Anamoly_detection2.ipynb

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      "source": [
        "! pip install pycaret"
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            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->IPython->pycaret) (0.1.9)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->IPython->pycaret) (0.6.0)\n",
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            "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.6/dist-packages (from widgetsnbextension~=3.5.0->ipywidgets->pycaret) (5.2.2)\n",
            "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.6/dist-packages (from ipykernel>=4.5.1->ipywidgets->pycaret) (5.3.4)\n",
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            "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.6/dist-packages (from nbformat>=4.2.0->ipywidgets->pycaret) (4.6.3)\n",
            "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /usr/local/lib/python3.6/dist-packages (from nbformat>=4.2.0->ipywidgets->pycaret) (2.6.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy->pycaret) (3.1.0)\n",
            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.6/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (5.6.1)\n",
            "Requirement already satisfied: terminado>=0.3.3; sys_platform != \"win32\" in /usr/local/lib/python3.6/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.8.3)\n",
            "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.6/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets->pycaret) (19.0.1)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.4.4)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.8.4)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (3.1.5)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.6.0)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.3)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (1.4.2)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.5.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (20.4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_6B_lQERqBkZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import collections\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BGdrwIdoqGze",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cross_bar_df1 = pd.read_csv('/content/Normalised_CroosBar_01.csv',index_col=False)\n",
        "cross_bar_df1 = cross_bar_df1[cross_bar_df1.columns[1:13]]\n",
        "\n",
        "cross_bar_df2 = pd.read_csv('/content/Normalised_CroosBar_04.csv',index_col=False)\n",
        "cross_bar_df2 = cross_bar_df2[cross_bar_df2.columns[1:13]]\n",
        "\n",
        "cross_bar_df3 = pd.read_csv('/content/Normalised_CroosBar_05.csv',index_col=False)\n",
        "cross_bar_df3 = cross_bar_df3[cross_bar_df3.columns[1:13]]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GRzT79J7r9_Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "ea91b03f-df75-4f95-9d8c-ffaa82ffa821"
      },
      "source": [
        "cross_bar_df1.head()"
      ],
      "execution_count": 4,
      "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>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-04-05 02:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.666534</td>\n",
              "      <td>2.080444</td>\n",
              "      <td>3.220836</td>\n",
              "      <td>10.105964</td>\n",
              "      <td>10.063987</td>\n",
              "      <td>0.666534</td>\n",
              "      <td>2.053527</td>\n",
              "      <td>3.307406</td>\n",
              "      <td>10.142075</td>\n",
              "      <td>10.063987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-04-05 03:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.716066</td>\n",
              "      <td>2.153494</td>\n",
              "      <td>3.135203</td>\n",
              "      <td>9.998847</td>\n",
              "      <td>10.075669</td>\n",
              "      <td>0.716066</td>\n",
              "      <td>2.120434</td>\n",
              "      <td>3.243347</td>\n",
              "      <td>10.053597</td>\n",
              "      <td>10.075669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-04-05 04:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.687551</td>\n",
              "      <td>2.142630</td>\n",
              "      <td>3.066270</td>\n",
              "      <td>9.954641</td>\n",
              "      <td>10.081478</td>\n",
              "      <td>0.687551</td>\n",
              "      <td>2.105328</td>\n",
              "      <td>3.181119</td>\n",
              "      <td>10.001306</td>\n",
              "      <td>10.081478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-04-05 05:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.686916</td>\n",
              "      <td>2.140013</td>\n",
              "      <td>3.071850</td>\n",
              "      <td>9.956670</td>\n",
              "      <td>10.054274</td>\n",
              "      <td>0.686916</td>\n",
              "      <td>2.101800</td>\n",
              "      <td>3.191233</td>\n",
              "      <td>10.008420</td>\n",
              "      <td>10.054274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-04-05 06:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.698125</td>\n",
              "      <td>2.156244</td>\n",
              "      <td>3.063865</td>\n",
              "      <td>9.961413</td>\n",
              "      <td>10.091478</td>\n",
              "      <td>0.698125</td>\n",
              "      <td>2.117328</td>\n",
              "      <td>3.183534</td>\n",
              "      <td>10.009700</td>\n",
              "      <td>10.091478</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  time  cyl  ...  d_pressure_mean   rpm_mean\n",
              "0  2019-04-05 02:30:00  1.0  ...        10.142075  10.063987\n",
              "1  2019-04-05 03:30:00  1.0  ...        10.053597  10.075669\n",
              "2  2019-04-05 04:30:00  1.0  ...        10.001306  10.081478\n",
              "3  2019-04-05 05:30:00  1.0  ...        10.008420  10.054274\n",
              "4  2019-04-05 06:30:00  1.0  ...        10.009700  10.091478\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uRebZEAyu4DL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9a15c735-0bda-49c2-d660-fee53b5f4e01"
      },
      "source": [
        "print(cross_bar_df1.shape,cross_bar_df2.shape,cross_bar_df3.shape,)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(13179, 12) (363, 12) (6199, 12)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mADFXTvr_xc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "21053f63-5351-49da-ac7f-06753119f49f"
      },
      "source": [
        "cross_bar_df2.head()"
      ],
      "execution_count": 6,
      "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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              "\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>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-09-18 12:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.209361</td>\n",
              "      <td>3.669231</td>\n",
              "      <td>2.841608</td>\n",
              "      <td>16.996985</td>\n",
              "      <td>11.766685</td>\n",
              "      <td>0.209361</td>\n",
              "      <td>3.693108</td>\n",
              "      <td>2.842620</td>\n",
              "      <td>17.174490</td>\n",
              "      <td>35.709109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-09-18 13:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.242446</td>\n",
              "      <td>3.693560</td>\n",
              "      <td>2.867769</td>\n",
              "      <td>17.005224</td>\n",
              "      <td>14.486232</td>\n",
              "      <td>0.242446</td>\n",
              "      <td>3.722898</td>\n",
              "      <td>2.861971</td>\n",
              "      <td>17.194612</td>\n",
              "      <td>32.329571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-09-19 21:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>3.330968</td>\n",
