RINL_blast_furnance.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyN/20nUeu016XrP+TRpTcZD",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JOSHITHA6/BHARAT_INTERN/blob/main/RINL_blast_furnance.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**IMPORTING ALL LIBRARIES**"
      ],
      "metadata": {
        "id": "gyBirhIJYmz2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pv4YL4RpX1Rs"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_error,mean_absolute_error,r2_score\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv('/content/bf3_data_2022.csv')"
      ],
      "metadata": {
        "id": "jRJiZMLFY_83"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop(columns=['SKIN_TEMP_AVG'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "3HZgVZm1ZLyu",
        "outputId": "bf8703f0-3548-48aa-a890-dfe2c81d4901"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            DATE_TIME   CB_FLOW  CB_PRESS  CB_TEMP  STEAM_FLOW  STEAM_TEMP  \\\n",
              "0      01-07-21 00:10  311727.0      3.15    129.0         4.0       213.0   \n",
              "1      01-07-21 00:20  315163.0      3.16    129.0         4.0       209.0   \n",
              "2      01-07-21 00:30  314595.0      3.16    128.0         4.0       205.0   \n",
              "3      01-07-21 00:40  312465.0      3.16    127.0         4.0       200.0   \n",
              "4      01-07-21 00:50  302981.0      3.11    126.0         4.0       194.0   \n",
              "...               ...       ...       ...      ...         ...         ...   \n",
              "25400  31-12-21 23:10  278198.0      2.75     76.0         2.0       189.0   \n",
              "25401  31-12-21 23:20  286486.0      2.80     77.0         1.0       190.0   \n",
              "25402  31-12-21 23:30  284500.0      2.81     77.0         0.0       191.0   \n",
              "25403  31-12-21 23:40  284455.0      2.83     77.0         1.0       190.0   \n",
              "25404  31-12-21 23:50  274728.0      2.73     77.0         2.0       189.0   \n",
              "\n",
              "       STEAM_PRESS  O2_PRESS  O2_FLOW  O2_PER  ...  TOP_TEMP1  TOP_TEMP2  \\\n",
              "0             3.34      3.20   7296.0   23.08  ...      112.0      135.0   \n",
              "1             3.35      3.20   7829.0   23.08  ...      120.0      143.0   \n",
              "2             3.35      3.21   7904.0   23.08  ...      123.0      138.0   \n",
              "3             3.35      3.21   7919.0   23.08  ...      119.0      128.0   \n",
              "4             3.29      3.16   7938.0   23.08  ...      125.0      139.0   \n",
              "...            ...       ...      ...     ...  ...        ...        ...   \n",
              "25400         2.92      2.79   2628.0   22.25  ...      100.0      116.0   \n",
              "25401         2.97      2.84   2590.0   22.22  ...      103.0      113.0   \n",
              "25402         2.98      2.85   2592.0   22.20  ...      101.0      116.0   \n",
              "25403         3.00      2.87   2582.0   22.21  ...      108.0      119.0   \n",
              "25404         2.90      2.78   2593.0   22.23  ...      112.0      120.0   \n",
              "\n",
              "       TOP_TEMP3  TOP_TEMP4  TOP_SPRAY  TOP_TEMP  TOP_PRESS_1     CO    CO2  \\\n",
              "0          107.0      130.0        0.0     121.0          2.0  22.22  21.00   \n",
              "1          109.0      128.0        0.0     125.0          1.0  22.56  21.00   \n",
              "2          110.0      124.0        0.0     124.0          1.0  22.49  21.08   \n",
              "3          102.0      110.0        0.0     115.0          1.0  22.36  21.13   \n",
              "4          112.0      124.0        0.0     125.0          1.0  22.25  21.30   \n",
              "...          ...        ...        ...       ...          ...    ...    ...   \n",
              "25400      108.0      122.0        0.2     111.0          1.0  21.90  20.25   \n",
              "25401      105.0      119.0        0.2     110.0          1.0  22.09  20.14   \n",
              "25402      108.0      115.0        0.1     110.0          1.0  22.04  20.20   \n",
              "25403      113.0      121.0        0.2     115.0          1.0  22.03  20.27   \n",
              "25404      111.0      121.0        0.1     116.0          1.0  22.03  20.35   \n",
              "\n",
              "         H2  \n",
              "0      3.88  \n",
              "1      3.94  \n",
              "2      3.94  \n",
              "3      3.99  \n",
              "4      4.10  \n",
              "...     ...  \n",
              "25400  3.10  \n",
              "25401  3.04  \n",
              "25402  2.99  \n",
              "25403  3.09  \n",
              "25404  3.23  \n",
              "\n",
              "[25405 rows x 25 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4d4bbe75-0b21-46b4-b34a-97270c54902f\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATE_TIME</th>\n",
              "      <th>CB_FLOW</th>\n",
              "      <th>CB_PRESS</th>\n",
              "      <th>CB_TEMP</th>\n",
              "      <th>STEAM_FLOW</th>\n",
              "      <th>STEAM_TEMP</th>\n",
              "      <th>STEAM_PRESS</th>\n",
              "      <th>O2_PRESS</th>\n",
              "      <th>O2_FLOW</th>\n",
              "      <th>O2_PER</th>\n",
              "      <th>...</th>\n",
              "      <th>TOP_TEMP1</th>\n",
              "      <th>TOP_TEMP2</th>\n",
              "      <th>TOP_TEMP3</th>\n",
              "      <th>TOP_TEMP4</th>\n",
              "      <th>TOP_SPRAY</th>\n",
              "      <th>TOP_TEMP</th>\n",
              "      <th>TOP_PRESS_1</th>\n",
              "      <th>CO</th>\n",
              "      <th>CO2</th>\n",
              "      <th>H2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>01-07-21 00:10</td>\n",
              "      <td>311727.0</td>\n",
              "      <td>3.15</td>\n",
              "      <td>129.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>3.34</td>\n",
              "      <td>3.20</td>\n",
              "      <td>7296.0</td>\n",
              "      <td>23.08</td>\n",
              "      <td>...</td>\n",
              "      <td>112.0</td>\n",
              "      <td>135.0</td>\n",
              "      <td>107.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>22.22</td>\n",
              "      <td>21.00</td>\n",
              "      <td>3.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01-07-21 00:20</td>\n",
              "      <td>315163.0</td>\n",
              "      <td>3.16</td>\n",
              "      <td>129.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>209.0</td>\n",
              "      <td>3.35</td>\n",
              "      <td>3.20</td>\n",
