AttemptedCROSS.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPeDRdGSnq30nPrvTDUNtAD",
      "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/Swelem/obesity_risk/blob/main/AttemptedCROSS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preparation"
      ],
      "metadata": {
        "id": "FQ1pMTHWQPHz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download and unzip"
      ],
      "metadata": {
        "id": "ohjOCEYmQmNn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_PY8xLGyInOJ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import lightgbm as lgb\n",
        "import os\n",
        "import glob\n",
        "#import optuna\n",
        "ran_optuna = False\n",
        "from tqdm import tqdm\n",
        "from sklearn.model_selection import train_test_split as tts\n",
        "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
        "import random\n",
        "from PIL import Image\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import GridSearchCV, train_test_split\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from imblearn.over_sampling import SMOTE\n",
        "#from optuna.samplers import TPESampler\n",
        "\n",
        "import torch\n",
        "from torch import nn\n",
        "from torchvision import datasets\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q kaggle"
      ],
      "metadata": {
        "id": "av-zrhNwN9tP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 93
        },
        "id": "ujuTSnxCN_fS",
        "outputId": "d1aae244-e390-4811-a97f-a390ef5eeed9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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              "\n",
              "     <input type=\"file\" id=\"files-2a104de5-3e63-48f0-83a0-c09279e84a78\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-2a104de5-3e63-48f0-83a0-c09279e84a78\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle (1).json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle (1).json': b'{\"username\":\"saifswelem\",\"key\":\"93c4723ccbde8c247af262277f428b4b\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p ~/.kaggle"
      ],
      "metadata": {
        "id": "eFUr_IOgOBVQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "!mkdir /content/Dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e5Hq2gTHOB2z",
        "outputId": "97e59dfe-aebd-4e7a-955f-596449bdec3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘/content/Dataset’: File exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp kaggle.json ~/.kaggle/"
      ],
      "metadata": {
        "id": "XfxCRJXfOGL3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "dpiIMOJ8OIGd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.chdir('/content/Dataset')"
      ],
      "metadata": {
        "id": "ALQAR5n1OI5F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle competitions download -c playground-series-s4e2\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jhrip1-AOKiO",
        "outputId": "dd888175-0936-41d6-f5a6-1a5cde9bf9ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "playground-series-s4e2.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip playground-series-s4e2.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G2FXaz4ZOOK5",
        "outputId": "2ff8c792-a96f-4046-8c82-136b68291909"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  playground-series-s4e2.zip\n",
            "replace sample_submission.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: sample_submission.csv   \n",
            "replace test.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: test.csv                \n",
            "replace train.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: train.csv               \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gFubLSCz-hs9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "given_test_data = pd.read_csv('/content/Dataset/test.csv')\n",
        "\n",
        "\n",
        "given_train_data = pd.read_csv('/content/Dataset/train.csv')"
      ],
      "metadata": {
        "id": "NkiPUOKxSKFZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Exploration"
      ],
      "metadata": {
        "id": "QdxMxfJPQwVb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "given_train_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "QhVnOf_ZSwO_",
        "outputId": "7d9cd212-0a4e-45a1-90bb-077e0ee49a03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  Gender        Age    Height      Weight family_history_with_overweight  \\\n",
              "0   0    Male  24.443011  1.699998   81.669950                            yes   \n",
              "1   1  Female  18.000000  1.560000   57.000000                            yes   \n",
              "2   2  Female  18.000000  1.711460   50.165754                            yes   \n",
              "3   3  Female  20.952737  1.710730  131.274851                            yes   \n",
              "4   4    Male  31.641081  1.914186   93.798055                            yes   \n",
              "\n",
              "  FAVC      FCVC       NCP        CAEC SMOKE      CH2O SCC       FAF  \\\n",
              "0  yes  2.000000  2.983297   Sometimes    no  2.763573  no  0.000000   \n",
              "1  yes  2.000000  3.000000  Frequently    no  2.000000  no  1.000000   \n",
              "2  yes  1.880534  1.411685   Sometimes    no  1.910378  no  0.866045   \n",
              "3  yes  3.000000  3.000000   Sometimes    no  1.674061  no  1.467863   \n",
              "4  yes  2.679664  1.971472   Sometimes    no  1.979848  no  1.967973   \n",
              "\n",
              "        TUE       CALC                 MTRANS           NObeyesdad  \n",
              "0  0.976473  Sometimes  Public_Transportation  Overweight_Level_II  \n",
              "1  1.000000         no             Automobile        Normal_Weight  \n",
              "2  1.673584         no  Public_Transportation  Insufficient_Weight  \n",
              "3  0.780199  Sometimes  Public_Transportation     Obesity_Type_III  \n",
              "4  0.931721  Sometimes  Public_Transportation  Overweight_Level_II  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6821d58c-205a-473c-bf8d-3c568d8f9910\" 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>id</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>family_history_with_overweight</th>\n",
              "      <th>FAVC</th>\n",
              "      <th>FCVC</th>\n",
              "      <th>NCP</th>\n",
              "      <th>CAEC</th>\n",
              "      <th>SMOKE</th>\n",
              "      <th>CH2O</th>\n",
              "      <th>SCC</th>\n",
              "      <th>FAF</th>\n",
              "      <th>TUE</th>\n",
              "      <th>CALC</th>\n",
              "      <th>MTRANS</th>\n",
              "      <th>NObeyesdad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Male</td>\n",
              "      <td>24.443011</td>\n",
              "      <td>1.699998</td>\n",
              "      <td>81.669950</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.983297</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>no</td>\n",
              "      <td>2.763573</td>\n",
              "      <td>no</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.976473</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>Public_Transportation</td>\n",
              "      <td>Overweight_Level_II</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Female</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1.560000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>Frequently</td>\n",
              "      <td>no</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>no</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>no</td>\n",
              "      <td>Automobile</td>\n",
              "      <td>Normal_Weight</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Female</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1.711460</td>\n",
              "      <td>50.165754</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>1.880534</td>\n",
              "      <td>1.411685</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>no</td>\n",
              "      <td>1.910378</td>\n",
              "      <td>no</td>\n",
              "      <td>0.866045</td>\n",
              "      <td>1.673584</td>\n",
              "      <td>no</td>\n",
              "      <td>Public_Transportation</td>\n",
              "      <td>Insufficient_Weight</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>20.952737</td>\n",
              "      <td>1.710730</td>\n",
              "      <td>131.274851</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>no</td>\n",
              "      <td>1.674061</td>\n",
              "      <td>no</td>\n",
              "      <td>1.467863</td>\n",
              "      <td>0.780199</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>Public_Transportation</td>\n",
              "      <td>Obesity_Type_III</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Male</td>\n",
              "      <td>31.641081</td>\n",
              "      <td>1.914186</td>\n",
              "      <td>93.798055</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>2.679664</td>\n",
              "      <td>1.971472</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>no</td>\n",
              "      <td>1.979848</td>\n",
              "      <td>no</td>\n",
              "      <td>1.967973</td>\n",
              "      <td>0.931721</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>Public_Transportation</td>\n",
              "      <td>Overweight_Level_II</td>\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "given_train_data",
              "summary": "{\n  \"name\": \"given_train_data\",\n  \"rows\": 20758,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5992,\n        \"min\": 0,\n        \"max\": 20757,\n        \"num_unique_values\": 20758,\n        \"samples\": [\n          10317,\n          4074,\n          9060\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.688071958787075,\n        \"min\": 14.0,\n        \"max\": 61.0,\n        \"num_unique_values\": 1703,\n        \"samples\": 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    \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FAVC\",\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\": \"FCVC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5332181544582983,\n        \"min\": 1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 934,\n        \"samples\": [\n          2.444599,\n          2.191429\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NCP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 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0.6021134769922342,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 1297,\n        \"samples\": [\n          0.076654,\n          0.586163\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CALC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Sometimes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MTRANS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Automobile\",\n          \"Bike\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NObeyesdad\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Overweight_Level_II\",\n          \"Normal_Weight\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "given_test_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "6Uk5qg91S-UN",
        "outputId": "f8c1a75e-6e82-467e-f5a2-c2b15edeee3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id  Gender        Age    Height      Weight  \\\n",
              "0  20758    Male  26.899886  1.848294  120.644178   \n",
              "1  20759  Female  21.000000  1.600000   66.000000   \n",
              "2  20760  Female  26.000000  1.643355  111.600553   \n",
              "3  20761    Male  20.979254  1.553127  103.669116   \n",
              "4  20762  Female  26.000000  1.627396  104.835346   \n",
              "\n",
              "  family_history_with_overweight FAVC      FCVC       NCP       CAEC SMOKE  \\\n",
              "0                            yes  yes  2.938616  3.000000  Sometimes    no   \n",
              "1                            yes  yes  2.000000  1.000000  Sometimes    no   \n",
              "2                            yes  yes  3.000000  3.000000  Sometimes    no   \n",
              "3                            yes  yes  2.000000  2.977909  Sometimes    no   \n",
              "4                            yes  yes  3.000000  3.000000  Sometimes    no   \n",
              "\n",
              "       CH2O SCC       FAF       TUE       CALC                 MTRANS  \n",
              "0  2.825629  no  0.855400  0.000000  Sometimes  Public_Transportation  \n",
              "1  3.000000  no  1.000000  0.000000  Sometimes  Public_Transportation  \n",
              "2  2.621877  no  0.000000  0.250502  Sometimes  Public_Transportation  \n",
              "3  2.786417  no  0.094851  0.000000  Sometimes  Public_Transportation  \n",
              "4  2.653531  no  0.000000  0.741069  Sometimes  Public_Transportation  "
            ],
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>Public_Transportation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20762</td>\n",
              "      <td>Female</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>1.627396</td>\n",
              "      <td>104.835346</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>no</td>\n",
              "      <td>2.653531</td>\n",
