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    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPIL+qDWG1DjkojNL3QOdB/",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Aditya-567/Lung-AND-Oral-Cancer-ML-Model/blob/lextrone/lung_cancer_graph.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Lung cancer Prediction**\n",
        "Graphs Analysis"
      ],
      "metadata": {
        "id": "hnJZ8QpEieeF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Employing exploratory data analysis (EDA) techniques, an effective cancer prediction machine learning model can accurately assess an individual's cancer risk at a relatively low cost. By leveraging EDA to uncover patterns and insights within the data, this model can provide reliable and personalized cancer risk evaluations. The data for this analysis is sourced from the online lung cancer prediction system's website, offering a convenient and accessible platform for gathering the necessary information to train and validate the model."
      ],
      "metadata": {
        "id": "KtQi29oNivt1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Importing Libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "#For ignoring warning\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "FT-92ToIjQUA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df=pd.read_csv('/content/sample_data/survey lung cancer.csv')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "0cuDNeMLks5O",
        "outputId": "b457d001-6fc7-4d94-8b82-6370eff4a90c"
      },
      "execution_count": null,
      "outputs": [
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          "data": {
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              "    GENDER  AGE SMOKING YELLOW_FINGERS ANXIETY PEER_PRESSURE CHRONIC DISEASE  \\\n",
              "0        M   69      No            Yes     Yes            No              No   \n",
              "1        M   74     Yes             No      No            No             Yes   \n",
              "2        F   59      No             No      No           Yes              No   \n",
              "3        M   63     Yes            Yes     Yes            No              No   \n",
              "4        F   63      No            Yes      No            No              No   \n",
              "..     ...  ...     ...            ...     ...           ...             ...   \n",
              "304      F   56      No             No      No           Yes             Yes   \n",
              "305      M   70     Yes             No      No            No              No   \n",
              "306      M   58     Yes             No      No            No              No   \n",
              "307      M   67     Yes             No     Yes            No              No   \n",
              "308      M   62      No             No      No           Yes              No   \n",
              "\n",
              "    FATIGUE  ALLERGY  WHEEZING ALCOHOL CONSUMING COUGHING SHORTNESS OF BREATH  \\\n",
              "0        Yes       No      Yes               Yes      Yes                 Yes   \n",
              "1        Yes      Yes       No                No       No                 Yes   \n",
              "2        Yes       No      Yes                No      Yes                 Yes   \n",
              "3         No       No       No               Yes       No                  No   \n",
              "4         No       No      Yes                No      Yes                 Yes   \n",
              "..       ...      ...      ...               ...      ...                 ...   \n",
              "304      Yes       No       No               Yes      Yes                 Yes   \n",
              "305      Yes      Yes      Yes               Yes      Yes                 Yes   \n",
              "306       No      Yes      Yes               Yes      Yes                  No   \n",
              "307      Yes      Yes       No               Yes      Yes                 Yes   \n",
              "308      Yes      Yes      Yes               Yes       No                  No   \n",
              "\n",
              "    SWALLOWING DIFFICULTY CHEST PAIN LUNG_CANCER  \n",
              "0                     Yes        Yes         YES  \n",
              "1                     Yes        Yes         YES  \n",
              "2                      No        Yes          NO  \n",
              "3                     Yes        Yes          NO  \n",
              "4                      No         No          NO  \n",
              "..                    ...        ...         ...  \n",
              "304                   Yes         No         YES  \n",
              "305                    No        Yes         YES  \n",
              "306                    No        Yes         YES  \n",
              "307                    No        Yes         YES  \n",
              "308                   Yes         No         YES  \n",
              "\n",
              "[309 rows x 16 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GENDER</th>\n",
              "      <th>AGE</th>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 309,\n  \"fields\": [\n    {\n      \"column\": \"GENDER\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"F\",\n          \"M\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 21,\n        \"max\": 87,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          81,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMOKING\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YELLOW_FINGERS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ANXIETY\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PEER_PRESSURE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CHRONIC DISEASE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FATIGUE \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ALLERGY \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WHEEZING\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ALCOHOL CONSUMING\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"COUGHING\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SHORTNESS OF BREATH\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SWALLOWING DIFFICULTY\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CHEST PAIN\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LUNG_CANCER\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"NO\",\n          \"YES\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RfAcgdgekx5l",
        "outputId": "d89a1ea2-8959-4049-bd83-81fc75eabaa6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(309, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#Checking for Duplicates\n",
