space_debris_lifetime_classification_ML.ipynb

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        "<a href=\"https://colab.research.google.com/github/ajgquional/space-debris-lifetime-classification/blob/main/space_debris_lifetime_classification_ML.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np"
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              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
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          "name": "stdout",
          "text": [
            "Saving final_debris.csv to final_debris (1).csv\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "\n",
        "data = files.upload()"
      ]
    },
    {
      "cell_type": "code",
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      "id": "ee76dcf3",
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        "id": "ee76dcf3",
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              "       ARGUMENT_OF_PERIGEE  MEAN_ANOMALY  MEAN_MOTION      DRAG LAUNCH_DATE  \\\n",
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              "\n",
              "       DECAY_DATE  ACTUAL_LIFETIME  LIFETIME_CLASS  \n",
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              "15197  2024-01-07                1               0  \n",
              "15198  2024-01-01                1               0  \n",
              "15199  2024-01-19                1               0  \n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "      margin-bottom: 4px;\n",
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-99ea8858-4123-4f23-821e-f1d443bd992f 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-99ea8858-4123-4f23-821e-f1d443bd992f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1b52a004-9975-4b20-930f-d8dfecc40ba3')\"\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-fill-color: #FFFFFF;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\n",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1b52a004-9975-4b20-930f-d8dfecc40ba3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "data = pd.read_csv('final_debris.csv')\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7efb247a",
      "metadata": {
        "id": "7efb247a"
      },
      "outputs": [],
      "source": [
        "selected_columns = ['OBJECT_NAME', 'INCLINATION', 'RAAN', 'ECCENTRICITY', 'ARGUMENT_OF_PERIGEE', 'MEAN_ANOMALY', 'MEAN_MOTION', 'DRAG', 'LIFETIME_CLASS']\n",
        "\n",
        "debris = data[selected_columns]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "87f1b209",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "87f1b209",
        "outputId": "57ef46ca-38d3-4a07-f469-c9116bed7fa4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             OBJECT_NAME  INCLINATION        RAAN  ECCENTRICITY  \\\n",
              "0            JUNO II R/B    50.267399  226.973404      0.001834   \n",
              "1          THOR ABLE R/B    48.342602  203.336899      0.000363   \n",
              "2      THOR ABLESTAR R/B    51.250000  356.489197      0.000000   \n",
              "3          SPUTNIK 4 DEB    64.889900  343.241089      0.019840   \n",
              "4            JUNO II R/B    49.885201  300.265198      0.000462   \n",
              "...                  ...          ...         ...           ...   \n",
              "15195          CZ-7A R/B    16.580099  217.119293      0.083127   \n",
              "15196          CZ-2F DEB    49.753399   83.155800      0.002256   \n",
              "15197          CZ-2F DEB    50.315899  149.068497      0.003953   \n",
              "15198          CZ-2F DEB    50.253101  188.243607      0.005231   \n",
              "15199  FIREFLY ALPHA R/B   140.006699  178.050995      0.003414   \n",
              "\n",
              "       ARGUMENT_OF_PERIGEE  MEAN_ANOMALY  MEAN_MOTION      DRAG  \\\n",
              "0                32.571201    327.563812    16.135613  0.003006   \n",
              "1               333.182892     26.922001    16.401144  0.001474   \n",
              "2               331.947296     28.052601    16.293583  0.000000   \n",
              "3               333.420105     27.084000    15.498003  0.000000   \n",
              "4               342.185089     17.966299    16.411893  0.001403   \n",
              "...                    ...           ...          ...       ...   \n",
              "15195           313.909698    175.890594    14.639418  0.002594   \n",
              "15196           346.752685     13.287000    16.208685  0.005305   \n",
              "15197           297.012695     62.683498    16.090190  0.005161   \n",
              "15198           256.066101    103.450798    15.988498  0.005506   \n",
              "15199            14.218700     53.426998    16.397195  0.000534   \n",
              "\n",
              "       LIFETIME_CLASS  \n",
