DCP_PreprocessNew.ipynb

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      "name": "DCP PreprocessNew.ipynb",
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        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/theAkhileshRai/DeepLearning/blob/master/DCP_PreprocessNew.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rd42eWa74LiP",
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          "height": 676
        },
        "outputId": "f80d101e-048c-43a4-ac3a-cab2ec430815"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import xlrd \n",
        "import os \n",
        "import glob\n",
        "import re \n",
        "from plotly.subplots import make_subplots\n",
        "import numpy as np\n",
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings(\"error\")\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
        "!pip install chart_studio\n",
        "import chart_studio.plotly as py\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.offline as py\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", message=\"numpy.ufunc size changed\")\n",
        "from sklearn.metrics import r2_score, median_absolute_error, mean_absolute_error\n",
        "from sklearn.metrics import median_absolute_error, mean_squared_error, mean_squared_log_error\n",
        "import plotly.graph_objs as go\n",
        "py.init_notebook_mode(connected=True)\n",
        "import plotly.graph_objects as go\n",
        "from plotly.offline import  download_plotlyjs,init_notebook_mode,plot,iplot\n",
        "import plotly.io as pio\n",
        "pio.renderers.default = 'colab'\n",
        "from itertools import groupby\n",
        "import plotly.express as px\n",
        "\n",
        "\n",
        "\n",
        "from glob import glob\n",
        "\n",
        "from datetime import datetime as dt\n",
        "import warnings\n",
        "from sklearn.svm import LinearSVR\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.svm import SVC\n",
        "from sklearn import ensemble\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
        "import xgboost as xgb\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from datetime import datetime\n",
        "warnings.filterwarnings('ignore')\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dense\n",
        "from numpy import hstack\n",
        "from numpy import array\n",
        "from random import random\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "pd.options.mode.chained_assignment = None \n",
        "!npm install -g electron@1.8.4 orca\n",
        "!pip install psutil requests"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: chart_studio in /usr/local/lib/python3.6/dist-packages (1.1.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from chart_studio) (1.12.0)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from chart_studio) (4.4.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from chart_studio) (2.23.0)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from chart_studio) (1.3.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->chart_studio) (2.9)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->chart_studio) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->chart_studio) (2020.4.5.1)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->chart_studio) (1.24.3)\n"
          ],
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              "        require.undef(\"plotly\");\n",
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          "text": [
            "\u001b[K\u001b[?25h\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[30;43mWARN\u001b[0m \u001b[0m\u001b[35mdeprecated\u001b[0m request@2.88.2: request has been deprecated, see https://github.com/request/request/issues/3142\n",
            "\u001b[K\u001b[?25h\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[30;43mWARN\u001b[0m \u001b[0m\u001b[35mdeprecated\u001b[0m left-pad@1.3.0: use String.prototype.padStart()\n",
            "\u001b[K\u001b[?25h/tools/node/bin/electron -> /tools/node/lib/node_modules/electron/cli.js\n",
            "/tools/node/bin/orca -> /tools/node/lib/node_modules/orca/bin/orca.js\n",
            "\u001b[K\u001b[?25h\n",
            "> electron@1.8.4 postinstall /tools/node/lib/node_modules/electron\n",
            "> node install.js\n",
            "\n",
            "/tools/node/lib/node_modules/electron/install.js:47\n",
            "  throw err\n",
            "  ^\n",
            "\n",
            "Error: EACCES: permission denied, mkdir '/tools/node/lib/node_modules/electron/.electron'\n",
            "\u001b[K\u001b[?25h\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[31;40mERR!\u001b[0m \u001b[0m\u001b[35mcode\u001b[0m ELIFECYCLE\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[31;40mERR!\u001b[0m \u001b[0m\u001b[35merrno\u001b[0m 1\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[31;40mERR!\u001b[0m\u001b[35m\u001b[0m electron@1.8.4 postinstall: `node install.js`\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[31;40mERR!\u001b[0m\u001b[35m\u001b[0m Exit status 1\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[31;40mERR!\u001b[0m\u001b[35m\u001b[0m \n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[31;40mERR!\u001b[0m\u001b[35m\u001b[0m Failed at the electron@1.8.4 postinstall script.\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[31;40mERR!\u001b[0m\u001b[35m\u001b[0m This is probably not a problem with npm. There is likely additional logging output above.\n",
            "\u001b[0m\n",
            "\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[31;40mERR!\u001b[0m\u001b[35m\u001b[0m A complete log of this run can be found in:\n",
            "\u001b[0m\u001b[37;40mnpm\u001b[0m \u001b[0m\u001b[31;40mERR!\u001b[0m\u001b[35m\u001b[0m     /root/.npm/_logs/2020-06-17T08_31_32_091Z-debug.log\n",
            "\u001b[0mRequirement already satisfied: psutil in /usr/local/lib/python3.6/dist-packages (5.4.8)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (2.23.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests) (2.9)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests) (2020.4.5.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_lmgqzWpenKB",
        "colab_type": "text"
      },
      "source": [
        "# Cross Unit 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qSBiV3Qq1Pmm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cross_df = pd.read_csv('/content/unnormalised_Cross1_df.csv')"
      ],
      "execution_count": 154,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hCa37LBr4bkl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "5ec89b91-4968-4225-e026-53fb76609927"
      },
      "source": [
        "cross_df.head()"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Unnamed: 0</th>\n",
              "      <th>time</th>\n",
              "      <th>cyl</th>\n",
              "      <th>s_temp_crank</th>\n",
              "      <th>d_temp_crank</th>\n",
              "      <th>s_pressure_crank</th>\n",
              "      <th>d_pressure_crank</th>\n",
              "      <th>rpm_crank</th>\n",
              "      <th>s_temp_mean</th>\n",
              "      <th>d_temp_mean</th>\n",
              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
              "      <th>rpm_mean</th>\n",
              "      <th>station</th>\n",
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              "      <th>0</th>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <td>955.180639</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2019-04-05 04:30:00</td>\n",
              "      <td>1.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-04-05 05:30:00</td>\n",
              "      <td>1.0</td>\n",
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              "      <th>4</th>\n",
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              "   Unnamed: 0                 time  cyl  ...    rpm_mean     station  machine\n",
              "0           0  2019-04-05 02:30:00  1.0  ...  956.126375  Cross Barr   Unit 1\n",
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              "3           3  2019-04-05 05:30:00  1.0  ...  955.242250  Cross Barr   Unit 1\n",
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              "\n",
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          "execution_count": 155
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          "height": 34
        },
        "outputId": "800ac8f7-c4dc-4fd7-d4da-ddf2bbd6523d"
      },
      "source": [
        "cross_df['cyl'].unique()"
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      "execution_count": 156,
      "outputs": [
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          "output_type": "execute_result",
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              "array([1., 2., 3., 4.])"
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    {
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      "metadata": {
        "id": "FwxxRHOC52B4",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "cross_df.loc[ (cross_df[\"time\"] > \"2019-06-25 0:00:00\") & (cross_df[\"time\"] < \"2019-06-28 0:00:00\") & (cross_df['cyl'] ==4), \"multi_class\"]=\"pre_1\"\n",
        "cross_df.loc[ (cross_df[\"time\"] > \"2019-06-25 0:00:00\") & (cross_df[\"time\"] < \"2019-06-28 0:00:00\") & (cross_df['cyl'] ==4), \"leak\"]=1\n",
        "cross_df.loc[ (cross_df[\"time\"] > \"2019-06-28 0:00:00\") & (cross_df[\"time\"] < \"2019-12-04 0:00:00\") & (cross_df['cyl'] ==4), \"multi_class\"]=\"1\"\n",
        "cross_df.loc[ (cross_df[\"time\"] > \"2019-06-28 0:00:00\") & (cross_df[\"time\"] < \"2019-12-04 0:00:00\") & (cross_df['cyl'] ==4), \"leak\"]=1"
      ],
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LOD9s64X6ShH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "39d4fb47-8433-49f6-e74a-f81139df61df"
      },
      "source": [
        "cross_df.head()"
      ],
      "execution_count": 158,
      "outputs": [
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          "data": {
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              "      <th>cyl</th>\n",
              "      <th>s_temp_crank</th>\n",
              "      <th>d_temp_crank</th>\n",
              "      <th>s_pressure_crank</th>\n",
              "      <th>d_pressure_crank</th>\n",
              "      <th>rpm_crank</th>\n",
              "      <th>s_temp_mean</th>\n",
              "      <th>d_temp_mean</th>\n",
              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
              "      <th>rpm_mean</th>\n",
              "      <th>station</th>\n",
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              "      <td>333.271093</td>\n",
              "      <td>959.947075</td>\n",
              "      <td>956.126375</td>\n",
              "      <td>100.781695</td>\n",
              "      <td>227.024053</td>\n",
              "      <td>341.150570</td>\n",
              "      <td>963.233788</td>\n",
              "      <td>956.126375</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2019-04-05 03:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>105.290034</td>\n",
              "      <td>236.122937</td>\n",
              "      <td>325.476945</td>\n",
              "      <td>950.197363</td>\n",
              "      <td>957.189625</td>\n",
              "      <td>105.290034</td>\n",
              "      <td>233.113909</td>\n",
              "      <td>335.320018</td>\n",
              "      <td>955.180639</td>\n",
              "      <td>957.189625</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2019-04-05 04:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>102.694591</td>\n",
              "      <td>235.134108</td>\n",
              "      <td>319.202676</td>\n",
              "      <td>946.173800</td>\n",
              "      <td>957.718375</td>\n",
              "      <td>102.694591</td>\n",
              "      <td>231.738927</td>\n",
              "      <td>329.656110</td>\n",
              "      <td>950.421229</td>\n",
              "      <td>957.718375</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-04-05 05:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>102.636799</td>\n",
              "      <td>234.895970</td>\n",
              "      <td>319.710641</td>\n",
              "      <td>946.358515</td>\n",
              "      <td>955.242250</td>\n",
              "      <td>102.636799</td>\n",
              "      <td>231.417846</td>\n",
              "      <td>330.576671</td>\n",
              "      <td>951.068734</td>\n",
              "      <td>955.242250</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-04-05 06:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>103.657053</td>\n",
              "      <td>236.373231</td>\n",
              "      <td>318.983829</td>\n",
              "      <td>946.790184</td>\n",
              "      <td>958.628500</td>\n",
              "      <td>103.657053</td>\n",
              "      <td>232.831218</td>\n",
              "      <td>329.875960</td>\n",
              "      <td>951.185184</td>\n",
              "      <td>958.628500</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                 time  cyl  ...  machine  multi_class  leak\n",
              "0           0  2019-04-05 02:30:00  1.0  ...   Unit 1          NaN   NaN\n",
              "1           1  2019-04-05 03:30:00  1.0  ...   Unit 1          NaN   NaN\n",
              "2           2  2019-04-05 04:30:00  1.0  ...   Unit 1          NaN   NaN\n",
              "3           3  2019-04-05 05:30:00  1.0  ...   Unit 1          NaN   NaN\n",
              "4           4  2019-04-05 06:30:00  1.0  ...   Unit 1          NaN   NaN\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qq2dE-D6-4EJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cross_df = cross_df.fillna(0)"
      ],
      "execution_count": 159,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HRwdJymkCny4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "db8c6021-a23e-4917-fdfe-f262cd560b57"
      },
      "source": [
        "cross_df['multi_class'].unique()"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, '1'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ahv70bMkE_SE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        },
        "outputId": "5e780bfd-b65a-421a-99ab-7f6ef8ef2d01"
      },
      "source": [
        "cross_df"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>time</th>\n",
              "      <th>cyl</th>\n",
              "      <th>s_temp_crank</th>\n",
              "      <th>d_temp_crank</th>\n",
              "      <th>s_pressure_crank</th>\n",
              "      <th>d_pressure_crank</th>\n",
              "      <th>rpm_crank</th>\n",
              "      <th>s_temp_mean</th>\n",
              "      <th>d_temp_mean</th>\n",
              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
              "      <th>rpm_mean</th>\n",
              "      <th>station</th>\n",
              "      <th>machine</th>\n",
              "      <th>multi_class</th>\n",
              "      <th>leak</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2019-04-05 02:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>100.781695</td>\n",
              "      <td>229.474039</td>\n",
              "      <td>333.271093</td>\n",
              "      <td>959.947075</td>\n",
              "      <td>956.126375</td>\n",
              "      <td>100.781695</td>\n",
              "      <td>227.024053</td>\n",
              "      <td>341.150570</td>\n",
              "      <td>963.233788</td>\n",
              "      <td>956.126375</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2019-04-05 03:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>105.290034</td>\n",
              "      <td>236.122937</td>\n",
              "      <td>325.476945</td>\n",
              "      <td>950.197363</td>\n",
              "      <td>957.189625</td>\n",
              "      <td>105.290034</td>\n",
              "      <td>233.113909</td>\n",
              "      <td>335.320018</td>\n",
              "      <td>955.180639</td>\n",
              "      <td>957.189625</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2019-04-05 04:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>102.694591</td>\n",
              "      <td>235.134108</td>\n",
              "      <td>319.202676</td>\n",
              "      <td>946.173800</td>\n",
              "      <td>957.718375</td>\n",
              "      <td>102.694591</td>\n",
              "      <td>231.738927</td>\n",
