kMeans_Clustering_Model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy\n",
        "import math\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error"
      ],
      "metadata": {
        "id": "L51gDtlPowvB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load data"
      ],
      "metadata": {
        "id": "OXdUFK1TG73w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "id": "HW-VrJguBvYJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "d9762df1-289f-477f-e1c1-6d8606324040"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-e7bcc8ea-4567-4782-b811-318928b1a582\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-e7bcc8ea-4567-4782-b811-318928b1a582\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving train_FD002.txt to train_FD002.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create headers\n",
        "index_col_names=['unit_id','time_cycle']\n",
        "operat_set_col_names=['op_conditions_{}'.format(i) for i in range(1,4)]\n",
        "sensor_measure_col_names=['sensor_{}'.format(i) for i in range(1,22)]\n",
        "all_col=index_col_names+operat_set_col_names+sensor_measure_col_names\n",
        "print(all_col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hp6ReiG3BE1_",
        "outputId": "ca81a71e-861a-4cd3-b321-b88636430d0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['unit_id', 'time_cycle', 'op_conditions_1', 'op_conditions_2', 'op_conditions_3', 'sensor_1', 'sensor_2', 'sensor_3', 'sensor_4', 'sensor_5', 'sensor_6', 'sensor_7', 'sensor_8', 'sensor_9', 'sensor_10', 'sensor_11', 'sensor_12', 'sensor_13', 'sensor_14', 'sensor_15', 'sensor_16', 'sensor_17', 'sensor_18', 'sensor_19', 'sensor_20', 'sensor_21']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import Data - NASA dataset 260 train trajectories, 259 tests and 6 conditions\n",
        "raw_train_df=pd.read_csv('/content/train_FD002.txt',delim_whitespace=True,names=all_col)\n",
        "raw_train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "yfc-U-V62GiI",
        "outputId": "2128f39f-7d3c-464f-b164-89d61473ce31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   unit_id  time_cycle  op_conditions_1  op_conditions_2  op_conditions_3  \\\n",
              "0        1           1          34.9983           0.8400            100.0   \n",
              "1        1           2          41.9982           0.8408            100.0   \n",
              "2        1           3          24.9988           0.6218             60.0   \n",
              "3        1           4          42.0077           0.8416            100.0   \n",
              "4        1           5          25.0005           0.6203             60.0   \n",
              "\n",
              "   sensor_1  sensor_2  sensor_3  sensor_4  sensor_5  ...  sensor_12  \\\n",
              "0    449.44    555.32   1358.61   1137.23      5.48  ...     183.06   \n",
              "1    445.00    549.90   1353.22   1125.78      3.91  ...     130.42   \n",
              "2    462.54    537.31   1256.76   1047.45      7.05  ...     164.22   \n",
              "3    445.00    549.51   1354.03   1126.38      3.91  ...     130.72   \n",
              "4    462.54    537.07   1257.71   1047.93      7.05  ...     164.31   \n",
              "\n",
              "   sensor_13  sensor_14  sensor_15  sensor_16  sensor_17  sensor_18  \\\n",
              "0    2387.72    8048.56     9.3461       0.02        334       2223   \n",
              "1    2387.66    8072.30     9.3774       0.02        330       2212   \n",
              "2    2028.03    7864.87    10.8941       0.02        309       1915   \n",
              "3    2387.61    8068.66     9.3528       0.02        329       2212   \n",
              "4    2028.00    7861.23    10.8963       0.02        309       1915   \n",
              "\n",
              "   sensor_19  sensor_20  sensor_21  \n",
              "0     100.00      14.73     8.8071  \n",
              "1     100.00      10.41     6.2665  \n",
              "2      84.93      14.08     8.6723  \n",
              "3     100.00      10.59     6.4701  \n",
              "4      84.93      14.13     8.5286  \n",
              "\n",
              "[5 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-7bb07f08-8d27-4cc0-90d4-df3c1ab102ee\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>unit_id</th>\n",
              "      <th>time_cycle</th>\n",
              "      <th>op_conditions_1</th>\n",