              "0                   2  \n",
              "1                   2  \n",
              "2                   0  \n",
              "3                   0  \n",
              "4                   2  \n",
              "...               ...  \n",
              "15195               0  \n",
              "15196               0  \n",
              "15197               0  \n",
              "15198               0  \n",
              "15199               0  \n",
              "\n",
              "[15200 rows x 9 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>OBJECT_NAME</th>\n",
              "      <th>INCLINATION</th>\n",
              "      <th>RAAN</th>\n",
              "      <th>ECCENTRICITY</th>\n",
              "      <th>ARGUMENT_OF_PERIGEE</th>\n",
              "      <th>MEAN_ANOMALY</th>\n",
              "      <th>MEAN_MOTION</th>\n",
              "      <th>DRAG</th>\n",
              "      <th>LIFETIME_CLASS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>JUNO II R/B</td>\n",
              "      <td>50.267399</td>\n",
              "      <td>226.973404</td>\n",
              "      <td>0.001834</td>\n",
              "      <td>32.571201</td>\n",
              "      <td>327.563812</td>\n",
              "      <td>16.135613</td>\n",
              "      <td>0.003006</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>THOR ABLE R/B</td>\n",
              "      <td>48.342602</td>\n",
              "      <td>203.336899</td>\n",
              "      <td>0.000363</td>\n",
              "      <td>333.182892</td>\n",
              "      <td>26.922001</td>\n",
              "      <td>16.401144</td>\n",
              "      <td>0.001474</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>THOR ABLESTAR R/B</td>\n",
              "      <td>51.250000</td>\n",
              "      <td>356.489197</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>331.947296</td>\n",
              "      <td>28.052601</td>\n",
              "      <td>16.293583</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SPUTNIK 4 DEB</td>\n",
              "      <td>64.889900</td>\n",
              "      <td>343.241089</td>\n",
              "      <td>0.019840</td>\n",
              "      <td>333.420105</td>\n",
              "      <td>27.084000</td>\n",
              "      <td>15.498003</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>JUNO II R/B</td>\n",
              "      <td>49.885201</td>\n",
              "      <td>300.265198</td>\n",
              "      <td>0.000462</td>\n",
              "      <td>342.185089</td>\n",
              "      <td>17.966299</td>\n",
              "      <td>16.411893</td>\n",
              "      <td>0.001403</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15195</th>\n",
              "      <td>CZ-7A R/B</td>\n",
              "      <td>16.580099</td>\n",
              "      <td>217.119293</td>\n",
              "      <td>0.083127</td>\n",
              "      <td>313.909698</td>\n",
              "      <td>175.890594</td>\n",
              "      <td>14.639418</td>\n",
              "      <td>0.002594</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15196</th>\n",
              "      <td>CZ-2F DEB</td>\n",
              "      <td>49.753399</td>\n",
              "      <td>83.155800</td>\n",
              "      <td>0.002256</td>\n",
              "      <td>346.752685</td>\n",
              "      <td>13.287000</td>\n",
              "      <td>16.208685</td>\n",
              "      <td>0.005305</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15197</th>\n",
              "      <td>CZ-2F DEB</td>\n",
              "      <td>50.315899</td>\n",
              "      <td>149.068497</td>\n",
              "      <td>0.003953</td>\n",
              "      <td>297.012695</td>\n",
              "      <td>62.683498</td>\n",
              "      <td>16.090190</td>\n",
              "      <td>0.005161</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15198</th>\n",
              "      <td>CZ-2F DEB</td>\n",
              "      <td>50.253101</td>\n",
              "      <td>188.243607</td>\n",
              "      <td>0.005231</td>\n",
              "      <td>256.066101</td>\n",
              "      <td>103.450798</td>\n",
              "      <td>15.988498</td>\n",
              "      <td>0.005506</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15199</th>\n",
              "      <td>FIREFLY ALPHA R/B</td>\n",
              "      <td>140.006699</td>\n",
              "      <td>178.050995</td>\n",
              "      <td>0.003414</td>\n",
              "      <td>14.218700</td>\n",
              "      <td>53.426998</td>\n",
              "      <td>16.397195</td>\n",
              "      <td>0.000534</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>15200 rows × 9 columns</p>\n",
              "</div>\n",