              "      <td>no</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.741069</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>Public_Transportation</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-d530a77a-6235-4d49-a475-4ab5d92c94af')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d530a77a-6235-4d49-a475-4ab5d92c94af 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-d530a77a-6235-4d49-a475-4ab5d92c94af');\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-5c3be633-0730-430d-bf03-176a892a0fff\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5c3be633-0730-430d-bf03-176a892a0fff')\"\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-5c3be633-0730-430d-bf03-176a892a0fff 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": "given_test_data",
              "summary": "{\n  \"name\": \"given_test_data\",\n  \"rows\": 13840,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3995,\n        \"min\": 20758,\n        \"max\": 34597,\n        \"num_unique_values\": 13840,\n        \"samples\": [\n          33716,\n          32341,\n          24886\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.79981353918079,\n        \"min\": 14.0,\n        \"max\": 61.0,\n        \"num_unique_values\": 1539,\n        \"samples\": [\n          34.462711,\n          20.519916\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0887605851825768,\n        \"min\": 1.45,\n        \"max\": 1.98,\n        \"num_unique_values\": 1739,\n        \"samples\": [\n          1.616467,\n          1.7931\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.111818557597687,\n        \"min\": 39.0,\n        \"max\": 165.057269,\n        \"num_unique_values\": 1798,\n        \"samples\": [\n          58.443049,\n          110.008636\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"family_history_with_overweight\",\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\": \"FAVC\",\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\": \"FCVC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5316064424734112,\n        \"min\": 1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 828,\n        \"samples\": [\n          2.923433,\n          1.813234\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NCP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7109268631672955,\n        \"min\": 1.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 649,\n        \"samples\": [\n          3.648194,\n          2.157164\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CAEC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Always\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMOKE\",\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\": \"CH2O\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6112295139643,\n        \"min\": 1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 1366,\n        \"samples\": [\n          1.152899,\n          2.232601\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SCC\",\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\": \"FAF\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8403611101051178,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 1260,\n        \"samples\": [\n          0.68183,\n          1.554817\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TUE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.608005301939806,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 1172,\n        \"samples\": [\n          1.281141,\n          0.66488\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CALC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"no\",\n          \"Always\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MTRANS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Automobile\",\n          \"Motorbike\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(given_train_data.shape)\n",
        "print(given_test_data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UXc-Fl73TIyo",
        "outputId": "d98bb00b-7232-4c75-a6d6-b6993e4c1dcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(20758, 18)\n",
            "(13840, 17)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(given_train_data[\"NObeyesdad\"].unique())\n",
        "class_counts = given_train_data[\"NObeyesdad\"].value_counts()\n",
        "class_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2MP7CMTYT7Lb",
        "outputId": "aca13b3b-84cf-476e-ec4f-a5877e9bc351"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Overweight_Level_II' 'Normal_Weight' 'Insufficient_Weight'\n",
            " 'Obesity_Type_III' 'Obesity_Type_II' 'Overweight_Level_I'\n",
            " 'Obesity_Type_I']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NObeyesdad\n",
              "Obesity_Type_III       4046\n",
              "Obesity_Type_II        3248\n",
              "Normal_Weight          3082\n",
              "Obesity_Type_I         2910\n",
              "Insufficient_Weight    2523\n",
              "Overweight_Level_II    2522\n",
              "Overweight_Level_I     2427\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Visualization\n"
      ],
      "metadata": {
        "id": "plB-d7IFQ1rC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(6,4))\n",
        "plt.barh(class_counts.index, class_counts.values, color=\"blue\")\n",
        "plt.xlabel(\"Count\")\n",
        "plt.title(\"Obesity Classes\")\n",
        "plt.gca().invert_yaxis() ## Invert the y-axis to show the highest frequency class at the top\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "oCyeDEsSTbO1",
        "outputId": "c6aa441d-72cf-4050-c134-9f237ad56dbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_features = given_train_data.select_dtypes(include=['object']).columns\n",
        "continuous_features = given_train_data.select_dtypes(include=['float64','int64']).columns\n",
        "## Export categorical and continuous data to separate DataFrames\n",
        "\n",
        "data_cat = given_train_data[categorical_features]\n",
        "data_cont = given_train_data[continuous_features]\n",
        "print(data_cat.columns)\n",
        "print(data_cont.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LRYUYQgsc2dh",
        "outputId": "dfdfc504-026a-4f57-c86d-e621538be8d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Gender', 'family_history_with_overweight', 'FAVC', 'CAEC', 'SMOKE',\n",
            "       'SCC', 'CALC', 'MTRANS', 'NObeyesdad'],\n",
            "      dtype='object')\n",
            "Index(['id', 'Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CH2O', 'FAF', 'TUE'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_id= data_cont[\"id\"]\n",
        "data_cont.drop('id',axis=1, inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q_INwWsccx24",
        "outputId": "4cd50c27-b7c8-46fc-f9a5-815ff3a0d042"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-150-6bfb34d38753>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data_cont.drop('id',axis=1, inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "## Drawing a frequency graph for each categorical variable\n",
        "fig, axes = plt.subplots(nrows=(len(data_cat.columns)+1) // 2, ncols=2,figsize=(14, len(data_cat.columns) * 2))\n",
        "fig.tight_layout(pad=5.0)\n",
        "\n",
        "## Adjustment for proper positioning of the axes\n",
        "axes = axes.ravel()\n",
        "\n",
        "for i, col in enumerate(data_cat.columns):\n",
        "    sns.countplot(x=data_cat[col], ax=axes[i], palette='viridis')\n",
        "    axes[i].set_title(f'Frequency of {col}')\n",
        "    axes[i].set_xlabel('')\n",
        "    axes[i].set_ylabel('Count')\n",
        "    axes[i].tick_params(axis='x', rotation=45)\n",
        "\n",
        "## Closing extra created axes\n",
        "for j in range(i + 1, len(axes)):\n",
        "    axes[j].axis('off')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "PNMl-qpXcVVF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4e6678ba-3ca4-4359-a594-d3f0f70fe5ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-151-96516fd721f9>:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x=data_cat[col], ax=axes[i], palette='viridis')\n",
            "<ipython-input-151-96516fd721f9>:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x=data_cat[col], ax=axes[i], palette='viridis')\n",
            "<ipython-input-151-96516fd721f9>:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x=data_cat[col], ax=axes[i], palette='viridis')\n",
            "<ipython-input-151-96516fd721f9>:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x=data_cat[col], ax=axes[i], palette='viridis')\n",
            "<ipython-input-151-96516fd721f9>:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x=data_cat[col], ax=axes[i], palette='viridis')\n",
            "<ipython-input-151-96516fd721f9>:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x=data_cat[col], ax=axes[i], palette='viridis')\n",
            "<ipython-input-151-96516fd721f9>:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x=data_cat[col], ax=axes[i], palette='viridis')\n",
            "<ipython-input-151-96516fd721f9>:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x=data_cat[col], ax=axes[i], palette='viridis')\n",
            "<ipython-input-151-96516fd721f9>:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x=data_cat[col], ax=axes[i], palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x1800 with 10 Axes>"
            ],
            "image/png": 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BmDBhAtWrV6dOnTqULl0ad3d3Vq9eba5va2vLhg0bsLW1xdPTk0aNGtGkSRMGDx5s1smaNSsbN27Ez8+PAgUKMG7cOObMmYOXl9db318RERERa7EYhmFYO4g/8+zZMwoXLsy0adMYOnQoBQsW5LvvviMwMJDUqVOzePFi6tatC8C5c+fIlSsXBw8epHjx4mzevJnq1atz584dc7bBGTNm0KtXLx48eICDgwO9evVi48aN/PLLL+Zr1q9fn6dPn7Jly5a/FGNQUBCurq4EBgb+J2PzVK43+M8rifwF25b1//NKb1nBoQOtHYK8J070HWjtEETivf/6GkXerr/7+VWv0PstRJWwbdgx3NohiIiIxAt/9TrlnWj56OPjg7e3NxUrVoxV7u/vT0RERKzyjz76iEyZMnHw4EEADh48SL58+czEI4CXlxdBQUGcOXPGrPPbbXt5eZnbeJOwsDCCgoJiPUREREREREREROR/7KwdwJ9ZunQpx44d48iRI68tCwgIwMHBgWTJksUqd3NzIyAgwKzzauIxZnnMsj+qExQUREhIyBtnIxwxYgSDBg36x/slIiIiIiIiIiLyvovXLR9v3rxJp06dWLRoEYkSJbJ2OLF8++23BAYGmo+bN29aOyQREREREREREZF4JV4nH/39/bl//z6FCxfGzs4OOzs79uzZw6RJk7Czs8PNzY3w8HCePn0aa7179+7h7u4OgLu7+2uzX8c8/7M6Li4ub2z1CODo6IiLi0ush4iIiIiIiIiIiPxPvE4+VqhQgdOnT3PixAnzUaRIERo2bGj+397enh07dpjrnD9/nhs3buDp6QmAp6cnp0+f5v79+2YdPz8/XFxcyJ07t1nn1W3E1InZhoiIiIiIiIiIiPx98XrMR2dnZ/LmzRurLGnSpKRMmdIsb9GiBV27diVFihS4uLjQoUMHPD09KV68OACVK1cmd+7cNG7cmNGjRxMQEEDfvn3x8fHB0dERgDZt2jBlyhR69uxJ8+bN2blzJ8uXL2fjxo1vd4dFRERERERERETeI/E6+fhXTJgwARsbG+rUqUNYWBheXl5MmzbNXG5ra8uGDRto27Ytnp6eJE2alKZNmzJ48GCzTtasWdm4cSNdunRh4sSJZMiQgTlz5uDl5WWNXRIREREREREREXkvvHPJx927d8d6nihRIqZOncrUqVN/d53MmTOzadOmP9xu2bJlOX78+H8RooiIiIiIiIiIiBDPx3wUERERERERERGRd5eSjyIiIiIiIiIiIhInlHwUERERERERERGROKHko4iIiIiIiIiIiMQJJR9FREREREREREQkTij5KCIiIiIiIiIiInHCztoBiIiIiIiIJHSftB5i7RDee3tn9rN2CCIiCZJaPoqIiIiIiIiIiEicUPJRRERERERERERE4oSSjyIiIiIiIiIiIhInlHwUERERERERERGROKHko4iIiIiIiIiIiMQJJR9FREREREREREQkTij5KCIiIiIiIiIiInFCyUcRERERERERERGJE0o+ioiIiIiIiIiISJxQ8lFEREREEpwRI0bw8ccf4+zsTJo0aahZsybnz5+PVads2bJYLJZYjzZt2sSqc+PGDby9vUmSJAlp0qShR48eREZGxqqze/duChcujKOjI9mzZ2fevHlxvXsiIiIi8YaSjyIiIiKS4OzZswcfHx8OHTqEn58fERERVK5cmefPn8eq16pVK+7evWs+Ro8ebS6LiorC29ub8PBwDhw4wPz585k3bx79+/c361y9ehVvb2/KlSvHiRMn6Ny5My1btmTr1q1vbV9FRERErMnO2gGIiIiIiLxtW7ZsifV83rx5pEmTBn9/f0qXLm2WJ0mSBHd39zduY9u2bZw9e5bt27fj5uZGwYIFGTJkCL169WLgwIE4ODgwY8YMsmbNyrhx4wDIlSsX+/btY8KECXh5ecXdDoqIiIjEE2r5KCIiIiIJXmBgIAApUqSIVb5o0SJSpUpF3rx5+fbbb3nx4oW57ODBg+TLlw83NzezzMvLi6CgIM6cOWPWqVixYqxtenl5cfDgwbjaFREREZF4JV4nH//KWDyhoaH4+PiQMmVKnJycqFOnDvfu3YtVR2PxiIiIiMjviY6OpnPnzpQsWZK8efOa5V9++SULFy5k165dfPvtt/zwww80atTIXB4QEBAr8QiYzwMCAv6wTlBQECEhIa/FEhYWRlBQUKyHiIiIyLssXicf/8pYPF26dGH9+vWsWLGCPXv2cOfOHWrXrm0u11g8IiIiIvJHfHx8+OWXX1i6dGms8q+//hovLy/y5ctHw4YNWbBgAWvWrOHy5ctxFsuIESNwdXU1HxkzZoyz1xIRERF5G+L1mI9/NhZPYGAgc+fOZfHixZQvXx4AX19fcuXKxaFDhyhevLjG4hERERGR39W+fXs2bNjATz/9RIYMGf6wbrFixQC4dOkS2bJlw93dncOHD8eqE9MDJ2acSHd399d65dy7dw8XFxcSJ0782mt8++23dO3a1XweFBSkBKSIiIi80+J1y8ff+u1YPP7+/kRERMQaR+ejjz4iU6ZM5jg6GotHRERERH7LMAzat2/PmjVr2LlzJ1mzZv3TdU6cOAFA2rRpAfD09OT06dPcv3/frOPn54eLiwu5c+c26+zYsSPWdvz8/PD09Hzjazg6OuLi4hLrISIiIvIui9ctH1/1prF4AgICcHBwIFmyZLHqurm5/ek4OzHL/qhOzFg8b7orHRYWRlhYmPlc4/GIiIiIvDt8fHxYvHgx69atw9nZ2bwudHV1JXHixFy+fJnFixdTrVo1UqZMyalTp+jSpQulS5cmf/78AFSuXJncuXPTuHFjRo8eTUBAAH379sXHxwdHR0cA2rRpw5QpU+jZsyfNmzdn586dLF++nI0bN1pt30VERETepnem5ePvjcVjLRqPR0REROTdNX36dAIDAylbtixp06Y1H8uWLQPAwcGB7du3U7lyZT766CO6detGnTp1WL9+vbkNW1tbNmzYgK2tLZ6enjRq1IgmTZowePBgs07WrFnZuHEjfn5+FChQgHHjxjFnzhwN7SMiIiIJxjvR8vH3xuJxd3cnPDycp0+fxmr9eO/evVjj7PzXY/GAxuMREREReZcZhvGHyzNmzMiePXv+dDuZM2dm06ZNf1inbNmyHD9+/G/FJyIiIvK+iNctH/9sLB4PDw/s7e1jjaNz/vx5bty4YY6jExdj8YDG4xEREREREREREfkz8brl45+NxePq6kqLFi3o2rUrKVKkwMXFhQ4dOuDp6Unx4sUBjcUjIiIiIiIiIiJiLfG65eOfjcUDMGHCBKpXr06dOnUoXbo07u7urF692lyusXhERERERERERESsI163fPyzsXgAEiVKxNSpU5k6derv1tFYPCIiIiIiIiIiIm9fvG75KCIiIiIiIiIiIu8uJR9FREREREREREQkTij5KCIiIiIiIiIiInFCyUcRERERERERERGJE0o+ioiIiIiIiIiISJxQ8lFERERERERERETihJKPIiIiIiIiIiIiEieUfBQREREREREREZE4oeSjiIiIiIiIiIiIxAklH0VERERERERERCROKPkoIiIiIiIiIiIicULJRxEREREREREREYkTSj6KiIiIiIiIiIhInFDyUUREREREREREROKEko8iIiIiIiIiIiISJ5R8FBERERERERERkTih5KOIiIiIiIiIiIjECTtrByAiIvI+6bCjk7VDkPfI5AoTrR2CiIiIiMi/ouSjiIiIiIiIyD9UcOhAa4fwXjvRd6C1QxCRf0ndrkVERERERERERCROKPkoIiIiIiIiIiIicULJx9+YOnUqWbJkIVGiRBQrVozDhw9bOyQRERERecfpGlNEREQSKiUfX7Fs2TK6du3KgAEDOHbsGAUKFMDLy4v79+9bOzQREREReUfpGlNEREQSMiUfXzF+/HhatWpFs2bNyJ07NzNmzCBJkiR8//331g5NRERERN5RusYUERGRhEyzXf+/8PBw/P39+fbbb80yGxsbKlasyMGDB1+rHxYWRlhYmPk8MDAQgKCgoP8knsiI0P9kOyL/1TH5X4oKDfvzSiJ/QXw8vsOf6/iW/85/cYzHbMMwjH+9Lfn73vY1ZkSkzkFxLa6+eyLDdf0f1+Lqs9O1bdyKq8+t5sqBcbJd+Z+1dQdaOwSJY3/1OlPJx//38OFDoqKicHNzi1Xu5ubGuXPnXqs/YsQIBg0a9Fp5xowZ4yxGkX/Cdc0Ia4cgEmdch420dggicWoWM/+zbQUHB+Pq6vqfbU/+Gl1jvn9cXcdbOwT5h1znDbd2CPIP6Hrv3eXaYoK1Q5C35M+uM5V8/Ie+/fZbunbtaj6Pjo7m8ePHpEyZEovFYsXIEoagoCAyZszIzZs3cXFxsXY4Iv85HePyPtPx/XYZhkFwcDDp0qWzdijyFyS0a0ydD95N+tzeXfrs3l367N5N7/vn9levM5V8/H+pUqXC1taWe/fuxSq/d+8e7u7ur9V3dHTE0dExVlmyZMniMkR5AxcXl/fyD1gkho5xeZ/p+H571OLRenSN+dfofPBu0uf27tJn9+7SZ/duep8/t79ynakJZ/6fg4MDHh4e7NixwyyLjo5mx44deHp6WjEyEREREXlX6RpTREREEjq1fHxF165dadq0KUWKFKFo0aJ89913PH/+nGbNmlk7NBERERF5R+kaU0RERBIyJR9fUa9ePR48eED//v0JCAigYMGCbNmy5bUBwsX6HB0dGTBgwGvdkkTeFzrG5X2m41sSGl1j/j6dD95N+tzeXfrs3l367N5N+txeshh/Nh+2iIiIiIiIiIiIyD+gMR9FREREREREREQkTij5KCIiIiIiIiIiInFCyUcRERERERERERGJE0o+ioiIiIiIiIiISJxQ8lFERERERERERETihJKPIiIiIiIiIiIiEieUfBT5HYZhxPpXRETip+jo6D98LiIiIv+cfg+9f179THXdJG+Dko8ir4g5CR88eJB58+YREhKCxWKxclQiIvJHbGxeXs7MnTuXoKAg87mISFz5vR/rStL8czHvXUhICIGBgW9cJm+fYRjm76GlS5eyfPlyK0ck/0bM31JQUBDPnj0D0HVTPPG+n+d0lIn8v5gv1lWrVlG9enWuX7/OlStXzGXv+8lA4p+bN29y48YNLl26FKtcx6LI627evMnIkSOZP38+oL8TEYk70dHR5o/1HTt2sGrVKq5cuUJERAQWi0WtiP6BmOvw9evXU6dOHQoVKkSTJk2YPXs2gBoDWEl0dLT53h85coTp06czduxYtm/fbuXI5J+I+TvbsGEDNWrU4JNPPqFgwYKsXLmSR48eWTu8BCPmGvXhw4fcvXuXhw8fArz33x9KPor8P4vFwp49e2jevDkjRoxg4MCB5MmTB/jfF69+zMrbsnr1aipWrEi5cuUoUqQIHTp04MKFC4AuwEXeJG3atHh4eODn5wfo70RE4oZhGGbisXv37jRu3JgWLVpQq1Ytxo0bx7Nnz7CxsXmvf0DGBYvFwsaNG6lfvz4lSpTA19eXwMBABg4cyJ49e6wdXoIVc6z369ePsWPH8uLFC06fPk3fvn3ZsGGDlaOTv8tisbB582bq1atHtWrVWLx4MTlz5qRly5acO3fO2uElCDEJ4HXr1lG7dm2KFy9O06ZN6dq1K/B+t0J9f/dM5B/YuHEjVapU4euvvyYoKIhdu3bRqlUrateuzenTp9/7uxESP+zZs4dGjRrRpUsX5s6di6+vL0uXLqVPnz5cvnzZ2uGJWJVhGERGRr5Wbmdnx4ABA9i/fz+LFi2yQmQi8r57tfvpvn37OHjwIKtXr+bMmTOULVuWtWvXMmLECCUg/4LQ0FDg5XsaHR1NcHAw06ZNo2/fvvTt2xcPDw+OHDlC3bp1KVOmjJWjTdhmzJjBxIkT6dChA5s2bWLNmjU4OzszduxYNm3aZO3w5C+Kjo4mLCyM2bNn07lzZ7755htSpkzJsWPHqF+/PiVLlrR2iAmCxWJhy5Yt1K9fn7p16/Ljjz9StGhRvvvuO9atW2ft8OKUko+S4L3amjF58uScPn2aZcuW0aJFC8aOHcvly5eJiIigfPnyBAcHv9d3IyR+2LZtG+XKlaNNmzaULVuWWrVq4efnx86dO5k2bZq1wxOxmrNnz2KxWLC1tQVg06ZN3L592zyPZ8yYkerVq5utZPTDX0T+SzGJx5UrVzJ37lyKFClC8eLFSZ8+PePGjaNcuXLs2LGDkSNHmglI9Zp53ffff0+/fv149OgRFosFGxsbkiZNyrNnz6hQoQI3btwgZ86c1KhRg4kTJwKwefNmTp06ZeXIE6YjR47g5eVFqVKlSJ06NVWqVKFPnz4EBgYyYMAAtm3bZu0Q5Q/EnIMsFguOjo7cvXuXTz/9lKdPn1KwYEHKly/PjBkzgJdjet68edOa4b73wsPDWblyJd988w0dO3YkXbp0zJkzh/bt2/PZZ59ZO7w4pSyKJFgxJ+KoqCizrHLlyuTNm5d27dqRJEkSOnXqxPbt2+nVqxcffPCBeZdWJK4YhsHdu3fNll3R0dGEh4dTsGBBJk6cyMKFC7l165aVoxR5+yZNmkTevHn56aefsFgsXLx4kerVq1OvXj0aNmzIzZs3cXJy4quvvmL+/PmcPn1aN4tE5D8XHh7OwoULWbFiRaxkmJ2dHUOGDKFcuXLs2rWLb7/9VhMX/kbMtffRo0fZtm0bU6dO5fHjxwA8e/aMkJAQ1qxZQ4UKFfD29jZvuAYEBDBv3jzOnj2rZO5bFPNep0yZkqdPn/L8+XNzWdmyZWnVqhWnT59m1KhRbN261Vphyu+IuQFrsVjYvXs3Bw4cACB16tSMHTuWQoUKUbNmTSZPngxAcHAwixYtUnf6OPDqecvBwYErV66QPn167t69S6FChahatSqTJk0CXt7c2rx5s7VCjVO6KpcEKabbjJ+fH507d6ZGjRoMHTqUdOnSsXz5co4ePcr8+fOpXLkyNjY2bNiwARsbGxwcHKwdurynHj9+zIsXL7BYLNSoUYM9e/awfft2bGxssLOzA8DJyYmUKVOSJEkSK0cr8vZVrFiRxo0bU7duXfbs2UOOHDm4ceMGzZs3586dO3h6etK0aVNCQkKoWbMm06ZNIzw83Nphi8g77rfJLgcHB3744Qfzpsd3331nnmtiEpAFChQgLCyMRIkSWSPkeOvs2bMATJs2DW9vb9atW8ekSZN48OABLi4udO7cmQkTJuDm5sasWbPMVu5Tpkzh1KlTFC9eXMncOPTb3gIx73X+/Pk5cOAAGzZsiFUnderUVKpUCScnJxYvXkxISMhbjVfe7NixY0RFRWFjY0NkZCTPnz+nadOmZqK/cePGnDhxgmTJkjFt2jTz9+3IkSM5f/48VapUsWb47yWLxcK2bdtYtmwZUVFR5MyZk6NHj1KyZEmqVavGrFmzAAgMDGTz5s1cvHjxjUMMvesshm4fSQK1Zs0aGjVqRIcOHQA4dOgQV65c4fjx46RMmRJ4eWd2wYIF/PDDD+zevZsCBQpYM2R5T61du5axY8dy//59GjRogKenJ1u2bGHr1q1MmjSJSpUqAfDtt9/i5+eHn58fyZMnt3LUIm/Hq2OsnTt3jiFDhrBlyxZWrFhB+fLlzVlnf/jhB/z9/Zk2bRr29vakS5eOw4cPkzx58lgz04qI/FWvnjtu3LhBkiRJiIiIIG3atAQFBZmTwX355Ze0adMGe3t7APOHf8xkhUqYwdy5c1m9ejULFy40r2F69OjBzp07qV69Oh07diRFihT069eP4cOH4+PjQ9KkSXn48CErVqxgz549FCxY0Lo78R579ThdunQpQUFBJE6cmMaNGwPQoUMH5s6dy5QpUyhWrBhp06aladOmVKxYkTRp0tCwYUPOnj3LRx99ZM3dSPDWrl1L79698fHxoU2bNtja2hIUFET+/PlZvnw5RYsWJTAwkFGjRrFu3ToyZcpEwYIFuXr1Klu3bmXnzp0UKlTI2rvxzjt69ChFihQBIDIyktDQUEqXLk2fPn2oU6cOfn5+VKlShbx587Jnzx6SJUuGYRj07duXxYsXs337drJly2blvfjvKfkoCdK9e/eoUaMGTZo0oX379ty5c4fChQtTt25dpkyZAsDVq1cZN24cZ86cYdKkSeTLl8/KUcv76NixY5QvX55u3brx6NEj9u3bR44cOShatCg3b95kypQpFC5cGHt7e3755RddFEiCExYWhqOjI/Cyu2NwcDCdO3dm06ZNrF69+rWJCE6cOMHatWuZO3cu9erVY+zYsdYIW0Teca8mYwYMGMD69et59OgRrq6udOvWjaZNmxIcHIyPjw+XLl2iYcOGtGrVKlYvGSUe/2f//v2kS5eOrFmzEhAQgLu7O/AyAbljxw4+/fRTunbtStKkSVmyZAkzZ84kceLEZMmShc6dO5M7d24r78H769XjtHv37vj6+uLm5sazZ8/Ily8fGzduNJctXryY6OhokiRJgqOjI6dPn+bChQvUqlWLTZs2vZcJk3fJw4cP8fHx4e7duzRo0ICvv/4aW1tb8ubNy8aNG8mcObNZb+fOnSxYsIDIyEiyZ8+Oj48PuXLlsvIevPsOHTpEiRIlmDBhAp06dTLLCxYsyNChQ6levToAixcvpnHjxtSsWdPsYblp06b3+reeko+SIF25coWKFSty8OBBIiIi8PT0pGrVqmaT540bN1KpUiXu3r1rdnUV+a9dvnyZJUuWYLFY6NOnDwDr169n0qRJJE+enEaNGuHq6srmzZtJkSIFtWrVIkeOHFaOWuTt2bx5M7dv36Zly5a0atWKM2fOcODAAc6dO8fw4cPZuHEja9asoXTp0kRHR2MYBra2toSGhjJlyhS2bt3K6tWrcXZ2tvauiMg7aujQoUyYMIE5c+YQHBzML7/8wrhx4xg5ciQ9evQgMDCQTp06sW/fPkaNGkWdOnWsHXK8duzYMbp27UqnTp2oVasWEDsB2aFDB1KmTMmLFy9IkiQJkZGR5vAz8t/6bXL84cOHNG3alFGjRpEmTRqOHj1K69atyZYtG7t37wbgwIEDBAYGEhYWRo0aNbC1taVz587s2rWLnTt36jeTlXz//fcUKVKE/Pnz8/TpU9q1a8f169dp0qQJ1apVo1q1amzYsMFMPv6WbpT8d0JDQ/nuu+/o378/48aNM3tZ5s2blylTplC2bFmzVf327dtZt24dd+7cIW/evHz55ZfkzJnTynsQd3QmlwQpUaJEZM+encOHD9OhQweqVavG1KlTAbh06RKrV6/G1dWVUqVKWTlSeV8FBQVRv359c8y6GDVq1ABgwoQJzJ8/n379+jFy5EhrhSliVT/88AMHDx5k5cqV+Pv7s3PnTgA++ugj+vbti8VioVatWmYLyKioKKKjo0mUKBFlypRh9OjR3Lt3T8lHEflLgoODY50vnj17xs6dOxk8eLCZKAPIkiUL7du3J3fu3Hh7ezNx4kQmT55MzZo1rRD1uyU4OBh7e3umT5+OnZ0dNWrUYMyYMfTo0YMff/wRGxsb2rRpQ5o0aQDMcR/lv3Xp0iWyZ89uPp88eTLz5s0jS5YsZM6cGWdnZ7y8vJg/fz5NmzY1J1IqUaKEuc7x48eZOHEiGzZsYMeOHUo8WsmxY8dYtWoV5cqVAyBZsmRMnjyZ9u3bs2TJEq5fv86dO3cYNWoUSZMmNceOf/z4Mbly5aJt27bWDP+9MX78eOrXr0+6dOno3Lkztra2dOrUiYiICLp27UpYWBhOTk4A5nAeFStWpEyZMuaQHe87DYAk7703Ne5Nly4dUVFRfPbZZ5QuXZqZM2ead1VnzZrFiRMn1G1A4pSLiwuzZs0iWbJk7N27lzNnzpjLatSoQffu3bly5Qpjx47lxYsXmt1REqTFixeTLFkytm3bRvv27WMNf/Hhhx/Sp08fatSowRdffMG2bduwtbU1L+h+/vlnAPNCT0Tkj9SoUYPevXvHKgsNDeWXX34xv4MNwyA6Opqvv/6amjVr8uOPPxIWFoarqyt9+/bF1taWqKgoa4Qf7/zedUuZMmXo06cPiRMnZsKECaxfvx6AMWPGULlyZebNm8f3338fa6Ze+W/17t2bZs2aAS8/p/DwcJIkSUJQUBC//PKLmYC3tbWlTJkyLFiwgKtXr5I/f35zGxEREYSFhRESEsKuXbs0Lr4VFS5cmEWLFpE1a1aOHTvGiRMnSJkyJVOmTMHd3Z3ly5eTKFEibt68yfXr1zl79iz+/v6cPXuWsmXLYrFY9Hf2LwUGBrJ48WKCg4OBlw2dOnbsyMiRI+nevTsjRowgVapUdOzYke7du+Pj40OrVq3w8fFh3LhxVo7+7VG3a3mvvTqr9YoVK3B3d6dUqVJUrlyZZ8+eUaJECezs7OjcuTOOjo7s27eP+fPns2/fvlhfsCJx5dSpUzRt2pSiRYvSsWNH8uTJYy7btm0bOXPm/N0uEiLvo5jzdsyPmpYtWxIeHs6lS5fo2rUr9evXj5VQvHDhAp07dwZg06ZNREdHExkZybBhw6hTp47O5SLyl1y4cIEsWbLg4OBAaGioOVN1ixYtuHbtGnPmzCFr1qzmOapJkyaEhoayfPlyK0cef0VERGBvb8/Jkye5f/8+kZGRVKlSBYvFwr59+xgzZgzBwcF06dLF7PnRv39/mjVrRtasWa0c/fvr4cOHJEuWDDs7O+7du4ebmxuBgYFs3LgRHx8fqlWrxqJFi8z6UVFRbNu2jVmzZrFq1apYE7i9Oi6zvH2vdpeOGefR0dGR0aNHU6BAAR4/fkyHDh24ffs2DRo0oHXr1laO+P0VM0TE/v37yZw5MxkyZCAsLIxJkybRv39/kiRJQqtWrXj06BEvXrwAwN7enm7duiWYuSWUfJT3np+fHzVr1qR8+fLcu3ePe/fu0adPH77++mvu379Po0aNuHfvHhEREXzwwQcMHz5cP1blrTp+/DgtW7akcOHCdOnSRYOqS4L16syyvx1/qFGjRhw5coQePXpQr149s2VGaGgoUVFRJE6cGBsbG3M9jV8kIn/Vq+ee7777jpUrV7Ju3TpSpkzJ8uXL+e677yhSpAg9evQgY8aMhISE4O3tTcGCBRk/fryVo49fZs2ahb+/PzNnzgRgyZIltGvXDmdnZyIjI0mWLBkLFiygSJEi7N27l7FjxxISEkLr1q01XuZbtnz5curXr4+/vz+FChUiODiYH3/8kR49elCxYkUWLFhg1n31byQqKkrd4eMpX19flixZgpOTEwMGDKBAgQI8evSI9u3bc/v2bT777DOzS7Cuk/6dmL+JmFbaNjY2hIWFkTdvXuzt7fHz8yN9+vQ8f/4cX19fOnfuzOzZs81Wx5DwxtpU8lHee9OmTQOgXbt2XLx4EV9fX6ZMmcLYsWP5+uuvAQgICMDBwYFEiRKZ42CIvE3Hjx+nTZs2fPDBBwwYMICPPvrI2iGJvFWvXoDNmDGDgwcP8uGHH1KqVClzRuvGjRvj7++Pj48PNWrUoEmTJiRNmtScifPVH0ciIn9XeHg4Z86coVKlSpQoUYLFixfj5OTE5MmTWbJkCXfu3KFgwYLcvn2bkJAQTpw4gZ2dXYL7Afl7QkNDGTlyJEuXLuXTTz+lT58+VK9enebNm1O2bFkiIyPx8fHhl19+YePGjRQqVIjdu3czePBgnJ2dWbRoEUmTJtV7GUdePU4Nw+DRo0c0b96cgwcP4ufnR8GCBQkKCmL9+vX06tWLSpUq4evra+Wo5U0iIyOxtbV9483WRYsWMWfOHJInT24mIB8/fkyTJk2IjIxk6dKlJEuWzHrBvwdirjcvXLjA5MmTuX37NiVKlKB79+7cuHGDatWqkSRJElavXk2GDBkICQkxW0D279/fnGg0oX13KPko752YP+Jff/3V7HpXqVIlWrRoAcDt27eZMmUKU6dOZfz48bRs2dLKEYu8FNOqa8mSJaRNm9ba4Yi8Na9efA0aNIiJEydSvnx5zpw5Q5o0aWjVqhWNGjUCXnaB3Lt3L1FRUaRIkYL9+/fj4OBgzfBF5B21a9cunj17Ro0aNejUqRNp0qShT58+nDp1isqVK1OoUCFWrVpFkiRJOHjwIIcOHeL8+fNkzJiRXr16YWdnp9mYf+PRo0f4+vryww8/8NFHHxEcHMy8efPMCWQAKlSowMOHDzl58iQABw8eJGPGjGTIkMFaYb/3Xj1OR40ahaurK23atOHOnTu0b9+eXbt2sWvXLjMBuWHDBho3bsygQYPo27evlaOXGMePH6dQoULm823btrFgwQLs7e354IMP6NevH/CyVev06dNJnjw5AwcOJH/+/Dx58oQXL16QPn16a4X/XohJPJ48eZJKlSpRsmRJEiVKxMqVKxk+fDg9evTg1q1bVKxYEScnJ9auXWt2wR45ciSTJk3i0qVLJE+e3Nq78vYZIu+hVatWGU5OTkaOHDkMV1dXo0+fPrGW37592+jTp49hsViMH374wUpRirwuJCTE2iGIWI2/v7/Rrl07Y9++fYZhGMbRo0eNJk2aGB4eHsaCBQvMen5+fsbGjRuNyMhIwzAMIyIiwirxisi76/79+4a3t7dRunRpo06dOoajo6Nx8uRJc/nJkycNNzc3o2rVqkZgYOAbtxFzDpKXoqOjDcMwjIcPHxojR4408ubNa7i7u5vLX7x4YRiGYRw/ftxImzat8dNPP1klzoRk/Pjx5v/DwsKM8PBwI1++fMamTZvM8jt37hi1atUykiVLZhw/ftwwDMN4+vSpsXXrVh3j8Yifn5+ROnVqY+rUqeZzi8Vi1KtXz/j888+NFClSGCVKlDAuXbpkGIZhLFy40KhUqZJRvnx54/Tp09YM/b0RFRVlGMbL74fEiRMbvXv3Nsvbt29vdOrUyXj+/LlhGIZx48YNo1ChQkbhwoWNW7duGYZhGKGhocbDhw+tE3w8oL5J8l4wDMOcVe/+/fsMHjyYSZMmMX36dDp06MDw4cOZPn26WT9dunS0bt2agQMH8vHHH1srbJHXxAxwL5LQrFq1ipYtW/Lzzz+TPXt2ADw8POjcuTN58uRh0qRJ5gD4FStWpFq1aubMsmp1JCJ/V+rUqRk2bBh37txhzZo1jB071hzzOzo6mvz587Nt2zZOnDhB48aNefDgwWvb0Lh3b5YyZUqaNWtG48aNCQ4OplWrVgAkTpwYAAcHB2xtbXXujmP79++nZ8+eNGzYEHj5voeHh/PixYtYk8SkTZuWqVOnUr58eSpVqsSRI0dwdXWlcuXKmsE9HsmQIQP169dnypQpTJ48mZMnTzJhwgSWLl3K8uXLOXXqFI8ePeKrr74CoGHDhtSrVw8nJyd1s/6P2NjYcPPmTSpUqED16tUZNmyYWf7gwQN2795N4cKFqVKlCgcOHGDdunUYhkHZsmW5c+cOjo6OpEyZ0sp7YT1KPso77datWwBYLBYsFgtbt25l/PjxFClShAYNGlChQgW+/fZbhg8fjo+PT6wEZMaMGenTpw85c+a0VvgiIvL/Yi7ILly4gL+/v1leqFAhunTpQr58+ejduzfbt2+PtZ5+/IvI3xVzw9rR0ZFs2bJRpkwZ1q1bx48//gi8/CEZGRlJ/vz52bJlC+vXr2f06NHWDDlei3k/Hz9+TFBQEMHBwaRJk4Y2bdowYMAAdu3aRYsWLXjx4gU3btxgyZIlREVFkSlTJitH/n7Lly8fCxcuZN++fTRo0ACApEmTYmdnZw5XEhERgWEYpE2blsmTJ5M7d26z624Mfc9an2EYfPTRR3Ts2JEqVaowZ84cJk6caCayIiIiSJ8+Pdu2bePMmTOMHTsWeDlUzQ8//KAhDf5DUVFRZM2albCwMPbv3w/AyJEjWb9+PXXr1qVHjx5cv36dvn378vz5c9asWUPy5MkJCwuzcuTWp9tN8s4aPnw4GzduZMeOHWZrsUuXLjF69GjSp0/P48ePSZcuHUmSJKFjx44AdO7cmZCQELp27Qroy1RExBreNDFM9erVSZYsGYMGDWL06NE4ODhQsWJFAAoWLEibNm3Ili0b5cqVs0bIIvIeiDn3xIwx+9FHH7FlyxYOHz7MiBEjmDBhAgCffvqp2SovT548XLx4kSxZslgr7HjN+P8xe9etW8fQoUN58eIFoaGhfPPNNzRs2JC2bdsCL8fz3bBhAyVKlCA4OJj169dr7Lk40qhRI9q2bUvJkiXx9vbGMAx69OhBvXr1WLZsGalSpTITxvb29uZ6rq6ubNiwgaRJk1ordPkd0dHR2NraYm9vT926dbFYLEyfPp0zZ84ALz/HyMhI0qdPz8cff8zt27fNdV1cXKwV9nspS5YsLFq0iI4dOzJ69GjSpEnDjz/+yJo1a6hcuTIAlSpVIkuWLOzevZs2bdpw4MABtfRGLR/lHda1a1fmzJlDokSJCAoKwjAMfHx8mDdvHnfu3GHu3LlmN4EkSZLQqVMnevXqxbBhw3j69Kl1gxcRSaBeTTwuW7aMiRMn0qNHD27cuEGpUqUYOHAgTk5OjBw5kh07dpjrFS1alD59+qgLmIj8I6+ee06dOsW+ffu4cuUK8PL80rlzZ5InT86kSZNYs2YNANWqVWPy5Mlky5ZN557fYbFY2LZtG1988QWff/45vXv35osvvqBLly4MGTIEOzs7vv76awYOHIijoyNp0qRh7dq1eHh4WDv091JgYCD37t3D29ubI0eO4OTkRPXq1RkzZgz79u2jTJkyXL16lbZt21KqVCk8PDzw8PCgUKFCDBkyBGdnZ2xsbIiOjrb2rsgrbG1tWbNmDdmzZ8fZ2Zmvv/6aFi1a8P333zN58mQA7OzssLW1JTo6msjISCtH/H7LkSMHEydOJCQkhEWLFtGzZ08qV66MYRhERERga2tLvnz5SJUqFYASj/9Ps13LOykiIsK8U7d3717atm2Lr68vRYoUwWKxMG3aNHOsxx49epgXmyEhIbx48SJBj7UgIhIf9OzZkyVLllC8eHHu3r3LuXPnmDBhAo0bN2bHjh1MnDiRsLAwOnXqRLVq1awdroi8w2Ja5wH06dOHdevWcevWLYoUKYKHhwejRo0CYM+ePUyfPp0DBw7g7OxMWFgYv/76a6zWYRKbYRg0bdoUW1tbfH19zfI5c+bQsWNHZsyYQZMmTQgICGDZsmV8+umnZM2a1YoRv//u3btH586dzR5iH3/8Mc+ePWPDhg2MGjWKy5cvM2fOHF68eEFwcDAODg6Ehobi4+OjJEk8FRwczJQpU3BwcKBbt24AXLlyhUmTJrFw4UIaN25M1qxZuXPnDhMmTOD48ePkzp3bylG//y5fvky7du2wtbXl22+/5ZNPPgGgf//+LFy4kD179pAxY0YrRxmPvP05bkT+mZjZpV714sULIzQ01MiQIYNRpEgRw9/f35xpb+rUqYaNjY0xevToN64rIiLWsXTpUiNdunTGqVOnDMMwjJ9++smwWCzGmjVrzDo7duwwPD09jU6dOlknSBF57wwdOtRIkyaNsWPHDuPRo0dGs2bNDBcXF6NVq1ZmnVOnThkrV640xo4da0RERBiGYZj/SmzR0dFGdHS04eXlZfj4+BiG8XJG5Zhr8U6dOhl58+Y1Z3/V9fjbExAQYHzxxReGs7OzcfjwYcMwDCMwMNBYvHix8cEHHxitW7d+43qa3Tr+OXbsmJE0aVIjf/78xvr162Mtu3z5stG+fXsjefLkRubMmY3p06cbZ86csVKkCdOFCxeMKlWqGF5eXsaxY8eMUaNGGYkSJTKOHTtm7dDiHXW7lneGjY0NV69eNe+qLl++nFatWuHo6MiFCxd49uwZLVq04Pjx4xiGQbt27Zg6dSq9evUym6OLiIj1BQQEUKlSJfLly8fixYupXr06U6dOpWbNmgQFBfH48WPKly/PxIkTGT9+vLXDFZF31KtdR0+fPs26detYuHAh5cuX59ixYyxfvpwqVaqwc+dOfHx8gJeTdNSpU4du3bphZ2dHVFSUWoP9jpgJH/PmzcvatWt5+PAhDg4OREREAPDBBx+QJEkSc3KT3471K/8d4/87M8b86+bmxsSJE6lWrRoVKlTg8OHDuLi4UL16dYYPH86WLVuoUKHCa9vRePjxT5o0aahRowanT582hw6L6Vb9wQcf0KVLFz7//HMyZszI559/rhaPb1mOHDmYNGkS9vb2VKlShb59+7Jv3z4KFSpk7dDiHX0DyDsjIiKC2bNn07dvX9q2bUv9+vWpVKkSAIkTJ+bYsWOEhobGSkC2adOG2bNnm4O/iojI2/WmcaOuXLnCixcvOHjwIG3atGHkyJHmpATz589n7NixREVF8fHHH2vsKRH5x15NduXLl4+WLVtSqFAhfvrpJxo3bsx3333HsmXLyJUrF3PmzOGLL754bRtKxvxPTGLr9u3bXL161RwDs127dmTJkoUvvvjCTEDCy4kgnZ2dCQ8Pt1rMCUF0dLQ5rIDFYjHfb3d3d7777juqVq1KxYoVOXz4MM7Oznh7ezNw4EBcXV31/foOSJ8+PePGjaNOnTq0b9+e48ePmzdG4GUCskePHqxcuVJDi1lJjhw5GDt2LMWLF+f48eMa0/Z3aMxHeacEBATQokULNm/ezFdffcX3338PQGhoKIkSJSIkJITChQvj5OTElClTKFq0qPllLCIi1rN//34++OAD0qZNy7Fjx6hfvz6XLl1i1qxZtGzZEoAXL15Qr149smTJwqRJk3T+FpF/ZM+ePTx9+pTPPvuMtm3bkixZMkaMGGGO/diuXTtsbGyYMGEC9vb29OrVi8OHD5M7d24mT56sFnp/YNWqVfTu3ZtHjx5RqVIlWrRoQcWKFdm2bRuDBw/m4sWLVKhQgeDgYPbs2cPevXspUKCAtcN+b706kdLUqVPZu3cvwcHBVK1alfbt2wNw//59OnTowJYtW9i+fTsff/wxISEhJE6c+LVtiHXFnKPOnDnDvXv3CAsLo1SpUjg7O/PkyRO++uor9u7dy65duyhQoACRkZFqmR2PvDovhbxOZxl5p7i4uODq6kr58uU5dOgQ06dPByBRokSEhoaaLSBv3bpFt27ddKdVRMTKDMPg8OHDfPLJJ8yePZtHjx6RM2dO6tSpQ86cObl9+zaPHz/m0KFD1K1bl1u3bjFhwgQsFgu6Pyoif4dhGDx58oS+ffsyffp06tSpw8KFC/nyyy8BzBsa165d4/r169jb2xMdHc3Vq1dp2LAhU6ZMUWvrP3DlyhV69+5N+/btmTJlCteuXWP48OGsWbOGypUrs2zZMlq3bo2trS3ZsmXj559/VuIxDj169MhMGn7zzTeMGDGCdOnSUapUKTp27Ej//v0JDQ0lTZo0TJ48GW9vb4oVK8bZs2fNxCOoO3x8YrFYWLlyJWXLlqVz5854e3tTvXp15syZQ/LkyZk3bx6lS5emcuXK+Pv7K/EYzyjx+MfU8lHiPeOVGQpj3Lp1izFjxrB582a6dOlidteLER4ezq1bt/jggw/eZqgiIvI7xowZw4gRI+jSpQvdunUjKCiIyZMns2DBAgIDA8maNStubm5s3LgRe3t7oqKi1N1RRP6WmPPG5cuXqVy5MlevXuW7776jY8eO5nIbGxsmTZrEvHnzSJMmDc+fP+fp06ecPHkSW1vbN153JlQxPxNj3o+AgAAzsWtvb8+VK1fo0KEDL168oF27dnz++efWDDdB+emnn6hduzbXr1/nwIEDtGrVimXLllGsWDH8/PyoUqUKAF9//TUTJkwgUaJEBAQEMG3aNPr376+kVTzl7+9P5cqVGTVqFJ999hnPnj2jd+/e3Llzh6+++opmzZoREBBAo0aNuHLlCr/++iuOjo7WDlvkL1HyUeK1mAvAffv24e/vz8WLF2natCl58uQhKCiIkSNHsm3bNtq3b0+7du0YOHAg165dY/bs2brzICJiBa/+cP9td6Bx48YxaNAgunfvTs+ePXFwcCA4OBh/f38yZcrEBx98gI2NjboRicjf9uq55/vvv2fHjh1cunSJNGnS0KJFC2rWrGnWvXfvHsuWLePnn3/G2dmZKVOmmGOo6abH/8S8p35+fixfvpznz58TFhbGqlWrzDqXLl2iY8eOREVFUb9+fZo1a2bFiBOOCxcu4O3tTZkyZShatCjwMtG4adMmvvzyS8aNG4erqytffPEFvXv3pk+fPrFaO+p7Nn6J+VubO3cukyZN4uDBgyROnBiLxcLNmzfp2rUrT548YevWrdja2nLv3j0iIiLIkCGDtUMX+cuUfJR4b/Xq1bRs2ZJKlSoRFBTEmTNnqFWrFuPGjePatWvMmjWL2bNn8+GHH3LmzBl2795NkSJFrB22iEiCNmrUKJydnWnWrFmsHzzjxo2jV69eDBw4kObNm5MuXbpY62nsKRH5u149b/Tu3ZuxY8dy5coVnj9/Trt27XBwcKB169axEpC/TTQqGfNmO3fupGLFitSqVYvDhw8TFBTEoEGD6Ny5s1nn8uXLNG3alFSpUvHDDz/g7OxsvYATiMjISAYPHoyfnx/dunWjSJEiODo64u3tTYMGDejRowcXLlygRIkSPH78mJEjR9KzZ09rhy3EPl/FnIfu379PmjRpWLBgAcOGDWP//v2kSpXKPC+dPXuWvHnzsmfPHj755BMr74HIP6Ore4nXfv31V7p168a4ceNYtmwZa9as4datWyRPnhw7OzuyZ89Ojx49WLRoEd7e3hw/flyJRxERK/jtvcxLly7Rvn17li1bRkhIiFnerVs3GjZsyKRJk5g6dSpPnjyJtZ4SjyLyd8WcN86ePUtQUBDbt28nQ4YM5MyZk/HjxxMeHs6cOXNYuXIlAOXLl2fkyJHm+oZhKPH4BpcuXeLChQtMmTKFVatW8dNPP1G3bl1WrlzJ1KlTzXrZsmXjhx9+YPLkyUo8xqFz586Z/7ezs6NLly48ePCA5cuXkyVLFh4/fsyzZ8+oXLkyAIkTJ6Z+/frs2bOHrl27Wits+Q0bGxsuXLjAmjVrsLW1ZcWKFTRt2pSgoCAKFSrE5cuXmTt3LoB5XnJ0dCR37tw4OTlZM3SRf0VX+BKvBQUFkSZNGpo1a8a5c+fImTMnLVq0YODAgQCcP3+eFClSUK1aNfr370+OHDmsG7CISAIV093x7NmzAMyePZsuXbrQunVrlixZwosXL8y6adOmJX369Pj7+5MsWTJrhCsi75lVq1bh5eXFjh07yJIlC9HR0URHR1OgQAEmTJhAdHQ0AwYMIGfOnNy9e5cePXqY62qMx9ddunSJWrVqMWTIEFKlSgVA1qxZ6d27Nx999BELFy5kxowZZv2sWbOSMWNGa4X73lu/fj25c+fG29ub69evExgYSPLkyZk9ezYbNmxg2rRppE6dmsuXL7Nx40Z+/vlnWrduzcWLFylVqhR2dnZERkZaezeEly0fFy9eTJ06dejevTv16tWjfv36uLi4kC9fPqZOnUrfvn0ZPnw4169f58mTJ/j6+vLs2TPc3d2tHb7IP6bko8RrAQEBhIaG8uDBA6pWrYqXlxczZ84EYPfu3UyePJl79+5ZOUoRkYTr1VlhV6xYQevWrVm+fDnwsot1u3btaNu2LUuWLOHmzZvAyxlTZ8yYwebNmzWrtYj8JxwcHChYsCBXr17l7t272NjYYBgG0dHR5M+fn8mTJzNgwADat2/P6dOncXBwUDLmDzg4OFClShXCw8M5cOCAWZ4tWzZ69+5N/vz5mThxInPmzLFilAlH5syZSZ8+PXv37qVdu3ZMnTqVEydOUK5cOZo2bcrixYsJDAxkxowZ9OvXj8aNG/Pw4UM2bNhgfs+qdW/8YGNjw8CBA6latSoTJkygXbt2NG3alKioKABatmzJjBkzGDp0KGXKlKFEiRL4+vqyevVq0qZNa+XoRf45jfko8cabZhcMDw+nSJEi/PLLL3z99dex7rD27NmTo0ePsmLFClKmTPm2wxURSfBeHbdo48aNbNy4kUWLFpEnTx569OhBrVq1AOjRowcLFizAzc3NbI106tQp7OzsNMajiPxtv3fe2L17NwMGDODhw4csXLiQQoUKERUVhcViea2+JpeJ7U3X4bdu3WL69OksXLiQ1q1b07t3b3PZxYsXmTx5Ml27diVLlixvOdqEIeY4j4yMJCoqiokTJxIUFISrqys3btxgx44djB49GkdHR1q1akXHjh3p2rUrFy9eJCQkhLx582oSt3gqKiqKBg0a8OjRI3bv3s2iRYuoX79+rBnmz58/z8WLFwkPD+fjjz9Wy2J55yn5KPFCzAXPoUOHOHToEDly5KBIkSK4ubmxcuVKBgwYQK5cuZgxYwaXL19mzZo1TJ8+nX379pEvXz5rhy8ikqD16tWLBQsW0L59e0JCQpg3bx45cuSgXbt2fP755wAsW7aMW7duERISwjfffKOZZUXkH3k18bhz505CQkIICwujdu3aAOzdu5eRI0fy4MEDZs2aRcGCBXWT40/EXIcfOXKEX3/9lcePH1OjRg2yZcvGgwcPmDhxojku3asJSCW14tbNmzdjJZy2bdvG8OHDGThwIGXLlmXWrFn079+fbt26sXLlSi5dusTevXvJnTu3uY6O/fjNMAx69uzJ+PHjWbhwIQ0aNDA/s7t376qlo7xfDJF4YsOGDYajo6Px8ccfG3Z2dkaTJk2Mw4cPG1FRUcaCBQuMnDlzGi4uLsZHH31kFClSxDh+/Li1QxYRSfDOnDljZMqUydi8ebNZdvLkSaNUqVKGp6ensWbNmjeuFxkZ+ZYiFJH3Ubdu3Yx06dIZOXLkMJImTWqULl3aOHTokGEYhrFr1y6jRo0aRrFixYzDhw9bOdJ3w4oVK4xkyZIZhQoVMrJly2YkTZrUmDx5shESEmLcu3fP6NOnj5E3b16jb9++1g41QTh8+LBhsViM7t27G+fOnTPL+/bta7i7uxt37twxDMMwDh06ZHTs2NEoW7asYbFYjE6dOlkpYvkj0dHRhmEYxtGjR41ly5YZ06ZNM27cuGGEh4cbhmEY3bt3N2xtbY3FixcbhmEYw4YNM6pXr24EBweb64q865R8FKuLjo42oqKijBYtWhgzZ840DMMwNm3aZBQrVsyoVauW8fPPPxuGYRhRUVHGjh07jEuXLhkPHjywZsgiIvL/bty4YWTMmNH48ccfDcN4ea42jJdJSScnJ+OTTz4xli9fbtaPWS4i8k/NmTPHSJ06teHv72/cuXPHuH79ulGwYEGjaNGixtmzZw3DMIytW7caJUqUMFq0aGHlaOO/X375xXBzczPmzZtnBAUFGYZhGH369DFSpUplTJs2zTAMw7h27ZrRpUsXo2jRosbDhw+tGW6C8OTJE2PSpElGqlSpjNKlSxvDhg0zlzVt2tRo166d+Vk9evTIOHz4sNG+fXsjIiLCWiHLn1ixYoXh6upqFC9e3EiaNKmRJ08eY/Dgwcbz588NwzCM3r17GxaLxShRooSROHFiw9/f38oRi/y31O1arMb4/y4ed+/excnJif79+9OwYUOKFCkCgJ+fH/379yddunR06NCBsmXLWjdgEZEELua8bbwyNtiNGzcoW7YszZo1o1+/frHGVytVqhRPnz4lS5YsNGvWjDp16sTajojIP9GzZ08uXbrE6tWrzeEbgoOD8fDwIF++fKxatQoAf39/ChUqpG6nv/Hbc/CuXbto06YN27ZtI2PGjOb71bt3b6ZOncqZM2fIkCEDd+7cwd7entSpU1sr9ATnwoULjBgxgj179uDu7s7kyZM5ceIEe/fupU2bNhQvXvy1z1Pd4a3rTV3df/nlFypXrszQoUOpV68eiRIlokePHhw5coQqVarQq1cv7Ozs2Lp1K+fPn8fb25ts2bJZaQ9E4oa+icVqLBYLK1eu5JNPPiF79uzMnz+f8+fPm8srVarEkCFDePDgAcOHD481056IiLxd0dHR5o+bW7ducfv2bZ49e0amTJno27cvAwcOZN68edja2mJjY0NoaCiZM2emV69eREREMGfOHBYvXgygxKOI/CPR0dEA3L17lydPngBga2tLSEgIzs7OjB07lgMHDnD16lUAPDw8sLGxMddLyF59D2LOwXfv3iUiIoLnz59z69YtkiRJgo2NDSEhIQAMGDAAFxcXdu7cCUC6dOmUeHzLPvzwQ7777jt8fX0xDIN69epx8uRJDhw4wIIFC4DXv1OVeLSemMTjtWvX+PHHH83yq1evkjhxYry8vEiaNCm2trYMHTqUwoULs3z5coKCggDw8vKiY8eOSjzKe0nJR3nrYhrb3rx5k65du+Lj40Pfvn3JnTs3kyZNYsOGDWbdihUr8u2332KxWMiUKZO1QhYRSdAMwzDv4g8ePJjPPvuMihUrUqBAAdasWUPdunUZPHgwzZs3p3HjxnTq1IkqVapw5swZGjduzNSpU3ny5AkrV64kODjYynsjIu+K3yYNY85DTZs25fDhw0ybNg2AxIkTAy9bfKVOnRoXF5c3rpeQxSREOnfuDMDq1aupW7cujx8/pmrVquTOnZtGjRoRHh5O4sSJMQyD4OBgnJ2dSZYsmVVjT+hcXV0pU6YMBw8epF69ely/fp0HDx4wY8YM1q5da+3w5BU2NjbcuXOHjz/+mG+++YaFCxcCkCRJEsLCwszEfkREBEmSJGH48OGcO3eObdu2WTNskbdCt0XkrbNYLBw8eJD169dTp04dunTpAry8Oz127FjGjRsHQPXq1QGoWrUqZcqUIUmSJFaLWUQkIYtpVTF48GCmTJnC3Llz8fT0pG7dunTo0IG9e/fSp08f8ubNyw8//MCFCxfImDGjeTGdPXt2FixYQKJEiXB2drbmrojIO+LVrot+fn7cvXuXnDlzkiFDBipWrEiHDh0YPXo0YWFhtGzZkqdPn/L999+TIUMGUqRIYeXo45/o6Gi2bdvGpk2bzGTHggULcHNzwzAM+vbty7Bhw6hatSpz587l+fPnrFixgqdPn1KgQAFrh5/gxfw9DBs2jMOHD1OgQAH8/PzM30sSf1y4cIHHjx+TNWtWVq5ciZ2dHTVr1sRisTBw4EAWLlyIvb09AM+fPyd37tykSpXKylGLxD2N+ShvXXBwMF27dmX58uWULFmSTZs2mcv279/P2LFjefbsGW3btqV27dpWjFREJGF7dYzHp0+fUrt2bdq1a8fnn3/O+vXradKkCcOGDaNdu3bmuGshISFmKyR4eXffzs5OXa1F5B/p3r07vr6+ODk5YWtrS5o0aZg6dSp58+Zl1KhRjBo1ChcXF5ycnEiWLBkHDhzA3t7+jeOuCbRp04ZZs2ZRpkwZdu3aZZaHhYXh5+fH8OHDOXHiBBkyZCAqKooVK1ZQuHBhK0YsMX5vvGSN8Rj/tGjRgmPHjpEtWzYePHhAr169SJEiBTVq1KBixYr06tULJycn5s+fz9y5czl06JB6+cl7T8lHeWte/cI8cuQIM2fOZNGiRcyfP58vvvjCrHfgwAH69etH0qRJWbx4MU5OTtYKWUQkwXr1h3tgYCC2trZky5aNU6dOcebMGT777DPGjBlDmzZteP78OePGjaNDhw4kT57c3IYmlhGRv+vV88auXbvo1q0bkydPpmDBguzfvx9fX18OHTrEmjVrKFiwINevX+fYsWO4uLhQtmxZbG1tlYz5jVff0wEDBnDt2jXOnDlDwYIFmTNnzmv19+3bR9KkSXF3dydt2rRvO1z5G/Q9a12/vckRFhaGo6MjmzZtYsWKFTRo0ICZM2fy8OFDvv76a3LmzEn9+vUJCQnB0dERgJUrVyrBLwmCko8S52K+FMPCwrCzs8PW1haAX3/9ldGjR/Pzzz8zePBg6tata67z888/kz59ejJkyGCtsEVEEqxXf8z07NmTu3fv8sMPP1C7dm0Mw8DPz49JkybRvHlz4OWM119++SWdO3eOdS4XEfmn5s2bx+HDhwkNDeX77783y0+ePEn//v1JmjQps2bNeu0mdUwrbInNz8+P5MmTU6RIEV68eMHs2bPx9fXFw8ODuXPnmvWuXr1KunTpzMSIiLxZTOLx5s2bHD16lFq1apnLHjx4QOnSpWnfvj2ff/45bdu25dGjR/Tq1YuKFSty6tQpnj9/To4cOZTglwRDfREkTsX8gN28eTN169alfPny1KpVixMnTpArVy6++eYbPD096devH6tXrzbXK1asmBKPIiJWcOfOnVitjvz8/GjXrh2GYeDp6cmRI0eoUqWKmXh8/vw5bdu2JVGiRLEuvEVE/o6Y9hAx//7444/MmDGDY8eOmTPBAhQoUICyZcuyd+9ewsLCXtuOEo+vCwsL4/vvv6do0aLs37+fJEmS0KRJE5o3b86xY8do3rw54eHhDBgwgK+++orQ0FBrhywS78UkHgsVKkSdOnXw9vZm+fLlXLhwgdSpUzN69GiWLVuGxWJh6NChpEyZkjFjxrBkyRI8PDwoXbq0Eo+SoCj5KHEmJvG4YcMGatasSY4cOShXrhwBAQF89tlnLFu2jJw5c9K5c2dKly5Nu3btWLdunbXDFhFJsCZNmoSnpyfR0dGsWbOGBQsWULJkSTw9PbFYLLRr145q1apx8eJFSpcuTfPmzalcuTK3bt1i8+bN2NraEhUVZe3dEJF3THR0tHnT4+bNm8DL2Zg7duzIjRs3mDt3Lo8fPzbrFy5cmKRJk/LkyROrxPuuiEnkOjo6MmrUKJo0aUL58uXZt28fyZMnp3HjxrRu3Zq9e/eSI0cOZs2axZgxY3B1dbVy5CLvhujoaLJmzUrx4sUJCAjAz8+PypUrM2vWLEJCQnB1deXo0aPkypWLIUOGYGtry8qVKwkMDLR26CJvnbpdy38mpun5q2NfBAcH8+mnn1KyZEmGDh1q1v3yyy/Zv38/W7ZsIVeuXBw+fJiFCxfSqVMnsmXLZq1dEBFJsGbOnEmnTp2YN28e9evXp1KlSuzfv5/ixYuzc+dOs96zZ8/YuHEjW7Zswd7enixZstCzZ0/s7Ow0zpqI/G2vXjeOGDGCw4cP0717d0qWLAm8nLhhz549NGjQgPr162NnZ0f79u0JCQnhp59+0qQyf+DFixckSZLEbBBw69Ytvv32W5YvX87OnTspWbIkz5494+bNm5w8eZLixYuTJUsWa4ct8k65ePEi33zzDdHR0TRp0gSLxcLEiRNJliwZ69ato2jRovz00084ODhw/vx5kiZNqh5+kiAp+Sj/iZgLx2vXrrFt2zYKFy5MkSJFCA0NxdPTk1atWtGuXTtzEF4ADw8PcuXKxcKFCwFiLRMRkbdn9uzZtG/fnmXLllGzZk3g5c2jpk2bcu7cOXx8fGjXrt0fDmqvcdZE5N/o2bMn8+bNY+bMmRQqVChWEqxly5bMnz8fV1dXypUrh2EYLFq0CEdHR81q/TuOHTtGjRo12L59O7ly5TITkDdv3qRr165s2LCB/fv3a6ILkf/A+fPn6dKlC1FRUUyePJn06dNz+vRphg0bRr169WjUqJEmB5IET9/U8q/FXPSdPn0aLy8vtmzZwv379wFIlCgRSZIkYdu2bcDLbh8x4/OUKFGC58+fm9tR4lFE5O3bvXs3rVu3pk+fPmbiEaBfv36kT5+eXLlysXTpUnx9fc1lkZGRr21HiUcR+ae2bNnCihUr2Lx5M7Vq1SJTpkwEBgayZ88eAObMmUPbtm2xtbWlQoUKzJ07F0dHR8LDwxN84jE6OjrW85ihLwzD4MMPP6R69eqcP38ei8VCdHQ0GTNmpG3btoSFhVGkSBEOHz5sjbBF3is5c+Zk4sSJAHTo0IETJ05QvHhx1q9fT6NGjQCUeJQEL2F/W8t/wsbGhnPnzlGmTBlq167NlClTqFatmrm8T58+/PLLL3Tp0gX4X5Lx8ePHODs7ExUVhRrgiohYR/r06SlVqhT+/v4cPXoUgDp16rBp0yZGjRrFzJkzcXNzY/78+WYCUl2rReS/9OjRIxwdHfHw8ODs2bMMHTqUIkWKUK1aNT777DPg5Zi0Xl5eTJgwgVWrVvHkyRMcHBysHLn1xVyH9+nTh+vXr5sJDg8PD8aPH0/OnDmpVKkS586dMxO16dKl4/PPP6d9+/Y4OztbM3yR90aOHDmYMmUKNjY2DBkyhH379lk7JJF4Rd2u5V8LDQ2lSZMmpEmThilTppjlERERPH78mGvXrnHgwAHmz59PqlSpKFOmDFevXmX58uX8/PPP5MmTx4rRi4jIxYsX6dixI7a2tgQGBvLixQtWrVpldnsMCAigY8eOnD59mrFjx+Lt7W3dgEXknfWmbtKHDx+mYcOGpEiRgjt37lCpUiWKFStGgQIFKFGiBJs3b8bLywuAVq1asXbtWsaPH0+jRo0SfGuiiIgISpYsydGjR8mePTufffYZRYoUoV69esDL7qCdOnXi9OnT/Pjjj2TJkoUpU6Zw/PhxFi1aRNKkSa28ByLvl4sXL9K1a1cePnzIhAkTKF68uLVDEokX1HRB/jU7OzsCAgIoXbq0WbZ161a2bNnCnDlzyJw5M4kTJ2bMmDHMmDGD7du3kyJFCg4ePKjEo4hIPJAjRw4mTZpEu3btOH36NLNnzzYTjxEREbi7u/Pdd98xdepUqlSpYt1gReSd9Wri8ZdffsHR0ZHMmTNTpEgRpk+fzvr16ylZsiRly5YlTZo03L59m6JFi5IyZUpzXNnZs2fj6OhIiRIlEnziEcDe3p7PP/+cBg0akDdvXvbv30/btm358ccfKVu2LC1btmTixIkMGzaMjz/+mFy5cnHr1i1++uknJR5F4kCOHDkYM2YM/fr1I126dNYORyTeUMtH+deCgoIoVqwYn3zyCd26dWP16tXMnz+fvHnz8sknn+Dk5MTYsWOpW7cuQ4YMwTAMIiIi1FVGRCSeuXz5Mj4+PtjY2NC7d29KlSoFvExA2tvbm/U0uYyI/Bu9evVi9erV3L59m7p16/LVV19Rvnx5c3lERARBQUF89dVXPH78mL1792JjY0NkZKSGfXiD3bt389lnn7Fjxw6KFCnC3bt3mTVrFiNHjsTDw4OmTZtSrlw57t27x8OHDylQoIBmtRaJY+Hh4fq9K/IKJR/lP7Fz5068vLxInz49jx8/ZsyYMVSoUIHs2bMTERFB9erVSZ06tTmztYiIxE8xXbAB+vbtS8mSJa0ckYi8615t8bh+/Xo6d+7M9OnTuXv3LvPnz8fOzo42bdpQu3ZtIiMjWbRoEfPmzePZs2ccOHAAe3t7zWr9J3r06MHdu3eZM2cOiRIlon79+pw8eZKPP/6Y69evc/DgQcaNG0eHDh2sHaqIiCRAunUo/4ny5ctz5coV7t+/T+bMmUmVKpW5zNbWFldXV7JmzWpOLKNuMiIi8VNMF+wuXbrQuXNn5s6dS/78+a0dloi8w2KShn5+fmzbto2OHTtSuXJlAPLkycOgQYOYMWMGNjY21KxZEycnJypXrkyPHj2ws7NTi8e/oFixYowfPx4HBwdatmzJ7t272bFjB3ny5OH8+fNs3bo1VutSERGRt0ktHyVOhYeHM2TIEL7//nt2795Njhw5rB2SiIj8Bb/++itz5sxhzJgxam0kIv/a5cuXqV69Ojdu3KBDhw6MHDnSXHb06FEGDRpEeHg4rVu3pnbt2uYyDfPw15UpU4Z9+/bh7u7Opk2bKFCggLVDEhERAZR8lDi0cOFCjhw5wrJly9i8eTOFChWydkgiIvIPqLujiPwXdu7cyTfffIPFYmHUqFGULVvWXObv70/79u0pWrQoEydOtF6Q7yDDMLBYLGzatIkuXbowatQoatasaZaLiIhYm35JSJw4f/48c+fO5ebNm+zatUuJRxGRd5gSjyLyd0RHR7+xvHz58gwZMgSLxcK0adPYu3evuczDwwNfX18mTJjwtsJ8b8QkGD08PIiOjsbf3z9WuYiIiLWp5aPEmfv37+Po6Iirq6u1QxERERGRt+DVltK+vr6cPn2aqKgovL29qVixIjY2NmzatIkhQ4aQMWNGOnbsSKlSpX53G/L3LFy4kDZt2rBz506KFi1q7XBEREQAtXyUOJQmTRolHkVEREQSkJikYa9evejVqxchISGcOnWKgQMHMmLECKKjo6lWrRr9+vXjzp07DBw4kJMnT75xG/L3lStXjo8//ph06dJZOxQRERGTpo0TEREREZH/zKxZs1ixYgWbN2/Gw8ODFStW0KBBA549e0ZoaCiDBg2iWrVqhIaGsnXrVvLly2ftkN8b6dOnZ/PmzSRKlMjaoYiIiJjU7VpERERERP6xV2ekjoiIYNq0aQQHB9O3b1/WrFlD8+bN6dOnD+fOnWPDhg34+PjQu3fvWLNYq6u1iIjI+0vf8CIiVnTx4kUqV66Mq6srFouFtWvXWjWesmXLkjdvXqvGEFeuXbuGxWJh3rx5/9k2Bw4cqAH9RSTBi0kiTps2jYMHD/Lll1/SokULbty4Qb9+/ejXrx/du3enS5cuREVFMXv2bGbOnAm8nKkZ1NVaRETkfaZveZEEYt68eVgsljc+vvnmG2uHl2A1bdqU06dPM2zYMH744QeKFCnyxnoxiTOLxcKqVateWx6TBHv48OFryzZs2ECVKlVImTIliRIl4sMPP6R79+48evToP98fERFJOLZu3cqvv/4KwNSpU+nVqxcpUqQgRYoUpE2blrNnzxIREUGdOnUAePr0KWXKlOGbb76hTZs2gGZkFhERSQg05qNIAjN48GCyZs0aq+x9bekW34WEhHDw4EH69OlD+/bt//J6gwcPpnbt2n/pB1v37t0ZN24cBQoUMH8UHjt2jClTprB06VJ27NhBzpw5/81uiIhIAjR9+nR69+7NqVOnOHLkCC9evGDGjBnkzZuX6Oho4GVrRovFwqZNm6hRowYjR44kU6ZMtG3bFovFEqu7toiIiLy/lHwUSWCqVq36u63rfis0NBQHBwd1hYojDx48ACBZsmR/eZ2CBQty4sQJ1qxZQ+3atf+w7pIlSxg3bhz16tVj0aJFsX7gffXVV5QrV47PP/+cY8eOYWenrwMREflrZs6cSadOnVi0aBEAxYoVA162foT/daEuXLgwHh4ejBo1iqFDh5I2bVpWr16NxWLBMAwlHkVERBIIZRREBIDdu3djsVhYunQpffv2JX369CRJkoSgoCAAfv75Z6pUqYKrqytJkiShTJky7N+//7Xt7Nu3j48//phEiRKRLVs2Zs6c+dq4eH809p7FYmHgwIGxym7fvk3z5s1xc3PD0dGRPHny8P33378x/uXLlzNs2DAyZMhAokSJqFChApcuXXrtdX7++WeqVatG8uTJSZo0Kfnz52fixIkA+Pr6YrFYOH78+GvrDR8+HFtbW27fvv2H7+fx48epWrUqLi4uODk5UaFCBQ4dOmQuHzhwIJkzZwagR48eWCwWsmTJ8ofbBKhfvz4ffvghgwcP5s/mCxs0aBDJkydn1qxZr/3AK1q0KL169eL06dOsXLnytXX9/f0pUaIEiRMnJmvWrMyYMeO1OmFhYQwYMIDs2bPj6OhIxowZ6dmzJ2FhYWadMmXKUKBAgTfGlzNnTry8vMzn0dHRfPfdd+TJk4dEiRLh5uZG69atefLkSaz1jh49ipeXF6lSpTLja968eaw6T58+5auvvsLV1ZVkyZLRtGlTnj59+loMp06d4quvvuKDDz4gUaJEuLu707x58zd2SX/TsS0iktAsW7aMtm3bsnLlSj7//HMyZszIihUrSJIkCYcOHeLZs2fAy7EcU6VKxaRJk1i+fDlz5szh559/xt7ensjISHW3FhERSUDU1EUkgQkMDHxtXMBUqVKZ/x8yZAgODg50796dsLAwHBwc2LlzJ1WrVsXDw4MBAwZgY2ODr68v5cuXZ+/evRQtWhSA06dPU7lyZVKnTs3AgQOJjIxkwIABuLm5/eN47927R/HixbFYLLRv357UqVOzefNmWrRoQVBQEJ07d45Vf+TIkdjY2NC9e3cCAwMZPXo0DRs25Oeffzbr+Pn5Ub16ddKmTUunTp1wd3fn119/ZcOGDXTq1Im6devi4+PDokWLKFSoUKztL1q0iLJly5I+ffrfjfnMmTN88sknuLi40LNnT+zt7Zk5cyZly5Zlz549FCtWjNq1a5MsWTK6dOlCgwYNqFatGk5OTn/6ftja2tK3b1+aNGnyh60fL168yPnz5/nqq69wcXF5Y50mTZowYMAANmzYQP369c3yJ0+eUK1aNb744gsaNGjA8uXLadu2LQ4ODmaSLzo6mk8//ZR9+/bx9ddfkytXLk6fPs2ECRO4cOGCOXFO48aNadWqFb/88kus7v1HjhzhwoUL9O3b1yxr3bo18+bNo1mzZnTs2JGrV68yZcoUjh8/zv79+7G3t+f+/fvmMfbNN9+QLFkyrl27xurVq83tGIbBZ599xr59+2jTpg25cuVizZo1NG3a9LX3wM/PjytXrtCsWTPc3d05c+YMs2bN4syZMxw6dMj8cRwXx7aIyLtm1qxZtGnTBnt7exYuXMinn34KQJ06dYiIiKBx48akT5+ewYMHY2dnh2EYpEyZkpQpU5rbiIqKUmt7ERGRhMYQkQTB19fXAN74MAzD2LVrlwEYH3zwgfHixQtzvejoaCNHjhyGl5eXER0dbZa/ePHCyJo1q1GpUiWzrGbNmkaiRImM69evm2Vnz541bG1tjVdPN1evXjUAw9fX97U4AWPAgAHm8xYtWhhp06Y1Hj58GKte/fr1DVdXVzPWmPhz5cplhIWFmfUmTpxoAMbp06cNwzCMyMhII2vWrEbmzJmNJ0+exNrmq/vXoEEDI126dEZUVJRZduzYsd+N+1U1a9Y0HBwcjMuXL5tld+7cMZydnY3SpUu/9j6MGTPmD7f327qRkZFGjhw5jAIFCpgxDxgwwACMBw8eGIZhGGvXrjUAY8KECX+4XRcXF6Nw4cLm8zJlyhiAMW7cOLMsLCzMKFiwoJEmTRojPDzcMAzD+OGHHwwbGxtj7969sbY3Y8YMAzD2799vGIZhPH361EiUKJHRq1evWPU6duxoJE2a1Hj27JlhGIaxd+9eAzAWLVoUq96WLVtila9Zs8YAjCNHjvzuPsXs++jRo82yyMhI45NPPnnt83v1WI+xZMkSAzB++ukns+yvHtsiIu+r6dOnG3Z2dsbq1auNY8eOGe7u7kb16tVj1Vm0aJFha2tr9O7d24iIiLBSpCIiIhLfqNu1SAIzdepU/Pz8Yj1e1bRpUxInTmw+P3HiBBcvXuTLL7/k0aNHPHz4kIcPH/L8+XMqVKjATz/9RHR0NFFRUWzdupWaNWuSKVMmc/1cuXLF6lr7dxiGwapVq6hRowaGYZiv/fDhQ7y8vAgMDOTYsWOx1mnWrBkODg7m808++QSAK1euAC+7Q1+9epXOnTu/Ntbiq13AmjRpwp07d9i1a5dZtmjRIhInTmzO2vkmUVFRbNu2jZo1a/LBBx+Y5WnTpuXLL79k3759Zlf2fyqm9ePJkyfNFoa/FRwcDICzs/MfbsvZ2fm1eOzs7GjdurX53MHBgdatW3P//n38/f0BWLFiBbly5eKjjz6K9bmUL18ewHzfXF1d+eyzz1iyZInZTTwqKoply5ZRs2ZNkiZNam7P1dWVSpUqxdqeh4cHTk5O5vZiPrMNGzYQERHxxn3atGkTdnZ2tG3bNtZ71qFDh9fqvnqsh4aG8vDhQ4oXLw5gHltxcWyLiLwrDMPg/Pnz9OjRg+XLl1OrVi0KFCjA0qVLOXr0KDVq1DDrfvnllyxYsIBx48bRpUsXc+IZERERSdiUfBRJYIoWLUrFihVjPV7125mwL168CLxMSqZOnTrWY86cOYSFhREYGMiDBw8ICQkhR44cr73mP51N+cGDBzx9+pRZs2a99trNmjUD4P79+7HWeTU5BJA8eXIAc9zAy5cvA38+w3elSpVImzatOZh+dHQ0S5Ys4bPPPvvDhN6DBw948eLFG/c5V65cREdHc/PmzT987b+iYcOGZM+e/XfHfoyJMSYJ+XuCg4Nf25906dKZScEYH374IfByvE54eVycOXPmtc8lpt6rn0uTJk24ceMGe/fuBWD79u3cu3ePxo0bm3UuXrxIYGAgadKkeW2bz549M7dXpkwZ6tSpw6BBg0iVKhWfffYZvr6+scaZvH79OmnTpn2tG/ubPpPHjx/TqVMn3NzcSJw4MalTpzb/BgIDAwHi5NgWEXlXWCwWcubMyZkzZ6hVqxaGYWBjY0Pp0qVZtmzZGxOQU6ZM4eTJkxrXUURERACN+Sgiv/FqSzDAbLUwZswYChYs+MZ1nJycYiV//szv/RiJiop642s3atTojeP1AeTPnz/W89+bOfNNCbo/Ymtry5dffsns2bOZNm0a+/fv586dOzRq1OhvbSeuxLR+/Oqrr1i3bt1ry3PlygW8nFDl91y/fp2goCBy5879t18/OjqafPnyMX78+Dcuz5gxo/l/Ly8v3NzcWLhwIaVLl2bhwoW4u7vHSnxHR0eTJk0aM9n7W6lTpwZeHjsrV67k0KFDrF+/nq1bt9K8eXPGjRvHoUOH/tK4ma/64osvOHDgAD169KBgwYI4OTkRHR1NlSpV1GJHRISX52cbGxvz5l7Md7jFYjETkPXq1ePTTz/lxx9/BKBly5a0bNkSePn9qySkiIhIwqbko4j8oWzZsgHg4uLyWivJV6VOnZrEiRObLSVfdf78+VjPY1oj/nb24evXr7+2TWdnZ6Kiov7wtf+OmP355Zdf/nSbTZo0Ydy4caxfv57NmzeTOnXqP+1mmzp1apIkSfLaPgOcO3cOGxubWIm5f6NRo0YMHTqUQYMGmYP+x/jwww/58MMPWbt2LRMnTnxja80FCxYAUL169Vjld+7c4fnz57FaP164cAHAnJE7W7ZsnDx5kgoVKvzpj8qYRO68efMYNWoUa9eupVWrVrESxdmyZWP79u2ULFnytQT4mxQvXpzixYszbNgwFi9eTMOGDVm6dCktW7Ykc+bM7Nixg2fPnsVKRv72M3ny5Ak7duxg0KBB9O/f3yz/7TH8d45tEZH3jY3NH3eUiklAfvnll5QoUYIDBw7EWq7Eo4iIiKjbtYj8IQ8PD7Jly8bYsWN59uzZa8sfPHgAvEwweXl5sXbtWm7cuGEu//XXX9m6dWusdVxcXEiVKhU//fRTrPJp06bFem5ra0udOnVYtWoVv/zyy+++9t9RuHBhsmbNynffffda8vO3rSPz589P/vz5mTNnDqtWraJ+/fp/OkOnra0tlStXZt26dWYXZXg5a/fixYspVarU784+/XfFtH48ceKE2drkVf379+fJkye0adPmtVal/v7+jBo1irx58742hmVkZCQzZ840n4eHhzNz5kxSp06Nh4cH8LLF4O3bt5k9e/ZrrxsSEsLz589jlTVu3JgnT57QunVrnj179loL0i+++IKoqCiGDBny2vYiIyPNz+rJkyevfU4xLXJjWt9Wq1aNyMhIpk+fbtaJiopi8uTJsdaLSX7+dnvffffda/X+6rEtIpIQlS5dGl9fX1KlSqVW4yIiIvIatXwUkT9kY2PDnDlzqFq1Knny5KFZs2akT5+e27dvs2vXLlxcXFi/fj0AgwYNYsuWLXzyySe0a9eOyMhIJk+eTJ48eV7r/tuyZUtGjhxJy5YtKVKkCD/99JPZuu5VI0eOZNeuXRQrVoxWrVqRO3duHj9+zLFjx9i+fTuPHz/+2/szffp0atSoQcGCBWnWrBlp06bl3LlznDlz5rVkUpMmTejevTvAX+5yPXToUPz8/ChVqhTt2rXDzs6OmTNnEhYWxujRo/9WvH+mYcOGDBkyhBMnTrxx2ZEjR5g4cSJnz56lYcOGJE+enGPHjvH999+TMmVKVq5cib29faz10qVLx6hRo7h27Roffvghy5Yt48SJE8yaNcus27hxY5YvX06bNm3YtWsXJUuWJCoqinPnzrF8+XK2bt1KkSJFzG0WKlSIvHnzmhPVFC5cONZrlilThtatWzNixAhOnDhB5cqVsbe35+LFi6xYsYKJEydSt25d5s+fz7Rp06hVqxbZsmUjODiY2bNn4+LiQrVq1QCoUaMGJUuW5JtvvuHatWvkzp2b1atXm2M4xnBxcaF06dKMHj2aiIgI0qdPz7Zt27h69epr7+XfObZFRBKiSpUqUalSJeB/XbVFREREALDSLNsi8pb5+voagHHkyJE3Lt+1a5cBGCtWrHjj8uPHjxu1a9c2UqZMaTg6OhqZM2c2vvjiC2PHjh2x6u3Zs8fw8PAwHBwcjA8++MCYMWOGMWDAAOO3p5sXL14YLVq0MFxdXQ1nZ2fjiy++MO7fv28AxoABA2LVvXfvnuHj42NkzJjRsLe3N9zd3Y0KFSoYs2bN+tP4r169agCGr69vrPJ9+/YZlSpVMpydnY2kSZMa+fPnNyZPnvzaft+9e9ewtbU1Pvzwwze+L7/n2LFjhpeXl+Hk5GQkSZLEKFeunHHgwIE3xjZmzJg/3d4f1Y35bAHjwYMHry1fu3atUalSJSN58uSGo6OjkT17dqNbt25vrFumTBkjT548xtGjRw1PT08jUaJERubMmY0pU6a8Vjc8PNwYNWqUkSdPHsPR0dFInjy54eHhYQwaNMgIDAx8rf7o0aMNwBg+fPjv7uesWbMMDw8PI3HixIazs7ORL18+o2fPnsadO3cMw3j5vjZo0MDIlCmT4ejoaKRJk8aoXr26cfTo0VjbefTokdG4cWPDxcXFcHV1NRo3bmwcP378tWPh1q1bRq1atYxkyZIZrq6uxueff27cuXPnjcfhXz22RUTeVVFRUdYOQURERN5DFsP4m7MwiIj8TQMHDmTQoEF/e9KX+ODhw4ekTZuW/v37069fP2uH806bOHEiXbp04dq1a6/NSi4iItb1amvFCxcukDlzZhwdHa0clYiIiLwP1B9CROQPzJs3j6ioKBo3bmztUN5phmEwd+5cypQpo8SjiEg882risX///jRp0oS9e/cSHh7+h+u9elPxt0NbiIiIiMTQmI8iIm+wc+dOzp49y7Bhw6hZs6Y5y7P8Pc+fP+fHH39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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preprocessing"
      ],
      "metadata": {
        "id": "HCmmw1StQ-3y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = pd.read_csv('train.csv')\n",
        "df_test = pd.read_csv('test.csv')"
      ],
      "metadata": {
        "id": "vRwYfijmhK9V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Add BMI column\n",
        "df_train['BMI'] = df_train['Weight'] / (df_train['Height']) ** 2\n",
        "df_test['BMI'] = df_test['Weight'] / (df_test['Height']) ** 2"
      ],
      "metadata": {
        "id": "62yCf-fTiQ-j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "print(df_train.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3tfeaCm2hUXi",
        "outputId": "3530eeeb-ba85-4479-e65c-137cfee2d4ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id                                0\n",
            "Gender                            0\n",
            "Age                               0\n",
            "Height                            0\n",
            "Weight                            0\n",
            "family_history_with_overweight    0\n",
            "FAVC                              0\n",
            "FCVC                              0\n",
            "NCP                               0\n",
            "CAEC                              0\n",
            "SMOKE                             0\n",
            "CH2O                              0\n",
            "SCC                               0\n",
            "FAF                               0\n",
            "TUE                               0\n",
            "CALC                              0\n",
            "MTRANS                            0\n",
            "NObeyesdad                        0\n",
            "BMI                               0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define function to round column\n",
        "def round_column(df: pd.DataFrame,\n",
        "                 column: str,\n",
        "                 n_decimals: int = 0) -> pd.DataFrame:\n",
        "\n",
        "    df[column] = df[column].round(n_decimals)\n",
        "    return df\n",
        "\n",
        "# Round columns 'FCVC' and 'NCP' to 1 decimal\n",
        "df_train = round_column(df_train, 'FCVC')\n",
        "df_train = round_column(df_train, 'NCP')\n",
        "df_test = round_column(df_test, 'FCVC')\n",
        "df_test = round_column(df_test, 'NCP')\n",
        "\n",
        "# print unique values of 'FCVC' and 'NCP' for train dataset\n",
        "print(f'{np.unique(df_train[\"FCVC\"]) = }')\n",
        "print(f'{np.unique(df_train[\"NCP\"]) = }')\n",
        "\n",
        "# print unique values of 'FCVC' and 'NCP' for test dataset\n",
        "print(f'{np.unique(df_test[\"FCVC\"]) = }')\n",
        "print(f'{np.unique(df_test[\"NCP\"]) = }')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4w2tvoqFiIdl",
        "outputId": "17e74847-843e-4f0c-da83-060f38da8561"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "np.unique(df_train[\"FCVC\"]) = array([1., 2., 3.])\n",
            "np.unique(df_train[\"NCP\"]) = array([1., 2., 3., 4.])\n",
            "np.unique(df_test[\"FCVC\"]) = array([1., 2., 3.])\n",
            "np.unique(df_test[\"NCP\"]) = array([1., 2., 3., 4.])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "print(df_train.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vre8E78vGbn3",
        "outputId": "12b0c393-c372-45a8-883c-426fd62adaef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id                                0\n",
            "Gender                            0\n",
            "Age                               0\n",
            "Height                            0\n",
            "Weight                            0\n",
            "family_history_with_overweight    0\n",
            "FAVC                              0\n",
            "FCVC                              0\n",
            "NCP                               0\n",
            "CAEC                              0\n",
            "SMOKE                             0\n",
            "CH2O                              0\n",
            "SCC                               0\n",
            "FAF                               0\n",
            "TUE                               0\n",
            "CALC                              0\n",
            "MTRANS                            0\n",
            "NObeyesdad                        0\n",
            "BMI                               0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert Gender to binary\n",
        "df_train['Gender'] = df_train['Gender'].map({'Male': 1, 'Female': 0})\n",
        "df_test['Gender'] = df_test['Gender'].map({'Male': 1, 'Female': 0})\n",
        "\n",
        "# convert age to categorical\n",
        "age_mapping = {'0-18': 0, '18-30': 1, '30-40': 2, '40-50': 3, '50-60': 4, '60-70': 5, '70-80': 6}\n",
        "df_train['Age'] = df_train['Age'].map(age_mapping)\n",
        "df_test['Age'] = df_train['Age'].map(age_mapping)\n",
        "\n",
        "df_train['Age'] = pd.cut(df_train['Age'], bins=[0, 18, 30, 40, 50, 60, 70, 80], labels=['0-18', '18-30', '30-40', '40-50', '50-60', '60-70', '70-80'])\n",
        "df_test['Age'] = pd.cut(df_test['Age'], bins=[0, 18, 30, 40, 50, 60, 70, 80], labels=['0-18', '18-30', '30-40', '40-50', '50-60', '60-70', '70-80'])\n",
        "\n",
        "# convert BMI to categorical\n",
        "df_train['BMI'] = pd.to_numeric(df_train['BMI'], errors='coerce')\n",
        "df_test['BMI'] = pd.to_numeric(df_train['BMI'], errors='coerce')\n",
        "\n",
        "df_train['BMI'] = pd.cut(df_train['BMI'], bins=[0, 18.5, 25, 30, 35, 40, 60], labels=['Underweight', 'Normal', 'Overweight', 'Obesity I', 'Obesity II', 'Obesity III'])\n",
        "df_test['BMI'] = pd.cut(df_test['BMI'], bins=[0, 18.5, 25, 30, 35, 40, 60], labels=['Underweight', 'Normal', 'Overweight', 'Obesity I', 'Obesity II', 'Obesity III'])\n",
        "\n",
        "# convert family_history_with_overweight to binary (yes=1, no=0)\n",
        "df_train['family_history_with_overweight'] = df_train['family_history_with_overweight'].map({'yes': 1, 'no': 0})\n",
        "df_test['family_history_with_overweight'] = df_test['family_history_with_overweight'].map({'yes': 1, 'no': 0})\n",
        "\n",
        "# convert SMOKE to binary (yes=1, no=0)\n",
        "df_train['SMOKE'] = df_train['SMOKE'].map({'yes': 1, 'no': 0})\n",
        "df_test['SMOKE'] = df_test['SMOKE'].map({'yes': 1, 'no': 0})\n",
        "\n",
        "# convert SCC to binary (yes=1, no=0)\n",
        "df_train['SCC'] = df_train['SCC'].map({'yes': 1, 'no': 0})\n",
        "df_test['SCC'] = df_test['SCC'].map({'yes': 1, 'no': 0})\n",
        "\n",
        "# convert FAVC to binary (yes=1, no=0)\n",
        "df_train['FAVC'] = df_train['FAVC'].map({'yes': 1, 'no': 0})\n",
        "df_test['FAVC'] = df_test['FAVC'].map({'yes': 1, 'no': 0})\n",
        "\n",
        "# convert CALC to binary (always=3, frequently=2, sometimes=1, no=0)\n",
        "df_train['CALC'] = df_train['CALC'].map({'Always': 3, 'Frequently': 2, 'Sometimes': 1, 'no': 0})\n",
        "df_test['CALC'] = df_test['CALC'].map({'Always': 3, 'Frequently': 2, 'Sometimes': 1, 'no': 0})\n",
        "\n",
        "# convert CAEC to binary (no=0, Sometimes=1, Frequently=2, Always=3)\n",
        "df_train['CAEC'] = df_train['CAEC'].map({'no': 0, 'Sometimes': 1, 'Frequently': 2, 'Always': 3})\n",
        "df_test['CAEC'] = df_test['CAEC'].map({'no': 0, 'Sometimes': 1, 'Frequently': 2, 'Always': 3})\n",
        "\n",
        "# convert MTRANS to dummy variables with fusioning motobike and bike into automobile\n",
        "df_train = pd.get_dummies(df_train, columns=['MTRANS'])\n",
        "df_test = pd.get_dummies(df_test, columns=['MTRANS'])\n",
        "\n",
        "# round CH2O to 0 decimals\n",
        "df_train = round_column(df_train, 'CH2O')\n",
        "df_test = round_column(df_test, 'CH2O')\n",
        "\n",
        "# round TUE to 1 decimal\n",
        "df_train = round_column(df_train, 'TUE', n_decimals=1)\n",
        "df_test = round_column(df_test, 'TUE', n_decimals=1)\n",
        "\n",
        "# round FAF to 1 decimal\n",
        "df_train = round_column(df_train, 'FAF', n_decimals=1)\n",
        "df_test = round_column(df_test, 'FAF', n_decimals=1)\n",
        "\n",
        "# Put BMI and Age to numerical values\n",
        "df_train['BMI'] = df_train['BMI'].cat.codes\n",
        "df_train['Age'] = df_train['Age'].cat.codes\n",
        "df_test['BMI'] = df_test['BMI'].cat.codes\n",
        "df_test['Age'] = df_test['Age'].cat.codes\n",
        "\n",
        "# delete height and weight columns\n",
        "# df_train = df_train.drop(['Height', 'Weight'], axis=1)\n",
        "# df_test = df_test.drop(['Height', 'Weight'], axis=1)\n",
        "\n",
        "# Add polynomial features\n",
        "polynomial_features = PolynomialFeatures(degree=2, include_bias=False)\n",
        "X_polynomial = polynomial_features.fit_transform(df_train[['Age', 'BMI']])\n",
        "df_train.drop(['Age', 'BMI'], axis=1, inplace=True)\n",
        "df_train = pd.concat([df_train, pd.DataFrame(X_polynomial, columns=['Age', 'BMI', 'Age^2', 'Age*BMI', 'BMI^2'])], axis=1)\n",
        "X_polynomial2 = polynomial_features.fit_transform(df_train[['Weight', 'Height']])\n",
        "df_train.drop(['Weight', 'Height'], axis=1, inplace=True)\n",
        "df_train = pd.concat([df_train, pd.DataFrame(X_polynomial2, columns=['Weight', 'Height', 'Weight^2', 'Weight*Height', 'Height^2'])], axis=1)\n",
        "\n",
        "X_polynomial = polynomial_features.fit_transform(df_test[['Age', 'BMI']])\n",
        "df_test.drop(['Age', 'BMI'], axis=1, inplace=True)\n",
        "df_test = pd.concat([df_test, pd.DataFrame(X_polynomial, columns=['Age', 'BMI', 'Age^2', 'Age*BMI', 'BMI^2'])], axis=1)\n",
        "X_polynomial2 = polynomial_features.fit_transform(df_test[['Weight', 'Height']])\n",
        "df_test.drop(['Weight', 'Height'], axis=1, inplace=True)\n",
        "df_test = pd.concat([df_test, pd.DataFrame(X_polynomial2, columns=['Weight', 'Height', 'Weight^2', 'Weight*Height', 'Height^2'])], axis=1)"
