machine_maintenance_prediction.ipynb

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      "authorship_tag": "ABX9TyP0rR5JamSgw2mom0JqAgGv",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/beratcmn/machine-maintenance-prediction/blob/main/machine_maintenance_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AoJq7DWlPW05",
        "outputId": "5ea37997-38e3-4e62-ccb7-d022ec4e652d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: line 1: nvidia-smi: command not found\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Getting the data"
      ],
      "metadata": {
        "id": "nEe6R1PbS3R7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/beratcmn/machine-maintenance-prediction/main/data/predictive_maintenance.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kT1FsmDXQFuj",
        "outputId": "c7b92745-1d73-4dab-d0e6-4c089a3db75f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-09-02 00:17:54--  https://raw.githubusercontent.com/beratcmn/machine-maintenance-prediction/main/data/predictive_maintenance.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 521014 (509K) [text/plain]\n",
            "Saving to: ‘predictive_maintenance.csv’\n",
            "\n",
            "predictive_maintena 100%[===================>] 508.80K  --.-KB/s    in 0.04s   \n",
            "\n",
            "2023-09-02 00:17:54 (12.6 MB/s) - ‘predictive_maintenance.csv’ saved [521014/521014]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing the required libraries"
      ],
      "metadata": {
        "id": "bO5L8MLsS6Y9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "MTzJ5j9eQEJU"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading and analyzing the data"
      ],
      "metadata": {
        "id": "vr8UWYt0S9Oq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"./predictive_maintenance.csv\")"
      ],
      "metadata": {
        "id": "vrJYBYMJQP_D"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
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        "id": "Sv6AvewSRzl1",
        "outputId": "053037cc-5d0a-46a4-fac0-d9e0ca417973"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 10 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   UDI                      10000 non-null  int64  \n",
            " 1   Product ID               10000 non-null  object \n",
            " 2   Type                     10000 non-null  object \n",
            " 3   Air temperature [K]      10000 non-null  float64\n",
            " 4   Process temperature [K]  10000 non-null  float64\n",
            " 5   Rotational speed [rpm]   10000 non-null  int64  \n",
            " 6   Torque [Nm]              10000 non-null  float64\n",
            " 7   Tool wear [min]          10000 non-null  int64  \n",
            " 8   Target                   10000 non-null  int64  \n",
            " 9   Failure Type             10000 non-null  object \n",
            "dtypes: float64(3), int64(4), object(3)\n",
            "memory usage: 781.4+ KB\n"
          ]
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      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
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        "outputId": "a82b9968-6545-4fe3-c287-502f4cdecb7a"
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      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "UDI                        0\n",
              "Product ID                 0\n",
              "Type                       0\n",
              "Air temperature [K]        0\n",
              "Process temperature [K]    0\n",
              "Rotational speed [rpm]     0\n",
              "Torque [Nm]                0\n",
              "Tool wear [min]            0\n",
              "Target                     0\n",
              "Failure Type               0\n",
              "dtype: int64"
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          "metadata": {},
          "execution_count": 5
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      "cell_type": "code",
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        "df.duplicated().sum()"
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              "0"
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          "metadata": {},
          "execution_count": 6
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        "df.head(n=10_000)"
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        "outputId": "a57a394b-fe85-4448-eb05-89daf5a1b8b1"
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      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        UDI Product ID Type  Air temperature [K]  Process temperature [K]  \\\n",
              "0         1     M14860    M                298.1                    308.6   \n",
              "1         2     L47181    L                298.2                    308.7   \n",
              "2         3     L47182    L                298.1                    308.5   \n",
              "3         4     L47183    L                298.2                    308.6   \n",
              "4         5     L47184    L                298.2                    308.7   \n",
              "...     ...        ...  ...                  ...                      ...   \n",
              "9995   9996     M24855    M                298.8                    308.4   \n",
              "9996   9997     H39410    H                298.9                    308.4   \n",
              "9997   9998     M24857    M                299.0                    308.6   \n",
              "9998   9999     H39412    H                299.0                    308.7   \n",
              "9999  10000     M24859    M                299.0                    308.7   \n",
              "\n",
              "      Rotational speed [rpm]  Torque [Nm]  Tool wear [min]  Target  \\\n",
              "0                       1551         42.8                0       0   \n",
              "1                       1408         46.3                3       0   \n",
              "2                       1498         49.4                5       0   \n",
              "3                       1433         39.5                7       0   \n",
              "4                       1408         40.0                9       0   \n",
              "...                      ...          ...              ...     ...   \n",
              "9995                    1604         29.5               14       0   \n",
              "9996                    1632         31.8               17       0   \n",
              "9997                    1645         33.4               22       0   \n",
              "9998                    1408         48.5               25       0   \n",
              "9999                    1500         40.2               30       0   \n",
              "\n",
              "     Failure Type  \n",
              "0      No Failure  \n",
              "1      No Failure  \n",
              "2      No Failure  \n",
              "3      No Failure  \n",
              "4      No Failure  \n",
              "...           ...  \n",
              "9995   No Failure  \n",
              "9996   No Failure  \n",
              "9997   No Failure  \n",
              "9998   No Failure  \n",
              "9999   No Failure  \n",
              "\n",
              "[10000 rows x 10 columns]"
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              "      <td>M14860</td>\n",
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              "      <td>42.8</td>\n",
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              "      <td>1408</td>\n",
              "      <td>46.3</td>\n",
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              "      <td>49.4</td>\n",
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              "      <td>298.2</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1433</td>\n",