              "      <td>329.656110</td>\n",
              "      <td>950.421229</td>\n",
              "      <td>957.718375</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-04-05 05:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>102.636799</td>\n",
              "      <td>234.895970</td>\n",
              "      <td>319.710641</td>\n",
              "      <td>946.358515</td>\n",
              "      <td>955.242250</td>\n",
              "      <td>102.636799</td>\n",
              "      <td>231.417846</td>\n",
              "      <td>330.576671</td>\n",
              "      <td>951.068734</td>\n",
              "      <td>955.242250</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-04-05 06:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>103.657053</td>\n",
              "      <td>236.373231</td>\n",
              "      <td>318.983829</td>\n",
              "      <td>946.790184</td>\n",
              "      <td>958.628500</td>\n",
              "      <td>103.657053</td>\n",
              "      <td>232.831218</td>\n",
              "      <td>329.875960</td>\n",
              "      <td>951.185184</td>\n",
              "      <td>958.628500</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13174</th>\n",
              "      <td>13174</td>\n",
              "      <td>2019-12-03 18:30:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>69.468218</td>\n",
              "      <td>234.194264</td>\n",
              "      <td>11.917698</td>\n",
              "      <td>86.638819</td>\n",
              "      <td>960.540375</td>\n",
              "      <td>69.468218</td>\n",
              "      <td>233.583070</td>\n",
              "      <td>11.510854</td>\n",
              "      <td>84.631886</td>\n",
              "      <td>960.081000</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13175</th>\n",
              "      <td>13175</td>\n",
              "      <td>2019-12-03 19:30:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>70.786694</td>\n",
              "      <td>234.678598</td>\n",
              "      <td>12.835046</td>\n",
              "      <td>89.306538</td>\n",
              "      <td>960.961000</td>\n",
              "      <td>70.786694</td>\n",
              "      <td>233.917543</td>\n",
              "      <td>12.484370</td>\n",
              "      <td>87.394472</td>\n",
              "      <td>961.192500</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13176</th>\n",
              "      <td>13176</td>\n",
              "      <td>2019-12-03 20:30:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>70.852788</td>\n",
              "      <td>235.354237</td>\n",
              "      <td>12.095686</td>\n",
              "      <td>86.979135</td>\n",
              "      <td>960.922875</td>\n",
              "      <td>70.852788</td>\n",
              "      <td>234.603897</td>\n",
              "      <td>11.773620</td>\n",
              "      <td>85.190095</td>\n",
              "      <td>961.115500</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13177</th>\n",
              "      <td>13177</td>\n",
              "      <td>2019-12-03 21:30:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>72.151680</td>\n",
              "      <td>238.102493</td>\n",
              "      <td>12.983520</td>\n",
              "      <td>91.361386</td>\n",
              "      <td>961.231250</td>\n",
              "      <td>72.151680</td>\n",
              "      <td>236.731073</td>\n",
              "      <td>12.731325</td>\n",
              "      <td>89.314398</td>\n",
              "      <td>960.885813</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13178</th>\n",
              "      <td>13178</td>\n",
              "      <td>2019-12-03 22:30:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>70.492729</td>\n",
              "      <td>234.548953</td>\n",
              "      <td>14.904049</td>\n",
              "      <td>97.300960</td>\n",
              "      <td>960.770375</td>\n",
              "      <td>70.492729</td>\n",
              "      <td>234.335206</td>\n",
              "      <td>14.321475</td>\n",
              "      <td>94.920884</td>\n",
              "      <td>960.865688</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13179 rows × 17 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Unnamed: 0                 time  cyl  ...  machine  multi_class  leak\n",
              "0               0  2019-04-05 02:30:00  1.0  ...   Unit 1            0   0.0\n",
              "1               1  2019-04-05 03:30:00  1.0  ...   Unit 1            0   0.0\n",
              "2               2  2019-04-05 04:30:00  1.0  ...   Unit 1            0   0.0\n",
              "3               3  2019-04-05 05:30:00  1.0  ...   Unit 1            0   0.0\n",
              "4               4  2019-04-05 06:30:00  1.0  ...   Unit 1            0   0.0\n",
              "...           ...                  ...  ...  ...      ...          ...   ...\n",
              "13174       13174  2019-12-03 18:30:00  4.0  ...   Unit 1            1   1.0\n",
              "13175       13175  2019-12-03 19:30:00  4.0  ...   Unit 1            1   1.0\n",
              "13176       13176  2019-12-03 20:30:00  4.0  ...   Unit 1            1   1.0\n",
              "13177       13177  2019-12-03 21:30:00  4.0  ...   Unit 1            1   1.0\n",
              "13178       13178  2019-12-03 22:30:00  4.0  ...   Unit 1            1   1.0\n",
              "\n",
              "[13179 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-t_Cv_rZ8587",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8fca39a2-3ace-4c1d-95bb-a2553e2bfe04"
      },
      "source": [
        "cross_df['cyl'].unique()"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 2., 3., 4.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gaEY3xZt-xuA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaler = MinMaxScaler()"
      ],
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o8-C32Jt304I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# cyl1 = cross_df.loc[cross_df.cyl==1.0]\n",
        "# cyl2 = cross_df.loc[cross_df.cyl==2.0]\n",
        "# cyl3 = cross_df.loc[cross_df.cyl==3.0]\n",
        "# cyl4 = cross_df.loc[cross_df.cyl==4.0]"
      ],
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O0XLnzmr-Hdw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "cc52e8c0-fe1c-45a6-bb87-a4da7be75bc8"
      },
      "source": [
        "cyl1.columns"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'time', 'cyl', 's_temp_crank', 'd_temp_crank',\n",
              "       's_pressure_crank', 'd_pressure_crank', 'rpm_crank', 's_temp_mean',\n",
              "       'd_temp_mean', 's_pressure_mean', 'd_pressure_mean', 'rpm_mean',\n",
              "       'station', 'machine', 'multi_class', 'leak'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vS0jYEEu-SLI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scale_columns = ['s_temp_crank', 'd_temp_crank',\n",
        "       's_pressure_crank', 'd_pressure_crank', 'rpm_crank', 's_temp_mean',\n",
        "       'd_temp_mean', 's_pressure_mean', 'd_pressure_mean', 'rpm_mean',]"
      ],
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3CJ9bHw7QQ6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# cyl1[scale_columns] = scaler.fit_transform(cyl1[scale_columns])\n",
        "# cyl2[scale_columns] = scaler.fit_transform(cyl2[scale_columns])\n",
        "# cyl3[scale_columns] = scaler.fit_transform(cyl3[scale_columns])\n",
        "# cyl4[scale_columns] = scaler.fit_transform(cyl4[scale_columns])"
      ],
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HSPBlNg2-8Bv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# normalised_cross1 = pd.concat([cyl1,cyl2,cyl3,cyl4], ignore_index=True)\n",
        "# normalised_cross1"
      ],
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qvxqJ3YD1qql",
        "colab_type": "text"
      },
      "source": [
        "# Cross Barr Unit 4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7RyedwAyJTnj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXLyqVBZC3lI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cross_df4 = pd.read_csv('/content/unnormalised_Cross4_df.csv')"
      ],
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bDCDyaW3DB3z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cross_df4.loc[ (cross_df4[\"time\"] > \"2019-03-30 0:00:00\") & (cross_df4[\"time\"] < \"2019-07-19 0:00:00\") & (cross_df4['cyl'] ==3), \"multi_class\"]=\"2\"\n",
        "cross_df4.loc[ (cross_df4[\"time\"] > \"2019-03-30 0:00:00\") & (cross_df4[\"time\"] < \"2019-07-19 0:00:00\") & (cross_df4['cyl'] ==3), \"leak\"]=1"
      ],
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9bJDV8EMOgus",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cross_df4 = cross_df4.fillna(0)"
      ],
      "execution_count": 171,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b6CFKCroOlJ9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        },
        "outputId": "28cfe7ee-8a64-47bf-c0f8-fef6de9dc94b"
      },
      "source": [
        "cross_df4"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>time</th>\n",
              "      <th>cyl</th>\n",
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              "      <th>d_temp_crank</th>\n",
              "      <th>s_pressure_crank</th>\n",
              "      <th>d_pressure_crank</th>\n",
              "      <th>rpm_crank</th>\n",
              "      <th>s_temp_mean</th>\n",
              "      <th>d_temp_mean</th>\n",
              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
              "      <th>rpm_mean</th>\n",
              "      <th>station</th>\n",
              "      <th>machine</th>\n",
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              "      <th>leak</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2019-09-18 12:30:00</td>\n",
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              "      <td>220.642275</td>\n",
              "      <td>947.695277</td>\n",
              "      <td>1887.952950</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 4</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>2019-09-18 13:30:00</td>\n",
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              "      <td>88.735953</td>\n",
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              "      <td>221.623952</td>\n",
              "      <td>948.716069</td>\n",
              "      <td>1716.509625</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 4</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>179.686328</td>\n",
              "      <td>740.734689</td>\n",
              "      <td>1894.732937</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 4</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-09-19 22:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>107.999395</td>\n",
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              "      <td>217.750079</td>\n",
              "      <td>906.799547</td>\n",
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              "      <td>217.806294</td>\n",
              "      <td>914.304280</td>\n",
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              "    <tr>\n",
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              "      <td>Cross Barr</td>\n",
              "      <td>Unit 4</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>358</td>\n",
              "      <td>2019-10-22 13:30:00</td>\n",
              "      <td>4.0</td>\n",
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              "      <td>61.112797</td>\n",
              "      <td>270.264230</td>\n",
              "      <td>3147.116875</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 4</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>359</th>\n",
              "      <td>359</td>\n",
              "      <td>2019-10-22 14:30:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>92.073668</td>\n",
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              "      <td>60.886815</td>\n",
              "      <td>271.841963</td>\n",
              "      <td>2786.828750</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 4</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>360</th>\n",
              "      <td>360</td>\n",
              "      <td>2019-11-01 21:30:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>90.976357</td>\n",
              "      <td>272.314380</td>\n",
              "      <td>48.188655</td>\n",
              "      <td>250.385622</td>\n",
              "      <td>2474.931250</td>\n",
              "      <td>90.976357</td>\n",
              "      <td>261.973639</td>\n",
              "      <td>57.049253</td>\n",
              "      <td>263.179566</td>\n",
              "      <td>1640.352562</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 4</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>361</th>\n",
              "      <td>361</td>\n",
              "      <td>2019-11-01 22:30:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>90.247726</td>\n",
              "      <td>271.338732</td>\n",
              "      <td>48.230119</td>\n",
              "      <td>250.281131</td>\n",
              "      <td>2952.010000</td>\n",
              "      <td>90.247726</td>\n",
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              "      <td>57.023545</td>\n",
              "      <td>262.869409</td>\n",
              "      <td>1768.053000</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 4</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>362</th>\n",
              "      <td>362</td>\n",
              "      <td>2019-11-01 23:30:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>89.182026</td>\n",
              "      <td>267.777127</td>\n",
              "      <td>49.215321</td>\n",
              "      <td>249.732138</td>\n",
              "      <td>2515.271250</td>\n",
              "      <td>89.182026</td>\n",
              "      <td>258.354327</td>\n",
              "      <td>57.807643</td>\n",
              "      <td>263.000853</td>\n",
              "      <td>2139.059062</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 4</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>363 rows × 17 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Unnamed: 0                 time  cyl  ...  machine  multi_class  leak\n",
              "0             0  2019-09-18 12:30:00  2.0  ...   Unit 4            0   0.0\n",
              "1             1  2019-09-18 13:30:00  2.0  ...   Unit 4            0   0.0\n",
              "2             2  2019-09-19 21:30:00  2.0  ...   Unit 4            0   0.0\n",
              "3             3  2019-09-19 22:30:00  2.0  ...   Unit 4            0   0.0\n",
              "4             4  2019-09-19 23:30:00  2.0  ...   Unit 4            0   0.0\n",
              "..          ...                  ...  ...  ...      ...          ...   ...\n",
              "358         358  2019-10-22 13:30:00  4.0  ...   Unit 4            0   0.0\n",
              "359         359  2019-10-22 14:30:00  4.0  ...   Unit 4            0   0.0\n",
              "360         360  2019-11-01 21:30:00  4.0  ...   Unit 4            0   0.0\n",
              "361         361  2019-11-01 22:30:00  4.0  ...   Unit 4            0   0.0\n",
              "362         362  2019-11-01 23:30:00  4.0  ...   Unit 4            0   0.0\n",
              "\n",
              "[363 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BqqLkHCDtT03",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "980547c9-dd5b-4eb7-b746-ff6623e742a4"
      },
      "source": [
        "cross_df4['cyl'].unique()"
      ],
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2., 4.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fYv5bC2XCJD4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# cyl2 = cross_df4.loc[(cross_df4.cyl==2.0) & (cross_df4.station == 'Cross Barr')]\n",
        "\n",
        "# cyl4 = cross_df4.loc[cross_df4.cyl==4.0]"
      ],
      "execution_count": 174,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-PIlbOdtMOI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# scaler = MinMaxScaler()"
      ],
      "execution_count": 175,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FtVlIeFWqtDK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# cyl2[scale_columns] = scaler.fit_transform(cyl2[scale_columns])\n",
        "\n",
        "# cyl4[scale_columns] = scaler.fit_transform(cyl4[scale_columns])"
      ],