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jFDrGO3Kk3NW",
        "outputId": "a13f4b9b-bd4e-4344-b9e8-abdcb76fb173"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "33"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#Removing Duplicates\n",
        "df=df.drop_duplicates()"
      ],
      "metadata": {
        "id": "YK5tF6myk67p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#Checking for null values\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XwjS-fqzliwa",
        "outputId": "b87fd675-b0bf-41f5-91dc-b0202404573b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GENDER                   0\n",
              "AGE                      0\n",
              "SMOKING                  0\n",
              "YELLOW_FINGERS           0\n",
              "ANXIETY                  0\n",
              "PEER_PRESSURE            0\n",
              "CHRONIC DISEASE          0\n",
              "FATIGUE                  0\n",
              "ALLERGY                  0\n",
              "WHEEZING                 0\n",
              "ALCOHOL CONSUMING        0\n",
              "COUGHING                 0\n",
              "SHORTNESS OF BREATH      0\n",
              "SWALLOWING DIFFICULTY    0\n",
              "CHEST PAIN               0\n",
              "LUNG_CANCER              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FpH3gLMgll2W",
        "outputId": "d53d859f-dd5b-4877-821c-7ecc8911b74d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 276 entries, 0 to 283\n",
            "Data columns (total 16 columns):\n",
            " #   Column                 Non-Null Count  Dtype \n",
            "---  ------                 --------------  ----- \n",
            " 0   GENDER                 276 non-null    object\n",
            " 1   AGE                    276 non-null    int64 \n",
            " 2   SMOKING                276 non-null    object\n",
            " 3   YELLOW_FINGERS         276 non-null    object\n",
            " 4   ANXIETY                276 non-null    object\n",
            " 5   PEER_PRESSURE          276 non-null    object\n",
            " 6   CHRONIC DISEASE        276 non-null    object\n",
            " 7   FATIGUE                276 non-null    object\n",
            " 8   ALLERGY                276 non-null    object\n",
            " 9   WHEEZING               276 non-null    object\n",
            " 10  ALCOHOL CONSUMING      276 non-null    object\n",
            " 11  COUGHING               276 non-null    object\n",
            " 12  SHORTNESS OF BREATH    276 non-null    object\n",
            " 13  SWALLOWING DIFFICULTY  276 non-null    object\n",
            " 14  CHEST PAIN             276 non-null    object\n",
            " 15  LUNG_CANCER            276 non-null    object\n",
            "dtypes: int64(1), object(15)\n",
            "memory usage: 36.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Let's check what's happened now\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "cnJrsZwUl0eZ",
        "outputId": "89ae25d2-e537-4aa6-cff3-38be80a25279"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    GENDER  AGE SMOKING YELLOW_FINGERS ANXIETY PEER_PRESSURE CHRONIC DISEASE  \\\n",
              "0        M   69      No            Yes     Yes            No              No   \n",
              "1        M   74     Yes             No      No            No             Yes   \n",
              "2        F   59      No             No      No           Yes              No   \n",
              "3        M   63     Yes            Yes     Yes            No              No   \n",
              "4        F   63      No            Yes      No            No              No   \n",
              "..     ...  ...     ...            ...     ...           ...             ...   \n",
              "279      F   59      No            Yes     Yes           Yes              No   \n",
              "280      F   59     Yes             No      No            No             Yes   \n",
              "281      M   55     Yes             No      No            No              No   \n",
              "282      M   46      No            Yes     Yes            No              No   \n",
              "283      M   60      No            Yes     Yes            No              No   \n",
              "\n",
              "    FATIGUE  ALLERGY  WHEEZING ALCOHOL CONSUMING COUGHING SHORTNESS OF BREATH  \\\n",
              "0        Yes       No      Yes               Yes      Yes                 Yes   \n",
              "1        Yes      Yes       No                No       No                 Yes   \n",
              "2        Yes       No      Yes                No      Yes                 Yes   \n",
              "3         No       No       No               Yes       No                  No   \n",
              "4         No       No      Yes                No      Yes                 Yes   \n",
              "..       ...      ...      ...               ...      ...                 ...   \n",
              "279       No      Yes      Yes                No      Yes                  No   \n",
              "280      Yes      Yes       No                No       No                 Yes   \n",
              "281      Yes      Yes       No                No       No                 Yes   \n",
              "282       No       No       No                No       No                  No   \n",
              "283      Yes       No      Yes               Yes      Yes                 Yes   \n",
              "\n",
              "    SWALLOWING DIFFICULTY CHEST PAIN LUNG_CANCER  \n",
              "0                     Yes        Yes         YES  \n",
              "1                     Yes        Yes         YES  \n",
              "2                      No        Yes          NO  \n",
              "3                     Yes        Yes          NO  \n",
              "4                      No         No          NO  \n",
              "..                    ...        ...         ...  \n",
              "279                   Yes         No         YES  \n",
              "280                    No         No          NO  \n",
              "281                    No        Yes          NO  \n",
              "282                   Yes        Yes          NO  \n",
              "283                   Yes        Yes         YES  \n",
              "\n",
              "[276 rows x 16 columns]"
            ],
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              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>YES</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280</th>\n",
              "      <td>F</td>\n",