              "      <td>39.5</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
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              "      <td>40.0</td>\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ad05207e-7563-47b2-9a11-eff90ee4da39 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x79cd89e39480>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Values</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def value_plot(df, y, figscale=1):\n",
              "  from matplotlib import pyplot as plt\n",
              "  df[y].plot(kind='line', figsize=(8 * figscale, 4 * figscale), title=y)\n",
              "  plt.gca().spines[['top', 'right']].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = value_plot(_df_0, *['index'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-8b25d01e-84e7-4d31-aeb6-daaea7ada711\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-8b25d01e-84e7-4d31-aeb6-daaea7ada711\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def value_plot(df, y, figscale=1):\n",
              "  from matplotlib import pyplot as plt\n",
              "  df[y].plot(kind='line', figsize=(8 * figscale, 4 * figscale), title=y)\n",
              "  plt.gca().spines[['top', 'right']].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = value_plot(_df_1, *['UDI'], **{})\n",
              "chart"
            ],
            "text/html": [
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-eed090e3-cd77-419a-a577-3de4a07c8787\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-eed090e3-cd77-419a-a577-3de4a07c8787\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def value_plot(df, y, figscale=1):\n",
              "  from matplotlib import pyplot as plt\n",
              "  df[y].plot(kind='line', figsize=(8 * figscale, 4 * figscale), title=y)\n",
              "  plt.gca().spines[['top', 'right']].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = value_plot(_df_2, *['Air temperature [K]'], **{})\n",
              "chart"
            ],
            "text/html": [
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-b4660644-bb46-4489-b589-eba61868f17f\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-b4660644-bb46-4489-b589-eba61868f17f\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def value_plot(df, y, figscale=1):\n",
              "  from matplotlib import pyplot as plt\n",
              "  df[y].plot(kind='line', figsize=(8 * figscale, 4 * figscale), title=y)\n",
              "  plt.gca().spines[['top', 'right']].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = value_plot(_df_3, *['Process temperature [K]'], **{})\n",
              "chart"
            ],
            "text/html": [
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              "SUVORK5CYII=\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-4f0328bb-b1a1-4a0b-b323-e2e1e28e392f\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-4f0328bb-b1a1-4a0b-b323-e2e1e28e392f\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x79cd858ad4b0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figscale=1):\n",
              "  from matplotlib import pyplot as plt\n",
              "  df[colname].plot(kind='hist', bins=num_bins, title=colname, figsize=(8*figscale, 4*figscale))\n",
              "  plt.gca().spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(_df_4, *['index'], **{})\n",
              "chart"
            ],
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              "AAAAAElFTkSuQmCC\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-9db7b46f-b38f-4c93-91f8-9b8691856b43\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-9db7b46f-b38f-4c93-91f8-9b8691856b43\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figscale=1):\n",
              "  from matplotlib import pyplot as plt\n",
              "  df[colname].plot(kind='hist', bins=num_bins, title=colname, figsize=(8*figscale, 4*figscale))\n",
              "  plt.gca().spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(_df_5, *['UDI'], **{})\n",
              "chart"
            ],
            "text/html": [
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              "TkSuQmCC\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-3e8e1a46-6abf-473f-995d-2135d7138087\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-3e8e1a46-6abf-473f-995d-2135d7138087\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figscale=1):\n",
              "  from matplotlib import pyplot as plt\n",
              "  df[colname].plot(kind='hist', bins=num_bins, title=colname, figsize=(8*figscale, 4*figscale))\n",
              "  plt.gca().spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(_df_6, *['Air temperature [K]'], **{})\n",
              "chart"
            ],
            "text/html": [
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              "SUVORK5CYII=\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-458f72d3-e4ca-421f-b510-c18cd7ec170e\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-458f72d3-e4ca-421f-b510-c18cd7ec170e\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figscale=1):\n",
              "  from matplotlib import pyplot as plt\n",
              "  df[colname].plot(kind='hist', bins=num_bins, title=colname, figsize=(8*figscale, 4*figscale))\n",
              "  plt.gca().spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(_df_7, *['Process temperature [K]'], **{})\n",
              "chart"
            ],
            "text/html": [
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-69d4db1f-4477-49a2-821b-ab910d883b0a\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-69d4db1f-4477-49a2-821b-ab910d883b0a\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x79cd85579120>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Categorical distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def categorical_histogram(df, colname, figscale=1, mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  df.groupby(colname).size().plot(kind='barh', color=sns.palettes.mpl_palette(mpl_palette_name), figsize=(8*figscale, 4.8*figscale))\n",
              "  plt.gca().spines[['top', 'right',]].set_visible(False)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = categorical_histogram(_df_8, *['Type'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-12f97c86-24e9-4963-91a9-c7365f4b2069\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-12f97c86-24e9-4963-91a9-c7365f4b2069\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def categorical_histogram(df, colname, figscale=1, mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  df.groupby(colname).size().plot(kind='barh', color=sns.palettes.mpl_palette(mpl_palette_name), figsize=(8*figscale, 4.8*figscale))\n",
              "  plt.gca().spines[['top', 'right',]].set_visible(False)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = categorical_histogram(_df_9, *['Failure Type'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-b43712b0-d8ea-4e63-b456-bc48d61fb3d0\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-b43712b0-d8ea-4e63-b456-bc48d61fb3d0\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x79cd8381ef80>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">2-d distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def scatter_plots(df, colname_pairs, figscale=1, alpha=.8):\n",
              "  from matplotlib import pyplot as plt\n",
              "  plt.figure(figsize=(len(colname_pairs) * 6 * figscale, 6 * figscale))\n",
              "  for plot_i, (x_colname, y_colname) in enumerate(colname_pairs, start=1):\n",
              "    ax = plt.subplot(1, len(colname_pairs), plot_i)\n",
              "    df.plot(kind='scatter', x=x_colname, y=y_colname, s=(32 * figscale), alpha=alpha, ax=ax)\n",
