Cpu usage prediction.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import glob"
      ]
    },
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      "source": [
        "df=pd.read_csv('/content/02.csv',delimiter=\";\",index_col=None, header=0)\n",
        "\n",
        "# Select the first 7750 rows of the DataFrame\n",
        "df_subset = df.iloc[:7750]"
      ]
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      "id": "aa907e8f",
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        "id": "aa907e8f"
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      "source": [
        "df=df.drop(['CPU cores','Disk size [GB]','CPU capacity provisioned [MHZ]','Network received throughput [KB/s]','Network transmitted throughput [KB/s]','CPU usage [MHZ]','Memory capacity provisioned [KB]','Memory usage [KB]','Memory usage [%]','Disk read throughput [KB/s]','Disk write throughput [KB/s]'],axis=1)"
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      "id": "40181d79",
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      "source": [
        "df['Datetime'] = pd.to_datetime(df['Timestamp'], format='%d.%m.%Y %H:%M:%S')\n",
        "df=df.drop(['Timestamp'],axis=1)"
      ]
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              "  CPU usage [%]            Datetime\n",
              "0           2,5 2015-11-05 00:00:00\n",
              "1          2,71 2015-11-05 00:05:00\n",
              "2          2,48 2015-11-05 00:10:00\n",
              "3          2,62 2015-11-05 00:15:00\n",
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              "   CPU usage [%]            Datetime\n",
              "0           2.50 2015-11-05 00:00:00\n",
              "1           2.71 2015-11-05 00:05:00\n",
              "2           2.48 2015-11-05 00:10:00\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-aeaa2be9-05a8-435a-bea6-61358f4ddb2b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8352,\n  \"fields\": [\n    {\n      \"column\": \"CPU usage [%]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.05223193147391,\n        \"min\": 2.01,\n        \"max\": 100.0,\n        \"num_unique_values\": 440,\n        \"samples\": [\n          2.15,\n          2.29,\n          56.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Datetime\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2015-11-05 00:00:00\",\n        \"max\": \"2015-12-03 23:55:00\",\n        \"num_unique_values\": 8352,\n        \"samples\": [\n          \"2015-11-14 07:45:00\",\n          \"2015-11-13 17:35:00\",\n          \"2015-12-01 15:05:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 98
        }
      ],
      "source": [
        "df['CPU usage [%]'] = df['CPU usage [%]'].str.replace(',', '.').astype(float)\n",
        "# df['Memory usage [%]'] = df['Memory usage [%]'].str.replace(',', '.').astype(float)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "id": "1c629441",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1c629441",
        "outputId": "1a1117b9-ae22-4ca4-a878-3e261bd47d6a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CPU usage [%]  CPU_10_min_before  CPU_20_min_before  CPU_30_min_before  \\\n",
              "3           2.62               2.48               2.71               2.50   \n",
              "4           2.44               2.62               2.48               2.71   \n",
              "5           2.45               2.44               2.62               2.48   \n",
              "6           2.61               2.45               2.44               2.62   \n",
              "7           2.42               2.61               2.45               2.44   \n",
              "\n",
              "   hour  dayofweek  dayodyear  \n",
              "3     0          3        309  \n",
              "4     0          3        309  \n",
              "5     0          3        309  \n",
              "6     0          3        309  \n",
              "7     0          3        309  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a00f62fe-e072-4115-8ca9-97ab4f70444b\" 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>CPU usage [%]</th>\n",
              "      <th>CPU_10_min_before</th>\n",
              "      <th>CPU_20_min_before</th>\n",
              "      <th>CPU_30_min_before</th>\n",
              "      <th>hour</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>dayodyear</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.62</td>\n",
              "      <td>2.48</td>\n",
              "      <td>2.71</td>\n",
              "      <td>2.50</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.44</td>\n",
              "      <td>2.62</td>\n",
              "      <td>2.48</td>\n",
              "      <td>2.71</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.45</td>\n",
              "      <td>2.44</td>\n",
              "      <td>2.62</td>\n",
              "      <td>2.48</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2.61</td>\n",
              "      <td>2.45</td>\n",
              "      <td>2.44</td>\n",
              "      <td>2.62</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2.42</td>\n",
              "      <td>2.61</td>\n",
              "      <td>2.45</td>\n",