              "      <td>7829.0</td>\n",
              "      <td>23.08</td>\n",
              "      <td>...</td>\n",
              "      <td>120.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>109.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>125.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>22.56</td>\n",
              "      <td>21.00</td>\n",
              "      <td>3.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>01-07-21 00:30</td>\n",
              "      <td>314595.0</td>\n",
              "      <td>3.16</td>\n",
              "      <td>128.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>205.0</td>\n",
              "      <td>3.35</td>\n",
              "      <td>3.21</td>\n",
              "      <td>7904.0</td>\n",
              "      <td>23.08</td>\n",
              "      <td>...</td>\n",
              "      <td>123.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>22.49</td>\n",
              "      <td>21.08</td>\n",
              "      <td>3.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01-07-21 00:40</td>\n",
              "      <td>312465.0</td>\n",
              "      <td>3.16</td>\n",
              "      <td>127.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>3.35</td>\n",
              "      <td>3.21</td>\n",
              "      <td>7919.0</td>\n",
              "      <td>23.08</td>\n",
              "      <td>...</td>\n",
              "      <td>119.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>22.36</td>\n",
              "      <td>21.13</td>\n",
              "      <td>3.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>01-07-21 00:50</td>\n",
              "      <td>302981.0</td>\n",
              "      <td>3.11</td>\n",
              "      <td>126.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>194.0</td>\n",
              "      <td>3.29</td>\n",
              "      <td>3.16</td>\n",
              "      <td>7938.0</td>\n",
              "      <td>23.08</td>\n",
              "      <td>...</td>\n",
              "      <td>125.0</td>\n",
              "      <td>139.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>125.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>22.25</td>\n",
              "      <td>21.30</td>\n",
              "      <td>4.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25400</th>\n",
              "      <td>31-12-21 23:10</td>\n",
              "      <td>278198.0</td>\n",
              "      <td>2.75</td>\n",
              "      <td>76.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>189.0</td>\n",
              "      <td>2.92</td>\n",
              "      <td>2.79</td>\n",
              "      <td>2628.0</td>\n",
              "      <td>22.25</td>\n",
              "      <td>...</td>\n",
              "      <td>100.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>122.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>111.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>21.90</td>\n",
              "      <td>20.25</td>\n",
              "      <td>3.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25401</th>\n",
              "      <td>31-12-21 23:20</td>\n",
              "      <td>286486.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>77.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>2.97</td>\n",
              "      <td>2.84</td>\n",
              "      <td>2590.0</td>\n",
              "      <td>22.22</td>\n",
              "      <td>...</td>\n",
              "      <td>103.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>119.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>110.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>22.09</td>\n",
              "      <td>20.14</td>\n",
              "      <td>3.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25402</th>\n",
              "      <td>31-12-21 23:30</td>\n",
              "      <td>284500.0</td>\n",
              "      <td>2.81</td>\n",
              "      <td>77.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>191.0</td>\n",
              "      <td>2.98</td>\n",
              "      <td>2.85</td>\n",
              "      <td>2592.0</td>\n",
              "      <td>22.20</td>\n",
              "      <td>...</td>\n",
              "      <td>101.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>110.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>22.04</td>\n",
              "      <td>20.20</td>\n",
              "      <td>2.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25403</th>\n",
              "      <td>31-12-21 23:40</td>\n",
              "      <td>284455.0</td>\n",
              "      <td>2.83</td>\n",
              "      <td>77.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>2.87</td>\n",
              "      <td>2582.0</td>\n",
              "      <td>22.21</td>\n",
              "      <td>...</td>\n",
              "      <td>108.0</td>\n",
              "      <td>119.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>115.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>22.03</td>\n",
              "      <td>20.27</td>\n",
              "      <td>3.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25404</th>\n",
              "      <td>31-12-21 23:50</td>\n",
              "      <td>274728.0</td>\n",
              "      <td>2.73</td>\n",
              "      <td>77.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>189.0</td>\n",
              "      <td>2.90</td>\n",
              "      <td>2.78</td>\n",
              "      <td>2593.0</td>\n",
              "      <td>22.23</td>\n",
              "      <td>...</td>\n",
              "      <td>112.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>116.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>22.03</td>\n",
              "      <td>20.35</td>\n",
              "      <td>3.23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>25405 rows × 25 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d4bbe75-0b21-46b4-b34a-97270c54902f')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-4d4bbe75-0b21-46b4-b34a-97270c54902f 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-4d4bbe75-0b21-46b4-b34a-97270c54902f');\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",
              "\n",
              "\n",
              "<div id=\"df-65dacdbe-70eb-4644-8792-2df225048b3c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-65dacdbe-70eb-4644-8792-2df225048b3c')\"\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",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-65dacdbe-70eb-4644-8792-2df225048b3c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['DATE_TIME']=pd.to_datetime(data['DATE_TIME'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0opVhDYyZ-cO",
