predictive_maintenance.ipynb

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        "<a href=\"https://colab.research.google.com/github/iyed-26/XO-game-project-/blob/main/predictive_maintenance.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "Elj5f_5-bWtp"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_path = pd.read_csv('/content/predictive_maintenance.csv')"
      ],
      "metadata": {
        "id": "PmL54wTcdGLk"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = data_path.copy()"
      ],
      "metadata": {
        "id": "-S3uLgOxddKi"
      },
      "execution_count": 3,
      "outputs": []
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        "df"
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        "outputId": "14a6f676-2517-4ea9-e6ee-68d1bfe1c9b0"
      },
      "execution_count": 4,
      "outputs": [
        {
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            "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",
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              "9995   No Failure  \n",
              "9996   No Failure  \n",
              "9997   No Failure  \n",
              "9998   No Failure  \n",
              "9999   No Failure  \n",
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              "[10000 rows x 10 columns]"
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"UDI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2886,\n        \"min\": 1,\n        \"max\": 10000,\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          6253,\n          4685,\n          1732\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          \"L53432\",\n          \"M19544\",\n          \"M16591\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"M\",\n          \"L\",\n          \"H\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Air temperature [K]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0002586829157574,\n        \"min\": 295.3,\n        \"max\": 304.5,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          299.3,\n          296.9,\n          300.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Process temperature [K]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4837342191657419,\n        \"min\": 305.7,\n        \"max\": 313.8,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          307.2,\n          308.6,\n          310.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rotational speed [rpm]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 179,\n        \"min\": 1168,\n        \"max\": 2886,\n        \"num_unique_values\": 941,\n        \"samples\": [\n          1274,\n          1576,\n          2010\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Torque [Nm]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.968933725121401,\n        \"min\": 3.8,\n        \"max\": 76.6,\n        \"num_unique_values\": 577,\n        \"samples\": [\n          36.1,\n          65.9,\n          12.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tool wear [min]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63,\n        \"min\": 0,\n        \"max\": 253,\n        \"num_unique_values\": 246,\n        \"samples\": [\n          93,\n          14,\n          215\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Failure Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"No Failure\",\n          \"Power Failure\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Tool wear [min]']=df['Tool wear [min]'].astype('float64')\n",
        "df['Rotational speed [rpm]']=df['Rotational speed [rpm]'].astype('float64')"
      ],
      "metadata": {
        "id": "nbwQjYYUdnoi"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u-ojYAkij81u",
        "outputId": "583a2c46-5cd6-4d33-96d5-c8f3b7d6ec77"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['UDI', 'Product ID', 'Type', 'Air temperature [K]',\n",
              "       'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]',\n",
              "       'Tool wear [min]', 'Target', 'Failure Type'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.rename(mapper={'Air temperature [K] ': 'Air temperature', 'Process temperature [K]': 'Process temperature',\n",
        "                 'Rotational speed [rpm]':'Rotational speed','Torque [Nm]':'Torque','Tool wear [min]':'Tool wear'\n",
        "\n",
        "} , axis =1 , inplace= True)"
      ],
      "metadata": {
        "id": "zRdeq6_gihsV"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "VBucayXglChr",
        "outputId": "31299c3b-a4b4-431e-c6ab-bc76a951959e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        UDI Product ID Type  Air temperature [K]  Process temperature  \\\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  Torque  Tool wear  Target Failure Type  \n",
              "0               1551.0    42.8        0.0       0   No Failure  \n",
              "1               1408.0    46.3        3.0       0   No Failure  \n",
              "2               1498.0    49.4        5.0       0   No Failure  \n",
              "3               1433.0    39.5        7.0       0   No Failure  \n",
              "4               1408.0    40.0        9.0       0   No Failure  \n",
              "...                ...     ...        ...     ...          ...  \n",
              "9995            1604.0    29.5       14.0       0   No Failure  \n",
              "9996            1632.0    31.8       17.0       0   No Failure  \n",
              "9997            1645.0    33.4       22.0       0   No Failure  \n",
              "9998            1408.0    48.5       25.0       0   No Failure  \n",
              "9999            1500.0    40.2       30.0       0   No Failure  \n",
              "\n",
              "[10000 rows x 10 columns]"
            ],
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              "      <th></th>\n",
              "      <th>UDI</th>\n",
              "      <th>Product ID</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>M14860</td>\n",
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              "      <td>298.1</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1551.0</td>\n",
              "      <td>42.8</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>L47181</td>\n",
              "      <td>L</td>\n",
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              "      <td>308.7</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>46.3</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>298.1</td>\n",
              "      <td>308.5</td>\n",
              "      <td>1498.0</td>\n",
              "      <td>49.4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>L47183</td>\n",
              "      <td>L</td>\n",
              "      <td>298.2</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1433.0</td>\n",
              "      <td>39.5</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>L47184</td>\n",
              "      <td>L</td>\n",
              "      <td>298.2</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>9996</td>\n",
              "      <td>M24855</td>\n",
              "      <td>M</td>\n",
              "      <td>298.8</td>\n",
              "      <td>308.4</td>\n",
              "      <td>1604.0</td>\n",
              "      <td>29.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
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              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>9997</td>\n",
              "      <td>H39410</td>\n",
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              "      <td>298.9</td>\n",
              "      <td>308.4</td>\n",
              "      <td>1632.0</td>\n",
              "      <td>31.8</td>\n",
              "      <td>17.0</td>\n",
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              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>9998</td>\n",
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              "      <td>299.0</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1645.0</td>\n",
              "      <td>33.4</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
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              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>9999</td>\n",