              "      <td>2.44</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>309</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-a00f62fe-e072-4115-8ca9-97ab4f70444b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a00f62fe-e072-4115-8ca9-97ab4f70444b 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-a00f62fe-e072-4115-8ca9-97ab4f70444b');\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-20037050-cfa2-4a28-b627-4fdb701fa4c1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-20037050-cfa2-4a28-b627-4fdb701fa4c1')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-20037050-cfa2-4a28-b627-4fdb701fa4c1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8349,\n  \"fields\": [\n    {\n      \"column\": \"CPU usage [%]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.054875423443548,\n        \"min\": 2.01,\n        \"max\": 100.0,\n        \"num_unique_values\": 440,\n        \"samples\": [\n          2.15,\n          2.29,\n          56.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPU_10_min_before\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.04592766832085,\n        \"min\": 2.01,\n        \"max\": 100.0,\n        \"num_unique_values\": 440,\n        \"samples\": [\n          2.15,\n          2.29,\n          56.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPU_20_min_before\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.036935006328374,\n        \"min\": 2.01,\n        \"max\": 100.0,\n        \"num_unique_values\": 440,\n        \"samples\": [\n          2.15,\n          2.29,\n          56.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPU_30_min_before\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.028201557519363,\n        \"min\": 2.01,\n        \"max\": 100.0,\n        \"num_unique_values\": 440,\n        \"samples\": [\n          2.15,\n          2.29,\n          56.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hour\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          8,\n          16,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dayofweek\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          3,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dayodyear\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 29,\n        \"samples\": [\n          336,\n          325,\n          321\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 99
        }
      ],
      "source": [
        "df['CPU_10_min_before'] = df['CPU usage [%]'].shift(1)\n",
        "df['CPU_20_min_before'] = df['CPU usage [%]'].shift(2)\n",
        "df['CPU_30_min_before'] = df['CPU usage [%]'].shift(3)\n",
        "df['hour']=df['Datetime'].dt.hour\n",
        "df['dayofweek']=df['Datetime'].dt.dayofweek\n",
        "df['dayodyear']=df['Datetime'].dt.dayofyear\n",
        "df=df.drop(['Datetime'],axis=1)\n",
        "df=df.dropna()\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "id": "1d1fc3ad",
      "metadata": {
        "id": "1d1fc3ad"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "id": "ba3fc676",
      "metadata": {
        "id": "ba3fc676"
      },
      "outputs": [],
      "source": [
        "scaler = MinMaxScaler()\n",
        "numerical_cols = df.select_dtypes(include=['float64', 'int64']).columns\n",
        "df[numerical_cols] = scaler.fit_transform(df[numerical_cols])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "id": "dfdee7c2",
      "metadata": {
        "id": "dfdee7c2"
      },
      "outputs": [],
      "source": [
        "train_size = int(len(df) * 0.8)\n",
        "train_df = df[:train_size]\n",
        "test_df = df[train_size:]\n",
        "X_train=train_df.drop(columns=['CPU usage [%]'])\n",
        "y_train=train_df['CPU usage [%]']\n",
        "X_test=test_df.drop(columns=['CPU usage [%]'])\n",
        "y_test=test_df['CPU usage [%]']\n",
        "# test_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "id": "fb568384",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "fb568384",
        "outputId": "0b4be6b0-fd1a-446d-9498-62ccad18cdbf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CPU_10_min_before  CPU_20_min_before  CPU_30_min_before  hour  dayofweek  \\\n",
              "3           0.004796           0.007144           0.005001     0          3   \n",
              "4           0.006225           0.004796           0.007144     0          3   \n",
              "5           0.004388           0.006225           0.004796     0          3   \n",
              "6           0.004490           0.004388           0.006225     0          3   \n",
              "7           0.006123           0.004490           0.004388     0          3   \n",
              "\n",
              "   dayodyear  \n",
              "3        309  \n",
              "4        309  \n",
              "5        309  \n",
              "6        309  \n",
              "7        309  "
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CPU_10_min_before</th>\n",
              "      <th>CPU_20_min_before</th>\n",
              "      <th>CPU_30_min_before</th>\n",
              "      <th>hour</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>dayodyear</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.004796</td>\n",
              "      <td>0.007144</td>\n",
              "      <td>0.005001</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.006225</td>\n",
              "      <td>0.004796</td>\n",
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              "      <th>5</th>\n",
              "      <td>0.004388</td>\n",
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              "      <th>6</th>\n",
              "      <td>0.004490</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.006123</td>\n",
              "      <td>0.004490</td>\n",
              "      <td>0.004388</td>\n",
              "      <td>0</td>\n",
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              "        document.querySelector('#df-f38e4448-e6cf-4349-8a5e-11386d5fa9a5 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-f38e4448-e6cf-4349-8a5e-11386d5fa9a5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\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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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_train",
