Dark_Triad_Detaction.ipynb

{
  "cells": [
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      "execution_count": 1,
      "metadata": {
        "id": "BgNaz5GItVB9"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
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        "colab": {
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              "       M1  M2  M3  M4  M5  M6  M7  M8  M9  N1  ...  P2  P3  P4  P5  P6  P7  \\\n",
              "0       4   4   4   4   4   4   4   3   4   2  ...   4   3   2   4   4   4   \n",
              "1       2   1   5   2   2   1   2   2   3   1  ...   1   1   5   4   1   5   \n",
              "2       3   3   3   5   1   1   5   5   3   2  ...   5   3   1   3   1   2   \n",
              "3       5   5   4   5   5   5   5   5   5   5  ...   1   5   2   5   5   5   \n",
              "4       4   4   2   5   5   5   4   1   4   3  ...   5   3   1   4   3   5   \n",
              "...    ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ...  ..  ..  ..  ..  ..  ..   \n",
              "18187   1   5   2   4   4   5   4   3   5   4  ...   3   3   3   5   3   2   \n",
              "18188   5   4   5   4   5   4   5   4   5   4  ...   2   2   1   5   2   2   \n",
              "18189   4   3   3   4   2   3   4   3   4   3  ...   5   3   1   1   1   2   \n",
              "18190   5   4   3   3   4   4   4   3   4   3  ...   3   3   4   4   3   4   \n",
              "18191   4   4   4   5   3   4   4   3   3   3  ...   3   3   2   4   3   3   \n",
              "\n",
              "       P8  P9  country  source  \n",
              "0       4   4       GB       1  \n",
              "1       3   2       US       1  \n",
              "2       3   1       US       1  \n",
              "3       1   5       GB       3  \n",
              "4       4   1       GB       3  \n",
              "...    ..  ..      ...     ...  \n",
              "18187   3   3       US       2  \n",
              "18188   3   4       GB       1  \n",
              "18189   4   1       US       2  \n",
              "18190   1   3       LK       2  \n",
              "18191   4   4       US       1  \n",
              "\n",
              "[18192 rows x 29 columns]"
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              "<p>18192 rows × 29 columns</p>\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
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              "</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-41819c05-0832-485a-8a33-7a17aadfffb0 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": 4
        }
      ],
      "source": [
        "df = pd.read_csv(\"SD3_data.csv\")\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Z2j36vsRtxy9"
      },
      "outputs": [],
      "source": [
        "# dropping the last two columns\n",
        "df = df.drop([\"country\",\"source\"], axis=1)\n",
        "#The dropna() method removes the rows that contains NULL values\n",
        "df.dropna(inplace = True)\n",
        "df=df.drop_duplicates(keep='last')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-IGf43hp4E7B",
        "outputId": "ffeb325a-1753-4c88-b341-2a9c7e7a7763"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(18133, 27)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "HTbYECS85bCc"
      },
      "outputs": [],
      "source": [
        "dfNumpy = df.to_numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "VsTSY-Q49TTq",
        "outputId": "5a7f87f4-1230-4e5a-d37f-7ce95ec33307"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       M1  M2  M3  M4  M5  M6  M7  M8  M9  N1  ...  N9  P1  P2  P3  P4  P5  \\\n",
              "0       4   4   4   4   4   4   4   3   4   2  ...   4   3   4   3   2   4   \n",
              "1       2   1   5   2   2   1   2   2   3   1  ...   2   1   1   1   5   4   \n",
              "2       3   3   3   5   1   1   5   5   3   2  ...   5   3   5   3   1   3   \n",
              "3       5   5   4   5   5   5   5   5   5   5  ...   5   5   1   5   2   5   \n",
              "4       4   4   2   5   5   5   4   1   4   3  ...   5   4   5   3   1   4   \n",
              "...    ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ...  ..  ..  ..  ..  ..  ..   \n",
              "18187   1   5   2   4   4   5   4   3   5   4  ...   2   3   3   3   3   5   \n",
              "18188   5   4   5   4   5   4   5   4   5   4  ...   3   3   2   2   1   5   \n",
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              "18190   5   4   3   3   4   4   4   3   4   3  ...   4   4   3   3   4   4   \n",
              "18191   4   4   4   5   3   4   4   3   3   3  ...   4   3   3   3   2   4   \n",
              "\n",
              "       P6  P7  P8  P9  \n",
              "0       4   4   4   4  \n",
              "1       1   5   3   2  \n",
              "2       1   2   3   1  \n",
              "3       5   5   1   5  \n",
              "4       3   5   4   1  \n",
              "...    ..  ..  ..  ..  \n",
              "18187   3   2   3   3  \n",
              "18188   2   2   3   4  \n",
              "18189   1   2   4   1  \n",
              "18190   3   4   1   3  \n",
              "18191   3   3   4   4  \n",
              "\n",
              "[18133 rows x 27 columns]"
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              "<p>18133 rows × 27 columns</p>\n",
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              "\n",
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              "    .colab-df-container {\n",
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              "\n",
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              "      width: 32px;\n",
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              "\n",
