MSc_ML_Lab_1.ipynb

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      "cell_type": "markdown",
      "source": [
        "Name : **Belo Abhigyan**\n",
        "\n",
        "Course :**MSc Computer Science**\n",
        "\n",
        "Roll No: **16**\n",
        "\n",
        "Sem : **Sem-2**\n",
        "\n",
        "## The given colab file consists of both parts of the 1st Assignment"
      ],
      "metadata": {
        "id": "vLi88yKW6HXk"
      }
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    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "KaMbC2N57Cik",
        "outputId": "e48705d7-1406-4b0d-d964-bbe912855166"
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "import os\n",
        "os.chdir('/content/drive/My Drive/MScML')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "XhpbT9_TYFlh"
      },
      "execution_count": 1,
      "outputs": []
    },
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      "cell_type": "markdown",
      "source": [
        "The MAGIC gamma telescope dataset: https://archive.ics.uci.edu/dataset/159/magic+gamma+telescope, it generated to simulate registration of high energy gamma particles in a ground-based atmospheric Cherenkov gamma telescope using the imaging technique allowing to discriminate statistically the information caused by primary gammas (signal) from the images of hadronic showers initiated by cosmic rays in the upper atmosphere (background). It is required to investigate the data deeper, split into train and test data with class labels g = gamma (signal) and h = hadron (background). You are asked to apply preprocessing and feature selection techniques.\n",
        "\n",
        "\n",
        "1. fLength: continuous # major axis of ellipse [mm]\n",
        "\n",
        "2. fWidth: continuous # minor axis of ellipse [mm]\n",
        "\n",
        "3. fSize: continuous # 10-log of sum of content of all pixels [in #phot]\n",
        "\n",
        "4. fConc: continuous # ratio of sum of two highest pixels over fSize [ratio]\n",
        "\n",
        "5. fConc1: continuous # ratio of highest pixel over fSize [ratio]\n",
        "\n",
        "6. fAsym: continuous # distance from highest pixel to center, projected onto major axis [mm]\n",
        "\n",
        "7. fM3Long: continuous # 3rd root of third moment along major axis [mm]\n",
        "\n",
        "8. fM3Trans: continuous # 3rd root of third moment along minor axis [mm]\n",
        "\n",
        "9. fAlpha: continuous # angle of major axis with vector to origin [deg]\n",
        "\n",
        "10. fDist: continuous # distance from origin to center of ellipse [mm]\n",
        "\n",
        "class: g,h # gamma (signal), hadron (background)\n",
        "\n",
        "g = gamma (signal): 12332 h = hadron (background): 6688"
      ],
      "metadata": {
        "id": "qjsuJM8RavJU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install ucimlrepo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zf0qwGXCcvTP",
        "outputId": "bbb1f450-02fd-48de-fbf4-b81c4e37495f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ucimlrepo\n",
            "  Downloading ucimlrepo-0.0.3-py3-none-any.whl (7.0 kB)\n",
            "Installing collected packages: ucimlrepo\n",
            "Successfully installed ucimlrepo-0.0.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from ucimlrepo import fetch_ucirepo\n",
        "\n",
        "# fetch dataset\n",
        "magicGammaTelescope = fetch_ucirepo(id=159)\n",
        "\n",
        "# data (as pandas dataframes)\n",
        "featuresData = magicGammaTelescope.data.features\n",
        "labelData = magicGammaTelescope.data.targets\n",
        "\n",
        "# metadata\n",
        "print(magicGammaTelescope.metadata)\n",
        "\n",
        "# variable information\n",
        "print(magicGammaTelescope.variables)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bNUxbwigXDif",
        "outputId": "cd4e8530-e46a-45ab-c5c0-23686b62b245"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'uci_id': 159, 'name': 'MAGIC Gamma Telescope', 'repository_url': 'https://archive.ics.uci.edu/dataset/159/magic+gamma+telescope', 'data_url': 'https://archive.ics.uci.edu/static/public/159/data.csv', 'abstract': 'Data are MC generated to simulate registration of high energy gamma particles in an atmospheric Cherenkov telescope', 'area': 'Physics and Chemistry', 'tasks': ['Classification'], 'characteristics': ['Multivariate'], 'num_instances': 19020, 'num_features': 10, 'feature_types': ['Real'], 'demographics': [], 'target_col': ['class'], 'index_col': None, 'has_missing_values': 'no', 'missing_values_symbol': None, 'year_of_dataset_creation': 2004, 'last_updated': 'Tue Dec 19 2023', 'dataset_doi': '10.24432/C52C8B', 'creators': ['R. Bock'], 'intro_paper': None, 'additional_info': {'summary': \"The data are MC generated (see below) to simulate registration of high energy gamma particles in a ground-based atmospheric Cherenkov gamma telescope using the imaging technique. Cherenkov gamma telescope observes high energy gamma rays, taking advantage of the radiation emitted by charged particles produced inside the electromagnetic showers initiated by the gammas, and developing in the atmosphere. This Cherenkov radiation (of visible to UV wavelengths) leaks through the atmosphere and gets recorded in the detector, allowing reconstruction of the shower parameters. The available information consists of pulses left by the incoming Cherenkov photons on the photomultiplier tubes, arranged in a plane, the camera. Depending on the energy of the primary gamma, a total of few hundreds to some 10000 Cherenkov photons get collected, in patterns (called the shower image), allowing to discriminate statistically those caused by primary gammas (signal) from the images of hadronic showers initiated by cosmic rays in the upper atmosphere (background).\\r\\n\\r\\nTypically, the image of a shower after some pre-processing is an elongated cluster. Its long axis is oriented towards the camera center if the shower axis is parallel to the telescope's optical axis, i.e. if the telescope axis is directed towards a point source. A principal component analysis is performed in the camera plane, which results in a correlation axis and defines an ellipse. If the depositions were distributed as a bivariate Gaussian, this would be an equidensity ellipse. The characteristic parameters of this ellipse (often called Hillas parameters) are among the image parameters that can be used for discrimination. The energy depositions are typically asymmetric along the major axis, and this asymmetry can also be used in discrimination. There are, in addition, further discriminating characteristics, like the extent of the cluster in the image plane, or the total sum of depositions.\\r\\n\\r\\nThe data set was generated by a Monte Carlo program, Corsika, described in:\\r\\n    D. Heck et al., CORSIKA, A Monte Carlo code to simulate extensive air showers,\\r\\n    Forschungszentrum Karlsruhe FZKA 6019 (1998).\\r\\nhttp://rexa.info/paper?id=ac6e674e9af20979b23d3ed4521f1570765e8d68\\r\\n\\r\\nThe program was run with parameters allowing to observe events with energies down to below 50 GeV.\", 'purpose': None, 'funded_by': None, 'instances_represent': None, 'recommended_data_splits': None, 'sensitive_data': None, 'preprocessing_description': None, 'variable_info': '    1.  fLength:  continuous  # major axis of ellipse [mm]\\r\\n    2.  fWidth:   continuous  # minor axis of ellipse [mm] \\r\\n    3.  fSize:    continuous  # 10-log of sum of content of all pixels [in #phot]\\r\\n    4.  fConc:    continuous  # ratio of sum of two highest pixels over fSize  [ratio]\\r\\n    5.  fConc1:   continuous  # ratio of highest pixel over fSize  [ratio]\\r\\n    6.  fAsym:    continuous  # distance from highest pixel to center, projected onto major axis [mm]\\r\\n    7.  fM3Long:  continuous  # 3rd root of third moment along major axis  [mm] \\r\\n    8.  fM3Trans: continuous  # 3rd root of third moment along minor axis  [mm]\\r\\n    9.  fAlpha:   continuous  # angle of major axis with vector to origin [deg]\\r\\n   10.  fDist:    continuous  # distance from origin to center of ellipse [mm]\\r\\n   11.  class:    g,h         # gamma (signal), hadron (background)\\r\\n\\r\\n   g = gamma (signal):     12332\\r\\n   h = hadron (background): 6688\\r\\n\\r\\n   For technical reasons, the number of h events is underestimated. In the real data, the h class represents the majority of the events.\\r\\n\\r\\n   The simple classification accuracy is not meaningful for this data, since classifying a background event as signal is worse than classifying a signal event as background. For comparison of different classifiers an ROC curve has to be used. The relevant points on this curve are those, where the probability of accepting a background event as signal is below one of the following thresholds: 0.01, 0.02, 0.05, 0.1, 0.2 depending on the required quality of the sample of the accepted events for different experiments.', 'citation': None}}\n",
            "        name     role        type demographic  \\\n",
            "0    fLength  Feature  Continuous        None   \n",
            "1     fWidth  Feature  Continuous        None   \n",
            "2      fSize  Feature  Continuous        None   \n",
            "3      fConc  Feature  Continuous        None   \n",
            "4     fConc1  Feature  Continuous        None   \n",
            "5      fAsym  Feature  Continuous        None   \n",
            "6    fM3Long  Feature  Continuous        None   \n",
            "7   fM3Trans  Feature  Continuous        None   \n",
            "8     fAlpha  Feature  Continuous        None   \n",
            "9      fDist  Feature  Continuous        None   \n",
            "10     class   Target      Binary        None   \n",
            "\n",
            "                                          description  units missing_values  \n",
            "0                               major axis of ellipse     mm             no  \n",
            "1                               minor axis of ellipse     mm             no  \n",
            "2              10-log of sum of content of all pixels  #phot             no  \n",
            "3       ratio of sum of two highest pixels over fSize   None             no  \n",
            "4                   ratio of highest pixel over fSize   None             no  \n",
            "5   distance from highest pixel to center, project...   None             no  \n",
            "6           3rd root of third moment along major axis     mm             no  \n",
            "7           3rd root of third moment along minor axis     mm             no  \n",
            "8           angle of major axis with vector to origin    deg             no  \n",
            "9           distance from origin to center of ellipse     mm             no  \n",
            "10                gamma (signal), hadron (background)   None             no  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://archive.ics.uci.edu/static/public/159/data.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qWgFNj9rycKy",
        "outputId": "de60fe39-377b-498e-db5c-fdfeb9d045e9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-02-03 16:48:02--  https://archive.ics.uci.edu/static/public/159/data.csv\n",
            "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
            "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified\n",
            "Saving to: ‘data.csv’\n",
            "\n",
            "data.csv                [  <=>               ]   1.43M  4.79MB/s    in 0.3s    \n",
            "\n",
            "2024-02-03 16:48:03 (4.79 MB/s) - ‘data.csv’ saved [1496488]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(featuresData.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qEmAkJhVAhTb",
        "outputId": "0772b272-e395-4ae1-9411-4386338fd0f7"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(19020, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(labelData.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T9l-y0LTAm2E",
        "outputId": "15fafdc3-4fc0-4c87-8752-2ef4f69ba287"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(19020, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.concat([featuresData,labelData],axis=1)"
      ],
      "metadata": {
        "id": "o8-P5P4vA3ac"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PImjf1d2trGO",
        "outputId": "5633e900-0af9-4adf-b971-03bf05dab9ad"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    fLength    fWidth   fSize   fConc  fConc1     fAsym  fM3Long  fM3Trans  \\\n",
              "0   28.7967   16.0021  2.6449  0.3918  0.1982   27.7004  22.0110   -8.2027   \n",
              "1   31.6036   11.7235  2.5185  0.5303  0.3773   26.2722  23.8238   -9.9574   \n",
              "2  162.0520  136.0310  4.0612  0.0374  0.0187  116.7410 -64.8580  -45.2160   \n",