              "      <td>59</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281</th>\n",
              "      <td>M</td>\n",
              "      <td>55</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>282</th>\n",
              "      <td>M</td>\n",
              "      <td>46</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>283</th>\n",
              "      <td>M</td>\n",
              "      <td>60</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>YES</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>276 rows × 16 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-61df481a-e676-463c-8f6e-040c5013b722')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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-61df481a-e676-463c-8f6e-040c5013b722 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-61df481a-e676-463c-8f6e-040c5013b722');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fac5326d-337e-4eca-a627-4cd19fcdaf15')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      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",
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              "    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-fac5326d-337e-4eca-a627-4cd19fcdaf15 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_a0417af2-07c5-4ea3-949e-d5674a611abb\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_a0417af2-07c5-4ea3-949e-d5674a611abb button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 276,\n  \"fields\": [\n    {\n      \"column\": \"GENDER\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"F\",\n          \"M\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 21,\n        \"max\": 87,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          81,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMOKING\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YELLOW_FINGERS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ANXIETY\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PEER_PRESSURE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CHRONIC DISEASE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FATIGUE \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ALLERGY \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WHEEZING\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ALCOHOL CONSUMING\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"COUGHING\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SHORTNESS OF BREATH\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SWALLOWING DIFFICULTY\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CHEST PAIN\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LUNG_CANCER\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"NO\",\n          \"YES\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sns.countplot(x='LUNG_CANCER', data=df, color='red')  # Change 'skyblue' to the desired color\n",
        "plt.title('Target Distribution')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "xjP1kTdql5DN",
        "outputId": "88db609d-bf93-4c83-da0c-9aaa206274a3"
      },
      "execution_count": null,
      "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": [
        "# prompt: generate pie chart\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "df_pie = df['GENDER'].value_counts()\n",
        "\n",
        "plt.pie(df_pie, autopct=\"%1.1f%%\", labels=df_pie.index, startangle=90)\n",
        "plt.title('Gender Distribution')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "czdakl-M-PZF",
        "outputId": "ab65056d-4bdb-4330-fa8c-d36f1b48d770"
      },
      "execution_count": null,
      "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": [
        "# prompt:\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "# Checking the distribution of the 'AGE' column\n",
        "sns.distplot(df['AGE'], color='red')\n",
        "plt.title('Distribution of Age')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Jq_Pd0iZl8c5",
        "outputId": "6b652868-d2b6-4570-c8c8-c5240b873e62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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OnIlt27ZhwIABKCkpqfA5Z8yYAS8vL/2NC6ESERHZNptcC2zo0KH6/fDwcNxzzz1o2bIltm7dir59+5Y7f/LkyUhISND/WV1MjYiIiGyT1CtAvr6+cHR0RGZmpsHxzMxM+Pv7V/gYf3//ap0PAC1atICvry9OnjxZ4f2urq76hU+5ACoREZHtkxqAXFxc0KlTJyQnJ+uP6XQ6JCcnIyoqqsLHREVFGZwPAElJSZWeDwB///03rly5gsaNGxuncCIiIrJq0ofBJyQk4IsvvsCSJUtw5MgRjB07Fnl5eRg1ahQAYPjw4Zg8ebL+/AkTJiAxMRGzZs3C0aNH8dZbb2Hv3r0YN24cAODGjRuYOHEidu7cibNnzyI5ORmDBw9Gq1atEBsbK+U9EhERkWWR3gcoPj4ely5dwrRp06DVahEREYHExER9R+eMjAw4OJTmtO7du2PFihWYOnUqpkyZgpCQEKxZswZhYWEAAEdHRxw8eBBLlixBdnY2AgIC0K9fP7zzzjtwdXWV8h6JiO5o0SLjPM+YMcZ5HiI7IH0eIEvEeYCIyKwYgIiMojq/v6VfASIisnsZGcDmzcCVK8D160CHDsBDDwHOzrIrI7JZDEBERDKtXQt89BFQWFh67OJF4MAB4JlngGbN5NVGZMOkd4ImIrJb//kP8MgjIvy0bQuMHg2MHAl4eooQNGsWcPmy7CqJbBIDEBGRDIcOAWPHAjod0LMnMH480LUrEBUFTJ8OBAcDt24BS5eKc4jIqBiAiIjMraQEeO45oLgYGDwYeOopwNGx9H4PD9H85eICHDsGbNsmr1YiG8UARERkbp99BuzaJZq65s8HNJry5zRqBDz2mNj/739FB2kiMhoGICIic8rKAqZMEfsffAA0aVL5ufffD4SEAEVFwG0z4BNR7TAAERGZ06efAjduAJ06Ac8/f+dzHRyAAQPE/vbtwM2bpq+PyE4wABERmUtenmj+AoDJk0XAuZt27YCAAKCgAPj9d9PWR2RHGICIiMzlq6+Aq1eBVq2AuLiqPUajAWJixP6WLaIDNRHVGgMQEZE5FBcDs2eL/YQEw1Ffd9O1q+gwfe0akJpqmvqI7AwDEBGROaxZA5w9C/j6AiNGVO+xzs6iQzQA7Nxp7MqI7BIDEBGROXz9tdiOGQPUrVv9x3ftKrZHjohO1ERUKwxARESmptUCv/4q9qt79Ufl5yfWBdPpgLQ049VGZKcYgIiITG3FCtF5uVs3oHXrmj9P585iu2ePceoismMMQEREprZ0qdgOH16751ED0IkTQHZ27Z6LyM4xABERmdKBA+Lm4gLEx9fuuRo0AFq2BBSFo8GIaokBiIjIlJYtE9uHHwZ8fGr/fOpVIAYgolphACIiMhVFAb7/Xuw/+aRxnjMiQmxPn+ZoMKJaYAAiIjKV/fuB9HSgTh0gNtY4z+njI5bGUBTgr7+M85xEdogBiIjIVNasEdv+/Ws2909lwsLE9vBh4z0nkZ1xkl0AEZHZLVpknOcZM+bO9//4o9hWdd2vqgoPBzZuBP78U8wLVJVFVYnIAP/XEBGZwqlTwKFDYs2vhx4y7nO3bAm4uYk+QOnpxn1uIjvBAEREZApq81evXsYZ/VWWoyPQrp3YZzMYUY0wABERmYKpmr9U7AdEVCsMQERExnblCrBjh9gfPNg0r9G+vdimp3M4PFENMAARERlbUpIYph4eDgQGmuY16tcvHQ5/7JhpXoPIhjEAEREZW2Ki2Pbvb9rXadNGbBmAiKqNAYiIyJgUBfj1V7Fv6gAUGiq2R4+a9nWIbBADEBGRMR08CGi1YuLDHj1M+1ohIYBGA2RmcnV4ompiACIiMia1+atPH8DV1bSv5e4ONGsm9tkMRlQtDEBERMZkrv4/KvYDIqoRBiAiImO5fh3Yvl3smysAsR8QUY0wABERGcvWrUBxsViqomVL87xmq1ZiLbArV4AzZ8zzmkQ2gAGIiMhYtmwR2759zfearq5AcLDY37zZfK9LZOUYgIiIjEUNIH36mPd1W7cW299+M+/rElkxBiAiImO4fBk4cEDs9+pl3tdmACKqNgYgIiJj2LpVbMPCAD8/8752ixaiH9DZs0BGhnlfm8hKMQARERmD2vzVu7f5X9vNrXQ+IF4FIqoSBiAiImNQO0Cbu/+PKiREbLdtk/P6RFaGAYiIqLYuXBDz8Gg0QHS0nBrYD4ioWhiAiIhqS73607Ej4O0tp4ZWrUQAO34cuHhRTg1EVoQBiIiottRmJ3OP/iqrbl2gQwex//vv8uogshIMQEREtaUGjvvvl1uH+vrsB0R0VwxARES1kZVVug5Xz55ya1H7H7EfENFdOckugIjIai1aBKSlif2AAOD77+XWc999Ynv4sJiY0ddXbj1EFoxXgIiIauPkSbFVh6HL1LAh0K6d2FdXpSeiCjEAERHVxokTYmsJAQhgPyCiKmIAIiKqqfx84Nw5sd+qldxaVOwHRFQlDEBERDV1+jSgKKKvjaz5f26nXgHavx/IyZFaCpElYwAiIqopS2v+AkRn7FatAJ0O+OMP2dUQWSwGICKimjp1SmwtpflLxX5ARHfFYfBERDVRUgKcOSP2LSUALVoktjqd2K5eDbRsWf3nGTPGeDURWSheASIiqolz54CiIrEERaNGsqsxpDbJpacDBQVyayGyUAxAREQ1cfq02LZoAThY2I9SX1/Ax0dcCVLrJCIDFva/lojISpQNQJZIvQp0/LjcOogsFAMQEVFNqAGoJn1szKF1a7FVR6oRkQEGICKi6srOBq5cATQaIChIdjUVU68AnTkj+ioRkQEGICKi6lKv/jRtCri5ya2lMo0aAZ6eQHFx6Wg1ItJjACIiqi51/h9L7f8DiKtT6lUgNoMRlWMRAWj+/PkICgqCm5sbIiMjsXv37juev3r1aoSGhsLNzQ3h4eHYsGFDpef+3//9HzQaDebMmWPkqonIbll6B2gVAxBRpaQHoFWrViEhIQHTp09HWloaOnTogNjYWGRlZVV4/o4dOzBs2DCMHj0a+/btQ1xcHOLi4nD48OFy5/7444/YuXMnAgICTP02iMheFBUBGRli39IDkNoR+tQp0RRGRHrSA9Ds2bPx3HPPYdSoUWjXrh0WLlyIunXr4quvvqrw/Llz56J///6YOHEi2rZti3feeQcdO3bEvHnzDM47f/48xo8fj+XLl8PZ2dkcb4WI7MHff4sw4eEBNGwou5o7a9xY1FlYCJw9K7saIosiNQAVFhYiNTUVMTEx+mMODg6IiYlBSkpKhY9JSUkxOB8AYmNjDc7X6XR4+umnMXHiRLRv3940xRORfVKDRFCQ6GdjyRwcgDZtxP6RI3JrIbIwUgPQ5cuXUVJSAj8/P4Pjfn5+0Gq1FT5Gq9Xe9fyZM2fCyckJL730UpXqKCgoQG5ursGNiKhC6ogqSx3+frt27cSWAYjIgPQmMGNLTU3F3LlzsXjxYmiq+O1sxowZ8PLy0t8CAwNNXCURWS31ClBwsNQyqqxtW7E9cwbIz5dbC5EFkRqAfH194ejoiMzMTIPjmZmZ8Pf3r/Ax/v7+dzz/999/R1ZWFpo1awYnJyc4OTkhPT0dr776KoIq+cY2efJk5OTk6G/nzp2r/ZsjItuTlweoP3+s5QpQgwZiTiCdjstiEJUhNQC5uLigU6dOSE5O1h/T6XRITk5GVFRUhY+JiooyOB8AkpKS9Oc//fTTOHjwIPbv36+/BQQEYOLEifj1118rfE5XV1d4enoa3IiIyklPF9uGDUXnYmuhXgX66y+5dRBZECfZBSQkJGDEiBHo3Lkzunbtijlz5iAvLw+jRo0CAAwfPhxNmjTBjBkzAAATJkxAdHQ0Zs2ahYEDB+Lbb7/F3r17sWjRIgBAgwYN0KBBA4PXcHZ2hr+/P9qonQGJiGrC2vr/qNq2BbZtYz8gojKkB6D4+HhcunQJ06ZNg1arRUREBBITE/UdnTMyMuDgUHqhqnv37lixYgWmTp2KKVOmICQkBGvWrEFYWJist0BE9sLa+v+o2rQRI9YyM4GrVwEfH9kVEUknPQABwLhx4zBu3LgK79u6dWu5Y0888QSeeOKJKj//Wc5/QUS1pSjWewWobl1R85kzohmsZ0/ZFRFJZ3OjwIiITOLqVeD6dTG3jjWOFFWvkh88KLcOIgvBAEREVBXqleQmTQAXF6ml1Mg994jtkSNiOQ8iO8cARERUFer6X82by62jpgIDAW9vsSzG0aOyqyGSjgGIiKgq1ADUrJncOmpKoym9CsRmMCIGICKiu1IUQJ0g1VoDEGAYgBRFbi1EkjEAERHdTXZ2aQfoJk1kV1NzbdoArq7i/XDGe7JzDEBERHejzgAdEGCdHaBVzs6li6MeOCC3FiLJGICIiO5G7f9jjcPfb9ehg9ju3ctmMLJrDEBERHdj7R2gy4qIEFeCtFo2g5FdYwAiIrobW+gArapTp7Qz9K5dcmshkogBiIjoTnJyRKdhjQZo2lR2NcbRtavY7tkD6HRyayGShAGIiOhO1Ks/fn6Am5vcWowlLEysD5aTAxw7JrsaIikYgIiI7sSW+v+onJyATp3E/u7dcmshkoQBiIjoTtQh8LYUgIDSZrDUVCA/X24tRBIwABER3YktdYAuKyQEaNwYKCgA/vhDdjVEZscARERUmRs3gCtXxL4tzAFUlkYD9Okj9rdsYWdosjsMQERElVGv/jRsKDoN25pu3QB3d+DyZS6QSnaHAYiIqDK22AG6LBcX4L77xH5ystxaiMyMAYiIqDK2HoAAoFcvscjr8ePAqVOyqyEyGwYgIqLK2EMA8vYGoqLE/urVXB+M7AYDEBFRRfLzgawssW/LAQgABg0CXF2BM2fEIqlEdsBJdgFERBZJ7QDt4wN4eMitxdTq1wf69QN+/hn48cfSBVNra8yY2j8HkYnwChARUUXsofmrrAceEEHoyhVg/XrZ1RCZHAMQEVFF7C0AuboCQ4aI/cRE4OhRufUQmRgDEBFRRdQmMFubAPFOOnUCevYUHaG//BLIzZVdEZHJMAAREd2uqAjQasW+PQUgAIiPF0tk5OYCn38OFBbKrojIJBiAiIhup9WKpSHq1hX9YuyJiwvw3HOAmxtw8iSwcKEIhEQ2hgGIiOh2f/8ttk2aiDWz7E2TJsD48SIM/fkn8MUXvBJENocBiIjodufPi22TJnLrkKlVK+CFFwAnJ+DAAWD2bPYJIpvCAEREdDs1ADVtKrcO2dq2BSZMEE2BZ84AM2eW9o0isnIMQEREt1ObwOw9AAFA69bA668Dvr5i1fiZM4Fjx2RXRVRrDEBERGXl5pY29TRuLLcWS+HvD0yaBAQHAzdvAnPnAnv2yK6KqFYYgIiIylKbvxo2FCOhSKhXD0hIADp2BEpKxDxBO3fKroqoxhiAiIjKYgfoyqlD5Hv0EJMlLl4MpKTIroqoRhiAiIjKYgfoO3NwAJ56CoiOFiFo6VLg+HHZVRFVGwMQEVFZZecAooo5OADDhgFduogJIz//XCyiSmRFGICIiFQ6HXDxotjnFaA702iA4cPFYrE3bgALFgDFxbKrIqoyBiAiIlVWllj2wcVFDPumO3NxAcaOBTw8xOKxv/4quyKiKmMAIiJSqf1/AgJEMw/dnY+PWEAVADZs4ESJZDX4P5yISMUJEGumSxegfXvRBPbNN6IpkcjCMQAREak4BL5mNBrgH/8QTWInTgCpqbIrIrorBiAiIhWHwNecry8QGyv216/nVSCyeAxAREQAkJ8v1roCeAWopvr2FQunXrzIq0Bk8RiAiIgA4MIFsa1fH3B3l1qK1apTB4iJEfvr1oklM4gsFAMQERHADtDG0qePuAqk1QLffSe7GqJKMQAREQHsAG0sZa8CzZ0rtxaiO2AAIiIC2AHamO67D3B0BHbtAtLSZFdDVCEGICIiReEaYMbk6Ql07Cj2FyyQWwtRJRiAiIiuXgVu3RJXLfz8ZFdjG6KjxXbFCiAnR24tRBVgACIiUq/+NG4MODnJrcVWtGolZoe+eRNYulR2NUTlMAAREakrwAcEyK3Dlmg0YqFUAFi0SG4tRBVgACIiUjtAN24stw5b8+STYnmMw4eBQ4dkV0NkgAGIiIhXgEyjfn1gwACxv3Kl1FKIbscARET2TacTk/YBDECm8I9/iO3KlWK0HZGFYAAiIvt26RJQVAQ4O4sFPcm4HnoI8PAAzp4Fdu6UXQ2RHgMQEdk3tfmrcWPAgT8Sja5uXWDwYLHPZjCyIPzfTkT2TV0Elc1fpjNsmNh+9x1QXCy3FqL/YQAiIvumBiCOADOdfv0AHx8gMxPYsUN2NUQAGICIyN5xBJjpOTsDAweK/bVr5dZC9D8MQERkv0pKOALMXAYNEtuffuJoMLIIDEBEZL8uXRJ9UlxcRBMNmU5srPh7PnkSOHJEdjVElhGA5s+fj6CgILi5uSEyMhK7d+++4/mrV69GaGgo3NzcEB4ejg0bNhjc/9ZbbyE0NBTu7u7w9vZGTEwMdu3aZcq3QETWqGz/H44AM6169YC+fcU+m8HIAkj/H79q1SokJCRg+vTpSEtLQ4cOHRAbG4usrKwKz9+xYweGDRuG0aNHY9++fYiLi0NcXBwOHz6sP6d169aYN28eDh06hO3btyMoKAj9+vXDpUuXzPW2iMgacASYeZVtBiOSTKMochtjIyMj0aVLF8ybNw8AoNPpEBgYiPHjx2PSpEnlzo+Pj0deXh7WrVunP9atWzdERERg4cKFFb5Gbm4uvLy8sGnTJvRVv4HcgXp+Tk4OPD09a/jOiMhiqYtzLloEpKYCjz4qmmjIuMaMMfzzhQtAkyZiodQLFwB/fzl1kc2qzu9vqVeACgsLkZqaipiYGP0xBwcHxMTEICUlpcLHpKSkGJwPALGxsZWeX1hYiEWLFsHLywsdOnSo8JyCggLk5uYa3IjIDnAEmHkFBABduohO0OvXy66G7JzUAHT58mWUlJTAz8/P4Lifnx+06siM22i12iqdv27dOnh4eMDNzQ0ff/wxkpKS4FvJNPczZsyAl5eX/hYYGFiLd0VEVqGkRMxLAzAAmdODD4rtr7/KrYPsnvQ+QKbSu3dv7N+/Hzt27ED//v0xZMiQSvsVTZ48GTk5OfrbuXPnzFwtEZldZqYIQa6uHAFmTv37i21SEmeFJqmkBiBfX184OjoiU/0W9j+ZmZnwr6Rt2N/fv0rnu7u7o1WrVujWrRu+/PJLODk54csvv6zwOV1dXeHp6WlwIyIbV3YNMI1Gbi32pEsXwNsbyM4G9uyRXQ3ZMakByMXFBZ06dUJycrL+mE6nQ3JyMqKioip8TFRUlMH5AJCUlFTp+WWft6CgoPZFE5Ft4AgwORwdgQceEPuJiXJrIbsmvQksISEBX3zxBZYsWYIjR45g7NixyMvLw6hRowAAw4cPx+TJk/XnT5gwAYmJiZg1axaOHj2Kt956C3v37sW4ceMAAHl5eZgyZQp27tyJ9PR0pKam4plnnsH58+fxxBNPSHmPRGSBGIDkUUfcsR8QSeQku4D4+HhcunQJ06ZNg1arRUREBBITE/UdnTMyMuBQZoKy7t27Y8WKFZg6dSqmTJmCkJAQrFmzBmFhYQAAR0dHHD16FEuWLMHly5fRoEEDdOnSBb///jvat28v5T0SkQXiCDB51AC0ezdw5QrQoIHcesgu1WgeoNOnT6NFixamqMcicB4gIhv32WfA+PGATgd88IHok0LGd/s8QGWFhwOHDwPffgvEx5uvJrJpJp8HqFWrVujduze++eYb3Lp1q0ZFEhFJk5kpwo+bG1C/vuxq7JM6GozNYCRJjQJQWloa7rnnHiQkJMDf3x/PP//8XdfvIiKyGGX7/3AEmBzqhLbJyVwdnqSoUQCKiIjA3LlzceHCBXz11Ve4ePEievbsibCwMMyePZtrbhGRZWMHaPl69gScnICMDODMGdnVkB2q1SgwJycnPProo1i9ejVmzpyJkydP4rXXXkNgYCCGDx+Oi2onQyIiS1J2FXiSw90d6NZN7G/eLLcWsku1CkB79+7FCy+8gMaNG2P27Nl47bXXcOrUKSQlJeHChQsYPHiwseokIjIejgCzDH36iC0DEElQowA0e/ZshIeHo3v37rhw4QKWLl2K9PR0vPvuuwgODsZ9992HxYsXIy0tzdj1EhHVTkEBoC6LwwAkV9kAxH5AZGY1mgdowYIFeOaZZzBy5Eg0ruQScqNGjSpdeoKISJpjx8Qv27p1AS8v2dXYt27dxEi8zEzg6FGgbVvZFZEdqVEASkpKQrNmzQwmKAQARVFw7tw5NGvWDC4uLhgxYoRRiiQiMpo//xRbrgEmn6ur6Ay9aZO4CsQARGZUoyawli1b4vLly+WOX716FcHBwbUuiojIZP76S2zZAdoysB8QSVKjAFTZ5NE3btyAm5tbrQoiIjIp9QoQ+/9YBjUAbdkiJqckMpNqNYElJCQAADQaDaZNm4a6devq7yspKcGuXbsQERFh1AKJiIyqbBMYydepE1CvHnDtGnDgAHDvvbIrIjtRrQC0b98+AOIK0KFDh+Di4qK/z8XFBR06dMBrr71m3AqJiIyloAA4eVLs8wqQZXByAu6/H1i/XjSDMQCRmVQrAG3ZsgUAMGrUKMydO5cLhRKRdTl2TDSz1KnDEWCWpE+f0gD06quyqyE7UaNRYF9//bWx6yAiMj21AzTXALMsaj+g334DiooAZ2e59ZBdqHIAevTRR7F48WJ4enri0UcfveO5P/zwQ60LIyIyOvb/sUz33AP4+ABXrwKpqaVLZBCZUJUDkJeXFzT/+8bkxUvHRGSNGIAsk4MD0Ls38N//imYwBiAygyoHoLLNXmwCIyKrVLYJjCxLnz6lAWjKFNnVkB2o0TxA+fn5uHnzpv7P6enpmDNnDjZu3Gi0woiIjIojwCyb2g/ojz+AW7fk1kJ2oUYBaPDgwVi6dCkAIDs7G127dsWsWbMwePBgLFiwwKgFEhEZxbFjQEmJGP3FZnzL06aNaJq8dQvYuVN2NWQHahSA0tLScN999wEAvv/+e/j7+yM9PR1Lly7FJ598YtQCiYiMQm3+at+eI8AskUYD9Ool9rdtk1oK2YcaBaCbN2+iXr16AICNGzfi0UcfhYODA7p164b09HSjFkhEZBRqB+j27eXWQZVTA9D/5pwjMqUaBaBWrVphzZo1OHfuHH799Vf069cPAJCVlcXJEYnIMqkBqF07uXVQ5Xr3FtudO9kPiEyuRgFo2rRpeO211xAUFITIyEhERUUBEFeD7uU05kRkico2gZFlatVKdFAvKGA/IDK5GgWgxx9/HBkZGdi7