        "outputId": "704ab5eb-3f0b-416a-d2cd-2850ed82b58d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-39-7e602554d981>:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
            "  data['DATE_TIME']=pd.to_datetime(data['DATE_TIME'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Formatting date time into millisec format using format fun**"
      ],
      "metadata": {
        "id": "IzrWlz1kcMkt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# Define the correct date-time format (adjust this based on your actual date-time format in the Excel file)\n",
        "date_format = '%Y-%m-%d %H:%M:%S.%f'  # Example format\n",
        "\n",
        "# Use pd.to_datetime to parse the DATE_TIME column with the specified format\n",
        "data['DATE_TIME'] = pd.to_datetime(data['DATE_TIME'], format=date_format, errors='coerce')\n",
        "\n",
        "# Display the first few rows of the dataframe to confirm changes\n",
        "print(data.head())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aq94t9p6b4mk",
        "outputId": "499b0001-12f6-4d38-867b-b939162fd65b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            DATE_TIME   CB_FLOW  CB_PRESS  CB_TEMP  STEAM_FLOW  STEAM_TEMP  \\\n",
            "0 2021-01-07 00:10:00  311727.0      3.15    129.0         4.0       213.0   \n",
            "1 2021-01-07 00:20:00  315163.0      3.16    129.0         4.0       209.0   \n",
            "2 2021-01-07 00:30:00  314595.0      3.16    128.0         4.0       205.0   \n",
            "3 2021-01-07 00:40:00  312465.0      3.16    127.0         4.0       200.0   \n",
            "4 2021-01-07 00:50:00  302981.0      3.11    126.0         4.0       194.0   \n",
            "\n",
            "   STEAM_PRESS  O2_PRESS  O2_FLOW  O2_PER  ...  TOP_TEMP2  TOP_TEMP3  \\\n",
            "0         3.34      3.20   7296.0   23.08  ...      135.0      107.0   \n",
            "1         3.35      3.20   7829.0   23.08  ...      143.0      109.0   \n",
            "2         3.35      3.21   7904.0   23.08  ...      138.0      110.0   \n",
            "3         3.35      3.21   7919.0   23.08  ...      128.0      102.0   \n",
            "4         3.29      3.16   7938.0   23.08  ...      139.0      112.0   \n",
            "\n",
            "   TOP_TEMP4  TOP_SPRAY  TOP_TEMP  TOP_PRESS_1     CO    CO2    H2  \\\n",
            "0      130.0        0.0     121.0          2.0  22.22  21.00  3.88   \n",
            "1      128.0        0.0     125.0          1.0  22.56  21.00  3.94   \n",
            "2      124.0        0.0     124.0          1.0  22.49  21.08  3.94   \n",
            "3      110.0        0.0     115.0          1.0  22.36  21.13  3.99   \n",
            "4      124.0        0.0     125.0          1.0  22.25  21.30  4.10   \n",
            "\n",
            "   SKIN_TEMP_AVG  \n",
            "0           69.9  \n",
            "1           71.5  \n",
            "2           70.6  \n",
            "3           70.2  \n",
            "4           70.7  \n",
            "\n",
            "[5 rows x 26 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Alternative to format function(without using format)**"
      ],
      "metadata": {
        "id": "Exzn3Yw9cXm6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Use pd.to_datetime to parse the DATE_TIME column without specifying format\n",
        "data['DATE_TIME'] = pd.to_datetime(data['DATE_TIME'], errors='coerce')\n",
        "\n",
        "# Display the first few rows of the dataframe to confirm changes\n",
        "print(data.head())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U9_GOGkzcF8T",
        "outputId": "5f9a34c9-ee97-4a8f-dcf8-8e8569bfc8e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            DATE_TIME   CB_FLOW  CB_PRESS  CB_TEMP  STEAM_FLOW  STEAM_TEMP  \\\n",
            "0 2021-01-07 00:10:00  311727.0      3.15    129.0         4.0       213.0   \n",
            "1 2021-01-07 00:20:00  315163.0      3.16    129.0         4.0       209.0   \n",
            "2 2021-01-07 00:30:00  314595.0      3.16    128.0         4.0       205.0   \n",
            "3 2021-01-07 00:40:00  312465.0      3.16    127.0         4.0       200.0   \n",
            "4 2021-01-07 00:50:00  302981.0      3.11    126.0         4.0       194.0   \n",
            "\n",
            "   STEAM_PRESS  O2_PRESS  O2_FLOW  O2_PER  ...  TOP_TEMP2  TOP_TEMP3  \\\n",
            "0         3.34      3.20   7296.0   23.08  ...      135.0      107.0   \n",
            "1         3.35      3.20   7829.0   23.08  ...      143.0      109.0   \n",
            "2         3.35      3.21   7904.0   23.08  ...      138.0      110.0   \n",
            "3         3.35      3.21   7919.0   23.08  ...      128.0      102.0   \n",
            "4         3.29      3.16   7938.0   23.08  ...      139.0      112.0   \n",
            "\n",
            "   TOP_TEMP4  TOP_SPRAY  TOP_TEMP  TOP_PRESS_1     CO    CO2    H2  \\\n",
            "0      130.0        0.0     121.0          2.0  22.22  21.00  3.88   \n",
            "1      128.0        0.0     125.0          1.0  22.56  21.00  3.94   \n",
            "2      124.0        0.0     124.0          1.0  22.49  21.08  3.94   \n",
            "3      110.0        0.0     115.0          1.0  22.36  21.13  3.99   \n",
            "4      124.0        0.0     125.0          1.0  22.25  21.30  4.10   \n",
            "\n",
            "   SKIN_TEMP_AVG  \n",
            "0           69.9  \n",
            "1           71.5  \n",
            "2           70.6  \n",
            "3           70.2  \n",
            "4           70.7  \n",
            "\n",
            "[5 rows x 26 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "jmgLIrtHch8N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**REGRESSION USING RANDOM_FOREST**"
      ],
      "metadata": {
        "id": "hJ5-EAzRcqmB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# def train_and_predict(X,y,shift_hours):\n",
        "#   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "#   # Initialize the Random Forest classifier\n",
        "#   rf = RandomForestRegressor(n_estimators=100, random_state=42)\n",
        "#   # Train the model on the training data\n",
        "#   rf.fit(X_train, y_train)\n",
        "#   # Predict the labels for the test set\n",
        "#   y_pred = rf.predict(X_test)\n",
        "#   # Evaluate the model's performance\n",
        "#   accuracy = accuracy_score(y_test, y_pred)\n",
        "#   mse = mean_squared_error(y_test, rf.predict(X_test))\n",
        "#   print(f\"Shift {shift_hours} hours - Mean Squared Error: {mse}\")\n",
        "#   mae = mean_absolute_error(y_test, rf.predict(X_test))\n",
        "#   print(f\"Shift {shift_hours} hours - Mean Absolute Error: {mae}\")\n",
        "#   r2 = r2_score(y_test, rf.predict(X_test))\n",
        "#   print(f\"Shift {shift_hours} hours - R-squared: {r2}\")\n",
