TransmissionLineFaultDetectionAndClassification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1GlgUqu1sPy_opoJ9jfMdj_MnJf4Khc4y",
      "authorship_tag": "ABX9TyP7ifLe9cvtAKf151RM6n2e",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Abhi3886/DSA/blob/main/TransmissionLineFaultDetectionAndClassification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "nnu5FtCEZRkq"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report"
      ],
      "metadata": {
        "id": "xe3o4Za6ZekB"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Fault(1) or Normal(0)\n",
        "\n",
        "# Types of faults to be detected\n",
        "# 0 0 0 0 - No Fault\n",
        "# 1 0 0 1 - Line:Ground fault (Phase A - Ground)\n",
        "# 0 0 1 1 - Line:Line fault (Phase A - Phase B)\n",
        "# 1 0 1 1 - Line:Line:Ground fault (Phase A,B - Ground)\n",
        "# 0 1 1 1 - Line:Line:Line fault (all 3 phases)\n",
        "# 1 1 1 1 - Line:Line:Line:Line:Ground fault (Three phase symmetrical fault)"
      ],
      "metadata": {
        "id": "sQXmOAUXZymo"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZT6H8sJk6acb",
        "outputId": "895d1557-d661-4fa8-e232-ea761d3c5e4c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "isFault = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/detect_dataset.csv')\n",
        "faultClassification = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/classData.csv')"
      ],
      "metadata": {
        "id": "ZzZXgU0sZ-CI"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "isFault.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "mUWJxwf7axyL",
        "outputId": "d6e342a2-05a9-4af1-8146-5ab1dd086b22"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Output (S)          Ia        Ib          Ic        Va        Vb        Vc  \\\n",
              "0           0 -170.472196  9.219613  161.252583  0.054490 -0.659921  0.605431   \n",
              "1           0 -122.235754  6.168667  116.067087  0.102000 -0.628612  0.526202   \n",
              "2           0  -90.161474  3.813632   86.347841  0.141026 -0.605277  0.464251   \n",
              "3           0  -79.904916  2.398803   77.506112  0.156272 -0.602235  0.445963   \n",
              "4           0  -63.885255  0.590667   63.294587  0.180451 -0.591501  0.411050   \n",
              "5           0  -55.954681 -1.001882   56.956562  0.193414 -0.590695  0.397281   \n",
              "6           0  -45.248446 -2.586980   47.835426  0.212393 -0.584136  0.371743   \n",
              "7           0  -47.845420 -3.428094   51.273513  0.216396 -0.590123  0.373727   \n",
              "8           0  -43.294259 -4.511300   47.805558  0.229748 -0.587588  0.357840   \n",
              "9           0  -43.474722 -5.388233   48.862955  0.235733 -0.591320  0.355587   \n",
              "\n",
              "   Unnamed: 7  Unnamed: 8  \n",
              "0         NaN         NaN  \n",
              "1         NaN         NaN  \n",
              "2         NaN         NaN  \n",
              "3         NaN         NaN  \n",
              "4         NaN         NaN  \n",
              "5         NaN         NaN  \n",
              "6         NaN         NaN  \n",
              "7         NaN         NaN  \n",
              "8         NaN         NaN  \n",
              "9         NaN         NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-809d2ada-b112-41c3-989e-ec70249d5a49\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Output (S)</th>\n",
              "      <th>Ia</th>\n",
              "      <th>Ib</th>\n",
              "      <th>Ic</th>\n",
              "      <th>Va</th>\n",
              "      <th>Vb</th>\n",
              "      <th>Vc</th>\n",
              "      <th>Unnamed: 7</th>\n",
              "      <th>Unnamed: 8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>-170.472196</td>\n",
              "      <td>9.219613</td>\n",
              "      <td>161.252583</td>\n",
              "      <td>0.054490</td>\n",
              "      <td>-0.659921</td>\n",
              "      <td>0.605431</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>-122.235754</td>\n",
              "      <td>6.168667</td>\n",
              "      <td>116.067087</td>\n",
              "      <td>0.102000</td>\n",
              "      <td>-0.628612</td>\n",
              "      <td>0.526202</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>-90.161474</td>\n",
              "      <td>3.813632</td>\n",
              "      <td>86.347841</td>\n",
              "      <td>0.141026</td>\n",
              "      <td>-0.605277</td>\n",
              "      <td>0.464251</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>-79.904916</td>\n",
              "      <td>2.398803</td>\n",
              "      <td>77.506112</td>\n",
              "      <td>0.156272</td>\n",
              "      <td>-0.602235</td>\n",
              "      <td>0.445963</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>-63.885255</td>\n",
              "      <td>0.590667</td>\n",
              "      <td>63.294587</td>\n",
              "      <td>0.180451</td>\n",
              "      <td>-0.591501</td>\n",
              "      <td>0.411050</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>-55.954681</td>\n",
              "      <td>-1.001882</td>\n",
              "      <td>56.956562</td>\n",
              "      <td>0.193414</td>\n",
              "      <td>-0.590695</td>\n",
              "      <td>0.397281</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>-45.248446</td>\n",
              "      <td>-2.586980</td>\n",
              "      <td>47.835426</td>\n",
              "      <td>0.212393</td>\n",
              "      <td>-0.584136</td>\n",
              "      <td>0.371743</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>-47.845420</td>\n",
              "      <td>-3.428094</td>\n",
              "      <td>51.273513</td>\n",
              "      <td>0.216396</td>\n",
              "      <td>-0.590123</td>\n",
              "      <td>0.373727</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0</td>\n",
              "      <td>-43.294259</td>\n",
              "      <td>-4.511300</td>\n",
              "      <td>47.805558</td>\n",
              "      <td>0.229748</td>\n",
              "      <td>-0.587588</td>\n",
              "      <td>0.357840</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0</td>\n",
              "      <td>-43.474722</td>\n",
              "      <td>-5.388233</td>\n",
              "      <td>48.862955</td>\n",
              "      <td>0.235733</td>\n",
              "      <td>-0.591320</td>\n",
              "      <td>0.355587</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-809d2ada-b112-41c3-989e-ec70249d5a49')\"\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-809d2ada-b112-41c3-989e-ec70249d5a49 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-809d2ada-b112-41c3-989e-ec70249d5a49');\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-d6d5766c-57cf-4bbb-b880-0407842042c8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d6d5766c-57cf-4bbb-b880-0407842042c8')\"\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-d6d5766c-57cf-4bbb-b880-0407842042c8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "isFault",
              "summary": "{\n  \"name\": \"isFault\",\n  \"rows\": 12001,\n  \"fields\": [\n    {\n      \"column\": \"Output (S)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ia\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 377.1584696606427,\n        \"min\": -883.5423165,\n        \"max\": 885.7385707,\n        \"num_unique_values\": 12001,\n        \"samples\": [\n          -3.616197874,\n          664.4536614\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ib\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 357.45861265833565,\n        \"min\": -900.5269515,\n        \"max\": 889.8688838,\n        \"num_unique_values\": 12001,\n        \"samples\": [\n          54.90147399,\n          -841.3494391\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ic\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 302.05280885125353,\n        \"min\": -883.3577621,\n        \"max\": 901.2742607,\n        \"num_unique_values\": 12001,\n        \"samples\": [\n          -54.9612497,\n          -26.12632658\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Va\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3462208166683564,\n        \"min\": -0.62074797,\n        \"max\": 0.609863587,\n        \"num_unique_values\": 11996,\n        \"samples\": [\n          -0.421158238,\n          0.444234389\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3576435032292448,\n        \"min\": -0.659920931,\n        \"max\": 0.627874757,\n        \"num_unique_values\": 11813,\n        \"samples\": [\n          0.45370005,\n          -0.12368055\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34927187675497695,\n        \"min\": -0.612709171,\n        \"max\": 0.608242799,\n        \"num_unique_values\": 11987,\n        \"samples\": [\n          -0.05057768,\n          -0.598466853\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unnamed: 7\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unnamed: 8\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop the last two columns\n",
        "isFault.drop(isFault.columns[[7,8]], axis=1, inplace=True)\n",
        "isFault.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IwWIIOaMa7Tg",