      "execution_count": 176,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "686CkNzQqys5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# normalised_cross4 = pd.concat([cyl2,cyl4], ignore_index=True)\n",
        "# normalised_cross4"
      ],
      "execution_count": 177,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PJ-Q5mIOcFOX",
        "colab_type": "text"
      },
      "source": [
        "# CrossBarr Unit 5"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gKRtmmsjJTLS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cross_df5 = pd.read_csv('/content/unnormalised_Cross5_df.csv')"
      ],
      "execution_count": 178,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7QK714DiOLyX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "72dfaf7f-421a-45cc-8983-a24b563e4f60"
      },
      "source": [
        "cross_df5.head()"
      ],
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>time</th>\n",
              "      <th>cyl</th>\n",
              "      <th>s_temp_crank</th>\n",
              "      <th>d_temp_crank</th>\n",
              "      <th>s_pressure_crank</th>\n",
              "      <th>d_pressure_crank</th>\n",
              "      <th>rpm_crank</th>\n",
              "      <th>s_temp_mean</th>\n",
              "      <th>d_temp_mean</th>\n",
              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
              "      <th>rpm_mean</th>\n",
              "      <th>station</th>\n",
              "      <th>machine</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2019-02-20 20:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>76.860068</td>\n",
              "      <td>77.168457</td>\n",
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              "      <td>775.040600</td>\n",
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              "      <td>76.860068</td>\n",
              "      <td>149.231320</td>\n",
              "      <td>399.349857</td>\n",
              "      <td>517.044327</td>\n",
              "      <td>89378.150000</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2019-02-22 06:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>54.422398</td>\n",
              "      <td>200.225056</td>\n",
              "      <td>270.711634</td>\n",
              "      <td>942.805205</td>\n",
              "      <td>1186.24000</td>\n",
              "      <td>54.422398</td>\n",
              "      <td>127.303732</td>\n",
              "      <td>160.565546</td>\n",
              "      <td>496.600721</td>\n",
              "      <td>1186.093125</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2019-02-22 07:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>55.862312</td>\n",
              "      <td>200.100648</td>\n",
              "      <td>269.542329</td>\n",
              "      <td>925.710125</td>\n",
              "      <td>1170.41125</td>\n",
              "      <td>55.862312</td>\n",
              "      <td>128.172733</td>\n",
              "      <td>151.254225</td>\n",
              "      <td>479.419394</td>\n",
              "      <td>1178.560625</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-02-22 08:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>57.811395</td>\n",
              "      <td>202.141609</td>\n",
              "      <td>270.960423</td>\n",
              "      <td>929.033938</td>\n",
              "      <td>1064.72750</td>\n",
              "      <td>57.811395</td>\n",
              "      <td>129.722597</td>\n",
              "      <td>150.794796</td>\n",
              "      <td>479.729550</td>\n",
              "      <td>1125.571875</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-02-22 09:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>59.892773</td>\n",
              "      <td>205.718807</td>\n",
              "      <td>270.089663</td>\n",
              "      <td>935.062910</td>\n",
              "      <td>1062.84375</td>\n",
              "      <td>59.892773</td>\n",
              "      <td>133.296460</td>\n",
              "      <td>144.235075</td>\n",
              "      <td>476.865996</td>\n",
              "      <td>1124.688750</td>\n",
              "      <td>Cross Barr</td>\n",
              "      <td>Unit 5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                 time  cyl  ...      rpm_mean     station  machine\n",
              "0           0  2019-02-20 20:30:00  2.0  ...  89378.150000  Cross Barr   Unit 5\n",
              "1           1  2019-02-22 06:30:00  2.0  ...   1186.093125  Cross Barr   Unit 5\n",
              "2           2  2019-02-22 07:30:00  2.0  ...   1178.560625  Cross Barr   Unit 5\n",
              "3           3  2019-02-22 08:30:00  2.0  ...   1125.571875  Cross Barr   Unit 5\n",
              "4           4  2019-02-22 09:30:00  2.0  ...   1124.688750  Cross Barr   Unit 5\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pL09Jl0UICiX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cross_df5.loc[ (cross_df5[\"time\"] > \"2019-02-22 0:00:00\") & (cross_df5[\"time\"] < \"2019-06-15 0:00:00\") & (cross_df5['cyl'] ==1), \"multi_class\"]=\"1\"\n",
        "cross_df5.loc[ (cross_df5[\"time\"] > \"2019-03-30 0:00:00\") & (cross_df5[\"time\"] < \"2019-06-15 0:00:00\") & (cross_df5['cyl'] ==1), \"leak\"]=1"
      ],
      "execution_count": 180,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7Kmt476OKBf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cross_df5 = cross_df5.fillna(0)"
      ],
      "execution_count": 181,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M8uhOwYeOwV3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "8f6450d0-c317-4e04-aa09-a396059dfab2"
      },
      "source": [
        "cross_df5.head()"
      ],
      "execution_count": 182,
      "outputs": [
        {
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          "data": {
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              "      <th>rpm_mean</th>\n",
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              "      <td>399.349857</td>\n",
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              "      <td>89378.150000</td>\n",
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              "      <td>Unit 5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2019-02-22 06:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>54.422398</td>\n",
              "      <td>200.225056</td>\n",
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              "      <td>160.565546</td>\n",
              "      <td>496.600721</td>\n",
              "      <td>1186.093125</td>\n",
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              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2019-02-22 07:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>55.862312</td>\n",
              "      <td>200.100648</td>\n",
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              "      <td>Unit 5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-02-22 08:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>57.811395</td>\n",
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              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-02-22 09:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>59.892773</td>\n",
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              "      <td>0.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "   Unnamed: 0                 time  cyl  ...  machine  multi_class  leak\n",
              "0           0  2019-02-20 20:30:00  2.0  ...   Unit 5            0   0.0\n",
              "1           1  2019-02-22 06:30:00  2.0  ...   Unit 5            0   0.0\n",
              "2           2  2019-02-22 07:30:00  2.0  ...   Unit 5            0   0.0\n",
              "3           3  2019-02-22 08:30:00  2.0  ...   Unit 5            0   0.0\n",
              "4           4  2019-02-22 09:30:00  2.0  ...   Unit 5            0   0.0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
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          "metadata": {
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          "execution_count": 182
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      "cell_type": "code",
      "metadata": {
        "id": "7g8ZF5TXO9AQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZmayUFHKPLPB",
        "colab_type": "text"
      },
      "source": [
        "# Reagan UNIT 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xYHWC8jMPKvo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reagon_df3 = pd.read_csv('/content/unnormalised_reagon_df3_df.csv')"
      ],
      "execution_count": 183,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R9JKKIuMTWs-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "f3aa7263-94f6-4138-9580-e191679e5806"
      },
      "source": [
        "reagon_df3.head()"
      ],
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\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>Unnamed: 0</th>\n",
              "      <th>time</th>\n",
              "      <th>cyl</th>\n",
              "      <th>s_temp_crank</th>\n",
              "      <th>d_temp_crank</th>\n",
              "      <th>s_pressure_crank</th>\n",
              "      <th>d_pressure_crank</th>\n",
              "      <th>rpm_crank</th>\n",
              "      <th>s_temp_mean</th>\n",
              "      <th>d_temp_mean</th>\n",
              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
              "      <th>rpm_mean</th>\n",
              "      <th>station</th>\n",
              "      <th>machine</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2019-04-24 16:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>67.068308</td>\n",
              "      <td>70.391468</td>\n",
              "      <td>20.764134</td>\n",
              "      <td>21.815709</td>\n",
              "      <td>1163.57875</td>\n",
              "      <td>67.460348</td>\n",
              "      <td>84.985394</td>\n",
              "      <td>19.963125</td>\n",
              "      <td>26.809810</td>\n",
              "      <td>1163.495000</td>\n",
              "      <td>Reagan</td>\n",
              "      <td>Unit 3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2019-04-24 18:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>71.555663</td>\n",
              "      <td>74.983800</td>\n",
              "      <td>20.433032</td>\n",
              "      <td>21.502441</td>\n",
              "      <td>1163.69250</td>\n",
              "      <td>71.565545</td>\n",
              "      <td>46.103862</td>\n",
              "      <td>18.870167</td>\n",
              "      <td>18.251083</td>\n",
              "      <td>1164.031250</td>\n",
              "      <td>Reagan</td>\n",
              "      <td>Unit 3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2019-04-24 19:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>77.317962</td>\n",
              "      <td>80.805322</td>\n",
              "      <td>20.224651</td>\n",
              "      <td>21.298848</td>\n",
              "      <td>1163.86375</td>\n",
              "      <td>77.317962</td>\n",
              "      <td>103.257663</td>\n",
              "      <td>19.141976</td>\n",
              "      <td>30.272669</td>\n",
              "      <td>1163.468750</td>\n",
              "      <td>Reagan</td>\n",
              "      <td>Unit 3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-04-24 20:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>80.424756</td>\n",
              "      <td>83.879016</td>\n",
              "      <td>20.146296</td>\n",
              "      <td>21.203918</td>\n",
              "      <td>1163.97500</td>\n",
              "      <td>80.424756</td>\n",
              "      <td>136.206616</td>\n",
              "      <td>18.860494</td>\n",
              "      <td>42.868381</td>\n",
              "      <td>1164.059375</td>\n",
              "      <td>Reagan</td>\n",
              "      <td>Unit 3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-04-24 21:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>78.738923</td>\n",
              "      <td>82.270082</td>\n",
              "      <td>19.976940</td>\n",
              "      <td>21.054990</td>\n",
              "      <td>1163.91750</td>\n",
              "      <td>78.738923</td>\n",
              "      <td>135.666382</td>\n",
              "      <td>18.927882</td>\n",
              "      <td>43.979829</td>\n",
              "      <td>1163.861875</td>\n",
              "      <td>Reagan</td>\n",
              "      <td>Unit 3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                 time  cyl  ...     rpm_mean  station  machine\n",
              "0           0  2019-04-24 16:30:00  1.0  ...  1163.495000   Reagan   Unit 3\n",
              "1           1  2019-04-24 18:30:00  1.0  ...  1164.031250   Reagan   Unit 3\n",
              "2           2  2019-04-24 19:30:00  1.0  ...  1163.468750   Reagan   Unit 3\n",
              "3           3  2019-04-24 20:30:00  1.0  ...  1164.059375   Reagan   Unit 3\n",
              "4           4  2019-04-24 21:30:00  1.0  ...  1163.861875   Reagan   Unit 3\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lL-2eaazUDui",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reagon_df3.loc[ (reagon_df3[\"time\"] > \"2019-04-18 0:00:00\") & (reagon_df3[\"time\"] < \"2019-06-21 0:00:00\") & (reagon_df3['cyl'] ==2), \"multi_class\"]=\"1\"\n",
        "reagon_df3.loc[ (reagon_df3[\"time\"] > \"2019-04-18 0:00:00\") & (reagon_df3[\"time\"] < \"2019-06-21 0:00:00\") & (reagon_df3['cyl'] ==2), \"leak\"]=1\n",
        "\n",
        "reagon_df3.loc[ (reagon_df3[\"time\"] > \"2019-04-18 0:00:00\") & (reagon_df3[\"time\"] < \"2019-06-21 0:00:00\") & (reagon_df3['cyl'] ==3), \"multi_class\"]=\"1\"\n",
        "reagon_df3.loc[ (reagon_df3[\"time\"] > \"2019-04-18 0:00:00\") & (reagon_df3[\"time\"] < \"2019-06-21 0:00:00\") & (reagon_df3['cyl'] ==3), \"leak\"]=1"
      ],
      "execution_count": 185,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i9soK4lwWriF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reagon_df3 = reagon_df3.fillna(0)"
      ],
      "execution_count": 186,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-SlikUc4Wucm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "11d05eaa-bef9-48bc-f2b7-1f8ce9704b84"
      },
      "source": [
        "reagon_df3.head()"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>s_pressure_crank</th>\n",
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              "      <th>d_temp_mean</th>\n",
              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
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              "      <th>machine</th>\n",
              "      <th>multi_class</th>\n",
              "      <th>leak</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2019-04-24 16:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>67.068308</td>\n",
              "      <td>70.391468</td>\n",
              "      <td>20.764134</td>\n",
              "      <td>21.815709</td>\n",
              "      <td>1163.57875</td>\n",
              "      <td>67.460348</td>\n",
              "      <td>84.985394</td>\n",
              "      <td>19.963125</td>\n",
              "      <td>26.809810</td>\n",
              "      <td>1163.495000</td>\n",
              "      <td>Reagan</td>\n",
              "      <td>Unit 3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2019-04-24 18:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>71.555663</td>\n",
              "      <td>74.983800</td>\n",
              "      <td>20.433032</td>\n",
              "      <td>21.502441</td>\n",
              "      <td>1163.69250</td>\n",
              "      <td>71.565545</td>\n",
              "      <td>46.103862</td>\n",
              "      <td>18.870167</td>\n",
              "      <td>18.251083</td>\n",
              "      <td>1164.031250</td>\n",
              "      <td>Reagan</td>\n",
              "      <td>Unit 3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2019-04-24 19:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>77.317962</td>\n",
              "      <td>80.805322</td>\n",
              "      <td>20.224651</td>\n",
              "      <td>21.298848</td>\n",
              "      <td>1163.86375</td>\n",
              "      <td>77.317962</td>\n",
              "      <td>103.257663</td>\n",
              "      <td>19.141976</td>\n",
              "      <td>30.272669</td>\n",
              "      <td>1163.468750</td>\n",
