OilyMLGO.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/abhishek-38/petroleum_ml/blob/main/OilyMLGO.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GOJWWzeqgnYf"
      },
      "outputs": [],
      "source": [
        "#https://drive.google.com/file/d/1QGX7yWYarF7G4FZLt6se3068KaW0-AA7/view?usp=sharing"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split, StratifiedShuffleSplit, StratifiedKFold\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "%config InLineBackend.figure_format = 'png'"
      ],
      "metadata": {
        "id": "fUq6wjwOg1cU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "File : https://drive.google.com/file/d/1QGX7yWYarF7G4FZLt6se3068KaW0-AA7/view?usp=share_link"
      ],
      "metadata": {
        "id": "a-VpQCt3g8z1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget --no-check-certificate 'https://docs.google.com/uc?export=download&id=1QGX7yWYarF7G4FZLt6se3068KaW0-AA7' -O input.zip"
      ],
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        "id": "8iIbYG6Zg7GD",
        "outputId": "57d77925-de9c-4d8a-adb5-533878aa57ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-01-21 06:03:47--  https://docs.google.com/uc?export=download&id=1QGX7yWYarF7G4FZLt6se3068KaW0-AA7\n",
            "Resolving docs.google.com (docs.google.com)... 108.177.125.102, 108.177.125.139, 108.177.125.138, ...\n",
            "Connecting to docs.google.com (docs.google.com)|108.177.125.102|:443... connected.\n",
            "HTTP request sent, awaiting response... 303 See Other\n",
            "Location: https://doc-0o-a0-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/l5kakk7221muq3vp00rpat3mq0bkecvm/1674281025000/04989237414475792611/*/1QGX7yWYarF7G4FZLt6se3068KaW0-AA7?e=download&uuid=dc4941df-90e4-4bb4-a093-ee95facfcaec [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2023-01-21 06:03:49--  https://doc-0o-a0-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/l5kakk7221muq3vp00rpat3mq0bkecvm/1674281025000/04989237414475792611/*/1QGX7yWYarF7G4FZLt6se3068KaW0-AA7?e=download&uuid=dc4941df-90e4-4bb4-a093-ee95facfcaec\n",
            "Resolving doc-0o-a0-docs.googleusercontent.com (doc-0o-a0-docs.googleusercontent.com)... 142.251.8.132, 2404:6800:4008:c15::84\n",
            "Connecting to doc-0o-a0-docs.googleusercontent.com (doc-0o-a0-docs.googleusercontent.com)|142.251.8.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 983552 (960K) [application/zip]\n",
            "Saving to: ‘input.zip’\n",
            "\n",
            "input.zip           100%[===================>] 960.50K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2023-01-21 06:03:50 (67.1 MB/s) - ‘input.zip’ saved [983552/983552]\n",
            "\n"
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    },
    {
      "cell_type": "code",
      "source": [
        "!unzip -q input.zip"
      ],
      "metadata": {
        "id": "P53BMO1HhPBs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train = pd.read_csv(\"/content/OILYMLgo/train.csv\")\n",
        "\n",
        "train_dtc = train.copy()\n",
        "train_dts = train.copy()"
      ],
      "metadata": {
        "id": "GYvItK2lhvRM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
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          "height": 206
        },
        "id": "tjJuPXiwiF2l",
        "outputId": "acb2e8c5-5ede-4d97-b4b0-0d587e205b78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "     CAL    CNC       GR    HRD    HRM     PE   ZDEN       DTC       DTS\n",
              "0 -999.0 -999.0  41.4699 -999.0 -999.0 -999.0 -999.0  128.0737  319.0654\n",
              "1 -999.0 -999.0  42.5053 -999.0 -999.0 -999.0 -999.0  127.8347  318.7825\n",
              "2 -999.0 -999.0  43.1548 -999.0 -999.0 -999.0 -999.0  127.2307  317.3323\n",
              "3 -999.0 -999.0  43.2410 -999.0 -999.0 -999.0 -999.0  126.2917  313.6486\n",
              "4 -999.0 -999.0  40.3218 -999.0 -999.0 -999.0 -999.0  125.3985  307.8903"
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              "      <td>43.2410</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>-999.0</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "test = pd.read_csv(\"/content/OILYMLgo/test.csv\")\n"
      ],
      "metadata": {
        "id": "xEAevWoeiHKO"
      },
      "execution_count": null,
      "outputs": []
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            "text/plain": [
              "      CAL     CNC       GR     HRD     HRM      PE    ZDEN\n",
              "0  8.5781  0.3521  55.1824  0.8121  0.7810  6.8291  2.3256\n",
              "1  8.5781  0.3639  57.0114  0.8038  0.7723  6.8100  2.3255\n",
              "2  8.5781  0.3703  58.9263  0.7444  0.7048  6.7766  2.3212\n",
              "3  8.5625  0.3667  57.3308  0.7169  0.6542  6.7219  2.3119\n",
              "4  8.5781  0.3500  53.0624  0.6845  0.6109  6.6384  2.2982"
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7f5f6051-e7e9-4648-b740-5224def8fbcc 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-7f5f6051-e7e9-4648-b740-5224def8fbcc');\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": 279
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample = pd.read_csv('/content/OILYMLgo/sample_submission.csv')"
      ],
      "metadata": {
        "id": "JXPc_HlGiRM2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_q6aDM63iXly",
        "outputId": "d79d1645-e9f3-482c-c1d0-a4db11bc2ca0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   DTC      DTS  \n",
              "0      100    200\n",
              "1      100    200\n",
              "2      100    200\n",
              "3      100    200\n",
              "4      100    200"
            ],
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              "\n",
              "  <div id=\"df-d60975ee-317c-46a0-9e73-cd89280fd82f\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DTC</th>\n",
              "      <th>DTS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100</td>\n",
              "      <td>200</td>\n",
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              "      <th>2</th>\n",
              "      <td>100</td>\n",
              "      <td>200</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100</td>\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d60975ee-317c-46a0-9e73-cd89280fd82f');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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              "    </div>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 281
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# graph a few of them\n",
        "# get correlation\n",
        "# pick best features\n",
        "# do some feature engineering\n",
        "# apply light gbm regressor\n"
      ],
      "metadata": {
        "id": "PKf4Fqy_iYh7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERE3BWXfwPRE",
        "outputId": "6fe8e345-587a-415a-cb52-37e39abd042e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(30143, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 283
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RwP6p1xiwerr",