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              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
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              "\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c78249ef-8045-40ea-8050-c63d1007a588\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c78249ef-8045-40ea-8050-c63d1007a588')\"\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-c78249ef-8045-40ea-8050-c63d1007a588 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "debris"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4e34dbd0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "4e34dbd0",
        "outputId": "a854f1b0-07e7-4c8a-f8ff-ba1511f6f8bc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        INCLINATION          RAAN  ECCENTRICITY  ARGUMENT_OF_PERIGEE  \\\n",
              "count  15200.000000  15200.000000  15200.000000         15200.000000   \n",
              "mean      74.743477    178.177950      0.014486           203.739029   \n",
              "std       21.290307    105.884317      0.068751            97.993573   \n",
              "min        0.014000      0.005600      0.000000             0.000000   \n",
              "25%       64.862450     83.994022      0.001312           129.815125   \n",
              "50%       81.190147    178.163200      0.002320           218.634445   \n",
              "75%       97.474998    269.586960      0.005209           286.179123   \n",
              "max      144.530197    359.944214      0.900278           359.999908   \n",
              "\n",
              "       MEAN_ANOMALY   MEAN_MOTION          DRAG  LIFETIME_CLASS  \n",
              "count  15200.000000  15200.000000  15200.000000    15200.000000  \n",
              "mean     156.882665     15.591032      0.038310        0.989803  \n",
              "std       98.381530      1.518888      0.180361        0.809324  \n",
              "min        0.058300      0.298106     -0.504960        0.000000  \n",
              "25%       73.738276     15.515929      0.001329        0.000000  \n",
              "50%      143.088547     15.898451      0.005821        1.000000  \n",
              "75%      231.500099     16.171861      0.020083        2.000000  \n",
              "max      359.999908     16.589265      5.433300        2.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4f6ee04d-467c-4c38-9075-82264aad3407\" 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>INCLINATION</th>\n",
              "      <th>RAAN</th>\n",
              "      <th>ECCENTRICITY</th>\n",
              "      <th>ARGUMENT_OF_PERIGEE</th>\n",
              "      <th>MEAN_ANOMALY</th>\n",
              "      <th>MEAN_MOTION</th>\n",
              "      <th>DRAG</th>\n",
              "      <th>LIFETIME_CLASS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>15200.000000</td>\n",
              "      <td>15200.000000</td>\n",
              "      <td>15200.000000</td>\n",
              "      <td>15200.000000</td>\n",
              "      <td>15200.000000</td>\n",
              "      <td>15200.000000</td>\n",
              "      <td>15200.000000</td>\n",
              "      <td>15200.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>74.743477</td>\n",
              "      <td>178.177950</td>\n",
              "      <td>0.014486</td>\n",
              "      <td>203.739029</td>\n",
              "      <td>156.882665</td>\n",
              "      <td>15.591032</td>\n",
              "      <td>0.038310</td>\n",
              "      <td>0.989803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>21.290307</td>\n",
              "      <td>105.884317</td>\n",
              "      <td>0.068751</td>\n",
              "      <td>97.993573</td>\n",
              "      <td>98.381530</td>\n",
              "      <td>1.518888</td>\n",
              "      <td>0.180361</td>\n",
              "      <td>0.809324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.014000</td>\n",
              "      <td>0.005600</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.058300</td>\n",
              "      <td>0.298106</td>\n",
              "      <td>-0.504960</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>64.862450</td>\n",
              "      <td>83.994022</td>\n",
              "      <td>0.001312</td>\n",
              "      <td>129.815125</td>\n",
              "      <td>73.738276</td>\n",
              "      <td>15.515929</td>\n",
              "      <td>0.001329</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>81.190147</td>\n",
              "      <td>178.163200</td>\n",
              "      <td>0.002320</td>\n",
              "      <td>218.634445</td>\n",
              "      <td>143.088547</td>\n",
              "      <td>15.898451</td>\n",
              "      <td>0.005821</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>97.474998</td>\n",