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              "      background-color: #E2EBFA;\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        document.querySelector('#df-9e9a8ba3-6483-44cd-ba5f-db62d9692560 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9e9a8ba3-6483-44cd-ba5f-db62d9692560');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  }\n",
              "\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-3ddec8ca-25f3-4be9-96ce-2b348d84b562 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
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "Sgx7UqvguRSb"
      },
      "outputs": [],
      "source": [
        "M_P = []\n",
        "N_P=[]\n",
        "P_P=[]\n",
        "class_label=[]\n",
        "M = 9\n",
        "N = 18\n",
        "P = 27\n",
        "columns = df.shape[1]\n",
        "rows = df.shape[0]\n",
        "# print(len(df.columns))\n",
        "for i in range(rows):\n",
        "  # minValue = 0\n",
        "  mScore = 0\n",
        "  nScore = 0\n",
        "  pScore = 0\n",
        "  for j in range(columns):\n",
        "    if j>=0 and j<M:\n",
        "      mScore+=dfNumpy[i][j]\n",
        "    elif j>=M and j<N:\n",
        "      nScore+=dfNumpy[i][j]\n",
        "    elif j>=N and j<P:\n",
        "      pScore+=dfNumpy[i][j]\n",
        "  M_P.append(mScore/9)\n",
        "  N_P.append(nScore/9)\n",
        "  P_P.append(pScore/9)\n",
        "  minValue = min((3.86-(mScore/9)),(3.68-(nScore/9)),(3.4-(pScore/9)))\n",
        "  if minValue == 3.86-(mScore/9):\n",
        "    class_label.append(0)\n",
        "  elif minValue == 3.68-(nScore/9):\n",
        "    class_label.append(1)\n",
        "  else:\n",
        "    class_label.append(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-4H1MKoFyMTi",
        "outputId": "e8bf4cbd-8a08-4511-87c1-b7dd12de595a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18133"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "len(class_label)\n",
        "# class_label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "LCiNqAgzmpY4",
        "outputId": "c3e53fa9-581f-44b1-f7c9-48f1e16339ff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       M1  M2  M3  M4  M5  M6  M7  M8  M9  N1  ...  N9  P1  P2  P3  P4  P5  \\\n",
              "0       4   4   4   4   4   4   4   3   4   2  ...   4   3   4   3   2   4   \n",
              "1       2   1   5   2   2   1   2   2   3   1  ...   2   1   1   1   5   4   \n",
              "2       3   3   3   5   1   1   5   5   3   2  ...   5   3   5   3   1   3   \n",
              "3       5   5   4   5   5   5   5   5   5   5  ...   5   5   1   5   2   5   \n",
              "4       4   4   2   5   5   5   4   1   4   3  ...   5   4   5   3   1   4   \n",
              "...    ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ...  ..  ..  ..  ..  ..  ..   \n",
              "18187   1   5   2   4   4   5   4   3   5   4  ...   2   3   3   3   3   5   \n",
              "18188   5   4   5   4   5   4   5   4   5   4  ...   3   3   2   2   1   5   \n",
              "18189   4   3   3   4   2   3   4   3   4   3  ...   3   2   5   3   1   1   \n",
              "18190   5   4   3   3   4   4   4   3   4   3  ...   4   4   3   3   4   4   \n",
              "18191   4   4   4   5   3   4   4   3   3   3  ...   4   3   3   3   2   4   \n",
              "\n",
              "       P6  P7  P8  P9  \n",
              "0       4   4   4   4  \n",
              "1       1   5   3   2  \n",
              "2       1   2   3   1  \n",
              "3       5   5   1   5  \n",
              "4       3   5   4   1  \n",
              "...    ..  ..  ..  ..  \n",
              "18187   3   2   3   3  \n",
              "18188   2   2   3   4  \n",
              "18189   1   2   4   1  \n",
              "18190   3   4   1   3  \n",
              "18191   3   3   4   4  \n",
              "\n",
              "[18133 rows x 27 columns]"
            ],
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              "  </tbody>\n",
              "</table>\n",
              "<p>18133 rows × 27 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-f7f0d654-5252-49bc-82b2-643c0040b65c')\"\n",
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              "  </svg>\n",
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              "\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",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      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-f7f0d654-5252-49bc-82b2-643c0040b65c 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-f7f0d654-5252-49bc-82b2-643c0040b65c');\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-2cd11aa1-51db-4616-9323-1e0dc8460a9c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2cd11aa1-51db-4616-9323-1e0dc8460a9c')\"\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-2cd11aa1-51db-4616-9323-1e0dc8460a9c 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": 11
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "Yw8106_C96q0"
      },
      "outputs": [],
      "source": [
        "df['Class_label'] = class_label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "Tb2-D5Zt9_Nj",
        "outputId": "afaa7846-ed90-452a-dd0e-be33f7cb45bc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   M1  M2  M3  M4  M5  M6  M7  M8  M9  N1  ...  P1  P2  P3  P4  P5  P6  P7  \\\n",
              "0   4   4   4   4   4   4   4   3   4   2  ...   3   4   3   2   4   4   4   \n",
              "1   2   1   5   2   2   1   2   2   3   1  ...   1   1   1   5   4   1   5   \n",
              "2   3   3   3   5   1   1   5   5   3   2  ...   3   5   3   1   3   1   2   \n",
              "3   5   5   4   5   5   5   5   5   5   5  ...   5   1   5   2   5   5   5   \n",
              "4   4   4   2   5   5   5   4   1   4   3  ...   4   5   3   1   4   3   5   \n",
              "5   4   2   2   4   2   3   5   2   2   2  ...   3   5   4   4   2   2   1   \n",
              "6   4   4   4   2   4   4   4   3   5   4  ...   3   2   4   2   4   3   4   \n",
              "7   5   5   5   5   5   5   5   4   5   4  ...   2   5   2   5   5   5   2   \n",