        "outputId": "867c7199-662c-4acd-9671-f85d3dac7b23"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Output (S)          Ia        Ib          Ic        Va        Vb        Vc\n",
              "0           0 -170.472196  9.219613  161.252583  0.054490 -0.659921  0.605431\n",
              "1           0 -122.235754  6.168667  116.067087  0.102000 -0.628612  0.526202\n",
              "2           0  -90.161474  3.813632   86.347841  0.141026 -0.605277  0.464251\n",
              "3           0  -79.904916  2.398803   77.506112  0.156272 -0.602235  0.445963\n",
              "4           0  -63.885255  0.590667   63.294587  0.180451 -0.591501  0.411050"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a5bb7117-f89e-4fcf-b3e1-c6d3499be0ce\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Output (S)</th>\n",
              "      <th>Ia</th>\n",
              "      <th>Ib</th>\n",
              "      <th>Ic</th>\n",
              "      <th>Va</th>\n",
              "      <th>Vb</th>\n",
              "      <th>Vc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>-170.472196</td>\n",
              "      <td>9.219613</td>\n",
              "      <td>161.252583</td>\n",
              "      <td>0.054490</td>\n",
              "      <td>-0.659921</td>\n",
              "      <td>0.605431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>-122.235754</td>\n",
              "      <td>6.168667</td>\n",
              "      <td>116.067087</td>\n",
              "      <td>0.102000</td>\n",
              "      <td>-0.628612</td>\n",
              "      <td>0.526202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>-90.161474</td>\n",
              "      <td>3.813632</td>\n",
              "      <td>86.347841</td>\n",
              "      <td>0.141026</td>\n",
              "      <td>-0.605277</td>\n",
              "      <td>0.464251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>-79.904916</td>\n",
              "      <td>2.398803</td>\n",
              "      <td>77.506112</td>\n",
              "      <td>0.156272</td>\n",
              "      <td>-0.602235</td>\n",
              "      <td>0.445963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>-63.885255</td>\n",
              "      <td>0.590667</td>\n",
              "      <td>63.294587</td>\n",
              "      <td>0.180451</td>\n",
              "      <td>-0.591501</td>\n",
              "      <td>0.411050</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a5bb7117-f89e-4fcf-b3e1-c6d3499be0ce')\"\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-a5bb7117-f89e-4fcf-b3e1-c6d3499be0ce 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-a5bb7117-f89e-4fcf-b3e1-c6d3499be0ce');\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-f32d15fe-88d0-4dc0-a0b8-23f69ac1cf0d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f32d15fe-88d0-4dc0-a0b8-23f69ac1cf0d')\"\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-f32d15fe-88d0-4dc0-a0b8-23f69ac1cf0d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "isFault",
              "summary": "{\n  \"name\": \"isFault\",\n  \"rows\": 12001,\n  \"fields\": [\n    {\n      \"column\": \"Output (S)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ia\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 377.1584696606427,\n        \"min\": -883.5423165,\n        \"max\": 885.7385707,\n        \"num_unique_values\": 12001,\n        \"samples\": [\n          -3.616197874,\n          664.4536614\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ib\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 357.45861265833565,\n        \"min\": -900.5269515,\n        \"max\": 889.8688838,\n        \"num_unique_values\": 12001,\n        \"samples\": [\n          54.90147399,\n          -841.3494391\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ic\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 302.05280885125353,\n        \"min\": -883.3577621,\n        \"max\": 901.2742607,\n        \"num_unique_values\": 12001,\n        \"samples\": [\n          -54.9612497,\n          -26.12632658\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Va\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3462208166683564,\n        \"min\": -0.62074797,\n        \"max\": 0.609863587,\n        \"num_unique_values\": 11996,\n        \"samples\": [\n          -0.421158238,\n          0.444234389\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3576435032292448,\n        \"min\": -0.659920931,\n        \"max\": 0.627874757,\n        \"num_unique_values\": 11813,\n        \"samples\": [\n          0.45370005,\n          -0.12368055\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34927187675497695,\n        \"min\": -0.612709171,\n        \"max\": 0.608242799,\n        \"num_unique_values\": 11987,\n        \"samples\": [\n          -0.05057768,\n          -0.598466853\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "isFault.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "lceQ5_uncofi",
        "outputId": "02f88222-7a15-41d6-ab37-aef6e548a94b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Output (S)            Ia            Ib            Ic            Va  \\\n",
              "count  12001.000000  12001.000000  12001.000000  12001.000000  12001.000000   \n",
              "mean       0.457962      6.709369    -26.557793     22.353043      0.010517   \n",
              "std        0.498250    377.158470    357.458613    302.052809      0.346221   \n",
              "min        0.000000   -883.542316   -900.526951   -883.357762     -0.620748   \n",
              "25%        0.000000    -64.348986    -51.421937    -54.562257     -0.237610   \n",
              "50%        0.000000     -3.239788      4.711283     -0.399419      0.002465   \n",
              "75%        1.000000     53.823453     69.637787     45.274542      0.285078   \n",
              "max        1.000000    885.738571    889.868884    901.274261      0.609864   \n",
              "\n",
              "                 Vb            Vc  \n",
              "count  12001.000000  12001.000000  \n",
              "mean      -0.015498      0.004980  \n",
              "std        0.357644      0.349272  \n",
              "min       -0.659921     -0.612709  \n",
              "25%       -0.313721     -0.278951  \n",
              "50%       -0.007192      0.008381  \n",
              "75%        0.248681      0.289681  \n",
              "max        0.627875      0.608243  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-432309ca-e3e5-446e-a7ff-9710bc74d95e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Output (S)</th>\n",
              "      <th>Ia</th>\n",
              "      <th>Ib</th>\n",
              "      <th>Ic</th>\n",
              "      <th>Va</th>\n",
              "      <th>Vb</th>\n",
              "      <th>Vc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>12001.000000</td>\n",
              "      <td>12001.000000</td>\n",
              "      <td>12001.000000</td>\n",
              "      <td>12001.000000</td>\n",
              "      <td>12001.000000</td>\n",
              "      <td>12001.000000</td>\n",
              "      <td>12001.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.457962</td>\n",
              "      <td>6.709369</td>\n",
              "      <td>-26.557793</td>\n",
              "      <td>22.353043</td>\n",
              "      <td>0.010517</td>\n",
              "      <td>-0.015498</td>\n",
              "      <td>0.004980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.498250</td>\n",
              "      <td>377.158470</td>\n",
              "      <td>357.458613</td>\n",
              "      <td>302.052809</td>\n",
              "      <td>0.346221</td>\n",
              "      <td>0.357644</td>\n",
              "      <td>0.349272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-883.542316</td>\n",
              "      <td>-900.526951</td>\n",
              "      <td>-883.357762</td>\n",
              "      <td>-0.620748</td>\n",
              "      <td>-0.659921</td>\n",
              "      <td>-0.612709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-64.348986</td>\n",
              "      <td>-51.421937</td>\n",
              "      <td>-54.562257</td>\n",
              "      <td>-0.237610</td>\n",
              "      <td>-0.313721</td>\n",
              "      <td>-0.278951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-3.239788</td>\n",
              "      <td>4.711283</td>\n",
              "      <td>-0.399419</td>\n",
              "      <td>0.002465</td>\n",
              "      <td>-0.007192</td>\n",
              "      <td>0.008381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>53.823453</td>\n",
              "      <td>69.637787</td>\n",
              "      <td>45.274542</td>\n",
              "      <td>0.285078</td>\n",
              "      <td>0.248681</td>\n",
              "      <td>0.289681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>885.738571</td>\n",
              "      <td>889.868884</td>\n",
              "      <td>901.274261</td>\n",
              "      <td>0.609864</td>\n",
              "      <td>0.627875</td>\n",