              "      <td>269.586960</td>\n",
              "      <td>0.005209</td>\n",
              "      <td>286.179123</td>\n",
              "      <td>231.500099</td>\n",
              "      <td>16.171861</td>\n",
              "      <td>0.020083</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>144.530197</td>\n",
              "      <td>359.944214</td>\n",
              "      <td>0.900278</td>\n",
              "      <td>359.999908</td>\n",
              "      <td>359.999908</td>\n",
              "      <td>16.589265</td>\n",
              "      <td>5.433300</td>\n",
              "      <td>2.000000</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-4f6ee04d-467c-4c38-9075-82264aad3407')\"\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-4f6ee04d-467c-4c38-9075-82264aad3407 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-4f6ee04d-467c-4c38-9075-82264aad3407');\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-66e35b10-5e59-4881-bd0e-ec79ec80d74f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-66e35b10-5e59-4881-bd0e-ec79ec80d74f')\"\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-66e35b10-5e59-4881-bd0e-ec79ec80d74f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "debris.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5eca106c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5eca106c",
        "outputId": "1b2e2ad2-a8ae-4fdc-dc43-033b26f85167"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    5243\n",
            "0    5056\n",
            "2    4901\n",
            "Name: LIFETIME_CLASS, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "lifetime_class_counts = debris['LIFETIME_CLASS'].value_counts()\n",
        "\n",
        "print(lifetime_class_counts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "70e948ae",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "id": "70e948ae",
        "outputId": "7118bfc1-5bc8-4e9e-f6eb-55e7f8119b5a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "sns.set()\n",
        "\n",
        "data= debris[['INCLINATION', 'RAAN', 'ECCENTRICITY', 'ARGUMENT_OF_PERIGEE', 'MEAN_ANOMALY', 'MEAN_MOTION', 'DRAG', 'LIFETIME_CLASS']]\n",
        "sns.heatmap(data.corr(),annot=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4549e0e2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4549e0e2",
        "outputId": "395bdec6-b1c7-4530-d485-65f3e913a90c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(12160, 7)\n",
            "(12160, 1)\n",
            "(3040, 7)\n",
            "(3040, 1)\n"
          ]
        }
      ],
      "source": [
        "# Separate Target Variable and Predictor Variables\n",
        "TargetVariable=['LIFETIME_CLASS']\n",
        "Predictors=['INCLINATION', 'RAAN', 'ECCENTRICITY', 'ARGUMENT_OF_PERIGEE',\n",
        "            'MEAN_ANOMALY', 'MEAN_MOTION', 'DRAG']\n",
        "\n",
        "X=debris[Predictors].values\n",
        "y=debris[TargetVariable].values\n",
        "\n",
        "### Sandardization of data ###\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "PredictorScaler=StandardScaler()\n",
        "\n",
        "# Storing the fit object for later reference\n",
        "PredictorScalerFit=PredictorScaler.fit(X)\n",
        "\n",
        "# Generating the standardized values of X and y\n",
        "X=PredictorScalerFit.transform(X)\n",
        "\n",
        "# Split the data into training and testing set\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bae1a665",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "bae1a665",
        "outputId": "6b273b72-d58d-4025-8323-daca3b243a96"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "608/608 [==============================] - 4s 4ms/step - loss: 1.0667 - accuracy: 0.4381 - val_loss: 1.0439 - val_accuracy: 0.4831\n",
            "Epoch 2/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 1.0198 - accuracy: 0.5199 - val_loss: 1.0136 - val_accuracy: 0.5374\n",
            "Epoch 3/100\n",
            "608/608 [==============================] - 2s 3ms/step - loss: 0.9877 - accuracy: 0.5398 - val_loss: 0.9908 - val_accuracy: 0.5280\n",
            "Epoch 4/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.9713 - accuracy: 0.5425 - val_loss: 0.9804 - val_accuracy: 0.5329\n",
            "Epoch 5/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.9629 - accuracy: 0.5492 - val_loss: 0.9774 - val_accuracy: 0.5280\n",
            "Epoch 6/100\n",
            "608/608 [==============================] - 2s 3ms/step - loss: 0.9575 - accuracy: 0.5512 - val_loss: 0.9700 - val_accuracy: 0.5469\n",
            "Epoch 7/100\n",
            "608/608 [==============================] - 2s 3ms/step - loss: 0.9537 - accuracy: 0.5554 - val_loss: 0.9650 - val_accuracy: 0.5432\n",
            "Epoch 8/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.9496 - accuracy: 0.5597 - val_loss: 0.9609 - val_accuracy: 0.5415\n",
