100\n",
            "48/48 [==============================] - 3s 64ms/step - loss: 4.3428e-04 - mean_absolute_error: 0.0121\n",
            "Epoch 36/100\n",
            "48/48 [==============================] - 3s 63ms/step - loss: 3.4489e-04 - mean_absolute_error: 0.0100\n",
            "Epoch 37/100\n",
            "48/48 [==============================] - 5s 98ms/step - loss: 3.5480e-04 - mean_absolute_error: 0.0103\n",
            "Epoch 38/100\n",
            "48/48 [==============================] - 4s 73ms/step - loss: 3.7624e-04 - mean_absolute_error: 0.0116\n",
            "Epoch 39/100\n",
            "48/48 [==============================] - 3s 63ms/step - loss: 3.3680e-04 - mean_absolute_error: 0.0098\n",
            "Epoch 40/100\n",
            "48/48 [==============================] - 3s 63ms/step - loss: 3.6552e-04 - mean_absolute_error: 0.0115\n",
            "Epoch 41/100\n",
            "48/48 [==============================] - 4s 88ms/step - loss: 3.4746e-04 - mean_absolute_error: 0.0107\n",
            "Epoch 42/100\n",
            "48/48 [==============================] - 4s 83ms/step - loss: 4.2966e-04 - mean_absolute_error: 0.0120\n",
            "Epoch 43/100\n",
            "48/48 [==============================] - 3s 65ms/step - loss: 3.8822e-04 - mean_absolute_error: 0.0122\n",
            "Epoch 44/100\n",
            "48/48 [==============================] - 3s 63ms/step - loss: 3.7999e-04 - mean_absolute_error: 0.0115\n",
            "Epoch 45/100\n",
            "48/48 [==============================] - 4s 85ms/step - loss: 3.6852e-04 - mean_absolute_error: 0.0106\n",
            "Epoch 46/100\n",
            "48/48 [==============================] - 4s 85ms/step - loss: 3.4470e-04 - mean_absolute_error: 0.0103\n",
            "Epoch 47/100\n",
            "48/48 [==============================] - 3s 65ms/step - loss: 3.5114e-04 - mean_absolute_error: 0.0112\n",
            "Epoch 48/100\n",
            "48/48 [==============================] - 3s 65ms/step - loss: 3.5700e-04 - mean_absolute_error: 0.0114\n",
            "Epoch 49/100\n",
            "48/48 [==============================] - 4s 77ms/step - loss: 3.7258e-04 - mean_absolute_error: 0.0115\n",
            "Epoch 50/100\n",
            "48/48 [==============================] - 4s 90ms/step - loss: 3.7358e-04 - mean_absolute_error: 0.0112\n",
            "Epoch 51/100\n",
            "48/48 [==============================] - 3s 65ms/step - loss: 3.5242e-04 - mean_absolute_error: 0.0104\n",
            "Epoch 52/100\n",
            "48/48 [==============================] - 3s 64ms/step - loss: 3.2391e-04 - mean_absolute_error: 0.0108\n",
            "Epoch 53/100\n",
            "48/48 [==============================] - 3s 68ms/step - loss: 3.9007e-04 - mean_absolute_error: 0.0119\n",
            "Epoch 54/100\n",
            "48/48 [==============================] - 5s 98ms/step - loss: 4.0022e-04 - mean_absolute_error: 0.0113\n",
            "Epoch 55/100\n",
            "48/48 [==============================] - 3s 65ms/step - loss: 3.2613e-04 - mean_absolute_error: 0.0104\n",
            "Epoch 56/100\n",
            "48/48 [==============================] - 3s 63ms/step - loss: 3.1043e-04 - mean_absolute_error: 0.0099\n",
            "Epoch 57/100\n",
            "48/48 [==============================] - 3s 63ms/step - loss: 3.3403e-04 - mean_absolute_error: 0.0112\n",
            "Epoch 58/100\n",
            "48/48 [==============================] - 5s 105ms/step - loss: 3.5414e-04 - mean_absolute_error: 0.0108\n",
            "Epoch 59/100\n",
            "48/48 [==============================] - 3s 61ms/step - loss: 3.3010e-04 - mean_absolute_error: 0.0105\n",
            "Epoch 60/100\n",
            "48/48 [==============================] - 3s 62ms/step - loss: 3.2726e-04 - mean_absolute_error: 0.0095\n",
            "Epoch 61/100\n",
            "48/48 [==============================] - 3s 64ms/step - loss: 3.2174e-04 - mean_absolute_error: 0.0100\n",
            "Epoch 62/100\n",
            "48/48 [==============================] - 5s 97ms/step - loss: 4.2885e-04 - mean_absolute_error: 0.0131\n",
            "Epoch 63/100\n",
            "48/48 [==============================] - 3s 67ms/step - loss: 3.9708e-04 - mean_absolute_error: 0.0120\n",
            "Epoch 64/100\n",
            "48/48 [==============================] - 3s 62ms/step - loss: 3.9502e-04 - mean_absolute_error: 0.0117\n",
            "Epoch 65/100\n",
            "48/48 [==============================] - 3s 65ms/step - loss: 4.5100e-04 - mean_absolute_error: 0.0129\n",
            "Epoch 66/100\n",
            "48/48 [==============================] - 4s 86ms/step - loss: 3.2982e-04 - mean_absolute_error: 0.0104\n",