      ],
      "metadata": {
        "id": "7N1RjkVFh9m4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "print(df_train.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "feRngFrbGd7-",
        "outputId": "1e5f3e0c-53cd-498e-ab41-c3ca4e2d4789"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id                                0\n",
            "Gender                            0\n",
            "family_history_with_overweight    0\n",
            "FAVC                              0\n",
            "FCVC                              0\n",
            "NCP                               0\n",
            "CAEC                              0\n",
            "SMOKE                             0\n",
            "CH2O                              0\n",
            "SCC                               0\n",
            "FAF                               0\n",
            "TUE                               0\n",
            "CALC                              0\n",
            "NObeyesdad                        0\n",
            "MTRANS_Automobile                 0\n",
            "MTRANS_Bike                       0\n",
            "MTRANS_Motorbike                  0\n",
            "MTRANS_Public_Transportation      0\n",
            "MTRANS_Walking                    0\n",
            "Age                               0\n",
            "BMI                               0\n",
            "Age^2                             0\n",
            "Age*BMI                           0\n",
            "BMI^2                             0\n",
            "Weight                            0\n",
            "Height                            0\n",
            "Weight^2                          0\n",
            "Weight*Height                     0\n",
            "Height^2                          0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_train['Age'].dtype)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TJNeQxuFjIJU",
        "outputId": "9c004826-9c57-434f-fcf7-89c2b505951e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_train['BMI'].dtype)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oCs6OFFSmMDV",
        "outputId": "4026d283-e0c5-43b3-cd01-dbfedc5f3b6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert NObeyesdad to numerical values\n",
        "df_train['NObeyesdad'] = df_train['NObeyesdad'].map({'Insufficient_Weight': 0, 'Normal_Weight': 1, 'Overweight_Level_I': 2, 'Overweight_Level_II': 3, 'Obesity_Type_I': 4, 'Obesity_Type_II': 5, 'Obesity_Type_III': 6})"
      ],
      "metadata": {
        "id": "g1lMMk6Om8kH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "print(df_train.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9PqN9KAfGgUw",
        "outputId": "59eb8dcd-9d4a-4328-a34c-fc2f9f266655"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id                                0\n",
            "Gender                            0\n",
            "family_history_with_overweight    0\n",
            "FAVC                              0\n",
            "FCVC                              0\n",
            "NCP                               0\n",
            "CAEC                              0\n",
            "SMOKE                             0\n",
            "CH2O                              0\n",
            "SCC                               0\n",
            "FAF                               0\n",
            "TUE                               0\n",
            "CALC                              0\n",
            "NObeyesdad                        0\n",
            "MTRANS_Automobile                 0\n",
            "MTRANS_Bike                       0\n",
            "MTRANS_Motorbike                  0\n",
            "MTRANS_Public_Transportation      0\n",
            "MTRANS_Walking                    0\n",
            "Age                               0\n",
            "BMI                               0\n",
            "Age^2                             0\n",
            "Age*BMI                           0\n",
            "BMI^2                             0\n",
            "Weight                            0\n",
            "Height                            0\n",
            "Weight^2                          0\n",
            "Weight*Height                     0\n",
            "Height^2                          0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "9N1v7OBIm_u_",
        "outputId": "aa337bb2-2c5d-4015-ce46-60422a4218ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          id  Gender  family_history_with_overweight  FAVC  FCVC  NCP  CAEC  \\\n",
              "0          0       1                               1     1   2.0  3.0     1   \n",
              "1          1       0                               1     1   2.0  3.0     2   \n",
              "2          2       0                               1     1   2.0  1.0     1   \n",
              "3          3       0                               1     1   3.0  3.0     1   \n",
              "4          4       1                               1     1   3.0  2.0     1   \n",
              "...      ...     ...                             ...   ...   ...  ...   ...   \n",
              "20753  20753       1                               1     1   3.0  3.0     1   \n",
              "20754  20754       1                               0     1   3.0  4.0     2   \n",
              "20755  20755       1                               1     1   2.0  3.0     1   \n",
              "20756  20756       1                               1     1   3.0  2.0     1   \n",
              "20757  20757       1                               1     1   3.0  3.0     1   \n",
              "\n",
              "       SMOKE  CH2O  SCC  ...  Age  BMI  Age^2  Age*BMI  BMI^2      Weight  \\\n",
              "0          0   3.0    0  ... -1.0  2.0    1.0     -2.0    4.0   81.669950   \n",
              "1          0   2.0    0  ... -1.0  1.0    1.0     -1.0    1.0   57.000000   \n",
              "2          0   2.0    0  ... -1.0  0.0    1.0     -0.0    0.0   50.165754   \n",
              "3          0   2.0    0  ... -1.0  5.0    1.0     -5.0   25.0  131.274851   \n",
              "4          0   2.0    0  ... -1.0  2.0    1.0     -2.0    4.0   93.798055   \n",
              "...      ...   ...  ...  ...  ...  ...    ...      ...    ...         ...   \n",
              "20753      0   2.0    0  ... -1.0  4.0    1.0     -4.0   16.0  114.187096   \n",
              "20754      0   1.0    0  ... -1.0  0.0    1.0     -0.0    0.0   50.000000   \n",
              "20755      0   2.0    0  ... -1.0  3.0    1.0     -3.0    9.0  105.580491   \n",
              "20756      0   2.0    0  ... -1.0  2.0    1.0     -2.0    4.0   83.520113   \n",
              "20757      0   2.0    0  ... -1.0  4.0    1.0     -4.0   16.0  118.134898   \n",
              "\n",
              "         Height      Weight^2  Weight*Height  Height^2  \n",
              "0      1.699998   6669.980733     138.838752  2.889993  \n",
              "1      1.560000   3249.000000      88.920000  2.433600  \n",
              "2      1.711460   2516.602874      85.856681  2.929095  \n",
              "3      1.710730  17233.086505     224.575826  2.926597  \n",
              "4      1.914186   8798.075122     179.546924  3.664108  \n",
              "...         ...           ...            ...       ...  \n",
              "20753  1.766626  13038.692893     201.725893  3.120967  \n",
              "20754  1.710000   2500.000000      85.500000  2.924100  \n",
              "20755  1.819557  11147.240080     192.109721  3.310788  \n",
              "20756  1.700000   6975.609276     141.984192  2.890000  \n",
              "20757  1.816547  13955.854125     214.597595  3.299843  \n",
              "\n",
              "[20758 rows x 29 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e9e2fbd7-3c73-469d-b615-d412a3540c74\" 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>id</th>\n",
              "      <th>Gender</th>\n",
              "      <th>family_history_with_overweight</th>\n",
              "      <th>FAVC</th>\n",
              "      <th>FCVC</th>\n",
              "      <th>NCP</th>\n",
              "      <th>CAEC</th>\n",
              "      <th>SMOKE</th>\n",
              "      <th>CH2O</th>\n",
              "      <th>SCC</th>\n",
              "      <th>...</th>\n",
              "      <th>Age</th>\n",
              "      <th>BMI</th>\n",
              "      <th>Age^2</th>\n",
              "      <th>Age*BMI</th>\n",
              "      <th>BMI^2</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight^2</th>\n",
              "      <th>Weight*Height</th>\n",
              "      <th>Height^2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>-1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>81.669950</td>\n",
              "      <td>1.699998</td>\n",
              "      <td>6669.980733</td>\n",
              "      <td>138.838752</td>\n",
              "      <td>2.889993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>1.560000</td>\n",
              "      <td>3249.000000</td>\n",
              "      <td>88.920000</td>\n",
              "      <td>2.433600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>-1.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.165754</td>\n",
              "      <td>1.711460</td>\n",
              "      <td>2516.602874</td>\n",
              "      <td>85.856681</td>\n",
              "      <td>2.929095</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1.710730</td>\n",
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              "      <td>224.575826</td>\n",
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              "      <th>4</th>\n",
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              "      <td>1</td>\n",
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              "      <td>3.0</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>20753</th>\n",
              "      <td>20753</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>-1.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>114.187096</td>\n",
              "      <td>1.766626</td>\n",
              "      <td>13038.692893</td>\n",
              "      <td>201.725893</td>\n",
              "      <td>3.120967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20754</th>\n",
              "      <td>20754</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1.710000</td>\n",
              "      <td>2500.000000</td>\n",
              "      <td>85.500000</td>\n",
              "      <td>2.924100</td>\n",
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              "    <tr>\n",
              "      <th>20755</th>\n",
              "      <td>20755</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>141.984192</td>\n",
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              "    <tr>\n",
              "      <th>20757</th>\n",
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              "      <td>13955.854125</td>\n",
              "      <td>214.597595</td>\n",
              "      <td>3.299843</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20758 rows × 29 columns</p>\n",
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              "\n",
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              "\n",
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              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      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",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "  <div id=\"id_05f0a786-9a4d-4185-9cf7-c6f44ad5045e\">\n",
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              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_train')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_05f0a786-9a4d-4185-9cf7-c6f44ad5045e 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_train');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_train"
            }
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply SMOTE to balance the dataset\n",
        "X_train = df_train.drop(columns=['NObeyesdad'])\n",
        "y_train = df_train['NObeyesdad']\n",
        "\n",
        "smote = SMOTE(sampling_strategy='auto', random_state=42)\n",
        "X_train, y_train = smote.fit_resample(X_train, y_train)\n",
        "\n",
        "# Create a new dataframe with the balanced dataset\n",
        "df_train = pd.concat([X_train, y_train], axis=1)"
      ],
      "metadata": {
        "id": "__8YnKwPnGsU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Round 1 Models"
      ],
      "metadata": {
        "id": "FEJ13GOzO0kf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1st Model - Random Forest\n"
      ],
      "metadata": {
        "id": "seZSPprFOhc8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_train.drop(['NObeyesdad', 'id'], axis=1)\n",
        "y = df_train['NObeyesdad']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "rf = RandomForestClassifier(random_state=42, max_depth=20, n_estimators=800, criterion='gini')\n",
        "rf.fit(X_train, y_train)\n",
        "\n",
        "y_pred = rf.predict(X_test)\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "# Plot confusion matrix\n",
        "#ax = plt.subplot(1, 1, 1)\n",
        "#_ = utilities.plot_confusion_matrix_heatmap(ax, y_test, y_pred)\n",
        "#from sklearn.metrics import plot_confusion_matrix\n",
        "\n",
        "# Plot confusion matrix\n",
        "#plt.figure(figsize=(10, 7))\n",