              "      <td>Reagan</td>\n",
              "      <td>Unit 3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-04-24 20:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>80.424756</td>\n",
              "      <td>83.879016</td>\n",
              "      <td>20.146296</td>\n",
              "      <td>21.203918</td>\n",
              "      <td>1163.97500</td>\n",
              "      <td>80.424756</td>\n",
              "      <td>136.206616</td>\n",
              "      <td>18.860494</td>\n",
              "      <td>42.868381</td>\n",
              "      <td>1164.059375</td>\n",
              "      <td>Reagan</td>\n",
              "      <td>Unit 3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-04-24 21:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>78.738923</td>\n",
              "      <td>82.270082</td>\n",
              "      <td>19.976940</td>\n",
              "      <td>21.054990</td>\n",
              "      <td>1163.91750</td>\n",
              "      <td>78.738923</td>\n",
              "      <td>135.666382</td>\n",
              "      <td>18.927882</td>\n",
              "      <td>43.979829</td>\n",
              "      <td>1163.861875</td>\n",
              "      <td>Reagan</td>\n",
              "      <td>Unit 3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                 time  cyl  ...  machine  multi_class  leak\n",
              "0           0  2019-04-24 16:30:00  1.0  ...   Unit 3            0   0.0\n",
              "1           1  2019-04-24 18:30:00  1.0  ...   Unit 3            0   0.0\n",
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              "3           3  2019-04-24 20:30:00  1.0  ...   Unit 3            0   0.0\n",
              "4           4  2019-04-24 21:30:00  1.0  ...   Unit 3            0   0.0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 187
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rCXyjFITY3j_",
        "colab_type": "text"
      },
      "source": [
        "# Reagon Unit 6"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "35jRwBiVW0H5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reagon_df6 = pd.read_csv('/content/unnormalised_reagon_df6_df.csv')"
      ],
      "execution_count": 188,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dAmXWwE7Z2gh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reagon_df6.loc[ (reagon_df6[\"time\"] > \"2019-04-04 0:00:00\") & (reagon_df6[\"time\"] < \"2020-05-06 0:00:00\") & (reagon_df6['cyl'] ==2), \"multi_class\"]=\"1\"\n",
        "reagon_df6.loc[ (reagon_df6[\"time\"] > \"2019-04-04 0:00:00\") & (reagon_df6[\"time\"] < \"2020-05-06 0:00:00\") & (reagon_df6['cyl'] ==2), \"leak\"]=1\n",
        "\n"
      ],
      "execution_count": 189,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61mrWwOcbgsr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "0236151a-fcf9-4b18-f65f-0b91babbd6bc"
      },
      "source": [
        "reagon_df6 = reagon_df6.fillna(0)\n",
        "reagon_df6.head()"
      ],
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>machine</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-01-29 22:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>63.441923</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-01-29 23:30:00</td>\n",
              "      <td>1.0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   Unnamed: 0                 time  cyl  ...  machine  multi_class  leak\n",
              "0           0  2019-01-29 19:30:00  1.0  ...   Unit 6            0   0.0\n",
              "1           1  2019-01-29 20:30:00  1.0  ...   Unit 6            0   0.0\n",
              "2           2  2019-01-29 21:30:00  1.0  ...   Unit 6            0   0.0\n",
              "3           3  2019-01-29 22:30:00  1.0  ...   Unit 6            0   0.0\n",
              "4           4  2019-01-29 23:30:00  1.0  ...   Unit 6            0   0.0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IDtiIsaKlUK9",
        "colab_type": "text"
      },
      "source": [
        "# West Turkey Track"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y2szj8Jvb0RG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "turkey_df2 = pd.read_csv('/content/unnormalised_west_turkey2_df.csv')"
      ],
      "execution_count": 191,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ozx3RJH3mOWc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Cyl 1\n",
        "turkey_df2.loc[ (turkey_df2[\"time\"] > \"2019-04-11 0:00:00\") & (turkey_df2[\"time\"] < \"2019-04-24 0:00:00\") & (turkey_df2['cyl'] ==1), \"multi_class\"]=\"pre_1\"\n",
        "turkey_df2.loc[ (turkey_df2[\"time\"] > \"2019-04-11 0:00:00\") & (turkey_df2[\"time\"] < \"2019-04-24 0:00:00\") & (turkey_df2['cyl'] ==1), \"leak\"]=1\n",
        "\n",
        "turkey_df2.loc[ (turkey_df2[\"time\"] > \"2019-04-24 0:00:00\") & (turkey_df2[\"time\"] < \"2019-06-09 0:00:00\") & (turkey_df2['cyl'] ==1), \"multi_class\"]=\"2\"\n",
        "turkey_df2.loc[ (turkey_df2[\"time\"] > \"2019-04-24 0:00:00\") & (turkey_df2[\"time\"] < \"2019-06-09 0:00:00\") & (turkey_df2['cyl'] ==1), \"leak\"]=1\n",
        "\n",
        "# Cyl 4\n",
        "turkey_df2.loc[ (turkey_df2[\"time\"] > \"2019-04-27 0:00:00\") & (turkey_df2[\"time\"] < \"2019-05-10 0:00:00\") & (turkey_df2['cyl'] ==4), \"multi_class\"]=\"pre_1\"\n",
        "turkey_df2.loc[ (turkey_df2[\"time\"] > \"2019-04-27 0:00:00\") & (turkey_df2[\"time\"] < \"2019-05-10 0:00:00\") & (turkey_df2['cyl'] ==4), \"leak\"]=1\n",
        "\n",
        "turkey_df2.loc[ (turkey_df2[\"time\"] > \"2019-05-10 0:00:00\") & (turkey_df2[\"time\"] < \"2019-05-23 0:00:00\") & (turkey_df2['cyl'] ==4), \"multi_class\"]=\"2\"\n",
        "turkey_df2.loc[ (turkey_df2[\"time\"] > \"2019-05-10 0:00:00\") & (turkey_df2[\"time\"] < \"2019-05-23 0:00:00\") & (turkey_df2['cyl'] ==4), \"leak\"]=1\n",
        "\n",
        "turkey_df2.loc[ (turkey_df2[\"time\"] > \"2019-05-23 0:00:00\") & (turkey_df2[\"time\"] < \"2019-06-09 0:00:00\") & (turkey_df2['cyl'] ==4), \"multi_class\"]=\"3\"\n",
        "turkey_df2.loc[ (turkey_df2[\"time\"] > \"2019-05-23 0:00:00\") & (turkey_df2[\"time\"] < \"2019-06-09 0:00:00\") & (turkey_df2['cyl'] ==4), \"leak\"]=1"
      ],
      "execution_count": 192,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mh2iaomOwu3U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "c51f2427-271b-4d4b-cf2c-d828b6c7926e"
      },
      "source": [
        "turkey_df2 = turkey_df2.fillna(0)\n",
        "turkey_df2.head()"
      ],
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <tbody>\n",
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              "    <tr>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>1152.13125</td>\n",
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              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-01-01 03:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>59.612113</td>\n",
              "      <td>69.365860</td>\n",
              "      <td>107.897634</td>\n",
              "      <td>117.815998</td>\n",
              "      <td>1151.63125</td>\n",
              "      <td>59.612113</td>\n",
              "      <td>161.399138</td>\n",
              "      <td>101.750428</td>\n",
              "      <td>279.490785</td>\n",
              "      <td>1151.63125</td>\n",
              "      <td>West Turkey Track</td>\n",
              "      <td>Unit 2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-01-01 07:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>55.314189</td>\n",
              "      <td>66.512616</td>\n",
              "      <td>112.050268</td>\n",
              "      <td>123.957754</td>\n",
              "      <td>1152.40875</td>\n",
              "      <td>55.314189</td>\n",
              "      <td>153.976086</td>\n",
              "      <td>106.750482</td>\n",
              "      <td>283.539875</td>\n",
              "      <td>1152.40875</td>\n",
              "      <td>West Turkey Track</td>\n",
              "      <td>Unit 2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                 time  cyl  ...  machine  multi_class  leak\n",
              "0           0  2019-01-01 00:30:00  1.0  ...   Unit 2            0   0.0\n",
              "1           1  2019-01-01 01:30:00  1.0  ...   Unit 2            0   0.0\n",
              "2           2  2019-01-01 02:30:00  1.0  ...   Unit 2            0   0.0\n",
              "3           3  2019-01-01 03:30:00  1.0  ...   Unit 2            0   0.0\n",
              "4           4  2019-01-01 07:30:00  1.0  ...   Unit 2            0   0.0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XTg-qD8LTu5v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9e0d286d-2f65-4526-cc23-791dc3c2f0b7"
      },
      "source": [
        "turkey_df2['station'].unique()"
      ],
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['West Turkey Track'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lpag9DRbxlfC",
        "colab_type": "text"
      },
      "source": [
        "# Hobit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7hRxojhdw8gT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hobit_df102 = pd.read_csv('/content/unnormalised_hobit_df102_df.csv')"
      ],
      "execution_count": 195,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "49nnUTiLzhKK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hobit_df102.loc[ (hobit_df102[\"time\"] > \"2019-03-25 0:00:00\") & (hobit_df102[\"time\"] < \"2019-03-26 0:00:00\") & (hobit_df102['cyl'] ==3), \"multi_class\"]=\"pre_1\"\n",
        "hobit_df102.loc[ (hobit_df102[\"time\"] > \"2019-03-25 0:00:00\") & (hobit_df102[\"time\"] < \"2019-03-26 0:00:00\") & (hobit_df102['cyl'] ==3), \"leak\"]=1\n",
        "\n",
        "hobit_df102.loc[ (hobit_df102[\"time\"] > \"2019-05-26 0:00:00\") & (hobit_df102[\"time\"] < \"2019-05-27 0:00:00\") & (hobit_df102['cyl'] ==3), \"multi_class\"]=\"1\"\n",
        "hobit_df102.loc[ (hobit_df102[\"time\"] > \"2019-05-26 0:00:00\") & (hobit_df102[\"time\"] < \"2019-05-27 0:00:00\") & (hobit_df102['cyl'] ==3), \"leak\"]=1\n",
        "\n",
        "hobit_df102.loc[ (hobit_df102[\"time\"] > \"2019-05-27 0:00:00\") & (hobit_df102[\"time\"] < \"2019-05-28 0:00:00\") & (hobit_df102['cyl'] ==3), \"multi_class\"]=\"2\"\n",
        "hobit_df102.loc[ (hobit_df102[\"time\"] > \"2019-05-27 0:00:00\") & (hobit_df102[\"time\"] < \"2019-05-28 0:00:00\") & (hobit_df102['cyl'] ==3), \"leak\"]=1\n",
        "\n",
        "hobit_df102.loc[ (turkey_df2[\"time\"] > \"2019-05-28 0:00:00\") & (hobit_df102[\"time\"] < \"2019-05-29 0:00:00\") & (hobit_df102['cyl'] ==3), \"multi_class\"]=\"3\"\n",
        "hobit_df102.loc[ (turkey_df2[\"time\"] > \"2019-05-28 0:00:00\") & (hobit_df102[\"time\"] < \"2019-05-29 0:00:00\") & (hobit_df102['cyl'] ==3), \"leak\"]=1"
      ],
      "execution_count": 196,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vSBDMRxrAOW4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "b8540425-c880-466a-ab9f-247d5688c7f6"
      },
      "source": [
        "hobit_df102 = hobit_df102.fillna(0)\n",
        "hobit_df102.head()"
      ],
      "execution_count": 197,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>time</th>\n",
              "      <th>cyl</th>\n",
              "      <th>s_temp_crank</th>\n",
              "      <th>d_temp_crank</th>\n",
              "      <th>s_pressure_crank</th>\n",
              "      <th>d_pressure_crank</th>\n",
              "      <th>rpm_crank</th>\n",
              "      <th>s_temp_mean</th>\n",
              "      <th>d_temp_mean</th>\n",
              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
              "      <th>rpm_mean</th>\n",
              "      <th>station</th>\n",
              "      <th>machine</th>\n",
              "      <th>multi_class</th>\n",
              "      <th>leak</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>2019-02-23 04:30:00</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>44.214429</td>\n",
              "      <td>174.244891</td>\n",
              "      <td>67.024913</td>\n",
              "      <td>222.744594</td>\n",
              "      <td>944.473250</td>\n",
              "      <td>Hobit LP</td>\n",
              "      <td>unit 102</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2019-02-23 05:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>45.326534</td>\n",
              "      <td>171.168598</td>\n",
              "      <td>66.450997</td>\n",
              "      <td>213.205321</td>\n",
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              "      <td>45.326534</td>\n",
              "      <td>174.286477</td>\n",
              "      <td>66.755155</td>\n",
              "      <td>219.735024</td>\n",
              "      <td>944.808063</td>\n",
              "      <td>Hobit LP</td>\n",
              "      <td>unit 102</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2019-02-23 06:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>45.474899</td>\n",
              "      <td>173.177438</td>\n",
              "      <td>66.491188</td>\n",
              "      <td>216.592279</td>\n",
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              "      <td>45.474899</td>\n",
              "      <td>175.749128</td>\n",
              "      <td>66.863127</td>\n",
              "      <td>222.385598</td>\n",
              "      <td>944.677875</td>\n",
              "      <td>Hobit LP</td>\n",
              "      <td>unit 102</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-02-23 07:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>45.289602</td>\n",
              "      <td>171.762360</td>\n",
              "      <td>66.233011</td>\n",
              "      <td>213.716226</td>\n",
              "      <td>944.176875</td>\n",
              "      <td>45.289602</td>\n",
              "      <td>174.207460</td>\n",
              "      <td>66.847118</td>\n",
              "      <td>219.920994</td>\n",
              "      <td>944.194875</td>\n",
              "      <td>Hobit LP</td>\n",
              "      <td>unit 102</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-02-23 08:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>46.675768</td>\n",
              "      <td>173.767152</td>\n",
              "      <td>66.224155</td>\n",
              "      <td>214.447160</td>\n",
              "      <td>943.953625</td>\n",
              "      <td>46.675768</td>\n",
              "      <td>176.276767</td>\n",
              "      <td>66.706109</td>\n",
              "      <td>220.396816</td>\n",
              "      <td>944.325000</td>\n",
              "      <td>Hobit LP</td>\n",
              "      <td>unit 102</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                 time  cyl  ...   machine  multi_class  leak\n",
              "0           0  2019-02-23 04:30:00  1.0  ...  unit 102            0   0.0\n",
              "1           1  2019-02-23 05:30:00  1.0  ...  unit 102            0   0.0\n",
              "2           2  2019-02-23 06:30:00  1.0  ...  unit 102            0   0.0\n",
              "3           3  2019-02-23 07:30:00  1.0  ...  unit 102            0   0.0\n",
              "4           4  2019-02-23 08:30:00  1.0  ...  unit 102            0   0.0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V8pjFTytCcFv",
        "colab_type": "text"
      },
      "source": [
        "# Hobit 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1jsrTCgqCE3l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hobit_df2 = pd.read_csv('/content/unnormalised_hobit_df2_df.csv')"
      ],
      "execution_count": 198,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S7FG4fIlCj91",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "b751a765-3e48-412b-c727-d2fb33be1361"
      },
      "source": [
        "hobit_df2.head()"
      ],
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "      <th>time</th>\n",
              "      <th>cyl</th>\n",
              "      <th>s_temp_crank</th>\n",
              "      <th>d_temp_crank</th>\n",
              "      <th>s_pressure_crank</th>\n",
              "      <th>d_pressure_crank</th>\n",
              "      <th>rpm_crank</th>\n",
              "      <th>s_temp_mean</th>\n",
              "      <th>d_temp_mean</th>\n",
              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
              "      <th>rpm_mean</th>\n",
              "      <th>station</th>\n",
              "      <th>machine</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2019-02-22 20:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>50.640029</td>\n",