            "Epoch 9/100\n",
            "608/608 [==============================] - 2s 2ms/step - loss: 0.9462 - accuracy: 0.5606 - val_loss: 0.9586 - val_accuracy: 0.5551\n",
            "Epoch 10/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.9429 - accuracy: 0.5669 - val_loss: 0.9565 - val_accuracy: 0.5625\n",
            "Epoch 11/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.9403 - accuracy: 0.5663 - val_loss: 0.9535 - val_accuracy: 0.5580\n",
            "Epoch 12/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.9380 - accuracy: 0.5671 - val_loss: 0.9508 - val_accuracy: 0.5559\n",
            "Epoch 13/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.9361 - accuracy: 0.5698 - val_loss: 0.9479 - val_accuracy: 0.5588\n",
            "Epoch 14/100\n",
            "608/608 [==============================] - 2s 3ms/step - loss: 0.9315 - accuracy: 0.5700 - val_loss: 0.9437 - val_accuracy: 0.5670\n",
            "Epoch 15/100\n",
            "608/608 [==============================] - 2s 3ms/step - loss: 0.9293 - accuracy: 0.5734 - val_loss: 0.9422 - val_accuracy: 0.5633\n",
            "Epoch 16/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.9269 - accuracy: 0.5742 - val_loss: 0.9410 - val_accuracy: 0.5559\n",
            "Epoch 17/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.9249 - accuracy: 0.5743 - val_loss: 0.9352 - val_accuracy: 0.5629\n",
            "Epoch 18/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.9231 - accuracy: 0.5773 - val_loss: 0.9340 - val_accuracy: 0.5646\n",
            "Epoch 19/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.9211 - accuracy: 0.5772 - val_loss: 0.9312 - val_accuracy: 0.5683\n",
            "Epoch 20/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.9184 - accuracy: 0.5801 - val_loss: 0.9346 - val_accuracy: 0.5678\n",
            "Epoch 21/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.9169 - accuracy: 0.5806 - val_loss: 0.9309 - val_accuracy: 0.5666\n",
            "Epoch 22/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.9160 - accuracy: 0.5811 - val_loss: 0.9279 - val_accuracy: 0.5670\n",
            "Epoch 23/100\n",
            "608/608 [==============================] - 2s 3ms/step - loss: 0.9139 - accuracy: 0.5821 - val_loss: 0.9278 - val_accuracy: 0.5736\n",
            "Epoch 24/100\n",
            "608/608 [==============================] - 2s 3ms/step - loss: 0.9114 - accuracy: 0.5836 - val_loss: 0.9248 - val_accuracy: 0.5732\n",
            "Epoch 25/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.9102 - accuracy: 0.5850 - val_loss: 0.9253 - val_accuracy: 0.5789\n",
            "Epoch 26/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.9083 - accuracy: 0.5813 - val_loss: 0.9220 - val_accuracy: 0.5757\n",
            "Epoch 27/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.9069 - accuracy: 0.5823 - val_loss: 0.9254 - val_accuracy: 0.5752\n",
            "Epoch 28/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.9058 - accuracy: 0.5847 - val_loss: 0.9207 - val_accuracy: 0.5806\n",
            "Epoch 29/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.9034 - accuracy: 0.5852 - val_loss: 0.9254 - val_accuracy: 0.5781\n",
            "Epoch 30/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.9025 - accuracy: 0.5831 - val_loss: 0.9155 - val_accuracy: 0.5794\n",
            "Epoch 31/100\n",
            "608/608 [==============================] - 2s 3ms/step - loss: 0.9020 - accuracy: 0.5844 - val_loss: 0.9153 - val_accuracy: 0.5785\n",
            "Epoch 32/100\n",
            "608/608 [==============================] - 2s 3ms/step - loss: 0.9003 - accuracy: 0.5858 - val_loss: 0.9144 - val_accuracy: 0.5806\n",
            "Epoch 33/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8982 - accuracy: 0.5887 - val_loss: 0.9169 - val_accuracy: 0.5835\n",
            "Epoch 34/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8981 - accuracy: 0.5856 - val_loss: 0.9127 - val_accuracy: 0.5843\n",
            "Epoch 35/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8967 - accuracy: 0.5865 - val_loss: 0.9102 - val_accuracy: 0.5917\n",
            "Epoch 36/100\n",
            "608/608 [==============================] - 2s 2ms/step - loss: 0.8962 - accuracy: 0.5900 - val_loss: 0.9092 - val_accuracy: 0.5855\n",
            "Epoch 37/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8949 - accuracy: 0.5902 - val_loss: 0.9099 - val_accuracy: 0.5900\n",
            "Epoch 38/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8930 - accuracy: 0.5897 - val_loss: 0.9077 - val_accuracy: 0.5888\n",
            "Epoch 39/100\n",
            "608/608 [==============================] - 2s 4ms/step - loss: 0.8924 - accuracy: 0.5909 - val_loss: 0.9107 - val_accuracy: 0.5818\n",
            "Epoch 40/100\n",