              "summary": "{\n  \"name\": \"X_train\",\n  \"rows\": 6679,\n  \"fields\": [\n    {\n      \"column\": \"CPU_10_min_before\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10037726227792955,\n        \"min\": 0.0005102561485865915,\n        \"max\": 1.0,\n        \"num_unique_values\": 250,\n        \"samples\": [\n          0.01142973772833963,\n          0.0038779467292580916,\n          0.012552301255230127\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPU_20_min_before\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10037722741667027,\n        \"min\": 0.0005102561485865915,\n        \"max\": 1.0,\n        \"num_unique_values\": 250,\n        \"samples\": [\n          0.01142973772833963,\n          0.004184100418410042,\n          0.012552301255230127\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPU_30_min_before\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10037726077325775,\n        \"min\": 0.0005102561485865915,\n        \"max\": 1.0,\n        \"num_unique_values\": 250,\n        \"samples\": [\n          0.01142973772833963,\n          0.006123073783039085,\n          0.012552301255230127\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hour\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          8,\n          16,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dayofweek\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          3,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dayodyear\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          317,\n          325,\n          309\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 103
        }
      ],
      "source": [
        "X_train.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "id": "8334680c",
      "metadata": {
        "id": "8334680c"
      },
      "outputs": [],
      "source": [
        "import xgboost as xgb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "id": "3ef17074",
      "metadata": {
        "id": "3ef17074"
      },
      "outputs": [],
      "source": [
        "reg=xgb.XGBRegressor(n_estimators=1000,early_stopping_round=50 )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "id": "e454f0ad",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "e454f0ad",
        "outputId": "d0a7e303-bf2b-4c2f-c244-5bbf2ee69043"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tvalidation_0-rmse:0.07157\tvalidation_1-rmse:0.31622\n",
            "[1]\tvalidation_0-rmse:0.05133\tvalidation_1-rmse:0.31581\n",
            "[2]\tvalidation_0-rmse:0.03731\tvalidation_1-rmse:0.23751\n",
            "[3]\tvalidation_0-rmse:0.02731\tvalidation_1-rmse:0.23728\n",
            "[4]\tvalidation_0-rmse:0.02039\tvalidation_1-rmse:0.23711\n",
            "[5]\tvalidation_0-rmse:0.01590\tvalidation_1-rmse:0.20805\n",
            "[6]\tvalidation_0-rmse:0.01270\tvalidation_1-rmse:0.21758\n",
            "[7]\tvalidation_0-rmse:0.01061\tvalidation_1-rmse:0.19566\n",
            "[8]\tvalidation_0-rmse:0.00926\tvalidation_1-rmse:0.18039\n",
            "[9]\tvalidation_0-rmse:0.00845\tvalidation_1-rmse:0.18231\n",
            "[10]\tvalidation_0-rmse:0.00801\tvalidation_1-rmse:0.18384\n",
            "[11]\tvalidation_0-rmse:0.00770\tvalidation_1-rmse:0.18533\n",
            "[12]\tvalidation_0-rmse:0.00749\tvalidation_1-rmse:0.17926\n",
            "[13]\tvalidation_0-rmse:0.00730\tvalidation_1-rmse:0.17441\n",
            "[14]\tvalidation_0-rmse:0.00723\tvalidation_1-rmse:0.17678\n",
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            "[16]\tvalidation_0-rmse:0.00704\tvalidation_1-rmse:0.18222\n",
            "[17]\tvalidation_0-rmse:0.00701\tvalidation_1-rmse:0.18434\n",
            "[18]\tvalidation_0-rmse:0.00695\tvalidation_1-rmse:0.18528\n",
            "[19]\tvalidation_0-rmse:0.00690\tvalidation_1-rmse:0.18543\n",
            "[20]\tvalidation_0-rmse:0.00687\tvalidation_1-rmse:0.18533\n",
            "[21]\tvalidation_0-rmse:0.00677\tvalidation_1-rmse:0.18540\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [09:42:30] WARNING: /workspace/src/learner.cc:740: \n",
            "Parameters: { \"early_stopping_round\" } are not used.\n",
            "\n",
            "  warnings.warn(smsg, UserWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "[957]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[958]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[959]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[960]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[961]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[962]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[963]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[964]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[965]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[966]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[967]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[968]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[969]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[970]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[971]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[972]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[973]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[974]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[975]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[976]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[977]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[978]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[979]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[980]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[981]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[982]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[983]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[984]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[985]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[986]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[987]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[988]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[989]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[990]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[991]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[992]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[993]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[994]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[995]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[996]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[997]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[998]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n",