        "#   return rf, pd.DataFrame(y_pred, columns=X.columns)\n"
      ],
      "metadata": {
        "id": "bmUhh9GsfAA7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_and_predict(X, y, shift_hours):\n",
        "  #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05, train_size=0.95, random_state=42)\n",
        "\n",
        "  model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
        "  model.fit(X_train, y_train)\n",
        "  predictions = model.predict(X)\n",
        "  predicted_df = pd.DataFrame(predictions, columns=X.columns)\n",
        "  predicted_df['DATE_TIME'] = X.index + pd.to_timedelta(shift_hours, unit='h')\n",
        "  mse = mean_squared_error(y_test, model.predict(X_test))\n",
        "  mae = mean_absolute_error(y_test, model.predict(X_test))\n",
        "  r2 = r2_score(y_test, model.predict(X_test))\n",
        "  print(f\"Shift {shift_hours} hours - Mean Squared Error: {mse}\")\n",
        "  print(f\"Shift {shift_hours} hours - Mean Absolute Error: {mae}\")\n",
        "  print(f\"Shift {shift_hours} hours - R-squared: {r2}\")\n",
        "  return model, predicted_df\n"
      ],
      "metadata": {
        "id": "9MS8w1IZx11L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop(columns=['DATE_TIME'])\n",
        "X.index = data['DATE_TIME']\n",
        "y = X.copy()\n",
        "    #print(X)\n",
        "    #print(y)\n",
        "\n",
        "\n",
        "# Proceed with your data processing and analysis\n",
        "\n",
        "    # result = train_and_predict(X, y, shift_hours=1)\n",
        "    # print(result)\n",
        "\n",
        "    # model_shift_1, predicted_df_shift_1 = result\n",
        "    # predicted_df_shift_1['CO_CO2_ratio'] = predicted_df_shift_1['CO'] /predicted_df_shift_1['CO2']\n",
        "\n",
        "    # predicted_df_shift_1.to_csv('predicted_data_after_1_hour.csv', index=False)\n",
        "    # print(predicted_df_shift_1)\n",
        "\n",
        "    # X_shift_2 = predicted_df_shift_1.drop(columns=['DATE_TIME'])\n",
        "    # X_shift_2.index = predicted_df_shift_1['DATE_TIME']\n",
        "    # model_shift_2, predicted_df_shift_2 = train_and_predict(X_shift_2, X_shift_2, shift_hours=1)\n",
        "    # predicted_df_shift_2['CO_CO2_ratio'] = predicted_df_shift_2['CO'] /predicted_df_shift_2['CO2']\n",
        "\n",
        "    # predicted_df_shift_2.to_csv('predicted_data_after_2_hours.csv', index=False)\n",
        "    # print(predicted_df_shift_2)\n",
        "\n",
        "    # X_shift_3 = predicted_df_shift_2.drop(columns=['DATE_TIME'])\n",
        "    # X_shift_3.index = predicted_df_shift_2['DATE_TIME']\n",
        "\n",
        "    # model_shift_3, predicted_df_shift_3 = train_and_predict(X_shift_3, X_shift_3, shift_hours=1)\n",
        "    # predicted_df_shift_3['CO_CO2_ratio'] = predicted_df_shift_3['CO'] /predicted_df_shift_3['CO2']\n",
        "\n",
        "    # predicted_df_shift_3.to_csv('predicted_data_after_3_hours.csv', index=False)\n",
        "    # predicted_df_shift_3\n",
        "\n",
        "    # X_shift_4 = predicted_df_shift_3.drop(columns=['DATE_TIME'])\n",
        "    # X_shift_4.index = predicted_df_shift_3['DATE_TIME']\n",
        "    # model_shift_4, predicted_df_shift_4 = train_and_predict(X_shift_4, X_shift_4, shift_hours=1)\n",
        "    # predicted_df_shift_4['CO_CO2_ratio'] = predicted_df_shift_4['CO'] /predicted_df_shift_4['CO2']\n",
        "\n",
        "    # predicted_df_shift_4.to_csv('predicted_data_after_4_hours.csv', index=False)\n",
        "    # predicted_df_shift_4\n"
      ],
      "metadata": {
        "id": "ViCX2xi9mvvx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_shift_1, predicted_df_shift_1 = train_and_predict(X, y, shift_hours=1)\n",
        "predicted_df_shift_1['CO_CO2_ratio'] = predicted_df_shift_1['CO'] /predicted_df_shift_1['CO2']\n",
        "predicted_df_shift_1.to_csv('predicted_data_after_1_hour.csv', index=False)\n",
        "print(predicted_df_shift_1)\n",
        "\n",
        "X_shift_2 = predicted_df_shift_1.drop(columns=['DATE_TIME'])\n",
        "X_shift_2.index = predicted_df_shift_1['DATE_TIME']\n",
        "model_shift_2, predicted_df_shift_2 = train_and_predict(X_shift_2, X_shift_2, shift_hours=1)\n",
        "predicted_df_shift_2['CO_CO2_ratio'] = predicted_df_shift_2['CO'] /predicted_df_shift_2['CO2']\n",
        "predicted_df_shift_2.to_csv('predicted_data_after_2_hours.csv', index=False)\n",
        "print(predicted_df_shift_2)\n",
        "\n",
        "X_shift_3 = predicted_df_shift_2.drop(columns=['DATE_TIME'])\n",
        "X_shift_3.index = predicted_df_shift_2['DATE_TIME']\n",
        "\n",
        "model_shift_3, predicted_df_shift_3 = train_and_predict(X_shift_3, X_shift_3, shift_hours=1)\n",
        "predicted_df_shift_3['CO_CO2_ratio'] = predicted_df_shift_3['CO'] /predicted_df_shift_3['CO2']\n",
        "predicted_df_shift_3.to_csv('predicted_data_after_3_hours.csv', index=False)\n",
        "predicted_df_shift_3\n",
        "\n",
        "X_shift_4 = predicted_df_shift_3.drop(columns=['DATE_TIME'])\n",
        "X_shift_4.index = predicted_df_shift_3['DATE_TIME']\n",
        "model_shift_4, predicted_df_shift_4 = train_and_predict(X_shift_4, X_shift_4, shift_hours=1)\n",
        "predicted_df_shift_4['CO_CO2_ratio'] = predicted_df_shift_4['CO'] /predicted_df_shift_4['CO2']\n",
        "predicted_df_shift_4.to_csv('predicted_data_after_4_hours.csv', index=False)\n",
        "predicted_df_shift_4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "br9WaV2-ybvE",
        "outputId": "feb049e7-1317-4d5b-b2dd-25bf74441844"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shift 1 hours - Mean Squared Error: 3697.2975984193563\n",
            "Shift 1 hours - Mean Absolute Error: 9.776020509293682\n",
            "Shift 1 hours - R-squared: 0.6700647107148657\n",
            "         CB_FLOW  CB_PRESS  CB_TEMP  STEAM_FLOW  STEAM_TEMP  STEAM_PRESS  \\\n",
            "0      311725.37    3.1582   128.19        3.68      209.87       3.3464   \n",
            "1      315161.62    3.1827   130.11        4.23      210.49       3.3782   \n",
            "2      314585.47    3.1843   128.37        4.01      205.93       3.3754   \n",
            "3      312463.37    3.1774   127.15        3.97      199.98       3.3692   \n",
            "4      302975.09    3.1339   125.78        3.60      193.92       3.3143   \n",
            "...          ...       ...      ...         ...         ...          ...   \n",
            "21510  278200.59    2.7472    77.20        2.02      189.47       2.9191   \n",
            "21511  286479.11    2.7922    77.68        1.69      190.38       2.9668   \n",
            "21512  284489.82    2.8016    77.44        0.73      190.48       2.9734   \n",