        "outputId": "f42d505a-00f3-4ab7-9ec7-67693fc982e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11088, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 284
        }
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    {
      "cell_type": "code",
      "source": [
        "train.describe()"
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          "height": 300
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        "id": "cBEo18NzwgdO",
        "outputId": "c4ebe062-ccae-4bef-9ff1-0bb4d803fe94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                CAL           CNC            GR           HRD           HRM  \\\n",
              "count  30143.000000  30143.000000  30143.000000  30143.000000  30143.000000   \n",
              "mean      -8.394576    -23.692615     38.959845      3.977690      1.547299   \n",
              "std      129.970219    157.142679    108.504554    365.112753    456.908969   \n",
              "min     -999.000000   -999.000000   -999.000000   -999.000000   -999.000000   \n",
              "25%        8.058350      0.122800     17.248750      0.717700      0.712050   \n",
              "50%        8.625000      0.193600     36.821800      1.623000      1.628100   \n",
              "75%        9.048850      0.337150     58.346150      3.158300      3.280600   \n",
              "max       21.064200   3490.158200   1470.253400  10000.000000  60467.761700   \n",
              "\n",
              "                 PE          ZDEN           DTC           DTS  \n",
              "count  30143.000000  30143.000000  30143.000000  30143.000000  \n",
              "mean     -17.446739    -20.229982    -54.891703     -9.737567  \n",
              "std      149.083136    148.809506    372.858812    440.314119  \n",
              "min     -999.000000   -999.000000   -999.000000   -999.000000  \n",
              "25%        0.053100      2.226700     66.304350    118.534350  \n",
              "50%        4.941500      2.432200     78.355100    137.689300  \n",
              "75%        7.856650      2.551350    107.022500    182.973150  \n",
              "max       28.106400      3.259700    155.980300    487.438400  "
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-8.394576</td>\n",
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              "      <td>-20.229982</td>\n",
              "      <td>-54.891703</td>\n",
              "      <td>-9.737567</td>\n",
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              "      <td>148.809506</td>\n",
              "      <td>372.858812</td>\n",
              "      <td>440.314119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.000000</td>\n",
              "      <td>-999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8.058350</td>\n",
              "      <td>0.122800</td>\n",
              "      <td>17.248750</td>\n",
              "      <td>0.717700</td>\n",
              "      <td>0.712050</td>\n",
              "      <td>0.053100</td>\n",
              "      <td>2.226700</td>\n",
              "      <td>66.304350</td>\n",
              "      <td>118.534350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.625000</td>\n",
              "      <td>0.193600</td>\n",
              "      <td>36.821800</td>\n",
              "      <td>1.623000</td>\n",
              "      <td>1.628100</td>\n",
              "      <td>4.941500</td>\n",
              "      <td>2.432200</td>\n",
              "      <td>78.355100</td>\n",
              "      <td>137.689300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9.048850</td>\n",
              "      <td>0.337150</td>\n",
              "      <td>58.346150</td>\n",
              "      <td>3.158300</td>\n",
              "      <td>3.280600</td>\n",
              "      <td>7.856650</td>\n",
              "      <td>2.551350</td>\n",
              "      <td>107.022500</td>\n",
              "      <td>182.973150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>21.064200</td>\n",
              "      <td>3490.158200</td>\n",
              "      <td>1470.253400</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>60467.761700</td>\n",
              "      <td>28.106400</td>\n",
              "      <td>3.259700</td>\n",
              "      <td>155.980300</td>\n",
              "      <td>487.438400</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d414f790-f8b2-4569-a595-2c60e8e2af43 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-d414f790-f8b2-4569-a595-2c60e8e2af43');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 285
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ColA = ['CAL','CNC','GR','HRD','HRM','PE','ZDEN']\n",
        "\n",
        "for col in train.columns.to_list():\n",
        "    train = train[train[col] != -999]\n",
        "\n",
        "for col in ColA + ['DTC']:\n",
        "    train_dtc = train_dtc[train_dtc[col] != -999]\n",
        "\n",
        "for col in ColA + ['DTS']:\n",
        "    train_dts = train_dts[train_dts[col] != -999]"
      ],
      "metadata": {
        "id": "XEkAJcd22vEV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "c3XcJX40N9Jh",
        "outputId": "92a0111a-5733-4a5c-d976-3f32d7e25d52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                CAL           CNC            GR           HRD           HRM  \\\n",
              "count  20525.000000  20525.000000  20525.000000  20525.000000  20525.000000   \n",
              "mean       8.426679      0.274416     49.889253      2.598719      5.835466   \n",
              "std        1.845912      3.062495     54.811017      3.465665    422.449589   \n",
              "min        5.930400      0.014500      1.038900      0.123600      0.134100   \n",
              "25%        6.629100      0.120300     16.036800      0.810000      0.797300   \n",
              "50%        8.578100      0.187700     37.498000      1.814900      1.829300   \n",
              "75%        8.671900      0.329000     61.140700      3.337400      3.463300   \n",
              "max       21.064200    365.885000   1470.253400    206.718200  60467.761700   \n",
              "\n",
              "                 PE          ZDEN           DTC           DTS  \n",
              "count  20525.000000  20525.000000  20525.000000  20525.000000  \n",
              "mean       3.833792      2.410734     88.312221    182.051067  \n",
              "std        4.375818      0.181713     23.542419     84.670122  \n",
              "min       -0.023200      0.680600     49.970500     80.580400  \n",
              "25%        0.049800      2.236100     70.423100    127.148800  \n",
              "50%        3.287800      2.466500     79.695400    142.678500  \n",
              "75%        7.061300      2.563700    102.482800    192.757800  \n",
              "max       28.106400      3.259700    155.980300    487.438400  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4f459a50-1773-479f-9d00-f2ce7bf1fa41\">\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>CAL</th>\n",
              "      <th>CNC</th>\n",
              "      <th>GR</th>\n",