              "      <td>2.005675</td>\n",
              "      <td>12.924733</td>\n",
              "      <td>14.523496</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>3.353176</td>\n",
              "      <td>2.035285</td>\n",
              "      <td>13.094825</td>\n",
              "      <td>35.842758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-09-19 22:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.622172</td>\n",
              "      <td>4.082144</td>\n",
              "      <td>2.785608</td>\n",
              "      <td>16.368342</td>\n",
              "      <td>14.479579</td>\n",
              "      <td>0.622172</td>\n",
              "      <td>4.102823</td>\n",
              "      <td>2.786716</td>\n",
              "      <td>16.516277</td>\n",
              "      <td>35.777038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-09-19 23:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.518412</td>\n",
              "      <td>3.959500</td>\n",
              "      <td>2.843085</td>\n",
              "      <td>16.561951</td>\n",
              "      <td>58.299788</td>\n",
              "      <td>0.518412</td>\n",
              "      <td>3.981560</td>\n",
              "      <td>2.839965</td>\n",
              "      <td>16.704652</td>\n",
              "      <td>53.010183</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  time  cyl  ...  d_pressure_mean   rpm_mean\n",
              "0  2019-09-18 12:30:00  2.0  ...        17.174490  35.709109\n",
              "1  2019-09-18 13:30:00  2.0  ...        17.194612  32.329571\n",
              "2  2019-09-19 21:30:00  2.0  ...        13.094825  35.842758\n",
              "3  2019-09-19 22:30:00  2.0  ...        16.516277  35.777038\n",
              "4  2019-09-19 23:30:00  2.0  ...        16.704652  53.010183\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWYlwITIsBxR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "5624ee76-aef1-4d3a-d4c9-42fa5b753fe8"
      },
      "source": [
        "cross_bar_df3.head()"
      ],
      "execution_count": 7,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-02-20 20:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.376892</td>\n",
              "      <td>0.380291</td>\n",
              "      <td>7.566670</td>\n",
              "      <td>8.072568</td>\n",
              "      <td>984.697912</td>\n",
              "      <td>0.376892</td>\n",
              "      <td>1.174602</td>\n",
              "      <td>3.931527</td>\n",
              "      <td>5.228811</td>\n",
              "      <td>984.697912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-02-22 06:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.129573</td>\n",
              "      <td>1.736679</td>\n",
              "      <td>2.513616</td>\n",
              "      <td>9.921748</td>\n",
              "      <td>12.605002</td>\n",
              "      <td>0.129573</td>\n",
              "      <td>0.932906</td>\n",
              "      <td>1.299533</td>\n",
              "      <td>5.003472</td>\n",
              "      <td>12.603383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-02-22 07:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.145445</td>\n",
              "      <td>1.735308</td>\n",
              "      <td>2.500727</td>\n",
              "      <td>9.733318</td>\n",
              "      <td>12.430530</td>\n",
              "      <td>0.145445</td>\n",
              "      <td>0.942484</td>\n",
              "      <td>1.196900</td>\n",
              "      <td>4.814091</td>\n",
              "      <td>12.520356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-02-22 08:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.166928</td>\n",
              "      <td>1.757804</td>\n",
              "      <td>2.516358</td>\n",
              "      <td>9.769955</td>\n",
              "      <td>11.265634</td>\n",
              "      <td>0.166928</td>\n",
              "      <td>0.959568</td>\n",
              "      <td>1.191835</td>\n",
              "      <td>4.817510</td>\n",
              "      <td>11.936289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-02-22 09:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.189870</td>\n",
              "      <td>1.797234</td>\n",
              "      <td>2.506760</td>\n",
              "      <td>9.836409</td>\n",
              "      <td>11.244870</td>\n",
              "      <td>0.189870</td>\n",
              "      <td>0.998961</td>\n",
              "      <td>1.119531</td>\n",
              "      <td>4.785946</td>\n",
              "      <td>11.926555</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  time  cyl  ...  d_pressure_mean    rpm_mean\n",
              "0  2019-02-20 20:30:00  2.0  ...         5.228811  984.697912\n",
              "1  2019-02-22 06:30:00  2.0  ...         5.003472   12.603383\n",
              "2  2019-02-22 07:30:00  2.0  ...         4.814091   12.520356\n",
              "3  2019-02-22 08:30:00  2.0  ...         4.817510   11.936289\n",
              "4  2019-02-22 09:30:00  2.0  ...         4.785946   11.926555\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eGTstrRzsKFV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "merge_df = pd.concat([cross_bar_df1, cross_bar_df2, cross_bar_df3],join = 'inner' )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dTMq1s4-xwmR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "merge_df = merge_df.reset_index(drop=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SGxYOSF8ux2l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "42f81a26-f954-4268-e906-c8831691fc6e"
      },
      "source": [
        "merge_df"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\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>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-04-05 02:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.666534</td>\n",
              "      <td>2.080444</td>\n",
              "      <td>3.220836</td>\n",
              "      <td>10.105964</td>\n",
              "      <td>10.063987</td>\n",
              "      <td>0.666534</td>\n",
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              "      <td>10.142075</td>\n",
              "      <td>10.063987</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-04-05 03:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.716066</td>\n",
              "      <td>2.153494</td>\n",
              "      <td>3.135203</td>\n",
              "      <td>9.998847</td>\n",
              "      <td>10.075669</td>\n",
              "      <td>0.716066</td>\n",
              "      <td>2.120434</td>\n",
              "      <td>3.243347</td>\n",
              "      <td>10.053597</td>\n",
              "      <td>10.075669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-04-05 04:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.687551</td>\n",
              "      <td>2.142630</td>\n",
              "      <td>3.066270</td>\n",
              "      <td>9.954641</td>\n",
              "      <td>10.081478</td>\n",
              "      <td>0.687551</td>\n",
              "      <td>2.105328</td>\n",
              "      <td>3.181119</td>\n",
              "      <td>10.001306</td>\n",
              "      <td>10.081478</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-04-05 05:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.686916</td>\n",
              "      <td>2.140013</td>\n",
              "      <td>3.071850</td>\n",
              "      <td>9.956670</td>\n",
              "      <td>10.054274</td>\n",
              "      <td>0.686916</td>\n",
              "      <td>2.101800</td>\n",
              "      <td>3.191233</td>\n",
              "      <td>10.008420</td>\n",
              "      <td>10.054274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-04-05 06:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.698125</td>\n",
              "      <td>2.156244</td>\n",