            "21513  284467.15    2.8118    77.60        1.18      189.92       2.9834   \n",
            "21514  274724.96    2.7194    79.93        2.43      189.96       2.8954   \n",
            "\n",
            "       O2_PRESS  O2_FLOW   O2_PER    PCI  ...  TOP_TEMP4  TOP_SPRAY  TOP_TEMP  \\\n",
            "0        3.2072  7320.20  23.3281  32.95  ...     128.40      0.086    122.28   \n",
            "1        3.2259  7853.49  23.3760  30.06  ...     136.64      0.080    132.30   \n",
            "2        3.2343  7889.03  23.2879  31.15  ...     128.70      0.058    125.94   \n",
            "3        3.2273  7939.72  23.3316  35.93  ...     120.98      0.067    120.67   \n",
            "4        3.1838  7950.33  23.3247  37.84  ...     132.35      0.066    129.32   \n",
            "...         ...      ...      ...    ...  ...        ...        ...       ...   \n",
            "21510    2.7883  2628.73  22.1776  12.50  ...     125.15      0.171    116.31   \n",
            "21511    2.8325  2567.48  22.2103  11.91  ...     121.77      0.174    114.42   \n",
            "21512    2.8421  2585.54  22.2032  13.69  ...     120.12      0.130    114.99   \n",
            "21513    2.8522  2577.82  22.2111  14.25  ...     123.58      0.175    118.36   \n",
            "21514    2.7680  2543.11  22.2296  14.21  ...     124.72      0.113    120.27   \n",
            "\n",
            "       TOP_PRESS_1       CO      CO2      H2  SKIN_TEMP_AVG  \\\n",
            "0             1.57  22.2248  21.1129  3.9575         70.765   \n",
            "1             1.08  22.5011  21.1286  4.0062         75.449   \n",
            "2             1.03  22.5026  21.0870  3.9690         73.089   \n",
            "3             1.07  22.4580  21.0946  4.0361         73.844   \n",
            "4             1.04  22.3448  21.2504  4.0927         73.923   \n",
            "...            ...      ...      ...     ...            ...   \n",
            "21510         1.00  22.1435  20.3381  3.0611         62.061   \n",
            "21511         1.00  22.2220  20.2018  3.0033         62.818   \n",
            "21512         1.00  22.1457  20.1880  3.0104         63.525   \n",
            "21513         1.00  22.1414  20.2266  3.0572         62.919   \n",
            "21514         1.00  22.1528  20.3915  3.1572         63.413   \n",
            "\n",
            "                DATE_TIME  CO_CO2_ratio  \n",
            "0     2021-01-07 01:10:00      1.052664  \n",
            "1     2021-01-07 01:20:00      1.064959  \n",
            "2     2021-01-07 01:30:00      1.067131  \n",
            "3     2021-01-07 01:40:00      1.064633  \n",
            "4     2021-01-07 01:50:00      1.051500  \n",
            "...                   ...           ...  \n",
            "21510 2022-01-01 00:10:00      1.088769  \n",
            "21511 2022-01-01 00:20:00      1.100001  \n",
            "21512 2022-01-01 00:30:00      1.096973  \n",
            "21513 2022-01-01 00:40:00      1.094667  \n",
            "21514 2022-01-01 00:50:00      1.086374  \n",
            "\n",
            "[21515 rows x 27 columns]\n",
            "Shift 1 hours - Mean Squared Error: 703.4306877684286\n",
            "Shift 1 hours - Mean Absolute Error: 4.196893206621782\n",
            "Shift 1 hours - R-squared: 0.9537011599138105\n",
            "           CB_FLOW  CB_PRESS   CB_TEMP  STEAM_FLOW  STEAM_TEMP  STEAM_PRESS  \\\n",
            "0      311707.9370  3.172035  127.6029      3.4482    208.2279     3.359296   \n",
            "1      315193.0974  3.200579  129.7778      4.3316    208.5786     3.397289   \n",
            "2      314585.7713  3.198128  128.5862      4.0411    206.6267     3.390246   \n",
            "3      312454.3885  3.188016  127.3109      4.0025    199.9404     3.381106   \n",
            "4      302965.9835  3.144698  125.4987      3.3685    193.5440     3.325053   \n",
            "...            ...       ...       ...         ...         ...          ...   \n",
            "21510  278217.1258  2.738955   77.9423      2.0401    189.5746     2.912783   \n",
            "21511  286470.2057  2.783174   78.5483      2.1732    190.6784     2.961657   \n",
            "21512  284491.1552  2.790352   78.0736      1.1856    190.4052     2.963861   \n",
            "21513  284480.0214  2.798093   78.5859      1.5259    190.0862     2.972361   \n",
            "21514  274727.6382  2.710975   80.5931      2.7425    190.4105     2.890832   \n",
            "\n",
            "       O2_PRESS    O2_FLOW     O2_PER      PCI  ...  TOP_TEMP4  TOP_SPRAY  \\\n",
            "0      3.220653  7285.8761  23.449563  33.0981  ...   127.7986    0.13371   \n",
            "1      3.245560  7802.6372  23.493706  29.8284  ...   140.8364    0.11986   \n",
            "2      3.247957  7890.8134  23.430931  31.6410  ...   130.3682    0.09760   \n",
            "3      3.237857  7954.2464  23.492024  35.4539  ...   127.7080    0.11048   \n",
            "4      3.194352  7964.8260  23.448267  39.2466  ...   136.6412    0.10037   \n",
            "...         ...        ...        ...      ...  ...        ...        ...   \n",
            "21510  2.781258  2632.4771  22.159341  12.2956  ...   127.0235    0.15769   \n",
            "21511  2.824145  2599.4154  22.209853  11.8607  ...   122.2162    0.15990   \n",
            "21512  2.831376  2577.1897  22.210636  13.2525  ...   123.6362    0.14107   \n",
            "21513  2.839115  2566.8880  22.211565  13.4414  ...   125.0325    0.16065   \n",
            "21514  2.758039  2506.3652  22.227809  13.0982  ...   127.2901    0.11957   \n",
            "\n",
            "       TOP_TEMP  TOP_PRESS_1         CO        CO2        H2  SKIN_TEMP_AVG  \\\n",
            "0      122.9302       1.3688  22.267925  21.138431  3.961559       69.84690   \n",
            "1      136.0657       1.1013  22.548122  21.095209  4.019387       76.46485   \n",
            "2      126.1240       1.0324  22.501405  21.111850  4.003082       74.63150   \n",
            "3      124.1728       1.1084  22.533338  21.081423  4.067722       77.01819   \n",
            "4      131.3750       1.0642  22.396331  21.224261  4.083538       75.31169   \n",
            "...         ...          ...        ...        ...       ...            ...   \n",
            "21510  119.0416       1.0000  22.331532  20.380113  3.036145       59.81098   \n",
            "21511  116.4475       1.0000  22.321011  20.259431  2.999189       62.86375   \n",
            "21512  118.4915       1.0000  22.248656  20.196347  2.999368       62.40272   \n",
            "21513  120.2514       1.0000  22.247952  20.218439  3.030830       61.87193   \n",
            "21514  122.7882       1.0000  22.219993  20.413177  3.107073       62.81932   \n",
            "\n",
            "       CO_CO2_ratio           DATE_TIME  \n",