              "    ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = scatter_plots(_df_10, *[[['index', 'UDI'], ['UDI', 'Air temperature [K]'], ['Air temperature [K]', 'Process temperature [K]'], ['Process temperature [K]', 'Rotational speed [rpm]']]], **{})\n",
              "chart"
            ],
            "text/html": [
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-fb0ca7cd-296f-4a1e-9386-54b24bec68d8\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-fb0ca7cd-296f-4a1e-9386-54b24bec68d8\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x79ce1620af50>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">2-d categorical distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def heatmap(df, x_colname, y_colname, figscale=1, mpl_palette_name='viridis'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  import pandas as pd\n",
              "  plt.subplots(figsize=(8 * figscale, 8 * figscale))\n",
              "  df_2dhist = pd.DataFrame({\n",
              "      x_label: grp[y_colname].value_counts()\n",
              "      for x_label, grp in df.groupby(x_colname)\n",
              "  })\n",
              "  sns.heatmap(df_2dhist, cmap=mpl_palette_name)\n",
              "  plt.xlabel(x_colname)\n",
              "  plt.ylabel(y_colname)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = heatmap(_df_11, *['Type', 'Failure Type'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-e9e08e4f-7805-4b8e-a9f5-39c4fb6f0195\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-e9e08e4f-7805-4b8e-a9f5-39c4fb6f0195\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x79cd835a1c90>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Faceted distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def violin_plot(df, value_colname, facet_colname, figscale=1, mpl_palette_name='Dark2', **kwargs):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  figsize = (12 * figscale, 1.2 * figscale * len(df[facet_colname].unique()))\n",
              "  plt.figure(figsize=figsize)\n",
              "  sns.violinplot(df, x=value_colname, y=facet_colname, palette=mpl_palette_name, **kwargs)\n",
              "  sns.despine(top=True, right=True, bottom=True, left=True)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = violin_plot(_df_12, *['index', 'Type'], **{'inner': 'box'})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-5fa9b394-e02c-4c8c-aa46-4636e8e0de79\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-5fa9b394-e02c-4c8c-aa46-4636e8e0de79\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def violin_plot(df, value_colname, facet_colname, figscale=1, mpl_palette_name='Dark2', **kwargs):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  figsize = (12 * figscale, 1.2 * figscale * len(df[facet_colname].unique()))\n",
              "  plt.figure(figsize=figsize)\n",
              "  sns.violinplot(df, x=value_colname, y=facet_colname, palette=mpl_palette_name, **kwargs)\n",
              "  sns.despine(top=True, right=True, bottom=True, left=True)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = violin_plot(_df_13, *['index', 'Failure Type'], **{'inner': 'box'})\n",
              "chart"
            ],
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              "/7cQhcdJvympAAAAAElFTkSuQmCC\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-aaab2df6-fec9-408c-a6e6-dac9de3e3dde\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-aaab2df6-fec9-408c-a6e6-dac9de3e3dde\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def violin_plot(df, value_colname, facet_colname, figscale=1, mpl_palette_name='Dark2', **kwargs):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  figsize = (12 * figscale, 1.2 * figscale * len(df[facet_colname].unique()))\n",
              "  plt.figure(figsize=figsize)\n",
              "  sns.violinplot(df, x=value_colname, y=facet_colname, palette=mpl_palette_name, **kwargs)\n",
              "  sns.despine(top=True, right=True, bottom=True, left=True)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = violin_plot(_df_14, *['UDI', 'Type'], **{'inner': 'box'})\n",
              "chart"
            ],
            "text/html": [
              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-8849ca1e-efc2-48db-97df-0864e3350dcd\">\n",
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              "SUVORK5CYII=\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-8849ca1e-efc2-48db-97df-0864e3350dcd\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-8849ca1e-efc2-48db-97df-0864e3350dcd\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def violin_plot(df, value_colname, facet_colname, figscale=1, mpl_palette_name='Dark2', **kwargs):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  figsize = (12 * figscale, 1.2 * figscale * len(df[facet_colname].unique()))\n",
              "  plt.figure(figsize=figsize)\n",
              "  sns.violinplot(df, x=value_colname, y=facet_colname, palette=mpl_palette_name, **kwargs)\n",
              "  sns.despine(top=True, right=True, bottom=True, left=True)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = violin_plot(_df_15, *['UDI', 'Failure Type'], **{'inner': 'box'})\n",
              "chart"
            ],
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              "AElFTkSuQmCC\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-39a3778a-1023-4cfb-a09f-72138cd35dfe\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-39a3778a-1023-4cfb-a09f-72138cd35dfe\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x79cd81241e10>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Time series</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def time_series_multiline(df, timelike_colname, value_colname, series_colname, figscale=1, mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  figsize = (10 * figscale, 5.2 * figscale)\n",
              "  palette = list(sns.palettes.mpl_palette(mpl_palette_name))\n",
              "  def _plot_series(series, series_name, series_index=0):\n",
              "    if value_colname == 'count()':\n",
              "      counted = (series[timelike_colname]\n",
              "                 .value_counts()\n",
              "                 .reset_index(name='counts')\n",
              "                 .rename({'index': timelike_colname}, axis=1)\n",
              "                 .sort_values(timelike_colname, ascending=True))\n",
              "      xs = counted[timelike_colname]\n",
              "      ys = counted['counts']\n",
              "    else:\n",
              "      xs = series[timelike_colname]\n",
              "      ys = series[value_colname]\n",
              "    plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "  fig, ax = plt.subplots(figsize=figsize, layout='constrained')\n",
              "  df = df.sort_values(timelike_colname, ascending=True)\n",
              "  if series_colname:\n",
              "    for i, (series_name, series) in enumerate(df.groupby(series_colname)):\n",
              "      _plot_series(series, series_name, i)\n",
              "    fig.legend(title=series_colname, bbox_to_anchor=(1, 1), loc='upper left')\n",
              "  else:\n",
              "    _plot_series(df, '')\n",
              "  sns.despine(fig=fig, ax=ax)\n",
              "  plt.xlabel(timelike_colname)\n",
              "  plt.ylabel(value_colname)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = time_series_multiline(_df_16, *['index', 'Air temperature [K]', 'Type'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-ac218ef9-a590-4e18-ad3e-7338c5f842e4\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-ac218ef9-a590-4e18-ad3e-7338c5f842e4\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def time_series_multiline(df, timelike_colname, value_colname, series_colname, figscale=1, mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  figsize = (10 * figscale, 5.2 * figscale)\n",