              "      <td>3.063865</td>\n",
              "      <td>9.961413</td>\n",
              "      <td>10.091478</td>\n",
              "      <td>0.698125</td>\n",
              "      <td>2.117328</td>\n",
              "      <td>3.183534</td>\n",
              "      <td>10.009700</td>\n",
              "      <td>10.091478</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6194</th>\n",
              "      <td>2019-10-08 14:30:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.367023</td>\n",
              "      <td>2.290135</td>\n",
              "      <td>0.203793</td>\n",
              "      <td>2.771141</td>\n",
              "      <td>12.087111</td>\n",
              "      <td>0.367023</td>\n",
              "      <td>2.348889</td>\n",
              "      <td>0.177171</td>\n",
              "      <td>2.796853</td>\n",
              "      <td>12.087111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6195</th>\n",
              "      <td>2019-10-08 15:30:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.438879</td>\n",
              "      <td>2.380740</td>\n",
              "      <td>0.202395</td>\n",
              "      <td>2.783980</td>\n",
              "      <td>12.266956</td>\n",
              "      <td>0.438879</td>\n",
              "      <td>2.432139</td>\n",
              "      <td>0.178642</td>\n",
              "      <td>2.804289</td>\n",
              "      <td>12.266956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6196</th>\n",
              "      <td>2019-10-08 16:30:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.480072</td>\n",
              "      <td>2.424573</td>\n",
              "      <td>0.195123</td>\n",
              "      <td>2.746196</td>\n",
              "      <td>12.353345</td>\n",
              "      <td>0.480072</td>\n",
              "      <td>2.481738</td>\n",
              "      <td>0.174627</td>\n",
              "      <td>2.793009</td>\n",
              "      <td>12.353345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6197</th>\n",
              "      <td>2019-10-08 17:30:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.507349</td>\n",
              "      <td>2.488776</td>\n",
              "      <td>0.177829</td>\n",
              "      <td>2.750903</td>\n",
              "      <td>12.217548</td>\n",
              "      <td>0.507349</td>\n",
              "      <td>2.522124</td>\n",
              "      <td>0.168590</td>\n",
              "      <td>2.789289</td>\n",
              "      <td>12.217548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6198</th>\n",
              "      <td>2019-10-08 18:30:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.561731</td>\n",
              "      <td>2.544242</td>\n",
              "      <td>0.164419</td>\n",
              "      <td>2.679422</td>\n",
              "      <td>12.063606</td>\n",
              "      <td>0.561731</td>\n",
              "      <td>2.568668</td>\n",
              "      <td>0.159972</td>\n",
              "      <td>2.716319</td>\n",
              "      <td>12.063606</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19741 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                     time  cyl  s_temp_crank  d_temp_crank  s_pressure_crank  \\\n",
              "0     2019-04-05 02:30:00  1.0      0.666534      2.080444          3.220836   \n",
              "1     2019-04-05 03:30:00  1.0      0.716066      2.153494          3.135203   \n",
              "2     2019-04-05 04:30:00  1.0      0.687551      2.142630          3.066270   \n",
              "3     2019-04-05 05:30:00  1.0      0.686916      2.140013          3.071850   \n",
              "4     2019-04-05 06:30:00  1.0      0.698125      2.156244          3.063865   \n",
              "...                   ...  ...           ...           ...               ...   \n",
              "6194  2019-10-08 14:30:00  4.0      0.367023      2.290135          0.203793   \n",
              "6195  2019-10-08 15:30:00  4.0      0.438879      2.380740          0.202395   \n",
              "6196  2019-10-08 16:30:00  4.0      0.480072      2.424573          0.195123   \n",
              "6197  2019-10-08 17:30:00  4.0      0.507349      2.488776          0.177829   \n",
              "6198  2019-10-08 18:30:00  4.0      0.561731      2.544242          0.164419   \n",
              "\n",
              "      d_pressure_crank  rpm_crank  s_temp_mean  d_temp_mean  s_pressure_mean  \\\n",
              "0            10.105964  10.063987     0.666534     2.053527         3.307406   \n",
              "1             9.998847  10.075669     0.716066     2.120434         3.243347   \n",
              "2             9.954641  10.081478     0.687551     2.105328         3.181119   \n",
              "3             9.956670  10.054274     0.686916     2.101800         3.191233   \n",
              "4             9.961413  10.091478     0.698125     2.117328         3.183534   \n",
              "...                ...        ...          ...          ...              ...   \n",
              "6194          2.771141  12.087111     0.367023     2.348889         0.177171   \n",
              "6195          2.783980  12.266956     0.438879     2.432139         0.178642   \n",
              "6196          2.746196  12.353345     0.480072     2.481738         0.174627   \n",
              "6197          2.750903  12.217548     0.507349     2.522124         0.168590   \n",
              "6198          2.679422  12.063606     0.561731     2.568668         0.159972   \n",
              "\n",
              "      d_pressure_mean   rpm_mean  \n",
              "0           10.142075  10.063987  \n",
              "1           10.053597  10.075669  \n",
              "2           10.001306  10.081478  \n",
              "3           10.008420  10.054274  \n",
              "4           10.009700  10.091478  \n",
              "...               ...        ...  \n",
              "6194         2.796853  12.087111  \n",
              "6195         2.804289  12.266956  \n",
              "6196         2.793009  12.353345  \n",
              "6197         2.789289  12.217548  \n",
              "6198         2.716319  12.063606  \n",
              "\n",
              "[19741 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5xz3jKW5vT0-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "merge_df.to_csv(\"/content/Normalised_Merge_df.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xui6dujpv5Z6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "dc73e8c5-1470-4234-8dc6-7db050ceca5a"
      },
      "source": [
        "merge_df.columns "
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['time', 'cyl', 's_temp_crank', 'd_temp_crank', 's_pressure_crank',\n",
              "       'd_pressure_crank', 'rpm_crank', 's_temp_mean', 'd_temp_mean',\n",
              "       's_pressure_mean', 'd_pressure_mean', 'rpm_mean'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mag8SVtjwg04",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "merge_df2 = merge_df.drop(labels = ['time','cyl'],axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ReQnKXrDw80h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "173cdbfa-4d35-4c39-a123-77c2d5504b0a"
      },
      "source": [
        "merge_df2.head()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></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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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <th>1</th>\n",
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              "      <td>2.120434</td>\n",
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              "      <td>9.956670</td>\n",
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              "      <td>10.009700</td>\n",
              "      <td>10.091478</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   s_temp_crank  d_temp_crank  s_pressure_crank  d_pressure_crank  rpm_crank  \\\n",