            "[999]\tvalidation_0-rmse:0.00595\tvalidation_1-rmse:0.18741\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_round=50,\n",
              "             early_stopping_rounds=None, enable_categorical=False,\n",
              "             eval_metric=None, feature_types=None, gamma=None, grow_policy=None,\n",
              "             importance_type=None, interaction_constraints=None,\n",
              "             learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
              "             max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
              "             max_leaves=None, min_child_weight=None, missing=nan,\n",
              "             monotone_constraints=None, multi_strategy=None, n_estimators=1000,\n",
              "             n_jobs=None, num_parallel_tree=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-6 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-6 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-6 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-6 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-6 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-6 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-6 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-6 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-6 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_round=50,\n",
              "             early_stopping_rounds=None, enable_categorical=False,\n",
              "             eval_metric=None, feature_types=None, gamma=None, grow_policy=None,\n",
              "             importance_type=None, interaction_constraints=None,\n",
              "             learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
              "             max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
              "             max_leaves=None, min_child_weight=None, missing=nan,\n",
              "             monotone_constraints=None, multi_strategy=None, n_estimators=1000,\n",
              "             n_jobs=None, num_parallel_tree=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_round=50,\n",
              "             early_stopping_rounds=None, enable_categorical=False,\n",
              "             eval_metric=None, feature_types=None, gamma=None, grow_policy=None,\n",
              "             importance_type=None, interaction_constraints=None,\n",
              "             learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
              "             max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
              "             max_leaves=None, min_child_weight=None, missing=nan,\n",
              "             monotone_constraints=None, multi_strategy=None, n_estimators=1000,\n",
              "             n_jobs=None, num_parallel_tree=None, ...)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ],
      "source": [
        "reg.fit(X_train,y_train,eval_set=[(X_train,y_train),(X_test,y_test)],\n",
        "       verbose=True)\n",
        "# y_pred=rfc.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "id": "ff9e2414",
      "metadata": {
        "id": "ff9e2414"
      },
      "outputs": [],
      "source": [
        "y_pred=reg.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "id": "80ca721c",
      "metadata": {
        "id": "80ca721c"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "id": "1a870fdd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1a870fdd",
        "outputId": "0d55c533-e89d-4537-e918-c92163fa9bbd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R-squared: 0.4836\n",
            "Mean Absolute Error: 0.1130\n",
            "Mean Squared Error: 0.0351\n",
            "Root Mean Squared Error: 0.1874\n"
          ]
        }
      ],
      "source": [
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "\n",
        "# Calculate Mean Squared Error\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "\n",
        "# Calculate Root Mean Squared Error\n",
        "rmse = np.sqrt(mse)\n",
        "\n",
        "# Print the metrics\n",
        "print(f\"R-squared: {r2:.4f}\")\n",
        "print(f\"Mean Absolute Error: {mae:.4f}\")\n",
        "print(f\"Mean Squared Error: {mse:.4f}\")\n",
        "print(f\"Root Mean Squared Error: {rmse:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "id": "e2c4f9a5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "e2c4f9a5",
        "outputId": "1cf9e275-da2f-438c-9aa2-0e8498d850f1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# plt.plot(y_test.index, y_test, label='True CPU Usage')\n",
        "# plt.plot(y_test.index, y_pred, label='Predicted CPU Usage')\n",
        "\n",
        "plt.plot(y_test.index, y_test, label='True CPU Usage', color='blue',linewidth=2)  # Solid line, dark color, high opacity\n",
        "plt.plot(y_test.index, y_pred, label='Predicted CPU Usage', color='orange', linestyle='--', linewidth=1.5, alpha=0.7)  # Dotted line, light color, lower opacity\n",
        "\n",
        "plt.xlim(df_subset.index[6600], df_subset.index[-1])\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0dab4d70",
      "metadata": {
        "id": "0dab4d70"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "id": "61cf86b7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "61cf86b7",
        "outputId": "363b1614-2011-4f58-93fe-77a9f1298bb5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest - R-squared: 0.9119\n",
            "Random Forest - Mean Absolute Error: 0.0473\n",
            "Random Forest - Root Mean Squared Error: 0.0774\n",
            "Random Forest - R-squared: 0.9119\n",
            "Random Forest - Mean Absolute Error: 0.0473\n",