              "      <td>0.608243</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-432309ca-e3e5-446e-a7ff-9710bc74d95e')\"\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-432309ca-e3e5-446e-a7ff-9710bc74d95e 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-432309ca-e3e5-446e-a7ff-9710bc74d95e');\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-11dbe1ce-7ac8-49e5-9cbc-b67126d52a87\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-11dbe1ce-7ac8-49e5-9cbc-b67126d52a87')\"\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-11dbe1ce-7ac8-49e5-9cbc-b67126d52a87 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"isFault\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Output (S)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4242.84494960579,\n        \"min\": 0.0,\n        \"max\": 12001.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.4579618365136239,\n          1.0,\n          0.49825041783899787\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ia\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4252.7504933446935,\n        \"min\": -883.5423165,\n        \"max\": 12001.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.709368569511546,\n          -3.239788336,\n          12001.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ib\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4254.6696692837995,\n        \"min\": -900.5269515,\n        \"max\": 12001.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -26.55779281444654,\n          4.711282775,\n          12001.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ic\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4254.38530171343,\n        \"min\": -883.3577621,\n        \"max\": 12001.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          22.353042564928337,\n          -0.399419207,\n          12001.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Va\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4242.974266966864,\n        \"min\": -0.62074797,\n        \"max\": 12001.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.010517366846596113,\n          0.002465466,\n          12001.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4242.982245107689,\n        \"min\": -0.659920931,\n        \"max\": 12001.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -0.015497891082576452,\n          -0.007192393,\n          12001.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4242.975625403812,\n        \"min\": -0.612709171,\n        \"max\": 12001.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.004980490080743271,\n          0.008381389,\n          12001.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "isFault.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AZCXgFWDeg-Q",
        "outputId": "5be6230a-d4fe-4f83-e834-81ccc7f4a464"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 12001 entries, 0 to 12000\n",
            "Data columns (total 7 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Output (S)  12001 non-null  int64  \n",
            " 1   Ia          12001 non-null  float64\n",
            " 2   Ib          12001 non-null  float64\n",
            " 3   Ic          12001 non-null  float64\n",
            " 4   Va          12001 non-null  float64\n",
            " 5   Vb          12001 non-null  float64\n",
            " 6   Vc          12001 non-null  float64\n",
            "dtypes: float64(6), int64(1)\n",
            "memory usage: 656.4 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "isFault.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CxQRsc3Jfp5q",
        "outputId": "04b0d00a-eb67-431d-c760-1ab5af97ece1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12001, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr = isFault.corr( method= \"pearson\")"
      ],
      "metadata": {
        "id": "IInGDOC1fwP_"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(corr, annot=True, fmt=\".1f\", linewidth =.5)\n",
        "# plt.display()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "tJExwSyegafX",
        "outputId": "7c94ba55-391c-435e-8ee2-ebd1c2985545"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(25,6))\n",
        "\n",
        "a1 = plt.subplot2grid((1,3),(0,0))\n",
        "a1.scatter(isFault['Ia'], isFault['Va'])\n",
        "a1.set_title('Line a condition')\n",
        "a1.set_xlabel('Ia')\n",
        "a1.set_ylabel('Va')\n",
        "\n",
        "a2 = plt.subplot2grid((1,3),(0,1))\n",
        "a2.scatter(isFault['Ib'], isFault['Vb'])\n",
        "a2.set_title('Line b condition')\n",
        "a2.set_xlabel('Ib')\n",
        "a2.set_ylabel('Vb')\n",
        "\n",
        "a3 = plt.subplot2grid((1,3),(0,2))\n",
        "a3.scatter(isFault['Ic'], isFault['Vc'])\n",
        "a3.set_title('Line c condition')\n",
        "a3.set_xlabel('Ic')\n",
        "a3.set_ylabel('Vc')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "j4cuYe05rm0F",
        "outputId": "0826f7c2-b207-4b2c-f853-a04ccb1ff8c7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x600 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(x=isFault['Output (S)'].value_counts(), labels=['No Fault', 'Fault'],\n",
        "        explode = [0, 0.2], autopct= '%1.1f%%', labeldistance=1.15,\n",
        "       colors=['green', 'red'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "JoOZpIMor38m",
        "outputId": "40dd92d2-47aa-494f-c1bc-7574d3c244e6"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAGZCAYAAABmNy2oAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAttklEQVR4nO3dd3xUVcLG8d+kQgokQCD0UATpRQFBelMR30VZQVdXWcuri+i66rq4uyiWV8Syigi6giy4KojouiA2OgYWpEWQ3kJEei+BJGTm/eOGSQCBlJmcufc+389nPklmJjdPKPPMuefcez0+n8+HiIgIEGY6gIiIhA6VgoiI+KkURETET6UgIiJ+KgUREfFTKYiIiJ9KQURE/FQKIiLip1IQERE/lYKIiPipFERExE+lICIifioFERHxUymIiIifSkFERPxUCiIi4qdSEBERP5WCiIj4qRQkpEycOJGEhATTMURcS6XgQIMGDcLj8fDSSy+dc//nn3+Ox+Mp0bYnTpyIx+O54DZ+/PgSbfdihg8fTsuWLYOybRG5UITpABIcZcqUYeTIkTzwwAMkJiYGdNvlypVj48aN59xXvnz5gP4METFDIwWH6tmzJ8nJyYwYMeKSz/v0009p0qQJ0dHRpKSk8Nprr1122x6Ph+Tk5HNuZcuW5euvv6Zjx44kJCRQsWJF+vbty9atW/3fN3/+fDweD0eOHPHfl5aWhsfjIT09/YKfM3HiRJ599ll++OEH/4hk4sSJhf0jEJFiUCk4VHh4OC+++CKjR49m586dv/icFStWMGDAAG677TbWrFnD8OHDGTZsWLFfeE+ePMljjz3G8uXLmTNnDmFhYdx88814vd5ibW/gwIE8/vjjNGnShN27d7N7924GDhxYrG2JSOFo95GD3XzzzbRs2ZJnnnmG995774LH//73v9OjRw+GDRsGQIMGDVi3bh2vvPIKgwYNuuh2jx49SlxcnP/ruLg49uzZQ//+/c953oQJE0hKSmLdunU0bdq0yPnLli1LXFwcERERJCcnF/n7RaToNFJwuJEjRzJp0iTWr19/wWPr16/n2muvPee+a6+9ls2bN5Obm3vRbcbHx5OWlua/LV68GIDNmzdz++23U7duXcqVK0dKSgoAGRkZgfuFRCSoNFJwuM6dO3Pdddfx1FNPXfLdf1GEhYVRv379C+6/6aabqF27NuPGjaNatWp4vV6aNm1Kdna2//sAfD6f/3tycnICkklEAkOl4AIvvfQSLVu2pGHDhufc36hRIxYtWnTOfYsWLaJBgwaEh4cX6WccPHiQjRs3Mm7cODp16gRAamrqOc9JSkoCYPfu3f4VUWlpaZfcblRU1CVHLSISWNp95ALNmjXjjjvu4M033zzn/scff5w5c+bw/PPPs2nTJiZNmsRbb73FE088UeSfkZiYSMWKFXn33XfZsmULc+fO5bHHHjvnOfXr16dmzZoMHz6czZs3M3PmzMuudkpJSWH79u2kpaVx4MABsrKyipxNRApPpeASzz333AWrgFq3bs3UqVOZMmUKTZs25emnn+a5554r1m6msLAwpkyZwooVK2jatCl//OMfeeWVV855TmRkJJMnT2bDhg00b96ckSNH8sILL1xyu/379+f666+nW7duJCUlMXny5CJnE5HC8/gK7uAVERFX00hBRET8VAoiIuKnUhARET+VgoiI+KkURETET6UgIiJ+KgUREfFTKYiIiJ9KQURE/FQKIiLip1IQERE/lYKIiPipFERExE+lICIifioFERHx0+U4xXGOnD7CgcwDHMg8wP6T+/2fH8s6RlZuFllnssjOzSYr99yPHjxER0QTHZ53y/u8TEQZoiOiSSiTQFJMEpViKpEUm0RSTBJJsUlEhUeZ/pVFAkalILZyxnuG9CPpbDm0ha2HtpJ+JJ30o+mkH0ln57GdHMw8SI43p1QzxUfFUzm2MrUTalMvsR51E+vmf6xQj4QyCaWaR6QkdOU1CVk7j+0kbU+a/7Zm3xq2Hd7GGe8Z09GKJLFMIldUvIIWVVrQKrkVLZNb0rxKc2KjYk1HE7mASkFCwq7ju1i4YyHLdy0nbU8aP+z9gQOZB0zHCpowTxhXVLiCVlVb0bJKS9pWb8s1Na6hbGRZ09HE5VQKYsT2w9tZsGMBC3csZOGOhWw9vNV0JOOiwqNoU60NXWp3oUtKFzrU7EBcVJzpWOIyKgUpFQcyDzBz00xmbZvFwh0L+enYT6YjhbyIsAhaV21Nl9pduK7edXSu3ZnI8EjTscThVAoSNGv3rWXGphnM2DSDJTuX4PV5TUeytfioeHrW7UnfBn3p26AvlWMrm44kDqRSkIDx+rzMT5/Pfzb8hxmbZrD9yHbTkRwrzBNGh5od6NewH7c0uoU6iXVMRxKHUClIia3avYoPVn/AlLVT2HV8l+k4rtShZgd+2/y3DGgygAplK5iOIzamUpBiyTiawYerP+TDNR+ydv9a03EkT1R4FH2u6MOdze6kb4O+REdEm44kNqNSkELLzs3mk7WfMG7lOBbuWIgP/dMJZYllErm18a08ePWDtKraynQcsQmVglzWzmM7eWf5O4xbOY59J/eZjiPF0KFmB4a0GcKvG/9aK5jkklQKclHzts/jrWVvMX3jdNsdRSy/rGpcVf73qv/lgaseoGp8VdNxJASpFOQcObk5vP/D+7y+5HXNFThYZFgk/Rv354n2T3BVtatMx5EQolIQAE6fOc34leN5ZfErZBzNMB1HStEN9W9gWOdhtK/Z3nQUCQEqBZc7mX2St5e/zWv/fY09J/aYjiMGda/TnWGdh9E1pavpKGKQSsGljmUdY/TS0by+5HUOnjpoOo6EkI61OvK3Tn/juvrXmY4iBqgUXCYnN4d/rPgHzy14jv2Z+03HkRDWqVYnXu39Km2rtzUdRUqRSsFFPlv/GUNnD2Xzoc2mo4hNePAwsOlARvQYQUpCiuk4UgpUCi6wdOdSnpj1BKkZqaajiE1Fh0fzcNuH+Wvnv+pKcg6nUnCwHUd28OTsJ5m6dqrpKOIQFctW5OkuTzO4zWAiwnQ1XydSKThQTm4Ory5+lRe+e4HMnEzTccSBmldpzrt936VdjXamo0iAqRQcZn76fAbPHMz6A+tNRxGHC/OE8eBVDzKi5wjKRZczHUcCRKXgEIdPHeaJb59gQtoE01HEZarGVWXU9aO4tcmtpqNIAKgUHGDq2qk88tUj7D2513QUcbEbr7iRMX3GUDuhtukoUgIqBRs7fOowD858UBPJEjLiouIYdf0o7ml1j+koUkwqBZuas20Od39+Nz8f/9l0FJEL3NLoFt7t+y4VYyqajiJFpFKwmdNnTvPU7KcYtXSULnIjIa1qXFUm9ptI73q9TUeRIlAp2Mjqvau547M7+HHfj6ajiBSKBw9D2g7h5V4vUyaijOk4UggqBZt4c+mb/GnWn8jOzTYdRaTIGic1Zuqvp9KkchPTUeQyVAoh7mT2Se6fcT+Tf5xsOopIicRGxjLhVxMY0GSA6ShyCSqFELb54GZumXqLdheJozx2zWOM7DVSp8kIUSqFEDV943Tu+vddHM06ajqKSMB1qd2FqbdOpXJsZdNR5DwqhRDj9XkZNncYI1JHaHWROFr1+OpMGzCNa2pcYzqKFKBSCCEnsk8wcNpAvtz8pekoIqUiKjyKsX3Gcm/re01HkTwqhRCx+/hubvzoRlbtWWU6ikipG3rtUF7s8SIej8d0FNdTKYSAtfvW0uejPmQczTAdRcSYAU0GMKnfJB3PYJhKwbC52+dyy8e3aEJZBOhYqyPTb5tOYtlE01FcS6Vg0Ps/vM990+8jx5tjOopIyGhUqRFf3/k1tcrXMh3FlcJMB3Crkakjufvzu1UIIudZf2A97d9rr+NzDFEpGPDMvGcYOmeo6RgiIWvX8V10m9SNtD1ppqO4jnYflbKhs4cyctFI0zFEbCGxTCLf/vZbrq52tekorqFSKEV//PqPvLH0DdMxRGylfHR5vr7zax3kVkpUCqXA5/Px0JcP8fbyt01HEbGl+Kh4Zv5mJp1qdzIdxfFUCkHm9Xm5f/r9TEibYDqKiK3FRsYy4/YZdKvTzXQUR9NEc5AN+XKICkEkAE7mnKTv5L4sylhkOoqjqRSCaNjcYdplJBJAmTmZ9J3clzV715iO4ljafRQkbyx5gz9+80fTMUQcqWpcVVLvSaVuYl3TURxHpRAE7//wPoM+H6RTX4sEUb3EeqTek0pyXLLpKI6i3UcBNn3jdO6dfq8KQSTIth7eynUfXMeR00dMR3EUlUIAfbfjOwZOG8gZ7xnTUURcYfXe1dw0+SZO5ZwyHcUxVAoBsvXQVm7++GZOnzltOoqIq6RmpDLoP4NMx3AMlUIAHMs6xv9M+R8OnjpoOoqIK01dO5XnFjxnOoYjqBRKKNeby23TbmPd/nWmo4i42vD5w/l03aemY9ieSqGE/jTrT3y15SvTMURcz4ePuz6/S2dWLSEtSS2B91a+x30z7jMdwxnmAQvOu68i8PB59/mAD4EtwECg0UW2lwvMBTYDh4FooC7QEyiX95wzwHRgAxAH3AjUK7CNRcBRoE+RfxsxqGa5miy7fxlV4qqYjmJLGikUU2pGKr+f+XvTMZwlCXi8wO2eX3jOkkJuKwfYDXQGHsAqkIPA5ALPWQHsAu4DrgI+Bf9K4sN5j3cv0m8gIeCnYz/R7+N+ZJ3JMh3FllQKxbD/5H4GThuoq6YFWhgQX+AWe97ju4HFwK8Ksa0ywF1AU6ASUBPrHf9u4Ejec/YDDYHKQFsgM+8G8AXQK287YjtLdi7hsW8eMx3DllQKReTzWfstdx3fZTqK8xwCXgXewHrXfqTAY9l5992IVRjFcXa18NkX+mQgA2tUsQVrF1IMsBqI4OK7psQWxi4fq4nnYlApFNHLi17m6y1fm47hPDWAfsCdQF+s3Tf/BM7uAfgG693+lcXcfg4wG2hGfim0wiqGMcB3wK3AKaz5jT7AHGAU8C/gWDF/rhh17/R72X54u+kYtqKJ5iJYlLGIrpO66ojl0nAKa8RwHda792+x5gai8x4fzqUnmgvKBT7GemEfxKV3CX2OVRQJWKVwP9aE8768nye207Z6W1J/l0pkeKTpKLagkUIhHTp1iNs/vV2FUFrKYq0+OgRsz/v4EvBs3g1gKtZo4lJygU+wVhHdxaULYTvWi39bIB24AogCmuR9Lbb0/c/fM3T2UNMxbCPCdAC7uPvzu/np2E+mY7hHFlYRNMd6UW593uNvY40iGl5iG2cL4SDWCCHmEs/NAWYC/bHeKvkAb4HteC/yfWILry95nW51utG3QV/TUUKeRgqFMHbZWL7Y9IXpGM72Dda78cNYk78fY/3rbIY1sVzlvBtAeSCxwDZGA+vzPs/FGknswnqh9wLH826/NNhbiDUyqJr3dc28be0BvgdqlezXE7N8+Bj0+SD2nthrOkrI00jhMrYe2sqTs540HcP5jgHTsOYSYrBehO/jwmWpl3KQ/BVGx4CNeZ+/c97z7gbqFPh6L7AWeLDAfY2xSuqfWLux+hchh4Skg6cOMvjLwXw6QCuSLkUTzZfg9XnpMrELqRmppqOISIB8/OuPGdBkgOkYIUu7jy5h1JJRKgQRhxny5RD2n9xvOkbIUilcxNZDW/nbvL+ZjiEiAbY/cz8PffmQ6RghS6XwC3w+H/fPuJ/MnMzLP1lEbOeTdZ8wbd000zFCkkrhF7y36j3mpc8zHUNEguihLx/iYKYujHU+lcJ5Dp06pANdRFxg38l9/GXOX0zHCDkqhfP8be7fdFlNEZcYv2o8K3evNB0jpKgUCkjbk8Y/VvzDdAwRKSVen5eHv3oYrczPp1Io4OGvHsbr0/kMRNxk8U+L+WD1B6ZjhAyVQp4PVn+gYxJEXOrJ2U9yPOu46RghQaUAHM86rlNZiLjYnhN7eG7Bc6ZjhASVAjAidQS7T+w2HUNEDBq1dBSbD242HcM415fC3hN7GbV0lOkYImJYjjeHp+c/bTqGca4vhf/77v905LKIAPDxjx+zeu9q0zGMcnUpZBzN0BJUEfHz4WPYvGGmYxjl6lJ4dv6zZOdmm44hIiFk+sbpLN251HQMY1xbCpsObmLSD5NMxxCREOTmMyS7thSenvc0ub5c0zFEJATN3jabBekLTMcwwpWlsHbfWqaunWo6hoiEMLeOFlxZCq/+91V86FwnInJxqRmprjzLgetKYdfxXXy05iPTMUTEBkYuGmk6QqlzXSm8ufRNrTgSkUKZuWkma/etNR2jVLmqFI5nHeed5e+YjiEiNuHDx2v/fc10jFLlqlIYt3IcR7OOmo4hIjby0ZqP2Htir+kYpcY1pXDGe0bnOBKRIsvKzWLssrGmY5Qa15TCtHXTyDiaYTqGiNjQ28vf5vSZ06ZjlArXlILOcSQixbU/cz+frf/MdIxS4YpS2HJoi2uPThSRwHhv1XumI5QKV5TC+JXjdbCaiJTIvO3z2HZ4m+kYQef4UjjjPaMT34lIifnw8d5K548WHF8KMzbOYM+JPaZjiIgDTPxhIrleZ59I0/GlMG7lONMRRMQhdh3fxZebvzQdI6gcXQo7j+3km63fmI4hIg4yftV40xGCytGl8PGPH+P1eU3HEBEH+XLzlxw6dch0jKBxdCl8su4T0xFExGHOeM/w+YbPTccIGseWQsbRDJb+7N7rrIpI8Dj5DadjS+GTtc79SxMRs+Zsm8PhU4dNxwgK55aCg5tcRMzK8ebwn43/MR0jKBxZCtp1JCLB5tQ3no4shWnrppmOICION3vbbI6cPmI6RsA5shScvDJAREJDdm42MzbOMB0j4BxXCseyjvHfnf81HUNEXMCJB8c6rhTmbp/LGe8Z0zFExAVmb5uNz+esMzA7rhS+3fqt6Qgi4hJ7T+7lh70/mI4RUI4rBScO50QkdDntjaijSmHroa2uuAiGiISOWdtmmY4QUI4qBY0SRKS0pWakcirnlOkYAeOoUnBaY4tI6Dt95jQLdyw0HSNgHFUKqRmppiOIiAvNT59vOkLAOKYUthzawoHMA6ZjiIgLOem0Oo4phSU7l5iOICIutXzXcsdc0EulICJSQsezj7Nu/zrTMQJCpSAiEgBOeQ1yRCmcyjnF6r2rTccQERdbutMZ8wqOKIUVu1eQ480xHUNEXMwpk82OKIVlPy8zHUFEXG7t/rWcyD5hOkaJOaIUVu/TriMRMcvr8zpistkRpfDjvh9NRxARUSmEAp/P54i/CBGxv/X715uOUGK2L4X0I+lk5mSajiEiwvoDKgXjnPCXICLO4ITXI9uXwsYDG01HEBEBYPvh7Zw+c9p0jBKxfSlsOLDBdAQREQByfblsOrjJdIwSsX0pbDm8xXQEERE/u79RtX0p/HT0J9MRRET87P6aZPtS+Pn4z6YjiIj42f01ydalcPjUYS1HFZGQsvPYTtMRSsTWpWD3P3wRcR6NFAxSKYhIqPn5mErBGJWCiISaXcd34fP5TMcoNpWCiEgA5Xhz2Hdyn+kYxWbrUtifud90BBGRC+w+sdt0hGKzdSkczTpqOoKIyAWOZR0zHaHYbF0Kdv6DFxHnsvNrk0pBRCTA7PzapFIQEQmw41nHTUcoNpWCiEiA2fm1SaUgIhJgdn5tsnUp2HmIJiLOpVIwJCs3y3QEEZEL2PlEnbYuBRGRUOT1eU1HKDbbloKdzy0iIs6W68s1HaHYbFsKIiKhys4jhQjTAYrLh0YKUjIRYRG0rd6WXnV70aNODyrHVjYdSRyifJnypiMUm21LQaQ4rqhwBb3q9qJXvV50S+lm6/+8IsFg21LQnIIURoWyFehRp4e/CFISUkxHEglpti0Fj8djOoKEoKjwKDrU7ECvur3oXa83rau2JsyjqTORwrJtKYR5wogMiyTHm2M6ihjWJKmJvwQ61+5MbFSs6UgitmXbUgCIj47n0KlDpmNIKasSW4Ve9XrRq24vetbtSbX4aqYjiTiGvUshSqXgBmUjytKpdid61+1Nr3q9aFa5mXYfigSJvUshOt50BAkCDx5aVW1lTQ7X7UXHWh2Jjog2HUvEFWxdCuWiy5mOIAFSs1xN/wqhHnV6kBSbZDqSiCvZuhTiozRSsKv4qHi6pnT1F8GVla40HUlEsHspaPeRbYR7wmlTvY1/l9A1Na4hMjzSdCwROY+tS6FclHYfhbJ6ifX8I4HudbqTUCbBdCQRuQxbl0JyXLLpCFJAYplEutfpTu96velVtxd1EuuYjiQiRWTrUtD6dLMiwyJpX7O9f6no1dWu1tHDIjZn61KoXq666Qiu0zipsX9eoEtKF+Ki4kxHEpEAsncpxKsUgq1ybGV61u3pLwIVsYiz2bsU9AIVcGUiytCpVif/BHGLKi109LCIi9i6FKrEViHcE27rS9+Z5sFDi+QW/pFAp9qdKBNRxnQsETHE1qUQHhZOlbgq7Dq+y3QUW6keX/2cE8rpimMicpatSwGs0yOoFC4tLiqOLrW7+E8v3SipkelIIhKibF8KDSs1ZOnPS03HCClhnjCurna1vwTa12ivo4dFpFBsXwqNKuldL0CdhDr+g8a61+lOYtlE05FExIZsXwpuPZFaQpkEutfp7p8grlehnulIIuIAti8Ft4wUIsMiuabGNf6lom2qtSE8LNx0LBFxGNuXQr0K9Rx7reYrK13pHwl0Temqs8KKSNDZvhQiwiKoX6E+6w+sNx2lxCrFVDrn6OGa5WuajiQiLmP7UgBolNTIlqUQHR5Nx1od/auEWia31NHDImKUI0qheeXmfLb+M9MxLsuDh2ZVmvlLoFOtTpSNLGs6loiInyNKoU31NqYjXFS1+Gr+3UE96/akSlwV05FERC7KEaXQtnpb0xH8YiJj6FK7i/+YgSaVm5iOJEXh9cLy5TB3Lhw+bDqNOEW3bnD99aZTFIojSqFSTCXqJdZj6+Gtpf6zwzxhXFX1Kv9S0Q41OxAVHlXqOaQEtm+Hb7+FWbNUBhIckZEqhdLWrka7UiuFlIQU/y6hHnV7UKFshVL5uRIgR45YL/6zZlm3raX/ZkJcJtw+xxQ5pxSqt+OjNR8FZdvlo8vTrU43fxFcUfGKoPwcCZKcHFiyJL8Eli2DXJ1uXUpRhH1eau2T9DICOa8QERZBu+rt/LuE2lVvp6OH7WbDhvwSmD8fjh83nUjcTKVQ+loltyIqPIrs3OxifX+Dig38I4FudbpRLrpcgBNKUB04ALNn5xfBTz+ZTiSST6VQ+qIjomlbvS2pGamFen7FshXpUbeH/5iBWuVrBTmhBFRWFqSm5k8Qp6WBz2c6lcgvUymY0aNOj4uWQlR4FNfWvNa/VLRV1VaEecJKOaEUm88Ha9bkl8B338GpU6ZTiRRObKzpBIXmqFLoXqc7zy541v91s8rN/PMCnWt3JiYyxmA6KbJdu/J3B82eDXv3mk4kUjxV7HPQqqNK4Zoa1zCo5SC6p3SnZ92eVI2vajqSFEVmpjUpfLYI1q41nUgkMGxUCh6fTztixRCvF1asyC+BxYshu3gLBURC2rZtUKeO6RSFolKQ0pWenl8Cc+bAoUOmE4kEX2YmlLXHyS8dtftIQtDRozBvnlUC334LW7aYTiRSuuLjbVMIoFKQQDtzBpYuzS+B77/X0cPibjaaTwCVggTCpk35JTB/Phw7ZjqRSOhQKYjjHTxozQecPWYgI8N0IpHQlZxsOkGRqBTk8rKzraOHz04Qr1plrRwSkcvTSEEcYc2a/BJYuNBaPSEiRadSEFvas+fco4d37zadSMQZVApiC5mZ1gjgbBGsWWM6kYgzaU5BQpLPBytX5q8SWrzYOtOoiARXtWqmExSJjmh2soyM/BKYM8daNSQipScszLrAU4x9TsapkYKTHD9uHT18dqnopk2mE4m4W716tioEUCnYW26udcTw2RJYutQ6olhEQkOLFqYTFJlKwW62bMkvgXnzrHMLiUhoUilIwB06ZM0HnF0llJ5uOpGIFJZKQUosO9taGXS2BFas0NHDInZlw1LQ6qNQsHZtfgksWAAnT5pOJCIllZAAhw+bTlFkGimYsHevddTw2SLYtct0IhEJtObNTScoFpVCaTh1Cr77Ln+CeM0a62AyEXEuG+46ApVCcPh8kJaWXwKLFsHp06ZTiUhpUim43M6d+SUwZw7s3286kYiYZNPdR5poLq4TJ/KvPTxrFmzYYDqRiISK8HDrDAM2ujbzWRopFFZuLixbll8CS5ZATo7pVCISiq680paFACqFS9u6Nb8E5s6FI0dMJxIRO+jZ03SCYlMpFHT4sPXif7YItm0znUhE7Kh3b9MJis3dcwo5OfDf/+afXnrFCms3kYhIcUVHW6ensdnZUc9y30hh/fr8EliwwJowFhEJlI4dbVsI4IZS2L/fOnr422+tjzt3mk4kIk523XWmE5SI80uhdWsVgYiUHpuXQpjpAEFn878gEbGRqlVte9DaWc4vhT59TCcQEbew8aqjs5xfCj17QmSk6RQi4gYO2DPh/FIoVw6uvdZ0ChFxurAw6NXLdIoSc34pANxwg+kEIuJ0rVtDpUqmU5SYO0rhpptMJxARp3PAriNwSyk0amS1uIhIsDjkzac7SgHgrrtMJxARp7rySmjXznSKgHBPKfzmN1qFJCLBcffdphMEjHtKISlJE84iEnjh4Y7aE+GeUgBHtbmIhIhevaBaNdMpAsZdpdC3L1SoYDqFiDjJoEGmEwSUu0ohKgpuv910ChFxioQE6NfPdIqAclcpgKP2/YmIYbffbl1Ux0HcVwpt21rLx0RESsphu47AjaUAmnAWkZJr3Nh6k+kw7iyF3/7WOnmViEhxOfTNpTtfGatXhx49TKcQEbsKD7feXDqQO0sBYMgQ0wlExK6uv966ypoDeXw+n890CCN8PuuyeT/+aDqJiNjNggXQubPpFEHh3pGCxwNDh5pOISJ206mTYwsB3DxSAMjNhQYNYNs200lExC6+/tox1074Je4dKYA1WfTkk6ZTiIhdXH21owsB3D5SAMjKgrp1Ydcu00lEJNT9+9+OO63F+dw9UgDrEPXHHjOdQkRCXdOm8KtfmU4RdBopAJw8CbVqwaFDppOISKj66CNXnFBTIwWA2Fh45BHTKUQkVNWvDwMGmE5RKlQKZz3yCMTFmU4hIqFo6FBrYYoLqBTOSkyEBx80nUJEQk2tWq465b5KoaDHH3fcudFFpIT+9CeIjDSdotSoFApKToZHHzWdQkRCRbVqcN99plOUKq0+Ot+JE9ZFeH7+2XQSETHtww/hN78xnaJUaaRwvrg4eOUV0ylExLQuXVxXCKCRwsV16QILF5pOISImRETAqlXWAWsuo5HCxYwe7ZolaCJyniFDXFkIoJHCpT38MLz1lukUIlKakpNh40YoV850EiNUCpdy5Ih1au39+00nEZHS8v77jr3UZmFo99GlJCTAiy+aTiEipaVjR1cXAmikcHk+H7RrB8uWmU4iIsEUHg4rV1qX6XUxjRQux+Ox5hU8HtNJRCSYBg92fSGARgqFd++9MGGC6RQiEgyVK8OmTVC+vOkkxmmkUFgvvQRJSaZTiEgwjBypQsijUiispCT45z9NpxCRQLvxRhg0yHSKkKFSKIobb7SOXRBHeQnwAI8WuK9r3n0Fb5c7sfoJYAhQAygLNAbeOe85jwEVgJrAh+c99glwU1HDS8lUrao3e+fRnEJRZWVBmzawZo3pJBIAy4ABQDmgG/BG3v1dgQbAcwWeG5P3vIv5X2AuMB5IAb4FBgOfAf8DzADuB74ANgP3AD8BlYCjQBtgNlCrhL+TFFJYGHz7LfToYTpJSNFIoaiio2HyZChb1nQSKaETwB3AOCDxFx6PAZIL3C53fOti4G6sQknBKokWwPd5j6/Pe+xq4Pa87W3Pe+xJ4PeoEErVk0+qEH6BSqE4mjSB114znUJK6CHgRqDnRR7/EOtdfFPgKSDzMtvrAEwHfgZ8wDxgE9A77/EWwHLgMLACOAXUB1KBlYCuEl6K2rWD5583nSIkRZgOYFu//z188w385z+mk0gxTMF6Ib7YIYm/AWoD1YDVwJ+BjVi7gi5mNNbooAbWf6wwrFFI57zHrwPuxNpNVBaYBMRijRAmAm/nbaMS8C7QpDi/mFxeuXLWaD9CL3+/RHMKJXHwoHWwy65dppNIEfyEtQtnFnD2UKWuQEvy5xTONxfoAWwB6l3kOa9ilcCrWIWyEGuE8W8uPhp5FjgC/A5rRLEGa87hLazRhATB5Mlw222mU4QslUJJzZ0LvXqB12s6iRTS58DNQMETo+dirTAKA7LOewzgJBAHfI31jv98p4DyWAVwY4H77wN25n3f+TZgrTZaBUzA2o00tcDPOgbEF/q3kkL53e90EOplaE6hpLp3ty7sLbbRA+sdeVqB29VYk85pXFgI5N0PUPUi28zJu53/Hyoc+KW3Cz7gAeDvWAWQm/f9FPiYe/FfQYqjYUPrOilySSqFQHj+eWuZqthCPNbkccFbLFAx7/OtwPNYu2/SsSaP78KaGyh4ZpwrsUYGYK0k6gL8CZiPtapoIvA+1qjkfOOBJPKPS7gWaxfVEuB1rGMcEkryS8q5oqNhyhSIjTWdJORppiUQIiNh6lS45hrYu9d0GimhKKzjBd7A2pVTE+gP/O28523EOr7grClYcwh3AIew5hX+jwsPetubd//iAve1BR7H2vVUGWsSWgJo5Eho2dJ0ClvQnEIgff89dO0Kp06ZTiIiZ2keoUhUCoH273/Dr3+tiWeRUNC1q3XUcmSk6SS2oTmFQLv5ZnjlFdMpRKRBA/j0UxVCEWmkECxDhsCYMaZTiLhThQqwdCnUr286ie2oFIIlNxf69YMvvjCdRMRdoqKsXUZduphOYkvafRQs4eHWErjWrU0nEXEPjwfef1+FUAIqhWCKjbVGCjVrmk4i4g6vvw4DB5pOYWsqhWCrWhW+/NI6CZeIBM+f/wx/+IPpFLanOYXSMmsW9OkDZ86YTiLiPIMG6QpqAaKRQmnp1Qs++ECn6xUJtD59YNw40ykcQ6VQmgYOtE6HERVlOomIM9x0E3z2md5sBZBKobTdfDN8/jmUKWM6iYi9DRxoFUJ0tOkkjqJSMOGGG6xVSTExppOI2NM998BHH2mEEAQqBVN69LAu5xmvy6iIFMkjj8D48RCml69g0J+qSR07WquSEhJMJxGxh6eeglGjrIPUJCi0JDUUrFoFvXvDgQOmk4iErhdftEpBgkqlECrWroWePWHPHtNJREKLx2ONDh5+2HQSV1AphJJNm6y5hp07TScRCQ1hYdb8we9+ZzqJa2hOIZQ0aAALF0KTJqaTiJgXGWmtMFIhlCqVQqipUweWLLGOZxBxq0qV4OuvdXI7A1QKoSguzrpi1LPPapWFuM9VV8GKFdC9u+kkrqQ5hVA3YwbceSccO2Y6iUjw3X03vPOOjvg3SKVgBxs2wK9+ZU1EizhRZKR1LYSHHjKdxPVUCnZx9CjccQfMnGk6iUhgJSfDJ59YB3OKcZpTsIvy5WH6dPjLX0wnEQmc9u2t+QMVQsjQSMGOpk2zLipy8qTpJCLF98AD8OabOpV8iFEp2NWaNXDLLbBli+kkIkUTHQ1jxsC995pOIr9Au4/sqlkzSEuz3m2J2EXjxpCaqkIIYSoFO4uNtZbvffUVVKtmOo3IxUVEwF//ap388eqrTaeRS9DuI6c4dAgGD4aPPzadRORcLVvChAnQqpXpJFIIGik4RYUKMGWKdatUyXQaEWsC+fnnYdkyFYKNaKTgRAcOwKOPwocfmk4ibtW2rTU60MkdbUcjBSeqVAk++MA60K1WLdNpxE3KlIGXX4bFi1UINqWRgtOdOGFdrWrsWPB6TacRJ+vYEd57zzoFvNiWRgpOFxcHo0dbp