              "      <td>H39412</td>\n",
              "      <td>H</td>\n",
              "      <td>299.0</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>48.5</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
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              "    <tr>\n",
              "      <th>9999</th>\n",
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              "      <td>40.2</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"UDI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2886,\n        \"min\": 1,\n        \"max\": 10000,\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          6253,\n          4685,\n          1732\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          \"L53432\",\n          \"M19544\",\n          \"M16591\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"M\",\n          \"L\",\n          \"H\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Air temperature [K]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0002586829157574,\n        \"min\": 295.3,\n        \"max\": 304.5,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          299.3,\n          296.9,\n          300.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Process temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4837342191657419,\n        \"min\": 305.7,\n        \"max\": 313.8,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          307.2,\n          308.6,\n          310.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rotational speed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 179.28409591342628,\n        \"min\": 1168.0,\n        \"max\": 2886.0,\n        \"num_unique_values\": 941,\n        \"samples\": [\n          1274.0,\n          1576.0,\n          2010.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Torque\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.968933725121401,\n        \"min\": 3.8,\n        \"max\": 76.6,\n        \"num_unique_values\": 577,\n        \"samples\": [\n          36.1,\n          65.9,\n          12.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tool wear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63.65414663663639,\n        \"min\": 0.0,\n        \"max\": 253.0,\n        \"num_unique_values\": 246,\n        \"samples\": [\n          93.0,\n          14.0,\n          215.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Failure Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"No Failure\",\n          \"Power Failure\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Product ID'] = df['Product ID'].apply(lambda x: x[1:])"
      ],
      "metadata": {
        "id": "-uPOLL0lldfH"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Product ID']=pd.to_numeric(df['Product ID'])"
      ],
      "metadata": {
        "id": "0HkU53OWliCf"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df\n"
      ],
      "metadata": {
        "id": "gE2Yvs7tl3V9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "e124841e-af10-4127-c2da-492f18d966b7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        UDI  Product ID Type  Air temperature [K]  Process temperature  \\\n",
              "0         1       14860    M                298.1                308.6   \n",
              "1         2       47181    L                298.2                308.7   \n",
              "2         3       47182    L                298.1                308.5   \n",
              "3         4       47183    L                298.2                308.6   \n",
              "4         5       47184    L                298.2                308.7   \n",
              "...     ...         ...  ...                  ...                  ...   \n",
              "9995   9996       24855    M                298.8                308.4   \n",
              "9996   9997       39410    H                298.9                308.4   \n",
              "9997   9998       24857    M                299.0                308.6   \n",
              "9998   9999       39412    H                299.0                308.7   \n",
              "9999  10000       24859    M                299.0                308.7   \n",
              "\n",
              "      Rotational speed  Torque  Tool wear  Target Failure Type  \n",
              "0               1551.0    42.8        0.0       0   No Failure  \n",
              "1               1408.0    46.3        3.0       0   No Failure  \n",
              "2               1498.0    49.4        5.0       0   No Failure  \n",
              "3               1433.0    39.5        7.0       0   No Failure  \n",
              "4               1408.0    40.0        9.0       0   No Failure  \n",
              "...                ...     ...        ...     ...          ...  \n",
              "9995            1604.0    29.5       14.0       0   No Failure  \n",
              "9996            1632.0    31.8       17.0       0   No Failure  \n",
              "9997            1645.0    33.4       22.0       0   No Failure  \n",
              "9998            1408.0    48.5       25.0       0   No Failure  \n",
              "9999            1500.0    40.2       30.0       0   No Failure  \n",
              "\n",
              "[10000 rows x 10 columns]"
            ],
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              "      <th></th>\n",
              "      <th>UDI</th>\n",
              "      <th>Product ID</th>\n",
              "      <th>Type</th>\n",
              "      <th>Air temperature [K]</th>\n",
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              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>298.2</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1433.0</td>\n",
              "      <td>39.5</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>47184</td>\n",
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              "      <td>40.0</td>\n",
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              "      <th>9996</th>\n",
              "      <td>9997</td>\n",
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              "      <td>31.8</td>\n",
              "      <td>17.0</td>\n",
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              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>9998</td>\n",
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              "      <td>33.4</td>\n",
              "      <td>22.0</td>\n",
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              "      <th>9998</th>\n",
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              "      <td>48.5</td>\n",
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              "      <td>0</td>\n",
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              "<p>10000 rows × 10 columns</p>\n",
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              "        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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              "\n",
              "\n",
              "<div id=\"df-80e5f635-b4e4-4782-b6cd-61bee1386f87\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-80e5f635-b4e4-4782-b6cd-61bee1386f87')\"\n",
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              "\n",
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              "  </button>\n",
              "\n",
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              "  .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",