              "8   5   3   4   4   4   4   4   2   4   3  ...   5   2   4   3   4   4   1   \n",
              "9   5   5   5   3   5   5   5   5   5   5  ...   5   2   5   5   5   5   5   \n",
              "\n",
              "   P8  P9  Class_label  \n",
              "0   4   4            2  \n",
              "1   3   2            1  \n",
              "2   3   1            0  \n",
              "3   1   5            0  \n",
              "4   4   1            2  \n",
              "5   1   5            2  \n",
              "6   4   3            0  \n",
              "7   5   3            0  \n",
              "8   2   1            0  \n",
              "9   3   5            2  \n",
              "\n",
              "[10 rows x 28 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-56ad03b5-2c0e-45ea-9aba-47783d35ce65\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>M1</th>\n",
              "      <th>M2</th>\n",
              "      <th>M3</th>\n",
              "      <th>M4</th>\n",
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              "      <th>3</th>\n",
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              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
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              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
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              "      <td>2</td>\n",
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              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
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              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
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              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
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              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 28 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-56ad03b5-2c0e-45ea-9aba-47783d35ce65')\"\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-56ad03b5-2c0e-45ea-9aba-47783d35ce65 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-56ad03b5-2c0e-45ea-9aba-47783d35ce65');\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-629ec69a-9cf2-44d6-9415-295a83e4c0fc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-629ec69a-9cf2-44d6-9415-295a83e4c0fc')\"\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-629ec69a-9cf2-44d6-9415-295a83e4c0fc 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
        }
      ],
      "source": [
        "df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "3ypUNroXBaqN",
        "outputId": "b936a23b-e4f1-41a0-d6a3-a59c941f82a5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       M1  M2  M3  M4  M5  M6  M7  M8  M9  N1  ...  P1  P2  P3  P4  P5  P6  \\\n",
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              "18186   4   5   4   5   5   5   5   4   5   2  ...   4   4   3   4   5   4   \n",
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              "18190   5   4   3   3   4   4   4   3   4   3  ...   4   3   3   4   4   3   \n",
              "18191   4   4   4   5   3   4   4   3   3   3  ...   3   3   3   2   4   3   \n",
              "\n",
              "       P7  P8  P9  Class_label  \n",
              "18182   1   3   1            0  \n",
              "18183   5   1   1            2  \n",
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              "18189   2   4   1            0  \n",
              "18190   4   1   3            0  \n",
              "18191   3   4   4            0  \n",
              "\n",
              "[10 rows x 28 columns]"
            ],
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              "      <th>18187</th>\n",
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              "      <td>5</td>\n",
              "      <td>3</td>\n",
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              "      <td>1</td>\n",
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              "      <td>4</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>18190</th>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>18191</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
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              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
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              "      <td>...</td>\n",
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              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 28 columns</p>\n",
              "</div>\n",
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              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13e3581b-cdb6-4538-a0f3-f9d8e3176b21')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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-13e3581b-cdb6-4538-a0f3-f9d8e3176b21 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-13e3581b-cdb6-4538-a0f3-f9d8e3176b21');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      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",
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              "    60% {\n",
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              "      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-ee48e86a-9827-445d-94a6-ca648e1311f1 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": 14
        }
      ],
      "source": [
        "df.tail(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cSX0sPS8jeF3",
        "outputId": "558dc16f-c5ba-43c7-b79c-663c225eed68"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.8 0.8 0.8 ... 0.8 0.8 0.8]\n",
            " [0.4 0.2 1.  ... 1.  0.6 0.4]\n",
            " [0.6 0.6 0.6 ... 0.4 0.6 0.2]\n",
            " ...\n",
            " [0.8 0.6 0.6 ... 0.4 0.8 0.2]\n",
            " [1.  0.8 0.6 ... 0.8 0.2 0.6]\n",
            " [0.8 0.8 0.8 ... 0.6 0.8 0.8]]\n"
