CBRE_Challenge.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.axes as ax\n",
        "from mpl_toolkits.mplot3d import Axes3D  # Import 3D plotting module\n",
        "import seaborn as sns\n",
        "import operator\n",
        "import json\n",
        "import plotly as py\n",
        "import plotly.express as px\n",
        "import plotly.graph_objs as go\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "from datetime import datetime\n",
        "current_date = datetime(2023, 9, 16)"
      ]
    },
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              "      <th>Asset ID</th>\n",
              "      <th>Asset Type</th>\n",
              "      <th>Floor</th>\n",
              "      <th>Room</th>\n",
              "      <th>Installation Date</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Elevator</td>\n",
              "      <td>7</td>\n",
              "      <td>103</td>\n",
              "      <td>1/6/2020</td>\n",
              "      <td>Manufacturer_4</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Plumbing System</td>\n",
              "      <td>6</td>\n",
              "      <td>109</td>\n",
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              "      <td>105</td>\n",
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              "      <td>5/23/2023</td>\n",
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            "text/plain": [
              "   Asset ID       Asset Type  Floor  Room Installation Date    Manufacturer  \\\n",
              "0         1         Elevator      7   103          1/6/2020  Manufacturer_4   \n",
              "1         2  Plumbing System      6   109         6/11/2022  Manufacturer_2   \n",
              "2         3       Fire Alarm      3   104         7/24/2019  Manufacturer_4   \n",
              "3         4         Elevator      1   105          1/4/2021  Manufacturer_4   \n",
              "4         5  Plumbing System      2   104        11/28/2022  Manufacturer_1   \n",
              "\n",
              "   Operational Time (hrs)  Work Orders  Repairs Last Serviced Date  \n",
              "0                   39313            0        1          6/19/2023  \n",
              "1                   20012            0        1           1/6/2023  \n",
              "2                   21546           10        5          9/24/2022  \n",
              "3                   47038            1        1          5/23/2023  \n",
              "4                   20022           10        3          1/29/2023  "
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv('/dataset/Dataset_-_CBRE_Challenge_-_HackSMU_2023.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-la1hARMyp7H"
      },
      "source": [
        "## Data Cleaning"
      ]
    },
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        "id": "ji3AgpkdTrkJ"
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      "source": [
        "df.set_index('Asset ID', inplace=True)\n",
        "df.dropna(inplace=True)"
      ]
    },
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        "id": "Bnnud75fTu-c",
        "outputId": "8fa9c174-dd61-4fbd-e701-5950c0cf78b6"
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              "      <th>1</th>\n",
              "      <td>Elevator</td>\n",
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              "      <td>Fire Alarm</td>\n",
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              "      <td>104</td>\n",
              "      <td>7/24/2019</td>\n",
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              "      <td>21546</td>\n",
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              "      <td>5</td>\n",
              "      <td>9/24/2022</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>Elevator</td>\n",
              "      <td>1</td>\n",
              "      <td>105</td>\n",
              "      <td>1/4/2021</td>\n",
              "      <td>Manufacturer_4</td>\n",
              "      <td>47038</td>\n",
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              "      <td>1</td>\n",
              "      <td>5/23/2023</td>\n",
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              "      <th>5</th>\n",
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              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>1/29/2023</td>\n",
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              "      <th>6</th>\n",
              "      <td>Fire Alarm</td>\n",
              "      <td>2</td>\n",
              "      <td>103</td>\n",
              "      <td>7/29/2021</td>\n",
              "      <td>Manufacturer_3</td>\n",
              "      <td>1602</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>10/3/2022</td>\n",
              "    </tr>\n",
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              "      <th>7</th>\n",
              "      <td>Fire Alarm</td>\n",
              "      <td>6</td>\n",
              "      <td>108</td>\n",
              "      <td>8/23/2020</td>\n",
              "      <td>Manufacturer_2</td>\n",
              "      <td>15650</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1/13/2023</td>\n",
              "    </tr>\n",
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              "      <th>8</th>\n",
              "      <td>HVAC</td>\n",
              "      <td>2</td>\n",
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              "      <td>8/9/2023</td>\n",
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              "      <th>9</th>\n",
              "      <td>Fire Alarm</td>\n",
              "      <td>6</td>\n",
              "      <td>108</td>\n",
              "      <td>7/30/2023</td>\n",
              "      <td>Manufacturer_5</td>\n",
              "      <td>24332</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2/13/2023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Elevator</td>\n",
              "      <td>7</td>\n",
              "      <td>101</td>\n",
              "      <td>7/24/2019</td>\n",
              "      <td>Manufacturer_4</td>\n",
              "      <td>18722</td>\n",
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              "      <td>5/29/2023</td>\n",
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              "    <div class=\"colab-df-buttons\">\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-78e2bacf-fa90-4f86-b135-0f5387567730');\n",