              "3   23.8172    9.5728  2.3385  0.6147  0.3922   27.2107  -6.4633   -7.1513   \n",
              "4   75.1362   30.9205  3.1611  0.3168  0.1832   -5.5277  28.5525   21.8393   \n",
              "\n",
              "    fAlpha     fDist class  \n",
              "0  40.0920   81.8828     g  \n",
              "1   6.3609  205.2610     g  \n",
              "2  76.9600  256.7880     g  \n",
              "3  10.4490  116.7370     g  \n",
              "4   4.6480  356.4620     g  "
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fLength</th>\n",
              "      <th>fWidth</th>\n",
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              "      <th>0</th>\n",
              "      <td>28.7967</td>\n",
              "      <td>16.0021</td>\n",
              "      <td>2.6449</td>\n",
              "      <td>0.3918</td>\n",
              "      <td>0.1982</td>\n",
              "      <td>27.7004</td>\n",
              "      <td>22.0110</td>\n",
              "      <td>-8.2027</td>\n",
              "      <td>40.0920</td>\n",
              "      <td>81.8828</td>\n",
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              "      <th>1</th>\n",
              "      <td>31.6036</td>\n",
              "      <td>11.7235</td>\n",
              "      <td>2.5185</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.3773</td>\n",
              "      <td>26.2722</td>\n",
              "      <td>23.8238</td>\n",
              "      <td>-9.9574</td>\n",
              "      <td>6.3609</td>\n",
              "      <td>205.2610</td>\n",
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              "      <th>2</th>\n",
              "      <td>162.0520</td>\n",
              "      <td>136.0310</td>\n",
              "      <td>4.0612</td>\n",
              "      <td>0.0374</td>\n",
              "      <td>0.0187</td>\n",
              "      <td>116.7410</td>\n",
              "      <td>-64.8580</td>\n",
              "      <td>-45.2160</td>\n",
              "      <td>76.9600</td>\n",
              "      <td>256.7880</td>\n",
              "      <td>g</td>\n",
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              "      <th>3</th>\n",
              "      <td>23.8172</td>\n",
              "      <td>9.5728</td>\n",
              "      <td>2.3385</td>\n",
              "      <td>0.6147</td>\n",
              "      <td>0.3922</td>\n",
              "      <td>27.2107</td>\n",
              "      <td>-6.4633</td>\n",
              "      <td>-7.1513</td>\n",
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              "      <td>0.1832</td>\n",
              "      <td>-5.5277</td>\n",
              "      <td>28.5525</td>\n",
              "      <td>21.8393</td>\n",
              "      <td>4.6480</td>\n",
              "      <td>356.4620</td>\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-fdc445cc-7185-4163-8da7-8a8f3c49e162\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fdc445cc-7185-4163-8da7-8a8f3c49e162')\"\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-fdc445cc-7185-4163-8da7-8a8f3c49e162 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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"class\"] = (df[\"class\"] == \"g\").astype(int)"
      ],
      "metadata": {
        "id": "Wqxvawa5hwao"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "2RUFHXwPuA4D",
        "outputId": "97d164f3-7bf9-4163-c08c-6f28ff28ed18"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        fLength    fWidth   fSize   fConc  fConc1     fAsym   fM3Long  \\\n",
              "0       28.7967   16.0021  2.6449  0.3918  0.1982   27.7004   22.0110   \n",
              "1       31.6036   11.7235  2.5185  0.5303  0.3773   26.2722   23.8238   \n",
              "2      162.0520  136.0310  4.0612  0.0374  0.0187  116.7410  -64.8580   \n",
              "3       23.8172    9.5728  2.3385  0.6147  0.3922   27.2107   -6.4633   \n",
              "4       75.1362   30.9205  3.1611  0.3168  0.1832   -5.5277   28.5525   \n",
              "...         ...       ...     ...     ...     ...       ...       ...   \n",
              "19015   21.3846   10.9170  2.6161  0.5857  0.3934   15.2618   11.5245   \n",
              "19016   28.9452    6.7020  2.2672  0.5351  0.2784   37.0816   13.1853   \n",
              "19017   75.4455   47.5305  3.4483  0.1417  0.0549   -9.3561   41.0562   \n",
              "19018  120.5135   76.9018  3.9939  0.0944  0.0683    5.8043  -93.5224   \n",
              "19019  187.1814   53.0014  3.2093  0.2876  0.1539 -167.3125 -168.4558   \n",
              "\n",
              "       fM3Trans   fAlpha     fDist  class  \n",
              "0       -8.2027  40.0920   81.8828      1  \n",
              "1       -9.9574   6.3609  205.2610      1  \n",
              "2      -45.2160  76.9600  256.7880      1  \n",
              "3       -7.1513  10.4490  116.7370      1  \n",
              "4       21.8393   4.6480  356.4620      1  \n",
              "...         ...      ...       ...    ...  \n",
              "19015    2.8766   2.4229  106.8258      0  \n",
              "19016   -2.9632  86.7975  247.4560      0  \n",
              "19017   -9.4662  30.2987  256.5166      0  \n",
              "19018  -63.8389  84.6874  408.3166      0  \n",
              "19019   31.4755  52.7310  272.3174      0  \n",
              "\n",
              "[19020 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1dab05cb-d327-4aa6-a682-a411bf869ca7\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fLength</th>\n",
              "      <th>fWidth</th>\n",
              "      <th>fSize</th>\n",
              "      <th>fConc</th>\n",
              "      <th>fConc1</th>\n",
              "      <th>fAsym</th>\n",
              "      <th>fM3Long</th>\n",
              "      <th>fM3Trans</th>\n",
              "      <th>fAlpha</th>\n",
              "      <th>fDist</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28.7967</td>\n",
              "      <td>16.0021</td>\n",
              "      <td>2.6449</td>\n",
              "      <td>0.3918</td>\n",
              "      <td>0.1982</td>\n",
              "      <td>27.7004</td>\n",
              "      <td>22.0110</td>\n",
              "      <td>-8.2027</td>\n",
              "      <td>40.0920</td>\n",
              "      <td>81.8828</td>\n",
              "      <td>1</td>\n",
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              "      <th>1</th>\n",
              "      <td>31.6036</td>\n",
              "      <td>11.7235</td>\n",
              "      <td>2.5185</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.3773</td>\n",
              "      <td>26.2722</td>\n",
              "      <td>23.8238</td>\n",
              "      <td>-9.9574</td>\n",
              "      <td>6.3609</td>\n",
              "      <td>205.2610</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>162.0520</td>\n",
              "      <td>136.0310</td>\n",
              "      <td>4.0612</td>\n",
              "      <td>0.0374</td>\n",
              "      <td>0.0187</td>\n",
              "      <td>116.7410</td>\n",
              "      <td>-64.8580</td>\n",
              "      <td>-45.2160</td>\n",
              "      <td>76.9600</td>\n",
              "      <td>256.7880</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23.8172</td>\n",
              "      <td>9.5728</td>\n",
              "      <td>2.3385</td>\n",
              "      <td>0.6147</td>\n",
              "      <td>0.3922</td>\n",
              "      <td>27.2107</td>\n",
              "      <td>-6.4633</td>\n",
              "      <td>-7.1513</td>\n",
              "      <td>10.4490</td>\n",
              "      <td>116.7370</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>75.1362</td>\n",
              "      <td>30.9205</td>\n",
              "      <td>3.1611</td>\n",
              "      <td>0.3168</td>\n",
              "      <td>0.1832</td>\n",
              "      <td>-5.5277</td>\n",
              "      <td>28.5525</td>\n",
              "      <td>21.8393</td>\n",
              "      <td>4.6480</td>\n",
              "      <td>356.4620</td>\n",
              "      <td>1</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19015</th>\n",
              "      <td>21.3846</td>\n",
              "      <td>10.9170</td>\n",
              "      <td>2.6161</td>\n",
              "      <td>0.5857</td>\n",
              "      <td>0.3934</td>\n",
              "      <td>15.2618</td>\n",
              "      <td>11.5245</td>\n",
              "      <td>2.8766</td>\n",
              "      <td>2.4229</td>\n",
              "      <td>106.8258</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19016</th>\n",
              "      <td>28.9452</td>\n",
              "      <td>6.7020</td>\n",
              "      <td>2.2672</td>\n",
              "      <td>0.5351</td>\n",
              "      <td>0.2784</td>\n",
              "      <td>37.0816</td>\n",
              "      <td>13.1853</td>\n",
              "      <td>-2.9632</td>\n",
              "      <td>86.7975</td>\n",
              "      <td>247.4560</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19017</th>\n",
              "      <td>75.4455</td>\n",
              "      <td>47.5305</td>\n",
              "      <td>3.4483</td>\n",
              "      <td>0.1417</td>\n",
              "      <td>0.0549</td>\n",
              "      <td>-9.3561</td>\n",
              "      <td>41.0562</td>\n",
              "      <td>-9.4662</td>\n",
              "      <td>30.2987</td>\n",
              "      <td>256.5166</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19018</th>\n",
              "      <td>120.5135</td>\n",
              "      <td>76.9018</td>\n",
              "      <td>3.9939</td>\n",
              "      <td>0.0944</td>\n",
              "      <td>0.0683</td>\n",
              "      <td>5.8043</td>\n",
              "      <td>-93.5224</td>\n",
              "      <td>-63.8389</td>\n",
              "      <td>84.6874</td>\n",
              "      <td>408.3166</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19019</th>\n",
              "      <td>187.1814</td>\n",
              "      <td>53.0014</td>\n",
              "      <td>3.2093</td>\n",
              "      <td>0.2876</td>\n",
              "      <td>0.1539</td>\n",
              "      <td>-167.3125</td>\n",
              "      <td>-168.4558</td>\n",
              "      <td>31.4755</td>\n",
              "      <td>52.7310</td>\n",
              "      <td>272.3174</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19020 rows × 11 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1dab05cb-d327-4aa6-a682-a411bf869ca7')\"\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",
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              "  </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-1dab05cb-d327-4aa6-a682-a411bf869ca7 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-1dab05cb-d327-4aa6-a682-a411bf869ca7');\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-3ef390f0-61af-461d-aebf-8a6dff92847e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3ef390f0-61af-461d-aebf-8a6dff92847e')\"\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-3ef390f0-61af-461d-aebf-8a6dff92847e 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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for feature in df.columns[:-1]:\n",
        "  print(feature)\n",
        "  plt.hist(df[df[\"class\"]==1][feature],color='blue',label='gamma',alpha=0.7,density=True)\n",
        "  plt.hist(df[df[\"class\"]==0][feature],color='red',label='hadron',alpha=0.7,density=True)\n",
        "  plt.title(feature)\n",
        "  plt.ylabel(\"Probability\")\n",
        "  plt.xlabel(feature)\n",
        "  plt.legend()\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Xjzpyer7jWBG",
        "outputId": "c741ca1f-a96e-48e0-ea8e-5e8012eca70f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fLength\n"
          ]
        },
        {
          "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": [
            "fWidth\n"
          ]
        },
        {
          "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": [
            "fSize\n"
          ]
        },
        {
          "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": [