              "0      0.666534      2.080444          3.220836         10.105964  10.063987   \n",
              "1      0.716066      2.153494          3.135203          9.998847  10.075669   \n",
              "2      0.687551      2.142630          3.066270          9.954641  10.081478   \n",
              "3      0.686916      2.140013          3.071850          9.956670  10.054274   \n",
              "4      0.698125      2.156244          3.063865          9.961413  10.091478   \n",
              "\n",
              "   s_temp_mean  d_temp_mean  s_pressure_mean  d_pressure_mean   rpm_mean  \n",
              "0     0.666534     2.053527         3.307406        10.142075  10.063987  \n",
              "1     0.716066     2.120434         3.243347        10.053597  10.075669  \n",
              "2     0.687551     2.105328         3.181119        10.001306  10.081478  \n",
              "3     0.686916     2.101800         3.191233        10.008420  10.054274  \n",
              "4     0.698125     2.117328         3.183534        10.009700  10.091478  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kJw8g8UUwxud",
        "colab_type": "text"
      },
      "source": [
        "# Anamoly Detection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pBgab1nDwnxJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586,
          "referenced_widgets": [
            "d38bd51c1a8948da907b30734d2d9baa",
            "7ee577c3de1b4d97949cb9a4c68c96c0",
            "ec29557336034f5b9b2b15d49056dc29",
            "087cc32865f5449b83e33b527c5f4a02",
            "0d219f8c03674c5bb21b369c38f187bc",
            "33ab2cbbef714418890b9db43e4eda87"
          ]
        },
        "outputId": "924193eb-d1b4-4347-8403-5eabea7b3f57"
      },
      "source": [
        "from pycaret.anomaly import *\n",
        "expo_anamoly = setup(merge_df2)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Setup Succesfully Completed!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style  type=\"text/css\" >\n",
              "</style><table id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row0_col0\" class=\"data row0 col0\" >session_id </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row0_col1\" class=\"data row0 col1\" >2831</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row1_col0\" class=\"data row1 col0\" >Original Data </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row1_col1\" class=\"data row1 col1\" >(19741, 10)</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row2_col0\" class=\"data row2 col0\" >Missing Values </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row2_col1\" class=\"data row2 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row3_col0\" class=\"data row3 col0\" >Numeric Features </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row3_col1\" class=\"data row3 col1\" >10</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row4_col0\" class=\"data row4 col0\" >Categorical Features </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row5_col0\" class=\"data row5 col0\" >Ordinal Features </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row5_col1\" class=\"data row5 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row6_col0\" class=\"data row6 col0\" >High Cardinality Features </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row6_col1\" class=\"data row6 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row7_col0\" class=\"data row7 col0\" >Transformed Data </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row7_col1\" class=\"data row7 col1\" >(19741, 10)</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row8_col0\" class=\"data row8 col0\" >Numeric Imputer </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row8_col1\" class=\"data row8 col1\" >mean</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row9_col0\" class=\"data row9 col0\" >Categorical Imputer </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row9_col1\" class=\"data row9 col1\" >constant</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row10_col0\" class=\"data row10 col0\" >Normalize </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row10_col1\" class=\"data row10 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row11_col0\" class=\"data row11 col0\" >Normalize Method </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row11_col1\" class=\"data row11 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row12_col0\" class=\"data row12 col0\" >Transformation </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row12_col1\" class=\"data row12 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row13_col0\" class=\"data row13 col0\" >Transformation Method </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row13_col1\" class=\"data row13 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row14_col0\" class=\"data row14 col0\" >PCA </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row14_col1\" class=\"data row14 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row15_col0\" class=\"data row15 col0\" >PCA Method </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row15_col1\" class=\"data row15 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row16_col0\" class=\"data row16 col0\" >PCA components </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row16_col1\" class=\"data row16 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row17_col0\" class=\"data row17 col0\" >Ignore Low Variance </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row17_col1\" class=\"data row17 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row18_col0\" class=\"data row18 col0\" >Combine Rare Levels </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row18_col1\" class=\"data row18 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row19_col0\" class=\"data row19 col0\" >Rare Level Threshold </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row19_col1\" class=\"data row19 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row20_col0\" class=\"data row20 col0\" >Numeric Binning </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row20_col1\" class=\"data row20 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row21_col0\" class=\"data row21 col0\" >Remove Multicollinearity </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row21_col1\" class=\"data row21 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row22_col0\" class=\"data row22 col0\" >Multicollinearity Threshold </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row22_col1\" class=\"data row22 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row23_col0\" class=\"data row23 col0\" >Group Features </td>\n",
              "                        <td id=\"T_48eeef24_a0a5_11ea_acd5_0242ac1c0002row23_col1\" class=\"data row23 col1\" >False</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f0af8b79630>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ciiwrwb31AMX",
        "colab_type": "text"
      },
      "source": [
        "# One Class SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FrwdntDZw0OZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "one_class_svm = create_model('svm')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FruhM0KMysCn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "one_class_svm_pred = predict_model(one_class_svm, data = merge_df2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WVLmJEtLyvDS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "467d7f3b-b4c4-493b-efaa-bd2beb9714d7"
      },
      "source": [
        "one_class_svm_pred"
      ],
      "execution_count": 30,
      "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>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>Label</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.666534</td>\n",
              "      <td>2.080444</td>\n",
              "      <td>3.220836</td>\n",
              "      <td>10.105964</td>\n",
              "      <td>10.063987</td>\n",
              "      <td>0.666534</td>\n",
              "      <td>2.053527</td>\n",