              "      <th>HRD</th>\n",
              "      <th>HRM</th>\n",
              "      <th>PE</th>\n",
              "      <th>ZDEN</th>\n",
              "      <th>DTC</th>\n",
              "      <th>DTS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>20525.000000</td>\n",
              "      <td>20525.000000</td>\n",
              "      <td>20525.000000</td>\n",
              "      <td>20525.000000</td>\n",
              "      <td>20525.000000</td>\n",
              "      <td>20525.000000</td>\n",
              "      <td>20525.000000</td>\n",
              "      <td>20525.000000</td>\n",
              "      <td>20525.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.426679</td>\n",
              "      <td>0.274416</td>\n",
              "      <td>49.889253</td>\n",
              "      <td>2.598719</td>\n",
              "      <td>5.835466</td>\n",
              "      <td>3.833792</td>\n",
              "      <td>2.410734</td>\n",
              "      <td>88.312221</td>\n",
              "      <td>182.051067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.845912</td>\n",
              "      <td>3.062495</td>\n",
              "      <td>54.811017</td>\n",
              "      <td>3.465665</td>\n",
              "      <td>422.449589</td>\n",
              "      <td>4.375818</td>\n",
              "      <td>0.181713</td>\n",
              "      <td>23.542419</td>\n",
              "      <td>84.670122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>5.930400</td>\n",
              "      <td>0.014500</td>\n",
              "      <td>1.038900</td>\n",
              "      <td>0.123600</td>\n",
              "      <td>0.134100</td>\n",
              "      <td>-0.023200</td>\n",
              "      <td>0.680600</td>\n",
              "      <td>49.970500</td>\n",
              "      <td>80.580400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6.629100</td>\n",
              "      <td>0.120300</td>\n",
              "      <td>16.036800</td>\n",
              "      <td>0.810000</td>\n",
              "      <td>0.797300</td>\n",
              "      <td>0.049800</td>\n",
              "      <td>2.236100</td>\n",
              "      <td>70.423100</td>\n",
              "      <td>127.148800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.578100</td>\n",
              "      <td>0.187700</td>\n",
              "      <td>37.498000</td>\n",
              "      <td>1.814900</td>\n",
              "      <td>1.829300</td>\n",
              "      <td>3.287800</td>\n",
              "      <td>2.466500</td>\n",
              "      <td>79.695400</td>\n",
              "      <td>142.678500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>8.671900</td>\n",
              "      <td>0.329000</td>\n",
              "      <td>61.140700</td>\n",
              "      <td>3.337400</td>\n",
              "      <td>3.463300</td>\n",
              "      <td>7.061300</td>\n",
              "      <td>2.563700</td>\n",
              "      <td>102.482800</td>\n",
              "      <td>192.757800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>21.064200</td>\n",
              "      <td>365.885000</td>\n",
              "      <td>1470.253400</td>\n",
              "      <td>206.718200</td>\n",
              "      <td>60467.761700</td>\n",
              "      <td>28.106400</td>\n",
              "      <td>3.259700</td>\n",
              "      <td>155.980300</td>\n",
              "      <td>487.438400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4f459a50-1773-479f-9d00-f2ce7bf1fa41')\"\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",
              "  <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-4f459a50-1773-479f-9d00-f2ce7bf1fa41 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-4f459a50-1773-479f-9d00-f2ce7bf1fa41');\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": 287
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "32xS81qC2gyj",
        "outputId": "912491d9-d0b2-4fc4-8b58-5a8b48849c33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                CAL           CNC            GR           HRD           HRM  \\\n",
              "count  11088.000000  11088.000000  11088.000000  11088.000000  11088.000000   \n",
              "mean       8.634049      0.158501     28.966414      4.028372    106.752210   \n",
              "std        0.044064      0.091298     43.648163      7.198112   2374.620246   \n",
              "min        8.500000      0.009800      0.852000      0.083900      0.102700   \n",
              "25%        8.625000      0.091700      8.449450      1.760775      1.865575   \n",
              "50%        8.625000      0.128300     18.168650      2.758900      3.176950   \n",
              "75%        8.671900      0.213500     36.470725      4.539675      5.034600   \n",
              "max        8.875000      0.559400   1124.442000    202.233000  62290.769500   \n",
              "\n",
              "                 PE          ZDEN  \n",
              "count  11088.000000  11088.000000  \n",
              "mean       7.353522      2.475884  \n",
              "std        1.239075      0.149440  \n",
              "min        4.760800      2.033400  \n",
              "25%        6.528075      2.378175  \n",
              "50%        7.881700      2.533600  \n",
              "75%        8.308325      2.581800  \n",
              "max       13.840700      3.024800  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a2764e83-3d64-4411-9a9a-e8c6c3c01712\">\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>CAL</th>\n",
              "      <th>CNC</th>\n",
              "      <th>GR</th>\n",
              "      <th>HRD</th>\n",
              "      <th>HRM</th>\n",
              "      <th>PE</th>\n",
              "      <th>ZDEN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11088.000000</td>\n",
              "      <td>11088.000000</td>\n",
              "      <td>11088.000000</td>\n",
              "      <td>11088.000000</td>\n",
              "      <td>11088.000000</td>\n",
              "      <td>11088.000000</td>\n",
              "      <td>11088.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.634049</td>\n",
              "      <td>0.158501</td>\n",
              "      <td>28.966414</td>\n",
              "      <td>4.028372</td>\n",
              "      <td>106.752210</td>\n",
              "      <td>7.353522</td>\n",
              "      <td>2.475884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.044064</td>\n",
              "      <td>0.091298</td>\n",
              "      <td>43.648163</td>\n",
              "      <td>7.198112</td>\n",
              "      <td>2374.620246</td>\n",
              "      <td>1.239075</td>\n",
              "      <td>0.149440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>8.500000</td>\n",
              "      <td>0.009800</td>\n",
              "      <td>0.852000</td>\n",
              "      <td>0.083900</td>\n",
              "      <td>0.102700</td>\n",
              "      <td>4.760800</td>\n",