          ]
        }
      ],
      "source": [
        "# normalizing data and converting to numpy\n",
        "from mlxtend.preprocessing import minmax_scaling\n",
        "df_normalized = df.to_numpy()\n",
        "df_normalized = np.delete(df_normalized, 27, axis=1)\n",
        "df_normalized = (df_normalized-np.min(df_normalized))/(np.max(df_normalized)-np.min(df_normalized))\n",
        "print(df_normalized)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "hLXjySS9kn2N",
        "outputId": "88a95bc6-87a6-4f6f-921f-74a8e9b53aad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        M1   M2   M3   M4   M5   M6   M7   M8   M9   N1  ...   N9   P1   P2  \\\n",
              "0      0.8  0.8  0.8  0.8  0.8  0.8  0.8  0.6  0.8  0.4  ...  0.8  0.6  0.8   \n",
              "1      0.4  0.2  1.0  0.4  0.4  0.2  0.4  0.4  0.6  0.2  ...  0.4  0.2  0.2   \n",
              "2      0.6  0.6  0.6  1.0  0.2  0.2  1.0  1.0  0.6  0.4  ...  1.0  0.6  1.0   \n",
              "3      1.0  1.0  0.8  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  0.2   \n",
              "4      0.8  0.8  0.4  1.0  1.0  1.0  0.8  0.2  0.8  0.6  ...  1.0  0.8  1.0   \n",
              "...    ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
              "18128  0.2  1.0  0.4  0.8  0.8  1.0  0.8  0.6  1.0  0.8  ...  0.4  0.6  0.6   \n",
              "18129  1.0  0.8  1.0  0.8  1.0  0.8  1.0  0.8  1.0  0.8  ...  0.6  0.6  0.4   \n",
              "18130  0.8  0.6  0.6  0.8  0.4  0.6  0.8  0.6  0.8  0.6  ...  0.6  0.4  1.0   \n",
              "18131  1.0  0.8  0.6  0.6  0.8  0.8  0.8  0.6  0.8  0.6  ...  0.8  0.8  0.6   \n",
              "18132  0.8  0.8  0.8  1.0  0.6  0.8  0.8  0.6  0.6  0.6  ...  0.8  0.6  0.6   \n",
              "\n",
              "        P3   P4   P5   P6   P7   P8   P9  \n",
              "0      0.6  0.4  0.8  0.8  0.8  0.8  0.8  \n",
              "1      0.2  1.0  0.8  0.2  1.0  0.6  0.4  \n",
              "2      0.6  0.2  0.6  0.2  0.4  0.6  0.2  \n",
              "3      1.0  0.4  1.0  1.0  1.0  0.2  1.0  \n",
              "4      0.6  0.2  0.8  0.6  1.0  0.8  0.2  \n",
              "...    ...  ...  ...  ...  ...  ...  ...  \n",
              "18128  0.6  0.6  1.0  0.6  0.4  0.6  0.6  \n",
              "18129  0.4  0.2  1.0  0.4  0.4  0.6  0.8  \n",
              "18130  0.6  0.2  0.2  0.2  0.4  0.8  0.2  \n",
              "18131  0.6  0.8  0.8  0.6  0.8  0.2  0.6  \n",
              "18132  0.6  0.4  0.8  0.6  0.6  0.8  0.8  \n",
              "\n",
              "[18133 rows x 27 columns]"
            ],
            "text/html": [
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18129</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>...</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18130</th>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>...</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18131</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>...</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18132</th>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>...</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>18133 rows × 27 columns</p>\n",
              "</div>\n",
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              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c40635b4-fa26-4afe-b662-0aaea3519424 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-c40635b4-fa26-4afe-b662-0aaea3519424');\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",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\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",
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              "      --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-30b35fc7-f157-4b79-8606-6ca11d271156 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": 16
        }
      ],
      "source": [
        "df_normalized = pd.DataFrame({'M1': df_normalized[:, 0],\n",
        "                              'M2': df_normalized[:, 1],\n",
        "                              'M3': df_normalized[:, 2],\n",
        "                              'M4': df_normalized[:, 3],\n",
        "                              'M5': df_normalized[:, 4],\n",
        "                              'M6': df_normalized[:, 5],\n",
        "                              'M7': df_normalized[:, 6],\n",
        "                              'M8': df_normalized[:, 7],\n",
        "                              'M9': df_normalized[:, 8],\n",
        "                              'N1': df_normalized[:, 9],\n",
        "                              'N2': df_normalized[:, 10],\n",
        "                              'N3': df_normalized[:, 11],\n",
        "                              'N4': df_normalized[:, 12],\n",
        "                              'N5': df_normalized[:, 13],\n",
        "                              'N6': df_normalized[:, 14],\n",
        "                              'N7': df_normalized[:, 15],\n",
        "                              'N8': df_normalized[:, 16],\n",
        "                              'N9': df_normalized[:, 17],\n",
        "                              'P1': df_normalized[:, 18],\n",
        "                              'P2': df_normalized[:, 19],\n",
        "                              'P3': df_normalized[:, 20],\n",
        "                              'P4': df_normalized[:, 21],\n",
        "                              'P5': df_normalized[:, 22],\n",
        "                              'P6': df_normalized[:, 23],\n",
        "                              'P7': df_normalized[:, 24],\n",
        "                              'P8': df_normalized[:, 25],\n",
        "                              'P9': df_normalized[:, 26]})\n",
        "df_normalized"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "Xn92sk6knZYL"
      },
      "outputs": [],
      "source": [