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              "      --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",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      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",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\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-80e5f635-b4e4-4782-b6cd-61bee1386f87 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_f80ad1dc-ae8b-4b70-b377-1e85b72015b1\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_f80ad1dc-ae8b-4b70-b377-1e85b72015b1 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"UDI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2886,\n        \"min\": 1,\n        \"max\": 10000,\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          6253,\n          4685,\n          1732\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14870,\n        \"min\": 14860,\n        \"max\": 57174,\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          53432,\n          19544,\n          16591\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"M\",\n          \"L\",\n          \"H\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Air temperature [K]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0002586829157574,\n        \"min\": 295.3,\n        \"max\": 304.5,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          299.3,\n          296.9,\n          300.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Process temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4837342191657419,\n        \"min\": 305.7,\n        \"max\": 313.8,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          307.2,\n          308.6,\n          310.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rotational speed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 179.28409591342628,\n        \"min\": 1168.0,\n        \"max\": 2886.0,\n        \"num_unique_values\": 941,\n        \"samples\": [\n          1274.0,\n          1576.0,\n          2010.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Torque\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.968933725121401,\n        \"min\": 3.8,\n        \"max\": 76.6,\n        \"num_unique_values\": 577,\n        \"samples\": [\n          36.1,\n          65.9,\n          12.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tool wear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63.65414663663639,\n        \"min\": 0.0,\n        \"max\": 253.0,\n        \"num_unique_values\": 246,\n        \"samples\": [\n          93.0,\n          14.0,\n          215.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Failure Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"No Failure\",\n          \"Power Failure\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns=['UDI'],inplace=True)\n",
        "\n"
      ],
      "metadata": {
        "id": "MZawS-cwl-a2"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df\n"
      ],
      "metadata": {
        "id": "HNP-J4DmnDoZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "84ae7f60-ad6a-428c-dfdb-f9f9837d94a9"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Product ID Type  Air temperature [K]  Process temperature  \\\n",
              "0          14860    M                298.1                308.6   \n",
              "1          47181    L                298.2                308.7   \n",
              "2          47182    L                298.1                308.5   \n",
              "3          47183    L                298.2                308.6   \n",
              "4          47184    L                298.2                308.7   \n",
              "...          ...  ...                  ...                  ...   \n",
              "9995       24855    M                298.8                308.4   \n",
              "9996       39410    H                298.9                308.4   \n",
              "9997       24857    M                299.0                308.6   \n",
              "9998       39412    H                299.0                308.7   \n",
              "9999       24859    M                299.0                308.7   \n",
              "\n",
              "      Rotational speed  Torque  Tool wear  Target Failure Type  \n",
              "0               1551.0    42.8        0.0       0   No Failure  \n",
              "1               1408.0    46.3        3.0       0   No Failure  \n",
              "2               1498.0    49.4        5.0       0   No Failure  \n",
              "3               1433.0    39.5        7.0       0   No Failure  \n",
              "4               1408.0    40.0        9.0       0   No Failure  \n",
              "...                ...     ...        ...     ...          ...  \n",
              "9995            1604.0    29.5       14.0       0   No Failure  \n",
              "9996            1632.0    31.8       17.0       0   No Failure  \n",
              "9997            1645.0    33.4       22.0       0   No Failure  \n",
              "9998            1408.0    48.5       25.0       0   No Failure  \n",
              "9999            1500.0    40.2       30.0       0   No Failure  \n",
              "\n",
              "[10000 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ae490d79-ed0a-4bb7-9be7-b010b79fb7c2\" 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>Product ID</th>\n",
              "      <th>Type</th>\n",
              "      <th>Air temperature [K]</th>\n",
              "      <th>Process temperature</th>\n",
              "      <th>Rotational speed</th>\n",
              "      <th>Torque</th>\n",
              "      <th>Tool wear</th>\n",
              "      <th>Target</th>\n",
              "      <th>Failure Type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14860</td>\n",
              "      <td>M</td>\n",
              "      <td>298.1</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1551.0</td>\n",
              "      <td>42.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47181</td>\n",
              "      <td>L</td>\n",
              "      <td>298.2</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>46.3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47182</td>\n",
              "      <td>L</td>\n",
              "      <td>298.1</td>\n",
              "      <td>308.5</td>\n",
              "      <td>1498.0</td>\n",
              "      <td>49.4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47183</td>\n",
              "      <td>L</td>\n",
              "      <td>298.2</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1433.0</td>\n",
              "      <td>39.5</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>47184</td>\n",
              "      <td>L</td>\n",
              "      <td>298.2</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>24855</td>\n",
              "      <td>M</td>\n",
              "      <td>298.8</td>\n",
              "      <td>308.4</td>\n",
              "      <td>1604.0</td>\n",
              "      <td>29.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>39410</td>\n",
              "      <td>H</td>\n",
              "      <td>298.9</td>\n",
              "      <td>308.4</td>\n",
              "      <td>1632.0</td>\n",
              "      <td>31.8</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>24857</td>\n",
              "      <td>M</td>\n",
              "      <td>299.0</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1645.0</td>\n",
              "      <td>33.4</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>39412</td>\n",
              "      <td>H</td>\n",
              "      <td>299.0</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>48.5</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>24859</td>\n",
              "      <td>M</td>\n",
              "      <td>299.0</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>40.2</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae490d79-ed0a-4bb7-9be7-b010b79fb7c2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "        width: 32px;\n",