              "  palette = list(sns.palettes.mpl_palette(mpl_palette_name))\n",
              "  def _plot_series(series, series_name, series_index=0):\n",
              "    if value_colname == 'count()':\n",
              "      counted = (series[timelike_colname]\n",
              "                 .value_counts()\n",
              "                 .reset_index(name='counts')\n",
              "                 .rename({'index': timelike_colname}, axis=1)\n",
              "                 .sort_values(timelike_colname, ascending=True))\n",
              "      xs = counted[timelike_colname]\n",
              "      ys = counted['counts']\n",
              "    else:\n",
              "      xs = series[timelike_colname]\n",
              "      ys = series[value_colname]\n",
              "    plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "  fig, ax = plt.subplots(figsize=figsize, layout='constrained')\n",
              "  df = df.sort_values(timelike_colname, ascending=True)\n",
              "  if series_colname:\n",
              "    for i, (series_name, series) in enumerate(df.groupby(series_colname)):\n",
              "      _plot_series(series, series_name, i)\n",
              "    fig.legend(title=series_colname, bbox_to_anchor=(1, 1), loc='upper left')\n",
              "  else:\n",
              "    _plot_series(df, '')\n",
              "  sns.despine(fig=fig, ax=ax)\n",
              "  plt.xlabel(timelike_colname)\n",
              "  plt.ylabel(value_colname)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = time_series_multiline(_df_17, *['index', 'Air temperature [K]', 'Failure Type'], **{})\n",
              "chart"
            ],
            "text/html": [
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-6d678bb5-ef21-4b97-be57-8f9174946835\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-6d678bb5-ef21-4b97-be57-8f9174946835\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def time_series_multiline(df, timelike_colname, value_colname, series_colname, figscale=1, mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  figsize = (10 * figscale, 5.2 * figscale)\n",
              "  palette = list(sns.palettes.mpl_palette(mpl_palette_name))\n",
              "  def _plot_series(series, series_name, series_index=0):\n",
              "    if value_colname == 'count()':\n",
              "      counted = (series[timelike_colname]\n",
              "                 .value_counts()\n",
              "                 .reset_index(name='counts')\n",
              "                 .rename({'index': timelike_colname}, axis=1)\n",
              "                 .sort_values(timelike_colname, ascending=True))\n",
              "      xs = counted[timelike_colname]\n",
              "      ys = counted['counts']\n",
              "    else:\n",
              "      xs = series[timelike_colname]\n",
              "      ys = series[value_colname]\n",
              "    plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "  fig, ax = plt.subplots(figsize=figsize, layout='constrained')\n",
              "  df = df.sort_values(timelike_colname, ascending=True)\n",
              "  if series_colname:\n",
              "    for i, (series_name, series) in enumerate(df.groupby(series_colname)):\n",
              "      _plot_series(series, series_name, i)\n",
              "    fig.legend(title=series_colname, bbox_to_anchor=(1, 1), loc='upper left')\n",
              "  else:\n",
              "    _plot_series(df, '')\n",
              "  sns.despine(fig=fig, ax=ax)\n",
              "  plt.xlabel(timelike_colname)\n",
              "  plt.ylabel(value_colname)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = time_series_multiline(_df_18, *['index', 'Process temperature [K]', 'Type'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-dbd4cc3b-05f1-4a05-aa6f-edc5c26cc186\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-dbd4cc3b-05f1-4a05-aa6f-edc5c26cc186\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def time_series_multiline(df, timelike_colname, value_colname, series_colname, figscale=1, mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  figsize = (10 * figscale, 5.2 * figscale)\n",
              "  palette = list(sns.palettes.mpl_palette(mpl_palette_name))\n",
              "  def _plot_series(series, series_name, series_index=0):\n",
              "    if value_colname == 'count()':\n",
              "      counted = (series[timelike_colname]\n",
              "                 .value_counts()\n",
              "                 .reset_index(name='counts')\n",
              "                 .rename({'index': timelike_colname}, axis=1)\n",
              "                 .sort_values(timelike_colname, ascending=True))\n",
              "      xs = counted[timelike_colname]\n",
              "      ys = counted['counts']\n",
              "    else:\n",
              "      xs = series[timelike_colname]\n",
              "      ys = series[value_colname]\n",
              "    plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "  fig, ax = plt.subplots(figsize=figsize, layout='constrained')\n",
              "  df = df.sort_values(timelike_colname, ascending=True)\n",
              "  if series_colname:\n",
              "    for i, (series_name, series) in enumerate(df.groupby(series_colname)):\n",
              "      _plot_series(series, series_name, i)\n",
              "    fig.legend(title=series_colname, bbox_to_anchor=(1, 1), loc='upper left')\n",
              "  else:\n",
              "    _plot_series(df, '')\n",
              "  sns.despine(fig=fig, ax=ax)\n",
              "  plt.xlabel(timelike_colname)\n",
              "  plt.ylabel(value_colname)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = time_series_multiline(_df_19, *['index', 'Process temperature [K]', 'Failure Type'], **{})\n",
              "chart"
            ],
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              "qlXccsstkduFEEKI4yFBrRBiQJ5++mkee+wxJk2axM9//nMWLVoU2fY///M/REdHM23aNKZMmcLi\n",
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              "EEIIIYQY8SSoFUIIIYQQI54EtUIIIYQQYsSToFYIIYQQQox4EtQKIYQQQogRT4JaIYQQQggx4klQ\n",
              "K4QQQgghRjwJaoUQQgghxIgnQa0QQgghhBjxJKgVQgghhBAj3v8Pg4rPvpNchksAAAAASUVORK5C\n",
              "YII=\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-56110443-1beb-4bce-a8d9-591df347e113\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-56110443-1beb-4bce-a8d9-591df347e113\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cleaning the data"
      ],
      "metadata": {
        "id": "AgYhmlarTEo4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = df.drop(columns=[\"UDI\", \"Product ID\", \"Target\"])"
      ],
      "metadata": {
        "id": "WrgjxGO0STq4"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "id": "1Q9HAXQySsuP",
        "outputId": "82c0da71-8655-4bf3-f6de-11d4bbb430d9"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Type  Air temperature [K]  Process temperature [K]  Rotational speed [rpm]  \\\n",
              "0    M                298.1                    308.6                    1551   \n",
              "1    L                298.2                    308.7                    1408   \n",
              "2    L                298.1                    308.5                    1498   \n",
              "3    L                298.2                    308.6                    1433   \n",
              "4    L                298.2                    308.7                    1408   \n",
              "\n",
              "   Torque [Nm]  Tool wear [min] Failure Type  \n",
              "0         42.8                0   No Failure  \n",
              "1         46.3                3   No Failure  \n",
              "2         49.4                5   No Failure  \n",