              "      <td>3.307406</td>\n",
              "      <td>10.142075</td>\n",
              "      <td>10.063987</td>\n",
              "      <td>0</td>\n",
              "      <td>29.599181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.716066</td>\n",
              "      <td>2.153494</td>\n",
              "      <td>3.135203</td>\n",
              "      <td>9.998847</td>\n",
              "      <td>10.075669</td>\n",
              "      <td>0.716066</td>\n",
              "      <td>2.120434</td>\n",
              "      <td>3.243347</td>\n",
              "      <td>10.053597</td>\n",
              "      <td>10.075669</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.228785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.687551</td>\n",
              "      <td>2.142630</td>\n",
              "      <td>3.066270</td>\n",
              "      <td>9.954641</td>\n",
              "      <td>10.081478</td>\n",
              "      <td>0.687551</td>\n",
              "      <td>2.105328</td>\n",
              "      <td>3.181119</td>\n",
              "      <td>10.001306</td>\n",
              "      <td>10.081478</td>\n",
              "      <td>0</td>\n",
              "      <td>-22.954512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.686916</td>\n",
              "      <td>2.140013</td>\n",
              "      <td>3.071850</td>\n",
              "      <td>9.956670</td>\n",
              "      <td>10.054274</td>\n",
              "      <td>0.686916</td>\n",
              "      <td>2.101800</td>\n",
              "      <td>3.191233</td>\n",
              "      <td>10.008420</td>\n",
              "      <td>10.054274</td>\n",
              "      <td>0</td>\n",
              "      <td>-9.307971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.698125</td>\n",
              "      <td>2.156244</td>\n",
              "      <td>3.063865</td>\n",
              "      <td>9.961413</td>\n",
              "      <td>10.091478</td>\n",
              "      <td>0.698125</td>\n",
              "      <td>2.117328</td>\n",
              "      <td>3.183534</td>\n",
              "      <td>10.009700</td>\n",
              "      <td>10.091478</td>\n",
              "      <td>0</td>\n",
              "      <td>-24.961897</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19736</th>\n",
              "      <td>0.367023</td>\n",
              "      <td>2.290135</td>\n",
              "      <td>0.203793</td>\n",
              "      <td>2.771141</td>\n",
              "      <td>12.087111</td>\n",
              "      <td>0.367023</td>\n",
              "      <td>2.348889</td>\n",
              "      <td>0.177171</td>\n",
              "      <td>2.796853</td>\n",
              "      <td>12.087111</td>\n",
              "      <td>0</td>\n",
              "      <td>-151.474938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19737</th>\n",
              "      <td>0.438879</td>\n",
              "      <td>2.380740</td>\n",
              "      <td>0.202395</td>\n",
              "      <td>2.783980</td>\n",
              "      <td>12.266956</td>\n",
              "      <td>0.438879</td>\n",
              "      <td>2.432139</td>\n",
              "      <td>0.178642</td>\n",
              "      <td>2.804289</td>\n",
              "      <td>12.266956</td>\n",
              "      <td>0</td>\n",
              "      <td>-84.013844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19738</th>\n",
              "      <td>0.480072</td>\n",
              "      <td>2.424573</td>\n",
              "      <td>0.195123</td>\n",
              "      <td>2.746196</td>\n",
              "      <td>12.353345</td>\n",
              "      <td>0.480072</td>\n",
              "      <td>2.481738</td>\n",
              "      <td>0.174627</td>\n",
              "      <td>2.793009</td>\n",
              "      <td>12.353345</td>\n",
              "      <td>0</td>\n",
              "      <td>-48.440336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19739</th>\n",
              "      <td>0.507349</td>\n",
              "      <td>2.488776</td>\n",
              "      <td>0.177829</td>\n",
              "      <td>2.750903</td>\n",
              "      <td>12.217548</td>\n",
              "      <td>0.507349</td>\n",
              "      <td>2.522124</td>\n",
              "      <td>0.168590</td>\n",
              "      <td>2.789289</td>\n",
              "      <td>12.217548</td>\n",
              "      <td>0</td>\n",
              "      <td>-114.905716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19740</th>\n",
              "      <td>0.561731</td>\n",
              "      <td>2.544242</td>\n",
              "      <td>0.164419</td>\n",
              "      <td>2.679422</td>\n",
              "      <td>12.063606</td>\n",
              "      <td>0.561731</td>\n",
              "      <td>2.568668</td>\n",
              "      <td>0.159972</td>\n",
              "      <td>2.716319</td>\n",
              "      <td>12.063606</td>\n",
              "      <td>0</td>\n",
              "      <td>-185.048929</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19741 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       s_temp_crank  d_temp_crank  ...  Label       Score\n",
              "0          0.666534      2.080444  ...      0   29.599181\n",
              "1          0.716066      2.153494  ...      0   -0.228785\n",
              "2          0.687551      2.142630  ...      0  -22.954512\n",
              "3          0.686916      2.140013  ...      0   -9.307971\n",
              "4          0.698125      2.156244  ...      0  -24.961897\n",
              "...             ...           ...  ...    ...         ...\n",
              "19736      0.367023      2.290135  ...      0 -151.474938\n",
              "19737      0.438879      2.380740  ...      0  -84.013844\n",
              "19738      0.480072      2.424573  ...      0  -48.440336\n",
              "19739      0.507349      2.488776  ...      0 -114.905716\n",
              "19740      0.561731      2.544242  ...      0 -185.048929\n",
              "\n",
              "[19741 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2cPiK02d1Zvp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "one_class_svm_pred.to_csv(\"/content/OneClassSVM_Pred_df.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ST6K6_Tqy-Wm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "59e9ef0a-500e-49e2-f449-8adf76813aaa"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "x = [i for i in range(len(one_class_svm_pred[\"Label\"]))]\n",
        "plt.scatter(x,one_class_svm_pred[\"Label\"])\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FBae-FECzv5q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "2ba90cea-6174-4b2e-ab49-877962ab7c92"
      },
      "source": [
        "num_bins = 2\n",
        "n, bins, patches = plt.hist(one_class_svm_pred[\"Label\"], num_bins, facecolor='blue', alpha=0.5)\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q8NyD2aCz2mZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "24c5d8cf-0c9e-4bc0-a69f-95754d484d6a"
      },
      "source": [
        "collections.Counter(one_class_svm_pred[\"Label\"])"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({0: 18754, 1: 987})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nFEYcfXdz8-V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9804cf23-22ac-4f34-e216-cb3ce8d8dc5b"
      },
      "source": [
        "987/(18754+987)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.04999746720024315"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ghjJupc1DnU",
        "colab_type": "text"
      },
      "source": [
        "# iforest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wyVqEYkP0JPB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " iforest = create_model('iforest')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q4T0SWX72fCd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "iforest_pred = predict_model(iforest, data = merge_df2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EtPMZw0K2ncG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "1238fd00-d1a1-49e7-d7af-d6adbef6c8a3"