            "0          1.053433 2021-01-07 02:10:00  \n",
            "1          1.068874 2021-01-07 02:20:00  \n",
            "2          1.065819 2021-01-07 02:30:00  \n",
            "3          1.068872 2021-01-07 02:40:00  \n",
            "4          1.055223 2021-01-07 02:50:00  \n",
            "...             ...                 ...  \n",
            "21510      1.095751 2022-01-01 01:10:00  \n",
            "21511      1.101759 2022-01-01 01:20:00  \n",
            "21512      1.101618 2022-01-01 01:30:00  \n",
            "21513      1.100379 2022-01-01 01:40:00  \n",
            "21514      1.088512 2022-01-01 01:50:00  \n",
            "\n",
            "[21515 rows x 27 columns]\n",
            "Shift 1 hours - Mean Squared Error: 313.51452354668766\n",
            "Shift 1 hours - Mean Absolute Error: 2.767369052863853\n",
            "Shift 1 hours - R-squared: 0.9867685788067776\n",
            "Shift 1 hours - Mean Squared Error: 250.70436721363052\n",
            "Shift 1 hours - Mean Absolute Error: 2.0340550636228767\n",
            "Shift 1 hours - R-squared: 0.9954980354441266\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             CB_FLOW  CB_PRESS     CB_TEMP  STEAM_FLOW  STEAM_TEMP  \\\n",
              "0      311690.412450  3.182409  125.940171    3.170233  207.067923   \n",
              "1      315187.670301  3.218607  130.445569    4.386141  208.937463   \n",
              "2      314585.901121  3.210289  128.141165    3.863666  207.446681   \n",
              "3      312445.408719  3.198305  127.257833    3.852060  199.281895   \n",
              "4      302944.007423  3.154052  125.706415    3.202897  194.622919   \n",
              "...              ...       ...         ...         ...         ...   \n",
              "21510  278240.729417  2.729649   78.876007    2.142820  189.785473   \n",
              "21511  286450.671527  2.782107   80.105770    2.640740  190.920214   \n",
              "21512  284479.341362  2.782221   79.682679    1.828972  190.554959   \n",
              "21513  284479.718450  2.783536   79.180948    1.872511  190.349363   \n",
              "21514  274755.400807  2.699739   80.785703    3.032051  191.011181   \n",
              "\n",
              "       STEAM_PRESS  O2_PRESS      O2_FLOW     O2_PER        PCI  ...  \\\n",
              "0         3.368184  3.230486  7296.874252  23.565640  33.502186  ...   \n",
              "1         3.417020  3.265471  7859.834139  23.653515  30.315161  ...   \n",
              "2         3.402104  3.259789  7766.061180  23.559828  31.291869  ...   \n",
              "3         3.390643  3.248067  7973.407165  23.645472  35.694368  ...   \n",
              "4         3.337236  3.203439  7942.584540  23.572528  39.643419  ...   \n",
              "...            ...       ...          ...        ...        ...  ...   \n",
              "21510     2.905615  2.772987  2669.331315  22.154264  12.027966  ...   \n",
              "21511     2.964653  2.824209  2662.632583  22.210592  11.918606  ...   \n",
              "21512     2.959002  2.823972  2571.400454  22.232746  12.569817  ...   \n",
              "21513     2.960272  2.825100  2554.913479  22.226706  12.684088  ...   \n",
              "21514     2.883583  2.745231  2477.338115  22.227691  12.203854  ...   \n",
              "\n",
              "        TOP_TEMP4  TOP_SPRAY    TOP_TEMP  TOP_PRESS_1         CO        CO2  \\\n",
              "0      128.276678   0.174447  123.751101     1.162930  22.322121  21.153526   \n",
              "1      141.372750   0.161214  135.546341     1.140441  22.550036  21.134648   \n",
              "2      131.924732   0.143162  126.057955     1.056284  22.522588  21.090480   \n",
              "3      134.966260   0.152496  127.932779     1.147905  22.602052  21.070116   \n",
              "4      141.432488   0.135016  133.791723     1.083663  22.432086  21.209034   \n",
              "...           ...        ...         ...          ...        ...        ...   \n",
              "21510  128.422522   0.141844  121.453962     1.000000  22.492613  20.386879   \n",
              "21511  124.939362   0.148102  120.409879     1.000001  22.435179  20.307543   \n",
              "21512  126.534129   0.144604  121.763235     1.000000  22.349958  20.246618   \n",
              "21513  126.861750   0.149454  122.152748     1.000000  22.332769  20.242775   \n",
              "21514  130.231377   0.129136  125.130555     1.000031  22.264699  20.460425   \n",
              "\n",
              "             H2  SKIN_TEMP_AVG  CO_CO2_ratio           DATE_TIME  \n",
              "0      3.970784      68.732834      1.055243 2021-01-07 04:10:00  \n",
              "1      4.058796      76.938042      1.066970 2021-01-07 04:20:00  \n",
              "2      4.005477      73.614340      1.067903 2021-01-07 04:30:00  \n",
              "3      4.074618      79.066440      1.072707 2021-01-07 04:40:00  \n",
              "4      4.091837      77.023846      1.057667 2021-01-07 04:50:00  \n",
              "...         ...            ...           ...                 ...  \n",
              "21510  3.016568      58.631909      1.103289 2022-01-01 03:10:00  \n",
              "21511  3.025227      62.287390      1.104771 2022-01-01 03:20:00  \n",
              "21512  2.989635      60.552542      1.103886 2022-01-01 03:30:00  \n",
              "21513  2.999920      60.592916      1.103246 2022-01-01 03:40:00  \n",
              "21514  3.070201      62.262303      1.088184 2022-01-01 03:50:00  \n",
              "\n",
              "[21515 rows x 27 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2cacc140-2b1c-479c-b3cd-1f541e9a181d\" 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>CB_FLOW</th>\n",
              "      <th>CB_PRESS</th>\n",
              "      <th>CB_TEMP</th>\n",
              "      <th>STEAM_FLOW</th>\n",
              "      <th>STEAM_TEMP</th>\n",
              "      <th>STEAM_PRESS</th>\n",
              "      <th>O2_PRESS</th>\n",
              "      <th>O2_FLOW</th>\n",
              "      <th>O2_PER</th>\n",
              "      <th>PCI</th>\n",
              "      <th>...</th>\n",
              "      <th>TOP_TEMP4</th>\n",
              "      <th>TOP_SPRAY</th>\n",
              "      <th>TOP_TEMP</th>\n",
              "      <th>TOP_PRESS_1</th>\n",
              "      <th>CO</th>\n",
              "      <th>CO2</th>\n",
              "      <th>H2</th>\n",
              "      <th>SKIN_TEMP_AVG</th>\n",
              "      <th>CO_CO2_ratio</th>\n",
              "      <th>DATE_TIME</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>311690.412450</td>\n",
              "      <td>3.182409</td>\n",
              "      <td>125.940171</td>\n",
              "      <td>3.170233</td>\n",
              "      <td>207.067923</td>\n",