            "608/608 [==============================] - 2s 3ms/step - loss: 0.8926 - accuracy: 0.5855 - val_loss: 0.9076 - val_accuracy: 0.5888\n",
            "Epoch 41/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8914 - accuracy: 0.5885 - val_loss: 0.9042 - val_accuracy: 0.5900\n",
            "Epoch 42/100\n",
            "608/608 [==============================] - 2s 2ms/step - loss: 0.8908 - accuracy: 0.5900 - val_loss: 0.9120 - val_accuracy: 0.5814\n",
            "Epoch 43/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8909 - accuracy: 0.5891 - val_loss: 0.9053 - val_accuracy: 0.5896\n",
            "Epoch 44/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8897 - accuracy: 0.5896 - val_loss: 0.9061 - val_accuracy: 0.5975\n",
            "Epoch 45/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8886 - accuracy: 0.5916 - val_loss: 0.9056 - val_accuracy: 0.5843\n",
            "Epoch 46/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8880 - accuracy: 0.5881 - val_loss: 0.9086 - val_accuracy: 0.5872\n",
            "Epoch 47/100\n",
            "608/608 [==============================] - 2s 4ms/step - loss: 0.8883 - accuracy: 0.5913 - val_loss: 0.9076 - val_accuracy: 0.5859\n",
            "Epoch 48/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8878 - accuracy: 0.5929 - val_loss: 0.9058 - val_accuracy: 0.5826\n",
            "Epoch 49/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8863 - accuracy: 0.5912 - val_loss: 0.9079 - val_accuracy: 0.5859\n",
            "Epoch 50/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8863 - accuracy: 0.5889 - val_loss: 0.9058 - val_accuracy: 0.5863\n",
            "Epoch 51/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8866 - accuracy: 0.5916 - val_loss: 0.9015 - val_accuracy: 0.5905\n",
            "Epoch 52/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8852 - accuracy: 0.5923 - val_loss: 0.9048 - val_accuracy: 0.5814\n",
            "Epoch 53/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8851 - accuracy: 0.5905 - val_loss: 0.9032 - val_accuracy: 0.5909\n",
            "Epoch 54/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8845 - accuracy: 0.5904 - val_loss: 0.9017 - val_accuracy: 0.5917\n",
            "Epoch 55/100\n",
            "608/608 [==============================] - 2s 3ms/step - loss: 0.8844 - accuracy: 0.5941 - val_loss: 0.9050 - val_accuracy: 0.5822\n",
            "Epoch 56/100\n",
            "608/608 [==============================] - 2s 3ms/step - loss: 0.8844 - accuracy: 0.5914 - val_loss: 0.9062 - val_accuracy: 0.5863\n",
            "Epoch 57/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8838 - accuracy: 0.5912 - val_loss: 0.9075 - val_accuracy: 0.5826\n",
            "Epoch 58/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8822 - accuracy: 0.5941 - val_loss: 0.8999 - val_accuracy: 0.5909\n",
            "Epoch 59/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8828 - accuracy: 0.5921 - val_loss: 0.9014 - val_accuracy: 0.5839\n",
            "Epoch 60/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8830 - accuracy: 0.5928 - val_loss: 0.9000 - val_accuracy: 0.5966\n",
            "Epoch 61/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8814 - accuracy: 0.5939 - val_loss: 0.9055 - val_accuracy: 0.5863\n",
            "Epoch 62/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8822 - accuracy: 0.5917 - val_loss: 0.9016 - val_accuracy: 0.5831\n",
            "Epoch 63/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8825 - accuracy: 0.5924 - val_loss: 0.8980 - val_accuracy: 0.5913\n",
            "Epoch 64/100\n",
            "608/608 [==============================] - 2s 3ms/step - loss: 0.8816 - accuracy: 0.5907 - val_loss: 0.9007 - val_accuracy: 0.5835\n",
            "Epoch 65/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8807 - accuracy: 0.5905 - val_loss: 0.8940 - val_accuracy: 0.5905\n",
            "Epoch 66/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8805 - accuracy: 0.5915 - val_loss: 0.8954 - val_accuracy: 0.5892\n",
            "Epoch 67/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8802 - accuracy: 0.5899 - val_loss: 0.8984 - val_accuracy: 0.5909\n",
            "Epoch 68/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8807 - accuracy: 0.5892 - val_loss: 0.9001 - val_accuracy: 0.5876\n",
            "Epoch 69/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8811 - accuracy: 0.5908 - val_loss: 0.8949 - val_accuracy: 0.5905\n",
            "Epoch 70/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8787 - accuracy: 0.5911 - val_loss: 0.8995 - val_accuracy: 0.5868\n",
            "Epoch 71/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8800 - accuracy: 0.5917 - val_loss: 0.8981 - val_accuracy: 0.5855\n",
            "Epoch 72/100\n",