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              "                                                    [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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              "            title=\"Suggest charts.\"\n",
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              "    </g>\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",
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              "      --disabled-fill-color: #AAA;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
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              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    0% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "      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",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "               Asset Type  Floor  Room Installation Date    Manufacturer  \\\n",
              "Asset ID                                                                   \n",
              "1                Elevator      7   103          1/6/2020  Manufacturer_4   \n",
              "2         Plumbing System      6   109         6/11/2022  Manufacturer_2   \n",
              "3              Fire Alarm      3   104         7/24/2019  Manufacturer_4   \n",
              "4                Elevator      1   105          1/4/2021  Manufacturer_4   \n",
              "5         Plumbing System      2   104        11/28/2022  Manufacturer_1   \n",
              "6              Fire Alarm      2   103         7/29/2021  Manufacturer_3   \n",
              "7              Fire Alarm      6   108         8/23/2020  Manufacturer_2   \n",
              "8                    HVAC      2   106          6/3/2021  Manufacturer_2   \n",
              "9              Fire Alarm      6   108         7/30/2023  Manufacturer_5   \n",
              "10               Elevator      7   101         7/24/2019  Manufacturer_4   \n",
              "\n",
              "          Operational Time (hrs)  Work Orders  Repairs Last Serviced Date  \n",
              "Asset ID                                                                   \n",
              "1                          39313            0        1          6/19/2023  \n",
              "2                          20012            0        1           1/6/2023  \n",
              "3                          21546           10        5          9/24/2022  \n",
              "4                          47038            1        1          5/23/2023  \n",
              "5                          20022           10        3          1/29/2023  \n",
              "6                           1602            9        4          10/3/2022  \n",
              "7                          15650            2        3          1/13/2023  \n",
              "8                          33932            1        4           8/9/2023  \n",
              "9                          24332            5        4          2/13/2023  \n",
              "10                         18722           10        2          5/29/2023  "
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3yGqDnFktrc2",
        "outputId": "380a18d3-1283-453d-a266-617edb4f3f65"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Manufacturer_5    131\n",
              "Manufacturer_3    100\n",
              "Manufacturer_4     92\n",
              "Manufacturer_2     90\n",
              "Manufacturer_1     87\n",
              "Name: Manufacturer, dtype: int64"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Manufacturer'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "8jB2G3OAvv0G",
        "outputId": "87432735-0281-4bef-c0bd-168780062a81"
      },
      "outputs": [
        {
          "data": {
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --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",
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              "      --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",
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              "    90% {\n",
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              "      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-0175126f-fe1a-4671-99a1-29f7e9ce4048 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "               Asset Type  Floor  Room Installation Date    Manufacturer  \\\n",
              "Asset ID                                                                   \n",
              "1                Elevator      7   103          1/6/2020  Manufacturer_4   \n",
              "2         Plumbing System      6   109         6/11/2022  Manufacturer_2   \n",
              "3              Fire Alarm      3   104         7/24/2019  Manufacturer_4   \n",
              "4                Elevator      1   105          1/4/2021  Manufacturer_4   \n",
              "5         Plumbing System      2   104        11/28/2022  Manufacturer_1   \n",
              "\n",
              "          Operational Time (hrs)  Work Orders  Repairs Last Serviced Date  \\\n",
              "Asset ID                                                                    \n",
              "1                          39313            0        1          6/19/2023   \n",
              "2                          20012            0        1           1/6/2023   \n",
              "3                          21546           10        5          9/24/2022   \n",
              "4                          47038            1        1          5/23/2023   \n",
              "5                          20022           10        3          1/29/2023   \n",
              "\n",
              "          Manufacturer_id  \n",
              "Asset ID                   \n",
              "1                       4  \n",
              "2                       2  \n",
              "3                       4  \n",
              "4                       4  \n",
              "5                       1  "
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Encoding Manufacturer into numeric type\n",
        "df['Manufacturer_id'] = df['Manufacturer'].str.extract('(\\d+)').astype(int)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iVf7iErykKl_",
        "outputId": "c5722c9f-c2ac-46b0-f474-f5271c9b4350"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(500, 10)"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r5ai9u7NxouU"
      },
      "source": [
        "### Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2E0KiXXKtDD7"
      },