            "Random Forest - Root Mean Squared Error: 0.0774\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Instantiate the RandomForestRegressor\n",
        "rf = RandomForestRegressor(n_estimators=1000)\n",
        "\n",
        "# Fit the model\n",
        "rf.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred_rf = rf.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "r2_rf = r2_score(y_test, y_pred_rf)\n",
        "mae_rf = mean_absolute_error(y_test, y_pred_rf)\n",
        "mse_rf = mean_squared_error(y_test, y_pred_rf)\n",
        "rmse_rf = np.sqrt(mse_rf)\n",
        "\n",
        "print(f\"Random Forest - R-squared: {r2_rf:.4f}\")\n",
        "print(f\"Random Forest - Mean Absolute Error: {mae_rf:.4f}\")\n",
        "print(f\"Random Forest - Root Mean Squared Error: {rmse_rf:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 130,
      "id": "43786271",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "43786271",
        "outputId": "eaee3cd8-3500-432f-a2c9-864d0b74b98d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# plt.plot(y_test.index, y_test, label='True CPU Usage')\n",
        "# plt.plot(y_test.index, y_pred_rf, label='Predicted CPU Usage')\n",
        "\n",
        "# Plot the true and predicted CPU usage with custom properties\n",
        "plt.plot(y_test.index, y_test, label='True CPU Usage', color='blue', linewidth=2)  # Solid line, dark color, high opacity\n",
        "plt.plot(y_test.index, y_pred_rf, label='Predicted CPU Usage', color='orange', linestyle='--', linewidth=1, alpha=0.7)  # Dotted line, light color, lower opacity\n",
        "\n",
        "plt.xlim(df_subset.index[6600], df_subset.index[-1])\n",
        "\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "id": "9db5555b",
      "metadata": {
        "id": "9db5555b"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 132,
      "id": "f603d71d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f603d71d",
        "outputId": "c2b2720a-611e-4c3b-a770-b711a73d45da"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gradient Boosting - R-squared: 0.7795\n",
            "Gradient Boosting - Mean Absolute Error: 0.0734\n",
            "Gradient Boosting - Root Mean Squared Error: 0.1225\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Instantiate the GradientBoostingRegressor\n",
        "gbr = GradientBoostingRegressor(n_estimators=1000)\n",
        "\n",
        "# Fit the model\n",
        "gbr.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred_gbr = gbr.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "r2_gbr = r2_score(y_test, y_pred_gbr)\n",
        "mae_gbr = mean_absolute_error(y_test, y_pred_gbr)\n",
        "mse_gbr = mean_squared_error(y_test, y_pred_gbr)\n",
        "rmse_gbr = np.sqrt(mse_gbr)\n",
        "\n",
        "print(f\"Gradient Boosting - R-squared: {r2_gbr:.4f}\")\n",
        "print(f\"Gradient Boosting - Mean Absolute Error: {mae_gbr:.4f}\")\n",
        "print(f\"Gradient Boosting - Root Mean Squared Error: {rmse_gbr:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "id": "733cdfc4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "733cdfc4",
        "outputId": "bf342f1d-b1e7-434d-cae9-b1c607a5f3cb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gradient Boosting - R-squared: 0.7795\n",
            "Gradient Boosting - Mean Absolute Error: 0.0734\n",
            "Gradient Boosting - Root Mean Squared Error: 0.1225\n"
          ]
        }
      ],
      "source": [
        "# Evaluate the model\n",
        "r2_gbr = r2_score(y_test, y_pred_gbr)\n",
        "mae_gbr = mean_absolute_error(y_test, y_pred_gbr)\n",
        "mse_gbr = mean_squared_error(y_test, y_pred_gbr)\n",
        "rmse_gbr = np.sqrt(mse_gbr)\n",
        "\n",
        "print(f\"Gradient Boosting - R-squared: {r2_gbr:.4f}\")\n",
        "print(f\"Gradient Boosting - Mean Absolute Error: {mae_gbr:.4f}\")\n",
        "print(f\"Gradient Boosting - Root Mean Squared Error: {rmse_gbr:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 135,
      "id": "7dda0fbe",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "7dda0fbe",
        "outputId": "11d1815a-502f-431f-834d-f9c3de1c89fc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# plt.plot(y_test.index, y_test, label='True CPU Usage')\n",
        "# plt.plot(y_test.index, y_pred_gbr, label='Predicted CPU Usage')\n",
        "\n",
        "# Plot the true and predicted CPU usage with custom properties\n",
        "plt.plot(y_test.index, y_test, label='True CPU Usage', color='blue', linewidth=2)  # Solid line, dark color, high opacity\n",
        "plt.plot(y_test.index, y_pred_gbr, label='Predicted CPU Usage', color='orange', linestyle='--', linewidth=1, alpha=0.7)  # Dotted line, light color, lower opacity\n",
        "\n",
        "# Set the x-axis limits from the 6600th index to the last index in your subset\n",
        "plt.xlim(df_subset.index[6600], df_subset.index[-1])\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 136,
      "id": "5d5cdddc",
      "metadata": {
        "id": "5d5cdddc"
      },
      "outputs": [],
      "source": [
        "  from sklearn.neighbors import KNeighborsRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 137,
      "id": "3c5f0c3f",
      "metadata": {
        "id": "3c5f0c3f"
      },
      "outputs": [],
      "source": [
        "knn = KNeighborsRegressor(n_neighbors=5)  # You can tune the number of neighbors\n",
        "\n",
        "# Fit the model\n",
        "knn.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred_knn = knn.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 138,
      "id": "a90a93aa",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a90a93aa",
        "outputId": "c1fa8d0d-f227-4f66-fc1e-0aca78be0fb5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN - R-squared: -0.5246\n",
            "KNN - Mean Absolute Error: 0.1921\n",
            "KNN - Root Mean Squared Error: 0.3220\n"
          ]
        }
      ],
      "source": [