              "      <td>183.845890</td>\n",
              "      <td>186.345355</td>\n",
              "      <td>560.763605</td>\n",
              "      <td>861.822000</td>\n",
              "      <td>50.640029</td>\n",
              "      <td>182.791927</td>\n",
              "      <td>177.146345</td>\n",
              "      <td>530.354554</td>\n",
              "      <td>861.822000</td>\n",
              "      <td>Hobit LP</td>\n",
              "      <td>Unit 2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2019-02-22 21:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>51.138342</td>\n",
              "      <td>187.128627</td>\n",
              "      <td>190.874015</td>\n",
              "      <td>585.733904</td>\n",
              "      <td>903.887500</td>\n",
              "      <td>51.138342</td>\n",
              "      <td>187.798994</td>\n",
              "      <td>180.399786</td>\n",
              "      <td>557.842592</td>\n",
              "      <td>903.887500</td>\n",
              "      <td>Hobit LP</td>\n",
              "      <td>Unit 2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2019-02-22 22:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>50.575318</td>\n",
              "      <td>186.352043</td>\n",
              "      <td>192.165582</td>\n",
              "      <td>588.913776</td>\n",
              "      <td>902.528750</td>\n",
              "      <td>50.575318</td>\n",
              "      <td>186.476177</td>\n",
              "      <td>181.568736</td>\n",
              "      <td>558.493826</td>\n",
              "      <td>902.528750</td>\n",
              "      <td>Hobit LP</td>\n",
              "      <td>Unit 2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-02-22 23:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>48.884332</td>\n",
              "      <td>184.334976</td>\n",
              "      <td>191.732335</td>\n",
              "      <td>587.292505</td>\n",
              "      <td>903.514375</td>\n",
              "      <td>48.884332</td>\n",
              "      <td>184.024408</td>\n",
              "      <td>181.526502</td>\n",
              "      <td>556.277180</td>\n",
              "      <td>903.514375</td>\n",
              "      <td>Hobit LP</td>\n",
              "      <td>Unit 2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-02-23 00:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>49.028440</td>\n",
              "      <td>184.422949</td>\n",
              "      <td>192.590655</td>\n",
              "      <td>589.447841</td>\n",
              "      <td>903.143625</td>\n",
              "      <td>49.028440</td>\n",
              "      <td>184.363702</td>\n",
              "      <td>181.839857</td>\n",
              "      <td>557.875290</td>\n",
              "      <td>903.143625</td>\n",
              "      <td>Hobit LP</td>\n",
              "      <td>Unit 2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                 time  cyl  ...    rpm_mean   station  machine\n",
              "0           0  2019-02-22 20:30:00  1.0  ...  861.822000  Hobit LP   Unit 2\n",
              "1           1  2019-02-22 21:30:00  1.0  ...  903.887500  Hobit LP   Unit 2\n",
              "2           2  2019-02-22 22:30:00  1.0  ...  902.528750  Hobit LP   Unit 2\n",
              "3           3  2019-02-22 23:30:00  1.0  ...  903.514375  Hobit LP   Unit 2\n",
              "4           4  2019-02-23 00:30:00  1.0  ...  903.143625  Hobit LP   Unit 2\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LXKksIPqC4mW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hobit_df2.loc[ (hobit_df2[\"time\"] > \"2019-12-22 0:00:00\") & (hobit_df2[\"time\"] < \"2020-03-01 0:00:00\") & (hobit_df2['cyl'] ==3), \"multi_class\"]=\"3\"\n",
        "hobit_df2.loc[ (hobit_df2[\"time\"] > \"2019-12-22 0:00:00\") & (hobit_df2[\"time\"] < \"2020-03-01 0:00:00\") & (hobit_df2['cyl'] ==3), \"leak\"]=1"
      ],
      "execution_count": 200,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-qAF2mmtFH4P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "8577a8e9-6312-45f7-d9cc-704cc1b3dcf2"
      },
      "source": [
        "hobit_df2 = hobit_df2.fillna(0)\n",
        "hobit_df2.head()"
      ],
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>time</th>\n",
              "      <th>cyl</th>\n",
              "      <th>s_temp_crank</th>\n",
              "      <th>d_temp_crank</th>\n",
              "      <th>s_pressure_crank</th>\n",
              "      <th>d_pressure_crank</th>\n",
              "      <th>rpm_crank</th>\n",
              "      <th>s_temp_mean</th>\n",
              "      <th>d_temp_mean</th>\n",
              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
              "      <th>rpm_mean</th>\n",
              "      <th>station</th>\n",
              "      <th>machine</th>\n",
              "      <th>multi_class</th>\n",
              "      <th>leak</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2019-02-22 20:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>50.640029</td>\n",
              "      <td>183.845890</td>\n",
              "      <td>186.345355</td>\n",
              "      <td>560.763605</td>\n",
              "      <td>861.822000</td>\n",
              "      <td>50.640029</td>\n",
              "      <td>182.791927</td>\n",
              "      <td>177.146345</td>\n",
              "      <td>530.354554</td>\n",
              "      <td>861.822000</td>\n",
              "      <td>Hobit LP</td>\n",
              "      <td>Unit 2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2019-02-22 21:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>51.138342</td>\n",
              "      <td>187.128627</td>\n",
              "      <td>190.874015</td>\n",
              "      <td>585.733904</td>\n",
              "      <td>903.887500</td>\n",
              "      <td>51.138342</td>\n",
              "      <td>187.798994</td>\n",
              "      <td>180.399786</td>\n",
              "      <td>557.842592</td>\n",
              "      <td>903.887500</td>\n",
              "      <td>Hobit LP</td>\n",
              "      <td>Unit 2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2019-02-22 22:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>50.575318</td>\n",
              "      <td>186.352043</td>\n",
              "      <td>192.165582</td>\n",
              "      <td>588.913776</td>\n",
              "      <td>902.528750</td>\n",
              "      <td>50.575318</td>\n",
              "      <td>186.476177</td>\n",
              "      <td>181.568736</td>\n",
              "      <td>558.493826</td>\n",
              "      <td>902.528750</td>\n",
              "      <td>Hobit LP</td>\n",
              "      <td>Unit 2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-02-22 23:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>48.884332</td>\n",
              "      <td>184.334976</td>\n",
              "      <td>191.732335</td>\n",
              "      <td>587.292505</td>\n",
              "      <td>903.514375</td>\n",
              "      <td>48.884332</td>\n",
              "      <td>184.024408</td>\n",
              "      <td>181.526502</td>\n",
              "      <td>556.277180</td>\n",
              "      <td>903.514375</td>\n",
              "      <td>Hobit LP</td>\n",
              "      <td>Unit 2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-02-23 00:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>49.028440</td>\n",
              "      <td>184.422949</td>\n",
              "      <td>192.590655</td>\n",
              "      <td>589.447841</td>\n",
              "      <td>903.143625</td>\n",
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              "      <td>184.363702</td>\n",
              "      <td>181.839857</td>\n",
              "      <td>557.875290</td>\n",
              "      <td>903.143625</td>\n",
              "      <td>Hobit LP</td>\n",
              "      <td>Unit 2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                 time  cyl  ...  machine  multi_class  leak\n",
              "0           0  2019-02-22 20:30:00  1.0  ...   Unit 2            0   0.0\n",
              "1           1  2019-02-22 21:30:00  1.0  ...   Unit 2            0   0.0\n",
              "2           2  2019-02-22 22:30:00  1.0  ...   Unit 2            0   0.0\n",
              "3           3  2019-02-22 23:30:00  1.0  ...   Unit 2            0   0.0\n",
              "4           4  2019-02-23 00:30:00  1.0  ...   Unit 2            0   0.0\n",
              "\n",
              "[5 rows x 17 columns]"
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WZDPFO4BJDm0",
        "colab_type": "text"
      },
      "source": [
        "# Katy 16"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8Ytl2kMJB0a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "katy16 = pd.read_csv('/content/unnormalised_katy_df16_df.csv')"
      ],
      "execution_count": 202,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vlbwHRWpJZZz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "c6494ccb-b751-40bd-ea32-802a212cc635"
      },
      "source": [
        "katy16.head()"
      ],
      "execution_count": 203,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   Unnamed: 0                 time  cyl  ...   rpm_mean  station  machine\n",
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              "2           2  2019-01-10 10:30:00  1.0  ...  920.88100     Katy   C-1116\n",
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              "4           4  2019-01-10 12:30:00  1.0  ...  921.87200     Katy   C-1116\n",
              "\n",
              "[5 rows x 15 columns]"
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          "metadata": {
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          "execution_count": 203
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9h-QNQr6KCcS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "katy16.loc[ (katy16[\"time\"] > \"2019-01-10 0:00:00\") & (katy16[\"time\"] < \"2019-01-19 0:00:00\") & (katy16['cyl'] ==1), \"multi_class\"]=\"pre_1\"\n",
        "katy16.loc[ (katy16[\"time\"] > \"2019-01-10 0:00:00\") & (katy16[\"time\"] < \"2019-01-19 0:00:00\") & (katy16['cyl'] ==1), \"leak\"]=1\n",
        "\n",
        "katy16.loc[ (katy16[\"time\"] > \"2019-01-19 0:00:00\") & (katy16[\"time\"] < \"2019-01-21 0:00:00\") & (katy16['cyl'] ==1), \"multi_class\"]=\"1\"\n",
        "katy16.loc[ (katy16[\"time\"] > \"2019-01-19 0:00:00\") & (katy16[\"time\"] < \"2019-01-21 0:00:00\") & (katy16['cyl'] ==1), \"leak\"]=1\n",
        "\n",
        "\n",
        "katy16.loc[ (katy16[\"time\"] > \"2019-01-21 0:00:00\") & (katy16[\"time\"] < \"2019-02-13 0:00:00\") & (katy16['cyl'] ==1), \"multi_class\"]=\"2\"\n",
        "katy16.loc[ (katy16[\"time\"] > \"2019-01-21 0:00:00\") & (katy16[\"time\"] < \"2019-02-13 0:00:00\") & (katy16['cyl'] ==1), \"leak\"]=1\n",
        "\n",
        "\n",
        "katy16.loc[ (katy16[\"time\"] > \"2019-02-13 0:00:00\") & (katy16[\"time\"] < \"2019-03-20 0:00:00\") & (katy16['cyl'] ==1), \"multi_class\"]=\"3\"\n",
        "katy16.loc[ (katy16[\"time\"] > \"2019-02-13 0:00:00\") & (katy16[\"time\"] < \"2019-03-20 0:00:00\") & (katy16['cyl'] ==1), \"leak\"]=1"
      ],
      "execution_count": 204,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tghSIX59UfWB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "65093956-9f6e-4b4f-d517-02cb0a665857"
      },
      "source": [
        "katy16.head()"
      ],
      "execution_count": 205,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>Katy</td>\n",
              "      <td>C-1116</td>\n",
              "      <td>pre_1</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                 time  cyl  ...  machine  multi_class  leak\n",
              "0           0  2019-01-10 08:30:00  1.0  ...   C-1116          NaN   NaN\n",
              "1           1  2019-01-10 09:30:00  1.0  ...   C-1116          NaN   NaN\n",
              "2           2  2019-01-10 10:30:00  1.0  ...   C-1116        pre_1   1.0\n",
              "3           3  2019-01-10 11:30:00  1.0  ...   C-1116        pre_1   1.0\n",
              "4           4  2019-01-10 12:30:00  1.0  ...   C-1116        pre_1   1.0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Z9NjkLZWdmj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "katy16.loc[ (katy16[\"time\"] > \"2019-10-12 0:00:00\") & (katy16[\"time\"] < \"2019-12-19 0:00:00\") & (katy16['cyl'] ==2), \"multi_class\"]=\"pre_1\"\n",
        "katy16.loc[ (katy16[\"time\"] > \"2019-10-12 0:00:00\") & (katy16[\"time\"] < \"2019-12-19 0:00:00\") & (katy16['cyl'] ==2), \"leak\"]=1\n",
        "\n",
        "\n",
        "katy16.loc[ (katy16[\"time\"] > \"2019-12-19 0:00:00\") & (katy16[\"time\"] < \"2019-01-06 0:00:00\") & (katy16['cyl'] ==2), \"multi_class\"]=\"1\"\n",
        "katy16.loc[ (katy16[\"time\"] > \"2019-12-19 0:00:00\") & (katy16[\"time\"] < \"2019-01-06 0:00:00\") & (katy16['cyl'] ==2), \"leak\"]=1"
      ],
      "execution_count": 206,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7BhTB7JSPwR_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "0b512c24-dc7c-4227-c9b9-0e098469013f"
      },
      "source": [
        "katy16 = katy16.fillna(0)\n",
        "katy16.head()"
      ],
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>rpm_mean</th>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <td>C-1116</td>\n",
              "      <td>pre_1</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-01-10 11:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>56.192569</td>\n",
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              "      <td>4</td>\n",
              "      <td>2019-01-10 12:30:00</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                 time  cyl  ...  machine  multi_class  leak\n",
              "0           0  2019-01-10 08:30:00  1.0  ...   C-1116            0   0.0\n",
              "1           1  2019-01-10 09:30:00  1.0  ...   C-1116            0   0.0\n",
              "2           2  2019-01-10 10:30:00  1.0  ...   C-1116        pre_1   1.0\n",
              "3           3  2019-01-10 11:30:00  1.0  ...   C-1116        pre_1   1.0\n",
              "4           4  2019-01-10 12:30:00  1.0  ...   C-1116        pre_1   1.0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z9Sh1z_yU6pl",
        "colab_type": "text"
      },
      "source": [
        "# Katy 17"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S5IA52ExQPVa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "katy17 = pd.read_csv('/content/unnormalised_katy_df17_df.csv')\n",
        "katy17['machine']= 'C-1117'"
      ],
      "execution_count": 208,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IT9D8SLiYhAP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "f999f670-5e51-42ce-9bdc-7dd20f44c37c"
      },
      "source": [
        "katy17 = katy17.fillna(0)\n",
        "katy17.head()"
      ],
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>Unnamed: 0</th>\n",
              "      <th>time</th>\n",
              "      <th>cyl</th>\n",
              "      <th>s_temp_crank</th>\n",
              "      <th>d_temp_crank</th>\n",
              "      <th>s_pressure_crank</th>\n",
              "      <th>d_pressure_crank</th>\n",
              "      <th>rpm_crank</th>\n",
              "      <th>s_temp_mean</th>\n",
              "      <th>d_temp_mean</th>\n",
              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
              "      <th>rpm_mean</th>\n",
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              "      <th>0</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2019-01-01 01:30:00</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>Katy</td>\n",
              "      <td>C-1117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2019-01-01 02:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>62.718960</td>\n",
              "      <td>90.105728</td>\n",
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              "      <td>713.207592</td>\n",
              "      <td>871.786069</td>\n",
              "      <td>782.83175</td>\n",
              "      <td>Katy</td>\n",
              "      <td>C-1117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-01-01 03:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>67.049133</td>\n",
              "      <td>92.769281</td>\n",
              "      <td>726.709101</td>\n",
              "      <td>895.771798</td>\n",