              "      <td>2.033400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8.625000</td>\n",
              "      <td>0.091700</td>\n",
              "      <td>8.449450</td>\n",
              "      <td>1.760775</td>\n",
              "      <td>1.865575</td>\n",
              "      <td>6.528075</td>\n",
              "      <td>2.378175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.625000</td>\n",
              "      <td>0.128300</td>\n",
              "      <td>18.168650</td>\n",
              "      <td>2.758900</td>\n",
              "      <td>3.176950</td>\n",
              "      <td>7.881700</td>\n",
              "      <td>2.533600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>8.671900</td>\n",
              "      <td>0.213500</td>\n",
              "      <td>36.470725</td>\n",
              "      <td>4.539675</td>\n",
              "      <td>5.034600</td>\n",
              "      <td>8.308325</td>\n",
              "      <td>2.581800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>8.875000</td>\n",
              "      <td>0.559400</td>\n",
              "      <td>1124.442000</td>\n",
              "      <td>202.233000</td>\n",
              "      <td>62290.769500</td>\n",
              "      <td>13.840700</td>\n",
              "      <td>3.024800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a2764e83-3d64-4411-9a9a-e8c6c3c01712')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a2764e83-3d64-4411-9a9a-e8c6c3c01712 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-a2764e83-3d64-4411-9a9a-e8c6c3c01712');\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": 288
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bqbHKpEfwnDj",
        "outputId": "f9a4b77a-71b2-4428-fda4-d8ba267cd1b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 20525 entries, 573 to 30142\n",
            "Data columns (total 9 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   CAL     20525 non-null  float64\n",
            " 1   CNC     20525 non-null  float64\n",
            " 2   GR      20525 non-null  float64\n",
            " 3   HRD     20525 non-null  float64\n",
            " 4   HRM     20525 non-null  float64\n",
            " 5   PE      20525 non-null  float64\n",
            " 6   ZDEN    20525 non-null  float64\n",
            " 7   DTC     20525 non-null  float64\n",
            " 8   DTS     20525 non-null  float64\n",
            "dtypes: float64(9)\n",
            "memory usage: 1.6 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from itertools import combinations\n",
        "def makeNewFeatures_Diff(train_data, colA):\n",
        "        for col1, col2 in combinations(colA, 2):\n",
        "            train_data['Diff_'+ col1 + \"_\" + col2] = train_data[col1] - train_data[col2]\n",
        "            # train_data['HarmonicMean_'+ col1 + \"_\" + col2] = 2*train_data[col1] * train_data[col2]/(train_data[col1] + train_data[col2])\n",
        "            # train_data['GeometricMean_'+ col1 + \"_\" + col2] = np.sqrt(train_data[col1] * train_data[col2])\n",
        "            # train_data['ArithmeticMean_'+ col1 + \"_\" + col2] = (train_data[col1] + train_data[col2])/2\n",
        "            # train_data['Strange_'+ col1 + \"_\" + col2] = (train_data['ArithmeticMean_'+ col1 + \"_\" + col2] - train_data['HarmonicMean_'+ col1 + \"_\" + col2] )/ (train_data['ArithmeticMean_'+ col1 + \"_\" + col2] - train_data['GeometricMean_'+ col1 + \"_\" + col2])\n",
        "\n",
        "\n",
        "            # if len(train_data[train_data[col2] == 0]) == 0:\n",
        "            #     train_data['Div_'+ col1 + \"_\" + col2] = train_data[col1] / train_data[col2]"
      ],
      "metadata": {
        "id": "-c0khIPcw4bF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ColA = ['CAL','CNC','GR','HRD','HRM','PE','ZDEN']\n",
        "makeNewFeatures_Diff(train, ColA)\n",
        "makeNewFeatures_Diff(train_dtc, ColA)\n",
        "makeNewFeatures_Diff(train_dts, ColA)\n",
        "makeNewFeatures_Diff(test, ColA)"
      ],
      "metadata": {
        "id": "OczAvPaJzsuY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "4e2uywod0Tln",
        "outputId": "b5a12df1-03ca-4ab0-cf41-900c091f8201"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         CAL     CNC       GR     HRD     HRM       PE    ZDEN       DTC  \\\n",
              "573  14.8824  0.5173  57.1903  1.9826  1.9726  27.9432  2.3510  125.9611   \n",
              "574  14.9570  0.4966  63.1593  2.0472  1.6373  28.1064  2.4055  125.9726   \n",
              "575  14.6341  0.4433  64.5217  2.0464  1.4866  26.5968  2.4579  125.8528   \n",
              "576  14.2318  0.4309  63.0144  2.0054  1.4613  19.5635  2.4219  125.7362   \n",
              "577  14.0163  0.4469  60.8421  1.9533  1.4438   9.8556  2.3138  125.9774   \n",
              "\n",
              "          DTS  Diff_CAL_CNC  ...  Diff_GR_HRD  Diff_GR_HRM  Diff_GR_PE  \\\n",
              "573  306.8676       14.3651  ...      55.2077      55.2177     29.2471   \n",
              "574  305.8347       14.4604  ...      61.1121      61.5220     35.0529   \n",
              "575  305.9089       14.1908  ...      62.4753      63.0351     37.9249   \n",
              "576  305.1737       13.8009  ...      61.0090      61.5531     43.4509   \n",
              "577  304.4476       13.5694  ...      58.8888      59.3983     50.9865   \n",
              "\n",
              "     Diff_GR_ZDEN  Diff_HRD_HRM  Diff_HRD_PE  Diff_HRD_ZDEN  Diff_HRM_PE  \\\n",
              "573       54.8393        0.0100     -25.9606        -0.3684     -25.9706   \n",
              "574       60.7538        0.4099     -26.0592        -0.3583     -26.4691   \n",
              "575       62.0638        0.5598     -24.5504        -0.4115     -25.1102   \n",
              "576       60.5925        0.5441     -17.5581        -0.4165     -18.1022   \n",
              "577       58.5283        0.5095      -7.9023        -0.3605      -8.4118   \n",
              "\n",
              "     Diff_HRM_ZDEN  Diff_PE_ZDEN  \n",
              "573        -0.3784       25.5922  \n",
              "574        -0.7682       25.7009  \n",
              "575        -0.9713       24.1389  \n",
              "576        -0.9606       17.1416  \n",
              "577        -0.8700        7.5418  \n",
              "\n",
              "[5 rows x 30 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-edbd4c36-ce4d-44e5-ab80-397ba20b508a\">\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",
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CAL</th>\n",
              "      <th>CNC</th>\n",
              "      <th>GR</th>\n",
              "      <th>HRD</th>\n",
              "      <th>HRM</th>\n",
              "      <th>PE</th>\n",
              "      <th>ZDEN</th>\n",
              "      <th>DTC</th>\n",
              "      <th>DTS</th>\n",
              "      <th>Diff_CAL_CNC</th>\n",
              "      <th>...</th>\n",
              "      <th>Diff_GR_HRD</th>\n",
              "      <th>Diff_GR_HRM</th>\n",
              "      <th>Diff_GR_PE</th>\n",