      "outputs": [],
      "source": [
        "# Calculate time since last service\n",
        "df['Last Serviced Date'] = pd.to_datetime(df['Last Serviced Date'])\n",
        "df['Time Since Last Service (days)'] = (current_date - df['Last Serviced Date']).dt.days\n",
        "\n",
        "# Calculate asset age\n",
        "df['Installation Date'] = pd.to_datetime(df['Installation Date'])\n",
        "df['Asset Age (days)'] = (current_date - df['Installation Date']).dt.days"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "9s_jJzBjttDy",
        "outputId": "0c8db8b0-3103-4c17-9443-0e664cb947c6"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>Operational Time (hrs)</th>\n",
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              "      <th>Asset ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th>2</th>\n",
              "      <td>20012</td>\n",
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              "      <td>2</td>\n",
              "      <td>253</td>\n",
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              "      <th>3</th>\n",
              "      <td>21546</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>357</td>\n",
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              "      <th>4</th>\n",
              "      <td>47038</td>\n",
              "      <td>1</td>\n",
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              "      <td>4</td>\n",
              "      <td>116</td>\n",
              "      <td>985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>20022</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>230</td>\n",
              "      <td>292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1602</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>348</td>\n",
              "      <td>779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>15650</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>246</td>\n",
              "      <td>1119</td>\n",
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              "      <th>8</th>\n",
              "      <td>33932</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>38</td>\n",
              "      <td>835</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>24332</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>215</td>\n",
              "      <td>48</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>18722</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>110</td>\n",
              "      <td>1515</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>29355</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>89</td>\n",
              "      <td>454</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>46525</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>295</td>\n",
              "      <td>490</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
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              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>290</td>\n",
              "      <td>1617</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>18211</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>189</td>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>29177</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>25</td>\n",
              "      <td>1386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>12953</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>303</td>\n",
              "      <td>1362</td>\n",
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              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>48382</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>246</td>\n",
              "      <td>1734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>24260</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>190</td>\n",
              "      <td>499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>45428</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>100</td>\n",
              "      <td>752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>29859</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>102</td>\n",
              "      <td>1635</td>\n",
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              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  <style>\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        document.querySelector('#df-32110e0c-cd59-42a0-97ed-2d86fa3e113a button.colab-df-convert');\n",
              "      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-32110e0c-cd59-42a0-97ed-2d86fa3e113a');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\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",
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              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\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",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    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",
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              "    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-b6ece7f0-84cb-4632-98a8-51a01369c638 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "          Operational Time (hrs)  Work Orders  Repairs  Manufacturer_id  \\\n",
              "Asset ID                                                                  \n",
              "1                          39313            0        1                4   \n",
              "2                          20012            0        1                2   \n",
              "3                          21546           10        5                4   \n",
              "4                          47038            1        1                4   \n",
              "5                          20022           10        3                1   \n",
              "6                           1602            9        4                3   \n",
              "7                          15650            2        3                2   \n",
              "8                          33932            1        4                2   \n",
              "9                          24332            5        4                5   \n",
              "10                         18722           10        2                4   \n",
              "11                         29355            6        1                2   \n",
              "12                         46525            8        1                2   \n",