        "df_normalized['Class_label'] = class_label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "i9-shNeondWU",
        "outputId": "95872a83-72a0-43de-f855-d1da38d5dfbb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        M1   M2   M3   M4   M5   M6   M7   M8   M9   N1  ...   P1   P2   P3  \\\n",
              "0      0.8  0.8  0.8  0.8  0.8  0.8  0.8  0.6  0.8  0.4  ...  0.6  0.8  0.6   \n",
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              "2      0.6  0.6  0.6  1.0  0.2  0.2  1.0  1.0  0.6  0.4  ...  0.6  1.0  0.6   \n",
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              "4      0.8  0.8  0.4  1.0  1.0  1.0  0.8  0.2  0.8  0.6  ...  0.8  1.0  0.6   \n",
              "...    ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
              "18128  0.2  1.0  0.4  0.8  0.8  1.0  0.8  0.6  1.0  0.8  ...  0.6  0.6  0.6   \n",
              "18129  1.0  0.8  1.0  0.8  1.0  0.8  1.0  0.8  1.0  0.8  ...  0.6  0.4  0.4   \n",
              "18130  0.8  0.6  0.6  0.8  0.4  0.6  0.8  0.6  0.8  0.6  ...  0.4  1.0  0.6   \n",
              "18131  1.0  0.8  0.6  0.6  0.8  0.8  0.8  0.6  0.8  0.6  ...  0.8  0.6  0.6   \n",
              "18132  0.8  0.8  0.8  1.0  0.6  0.8  0.8  0.6  0.6  0.6  ...  0.6  0.6  0.6   \n",
              "\n",
              "        P4   P5   P6   P7   P8   P9  Class_label  \n",
              "0      0.4  0.8  0.8  0.8  0.8  0.8            2  \n",
              "1      1.0  0.8  0.2  1.0  0.6  0.4            1  \n",
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              "4      0.2  0.8  0.6  1.0  0.8  0.2            2  \n",
              "...    ...  ...  ...  ...  ...  ...          ...  \n",
              "18128  0.6  1.0  0.6  0.4  0.6  0.6            0  \n",
              "18129  0.2  1.0  0.4  0.4  0.6  0.8            0  \n",
              "18130  0.2  0.2  0.2  0.4  0.8  0.2            0  \n",
              "18131  0.8  0.8  0.6  0.8  0.2  0.6            0  \n",
              "18132  0.4  0.8  0.6  0.6  0.8  0.8            0  \n",
              "\n",
              "[18133 rows x 28 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>M1</th>\n",
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              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>...</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>...</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>...</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18128</th>\n",
              "      <td>0.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>...</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18129</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>...</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18130</th>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18131</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>...</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18132</th>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>...</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>18133 rows × 28 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-3ac396cb-77b3-4865-9158-0b8c28b952ff')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3ac396cb-77b3-4865-9158-0b8c28b952ff 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-3ac396cb-77b3-4865-9158-0b8c28b952ff');\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-8c7ca1ea-6f3a-48d6-9039-cc4e80d1b6fd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8c7ca1ea-6f3a-48d6-9039-cc4e80d1b6fd')\"\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-8c7ca1ea-6f3a-48d6-9039-cc4e80d1b6fd 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": 18
        }
      ],
      "source": [
        "df_normalized"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "wA0EGSp5-fge"
      },
      "outputs": [],
      "source": [
        "x=df_normalized.drop('Class_label',axis=1)\n",
        "y=df_normalized['Class_label']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "LRc-EVO7-9Eg"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "from sklearn.model_selection import train_test_split\n",
        "train_X ,test_X , train_y ,test_y = train_test_split(x,y,test_size=0.2,random_state=random.randint(0,100))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y7M9LWU2-gUM"
      },
      "source": [
        "# **Built in Logistic Regression**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "E5tiXn64_SJx"
      },
      "outputs": [],
      "source": [
        "iter=5000\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "reg = LogisticRegression(max_iter=iter).fit(train_X,train_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xL8nFhrJ_s5X",
        "outputId": "91cd59e4-7d2b-4800-a6c4-75b1e783b4b3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9986212601682063"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "reg.score(train_X,train_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FkGk3XykBLZc",
        "outputId": "0feb5f45-bb6a-4f30-c9f5-c5cda3527ee6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 2 ... 0 0 1]\n"
          ]
        }
      ],
      "source": [
        "y_p=reg.predict(test_X)\n",
        "print(y_p)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JfOHUPjuBgTG",
        "outputId": "1d70079a-1c37-4c68-9302-562cf81ec99a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.67747463e-01, 2.66163467e-01, 1.66089071e-01],\n",
              "       [9.99955673e-01, 1.60212527e-08, 4.43112066e-05],\n",
              "       [8.14493203e-03, 5.83511734e-04, 9.91271556e-01],\n",
              "       ...,\n",
              "       [7.56845954e-01, 5.57316660e-02, 1.87422380e-01],\n",
              "       [9.84853289e-01, 1.49076788e-02, 2.39032374e-04],\n",
              "       [2.42954883e-03, 9.97570026e-01, 4.25430590e-07]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "reg.predict_proba(test_X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PCbcZQKeBkrM",
        "outputId": "39cfdb06-e879-4a93-d9d4-333120d7c19d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2159    0    0]\n",