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              "\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"Product ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14870,\n        \"min\": 14860,\n        \"max\": 57174,\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          53432,\n          19544,\n          16591\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"M\",\n          \"L\",\n          \"H\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Air temperature [K]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0002586829157574,\n        \"min\": 295.3,\n        \"max\": 304.5,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          299.3,\n          296.9,\n          300.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Process temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4837342191657419,\n        \"min\": 305.7,\n        \"max\": 313.8,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          307.2,\n          308.6,\n          310.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rotational speed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 179.28409591342628,\n        \"min\": 1168.0,\n        \"max\": 2886.0,\n        \"num_unique_values\": 941,\n        \"samples\": [\n          1274.0,\n          1576.0,\n          2010.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Torque\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.968933725121401,\n        \"min\": 3.8,\n        \"max\": 76.6,\n        \"num_unique_values\": 577,\n        \"samples\": [\n          36.1,\n          65.9,\n          12.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tool wear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63.65414663663639,\n        \"min\": 0.0,\n        \"max\": 253.0,\n        \"num_unique_values\": 246,\n        \"samples\": [\n          93.0,\n          14.0,\n          215.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Failure Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"No Failure\",\n          \"Power Failure\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features = ['Type',\t'Air temperature [K]'\t,'Process temperature',\t'Rotational speed'\t,'Torque',\t'Tool wear'\t]"
      ],
      "metadata": {
        "id": "sHcGHl4NoFBn"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target=['Target'\t,'Failure Type']"
      ],
      "metadata": {
        "id": "uGL74qeiDPPr"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "supr1=df.loc[df['Failure Type']=='Random Failures'].index"
      ],
      "metadata": {
        "id": "_9aeJhLcDZbf"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[supr1,target]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "GmwcZv73F1z1",
        "outputId": "58f52672-58cf-409c-d564-cc6a21758358"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Target     Failure Type\n",
              "1221       0  Random Failures\n",
              "1302       0  Random Failures\n",
              "1748       0  Random Failures\n",
              "2072       0  Random Failures\n",
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              "3452       0  Random Failures\n",
              "5471       0  Random Failures\n",
              "5489       0  Random Failures\n",
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              "5509       0  Random Failures\n",
              "5553       0  Random Failures\n",
              "5639       0  Random Failures\n",
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              "6913       0  Random Failures\n",
              "6960       0  Random Failures\n",
              "7488       0  Random Failures\n",
              "7868       0  Random Failures"
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              "</div>\n",
              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 18,\n  \"fields\": [\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Failure Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Random Failures\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(supr1,inplace=True)"
      ],
      "metadata": {
        "id": "kNsR_hCEHIXx"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ZWPP1jjCHSFd",
        "outputId": "c4be8548-9e9a-4c3c-a8ee-ff7bbfd68365"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Product ID Type  Air temperature [K]  Process temperature  \\\n",
              "0          14860    M                298.1                308.6   \n",
              "1          47181    L                298.2                308.7   \n",
              "2          47182    L                298.1                308.5   \n",
              "3          47183    L                298.2                308.6   \n",
              "4          47184    L                298.2                308.7   \n",
              "...          ...  ...                  ...                  ...   \n",
              "9995       24855    M                298.8                308.4   \n",
              "9996       39410    H                298.9                308.4   \n",
              "9997       24857    M                299.0                308.6   \n",
              "9998       39412    H                299.0                308.7   \n",
              "9999       24859    M                299.0                308.7   \n",
              "\n",
              "      Rotational speed  Torque  Tool wear  Target Failure Type  \n",
              "0               1551.0    42.8        0.0       0   No Failure  \n",
              "1               1408.0    46.3        3.0       0   No Failure  \n",
              "2               1498.0    49.4        5.0       0   No Failure  \n",
              "3               1433.0    39.5        7.0       0   No Failure  \n",
              "4               1408.0    40.0        9.0       0   No Failure  \n",
              "...                ...     ...        ...     ...          ...  \n",
              "9995            1604.0    29.5       14.0       0   No Failure  \n",
              "9996            1632.0    31.8       17.0       0   No Failure  \n",
              "9997            1645.0    33.4       22.0       0   No Failure  \n",
              "9998            1408.0    48.5       25.0       0   No Failure  \n",
              "9999            1500.0    40.2       30.0       0   No Failure  \n",
              "\n",
              "[9982 rows x 9 columns]"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Product ID</th>\n",
              "      <th>Type</th>\n",
              "      <th>Air temperature [K]</th>\n",
              "      <th>Process temperature</th>\n",
              "      <th>Rotational speed</th>\n",
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              "      <th>Tool wear</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14860</td>\n",
              "      <td>M</td>\n",
              "      <td>298.1</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1551.0</td>\n",
              "      <td>42.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47181</td>\n",
              "      <td>L</td>\n",
              "      <td>298.2</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>46.3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47182</td>\n",
              "      <td>L</td>\n",
              "      <td>298.1</td>\n",
              "      <td>308.5</td>\n",
              "      <td>1498.0</td>\n",
              "      <td>49.4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47183</td>\n",
              "      <td>L</td>\n",
              "      <td>298.2</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1433.0</td>\n",
              "      <td>39.5</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>47184</td>\n",
              "      <td>L</td>\n",
              "      <td>298.2</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>24855</td>\n",
              "      <td>M</td>\n",
              "      <td>298.8</td>\n",
              "      <td>308.4</td>\n",
              "      <td>1604.0</td>\n",
              "      <td>29.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>39410</td>\n",
              "      <td>H</td>\n",
              "      <td>298.9</td>\n",
              "      <td>308.4</td>\n",
              "      <td>1632.0</td>\n",
              "      <td>31.8</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>24857</td>\n",
              "      <td>M</td>\n",
              "      <td>299.0</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1645.0</td>\n",
              "      <td>33.4</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>39412</td>\n",
              "      <td>H</td>\n",
              "      <td>299.0</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>48.5</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>24859</td>\n",
              "      <td>M</td>\n",
              "      <td>299.0</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>40.2</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9982 rows × 9 columns</p>\n",