              "3         39.5                7   No Failure  \n",
              "4         40.0                9   No Failure  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d99de462-20be-478f-ad31-2ba3cf6cf449\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Type</th>\n",
              "      <th>Air temperature [K]</th>\n",
              "      <th>Process temperature [K]</th>\n",
              "      <th>Rotational speed [rpm]</th>\n",
              "      <th>Torque [Nm]</th>\n",
              "      <th>Tool wear [min]</th>\n",
              "      <th>Failure Type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M</td>\n",
              "      <td>298.1</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1551</td>\n",
              "      <td>42.8</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>L</td>\n",
              "      <td>298.2</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1408</td>\n",
              "      <td>46.3</td>\n",
              "      <td>3</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>L</td>\n",
              "      <td>298.1</td>\n",
              "      <td>308.5</td>\n",
              "      <td>1498</td>\n",
              "      <td>49.4</td>\n",
              "      <td>5</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>L</td>\n",
              "      <td>298.2</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1433</td>\n",
              "      <td>39.5</td>\n",
              "      <td>7</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>L</td>\n",
              "      <td>298.2</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1408</td>\n",
              "      <td>40.0</td>\n",
              "      <td>9</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d99de462-20be-478f-ad31-2ba3cf6cf449')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d99de462-20be-478f-ad31-2ba3cf6cf449 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d99de462-20be-478f-ad31-2ba3cf6cf449');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-341cfcb5-d04f-4a5e-824a-7a484204db4d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-341cfcb5-d04f-4a5e-824a-7a484204db4d')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-341cfcb5-d04f-4a5e-824a-7a484204db4d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Mapping each quality to a number.\n",
        "\n",
        "Low (L) = 0\n",
        "\n",
        "Medium (M) = 1\n",
        "\n",
        "High (H) = 2"
      ],
      "metadata": {
        "id": "PU6z9wTiT8M-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"Type\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8L-EV69KUfQc",
        "outputId": "3b47476c-b5e4-47fa-89cd-b35c958b1a83"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['M', 'L', 'H'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "quality_mapping = {'L': 0, 'M': 1, 'H': 2}\n",
        "data['Type'] = data['Type'].replace(quality_mapping)"
      ],
      "metadata": {
        "id": "KJL5Uc7WTqz0"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Mapping Failure Type to a number.\n",
        "\n",
        "No Failure = 0\n",
        "\n",
        "Power Failure = 1\n",
        "\n",
        "Tool Wear Failure = 2\n",
        "\n",
        "Overstrain Failure = 3\n",
        "\n",
        "Random Failure = 4\n",
        "\n",
        "Heat Dissipation Failure = 5"
      ],
      "metadata": {
        "id": "Up4SnN1hUom5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"Failure Type\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YuaUGNuvUMKz",
        "outputId": "2419ae2c-0076-485a-d556-78ca07bc2cf5"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['No Failure', 'Power Failure', 'Tool Wear Failure',\n",
              "       'Overstrain Failure', 'Random Failures',\n",
              "       'Heat Dissipation Failure'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "failure_type_mapping = {\n",
        "    \"No Failure\": 0,\n",
        "    \"Power Failure\": 1,\n",
        "    \"Tool Wear Failure\": 2,\n",
        "    \"Overstrain Failure\": 3,\n",
        "    \"Random Failures\": 4,\n",
        "    \"Heat Dissipation Failure\": 5,\n",
        "}\n",
        "data['Failure Type'] = data['Failure Type'].replace(failure_type_mapping)"
      ],
      "metadata": {
        "id": "b1Fkq00rVIpy"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(n=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 687
        },
        "id": "79jBWhFPTw16",
        "outputId": "cdddee56-ee66-4a8b-eba2-fe36e79733ed"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Type  Air temperature [K]  Process temperature [K]  \\\n",
              "0      1                298.1                    308.6   \n",
              "1      0                298.2                    308.7   \n",
              "2      0                298.1                    308.5   \n",
              "3      0                298.2                    308.6   \n",
              "4      0                298.2                    308.7   \n",
              "..   ...                  ...                      ...   \n",
              "95     1                299.0                    309.0   \n",
              "96     1                299.0                    309.0   \n",
              "97     1                298.9                    308.9   \n",
              "98     0                298.9                    308.8   \n",
              "99     0                298.8                    308.8   \n",
              "\n",
              "    Rotational speed [rpm]  Torque [Nm]  Tool wear [min]  Failure Type  \n",
              "0                     1551         42.8                0             0  \n",
              "1                     1408         46.3                3             0  \n",
              "2                     1498         49.4                5             0  \n",
              "3                     1433         39.5                7             0  \n",
              "4                     1408         40.0                9             0  \n",
              "..                     ...          ...              ...           ...  \n",
              "95                    1351         52.2               44             0  \n",
              "96                    1575         35.3               47             0  \n",
              "97                    1750         29.9               50             0  \n",
              "98                    1529         32.7               53             0  \n",
              "99                    1523         44.4               55             0  \n",
              "\n",
              "[100 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-be6e3141-220b-4029-83c8-176469b9b702\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Type</th>\n",
              "      <th>Air temperature [K]</th>\n",
              "      <th>Process temperature [K]</th>\n",
              "      <th>Rotational speed [rpm]</th>\n",
              "      <th>Torque [Nm]</th>\n",
              "      <th>Tool wear [min]</th>\n",
              "      <th>Failure Type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>298.1</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1551</td>\n",
              "      <td>42.8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>298.2</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1408</td>\n",
              "      <td>46.3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>298.1</td>\n",
              "      <td>308.5</td>\n",
              "      <td>1498</td>\n",
              "      <td>49.4</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>298.2</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1433</td>\n",
              "      <td>39.5</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>298.2</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1408</td>\n",