            " [   4  621    0]\n",
            " [   0    0  843]]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm1=confusion_matrix(test_y, y_p)\n",
        "print(cm1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "QhhUrFAsBpJg",
        "outputId": "ad602203-f526-4338-d8b5-24709f124940"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(50.722222222222214, 0.5, 'Truth or Actual Value')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sn\n",
        "from matplotlib import pyplot as plt\n",
        "plt.figure(figsize = (5,4))\n",
        "sn.heatmap(cm1, annot=True)\n",
        "plt.xlabel('Predicted Value')\n",
        "plt.ylabel('Truth or Actual Value')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QBXppGrfEJxL"
      },
      "source": [
        "# **Decision Tree**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0rh01R3QEM4P",
        "outputId": "1c01e9bf-748b-48a4-b94e-9e1f8f158adc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9986212601682063"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "dtree_model = DecisionTreeClassifier().fit(train_X, train_y)\n",
        "reg.score(train_X,train_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_aKTLpyMV5hC",
        "outputId": "758fe230-32d7-4c6f-c02b-3b38e7c5a048"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 2 ... 2 0 1]\n"
          ]
        }
      ],
      "source": [
        "y_predD = dtree_model.predict(test_X)\n",
        "print(y_predD)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S_6EGFedQWvz",
        "outputId": "ed08502b-57a9-4042-8c90-30f8464b38c7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1758  132  269]\n",
            " [ 145  340  140]\n",
            " [ 264  121  458]]\n"
          ]
        }
      ],
      "source": [
        "# creating a confusion matrix\n",
        "cm2 = confusion_matrix(test_y, y_predD)\n",
        "print(cm2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "o8EojpkEPA8U",
        "outputId": "84265fc1-e9e9-4ca8-df9e-c30b8544bceb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(33.22222222222222, 0.5, 'Truth or Actual Value')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (5,4))\n",
        "sn.heatmap(cm2, annot=True,cmap='Oranges_r')\n",
        "plt.xlabel('Predicted Value')\n",
        "plt.ylabel('Truth or Actual Value')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gEehy_O0ETcF"
      },
      "source": [
        "# **Random Forest**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "nufonLRHEhKd"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "clf=RandomForestClassifier(n_estimators=iter).fit(train_X ,train_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3MHbWp0KJygt",
        "outputId": "49448049-a02a-4dad-f2d9-92d3b8b38e62"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "clf.score(train_X,train_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FLjhZ6WDJR3o",
        "outputId": "70f37ef0-213b-4673-d0c4-f984072ac6b7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 2 ... 0 0 1]\n"
          ]
        }
      ],
      "source": [
        "y_predR=clf.predict(test_X)\n",
        "print(y_predR)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZKagPGIDQTpY",
        "outputId": "599949de-bbe0-40bc-d36a-cf88e93bb793"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2089   32   38]\n",
            " [ 123  444   58]\n",
            " [ 282   70  491]]\n"
          ]
        }
      ],
      "source": [
        "# creating a confusion matrix\n",
        "cm3 = confusion_matrix(test_y, y_predR)\n",
        "print(cm3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "6Rw1w9GyPrKb",
        "outputId": "d93382f6-2bfd-4926-9192-370b975ede70"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(33.22222222222222, 0.5, 'Truth or Actual Value')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (5,4))\n",
        "sn.heatmap(cm3, annot=True,cmap='Greens_r')\n",
        "plt.xlabel('Predicted Value')\n",
        "plt.ylabel('Truth or Actual Value')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ejtSr0DBQFtN"
      },
      "source": [
        "# **SVM**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "7oVWwjgrRYTL"
      },
      "outputs": [],
      "source": [
        "from sklearn.svm import SVC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nfymzzMCQssX",
        "outputId": "67a4a828-8a39-4c85-c66c-019ef03415e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9987591341513856"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "classifier=SVC(kernel=\"linear\",gamma=\"auto\",C=2).fit(train_X,train_y)\n",
        "classifier.score(train_X,train_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xW-09nBxqNTd",
        "outputId": "c31565f8-1c15-458f-ded6-d0663fef9d8c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 2 ... 0 0 1]\n"
          ]
        }
      ],
      "source": [
        "ypredsvm=classifier.predict(test_X)\n",
        "print(ypredsvm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CEshX6NBqfYM",
        "outputId": "5c8ce453-6900-474f-d723-52583105a547"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2159    0    0]\n",
            " [   1  621    3]\n",
            " [   0    2  841]]\n"
          ]
        }
      ],
      "source": [
        "# creating a confusion matrix\n",
        "cm4 = confusion_matrix(test_y, ypredsvm)\n",
        "print(cm4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "O68ocG6sqzvu",
        "outputId": "9eee3c7d-0ea2-47b8-ef69-349818889317"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(33.22222222222222, 0.5, 'Truth or Actual Value')"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (5,4))\n",
        "sn.heatmap(cm4, annot=True,cmap='Blues_r')\n",
        "plt.xlabel('Predicted Value')\n",
        "plt.ylabel('Truth or Actual Value')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(class_label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-xcMaAC2clCJ",