      },
      "source": [
        "iforest_pred"
      ],
      "execution_count": 39,
      "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>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>Label</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.666534</td>\n",
              "      <td>2.080444</td>\n",
              "      <td>3.220836</td>\n",
              "      <td>10.105964</td>\n",
              "      <td>10.063987</td>\n",
              "      <td>0.666534</td>\n",
              "      <td>2.053527</td>\n",
              "      <td>3.307406</td>\n",
              "      <td>10.142075</td>\n",
              "      <td>10.063987</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.109432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.716066</td>\n",
              "      <td>2.153494</td>\n",
              "      <td>3.135203</td>\n",
              "      <td>9.998847</td>\n",
              "      <td>10.075669</td>\n",
              "      <td>0.716066</td>\n",
              "      <td>2.120434</td>\n",
              "      <td>3.243347</td>\n",
              "      <td>10.053597</td>\n",
              "      <td>10.075669</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.104707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.687551</td>\n",
              "      <td>2.142630</td>\n",
              "      <td>3.066270</td>\n",
              "      <td>9.954641</td>\n",
              "      <td>10.081478</td>\n",
              "      <td>0.687551</td>\n",
              "      <td>2.105328</td>\n",
              "      <td>3.181119</td>\n",
              "      <td>10.001306</td>\n",
              "      <td>10.081478</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.127592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.686916</td>\n",
              "      <td>2.140013</td>\n",
              "      <td>3.071850</td>\n",
              "      <td>9.956670</td>\n",
              "      <td>10.054274</td>\n",
              "      <td>0.686916</td>\n",
              "      <td>2.101800</td>\n",
              "      <td>3.191233</td>\n",
              "      <td>10.008420</td>\n",
              "      <td>10.054274</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.126017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.698125</td>\n",
              "      <td>2.156244</td>\n",
              "      <td>3.063865</td>\n",
              "      <td>9.961413</td>\n",
              "      <td>10.091478</td>\n",
              "      <td>0.698125</td>\n",
              "      <td>2.117328</td>\n",
              "      <td>3.183534</td>\n",
              "      <td>10.009700</td>\n",
              "      <td>10.091478</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.122679</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19736</th>\n",
              "      <td>0.367023</td>\n",
              "      <td>2.290135</td>\n",
              "      <td>0.203793</td>\n",
              "      <td>2.771141</td>\n",
              "      <td>12.087111</td>\n",
              "      <td>0.367023</td>\n",
              "      <td>2.348889</td>\n",
              "      <td>0.177171</td>\n",
              "      <td>2.796853</td>\n",
              "      <td>12.087111</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.099290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19737</th>\n",
              "      <td>0.438879</td>\n",
              "      <td>2.380740</td>\n",
              "      <td>0.202395</td>\n",
              "      <td>2.783980</td>\n",
              "      <td>12.266956</td>\n",
              "      <td>0.438879</td>\n",
              "      <td>2.432139</td>\n",
              "      <td>0.178642</td>\n",
              "      <td>2.804289</td>\n",
              "      <td>12.266956</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.116588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19738</th>\n",
              "      <td>0.480072</td>\n",
              "      <td>2.424573</td>\n",
              "      <td>0.195123</td>\n",
              "      <td>2.746196</td>\n",
              "      <td>12.353345</td>\n",
              "      <td>0.480072</td>\n",
              "      <td>2.481738</td>\n",
              "      <td>0.174627</td>\n",
              "      <td>2.793009</td>\n",
              "      <td>12.353345</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.121868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19739</th>\n",
              "      <td>0.507349</td>\n",
              "      <td>2.488776</td>\n",
              "      <td>0.177829</td>\n",
              "      <td>2.750903</td>\n",
              "      <td>12.217548</td>\n",
              "      <td>0.507349</td>\n",
              "      <td>2.522124</td>\n",
              "      <td>0.168590</td>\n",
              "      <td>2.789289</td>\n",
              "      <td>12.217548</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.121928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19740</th>\n",
              "      <td>0.561731</td>\n",
              "      <td>2.544242</td>\n",
              "      <td>0.164419</td>\n",
              "      <td>2.679422</td>\n",
              "      <td>12.063606</td>\n",
              "      <td>0.561731</td>\n",
              "      <td>2.568668</td>\n",
              "      <td>0.159972</td>\n",
              "      <td>2.716319</td>\n",
              "      <td>12.063606</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.120309</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19741 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       s_temp_crank  d_temp_crank  s_pressure_crank  ...   rpm_mean  Label     Score\n",
              "0          0.666534      2.080444          3.220836  ...  10.063987      0 -0.109432\n",
              "1          0.716066      2.153494          3.135203  ...  10.075669      0 -0.104707\n",
              "2          0.687551      2.142630          3.066270  ...  10.081478      0 -0.127592\n",
              "3          0.686916      2.140013          3.071850  ...  10.054274      0 -0.126017\n",
              "4          0.698125      2.156244          3.063865  ...  10.091478      0 -0.122679\n",
              "...             ...           ...               ...  ...        ...    ...       ...\n",
              "19736      0.367023      2.290135          0.203793  ...  12.087111      0 -0.099290\n",
              "19737      0.438879      2.380740          0.202395  ...  12.266956      0 -0.116588\n",
              "19738      0.480072      2.424573          0.195123  ...  12.353345      0 -0.121868\n",
              "19739      0.507349      2.488776          0.177829  ...  12.217548      0 -0.121928\n",
              "19740      0.561731      2.544242          0.164419  ...  12.063606      0 -0.120309\n",
              "\n",
              "[19741 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q5DbHZBW2yNK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "iforest_pred.to_csv(\"/content/iforest_df.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w5Zmes4_3UUI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "274a957b-16dd-4419-ae77-07eb407c3e26"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "x = [i for i in range(len(iforest_pred[\"Label\"]))]\n",
        "plt.scatter(x,iforest_pred[\"Label\"])\n",
        "plt.show()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8kjJvh0e3bB8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "1a3bb9f4-3a09-48ad-aed4-dd1f1472a1dc"
      },
      "source": [
        "num_bins = 2\n",
        "n, bins, patches = plt.hist(iforest_pred[\"Label\"], num_bins, facecolor='blue', alpha=0.5)\n",
        "plt.show()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ta1HA7mD4FxZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "915cd82e-c59c-47a2-8a3e-3b896609bd15"