              "      <td>40.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>1</td>\n",
              "      <td>299.0</td>\n",
              "      <td>309.0</td>\n",
              "      <td>1351</td>\n",
              "      <td>52.2</td>\n",
              "      <td>44</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>1</td>\n",
              "      <td>299.0</td>\n",
              "      <td>309.0</td>\n",
              "      <td>1575</td>\n",
              "      <td>35.3</td>\n",
              "      <td>47</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>1</td>\n",
              "      <td>298.9</td>\n",
              "      <td>308.9</td>\n",
              "      <td>1750</td>\n",
              "      <td>29.9</td>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>0</td>\n",
              "      <td>298.9</td>\n",
              "      <td>308.8</td>\n",
              "      <td>1529</td>\n",
              "      <td>32.7</td>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>0</td>\n",
              "      <td>298.8</td>\n",
              "      <td>308.8</td>\n",
              "      <td>1523</td>\n",
              "      <td>44.4</td>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 7 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-be6e3141-220b-4029-83c8-176469b9b702 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-be6e3141-220b-4029-83c8-176469b9b702');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-fa0f7221-cc1b-4849-809f-8fdd79f9e73b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fa0f7221-cc1b-4849-809f-8fdd79f9e73b')\"\n",
              "            title=\"Suggest charts.\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fa0f7221-cc1b-4849-809f-8fdd79f9e73b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Splitting Features and Targets"
      ],
      "metadata": {
        "id": "hjQV4badWpnR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.sample(frac=1, random_state=36)"
      ],
      "metadata": {
        "id": "SHOu109CWs7s"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop(columns=['Failure Type'])\n",
        "y = data['Failure Type']"
      ],
      "metadata": {
        "id": "osiXw-gPW7oY"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "id": "Ez6HxXlbXFUK",
        "outputId": "2b7d14d4-e407-4253-d96b-cc04c503e4d1"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Type  Air temperature [K]  Process temperature [K]  \\\n",
              "3413     0                301.3                    310.3   \n",
              "5581     1                302.5                    312.1   \n",
              "3466     0                301.7                    310.6   \n",
              "4483     0                302.7                    310.4   \n",
              "5366     2                303.1                    312.6   \n",
              "\n",
              "      Rotational speed [rpm]  Torque [Nm]  Tool wear [min]  \n",
              "3413                    1560         36.3              129  \n",
              "5581                    1454         47.6               37  \n",
              "3466                    1447         56.9               47  \n",
              "4483                    1489         42.7               33  \n",
              "5366                    1404         40.6              152  "
            ],
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              "\n",
              "  <div id=\"df-522c637f-56e2-4425-abc0-bec94d55eb68\" class=\"colab-df-container\">\n",
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              "      <th>Type</th>\n",
              "      <th>Air temperature [K]</th>\n",
              "      <th>Process temperature [K]</th>\n",
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              "    <tr>\n",
              "      <th>4483</th>\n",
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              "      <td>312.6</td>\n",
              "      <td>1404</td>\n",
              "      <td>40.6</td>\n",
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              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-522c637f-56e2-4425-abc0-bec94d55eb68 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-522c637f-56e2-4425-abc0-bec94d55eb68');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2a3f2960-2a24-4f55-9c3b-b252b69c0426\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2a3f2960-2a24-4f55-9c3b-b252b69c0426')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2a3f2960-2a24-4f55-9c3b-b252b69c0426 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eXP5kMx7XJsY",
        "outputId": "4f41b943-d2a8-42ad-bcca-c5cd7a2cf53a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3413    0\n",
              "5581    0\n",
              "3466    0\n",
              "4483    0\n",
              "5366    0\n",
              "Name: Failure Type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Splitting the data for training and testing\n",
        "\n",
        "I want to use %80 percent of the data for training and remaining %20 percent for testing the model."
      ],
      "metadata": {
        "id": "M1MvI17RXn6m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_proportion = 0.8"
      ],
      "metadata": {
        "id": "2tmeNDcDXrKj"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_rows = int(train_proportion * len(X))\n",
        "X_test_rows = len(X) - X_train_rows\n",
        "X_train_rows, X_test_rows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lZFs58wiX4eo",
        "outputId": "1f9f0aab-ad96-4721-fac2-895d0327a0cd"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8000, 2000)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_rows = int(train_proportion * len(y))\n",
        "y_test_rows = len(y) - y_train_rows\n",
        "y_train_rows, y_test_rows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mRxoZyFOX7rY",
        "outputId": "fe8cfd41-72ce-4063-9496-6556cbf21278"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8000, 2000)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X[:X_train_rows]\n",
        "X_test = X[X_test_rows:]"
      ],
      "metadata": {
        "id": "3am9PGR9YCWC"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "id": "LOBlzJLEYEOQ",
        "outputId": "b1690e72-9d1e-490b-b638-cde1c9d46133"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Type  Air temperature [K]  Process temperature [K]  \\\n",
              "3413     0                301.3                    310.3   \n",
              "5581     1                302.5                    312.1   \n",
              "3466     0                301.7                    310.6   \n",
              "4483     0                302.7                    310.4   \n",
              "5366     2                303.1                    312.6   \n",
              "\n",
              "      Rotational speed [rpm]  Torque [Nm]  Tool wear [min]  \n",
              "3413                    1560         36.3              129  \n",
              "5581                    1454         47.6               37  \n",
              "3466                    1447         56.9               47  \n",
              "4483                    1489         42.7               33  \n",
              "5366                    1404         40.6              152  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cd89fb55-3d9e-429d-944a-85655780b864\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Type</th>\n",
              "      <th>Air temperature [K]</th>\n",
              "      <th>Process temperature [K]</th>\n",
              "      <th>Rotational speed [rpm]</th>\n",
              "      <th>Torque [Nm]</th>\n",
              "      <th>Tool wear [min]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3413</th>\n",
              "      <td>0</td>\n",