              "</div>\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-dabe0077-618c-4dfa-bc0c-6aae4516d2a9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "\n",
              "<div id=\"df-4a841c82-24a4-41c1-9f72-f67c1106f9d5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4a841c82-24a4-41c1-9f72-f67c1106f9d5')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\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",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
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              "  }\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",
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              "  }\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",
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              "      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",
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              "      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-4a841c82-24a4-41c1-9f72-f67c1106f9d5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_bb6bbf35-4193-4927-bd43-ac81dd12e2c1\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_bb6bbf35-4193-4927-bd43-ac81dd12e2c1 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 9982,\n  \"fields\": [\n    {\n      \"column\": \"Product ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14873,\n        \"min\": 14860,\n        \"max\": 57174,\n        \"num_unique_values\": 9982,\n        \"samples\": [\n          30668,\n          57128,\n          21922\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"M\",\n          \"L\",\n          \"H\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Air temperature [K]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.000506492016775,\n        \"min\": 295.3,\n        \"max\": 304.5,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          299.3,\n          296.9,\n          300.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Process temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4834356854657407,\n        \"min\": 305.7,\n        \"max\": 313.8,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          307.2,\n          308.6,\n          310.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rotational speed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 179.3751232735624,\n        \"min\": 1168.0,\n        \"max\": 2886.0,\n        \"num_unique_values\": 941,\n        \"samples\": [\n          1274.0,\n          1576.0,\n          2010.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Torque\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.966712395302359,\n        \"min\": 3.8,\n        \"max\": 76.6,\n        \"num_unique_values\": 577,\n        \"samples\": [\n          36.1,\n          65.9,\n          12.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tool wear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63.646655361506305,\n        \"min\": 0.0,\n        \"max\": 253.0,\n        \"num_unique_values\": 246,\n        \"samples\": [\n          93.0,\n          14.0,\n          215.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Failure Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Power Failure\",\n          \"Heat Dissipation Failure\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QfQiRwUpHT7F",
        "outputId": "8eeaaee0-2da9-45b4-adcb-179b5fadea81"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9982, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "supr2=df.loc[(df['Failure Type']=='No Failure') & (df['Target']==1)].index"
      ],
      "metadata": {
        "id": "yWpeAXbAHfNt"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[supr2,target]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "JYsgPcf7MCWH",
        "outputId": "ce77cd1e-9e80-41fe-b810-2de44f5eb857"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Target Failure Type\n",
              "1437       1   No Failure\n",
              "2749       1   No Failure\n",
              "4044       1   No Failure\n",
              "4684       1   No Failure\n",
              "5536       1   No Failure\n",
              "5941       1   No Failure\n",
              "6478       1   No Failure\n",
              "8506       1   No Failure\n",
              "9015       1   No Failure"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-05c4c916-ad72-4285-a53e-c24ad42e3ed2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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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              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-05c4c916-ad72-4285-a53e-c24ad42e3ed2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-05c4c916-ad72-4285-a53e-c24ad42e3ed2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --hover-fill-color: #174EA6;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    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-b5daf845-89fd-49a6-b61b-b8c1cf2bf23e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Failure Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"No Failure\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(supr2 , inplace=True)"
      ],
      "metadata": {
        "id": "aC9D_w91MjTO"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "uuvrOnkCMopm",
        "outputId": "cf99ec60-0845-478e-d61e-f6bb76688ee6"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Product ID Type  Air temperature [K]  Process temperature  \\\n",
              "0          14860    M                298.1                308.6   \n",
              "1          47181    L                298.2                308.7   \n",
              "2          47182    L                298.1                308.5   \n",
              "3          47183    L                298.2                308.6   \n",
              "4          47184    L                298.2                308.7   \n",
              "...          ...  ...                  ...                  ...   \n",
              "9995       24855    M                298.8                308.4   \n",
              "9996       39410    H                298.9                308.4   \n",
              "9997       24857    M                299.0                308.6   \n",
              "9998       39412    H                299.0                308.7   \n",
              "9999       24859    M                299.0                308.7   \n",
              "\n",
              "      Rotational speed  Torque  Tool wear  Target Failure Type  \n",
              "0               1551.0    42.8        0.0       0   No Failure  \n",
              "1               1408.0    46.3        3.0       0   No Failure  \n",
              "2               1498.0    49.4        5.0       0   No Failure  \n",
              "3               1433.0    39.5        7.0       0   No Failure  \n",
              "4               1408.0    40.0        9.0       0   No Failure  \n",
              "...                ...     ...        ...     ...          ...  \n",
              "9995            1604.0    29.5       14.0       0   No Failure  \n",
              "9996            1632.0    31.8       17.0       0   No Failure  \n",
              "9997            1645.0    33.4       22.0       0   No Failure  \n",
              "9998            1408.0    48.5       25.0       0   No Failure  \n",
              "9999            1500.0    40.2       30.0       0   No Failure  \n",
              "\n",
              "[9973 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2417cf97-bb52-491b-9cd7-446eccf665aa\" 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>Product ID</th>\n",
              "      <th>Type</th>\n",
              "      <th>Air temperature [K]</th>\n",
              "      <th>Process temperature</th>\n",
              "      <th>Rotational speed</th>\n",
              "      <th>Torque</th>\n",
              "      <th>Tool wear</th>\n",
              "      <th>Target</th>\n",
              "      <th>Failure Type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14860</td>\n",
              "      <td>M</td>\n",
              "      <td>298.1</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1551.0</td>\n",