      },
      "source": [
        "collections.Counter(iforest_pred[\"Label\"])"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({0: 18754, 1: 987})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KkC1apsw4RtR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "333fcfc6-9d73-4f7d-9471-d6cb36552949"
      },
      "source": [
        "987/(18754+987)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.04999746720024315"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G2izCy5Y4d17",
        "colab_type": "text"
      },
      "source": [
        "# Angle-base Outlier Detection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8MIaOhAS4Xfb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "abod = create_model('abod')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7-NPfVkR4vsl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "abod_pred = predict_model(abod, data = merge_df2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5GvrJ4gx46M7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "abe0c394-a858-4978-ef22-688e469be57d"
      },
      "source": [
        "abod_pred"
      ],
      "execution_count": 50,
      "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>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>Label</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.666534</td>\n",
              "      <td>2.080444</td>\n",
              "      <td>3.220836</td>\n",
              "      <td>10.105964</td>\n",
              "      <td>10.063987</td>\n",
              "      <td>0.666534</td>\n",
              "      <td>2.053527</td>\n",
              "      <td>3.307406</td>\n",
              "      <td>10.142075</td>\n",
              "      <td>10.063987</td>\n",
              "      <td>0</td>\n",
              "      <td>-2603.248592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.716066</td>\n",
              "      <td>2.153494</td>\n",
              "      <td>3.135203</td>\n",
              "      <td>9.998847</td>\n",
              "      <td>10.075669</td>\n",
              "      <td>0.716066</td>\n",
              "      <td>2.120434</td>\n",
              "      <td>3.243347</td>\n",
              "      <td>10.053597</td>\n",
              "      <td>10.075669</td>\n",
              "      <td>0</td>\n",
              "      <td>-614.507996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.687551</td>\n",
              "      <td>2.142630</td>\n",
              "      <td>3.066270</td>\n",
              "      <td>9.954641</td>\n",
              "      <td>10.081478</td>\n",
              "      <td>0.687551</td>\n",
              "      <td>2.105328</td>\n",
              "      <td>3.181119</td>\n",
              "      <td>10.001306</td>\n",
              "      <td>10.081478</td>\n",
              "      <td>0</td>\n",
              "      <td>-34626.695133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.686916</td>\n",
              "      <td>2.140013</td>\n",
              "      <td>3.071850</td>\n",
              "      <td>9.956670</td>\n",
              "      <td>10.054274</td>\n",
              "      <td>0.686916</td>\n",
              "      <td>2.101800</td>\n",
              "      <td>3.191233</td>\n",
              "      <td>10.008420</td>\n",
              "      <td>10.054274</td>\n",
              "      <td>0</td>\n",
              "      <td>-8128.370255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.698125</td>\n",
              "      <td>2.156244</td>\n",
              "      <td>3.063865</td>\n",
              "      <td>9.961413</td>\n",
              "      <td>10.091478</td>\n",
              "      <td>0.698125</td>\n",
              "      <td>2.117328</td>\n",
              "      <td>3.183534</td>\n",
              "      <td>10.009700</td>\n",
              "      <td>10.091478</td>\n",
              "      <td>0</td>\n",
              "      <td>-19414.185730</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19736</th>\n",
              "      <td>0.367023</td>\n",
              "      <td>2.290135</td>\n",
              "      <td>0.203793</td>\n",
              "      <td>2.771141</td>\n",
              "      <td>12.087111</td>\n",
              "      <td>0.367023</td>\n",
              "      <td>2.348889</td>\n",
              "      <td>0.177171</td>\n",
              "      <td>2.796853</td>\n",
              "      <td>12.087111</td>\n",
              "      <td>1</td>\n",
              "      <td>-311.681094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19737</th>\n",
              "      <td>0.438879</td>\n",
              "      <td>2.380740</td>\n",
              "      <td>0.202395</td>\n",
              "      <td>2.783980</td>\n",
              "      <td>12.266956</td>\n",
              "      <td>0.438879</td>\n",
              "      <td>2.432139</td>\n",
              "      <td>0.178642</td>\n",
              "      <td>2.804289</td>\n",
              "      <td>12.266956</td>\n",
              "      <td>1</td>\n",
              "      <td>-23.792976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19738</th>\n",
              "      <td>0.480072</td>\n",
              "      <td>2.424573</td>\n",
              "      <td>0.195123</td>\n",
              "      <td>2.746196</td>\n",
              "      <td>12.353345</td>\n",
              "      <td>0.480072</td>\n",
              "      <td>2.481738</td>\n",
              "      <td>0.174627</td>\n",
              "      <td>2.793009</td>\n",
              "      <td>12.353345</td>\n",
              "      <td>0</td>\n",
              "      <td>-798.582223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19739</th>\n",
              "      <td>0.507349</td>\n",
              "      <td>2.488776</td>\n",
              "      <td>0.177829</td>\n",
              "      <td>2.750903</td>\n",
              "      <td>12.217548</td>\n",
              "      <td>0.507349</td>\n",
              "      <td>2.522124</td>\n",
              "      <td>0.168590</td>\n",
              "      <td>2.789289</td>\n",
              "      <td>12.217548</td>\n",
              "      <td>0</td>\n",
              "      <td>-2611.331521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19740</th>\n",
              "      <td>0.561731</td>\n",
              "      <td>2.544242</td>\n",
              "      <td>0.164419</td>\n",
              "      <td>2.679422</td>\n",
              "      <td>12.063606</td>\n",
              "      <td>0.561731</td>\n",
              "      <td>2.568668</td>\n",
              "      <td>0.159972</td>\n",
              "      <td>2.716319</td>\n",
              "      <td>12.063606</td>\n",
              "      <td>1</td>\n",
              "      <td>-9.473712</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19741 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       s_temp_crank  d_temp_crank  ...  Label         Score\n",
              "0          0.666534      2.080444  ...      0  -2603.248592\n",
              "1          0.716066      2.153494  ...      0   -614.507996\n",
              "2          0.687551      2.142630  ...      0 -34626.695133\n",
              "3          0.686916      2.140013  ...      0  -8128.370255\n",
              "4          0.698125      2.156244  ...      0 -19414.185730\n",
              "...             ...           ...  ...    ...           ...\n",
              "19736      0.367023      2.290135  ...      1   -311.681094\n",
              "19737      0.438879      2.380740  ...      1    -23.792976\n",
              "19738      0.480072      2.424573  ...      0   -798.582223\n",
              "19739      0.507349      2.488776  ...      0  -2611.331521\n",
              "19740      0.561731      2.544242  ...      1     -9.473712\n",
              "\n",
              "[19741 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i7Y3urw25FEV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "abod_pred.to_csv(\"/content/abod_pred_df.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wuDYejC86Ve-",