dy8SExP1x/v27YuPP/7YaMURERlF2RFgvAJkucr2A2IzGJlYjZbCAAB/f3/4+/sbHOvatWutCyIiMrqyI8A4BN6y9e4NrFgBbN0quxKycTUKQHl5efjggw+QnJyMrKws6HQ6g/tPnz5tlOKIiIyCI8Csh3oFaOdOID9fLFxLZAI1CkDPPvsstm3bhqeffhqNGzfWL5FBRGSR2AHaerRsCTRpApw/D6SklE6QSGRkNQpAv/zyC9avX48ePXoYux4iIuPjEHg5Fi2q2eOaNhUBaPbs0r5bxjJmjHGfj6xWjTpBe3t7w8fHx9i1EBGZBkeAWZfWrcX22DG5dZBNq1EAeueddzBt2jSD9cCIiCwSR4BZnzZtxPbMGaCwUG4tZLNq1AQ2a9YsnDp1Cn5+fggKCoKzs7PB/WlpaUYpjoio1o4f5wgwa+PrC3h7A9euAadOAW3byq6IbFCNAlBcXJyRyyAiMpGyHaA5YMM6aDTiKtDOnSLAMgCRCdQoAE2fPt3YdRARmQY7QFun1q1FAGI/IDKRGvUBAoDs7Gz85z//weTJk3H16lUAounr/PnzRiuOiKjW2AHaOqn9gM6eFf24iIysRleADh48iJiYGHh5eeHs2bN47rnn4OPjgx9++AEZGRlYunSpseskIqoZzgFknRo0AHx8gKtXRT8gfn5kZDW6ApSQkICRI0fixIkTcHNz0x9/8MEH8dtvvxmtOCKiWik7AoxXgKyLRlM6HP74cbm1kE2qUQDas2cPnn/++XLHmzRpAq1WW+uiiIiMgiPArJvaDMYARCZQowDk6uqK3NzccsePHz+Ohg0b1rooIiKj4Agw66ZeATpzBrh1S24tZHNqFIAGDRqEt99+G0VFRQAAjUaDjIwMvP7663jssceMWiARUY1xBJh18/UVfYF0OoCLbJOR1SgAzZo1Czdu3EDDhg2Rn5+P6OhotGrVCvXq1cN7771n7BqJiGqGI8CsH5fFIBOp0SgwLy8vJCUl4Y8//sCBAwdw48YNdOzYETExMcauj4io5jgCzPq1aSNWhWcAIiOrdgDS6XRYvHgxfvjhB5w9exYajQbBwcHw9/eHoijQsJ2diCwBR4DZBvUKUHq66AdUZuQxUW1UqwlMURQMGjQIzz77LM6fP4/w8HC0b98e6enpGDlyJB555BFT1UlEVD3qCDBPT44As2YNGoi+QDqdmA+IyEiqdQVo8eLF+O2335CcnIzevXsb3Ld582bExcVh6dKlGD58uFGLJCKqtrIdoHll2rq1bg1cviyawXg1j4ykWleAVq5ciSlTppQLPwDQp08fTJo0CcuXLzdacURENcYRYLaDEyKSCVQrAB08eBD9+/ev9P4BAwbgwIEDtS6KiKjW2AHadqgTIqr9gIiMoFoB6OrVq/Dz86v0fj8/P1y7dq3WRRER1dqhQ2IbHi63Dqo9H5/SfkBqx3aiWqpWACopKYGTU+XdhhwdHVFcXFzrooiIauXGjdIOs/fcI7cWMg71KhCHw5ORVKsTtKIoGDlyJFxdXSu8v6CgwChFERHVyp9/AooC+PkBjRrJroaMoXVr4I8/2A+IjKZaAWjEiBF3PYcjwIhIOrX5i1d/bIfaETojA8jPB+rUkVsPWb1qBaCvv/7aVHUQERnPwYNiywBkO3x8gIYNgUuXRD8g9u2iWqrRWmBERBaNAcg2qf2A2AxGRsAARES2RVFKAxCvEtgWLoxKRsQARES25fx54No1wNERaNtWdjVkTLf3AyKqBQYgIrItagfoNm24cKat8fYWo/oUhc1gVGsMQERkW9j/x7aFhort0aNy6yCrxwBERLaFAci2qc2aR47IrYOsHgMQEdkWBiDb1qYNoNEAFy8C2dmyqyErxgBERLajsLC0aYQjwGyTuzvQvLnYZzMY1QIDEBHZjqNHgeJiwMsLCAyUXQ2ZitoPiM1gVAsWEYDmz5+PoKAguLm5ITIyErt3777j+atXr0ZoaCjc3NwQHh6ODRs26O8rKirC66+/jvDwcLi7uyMgIADDhw/HhQsXTP02iEi2ss1fGo3cWsh0yvYDUhS5tZDVkh6AVq1ahYSEBEyfPh1paWno0KEDYmNjkZWVVeH5O3bswLBhwzB69Gjs27cPcXFxiIuLw+HDhwEAN2/eRFpaGt58802kpaXhhx9+wLFjxzBo0CBzvi0ikoH9f+xDy5aAszOQkyP6AhHVgEZR5MbnyMhIdOnSBfPmzQMA6HQ6BAYGYvz48Zg0aVK58+Pj45GXl4d169bpj3Xr1g0RERFYuHBhha+xZ88edO3aFenp6WjWrNlda8rNzYWXlxdycnLg6elZw3dGRGbXvz/w66/A558DY8ZUft6iReariUxjzhxxBSg+HujTp+qPu9O/C7J61fn9LfUKUGFhIVJTUxETE6M/5uDggJiYGKSkpFT4mJSUFIPzASA2NrbS8wEgJycHGo0G9evXr/D+goIC5ObmGtyIyArxCpD94HB4qiWpAejy5csoKSmBn5+fwXE/Pz9otdoKH6PVaqt1/q1bt/D6669j2LBhlabBGTNmwMvLS38LZOdJIutz+XJpc0j79nJrIdNTA9Dx40BJidxayCpJ7wNkSkVFRRgyZAgURcGCBQsqPW/y5MnIycnR386dO2fGKonIKNQlMFq0AOrVk1sLmV7TpmJI/K1bwNmzsqshKyQ1APn6+sLR0RGZmZkGxzMzM+Hv71/hY/z9/at0vhp+0tPTkZSUdMe2QFdXV3h6ehrciMjKsPnLvjg4lA6H/+svubWQVZIagFxcXNCpUyckJyfrj+l0OiQnJyMqKqrCx0RFRRmcDwBJSUkG56vh58SJE9i0aRMaNGhgmjdARJaDAcj+qM1gnBCRasBJdgEJCQkYMWIEOnfujK5du2LOnDnIy8vDqFGjAADDhw9HkyZNMGPGDADAhAkTEB0djVmzZmHgwIH49ttvsXfvXiz636iOoqIiPP7440hLS8O6detQUlKi7x/k4+MDFxcXOW+UiEyLAcj+qAHo9GnRFObmJrcesirSA1B8fDwuXbqEadOmQavVIiIiAomJifqOzhkZGXBwKL1Q1b17d6xYsQJTp07FlClTEBISgjVr1iAsLAwAcP78eaxduxYAEBERYfBaW7ZsQa9evczyvojIjEpKgD//FPsMQPbD11fcLl8GTpzg8idULdLnAbJEnAeIyMocPy4WyaxTB7h+HXB0vPP5nAfIdnzzDfD770DfvsCQIXc/n/MA2TSrmQeIiMgo9u0T2/Dwu4cfsi1qMxg7QlM1MQARkfVLSxPbTp3k1kHmFxoq1n27eBG4dk12NWRFGICIyPqpAahjR7l1kPm5uwNBQWKfV4GoGhiAiMi6KQoDkL1r105s1Y7wRFXAAERE1i0jA7h6VawOziUw7JMagI4cAXQ6ubWQ1WAAIiLrpl79CQsDXF3l1kJyBAeLEYA3bwLp6bKrISvBAERE1o3NX+ToyGUxqNoYgIjIujEAEcB+QFRtDEBEZN0YgAgoDUBnzgD5+XJrIavAAERE1uviRUCrFSuDcwkM++brC/j5iU7QXByVqoABiIisl3r1p21boG5dubWQfOpVIPYDoipgACIi65WaKrb33iu3DrIMZfsBcZlLugsGICKyXrt3i23XrnLrIMvQurUYEXblCpCVJbsasnAMQERknRSFAYgMubkBrVqJfTaD0V0wABGRdUpPBy5dApycgA4dZFdDloLD4amKGICIyDqpV386dBDf/ImA0gB0/DhQXCy3FrJoDEBEZJ3Y/EUVadoUqFcPKCgATp2SXQ1ZMAYgIrJODEBUEQcHDoenKmEAIiLrU1xcOgSeAYhux35AVAUMQERkfY4cESt/16sHtGkjuxqyNGoAOncOyM2VWwtZLAYgIrI+avNX585i3heisjw9gcBAsc9mMKoEAxARWR/2/6G7Ua8CHTkitw6yWAxARGR9UlLElgGIKtO+vdj+9ZdYIJXoNgxARGRdcnKAw4fFfvfucmshy9WiBeDiIvoAnT8vuxqyQAxARGRddu4Uy2C0aAH4+8uuhiyVs3NpB3mOBqMKMAARkXX54w+x7dFDbh1k+TgfEN0BAxARWRcGIKoqtR/QqVNiZmiiMhiAiMh6FBcDu3aJffb/obtp1Aho0ED8uzl+XHY1ZGEYgIjIehw4AOTlAV5epd/uiSqj0XBWaKoUAxARWQ+1+SsqSqz5RHQ37AdEleBPECKyHjt2iC37/1BVhYaKsJyZCVy5IrsasiAMQERkHRSFHaCp+urWBYKDxT6bwagMBiAisg5nzgB//w04OXEGaKoeNoNRBRiAiMg6bNkitl27Au7ucmsh66J2mD96VIwIIwLgJLsAIqIq2bpVbL28gEWLpJZCVqZ5c9EUdvOmWEiXUygQeAWIiKyBopReAVKXNyCqKgcHoG1bsf/rr3JrIYvBAERElu/kSbGgpZMT0LKl7GrIGqn9gBiA6H8YgIjI8qlXf4KDxQrfRNWl9gPaswe4elVuLWQRGICIyPKx+Ytqy9sbaNwY0OmA5GTZ1ZAFYAAiIsvG/j9kLGwGozIYgIjIsh09KmbxdXMrndCOqCbUALRxowjWZNcYgIjIsiUliW2PHoCzs9xayLq1bg24ugLnzgFHjsiuhiRjACIiy5aYKLb9+8utg6yfiwsQHS322Qxm9xiAiMhy5eeXToDIAETGoP47UoM12S0GICKyXL//LkJQkyalw5iJaiM2Vmy3bRMzQ5PdYgAiIsv1yy9iO2AAoNHIrYVsQ9u2QGAgUFAA/Pab7GpIIgYgIrJc7P9DxqbRsBmMADAAEZGlOntWDIF3dAT69pVdDdkSBiACAxARWSp1lE5UFFC/vtRSyMb07SuC9bFjwJkzsqshSRiAiMgyrV0rtgMGyK2DbI+XlwjWAIfD2zEGICKyPLm5wKZNYj8uTmopZKPUZjAGILvFAERElicxESgsFDP3tm0ruxqyRWoASk4W/9bI7jAAEZHl+fFHsY2L4/B3Mo177wUaNgSuXwdSUmRXQxIwABGRZSkoANavF/uPPCK3FrJdDg5Av35in6PB7BIDEBFZls2bxbfyxo2Brl1lV0O2jP2A7BoDEBFZljVrxHbwYPEtnchU1CtA+/YBWq3cWsjs+NOFiCxHUVFp/x82f5GpNWoEdOok9jdulFsLmR0DEBFZjk2bgEuXROfUPn1kV0P2gLNC2y0GICKyHCtWiG18PODkJLcWsg/q6vAbNwIlJXJrIbNiACIiy3DzZmnz1z/+IbcWsh/dugGensCVK0BamuxqyIykB6D58+cjKCgIbm5uiIyMxO7du+94/urVqxEaGgo3NzeEh4djw4YNBvf/8MMP6NevHxo0aACNRoP9+/ebsHoiMpqffwby8oCgIPFLicgcnJ2BmBixz2YwuyI1AK1atQoJCQmYPn060tLS0KFDB8TGxiIrK6vC83fs2IFhw4Zh9OjR2LdvH+Li4hAXF4fDhw/rz8nLy0PPnj0xc+ZMc70NIjIGtfnrH//g5IdkXuwHZJc0iqIosl48MjISXbp0wbx58wAAOp0OgYGBGD9+PCZNmlTu/Pj4eOTl5WHdunX6Y926dUNERAQWLlxocO7Zs2cRHByMffv2ISIiolp15ebmwsvLCzk5OfD09Kz+GyOi6rl8GQgIEKPADh8G2rev+LxFi8xbF9meMWPKH8vIAJo3F9MuXL4MeHubvy4yiur8/pZ2BaiwsBCpqamIUS89AnBwcEBMTAxSKpmWPCUlxeB8AIiNja30/KoqKChAbm6uwY2IzGjZMhF+OnasPPwQmUqzZmLNOZ1OrA1GdkFaALp8+TJKSkrg5+dncNzPzw/aSiak0mq11Tq/qmbMmAEvLy/9LTAwsFbPR0TVoCjAf/4j9p97Tm4tZL/YDGZ3pHeCtgSTJ09GTk6O/nbu3DnZJRHZj5QU4K+/gLp1gWHDZFdD9koNQL/8IkI52TxpE234+vrC0dERmZmZBsczMzPh7+9f4WP8/f2rdX5Vubq6wtXVtVbPQUQ1pF79GTIE8PKSWwvZr/vvB9zdgQsXgP37xWrxZNOkXQFycXFBp06dkFymvVWn0yE5ORlRUVEVPiYqKsrgfABISkqq9HwisnC5ucCqVWL/2Wfl1kL2zc0NeOABsf/zz3JrIbOQ2gSWkJCAL774AkuWLMGRI0cwduxY5OXlYdSoUQCA4cOHY/LkyfrzJ0yYgMTERMyaNQtHjx7FW2+9hb1792LcuHH6c65evYr9+/fjr7/+AgAcO3YM+/fvr3U/ISIygWXLxASIbdsC3bvLrobs3UMPiS0DkF2QOtd8fHw8Ll26hGnTpkGr1SIiIgKJiYn6js4ZGRlwKLMadPfu3bFixQpMnToVU6ZMQUhICNasWYOwsDD9OWvXrtUHKAAYOnQoAGD69Ol46623zPPGiOjudDpg7lyx/8ILnPuH5Bs4UGz37gUuXgQaN5ZbD5mU1HmALBXnASIygw0bxC8cLy/g778BD4+7P4bzAFFtVTQPUFmRkcDu3cAXX7BZ1gpZxTxARGTn5swR22efrVr4ITIHNoPZDQYgIjK/P/8EkpLEzLtl+vARSffww2K7aROQny+3FjIpBiAiMj/16k9cnFj8lMhSdOgANG0qOudv2SK7GjIhBiAiMq+//waWLBH7r74qtxai22k0bAazEwxARGRe//63WPcrOppD38kyqc1g69ZxVmgbxgBEROZz6VLpSK433