              "13                         43437            7        2                2   \n",
              "14                         18211            0        4                4   \n",
              "15                         29177            8        4                5   \n",
              "16                         12953            2        4                1   \n",
              "17                         48382            9        5                5   \n",
              "18                         24260            5        0                2   \n",
              "19                         45428            2        3                5   \n",
              "20                         29859            1        5                2   \n",
              "\n",
              "          Time Since Last Service (days)  Asset Age (days)  \n",
              "Asset ID                                                    \n",
              "1                                     89              1349  \n",
              "2                                    253               462  \n",
              "3                                    357              1515  \n",
              "4                                    116               985  \n",
              "5                                    230               292  \n",
              "6                                    348               779  \n",
              "7                                    246              1119  \n",
              "8                                     38               835  \n",
              "9                                    215                48  \n",
              "10                                   110              1515  \n",
              "11                                    89               454  \n",
              "12                                   295               490  \n",
              "13                                   290              1617  \n",
              "14                                    12               189  \n",
              "15                                    25              1386  \n",
              "16                                   303              1362  \n",
              "17                                   246              1734  \n",
              "18                                   190               499  \n",
              "19                                   100               752  \n",
              "20                                   102              1635  "
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# drop unnecessary features\n",
        "df.drop(columns=['Manufacturer', 'Floor', 'Room', 'Installation Date', 'Last Serviced Date', 'Asset Type'], inplace=True)\n",
        "df.head(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xfvsnuxG0Isp"
      },
      "outputs": [],
      "source": [
        "# drop the rows whose asset age is greater than last service date\n",
        "\n",
        "df = df[df['Asset Age (days)'] >= df['Time Since Last Service (days)']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "an80FGR4k23d",
        "outputId": "c397c340-0a35-4556-a566-99fb02d91a86"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Operational Time (hrs)</th>\n",
              "      <th>Work Orders</th>\n",
              "      <th>Repairs</th>\n",
              "      <th>Manufacturer_id</th>\n",
              "      <th>Time Since Last Service (days)</th>\n",
              "      <th>Asset Age (days)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>435.000000</td>\n",
              "      <td>435.000000</td>\n",
              "      <td>435.000000</td>\n",
              "      <td>435.000000</td>\n",
              "      <td>435.000000</td>\n",
              "      <td>435.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>26134.347126</td>\n",
              "      <td>4.868966</td>\n",
              "      <td>2.404598</td>\n",
              "      <td>3.181609</td>\n",
              "      <td>182.579310</td>\n",
              "      <td>1000.563218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
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              "      <td>3.262527</td>\n",
              "      <td>1.731246</td>\n",
              "      <td>1.443764</td>\n",
              "      <td>105.690859</td>\n",
              "      <td>461.734650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>459.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>66.000000</td>\n",
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              "    <tr>\n",
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              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>625.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>26191.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>991.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>38726.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>265.000000</td>\n",
              "      <td>1401.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>49976.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>367.000000</td>\n",
              "      <td>1819.000000</td>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "        document.querySelector('#df-e3e0ea04-a525-4e1e-af27-7d8a99cdce4d 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-e3e0ea04-a525-4e1e-af27-7d8a99cdce4d');\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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              "  .colab-df-quickchart {\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\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",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      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",
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              "    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-d67a5d59-5905-4ec5-8a59-6afad52ab38b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       Operational Time (hrs)  Work Orders     Repairs  Manufacturer_id  \\\n",
              "count              435.000000   435.000000  435.000000       435.000000   \n",
              "mean             26134.347126     4.868966    2.404598         3.181609   \n",
              "std              14151.171313     3.262527    1.731246         1.443764   \n",
              "min                459.000000     0.000000    0.000000         1.000000   \n",
              "25%              14874.500000     2.000000    1.000000         2.000000   \n",
              "50%              26191.000000     5.000000    2.000000         3.000000   \n",