              "      <th>Diff_GR_ZDEN</th>\n",
              "      <th>Diff_HRD_HRM</th>\n",
              "      <th>Diff_HRD_PE</th>\n",
              "      <th>Diff_HRD_ZDEN</th>\n",
              "      <th>Diff_HRM_PE</th>\n",
              "      <th>Diff_HRM_ZDEN</th>\n",
              "      <th>Diff_PE_ZDEN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>573</th>\n",
              "      <td>14.8824</td>\n",
              "      <td>0.5173</td>\n",
              "      <td>57.1903</td>\n",
              "      <td>1.9826</td>\n",
              "      <td>1.9726</td>\n",
              "      <td>27.9432</td>\n",
              "      <td>2.3510</td>\n",
              "      <td>125.9611</td>\n",
              "      <td>306.8676</td>\n",
              "      <td>14.3651</td>\n",
              "      <td>...</td>\n",
              "      <td>55.2077</td>\n",
              "      <td>55.2177</td>\n",
              "      <td>29.2471</td>\n",
              "      <td>54.8393</td>\n",
              "      <td>0.0100</td>\n",
              "      <td>-25.9606</td>\n",
              "      <td>-0.3684</td>\n",
              "      <td>-25.9706</td>\n",
              "      <td>-0.3784</td>\n",
              "      <td>25.5922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>574</th>\n",
              "      <td>14.9570</td>\n",
              "      <td>0.4966</td>\n",
              "      <td>63.1593</td>\n",
              "      <td>2.0472</td>\n",
              "      <td>1.6373</td>\n",
              "      <td>28.1064</td>\n",
              "      <td>2.4055</td>\n",
              "      <td>125.9726</td>\n",
              "      <td>305.8347</td>\n",
              "      <td>14.4604</td>\n",
              "      <td>...</td>\n",
              "      <td>61.1121</td>\n",
              "      <td>61.5220</td>\n",
              "      <td>35.0529</td>\n",
              "      <td>60.7538</td>\n",
              "      <td>0.4099</td>\n",
              "      <td>-26.0592</td>\n",
              "      <td>-0.3583</td>\n",
              "      <td>-26.4691</td>\n",
              "      <td>-0.7682</td>\n",
              "      <td>25.7009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>575</th>\n",
              "      <td>14.6341</td>\n",
              "      <td>0.4433</td>\n",
              "      <td>64.5217</td>\n",
              "      <td>2.0464</td>\n",
              "      <td>1.4866</td>\n",
              "      <td>26.5968</td>\n",
              "      <td>2.4579</td>\n",
              "      <td>125.8528</td>\n",
              "      <td>305.9089</td>\n",
              "      <td>14.1908</td>\n",
              "      <td>...</td>\n",
              "      <td>62.4753</td>\n",
              "      <td>63.0351</td>\n",
              "      <td>37.9249</td>\n",
              "      <td>62.0638</td>\n",
              "      <td>0.5598</td>\n",
              "      <td>-24.5504</td>\n",
              "      <td>-0.4115</td>\n",
              "      <td>-25.1102</td>\n",
              "      <td>-0.9713</td>\n",
              "      <td>24.1389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>576</th>\n",
              "      <td>14.2318</td>\n",
              "      <td>0.4309</td>\n",
              "      <td>63.0144</td>\n",
              "      <td>2.0054</td>\n",
              "      <td>1.4613</td>\n",
              "      <td>19.5635</td>\n",
              "      <td>2.4219</td>\n",
              "      <td>125.7362</td>\n",
              "      <td>305.1737</td>\n",
              "      <td>13.8009</td>\n",
              "      <td>...</td>\n",
              "      <td>61.0090</td>\n",
              "      <td>61.5531</td>\n",
              "      <td>43.4509</td>\n",
              "      <td>60.5925</td>\n",
              "      <td>0.5441</td>\n",
              "      <td>-17.5581</td>\n",
              "      <td>-0.4165</td>\n",
              "      <td>-18.1022</td>\n",
              "      <td>-0.9606</td>\n",
              "      <td>17.1416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>577</th>\n",
              "      <td>14.0163</td>\n",
              "      <td>0.4469</td>\n",
              "      <td>60.8421</td>\n",
              "      <td>1.9533</td>\n",
              "      <td>1.4438</td>\n",
              "      <td>9.8556</td>\n",
              "      <td>2.3138</td>\n",
              "      <td>125.9774</td>\n",
              "      <td>304.4476</td>\n",
              "      <td>13.5694</td>\n",
              "      <td>...</td>\n",
              "      <td>58.8888</td>\n",
              "      <td>59.3983</td>\n",
              "      <td>50.9865</td>\n",
              "      <td>58.5283</td>\n",
              "      <td>0.5095</td>\n",
              "      <td>-7.9023</td>\n",
              "      <td>-0.3605</td>\n",
              "      <td>-8.4118</td>\n",
              "      <td>-0.8700</td>\n",
              "      <td>7.5418</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 30 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-edbd4c36-ce4d-44e5-ab80-397ba20b508a')\"\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",
              "  <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-edbd4c36-ce4d-44e5-ab80-397ba20b508a 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-edbd4c36-ce4d-44e5-ab80-397ba20b508a');\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": 292
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# corr_map = sns.heatmap(train.corr(), cmap=\"YlGnBu\", annot=True)\n",
        "# plt.show()"
      ],
      "metadata": {
        "id": "K76O-JFDxL4s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import lightgbm as lgb\n",
        "from sklearn.model_selection import train_test_split, StratifiedShuffleSplit, StratifiedKFold\n"
      ],
      "metadata": {
        "id": "bSFQW6DFzAFQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install catboost"
      ],
      "metadata": {
        "id": "0TsF85Tj40nP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from catboost import CatBoostRegressor\n",
        "# import catboost\n",
        "from sklearn import metrics"
      ],
      "metadata": {
        "id": "4rVkz7A942SW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "pi_8ZeM97ATd",
        "outputId": "f0d8a713-9a85-46a4-de73-950978069b91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           CAL     CNC        GR     HRD     HRM       PE    ZDEN       DTC  \\\n",
              "573    14.8824  0.5173   57.1903  1.9826  1.9726  27.9432  2.3510  125.9611   \n",
              "574    14.9570  0.4966   63.1593  2.0472  1.6373  28.1064  2.4055  125.9726   \n",
              "575    14.6341  0.4433   64.5217  2.0464  1.4866  26.5968  2.4579  125.8528   \n",
              "576    14.2318  0.4309   63.0144  2.0054  1.4613  19.5635  2.4219  125.7362   \n",
              "577    14.0163  0.4469   60.8421  1.9533  1.4438   9.8556  2.3138  125.9774   \n",
              "...        ...     ...       ...     ...     ...      ...     ...       ...   \n",