        "r2_knn = r2_score(y_test, y_pred_knn)\n",
        "mae_knn = mean_absolute_error(y_test, y_pred_knn)\n",
        "mse_knn = mean_squared_error(y_test, y_pred_knn)\n",
        "rmse_knn = np.sqrt(mse_knn)\n",
        "\n",
        "print(f\"KNN - R-squared: {r2_knn:.4f}\")\n",
        "print(f\"KNN - Mean Absolute Error: {mae_knn:.4f}\")\n",
        "print(f\"KNN - Root Mean Squared Error: {rmse_knn:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 143,
      "id": "cc9e1428",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "cc9e1428",
        "outputId": "bb1b39d4-b414-4af1-ea3b-7fcf60dad6ba"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# plt.plot(y_test.index, y_test, label='True CPU Usage')\n",
        "# plt.plot(y_test.index, y_pred_knn, label='Predicted CPU Usage')\n",
        "\n",
        "# Plot the true and predicted CPU usage with custom properties\n",
        "plt.plot(y_test.index, y_test, label='True CPU Usage', color='blue', linewidth=2)\n",
        "plt.plot(y_test.index, y_pred_knn, label='Predicted CPU Usage', color='orange', linestyle='--', linewidth=1, alpha=0.7)\n",
        "\n",
        "# Set the x-axis limits from the 6600th index to the last index in your subset\n",
        "plt.xlim(df_subset.index[6600], df_subset.index[-1])\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 146,
      "id": "770c4a37",
      "metadata": {
        "id": "770c4a37"
      },
      "outputs": [],
      "source": [
        "\n",
        "import lightgbm as lgb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 147,
      "id": "c23ac286",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c23ac286",
        "outputId": "768daec6-57ba-48b6-a9af-1e1860ced6bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000260 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 539\n",
            "[LightGBM] [Info] Number of data points in the train set: 6679, number of used features: 6\n",
            "[LightGBM] [Info] Start training from score 0.016976\n"
          ]
        }
      ],
      "source": [
        "lgb_reg = lgb.LGBMRegressor(n_estimators=1000)\n",
        "\n",
        "# Fit the model\n",
        "lgb_reg.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)])\n",
        "\n",
        "# Make predictions\n",
        "y_pred_lgb = lgb_reg.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 153,
      "id": "79c3c6d9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "79c3c6d9",
        "outputId": "5d37433a-12bb-407f-8bb1-62917eb0b129"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# plt.plot(y_test.index, y_test, label='True CPU Usage')\n",
        "# plt.plot(y_test.index, y_pred_lgb, label='Predicted CPU Usage')\n",
        "\n",
        "# Plot the true and predicted CPU usage with custom properties\n",
        "plt.plot(y_test.index, y_test, label='True CPU Usage', color='blue', linewidth=2)  # Solid line, dark color, high opacity\n",
        "plt.plot(y_test.index, y_pred_lgb, label='Predicted CPU Usage', color='orange', linestyle='--', linewidth=1, alpha=0.7)  # Dotted line, light color, lower opacity\n",
        "\n",
        "# Set the x-axis limits from the 6600th index to the last index in your subset\n",
        "plt.xlim(df_subset.index[6600], df_subset.index[-1])\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---------------------------------------------------------------------------------------\n"
      ],
      "metadata": {
        "id": "FkdzHf-Vr46B"
      },
      "id": "FkdzHf-Vr46B"
    },
    {
      "cell_type": "code",
      "execution_count": 149,
      "id": "8e0295ed",
      "metadata": {
        "id": "8e0295ed"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.ensemble import VotingRegressor, StackingRegressor\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
        "from lightgbm import LGBMRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "\n",
        "# Initialize individual models\n",
        "xgb_model = XGBRegressor(n_estimators=1000)\n",
        "rf_model = RandomForestRegressor(n_estimators=1000)\n",
        "gbr_model = GradientBoostingRegressor(n_estimators=1000)\n",
        "knn_model = KNeighborsRegressor(n_neighbors=5)\n",
        "lgb_model = LGBMRegressor(n_estimators=1000)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import VotingRegressor, StackingRegressor\n",
        "# Create the voting regressor\n",
        "voting_reg = VotingRegressor(estimators=[\n",
        "    ('xgb', xgb_model),\n",
        "    ('rf', rf_model),\n",
        "    ('gbr', gbr_model),\n",
        "    ('knn', knn_model),\n",
        "    ('lgb', lgb_model)\n",
        "])\n",
        "\n",
        "# Train the voting regressor\n",
        "voting_reg.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions with the voting regressor\n",
        "y_pred_voting = voting_reg.predict(X_test)\n",
        "\n",
        "# Filter data up to index 7750\n",
        "y_test_filtered_voting = y_test[y_test.index <= 7750]\n",
        "y_pred_voting_filtered = y_pred_voting[y_test.index <= 7750]\n",
        "\n",
        "# Evaluate the voting model\n",
        "r2_voting = r2_score(y_test, y_pred_voting)\n",
        "mae_voting = mean_absolute_error(y_test, y_pred_voting)\n",
        "rmse_voting = np.sqrt(mean_squared_error(y_test, y_pred_voting))\n",
        "\n",
        "print(f\"Voting Regressor - R-squared: {r2_voting:.4f}\")\n",
        "print(f\"Voting Regressor - Mean Absolute Error: {mae_voting:.4f}\")\n",
        "print(f\"Voting Regressor - Root Mean Squared Error: {rmse_voting:.4f}\")\n",
        "\n",
        "# plt.plot(y_test.index, y_test, label='True CPU Usage')\n",
        "# plt.plot(y_test.index, y_pred_voting, label='Predicted CPU Usage')\n",
        "\n",
        "# Plot the true vs predicted CPU usage with customizations\n",
        "plt.plot(y_test.index, y_test, label='True CPU Usage', color='blue', linewidth=2)  # Solid line, dark color, high opacity\n",