              "      <td>42.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47181</td>\n",
              "      <td>L</td>\n",
              "      <td>298.2</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>46.3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47182</td>\n",
              "      <td>L</td>\n",
              "      <td>298.1</td>\n",
              "      <td>308.5</td>\n",
              "      <td>1498.0</td>\n",
              "      <td>49.4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47183</td>\n",
              "      <td>L</td>\n",
              "      <td>298.2</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1433.0</td>\n",
              "      <td>39.5</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>47184</td>\n",
              "      <td>L</td>\n",
              "      <td>298.2</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>24855</td>\n",
              "      <td>M</td>\n",
              "      <td>298.8</td>\n",
              "      <td>308.4</td>\n",
              "      <td>1604.0</td>\n",
              "      <td>29.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>39410</td>\n",
              "      <td>H</td>\n",
              "      <td>298.9</td>\n",
              "      <td>308.4</td>\n",
              "      <td>1632.0</td>\n",
              "      <td>31.8</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>24857</td>\n",
              "      <td>M</td>\n",
              "      <td>299.0</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1645.0</td>\n",
              "      <td>33.4</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>39412</td>\n",
              "      <td>H</td>\n",
              "      <td>299.0</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>48.5</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>24859</td>\n",
              "      <td>M</td>\n",
              "      <td>299.0</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>40.2</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Failure</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9973 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2417cf97-bb52-491b-9cd7-446eccf665aa')\"\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-2417cf97-bb52-491b-9cd7-446eccf665aa 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-2417cf97-bb52-491b-9cd7-446eccf665aa');\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-c84d9ba7-2186-4f3b-9908-23f66c765a4e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c84d9ba7-2186-4f3b-9908-23f66c765a4e')\"\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-c84d9ba7-2186-4f3b-9908-23f66c765a4e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
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              "\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 9973,\n  \"fields\": [\n    {\n      \"column\": \"Product ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14870,\n        \"min\": 14860,\n        \"max\": 57174,\n        \"num_unique_values\": 9973,\n        \"samples\": [\n          50543,\n          49590,\n          51563\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"M\",\n          \"L\",\n          \"H\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Air temperature [K]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.000547560650067,\n        \"min\": 295.3,\n        \"max\": 304.5,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          299.3,\n          296.9,\n          300.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Process temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4836918656496663,\n        \"min\": 305.7,\n        \"max\": 313.8,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          307.2,\n          308.6,\n          310.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rotational speed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 179.4121706087358,\n        \"min\": 1168.0,\n        \"max\": 2886.0,\n        \"num_unique_values\": 941,\n        \"samples\": [\n          1274.0,\n          1576.0,\n          2010.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Torque\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.966804857457536,\n        \"min\": 3.8,\n        \"max\": 76.6,\n        \"num_unique_values\": 577,\n        \"samples\": [\n          36.1,\n          65.9,\n          12.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tool wear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63.64915152705582,\n        \"min\": 0.0,\n        \"max\": 253.0,\n        \"num_unique_values\": 246,\n        \"samples\": [\n          93.0,\n          14.0,\n          215.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Failure Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Power Failure\",\n          \"Heat Dissipation Failure\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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    {
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        "df.drop('Product ID',axis=1,inplace=True)"
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        "id": "EiuZ76VTM7dO"
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      "outputs": []
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        "outputId": "9fe8b640-8ad7-429d-f7f4-fa9b0be6e415"
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      "execution_count": 26,
      "outputs": [
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          "data": {
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          "metadata": {},
          "execution_count": 26
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    {
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        "id": "N3wjIhCmOp7f",
        "outputId": "152510fa-4e44-47f2-fcf6-0d67b43926d2"
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      "execution_count": 27,
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          "data": {
            "text/plain": [
              "     Type  Air temperature [K]  Process temperature  Rotational speed  Torque  \\\n",
              "0       M                298.1                308.6            1551.0    42.8   \n",
              "1       L                298.2                308.7            1408.0    46.3   \n",
              "2       L                298.1                308.5            1498.0    49.4   \n",
              "3       L                298.2                308.6            1433.0    39.5   \n",
              "4       L                298.2                308.7            1408.0    40.0   \n",
              "...   ...                  ...                  ...               ...     ...   \n",
              "9995    M                298.8                308.4            1604.0    29.5   \n",
              "9996    H                298.9                308.4            1632.0    31.8   \n",
              "9997    M                299.0                308.6            1645.0    33.4   \n",
              "9998    H                299.0                308.7            1408.0    48.5   \n",
              "9999    M                299.0                308.7            1500.0    40.2   \n",
              "\n",
              "      Tool wear  Target Failure Type  \n",
              "0           0.0       0   No Failure  \n",
              "1           3.0       0   No Failure  \n",
              "2           5.0       0   No Failure  \n",
              "3           7.0       0   No Failure  \n",
              "4           9.0       0   No Failure  \n",
              "...         ...     ...          ...  \n",
              "9995       14.0       0   No Failure  \n",
              "9996       17.0       0   No Failure  \n",
              "9997       22.0       0   No Failure  \n",
              "9998       25.0       0   No Failure  \n",
              "9999       30.0       0   No Failure  \n",
              "\n",