              "      <td>301.3</td>\n",
              "      <td>310.3</td>\n",
              "      <td>1560</td>\n",
              "      <td>36.3</td>\n",
              "      <td>129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5581</th>\n",
              "      <td>1</td>\n",
              "      <td>302.5</td>\n",
              "      <td>312.1</td>\n",
              "      <td>1454</td>\n",
              "      <td>47.6</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3466</th>\n",
              "      <td>0</td>\n",
              "      <td>301.7</td>\n",
              "      <td>310.6</td>\n",
              "      <td>1447</td>\n",
              "      <td>56.9</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4483</th>\n",
              "      <td>0</td>\n",
              "      <td>302.7</td>\n",
              "      <td>310.4</td>\n",
              "      <td>1489</td>\n",
              "      <td>42.7</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5366</th>\n",
              "      <td>2</td>\n",
              "      <td>303.1</td>\n",
              "      <td>312.6</td>\n",
              "      <td>1404</td>\n",
              "      <td>40.6</td>\n",
              "      <td>152</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cd89fb55-3d9e-429d-944a-85655780b864')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cd89fb55-3d9e-429d-944a-85655780b864 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cd89fb55-3d9e-429d-944a-85655780b864');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8470d024-f1fd-44c9-a43f-b17292d7e2a0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8470d024-f1fd-44c9-a43f-b17292d7e2a0')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8470d024-f1fd-44c9-a43f-b17292d7e2a0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "id": "vKC7htbiYNj_",
        "outputId": "a5db22e2-3e1f-4e86-c949-cad99903ee18"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Type  Air temperature [K]  Process temperature [K]  \\\n",
              "1246     2                297.2                    308.5   \n",
              "5207     1                303.8                    312.7   \n",
              "6157     0                301.1                    311.2   \n",
              "4275     1                302.4                    310.5   \n",
              "2125     0                299.3                    308.9   \n",
              "\n",
              "      Rotational speed [rpm]  Torque [Nm]  Tool wear [min]  \n",
              "1246                    1653         33.3              207  \n",
              "5207                    1488         41.3              175  \n",
              "6157                    1774         29.6              186  \n",
              "4275                    1500         36.7              164  \n",
              "2125                    1258         69.4              119  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c82b9fcb-4cf4-4657-9592-9855a10e8d96\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Type</th>\n",
              "      <th>Air temperature [K]</th>\n",
              "      <th>Process temperature [K]</th>\n",
              "      <th>Rotational speed [rpm]</th>\n",
              "      <th>Torque [Nm]</th>\n",
              "      <th>Tool wear [min]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1246</th>\n",
              "      <td>2</td>\n",
              "      <td>297.2</td>\n",
              "      <td>308.5</td>\n",
              "      <td>1653</td>\n",
              "      <td>33.3</td>\n",
              "      <td>207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5207</th>\n",
              "      <td>1</td>\n",
              "      <td>303.8</td>\n",
              "      <td>312.7</td>\n",
              "      <td>1488</td>\n",
              "      <td>41.3</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6157</th>\n",
              "      <td>0</td>\n",
              "      <td>301.1</td>\n",
              "      <td>311.2</td>\n",
              "      <td>1774</td>\n",
              "      <td>29.6</td>\n",
              "      <td>186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4275</th>\n",
              "      <td>1</td>\n",
              "      <td>302.4</td>\n",
              "      <td>310.5</td>\n",
              "      <td>1500</td>\n",
              "      <td>36.7</td>\n",
              "      <td>164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2125</th>\n",
              "      <td>0</td>\n",
              "      <td>299.3</td>\n",
              "      <td>308.9</td>\n",
              "      <td>1258</td>\n",
              "      <td>69.4</td>\n",
              "      <td>119</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c82b9fcb-4cf4-4657-9592-9855a10e8d96')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c82b9fcb-4cf4-4657-9592-9855a10e8d96 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c82b9fcb-4cf4-4657-9592-9855a10e8d96');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0965579f-0956-4bc2-a680-e824b06e6640\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0965579f-0956-4bc2-a680-e824b06e6640')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0965579f-0956-4bc2-a680-e824b06e6640 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = y[:y_train_rows]\n",
        "y_test = y[y_test_rows:]"
      ],
      "metadata": {
        "id": "d9LVvaFtYG85"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6zfPMuhsYIIw",
        "outputId": "b5a20296-73d4-42d1-e305-64930b13b5b8"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3413    0\n",
              "5581    0\n",
              "3466    0\n",
              "4483    0\n",
              "5366    0\n",
              "Name: Failure Type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "StpXOMG8YQd_",
        "outputId": "4b8dca6f-b8a0-4d8a-fc20-f8973a75447f"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1246    0\n",
              "5207    0\n",
              "6157    0\n",
              "4275    0\n",
              "2125    1\n",
              "Name: Failure Type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dEZ_uxrdYSsG",
        "outputId": "a306ea82-6643-4249-cf94-f7e27f211f9a"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8000, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mOI2lALAYTSn",
        "outputId": "17e7e4b4-2af7-4493-b561-0a77e11ec862"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8000,)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating a model"
      ],
      "metadata": {
        "id": "93cwKCmMYeTn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = None"
      ],
      "metadata": {
        "id": "XPri-yXhYfmT"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Input(shape=(X_train.shape[1],)),\n",
        "    tf.keras.layers.BatchNormalization(),\n",
        "    tf.keras.layers.Dense(1024, activation='relu'),\n",
        "    tf.keras.layers.Dense(512, activation='relu'),\n",
        "    tf.keras.layers.Dense(256, activation='relu'),\n",
        "    tf.keras.layers.Dense(128, activation='relu'),\n",
        "    tf.keras.layers.Dense(6, activation='softmax')\n",
        "])\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HfqLYXVLYy6e",
        "outputId": "075f7e96-928f-4b3c-9912-78e006025536"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " batch_normalization (BatchN  (None, 6)                24        \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1024)              7168      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 512)               524800    \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 256)               131328    \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 6)                 774       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 696,990\n",
            "Trainable params: 696,978\n",