pBbC1FlevcG6tQRVysPHJBdDZkIAxARmc+cOaJjaefOQEyM7GqIKlanDmeFtgMMQERkHleuAJ9+KvanTOHEh2TZ1H5A69bJrYNMhgGIiMzjww+B69fFKJvBg2VXQ3RnagDavRvgUko2iQGIiEzv4sXSqz/vvivm/yGyZI0bi6ZagFeBbBR/ChGR6b3/vuj7061b6XpLRJZOvVL5ww9y6yCTYAAiItM6c6Z05Nd777HvD1mPxx4T202bgOxsqaWQ8TEAEZFpTZkCFBYCffsCffrIroao6tq2FbeiIjaD2SAGICIynV27gG+/FVd9/v1v2dUQVZ96Fej77+XWQUbHAEREpqEowGuvif3hw4GICKnlENWIGoB+/RW4cUNuLWRUDEBEZBo//ABs3y4mlXv3XdnVENVMhw5AixbArVvAhg2yqyEjYgAiIuPLywNeeUXsv/aaWF2byBppNGwGs1EMQERkfO+9B5w7BzRvDkyaJLsaotoZMkRsf/5ZTOZJNoEBiIiM69ix0g7Pc+cCdevKrYeotjp1AkJCRDPYTz/JroaMhAGIiIxHUYDx48Ww4QcfBAYNkl0RUe1pNMA//iH2V6yQWwsZDQMQERnPf/8LJCUBrq7AJ59w0kOyHcOGie3GjcClS3JrIaNgACIi4yjb8fn114GWLeXWQ2RMbdoAHTsCJSXsDG0jGICIyDjeeQf4+28gKIgdn8k2sRnMpjAAEVHtHTgAzJol9ufOFXP/ENmaoUNFs+727cCpU7KroVpiACKi2ikqAkaNAoqLgUceAR5+WHZFRKbRpAnQr5/YX7xYailUewxARFQ7//43sG8f4O0NfPYZOz6TbXvmGbFdvFj0ByKr5SS7ACKyYkeOAG+9JfbnzgX8/Ss+b9Eis5VEZFKDBwM+PqK/26ZNQGys7IqohngFiIhqpqREfBsuLBRz/jz1lOyKiEzP1RV48kmx/9VXcmuhWmEAIqKa+eQTYOdOwNMT+PxzNn2R/VCbwdasAa5ckVoK1RwDEBFV38mTwBtviP1//5uLnZJ9iYgQcwIVFvIqkBVjACKi6ikqEs1d+flAnz7As8/KrojI/F58UWznz2dnaCvFAERE1fOvfwG7dgFeXsDXX7Ppi+zTsGFAgwZAerpYJZ6sDgMQEVXd1q3A+++L/UWLgGbNpJZDJE2dOqVXPz/9VG4tVCMcBk9ElSs7fD0vD3j7bbHie48eQHY2h7eTfRs7FvjoI2DzZuDPP4H27WVXRNXAK0BEdHeKAixbJkJPo0bAkCGyKyKSr3lzIC5O7KtLwZDVYAAiorvbvl3M9uzoKC77u7nJrojIMkycKLbLlon+QGQ1GICI6M7OnQNWrRL7gweLb71EJHTrBvTtK9bC+/BD2dVQNTAAEVHl8vKAhQvF0Pd27YAHHpBdEZHlUefE+vJL4OJFubVQlTEAEVHFdDoxydvly4Cvr2j6cuCPDKJyevUCuncHCgrExKBkFfjTjIgqNmkScPgw4OwMPP884O4uuyIiy6TRAG++Kfbnz2dfICvBAERE5f3nP2J4LwAMH875fojuJjYW6N1bXAVSm8TIomkURVFkF2FpcnNz4eXlhZycHHh6esouh8i8Nm0CBgwQnTofegh4+GHZFRFZnjFjyh9LSwM6dRL7e/YAnTubtyaq1u9vXgEiolK7dol5TYqLxVT/Dz0kuyIi69GxI/D002L/1VfF/FlksRiAiEg4fFhc+cnLE6O9uM4XUfW9955YJuO334AlS2RXQ3fAAEREwIEDYmX3a9fEvCY//AC4usquisj6BAaKJWMA4JVXAK1Wbj1UKQYgInu3e7cYxnvpkriEv2ED4OEhuyoi6/Xyy6L/T3Y2MG6c7GqoEgxARPZs7Vpx5Sc7W8xjsnkz4O0tuyoi6+bkJCZFdHIC/vtfYOlS2RVRBRiAiOyRoohp++PiRJ+fmBjg118BLy/ZlRHZhnvuAaZPF/v/93/AwYNy66FyGICI7E1WFjBoEPD66yIIvfACm72ITGHKFKB/fyA/H3jsMSAnR3ZFVAbnAaoA5wEiaRYtMt1zKwqQmgp8+y1w/bq4PP/EE6L/DxGZxo0bwLvvigEGoaGiT5Czc+n9Fc0nRDXGeYCIyFBGBjB7NvDFFyL8BASIb6cMP0Sm5eEBjB0rRlUePSpmWS8pkV0VAXCSXQARmYhOB5w4AWzcKOb4AcQ3z9hYcVm+7LdQIjKd5s1FU/OnnwL794s5tkaOFFdhSRr+7RPZEp1OXO05cEDM6nzlijiu0YhhuY88AjRoILdGInsUGgo89xzw+edimYzcXLHIMEnDAERkrXQ60a9AqwXOnQNOnwZOnRJ9DlRubkDXrkC/fkDDhvJqJSIgIkL0AVq0CDh2DJg5E4iOFvNvkdkxABFZupISIDMTuHBBhJ2yt6Ki8ue7uYlvm506iR+4Li5mL5mIKtG+PfDaa8D8+eL/dWSkGC4/cSJnXzczjgKrAEeBkTSffSaasE6dEld1zp8XQae4uOLzHR2BRo1Ep+bg4NKbo6N56yai6rlxA1i+XKwgDwBBQcA774hFiPn/t8aq8/ubAagCDEBkNsXFwI4dQFISsH078McfFV/VcXUVIadxY8Dfv/Tm68sflkTWSlHEKLGJE4GLF8WxoCDRN2jECPH/naqFAaiWGIBuY6y5aTjfhXD9uph1ee1aYP164OpVw/s9PIBWrcTIkSZNxM3HB3DgrBVENqmwEEhOFiM2b94sPR4cDISHAyEhIhjdqTmbP18BVO/3N/sAEZlDRgbw888i9GzdKn7gqXx8xLD0Xr3Et0A/PzFqi4jsg4sLMGAA0LcvsHcv8PvvYlDDmTPiBogvQI0aiatC6s3fXwxuqFNHbv1WigGIKqbTAQUF4pabK5pZXF05b0VVKYpo21+7Vtz27ze8PyQEGDwYePhhsQip+vdqypmgiciyubiInwfdu4sFig8eFJMnnjwpltFQBz/s22f4OHd3sfhqy5blb40b8wtVJSzit9n8+fPx0UcfQavVokOHDvj000/RtWvXSs9fvXo13nzzTZw9exYhISGYOXMmHnzwQf39iqJg+vTp+OKLL5CdnY0ePXpgwYIFCAkJMcfbsXzXrokJ8o4fF9vz58WVB/U/16VLFfdDAUqDkJubWDjTxweoX1+sIN6ggfg20rChuN/eaLXApk3iMvamTaVt+oD49ta9u1iDa9AgoE0beXUSkeWrXx+4/35xUxQRiC5eLL1duCDW9bt+XSxovHu3uN2uTh2gRQvRrB4aanirX9/Mb8qySO8DtGrVKgwfPhwLFy5EZGQk5syZg9WrV+PYsWNo1KhRufN37NiB+++/HzNmzMBDDz2EFStWYObMmUhLS0NYWBgAYObMmZgxYwaWLFmC4OBgvPnmmzh06BD++usvuFXhF7NN9AHKyRGXUE+cMAw7J04Aly+b/vXr1RNBqFEj0VG3YUPRRt2ypfiztX8jKSkR38x27xaTmm3fDhw6ZHiOu7uYdXnQIODBB6s2Dw+vABFRddy6Jb603nuvGD1a9paRcedlN/z8yoei0FCgWTOr7XNoVZ2gIyMj0aVLF8ybNw8AoNPpEBgYiPHjx2PSpEnlzo+Pj0deXh7WrVunP9atWzdERERg4cKFUBQFAQEBePXVV/Haa68BAHJycuDn54fFixdj6NChd63J4gNQcbH4B6/Vinkk0tNFO/Hp06Xtxrd3rL1dQIBohgkJEZ1t/f1L25QbNRLfGlxdxe3rr0WTWGGh+M9WUCA66mVni9u1a+J2+bKoq+xEfBWpV098G1Ev0ar7AQEiHNWvbxkjm3Q68fd4/nxpeFTD5IEDFb/Pjh2BBx4Qtx49qn8ljAGIiGqiok7QRUXi98OpU+Jn17Fj4ovb0aPA339X/lx16gCtW4sw1KIF0LRp6S0gQFz5t9D5xaymE3RhYSFSU1MxefJk/TEHBwfExMQgJSWlwsekpKQgISHB4FhsbCzWrFkDADhz5gy0Wi1iYmL093t5eSEyMhIpKSlVCkAmc/iwaLstKrr77cYN0fcmN1dc4lT3s7PF8gZVya0NG4pw0bp1adhp3Voc8/Coet0ajQgkdepUrbNdfr4IQpcuiUu06v7Nm+I/3fXr4u/h9nbssq/n4yOa1NSbu3vp69epI4KFunVwEI9xcDDcV7eACI3FxYZ/x8XFIszduCFqUreXL4tgeelS5fPvAKKmTp3ETMtdu4pOzJxtmYgshbOz+HnfqpW4Gl3W9eviy5waiNTb8ePiZ/iBA+JWGXd38XPax0d0gfDxATw9S788u7kZ3pydxe8RR0fR59HREQgLE1euJJEagC5fvoySkhL4+fkZHPfz88PRo0crfIxWq63wfK1Wq79fPVbZObcrKChAQUGB/s85OTkARJI0qtWrgbffNs5zaTTil62fn7hyExwshkkGBYkrOs2bVx5ydDoRpqoqP7/69al9gdq1Kz02apR4LvWKlXrVSt1euiT+UyqKCHnqOlayeXuLb0FlOxa2aye+Hd1+paq2/2Zq8ndNRFSTnz3qF+OHHy49VlIifkarV7vVCVnVW1aWOC8vT9zOnat5za+8In6eGpH6e7sqjVsW0QlathkzZuBf//pXueOBgYESqqkiRRH/ELOyyvc9sVQvvyy7gpq5dg1ITRU3IiJLZI0/Xz/+WNxM4Pr16/Dy8rrjOVIDkK+vLxwdHZGZmWlwPDMzE/7+/hU+xt/f/47nq9vMzEw0LjOLZmZmJiIiIip8zsmTJxs0q+l0Oly9ehUNGjSAxto761ZDbm4uAgMDce7cOcvs+0Tl8DOzTvzcrA8/M+ugKAquX7+OgICAu54rNQC5uLigU6dOSE5ORlxcHAARPpKTkzFu3LgKHxMVFYXk5GS8XCbtJiUlISoqCgAQHBwMf39/JCcn6wNPbm4udu3ahbFjx1b4nK6urnC9bRG6+nY8PNDT05P/wa0MPzPrxM/N+vAzs3x3u/Kjkt4ElpCQgBEjRqBz587o2rUr5syZg7y8PIwaNQoAMHz4cDRp0gQzZswAAEyYMAHR0dGYNWsWBg4ciG+//RZ79+7Fov+NntFoNHj55Zfx7rvvIiQkRD8MPiAgQB+yiIiIyL5JD0Dx8fG4dOkSpk2bBq1Wi4iICCQmJuo7MWdkZMChzHwE3bt3x4oVKzB16lRMmTIFISEhWLNmjX4OIAD45z//iby8PIwZMwbZ2dno2bMnEhMTqzQHEBEREdk+6fMAkeUoKCjAjBkzMHny5HJNgmSZ+JlZJ35u1oefme1hACIiIiK7Y51zXRMRERHVAgMQERER2R0GICIiIrI7DEBERERkdxiA7MyMGTPQpUsX1KtXD40aNUJcXByOHTtmcM6tW7fw4osvokGDBvDw8MBjjz1WbvZtkueDDz7Qz3el4mdmmc6fP4+nnnoKDRo0QJ06dRAeHo69e/fq71cUBdOmTUPjxo1Rp04dxMTE4MSJExIrppKSErz55psIDg5GnTp10LJlS7zzzjsGa0vxc7MNDEB2Ztu2bXjxxRexc+dOJCUloaioCP369UNeXp7+nFdeeQU///wzVq9ejW3btuHChQt49NFHJVZNqj179uDzzz/HPffcY3Ccn5nluXbtGnr06AFnZ2f88ssv+OuvvzBr1ix4e3vrz/nwww/xySefYOHChdi1axfc3d0RGxuLW7duSazcvs2cORMLFizAvHnzcOTIEcycORMffvghPv30U/05/NxshEJ2LSsrSwGgbNu2TVEURcnOzlacnZ2V1atX6885cuSIAkBJSUmRVSYpinL9+nUlJCRESUpKUqKjo5UJEyYoisLPzFK9/vrrSs+ePSu9X6fTKf7+/spHH32kP5adna24uroqK1euNEeJVIGBAwcqzzzzjMGxRx99VHnyyScVReHnZkt4BcjO5eTkAAB8fHwAAKmpqSgqKkJMTIz+nNDQUDRr1gwpKSlSaiThxRdfxMCBAw0+G4CfmaVau3YtOnfujCeeeAKNGjXCvffeiy+++EJ//5kzZ6DVag0+Ny8vL0RGRvJzk6h79+5ITk7G8ePHAQAHDhzA9u3bMWDAAAD83GyJ9KUwSB6dToeXX34ZPXr00C8lotVq4eLiUm4xWD8/P2i1WglVEgB8++23SEtLw549e8rdx8/MMp0+fRoLFixAQkICpkyZgj179uCll16Ci4sLRowYof9s1GV/VPzc5Jo0aRJyc3MRGhoKR0dHlJSU4L333sOTTz4JAPzcbAgDkB178cUXcfjwYWzfvl12KXQH586dw4QJE5CUlMT17KyITqdD586d8f777wMA7r33Xhw+fBgLFy7EiBEjJFdHlfnuu++wfPlyrFixAu3bt8f+/fvx8ssvIyAggJ+bjWETmJ0aN24c1q1bhy1btqBp06b64/7+/igsLER2drbB+ZmZmfD39zdzlQSIJq6srCx07NgRTk5OcHJywrZt2/DJJ5/AyckJfn5+/MwsUOPGjdGuXTuDY23btkVGRgYA6D+b20fr8XOTa+LEiZg0aRKGDh2K8PBwPP3003jllVcwY8YMAPzcbAkDkJ1RFAXjxo3Djz/+iM2bNyM4ONjg/k6dOsHZ2RnJycn6Y8eOHUNGRgaioqLMXS4B6Nu3Lw4dOoT9+/frb507d8aTTz6p3+dnZnl69OhRboqJ48ePo3nz5gCA4OBg+Pv7G3xuubm52LVrFz83iW7evAkHB8NfjY6OjtDpdAD4udkU2b2wybzGjh2reHl5KVu3blUuXryov928eVN/zv/93/8pzZo1UzZv3qzs3btXiYqKUqKioiRWTbcrOwpMUfiZWaLdu3crTk5OynvvvaecOHFCWb58uVK3bl3lm2++0Z/zwQcfKPXr11d++ukn5eDBg8rgwYOV4OBgJT8/X2Ll9m3EiBFKkyZNlHXr1ilnzpxRfvjhB8XX11f55z//qT+Hn5ttYACyMwAqvH399df6c/Lz85UXXnhB8fb2VurWras88sgjysWLF+UVTeXcHoD4mVmmn3/+WQkLC1NcXV2V0NBQZdGiRQb363Q65c0331T8/PwUV1dXpW/fvsqxY8ckVUuKoii5ubnKhAkTlGbNmilubm5KixYtlDfeeEMpKCjQn8PPzTZoFKXM9JZEREREdoB9gIiIiMjuMAARERGR3WEAIiIiIrvDAERERER2hwGIiIiI7A4DEBEREdkdBiAiIiKyOwxAREREZHcYgIjIJqSkpMDR0REDBw4sd19hYSE++ugjdOzYEe7u7vDy8kKHDh0wdepUXLhwQX/eyJEjodFoyt369+9vzrdCRGbAmaCJyCY8++yz8PDwwJdffoljx44hICAAAFBQUIB+/frh4MGD+Ne//oUePXqgYcOGOHPmDFauXAlvb2/9St8jR45EZmYmvv76a4PndnV1hbe3t9nfExGZjpPsAoiIauvGjRtYtWoV9u7dC61Wi8WLF2PKlCkAgI8//hjbt2/H3r17ce+99+of06xZM0RHR+P274Curq7w9/c3a/1EZH5sAiMiq/fdd98hNDQUbdq0wVNPPYWvvvpKH2xWrlyJBx54wCD8lKXRaMxZKhFZCAYgIrJ6X375JZ566ikAQP/+/ZGTk4Nt27YBAI4fP442bdoYnP/II4/Aw8MDHh4e6N69u8F969at09+n3t5//33zvBEiMhs2gRGRVTt27Bh2796NH3/8EQDg5OSE+Ph4fPnll+jVq1eFj/nss8+Ql5eHTz75BL/99pvBfb1798aCBQsMjvn4+JikdiKShwGIiKzal19+ieLiYn2nZwBQFAWurq6YN28eQkJCcOzYMYPHNG7cGEDFwcbd3R2tWrUybdFEJB2bwIjIahUXF2Pp0qWYNWsW9u/fr78dOHAAAQEBWLlyJYYNG4akpCTs27dPdrlEZEF4BYiIrNa6detw7do1jB49Gl5eXgb3PfbYY/jyyy/x+++/Y/369ejbty+mT5+O++67D97e3jh+/Dh++eUXODo6GjyuoKAAWq3W4JiTkxN8fX1N/n6IyHw4DxARWa2HH34YOp0O69evL3ff7t27ERkZiQMHDqBNmzaYM2cOVq5ciePHj0On0yE4OBgDBgzAK6+8gsDAQABiHqAlS5aUe642bdrg6NGjJn8/RGQ+DEBERERkd9gHiIiIiOwOAxARERHZHQYgIiIisjsMQERERGR3GICIiIjI7jAAERERkd1hACIiIiK7wwBEREREdocBiIiIiOwOAxARERHZHQYgIiIisjsMQERERGR3/h90C5VDY6HzfAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: generate pie chart how much male have lung cancer nad how much female have lunc cancer\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "df_pie = df.groupby('GENDER')['LUNG_CANCER'].value_counts()\n",
        "\n",
        "plt.pie(df_pie, autopct=\"%1.1f%%\", labels=df_pie.index, startangle=90)\n",
        "plt.title('Gender Distribution of Lung Cancer')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "GVmof373-hVb",
        "outputId": "a07c3e6e-3003-47b6-c8c0-75a636395e0b"
      },
      "execution_count": null,
      "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": [
        "# function for plotting\n",
        "def plot(col, df=df):\n",
        "    return df.groupby(col)['LUNG_CANCER'].value_counts(normalize=True).unstack().plot(kind='bar', figsize=(8,5), color=['green', 'red'])\n"
      ],
      "metadata": {
        "id": "_TgOk6yt8IBY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt:\n",
        "\n",
        "# Checking the relationship between 'SMOKING' and 'LUNG_CANCER'\n",
        "plot('SMOKING')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "5zdalHid8gnb",
        "outputId": "9babfa09-b753-4ba1-d88a-7c4c0fdc4738"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='SMOKING'>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt:\n",
        "\n",
        "# Checking the relationship between 'YELLOW_FINGERS' and 'LUNG_CANCER'\n",
        "plot('YELLOW_FINGERS')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "kMU23lxA8rmJ",
        "outputId": "cfb969bb-6a02-40f3-aecc-7f7d9ab18af9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='YELLOW_FINGERS'>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt:\n",
        "\n",
        "# Checking the relationship between 'ALCOHOL_CONSUMING' and 'LUNG_CANCER'\n",
        "plot('ALCOHOL CONSUMING')\n",
        "\n",
        "\n",
        "# Checking the relationship between 'COUGHING' and 'LUNG_CANCER'\n",
        "plot('COUGHING')\n",
        "\n",
        "\n",
        "# Checking the relationship between 'SHORTNESS_OF_BREATH' and 'LUNG_CANCER'\n",
        "plot('SHORTNESS OF BREATH')\n",
        "\n",
        "\n",
        "# Checking the relationship between 'CHRONIC DISEASE' and 'LUNG_CANCER'\n",
        "plot('CHRONIC DISEASE')\n",
        "\n",
        "\n",
        "# Checking the relationship between 'ANXIETY' and 'LUNG_CANCER'\n",
        "plot('ANXIETY')\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "h7zRhn5B8ydo",
        "outputId": "232aed7a-5136-4087-e352-f1df9a375e00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='ANXIETY'>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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VvXp17dy5UyNGjNBjjz1W4PJMAAAAJUV8FkcZ+cahZs2aFViWmpqqhx56SJGRkVqwYIFGjBihCxcuqE6dOnr66ac1evRoN0wKAADKKptl3flllZ2dLT8/P2VlZRU4Cnfx4kVlZGSoYcOGstvtbpoQN4s/RwC4jUx/TXRZcOfn0R3ner32ayV6z+fs2bPVoEED2e12hYWFKS0t7brbJyYmqlmzZqpYsaKCgoI0ZMgQ3kcIAABQDrkcn8nJyYqNjVV8fLzS09MVHBysyMhInThxotDtFy1apOHDhys+Pl67du1SUlKSkpOTNWLEiJseHgAAAKWLy/E5bdo09e3bV71799Y999yjOXPmqFKlSvrggw8K3X7Tpk1q27atevTooQYNGqhjx46Kjo6+4dFSAAAAlD0uxWdubq62bNmiiIiIX+7Aw0MRERFKTU0tdJ+HH35YW7ZsccTmgQMHtGrVKsd3iAMAAKD8cOnT7qdOnVJeXp4CAgKclgcEBGj37t2F7tOjRw+dOnVKjzzyiCzL0pUrV/Taa69d97T7pUuXdOnSJcfP2dnZN5ytFHxuCtfBnx8AAOXDbb/I/IYNGzRx4kS99957Sk9P1+eff66VK1de92sbExIS5Ofn57gFBQUVua2np6ekq0dlUXpduHBB0tVvWQIAAGWXS0c+a9SoIU9PTx0/ftxp+fHjxxUYGFjoPm+99ZZefPFFvfLKK5Kkli1bKicnR6+++qpGjhwpD4+C/RsXF6fY2FjHz9nZ2UUGqJeXlypVqqSTJ0/K29u70PvDncuyLF24cEEnTpyQv7+/4x8TAACgbHIpPitUqKDQ0FClpKSoa9eukqT8/HylpKTojTfeKHSfCxcuFAjCa4FR1KlWHx8f+fj4FGsmm82m2rVrKyMjQ4cOHSrmM8Gdxt/fv8h/wAAAgLLD5W84io2NVa9evdS6dWu1adNGiYmJysnJUe/evSVJMTExqlu3rhISEiRJnTt31rRp03T//fcrLCxM+/fv11tvvaXOnTvfsqNcFSpUUJMmTTj1Xkp5e3tzxBMAgHLC5fiMiorSyZMnNXr0aGVmZiokJESrV692fAjp8OHDTkc6R40aJZvNplGjRunf//63atasqc6dO2vChAm37lno6qfu+WYcAACAO1up/3pNAABwA3y9puvu/Dy649zWr9cEAAAASoL4BAAAgDHEJwAAAIwhPgEAAGAM8QkAAABjiE8AAAAYQ3wCAADAGOITAAAAxhCfAAAAMMblr9cEyjy+CcR1fBMIAKCYOPIJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGFOi+Jw9e7YaNGggu92usLAwpaWlXXf7s2fPasCAAapdu7Z8fHzUtGlTrVq1qkQDAwAAoPTycnWH5ORkxcbGas6cOQoLC1NiYqIiIyO1Z88e1apVq8D2ubm56tChg2rVqqVPP/1UdevW1aFDh+Tv738r5gcAAEApYrMsy3Jlh7CwMD344IOaNWuWJCk/P19BQUEaOHCghg8fXmD7OXPm6J133tHu3bvl7e1doiGzs7Pl5+enrKws+fr6lug+gGKz2dw9Qenj2ssIANN4XXMdr2suK26vuXTaPTc3V1u2bFFERMQvd+DhoYiICKWmpha6z/LlyxUeHq4BAwYoICBA9913nyZOnKi8vLwiH+fSpUvKzs52ugEAAKD0cyk+T506pby8PAUEBDgtDwgIUGZmZqH7HDhwQJ9++qny8vK0atUqvfXWW5o6darGjx9f5OMkJCTIz8/PcQsKCnJlTAAAANyhbvun3fPz81WrVi3NmzdPoaGhioqK0siRIzVnzpwi94mLi1NWVpbjduTIkds9JgAAAAxw6QNHNWrUkKenp44fP+60/Pjx4woMDCx0n9q1a8vb21uenp6OZS1atFBmZqZyc3NVoUKFAvv4+PjIx8fHldEAAABQCrh05LNChQoKDQ1VSkqKY1l+fr5SUlIUHh5e6D5t27bV/v37lZ+f71i2d+9e1a5du9DwBAAAQNnl8mn32NhYzZ8/XwsWLNCuXbv0+uuvKycnR71795YkxcTEKC4uzrH966+/rjNnzmjQoEHau3evVq5cqYkTJ2rAgAG37lkAAACgVHD5Op9RUVE6efKkRo8erczMTIWEhGj16tWODyEdPnxYHh6/NG1QUJDWrFmjIUOGqFWrVqpbt64GDRqkYcOG3bpnAQAAgFLB5et8ugPX+YRRXA/PdXf+ywhQvvG65jpe11x2W67zCQAAANwM4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwpkTxOXv2bDVo0EB2u11hYWFKS0sr1n6LFy+WzWZT165dS/KwAAAAKOVcjs/k5GTFxsYqPj5e6enpCg4OVmRkpE6cOHHd/Q4ePKg///nPateuXYmHBQAAQOnmcnxOmzZNffv2Ve/evXXPPfdozpw5qlSpkj744IMi98nLy1PPnj01duxYNWrU6KYGBgAAQOnlUnzm5uZqy5YtioiI+OUOPDwUERGh1NTUIvcbN26catWqpT59+hTrcS5duqTs7GynGwAAAEo/l+Lz1KlTysvLU0BAgNPygIAAZWZmFrrPV199paSkJM2fP7/Yj5OQkCA/Pz/HLSgoyJUxAQAAcIe6rZ92P3funF588UXNnz9fNWrUKPZ+cXFxysrKctyOHDlyG6cEAACAKV6ubFyjRg15enrq+PHjTsuPHz+uwMDAAtv/8MMPOnjwoDp37uxYlp+ff/WBvby0Z88e3X333QX28/HxkY+PjyujAQAAoBRw6chnhQoVFBoaqpSUFMey/Px8paSkKDw8vMD2zZs31/fff69t27Y5bl26dNFjjz2mbdu2cTodAACgnHHpyKckxcbGqlevXmrdurXatGmjxMRE5eTkqHfv3pKkmJgY1a1bVwkJCbLb7brvvvuc9vf395ekAssBAABQ9rkcn1FRUTp58qRGjx6tzMxMhYSEaPXq1Y4PIR0+fFgeHnxxEgAAAAqyWZZluXuIG8nOzpafn5+ysrLk6+vr7nFQ1tls7p6g9LnzX0aA8o3XNdfxuuay4vYahygBAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACM8XL3ALh9bGNt7h6hVLLcPQAAAGUYRz4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY0oUn7Nnz1aDBg1kt9sVFhamtLS0IredP3++2rVrp6pVq6pq1aqKiIi47vYAAAAou1yOz+TkZMXGxio+Pl7p6ekKDg5WZGSkTpw4Uej2GzZsUHR0tNavX6/U1FQFBQWpY8eO+ve//33TwwMAAKB0sVmWZbmyQ1hYmB588EHNmjVLkpSfn6+goCANHDhQw4cPv+H+eXl5qlq1qmbNmqWYmJhiPWZ2drb8/PyUlZUlX19fV8Yt12xjbe4eoVSyxrh7glLItZcRAKbZ+P+By3hdc1lxe82lI5+5ubnasmWLIiIifrkDDw9FREQoNTW1WPdx4cIFXb58WdWqVStym0uXLik7O9vpBgAAgNLPpfg8deqU8vLyFBAQ4LQ8ICBAmZmZxbqPYcOGqU6dOk4B+1sJCQny8/Nz3IKCglwZEwAAAHcoo592nzRpkhYvXqylS5fKbrcXuV1cXJyysrIctyNHjhicEgAAALeLlysb16hRQ56enjp+/LjT8uPHjyswMPC6+/7lL3/RpEmT9I9//EOtWrW67rY+Pj7y8fFxZTQAAACUAi4d+axQoYJCQ0OVkpLiWJafn6+UlBSFh4cXud+UKVP09ttva/Xq1WrdunXJpwUAAECp5tKRT0mKjY1Vr1691Lp1a7Vp00aJiYnKyclR7969JUkxMTGqW7euEhISJEmTJ0/W6NGjtWjRIjVo0MDx3tDKlSurcuXKt/CpAAAA4E7ncnxGRUXp5MmTGj16tDIzMxUSEqLVq1c7PoR0+PBheXj8ckD1/fffV25urrp37+50P/Hx8RozZszNTQ8AAIBSxeXrfLoD1/ksGa7zWTJc57ME7vyXEaB84zqfruN1zWW35TqfAAAAwM0gPgEAAGAM8QkAAABjiE8AAAAYQ3wCAADAGOITAAAAxhCfAAAAMIb4BAAAgDHEJwAAAIwhPgEAAGAM8QkAAABjiE8AAAAYQ3wCAADAGOITAAAAxhCfAAAAMIb4BAAAgDHEJwAAAIwhPgEAAGAM8QkAAABjiE8AAAAYQ3wCAADAGOITAAAAxhCfAAAAMIb4BAAAgDHEJwAAAIwhPgEAAGAM8QkAAABjiE8AAAAYQ3wCAADAGOITAAAAxhCfAAAAMIb4BAAAgDHEJwAAAIwhPgEAAGAM8QkAAABjiE8AAAAYQ3wCAADAGOITAAAAxhCfAAAAMIb4BAAAgDHEJwAAAIwhPgEAAGAM8QkAAABjiE8AAAAYQ3wCAADAGOITAAAAxhCfAAAAMMbL3QMAAFBctrE2d49QKlnuHgD4FY58AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCmRPE5e/ZsNWjQQHa7XWFhYUpLS7vu9v/zP/+j5s2by263q2XLllq1alWJhgUAAEDp5nJ8JicnKzY2VvHx8UpPT1dwcLAiIyN14sSJQrfftGmToqOj1adPH23dulVdu3ZV165dtX379pseHgAAAKWLzbIsy5UdwsLC9OCDD2rWrFmSpPz8fAUFBWngwIEaPnx4ge2joqKUk5Ojv/3tb45lDz30kEJCQjRnzpxiPWZ2drb8/PyUlZUlX19fV8Yt12xjbe4eoVSyxrh7glLItZcRoMR4XSsZXtdKgNc1lxW311w68pmbm6stW7YoIiLilzvw8FBERIRSU1ML3Sc1NdVpe0mKjIwscnsAAACUXV6ubHzq1Cnl5eUpICDAaXlAQIB2795d6D6ZmZmFbp+ZmVnk41y6dEmXLl1y/JyVlSXpalHDBRfdPUDpxH9lJcDfTZjC61qJ8De0BHhdc9m1TrvRSXWX4tOUhIQEjR07tsDyoKAgN0yD8sbP3QOURn781oA7GX9DS4DXtRI7d+6c/K7z+3MpPmvUqCFPT08dP37cafnx48cVGBhY6D6BgYEubS9JcXFxio2Ndfycn5+vM2fOqHr16rLZeL8Pbp/s7GwFBQXpyJEjvL8YQJnA6xpMsSxL586dU506da67nUvxWaFCBYWGhiolJUVdu3aVdDUMU1JS9MYbbxS6T3h4uFJSUjR48GDHsnXr1ik8PLzIx/Hx8ZGPj4/TMn9/f1dGBW6Kr68vL9IAyhRe12DC9Y54XuPyaffY2Fj16tVLrVu3Vps2bZSYmKicnBz17t1bkhQTE6O6desqISFBkjRo0CA9+uijmjp1qjp16qTFixdr8+bNmjdvnqsPDQAAgFLO5fiMiorSyZMnNXr0aGVmZiokJESrV692fKjo8OHD8vD45UP0Dz/8sBYtWqRRo0ZpxIgRatKkiZYtW6b77rvv1j0LAAAAlAouX+cTKMsuXbqkhIQExcXFFXjrBwCURryu4U5DfAIAAMCYEn23OwAAAFASxCcAAACMIT4BAABgDPEJAAAAY4hPAADKkJ9//lkXLlxw/Hzo0CElJiZq7dq1bpwK+AXxCQBAGfLMM89o4cKFkqSzZ88qLCxMU6dO1TPPPKP333/fzdMBxCfgxLIscfUxAKVZenq62rVrJ0n69NNPFRAQoEOHDmnhwoWaMWOGm6cDiE9AkrRw4UK1bNlSFStWVMWKFdWqVSv913/9l7vHAgCXXbhwQVWqVJEkrV27Vs8++6w8PDz00EMP6dChQ26eDiA+AU2bNk2vv/66nnrqKS1ZskRLlizRk08+qddee03Tp09393gA4JLGjRtr2bJlOnLkiNasWaOOHTtKkk6cOCFfX183TwfwDUeAGjZsqLFjxyomJsZp+YIFCzRmzBhlZGS4aTIAcN2nn36qHj16KC8vT48//rjWrVsnSUpISNDGjRv197//3c0TorwjPlHu2e12bd++XY0bN3Zavm/fPrVs2VIXL15002QAUDKZmZk6duyYgoOD5eFx9SRnWlqafH191bx5czdPh/KO0+4o9xo3bqwlS5YUWJ6cnKwmTZq4YSIAuDmBgYGqUqWK1q1bp59//lmS9OCDDxKeuCN4uXsAwN3Gjh2rqKgobdy4UW3btpUkff3110pJSSk0SgHgTnb69Gk999xzWr9+vWw2m/bt26dGjRqpT58+qlq1qqZOneruEVHOceQT5d4f//hHffPNN6pevbqWLVumZcuWqUaNGkpLS1O3bt3cPR4AuGTIkCHy9vbW4cOHValSJcfyqKgorV692o2TAVfxnk8AAMqQwMBArVmzRsHBwapSpYq+/fZbNWrUSAcOHFCrVq10/vx5d4+Ico7T7ii3PDw8ZLPZrruNzWbTlStXDE0EADcvJyfH6YjnNWfOnJGPj48bJgKcEZ8ot5YuXVrkutTUVM2YMUP5+fkGJwKAkjt69Kjq1Kmjdu3aaeHChXr77bclXf1HdH5+vqZMmaLHHnvMzVMCnHYHnOzZs0fDhw/XihUr1LNnT40bN07169d391gAcENVq1bV7NmzFRwcrMcff1wPPPCA/vnPf6pLly7asWOHzpw5o6+//lp33323u0dFOccHjgBdPWLQt29ftWzZUleuXNG2bdu0YMECwhNAqTFhwgT169dPY8aM0c6dO/XII4/omWeeUU5Ojp599llt3bqV8MQdgSOfKNeysrI0ceJEzZw5UyEhIZo8ebLatWvn7rEAoEQyMjLUp08f7dy5U/PmzVOXLl3cPRJQAPGJcmvKlCmaPHmyAgMDNXHiRD3zzDPuHgkAbolZs2ZpyJAhatGihby8nD/ekZ6e7qapgKuIT5RbHh4eqlixoiIiIuTp6Vnkdp9//rnBqQDg5hw6dEi9e/fW9u3b1a9fvwLxGR8f76bJgKv4tDvKrZiYmBteagkASpP58+dr6NChioiI0I4dO1SzZk13jwQUwJFPAADKgCeffFJpaWlKTExUTEyMu8cBisSRTwAAyoC8vDx99913qlevnrtHAa6LI58AAAAwhut8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BlBuZmZkaOHCgGjVqJB8fHwUFBalz585KSUlxbLNp0yY99dRTqlq1qux2u1q2bKlp06YpLy/Psc3Bgwdls9m0bdu2Ao/Rvn17DR482GnZ1q1bFRUVpdq1a8vHx0f169fX008/rRUrVujaBUdcuc/CfrbZbFq8eLHTfomJiWrQoIHTstzcXL3zzjt64IEHdNddd8nPz0/BwcEaNWqUjh49ev1fIADcAsQngHLh4MGDCg0N1T//+U+98847+v7777V69Wo99thjGjBggCRp6dKlevTRR1WvXj2tX79eu3fv1qBBgzR+/Hg9//zzKsmV6b744gs99NBDOn/+vBYsWKBdu3Zp9erV6tatm0aNGqWsrKxb8vzsdrtGjRqly5cvF7nNpUuX1KFDB02cOFEvvfSSNm7cqO+//14zZszQqVOnNHPmzFsyCwBcDxeZB1Au9O/fXzabTWlpabrrrrscy++99169/PLLysnJUd++fdWlSxfNmzfPsf6VV15RQECAunTpoiVLligqKqrYj5mTk6M+ffqoU6dO+vzzz53WtWjRQn369ClR0BYmOjpay5cv1/z589W/f/9Ct5k+fbq++uorbd68Wffff79j+e9+9zs9+uijt2wWALgejnwCKPPOnDmj1atXa8CAAU7heY2/v7/Wrl2r06dP689//nOB9Z07d1bTpk31ySefuPS41+7zzTffLHIbm83m0n0WxdfXVyNHjtS4ceOUk5NT6DaffPKJOnTo4BSet2MWALge4hNAmbd//35ZlqXmzZsXuc3evXslXT0iWZjmzZs7timua9s3a9bMsez//u//VLlyZcftb3/7m9M+Dz/8sNP6ypUr68svvyzW4/Xv3192u13Tpk0rcp5fzyJJ3bp1czzOww8/7MrTA4AS4bQ7gDLPldPJt/vUc6tWrRwfKmrSpImuXLnitD45OblAAPfs2bNY9+3j46Nx48Zp4MCBev3114u1z3vvvaecnBzNmDFDGzduLNY+AHAzOPIJoMxr0qSJbDabdu/eXeQ2TZs2lSTt2rWr0PW7du1ybOPr6ytJhX5Y6OzZs/Lz83M8riTt2bPHsd7Hx0eNGzdW48aNC32coKAgx/prt4oVK97oKTq88MILql+/vsaPH19gXZMmTZxmkaTatWurcePGqlatWrEfAwBuBvEJoMyrVq2aIiMjNXv27ELfD3n27Fl17NhR1apV09SpUwusX758ufbt26fo6GjH/dWoUUNbtmxx2i47O1v79+93ROq1+5w8efJteFaF8/DwUEJCgt5//30dPHjQaV10dLTWrVunrVu3GpsHAH6L+ARQLsyePVt5eXlq06aNPvvsM+3bt0+7du3SjBkzFB4errvuuktz587VF198oVdffVXfffedDh48qKSkJL300kvq3r27nnvuOcf9xcbGauLEifr444/1ww8/KC0tTT179lTNmjX17LPPSpIqV66sv/71r1q5cqU6deqkNWvW6MCBA/ruu+80ZcoUSZKnp+ctf66dOnVSWFiY5s6d67R8yJAhCg8P1xNPPKF3331X6enpysjI0Jo1a/T3v//9tswCAL/Fez4BlAuNGjVSenq6JkyYoKFDh+rYsWOqWbOmQkND9f7770uSunfvrvXr12vChAlq166dLl68qCZNmmjkyJEaPHiw06fB33zzTVWuXFmTJ0/WDz/8oGrVqqlt27Zav36902nybt26adOmTZo8ebJiYmJ05swZ+fn5qXXr1lq8eLGefvrp2/J8J0+eXOADRHa7XSkpKUpMTNSHH36ouLg45efnq2HDhvrDH/6gIUOG3JZZAODXbBYXdgMAAIAhnHYHAACAMcQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYAzxCQAAAGP+Hw2JJ/2D4gvlAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}