        "plt.plot(y_test.index, y_pred_voting, label='Predicted CPU Usage', color='orange', linestyle='--', linewidth=1, alpha=0.7)  # Dotted line, light color, lower opacity\n",
        "\n",
        "# Set the x-axis limits from the 6600th index to the last index in your subset\n",
        "plt.xlim(df_subset.index[6600], df_subset.index[-1])\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "id": "2aM5tGoor3aI",
        "outputId": "822d9d44-87b6-4f90-9975-7b4e6e36a701"
      },
      "id": "2aM5tGoor3aI",
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000280 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 539\n",
            "[LightGBM] [Info] Number of data points in the train set: 6679, number of used features: 6\n",
            "[LightGBM] [Info] Start training from score 0.016976\n",
            "Voting Regressor - R-squared: 0.6330\n",
            "Voting Regressor - Mean Absolute Error: 0.0954\n",
            "Voting Regressor - Root Mean Squared Error: 0.1580\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the stacking regressor\n",
        "stacking_reg = StackingRegressor(\n",
        "    estimators=[\n",
        "        ('xgb', xgb_model),\n",
        "        ('rf', rf_model),\n",
        "        ('gbr', gbr_model),\n",
        "        ('knn', knn_model),\n",
        "        ('lgb', lgb_model)\n",
        "    ],\n",
        "    final_estimator=Ridge()  # The meta-model (you can use any other regressor)\n",
        ")\n",
        "\n",
        "# Train the stacking regressor\n",
        "stacking_reg.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions with the stacking regressor\n",
        "y_pred_stacking = stacking_reg.predict(X_test)\n",
        "\n",
        "# Evaluate the stacking model\n",
        "r2_stacking = r2_score(y_test, y_pred_stacking)\n",
        "mae_stacking = mean_absolute_error(y_test, y_pred_stacking)\n",
        "rmse_stacking = np.sqrt(mean_squared_error(y_test, y_pred_stacking))\n",
        "\n",
        "print(f\"Stacking Regressor - R-squared: {r2_stacking:.4f}\")\n",
        "print(f\"Stacking Regressor - Mean Absolute Error: {mae_stacking:.4f}\")\n",
        "print(f\"Stacking Regressor - Root Mean Squared Error: {rmse_stacking:.4f}\")\n",
        "\n",
        "# plt.plot(y_test.index, y_test, label='True CPU Usage')\n",
        "# plt.plot(y_test.index, y_pred_stacking, label='Predicted CPU Usage')\n",
        "\n",
        "# Plot the true vs predicted CPU usage with customizations\n",
        "plt.plot(y_test.index, y_test, label='True CPU Usage', color='blue', linewidth=2)  # Solid line, dark color, high opacity\n",
        "plt.plot(y_test.index, y_pred_stacking, label='Predicted CPU Usage', color='orange', linestyle='--', linewidth=1, alpha=0.7)  # Dotted line, light color, lower opacity\n",
        "\n",
        "# Set the x-axis limits from the 6600th index to the last index in your subset\n",
        "plt.xlim(df_subset.index[6600], df_subset.index[-1])\n",
        "\n",
        "\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "d2qf6582tgDY",
        "outputId": "18c0afdd-40ef-40c9-bca4-f3d45a42abda"
      },
      "id": "d2qf6582tgDY",
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000164 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 539\n",
            "[LightGBM] [Info] Number of data points in the train set: 6679, number of used features: 6\n",
            "[LightGBM] [Info] Start training from score 0.016976\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000140 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 520\n",
            "[LightGBM] [Info] Number of data points in the train set: 5343, number of used features: 6\n",
            "[LightGBM] [Info] Start training from score 0.019831\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000137 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 523\n",
            "[LightGBM] [Info] Number of data points in the train set: 5343, number of used features: 6\n",
            "[LightGBM] [Info] Start training from score 0.019938\n",
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000385 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 536\n",
            "[LightGBM] [Info] Number of data points in the train set: 5343, number of used features: 6\n",
            "[LightGBM] [Info] Start training from score 0.019921\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000126 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 415\n",
            "[LightGBM] [Info] Number of data points in the train set: 5343, number of used features: 6\n",
            "[LightGBM] [Info] Start training from score 0.005887\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000130 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 453\n",
            "[LightGBM] [Info] Number of data points in the train set: 5344, number of used features: 6\n",
            "[LightGBM] [Info] Start training from score 0.019302\n",
            "Stacking Regressor - R-squared: 0.6359\n",
            "Stacking Regressor - Mean Absolute Error: 0.0838\n",
            "Stacking Regressor - Root Mean Squared Error: 0.1574\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Blending"
      ],
      "metadata": {
        "id": "Lk-7L-ZMxhB9"
      },
      "id": "Lk-7L-ZMxhB9"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the training data into two parts for blending\n",
        "X_blend_train, X_blend_meta, y_blend_train, y_blend_meta = train_test_split(X_train, y_train, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "3qvyc7YryeRo"
      },
      "id": "3qvyc7YryeRo",
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the base models on X_blend_train\n",
        "xgb_model = XGBRegressor(n_estimators=1000)\n",
        "rf_model = RandomForestRegressor(n_estimators=1000)\n",
        "gbr_model = GradientBoostingRegressor(n_estimators=1000)\n",
        "knn_model = KNeighborsRegressor(n_neighbors=5)\n",
        "lgb_model = LGBMRegressor(n_estimators=1000)\n",