              "      <td>3.368184</td>\n",
              "      <td>3.230486</td>\n",
              "      <td>7296.874252</td>\n",
              "      <td>23.565640</td>\n",
              "      <td>33.502186</td>\n",
              "      <td>...</td>\n",
              "      <td>128.276678</td>\n",
              "      <td>0.174447</td>\n",
              "      <td>123.751101</td>\n",
              "      <td>1.162930</td>\n",
              "      <td>22.322121</td>\n",
              "      <td>21.153526</td>\n",
              "      <td>3.970784</td>\n",
              "      <td>68.732834</td>\n",
              "      <td>1.055243</td>\n",
              "      <td>2021-01-07 04:10:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>315187.670301</td>\n",
              "      <td>3.218607</td>\n",
              "      <td>130.445569</td>\n",
              "      <td>4.386141</td>\n",
              "      <td>208.937463</td>\n",
              "      <td>3.417020</td>\n",
              "      <td>3.265471</td>\n",
              "      <td>7859.834139</td>\n",
              "      <td>23.653515</td>\n",
              "      <td>30.315161</td>\n",
              "      <td>...</td>\n",
              "      <td>141.372750</td>\n",
              "      <td>0.161214</td>\n",
              "      <td>135.546341</td>\n",
              "      <td>1.140441</td>\n",
              "      <td>22.550036</td>\n",
              "      <td>21.134648</td>\n",
              "      <td>4.058796</td>\n",
              "      <td>76.938042</td>\n",
              "      <td>1.066970</td>\n",
              "      <td>2021-01-07 04:20:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>314585.901121</td>\n",
              "      <td>3.210289</td>\n",
              "      <td>128.141165</td>\n",
              "      <td>3.863666</td>\n",
              "      <td>207.446681</td>\n",
              "      <td>3.402104</td>\n",
              "      <td>3.259789</td>\n",
              "      <td>7766.061180</td>\n",
              "      <td>23.559828</td>\n",
              "      <td>31.291869</td>\n",
              "      <td>...</td>\n",
              "      <td>131.924732</td>\n",
              "      <td>0.143162</td>\n",
              "      <td>126.057955</td>\n",
              "      <td>1.056284</td>\n",
              "      <td>22.522588</td>\n",
              "      <td>21.090480</td>\n",
              "      <td>4.005477</td>\n",
              "      <td>73.614340</td>\n",
              "      <td>1.067903</td>\n",
              "      <td>2021-01-07 04:30:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>312445.408719</td>\n",
              "      <td>3.198305</td>\n",
              "      <td>127.257833</td>\n",
              "      <td>3.852060</td>\n",
              "      <td>199.281895</td>\n",
              "      <td>3.390643</td>\n",
              "      <td>3.248067</td>\n",
              "      <td>7973.407165</td>\n",
              "      <td>23.645472</td>\n",
              "      <td>35.694368</td>\n",
              "      <td>...</td>\n",
              "      <td>134.966260</td>\n",
              "      <td>0.152496</td>\n",
              "      <td>127.932779</td>\n",
              "      <td>1.147905</td>\n",
              "      <td>22.602052</td>\n",
              "      <td>21.070116</td>\n",
              "      <td>4.074618</td>\n",
              "      <td>79.066440</td>\n",
              "      <td>1.072707</td>\n",
              "      <td>2021-01-07 04:40:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>302944.007423</td>\n",
              "      <td>3.154052</td>\n",
              "      <td>125.706415</td>\n",
              "      <td>3.202897</td>\n",
              "      <td>194.622919</td>\n",
              "      <td>3.337236</td>\n",
              "      <td>3.203439</td>\n",
              "      <td>7942.584540</td>\n",
              "      <td>23.572528</td>\n",
              "      <td>39.643419</td>\n",
              "      <td>...</td>\n",
              "      <td>141.432488</td>\n",
              "      <td>0.135016</td>\n",
              "      <td>133.791723</td>\n",
              "      <td>1.083663</td>\n",
              "      <td>22.432086</td>\n",
              "      <td>21.209034</td>\n",
              "      <td>4.091837</td>\n",
              "      <td>77.023846</td>\n",
              "      <td>1.057667</td>\n",
              "      <td>2021-01-07 04:50:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21510</th>\n",
              "      <td>278240.729417</td>\n",
              "      <td>2.729649</td>\n",
              "      <td>78.876007</td>\n",
              "      <td>2.142820</td>\n",
              "      <td>189.785473</td>\n",
              "      <td>2.905615</td>\n",
              "      <td>2.772987</td>\n",
              "      <td>2669.331315</td>\n",
              "      <td>22.154264</td>\n",
              "      <td>12.027966</td>\n",
              "      <td>...</td>\n",
              "      <td>128.422522</td>\n",
              "      <td>0.141844</td>\n",
              "      <td>121.453962</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>22.492613</td>\n",
              "      <td>20.386879</td>\n",
              "      <td>3.016568</td>\n",
              "      <td>58.631909</td>\n",
              "      <td>1.103289</td>\n",
              "      <td>2022-01-01 03:10:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21511</th>\n",
              "      <td>286450.671527</td>\n",
              "      <td>2.782107</td>\n",
              "      <td>80.105770</td>\n",
              "      <td>2.640740</td>\n",
              "      <td>190.920214</td>\n",
              "      <td>2.964653</td>\n",
              "      <td>2.824209</td>\n",
              "      <td>2662.632583</td>\n",
              "      <td>22.210592</td>\n",
              "      <td>11.918606</td>\n",
              "      <td>...</td>\n",
              "      <td>124.939362</td>\n",
              "      <td>0.148102</td>\n",
              "      <td>120.409879</td>\n",
              "      <td>1.000001</td>\n",
              "      <td>22.435179</td>\n",
              "      <td>20.307543</td>\n",
              "      <td>3.025227</td>\n",
              "      <td>62.287390</td>\n",
              "      <td>1.104771</td>\n",
              "      <td>2022-01-01 03:20:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21512</th>\n",
              "      <td>284479.341362</td>\n",
              "      <td>2.782221</td>\n",
              "      <td>79.682679</td>\n",
              "      <td>1.828972</td>\n",
              "      <td>190.554959</td>\n",
              "      <td>2.959002</td>\n",
              "      <td>2.823972</td>\n",
              "      <td>2571.400454</td>\n",