        "outputId": "504bf475-fc44-4ba5-cf62-c45913de6c4d"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 2, 0, 2, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 1, 0, 0, 2, 1, 1, 0, 2, 2, 1, 0, 1, 2, 0, 2, 0, 0, 0, 0, 1, 1, 2, 0, 0, 1, 0, 0, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2, 0, 2, 0, 0, 2, 2, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 2, 0, 0, 2, 0, 1, 2, 0, 2, 0, 1, 1, 2, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 2, 0, 0, 0, 2, 2, 0, 0, 0, 1, 0, 1, 0, 1, 0, 2, 0, 0, 0, 2, 1, 0, 2, 1, 0, 1, 0, 2, 0, 0, 0, 2, 2, 0, 0, 2, 0, 2, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "qcdiLkfmnvkh",
        "outputId": "63bae6d8-c8d6-43b2-aa36-a3d9c20030af"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10931\n",
            "2995\n",
            "4207\n",
            "['Machiavellianism', 'Narcissism', 'Psychopathy']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "M,N,P = 0,0,0\n",
        "for i in range(len(class_label)):\n",
        "  if class_label[i] == 0: M+=1\n",
        "  elif class_label[i] == 1: N+=1\n",
        "  elif class_label[i] == 2: P+=1\n",
        "print(M)\n",
        "print(N)\n",
        "print(P)\n",
        "\n",
        "BarChart = {\n",
        "    'Machiavellianism':M,\n",
        "    'Narcissism':N,\n",
        "    'Psychopathy':P\n",
        "}\n",
        "\n",
        "\n",
        "\n",
        "triads = list(BarChart.keys())\n",
        "values = list(BarChart.values())\n",
        "print(triads)\n",
        "\n",
        "fig = plt.figure()\n",
        "plt.bar(triads, values,width = 0.4)\n",
        "plt.xlabel(\"The 3 Short Dark Triads\")\n",
        "plt.ylabel(\"No. of People Who Fall in The Catagories\")\n",
        "plt.title(\"Chart Showing The Overall Number Of People Who Fall In Either Catagory\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "hJzTU0wBKNtn",
        "outputId": "4f1fb9fa-4dd3-4bd9-fafb-89b109e6c9fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[75789 63015 60944 63480 64701 66156 79383 57258 73882 59241 56578 47900\n",
            " 48464 63587 56195 51989 47125 62436 55541 58196 47437 42047 67242 53501\n",
            " 64211 46992 50628]\n",
            "['M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'M8', 'M9']\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": [
            "['N1', 'N2', 'N3', 'N4', 'N5', 'N6', 'N7', 'N8', 'N9']\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": [
            "['P1', 'P2', 'P3', 'P4', 'P5', 'P6', 'P7', 'P8', 'P9']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "df_numpy = df.to_numpy()\n",
        "# print(df_numpy)\n",
        "df_numpy = np.delete(df_numpy, 27, axis=1)\n",
        "# print(df_numpy)\n",
        "# print(df_numpy.shape)\n",
        "MAnswer = []\n",
        "NAnswer = []\n",
        "PAnswer = []\n",
        "\n",
        "sum = df_numpy.sum(axis=0)\n",
        "print(sum)\n",
        "\n",
        "MBarChart = {\n",
        "    'M1':sum[0]/len(df_normalized),'M2':sum[1]/len(df_normalized),'M3':sum[2]/len(df_normalized),\n",
        "    'M4':sum[3]/len(df_normalized),'M5':sum[4]/len(df_normalized),'M6':sum[5]/len(df_normalized),\n",
        "    'M7':sum[6]/len(df_normalized),'M8':sum[7]/len(df_normalized),'M9':sum[8]/len(df_normalized)\n",
        "}\n",
        "M = list(MBarChart.keys())\n",
        "Pvalues = list(MBarChart.values())\n",
        "print(M)\n",
        "fig = plt.figure()\n",
        "plt.bar(M, Pvalues,width = 0.4)\n",
        "plt.xlabel(\"Machiavellianism Questions\")\n",
        "plt.ylabel(\"Scores\")\n",
        "plt.title(\"Average Response Analysis for Machiavellianism\")\n",
        "plt.show()\n",
        "NBarChart = {\n",
        "    'N1':sum[9]/len(df_normalized),'N2':sum[10]/len(df_normalized),'N3':sum[11]/len(df_normalized),\n",
        "    'N4':sum[12]/len(df_normalized),'N5':sum[13]/len(df_normalized),'N6':sum[14]/len(df_normalized),\n",
        "    'N7':sum[15]/len(df_normalized),'N8':sum[16]/len(df_normalized),'N9':sum[17]/len(df_normalized)\n",
        "}\n",
        "N = list(NBarChart.keys())\n",
        "Pvalues = list(NBarChart.values())\n",
        "print(N)\n",
        "fig = plt.figure()\n",
        "plt.bar(N, Pvalues,width = 0.4)\n",
        "plt.xlabel(\"Narcissism Questions\")\n",
        "plt.ylabel(\"Scores\")\n",
        "plt.title(\"Average Response Analysis for Narcissism \")\n",
        "plt.show()\n",
        "PBarChart = {\n",
        "    'P1':sum[18]/len(df_normalized),'P2':sum[19]/len(df_normalized),'P3':sum[20]/len(df_normalized),\n",
        "    'P4':sum[21]/len(df_normalized),'P5':sum[22]/len(df_normalized),'P6':sum[23]/len(df_normalized),\n",
        "    'P7':sum[24]/len(df_normalized),'P8':sum[25]/len(df_normalized),'P9':sum[26]/len(df_normalized)\n",
        "}\n",
        "P = list(PBarChart.keys())\n",
        "Pvalues = list(PBarChart.values())\n",
        "print(P)\n",
        "fig = plt.figure()\n",
        "plt.bar(P, Pvalues,width = 0.4)\n",
        "plt.xlabel(\"Psychopathy Questions\")\n",
        "plt.ylabel(\"Scores\")\n",
        "plt.title(\"Average Response Analysis for Psychopathy\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "HCYK0QXJHcIo"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "\n",
        "# BLR Built in Logistic Regression\n",
        "# SVM Support Vector Machine\n",
        "# RF  Random Forest\n",
        "# DT  Dession Tree\n",
        "\n",
        "BLRa=accuracy_score(test_y, y_p)\n",
        "SVMa=accuracy_score(test_y, ypredsvm)\n",
        "RFa=accuracy_score(test_y, y_predR)\n",
        "DTa=accuracy_score(test_y, y_predD)\n",
        "\n",
        "BLRp=precision_score(test_y, y_p,average='weighted')\n",
        "SVMp=precision_score(test_y, ypredsvm,average='weighted')\n",
        "RFp=precision_score(test_y, y_predR,average='weighted')\n",
        "DTp=precision_score(test_y, y_predD,average='weighted')\n",
        "\n",