              "75%              38726.000000     8.000000    4.000000         5.000000   \n",
              "max              49976.000000    10.000000    5.000000         5.000000   \n",
              "\n",
              "       Time Since Last Service (days)  Asset Age (days)  \n",
              "count                      435.000000        435.000000  \n",
              "mean                       182.579310       1000.563218  \n",
              "std                        105.690859        461.734650  \n",
              "min                          4.000000         66.000000  \n",
              "25%                         93.000000        625.000000  \n",
              "50%                        184.000000        991.000000  \n",
              "75%                        265.000000       1401.500000  \n",
              "max                        367.000000       1819.000000  "
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "rbZxOqiDo9Ct",
        "outputId": "18ab2d90-1cfe-4acf-fbd8-cd7273868798"
      },
      "outputs": [
        {
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Operational Time (hrs)</th>\n",
              "      <th>Work Orders</th>\n",
              "      <th>Repairs</th>\n",
              "      <th>Manufacturer_id</th>\n",
              "      <th>Time Since Last Service (days)</th>\n",
              "      <th>Asset Age (days)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Asset ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.061938</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.8</td>\n",
              "      <td>7.75</td>\n",
              "      <td>3.107438</td>\n",
              "      <td>7.586994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.553870</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.8</td>\n",
              "      <td>3.25</td>\n",
              "      <td>7.173554</td>\n",
              "      <td>3.033086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.832684</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7.75</td>\n",
              "      <td>9.752066</td>\n",
              "      <td>8.439247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9.466002</td>\n",
              "      <td>1.9</td>\n",
              "      <td>2.8</td>\n",
              "      <td>7.75</td>\n",
              "      <td>3.776860</td>\n",
              "      <td>5.718197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4.555688</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6.4</td>\n",
              "      <td>1.00</td>\n",
              "      <td>6.603306</td>\n",
              "      <td>2.160297</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e3137ee8-94aa-40e9-b199-d58149346929')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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-e3137ee8-94aa-40e9-b199-d58149346929 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-e3137ee8-94aa-40e9-b199-d58149346929');\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-876bfffa-01c1-4395-b50f-4dd6d10c8240\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-876bfffa-01c1-4395-b50f-4dd6d10c8240')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --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",
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              "    20% {\n",
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              "      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-876bfffa-01c1-4395-b50f-4dd6d10c8240 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "          Operational Time (hrs)  Work Orders  Repairs  Manufacturer_id  \\\n",
              "Asset ID                                                                  \n",
              "1                       8.061938          1.0      2.8             7.75   \n",
              "2                       4.553870          1.0      2.8             3.25   \n",
              "3                       4.832684         10.0     10.0             7.75   \n",
              "4                       9.466002          1.9      2.8             7.75   \n",
              "5                       4.555688         10.0      6.4             1.00   \n",
              "\n",
              "          Time Since Last Service (days)  Asset Age (days)  \n",
              "Asset ID                                                    \n",
              "1                               3.107438          7.586994  \n",
              "2                               7.173554          3.033086  \n",
              "3                               9.752066          8.439247  \n",
              "4                               3.776860          5.718197  \n",
              "5                               6.603306          2.160297  "
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = ((df - df.min ()) / (df.max() - df.min ())) * 9 + 1\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "MlhhbvqqXJu-",
        "outputId": "19a80ef3-0c46-40d8-9d4c-6b1ec9243e0e"
      },
      "outputs": [
        {
          "data": {
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              "      <th>Operational Time (hrs)</th>\n",
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              "      <th>1</th>\n",
              "      <td>8.061938</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\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",
              "    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",
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              "  }\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",
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              "    60% {\n",
              "      border-color: transparent;\n",
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              "    }\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-eb1691a9-db3d-4f93-8fcd-003f629412ab button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "          Operational Time (hrs)  Work Orders  Repairs  Manufacturer_id  \\\n",
              "Asset ID                                                                  \n",
              "1                       8.061938          1.0      2.8             7.75   \n",
              "2                       4.553870          1.0      2.8             3.25   \n",
              "3                       4.832684         10.0     10.0             7.75   \n",
              "4                       9.466002          1.9      2.8             7.75   \n",