            "fConc\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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6dNB7772nt99+Wy1bttSGDRv08ssv3/K9LRaLVq1apapVq6pz586Kjo5W3bp1tWTJkpL6OLfFYhiG4eoiSlNWVpb8/f2VmZkpPz8/V5cDB5XF06rLIk4FhxlduXJFJ0+etLmGC9zDb42NPd/fzNwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAMqlcnY+TZngrDEh3AAAypUbV9G9cUsCuI8bV1L29PS8rffhCsUAgHLF09NTAQEB1nseVa5cWRaLxcVVoaCgQOfOnVPlypVVocLtxRPCDQCg3AkODpYkl9zUETfn4eGh2rVr33bYJNwAAModi8WikJAQBQYGKi8vz9Xl4L+8vLys98W6HYQbAEC55enpedvrO+B+WFAMAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMxaXhJjExUe3atZOvr68CAwPVs2dPHT169JbbLV26VI0aNZKPj4+aN2+utWvXlkK1AACgLHBpuNm6davi4+O1c+dObdy4UXl5efrd736nnJycm26zY8cOPf7444qLi9PXX3+tnj17qmfPnvr2229LsXIAAOCuLIZhGK4u4oZz584pMDBQW7duVefOnYvs07dvX+Xk5GjNmjXWtg4dOqhVq1aaPXv2LfeRlZUlf39/ZWZmys/Pz2m1o3TFxrq6gvJh9WpXVwAA19nz/e1Wa24yMzMlSdWqVbtpn+TkZEVHR9u0xcTEKDk5uURrAwAAZYPb3DizoKBAI0eOVKdOndSsWbOb9ktPT1dQUJBNW1BQkNLT04vsf/XqVV29etX6PCsryzkFAwAAt+Q2Mzfx8fH69ttvtXjxYqe+b2Jiovz9/a2PsLAwp74/AABwL24RboYNG6Y1a9Zo8+bNuvPOO3+zb3BwsDIyMmzaMjIyFBwcXGT/0aNHKzMz0/pITU11Wt0AAMD9uDTcGIahYcOG6ZNPPtGmTZsUERFxy22ioqKUlJRk07Zx40ZFRUUV2d/b21t+fn42DwAAYF4uXXMTHx+vRYsWadWqVfL19bWum/H391elSpUkSf3791etWrWUmJgoSXr++efVpUsXTZkyRQ8//LAWL16svXv3as6cOS77HAAAwH24dObm3XffVWZmprp27aqQkBDrY8mSJdY+KSkpSktLsz7v2LGjFi1apDlz5qhly5ZatmyZVq5c+ZuLkAEAQPnhVte5KQ1c58YcuM5N6eA6NwDcRZm9zg0AAMDtItwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTqeDqAgC4r9hYV1dgn9WrXV0BAHdAuEGZNGZ3GfvWlfRaJN+8AFAaOCwFAABMhXADAABMhXADAABMhTU3QClhnRAAlA5mbgAAgKkwcwPgpsrcbFOsOB8cADM3AADAXAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVAg3AADAVCq4ugC4gdhYV1cAAIDTMHMDAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMxaXhZtu2bYqNjVVoaKgsFotWrlz5m/23bNkii8VS6JGenl46BQMAALfn0nCTk5Ojli1batasWXZtd/ToUaWlpVkfgYGBJVQhAAAoa1x6V/Bu3bqpW7dudm8XGBiogIAA5xcEAADKvDK55qZVq1YKCQnRgw8+qK+++uo3+169elVZWVk2DwAAYF5lKtyEhIRo9uzZWr58uZYvX66wsDB17dpV+/fvv+k2iYmJ8vf3tz7CwsJKsWIAAFDaXHpYyl4NGzZUw4YNrc87duyo48ePa+rUqVqwYEGR24wePVoJCQnW51lZWQQcAABMrEyFm6JERkZq+/btN33d29tb3t7epVgRAABwpTJ1WKooBw4cUEhIiKvLAAAAbsKlMzeXLl3SsWPHrM9PnjypAwcOqFq1aqpdu7ZGjx6t06dPa/78+ZKkadOmKSIiQk2bNtWVK1f0/vvva9OmTdqwYYOrPgIAAHAzLg03e/fu1X333Wd9fmNtzIABAzRv3jylpaUpJSXF+npubq5GjRql06dPq3LlymrRooW++OILm/cAAADlm8UwDMPVRZSmrKws+fv7KzMzU35+fq4uxz3Exrq6Arvt3u3qCuCOIiMlrV7t6jIAlAB7vr8dWnOzefNmhwoDAAAoaQ6Fm4ceekj16tXT66+/rtTUVGfXBAAA4DCHws3p06c1bNgwLVu2THXr1lVMTIw+/vhj5ebmOrs+AAAAuzgUbmrUqKEXXnhBBw4c0K5du3TXXXdp6NChCg0N1YgRI/TNN984u04AAIBiue3r3Nx9990aPXq0hg0bpkuXLunDDz9UmzZtdO+99+q7775zRo0AAADF5nC4ycvL07Jly9S9e3fVqVNH69ev18yZM5WRkaFjx46pTp066t27tzNrBQAAuCWHrnMzfPhw/fOf/5RhGOrXr58mTZqkZs2aWV+vUqWKJk+erNDQUKcVCgAAUBwOhZtDhw5pxowZ6tWr103v21SjRg1OGQcAAKXOocNS48aNU+/evQsFm2vXrmnbtm2SpAoVKqhLly63XyEAAIAdHAo39913n37++edC7ZmZmdwKAQAAuJRD4cYwDFkslkLt58+fV5UqVW67KAAAAEfZteamV69ekiSLxaKBAwfaHJbKz8/Xv/71L3Xs2NG5FQIAANjBrnDj7+8v6frMja+vrypVqmR9zcvLSx06dNDgwYOdWyEAAIAd7Ao3c+fOlSSFh4frz3/+M4egALifMniXe+5kDjiXQ6eCjxs3ztl1AAAAOEWxw83dd9+tpKQkVa1aVa1bty5yQfEN+/fvd0pxAAAA9ip2uOnRo4d1AXHPnj1Lqh4AAIDbUuxw88tDURyWAgAA7uq27woOAADgToo9c1O1atXfXGfzS0VdvRgAAKA0FDvcTJs2rQTLAAAAcI5ih5sBAwaUZB0AAABOUexwk5WVJT8/P+vPv+VGPwAoTbt3u7oC+0VGuroCwHzsWnOTlpamwMBABQQEFLn+5sYNNfPz851aJAAAQHEVO9xs2rRJ1apVkyRt3ry5xAoCAAC4HcUON126dCnyZwAAAHfi0L2lJOnChQv64IMPdPjwYUlSkyZNNGjQIOvsDgAAgCs4dBG/bdu2KTw8XNOnT9eFCxd04cIFTZ8+XREREdq2bZuzawQAACg2h2Zu4uPj1bdvX7377rvy9PSUJOXn52vo0KGKj4/XwYMHnVokAABAcTk0c3Ps2DGNGjXKGmwkydPTUwkJCTp27JjTigMAALCXQ+Hm7rvvtq61+aXDhw+rZcuWt10UAACAo4p9WOpf//qX9ecRI0bo+eef17Fjx9ShQwdJ0s6dOzVr1ixNnDjR+VWiRJXFC58BAHAzFsMwjOJ09PDwkMVi0a26u/tF/LKysuTv76/MzEyupPxfu4NiXV0CUG5FRkpavdrVZQBuz57v72LP3Jw8efK2CwMAAChpxQ43derUKck6AAAAnMLhi/hJ0qFDh5SSkqLc3Fyb9kcfffS2igIAAHCUQ+HmxIkT+v3vf6+DBw/arMO5cTNNd15zAwAAzM2hU8Gff/55RURE6OzZs6pcubK+++47bdu2TW3bttWWLVucXCIAAEDxOTRzk5ycrE2bNqlGjRry8PCQh4eH7rnnHiUmJmrEiBH6+uuvnV0nAABAsTg0c5Ofny9fX19JUo0aNXTmzBlJ1xcdHz161HnVAQAA2MmhmZtmzZrpm2++UUREhNq3b69JkybJy8tLc+bMUd26dZ1dIwAAQLE5FG5efvll5eTkSJJeffVVPfLII7r33ntVvXp1LVmyxKkFAgAA2MOhcBMTE2P9uX79+jpy5Ih+/vlnVa1a1XrGFAAAgCvc1nVuJCk1NVWSFBYWdtvFAEC5FFsGb4HCLSPgxhxaUHzt2jWNGTNG/v7+Cg8PV3h4uPz9/fXyyy8rLy/P2TUCAAAUm0MzN8OHD9eKFSs0adIkRUVFSbp+evj48eN1/vx5vfvuu04tEgAAoLgcCjeLFi3S4sWL1a1bN2tbixYtFBYWpscff5xwAwAAXMahw1Le3t4KDw8v1B4RESEvL6/brQkAAMBhDoWbYcOG6bXXXtPVq1etbVevXtWECRM0bNgwpxUHAABgr2IflurVq5fN8y+++EJ33nmnWrZsKUn65ptvlJubqwceeMC5FQIAANih2OHG39/f5vljjz1m85xTwQEAgDsodriZO3duSdYBAADgFLd1Eb9z585Zb5TZsGFD1axZ0ylFAQAAOMqhBcU5OTl6+umnFRISos6dO6tz584KDQ1VXFycLl++7OwaAQAAis2hcJOQkKCtW7dq9erVunjxoi5evKhVq1Zp69atGjVqlLNrBAAAKDaHDkstX75cy5YtU9euXa1t3bt3V6VKldSnTx8u4gcAAFzGoZmby5cvKygoqFB7YGAgh6UAAIBLORRuoqKiNG7cOF25csXa9p///EevvPKK9V5TAAAAruDQYalp06bpoYceKnQRPx8fH61fv96pBQIAANjDoXDTvHlz/fDDD1q4cKGOHDkiSXr88cf15JNPqlKlSk4tEAAAwB52h5u8vDw1atRIa9as0eDBg0uiJgAAAIfZHW4qVqxos9YGAOC43btdXYH9IiNdXQHw2xxaUBwfH69//OMfunbtmrPrAQAAuC0OrbnZs2ePkpKStGHDBjVv3lxVqlSxeX3FihVOKQ4AAMBeDs3cBAQE6LHHHlNMTIxCQ0Pl7+9v8yiubdu2KTY2VqGhobJYLFq5cuUtt9myZYvuvvtueXt7q379+po3b54jHwEAAJiUXTM3BQUFevPNN/X9998rNzdX999/v8aPH+/wGVI5OTlq2bKlnn76afXq1euW/U+ePKmHH35YQ4YM0cKFC5WUlKRnnnlGISEhiomJcagGAABgLnaFmwkTJmj8+PGKjo5WpUqVNH36dJ07d04ffvihQzvv1q2bunXrVuz+s2fPVkREhKZMmSJJaty4sbZv366pU6cSbgAAgCQ7D0vNnz9f77zzjtavX6+VK1dq9erVWrhwoQoKCkqqPhvJycmKjo62aYuJiVFycvJNt7l69aqysrJsHgAAwLzsCjcpKSnq3r279Xl0dLQsFovOnDnj9MKKkp6eXuieVkFBQcrKytJ//vOfIrdJTEy0WQ8UFhZWGqUCAAAXsSvcXLt2TT4+PjZtFStWVF5enlOLcqbRo0crMzPT+khNTXV1SQAAoATZtebGMAwNHDhQ3t7e1rYrV65oyJAhNqeDl9Sp4MHBwcrIyLBpy8jIkJ+f300XNXt7e9vUCwAAzM2ucDNgwIBCbU899ZTTirmVqKgorV271qZt48aN3IkcAABY2RVu5s6d69SdX7p0SceOHbM+P3nypA4cOKBq1aqpdu3aGj16tE6fPq358+dLkoYMGaKZM2fqxRdf1NNPP61Nmzbp448/1meffebUugAAQNnl0EX8nGXv3r1q3bq1WrduLUlKSEhQ69atNXbsWElSWlqaUlJSrP0jIiL02WefaePGjWrZsqWmTJmi999/n9PAAQCAlcUwDMPVRZSmrKws+fv7KzMzU35+fq4uxy3sDop1dQkAypDISEmrV7u6DJQz9nx/u3TmBgAAwNkINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQquLoAAABKWmysqyuw3+rVrq6g7GLmBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmAq3X3C2sniNbwCwVxn7t27M7ut/vhbJPQ3KA2ZuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqRBuAACAqbhFuJk1a5bCw8Pl4+Oj9u3ba/fu3TftO2/ePFksFpuHj49PKVYLAADcmcvvCr5kyRIlJCRo9uzZat++vaZNm6aYmBgdPXpUgYGBRW7j5+eno0ePWp9bLJbSKhcAyr3f+P9PwC24fObmrbfe0uDBgzVo0CA1adJEs2fPVuXKlfXhhx/edBuLxaLg4GDrIygoqBQrBgAA7syl4SY3N1f79u1TdHS0tc3Dw0PR0dFKTk6+6XaXLl1SnTp1FBYWph49eui77767ad+rV68qKyvL5gEAAMzLpeHmp59+Un5+fqGZl6CgIKWnpxe5TcOGDfXhhx9q1apV+t///V8VFBSoY8eO+ve//11k/8TERPn7+1sfYWFhTv8cAADAfbj8sJS9oqKi1L9/f7Vq1UpdunTRihUrVLNmTf3P//xPkf1Hjx6tzMxM6yM1NbWUKwYAAKXJpQuKa9SoIU9PT2VkZNi0Z2RkKDg4uFjvUbFiRbVu3VrHjh0r8nVvb295e3vfdq0AAKBscOnMjZeXl9q0aaOkpCRrW0FBgZKSkhQVFVWs98jPz9fBgwcVEhJSUmUCAIAyxOWngickJGjAgAFq27atIiMjNW3aNOXk5GjQoEGSpP79+6tWrVpKTEyUJL366qvq0KGD6tevr4sXL+rNN9/Ujz/+qGeeecaVHwMAALgJl4ebvn376ty5cxo7dqzS09PVqlUrrVu3zrrIOCUlRR4e/z/BdOHCBQ0ePFjp6emqWrWq2rRpox07dqhJkyau+ggAAMCNWAzDMFxdRGnKysqSv7+/MjMz5efn5/wdxMY6/z1LGBfkAgD3Exn53x9Wr3ZpHe7Cnu/vMne2FAAAwG8h3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFOp4OoCzGb3bldXAABA+Ua4AQDAncXGuroC+61e7dLdc1gKAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCrdfAADADZXFexVGRrq6guuYuQEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKbiFuFm1qxZCg8Pl4+Pj9q3b6/dt7hy0dKlS9WoUSP5+PioefPmWrt2bSlVCgAA3J3Lw82SJUuUkJCgcePGaf/+/WrZsqViYmJ09uzZIvvv2LFDjz/+uOLi4vT111+rZ8+e6tmzp7799ttSrhwAALgji2EYhisLaN++vdq1a6eZM2dKkgoKChQWFqbhw4frb3/7W6H+ffv2VU5OjtasWWNt69Chg1q1aqXZs2ffcn9ZWVny9/dXZmam/Pz8nPdB/mt3UKzT3xMAgLLAevuF1aud/t72fH+7dOYmNzdX+/btU3R0tLXNw8ND0dHRSk5OLnKb5ORkm/6SFBMTc9P+AACgfHHpjTN/+ukn5efnKygoyKY9KChIR44cKXKb9PT0Ivunp6cX2f/q1au6evWq9XlmZqak6wmwJFwqyCuR9wUAwN1l3fgKLIHv2Bvf28U54GT6u4InJibqlVdeKdQeFhbmgmoAADCx9f/909+/xHaRnZ0t/1u8v0vDTY0aNeTp6amMjAyb9oyMDAUHBxe5TXBwsF39R48erYSEBOvzgoIC/fzzz6pevbosFovdNWdlZSksLEypqaklsmYHjmNs3BPj4r4YG/fEuBTNMAxlZ2crNDT0ln1dGm68vLzUpk0bJSUlqWfPnpKuh4+kpCQNGzasyG2ioqKUlJSkkSNHWts2btyoqKioIvt7e3vL29vbpi0gIOC2a/fz8+M/OjfF2LgnxsV9MTbuiXEp7FYzNje4/LBUQkKCBgwYoLZt2yoyMlLTpk1TTk6OBg0aJEnq37+/atWqpcTEREnS888/ry5dumjKlCl6+OGHtXjxYu3du1dz5sxx5ccAAABuwuXhpm/fvjp37pzGjh2r9PR0tWrVSuvWrbMuGk5JSZGHx/+f1NWxY0ctWrRIL7/8sl566SU1aNBAK1euVLNmzVz1EQAAgBtxebiRpGHDht30MNSWLVsKtfXu3Vu9e/cu4aqK5u3trXHjxhU61AXXY2zcE+Pivhgb98S43D6XX8QPAADAmVx++wUAAABnItwAAABTIdwAAABTIdwAAABTIdwUYdasWQoPD5ePj4/at2+v3bt3/2b/pUuXqlGjRvLx8VHz5s21du3aUqq0/LFnbN577z3de++9qlq1qqpWraro6OhbjiUcY+/fmRsWL14si8VivYgnnMvecbl48aLi4+MVEhIib29v3XXXXfx7VkLsHZtp06apYcOGqlSpksLCwvTCCy/oypUrpVRtGWTAxuLFiw0vLy/jww8/NL777jtj8ODBRkBAgJGRkVFk/6+++srw9PQ0Jk2aZBw6dMh4+eWXjYoVKxoHDx4s5crNz96xeeKJJ4xZs2YZX3/9tXH48GFj4MCBhr+/v/Hvf/+7lCs3N3vH5YaTJ08atWrVMu69916jR48epVNsOWLvuFy9etVo27at0b17d2P79u3GyZMnjS1bthgHDhwo5crNz96xWbhwoeHt7W0sXLjQOHnypLF+/XojJCTEeOGFF0q58rKDcPMrkZGRRnx8vPV5fn6+ERoaaiQmJhbZv0+fPsbDDz9s09a+fXvjueeeK9E6yyN7x+bXrl27Zvj6+hofffRRSZVYLjkyLteuXTM6duxovP/++8aAAQMINyXA3nF59913jbp16xq5ubmlVWK5Ze/YxMfHG/fff79NW0JCgtGpU6cSrbMs47DUL+Tm5mrfvn2Kjo62tnl4eCg6OlrJyclFbpOcnGzTX5JiYmJu2h+OcWRsfu3y5cvKy8tTtWrVSqrMcsfRcXn11VcVGBiouLi40iiz3HFkXD799FNFRUUpPj5eQUFBatasmd544w3l5+eXVtnlgiNj07FjR+3bt8966OrEiRNau3atunfvXio1l0VucYVid/HTTz8pPz/feuuHG4KCgnTkyJEit0lPTy+yf3p6eonVWR45Mja/9te//lWhoaGFwigc58i4bN++XR988IEOHDhQChWWT46My4kTJ7Rp0yY9+eSTWrt2rY4dO6ahQ4cqLy9P48aNK42yywVHxuaJJ57QTz/9pHvuuUeGYejatWsaMmSIXnrppdIouUxi5gblwsSJE7V48WJ98skn8vHxcXU55VZ2drb69eun9957TzVq1HB1OfiFgoICBQYGas6cOWrTpo369u2rv//975o9e7arSyv3tmzZojfeeEPvvPOO9u/frxUrVuizzz7Ta6+95urS3BYzN79Qo0YNeXp6KiMjw6Y9IyNDwcHBRW4THBxsV384xpGxuWHy5MmaOHGivvjiC7Vo0aIkyyx37B2X48eP69SpU4qNjbW2FRQUSJIqVKigo0ePql69eiVbdDngyN+XkJAQVaxYUZ6enta2xo0bKz09Xbm5ufLy8irRmssLR8ZmzJgx6tevn5555hlJUvPmzZWTk6Nnn31Wf//7321uLo3r+I38gpeXl9q0aaOkpCRrW0FBgZKSkhQVFVXkNlFRUTb9JWnjxo037Q/HODI2kjRp0iS99tprWrdundq2bVsapZYr9o5Lo0aNdPDgQR04cMD6ePTRR3XffffpwIEDCgsLK83yTcuRvy+dOnXSsWPHrGFTkr7//nuFhIQQbJzIkbG5fPlyoQBzI4Qa3B6yaK5e0exuFi9ebHh7exvz5s0zDh06ZDz77LNGQECAkZ6ebhiGYfTr18/429/+Zu3/1VdfGRUqVDAmT55sHD582Bg3bhyngpcQe8dm4sSJhpeXl7Fs2TIjLS3N+sjOznbVRzAle8fl1zhbqmTYOy4pKSmGr6+vMWzYMOPo0aPGmjVrjMDAQOP111931UcwLXvHZty4cYavr6/xz3/+0zhx4oSxYcMGo169ekafPn1c9RHcHuGmCDNmzDBq165teHl5GZGRkcbOnTutr3Xp0sUYMGCATf+PP/7YuOuuuwwvLy+jadOmxmeffVbKFZcf9oxNnTp1DEmFHuPGjSv9wk3O3r8zv0S4KTn2jsuOHTuM9u3bG97e3kbdunWNCRMmGNeuXSvlqssHe8YmLy/PGD9+vFGvXj3Dx8fHCAsLM4YOHWpcuHCh9AsvIyyGwZwWAAAwD9bcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAHALhmHo2WefVbVq1WSxWLhrOACHEW4AuIV169Zp3rx5WrNmjdLS0tSsWTOlp6dr+PDhqlu3rry9vRUWFqbY2NhC93MDgF/iruAA3MLx48cVEhKijh07SpJOnTqlTp06KSAgQG+++aaaN2+uvLw8rV+/XvHx8Tpy5IiLKwbgrpi5AeByAwcO1PDhw5WSkiKLxaLw8HANHTpUFotFu3fv1mOPPaa77rpLTZs2VUJCgnbu3GndNiUlRT169NAdd9whPz8/9enTRxkZGdbXx48fr1atWmnBggUKDw+Xv7+//vjHPyo7O9vap6CgQJMmTVL9+vXl7e2t2rVra8KECaX6OwDgPIQbAC739ttv69VXX9Wdd96ptLQ07dq1S+vWrVN8fLyqVKlSqH9AQICk66GkR48e+vnnn7V161Zt3LhRJ06cUN++fW36Hz9+XCtXrtSaNWu0Zs0abd26VRMnTrS+Pnr0aE2cOFFjxozRoUOHtGjRIgUFBZXoZwZQcjgsBcDl/P395evrK09PTwUHB2v37t0yDEONGjX6ze2SkpJ08OBBnTx5UmFhYZKk+fPnq2nTptqzZ4/atWsn6XoImjdvnnx9fSVJ/fr1U1JSkiZMmKDs7Gy9/fbbmjlzpgYMGCBJqlevnu65554S/MQAShIzNwDcjmEYxep3+PBhhYWFWYONJDVp0kQBAQE6fPiwtS08PNwabCQpJCREZ8+etb7H1atX9cADDzipegCuRrgB4HYaNGggi8XitEXDFStWtHlusVhUUFAgSapUqZJT9gHAfRBuALidatWqKSYmRrNmzVJOTk6h1y9evChJaty4sVJTU5Wammp97dChQ7p48aKaNGlSrH01aNBAlSpV4vRywEQINwDc0qxZs5Sfn6/IyEgtX75cP/zwgw4fPqzp06crKipKkhQdHa3mzZvrySef1P79+7V79271799fXbp0Udu2bYu1Hx8fH/31r3/Viy++qPnz5+v48ePauXOnPvjgg5L8eABKEAuKAbilunXrav/+/ZowYYJGjRqltLQ01axZU23atNG7774r6frhpVWrVmn48OHq3LmzPDw89NBDD2nGjBl27WvMmDGqUKGCxo4dqzNnzigkJERDhgwpiY8FoBRYjOKu3AMAACgDOCwFAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABM5f8AQJPi9sXQ89wAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fConc1\n"
          ]
        },
        {
          "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": [
            "fAsym\n"
          ]
        },
        {
          "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": [
            "fM3Long\n"
          ]
        },
        {
          "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": [