              "      <th>op_conditions_2</th>\n",
              "      <th>op_conditions_3</th>\n",
              "      <th>sensor_1</th>\n",
              "      <th>sensor_2</th>\n",
              "      <th>sensor_3</th>\n",
              "      <th>sensor_4</th>\n",
              "      <th>sensor_5</th>\n",
              "      <th>...</th>\n",
              "      <th>sensor_12</th>\n",
              "      <th>sensor_13</th>\n",
              "      <th>sensor_14</th>\n",
              "      <th>sensor_15</th>\n",
              "      <th>sensor_16</th>\n",
              "      <th>sensor_17</th>\n",
              "      <th>sensor_18</th>\n",
              "      <th>sensor_19</th>\n",
              "      <th>sensor_20</th>\n",
              "      <th>sensor_21</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>34.9983</td>\n",
              "      <td>0.8400</td>\n",
              "      <td>100.0</td>\n",
              "      <td>449.44</td>\n",
              "      <td>555.32</td>\n",
              "      <td>1358.61</td>\n",
              "      <td>1137.23</td>\n",
              "      <td>5.48</td>\n",
              "      <td>...</td>\n",
              "      <td>183.06</td>\n",
              "      <td>2387.72</td>\n",
              "      <td>8048.56</td>\n",
              "      <td>9.3461</td>\n",
              "      <td>0.02</td>\n",
              "      <td>334</td>\n",
              "      <td>2223</td>\n",
              "      <td>100.00</td>\n",
              "      <td>14.73</td>\n",
              "      <td>8.8071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>41.9982</td>\n",
              "      <td>0.8408</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>549.90</td>\n",
              "      <td>1353.22</td>\n",
              "      <td>1125.78</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>130.42</td>\n",
              "      <td>2387.66</td>\n",
              "      <td>8072.30</td>\n",
              "      <td>9.3774</td>\n",
              "      <td>0.02</td>\n",
              "      <td>330</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.41</td>\n",
              "      <td>6.2665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>24.9988</td>\n",
              "      <td>0.6218</td>\n",
              "      <td>60.0</td>\n",
              "      <td>462.54</td>\n",
              "      <td>537.31</td>\n",
              "      <td>1256.76</td>\n",
              "      <td>1047.45</td>\n",
              "      <td>7.05</td>\n",
              "      <td>...</td>\n",
              "      <td>164.22</td>\n",
              "      <td>2028.03</td>\n",
              "      <td>7864.87</td>\n",
              "      <td>10.8941</td>\n",
              "      <td>0.02</td>\n",
              "      <td>309</td>\n",
              "      <td>1915</td>\n",
              "      <td>84.93</td>\n",
              "      <td>14.08</td>\n",
              "      <td>8.6723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>42.0077</td>\n",
              "      <td>0.8416</td>\n",
              "      <td>100.0</td>\n",
              "      <td>445.00</td>\n",
              "      <td>549.51</td>\n",
              "      <td>1354.03</td>\n",
              "      <td>1126.38</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>130.72</td>\n",
              "      <td>2387.61</td>\n",
              "      <td>8068.66</td>\n",
              "      <td>9.3528</td>\n",
              "      <td>0.02</td>\n",
              "      <td>329</td>\n",
              "      <td>2212</td>\n",
              "      <td>100.00</td>\n",
              "      <td>10.59</td>\n",
              "      <td>6.4701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>25.0005</td>\n",
              "      <td>0.6203</td>\n",
              "      <td>60.0</td>\n",
              "      <td>462.54</td>\n",
              "      <td>537.07</td>\n",
              "      <td>1257.71</td>\n",
              "      <td>1047.93</td>\n",
              "      <td>7.05</td>\n",
              "      <td>...</td>\n",
              "      <td>164.31</td>\n",
              "      <td>2028.00</td>\n",
              "      <td>7861.23</td>\n",
              "      <td>10.8963</td>\n",
              "      <td>0.02</td>\n",
              "      <td>309</td>\n",
              "      <td>1915</td>\n",
              "      <td>84.93</td>\n",