        "\n",
        "BLRr=recall_score(test_y, y_p,average='weighted')\n",
        "SVMr=recall_score(test_y, ypredsvm,average='weighted')\n",
        "RFr=recall_score(test_y, y_predR,average='weighted')\n",
        "DTr=recall_score(test_y, y_predD,average='weighted')\n",
        "\n",
        "\n",
        "\n",
        "BLRf=f1_score(test_y, y_p,average='weighted')\n",
        "SVMf=f1_score(test_y, ypredsvm,average='weighted')\n",
        "RFf=f1_score(test_y, y_predR,average='weighted')\n",
        "DTf=f1_score(test_y, y_predD,average='weighted')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "5iYbBudrL0yh"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "Vfoy_JUfTY7H",
        "outputId": "22759419-db5e-4729-b99e-a31fd7ba3393"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Import Libraries\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define Data\n",
        "\n",
        "team = ['Logistic Regression','SVM','Random Forest','Decision Tree']\n",
        "Accuracy = [BLRa,SVMa,RFa,DTa]\n",
        "Precision = [BLRp,SVMp,RFp,DTp]\n",
        "Recall = [BLRr,SVMr,RFr,DTr]\n",
        "F1=[BLRf,SVMf,RFf,DTf]\n",
        "x_axis = np.arange(len(team))\n",
        "\n",
        "# Multi bar Chart\n",
        "\n",
        "plt.bar(x_axis +0.20, Accuracy, width=0.2, label = 'Accuracy')\n",
        "plt.bar(x_axis +0.20*2, Precision, width=0.2, label = 'Precision')\n",
        "plt.bar(x_axis +0.20*3, Recall, width=0.2, label = 'Recall')\n",
        "plt.bar(x_axis +0.20*4, F1, width=0.2, label = 'F1')\n",
        "\n",
        "\n",
        "\n",
        "plt.xticks(x_axis,team)\n",
        "\n",
        "\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eLxU90wyqVcU",
        "outputId": "e6ebe7ef-76dd-490f-b9d2-45db4d553eae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression's evaluation metrics\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      2159\n",
            "           1       1.00      0.99      1.00       625\n",
            "           2       1.00      1.00      1.00       843\n",
            "\n",
            "    accuracy                           1.00      3627\n",
            "   macro avg       1.00      1.00      1.00      3627\n",
            "weighted avg       1.00      1.00      1.00      3627\n",
            "\n",
            "SVM evaluation metrics\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      2159\n",
            "           1       1.00      0.99      1.00       625\n",
            "           2       1.00      1.00      1.00       843\n",
            "\n",
            "    accuracy                           1.00      3627\n",
            "   macro avg       1.00      1.00      1.00      3627\n",
            "weighted avg       1.00      1.00      1.00      3627\n",
            "\n",
            "Random Forest evaluation metrics\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.97      0.90      2159\n",
            "           1       0.81      0.71      0.76       625\n",
            "           2       0.84      0.58      0.69       843\n",
            "\n",
            "    accuracy                           0.83      3627\n",
            "   macro avg       0.83      0.75      0.78      3627\n",
            "weighted avg       0.83      0.83      0.82      3627\n",
            "\n",
            "Decision Tree evaluation metrics\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.81      0.81      2159\n",
            "           1       0.57      0.54      0.56       625\n",
            "           2       0.53      0.54      0.54       843\n",
            "\n",
            "    accuracy                           0.70      3627\n",
            "   macro avg       0.64      0.63      0.64      3627\n",
            "weighted avg       0.70      0.70      0.70      3627\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(\"Logistic Regression's evaluation metrics\")\n",
        "print(classification_report(test_y,y_p))\n",
        "print(\"SVM evaluation metrics\")\n",
        "print(classification_report(test_y,ypredsvm))\n",
        "print(\"Random Forest evaluation metrics\")\n",
        "print(classification_report(test_y,y_predR))\n",
        "print(\"Decision Tree evaluation metrics\")\n",
        "print(classification_report(test_y,y_predD))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "JIeH3djpagze"
      },
      "outputs": [],
      "source": [
        "list_of_column_names = list(df_normalized.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "h24ur7peaHjT",
        "outputId": "73092e55-d063-4b6b-c289-ca0db931d1ac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x7e2dea0e3850>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "a=df_normalized.corr().to_numpy()\n",
        "heatmap=plt.imshow(a, cmap='flag')\n",
        "plt.colorbar(heatmap)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "HNM9p2ZV08YB",
        "outputId": "535f0122-fcca-43cc-f53d-21556de66422"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'ROC Curves for LogisticRegression'}, xlabel='False Positive Rate', ylabel='True Positive Rate'>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "from yellowbrick.classifier import ROCAUC\n",
        "model = LogisticRegression(multi_class=\"auto\", solver=\"liblinear\")\n",
        "visualizer = ROCAUC(model, classes=[\"0\", \"1\", \"2\"])\n",
        "visualizer.fit(train_X,train_y)\n",
        "visualizer.score(test_X,test_y)\n",
        "visualizer.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "pxL0hivd09T2",
        "outputId": "60648f07-5f72-4e96-b683-b556901d4dff"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'ROC Curves for SVC'}, xlabel='False Positive Rate', ylabel='True Positive Rate'>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "viz = ROCAUC(SVC(probability=True))\n",
        "viz.fit(train_X,train_y)\n",
        "viz.score(test_X,test_y)\n",
        "viz.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}