              "      <td>814.71975</td>\n",
              "      <td>67.049133</td>\n",
              "      <td>91.310056</td>\n",
              "      <td>723.884366</td>\n",
              "      <td>882.037770</td>\n",
              "      <td>814.71975</td>\n",
              "      <td>Katy</td>\n",
              "      <td>C-1117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-01-01 04:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>64.021577</td>\n",
              "      <td>89.330696</td>\n",
              "      <td>725.771761</td>\n",
              "      <td>892.378482</td>\n",
              "      <td>804.23700</td>\n",
              "      <td>64.021577</td>\n",
              "      <td>87.835753</td>\n",
              "      <td>722.038751</td>\n",
              "      <td>877.314739</td>\n",
              "      <td>804.23700</td>\n",
              "      <td>Katy</td>\n",
              "      <td>C-1117</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                 time  cyl  ...   rpm_mean  station  machine\n",
              "0           0  2019-01-01 00:30:00  1.0  ...  870.26175     Katy   C-1117\n",
              "1           1  2019-01-01 01:30:00  1.0  ...  847.99725     Katy   C-1117\n",
              "2           2  2019-01-01 02:30:00  1.0  ...  782.83175     Katy   C-1117\n",
              "3           3  2019-01-01 03:30:00  1.0  ...  814.71975     Katy   C-1117\n",
              "4           4  2019-01-01 04:30:00  1.0  ...  804.23700     Katy   C-1117\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 209
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a2gd8n-SY8ho",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "katy17.loc[ (katy17[\"time\"] > \"2018-08-27 0:00:00\") & (katy17[\"time\"] < \"2019-06-02 0:00:00\") & (katy17['cyl'] ==1), \"multi_class\"]=\"3\"\n",
        "katy17.loc[ (katy17[\"time\"] > \"2018-08-27 0:00:00\") & (katy17[\"time\"] < \"2019-06-02 0:00:00\") & (katy17['cyl'] ==1), \"leak\"]=1\n",
        "\n",
        "\n",
        "katy17.loc[ (katy17[\"time\"] > \"2018-08-27 0:00:00\") & (katy17[\"time\"] < \"2019-06-02 0:00:00\") & (katy17['cyl'] ==2), \"multi_class\"]=\"3\"\n",
        "katy17.loc[ (katy17[\"time\"] > \"2018-08-27 0:00:00\") & (katy17[\"time\"] < \"2019-06-02 0:00:00\") & (katy17['cyl'] ==2), \"leak\"]=1"
      ],
      "execution_count": 210,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jvCLOcCPlxTf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "06094b6c-e063-491b-ab2a-5a4afa89ae40"
      },
      "source": [
        "katy17 = katy17.fillna(0)\n",
        "katy17.head()"
      ],
      "execution_count": 211,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>d_pressure_crank</th>\n",
              "      <th>rpm_crank</th>\n",
              "      <th>s_temp_mean</th>\n",
              "      <th>d_temp_mean</th>\n",
              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
              "      <th>rpm_mean</th>\n",
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              "      <th>multi_class</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <td>868.228355</td>\n",
              "      <td>847.99725</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2019-01-01 02:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>62.718960</td>\n",
              "      <td>90.105728</td>\n",
              "      <td>714.892442</td>\n",
              "      <td>894.136902</td>\n",
              "      <td>782.83175</td>\n",
              "      <td>62.718960</td>\n",
              "      <td>87.211628</td>\n",
              "      <td>713.207592</td>\n",
              "      <td>871.786069</td>\n",
              "      <td>782.83175</td>\n",
              "      <td>Katy</td>\n",
              "      <td>C-1117</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-01-01 03:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>67.049133</td>\n",
              "      <td>92.769281</td>\n",
              "      <td>726.709101</td>\n",
              "      <td>895.771798</td>\n",
              "      <td>814.71975</td>\n",
              "      <td>67.049133</td>\n",
              "      <td>91.310056</td>\n",
              "      <td>723.884366</td>\n",
              "      <td>882.037770</td>\n",
              "      <td>814.71975</td>\n",
              "      <td>Katy</td>\n",
              "      <td>C-1117</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-01-01 04:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>64.021577</td>\n",
              "      <td>89.330696</td>\n",
              "      <td>725.771761</td>\n",
              "      <td>892.378482</td>\n",
              "      <td>804.23700</td>\n",
              "      <td>64.021577</td>\n",
              "      <td>87.835753</td>\n",
              "      <td>722.038751</td>\n",
              "      <td>877.314739</td>\n",
              "      <td>804.23700</td>\n",
              "      <td>Katy</td>\n",
              "      <td>C-1117</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                 time  cyl  ...  machine  multi_class  leak\n",
              "0           0  2019-01-01 00:30:00  1.0  ...   C-1117            3   1.0\n",
              "1           1  2019-01-01 01:30:00  1.0  ...   C-1117            3   1.0\n",
              "2           2  2019-01-01 02:30:00  1.0  ...   C-1117            3   1.0\n",
              "3           3  2019-01-01 03:30:00  1.0  ...   C-1117            3   1.0\n",
              "4           4  2019-01-01 04:30:00  1.0  ...   C-1117            3   1.0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iPdgb9RNUONv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1d64bda1-ca88-43b5-a9db-cc77003a226a"
      },
      "source": [
        "katy17['multi_class'].unique()"
      ],
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['3', 0], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9oo07d2anbmA",
        "colab_type": "text"
      },
      "source": [
        "# Goliad"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cz9TdbO1na5n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "goliad_df = pd.read_csv('/content/unnormalised_goliad3_df.csv')"
      ],
      "execution_count": 213,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OMrMuWQssy-D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "55ba1e2a-6140-43e8-bcb1-5fe203eddd30"
      },
      "source": [
        "goliad_df.head()"
      ],
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>time</th>\n",
              "      <th>cyl</th>\n",
              "      <th>s_temp_crank</th>\n",
              "      <th>d_temp_crank</th>\n",
              "      <th>s_pressure_crank</th>\n",
              "      <th>d_pressure_crank</th>\n",
              "      <th>rpm_crank</th>\n",
              "      <th>s_temp_mean</th>\n",
              "      <th>d_temp_mean</th>\n",
              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
              "      <th>rpm_mean</th>\n",
              "      <th>station</th>\n",
              "      <th>machine</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>01/01/19 0:30</td>\n",
              "      <td>1</td>\n",
              "      <td>58.698929</td>\n",
              "      <td>129.865698</td>\n",
              "      <td>520.763490</td>\n",
              "      <td>963.504010</td>\n",
              "      <td>849.924375</td>\n",
              "      <td>58.698929</td>\n",
              "      <td>129.275902</td>\n",
              "      <td>522.702283</td>\n",
              "      <td>962.304606</td>\n",
              "      <td>849.924375</td>\n",
              "      <td>Goliad</td>\n",
              "      <td>unit 1100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>01/01/19 1:30</td>\n",
              "      <td>1</td>\n",
              "      <td>58.051611</td>\n",
              "      <td>128.758957</td>\n",
              "      <td>521.981967</td>\n",
              "      <td>962.444854</td>\n",
              "      <td>846.270375</td>\n",
              "      <td>58.051611</td>\n",
              "      <td>128.325610</td>\n",
              "      <td>523.642508</td>\n",
              "      <td>961.984840</td>\n",
              "      <td>846.270375</td>\n",
              "      <td>Goliad</td>\n",
              "      <td>unit 1100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>01/01/19 2:30</td>\n",
              "      <td>1</td>\n",
              "      <td>57.415043</td>\n",
              "      <td>128.049603</td>\n",
              "      <td>520.491969</td>\n",
              "      <td>959.543396</td>\n",
              "      <td>849.385375</td>\n",
              "      <td>57.415043</td>\n",
              "      <td>127.925926</td>\n",
              "      <td>522.036945</td>\n",
              "      <td>961.362137</td>\n",
              "      <td>849.385375</td>\n",
              "      <td>Goliad</td>\n",
              "      <td>unit 1100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>01/01/19 3:30</td>\n",
              "      <td>1</td>\n",
              "      <td>56.263346</td>\n",
              "      <td>126.684411</td>\n",
              "      <td>522.533985</td>\n",
              "      <td>962.263092</td>\n",
              "      <td>851.701000</td>\n",
              "      <td>56.263346</td>\n",
              "      <td>126.420956</td>\n",
              "      <td>524.055399</td>\n",
              "      <td>962.907113</td>\n",
              "      <td>851.701000</td>\n",
              "      <td>Goliad</td>\n",
              "      <td>unit 1100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>01/01/19 7:30</td>\n",
              "      <td>1</td>\n",
              "      <td>65.296416</td>\n",
              "      <td>137.483740</td>\n",
              "      <td>531.240603</td>\n",
              "      <td>986.845826</td>\n",
              "      <td>804.600000</td>\n",
              "      <td>65.296416</td>\n",
              "      <td>137.573636</td>\n",
              "      <td>534.782716</td>\n",
              "      <td>994.087129</td>\n",
              "      <td>804.600000</td>\n",
              "      <td>Goliad</td>\n",
              "      <td>unit 1100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0           time  cyl  ...    rpm_mean  station    machine\n",
              "0           0  01/01/19 0:30    1  ...  849.924375   Goliad  unit 1100\n",
              "1           1  01/01/19 1:30    1  ...  846.270375   Goliad  unit 1100\n",
              "2           2  01/01/19 2:30    1  ...  849.385375   Goliad  unit 1100\n",
              "3           3  01/01/19 3:30    1  ...  851.701000   Goliad  unit 1100\n",
              "4           4  01/01/19 7:30    1  ...  804.600000   Goliad  unit 1100\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYyDAAX5s-MK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-04-26 0:00:00\") & (goliad_df[\"time\"] < \"2019-04-26 0:00:00\") & (goliad_df['cyl'] ==4), \"multi_class\"]=\"pre_1\"\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-04-26 0:00:00\") & (goliad_df[\"time\"] < \"2019-04-26 0:00:00\") & (goliad_df['cyl'] ==4), \"leak\"]=1\n",
        "\n",
        "\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-04-27 0:00:00\") & (goliad_df[\"time\"] < \"2019-07-11 0:00:00\") & (goliad_df['cyl'] ==4), \"multi_class\"]=\"1\"\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-04-27 0:00:00\") & (goliad_df[\"time\"] < \"2019-07-11 0:00:00\") & (goliad_df['cyl'] ==4), \"leak\"]=1"
      ],
      "execution_count": 215,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Os6CTvhd8Hxt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-05-27 0:00:00\") & (goliad_df[\"time\"] < \"2019-07-11 0:00:00\") & (goliad_df['cyl'] ==1), \"multi_class\"]=\"1\"\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-05-27 0:00:00\") & (goliad_df[\"time\"] < \"2019-07-11 0:00:00\") & (goliad_df['cyl'] ==1), \"leak\"]=1\n",
        "\n",
        "\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-07-19 0:00:00\") & (goliad_df[\"time\"] < \"2019-07-31 0:00:00\") & (goliad_df['cyl'] ==1), \"multi_class\"]=\"pre_1\"\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-07-19 0:00:00\") & (goliad_df[\"time\"] < \"2019-07-31 0:00:00\") & (goliad_df['cyl'] ==1), \"leak\"]=1\n",
        "\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-07-31 0:00:00\") & (goliad_df[\"time\"] < \"2019-08-04 0:00:00\") & (goliad_df['cyl'] ==1), \"multi_class\"]=\"1\"\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-07-31 0:00:00\") & (goliad_df[\"time\"] < \"2019-08-04 0:00:00\") & (goliad_df['cyl'] ==1), \"leak\"]=1\n",
        "\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-08-04 0:00:00\") & (goliad_df[\"time\"] < \"2019-08-09 0:00:00\") & (goliad_df['cyl'] ==1), \"multi_class\"]=\"2\"\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-08-04 0:00:00\") & (goliad_df[\"time\"] < \"2019-08-09 0:00:00\") & (goliad_df['cyl'] ==1), \"leak\"]=1"
      ],
      "execution_count": 216,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "THReTMOICQ6_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-08-10 0:00:00\") & (goliad_df[\"time\"] < \"2019-08-11 0:00:00\") & (goliad_df['cyl'] ==4), \"multi_class\"]=\"pre_1\"\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-08-10 0:00:00\") & (goliad_df[\"time\"] < \"2019-08-11 0:00:00\") & (goliad_df['cyl'] ==4), \"leak\"]=1\n",
        "\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-08-11 0:00:00\") & (goliad_df[\"time\"] < \"2019-08-12 0:00:00\") & (goliad_df['cyl'] ==4), \"multi_class\"]=\"1\"\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-08-11 0:00:00\") & (goliad_df[\"time\"] < \"2019-08-12 0:00:00\") & (goliad_df['cyl'] ==4), \"leak\"]=1\n",
        "\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-08-12 0:00:00\") & (goliad_df[\"time\"] < \"2019-08-27 0:00:00\") & (goliad_df['cyl'] ==4), \"multi_class\"]=\"2\"\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-08-12 0:00:00\") & (goliad_df[\"time\"] < \"2019-08-27 0:00:00\") & (goliad_df['cyl'] ==4), \"leak\"]=1"
      ],
      "execution_count": 217,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h7p_FbY-FcoM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-08-12 0:00:00\") & (goliad_df[\"time\"] < \"2019-08-23 0:00:00\") & (goliad_df['cyl'] ==2), \"multi_class\"]=\"pre_1\"\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-08-12 0:00:00\") & (goliad_df[\"time\"] < \"2019-08-23 0:00:00\") & (goliad_df['cyl'] ==2), \"leak\"]=1\n",
        "\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-08-23 0:00:00\") & (goliad_df[\"time\"] < \"2019-08-24 0:00:00\") & (goliad_df['cyl'] ==2), \"multi_class\"]=\"1\"\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-08-23 0:00:00\") & (goliad_df[\"time\"] < \"2019-08-24 0:00:00\") & (goliad_df['cyl'] ==2), \"leak\"]=1\n",
        "\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-08-24 0:00:00\") & (goliad_df[\"time\"] < \"2019-08-27 0:00:00\") & (goliad_df['cyl'] ==2), \"multi_class\"]=\"2\"\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-08-24 0:00:00\") & (goliad_df[\"time\"] < \"2019-08-27 0:00:00\") & (goliad_df['cyl'] ==2), \"leak\"]=1"
      ],
      "execution_count": 218,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G2HNJwqLGa4v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-09-11 0:00:00\") & (goliad_df[\"time\"] < \"2019-12-02 0:00:00\") & (goliad_df['cyl'] ==3), \"multi_class\"]=\"pre_1\"\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-09-11 0:00:00\") & (goliad_df[\"time\"] < \"2019-12-02 0:00:00\") & (goliad_df['cyl'] ==3), \"leak\"]=1\n",
        "\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-12-02 0:00:00\") & (goliad_df[\"time\"] < \"2019-12-16 0:00:00\") & (goliad_df['cyl'] ==3), \"multi_class\"]=\"1\"\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-12-02 0:00:00\") & (goliad_df[\"time\"] < \"2019-12-16 0:00:00\") & (goliad_df['cyl'] ==3), \"leak\"]=1\n",
        "\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-12-16 0:00:00\") & (goliad_df[\"time\"] < \"2019-12-27 0:00:00\") & (goliad_df['cyl'] ==3), \"multi_class\"]=\"2\"\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-12-16 0:00:00\") & (goliad_df[\"time\"] < \"2019-12-27 0:00:00\") & (goliad_df['cyl'] ==3), \"leak\"]=1"
      ],
      "execution_count": 219,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1qqCxkF1KoiK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-09-11 0:00:00\") & (goliad_df[\"time\"] < \"2019-12-02 0:00:00\") & (goliad_df['cyl'] ==3), \"multi_class\"]=\"pre_1\"\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-09-11 0:00:00\") & (goliad_df[\"time\"] < \"2019-12-02 0:00:00\") & (goliad_df['cyl'] ==3), \"leak\"]=1\n",