        "#plot_confusion_matrix(rf, X_test, y_test, ax=plt.gca(), cmap=plt.cm.Blues, values_format='.0f')\n",
        "#plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bGWZs0KunLwg",
        "outputId": "6f4824b0-753d-4c84-9b3b-a1a549414d4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.94      0.94       809\n",
            "           1       0.86      0.87      0.86       841\n",
            "           2       0.82      0.79      0.80       829\n",
            "           3       0.80      0.84      0.82       801\n",
            "           4       0.89      0.86      0.87       818\n",
            "           5       0.96      0.96      0.96       806\n",
            "           6       1.00      1.00      1.00       761\n",
            "\n",
            "    accuracy                           0.89      5665\n",
            "   macro avg       0.89      0.89      0.89      5665\n",
            "weighted avg       0.89      0.89      0.89      5665\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_confusion_matrix(cm, classes, normalize=False, cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        title = 'Normalized confusion matrix'\n",
        "    else:\n",
        "        title = 'Confusion matrix, without normalization'\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i in range(cm.shape[0]):\n",
        "        for j in range(cm.shape[1]):\n",
        "            plt.text(j, i, format(cm[i, j], fmt),\n",
        "                     horizontalalignment=\"center\",\n",
        "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "    plt.tight_layout()\n",
        "\n",
        "# Compute confusion matrix manually\n",
        "def compute_confusion_matrix(y_true, y_pred, classes):\n",
        "    num_classes = len(classes)\n",
        "    cm = np.zeros((num_classes, num_classes), dtype=int)\n",
        "    for true_label, pred_label in zip(y_true, y_pred):\n",
        "        cm[true_label, pred_label] += 1\n",
        "    return cm\n",
        "\n",
        "# Assuming you have defined rf.classes_ as the list of class labels in your RandomForestClassifier\n",
        "classes = rf.classes_\n",
        "\n",
        "# Assuming you have computed y_pred using your RandomForestClassifier\n",
        "cm = compute_confusion_matrix(y_test, y_pred, classes)\n",
        "\n",
        "# Plot confusion matrix\n",
        "plt.figure(figsize=(10, 7))\n",
        "plot_confusion_matrix(cm, classes=classes)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "id": "kB7HiFt9tuFh",
        "outputId": "431b202b-7a7b-4138-b523-d358a1268121"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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oLi7OMvNQRsiZM2eaCxz9/Vs+SXJxcVH9+vVVv359ffLJJxoxYoTeeustrVmzRg0aNEjzdUh3Bun+3R9//KE8efIYPg3kPd9//70iIiL08ccfW8pu3LiR6to8THecvzt37px69eqlhg0bys3NTa+//rrCw8Mf+P2996F/xYoV2rp1q958801JdwYfT5w4Ufnz55eXl5cqVar0j8d52NeYnvc9PfdfRl77e/4aa7169az2HThwIEN/1u5nz549OnjwoGbMmKHOnTtbylesWPHAx+zdu7d27dql9evXKzAw0Grf5cuXtWrVKg0dOlSDBw+2lN/revZX6bnmhQoVuu97fm8/AKQX3YrgtN544w15eXnphRdeUExMTKr9R44c0aeffipJaty4sSRpzJgxVnXudQ1o0qRJhsVVtGhRxcfH67fffrOUnTt3LtWMSJcuXUr13AoVKkhSqulV78mXL58qVKigGTNmWH0A/P3337V8+XLL68wKXF1dU31jOW7cuFTfYN/7UJsRK8Z2795dKSkpmjp1qiZPnqxs2bKpW7duD/zNaUhIiB555BGNHj1at27dUs2aNSXdaTQcOXJE33//vapXr57qW+S/e9jXmJ733db7715cGb1Sb+XKlRUQEKBJkyZZ3cdLlizR/v37M/Rn7X7ufYP+1/fdbDZbfh+k17Rp0/T5559r/Pjxqlq1qk3nk1L/vpHSdy80btxYW7ZsUVRUlKUsMTFRkydPVuHChRUaGpqOVwEAd5A5gNMqWrSoZs2apbZt26pUqVJWKyRv3LhRc+fOtcwZXr58eUVERGjy5MmKi4tT3bp1tWXLFs2YMUMtW7bUE088kWFxtWvXTgMGDFCrVq3Uu3dvXbt2TRMnTlTx4sWtBiYOGzZM69evV5MmTVSoUCHFxsZqwoQJKlCggNUAxL/78MMP1ahRI4WFhalbt26WKS39/Pyy1Aq8TZs21VdffSU/Pz+FhoYqKipKK1euVO7cua3qVahQQa6urvrggw8UHx8vd3d31atXTwEBAek637Rp07Ro0SJNnz7d0od93Lhx6tixoyZOnKhXXnnFUtdkMqlu3bqW9RT+Se3atTV79myVLVvW0of7sccek5eXlw4ePKjnnnvuX49xL7PQu3dvhYeHy9XV1Wqgty1sfd9tvf/uxbVy5Up98sknyp8/v0JCQqwGmj+I7Nmz64MPPlDXrl1Vt25dtW/f3jKVaeHChdW3b9+HOr4tSpYsqaJFi+r111/XmTNn5Ovrqx9++OGB+t5fuHBBr7zyikJDQ+Xu7q6vv/7aan+rVq3k6+urOnXqaNSoUbp165YeeeQRLV++XMeOHUt1vHv3wltvvaV27dope/bsatasWZoZvzfffFPffvutGjVqpN69eytXrlyaMWOGjh07ph9++IHVlAE8GEPmSALs6ODBg+bu3bubCxcubHZzczP7+PiYa9asaR43bpz5xo0blnq3bt0yDx061BwSEmLOnj27uWDBguaBAwda1TGb70wn2KRJk1TnqVu3rrlu3bqWx/ebytRsNpuXL19uLlOmjNnNzc1cokQJ89dff51qysRVq1aZW7RoYc6fP7/Zzc3NnD9/fnP79u3NBw8eTHWOv06LaTabzStXrjTXrFnT7Onpafb19TU3a9bMvG/fPqs6987396lSp02bdt+pFP/+ekuXLp2q/H7XR5I5MjLS8vjy5cvmrl27mvPkyWP29vY2h4eHm//44480p4mcMmWKuUiRImZXV1eraU3vd657++4d59SpU2Y/Pz9zs2bNUtVr1aqV2cvLy3z06FGz2XxnKkhJ5nbt2v3j679n/PjxZknmHj16WJU3aNDALMm8atUqq/K03rPbt2+be/XqZc6bN6/ZZDJZ7oN/uockmd955x2rMlved7PZtvvPbL4zNW2dOnXMnp6eZkn/OH3nveky586d+6+v12w2m+fMmWOuWLGi2d3d3ZwrVy5zhw4dzKdPn7aqExERYfby8krzfH+/n/56rr9fr7Ri27dvn7lBgwZmb29vc548eczdu3c37969O1Ws/zaV6b1z3m+793N0+vRpc6tWrcz+/v5mPz8/c5s2bcxnz55N83189913zY888ojZxcXF6hhp/WwcOXLE/Mwzz5j9/f3NHh4e5qpVq5oXLlz4r6//r7H//b0B8N9mMpsZiQQA9yxevFhNmzbV7t27VbZsWaPDAQDArsg5AsBfrFmzRu3ataNhAAD4TyJzAAAAAEASmQMAAAAAd9E4AAAAACCJxgEAAACAu2gcAAAAAJDk4IugpaSk6OzZs/Lx8UnXkvMAAAD4Z2azWVeuXFH+/PkdelG9GzduKCkpye7ndXNzk4eHh93P+7AcunFw9uxZFSxY0OgwAAAAnNapU6csK8s7mhs3bsjTJ7d0+5rdzx0UFKRjx445XAPBoRsHPj4+kiS3Ml1lcnUzOBrn8seSEUaH4JQ83VyNDsEpkTkEgIx3JSFBxUIKWj5vOaKkpCTp9jW5h0ZI9vysmJyk6H0zlJSUROPAnu59IDC5usnk6m5wNM7Fx9fX6BCcUg4aB5mCxgEAZB6n+B3r6mbXL5IdeRExh24cAAAAAP/K5HJns+f5HJTjRg4AAAAgQ5E5AAAAgHMzSbJn9ygH7olF5gAAAACAJDIHAAAAcHaMObCZ40YOAAAAIEPROAAAAAAgiW5FAAAAcHYmk50HJDvuiGQyBwAAAAAkkTkAAACAs2NAss0cN3IAAAAAGYrMAQAAAJwbYw5sRuYAAAAAgCQaBwAAAADuolsRAAAAnJydByQ78Pfvjhs5AAAAgAxF5gAAAADOjQHJNiNzAAAAAEASjQMAAAAAd9GtCAAAAM6NFZJt5riRAwAAAMhQZA4AAADg3BiQbDMyBwAAAAAkkTkAAACAs2PMgc0cN3IAAAAAGYrGAQAAAABJdCsCAACAs2NAss3IHAAAAACQROYAAAAAzo4ByTZz3MgBAAAAZCgaBw/hjwWDdX37p6m20QOesdSpVrawlkyK1IVfRilm3QdaMaWXPNyzpzqWW3ZXbZrVX9e3f6pyxR+x58twOJ9+PEp5vLPrrTf6Wcpu3LihN/r20qPBgSoU6K8uzz2r2JgYA6N0Dh+Nel853FzU/7U+Rofi0H7ZsF6tWzZTSHB+eWY36ef5PxkdklOZNGG8ShQrLH9vD9WuUU1bt2wxOiSnwHXNHFxXg5hMf2YP7LIx5uA/qVanj1W44SDL1rjHeEnSjyt3SbrTMJj/2ctatemAanf+RLU6f6xJ321QSkpKqmONeLWFzp1PsGf4DmnH9q2a8eUUlS5T1qp80IDXtGzJIk2dOVvzl65SdPRZdenQxqAoncO2bVs19YvJKlu2nNGhOLzExESVLVdeY8aONzoUpzP3uzka0L+f3hr0jqK27FC5cuXVvEm4YmNjjQ7NoXFdMwfXFY6AxsFDuBCXqJiLVyxb49qldeTUeW3YfliSNOq1Vpowe70+mr5S+49G69CJWP2wYpeSbiVbHadhjVKqX72EBo75yYBX4TiuXr2ql7tFaPRnk+Tnn9NSnhAfr29mTtO7Iz9UncefUIWKlTRu4hfasilK27ZsMjBix3X16lU937mjxk+cLP+cOf/9CfhH4U810pBhw9WiZSujQ3E6Y8d8oq7duqtzl64qFRqqcRMmyTNHDs2Y/qXRoTk0rmvm4LrCEdA4yCDZs7mqXePKmjF/syQpb05vVS1bWOcvXdGaL/vo+PLhWj65l2pUKGL1vIBcPpowqJ26vf21rt24ZUToDmNAv156MryR6j5R36p8184dunXrllX5oyVKqkDBYG2lcfBA+vbuqacaN1a9+g2MDgW4r6SkJO3csd3qPnVxcVG9eg20ZVOUgZE5Nq5r5uC6GszFZP/NQdE4yCDNnygrf29Pfb3gTuMg5JHckqS3XmykL+dFqUWvidr1x2ktnhipogXzWp43echzmvLDr9qx/5QhcTuKH+fO0W+7durtoe+l2hcbGy03Nzf5+ftblecNCGDcwQOYO2e2du3coWHDRxodCvCPLly4oOTkZAUEBFqVBwQGKjo62qCoHB/XNXNwXeEomMo0g0S0qK5lG/fr3IU74wZc7rYYp/64UV/dbTDsPjBPj1ctrogW1TT4s4V6pV0d+Xh56MNpKwyL2xGcOX1Kb73RT98vWCIPDw+jw3Fqp0+dUv/X+mjB4uVcawCA82AqU5tlicjHjx+vwoULy8PDQ9WqVdMWBxu5HxyUU/WqltD0n/5MC95rJOw/av1twIFj0SoYdKcP9+NVHlW1soUVH/Wxrmz+RHt/GiRJ+vWr1zRlaAc7RZ/17d65Q+fPx6pezaoK9PNQoJ+HNv6yXpMnfqZAPw8F5A1UUlKS4uPirJ53PjZWAYGBaR8UadqxY7tiY2NVo1ol+Xhml49ndm1Yv04TPhsnH8/sSk5O/veDAHaSJ08eubq6KjbWOkMYGxOjoKAgg6JyfFzXzMF1haMwvHEwZ84c9evXT++884527Nih8uXLKzzcsUbud2peTbGXr2jJL/ssZSfOXtLZ2DgVLxxgVbdYcIBOnrssSXrtwx9Vtf0oVXvuQ1V77kO1fPXzO8cbOENDxi+03wvI4mo/Xk8bNu/U2o3bLFuFxyrpmbbtLf+fPXt2rV+72vKcQwcP6PSpk6pStbqBkTueJ+rV19Ydv2nT1p2W7bFKldWufQdt2rpTrq6uRocIWLi5uaniY5W0ZvUqS1lKSorWrFmlqtXDDIzMsXFdMwfX1WAmk/03B2V4t6JPPvlE3bt3V9euXSVJkyZN0qJFi/Tll1/qzTffNDi6f2cymdS5eTV9s3CrkpOtpygdPXO1Br3cSHsOntHuA2fUsVlVlSgcoOcG3JmV4FT0Zav6V6/dlCQdPX1BZ2Lj7fMCHICPj49KlS5jVZYjh5dy5cptKe/QuaveHthf/jlzycfXRwNf76Mq1aqrMo2DdPHx8VHpMtbX2svLS7ly50pVDttdvXpVRw4ftjw+fuyYdu/apZy5cik4ONjAyBxf7z791P35CFWqVFmVq1TVZ2PH6FpiojpHdDU6NIfGdc0cXFc4AkMbB0lJSdq+fbsGDhxoKXNxcVGDBg0UFZV65P7Nmzd18+ZNy+OEBOPXBahXrbiC8+XSjPmpZ8X57Nt18nDPrlH9WimnXw7tOXhWTSMn6tjpiwZE6tyGf/CxXFxc1LXjs0q6eVNP1G+oUWPGGR0WIEnasX2bwhs8YXk8oP+dBfw6dorQlC+nGxSVc2jzbFtdOH9ew4YOVkx0tMqVr6D5C5cqkC6FD4Xrmjm4rnAEJrPZbDbq5GfPntUjjzyijRs3Kizsz5TaG2+8oXXr1mnz5s1W9YcMGaKhQ4emOo57+ZdkcnXP9Hj/S06v/9joEJxSDje65WQGkwOnbwEgq0pISFBgbj/Fx8fL19fX6HAeSEJCgvz8/ORe9x2Zstlvog3z7Ru6uW6oQ147w8ccpMfAgQMVHx9v2U6dYvpPAAAAIKMY2q3o3sj9mL/NRR9zn5H77u7ucncnQwAAAIB0sPcgYQfOaBuaOXBzc1OlSpW0apX1yP1Vq1ZZdTMCAAAAkPkMn62oX79+ioiIUOXKlVW1alWNGTNGiYmJltmLAAAAgIfCImg2M7xx0LZtW50/f16DBw9WdHS0KlSooKVLGbkPAAAA2JvhjQNJ6tmzp3r27Gl0GAAAAMB/WpZoHAAAAACZhgHJNnPcDlEAAAAAMhSNAwAAADi3ewOS7bmlQ+HChWUymVJtkZGRkqQbN24oMjJSuXPnlre3t1q3bp1qKYCTJ0+qSZMmypEjhwICAtS/f3/dvn073ZeKxgEAAABgoK1bt+rcuXOWbcWKFZKkNm3aSJL69u2rBQsWaO7cuVq3bp3Onj2rp59+2vL85ORkNWnSRElJSdq4caNmzJih6dOna/DgwemOhTEHAAAAcG5ZfMxB3rx5rR6///77Klq0qOrWrav4+HhNnTpVs2bNUr169SRJ06ZNU6lSpbRp0yZVr15dy5cv1759+7Ry5UoFBgaqQoUKevfddzVgwAANGTJEbm5uNsdC5gAAAADIBAkJCVbbzZs3//U5SUlJ+vrrr/X888/LZDJp+/btunXrlho0aGCpU7JkSQUHBysqKkqSFBUVpbJly1otBRAeHq6EhATt3bs3XTHTOAAAAAAyQcGCBeXn52fZRo4c+a/P+emnnxQXF6cuXbpIkqKjo+Xm5iZ/f3+reoGBgYqOjrbU+fsaYfce36tjK7oVAQAAwMnZeYXku9+/nzp1Sr6+vpZSd3f3f33m1KlT1ahRI+XPnz/TovsnNA4AAACATODr62vVOPg3J06c0MqVK/Xjjz9ayoKCgpSUlKS4uDir7EFMTIyCgoIsdbZs2WJ1rHuzGd2rYyu6FQEAAMC53RuQbM/tAUybNk0BAQFq0qSJpaxSpUrKnj27Vq1aZSk7cOCATp48qbCwMElSWFiY9uzZo9jYWEudFStWyNfXV6GhoemKgcwBAAAAYLCUlBRNmzZNERERypbtz4/ofn5+6tatm/r166dcuXLJ19dXvXr1UlhYmKpXry5JatiwoUJDQ9WpUyeNGjVK0dHRGjRokCIjI23qyvRXNA4AAADg3Ewm+445eIDMwcqVK3Xy5Ek9//zzqfaNHj1aLi4uat26tW7evKnw8HBNmDDBst/V1VULFy5Ujx49FBYWJi8vL0VERGjYsGHpjoPGAQAAAGCwhg0bymw2p7nPw8ND48eP1/jx4+/7/EKFCmnx4sUPHQdjDgAAAABIInMAAAAAZ2ey81Smdp02NWM5buQAAAAAMhSZAwAAADi3h5he9IHP56DIHAAAAACQROMAAAAAwF10KwIAAIBzY0CyzRw3cgAAAAAZiswBAAAAnBsDkm1G5gAAAACAJDIHAAAAcHaMObCZ40YOAAAAIEPROAAAAAAgiW5FAAAAcHYMSLYZmQMAAAAAksgcAAAAwMmZTCaZyBzYhMwBAAAAAElkDgAAAODkyBzYjswBAAAAAEk0DgAAAADcRbciAAAAODfT3c2e53NQZA4AAAAASCJzAAAAACfHgGTbOUXjYO+i9+Tj62t0GE6lQOP3jA7BKZ1a9JbRITglTzdXo0NwSq4ujvvHDQDwYJyicQAAAADcD5kD2zHmAAAAAIAkGgcAAAAA7qJbEQAAAJwa3YpsR+YAAAAAgCQyBwAAAHByZA5sR+YAAAAAgCQyBwAAAHB2prubPc/noMgcAAAAAJBE4wAAAADAXXQrAgAAgFNjQLLtyBwAAAAAkETmAAAAAE7OZJKdMwf2O1VGI3MAAAAAQBKZAwAAADg5k+w85sCBUwdkDgAAAABIonEAAAAA4C66FQEAAMCpMZWp7cgcAAAAAJBE5gAAAADOziT7jhF23MQBmQMAAAAAd9A4AAAAACCJbkUAAABwdnYekGxmQDIAAAAAR0fmAAAAAE7N3lOZ2nc15oxF5gAAAACAJDIHAAAAcHJkDmxH5gAAAACAJBoHAAAAAO6iWxEAAACcGysk24zMAQAAAABJZA4AAADg5BiQbDsyBxnowxHDFOjrZrXVrFTGsv/1V19R1XIlVSjAV6Eh+dW53dM6dPAPAyPOmv6Y/aqur3sn1Ta6T2Pl9PHQJ6820u6vInVp+f908Ls++rj3U/L1crc6RqWS+bX4k046t3CAzi58Qz9/2EFliwYa9Iocw6cfj1Jen+x6a0A/S1lMTLRe6R6h0KIFVCjQT/VqVdGC+T8aGKVj+GXDerVp1VzFCj8ib3cXLZj/k9V+s9msd4cOVtFC+ZXHL4eaPvWkDh86ZEywDuzDD0aqZvUqypvTR8H5A9SmdUsdPHDA6LCcxqQJ41WiWGH5e3uodo1q2rpli9EhOQWuK7I6GgcZrESpUO05dNKy/bx8rWVfuQqP6dOJU7Rh62+aPW+RzGaz2rZsouTkZOMCzoJqvTRFhVt9ZNka95spSfpx7V7ly+OjfLm9NXDiClXqMlHdR/6kJ6sW06Q3mlue7+WZXfNHddCp2HjV6fGF6vecpqvXkvTzhx2VzZVbPi07t2/VzGlTVLpMWavyni921eFDB/X1nB+1btNONWneSi90bq/fdu80KFLHcC0xUWXKldMnn36W5v7RH4/SpPHj9Om4iVr7yyZ5eXmpZdOndOPGDTtH6tg2rF+nl3tEat0vm7RwyQrdvnVLTRs3VGJiotGhOby5383RgP799NagdxS1ZYfKlSuv5k3CFRsba3RoDo3rapx7mQN7bo6KT0oZLFu2bAoIDLJsuXPnsezr3PUFhdWsreBChVWuQkW9+fZQnTl9SqdOHDcu4CzoQvw1xVxKtGyNw4rryOlL2rDrhPYdO6/2g+dq8caDOnb2stbtPK4hX6xW4xrF5ep65wexRHAe5fbLoXenrtWhUxe1//h5vTdjnYJyeys4yM/gV5f1XL16VS93i9An4ybJzz+n1b4tm6P0wkuReqxyVRUOKaLX3vif/Pz8tXvnDoOidQwNn2qkd4YOV/MWrVLtM5vNGj/uU73x5ltq2ryFypQtp8lfztC5c2e14Oef7B+sA/t50VJ1iuii0NKlVa58eU2eOl2nTp7Uzh3bjQ7N4Y0d84m6duuuzl26qlRoqMZNmCTPHDk0Y/qXRofm0LiucAQ0DjLY0SOHVa54IVUpV0I9unXW6VMn06yXmJio2V/PVHDhEOUvUNDOUTqO7Nlc1O7Jcpqx5P7fVPt6uSvh2k0lJ5slSQdPXtSFuGuKaFJR2bO5yMMtm7o0rqj9x8/rRHScnSJ3HAP69dKTTzVS3Sfqp9pXtVqYfvphri5fuqSUlBTN+36Obt68oZq16xoQqXM4fuyYYqKj9UT9BpYyPz8/Va5aTVs2RRkYmeNLiI+XJOXMmcvgSBxbUlKSdu7Yrnp/uUddXFxUr14D7tGHwHWFo2BAcgZ6rHJVjZ34hYo+Wlyx0dH66P3havFUPa3btFPePj6SpGlTJmnY4IG6lpioYo8W19yfFsvNzc3gyLOu5rVLyt/bQ18v2ZXm/tx+nhrYuY6+XPDnN9lXrycpvM90fTe8nQZ2riNJOnz6kpr3/9rSgMAd876foz27d2r5uk1p7v9ixrd6octzKl4oUNmyZZNnjhyaPut7FSlazM6ROo+YmGhJUkCA9RiYgIBAxcTEGBGSU0hJSVH/1/oorEZNlS5T5t+fgPu6cOGCkpOTU9+jgYE6cIBxcg+K62osBiTbjsxBBqrf8Ck1b/WMSpcppycaNNSs739WfHyc5s/73lKn9bPttWrDFv20ZJWKFHtU3bs8Rz/jfxDRuKKWbTmkcxevptrnk8NN895/TvtPnNfwaWst5R5u2TTpjeaK+v2k6r4yVfV6fql9x2L14/vPycON9vA9Z06f0ltv9NPEqTPl4eGRZp2Rw99RQnycfliwTCvWb1KPnn30QkR77du7x87RAv+sT69I7d37u2Z+M9voUADAoRnaOFi/fr2aNWum/Pnzy2Qy6aeffjIynAzn5++vokUf1bGjhy1lvn5+KlLsUYXVrK2pX83RoYMHtHjBT8YFmYUFB/qpXqUimr4wdZcib083/fxhR125lqS2g+bodnKKZV/bBmUVHOSvF9+fr+1/nNWWfWcU8e4PKpzPX81qlbDnS8jSdu/cofPnY1W/VlUF+XsoyN9DG39ZrykTP1OQv4eOHT2iqZ9P0KcTpqjO4/VUpmx59R/4tipUrKQvJ080OnyHFRgYJEmKjbXOEsTGxigwkBm1HkSf3j21ePFCLVuxRgUKFDA6HIeXJ08eubq6pr5HY2IUFBRkUFSOj+tqLAYk287QxkFiYqLKly+v8ePHGxlGpkm8elXHjx1VYGC+NPebzWbJbFZSUpKdI3MMnRpVUGxcopZsOmhV7pPDTQs/7qikW8l65n/f6maS9WxPOTyyK8VslvkvPYjuPXZxcdwf1oxW5/F6Wr95p9Zs3GbZKjxWSc+0ba81G7fp+vVrkiQXk/WvCRdXV6WkpKR1SNigcEiIAoOCtHb1KktZQkKCtm3ZrKrVwwyMzPGYzWb16d1TP8+fp6XLV6twSIjRITkFNzc3VXysktb85R5NSUnRmjWruEcfAtcVjsLQPhaNGjVSo0aNjAwhQw15a4AaNmqiAgWDFRN9TqNGDJOrq6tatWmr48eOav6Pc/V4vSeVO08enTt7RmM/GSUPD0/Vb/iU0aFnOSaT1LlRBX2zdLfVOAGfHG5a+FEneXpkV9fhc+Tr5W5Z4+B83DWlpJi1atsRjXj5SY3p21gTf9wiF5NJr3eoqdvJKVq347hBryjr8fbxUalQ677ZOXJ4KWeu3CoVWka3bt1SSNFieu3VVzT0vQ+UM1duLVn4s9atXqlv5s43KGrHcPXqVR098mfG8MTxY/pt9y7lzJlLBYODFdnrVY16/z0VLfaoCoWEaPiQwcqXL7+aNW9pXNAOqE+vSM2ZPUtzf5wvbx8fRUffGc/h5+cnT09Pg6NzbL379FP35yNUqVJlVa5SVZ+NHaNriYnqHNHV6NAcGtfVQKa7mz3P56AcqgP2zZs3dfPmTcvjhIQEA6NJ7eyZ03r5+U66fOmicufJq6rVa2jxqg3Kkyevbt+6pc0bf9XkCeMUH3dZeQMCVb1GLS1cuU558wYYHXqWU69SEQUH+WvGYusuRRWK51PV0ne6Dez7trfVvhJtx+hkdLwOnryo1v/7Vm9F1NXa8d2UYjZr96FzavHG14q+lHrsAtKWPXt2ffv9z3r3nbfU8dlWSky8qpAiRfXZ51/qyXDnadRnhh3bt6lxw3qWx2++8ZokqUOnCH3+xTT1fe0NJSYmqlfkS4qPi1NYjVqat2DJfcd+IG2TP7/Tva1h/cety7+Ypk4RXewfkBNp82xbXTh/XsOGDlZMdLTKla+g+QuX0vXtIXFd4QhMZrM5S0zfYjKZNG/ePLVs2fK+dYYMGaKhQ4emKj98