        "colab_type": "text"
      },
      "source": [
        "# Clustering-Based Local Outlier\t"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fc_aFuwY6U2K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cluster = create_model('cluster')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PQnMZgp27NxL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cluster_pred = predict_model(cluster, data = merge_df2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0y3-CQ038e3m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "f4413787-6655-4ac7-da98-e1da49a18fcb"
      },
      "source": [
        "cluster_pred"
      ],
      "execution_count": 54,
      "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>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>Label</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.666534</td>\n",
              "      <td>2.080444</td>\n",
              "      <td>3.220836</td>\n",
              "      <td>10.105964</td>\n",
              "      <td>10.063987</td>\n",
              "      <td>0.666534</td>\n",
              "      <td>2.053527</td>\n",
              "      <td>3.307406</td>\n",
              "      <td>10.142075</td>\n",
              "      <td>10.063987</td>\n",
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              "      <th>1</th>\n",
              "      <td>0.716066</td>\n",
              "      <td>2.153494</td>\n",
              "      <td>3.135203</td>\n",
              "      <td>9.998847</td>\n",
              "      <td>10.075669</td>\n",
              "      <td>0.716066</td>\n",
              "      <td>2.120434</td>\n",
              "      <td>3.243347</td>\n",
              "      <td>10.053597</td>\n",
              "      <td>10.075669</td>\n",
              "      <td>0</td>\n",
              "      <td>7.961673</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.687551</td>\n",
              "      <td>2.142630</td>\n",
              "      <td>3.066270</td>\n",
              "      <td>9.954641</td>\n",
              "      <td>10.081478</td>\n",
              "      <td>0.687551</td>\n",
              "      <td>2.105328</td>\n",
              "      <td>3.181119</td>\n",
              "      <td>10.001306</td>\n",
              "      <td>10.081478</td>\n",
              "      <td>0</td>\n",
              "      <td>7.862128</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.686916</td>\n",
              "      <td>2.140013</td>\n",
              "      <td>3.071850</td>\n",
              "      <td>9.956670</td>\n",
              "      <td>10.054274</td>\n",
              "      <td>0.686916</td>\n",
              "      <td>2.101800</td>\n",
              "      <td>3.191233</td>\n",
              "      <td>10.008420</td>\n",
              "      <td>10.054274</td>\n",
              "      <td>0</td>\n",
              "      <td>7.877663</td>\n",
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              "      <th>4</th>\n",
              "      <td>0.698125</td>\n",
              "      <td>2.156244</td>\n",
              "      <td>3.063865</td>\n",
              "      <td>9.961413</td>\n",
              "      <td>10.091478</td>\n",
              "      <td>0.698125</td>\n",
              "      <td>2.117328</td>\n",
              "      <td>3.183534</td>\n",
              "      <td>10.009700</td>\n",
              "      <td>10.091478</td>\n",
              "      <td>0</td>\n",
              "      <td>7.870229</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>19736</th>\n",
              "      <td>0.367023</td>\n",
              "      <td>2.290135</td>\n",
              "      <td>0.203793</td>\n",
              "      <td>2.771141</td>\n",
              "      <td>12.087111</td>\n",
              "      <td>0.367023</td>\n",
              "      <td>2.348889</td>\n",
              "      <td>0.177171</td>\n",
              "      <td>2.796853</td>\n",
              "      <td>12.087111</td>\n",
              "      <td>0</td>\n",
              "      <td>3.669514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19737</th>\n",
              "      <td>0.438879</td>\n",
              "      <td>2.380740</td>\n",
              "      <td>0.202395</td>\n",
              "      <td>2.783980</td>\n",
              "      <td>12.266956</td>\n",
              "      <td>0.438879</td>\n",
              "      <td>2.432139</td>\n",
              "      <td>0.178642</td>\n",
              "      <td>2.804289</td>\n",
              "      <td>12.266956</td>\n",
              "      <td>0</td>\n",
              "      <td>3.788660</td>\n",
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              "    <tr>\n",
              "      <th>19738</th>\n",
              "      <td>0.480072</td>\n",
              "      <td>2.424573</td>\n",
              "      <td>0.195123</td>\n",
              "      <td>2.746196</td>\n",
              "      <td>12.353345</td>\n",
              "      <td>0.480072</td>\n",
              "      <td>2.481738</td>\n",
              "      <td>0.174627</td>\n",
              "      <td>2.793009</td>\n",
              "      <td>12.353345</td>\n",
              "      <td>0</td>\n",
              "      <td>3.888200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19739</th>\n",
              "      <td>0.507349</td>\n",
              "      <td>2.488776</td>\n",
              "      <td>0.177829</td>\n",
              "      <td>2.750903</td>\n",
              "      <td>12.217548</td>\n",
              "      <td>0.507349</td>\n",
              "      <td>2.522124</td>\n",
              "      <td>0.168590</td>\n",
              "      <td>2.789289</td>\n",
              "      <td>12.217548</td>\n",
              "      <td>0</td>\n",
              "      <td>3.796066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19740</th>\n",
              "      <td>0.561731</td>\n",
              "      <td>2.544242</td>\n",
              "      <td>0.164419</td>\n",
              "      <td>2.679422</td>\n",
              "      <td>12.063606</td>\n",
              "      <td>0.561731</td>\n",
              "      <td>2.568668</td>\n",
              "      <td>0.159972</td>\n",
              "      <td>2.716319</td>\n",
              "      <td>12.063606</td>\n",
              "      <td>0</td>\n",
              "      <td>3.785228</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19741 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       s_temp_crank  d_temp_crank  s_pressure_crank  ...   rpm_mean  Label     Score\n",
              "0          0.666534      2.080444          3.220836  ...  10.063987      0  8.130700\n",
              "1          0.716066      2.153494          3.135203  ...  10.075669      0  7.961673\n",
              "2          0.687551      2.142630          3.066270  ...  10.081478      0  7.862128\n",
              "3          0.686916      2.140013          3.071850  ...  10.054274      0  7.877663\n",
              "4          0.698125      2.156244          3.063865  ...  10.091478      0  7.870229\n",
              "...             ...           ...               ...  ...        ...    ...       ...\n",
              "19736      0.367023      2.290135          0.203793  ...  12.087111      0  3.669514\n",
              "19737      0.438879      2.380740          0.202395  ...  12.266956      0  3.788660\n",
              "19738      0.480072      2.424573          0.195123  ...  12.353345      0  3.888200\n",
              "19739      0.507349      2.488776          0.177829  ...  12.217548      0  3.796066\n",
              "19740      0.561731      2.544242          0.164419  ...  12.063606      0  3.785228\n",
              "\n",
              "[19741 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0j1T__BE8q6z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cluster_pred.to_csv(\"/content/cluster_pred_df.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}