        "\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-12-02 0:00:00\") & (goliad_df[\"time\"] < \"2019-12-16 0:00:00\") & (goliad_df['cyl'] ==3), \"multi_class\"]=\"1\"\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-12-02 0:00:00\") & (goliad_df[\"time\"] < \"2019-12-16 0:00:00\") & (goliad_df['cyl'] ==3), \"leak\"]=1\n",
        "\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-12-16 0:00:00\") & (goliad_df[\"time\"] < \"2019-12-19 0:00:00\") & (goliad_df['cyl'] ==3), \"multi_class\"]=\"2\"\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-12-16 0:00:00\") & (goliad_df[\"time\"] < \"2019-12-19 0:00:00\") & (goliad_df['cyl'] ==3), \"leak\"]=1"
      ],
      "execution_count": 220,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VyuaQ3M_LYdg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-12-24 0:00:00\") & (goliad_df[\"time\"] < \"2020-01-01 0:00:00\") & (goliad_df['cyl'] ==1), \"multi_class\"]=\"pre_1\"\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2019-12-24 0:00:00\") & (goliad_df[\"time\"] < \"2020-01-01 0:00:00\") & (goliad_df['cyl'] ==1), \"leak\"]=1\n",
        "\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2020-01-01 0:00:00\") & (goliad_df[\"time\"] < \"2020-02-07 0:00:00\") & (goliad_df['cyl'] ==1), \"multi_class\"]=\"1\"\n",
        "goliad_df.loc[ (goliad_df[\"time\"] > \"2020-01-01 0:00:00\") & (goliad_df[\"time\"] < \"2020-02-07 0:00:00\") & (goliad_df['cyl'] ==1), \"leak\"]=1\n"
      ],
      "execution_count": 221,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QnIhudto6QqM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "6afc9fd4-3766-4e9a-dcbe-ff65da7968d8"
      },
      "source": [
        "goliad_df = goliad_df.fillna(0)\n",
        "goliad_df.head()"
      ],
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>time</th>\n",
              "      <th>cyl</th>\n",
              "      <th>s_temp_crank</th>\n",
              "      <th>d_temp_crank</th>\n",
              "      <th>s_pressure_crank</th>\n",
              "      <th>d_pressure_crank</th>\n",
              "      <th>rpm_crank</th>\n",
              "      <th>s_temp_mean</th>\n",
              "      <th>d_temp_mean</th>\n",
              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
              "      <th>rpm_mean</th>\n",
              "      <th>station</th>\n",
              "      <th>machine</th>\n",
              "      <th>multi_class</th>\n",
              "      <th>leak</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>01/01/19 0:30</td>\n",
              "      <td>1</td>\n",
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              "      <td>963.504010</td>\n",
              "      <td>849.924375</td>\n",
              "      <td>58.698929</td>\n",
              "      <td>129.275902</td>\n",
              "      <td>522.702283</td>\n",
              "      <td>962.304606</td>\n",
              "      <td>849.924375</td>\n",
              "      <td>Goliad</td>\n",
              "      <td>unit 1100</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>01/01/19 1:30</td>\n",
              "      <td>1</td>\n",
              "      <td>58.051611</td>\n",
              "      <td>128.758957</td>\n",
              "      <td>521.981967</td>\n",
              "      <td>962.444854</td>\n",
              "      <td>846.270375</td>\n",
              "      <td>58.051611</td>\n",
              "      <td>128.325610</td>\n",
              "      <td>523.642508</td>\n",
              "      <td>961.984840</td>\n",
              "      <td>846.270375</td>\n",
              "      <td>Goliad</td>\n",
              "      <td>unit 1100</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>01/01/19 2:30</td>\n",
              "      <td>1</td>\n",
              "      <td>57.415043</td>\n",
              "      <td>128.049603</td>\n",
              "      <td>520.491969</td>\n",
              "      <td>959.543396</td>\n",
              "      <td>849.385375</td>\n",
              "      <td>57.415043</td>\n",
              "      <td>127.925926</td>\n",
              "      <td>522.036945</td>\n",
              "      <td>961.362137</td>\n",
              "      <td>849.385375</td>\n",
              "      <td>Goliad</td>\n",
              "      <td>unit 1100</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>01/01/19 3:30</td>\n",
              "      <td>1</td>\n",
              "      <td>56.263346</td>\n",
              "      <td>126.684411</td>\n",
              "      <td>522.533985</td>\n",
              "      <td>962.263092</td>\n",
              "      <td>851.701000</td>\n",
              "      <td>56.263346</td>\n",
              "      <td>126.420956</td>\n",
              "      <td>524.055399</td>\n",
              "      <td>962.907113</td>\n",
              "      <td>851.701000</td>\n",
              "      <td>Goliad</td>\n",
              "      <td>unit 1100</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>01/01/19 7:30</td>\n",
              "      <td>1</td>\n",
              "      <td>65.296416</td>\n",
              "      <td>137.483740</td>\n",
              "      <td>531.240603</td>\n",
              "      <td>986.845826</td>\n",
              "      <td>804.600000</td>\n",
              "      <td>65.296416</td>\n",
              "      <td>137.573636</td>\n",
              "      <td>534.782716</td>\n",
              "      <td>994.087129</td>\n",
              "      <td>804.600000</td>\n",
              "      <td>Goliad</td>\n",
              "      <td>unit 1100</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0           time  cyl  ...    machine  multi_class  leak\n",
              "0           0  01/01/19 0:30    1  ...  unit 1100            0   0.0\n",
              "1           1  01/01/19 1:30    1  ...  unit 1100            0   0.0\n",
              "2           2  01/01/19 2:30    1  ...  unit 1100            0   0.0\n",
              "3           3  01/01/19 3:30    1  ...  unit 1100            0   0.0\n",
              "4           4  01/01/19 7:30    1  ...  unit 1100            0   0.0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d4xoI1iYSGDV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5c1e93b5-e62a-4280-a579-94ec2a2b1f3b"
      },
      "source": [
        "goliad_df['multi_class'].unique()"
      ],
      "execution_count": 223,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 223
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NZR2z2pO1ZMM",
        "colab_type": "text"
      },
      "source": [
        "# Lighting 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mc1EL7-vTXpL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lighting_df2 = pd.read_csv('/content/unnormalised_lightning_df2_df.csv')"
      ],
      "execution_count": 224,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jB6ULbAEUX8_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "379f3aa0-04ff-4940-a546-0fa6418a3493"
      },
      "source": [
        "lighting_df2.head()"
      ],
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>time</th>\n",
              "      <th>cyl</th>\n",
              "      <th>s_temp_crank</th>\n",
              "      <th>d_temp_crank</th>\n",
              "      <th>s_pressure_crank</th>\n",
              "      <th>d_pressure_crank</th>\n",
              "      <th>rpm_crank</th>\n",
              "      <th>s_temp_mean</th>\n",
              "      <th>d_temp_mean</th>\n",
              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
              "      <th>rpm_mean</th>\n",
              "      <th>station</th>\n",
              "      <th>machine</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2019-01-01 00:30:00</td>\n",
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              "      <td>68.089715</td>\n",
              "      <td>204.941507</td>\n",
              "      <td>266.400316</td>\n",
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              "      <td>203.544910</td>\n",
              "      <td>280.540890</td>\n",
              "      <td>903.529728</td>\n",
              "      <td>1095.10625</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>Unit 2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2019-01-01 01:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>67.034445</td>\n",
              "      <td>201.785386</td>\n",
              "      <td>271.893564</td>\n",
              "      <td>873.009268</td>\n",
              "      <td>1099.30625</td>\n",
              "      <td>67.034445</td>\n",
              "      <td>200.788049</td>\n",
              "      <td>285.984770</td>\n",
              "      <td>908.956779</td>\n",
              "      <td>1099.30625</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>Unit 2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2019-01-01 02:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>65.589635</td>\n",
              "      <td>194.116914</td>\n",
              "      <td>290.491619</td>\n",
              "      <td>886.026560</td>\n",
              "      <td>1097.34625</td>\n",
              "      <td>65.589635</td>\n",
              "      <td>192.720773</td>\n",
              "      <td>305.612809</td>\n",
              "      <td>920.744373</td>\n",
              "      <td>1097.34625</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>Unit 2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-01-01 03:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>64.556183</td>\n",
              "      <td>189.472267</td>\n",
              "      <td>300.252905</td>\n",
              "      <td>891.113648</td>\n",
              "      <td>1092.95250</td>\n",
              "      <td>64.556183</td>\n",
              "      <td>188.680634</td>\n",
              "      <td>316.594817</td>\n",
              "      <td>931.756675</td>\n",
              "      <td>1092.95250</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>Unit 2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-01-01 04:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>56.654270</td>\n",
              "      <td>185.580377</td>\n",
              "      <td>286.851893</td>\n",
              "      <td>888.180775</td>\n",
              "      <td>1090.30500</td>\n",
              "      <td>56.654270</td>\n",
              "      <td>184.180057</td>\n",
              "      <td>301.712783</td>\n",
              "      <td>921.538740</td>\n",
              "      <td>1090.30500</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>Unit 2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                 time  cyl  ...    rpm_mean    station  machine\n",
              "0           0  2019-01-01 00:30:00  1.0  ...  1095.10625  Lightning   Unit 2\n",
              "1           1  2019-01-01 01:30:00  1.0  ...  1099.30625  Lightning   Unit 2\n",
              "2           2  2019-01-01 02:30:00  1.0  ...  1097.34625  Lightning   Unit 2\n",
              "3           3  2019-01-01 03:30:00  1.0  ...  1092.95250  Lightning   Unit 2\n",
              "4           4  2019-01-01 04:30:00  1.0  ...  1090.30500  Lightning   Unit 2\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 225
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzyOzfoVVFDf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lighting_df2.loc[ (lighting_df2[\"time\"] > \"2019-11-12 0:00:00\") & (lighting_df2[\"time\"] < \"2019-11-14 0:00:00\") & (lighting_df2['cyl'] ==1), \"multi_class\"]=\"pre_1\"\n",
        "lighting_df2.loc[ (lighting_df2[\"time\"] > \"2019-11-12 0:00:00\") & (lighting_df2[\"time\"] < \"2019-11-14 0:00:00\") & (lighting_df2['cyl'] ==1), \"leak\"]=1\n",
        "\n",
        "lighting_df2.loc[ (lighting_df2[\"time\"] > \"2019-11-14 0:00:00\") & (lighting_df2[\"time\"] < \"2019-11-15 0:00:00\") & (lighting_df2['cyl'] ==1), \"multi_class\"]=\"1\"\n",
        "lighting_df2.loc[ (lighting_df2[\"time\"] > \"2019-11-14 0:00:00\") & (lighting_df2[\"time\"] < \"2019-11-15 0:00:00\") & (lighting_df2['cyl'] ==1), \"leak\"]=1\n",
        "\n",
        "lighting_df2.loc[ (lighting_df2[\"time\"] > \"2019-11-15 0:00:00\") & (lighting_df2[\"time\"] < \"2019-11-16 0:00:00\") & (lighting_df2['cyl'] ==1), \"multi_class\"]=\"2\"\n",
        "lighting_df2.loc[ (lighting_df2[\"time\"] > \"2019-11-15 0:00:00\") & (lighting_df2[\"time\"] < \"2019-11-16 0:00:00\") & (lighting_df2['cyl'] ==1), \"leak\"]=1\n",
        "\n",
        "lighting_df2.loc[ (lighting_df2[\"time\"] > \"2019-11-16 0:00:00\") & (lighting_df2[\"time\"] < \"2020-01-22 0:00:00\") & (lighting_df2['cyl'] ==1), \"multi_class\"]=\"2\"\n",
        "lighting_df2.loc[ (lighting_df2[\"time\"] > \"2019-11-16 0:00:00\") & (lighting_df2[\"time\"] < \"2020-01-22 0:00:00\") & (lighting_df2['cyl'] ==1), \"leak\"]=1"
      ],
      "execution_count": 226,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymL0QfKXZYh9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "90176091-ea2a-440a-b327-ba7ebffa7a90"
      },
      "source": [
        "lighting_df2 = lighting_df2.fillna(0)\n",
        "lighting_df2.head()"
      ],
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>time</th>\n",
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              "      <th>s_pressure_crank</th>\n",
              "      <th>d_pressure_crank</th>\n",
              "      <th>rpm_crank</th>\n",
              "      <th>s_temp_mean</th>\n",
              "      <th>d_temp_mean</th>\n",
              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
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              "      <th>machine</th>\n",
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              "      <th>leak</th>\n",
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              "      <th>0</th>\n",
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              "      <td>2019-01-01 00:30:00</td>\n",
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              "      <td>1095.10625</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2019-01-01 01:30:00</td>\n",
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              "      <td>67.034445</td>\n",
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              "      <td>873.009268</td>\n",
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              "      <td>67.034445</td>\n",
              "      <td>200.788049</td>\n",
              "      <td>285.984770</td>\n",
              "      <td>908.956779</td>\n",
              "      <td>1099.30625</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>Unit 2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2019-01-01 02:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>65.589635</td>\n",
              "      <td>194.116914</td>\n",
              "      <td>290.491619</td>\n",
              "      <td>886.026560</td>\n",
              "      <td>1097.34625</td>\n",
              "      <td>65.589635</td>\n",
              "      <td>192.720773</td>\n",
              "      <td>305.612809</td>\n",
              "      <td>920.744373</td>\n",
              "      <td>1097.34625</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>Unit 2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-01-01 03:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>64.556183</td>\n",
              "      <td>189.472267</td>\n",
              "      <td>300.252905</td>\n",
              "      <td>891.113648</td>\n",
              "      <td>1092.95250</td>\n",
              "      <td>64.556183</td>\n",
              "      <td>188.680634</td>\n",
              "      <td>316.594817</td>\n",
              "      <td>931.756675</td>\n",
              "      <td>1092.95250</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>Unit 2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-01-01 04:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>56.654270</td>\n",
              "      <td>185.580377</td>\n",
              "      <td>286.851893</td>\n",
              "      <td>888.180775</td>\n",
              "      <td>1090.30500</td>\n",
              "      <td>56.654270</td>\n",
              "      <td>184.180057</td>\n",
              "      <td>301.712783</td>\n",
              "      <td>921.538740</td>\n",
              "      <td>1090.30500</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>Unit 2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                 time  cyl  ...  machine  multi_class  leak\n",