              "      <td>14.13</td>\n",
              "      <td>8.5286</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7bb07f08-8d27-4cc0-90d4-df3c1ab102ee')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-65ff13cb-da9c-49c9-a945-34de30b3aaa7\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-65ff13cb-da9c-49c9-a945-34de30b3aaa7')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-65ff13cb-da9c-49c9-a945-34de30b3aaa7 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7bb07f08-8d27-4cc0-90d4-df3c1ab102ee 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-7bb07f08-8d27-4cc0-90d4-df3c1ab102ee');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "K-means clustering model.\n",
        "\n",
        "*   Develop a k-means clustering algorithm to find clusters.\n",
        "*   Save model and centroids.\n",
        "\n",
        "*   Use model to predict the cluster every data point belongs to.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "DiKs4NqzrKdR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "op_condts = raw_train_df[['op_conditions_1'\t, 'op_conditions_2', 'op_conditions_3']]\n",
        "op_condts.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P8tpR4v-JXPJ",
        "outputId": "b87c62f5-e0ea-44ff-f55a-a55ea5410c36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(53759, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use K-means to find clusters\n",
        "kmeans = KMeans(n_clusters = 6) # Greedy k-means is used when the n_clusters is not specified, it enables faster convergence\n",
        "kmeans.fit(op_condts)\n",
        "cluster_labels = kmeans.labels_\n",
        "centroids = kmeans.cluster_centers_"
      ],
      "metadata": {
        "id": "qEQl5dvWbQR2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "centroids"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4mxv4w1kJ9ZB",
        "outputId": "5d10b634-a797-4b0a-9062-9271f9e26b5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4.20029763e+01, 8.40485184e-01, 1.00000000e+02],\n",
              "       [1.00029713e+01, 2.50495035e-01, 1.00000000e+02],\n",
              "       [2.50030380e+01, 6.20501875e-01, 6.00000000e+01],\n",
              "       [2.00029990e+01, 7.00515193e-01, 1.00000000e+02],\n",
              "       [1.50451268e-03, 4.94343610e-04, 1.00000000e+02],\n",
              "       [3.50030490e+01, 8.40500585e-01, 1.00000000e+02]])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cluster_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G_CGKt2LK69E",
        "outputId": "a5b78cbc-b09d-480f-dc82-65732f0fe0ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 2, ..., 2, 2, 5], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Save Kmeans model"
      ],
      "metadata": {
        "id": "C7clb7BzLA3J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save Model Using joblib\n",
        "import joblib\n",
        "# save the model to disk\n",
        "filename = 'kmeans_model'\n",
        "joblib.dump(kmeans, filename)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KaI4IqabLAND",
        "outputId": "1b5df268-df28-4b19-bfb1-9f10c6cb7566"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['kmeans_model']"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use Model to predict clusters of other NASA datasets"
      ],
      "metadata": {
        "id": "2GRF1S6AMGUj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model = joblib.load('/content/kmeans_model')"
      ],
      "metadata": {
        "id": "Boxzt7JQMMoZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import Data - NASA dataset 260 train trajectories, 259 tests and 6 conditions\n",
        "raw_test_df=pd.read_csv('/content/train_FD004.txt',delim_whitespace=True,names=all_col)\n",
        "raw_test_df.head()"
      ],
      "metadata": {
        "id": "50z1r2RUMF0D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "op_condts = raw_test_df[['op_conditions_1'\t, 'op_conditions_2', 'op_conditions_3']]\n",
        "cluster_labels = loaded_model.predict(op_condts)\n",
        "print(cluster_labels.shape)"
      ],
      "metadata": {
        "id": "XuB8tAAQMpiE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.unique(cluster_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g0yTLhBkNFy0",
        "outputId": "eb11897f-e370-462d-c380-3b0a3d9e9c59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_DEoodBKNLvE"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}