              "30138   8.6719  0.1879  119.9486  2.5130  2.5187   0.0597  2.6240   74.2133   \n",
              "30139   8.6719  0.1825  122.5286  2.5011  2.4818   0.0582  2.6194   74.3503   \n",
              "30140   8.6797  0.1774  122.0544  2.5010  2.4717   0.0571  2.6169   74.4752   \n",
              "30141   8.6719  0.1744  120.6518  2.5150  2.5007   0.0562  2.6159   74.5917   \n",
              "30142   8.6719  0.1742  120.5270  2.5211  2.5068   0.0552  2.6155   74.7082   \n",
              "\n",
              "            DTS  Diff_CAL_CNC  ...  Diff_GR_HRD  Diff_GR_HRM  Diff_GR_PE  \\\n",
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              "30139  132.2201        8.4894  ...     120.0275     120.0468    122.4704   \n",
              "30140  132.5171        8.5023  ...     119.5534     119.5827    121.9973   \n",
              "30141  132.7115        8.4975  ...     118.1368     118.1511    120.5956   \n",
              "30142  132.9060        8.4977  ...     118.0059     118.0202    120.4718   \n",
              "\n",
              "       Diff_GR_ZDEN  Diff_HRD_HRM  Diff_HRD_PE  Diff_HRD_ZDEN  Diff_HRM_PE  \\\n",
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              "...             ...           ...          ...            ...          ...   \n",
              "30138      117.3246       -0.0057       2.4533        -0.1110       2.4590   \n",
              "30139      119.9092        0.0193       2.4429        -0.1183       2.4236   \n",
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              "       Diff_HRM_ZDEN  Diff_PE_ZDEN  \n",
              "573          -0.3784       25.5922  \n",
              "574          -0.7682       25.7009  \n",
              "575          -0.9713       24.1389  \n",
              "576          -0.9606       17.1416  \n",
              "577          -0.8700        7.5418  \n",
              "...              ...           ...  \n",
              "30138        -0.1053       -2.5643  \n",
              "30139        -0.1376       -2.5612  \n",
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              "      <td>117.4299</td>\n",
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              "      <td>-0.1053</td>\n",
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              "      <th>30139</th>\n",
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              "      <td>2.4818</td>\n",
              "      <td>0.0582</td>\n",
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              "      <td>74.3503</td>\n",
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              "      <td>8.4894</td>\n",
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              "      <th>30140</th>\n",
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              "      <td>2.4717</td>\n",
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              "      <td>2.6169</td>\n",
              "      <td>74.4752</td>\n",
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              "      <td>8.5023</td>\n",
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              "      <td>119.5827</td>\n",
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              "      <td>119.4375</td>\n",
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              "      <td>-0.1159</td>\n",
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              "      <td>-0.1452</td>\n",
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              "      <th>30141</th>\n",
              "      <td>8.6719</td>\n",
              "      <td>0.1744</td>\n",
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              "      <td>74.5917</td>\n",
              "      <td>132.7115</td>\n",
              "      <td>8.4975</td>\n",
              "      <td>...</td>\n",
              "      <td>118.1368</td>\n",
              "      <td>118.1511</td>\n",
              "      <td>120.5956</td>\n",
              "      <td>118.0359</td>\n",
              "      <td>0.0143</td>\n",
              "      <td>2.4588</td>\n",
              "      <td>-0.1009</td>\n",
              "      <td>2.4445</td>\n",
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              "      <td>-2.5597</td>\n",
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              "    <tr>\n",
              "      <th>30142</th>\n",
              "      <td>8.6719</td>\n",
              "      <td>0.1742</td>\n",
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              "      <td>132.9060</td>\n",
              "      <td>8.4977</td>\n",
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              "      <td>120.4718</td>\n",
              "      <td>117.9115</td>\n",
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              "      <td>2.4516</td>\n",
              "      <td>-0.1087</td>\n",
              "      <td>-2.5603</td>\n",
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              "  </tbody>\n",
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              "<p>20525 rows × 30 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 297
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data, val = train_test_split(train, train_size= 0.8, shuffle = True)\n",
        "train_data_dtc, val_dtc = train_test_split(train_dtc, train_size= 0.8, shuffle = True)\n",
        "train_data_dts, val_dts = train_test_split(train_dts, train_size= 0.8, shuffle = True)"
      ],
      "metadata": {
        "id": "d-gNA1D62WNZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_x = train_data.drop(['DTC', 'DTS'], axis = 1)\n",
        "train_y = train_data[['DTC', 'DTS']]\n",
        "val_x = val.drop(['DTC', 'DTS'], axis = 1)\n",
        "val_y = val[['DTC', 'DTS']]\n",
        "\n",
        "train_x_dtc = train_data_dtc.drop(['DTC', 'DTS'], axis = 1)\n",
        "train_y_dtc = train_data_dtc[['DTC', 'DTS']]\n",
        "val_x_dtc = val_dtc.drop(['DTC', 'DTS'], axis = 1)\n",
        "val_y_dtc = val_dtc[['DTC', 'DTS']]\n",
        "\n",
        "train_x_dts = train_data_dts.drop(['DTC', 'DTS'], axis = 1)\n",
        "train_y_dts = train_data_dts[['DTC', 'DTS']]\n",
        "val_x_dts = val_dts.drop(['DTC', 'DTS'], axis = 1)\n",
        "val_y_dts = val_dts[['DTC', 'DTS']]"
      ],
      "metadata": {
        "id": "hqRSFyHL7Jbn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_dtc = lgb.LGBMRegressor(\n",
        "    n_estimators = 1200,\n",
        "    learning_rate= 0.05,\n",
        "    reg_alpha=0.03,\n",
        "    reg_lambda=0.001,\n",
        ")\n",
        "model_dts = lgb.LGBMRegressor(\n",
        "    n_estimators = 1200,\n",
        "    learning_rate= 0.05,\n",
        "    reg_alpha=0.03,\n",
        "    reg_lambda=0.001\n",
        ")\n"
      ],
      "metadata": {
        "id": "OqeMeWRu5aXp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_dtc.fit(\n",
        "        X = train_x_dtc,\n",
        "        y = train_y_dtc['DTC'],\n",
        "        eval_set = (val_x_dtc, val_y_dtc['DTC']),\n",
        "        eval_metric='rmse',\n",
        "        verbose = 200,\n",
        "        early_stopping_rounds=100\n",