+Pu0sV0GnGiChWsf2MLF6oQHEAjBbeZOROGDoUffzSdROwuJgb+8Af485+t07CII6gU3MjrhUmT4OmndelPKbqICOvgs2eegapVTaeRAFMpuNnp09a5Z0aMgCNHTKcRO7j1VnjhBe0mcjCVglgHvr34Irz1FmRlmU4joah7d3jpJWjTxnQSCTKVguTbsQOGDbOOb9BKJQHroLOXXoLevU0nkVKi1UeSr3ZteP99WLkS+veH8HDTicSUK66Ajz6yrnWgQnAVjRTk4nbssE5xPH48HD5sOo0Em8djFcDDD8MNN0CY3jO6kUpBLi8z0zpCevRoLWV1ovh466JNQ4ZoAllUClJEc+ZYK5a++ELzDnbXsKFVBHffbRWDCCoFKa7t263VShMmaDmrnYSFQZ8+1i6iXr2sXUYiBagUpGROnrQmp99+27pEqISmhATrgLPBg6FuXdNpJISpFCRwNmyAadOs2w8/mE4jiYlw003WSrLeva0zmIpchkpBgmPLlvyCWLHCdBr3qFwZ+vWziqBbN4iMNJ1IbEalIMGXnp5fEN9/D/onF1g1asAtt1hF0LGjlpJKiagUpHT99BN8+qlVEEuWQG6u6UT2VK+eVQL9+1unntCEsQSISkHMOX4cli6F1FRYtMgqiRMnTKcKPR4PNGoEHTrk3xo2NJ1KHEqlIKEjNxdWr84viUWL3Hlq77g46xrHZwugfXtr9ZBIKVApSGjbsSO/IBYtgvXrITvbdKrASkk5dxTQvLnOOyXGqBTEXrxea15i61ZrhdPZj2c/P3nSdMJfFhNjnWTuiiusU0mcvTVsaF3OUiREqBTEWfbsubAw9u2z5iqOH7c+nv08J6dkP6tMGYiNtW4xMdapImrUgFq1zr3Vrm0tFdVksNiASkHcKzs7vyjO/5iTk/9iX/CF/+zHmBgt/RRHUimIiIif3uqIiIifSkFERPxUCiIi4qdSEBERP5WCiIj4qRRERMRPpSAiIn4qBRER8VMpiIiIn0pBRET8VAoiIuKnUhARET+VgoiI+KkURBxm0KBB9OvXz3QMsSmVgogBgwYNwuPxXHDbsmVLwH9W165defTRRwO+XXGmCNMBRNzq+uuv55///Oc59yUlJRlKI2LRSEHEkOjoaJKTk8+5jRo1imbNmhEbG0vNmjUZPHgwJ06c8H/P8OHDadmy5TnbeeONN0hJSfnFnzFo0CAWLFjAqFGj/KOR9PT04P1SYnsqBZEQEhYWxptvvsnatWuZNGkSc+fO5cknnyz29kaNGkX79u25//772b17N7t376ZmzZoBTCxOo91HIoZ88cUXxMXF+b++4YYb+OSTT/xfp6Sk8MILL/Dggw8yduzYYv2M8uXLExUVRUxMDMnJySXOLM6nUhAxpFu3brz99tv+r2NjY5k9ezYjRoxgw4YNHDt2jDNnznD69GkyMzOJiYkxmFbcQruPRAyJjY2lfv36/ltWVhZ9+/alefPmfPrpp6xYsYIxY8YAkJ2dDVi7l3w+3znbycnJKfXs4lwaKYiEiBUrVuD1ennttdcIC7Per02dOvWc5yQlJbFnzx58Ph8ejweAtLS0S243KiqK3NzcoGQW59FIQSRE1K9fn5ycHEaPHs22bdv417/+xTvvvHPOc7p27cr+/ft5+eWX2bp1K2PGjOGrr7665HZTUlJYunQp6enpHDhwAK/XG8xfQ2xOpSASIlq0aMHf//53Ro4cSdOmTfnwww8ZMWLEOc9p1KgRY8eOZcyYMbRo0YLvv/+eJ5544pLbfeKJJwgPD6dx48YkJSWRkZERzF9DbM7jO38HpYiIuJZGCiIi4qdSEBERP5WCiIj4qRRERMRPpSAiIn4qBRER8VMpiIiIn0pBRET8VAoiIuKnUhARET+VgoiI+P0/pxrzO1u59aAAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Building the binary classifier model\n",
        "\n",
        "y = isFault.iloc[:,0]\n",
        "X = isFault.iloc[:,1:7]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=1)\n",
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ovk3vh0asmtx",
        "outputId": "5ab3736e-0e52-4e1a-bc38-478d8c952fc5"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((9600, 6), (2401, 6), (9600,), (2401,))"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Forest Classifier\n",
        "model_1 = RandomForestClassifier()\n",
        "model_1.fit(X_train, y_train)\n",
        "y_pred = model_1.predict(X_test)"
      ],
      "metadata": {
        "id": "M9CqwEw3szWy"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Classification Report:\\n{classification_report(y_test, y_pred)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I-uXK_-otOHL",
        "outputId": "edba7447-8380-4ff8-ab0d-e496a1aa2fe7"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      1297\n",
            "           1       1.00      0.99      1.00      1104\n",
            "\n",
            "    accuracy                           1.00      2401\n",
            "   macro avg       1.00      1.00      1.00      2401\n",
            "weighted avg       1.00      1.00      1.00      2401\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Troxrw1s9if",
        "outputId": "c38e378e-3bf8-40c0-9ca2-f4640d9126df"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1294,    3],\n",
              "       [   6, 1098]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Accuracy Score: {accuracy_score(y_test, y_pred)*100:.03f}%')\n",
        "print(f'Precision Score: {precision_score(y_test, y_pred)*100:.03f}%')\n",
        "print(f'Recall Score: {recall_score(y_test, y_pred)*100:.03f}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zSZc4sJbtHUY",
        "outputId": "8025dda2-922d-4311-9855-0f26302cdb9e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score: 99.625%\n",
            "Precision Score: 99.728%\n",
            "Recall Score: 99.457%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**# Mutli Class Classification using Random Forest**"
      ],
      "metadata": {
        "id": "1shC1y5ctVwJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "faultClassification.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "vx3rNS3qtfV6",
        "outputId": "0d3745ec-7957-4a09-924d-02d487bc0916"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   G  C  B  A          Ia          Ib          Ic        Va        Vb  \\\n",
              "0  1  0  0  1 -151.291812   -9.677452   85.800162  0.400750 -0.132935   \n",
              "1  1  0  0  1 -336.186183  -76.283262   18.328897  0.312732 -0.123633   \n",
              "2  1  0  0  1 -502.891583 -174.648023  -80.924663  0.265728 -0.114301   \n",
              "3  1  0  0  1 -593.941905 -217.703359 -124.891924  0.235511 -0.104940   \n",
              "4  1  0  0  1 -643.663617 -224.159427 -132.282815  0.209537 -0.095554   \n",
              "\n",
              "         Vc  \n",
              "0 -0.267815  \n",
              "1 -0.189099  \n",
              "2 -0.151428  \n",
              "3 -0.130570  \n",
              "4 -0.113983  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b2a92262-89f5-42f4-a7c4-ba84ebcce51f\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>G</th>\n",
              "      <th>C</th>\n",
              "      <th>B</th>\n",
              "      <th>A</th>\n",
              "      <th>Ia</th>\n",
              "      <th>Ib</th>\n",
              "      <th>Ic</th>\n",
              "      <th>Va</th>\n",
              "      <th>Vb</th>\n",
              "      <th>Vc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-151.291812</td>\n",
              "      <td>-9.677452</td>\n",
              "      <td>85.800162</td>\n",
              "      <td>0.400750</td>\n",
              "      <td>-0.132935</td>\n",
              "      <td>-0.267815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-336.186183</td>\n",
              "      <td>-76.283262</td>\n",
              "      <td>18.328897</td>\n",
              "      <td>0.312732</td>\n",
              "      <td>-0.123633</td>\n",
              "      <td>-0.189099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-502.891583</td>\n",
              "      <td>-174.648023</td>\n",
              "      <td>-80.924663</td>\n",
              "      <td>0.265728</td>\n",
              "      <td>-0.114301</td>\n",
              "      <td>-0.151428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-593.941905</td>\n",
              "      <td>-217.703359</td>\n",
              "      <td>-124.891924</td>\n",
              "      <td>0.235511</td>\n",
              "      <td>-0.104940</td>\n",
              "      <td>-0.130570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-643.663617</td>\n",
              "      <td>-224.159427</td>\n",
              "      <td>-132.282815</td>\n",
              "      <td>0.209537</td>\n",
              "      <td>-0.095554</td>\n",
              "      <td>-0.113983</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b2a92262-89f5-42f4-a7c4-ba84ebcce51f')\"\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-b2a92262-89f5-42f4-a7c4-ba84ebcce51f 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-b2a92262-89f5-42f4-a7c4-ba84ebcce51f');\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-a406f60b-463b-45a1-8077-4b8d627f341f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a406f60b-463b-45a1-8077-4b8d627f341f')\"\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-a406f60b-463b-45a1-8077-4b8d627f341f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "faultClassification",
              "summary": "{\n  \"name\": \"faultClassification\",\n  \"rows\": 7861,\n  \"fields\": [\n    {\n      \"column\": \"G\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"C\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"B\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"A\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ia\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 464.74167085071804,\n        \"min\": -883.5423165,\n        \"max\": 885.7385707,\n        \"num_unique_values\": 7861,\n        \"samples\": [\n          -41.38147645,\n          711.6652033\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ib\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 439.26919548763203,\n        \"min\": -900.5269515,\n        \"max\": 889.8688838,\n        \"num_unique_values\": 7861,\n        \"samples\": [\n          2.186932811,\n          70.7179672\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ic\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 371.10741227146906,\n        \"min\": -883.3577621,\n        \"max\": 901.2742607,\n        \"num_unique_values\": 7861,\n        \"samples\": [\n          36.10854133,\n          43.41773192\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Va\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28915019082376264,\n        \"min\": -0.62074797,\n        \"max\": 0.595342181,\n        \"num_unique_values\": 7856,\n        \"samples\": [\n          0.481916146,\n          -0.029941053\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.31343704879663753,\n        \"min\": -0.608016002,\n        \"max\": 0.627874757,\n        \"num_unique_values\": 7861,\n        \"samples\": [\n          -0.562912543,\n          -0.37593857\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3078971302786381,\n        \"min\": -0.612709171,\n        \"max\": 0.600178804,\n        \"num_unique_values\": 7847,\n        \"samples\": [\n          0.497317077,\n          0.278263538\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "faultClassification['faultType'] = faultClassification['G'].astype(str) + faultClassification['C'].astype(str) + faultClassification['B'].astype(str) + faultClassification['A'].astype(str)\n",