            "fM3Trans\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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7BSkC5yABAHCdadOmjeLi4iRJTZo00YIFC5SYmKg77rjD7gTu4OBgvfjiixo+fLhef/11SdK8efPUvXt3MzQ1bdpUW7Zs0bp16+y2kZubq3feeUe+vr6SpISEBO3atUuHDx9WUFCQJOmdd97RTTfdpG3btunmm2+WdHHv1rJly1S7dm1J0qBBg5SYmKiXXnqp/L4gRWAPEgAA15k2bdrYfQ4ICFBGRoYk6csvv1S3bt1Uv3591a5dW4MGDdKpU6d07tw5SdKePXsUERFht35kZGShbTRs2NAMR5fWCwoKMsORJLVs2VJeXl7as2eP2RYcHGyGI2ttFYmABADAdaZ69ep2n202mwoKCnTkyBHdfffdatOmjT766COlpKRo4cKFkkp+onXNmjXLtLaKxiE2AAAgSUpJSVFBQYFmzZolJ6eL+1A++OADuz4tWrRQcnKyXds333zzl2O3aNFCR48e1dGjR829SD/88IPOnDmjli1bltEMyg57kAAAgCSpcePGysvL02uvvaZDhw7p3Xff1aJFi+z6jB49WuvWrdOrr76q/fv3a8GCBYXOPypKVFSUWrdurYEDB2rHjh3aunWrBg8erM6dO6tDhw7lNaVSIyABAABJUmhoqGbPnq2XX35ZrVq10nvvvafp06fb9bnlllv05ptvat68eQoNDdUXX3yhSZMm/eXYNptNn3zyierUqaPbbrtNUVFRatSokVasWFFe07kqNsMwDEcXURVlZWXJ09NTmZmZ8vDwcHQ5AMpIRd8EuSxwI2XHOH/+vA4fPmx3nx9UDlf63hT37zd7kAAAACwISAAAABYEJAAAAItKEZAWLlyo4OBgubm5KSIiQlu3br1i/5UrV6p58+Zyc3NT69at9dlnn5nL8vLy9Mwzz5jPkQkMDNTgwYN17NgxuzFOnz6tgQMHysPDQ15eXoqJiSn0rBkAAHB9cnhAWrFihWJjYxUXF6cdO3YoNDRU0dHRl71r5pYtW9S/f3/FxMRo586d6tOnj/r06aPvv/9eknTu3Dnt2LFDkydP1o4dO7Rq1Srt27dPf/vb3+zGGThwoHbv3q2EhAStXbtWmzdv1qOPPlru8wUAXFu41qnyKYvvicOvYouIiNDNN9+sBQsWSLr4DJagoCCNGjVKzz77bKH+/fr1U3Z2ttauXWu23XLLLQoLCyt0r4ZLtm3bpvDwcP30009q0KCB9uzZo5YtW2rbtm3mvRfWrVunHj166Oeff1ZgYOBf1s1VbMC1iavYUFx5eXk6cOCAAgMD5enp6ehy8CenTp1SRkaGmjZtKmdnZ7tlxf377dA7aefm5iolJUUTJkww25ycnBQVFaWkpKQi10lKSlJsbKxdW3R0tFavXn3Z7WRmZspms8nLy8scw8vLy+7GVFFRUXJyclJycrLuueeeQmPk5OQoJyfH/JyVlVWcKQIArlHVqlVTjRo19Ouvv6p69ermnafhOIZh6Ny5c8rIyJCXl1ehcFQSDg1IJ0+eVH5+vvz8/Oza/fz8tHfv3iLXSU9PL7J/enp6kf3Pnz+vZ555Rv379zeTYnp6uurVq2fXr1q1aqpbt+5lx5k+fbqmTZtWrHkBAK59NptNAQEBOnz4sH766SdHl4M/8fLykr+//1WNcU0/iy0vL08PPPCADMPQG2+8cVVjTZgwwW7PVVZWlt0TiQEA1x8XFxc1adKkxA9yRfmpXr36Ve05usShAcnHx0fOzs46ceKEXfuJEycum/z8/f2L1f9SOPrpp5+0YcMGu+OM/v7+hU4Cv3Dhgk6fPn3Z7bq6usrV1bXYcwMAXB+cnJy4k/Y1yKEHTF1cXNS+fXslJiaabQUFBUpMTFRkZGSR60RGRtr1l6SEhAS7/pfC0f79+/Xll1/K29u70BhnzpxRSkqK2bZhwwYVFBQoIiKiLKYGAACqMIcfYouNjdWQIUPUoUMHhYeHa+7cucrOztbQoUMlSYMHD1b9+vXNh+WNGTNGnTt31qxZs9SzZ08tX75c27dv1+LFiyVdDEf33XefduzYobVr1yo/P988r6hu3bpycXFRixYt1L17dw0bNkyLFi1SXl6eRo4cqQcffLBYV7ABAIBrm8MDUr9+/fTrr79qypQpSk9PV1hYmNatW2eeiJ2WlmZ3ZUDHjh0VHx+vSZMmaeLEiWrSpIlWr16tVq1aSZJ++eUXffrpp5KksLAwu21t3LhRXbp0kSS99957GjlypLp16yYnJyf17dtX8+fPL/8JAwCASs/h90GqqrgPEnBt4j5IwLWtuH+/uWkDAACABQEJAADAgoAEAABgQUACAACwICABAABYEJAAAAAsCEgAAAAWBCQAAAALAhIAAIAFAQkAAMCCgAQAAGBBQAIAALAgIAEAAFgQkAAAACwISAAAABYEJAAAAAsCEgAAgAUBCQAAwIKABAAAYEFAAgAAsCAgAQAAWBCQAAAALAhIAAAAFgQkAAAACwISAACABQEJAADAgoAEAABgQUACAACwICABAABYEJAAAAAsCEgAAAAWBCQAAAALAhIAAIAFAQkAAMCCgAQAAGBBQAIAALAgIAEAAFgQkAAAACwISAAAABYEJAAAAAsCEgAAgAUBCQAAwKKaowsAgMpo8tZeji6h+C6VumaNQ8sAriXsQQIAALAgIAEAAFgQkAAAACwISAAAABYEJAAAAAsCEgAAgAUBCQAAwIKABAAAYEFAAgAAsCAgAQAAWBCQAAAALAhIAAAAFgQkAAAACwISAACABQEJAADAgoAEAABgQUACAACwICABAABYEJAAAAAsCEgAAAAWBCQAAAALAhIAAIAFAQkAAMCCgAQAAGBBQAIAALBweEBauHChgoOD5ebmpoiICG3duvWK/VeuXKnmzZvLzc1NrVu31meffWa3fNWqVbrzzjvl7e0tm82m1NTUQmN06dJFNpvN7jV8+PCynBYAAKjCHBqQVqxYodjYWMXFxWnHjh0KDQ1VdHS0MjIyiuy/ZcsW9e/fXzExMdq5c6f69OmjPn366Pvvvzf7ZGdnq1OnTnr55ZevuO1hw4bp+PHj5mvmzJllOjcAAFB1OTQgzZ49W8OGDdPQoUPVsmVLLVq0SDVq1NCSJUuK7D9v3jx1795dTz31lFq0aKEXXnhB7dq104IFC8w+gwYN0pQpUxQVFXXFbdeoUUP+/v7my8PDo0znBgAAqi6HBaTc3FylpKTYBRknJydFRUUpKSmpyHWSkpIKBZ/o6OjL9r+S9957Tz4+PmrVqpUmTJigc+fOXbF/Tk6OsrKy7F4AAODaVM1RGz558qTy8/Pl5+dn1+7n56e9e/cWuU56enqR/dPT00u07QEDBqhhw4YKDAzUd999p2eeeUb79u3TqlWrLrvO9OnTNW3atBJtBwAAVE0OC0iO9Oijj5rvW7durYCAAHXr1k0HDx7UjTfeWOQ6EyZMUGxsrPk5KytLQUFB5V4rAACoeA4LSD4+PnJ2dtaJEyfs2k+cOCF/f/8i1/H39y9R/+KKiIiQJB04cOCyAcnV1VWurq5XtR0AAFA1OOwcJBcXF7Vv316JiYlmW0FBgRITExUZGVnkOpGRkXb9JSkhIeGy/Yvr0q0AAgICrmocAABwbXDoIbbY2FgNGTJEHTp0UHh4uObOnavs7GwNHTpUkjR48GDVr19f06dPlySNGTNGnTt31qxZs9SzZ08tX75c27dv1+LFi80xT58+rbS0NB07dkyStG/fPkkyr1Y7ePCg4uPj1aNHD3l7e+u7777Tk08+qdtuu01t2rSp4K8AAACojBwakPr166dff/1VU6ZMUXp6usLCwrRu3TrzROy0tDQ5Of3fTq6OHTsqPj5ekyZN0sSJE9WkSROtXr1arVq1Mvt8+umnZsCSpAcffFCSFBcXp6lTp8rFxUVffvmlGcaCgoLUt29fTZo0qYJmDQAAKjubYRiGo4uoirKysuTp6anMzEzuoQRcQ3r1uvjv5K29HFtICYSH/++bNWscWgdQFRT377fDHzUCAABQ2RCQAAAALAhIAAAAFgQkAAAACwISAACABQEJAADAgoAEAABgQUACAACwICABAABYEJAAAAAsCEgAAAAWBCQAAACLUgWkjRs3lnUdAAAAlUapAlL37t1144036sUXX9TRo0fLuiYAAACHKlVA+uWXXzRy5Eh9+OGHatSokaKjo/XBBx8oNze3rOsDAACocKUKSD4+PnryySeVmpqq5ORkNW3aVE888YQCAwM1evRoffvtt2VdJwAAQIW56pO027VrpwkTJmjkyJH6/ffftWTJErVv31633nqrdu/eXRY1AgAAVKhSB6S8vDx9+OGH6tGjhxo2bKj169drwYIFOnHihA4cOKCGDRvq/vvvL8taAQAAKkS10qw0atQovf/++zIMQ4MGDdLMmTPVqlUrc3nNmjX16quvKjAwsMwKBQAAqCilCkg//PCDXnvtNd17771ydXUtso+Pjw+3AwAAAFVSqQ6xxcXF6f777y8Uji5cuKDNmzdLkqpVq6bOnTtffYUAAAAVrFQBqWvXrjp9+nSh9szMTHXt2vWqiwIAAHCkUgUkwzBks9kKtZ86dUo1a9a86qIAAAAcqUTnIN17772SJJvNpocfftjuEFt+fr6+++47dezYsWwrBAAAqGAlCkienp6SLu5Bql27ttzd3c1lLi4uuuWWWzRs2LCyrRAAAKCClSggLV26VJIUHBys8ePHczgNAABck0p1mX9cXFxZ1wEAAFBpFDsgtWvXTomJiapTp47atm1b5Enal+zYsaNMigMAAHCEYgek3r17mydl9+nTp7zqAQAAcLhiB6Q/H1bjEBsAALiWlfphtQAAANeqYu9BqlOnzhXPO/qzou6yDQAAUFUUOyDNnTu3HMsAAACoPIodkIYMGVKedQAAAFQaxQ5IWVlZ8vDwMN9fyaV+AAAAVVGJzkE6fvy46tWrJy8vryLPR7r0ENv8/PwyLRIAAKAiFTsgbdiwQXXr1pUkbdy4sdwKAgAAcLRiB6TOnTsX+R4AAOBaU6pnsUnSb7/9pv/3//6f9uzZI0lq2bKlhg4dau5lAgAAqKpKdaPIzZs3Kzg4WPPnz9dvv/2m3377TfPnz1dISIg2b95c1jUCAABUqFLtQRoxYoT69eunN954Q87OzpKk/Px8PfHEExoxYoR27dpVpkUCAABUpFLtQTpw4IDGjRtnhiNJcnZ2VmxsrA4cOFBmxQEAADhCqQJSu3btzHOP/mzPnj0KDQ296qIAAAAcqdiH2L777jvz/ejRozVmzBgdOHBAt9xyiyTpm2++0cKFCzVjxoyyrxIAAKACFTsghYWFyWazyTAMs+3pp58u1G/AgAHq169f2VQHAADgAMUOSIcPHy7POgAAACqNYgekhg0blmcdAAAAlUapbxQpST/88IPS0tKUm5tr1/63v/3tqooCAABwpFIFpEOHDumee+7Rrl277M5LuvQAWx5WCwAAqrJSXeY/ZswYhYSEKCMjQzVq1NDu3bu1efNmdejQQZs2bSrjEgEAACpWqfYgJSUlacOGDfLx8ZGTk5OcnJzUqVMnTZ8+XaNHj9bOnTvLuk4AAIAKU6o9SPn5+apdu7YkycfHR8eOHZN08UTuffv2lV11AAAADlCqPUitWrXSt99+q5CQEEVERGjmzJlycXHR4sWL1ahRo7KuEQAAoEKVKiBNmjRJ2dnZkqTnn39ed999t2699VZ5e3trxYoVZVogAABARStVQIqOjjbfN27cWHv37tXp06dVp04d80o2AACAquqq7oMkSUePHpUkBQUFXXUxAAAAlUGpTtK+cOGCJk+eLE9PTwUHBys4OFienp6aNGmS8vLyyrpGAACAClWqPUijRo3SqlWrNHPmTEVGRkq6eOn/1KlTderUKb3xxhtlWiQAAEBFKlVAio+P1/Lly3XXXXeZbW3atFFQUJD69+9PQAIAAFVaqQ6xubq6Kjg4uFB7SEiIXFxcrrYmAAAAhypVQBo5cqReeOEF5eTkmG05OTl66aWXNHLkyDIrDgAAwBGKfYjt3nvvtfv85Zdf6oYbblBoaKgk6dtvv1Vubq66detWthUCAABUsGIHJE9PT7vPffv2tfvMZf4AAOBaUeyAtHTp0vKsAwAAoNK4qhtF/vrrr+bDaZs1ayZfX98yKQoAAMCRSnWSdnZ2th555BEFBATotttu02233abAwEDFxMTo3LlzZV0jAABAhSpVQIqNjdVXX32lNWvW6MyZMzpz5ow++eQTffXVVxo3blxZ1wgAAFChSnWI7aOPPtKHH36oLl26mG09evSQu7u7HnjgAW4UCQAAqrRS7UE6d+6c/Pz8CrXXq1evxIfYFi5cqODgYLm5uSkiIkJbt269Yv+VK1eqefPmcnNzU+vWrfXZZ5/ZLV+1apXuvPNOeXt7y2azKTU1tdAY58+f14gRI+Tt7a1atWqpb9++OnHiRInqBgAA165SBaTIyEjFxcXp/PnzZtsff/yhadOmmc9mK44VK1YoNjZWcXFx2rFjh0JDQxUdHa2MjIwi+2/ZskX9+/dXTEyMdu7cqT59+qhPnz76/vvvzT7Z2dnq1KmTXn755ctu98knn9SaNWu0cuVKffXVVzp27Fih+zwBAIDrl80wDKOkK+3atUvdu3dXTk6O3Y0i3dzctH79et10003FGiciIkI333yzFixYIEkqKChQUFCQRo0apWeffbZQ/379+ik7O1tr164122655RaFhYVp0aJFdn2PHDmikJAQ7dy5U2FhYWZ7ZmamfH19FR8fr/vuu0+StHfvXrVo0UJJSUm65ZZbilV7VlaWPD09lZmZKQ8Pj2KtA6Dy69Xr4r+Tt/ZybCElEB7+v2/WrHFoHUBVUNy/36Xag9S6dWvt379f06dPV1hYmMLCwjRjxgzt37+/2OEoNzdXKSkpioqK+r9inJwUFRWlpKSkItdJSkqy6y9J0dHRl+1flJSUFOXl5dmN07x5czVo0KBE4wAAgGtXiU/SzsvLU/PmzbV27VoNGzas1Bs+efKk8vPzC53L5Ofnp7179xa5Tnp6epH909PTi73d9PR0ubi4yMvLq0Tj5OTk2D17Lisrq9jbBAAAVUuJ9yBVr17d7tyj68X06dPl6elpvni0CgAA165SHWIbMWKEXn75ZV24cKHUG/bx8ZGzs3Ohq8dOnDghf3//Itfx9/cvUf/LjZGbm6szZ86UaJwJEyYoMzPTfB09erTY2wQAAFVLqQLStm3btGrVKjVo0EDR0dG699577V7F4eLiovbt2ysxMdFsKygoUGJi4mWvhIuMjLTrL0kJCQklunKuffv2ql69ut04+/btU1pa2hXHcXV1lYeHh90LAABcm0p1o0gvLy/17dv3qjceGxurIUOGqEOHDgoPD9fcuXOVnZ2toUOHSpIGDx6s+vXra/r06ZKkMWPGqHPnzpo1a5Z69uyp5cuXa/v27Vq8eLE55unTp5WWlqZjx45JkvmsOH9/f/n7+8vT01MxMTGKjY1V3bp15eHhoVGjRikyMrLYV7ABAIBrW4kCUkFBgV555RX9+OOPys3N1e23366pU6fK3d29VBvv16+ffv31V02ZMkXp6ekKCwvTunXrzBOx09LS5OT0fzu5OnbsqPj4eE2aNEkTJ05UkyZNtHr1arVq1crs8+mnn5oBS5IefPBBSVJcXJymTp0qSZozZ46cnJzUt29f5eTkKDo6Wq+//nqp5gAAAK49JboP0gsvvKCpU6cqKipK7u7uWr9+vfr3768lS5aUZ42VEvdBAq5N3AcJuLaVy32Q3nnnHb3++utav369Vq9erTVr1ui9995TQUHBVRcMAABQWZQoIKWlpalHjx7m56ioKNlsNvN8HwAAgGtBiQLShQsX5ObmZtdWvXp15eXllWlRAAAAjlSik7QNw9DDDz8sV1dXs+38+fMaPny4atasabatWrWq7CoEAACoYCUKSEOGDCnU9tBDD5VZMQAAAJVBiQLS0qVLy6sOAACASqNUd9IGAAC4lhGQAAAALAhIAAAAFgQkAAAACwISAACABQEJAADAgoAEAABgQUACAACwICABAABYEJAAAAAsCEgAAAAWBCQAAAALAhIAAIAFAQkAAMCCgAQAAGBBQAIAALAgIAEAAFgQkAAAACwISAAAABYEJAAAAAsCEgAAgAUBCQAAwIKABAAAYEFAAgAAsCAgAQAAWBCQAAAALAhIAAAAFgQkAAAACwISAACABQEJAADAgoAEAABgQUACAACwICABAABYEJAAAAAsCEgAAAAWBCQAAAALAhIAAIAFAQkAAMCCgAQAAGBBQAIAALAgIAEAAFgQkAAAACwISAAAABYEJAAAAAsCEgAAgEU1RxcAAKXRq5ejKwBwLWMPEgAAgAUBCQAAwIKABAAAYEFAAgAAsCAgAQAAWBCQAAAALAhIAAAAFgQkAAAACwISAACABQEJAADAgoAEAABgQUACAACwICABAABYEJAAAAAsCEgAAAAWBCQAAACLShGQFi5cqODgYLm5uSkiIkJbt269Yv+VK1eqefPmcnNzU+vWrfXZZ5/ZLTcMQ1OmTFFAQIDc3d0VFRWl/fv32/UJDg6WzWaze82YMaPM5wYAAKoehwekFStWKDY2VnFxcdqxY4dCQ0MVHR2tjIyMIvtv2bJF/fv3V0xMjHbu3Kk+ffqoT58++v77780+M2fO1Pz587Vo0SIlJyerZs2aio6O1vnz5+3Gev7553X8+HHzNWrUqHKdKwAAqBocHpBmz56tYcOGaejQoWrZsqUWLVqkGjVqaMmSJUX2nzdvnrp3766nnnpKLVq00AsvvKB27dppwYIFki7uPZo7d64mTZqk3r17q02bNnrnnXd07NgxrV692m6s2rVry9/f33zVrFmzvKcLAACqAIcGpNzcXKWkpCgqKspsc3JyUlRUlJKSkopcJykpya6/JEVHR5v9Dx8+rPT0dLs+np6eioiIKDTmjBkz5O3trbZt2+qVV17RhQsXympqAACgCqvmyI2fPHlS+fn58vPzs2v38/PT3r17i1wnPT29yP7p6enm8kttl+sjSaNHj1a7du1Ut25dbdmyRRMmTNDx48c1e/bsIrebk5OjnJwc83NWVlYxZwkAAKoahwYkR4qNjTXft2nTRi4uLnrsscc0ffp0ubq6Fuo/ffp0TZs2rSJLBAAADuLQQ2w+Pj5ydnbWiRMn7NpPnDghf3//Itfx9/e/Yv9L/5ZkTEmKiIjQhQsXdOTIkSKXT5gwQZmZmebr6NGjV5wbAACouhwakFxcXNS+fXslJiaabQUFBUpMTFRkZGSR60RGRtr1l6SEhASzf0hIiPz9/e36ZGVlKTk5+bJjSlJqaqqcnJxUr169Ipe7urrKw8PD7gUAAK5NDj/EFhsbqyFDhqhDhw4KDw/X3LlzlZ2draFDh0qSBg8erPr162v69OmSpDFjxqhz586aNWuWevbsqeXLl2v79u1avHixJMlms2ns2LF68cUX1aRJE4WEhGjy5MkKDAxUnz59JF080Ts5OVldu3ZV7dq1lZSUpCeffFIPPfSQ6tSp45CvA3BN69WrzIecfOXbpQHAVXF4QOrXr59+/fVXTZkyRenp6QoLC9O6devMk6zT0tLk5PR/O7o6duyo+Ph4TZo0SRMnTlSTJk20evVqtWrVyuzz9NNPKzs7W48++qjOnDmjTp06ad26dXJzc5N0cW/Q8uXLNXXqVOXk5CgkJERPPvmk3XlJAADg+mUzDMNwdBFVUVZWljw9PZWZmcnhNuCvlMMepL+44f51JTz8f9+sWePQOoCqoLh/vx1+o0gAAIDKhoAEAABgQUACAACwICABAABYEJAAAAAsCEgAAAAWBCQAAAALAhIAAIAFAQkAAMCCgAQAAGBBQAIAALAgIAEAAFgQkAAAACwISAAAABYEJAAAAAsCEgAAgAUBCQAAwIKABAAAYEFAAgAAsCAgAQAAWBCQAAAALAhIAAAAFgQkAAAACwISAACABQEJAADAgoAEAABgQUACAACwICABAABYEJAAAAAsCEgAAAAWBCQAAAALAhIAAIAFAQkAAMCCgAQAAGBBQAIAALCo5ugCAABlpFcvR1dQcmvWOLoCoEjsQQIAALAgIAEAAFgQkAAAACwISAAAABYEJAAAAAsCEgAAgAUBCQAAwIKABAAAYEFAAgAAsCAgAQAAWBCQAAAALAhIAAAAFgQkAAAACwISAACABQEJAADAgoAEAABgQUACAACwICABAABYEJAAAAAsqjm6AADA1dm6tfzGDg8vv7GByow9SAAAABYEJAAAAAsOsQFVTa9ejq4AAK557EECAACwICABAABYEJAAAAAsCEgAAAAWBCQAAAALrmIDADhOVbwqc80aR1eACsAeJAAAAAsCEgAAgAUBCQAAwKJSBKSFCxcqODhYbm5uioiI0Na/ePLiypUr1bx5c7m5ual169b67LPP7JYbhqEpU6YoICBA7u7uioqK0v79++36nD59WgMHDpSHh4e8vLwUExOj33//vcznBgAAqh6HB6QVK1YoNjZWcXFx2rFjh0JDQxUdHa2MjIwi+2/ZskX9+/dXTEyMdu7cqT59+qhPnz76/vvvzT4zZ87U/PnztWjRIiUnJ6tmzZqKjo7W+fPnzT4DBw7U7t27lZCQoLVr12rz5s169NFHy32+AACg8rMZhmE4soCIiAjdfPPNWrBggSSpoKBAQUFBGjVqlJ599tlC/fv166fs7GytXbvWbLvlllsUFhamRYsWyTAMBQYGaty4cRo/frwkKTMzU35+flq2bJkefPBB7dmzRy1bttS2bdvUoUMHSdK6devUo0cP/fzzzwoMDPzLurOysuTp6anMzEx5eHiUxZcCjlAVr6CpQv5iZzCqgPBwR1eAMsGVd6bi/v126GX+ubm5SklJ0YQJE8w2JycnRUVFKSkpqch1kpKSFBsba9cWHR2t1atXS5IOHz6s9PR0RUVFmcs9PT0VERGhpKQkPfjgg0pKSpKXl5cZjiQpKipKTk5OSk5O1j333FOGswSAqqu8Qi7BC5WdQwPSyZMnlZ+fLz8/P7t2Pz8/7d27t8h10tPTi+yfnp5uLr/UdqU+9erVs1terVo11a1b1+xjlZOTo5ycHPNzZmampItJFJIeeMDRFVzztqc4ugKg7Gz4xtEVlE6H9o6uoJS6d3d0BSX3wQflMuylv9t/dQCNG0UW0/Tp0zVt2rRC7UFBQQ6oBgDgEOsdXcB1xNOzXIc/e/asPK+wDYcGJB8fHzk7O+vEiRN27SdOnJC/v3+R6/j7+1+x/6V/T5w4oYCAALs+YWFhZh/rSeAXLlzQ6dOnL7vdCRMm2B3aKygo0OnTp+Xt7S2bzVaM2V69rKwsBQUF6ejRo9f8eU/Xy1yvl3lK189cmee153qZ6/UyT8MwdPbs2b8839ihAcnFxUXt27dXYmKi+vTpI+li8EhMTNTIkSOLXCcyMlKJiYkaO3as2ZaQkKDIyEhJUkhIiPz9/ZWYmGgGoqysLCUnJ+vxxx83xzhz5oxSUlLUvv3F/aUbNmxQQUGBIiIiityuq6urXF1d7dq8vLxKOfOr4+HhcU3/8P7Z9TLX62We0vUzV+Z57ble5no9zPNKe44ucfghttjYWA0ZMkQdOnRQeHi45s6dq+zsbA0dOlSSNHjwYNWvX1/Tp0+XJI0ZM0adO3fWrFmz1LNnTy1fvlzbt2/X4sWLJUk2m01jx47Viy++qCZNmigkJESTJ09WYGCgGcJatGih7t27a9iwYVq0aJHy8vI0cuRIPfjgg8W6gg0AAFzbHB6Q+vXrp19//VVTpkxRenq6wsLCtG7dOvMk67S0NDk5/d/tmjp27Kj4+HhNmjRJEydOVJMmTbR69Wq1atXK7PP0008rOztbjz76qM6cOaNOnTpp3bp1cnNzM/u89957GjlypLp16yYnJyf17dtX8+fPr7iJAwCAystAlXH+/HkjLi7OOH/+vKNLKXfXy1yvl3kaxvUzV+Z57ble5nq9zLO4HH6jSAAAgMrG4Y8aAQAAqGwISAAAABYEJAAAAAsCEgAAgAUBqRI6cuSIYmJiFBISInd3d914442Ki4tTbm6uXb/vvvtOt956q9zc3BQUFKSZM2cWGmvlypVq3ry53Nzc1Lp1a3322WcVNY1ieemll9SxY0fVqFHjsjfetNlshV7Lly+367Np0ya1a9dOrq6uaty4sZYtW1b+xZdQceaalpamnj17qkaNGqpXr56eeuopXbhwwa5PVZjrnwUHBxf6/s2YMcOuT3F+lquChQsXKjg4WG5uboqIiNDW8nrSawWaOnVqoe9f8+bNzeXnz5/XiBEj5O3trVq1aqlv376FnnZQGW3evFm9evVSYGCgbDab+cDzSwzD0JQpUxQQECB3d3dFRUVp//79dn1Onz6tgQMHysPDQ15eXoqJidHvv/9egbMonr+a68MPP1zoe9zd8uy2qjLXskRAqoT27t2rgoIC/fOf/9Tu3bs1Z84cLVq0SBMnTjT7ZGVl6c4771TDhg2VkpKiV155RVOnTjVvmClJW7ZsUf/+/RUTE6OdO3eqT58+6tOnj77//ntHTKtIubm5uv/++827nF/O0qVLdfz4cfN16aafknT48GH17NlTXbt2VWpqqsaOHau///3vWr++cj006a/mmp+fr549eyo3N1dbtmzR22+/rWXLlmnKlClmn6oyV6vnn3/e7vs3atQoc1lxfparghUrVig2NlZxcXHasWOHQkNDFR0dXeixRlXRTTfdZPf9++9//2sue/LJJ7VmzRqtXLlSX331lY4dO6Z7773XgdUWT3Z2tkJDQ7Vw4cIil8+cOVPz58/XokWLlJycrJo1ayo6Olrnz583+wwcOFC7d+9WQkKC1q5dq82bN+vRRx+tqCkU21/NVZK6d+9u9z1+//337ZZXlbmWKQffZgDFNHPmTCMkJMT8/Prrrxt16tQxcnJyzLZnnnnGaNasmfn5gQceMHr27Gk3TkREhPHYY4+Vf8EltHTpUsPT07PIZZKMjz/++LLrPv3008ZNN91k19avXz8jOjq6DCssO5eb62effWY4OTkZ6enpZtsbb7xheHh4mN/nqjZXwzCMhg0bGnPmzLns8uL8LFcF4eHhxogRI8zP+fn5RmBgoDF9+nQHVnX14uLijNDQ0CKXnTlzxqhevbqxcuVKs23Pnj2GJCMpKamCKrx61t8xBQUFhr+/v/HKK6+YbWfOnDFcXV2N999/3zAMw/jhhx8MSca2bdvMPp9//rlhs9mMX375pcJqL6mifp8OGTLE6N2792XXqapzvVrsQaoiMjMzVbduXfNzUlKSbrvtNrm4uJht0dHR2rdvn3777TezT1RUlN040dHRSkpKqpiiy9CIESPk4+Oj8PBwLVmyRMafbt91rcwzKSlJrVu3Nu8iL12cR1ZWlnbv3m32qYpznTFjhry9vdW2bVu98sordocNi/OzXNnl5uYqJSXF7nvj5OSkqKioSv+9KY79+/crMDBQjRo10sCBA5WWliZJSklJUV5ent28mzdvrgYNGlTpeR8+fFjp6el28/L09FRERIQ5r6SkJHl5ealDhw5mn6ioKDk5OSk5ObnCa75amzZtUr169dSsWTM9/vjjOnXqlLnsWptrcTn8USP4awcOHNBrr72mV1991WxLT09XSEiIXb9Lf1jT09NVp04dpaen2/2xvdQnPT29/IsuQ88//7xuv/121ahRQ1988YWeeOIJ/f777xo9erQkXXaeWVlZ+uOPP+Tu7u6IskvscvO4tOxKfSrzXEePHq127dqpbt262rJliyZMmKDjx49r9uzZkor3s1zZnTx5Uvn5+UV+b/bu3eugqspGRESEli1bpmbNmun48eOaNm2abr31Vn3//fdKT0+Xi4tLoXPqquLvmT+7VPuVfn+mp6erXr16dsurVaumunXrVrm5d+/eXffee69CQkJ08OBBTZw4UXfddZeSkpLk7Ox8Tc21JAhIFejZZ5/Vyy+/fMU+e/bssTsB8pdfflH37t11//33a9iwYeVdYpkozTyvZPLkyeb7tm3bKjs7W6+88ooZkByprOdaVZRk3rGxsWZbmzZt5OLioscee0zTp0+Xq6treZeKq3TXXXeZ79u0aaOIiAg1bNhQH3zwQaUM5Ci5Bx980HzfunVrtWnTRjfeeKM2bdqkbt26ObAyxyIgVaBx48bp4YcfvmKfRo0ame+PHTumrl27qmPHjoVOWPX39y90pcilz/7+/lfsc2l5eSnpPEsqIiJCL7zwgnJycuTq6nrZeXp4eJT7L/CynKu/v3+hq56K+z2tiLn+2dXMOyIiQhcuXNCRI0fUrFmzYv0sV3Y+Pj5ydnZ2yH9vFc3Ly0tNmzbVgQMHdMcddyg3