        "        )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0c7iLeuM58Ca",
        "outputId": "5fdc4e2e-87fc-44c9-83a5-ea501ec08152"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 100 rounds.\n",
            "[200]\tvalid_0's rmse: 3.75496\tvalid_0's l2: 14.0997\n",
            "[400]\tvalid_0's rmse: 3.55902\tvalid_0's l2: 12.6666\n",
            "[600]\tvalid_0's rmse: 3.45823\tvalid_0's l2: 11.9593\n",
            "[800]\tvalid_0's rmse: 3.38281\tvalid_0's l2: 11.4434\n",
            "[1000]\tvalid_0's rmse: 3.32886\tvalid_0's l2: 11.0813\n",
            "[1200]\tvalid_0's rmse: 3.29734\tvalid_0's l2: 10.8724\n",
            "Did not meet early stopping. Best iteration is:\n",
            "[1199]\tvalid_0's rmse: 3.29729\tvalid_0's l2: 10.8722\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMRegressor(learning_rate=0.05, n_estimators=1200, reg_alpha=0.03,\n",
              "              reg_lambda=0.001)"
            ]
          },
          "metadata": {},
          "execution_count": 301
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# train_x['DTC'] = model_dtc.predict(train_x)\n",
        "# val_x['DTC'] = model_dtc.predict(val_x)\n",
        "# makeNewFeatures_Diff(train, ColA + ['DTC'])\n",
        "\n",
        "# train_data, val = train_test_split(train, train_size= 0.8, shuffle = True)\n",
        "\n",
        "# train_x = train_data.drop(['DTS'], axis = 1)\n",
        "# train_y = train_data[['DTS']]\n",
        "# val_x = val.drop(['DTS'], axis = 1)\n",
        "# val_y = val[['DTS']]"
      ],
      "metadata": {
        "id": "9oBRHGqGfuvT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_dts.fit(\n",
        "        X = train_x_dts,\n",
        "        y = train_y_dts['DTS'],\n",
        "        eval_set = (val_x_dts, val_y_dts['DTS']),\n",
        "        eval_metric='rmse',\n",
        "        verbose = 200,\n",
        "        early_stopping_rounds=100\n",
        "        )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BwBT77lF79T-",
        "outputId": "6be494bb-63e9-4293-8c90-36affaf534f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training until validation scores don't improve for 100 rounds.\n",
            "[200]\tvalid_0's rmse: 9.45762\tvalid_0's l2: 89.4465\n",
            "[400]\tvalid_0's rmse: 8.96593\tvalid_0's l2: 80.3878\n",
            "[600]\tvalid_0's rmse: 8.71828\tvalid_0's l2: 76.0083\n",
            "[800]\tvalid_0's rmse: 8.54225\tvalid_0's l2: 72.9701\n",
            "[1000]\tvalid_0's rmse: 8.44699\tvalid_0's l2: 71.3516\n",
            "[1200]\tvalid_0's rmse: 8.38662\tvalid_0's l2: 70.3354\n",
            "Did not meet early stopping. Best iteration is:\n",
            "[1200]\tvalid_0's rmse: 8.38662\tvalid_0's l2: 70.3354\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMRegressor(learning_rate=0.05, n_estimators=1200, reg_alpha=0.03,\n",
              "              reg_lambda=0.001)"
            ]
          },
          "metadata": {},
          "execution_count": 303
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lgb.plot_importance(model_dtc, figsize = (20,20))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "bPgUBQXm6631",
        "outputId": "3b30273e-7fd1-4be0-d4d6-3c711d5e1b01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a8cc33ee0>"
            ]
          },
          "metadata": {},
          "execution_count": 304
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lgb.plot_importance(model_dts, figsize = (20,20))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "JL8_SFua-4y3",
        "outputId": "335802f3-0699-412f-d193-1f9913ad8178"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6a8c35e0a0>"
            ]
          },
          "metadata": {},
          "execution_count": 305
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bad_cols = [x for x in test.columns.to_list() if x not in train.columns.to_list()]"
      ],
      "metadata": {
        "id": "UAsE2EdxMyna"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "[x for x in train.columns.to_list() if x not in test.columns.to_list()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0m07hmA4fXRD",
        "outputId": "37866e1b-9d1e-438d-da18-191a76aef3c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['DTC', 'DTS']"
            ]
          },
          "metadata": {},
          "execution_count": 307
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission = pd.DataFrame()"
      ],
      "metadata": {
        "id": "qYJzr9p5ZHJL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission['DTC'] = model_dtc.predict(test.drop(bad_cols, axis =1))\n",
        "# test['DTC'] = submission['DTC']\n",
        "\n",
        "# makeNewFeatures_Diff(test, ColA + ['DTC'])\n",
        "submission['DTS'] = model_dts.predict(test.drop(bad_cols, axis =1))"
      ],
      "metadata": {
        "id": "q4YV64zM_UMC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "AKS20a76MYMB",
        "outputId": "45f25865-8385-4a91-b087-f4c4d26cebb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              DTC         DTS\n",
              "0      109.461839  212.197767\n",
              "1      109.732446  217.514073\n",
              "2      115.600926  219.943623\n",
              "3      114.303990  216.778920\n",
              "4      111.002827  213.477069\n",
              "...           ...         ...\n",
              "11083   92.314238  165.290293\n",
              "11084   89.722195  158.175316\n",
              "11085   91.812899  150.582727\n",
              "11086   93.887594  155.403500\n",
              "11087   94.666074  155.195797\n",
              "\n",
              "[11088 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-33f31d3c-ef70-45fe-bb5b-13dd59dee310\">\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>DTC</th>\n",
              "      <th>DTS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>109.461839</td>\n",
              "      <td>212.197767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>109.732446</td>\n",
              "      <td>217.514073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>115.600926</td>\n",
              "      <td>219.943623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>114.303990</td>\n",
              "      <td>216.778920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>111.002827</td>\n",
              "      <td>213.477069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11083</th>\n",
              "      <td>92.314238</td>\n",
              "      <td>165.290293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11084</th>\n",
              "      <td>89.722195</td>\n",
              "      <td>158.175316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11085</th>\n",
              "      <td>91.812899</td>\n",
              "      <td>150.582727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11086</th>\n",
              "      <td>93.887594</td>\n",
              "      <td>155.403500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11087</th>\n",
              "      <td>94.666074</td>\n",