        "\n",
        "xgb_model.fit(X_blend_train, y_blend_train)\n",
        "rf_model.fit(X_blend_train, y_blend_train)\n",
        "gbr_model.fit(X_blend_train, y_blend_train)\n",
        "knn_model.fit(X_blend_train, y_blend_train)\n",
        "lgb_model.fit(X_blend_train, y_blend_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        },
        "id": "v8CDY8M1ygHT",
        "outputId": "5fde9bd5-542c-4a3a-f3a5-b5b0c359edda"
      },
      "id": "v8CDY8M1ygHT",
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000119 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 503\n",
            "[LightGBM] [Info] Number of data points in the train set: 5343, number of used features: 6\n",
            "[LightGBM] [Info] Start training from score 0.016259\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMRegressor(n_estimators=1000)"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-7 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-7 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-7 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-7 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-7 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-7 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-7 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-7 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-7 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-7 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-7 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-7 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor(n_estimators=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LGBMRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LGBMRegressor(n_estimators=1000)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate meta-features (predictions) for the meta-model using X_blend_meta\n",
        "meta_features = np.column_stack([\n",
        "    xgb_model.predict(X_blend_meta),\n",
        "    rf_model.predict(X_blend_meta),\n",
        "    gbr_model.predict(X_blend_meta),\n",
        "    knn_model.predict(X_blend_meta),\n",
        "    lgb_model.predict(X_blend_meta)\n",
        "])\n"
      ],
      "metadata": {
        "id": "QnNMNkry117N"
      },
      "id": "QnNMNkry117N",
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "\n",
        "# Train the meta-model on the meta-features\n",
        "meta_model = Ridge()\n",
        "meta_model.fit(meta_features, y_blend_meta)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 79
        },
        "id": "1k9e7VR-14ap",
        "outputId": "ba65b43b-19a6-4ebc-b274-dc8d6dd1ffc4"
      },
      "id": "1k9e7VR-14ap",
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge()"
            ],
            "text/html": [
              "<style>#sk-container-id-8 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-8 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-8 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-8 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-8 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-8 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-8 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-8 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-8 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-8 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-8 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-8 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Ridge<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Ridge()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate base model predictions on the test set\n",
        "meta_features_test = np.column_stack([\n",
        "    xgb_model.predict(X_test),\n",
        "    rf_model.predict(X_test),\n",
        "    gbr_model.predict(X_test),\n",
        "    knn_model.predict(X_test),\n",
        "    lgb_model.predict(X_test)\n",
        "])\n",
        "\n",
        "# Use the meta-model to make final predictions on the test set\n",
        "y_pred_blending = meta_model.predict(meta_features_test)\n"
      ],
      "metadata": {
        "id": "6V0wA3a916u9"
      },
      "id": "6V0wA3a916u9",
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the blending model\n",
        "r2_blending = r2_score(y_test, y_pred_blending)\n",
        "mae_blending = mean_absolute_error(y_test, y_pred_blending)\n",
        "rmse_blending = np.sqrt(mean_squared_error(y_test, y_pred_blending))\n",
        "\n",
        "print(f\"Blending - R-squared: {r2_blending:.4f}\")\n",
        "print(f\"Blending - Mean Absolute Error: {mae_blending:.4f}\")\n",
        "print(f\"Blending - Root Mean Squared Error: {rmse_blending:.4f}\")\n",
        "\n",
        "# plt.plot(y_test.index, y_test, label='True CPU Usage')\n",
        "# plt.plot(y_test.index, y_pred_blending, label='Blended Predicted CPU Usage')\n",
        "\n",
        "# Plot the true vs blended predicted CPU usage\n",
        "plt.plot(y_test.index, y_test, label='True CPU Usage', color='blue', linewidth=2)  # Solid line, dark color, high opacity\n",
        "plt.plot(y_test.index, y_pred_blending, label='Blended Predicted CPU Usage', color='orange', linestyle='--', linewidth=1, alpha=0.7)  # Dotted line, light color, lower opacity\n",
        "\n",
        "# Set the x-axis limits to the desired range\n",
        "plt.xlim(df_subset.index[6600], df_subset.index[-1])\n",
        "\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "lqNsBJ0F18l2",
        "outputId": "8a84f7c5-ab3d-4d60-e9fe-a37e6e5e0b5f"
      },
      "id": "lqNsBJ0F18l2",
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Blending - R-squared: 0.2088\n",
            "Blending - Mean Absolute Error: 0.1396\n",
            "Blending - Root Mean Squared Error: 0.2320\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EX_actlwxBfW"
      },
      "id": "EX_actlwxBfW",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.5"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}