        "faultClassification.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zUm3y25jt6Mg",
        "outputId": "056dbb84-86de-44a5-c4e4-fd06a137c1d4"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   G  C  B  A          Ia          Ib          Ic        Va        Vb  \\\n",
              "0  1  0  0  1 -151.291812   -9.677452   85.800162  0.400750 -0.132935   \n",
              "1  1  0  0  1 -336.186183  -76.283262   18.328897  0.312732 -0.123633   \n",
              "2  1  0  0  1 -502.891583 -174.648023  -80.924663  0.265728 -0.114301   \n",
              "3  1  0  0  1 -593.941905 -217.703359 -124.891924  0.235511 -0.104940   \n",
              "4  1  0  0  1 -643.663617 -224.159427 -132.282815  0.209537 -0.095554   \n",
              "\n",
              "         Vc faultType  \n",
              "0 -0.267815      1001  \n",
              "1 -0.189099      1001  \n",
              "2 -0.151428      1001  \n",
              "3 -0.130570      1001  \n",
              "4 -0.113983      1001  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-624872ff-2802-45ff-ad53-4ed3ae802aef\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>G</th>\n",
              "      <th>C</th>\n",
              "      <th>B</th>\n",
              "      <th>A</th>\n",
              "      <th>Ia</th>\n",
              "      <th>Ib</th>\n",
              "      <th>Ic</th>\n",
              "      <th>Va</th>\n",
              "      <th>Vb</th>\n",
              "      <th>Vc</th>\n",
              "      <th>faultType</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-151.291812</td>\n",
              "      <td>-9.677452</td>\n",
              "      <td>85.800162</td>\n",
              "      <td>0.400750</td>\n",
              "      <td>-0.132935</td>\n",
              "      <td>-0.267815</td>\n",
              "      <td>1001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-336.186183</td>\n",
              "      <td>-76.283262</td>\n",
              "      <td>18.328897</td>\n",
              "      <td>0.312732</td>\n",
              "      <td>-0.123633</td>\n",
              "      <td>-0.189099</td>\n",
              "      <td>1001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-502.891583</td>\n",
              "      <td>-174.648023</td>\n",
              "      <td>-80.924663</td>\n",
              "      <td>0.265728</td>\n",
              "      <td>-0.114301</td>\n",
              "      <td>-0.151428</td>\n",
              "      <td>1001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-593.941905</td>\n",
              "      <td>-217.703359</td>\n",
              "      <td>-124.891924</td>\n",
              "      <td>0.235511</td>\n",
              "      <td>-0.104940</td>\n",
              "      <td>-0.130570</td>\n",
              "      <td>1001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-643.663617</td>\n",
              "      <td>-224.159427</td>\n",
              "      <td>-132.282815</td>\n",
              "      <td>0.209537</td>\n",
              "      <td>-0.095554</td>\n",
              "      <td>-0.113983</td>\n",
              "      <td>1001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-624872ff-2802-45ff-ad53-4ed3ae802aef')\"\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-624872ff-2802-45ff-ad53-4ed3ae802aef 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-624872ff-2802-45ff-ad53-4ed3ae802aef');\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-9eee73bc-e9be-4636-bf48-bf26f3bec3cc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9eee73bc-e9be-4636-bf48-bf26f3bec3cc')\"\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-9eee73bc-e9be-4636-bf48-bf26f3bec3cc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "faultClassification",
              "summary": "{\n  \"name\": \"faultClassification\",\n  \"rows\": 7861,\n  \"fields\": [\n    {\n      \"column\": \"G\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"C\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"B\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"A\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ia\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 464.74167085071804,\n        \"min\": -883.5423165,\n        \"max\": 885.7385707,\n        \"num_unique_values\": 7861,\n        \"samples\": [\n          -41.38147645,\n          711.6652033\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ib\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 439.26919548763203,\n        \"min\": -900.5269515,\n        \"max\": 889.8688838,\n        \"num_unique_values\": 7861,\n        \"samples\": [\n          2.186932811,\n          70.7179672\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ic\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 371.10741227146906,\n        \"min\": -883.3577621,\n        \"max\": 901.2742607,\n        \"num_unique_values\": 7861,\n        \"samples\": [\n          36.10854133,\n          43.41773192\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Va\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28915019082376264,\n        \"min\": -0.62074797,\n        \"max\": 0.595342181,\n        \"num_unique_values\": 7856,\n        \"samples\": [\n          0.481916146,\n          -0.029941053\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.31343704879663753,\n        \"min\": -0.608016002,\n        \"max\": 0.627874757,\n        \"num_unique_values\": 7861,\n        \"samples\": [\n          -0.562912543,\n          -0.37593857\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3078971302786381,\n        \"min\": -0.612709171,\n        \"max\": 0.600178804,\n        \"num_unique_values\": 7847,\n        \"samples\": [\n          0.497317077,\n          0.278263538\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"faultType\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"1001\",\n          \"1011\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(faultClassification['faultType'].value_counts(), autopct='%1.1f%%',\n",
        "       labels=['No Fault', 'LLG Fault', 'LLLG Fault', 'LG Fault', 'LLL Fault', 'LL Fault'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "OkU3hF1DuDN-",
        "outputId": "e4db1c81-98a5-41bf-ab9a-87d1bdd647fd"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## seperate i/p & o/p\n",
        "X = faultClassification.drop(['G','C','B','A','faultType'], axis=1)\n",
        "y = faultClassification['faultType']\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ],
      "metadata": {
        "id": "rjXqZQ3IuKZf"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Splting train and test data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=1)\n",
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_BMDx12juXtT",
        "outputId": "3ed3f671-74ee-4245-e136-a31357d88f06"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((6288, 6), (1573, 6), (6288,), (1573,))"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## RF Algo\n",
        "model_2 = RandomForestClassifier()\n",
        "model_2.fit(X_train, y_train)\n",
        "y_pred = model_2.predict(X_test)"
      ],
      "metadata": {
        "id": "WvQIQEV-uh5E"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "43gPdUUjuoie",
        "outputId": "e90b8c39-66ab-4978-e399-1aef39460432"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[468,   0,   0,   0,   0,   0],\n",
              "       [  0, 202,   0,   0,   0,   0],\n",
              "       [  0,   0, 139,   0,   0,  92],\n",
              "       [  0,   0,   0, 220,   0,   0],\n",
              "       [  0,   0,   0,   2, 224,   0],\n",
              "       [  0,   0,  95,   0,   0, 131]])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O7NItT5Jut1F",
        "outputId": "b4c632bc-3802-4275-ac14-a4e6fda23f02"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       468\n",
            "           1       1.00      1.00      1.00       202\n",
            "           2       0.59      0.60      0.60       231\n",
            "           3       0.99      1.00      1.00       220\n",
            "           4       1.00      0.99      1.00       226\n",
            "           5       0.59      0.58      0.58       226\n",
            "\n",
            "    accuracy                           0.88      1573\n",
            "   macro avg       0.86      0.86      0.86      1573\n",
            "weighted avg       0.88      0.88      0.88      1573\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Accuracy Score: {accuracy_score(y_test, y_pred)*100:.03f}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JC5opEXruzCf",
        "outputId": "55333e33-c304-4d57-e517-c0750b6692a5"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score: 87.985%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res = classification_report(y_test, y_pred, output_dict=True)\n",
        "res.values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ACo8xnRju-NN",