              "      <td>155.195797</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11088 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-33f31d3c-ef70-45fe-bb5b-13dd59dee310')\"\n",
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              "          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",
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              "    </div>\n",
              "  </div>\n",
              "  "
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          "execution_count": 310
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      ]
    },
    {
      "cell_type": "code",
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        "submission.describe()"
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        "colab": {
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          "height": 300
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        "outputId": "db9608ab-db3b-4675-e3e6-894013a7e940"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                DTC           DTS\n",
              "count  11088.000000  11088.000000\n",
              "mean      76.930878    141.955189\n",
              "std       14.581475     30.496042\n",
              "min       55.442495    105.899919\n",
              "25%       65.739845    121.225975\n",
              "50%       71.384379    129.582485\n",
              "75%       85.227901    155.415503\n",
              "max      122.570290    254.901339"
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DTC</th>\n",
              "      <th>DTS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11088.000000</td>\n",
              "      <td>11088.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>76.930878</td>\n",
              "      <td>141.955189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>14.581475</td>\n",
              "      <td>30.496042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>55.442495</td>\n",
              "      <td>105.899919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>65.739845</td>\n",
              "      <td>121.225975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>71.384379</td>\n",
              "      <td>129.582485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>85.227901</td>\n",
              "      <td>155.415503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>122.570290</td>\n",
              "      <td>254.901339</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f3c36c9c-0120-4b84-8a70-d20de8440a5c')\"\n",
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              "  </style>\n",
              "\n",
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              "          const element = document.querySelector('#df-f3c36c9c-0120-4b84-8a70-d20de8440a5c');\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",
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              "  "
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "submission['DTC']"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2LHoM0vKbzaR",
        "outputId": "a72b8dc3-4d63-4cf7-c893-6f489cfa324d"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        109.461839\n",
              "1        109.732446\n",
              "2        115.600926\n",
              "3        114.303990\n",
              "4        111.002827\n",
              "            ...    \n",
              "11083     92.314238\n",
              "11084     89.722195\n",
              "11085     91.812899\n",
              "11086     93.887594\n",
              "11087     94.666074\n",
              "Name: DTC, Length: 11088, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 312
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -O final.csv https://raw.githubusercontent.com/pddasig/Machine-Learning-Competition-2020/master/real_test_result.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H3QBwlm8Nwah",
        "outputId": "eb44e4e7-ccc4-4a65-87a9-0aba40c9ec1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-01-21 06:23:59--  https://raw.githubusercontent.com/pddasig/Machine-Learning-Competition-2020/master/real_test_result.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.111.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 193080 (189K) [text/plain]\n",
            "Saving to: ‘final.csv’\n",
            "\n",
            "final.csv           100%[===================>] 188.55K  --.-KB/s    in 0.03s   \n",
            "\n",
            "2023-01-21 06:23:59 (6.28 MB/s) - ‘final.csv’ saved [193080/193080]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final = pd.read_csv('/content/final.csv')"
      ],
      "metadata": {
        "id": "XkB9b1DganWy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final.columns.to_list()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "36fz6iRBb3Al",
        "outputId": "65235c1c-d005-487a-a485-798d62ecbb0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['DTC    ', 'DTS  ']"
            ]
          },
          "metadata": {},
          "execution_count": 315
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "nXy0IyTTbIwO",
        "outputId": "f81b61fa-0299-4c58-f925-6d56fae8a78c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    DTC         DTS  \n",
              "0  107.0669  261.0363\n",
              "1  107.8095  260.1601\n",
              "2  108.2305  261.0667\n",
              "3  108.4281  262.5100\n",
              "4  108.2227  263.3599"
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b67c45b6-4042-4bea-8872-a1efae7a9b4a');\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": 316
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "RMSE_0 = np.sqrt(mean_squared_error(final['DTC    '], submission['DTC']))\n",
        "RMSE_0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5vWu7ocIaxPo",
        "outputId": "d621d3e6-2649-411b-9698-83f4a94373ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.8147612809083675"
            ]
          },
          "metadata": {},
          "execution_count": 317
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "RMSE_1 = np.sqrt(mean_squared_error(final['DTS  '], submission['DTS']))\n",
        "RMSE_1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n9lmTIsTbQjB",
        "outputId": "c05e15b3-452e-4d37-eb53-e23063fd4c28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22.81602938092548"
            ]
          },
          "metadata": {},
          "execution_count": 318
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "net = np.sqrt(((RMSE_0**2 + RMSE_1**2)/2))\n",
        "net"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d27bUe8xcYzh",
        "outputId": "d665f872-856f-408d-edd2-2ac723b8b5df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16.48867979711216"
            ]
          },
          "metadata": {},
          "execution_count": 319
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission.to_csv('best_oilyMLGO.csv', index = False)"
      ],
      "metadata": {
        "id": "houTtG7hhafm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Q3Nx5SU2mm1X"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}