              "      <td>22.232746</td>\n",
              "      <td>12.569817</td>\n",
              "      <td>...</td>\n",
              "      <td>126.534129</td>\n",
              "      <td>0.144604</td>\n",
              "      <td>121.763235</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>22.349958</td>\n",
              "      <td>20.246618</td>\n",
              "      <td>2.989635</td>\n",
              "      <td>60.552542</td>\n",
              "      <td>1.103886</td>\n",
              "      <td>2022-01-01 03:30:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21513</th>\n",
              "      <td>284479.718450</td>\n",
              "      <td>2.783536</td>\n",
              "      <td>79.180948</td>\n",
              "      <td>1.872511</td>\n",
              "      <td>190.349363</td>\n",
              "      <td>2.960272</td>\n",
              "      <td>2.825100</td>\n",
              "      <td>2554.913479</td>\n",
              "      <td>22.226706</td>\n",
              "      <td>12.684088</td>\n",
              "      <td>...</td>\n",
              "      <td>126.861750</td>\n",
              "      <td>0.149454</td>\n",
              "      <td>122.152748</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>22.332769</td>\n",
              "      <td>20.242775</td>\n",
              "      <td>2.999920</td>\n",
              "      <td>60.592916</td>\n",
              "      <td>1.103246</td>\n",
              "      <td>2022-01-01 03:40:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21514</th>\n",
              "      <td>274755.400807</td>\n",
              "      <td>2.699739</td>\n",
              "      <td>80.785703</td>\n",
              "      <td>3.032051</td>\n",
              "      <td>191.011181</td>\n",
              "      <td>2.883583</td>\n",
              "      <td>2.745231</td>\n",
              "      <td>2477.338115</td>\n",
              "      <td>22.227691</td>\n",
              "      <td>12.203854</td>\n",
              "      <td>...</td>\n",
              "      <td>130.231377</td>\n",
              "      <td>0.129136</td>\n",
              "      <td>125.130555</td>\n",
              "      <td>1.000031</td>\n",
              "      <td>22.264699</td>\n",
              "      <td>20.460425</td>\n",
              "      <td>3.070201</td>\n",
              "      <td>62.262303</td>\n",
              "      <td>1.088184</td>\n",
              "      <td>2022-01-01 03:50:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21515 rows × 27 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2cacc140-2b1c-479c-b3cd-1f541e9a181d')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-2cacc140-2b1c-479c-b3cd-1f541e9a181d 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-2cacc140-2b1c-479c-b3cd-1f541e9a181d');\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",
              "\n",
              "\n",
              "<div id=\"df-abc5d48f-419c-416c-8e02-79710f82b808\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-abc5d48f-419c-416c-8e02-79710f82b808')\"\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",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-abc5d48f-419c-416c-8e02-79710f82b808 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "predicted_df_shift_4"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming predicted_df_shift_1 contains your predicted data\n",
        "# Extract the predicted and actual values for CO and CO2\n",
        "predicted_CO = predicted_df_shift_1['CO']\n",
        "predicted_CO2 = predicted_df_shift_1['CO2']\n",
        "actual_CO = y['CO']  # Use the entire y dataset instead of y_test\n",
        "actual_CO2 = y['CO2']  # Use the entire y dataset instead of y_test\n",
        "\n",
        "# Create a scatter plot\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(actual_CO2, predicted_CO2, color='blue', label='CO2 Predicted vs Actual')\n",
        "plt.scatter(actual_CO, predicted_CO, color='red', label='CO Predicted vs Actual')\n",
        "plt.title('Predicted vs Actual CO and CO2')\n",
        "plt.xlabel('Actual')\n",
        "plt.ylabel('Predicted')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "zPiXO4WY2YMB",
        "outputId": "7d9299a9-d360-42ef-8149-e05fbf8356af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming predicted_df_shift_1 contains your predicted data\n",
        "# Extract the predicted and actual values for CO and CO2\n",
        "predicted_CO = predicted_df_shift_1['CO']\n",
        "predicted_CO2 = predicted_df_shift_1['CO2']\n",
        "actual_CO = y['CO']  # Use the entire y dataset instead of y_test\n",
        "actual_CO2 = y['CO2']  # Use the entire y dataset instead of y_test\n",
        "\n",
        "# Calculate CO/CO2 ratios for both actual and predicted values\n",
        "actual_CO_CO2_ratio = actual_CO / actual_CO2\n",
        "predicted_CO_CO2_ratio = predicted_CO / predicted_CO2\n",
        "\n",
        "# Create a scatter plot\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(actual_CO2, predicted_CO2, color='blue', label='CO2 Predicted vs Actual')\n",
        "plt.scatter(actual_CO, predicted_CO, color='red', label='CO Predicted vs Actual')\n",
        "plt.scatter(actual_CO_CO2_ratio, predicted_CO_CO2_ratio, color='green', label='CO/CO2 Ratio')\n",
        "plt.title('Predicted vs Actual CO and CO2')\n",
        "plt.xlabel('Actual')\n",
        "plt.ylabel('Predicted')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "4d_EHyeM3Gnr",
        "outputId": "be038f68-8803-46f6-a153-5d0340319732"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming predicted_df_shift_1 contains your predicted data\n",
        "# Extract the predicted and actual values for CO and CO2\n",
        "predicted_CO = predicted_df_shift_1['CO']\n",
        "predicted_CO2 = predicted_df_shift_1['CO2']\n",
        "actual_CO = y['CO']  # Use the entire y dataset instead of y_test\n",
        "actual_CO2 = y['CO2']  # Use the entire y dataset instead of y_test\n",
        "\n",
        "# Calculate CO/CO2 ratios for both actual and predicted values\n",
        "actual_CO_CO2_ratio = actual_CO / actual_CO2\n",
        "predicted_CO_CO2_ratio = predicted_CO / predicted_CO2\n",
        "\n",
        "# Create a histogram for CO/CO2 ratios\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.hist(actual_CO_CO2_ratio, bins=20, color='blue', alpha=0.5, label='Actual CO/CO2 Ratio')\n",
        "plt.hist(predicted_CO_CO2_ratio, bins=20, color='red', alpha=0.5, label='Predicted CO/CO2 Ratio')\n",
        "plt.title('Histogram of CO/CO2 Ratios')\n",
        "plt.xlabel('CO/CO2 Ratio')\n",
        "plt.ylabel('Frequency')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "k6Z9cNX63RzL",
        "outputId": "47bf66bf-8e44-4a91-e211-1a9d76703238",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}