        "outputId": "2ad4fb25-5d79-4396-90b3-8b8437ec96e7"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_values([{'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 468.0}, {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 202.0}, {'precision': 0.594017094017094, 'recall': 0.6017316017316018, 'f1-score': 0.5978494623655914, 'support': 231.0}, {'precision': 0.990990990990991, 'recall': 1.0, 'f1-score': 0.995475113122172, 'support': 220.0}, {'precision': 1.0, 'recall': 0.9911504424778761, 'f1-score': 0.9955555555555555, 'support': 226.0}, {'precision': 0.5874439461883408, 'recall': 0.5796460176991151, 'f1-score': 0.5835189309576837, 'support': 226.0}, 0.8798474253019708, {'precision': 0.8620753385327377, 'recall': 0.8620880103180989, 'f1-score': 0.8620665103335005, 'support': 1573.0}, {'precision': 0.8798463436583164, 'recall': 0.8798474253019708, 'f1-score': 0.8798338109633321, 'support': 1573.0}])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the dictionary to a DataFrame\n",
        "df = pd.DataFrame(res).transpose()\n",
        "\n",
        "# Print the DataFrame\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "--QQHTDdvfUK",
        "outputId": "4facfbdd-b874-46a9-a295-8746996b3dc1"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score      support\n",
            "0              1.000000  1.000000  1.000000   468.000000\n",
            "1              1.000000  1.000000  1.000000   202.000000\n",
            "2              0.594017  0.601732  0.597849   231.000000\n",
            "3              0.990991  1.000000  0.995475   220.000000\n",
            "4              1.000000  0.991150  0.995556   226.000000\n",
            "5              0.587444  0.579646  0.583519   226.000000\n",
            "accuracy       0.879847  0.879847  0.879847     0.879847\n",
            "macro avg      0.862075  0.862088  0.862067  1573.000000\n",
            "weighted avg   0.879846  0.879847  0.879834  1573.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=df.index[:-3], y=df['precision'][:-3])\n",
        "plt.xlabel(\"Class\")\n",
        "plt.title(\"Class-wise Precision\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "nXkhv_E-vsK2",
        "outputId": "74e9a909-310f-45e7-cb60-cebcc89bb970"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Class-wise Precision')"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=df.index[:-3], y=df['f1-score'][:-3])\n",
        "plt.xlabel(\"Class Label\")\n",
        "plt.title(\"Class-wise F1 Score\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "Hh1CaJcJvyWy",
        "outputId": "dc0fa018-d935-4c6c-96b7-9b55059e29c0"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Class-wise F1 Score')"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAHHCAYAAABDUnkqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA0UElEQVR4nO3de1xVVf7/8fcB5aDIxRsIxoCa5h0VkiE0ulBkZjnlJesbDGP2LSE1ysos0ayoLNPJW5mm3yZH00lnStOINHWivFJmqXnFGyiZophgsH9/+PNMDKgcBA+sXs/HYz8enbXX2uezt6Rv1l77HJtlWZYAAAAM4ebqAgAAAKoS4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBqjFQkND9ec//9nVZVSJvXv3ymazac6cOa4uBUAtR7gBaqBdu3bpf//3f9WyZUt5enrKx8dH0dHRmjx5sn755RdXl2eMVatWyWazlbvde++9jn7r1q3T0KFDFR4errp168pmszn1PkVFRZo8ebK6du0qHx8f+fn5qUOHDnrooYe0bdu2qj4t4HevjqsLAFDa0qVL1b9/f9ntdsXHx6tjx44qKirS2rVrNXLkSG3dulVvv/22q8usciEhIfrll19Ut27dK/7ew4YN07XXXluqLTQ01PHfy5Yt0zvvvKPOnTurZcuW2rFjh1PHv+eee/TJJ59o0KBBGjJkiM6ePatt27bp448/1nXXXae2bdtWxWkA+P8IN0ANsmfPHt17770KCQnR559/rsDAQMe+pKQk7dy5U0uXLnVhhdXHZrPJ09PTJe/ds2dP9evX74L7H3nkET311FOqV6+ekpOTnQo369ev18cff6wXX3xRzzzzTKl9U6ZM0fHjxytbttPOnDkjDw8PubkxaQ+z8RMO1CCvvvqqTp06pVmzZpUKNuddffXVGj58+AXHHzt2TE888YQ6deqkBg0ayMfHR7169dI333xTpu+bb76pDh06qH79+mrYsKEiIiI0b948x/6TJ09qxIgRCg0Nld1ul7+/v2655RZt2rTpoueQkpKixo0by7IsR9ujjz4qm82mv/71r4623Nxc2Ww2TZ8+XVL5a25ycnKUmJioq666Sna7XYGBgbrrrru0d+/eUu/5ySefqGfPnvLy8pK3t7d69+6trVu3XrROZwQEBKhevXqVGrtr1y5JUnR0dJl97u7uaty4cam2gwcPavDgwQoKCpLdbleLFi30yCOPqKioyNFn9+7d6t+/vxo1aqT69evrj3/8Y5nQe/6W2/z58/Xss8+qefPmql+/vvLz8yVJX3/9tW677Tb5+vqqfv36iomJ0b///e9KnSNQ0zBzA9QgH330kVq2bKnrrruuUuN3796tJUuWqH///mrRooVyc3P11ltvKSYmRt9//72CgoIkSTNnztSwYcPUr18/DR8+XGfOnNG3336rr7/+Wvfdd58k6eGHH9aiRYuUnJys9u3b66efftLatWv1ww8/qFu3bhesoWfPnnrjjTe0detWdezYUZK0Zs0aubm5ac2aNRo2bJijTZKuv/76Cx7rnnvu0datW/Xoo48qNDRUR44cUXp6urKzsx23jd577z0lJCQoLi5Or7zyik6fPq3p06erR48e2rx5c6nbSxdy8uRJ5eXllWpr1KhRlcxwhISESJLef/99RUdHq06dC/+1e+jQIXXv3l3Hjx/XQw89pLZt2+rgwYNatGiRTp8+LQ8PD+Xm5uq6667T6dOnNWzYMDVu3Fhz587VnXfeqUWLFulPf/pTqWOOHz9eHh4eeuKJJ1RYWCgPDw99/vnn6tWrl8LDw5Wamio3Nze9++67uummm7RmzRp17979ss8bcCkLQI1w4sQJS5J11113VXhMSEiIlZCQ4Hh95swZq7i4uFSfPXv2WHa73Xr++ecdbXfddZfVoUOHix7b19fXSkpKqnAt5x05csSSZE2bNs2yLMs6fvy45ebmZvXv398KCAhw9Bs2bJjVqFEjq6SkxFGnJOvdd9+1LMuyfv75Z0uSNWHChAu+18mTJy0/Pz9ryJAhpdpzcnIsX1/fMu3/beXKlZakcrc9e/aUOyYpKcly5q/OkpISKyYmxpJkBQQEWIMGDbKmTp1q7du3r0zf+Ph4y83NzVq/fn25x7EsyxoxYoQlyVqzZo1j38mTJ60WLVpYoaGhjj//8+fWsmVL6/Tp06WO07p1aysuLs5xTMuyrNOnT1stWrSwbrnllgqfG1BTcVsKqCHO3y7w9vau9DHsdrtjtqG4uFg//fSTGjRooGuuuabU7SQ/Pz8dOHBA69evv+Cx/Pz89PXXX+vQoUNO1dC0aVO1bdtWq1evliT9+9//lru7u0aOHKnc3Fz9+OOPks7N3PTo0eOCTx7Vq1dPHh4eWrVqlX7++edy+6Snp+v48eMaNGiQ8vLyHJu7u7siIyO1cuXKCtU8ZswYpaenl9qaNWvm1HlfiM1m04oVK/TCCy+oYcOG+vvf/66kpCSFhIRo4MCBjjU3JSUlWrJkifr06aOIiIhyjyOdW9zcvXt39ejRw7GvQYMGeuihh7R37159//33pcYlJCSUuqWWlZWlH3/8Uffdd59++uknxzUrKCjQzTffrNWrV6ukpKRKzh1wFW5LATWEj4+PpHO3SCqrpKREkydP1rRp07Rnzx4VFxc79v12bcdTTz2lzz77TN27d9fVV1+tW2+9Vffdd1+pdSGvvvqqEhISFBwcrPDwcN1+++2Kj49Xy5YtJUmnTp3SqVOnHP3d3d3VtGlTSeduTS1btkzSuRATERGhiIgINWrUSGvWrFFAQIC++eYbxy2w8tjtdr3yyit6/PHHFRAQoD/+8Y+64447FB8f7wge54PSTTfdVO4xzl/TS+nUqZNiY2Mr1Lcy7Ha7Ro8erdGjR+vw4cP64osvNHnyZH3wwQeqW7eu/va3v+no0aPKz8933Mq7kH379ikyMrJMe7t27Rz7f3uMFi1alOp3/polJCRc8D1OnDihhg0bVvj8gJqGmRughvDx8VFQUJC+++67Sh/jpZdeUkpKiq6//nr97W9/04oVK5Senq4OHTqU+m28Xbt22r59u+bPn68ePXroH//4h3r06KHU1FRHnwEDBmj37t168803FRQUpAkTJqhDhw765JNPJEmvvfaaAgMDHdtvH6Xu0aOHDh48qN27d2vNmjXq2bOnbDabevTooTVr1ujLL79USUmJevbsedHzGTFihHbs2KG0tDR5enrqueeeU7t27bR582ZJcpzTe++9V2bmJT09Xf/85z8rfS2rS2BgoO69916tXr1arVu31gcffKBff/212t7vvxdCn79mEyZMKPeapaenq0GDBtVWD3AlMHMD1CB33HGH3n77bWVmZioqKsrp8YsWLdKNN96oWbNmlWo/fvy4mjRpUqrNy8tLAwcO1MCBA1VUVKS7775bL774okaNGuV4JDswMFBDhw7V0KFDdeTIEXXr1k0vvviievXqpfj4+FK3Rn77j+j50JKenq7169fr6aeflnRu8fD06dMVFBQkLy8vhYeHX/KcWrVqpccff1yPP/64fvzxR3Xp0kWvv/66/va3v6lVq1aSJH9//2qdeakOdevWVefOnfXjjz8qLy9P/v7+8vHxuWS4DQkJ0fbt28u0n/8wwPMLmC/k/DXz8fGpddcMqChmboAa5Mknn5SXl5cefPBB5ebmltm/a9cuTZ48+YLj3d3dSz2CLUkLFy7UwYMHS7X99NNPpV57eHioffv2sixLZ8+eVXFxsU6cOFGqj7+/v4KCglRYWChJatmypWJjYx3bb29ptWjRQs2bN9cbb7yhs2fPOvb17NlTu3bt0qJFi/THP/7xok8OnT59WmfOnCnV1qpVK3l7eztqiIuLk4+Pj1566SWdPXu2zDGOHj16weNfKT/++KOys7PLtB8/flyZmZlq2LChmjZtKjc3N/Xt21cfffSRNmzYUKb/+T/X22+/XevWrVNmZqZjX0FBgd5++22Fhoaqffv2F60nPDxcrVq10muvvVbqtuJ5NeGaAZeLmRugBmnVqpXmzZungQMHql27dqU+ofjLL7/UwoULL/pdUnfccYeef/55JSYm6rrrrtOWLVv0/vvvO9bJnHfrrbeqWbNmio6OVkBAgH744QdNmTJFvXv3lre3t44fP66rrrpK/fr1U1hYmBo0aKDPPvtM69ev1+uvv16hc+nZs6fmz5+vTp06OdZvdOvWTV5eXtqxY8dF19tI0o4dO3TzzTdrwIABat++verUqaPFixcrNzfX8dUIPj4+mj59uh544AF169ZN9957r5o2bars7GwtXbpU0dHRmjJlSoXqvZh9+/bpvffekyRH8HjhhRcknZspeeCBBy449vzaol69eqlnz55q1KiRDh48qLlz5+rQoUOaNGmS3N3dJZ27rfjpp58qJiZGDz30kNq1a6fDhw9r4cKFWrt2rfz8/PT000/r73//u3r16qVhw4apUaNGmjt3rvbs2aN//OMfl3x83c3NTe+884569eqlDh06KDExUc2bN9fBgwe1cuVK+fj46KOPPrrsawa4lIuf1gJQjh07dlhDhgyxQkNDLQ8PD8vb29uKjo623nzzTevMmTOOfuU9Cv74449bgYGBVr169azo6GgrMzPTiomJsWJiYhz93nrrLev666+3GjdubNntdqtVq1bWyJEjrRMnTliWZVmFhYXWyJEjrbCwMMvb29vy8vKywsLCHI93V8TUqVMtSdYjjzxSqj02NtaSZGVkZJRq/+9HwfPy8qykpCSrbdu2lpeXl+Xr62tFRkZaH3zwQZn3WrlypRUXF2f5+vpanp6eVqtWraw///nP1oYNGy5a4/nHpRcuXFihfuVtv72u5cnNzbVefvllKyYmxgoMDLTq1KljNWzY0LrpppusRYsWlem/b98+Kz4+3mratKllt9utli1bWklJSVZhYaGjz65du6x+/fpZfn5+lqenp9W9e3fr448/durcNm/ebN19992On4GQkBBrwIABZf5cgNrIZln/NYcNAABQi7HmBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKL+7D/ErKSnRoUOH5O3tfcFvIwYAADWLZVk6efKkgoKCLvlhlb+7cHPo0CEFBwe7ugwAAFAJ+/fv11VXXXXRPr+7cOPt7S3p3MXx8fFxcTUAAKAi8vPzFRwc7Ph3/GJ+d+Hm/K0oHx8fwg0AALVMRZaUsKAYAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIzi0nCzevVq9enTR0FBQbLZbFqyZMklx6xatUrdunWT3W7X1VdfrTlz5lR7nQAAoPZwabgpKChQWFiYpk6dWqH+e/bsUe/evXXjjTcqKytLI0aM0IMPPqgVK1ZUc6UAAKC2cOkXZ/bq1Uu9evWqcP8ZM2aoRYsWev311yVJ7dq109q1a/XGG28oLi6uusoEAAC1SK1ac5OZmanY2NhSbXFxccrMzHRRRQAAoKZx6cyNs3JychQQEFCqLSAgQPn5+frll19Ur169MmMKCwtVWFjoeJ2fn1/tdQIAANepVeGmMtLS0jRu3Dinx4WP/L9qqKb22Tgh/rLGcx3PudzrCACouFoVbpo1a6bc3NxSbbm5ufLx8Sl31kaSRo0apZSUFMfr/Px8BQcHV2udAGo2Qvc5/PJSNfjlpeapVeEmKipKy5YtK9WWnp6uqKioC46x2+2y2+3VXRoAAKghXLqg+NSpU8rKylJWVpakc496Z2VlKTs7W9K5WZf4+P8k4ocffli7d+/Wk08+qW3btmnatGn64IMP9Nhjj7mifAAAUAO5NNxs2LBBXbt2VdeuXSVJKSkp6tq1q8aMGSNJOnz4sCPoSFKLFi20dOlSpaenKywsTK+//rreeecdHgMHAAAOLr0tdcMNN8iyrAvuL+/Th2+44QZt3ry5GqsCAAC1Wa36nBsAAIBLIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCguDzdTp05VaGioPD09FRkZqXXr1l20/6RJk3TNNdeoXr16Cg4O1mOPPaYzZ85coWoBAEBN59Jws2DBAqWkpCg1NVWbNm1SWFiY4uLidOTIkXL7z5s3T08//bRSU1P1ww8/aNasWVqwYIGeeeaZK1w5AACoqVwabiZOnKghQ4YoMTFR7du314wZM1S/fn3Nnj273P5ffvmloqOjdd999yk0NFS33nqrBg0adMnZHgAA8PvhsnBTVFSkjRs3KjY29j/FuLkpNjZWmZmZ5Y657rrrtHHjRkeY2b17t5YtW6bbb7/9gu9TWFio/Pz8UhsAADBXHVe9cV5enoqLixUQEFCqPSAgQNu2bSt3zH333ae8vDz16NFDlmXp119/1cMPP3zR21JpaWkaN25cldYOAABqLpcvKHbGqlWr9NJLL2natGnatGmTPvzwQy1dulTjx4+/4JhRo0bpxIkTjm3//v1XsGIAAHCluWzmpkmTJnJ3d1dubm6p9tzcXDVr1qzcMc8995weeOABPfjgg5KkTp06qaCgQA899JBGjx4tN7eyWc1ut8tut1f9CQAAgBrJZTM3Hh4eCg8PV0ZGhqOtpKREGRkZioqKKnfM6dOnywQYd3d3SZJlWdVXLAAAqDVcNnMjSSkpKUpISFBERIS6d++uSZMmqaCgQImJiZKk+Ph4NW/eXGlpaZKkPn36aOLEieratasiIyO1c+dOPffcc+rTp48j5AAAgN83l4abgQMH6ujRoxozZoxycnLUpUsXLV++3LHIODs7u9RMzbPPPiubzaZnn31WBw8eVNOmTdWnTx+9+OKLrjoFAABQw7g03EhScnKykpOTy923atWqUq/r1Kmj1NRUpaamXoHKAABAbVSrnpYCAAC4FMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABjF5eFm6tSpCg0NlaenpyIjI7Vu3bqL9j9+/LiSkpIUGBgou92uNm3aaNmyZVeoWgAAUNPVceWbL1iwQCkpKZoxY4YiIyM1adIkxcXFafv27fL39y/Tv6ioSLfccov8/f21aNEiNW/eXPv27ZOfn9+VLx4AANRILg03EydO1JAhQ5SYmChJmjFjhpYuXarZs2fr6aefLtN/9uzZOnbsmL788kvVrVtXkhQaGnolSwYAADWcy25LFRUVaePGjYqNjf1PMW5uio2NVWZmZrlj/vWvfykqKkpJSUkKCAhQx44d9dJLL6m4uPhKlQ0AAGo4l83c5OXlqbi4WAEBAaXaAwICtG3btnLH7N69W59//rnuv/9+LVu2TDt37tTQoUN19uxZpaamljumsLBQhYWFjtf5+flVdxIAAKDGqXS42blzp3bt2qXrr79e9erVk2VZstlsVVlbGSUlJfL399fbb78td3d3hYeH6+DBg5owYcIFw01aWprGjRtXrXUBV0L4yP9zdQk1wsYJ8a4uAUAN5/RtqZ9++kmxsbFq06aNbr/9dh0+fFiSNHjwYD3++OMVPk6TJk3k7u6u3NzcUu25ublq1qxZuWMCAwPVpk0bubu7O9ratWunnJwcFRUVlTtm1KhROnHihGPbv39/hWsEAAC1j9Ph5rHHHlOdOnWUnZ2t+vXrO9oHDhyo5cuXV/g4Hh4eCg8PV0ZGhqOtpKREGRkZioqKKndMdHS0du7cqZKSEkfbjh07FBgYKA8Pj3LH2O12+fj4lNoAAIC5nA43n376qV555RVdddVVpdpbt26tffv2OXWslJQUzZw5U3PnztUPP/ygRx55RAUFBY6np+Lj4zVq1ChH/0ceeUTHjh3T8OHDtWPHDi1dulQvvfSSkpKSnD0NAABgKKfX3BQUFJSasTnv2LFjstvtTh1r4MCBOnr0qMaMGaOcnBx16dJFy5cvdywyzs7Olpvbf/JXcHCwVqxYoccee0ydO3dW8+bNNXz4cD311FPOngYAADCU0+GmZ8+e+r//+z+NHz9ekmSz2VRSUqJXX31VN954o9MFJCcnKzk5udx9q1atKtMWFRWlr776yun3AQCgpuKBgXOq6oEBp8PNq6++qptvvlkbNmxQUVGRnnzySW3dulXHjh3Tv//97yopCgAAoLKcXnPTsWNH7dixQz169NBdd92lgoIC3X333dq8ebNatWpVHTUCAABUmFMzN2fPntVtt92mGTNmaPTo0dVVEwAAQKU5NXNTt25dffvtt9VVCwAAwGVz+rbU//zP/2jWrFnVUQsAAMBlc3pB8a+//qrZs2frs88+U3h4uLy8vErtnzhxYpUVBwAA4Cynw813332nbt26STr36cC/Vd3fLQUAAHApToeblStXVkcdAAAAVcLpNTe/deDAAR04cKCqagEAALhsToebkpISPf/88/L19VVISIhCQkLk5+en8ePHl/pCSwAAAFdw+rbU6NGjNWvWLL388suKjo6WJK1du1Zjx47VmTNn9OKLL1Z5kQAAABXldLiZO3eu3nnnHd15552OtvNfYjl06FDCDQAAcCmnb0sdO3ZMbdu2LdPetm1bHTt2rEqKAgAAqCynw01YWJimTJlSpn3KlCkKCwurkqIAAAAqq1LfCt67d2999tlnioqKkiRlZmZq//79WrZsWZUXCAAA4AynZ25iYmK0fft2/elPf9Lx48d1/Phx3X333dq+fbt69uxZHTUCAABUmNMzN5LUvHlzFg4DAIAayemZm3fffVcLFy4s075w4ULNnTu3SooCAACoLKfDTVpampo0aVKm3d/fXy+99FKVFAUAAFBZToeb7OxstWjRokx7SEiIsrOzq6QoAACAynI63Pj7++vbb78t0/7NN9+ocePGVVIUAABAZTkdbgYNGqRhw4Zp5cqVKi4uVnFxsT7//HMNHz5c9957b3XUCAAAUGFOPy01fvx47d27VzfffLPq1Dk3vKSkRPHx8ay5AQAALud0uPHw8NCCBQv0wgsvKCsrS/Xq1VOnTp0UEhJSHfUBAAA4pVKfcyNJrVu3VuvWrVVcXKwtW7bIx8dHDRs2rMraAAAAnOb0mpsRI0Zo1qxZkqTi4mLFxMSoW7duCg4O1qpVq6q6PgAAAKc4HW4WLVrk+ILMjz76SLt379a2bdv02GOPafTo0VVeIAAAgDOcDjd5eXlq1qyZJGnZsmUaMGCA2rRpo7/85S/asmVLlRcIAADgDKfDTUBAgL7//nsVFxdr+fLluuWWWyRJp0+flru7e5UXCAAA4AynFxQnJiZqwIABCgwMlM1mU2xsrCTp66+/Vtu2bau8QAAAAGc4HW7Gjh2rjh07av/+/erfv7/sdrskyd3dXU8//XSVFwgAAOCMSj0K3q9fP0nSgQMHVFJSIjc3NyUkJFRpYQAAAJXh9Jqb32rfvr327t1bRaUAAABcvssKN5ZlVVUdAAAAVeKywg0AAEBNc1nh5plnnlGjRo2qqhYAAIDLVunvlpKkUaNGVVUdAAAAVaLKbkvt379ff/nLX6rqcAAAAJVSZeHm2LFjmjt3blUdDgAAoFIqfFvqX//610X37969+7KLAQAAuFwVDjd9+/aVzWa76OPfNputSooCAACorArflgoMDNSHH36okpKScrdNmzZVZ50AAAAVUuFwEx4ero0bN15w/6VmdQAAAK6ECt2W+vbbbzVy5EgVFBRcsM/VV1+tlStXVllhAAAAlVGhcNO1a1cdPnxY/v7+atmypdavX6/GjRuX6uPl5aWYmJhqKRIAAKCiKnRbys/PT3v27JEk7d27VyUlJdVaFAAAQGVVaObmnnvuUUxMjAIDA2Wz2RQRESF3d/dy+/JIOAAAcKUKhZu3335bd999t3bu3Klhw4ZpyJAh8vb2ru7aAAAAnFbhz7m57bbbJEkbN27U8OHDCTcAAKBGcvqLM999993qqAMAAKBKVNl3SwEAANQEhBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYJQaEW6mTp2q0NBQeXp6KjIyUuvWravQuPnz58tms6lv377VWyAAAKg1XB5uFixYoJSUFKWmpmrTpk0KCwtTXFycjhw5ctFxe/fu1RNPPKGePXteoUoBAEBt4PJwM3HiRA0ZMkSJiYlq3769ZsyYofr162v27NkXHFNcXKz7779f48aNU8uWLa9gtQAAoKZzabgpKirSxo0bFRsb62hzc3NTbGysMjMzLzju+eefl7+/vwYPHnzJ9ygsLFR+fn6pDQAAmMul4SYvL0/FxcUKCAgo1R4QEKCcnJxyx6xdu1azZs3SzJkzK/QeaWlp8vX1dWzBwcGXXTcAAKi5XH5byhknT57UAw88oJkzZ6pJkyYVGjNq1CidOHHCse3fv7+aqwQAAK5Ux5Vv3qRJE7m7uys3N7dUe25urpo1a1am/65du7R371716dPH0VZSUiJJqlOnjrZv365WrVqVGmO322W326uhegAAUBO5dObGw8ND4eHhysjIcLSVlJQoIyNDUVFRZfq3bdtWW7ZsUVZWlmO78847deONNyorK4tbTgAAwLUzN5KUkpKihIQERUREqHv37po0aZIKCgqUmJgoSYqPj1fz5s2VlpYmT09PdezYsdR4Pz8/SSrTDgAAfp9cHm4GDhyoo0ePasyYMcrJyVGXLl20fPlyxyLj7OxsubnVqqVBAADAhVwebiQpOTlZycnJ5e5btWrVRcfOmTOn6gsCAAC1FlMiAADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADBKjQg3U6dOVWhoqDw9PRUZGal169ZdsO/MmTPVs2dPNWzYUA0bNlRsbOxF+wMAgN8Xl4ebBQsWKCUlRampqdq0aZPCwsIUFxenI0eOlNt/1apVGjRokFauXKnMzEwFBwfr1ltv1cGDB69w5QAAoCZyebiZOHGihgwZosTERLVv314zZsxQ/fr1NXv27HL7v//++xo6dKi6dOmitm3b6p133lFJSYkyMjKucOUAAKAmcmm4KSoq0saNGxUbG+toc3NzU2xsrDIzMyt0jNOnT+vs2bNq1KhRufsLCwuVn59fagMAAOZyabjJy8tTcXGxAgICSrUHBAQoJyenQsd46qmnFBQUVCog/VZaWpp8fX0dW3Bw8GXXDQAAai6X35a6HC+//LLmz5+vxYsXy9PTs9w+o0aN0okTJxzb/v37r3CVAADgSqrjyjdv0qSJ3N3dlZubW6o9NzdXzZo1u+jY1157TS+//LI+++wzde7c+YL97Ha77HZ7ldQLAABqPpfO3Hh4eCg8PLzUYuDzi4OjoqIuOO7VV1/V+PHjtXz5ckVERFyJUgEAQC3h0pkbSUpJSVFCQoIiIiLUvXt3TZo0SQUFBUpMTJQkxcfHq3nz5kpLS5MkvfLKKxozZozmzZun0NBQx9qcBg0aqEGDBi47DwAAUDO4PNwMHDhQR48e1ZgxY5STk6MuXbpo+fLljkXG2dnZcnP7zwTT9OnTVVRUpH79+pU6TmpqqsaOHXslSwcAADWQy8ONJCUnJys5ObncfatWrSr1eu/evdVfEAAAqLVq9dNSAAAA/41wAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGqRHhZurUqQoNDZWnp6ciIyO1bt26i/ZfuHCh2rZtK09PT3Xq1EnLli27QpUCAICazuXhZsGCBUpJSVFqaqo2bdqksLAwxcXF6ciRI+X2//LLLzVo0CANHjxYmzdvVt++fdW3b1999913V7hyAABQE7k83EycOFFDhgxRYmKi2rdvrxkzZqh+/fqaPXt2uf0nT56s2267TSNHjlS7du00fvx4devWTVOmTLnClQMAgJrIpeGmqKhIGzduVGxsrKPNzc1NsbGxyszMLHdMZmZmqf6SFBcXd8H+AADg96WOK988Ly9PxcXFCggIKNUeEBCgbdu2lTsmJyen3P45OTnl9i8sLFRhYaHj9YkTJyRJ+fn5F62tuPCXS9b/e3Cp63QpXMdzLvc6SlzL87iWVYf/v6sGP5NV52LX8vw+y7IueRyXhpsrIS0tTePGjSvTHhwc7IJqah/fNx92dQlG4DpWHa5l1eFaVg2uY9WpyLU8efKkfH19L9rHpeGmSZMmcnd3V25ubqn23NxcNWvWrNwxzZo1c6r/qFGjlJKS4nhdUlKiY8eOqXHjxrLZbJd5BtUnPz9fwcHB2r9/v3x8fFxdTq3Fdaw6XMuqw7WsGlzHqlMbrqVlWTp58qSCgoIu2del4cbDw0Ph4eHKyMhQ3759JZ0LHxkZGUpOTi53TFRUlDIyMjRixAhHW3p6uqKiosrtb7fbZbfbS7X5+flVRflXhI+PT439QatNuI5Vh2tZdbiWVYPrWHVq+rW81IzNeS6/LZWSkqKEhARFRESoe/fumjRpkgoKCpSYmChJio+PV/PmzZWWliZJGj58uGJiYvT666+rd+/emj9/vjZs2KC3337blacBAABqCJeHm4EDB+ro0aMaM2aMcnJy1KVLFy1fvtyxaDg7O1tubv95qOu6667TvHnz9Oyzz+qZZ55R69attWTJEnXs2NFVpwAAAGoQl4cbSUpOTr7gbahVq1aVaevfv7/69+9fzVW5lt1uV2pqaplbanAO17HqcC2rDteyanAdq45p19JmVeSZKgAAgFrC5Z9QDAAAUJUINwAAwCiEGwAAYBTCDQAAMArhpgaaOnWqQkND5enpqcjISK1bt87VJdU6q1evVp8+fRQUFCSbzaYlS5a4uqRaKy0tTddee628vb3l7++vvn37avv27a4uq9aZPn26Onfu7PiQtKioKH3yySeuLssIL7/8smw2W6kPd0XFjB07VjabrdTWtm1bV5d12Qg3NcyCBQuUkpKi1NRUbdq0SWFhYYqLi9ORI0dcXVqtUlBQoLCwME2dOtXVpdR6X3zxhZKSkvTVV18pPT1dZ8+e1a233qqCggJXl1arXHXVVXr55Ze1ceNGbdiwQTfddJPuuusubd261dWl1Wrr16/XW2+9pc6dO7u6lFqrQ4cOOnz4sGNbu3atq0u6bDwKXsNERkbq2muv1ZQpUySd+zqK4OBgPfroo3r66addXF3tZLPZtHjxYsdXfODyHD16VP7+/vriiy90/fXXu7qcWq1Ro0aaMGGCBg8e7OpSaqVTp06pW7dumjZtml544QV16dJFkyZNcnVZtcrYsWO1ZMkSZWVlubqUKsXMTQ1SVFSkjRs3KjY21tHm5uam2NhYZWZmurAy4D9OnDgh6dw/zKic4uJizZ8/XwUFBRf8XjxcWlJSknr37l3q70w478cff1RQUJBatmyp+++/X9nZ2a4u6bLViE8oxjl5eXkqLi52fPXEeQEBAdq2bZuLqgL+o6SkRCNGjFB0dDRfeVIJW7ZsUVRUlM6cOaMGDRpo8eLFat++vavLqpXmz5+vTZs2af369a4upVaLjIzUnDlzdM011+jw4cMaN26cevbsqe+++07e3t6uLq/SCDcAKiwpKUnfffedEffkXeGaa65RVlaWTpw4oUWLFikhIUFffPEFAcdJ+/fv1/Dhw5Weni5PT09Xl1Or9erVy/HfnTt3VmRkpEJCQvTBBx/U6tulhJsapEmTJnJ3d1dubm6p9tzcXDVr1sxFVQHnJCcn6+OPP9bq1at11VVXubqcWsnDw0NXX321JCk8PFzr16/X5MmT9dZbb7m4stpl48aNOnLkiLp16+ZoKy4u1urVqzVlyhQVFhbK3d3dhRXWXn5+fmrTpo127tzp6lIuC2tuahAPDw+Fh4crIyPD0VZSUqKMjAzuy8NlLMtScnKyFi9erM8//1wtWrRwdUnGKCkpUWFhoavLqHVuvvlmbdmyRVlZWY4tIiJC999/v7Kysgg2l+HUqVPatWuXAgMDXV3KZWHmpoZJSUlRQkKCIiIi1L17d02aNEkFBQVKTEx0dWm1yqlTp0r95rFnzx5lZWWpUaNG+sMf/uDCymqfpKQkzZs3T//85z/l7e2tnJwcSZKvr6/q1avn4upqj1GjRqlXr176wx/+oJMnT2revHlatWqVVqxY4erSah1vb+8ya768vLzUuHFj1oI56YknnlCfPn0UEhKiQ4cOKTU1Ve7u7ho0aJCrS7sshJsaZuDAgTp69KjGjBmjnJwcdenSRcuXLy+zyBgXt2HDBt14442O1ykpKZKkhIQEzZkzx0VV1U7Tp0+XJN1www2l2t999139+c9/vvIF1VJHjhxRfHy8Dh8+LF9fX3Xu3FkrVqzQLbfc4urS8Dt24MABDRo0SD/99JOaNm2qHj166KuvvlLTpk1dXdpl4XNuAACAUVhzAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGQLWy2WxasmSJq8uolLFjx6pLly6XdYy9e/fKZrMpKyurSmoCcGmEGwCVlpOTo0cffVQtW7aU3W5XcHCw+vTpU+r70Vzphhtu0IgRI1xdBoArjK9fAFApe/fuVXR0tPz8/DRhwgR16tRJZ8+e1YoVK5SUlKRt27a5ukQAv1PM3AColKFDh8pms2ndunW655571KZNG3Xo0EEpKSn66quvLjjuqaeeUps2bVS/fn21bNlSzz33nM6ePevY/8033+jGG2+Ut7e3fHx8FB4erg0bNkiS9u3bpz59+qhhw4by8vJShw4dtGzZskqfw6VqOe+tt95ScHCw6tevrwEDBujEiROl9r/zzjtq166dPD091bZtW02bNq3SNQG4fMzcAHDasWPHtHz5cr344ovy8vIqs9/Pz++CY729vTVnzhwFBQVpy5YtGjJkiLy9vfXkk09Kku6//3517dpV06dPl7u7u7KyslS3bl1J576hvKioSKtXr5aXl5e+//57NWjQoNLncalaJGnnzp364IMP9NFHHyk/P1+DBw/W0KFD9f7770uS3n//fY0ZM0ZTpkxR165dtXnzZg0ZMkReXl5KSEiodG0ALoMFAE76+uuvLUnWhx9+eMm+kqzFixdfcP+ECROs8PBwx2tvb29rzpw55fbt1KmTNXbs2ArXGRMTYw0fPrzC/f+7ltTUVMvd3d06cOCAo+2TTz6x3NzcrMOHD1uWZVmtWrWy5s2bV+o448ePt6KioizLsqw9e/ZYkqzNmzdXuA4Al4eZGwBOsyyr0mMXLFigv/71r9q1a5dOnTqlX3/9VT4+Po79KSkpevDBB/Xee+8pNjZW/fv3V6tWrSRJw4YN0yOPPKJPP/1UsbGxuueee9S5c+dqq0WS/vCHP6h58+aO11FRUSopKdH27dvl7e2tXbt2afDgwRoyZIijz6+//ipfX99K1wXg8rDmBoDTWrduLZvN5vSi4czMTN1///26/fbb9fHHH2vz5s0aPXq0ioqKHH3Gjh2rrVu3qnfv3vr888/Vvn17LV68WJL04IMPavfu3XrggQe0ZcsWRURE6M0336zUOVSklks5deqUJGnmzJnKyspybN99991F1x0BqF6EGwBOa9SokeLi4jR16lQVFBSU2X/8+PFyx3355ZcKCQnR6NGjFRERodatW2vfvn1l+rVp00aPPfaYPv30U91999169913HfuCg4P18MMP68MPP9Tjjz+umTNnVuocKlpLdna2Dh065Hj91Vdfyc3NTddcc40CAgIUFBSk3bt36+qrry61tWjRolJ1Abh83JYCUClTp05VdHS0unfvrueff16dO3fWr7/+qvT0dE2fPl0//PBDmTGtW7dWdna25s+fr2uvvVZLly51zMpI0i+//KKRI0eqX79+atGihQ4cOKD169frnnvukSSNGDFCvXr1Ups2bfTzzz9r5cqVateu3UXrPHr0aJkP0AsMDLxkLed5enoqISFBr732mvLz8zVs2DANGDBAzZo1kySNGzdOw4YNk6+vr2677TYVFhZqw4YN+vnnn5WSkuLsZQVQFVy96AdA7XXo0CErKSnJCgkJsTw8PKzmzZtbd955p7Vy5UpHH/3XguKRI0dajRs3tho0aGANHDjQeuONNyxfX1/LsiyrsLDQuvfee63g4GDLw8PDCgoKspKTk61ffvnFsizLSk5Otlq1amXZ7XaradOm1gMPPGDl5eVdsL6YmBhLUplt/Pjxl6zFss4tKA4LC7OmTZtmBQUFWZ6enla/fv2sY8eOlXqf999/3+rSpYvl4eFhNWzY0Lr++usdi61ZUAxceTbLuoyVgQAAADUMa24AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMMr/A281IV7Ma2+oAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}