            "608/608 [==============================] - 2s 3ms/step - loss: 0.8795 - accuracy: 0.5933 - val_loss: 0.8958 - val_accuracy: 0.5909\n",
            "Epoch 73/100\n",
            "608/608 [==============================] - 2s 3ms/step - loss: 0.8785 - accuracy: 0.5926 - val_loss: 0.8936 - val_accuracy: 0.5921\n",
            "Epoch 74/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8794 - accuracy: 0.5906 - val_loss: 0.8952 - val_accuracy: 0.5958\n",
            "Epoch 75/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8786 - accuracy: 0.5924 - val_loss: 0.8983 - val_accuracy: 0.5847\n",
            "Epoch 76/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8790 - accuracy: 0.5935 - val_loss: 0.8956 - val_accuracy: 0.5876\n",
            "Epoch 77/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8780 - accuracy: 0.5927 - val_loss: 0.8973 - val_accuracy: 0.5880\n",
            "Epoch 78/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8771 - accuracy: 0.5920 - val_loss: 0.9004 - val_accuracy: 0.5822\n",
            "Epoch 79/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8781 - accuracy: 0.5932 - val_loss: 0.8941 - val_accuracy: 0.5896\n",
            "Epoch 80/100\n",
            "608/608 [==============================] - 2s 3ms/step - loss: 0.8778 - accuracy: 0.5919 - val_loss: 0.8953 - val_accuracy: 0.5880\n",
            "Epoch 81/100\n",
            "608/608 [==============================] - 2s 3ms/step - loss: 0.8772 - accuracy: 0.5926 - val_loss: 0.8995 - val_accuracy: 0.5876\n",
            "Epoch 82/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8779 - accuracy: 0.5915 - val_loss: 0.8912 - val_accuracy: 0.5950\n",
            "Epoch 83/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8765 - accuracy: 0.5949 - val_loss: 0.8935 - val_accuracy: 0.5888\n",
            "Epoch 84/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8765 - accuracy: 0.5888 - val_loss: 0.8931 - val_accuracy: 0.5905\n",
            "Epoch 85/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8766 - accuracy: 0.5934 - val_loss: 0.8914 - val_accuracy: 0.5925\n",
            "Epoch 86/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8762 - accuracy: 0.5922 - val_loss: 0.8922 - val_accuracy: 0.5917\n",
            "Epoch 87/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8751 - accuracy: 0.5929 - val_loss: 0.8937 - val_accuracy: 0.5938\n",
            "Epoch 88/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8758 - accuracy: 0.5906 - val_loss: 0.8902 - val_accuracy: 0.5900\n",
            "Epoch 89/100\n",
            "608/608 [==============================] - 2s 3ms/step - loss: 0.8746 - accuracy: 0.5932 - val_loss: 0.8919 - val_accuracy: 0.5909\n",
            "Epoch 90/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8742 - accuracy: 0.5929 - val_loss: 0.8918 - val_accuracy: 0.5921\n",
            "Epoch 91/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8740 - accuracy: 0.5913 - val_loss: 0.8880 - val_accuracy: 0.5921\n",
            "Epoch 92/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8731 - accuracy: 0.5944 - val_loss: 0.8927 - val_accuracy: 0.5822\n",
            "Epoch 93/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8733 - accuracy: 0.5966 - val_loss: 0.8924 - val_accuracy: 0.5896\n",
            "Epoch 94/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8727 - accuracy: 0.5915 - val_loss: 0.8925 - val_accuracy: 0.5835\n",
            "Epoch 95/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8734 - accuracy: 0.5926 - val_loss: 0.8910 - val_accuracy: 0.5896\n",
            "Epoch 96/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8726 - accuracy: 0.5921 - val_loss: 0.8898 - val_accuracy: 0.5868\n",
            "Epoch 97/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8715 - accuracy: 0.5954 - val_loss: 0.8930 - val_accuracy: 0.5900\n",
            "Epoch 98/100\n",
            "608/608 [==============================] - 2s 3ms/step - loss: 0.8713 - accuracy: 0.5943 - val_loss: 0.8868 - val_accuracy: 0.5921\n",
            "Epoch 99/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8718 - accuracy: 0.5933 - val_loss: 0.8891 - val_accuracy: 0.5884\n",
            "Epoch 100/100\n",
            "608/608 [==============================] - 1s 2ms/step - loss: 0.8719 - accuracy: 0.5936 - val_loss: 0.8917 - val_accuracy: 0.5905\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "95/95 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "     Class 0       0.57      0.67      0.62       964\n",
            "     Class 1       0.55      0.53      0.54      1083\n",
            "     Class 2       0.69      0.60      0.64       993\n",
            "\n",
            "    accuracy                           0.60      3040\n",
            "   macro avg       0.60      0.60      0.60      3040\n",