N1dnzpyx24tU1ed9qfYTJ04oICDAbD9x4oTCwsLMPtYT8C9cuKDTp09X6blLF//b9fHx0YEDB9StW7dreq5XQkCqQL6+vvL19S1W319++UVdu3ZV+/bttXTpUjk52Z8uFhkZqeeee055eXmqXr26JCkhIUHNmjUzD0lERkYqMTFRY8eONddLSEhQZGRk2UzoMkoyz9JITU1VnTp1zL0PkZGRhW5fUBHzlMp2rpGRkXrppZeUkZFh7s5OSEiQh4eHWrZsafZx1Fz/7GrmnZqaKicnJ3OOxflZruxcXFzUvn17JSYmmldYFhQUKDExUSNHjnRscWXs999/18GDBzVo0CC1b99e1atXV2Jiovr27StJ2rdvn9LS0ir8Z7IshYSEyN/fX4mJiWYgysrKUnJysnkVamRkpM6cOaOUlBS1b99ekrRhwwYVFBQoIiLCUaWXiZ9//lmnTp0yw+G1PNcrcvRZ4ijs559/Nho3bmx069bN+Pnnn43jx4+br0vOnDlj+Pn5GYMGDTK+//57Y/ny5UaNGjWMf/7zn2afr7/+2qhWrZrx6quvGnv27DHi4uKM6tWrG7t27XLEtIr0008/GTt37jSmTZtm1KpVy9i5c6exc+dO4+zZs4ZhGMann35qvPnmm8auXbuM/fv3G6+//rpRo0YNY8qUKeYYhw4dMmrUqGE89dRTxp49e4yFCxcazs7Oxrp16xw1rSL91VwvXLhgtGrVyrjzzjuN1NRUY926dYavr68xYcIEc4yqMtdLtmzZYsyZM8dITU01Dh48aPzrX/8yfH19jcGDB5t9ivOzXBUsX77ccHV1NZYtW2b88MMPxqOPPmp4eXnZXZVYFY0bN87YtGmTcfjwYePrr782oqKiDB8fHyMjI8MwDMMYPny40aBBA2PDhg3G9u3bjcjISCMyMtLBVf+1s2fPmv8NSjJmz55t7Ny50/jpp58MwzCMGTNmGF5eXsYnn3xifPfdd0bv3r2NkJAQ448//jDH6N69u9G2bVsjOTnZ+O9//2s0adLE6N+/v6OmdFlXmuvZs2eN8ePHG0lJScbhw4eNL7/80mjXrp3RpEkT4/z58+YYVWWuZYmAVAktXbrUkFTk68++/fZbo1OnToarq6tRv359Y8aMGYXG+uCDD4ymTZsaLi4uxk033WT8+9//rqhpFMuQIUOKnOfGjRsNw7h4KWlYWJhRq1Yto2bNmkZoaKixaNEiIz8/326cjRs3GmFhYYaLi4vRqFEjY+nSpRU/mb/wV3M1DMM4cuSIcddddxnu7u6Gj4+PMW7cOCMvL89unKow10tSUlKMiIgIw9PT03BzczNatGhh/OMf/7D7xWsYxftZrgpee+01o0GDBoaLi4sRHh5ufPPNN44u6ar169fPCAgIMFxcXIz69esb/fr1Mw4cOGAu/+OPP4wnnnjCqFOnjlGjRg3jnnvusfufucpq48aNRf73OGTIEMMwLl7qP3nyZMPPz89wdXU1unXrZuzbt89ujFOnThn9+/c3atWqZXh4eBhDhw41/4enMrnSXM+dO2fceeedhq+vr1G9enWjYcOGxrBhwwoF+6oy17JkM4w/XS8NAAAA7qQNAABgRUACAACwICABAABYEJAAAAAsCEgAAAAWBCQAAAALAhIAAIAFAQlApWYYhh599FHVrVtXNptNqampji4JwHWAgASgUlu3bp2WLVumtWvX6vjx43r11Vdls9k0fPjwQn1HjBghm81m9yDdN954Q23atJGHh4c8PDwUGRmpzz//XJK0adMm2Wy2K742bdpUQTMFUJnwsFoAldrBgwcVEBCgjh07SpKqVaumoKAgLV++XHPmzJG7u7sk6fz584qPj1eDBg3s1r/hhhs0Y8YMNWnSRIZh6O2331bv3r21c+dOdezYUcePHzf7jhkzRllZWVq6dKnZVrduXfN9bm6uXFxcynO6ACoJ9iABqLQefvhhjRo1SmlpabLZbAoODpYktWvXTkFBQVq1apXZd9WqVWrQoIHatm1rN0avXr3Uo0cPNWnSRE2bNtVLL72kWrVq6ZtvvpGLi4v8/f3Nl7u7u1xdXc3PixYtUnh4uN566y2FhITIzc1N0sW9Wp06dZKXl5e8vb1199136+DBg+Y2jxw5IpvNplWrVqlr166qUaOGQkNDlZSUZPb56aef1KtXL9WpU0c1a9bUTTfdpM8++6wcv5oASoKABKDSmjdvnp5//nndcMMNOn78uLZt22Yue+SRR+z29CxZskRDhw694nj5+flavny5srOzFRkZWawaDhw4oI8++kirVq0yz3/Kzs5WbGystm/frsTERDk5Oemee+5RQUGB3brPPfecxo8fr9TUVDVt2lT9+/fXhQsXJF08HJiTk6PNmzdr165devnll1WrVq1i1QSg/HGIDUCl5enpqdq1a8vZ2Vn+/v52yx566CFNmDBBP/30kyTp66+/1vLly4s8Z2jXrl2KjIzU+fPnVatWLX388cdq2bJlsWrIzc3VO++8I19fX7Otb9++dn2WLFkiX19f/fDDD2rVqpXZPn78ePXs2VOSNG3aNN100006cOCAmjdvrrS0NPXt21etW7eWJDVq1KhY9QCoGOxBAlAl+fr6qmfPnlq2bJmWLl2qnj17ysfHp8i+zZo1U2pqqpKTk/X4449ryJAh+uGHH4q1nYYNG9qFI0nav3+/+vfvr0aNGsnDw8M89JeWlmbXr02bNub7gIAASVJGRoYkafTo0XrxxRf1P//zP4qLi9N3331XrHoAVAwCEoAq65FHHtGyZcv09ttv65FHHrlsPxcXFzVu3Fjt27fX9OnTFRoaqnnz5hVrGzVr1izU1qtXL50+fVpvvvmmkpOTlZycLOni3qY/q169uvneZrNJknkY7u9//7sOHTqkQYMGadeuXerQoYNee+21YtUEoPwRkABUWd27d1dubq7y8vIUHR1d7PUKCgqUk5NTqm2eOnVK+/bt06RJk9StWze1aNFCv/32W6nGCgoK0vDhw7Vq1SqNGzdOb775ZqnGAVD2OAcJQJXl7OysPXv2mO+LMmHCBN11111q0KCBzp49q/j4eG3atEnr168v1Tbr1Kkjb29vLV68WAEBAUpLS9Ozzz5b4nHGjh2ru+66S02bNtVvv/2mjRs3qkWLFqWqCUDZIyABqNI8PDyuuDwjI0ODBw/W8ePH5enpqTZt2mj9+vW64447SrU9JycnLV++XKNHj1arVq3UrFkzzZ8/X126dCnROPn5+RoxYoR+/vlneXh4qHv37pozZ06pagJQ9myGYRiOLgIAAKAy4RwkAAAACwISAACABQEJAADAgoAEAABgQUACAACwICABAABYEJAAAAAsCEgAAAAWBCQAAAALAhIAAIAFAQkAAMCCgAQAAGDx/wE9Wg3gUVYJvgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fAlpha\n"
          ]
        },
        {
          "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": [
            "fDist\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": [
        "### Train, validation, and test sets"
      ],
      "metadata": {
        "id": "avtECLCTlszN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "renamedLabel = df.iloc[:,-1]\n",
        "renamedLabel"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tjMG9pZYuRIM",
        "outputId": "eebc470d-0f31-4a07-e1d2-b13ebb01e6e8"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        1\n",
              "1        1\n",
              "2        1\n",
              "3        1\n",
              "4        1\n",
              "        ..\n",
              "19015    0\n",
              "19016    0\n",
              "19017    0\n",
              "19018    0\n",
              "19019    0\n",
              "Name: class, Length: 19020, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "featuresData"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "SrGn1kKzCqkv",
        "outputId": "6211bf43-7874-4f28-881b-b3d5327f8393"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        fLength    fWidth   fSize   fConc  fConc1     fAsym   fM3Long  \\\n",
              "0       28.7967   16.0021  2.6449  0.3918  0.1982   27.7004   22.0110   \n",
              "1       31.6036   11.7235  2.5185  0.5303  0.3773   26.2722   23.8238   \n",
              "2      162.0520  136.0310  4.0612  0.0374  0.0187  116.7410  -64.8580   \n",
              "3       23.8172    9.5728  2.3385  0.6147  0.3922   27.2107   -6.4633   \n",
              "4       75.1362   30.9205  3.1611  0.3168  0.1832   -5.5277   28.5525   \n",
              "...         ...       ...     ...     ...     ...       ...       ...   \n",
              "19015   21.3846   10.9170  2.6161  0.5857  0.3934   15.2618   11.5245   \n",
              "19016   28.9452    6.7020  2.2672  0.5351  0.2784   37.0816   13.1853   \n",
              "19017   75.4455   47.5305  3.4483  0.1417  0.0549   -9.3561   41.0562   \n",
              "19018  120.5135   76.9018  3.9939  0.0944  0.0683    5.8043  -93.5224   \n",
              "19019  187.1814   53.0014  3.2093  0.2876  0.1539 -167.3125 -168.4558   \n",
              "\n",
              "       fM3Trans   fAlpha     fDist  \n",
              "0       -8.2027  40.0920   81.8828  \n",
              "1       -9.9574   6.3609  205.2610  \n",
              "2      -45.2160  76.9600  256.7880  \n",
              "3       -7.1513  10.4490  116.7370  \n",
              "4       21.8393   4.6480  356.4620  \n",
              "...         ...      ...       ...  \n",
              "19015    2.8766   2.4229  106.8258  \n",
              "19016   -2.9632  86.7975  247.4560  \n",
              "19017   -9.4662  30.2987  256.5166  \n",
              "19018  -63.8389  84.6874  408.3166  \n",
              "19019   31.4755  52.7310  272.3174  \n",
              "\n",
              "[19020 rows x 10 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fLength</th>\n",
              "      <th>fWidth</th>\n",
              "      <th>fSize</th>\n",
              "      <th>fConc</th>\n",
              "      <th>fConc1</th>\n",
              "      <th>fAsym</th>\n",
              "      <th>fM3Long</th>\n",
              "      <th>fM3Trans</th>\n",
              "      <th>fAlpha</th>\n",
              "      <th>fDist</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28.7967</td>\n",
              "      <td>16.0021</td>\n",
              "      <td>2.6449</td>\n",
              "      <td>0.3918</td>\n",
              "      <td>0.1982</td>\n",
              "      <td>27.7004</td>\n",
              "      <td>22.0110</td>\n",
              "      <td>-8.2027</td>\n",
              "      <td>40.0920</td>\n",
              "      <td>81.8828</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31.6036</td>\n",
              "      <td>11.7235</td>\n",
              "      <td>2.5185</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.3773</td>\n",
              "      <td>26.2722</td>\n",
              "      <td>23.8238</td>\n",
              "      <td>-9.9574</td>\n",
              "      <td>6.3609</td>\n",
              "      <td>205.2610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>162.0520</td>\n",
              "      <td>136.0310</td>\n",
              "      <td>4.0612</td>\n",
              "      <td>0.0374</td>\n",
              "      <td>0.0187</td>\n",
              "      <td>116.7410</td>\n",
              "      <td>-64.8580</td>\n",
              "      <td>-45.2160</td>\n",
              "      <td>76.9600</td>\n",
              "      <td>256.7880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23.8172</td>\n",
              "      <td>9.5728</td>\n",
              "      <td>2.3385</td>\n",
              "      <td>0.6147</td>\n",
              "      <td>0.3922</td>\n",
              "      <td>27.2107</td>\n",
              "      <td>-6.4633</td>\n",
              "      <td>-7.1513</td>\n",
              "      <td>10.4490</td>\n",
              "      <td>116.7370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>75.1362</td>\n",
              "      <td>30.9205</td>\n",
              "      <td>3.1611</td>\n",
              "      <td>0.3168</td>\n",
              "      <td>0.1832</td>\n",
              "      <td>-5.5277</td>\n",
              "      <td>28.5525</td>\n",
              "      <td>21.8393</td>\n",
              "      <td>4.6480</td>\n",
              "      <td>356.4620</td>\n",
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              "      <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>19015</th>\n",
              "      <td>21.3846</td>\n",
              "      <td>10.9170</td>\n",
              "      <td>2.6161</td>\n",
              "      <td>0.5857</td>\n",
              "      <td>0.3934</td>\n",
              "      <td>15.2618</td>\n",
              "      <td>11.5245</td>\n",
              "      <td>2.8766</td>\n",
              "      <td>2.4229</td>\n",
              "      <td>106.8258</td>\n",
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              "    <tr>\n",
              "      <th>19016</th>\n",
              "      <td>28.9452</td>\n",
              "      <td>6.7020</td>\n",
              "      <td>2.2672</td>\n",
              "      <td>0.5351</td>\n",
              "      <td>0.2784</td>\n",
              "      <td>37.0816</td>\n",
              "      <td>13.1853</td>\n",
              "      <td>-2.9632</td>\n",
              "      <td>86.7975</td>\n",
              "      <td>247.4560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19017</th>\n",
              "      <td>75.4455</td>\n",
              "      <td>47.5305</td>\n",
              "      <td>3.4483</td>\n",
              "      <td>0.1417</td>\n",
              "      <td>0.0549</td>\n",
              "      <td>-9.3561</td>\n",
              "      <td>41.0562</td>\n",
              "      <td>-9.4662</td>\n",
              "      <td>30.2987</td>\n",
              "      <td>256.5166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19018</th>\n",
              "      <td>120.5135</td>\n",
              "      <td>76.9018</td>\n",
              "      <td>3.9939</td>\n",
              "      <td>0.0944</td>\n",
              "      <td>0.0683</td>\n",
              "      <td>5.8043</td>\n",
              "      <td>-93.5224</td>\n",
              "      <td>-63.8389</td>\n",
              "      <td>84.6874</td>\n",
              "      <td>408.3166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19019</th>\n",
              "      <td>187.1814</td>\n",
              "      <td>53.0014</td>\n",
              "      <td>3.2093</td>\n",
              "      <td>0.2876</td>\n",
              "      <td>0.1539</td>\n",
              "      <td>-167.3125</td>\n",
              "      <td>-168.4558</td>\n",
              "      <td>31.4755</td>\n",
              "      <td>52.7310</td>\n",
              "      <td>272.3174</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19020 rows × 10 columns</p>\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      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-02223583-ee3e-4c5f-ac14-20f98303dc06 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-02223583-ee3e-4c5f-ac14-20f98303dc06');\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-1f355700-9ca4-4fdb-866b-b97838af9d8e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1f355700-9ca4-4fdb-866b-b97838af9d8e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    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-1f355700-9ca4-4fdb-866b-b97838af9d8e 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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(featuresData, renamedLabel, test_size=0.2, random_state=42)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=1)"
      ],
      "metadata": {
        "id": "ooA-iv-ulnNL"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "_v59mF2jq1sg",
        "outputId": "7c91c7ac-ec8e-4a26-dd3a-a1ecfde5009f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        fLength   fWidth   fSize   fConc  fConc1     fAsym   fM3Long  \\\n",
              "616     25.0822  10.9047  2.5775  0.5212  0.2712   21.7270  -17.8951   \n",
              "10725   13.1598  10.4453  2.1414  0.7870  0.4874   13.1831    5.6563   \n",