            "Epoch 67/100\n",
            "48/48 [==============================] - 4s 84ms/step - loss: 2.9943e-04 - mean_absolute_error: 0.0091\n",
            "Epoch 68/100\n",
            "48/48 [==============================] - 3s 64ms/step - loss: 4.0002e-04 - mean_absolute_error: 0.0115\n",
            "Epoch 69/100\n",
            "48/48 [==============================] - 3s 64ms/step - loss: 4.6801e-04 - mean_absolute_error: 0.0138\n",
            "Epoch 70/100\n",
            "48/48 [==============================] - 4s 79ms/step - loss: 3.2181e-04 - mean_absolute_error: 0.0101\n",
            "Epoch 71/100\n",
            "48/48 [==============================] - 4s 88ms/step - loss: 3.1956e-04 - mean_absolute_error: 0.0104\n",
            "Epoch 72/100\n",
            "48/48 [==============================] - 3s 64ms/step - loss: 2.9391e-04 - mean_absolute_error: 0.0092\n",
            "Epoch 73/100\n",
            "48/48 [==============================] - 3s 63ms/step - loss: 3.7629e-04 - mean_absolute_error: 0.0120\n",
            "Epoch 74/100\n",
            "48/48 [==============================] - 3s 71ms/step - loss: 3.6609e-04 - mean_absolute_error: 0.0115\n",
            "Epoch 75/100\n",
            "48/48 [==============================] - 5s 97ms/step - loss: 3.2224e-04 - mean_absolute_error: 0.0094\n",
            "Epoch 76/100\n",
            "48/48 [==============================] - 3s 64ms/step - loss: 3.3352e-04 - mean_absolute_error: 0.0105\n",
            "Epoch 77/100\n",
            "48/48 [==============================] - 3s 64ms/step - loss: 3.2682e-04 - mean_absolute_error: 0.0101\n",
            "Epoch 78/100\n",
            "48/48 [==============================] - 3s 65ms/step - loss: 3.9995e-04 - mean_absolute_error: 0.0126\n",
            "Epoch 79/100\n",
            "48/48 [==============================] - 5s 103ms/step - loss: 3.0191e-04 - mean_absolute_error: 0.0092\n",
            "Epoch 80/100\n",
            "48/48 [==============================] - 3s 63ms/step - loss: 4.0212e-04 - mean_absolute_error: 0.0130\n",
            "Epoch 81/100\n",
            "48/48 [==============================] - 3s 64ms/step - loss: 3.1554e-04 - mean_absolute_error: 0.0100\n",
            "Epoch 82/100\n",
            "48/48 [==============================] - 3s 65ms/step - loss: 3.4480e-04 - mean_absolute_error: 0.0111\n",
            "Epoch 83/100\n",
            "48/48 [==============================] - 5s 102ms/step - loss: 3.3628e-04 - mean_absolute_error: 0.0106\n",
            "Epoch 84/100\n",
            "48/48 [==============================] - 3s 68ms/step - loss: 3.7403e-04 - mean_absolute_error: 0.0111\n",
            "Epoch 85/100\n",
            "48/48 [==============================] - 3s 65ms/step - loss: 3.9913e-04 - mean_absolute_error: 0.0121\n",
            "Epoch 86/100\n",
            "48/48 [==============================] - 3s 65ms/step - loss: 2.9872e-04 - mean_absolute_error: 0.0088\n",
            "Epoch 87/100\n",
            "48/48 [==============================] - 5s 96ms/step - loss: 3.2566e-04 - mean_absolute_error: 0.0096\n",
            "Epoch 88/100\n",
            "48/48 [==============================] - 3s 70ms/step - loss: 3.3761e-04 - mean_absolute_error: 0.0110\n",
            "Epoch 89/100\n",
            "48/48 [==============================] - 3s 63ms/step - loss: 2.9991e-04 - mean_absolute_error: 0.0092\n",
            "Epoch 90/100\n",
            "48/48 [==============================] - 3s 64ms/step - loss: 3.0553e-04 - mean_absolute_error: 0.0094\n",
            "Epoch 91/100\n",
            "48/48 [==============================] - 4s 87ms/step - loss: 5.0994e-04 - mean_absolute_error: 0.0141\n",
            "Epoch 92/100\n",
            "48/48 [==============================] - 4s 82ms/step - loss: 3.2384e-04 - mean_absolute_error: 0.0104\n",
            "Epoch 93/100\n",
            "48/48 [==============================] - 3s 65ms/step - loss: 3.1399e-04 - mean_absolute_error: 0.0099\n",
            "Epoch 94/100\n",
            "48/48 [==============================] - 3s 67ms/step - loss: 4.2065e-04 - mean_absolute_error: 0.0137\n",
            "Epoch 95/100\n",
            "48/48 [==============================] - 4s 82ms/step - loss: 3.1794e-04 - mean_absolute_error: 0.0097\n",
            "Epoch 96/100\n",
            "48/48 [==============================] - 4s 86ms/step - loss: 3.0047e-04 - mean_absolute_error: 0.0092\n",
            "Epoch 97/100\n",
            "48/48 [==============================] - 3s 64ms/step - loss: 2.9541e-04 - mean_absolute_error: 0.0092\n",
            "Epoch 98/100\n",