              "5                       4.555688         10.0      6.4             1.00   \n",
              "\n",
              "          Time Since Last Service (days)  Asset Age (days)  \n",
              "Asset ID                                                    \n",
              "1                               3.107438          7.586994  \n",
              "2                               7.173554          3.033086  \n",
              "3                               9.752066          8.439247  \n",
              "4                               3.776860          5.718197  \n",
              "5                               6.603306          2.160297  "
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Splitting the dataset into Train (80%) and Test (20%)\n",
        "df_train, df_test = train_test_split(df, test_size=0.2, train_size=0.8, random_state=42)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "RgsTdIu1qPmB",
        "outputId": "2e11fb73-7087-4806-9a67-c0ec2e46a542"
      },
      "outputs": [
        {
          "data": {
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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-a4c94016-ccf7-4947-bcb9-53d8a72cc865');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "</svg>\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --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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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\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",
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              "    20% {\n",
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              "      border-left-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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2991366b-5c20-4dbd-9e79-7d9107fdc62e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "          Operational Time (hrs)  Work Orders  Repairs  Manufacturer_id  \\\n",
              "Asset ID                                                                  \n",
              "74                      1.789547          9.1      2.8             7.75   \n",
              "324                     2.472767          1.9      8.2             1.00   \n",
              "270                     9.804249          2.8      2.8            10.00   \n",
              "35                      5.603146          2.8     10.0             7.75   \n",
              "97                      9.532342          8.2      1.0            10.00   \n",
              "\n",
              "          Time Since Last Service (days)  Asset Age (days)  Cluster  \n",
              "Asset ID                                                             \n",
              "74                              1.297521          4.578437        3  \n",
              "324                             7.719008          7.607530        1  \n",
              "270                             1.223140          2.653166        0  \n",
              "35                              6.504132          9.969196        1  \n",
              "97                              3.057851          8.598403        3  "
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Unsupervised learning using K-means Algorithm\n",
        "features = df_train[['Operational Time (hrs)', 'Work Orders', 'Repairs', 'Time Since Last Service (days)', 'Asset Age (days)']]\n",
        "\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaled_data = scaler.fit_transform(features)\n",
        "\n",
        "# Initialize the K-Means model with 3 clusters\n",
        "kmeans = KMeans(n_clusters=4, random_state=42, n_init=100)\n",
        "\n",
        "# Fit the K-Means model to the scaled data\n",
        "kmeans.fit(scaled_data)\n",
        "\n",
        "# Add the cluster labels to the original dataset\n",
        "df_train['Cluster'] = kmeans.fit_predict(features)\n",
        "\n",
        "df_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "EsqYM-IifAxn",
        "outputId": "4a0aab31-78d8-427f-90f8-6aa51b5a395f"
      },
      "outputs": [
        {
          "data": {
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              "      <th>Operational Time (hrs)</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>1.789547</td>\n",
              "      <td>9.1</td>\n",
              "      <td>2.8</td>\n",
              "      <td>7.75</td>\n",
              "      <td>1.297521</td>\n",
              "      <td>4.578437</td>\n",
              "      <td>3</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>324</th>\n",
              "      <td>2.472767</td>\n",
              "      <td>1.9</td>\n",
              "      <td>8.2</td>\n",
              "      <td>1.00</td>\n",
              "      <td>7.719008</td>\n",
              "      <td>7.607530</td>\n",
              "      <td>1</td>\n",
              "      <td>Moderate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270</th>\n",
              "      <td>9.804249</td>\n",
              "      <td>2.8</td>\n",
              "      <td>2.8</td>\n",
              "      <td>10.00</td>\n",
              "      <td>1.223140</td>\n",
              "      <td>2.653166</td>\n",
              "      <td>0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>5.603146</td>\n",
              "      <td>2.8</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7.75</td>\n",
              "      <td>6.504132</td>\n",
              "      <td>9.969196</td>\n",
              "      <td>1</td>\n",
              "      <td>Moderate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>9.532342</td>\n",
              "      <td>8.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.00</td>\n",
              "      <td>3.057851</td>\n",
              "      <td>8.598403</td>\n",
              "      <td>3</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "  </tbody>\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-18453b04-046d-40e8-819d-ea7107eb72da');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
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              "            title=\"Suggest charts.\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --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",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      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",