              "[9973 rows x 8 columns]"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 9973,\n  \"fields\": [\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"M\",\n          \"L\",\n          \"H\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Air temperature [K]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.000547560650067,\n        \"min\": 295.3,\n        \"max\": 304.5,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          299.3,\n          296.9,\n          300.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Process temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4836918656496663,\n        \"min\": 305.7,\n        \"max\": 313.8,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          307.2,\n          308.6,\n          310.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rotational speed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 179.4121706087358,\n        \"min\": 1168.0,\n        \"max\": 2886.0,\n        \"num_unique_values\": 941,\n        \"samples\": [\n          1274.0,\n          1576.0,\n          2010.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Torque\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.966804857457536,\n        \"min\": 3.8,\n        \"max\": 76.6,\n        \"num_unique_values\": 577,\n        \"samples\": [\n          36.1,\n          65.9,\n          12.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tool wear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63.64915152705582,\n        \"min\": 0.0,\n        \"max\": 253.0,\n        \"num_unique_values\": 246,\n        \"samples\": [\n          93.0,\n          14.0,\n          215.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Failure Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Power Failure\",\n          \"Heat Dissipation Failure\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chang1={'L':1 ,'M':2 ,'H':3}\n",
        "chang2={'No Failure':0 ,'Power Failure':1 ,'Heat Dissipation Failure':2,'Tool Wear Failure':3,'Overstrain Failure':4}\n",
        "df['Type'].replace(to_replace=chang1 ,inplace=True)\n",
        "df['Failure Type'].replace(to_replace=chang2 ,inplace=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z7HFyDIAOywm",
        "outputId": "2b8328fb-4854-4bee-f255-aaaedbc96ba7"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-28-b4958583382b>:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  df['Type'].replace(to_replace=chang1 ,inplace=True)\n",
            "<ipython-input-28-b4958583382b>:3: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
            "  df['Type'].replace(to_replace=chang1 ,inplace=True)\n",
            "<ipython-input-28-b4958583382b>:4: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  df['Failure Type'].replace(to_replace=chang2 ,inplace=True)\n",
            "<ipython-input-28-b4958583382b>:4: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
            "  df['Failure Type'].replace(to_replace=chang2 ,inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "hWYcHK8gWWCY",
        "outputId": "12fd0f72-d1c8-4d97-8542-e98fe4535cfe"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Type  Air temperature [K]  Process temperature  Rotational speed  \\\n",
              "0        2                298.1                308.6            1551.0   \n",
              "1        1                298.2                308.7            1408.0   \n",
              "2        1                298.1                308.5            1498.0   \n",
              "3        1                298.2                308.6            1433.0   \n",
              "4        1                298.2                308.7            1408.0   \n",
              "...    ...                  ...                  ...               ...   \n",
              "9995     2                298.8                308.4            1604.0   \n",
              "9996     3                298.9                308.4            1632.0   \n",
              "9997     2                299.0                308.6            1645.0   \n",
              "9998     3                299.0                308.7            1408.0   \n",
              "9999     2                299.0                308.7            1500.0   \n",
              "\n",
              "      Torque  Tool wear  Target  Failure Type  \n",
              "0       42.8        0.0       0             0  \n",
              "1       46.3        3.0       0             0  \n",
              "2       49.4        5.0       0             0  \n",
              "3       39.5        7.0       0             0  \n",
              "4       40.0        9.0       0             0  \n",
              "...      ...        ...     ...           ...  \n",
              "9995    29.5       14.0       0             0  \n",
              "9996    31.8       17.0       0             0  \n",
              "9997    33.4       22.0       0             0  \n",
              "9998    48.5       25.0       0             0  \n",
              "9999    40.2       30.0       0             0  \n",
              "\n",
              "[9973 rows x 8 columns]"
            ],
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              "      <th></th>\n",
              "      <th>Type</th>\n",
              "      <th>Air temperature [K]</th>\n",
              "      <th>Process temperature</th>\n",
              "      <th>Rotational speed</th>\n",
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              "      <td>3.0</td>\n",
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              "    <tr>\n",
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              "      <td>298.1</td>\n",
              "      <td>308.5</td>\n",
              "      <td>1498.0</td>\n",
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              "      <th>3</th>\n",
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              "      <td>298.2</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1433.0</td>\n",
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              "      <td>298.2</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1408.0</td>\n",
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              "      <td>9.0</td>\n",
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              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>2</td>\n",
              "      <td>298.8</td>\n",
              "      <td>308.4</td>\n",
              "      <td>1604.0</td>\n",
              "      <td>29.5</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>3</td>\n",
              "      <td>298.9</td>\n",
              "      <td>308.4</td>\n",
              "      <td>1632.0</td>\n",
              "      <td>31.8</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>2</td>\n",
              "      <td>299.0</td>\n",
              "      <td>308.6</td>\n",
              "      <td>1645.0</td>\n",
              "      <td>33.4</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>3</td>\n",
              "      <td>299.0</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1408.0</td>\n",
              "      <td>48.5</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>2</td>\n",
              "      <td>299.0</td>\n",
              "      <td>308.7</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>40.2</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9973 rows × 8 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "    .colab-df-convert:hover {\n",
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              "        element.innerHTML = '';\n",
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              "  </button>\n",
              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\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",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    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",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_c8228190-6aa7-494c-bccc-6031956308fa\">\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "    </button>\n",