            "Non-trainable params: 12\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(), metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "us-hsM9uZnRv"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training the model"
      ],
      "metadata": {
        "id": "FGIHuWn3e4_v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
        "    filepath='model.h5',\n",
        "    monitor='val_accuracy',\n",
        "    save_best_only=True,\n",
        "    mode='max',\n",
        "    verbose=1,\n",
        ")"
      ],
      "metadata": {
        "id": "YhKyE4yWaAST"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, epochs=16, batch_size=128, validation_split=0.1, callbacks=[checkpoint_callback])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jooob8RcaB3F",
        "outputId": "4e367c41-f32d-4d36-e2c0-4ca9ba5c7be1"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/16\n",
            "57/57 [==============================] - ETA: 0s - loss: 0.3499 - accuracy: 0.9456\n",
            "Epoch 1: val_accuracy improved from -inf to 0.97375, saving model to model.h5\n",
            "57/57 [==============================] - 4s 30ms/step - loss: 0.3499 - accuracy: 0.9456 - val_loss: 6.8971 - val_accuracy: 0.9737\n",
            "Epoch 2/16\n",
            "56/57 [============================>.] - ETA: 0s - loss: 0.1587 - accuracy: 0.9628\n",
            "Epoch 2: val_accuracy did not improve from 0.97375\n",
            "57/57 [==============================] - 1s 25ms/step - loss: 0.1583 - accuracy: 0.9629 - val_loss: 3.4328 - val_accuracy: 0.9737\n",
            "Epoch 3/16\n",
            "57/57 [==============================] - ETA: 0s - loss: 0.1266 - accuracy: 0.9647\n",
            "Epoch 3: val_accuracy did not improve from 0.97375\n",
            "57/57 [==============================] - 1s 23ms/step - loss: 0.1266 - accuracy: 0.9647 - val_loss: 2.1308 - val_accuracy: 0.9737\n",
            "Epoch 4/16\n",
            "56/57 [============================>.] - ETA: 0s - loss: 0.1143 - accuracy: 0.9664\n",
            "Epoch 4: val_accuracy did not improve from 0.97375\n",
            "57/57 [==============================] - 1s 23ms/step - loss: 0.1147 - accuracy: 0.9664 - val_loss: 0.7060 - val_accuracy: 0.9737\n",
            "Epoch 5/16\n",
            "56/57 [============================>.] - ETA: 0s - loss: 0.1077 - accuracy: 0.9685\n",
            "Epoch 5: val_accuracy did not improve from 0.97375\n",
            "57/57 [==============================] - 1s 24ms/step - loss: 0.1072 - accuracy: 0.9686 - val_loss: 0.4353 - val_accuracy: 0.9737\n",
            "Epoch 6/16\n",
            "56/57 [============================>.] - ETA: 0s - loss: 0.0983 - accuracy: 0.9697\n",
            "Epoch 6: val_accuracy did not improve from 0.97375\n",
            "57/57 [==============================] - 2s 39ms/step - loss: 0.0986 - accuracy: 0.9697 - val_loss: 0.1828 - val_accuracy: 0.9737\n",
            "Epoch 7/16\n",
            "56/57 [============================>.] - ETA: 0s - loss: 0.0927 - accuracy: 0.9727\n",
            "Epoch 7: val_accuracy improved from 0.97375 to 0.97500, saving model to model.h5\n",
            "57/57 [==============================] - 2s 30ms/step - loss: 0.0925 - accuracy: 0.9728 - val_loss: 0.0740 - val_accuracy: 0.9750\n",
            "Epoch 8/16\n",
            "56/57 [============================>.] - ETA: 0s - loss: 0.0887 - accuracy: 0.9724\n",
            "Epoch 8: val_accuracy improved from 0.97500 to 0.97750, saving model to model.h5\n",
            "57/57 [==============================] - 1s 25ms/step - loss: 0.0884 - accuracy: 0.9725 - val_loss: 0.0907 - val_accuracy: 0.9775\n",
            "Epoch 9/16\n",
            "56/57 [============================>.] - ETA: 0s - loss: 0.0930 - accuracy: 0.9722\n",
            "Epoch 9: val_accuracy improved from 0.97750 to 0.98250, saving model to model.h5\n",
            "57/57 [==============================] - 1s 26ms/step - loss: 0.0933 - accuracy: 0.9722 - val_loss: 0.0616 - val_accuracy: 0.9825\n",
            "Epoch 10/16\n",
            "56/57 [============================>.] - ETA: 0s - loss: 0.0948 - accuracy: 0.9686\n",
            "Epoch 10: val_accuracy did not improve from 0.98250\n",
            "57/57 [==============================] - 1s 25ms/step - loss: 0.0947 - accuracy: 0.9686 - val_loss: 0.0749 - val_accuracy: 0.9800\n",
            "Epoch 11/16\n",
            "56/57 [============================>.] - ETA: 0s - loss: 0.0883 - accuracy: 0.9722\n",
            "Epoch 11: val_accuracy improved from 0.98250 to 0.98875, saving model to model.h5\n",
            "57/57 [==============================] - 1s 25ms/step - loss: 0.0887 - accuracy: 0.9722 - val_loss: 0.0464 - val_accuracy: 0.9887\n",
            "Epoch 12/16\n",
            "56/57 [============================>.] - ETA: 0s - loss: 0.0829 - accuracy: 0.9724\n",
            "Epoch 12: val_accuracy did not improve from 0.98875\n",
            "57/57 [==============================] - 1s 25ms/step - loss: 0.0828 - accuracy: 0.9724 - val_loss: 0.0494 - val_accuracy: 0.9787\n",
            "Epoch 13/16\n",
            "55/57 [===========================>..] - ETA: 0s - loss: 0.0801 - accuracy: 0.9734\n",
            "Epoch 13: val_accuracy did not improve from 0.98875\n",
            "57/57 [==============================] - 1s 25ms/step - loss: 0.0796 - accuracy: 0.9735 - val_loss: 0.0667 - val_accuracy: 0.9787\n",
            "Epoch 14/16\n",
            "57/57 [==============================] - ETA: 0s - loss: 0.0928 - accuracy: 0.9719\n",
            "Epoch 14: val_accuracy did not improve from 0.98875\n",
            "57/57 [==============================] - 2s 36ms/step - loss: 0.0928 - accuracy: 0.9719 - val_loss: 0.0452 - val_accuracy: 0.9750\n",
            "Epoch 15/16\n",
            "57/57 [==============================] - ETA: 0s - loss: 0.0764 - accuracy: 0.9764\n",
            "Epoch 15: val_accuracy did not improve from 0.98875\n",
            "57/57 [==============================] - 2s 43ms/step - loss: 0.0764 - accuracy: 0.9764 - val_loss: 0.0394 - val_accuracy: 0.9862\n",
            "Epoch 16/16\n",
            "55/57 [===========================>..] - ETA: 0s - loss: 0.0802 - accuracy: 0.9737\n",
            "Epoch 16: val_accuracy did not improve from 0.98875\n",
            "57/57 [==============================] - 3s 44ms/step - loss: 0.0794 - accuracy: 0.9740 - val_loss: 0.0325 - val_accuracy: 0.9875\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Showing training results"
      ],
      "metadata": {
        "id": "Kg5sA6NHe7Nu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot training & validation accuracy values\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "print(\"\\n\")\n",
        "\n",
        "# Plot training & validation loss values\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 962
        },
        "id": "s5nCzM3pdrPs",
        "outputId": "14cee8e8-cd37-4fe0-dde9-1601e3a34be3"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Testing the model on Test Dataset"
      ],
      "metadata": {
        "id": "HbX8UsY_fANM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_accuracy = model.evaluate(X_test, y_test)\n",
        "print(f\"Test Accuracy: {(test_accuracy * 100):.4f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "amNRuRHHduU9",
        "outputId": "e56921f4-8416-470a-c167-270b238c652c"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "250/250 [==============================] - 2s 6ms/step - loss: 0.0552 - accuracy: 0.9856\n",
            "Test Accuracy: 98.5625%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### At the end model was 98.5%  right about its predictions. Which is quite good :)"
      ],
      "metadata": {
        "id": "A-Rn7ZyDfGUj"
      }
    }
  ]
}