              "16645   18.4268  10.4534  2.1584  0.5625  0.3090   23.2750    9.8267   \n",
              "500     17.0875  11.2666  2.2553  0.5667  0.3083    0.7686   -5.2321   \n",
              "17002  214.9680  52.8938  3.9427  0.0973  0.0508 -113.1400 -243.2760   \n",
              "...         ...      ...     ...     ...     ...       ...       ...   \n",
              "3027    45.1020  20.0974  3.1647  0.1930  0.0975   34.5486   31.4351   \n",
              "2732    27.2726  12.6129  2.7288  0.3978  0.2512  -10.4679  -17.5545   \n",
              "2100    97.0464  12.2902  2.7214  0.2811  0.1491 -122.3730   84.5137   \n",
              "8144    19.9527   6.5626  2.2292  0.8201  0.5162   17.6483   16.5275   \n",
              "10145   21.5493  13.4295  2.5999  0.5176  0.3178   12.8776   -8.3443   \n",
              "\n",
              "       fM3Trans   fAlpha    fDist  \n",
              "616     -7.3121   6.2190  180.699  \n",
              "10725  -10.5963  34.5856  179.096  \n",
              "16645   -3.2842  73.1980  172.494  \n",
              "500      4.9075  56.4005  142.859  \n",
              "17002   19.1703  15.6930  216.928  \n",
              "...         ...      ...      ...  \n",
              "3027    -8.0756   1.2698  171.526  \n",
              "2732    -9.9008   3.7890  185.431  \n",
              "2100     3.9472  30.2970  231.445  \n",
              "8144    -5.1195  56.6840  189.301  \n",
              "10145   11.3953  15.6820  238.771  \n",
              "\n",
              "[11412 rows x 10 columns]"
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              "      <td>-3.2842</td>\n",
              "      <td>73.1980</td>\n",
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              "      <th>500</th>\n",
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              "      <td>0.5667</td>\n",
              "      <td>0.3083</td>\n",
              "      <td>0.7686</td>\n",
              "      <td>-5.2321</td>\n",
              "      <td>4.9075</td>\n",
              "      <td>56.4005</td>\n",
              "      <td>142.859</td>\n",
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              "      <th>17002</th>\n",
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              "      <td>-243.2760</td>\n",
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              "      <td>15.6930</td>\n",
              "      <td>216.928</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>3027</th>\n",
              "      <td>45.1020</td>\n",
              "      <td>20.0974</td>\n",
              "      <td>3.1647</td>\n",
              "      <td>0.1930</td>\n",
              "      <td>0.0975</td>\n",
              "      <td>34.5486</td>\n",
              "      <td>31.4351</td>\n",
              "      <td>-8.0756</td>\n",
              "      <td>1.2698</td>\n",
              "      <td>171.526</td>\n",
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              "    <tr>\n",
              "      <th>2732</th>\n",
              "      <td>27.2726</td>\n",
              "      <td>12.6129</td>\n",
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              "      <td>0.3978</td>\n",
              "      <td>0.2512</td>\n",
              "      <td>-10.4679</td>\n",
              "      <td>-17.5545</td>\n",
              "      <td>-9.9008</td>\n",
              "      <td>3.7890</td>\n",
              "      <td>185.431</td>\n",
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              "    <tr>\n",
              "      <th>2100</th>\n",
              "      <td>97.0464</td>\n",
              "      <td>12.2902</td>\n",
              "      <td>2.7214</td>\n",
              "      <td>0.2811</td>\n",
              "      <td>0.1491</td>\n",
              "      <td>-122.3730</td>\n",
              "      <td>84.5137</td>\n",
              "      <td>3.9472</td>\n",
              "      <td>30.2970</td>\n",
              "      <td>231.445</td>\n",
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              "    <tr>\n",
              "      <th>8144</th>\n",
              "      <td>19.9527</td>\n",
              "      <td>6.5626</td>\n",
              "      <td>2.2292</td>\n",
              "      <td>0.8201</td>\n",
              "      <td>0.5162</td>\n",
              "      <td>17.6483</td>\n",
              "      <td>16.5275</td>\n",
              "      <td>-5.1195</td>\n",
              "      <td>56.6840</td>\n",
              "      <td>189.301</td>\n",
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              "    <tr>\n",
              "      <th>10145</th>\n",
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              "      <td>0.5176</td>\n",
              "      <td>0.3178</td>\n",
              "      <td>12.8776</td>\n",
              "      <td>-8.3443</td>\n",
              "      <td>11.3953</td>\n",
              "      <td>15.6820</td>\n",
              "      <td>238.771</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>11412 rows × 10 columns</p>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  @keyframes spin {\n",
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              "\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_val.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HRU6Ov8nq8mH",
        "outputId": "13bdfe69-8579-43ce-87df-f9f48f6dea44"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3804, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_wLhxY2IrDFv",
        "outputId": "7c58b663-4b15-4920-9a58-3b37c7df6720"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3804, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "help(scaler.fit)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SGParyk1oyCM",
        "outputId": "2bfb9ed2-3e27-44df-9051-c92f7f521a06"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help on method fit in module sklearn.preprocessing._data:\n",
            "\n",
            "fit(X, y=None, sample_weight=None) method of sklearn.preprocessing._data.StandardScaler instance\n",
            "    Compute the mean and std to be used for later scaling.\n",
            "    \n",
            "    Parameters\n",
            "    ----------\n",
            "    X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
            "        The data used to compute the mean and standard deviation\n",
            "        used for later scaling along the features axis.\n",
            "    \n",
            "    y : None\n",
            "        Ignored.\n",
            "    \n",
            "    sample_weight : array-like of shape (n_samples,), default=None\n",
            "        Individual weights for each sample.\n",
            "    \n",
            "        .. versionadded:: 0.24\n",
            "           parameter *sample_weight* support to StandardScaler.\n",
            "    \n",
            "    Returns\n",
            "    -------\n",
            "    self : object\n",
            "        Fitted scaler.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "from sklearn.metrics import classification_report as cr\n",
        "classifier = tree.DecisionTreeClassifier()"
      ],
      "metadata": {
        "id": "WKjNJ-vvzKR1"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Not scaled/ normalized"
      ],
      "metadata": {
        "id": "BoRHL8m9zQRx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = classifier.fit(X_train,y_train)"
      ],
      "metadata": {
        "id": "eVl0qqULzPUa"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = classifier.predict(X_test)"
      ],
      "metadata": {
        "id": "QBhZws2Pz5a3"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(cr(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qVquno_a0ayv",
        "outputId": "0685a88a-b5e6-491b-99fb-da8133afaee7"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.73      0.72      1344\n",
            "           1       0.85      0.84      0.84      2460\n",
            "\n",
            "    accuracy                           0.80      3804\n",
            "   macro avg       0.78      0.78      0.78      3804\n",
            "weighted avg       0.80      0.80      0.80      3804\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. scaled but not oversampled"
      ],
      "metadata": {
        "id": "5Jc1veCs1Y_G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "def scaleDataset(features, labels):\n",
        "  X = features.values\n",
        "  y = labels.values\n",
        "  scaler = StandardScaler()\n",
        "  X = scaler.fit_transform (X)\n",
        "  return X, y"
      ],
      "metadata": {
        "id": "LyHd1may7-oJ"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_trainNormalized, y_trainNormalized = scaleDataset(X_train, y_train)\n",
        "X_validNormalized, y_validNormalized = scaleDataset(X_val, y_val)\n",
        "X_testNormalized, y_testNormalized = scaleDataset(X_test, y_test)"
      ],
      "metadata": {
        "id": "AKLIcg4x8W-i"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = classifier.fit(X_trainNormalized, y_trainNormalized)\n",
        "y_pred1 = classifier.predict(X_testNormalized)\n",
        "print(cr(y_testNormalized,y_pred1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HB7OGXDo1TOY",
        "outputId": "e9978f02-d28e-4424-a684-06fe493c7bdc"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.72      0.71      1344\n",
            "           1       0.84      0.84      0.84      2460\n",
            "\n",
            "    accuracy                           0.80      3804\n",
            "   macro avg       0.78      0.78      0.78      3804\n",
            "weighted avg       0.80      0.80      0.80      3804\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(y_trainNormalized == 1).sum(), (y_trainNormalized == 0).sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HzystecO9Bkv",
        "outputId": "736da2ab-d0a3-4146-9913-c8faeab5fb5f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7418, 3994)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. scaled and oversampled"
      ],
      "metadata": {
        "id": "WgCUDr-t3ZS7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "def scaleDataset(features, labels,oversample=False):\n",
        "  X = features.values\n",
        "  y = labels.values\n",
        "  scaler = StandardScaler()\n",
        "  X = scaler.fit_transform (X)\n",
        "  if oversample:\n",
        "    ros = RandomOverSampler()\n",
        "    X, y = ros.fit_resample(X, y)\n",
        "  return X, y"
      ],
      "metadata": {
        "id": "xCRT6IlXmsCl"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_trainOversampledScaled, y_trainOversampledScaled = scaleDataset(X_train, y_train, oversample=True)\n",
        "X_validOversampledScaled, y_validOversampledScaled = scaleDataset(X_val, y_val, oversample=False)\n",
        "X_testOversampledScaled, y_testOversampledScaled = scaleDataset (X_test, y_test, oversample=False)"
      ],
      "metadata": {
        "id": "K2Vv2PqopBzC"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(y_trainOversampledScaled == 1).sum(), (y_trainOversampledScaled == 0).sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hHvoK6m_u09v",
        "outputId": "8229cf26-ded4-4e02-dc8f-eb7263a381dd"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7418, 7418)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = classifier.fit(X_trainOversampledScaled, y_trainOversampledScaled)\n",
        "y_pred1 = classifier.predict(X_testOversampledScaled)\n",
        "print(cr(y_testOversampledScaled,y_pred1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jJYT748fyNy4",
        "outputId": "c490ea15-4411-4d30-f8a7-bd9d3fb829f5"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.72      0.71      0.72      1344\n",
            "           1       0.84      0.85      0.85      2460\n",
            "\n",
            "    accuracy                           0.80      3804\n",
            "   macro avg       0.78      0.78      0.78      3804\n",
            "weighted avg       0.80      0.80      0.80      3804\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***SpamBase Dataset***"
      ],
      "metadata": {
        "id": "TZsptc9nR_X-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from ucimlrepo import fetch_ucirepo\n",
        "\n",
        "# fetch dataset\n",
        "spam_base = fetch_ucirepo(id=94)\n",
        "\n",
        "# data (as pandas dataframes)\n",
        "featuresData = spam_base.data.features\n",
        "labelData = spam_base.data.targets\n",
        "\n",
        "# metadata\n",
        "print(spam_base.metadata)\n",
        "\n",
        "# variable information\n",
        "print(spam_base.variables)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZYj5OJsJO-Ux",