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              "    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-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-534115f0-8108-4eb8-869a-3de113b6606d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "          Operational Time (hrs)  Work Orders  Repairs  Manufacturer_id  \\\n",
              "Asset ID                                                                  \n",
              "74                      1.789547          9.1      2.8             7.75   \n",
              "324                     2.472767          1.9      8.2             1.00   \n",
              "270                     9.804249          2.8      2.8            10.00   \n",
              "35                      5.603146          2.8     10.0             7.75   \n",
              "97                      9.532342          8.2      1.0            10.00   \n",
              "\n",
              "          Time Since Last Service (days)  Asset Age (days)  Cluster  \\\n",
              "Asset ID                                                              \n",
              "74                              1.297521          4.578437        3   \n",
              "324                             7.719008          7.607530        1   \n",
              "270                             1.223140          2.653166        0   \n",
              "35                              6.504132          9.969196        1   \n",
              "97                              3.057851          8.598403        3   \n",
              "\n",
              "         Maintenance Priority  \n",
              "Asset ID                       \n",
              "74                       High  \n",
              "324                  Moderate  \n",
              "270                       Low  \n",
              "35                   Moderate  \n",
              "97                       High  "
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Assuming df_train has the 'Cluster' feature\n",
        "df_train['Maintenance Priority'] = df_train['Cluster'].map({0: 'Low', 1: 'Moderate', 2: 'Moderate', 3: 'High'})\n",
        "df_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lsXy2zVCwXiT",
        "outputId": "8b9ec74d-25a9-4699-e35d-9a01e7634f25"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Moderate    164\n",
              "Low          98\n",
              "High         86\n",
              "Name: Maintenance Priority, dtype: int64"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_train['Maintenance Priority'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "K5kapPgYKdLc",
        "outputId": "77e3b809-bd0d-47e5-a176-cfcd44e3aca3"
      },
      "outputs": [
        {
          "data": {
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              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visualize the clusters\n",
        "trace1 = go.Scatter3d(\n",
        "    x= df_train['Time Since Last Service (days)'],\n",
        "    y= df_train['Repairs'],\n",
        "    z= df_train['Work Orders'],\n",
        "    mode='markers',\n",
        "     marker=dict(\n",
        "        color = df_train['Cluster'],\n",
        "        size= 10,\n",
        "        line=dict(\n",
        "            color= df_train['Cluster'],\n",
        "            width= 2\n",
        "        ),\n",
        "        opacity=1\n",
        "     )\n",
        ")\n",
        "data = [trace1]\n",
        "layout = go.Layout(\n",
        "    title= 'Clusters',\n",
        "    scene = dict(\n",
        "            xaxis = dict(title  = 'Time Since Last Service (days)'),\n",
        "            yaxis = dict(title  = 'Repairs'),\n",
        "            zaxis = dict(title  = 'Work Orders')\n",
        "        )\n",
        ")\n",
        "fig = go.Figure(data=data, layout=layout)\n",
        "py.offline.iplot(fig)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "9tpTsTf6sBKZ",
        "outputId": "a369e058-fc54-4f92-dfb4-52f11594d5c7"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visual for Operational Time (hrs) and Time Since Last Service (days)\n",
        "features = df_train[['Operational Time (hrs)', 'Time Since Last Service (days)']]\n",
        "\n",
        "# Perform K-Means clustering\n",
        "df_train['Cluster'] = kmeans.fit_predict(features)\n",
        "\n",
        "# Visualize the clusters\n",
        "plt.scatter(features.iloc[:, 0], features.iloc[:, 1], c=df_train['Cluster'], cmap='rainbow')\n",
        "plt.xlabel('Operational Time (hrs)')\n",
        "plt.ylabel('Time Since Last Service (days)')\n",
        "plt.title('K-Means Clustering (Operational Time vs. Time Since Last Service)')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "DdwXSU3YyHZ-",
        "outputId": "36987896-a088-4f9b-b48f-27b50ebfe245"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visual for Operational Time and Asset Age\n",
        "features = df_train[['Repairs', 'Work Orders']]\n",
        "\n",
        "# Perform K-Means clustering\n",
        "df_train['Cluster'] = kmeans.fit_predict(features)\n",
        "\n",
        "# Visualize the clusters\n",
        "plt.scatter(features.iloc[:, 0], features.iloc[:, 1], c=df_train['Cluster'], cmap='rainbow')\n",
        "plt.xlabel('Operational Time (hrs)')\n",
        "plt.ylabel('Asset Age (days)')\n",
        "plt.title('K-Means Clustering (Operational Time vs. Asset Age (days)')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "TpZXZ_hxRQ6L",
        "outputId": "5abe5dfb-1193-486b-f31c-20d93579ce3f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visual for Work Orders and Repairs\n",
        "features = df_train[['Work Orders', 'Repairs']]\n",
        "\n",
        "# Perform K-Means clustering\n",
        "df_train['Cluster'] = kmeans.fit_predict(features)\n",
        "\n",
        "# Visualize the clusters\n",
        "plt.scatter(features.iloc[:, 0], features.iloc[:, 1], c=df_train['Cluster'], cmap='rainbow')\n",
        "plt.xlabel('Work Orders')\n",
        "plt.ylabel('Repairs')\n",
        "plt.title('K-Means Clustering (Work Orders vs. Repairs)')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9NhT7YpNx0pF"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "\n",
        "# Assuming you have df_train and its 'Maintenance Priority' column\n",
        "maintenance_priority_counts = df_train['Maintenance Priority'].value_counts().to_dict()\n",
        "\n",
        "# Save as a JSON file\n",
        "with open('maintenance_priority_counts.json', 'w') as f:\n",
        "    json.dump(maintenance_priority_counts, f)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}