            "48/48 [==============================] - 3s 65ms/step - loss: 2.9079e-04 - mean_absolute_error: 0.0090\n",
            "Epoch 99/100\n",
            "48/48 [==============================] - 4s 76ms/step - loss: 4.5099e-04 - mean_absolute_error: 0.0124\n",
            "Epoch 100/100\n",
            "48/48 [==============================] - 4s 91ms/step - loss: 3.4741e-04 - mean_absolute_error: 0.0109\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.plot(history.history[\"loss\"])\n",
        "plt.plot(history.history[\"mean_absolute_error\"])\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epochs')\n",
        "plt.title('Losses')\n",
        "plt.legend(['Mean Squared Error','Mean Absolute Error'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "outputId": "c7b7167c-5327-46a9-b572-d822737ed511",
        "id": "YQGmR6gHqSVG"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "time_steps = 60\n",
        "test_data = scaled_data[train_size - time_steps:, :]\n",
        "\n",
        "x_test = []\n",
        "y_test = []\n",
        "n_cols = 1\n",
        "\n",
        "for i in range(time_steps, len(test_data)):\n",
        "  x_test.append(test_data[i-time_steps:i, 0:n_cols])\n",
        "  y_test.append(test_data[i,0:n_cols])\n",
        "\n",
        "x_test,y_test = np.array(x_test), np.array(y_test)\n",
        "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], n_cols))"
      ],
      "metadata": {
        "id": "Hx1DWCzYrBwd"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4cjiwYXcsl8u",
        "outputId": "35ac20fa-ea9c-4027-fedf-23c736bcd931"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((524, 60, 1), (524, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(x_test)\n",
        "predictions = scaler.inverse_transform(predictions)\n",
        "predictions.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MCJSigycsqfX",
        "outputId": "72995420-0e29-4a87-fcea-425d8b069614"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "17/17 [==============================] - 2s 27ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(524, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test = scaler.inverse_transform(y_test)\n",
        "rmse = np.sqrt(np.mean(y_test - predictions)**2).round(2)\n",
        "rmse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wGbeEsxntVoJ",
        "outputId": "57d31550-621e-4694-9826-494eb05600a1"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3911.21"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds_acts = pd.DataFrame(data = {'Predictions': predictions.flatten(), 'Actuals': y_test.flatten()})\n",
        "\n",
        "s = pd.Series([None, None])\n",
        "for i in range(0,len(train_data)):\n",
        "  preds_acts = preds_acts.append(s,ignore_index=True)\n",
        "preds_acts"
      ],
      "metadata": {
        "id": "jY0qiS5Dtqc_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "b729b1c2-7440-4ef1-bcce-21a0aa294681"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Predictions      Actuals    0    1\n",
              "0     215884.062500  196813.1719  NaN  NaN\n",
              "1     205591.375000  219772.2031  NaN  NaN\n",
              "2     224553.859375  225318.6719  NaN  NaN\n",
              "3     232394.781250  222983.5313  NaN  NaN\n",
              "4     231293.468750  215099.4844  NaN  NaN\n",
              "...             ...          ...  ...  ...\n",
              "2088            NaN          NaN  NaN  NaN\n",
              "2089            NaN          NaN  NaN  NaN\n",
              "2090            NaN          NaN  NaN  NaN\n",
              "2091            NaN          NaN  NaN  NaN\n",
              "2092            NaN          NaN  NaN  NaN\n",
              "\n",
              "[2093 rows x 4 columns]"
            ],
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              "      <td>NaN</td>\n",
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              "      <th>2089</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2090</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "<p>2093 rows × 4 columns</p>\n",
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              "              style=\"display:none;\">\n",
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              "              title=\"Suggest charts.\"\n",
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              "\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
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              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-bfc73dce-f2cd-4ede-afff-0265d15eefdb button.colab-df-quickchart');\n",
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              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