              "    <script>\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#id_c8228190-6aa7-494c-bccc-6031956308fa button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 9973,\n  \"fields\": [\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Air temperature [K]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.000547560650067,\n        \"min\": 295.3,\n        \"max\": 304.5,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          299.3,\n          296.9,\n          300.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Process temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4836918656496663,\n        \"min\": 305.7,\n        \"max\": 313.8,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          307.2,\n          308.6,\n          310.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rotational speed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 179.4121706087358,\n        \"min\": 1168.0,\n        \"max\": 2886.0,\n        \"num_unique_values\": 941,\n        \"samples\": [\n          1274.0,\n          1576.0,\n          2010.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Torque\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.966804857457536,\n        \"min\": 3.8,\n        \"max\": 76.6,\n        \"num_unique_values\": 577,\n        \"samples\": [\n          36.1,\n          65.9,\n          12.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tool wear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63.64915152705582,\n        \"min\": 0.0,\n        \"max\": 253.0,\n        \"num_unique_values\": 246,\n        \"samples\": [\n          93.0,\n          14.0,\n          215.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Failure Type\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "sc = StandardScaler()"
      ],
      "metadata": {
        "id": "z5z6FtkuWa5S"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_features = [feature for feature in features if df[feature].dtype=='float64']"
      ],
      "metadata": {
        "id": "ulcASw5bYzV6"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[num_features] = sc.fit_transform(df[num_features])"
      ],
      "metadata": {
        "id": "ickCyT9hWdxj"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pca = PCA(n_components=len(num_features))\n",
        "X_pca = pd.DataFrame(data=pca.fit_transform(df[num_features]), columns=['PC'+str(i+1) for i in range(len(num_features))])\n",
        "var_exp = pd.Series(data=100*pca.explained_variance_ratio_, index=['PC'+str(i+1) for i in range(len(num_features))])\n",
        "print('Explained variance ratio per component:', round(var_exp,2), sep='\\n')\n",
        "print('Explained variance ratio with 3 components: '+str(round(var_exp.values[:3].sum(),2)))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vMJayIfPaB-L",
        "outputId": "c2b7c383-6003-40fa-de38-c7cfaedb558b"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Explained variance ratio per component:\n",
            "PC1    38.25\n",
            "PC2    36.78\n",
            "PC3    19.99\n",
            "PC4     2.53\n",
            "PC5     2.45\n",
            "dtype: float64\n",
            "Explained variance ratio with 3 components: 95.02\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# PCA for Data visualization\n",
        "pca3 = PCA(n_components=3)\n",
        "X_pca3 = pd.DataFrame(data=pca3.fit_transform(df[num_features]), columns=['PC1','PC2','PC3'])\n",
        "\n",
        "# Loadings Analysis\n",
        "fig, axs = plt.subplots(ncols=3, figsize=(18,4))\n",
        "fig.suptitle('Loadings magnitude')\n",
        "pca_loadings = pd.DataFrame(data=pca3.components_, columns=num_features)\n",
        "for j in range(3):\n",
        "    ax = axs[j]\n",
        "    sns.barplot(ax=ax, x=pca_loadings.columns, y=pca_loadings.values[j])\n",
        "    ax.tick_params(axis='x', rotation=90)\n",
        "    ax.title.set_text('PC'+str(j+1))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "yh97bajWpdvm",
        "outputId": "67b82b98-f069-4c4b-c840-8b5849a0fb08"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x400 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(7,4))\n",
        "sns.heatmap(data=df.corr(), mask=np.triu(df.corr()), annot=True, cmap='BrBG')\n",
        "plt.title('Correlation Heatmap')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "vDekC0ump7Iv",
        "outputId": "03b9c1b4-19de-4d8e-f23e-7dcf1d2fa83b"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score, fbeta_score\n",
        "from sklearn.metrics import confusion_matrix, make_scorer\n",
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "5sevCEz7snnO"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x , y =df[features] , df[['Target','Failure Type']]\n",
        "x_trainval , x_test ,y_trainval , y_test = train_test_split(x,y,test_size=0.1,stratify=df['Failure Type'],random_state=0)\n",
        "x_train , x_val ,y_train , y_val = train_test_split(x_trainval,y_trainval,test_size=0.11,stratify=y_trainval['Failure Type'],random_state=0)\n"
      ],
      "metadata": {
        "id": "lg8x9XOAtP82"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr=LogisticRegression(random_state=0)\n",
        "lr.fit(x_train , y_train['Target'])\n",
        "y_val_lr = lr.predict(x_val)\n",
        "y_test_lr = lr.predict(x_test)"
      ],
      "metadata": {
        "id": "mNLVmqNQwayT"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def eval_preds(model,X,y_true,y_pred):\n",
        "\n",
        "        y_true = y_true['Target']\n",
        "        cm = confusion_matrix(y_true, y_pred)\n",
        "        proba = model.predict_proba(X)[:,1]\n",
        "        acc = accuracy_score(y_true, y_pred)\n",
        "        auc = roc_auc_score(y_true, proba)\n",
        "        f1 = f1_score(y_true, y_pred, pos_label=1)\n",
        "        f2 = fbeta_score(y_true, y_pred, pos_label=1, beta=2)\n",
        "        metrics = pd.Series(data={'ACC':acc, 'AUC':auc, 'F1':f1, 'F2':f2})\n",
        "        metrics = round(metrics,3)\n",
        "        return cm, metrics"
      ],
      "metadata": {
        "id": "3aG5fJTB6qE5"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm_val_lr, metrics_val_lr = eval_preds(lr,x_val,y_val,y_val_lr)\n",
        "cm_test_lr, metrics_test_lr = eval_preds(lr,x_test,y_test,y_test_lr)\n",
        "print('Validation set metrics:',metrics_val_lr, sep='\\n')\n",
        "print('Test set metrics:',metrics_test_lr, sep='\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YKcrxWFX7d2p",
        "outputId": "5a62af19-026a-428d-a536-6b1142fca7c7"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation set metrics:\n",
            "ACC    0.968\n",
            "AUC    0.925\n",
            "F1     0.158\n",
            "F2     0.109\n",
            "dtype: float64\n",
            "Test set metrics:\n",
            "ACC    0.973\n",
            "AUC    0.928\n",
            "F1     0.341\n",
            "F2     0.250\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x , y =df[features] , df[['Target','Failure Type']]\n",
        "xx_trainval , xx_test ,yy_trainval , yy_test = train_test_split(x,y,test_size=0.1,stratify=df['Failure Type'],random_state=0)\n",
        "xx_train , xx_val ,yy_train , yy_val = train_test_split(xx_trainval,yy_trainval,test_size=0.11,stratify=y_trainval['Failure Type'],random_state=0)\n"
      ],
      "metadata": {
        "id": "YtBuMzrYBmdi"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "xg=RandomForestClassifier(random_state=0)\n",
        "xg.fit(xx_train , yy_train['Target'])\n",
        "yy_val_xg = xg.predict(xx_val)\n",
        "yy_test_xg = xg.predict(xx_test)"
      ],
      "metadata": {
        "id": "vCwHd_SVCH_y"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm_val_xg, metrics_val_xg = eval_preds(xg,xx_val,yy_val,yy_val_xg)\n",
        "cm_test_xg, metrics_test_xg = eval_preds(xg,xx_test,yy_test,yy_test_xg)\n",
        "print('Validation set metrics:',metrics_val_xg, sep='\\n')\n",
        "print('Test set metrics:',metrics_test_xg, sep='\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "akMs0wryBp95",
        "outputId": "ca932410-569d-4e3c-96e0-7efd8de23cdd"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation set metrics:\n",
            "ACC    0.983\n",
            "AUC    0.989\n",
            "F1     0.702\n",
            "F2     0.641\n",
            "dtype: float64\n",
            "Test set metrics:\n",
            "ACC    0.983\n",
            "AUC    0.958\n",
            "F1     0.679\n",
            "F2     0.592\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YtfdY86dyacB"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}