            "weighted avg       0.60      0.60      0.60      3040\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.utils import to_categorical\n",
        "from keras.callbacks import EarlyStopping\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "\n",
        "# Assuming y_train and y_test are categorical (one-hot encoded), if not, convert them\n",
        "y_train_categorical = to_categorical(y_train)\n",
        "y_test_categorical = to_categorical(y_test)\n",
        "\n",
        "classifier = Sequential()\n",
        "classifier.add(Dense(units=10, input_dim=7, kernel_initializer='uniform', activation='relu'))\n",
        "classifier.add(Dense(units=10, kernel_initializer='uniform', activation='relu'))\n",
        "classifier.add(Dense(units=3, kernel_initializer='uniform', activation='softmax'))\n",
        "classifier.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
        "\n",
        "ANN_Model = classifier.fit(X_train, y_train_categorical, validation_split=0.2,\n",
        "                            batch_size=16, epochs=100, verbose=1, callbacks=[early_stopping])\n",
        "\n",
        "# Plot learning curves (Loss)\n",
        "plt.plot(ANN_Model.history['loss'], label='Training Loss')\n",
        "plt.plot(ANN_Model.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Learning Curves (Loss)')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Plot learning curves (Accuracy)\n",
        "plt.plot(ANN_Model.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(ANN_Model.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Learning Curves (Accuracy)')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "y_pred = np.argmax(classifier.predict(X_test), axis=1)\n",
        "y_test_argmax = np.argmax(y_test_categorical, axis=1)\n",
        "\n",
        "# Confusion matrix\n",
        "cm = confusion_matrix(y_test_argmax, y_pred)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1', 'Class 2'],\n",
        "            yticklabels=['Class 0', 'Class 1', 'Class 2'])\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.show()\n",
        "\n",
        "# Classification Performance\n",
        "class_report = classification_report(y_test_argmax, y_pred, target_names=['Class 0', 'Class 1', 'Class 2'])\n",
        "print(\"Classification Report:\\n\", class_report)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "84a5078f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "84a5078f",
        "outputId": "33aabcff-2418-4e09-e3e6-1d68e2d0c774"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:160: UserWarning: [05:29:48] WARNING: /workspace/src/learner.cc:742: \n",
            "Parameters: { \"num_classes\" } are not used.\n",
            "\n",
            "  warnings.warn(smsg, UserWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "     Class 0       0.72      0.71      0.71       964\n",
            "     Class 1       0.64      0.67      0.65      1083\n",
            "     Class 2       0.78      0.74      0.76       993\n",
            "\n",
            "    accuracy                           0.71      3040\n",
            "   macro avg       0.71      0.71      0.71      3040\n",
            "weighted avg       0.71      0.71      0.71      3040\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.model_selection import learning_curve\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "\n",
        "xgb_model = XGBClassifier(objective='multi:softmax', num_classes=3, random_state=42)\n",
        "xgb_model.fit(X_train, y_train)\n",
        "\n",
        "train_sizes, train_scores, test_scores, fit_times, _ = learning_curve(\n",
        "    xgb_model, X_train, y_train, cv=5, scoring='accuracy', n_jobs=-1,\n",
        "    train_sizes=np.linspace(0.1, 1.0, 10), return_times=True\n",
        ")\n",
        "\n",
        "# Plot learning curves\n",
        "plt.figure(figsize=(12, 8))\n",
        "\n",
        "# Plot training & validation accuracy values\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Training Accuracy', marker='o')\n",
        "plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Validation Accuracy', marker='o')\n",
        "plt.title('Learning Curves - Training and Validation Accuracy')\n",
        "plt.xlabel('Training Examples')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "# Predictions on the test set\n",
        "y_pred = xgb_model.predict(X_test)\n",
        "\n",
        "# Confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1', 'Class 2'],\n",
        "            yticklabels=['Class 0', 'Class 1', 'Class 2'])\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.show()\n",
        "\n",
        "# Classification Performance\n",
        "class_report = classification_report(y_test, y_pred, target_names=['Class 0', 'Class 1', 'Class 2'])\n",
        "print(\"Classification Report:\\n\", class_report)\n"
      ]
    }
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