              "0           0  2019-01-01 00:30:00  1.0  ...   Unit 2            0   0.0\n",
              "1           1  2019-01-01 01:30:00  1.0  ...   Unit 2            0   0.0\n",
              "2           2  2019-01-01 02:30:00  1.0  ...   Unit 2            0   0.0\n",
              "3           3  2019-01-01 03:30:00  1.0  ...   Unit 2            0   0.0\n",
              "4           4  2019-01-01 04:30:00  1.0  ...   Unit 2            0   0.0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IQYqIzFFbYMS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5c074796-81ee-4aac-9873-89fc87611a93"
      },
      "source": [
        "lighting_df2['leak'].unique()"
      ],
      "execution_count": 228,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bLPtbE8HczoH",
        "colab_type": "text"
      },
      "source": [
        "# Lighting 4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CEui3yA3ci7k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lighting_df4 = pd.read_csv('/content/unnormalised_lightning_df4_df.csv')"
      ],
      "execution_count": 229,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_NP0tkEDcy8H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-05-25 0:00:00\") & (lighting_df4[\"time\"] < \"2019-05-29 0:00:00\") & (lighting_df4['cyl'] ==2), \"multi_class\"]=\"pre_1\"\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-05-22 0:00:00\") & (lighting_df4[\"time\"] < \"2019-05-29 0:00:00\") & (lighting_df4['cyl'] ==2), \"leak\"]=1\n",
        "\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-05-29 0:00:00\") & (lighting_df4[\"time\"] < \"2019-06-21 0:00:00\") & (lighting_df4['cyl'] ==2), \"multi_class\"]=\"1\"\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-05-29 0:00:00\") & (lighting_df4[\"time\"] < \"2019-06-21 0:00:00\") & (lighting_df4['cyl'] ==2), \"leak\"]=1\n",
        "\n",
        "\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-06-21 0:00:00\") & (lighting_df4[\"time\"] < \"2019-06-23 0:00:00\") & (lighting_df4['cyl'] ==2), \"multi_class\"]=\"2\"\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-06-21 0:00:00\") & (lighting_df4[\"time\"] < \"2019-06-23 0:00:00\") & (lighting_df4['cyl'] ==2), \"leak\"]=1\n",
        "\n",
        "\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-06-23 0:00:00\") & (lighting_df4[\"time\"] < \"2019-06-24 0:00:00\") & (lighting_df4['cyl'] ==2), \"multi_class\"]=\"3\"\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-06-23 0:00:00\") & (lighting_df4[\"time\"] < \"2019-06-24 0:00:00\") & (lighting_df4['cyl'] ==2), \"leak\"]=1"
      ],
      "execution_count": 230,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aH_oSVZjfg2d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-04-25 0:00:00\") & (lighting_df4[\"time\"] < \"2019-05-02 0:00:00\") & (lighting_df4['cyl'] ==2), \"multi_class\"]=\"pre_1\"\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-04-25 0:00:00\") & (lighting_df4[\"time\"] < \"2019-05-02 0:00:00\") & (lighting_df4['cyl'] ==2), \"leak\"]=1\n",
        "\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-05-02 0:00:00\") & (lighting_df4[\"time\"] < \"2019-05-31 0:00:00\") & (lighting_df4['cyl'] ==2), \"multi_class\"]=\"1\"\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-05-02 0:00:00\") & (lighting_df4[\"time\"] < \"2019-05-31 0:00:00\") & (lighting_df4['cyl'] ==2), \"leak\"]=1\n",
        "\n",
        "\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-05-31 0:00:00\") & (lighting_df4[\"time\"] < \"2019-06-12 0:00:00\") & (lighting_df4['cyl'] ==2), \"multi_class\"]=\"1\"\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-05-31 0:00:00\") & (lighting_df4[\"time\"] < \"2019-06-12 0:00:00\") & (lighting_df4['cyl'] ==2), \"leak\"]=1\n"
      ],
      "execution_count": 231,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5FDfekKpoiKE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-09-06 0:00:00\") & (lighting_df4[\"time\"] < \"2019-10-29 0:00:00\") & (lighting_df4['cyl'] ==2), \"multi_class\"]=\"1\"\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-09-06 0:00:00\") & (lighting_df4[\"time\"] < \"2019-10-29 0:00:00\") & (lighting_df4['cyl'] ==2), \"leak\"]=1\n",
        "\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-10-29 0:00:00\") & (lighting_df4[\"time\"] < \"2019-12-06 0:00:00\") & (lighting_df4['cyl'] ==2), \"multi_class\"]=\"2\"\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-10-29 0:00:00\") & (lighting_df4[\"time\"] < \"2019-12-06 0:00:00\") & (lighting_df4['cyl'] ==2), \"leak\"]=1\n",
        "\n",
        "\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-12-06 0:00:00\") & (lighting_df4[\"time\"] < \"2019-12-27 0:00:00\") & (lighting_df4['cyl'] ==2), \"multi_class\"]=\"3\"\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-12-06 0:00:00\") & (lighting_df4[\"time\"] < \"2019-12-27 0:00:00\") & (lighting_df4['cyl'] ==2), \"leak\"]=1\n"
      ],
      "execution_count": 232,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sHl8IbT8p45D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-06-25 0:00:00\") & (lighting_df4[\"time\"] < \"2019-08-17 0:00:00\") & (lighting_df4['cyl'] ==2), \"multi_class\"]=\"pre_1\"\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-06-25 0:00:00\") & (lighting_df4[\"time\"] < \"2019-08-17 0:00:00\") & (lighting_df4['cyl'] ==2), \"leak\"]=1\n",
        "\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-08-17 0:00:00\") & (lighting_df4[\"time\"] < \"2019-09-17 0:00:00\") & (lighting_df4['cyl'] ==2), \"multi_class\"]=\"1\"\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-08-17 0:00:00\") & (lighting_df4[\"time\"] < \"2019-09-17 0:00:00\") & (lighting_df4['cyl'] ==2), \"leak\"]=1\n",
        "\n",
        "\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-09-17 0:00:00\") & (lighting_df4[\"time\"] < \"2019-09-20 0:00:00\") & (lighting_df4['cyl'] ==2), \"multi_class\"]=\"2\"\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-09-17 0:00:00\") & (lighting_df4[\"time\"] < \"2019-09-20 0:00:00\") & (lighting_df4['cyl'] ==2), \"leak\"]=1\n",
        "\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-09-20 0:00:00\") & (lighting_df4[\"time\"] < \"2019-12-02 0:00:00\") & (lighting_df4['cyl'] ==2), \"multi_class\"]=\"3\"\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-09-20 0:00:00\") & (lighting_df4[\"time\"] < \"2019-12-02 0:00:00\") & (lighting_df4['cyl'] ==2), \"leak\"]=1\n"
      ],
      "execution_count": 233,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RF1XibZWvSd4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-12-07 0:00:00\") & (lighting_df4[\"time\"] < \"2019-12-08 0:00:00\") & (lighting_df4['cyl'] ==2), \"multi_class\"]=\"1\"\n",
        "lighting_df4.loc[ (lighting_df4[\"time\"] > \"2019-12-07 0:00:00\") & (lighting_df4[\"time\"] < \"2019-12-08 0:00:00\") & (lighting_df4['cyl'] ==2), \"leak\"]=1"
      ],
      "execution_count": 234,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hb7gg1Qpve2P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "a053563e-8989-4a27-dd80-61ce4964244c"
      },
      "source": [
        "lighting_df4 = lighting_df4.fillna(0)\n",
        "lighting_df4.head()"
      ],
      "execution_count": 235,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>time</th>\n",
              "      <th>cyl</th>\n",
              "      <th>s_temp_crank</th>\n",
              "      <th>d_temp_crank</th>\n",
              "      <th>s_pressure_crank</th>\n",
              "      <th>d_pressure_crank</th>\n",
              "      <th>rpm_crank</th>\n",
              "      <th>s_temp_mean</th>\n",
              "      <th>d_temp_mean</th>\n",
              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
              "      <th>rpm_mean</th>\n",
              "      <th>station</th>\n",
              "      <th>machine</th>\n",
              "      <th>multi_class</th>\n",
              "      <th>leak</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2019-01-01 00:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>71.360254</td>\n",
              "      <td>208.072222</td>\n",
              "      <td>260.899487</td>\n",
              "      <td>848.638863</td>\n",
              "      <td>1053.76625</td>\n",
              "      <td>71.360254</td>\n",
              "      <td>202.279267</td>\n",
              "      <td>285.027334</td>\n",
              "      <td>879.467963</td>\n",
              "      <td>1053.76625</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>Unit 4</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2019-01-01 01:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>69.513345</td>\n",
              "      <td>204.151695</td>\n",
              "      <td>266.082104</td>\n",
              "      <td>852.073959</td>\n",
              "      <td>1045.87250</td>\n",
              "      <td>69.513345</td>\n",
              "      <td>198.443762</td>\n",
              "      <td>289.942010</td>\n",
              "      <td>882.502510</td>\n",
              "      <td>1045.87250</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>Unit 4</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2019-01-01 02:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>65.506618</td>\n",
              "      <td>190.650585</td>\n",
              "      <td>288.418406</td>\n",
              "      <td>857.892369</td>\n",
              "      <td>1047.72125</td>\n",
              "      <td>65.506618</td>\n",
              "      <td>186.275792</td>\n",
              "      <td>312.004013</td>\n",
              "      <td>891.321862</td>\n",
              "      <td>1047.72125</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>Unit 4</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-01-01 03:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>68.095249</td>\n",
              "      <td>189.027238</td>\n",
              "      <td>300.204184</td>\n",
              "      <td>859.385574</td>\n",
              "      <td>1046.16750</td>\n",
              "      <td>68.095249</td>\n",
              "      <td>185.116650</td>\n",
              "      <td>324.099512</td>\n",
              "      <td>894.828712</td>\n",
              "      <td>1046.16750</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>Unit 4</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2019-01-01 07:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>50.003248</td>\n",
              "      <td>180.027393</td>\n",
              "      <td>266.663401</td>\n",
              "      <td>841.167391</td>\n",
              "      <td>1050.88750</td>\n",
              "      <td>50.003248</td>\n",
              "      <td>175.478650</td>\n",
              "      <td>288.933825</td>\n",
              "      <td>873.465173</td>\n",
              "      <td>1050.88750</td>\n",
              "      <td>Lightning</td>\n",
              "      <td>Unit 4</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                 time  cyl  ...  machine  multi_class  leak\n",
              "0           0  2019-01-01 00:30:00  1.0  ...   Unit 4            0   0.0\n",
              "1           1  2019-01-01 01:30:00  1.0  ...   Unit 4            0   0.0\n",
              "2           2  2019-01-01 02:30:00  1.0  ...   Unit 4            0   0.0\n",
              "3           3  2019-01-01 03:30:00  1.0  ...   Unit 4            0   0.0\n",
              "4           4  2019-01-01 07:30:00  1.0  ...   Unit 4            0   0.0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tfTWn0Z-vn-7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "337a0dba-ce8d-49dd-c595-1321259263db"
      },
      "source": [
        "lighting_df4['multi_class'].unique()"
      ],
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, '1', '2', '3'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WqncAXI3vuH4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "alldfs = [var for var in dir() if isinstance(eval(var), pd.core.frame.DataFrame)]\n"
      ],
      "execution_count": 237,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEVRm-mS1wwS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "merge_df = pd.concat([cross_df,cross_df4,cross_df5,goliad_df,hobit_df102,hobit_df2,katy16,katy17,lighting_df2,lighting_df4,reagon_df3,reagon_df6,turkey_df2],ignore_index=True)"
      ],
      "execution_count": 238,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4yH0LtmQ2lrk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        },
        "outputId": "844cf123-4b01-4b58-e161-dfa13d392c11"
      },
      "source": [
        "merge_df"
      ],
      "execution_count": 239,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Unnamed: 0</th>\n",
              "      <th>time</th>\n",
              "      <th>cyl</th>\n",
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              "      <th>165856</th>\n",
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              "      <td>4.0</td>\n",
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              "      <td>56.944537</td>\n",
              "      <td>254.070782</td>\n",
              "      <td>1152.238750</td>\n",
              "      <td>West Turkey Track</td>\n",
              "      <td>Unit 2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165857</th>\n",
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              "      <td>253.641830</td>\n",
              "      <td>1152.350000</td>\n",
              "      <td>West Turkey Track</td>\n",
              "      <td>Unit 2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
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              "      <td>4.0</td>\n",
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              "      <td>100.061720</td>\n",
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              "      <td>253.938337</td>\n",
              "      <td>1151.686250</td>\n",
              "      <td>West Turkey Track</td>\n",
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              "      <th>165859</th>\n",
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              "      <td>2019-08-01 05:30:00</td>\n",
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              "      <td>253.942553</td>\n",
              "      <td>1151.798750</td>\n",
              "      <td>West Turkey Track</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>165860</th>\n",
              "      <td>15790</td>\n",
              "      <td>2019-08-01 06:30:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>97.748818</td>\n",
              "      <td>266.430155</td>\n",
              "      <td>69.404583</td>\n",
              "      <td>264.510459</td>\n",
              "      <td>1152.183750</td>\n",
              "      <td>97.748818</td>\n",
              "      <td>289.281506</td>\n",
              "      <td>56.225884</td>\n",
              "      <td>254.396108</td>\n",
              "      <td>1152.183750</td>\n",
              "      <td>West Turkey Track</td>\n",
              "      <td>Unit 2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>165861 rows × 17 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Unnamed: 0                 time  cyl  ...  machine  multi_class  leak\n",
              "0                0  2019-04-05 02:30:00  1.0  ...   Unit 1            0   0.0\n",
              "1                1  2019-04-05 03:30:00  1.0  ...   Unit 1            0   0.0\n",
              "2                2  2019-04-05 04:30:00  1.0  ...   Unit 1            0   0.0\n",
              "3                3  2019-04-05 05:30:00  1.0  ...   Unit 1            0   0.0\n",
              "4                4  2019-04-05 06:30:00  1.0  ...   Unit 1            0   0.0\n",
              "...            ...                  ...  ...  ...      ...          ...   ...\n",
              "165856       15786  2019-08-01 02:30:00  4.0  ...   Unit 2            0   0.0\n",
              "165857       15787  2019-08-01 03:30:00  4.0  ...   Unit 2            0   0.0\n",
              "165858       15788  2019-08-01 04:30:00  4.0  ...   Unit 2            0   0.0\n",
              "165859       15789  2019-08-01 05:30:00  4.0  ...   Unit 2            0   0.0\n",
              "165860       15790  2019-08-01 06:30:00  4.0  ...   Unit 2            0   0.0\n",
              "\n",
              "[165861 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 239
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BlplC1hG3gDx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "merge_df.to_csv('new_unnormalised.csv', index=False)"
      ],
      "execution_count": 240,
      "outputs": []
    }
  ]
}