        "outputId": "1ef109bc-a3d4-48a0-f3e5-15eb89afc200"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'uci_id': 94, 'name': 'Spambase', 'repository_url': 'https://archive.ics.uci.edu/dataset/94/spambase', 'data_url': 'https://archive.ics.uci.edu/static/public/94/data.csv', 'abstract': 'Classifying Email as Spam or Non-Spam', 'area': 'Computer Science', 'tasks': ['Classification'], 'characteristics': ['Multivariate'], 'num_instances': 4601, 'num_features': 57, 'feature_types': ['Integer', 'Real'], 'demographics': [], 'target_col': ['Class'], 'index_col': None, 'has_missing_values': 'no', 'missing_values_symbol': None, 'year_of_dataset_creation': 1999, 'last_updated': 'Mon Aug 28 2023', 'dataset_doi': '10.24432/C53G6X', 'creators': ['Mark Hopkins', 'Erik Reeber', 'George Forman', 'Jaap Suermondt'], 'intro_paper': None, 'additional_info': {'summary': 'The \"spam\" concept is diverse: advertisements for products/web sites, make money fast schemes, chain letters, pornography...\\n\\nThe classification task for this dataset is to determine whether a given email is spam or not.\\n\\t\\nOur collection of spam e-mails came from our postmaster and individuals who had filed spam.  Our collection of non-spam e-mails came from filed work and personal e-mails, and hence the word \\'george\\' and the area code \\'650\\' are indicators of non-spam.  These are useful when constructing a personalized spam filter.  One would either have to blind such non-spam indicators or get a very wide collection of non-spam to generate a general purpose spam filter.\\n\\nFor background on spam: Cranor, Lorrie F., LaMacchia, Brian A.  Spam!, Communications of the ACM, 41(8):74-83, 1998.\\n\\nTypical performance is around ~7% misclassification error. False positives (marking good mail as spam) are very undesirable.If we insist on zero false positives in the training/testing set, 20-25% of the spam passed through the filter. See also Hewlett-Packard Internal-only Technical Report. External version forthcoming. ', 'purpose': None, 'funded_by': None, 'instances_represent': 'Emails', 'recommended_data_splits': None, 'sensitive_data': None, 'preprocessing_description': None, 'variable_info': 'The last column of \\'spambase.data\\' denotes whether the e-mail was considered spam (1) or not (0), i.e. unsolicited commercial e-mail.  Most of the attributes indicate whether a particular word or character was frequently occuring in the e-mail.  The run-length attributes (55-57) measure the length of sequences of consecutive capital letters.  For the statistical measures of each attribute, see the end of this file.  Here are the definitions of the attributes:\\r\\n\\r\\n48 continuous real [0,100] attributes of type word_freq_WORD \\r\\n= percentage of words in the e-mail that match WORD, i.e. 100 * (number of times the WORD appears in the e-mail) / total number of words in e-mail.  A \"word\" in this case is any string of alphanumeric characters bounded by non-alphanumeric characters or end-of-string.\\r\\n\\r\\n6 continuous real [0,100] attributes of type char_freq_CHAR] \\r\\n= percentage of characters in the e-mail that match CHAR, i.e. 100 * (number of CHAR occurences) / total characters in e-mail\\r\\n\\r\\n1 continuous real [1,...] attribute of type capital_run_length_average \\r\\n= average length of uninterrupted sequences of capital letters\\r\\n\\r\\n1 continuous integer [1,...] attribute of type capital_run_length_longest \\r\\n= length of longest uninterrupted sequence of capital letters\\r\\n\\r\\n1 continuous integer [1,...] attribute of type capital_run_length_total \\r\\n= sum of length of uninterrupted sequences of capital letters \\r\\n= total number of capital letters in the e-mail\\r\\n\\r\\n1 nominal {0,1} class attribute of type spam\\r\\n= denotes whether the e-mail was considered spam (1) or not (0), i.e. unsolicited commercial e-mail.  \\r\\n', 'citation': None}}\n",
            "                          name     role        type demographic  \\\n",
            "0               word_freq_make  Feature  Continuous        None   \n",
            "1            word_freq_address  Feature  Continuous        None   \n",
            "2                word_freq_all  Feature  Continuous        None   \n",
            "3                 word_freq_3d  Feature  Continuous        None   \n",
            "4                word_freq_our  Feature  Continuous        None   \n",
            "5               word_freq_over  Feature  Continuous        None   \n",
            "6             word_freq_remove  Feature  Continuous        None   \n",
            "7           word_freq_internet  Feature  Continuous        None   \n",
            "8              word_freq_order  Feature  Continuous        None   \n",
            "9               word_freq_mail  Feature  Continuous        None   \n",
            "10           word_freq_receive  Feature  Continuous        None   \n",
            "11              word_freq_will  Feature  Continuous        None   \n",
            "12            word_freq_people  Feature  Continuous        None   \n",
            "13            word_freq_report  Feature  Continuous        None   \n",
            "14         word_freq_addresses  Feature  Continuous        None   \n",
            "15              word_freq_free  Feature  Continuous        None   \n",
            "16          word_freq_business  Feature  Continuous        None   \n",
            "17             word_freq_email  Feature  Continuous        None   \n",
            "18               word_freq_you  Feature  Continuous        None   \n",
            "19            word_freq_credit  Feature  Continuous        None   \n",
            "20              word_freq_your  Feature  Continuous        None   \n",
            "21              word_freq_font  Feature  Continuous        None   \n",
            "22               word_freq_000  Feature  Continuous        None   \n",
            "23             word_freq_money  Feature  Continuous        None   \n",
            "24                word_freq_hp  Feature  Continuous        None   \n",
            "25               word_freq_hpl  Feature  Continuous        None   \n",
            "26            word_freq_george  Feature  Continuous        None   \n",
            "27               word_freq_650  Feature  Continuous        None   \n",
            "28               word_freq_lab  Feature  Continuous        None   \n",
            "29              word_freq_labs  Feature  Continuous        None   \n",
            "30            word_freq_telnet  Feature  Continuous        None   \n",
            "31               word_freq_857  Feature  Continuous        None   \n",
            "32              word_freq_data  Feature  Continuous        None   \n",
            "33               word_freq_415  Feature  Continuous        None   \n",
            "34                word_freq_85  Feature  Continuous        None   \n",
            "35        word_freq_technology  Feature  Continuous        None   \n",
            "36              word_freq_1999  Feature  Continuous        None   \n",
            "37             word_freq_parts  Feature  Continuous        None   \n",
            "38                word_freq_pm  Feature  Continuous        None   \n",
            "39            word_freq_direct  Feature  Continuous        None   \n",
            "40                word_freq_cs  Feature  Continuous        None   \n",
            "41           word_freq_meeting  Feature  Continuous        None   \n",
            "42          word_freq_original  Feature  Continuous        None   \n",
            "43           word_freq_project  Feature  Continuous        None   \n",
            "44                word_freq_re  Feature  Continuous        None   \n",
            "45               word_freq_edu  Feature  Continuous        None   \n",
            "46             word_freq_table  Feature  Continuous        None   \n",
            "47        word_freq_conference  Feature  Continuous        None   \n",
            "48                 char_freq_;  Feature  Continuous        None   \n",
            "49                 char_freq_(  Feature  Continuous        None   \n",
            "50                 char_freq_[  Feature  Continuous        None   \n",
            "51                 char_freq_!  Feature  Continuous        None   \n",
            "52                 char_freq_$  Feature  Continuous        None   \n",
            "53                 char_freq_#  Feature  Continuous        None   \n",
            "54  capital_run_length_average  Feature  Continuous        None   \n",
            "55  capital_run_length_longest  Feature  Continuous        None   \n",
            "56    capital_run_length_total  Feature  Continuous        None   \n",
            "57                       Class   Target      Binary        None   \n",
            "\n",
            "                 description units missing_values  \n",
            "0                       None  None             no  \n",
            "1                       None  None             no  \n",
            "2                       None  None             no  \n",
            "3                       None  None             no  \n",
            "4                       None  None             no  \n",
            "5                       None  None             no  \n",
            "6                       None  None             no  \n",
            "7                       None  None             no  \n",
            "8                       None  None             no  \n",
            "9                       None  None             no  \n",
            "10                      None  None             no  \n",
            "11                      None  None             no  \n",
            "12                      None  None             no  \n",
            "13                      None  None             no  \n",
            "14                      None  None             no  \n",
            "15                      None  None             no  \n",
            "16                      None  None             no  \n",
            "17                      None  None             no  \n",
            "18                      None  None             no  \n",
            "19                      None  None             no  \n",
            "20                      None  None             no  \n",
            "21                      None  None             no  \n",
            "22                      None  None             no  \n",
            "23                      None  None             no  \n",
            "24                      None  None             no  \n",
            "25                      None  None             no  \n",
            "26                      None  None             no  \n",
            "27                      None  None             no  \n",
            "28                      None  None             no  \n",
            "29                      None  None             no  \n",
            "30                      None  None             no  \n",
            "31                      None  None             no  \n",
            "32                      None  None             no  \n",
            "33                      None  None             no  \n",
            "34                      None  None             no  \n",
            "35                      None  None             no  \n",
            "36                      None  None             no  \n",
            "37                      None  None             no  \n",
            "38                      None  None             no  \n",
            "39                      None  None             no  \n",
            "40                      None  None             no  \n",
            "41                      None  None             no  \n",
            "42                      None  None             no  \n",
            "43                      None  None             no  \n",
            "44                      None  None             no  \n",
            "45                      None  None             no  \n",
            "46                      None  None             no  \n",
            "47                      None  None             no  \n",
            "48                      None  None             no  \n",
            "49                      None  None             no  \n",
            "50                      None  None             no  \n",
            "51                      None  None             no  \n",
            "52                      None  None             no  \n",
            "53                      None  None             no  \n",
            "54                      None  None             no  \n",
            "55                      None  None             no  \n",
            "56                      None  None             no  \n",
            "57  spam (1) or not spam (0)  None             no  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://archive.ics.uci.edu/static/public/94/data.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dk0f_fO4SJ_z",