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              "\n",
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              "\n",
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              "    }\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-25a29376-91f9-4537-9cca-ff01b141acce 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-25a29376-91f9-4537-9cca-ff01b141acce');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (16,6))\n",
        "plt.plot(preds_acts['Predictions'], linewidth=1, color='green')\n",
        "plt.plot(preds_acts['Actuals'], linewidth=2, color='black' )\n",
        "plt.plot(unseen_predictions['prediction_label'], linewidth='1', color='red')\n",
        "plt.legend(['Predictions', 'Actuals', 'preditionjinja'])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "j5MoWJ_LuIQl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "outputId": "77c3ed25-5e7e-4147-aa46-424a602f6b45"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds_acts['Predictions'].shift(len(train_data))\n",
        "unseen_predictions.shift(len(train_1_data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "j8KLdsuJzCgt",
        "outputId": "7ea88ea4-5e60-465f-d684-afd9405ee436"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Close  Future_Price  prediction_label     0     1\n",
              "0       NaN           NaN               NaN  None  None\n",
              "1       NaN           NaN               NaN  None  None\n",
              "2       NaN           NaN               NaN  None  None\n",
              "3       NaN           NaN               NaN  None  None\n",
              "4       NaN           NaN               NaN  None  None\n",
              "...     ...           ...               ...   ...   ...\n",
              "3656    NaN           NaN               NaN   NaN   NaN\n",
              "3657    NaN           NaN               NaN   NaN   NaN\n",
              "3658    NaN           NaN               NaN   NaN   NaN\n",
              "3659    NaN           NaN               NaN   NaN   NaN\n",
              "3660    NaN           NaN               NaN   NaN   NaN\n",
              "\n",
              "[3661 rows x 5 columns]"
            ],
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              "\n",
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "      <th>Future_Price</th>\n",
              "      <th>prediction_label</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>None</td>\n",
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              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>None</td>\n",
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              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>None</td>\n",
              "      <td>None</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>3656</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3657</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3658</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3659</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3660</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3661 rows × 5 columns</p>\n",
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              "  .colab-df-quickchart {\n",
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              "    border: none;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-03a29980-f7a2-4c37-938a-e66756e4a12b button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1f3636ae-96d6-4568-84f3-8ca2249f0bf1 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-1f3636ae-96d6-4568-84f3-8ca2249f0bf1');\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": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16, 6))\n",
        "plt.title(\"Ethereum predictions\", fontsize=20)\n",
        "plt.xlabel('Date', fontsize=20)\n",
        "plt.ylabel('Close Price', fontsize=18)\n",
        "plt.plot(dataset.iloc[:train_size]['Close'], linewidth=1)\n",
        "plt.plot(dataset.iloc[train_size:]['Close'], linewidth=1,color='black')\n",
        "plt.plot(unseen_predictions[r'prediction_label'].shift(len(train_data)), linewidth = 1, color='pink')\n",
        "plt.plot(preds_acts['Predictions'].shift(len(train_data)), linewidth = 1, color='green')\n",
        "plt.legend(['Train', 'Test', 'Predictions','prediction_label'])"
      ],
      "metadata": {
        "id": "Y9H2V8wRu_1j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "3ec53ae0-ffc2-4f63-f0ef-8e943db334e2"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ee2184d8280>"
            ]
          },
          "metadata": {},
          "execution_count": 134
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}