        "outputId": "37279e48-c5f1-4561-e359-c58757e9523b"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-02-03 16:54:48--  https://archive.ics.uci.edu/static/public/94/data.csv\n",
            "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
            "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified\n",
            "Saving to: ‘data.csv.1’\n",
            "\n",
            "data.csv.1              [  <=>               ] 687.36K  2.77MB/s    in 0.2s    \n",
            "\n",
            "2024-02-03 16:54:48 (2.77 MB/s) - ‘data.csv.1’ saved [703858]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(featuresData.shape)\n",
        "print(labelData.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lu75dXFeSdjk",
        "outputId": "8578384c-3c57-483d-e4d2-b95c41532d9b"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4601, 57)\n",
            "(4601, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.concat([featuresData,labelData],axis=1)"
      ],
      "metadata": {
        "id": "HNAHwoHNSjT5"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "xqkgVshGSlf0",
        "outputId": "f7abb049-b73b-4051-e37a-54022756570a"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   word_freq_make  word_freq_address  word_freq_all  word_freq_3d  \\\n",
              "0            0.00               0.64           0.64           0.0   \n",
              "1            0.21               0.28           0.50           0.0   \n",
              "2            0.06               0.00           0.71           0.0   \n",
              "3            0.00               0.00           0.00           0.0   \n",
              "4            0.00               0.00           0.00           0.0   \n",
              "\n",
              "   word_freq_our  word_freq_over  word_freq_remove  word_freq_internet  \\\n",
              "0           0.32            0.00              0.00                0.00   \n",
              "1           0.14            0.28              0.21                0.07   \n",
              "2           1.23            0.19              0.19                0.12   \n",
              "3           0.63            0.00              0.31                0.63   \n",
              "4           0.63            0.00              0.31                0.63   \n",
              "\n",
              "   word_freq_order  word_freq_mail  ...  char_freq_;  char_freq_(  \\\n",
              "0             0.00            0.00  ...         0.00        0.000   \n",
              "1             0.00            0.94  ...         0.00        0.132   \n",
              "2             0.64            0.25  ...         0.01        0.143   \n",
              "3             0.31            0.63  ...         0.00        0.137   \n",
              "4             0.31            0.63  ...         0.00        0.135   \n",
              "\n",
              "   char_freq_[  char_freq_!  char_freq_$  char_freq_#  \\\n",
              "0          0.0        0.778        0.000        0.000   \n",
              "1          0.0        0.372        0.180        0.048   \n",
              "2          0.0        0.276        0.184        0.010   \n",
              "3          0.0        0.137        0.000        0.000   \n",
              "4          0.0        0.135        0.000        0.000   \n",
              "\n",
              "   capital_run_length_average  capital_run_length_longest  \\\n",
              "0                       3.756                          61   \n",
              "1                       5.114                         101   \n",
              "2                       9.821                         485   \n",
              "3                       3.537                          40   \n",
              "4                       3.537                          40   \n",
              "\n",
              "   capital_run_length_total  Class  \n",
              "0                       278      1  \n",
              "1                      1028      1  \n",
              "2                      2259      1  \n",
              "3                       191      1  \n",
              "4                       191      1  \n",
              "\n",
              "[5 rows x 58 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-32d56dcc-f087-419e-9f90-27bf7b914e05\" 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>word_freq_make</th>\n",
              "      <th>word_freq_address</th>\n",
              "      <th>word_freq_all</th>\n",
              "      <th>word_freq_3d</th>\n",
              "      <th>word_freq_our</th>\n",
              "      <th>word_freq_over</th>\n",
              "      <th>word_freq_remove</th>\n",
              "      <th>word_freq_internet</th>\n",
              "      <th>word_freq_order</th>\n",
              "      <th>word_freq_mail</th>\n",
              "      <th>...</th>\n",
              "      <th>char_freq_;</th>\n",
              "      <th>char_freq_(</th>\n",
              "      <th>char_freq_[</th>\n",
              "      <th>char_freq_!</th>\n",
              "      <th>char_freq_$</th>\n",
              "      <th>char_freq_#</th>\n",
              "      <th>capital_run_length_average</th>\n",
              "      <th>capital_run_length_longest</th>\n",
              "      <th>capital_run_length_total</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.778</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.756</td>\n",
              "      <td>61</td>\n",
              "      <td>278</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.21</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.94</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.132</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.372</td>\n",
              "      <td>0.180</td>\n",
              "      <td>0.048</td>\n",
              "      <td>5.114</td>\n",
              "      <td>101</td>\n",
              "      <td>1028</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.23</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.25</td>\n",
              "      <td>...</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.143</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.276</td>\n",
              "      <td>0.184</td>\n",
              "      <td>0.010</td>\n",
              "      <td>9.821</td>\n",
              "      <td>485</td>\n",
              "      <td>2259</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.63</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.137</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.137</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.537</td>\n",
              "      <td>40</td>\n",
              "      <td>191</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.63</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.135</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.135</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.537</td>\n",
              "      <td>40</td>\n",
              "      <td>191</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 58 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-32d56dcc-f087-419e-9f90-27bf7b914e05')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </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-32d56dcc-f087-419e-9f90-27bf7b914e05 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-32d56dcc-f087-419e-9f90-27bf7b914e05');\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-e728abc2-6042-46cd-ae45-4a09b092262a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e728abc2-6042-46cd-ae45-4a09b092262a')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\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-e728abc2-6042-46cd-ae45-4a09b092262a 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": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "renamedLabel = df.iloc[:,-1]\n",
        "renamedLabel"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HKUtGcjzTenq",
        "outputId": "cd8c69da-be6c-4843-c708-c8b6628f3180"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       1\n",
              "1       1\n",
              "2       1\n",
              "3       1\n",
              "4       1\n",
              "       ..\n",
              "4596    0\n",
              "4597    0\n",
              "4598    0\n",
              "4599    0\n",
              "4600    0\n",
              "Name: Class, Length: 4601, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(featuresData, renamedLabel, test_size=0.2, random_state=42)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=1)"
      ],
      "metadata": {
        "id": "ILM6bLRFTmQu"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_val.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gVUyVUWtTx_R",
        "outputId": "7ce1b9bc-6d5c-42ff-8b37-669b583f6b98"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(920, 57)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LzRKZJwTTzUE",
        "outputId": "4b6a14e8-ec39-463d-8a7e-9c5dc9f48a86"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(921, 57)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Not scaled/ normalized"
      ],
      "metadata": {
        "id": "SpdKPKHSUGOB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = classifier.fit(X_train,y_train)\n",
        "y_pred = classifier.predict(X_test)\n",
        "print(cr(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ToGSnIYUELR",
        "outputId": "d4f43093-06c4-4d7f-aa48-7539bd926316"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.92      0.91       531\n",
            "           1       0.88      0.87      0.88       390\n",
            "\n",
            "    accuracy                           0.90       921\n",
            "   macro avg       0.89      0.89      0.89       921\n",
            "weighted avg       0.90      0.90      0.90       921\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. scaled but not oversampled"
      ],
      "metadata": {
        "id": "uSf24mPfUe8t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_trainNormalized, y_trainNormalized = scaleDataset(X_train, y_train)\n",
        "X_validNormalized, y_validNormalized = scaleDataset(X_val, y_val)\n",
        "X_testNormalized, y_testNormalized = scaleDataset(X_test, y_test)"
      ],
      "metadata": {
        "id": "NKzdiQ9aUd6O"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = classifier.fit(X_trainNormalized, y_trainNormalized)\n",
        "y_pred1 = classifier.predict(X_testNormalized)\n",
        "print(cr(y_testNormalized,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3bvVhFATUgqt",
        "outputId": "d0ce43f6-bb8f-4280-fc31-49ded05771e1"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.92      0.91       531\n",
            "           1       0.88      0.87      0.88       390\n",
            "\n",
            "    accuracy                           0.90       921\n",
            "   macro avg       0.89      0.89      0.89       921\n",
            "weighted avg       0.90      0.90      0.90       921\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Scaled and oversampled"
      ],
      "metadata": {
        "id": "YAydqhbeUsM0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_trainOversampledScaled, y_trainOversampledScaled = scaleDataset(X_train, y_train, oversample=True)\n",
        "X_validOversampledScaled, y_validOversampledScaled = scaleDataset(X_val, y_val, oversample=False)\n",
        "X_testOversampledScaled, y_testOversampledScaled = scaleDataset (X_test, y_test, oversample=False)"
      ],
      "metadata": {
        "id": "HOxhuVO_UuWS"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(y_trainOversampledScaled == 1).sum(), (y_trainOversampledScaled == 0).sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4yMGVo44VTmp",
        "outputId": "5b63d59d-8baa-49e4-8369-21934811a000"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1688, 1688)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = classifier.fit(X_trainOversampledScaled, y_trainOversampledScaled)\n",
        "y_pred1 = classifier.predict(X_testOversampledScaled)\n",
        "print(cr(y_testOversampledScaled,y_pred1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tV1Yb-EtUuc6",
        "outputId": "7ae7b97b-bb3b-456a-b898-fd3fb3dceecf"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.93      0.90       531\n",
            "           1       0.90      0.81      0.85       390\n",
            "\n",
            "    accuracy                           0.88       921\n",
            "   macro avg       0.88      0.87      0.87       921\n",
            "weighted avg       0.88      0.88      0.88       921\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5D2X3BeO5rde"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}