+oJ8fH0zMbr/nkJNRxgdglM6tegto0NwSp5urkaH4JRc6YYH/KclJCQoMLef4uPj5eugn7MSEhLk5+enwK5fycUth93Om5J0TTHTOjnktXOo2YoGDhyo+Ph4y3bq1CmjQwIAAEAW5wgDks+cOaOOHTsqd+7c8vT0VNmyZbVt2zbLfrPZrMGDBytfvnzy9PRUgwYNdOjQIatjXLp0SR06dJCvr6/8/f3VrVs3Xb2avl4TDtU4cHd3l6+vr9UGAAAAOLLLly+rZs2ayp49u5YsWaJ9+/bp448/Vs6cOS11Ro0apbFjx2rSpEnavHmzvLy8FB4ebjUlfocOHbR3716tWLFCCxcu1Pr16/Xiiy+mKxaHGnMAAAAApFdWXwTtgw8+UMGCBTVt2jRLWchfZmAzm80aM2aMBg0apBYtWkiSZs6cqcDAQP30009q166d9u/fr6VLl2rr1q2qXLmyJGncuHFq3LixPvroI+XPn9+mWAzNHFy9elW7du3Srl27JEnHjh3Trl27dPLkSSPDAgAAAOzm559/VuXKldWmTRsFBASoYsWKmjJlimX/sWPHFB0drQYNGljK/Pz8VK1aNUVFRUmSoqKi5O/vb2kYSFKDBg3k4uKizZs32xyLoY2Dbdu2qWLFiqpYsaIkqV+/fqpYsaIGDx5sZFgAAABwIkaNOUhISLDa/jrr5l8dPXpUEydO1KOPPqply5apR48e6t27t2bMmCFJlqma/z6zVWBgoGVfdHS0AgKsZ8DMli2bcuXKZaljC0O7FT3++OPKIpMlAQAAABmqYMGCVo/feecdDRkyJFW9lJQUVa5cWSNG3JktsmLFivr99981adIkRURE2CNUC8YcAAAAAJng1KlTVhPouLu7p1kvX758Cg0NtSorVaqUfvjhB0lSUFCQJCkmJkb58uWz1ImJiVGFChUsdWJjY62Ocfv2bV26dMnyfFs41GxFAAAAQLqZDNikVLNs3q9xULNmTR04cMCq7ODBgypUqJCkO4OTg4KCtGrVKsv+hIQEbd68WWFhYZKksLAwxcXFafv27ZY6q1evVkpKiqpVq2bzpSJzAAAAABiob9++qlGjhkaMGKFnn31WW7Zs0eTJkzV58mRJd8ZM9OnTR8OHD9ejjz6qkJAQvf3228qfP79lAeFSpUrpqaeeUvfu3TVp0iTdunVLPXv2VLt27WyeqUiicQAAAAAnl9WnMq1SpYrmzZungQMHatiwYQoJCdGYMWPUoUMHS5033nhDiYmJevHFFxUXF6datWpp6dKl8vDwsNT55ptv1LNnT9WvX18uLi5q3bq1xo4dm77YzQ48IvjektiHT1+QDwuiZahCTUcYHYJTOrXoLaNDcEqebq5Gh+CUXF3s94cUQNaTkJCgwNx+io+Pd9iFZ+99VnzkxW/l4pbDbudNSbqmM5PbO+S1I3MAAAAAp5bVMwdZCQOSAQAAAEiicQAAAADgLroVAQAAwKmZZOduRaJbEQAAAAAHR+YAAAAATo0BybYjcwAAAABAEo0DAAAAAHfRrQgAAADOzXR3s+f5HBSZAwAAAACSyBwAAADAyTEg2XZkDgAAAABIInMAAAAAJ0fmwHZkDgAAAABIonEAAAAA4C66FQEAAMCpmUx3Nnuez1GROQAAAAAgicwBAAAAnNydzIE9ByTb7VQZjswBAAAAAElkDgAAAODs7DzmQGQOAAAAADg6GgcAAAAAJNGtCAAAAE6OFZJtR+YAAAAAgCQyBwAAAHByLIJmOzIHAAAAACSROQAAAICTc3ExycXFfl/nm+14roxG5gAAAACAJBoHAAAAAO6iWxEAAACcGgOSbUfmAAAAAIAkJ8kc+Hhml69ndqPDcCpH5g80OgSnFPLCN0aH4JT2jm9rdAhOKZe3m9EhOK1srnw3B9gTi6DZjt9OAAAAACQ5SeYAAAAAuB/GHNiOzAEAAAAASTQOAAAAANxFtyIAAAA4NQYk247MAQAAAABJZA4AAADg5Mgc2I7MAQAAAABJZA4AAADg5JjK1HZkDgAAAABIonEAAAAA4C66FQEAAMCpmWTnAcly3H5FZA4AAAAASCJzAAAAACfHgGTbkTkAAAAAIInGAQAAAIC76FYEAAAAp8YKybYjcwAAAABAEpkDAAAAODkGJNuOzAEAAAAASWQOAAAA4OQYc2A7MgcAAAAAJNE4AAAAAHAX3YoAAADg1BiQbDsyBwAAAAAkkTkAAACAk2NAsu3IHAAAAACQROYAAAAAzs7OYw7kuIkDMgcAAAAA7qBxAAAAAEAS3YoAAADg5BiQbDsyBwAAAAAk0TjIdJMnTVSViuUUkMtXAbl8VbdWmJYtXWJ0WA7n3Nkz6vViF5Uukk9F8/mpfo3HtHvndsv+xKtX9Vb/V1WpdBEVzeenx6uX18wvJxsYcdaUL2cOTYmspRNT2ip2ZgdtGtVcFYvktuyf1KOmrsyOsNp+fLNBquOEV3xEq4c3VuzMDjr5RTt9+9oT9nwZWVpycrI+GjlUNR8rqeIFcqp25VB9+tFImc1mS51CeTzT3CaN+8TAyLO+XzasV5unm+vRkALy8XDVgp9/str/0gtd5ePharW1atbImGCdwKQJ41WiWGH5e3uodo1q2rpli9EhOQWuqzHuLYJmz81R0a0okz1SoIDeHfG+ihV7VGazWV9/NUNtnm6hTVt3KrR0aaPDcwhxcZfV8qknVKN2XX0992flzpNHx44clp+/v6XO0EH99ev6tRr3+TQVDC6kdatX6n+v91ZQUD41bNzMuOCzEH8vN60Y1kgb9kbr6fdX6ULCDRXN56u4xCSrest3nVaPib9aHifdTrHa37xqsMa9WENDZ+/Q+r3RcnUxKbSgvz1egkOYOPZjfT1tij7+bIqKlwzVb7u2q3+vl+Tr66uuL0ZKkrbuPWb1nLWrluuNV19W42atjAjZYVy7lqiyZcurU0RXdWj7TJp1nmwYromTv7Q8dnN3t1d4TmXud3M0oH8/jRs/SVWqVtNnY8eoeZNw7d57QAEBAUaH57C4rnAENA4yWZOm1h9Mh777nqZ8PlFbNm+icWCjCWM+Uv5HCmj0+CmWsuBCIVZ1tm3epGfad1KNWnUlSR27vKCvp3+hnTu20Ti4q2/zMjpzMVE9Jv35wf/E+aup6iXdSlFs/I00j+HqYtKoiKp6+5ttmrnmsKX8wJn4jA/YQW3fsklPNmqq+g3vfGNdMLiQfv7xO+3asc1SJyAwyOo5K5YsUFitugoubH1fw1rD8EZqGP7PmQA3d3cFBgX9Yx38u7FjPlHXbt3VuUtXSdK4CZO0ZMkizZj+pfq/8abB0TkurqtxGHNgO7oV2VFycrK+mzNbiYmJqlY9zOhwHMbypQtVruJjerFLe5V7tIAa1qmqb2ZMtapTuVp1rViyUOfOnpHZbNavG9bq6JFDqvtE6i4x/1WNKxXUjqMXNbNPXR39/Fn9MrKputR7NFW9WqFBOvr5s9rxSUuN7lZdubz//Oa1QkhuPZLbSykp0i8jm+rQxDb64c36KlXA346vJGurVLW6Nq5fo6OHD0mS9v3+m7ZtjtLj9RumWf98bIxWr1iqth0i7Bmm0/pl/TqFFAxSxbKl1KfXK7p48aLRITmcpKQk7dyxXfXq//n708XFRfXqNdCWTVEGRubYuK74J0OGDLE0YO5tJUuWtOy/ceOGIiMjlTt3bnl7e6t169aKiYmxOsbJkyfVpEkT5ciRQwEBAerfv79u376d7ljIHNjB73v26PHaYbpx44a8vb015/t5KhUaanRYDuPk8WP66svJ6v7Kq+rdb4B27dimwW/2U3Y3Nz3bvpMk6d0PxuiNPq+ocukiypYtm1xcXDTq04mqXrO2wdFnHYUDfPRCgxL6bPFeffTTHlUqmlujulRV0u0UzVp/RJK0YtcZ/bzlpI7HXlGRQB+90+4x/fBmA9V/e7FSzGYVDvCWJA18prwGfrVVJ89fVa+mpbVkcLgq9p2ny3/rovRf9Mqrr+vqlQTVCysvV1dXJScnq/9bQ9WqTfs06/8w+2t5efvoqaYt7RuoE3qyYbiat2ylwoVDdPToEQ0dPEitWzTRqnW/ytXV1ejwHMaFCxeUnJysgIBAq/KAwEAdOPCHQVE5Pq4r/k3p0qW1cuVKy+Ns2f78mN63b18tWrRIc+fOlZ+fn3r27Kmnn35av/56pzdAcnKymjRpoqCgIG3cuFHnzp1T586dlT17do0YMSJdcdA4sIPiJUpo87Zdio+P17wfv1f35yO0fNU6Ggg2SklJUbkKlTRw8LuSpDLlKujA/r36atoUS+Ng2uTx2rFts6bN+kEFChbS5o0b9Fb/VxUYlE91Hq9vZPhZhouLtPPoRQ2dvVOS9NvxSypVIKe6NShuaRz8EHXcUn/fqTj9fvKy9oxtrdqlA7Xu92i5uNxJk37002/6ectJSVKPib/qwIQ2alm9sKatOmjfF5UFLfzpe/30/WyN/Xy6ipcM1b7ff9PQt/orMCifnmnXMVX972bNVMtn2srDw8OAaJ3LM8+2s/x/6TJlVaZMOZULfVQb1q3V4/X4PQD8l9l7kPCDnCtbtmwKSqNbZHx8vKZOnapZs2apXr16kqRp06apVKlS2rRpk6pXr67ly5dr3759WrlypQIDA1WhQgW9++67GjBggIYMGSI3Nzeb4zC0W9HIkSNVpUoV+fj4KCAgQC1bttSBAweMDClTuLm5qWixYnqsUiW9+95IlS1XXuPHfWp0WA4jIDCfipcsZVVWrHhJnT19SpJ0/fp1vf/uYL0zfJQaNmqq0DJl1fXFV9S8VRt9/tloI0LOkqIvX9cfp+Osyg6cjVeBPN73fc7x2Ku6kHBDRQJ9LceQpD9O/znGIOl2io7FXlHBPF4ZH7QDGjHkf+rx6utq/vSzKhlaRk8/+5y6vdxLE8Z8mKrulqhfdOTwQbXr2NWASJ1fSJEiyp0nj44eOfzvlWGRJ08eubq6KjbWustCbExMmh9cYBuuK/7NoUOHlD9/fhUpUkQdOnTQyZN3voTbvn27bt26pQYN/uySVrJkSQUHBysq6k6XtKioKJUtW1aBgX9mpsLDw5WQkKC9e/emKw5DGwfr1q1TZGSkNm3apBUrVujWrVtq2LChEhMTjQwr06WkpOjmzZtGh+EwqlQL05FD1t9IHz1ySI8UCJYk3b51S7du3ZKLi/Xt7OLiopQU65l2/ss2HYzVo/n9rMqK5fPVqQupByXfkz9XDuXydldM3J1Gwa5jF3UjKVmP5ve11MnmalKhPN7/eJz/kuvXr8vFZH0vurq6pnkvzvlmhsqWf0yhZcrZK7z/lDOnT+vSxYsKzJfP6FAcipubmyo+VklrVq+ylKWkpGjNmlWqyni5B8Z1Ndbf+/PbY5OkhIQEq+1+n/+qVaum6dOna+nSpZo4caKOHTum2rVr68qVK4qOjpabm5v8/zJLoyQFBgYqOjpakhQdHW3VMLi3/96+9DC0W9HSpUutHk+fPl0BAQHavn276tSpY1BUGevttwYq/KlGKlgwWFeuXNGc2bO0ft1aLVi8zOjQHEb3V3qrRXhdjf34AzVr1Vq7tm/TNzOmatToCZIkH19fhdWso+GDB8rD01MFCgYr6tcN+mHONxo8fJTB0Wcd4xft08phjfV6y7L6Meq4KhXLo671HlXvKXe+dfByz6aBz5TX/M0nFBN/XSGBPnr3uco6GpOglbvPSJKuXL+lqSsP6H/PVNDpi9d06vxVvdrszqxb8zadMOy1ZSUNwhvrs9EfKH+BgipeMlR79+zSFxPH6tnnOlvVu3IlQYt+/lGDhr5vUKSO5+rVq1ZZgBPHj+u33buUM2cu5cyVSyPfG6YWLZ9WYGCQjh09orffelNFihZTgyfDDYzaMfXu00/dn49QpUqVVblKVX02doyuJSaqcwRZrofBdf3vKViwoNXjd955R0OGDElVr1GjP2diK1eunKpVq6ZChQrpu+++k6enZ2aHaSVLjTmIj7/TVSFXrlxp7r9586ZViyshIcEucT2M87Gx6ta1s6LPnZOfn5/KlC2nBYuXqX6DJ40OzWFUeKyyvvjqO70/7G2N+fA9FSxUWENHfKSnn/1zgOeEqV9p5LC31evFLoq7fEmPFAzWG4OGqvPzLxoYeday4+hFPffJGg1p95gGPF1eJ85f0Zszt+q7X+/MuZ+cYlbp4Jx6rk5R+Xm56dzl61r921m9+91Oq7UOBn2zTckpZk15pZY83Fy17fAFNRm+PNV6Cf9VQ0d+oo/fH6q333hVFy6cV2BQPj0X0U2vvv4/q3oLfpwrs9ms5q2fNShSx7Nz+zY1Dv9z7MDAN16TJD3XsbPGjJugvXt+06yvZyo+Lk758uVXvQZP6u13hsmdtQ7Src2zbXXh/HkNGzpYMdHRKle+guYvXJrqm0mkD9fVOEZNZXrq1Cn5+v6Zbbf195G/v7+KFy+uw4cP68knn1RSUpLi4uKssgcxf+mSFhQUpC1/W1Dv3mxG6e22ZjL/ddlOA6WkpKh58+aKi4vTL7/8kmadIUOGaOjQoanKYy7GW114PLxLV/mglxlKvPyt0SE4pb3j2xodglPK5W37ADakTzZXZhJH1peQkKDA3H6Kj3fcz1kJCQny8/NT2HvLlM3DfmPjbt9IVNRb4Q987a5evarg4GANGTJEERERyps3r7799lu1bt1aknTgwAGVLFlSUVFRql69upYsWaKmTZvq3LlzlgX1Jk+erP79+ys2NjZdX5Jkmd9OkZGR+v333zV79uz71hk4cKDi4+Mt26lTp+wYIQAAAJDxXn/9da1bt07Hjx/Xxo0b1apVK7m6uqp9+/by8/NTt27d1K9fP61Zs0bbt29X165dFRYWpurVq0uSGjZsqNDQUHXq1Em7d+/WsmXLNGjQIEVGRqY7e5oluhX17NlTCxcu1Pr161WgQIH71nN3dyc9DAAAgHTJ6lOZnj59Wu3bt9fFixeVN29e1apVS5s2bVLevHklSaNHj5aLi4tat26tmzdvKjw8XBMmTLA839XVVQsXLlSPHj0UFhYmLy8vRUREaNiwYemO3dDGgdlsVq9evTRv3jytXbtWISEhRoYDAAAA2N0/9ZyRJA8PD40fP17jx4+/b51ChQpp8eLFDx2LoY2DyMhIzZo1S/Pnz5ePj49lqiU/Pz+7j8wGAACAczJqQLIjMnTMwcSJExUfH6/HH39c+fLls2xz5swxMiwAAADgP8nwbkUAAABAZsrqYw6ykiwzWxEAAAAAY9E4AAAAACApi0xlCgAAAGQWBiTbjswBAAAAAElkDgAAAODkTLLzgGT7nSrDkTkAAAAAIInGAQAAAIC76FYEAAAAp+ZiMsnFjv2K7HmujEbmAAAAAIAkMgcAAABwcqyQbDsyBwAAAAAkkTkAAACAk2MRNNuROQAAAAAgicYBAAAAgLvoVgQAAACn5mK6s9nzfI6KzAEAAAAASWQOAAAA4OxMdh4kTOYAAAAAgKMjcwAAAACnxiJotiNzAAAAAEASjQMAAAAAd9GtCAAAAE7NdPefPc/nqMgcAAAAAJBE5gAAAABOjkXQbEfmAAAAAIAkMgcAAABwciaTya6LoNl1wbUMRuYAAAAAgCQaBwAAAADuolsRAAAAnBorJNuOzAEAAAAASWQOAAAA4ORcTCa52PHrfHueK6OROQAAAAAgicwBAAAAnBxjDmxH5gAAAACAJBoHAAAAAO6iWxEAAACcGisk247GAdLknyO70SE4pT8mtTc6BKdUrNtXRofglE7PiDA6BKfl6uK4HxyyMkf+QAZkFTQOAAAA4NQYkGw7xhwAAAAAkETmAAAAAE6ORdBsR+YAAAAAgCQaBwAAAADuolsRAAAAnJrp7mbP8zkqMgcAAAAAJJE5AAAAgJNjETTbkTkAAAAAIInGAQAAAIC7bOpW9PPPP9t8wObNmz9wMAAAAEBGczHd2ex5PkdlU+OgZcuWNh3MZDIpOTn5YeIBAAAAYBCbGgcpKSmZHQcAAACQKRiQbLuHGnNw48aNjIoDAAAAgMHS3ThITk7Wu+++q0ceeUTe3t46evSoJOntt9/W1KlTMzxAAAAA4GGZTPbbHFm6Gwfvvfeepk+frlGjRsnNzc1SXqZMGX3xxRcZGhwAAAAA+0l342DmzJmaPHmyOnToIFdXV0t5+fLl9ccff2RocAAAAADsJ90rJJ85c0bFihVLVZ6SkqJbt25lSFAAAABARmFAsu3SnTkIDQ3Vhg0bUpV///33qlixYoYEBQAAAMD+0p05GDx4sCIiInTmzBmlpKToxx9/1IEDBzRz5kwtXLgwM2IEAAAAHhiLoNku3ZmDFi1aaMGCBVq5cqW8vLw0ePBg7d+/XwsWLNCTTz6ZGTECAAAAsIN0Zw4kqXbt2lqxYkVGxwIAAABkOMYc2O6BGgeStG3bNu3fv1/SnXEIlSpVyrCgAAAAANhfuhsHp0+fVvv27fXrr7/K399fkhQXF6caNWpo9uzZKlCgQEbHCAAAAMAO0j3m4IUXXtCtW7e0f/9+Xbp0SZcuXdL+/fuVkpKiF154ITNiBAAAAB6YyYDNUaU7c7Bu3Tpt3LhRJUqUsJSVKFFC48aNU+3atTM0OAAAAAD2k+7GQcGCBdNc7Cw5OVn58+fPkKAAAACAjOJiMsnFjoOE7XmujJbubkUffvihevXqpW3btlnKtm3bpldffVUfffRRhgYHAAAA/Je8//77MplM6tOnj6Xsxo0bioyMVO7cueXt7a3WrVsrJibG6nknT55UkyZNlCNHDgUEBKh///66fft2us9vU+YgZ86cVlMyJSYmqlq1asqW7c7Tb9++rWzZsun5559Xy5Yt0x0EAAAAkFlMpjubPc/3ILZu3arPP/9c5cqVsyrv27evFi1apLlz58rPz089e/bU008/rV9//VXSnR48TZo0UVBQkDZu3Khz586pc+fOyp49u0aMGJGuGGxqHIwZMyZdBwUAAABgu6tXr6pDhw6aMmWKhg8fbimPj4/X1KlTNWvWLNWrV0+SNG3aNJUqVUqbNm1S9erVtXz5cu3bt08rV65UYGCgKlSooHfffVcDBgzQkCFD5ObmZnMcNjUOIiIi0vnyAAAAANgqMjJSTZo0UYMGDawaB9u3b9etW7fUoEEDS1nJkiUVHBysqKgoVa9eXVFRUSpbtqwCAwMtdcLDw9WjRw/t3btXFStWtDmOB14ETbrT/ykpKcmqzNfX92EOCQAAAGQoo1ZITkhIsCp3d3eXu7t7qvqzZ8/Wjh07tHXr1lT7oqOj5ebmZllf7J7AwEBFR0db6vy1YXBv/7196ZHuAcmJiYnq2bOnAgIC5OXlpZw5c1ptSNukCeNVolhh+Xt7qHaNatq6ZYvRITm8K1euqP9rfVTy0cLK7ZdD9erW1PZtqX+ocH/VyxVXgZzuqba3Xu+ty5cvadAbfVSnShkVzeenqmWK6e0BfZUQH2902FlO/lw59GXvujo9vYMuzYrQ1k9a6bGieSz7r//QLc2tb4uyljpvtC6vNe811cVZETo3s6MRL8OhfPrxKOXxya63BvRLtc9sNqvt002Vxye7Fi+Yb0B0jm34sCHK4eZitVUoU8rosJwGnwf+WwoWLCg/Pz/LNnLkyFR1Tp06pVdffVXffPONPDw8DIjSWrozB2+88YbWrFmjiRMnqlOnTho/frzOnDmjzz//XO+//35mxOjw5n43RwP699O48ZNUpWo1fTZ2jJo3CdfuvQcUEBBgdHgOK/Ll7tq393d98eVM5cuXX7O//VpNGz2p7bv2Kv8jjxgdnkNYtPpXJScnWx4f2L9X7Vs1VpOWrRVz7pxios/p7WHv69GSpXTm1Em92a+nYqLPafKM2QZGnbX4e7lp9XtNte73c2o5fJnOJ9xQsXx+unz1pqVO4W6zrJ7TsGIBTXqltuZtOm4pc8vmoh+jjmnzwVhF1C9ur/Ad0o7tWzVj2hSVLlM2zf2Txn9q128InVFoaGktXLrC8vjeBCR4OHweMI5RA5JPnTpl1asmrazB9u3bFRsbq8cee8xSlpycrPXr1+uzzz7TsmXLlJSUpLi4OKvsQUxMjIKCgiRJQUFB2vK3hua92Yzu1bFVujMHCxYs0IQJE9S6dWtly5ZNtWvX1qBBgzRixAh988036T3cf8LYMZ+oa7fu6tylq0qFhmrchEnyzJFDM6Z/aXRoDuv69ev6ad4PGj7iA9WqXUdFixXTW28PUZGixTRl8kSjw3MYufPkVUBgkGVbuWyxCoUUUVjNOioZWlpTZs7Rk42aqnBIUdWs84QGDBqmlUsXPdDUaM7qtVbldPpCol4av0HbDl/QidirWrX7jI7FXLHUiYm7brU1q1pI634/p+N/qTN8zk6NW7hXv5+8bMTLcBhXr17Vy90iNHrcJPn5p85W7/ltlyaMG6NPJ0wxIDrn4Zotm4KCgixbnjx5/v1J+Fd8Hvjv8fX1tdrSahzUr19fe/bs0a5duyxb5cqV1aFDB8v/Z8+eXatWrbI858CBAzp58qTCwsIkSWFhYdqzZ49iY2MtdVasWCFfX1+FhoamK+Z0Nw4uXbqkIkWKWF7wpUuXJEm1atXS+vXr03s4p5eUlKSdO7arXv0/B5G4uLioXr0G2rIpysDIHNvt27eVnJws97+l3zw9PRW18VeDonJsSUlJ+vG7b9WuQ5f7fuuakBAvbx9fvkX8iyaVg7XjyAV981o9nfjyOUV92FJdG5S4b/0APw899VhBzVh1wI5ROo8B/Xrpyacaqe4T9VPtu3btml56vrM++HisAgPT900ZrB05fEhFCj2i0BJF1bVzR506edLokBwenweMdW8RNHtutvLx8VGZMmWsNi8vL+XOnVtlypSRn5+funXrpn79+mnNmjXavn27unbtqrCwMFWvXl2S1LBhQ4WGhqpTp07avXu3li1bpkGDBikyMjLNBsk/Xqt01ZZUpEgRHTt2TNKdkdLfffedpDsZhb8PlIB04cIFJScnKyDAepBIwF8GkSD9fHx8VK16mD4YOVznzp5VcnKyvp31tTZvilL0uXNGh+eQli36WQnxcWrzXKc091+6eEGffjhSHSK62TmyrC0k0Efdw0vq8Ll4NX93maYs36+Pn6+uDo8XS7N+x8cf1ZXrt/TT5hN2jtTx/fj9HP22e6feHvJemvsHvfmaqlSrrsZNm9s5MudSpWo1Tf5imuYvWKJPx03Q8ePH1KBeHV25cuXfn4z74vMAHsbo0aPVtGlTtW7dWnXq1FFQUJB+/PFHy35XV1ctXLhQrq6uCgsLU8eOHdW5c2cNGzYs3edK99d/Xbt21e7du1W3bl29+eabatasmT777DPdunVLn3zySboDAB7UF1/OVI+XuqlYSAG5urqqQsXH1KZte+3asd3o0BzS7K+n6YkG4QrKlz/VvisJCerctqUeLVFS/d5824Dosi4Xk0k7jlzQO7Pu3He7j11U6YI51b1hKX2z9nCq+p3rF9ecDYd181Zyqn24vzOnT+mtN/rp+5+XpDlgb8miBdqwfq3W/MKkBA8r/KlGlv8vW66cqlStppLFCuuH779Tl658OQDYw9q1a60ee3h4aPz48Ro/fvx9n1OoUCEtXrz4oc+d7sxB37591bt3b0lSgwYN9Mcff2jWrFnauXOnXn311XQda+LEiSpXrpylH1ZYWJiWLFmS3pCytDx58sjV1VWxsdZLXMf+ZRAJHkyRokW1bOVaxV66ogNHTmr9r5t1+9YtFQ4pYnRoDuf0yRPasHa12nfummrf1StX1PGZZvL29tYXX89V9uzZDYgw64qOu679p+Osyv44E6eCebxS1a1ZKlAlHvHXtJUH7RSd89i9c4fOn49VvVpVFejvoUB/D238Zb0mT/xMgf4eWrdmpY4fPaKiBfJY9ktSl47Pqnmj1F2QYDt/f38Ve7S4jh5O3diF7fg8YKx7A5LtuTmqh+44XKhQIRUqVOiBnlugQAG9//77evTRR2U2mzVjxgy1aNFCO3fuVOnSpR82tCzBzc1NFR+rpDWrV6l5i5aSpJSUFK1Zs0ovv9LT2OCchJeXl7y8vHT58mWtXLFMw0d8YHRIDmfOrJnKkzdA9Rs2tiq/kpCgDs80lZubu6bN+jFLTLGW1UT9EaPi+f2syh7N56eT56+mqhtRv7i2Hz6vPScu2Ss8p1H78XrasHmnVVmvHi/o0eIl1Ltvf+XKnUcRz3e3fk61ihr+/kcKb9TUnqE6natXr+rY0SMK6sAUuw+DzwNwFDY1DsaOHWvzAe9lFWzRrFkzq8fvvfeeJk6cqE2bNjlN40CSevfpp+7PR6hSpcqqXKWqPhs7RtcSE9U5IvW3tLDdiuXLZDabVbx4CR05clhvDXxDxUuUVCeua7qkpKTou29m6pl2Ha0GGl9JSNBzrZvo+rVrGvv5NF25kqArV+4s5pI7T165uroaFXKWMm7B71ozopn6P11eP2w8qirF8ur5J0uo5yTrgfE+ntn1dFiI3pyR9pzmBfN4Kae3uwrm8ZKri0nlCueSJB2JTlDiDWaH8vHxUanQMlZlOXJ4KVeu3JbytAYhFygQrEKFQ+wSo7MYOOB1NW7STMHBhXTu3FkNHzZErq6uatO2vdGhOTw+DxjHqEXQHJFNjYPRo0fbdDCTyZSuxsFfJScna+7cuUpMTLRMy/R3N2/e1M2bf84d/vdV57KqNs+21YXz5zVs6GDFREerXPkKmr9waaqV7JA+CQnxemfQ/3TmzGnlzJVLLVs+rXeGvUe3l3TasHaVzpw+qXYdI6zK9/y2Uzu33fkgW+sx62nQonYfUMHgwvYKMUvbfuSC2o5aqWEdKut/bSroeOxV9Z+2WbM3HLGq16ZWEZlMJn33y5E0j/N2u8fU6Yk/1zfY/HErSVLDwYu0YS+DFWE/Z06fUUSn53Tp4kXlyZtXNWrU0toNUcqbN6/RoTk8Pg/AEZjMZrPZyAD27NmjsLAw3bhxQ97e3po1a5YaN26cZt0hQ4Zo6NChqcpjLsZbLTCBh5eSYuht4bQuX7tldAhOqVi3r4wOwSmdnhHx75XwQHK4k3nLDI78bW1WlJCQoMDcfoqPd9zPWQkJCfLz89OLX2+RWw5vu5036dpVTe5Y1SGvXboHJGe0EiVKaNeuXdq8ebN69OihiIgI7du3L826AwcOVHx8vGU7deqUnaMFAAAAnJfhKxm5ubmpWLE784FXqlRJW7du1aeffqrPP/88VV13d/d0L+QAAAAAwDaGNw7+LiUlxWpcAQAAAPAwGJBsO0MbBwMHDlSjRo0UHBysK1euaNasWVq7dq2WLVtmZFgAAADAf5KhjYPY2Fh17txZ586dk5+fn8qVK6dly5bpySefNDIsAAAAOBGTSXKx45f5Dpw4eLDGwYYNG/T555/ryJEj+v777/XII4/oq6++UkhIiGrVqmXzcaZOnfogpwcAAACQCdI9W9EPP/yg8PBweXp6aufOnZbxAfHx8RoxYkSGBwgAAAA8DBeT/TdHle7GwfDhwzVp0iRNmTLFarGpmjVraseOHRkaHAAAAAD7SXfj4MCBA6pTp06qcj8/P8XFxWVETAAAAAAMkO7GQVBQkA4fPpyq/JdfflGRIkUyJCgAAAAgo9ybytSem6NKd+Oge/fuevXVV7V582aZTCadPXtW33zzjV5//XX16NEjM2IEAAAAYAfpnq3ozTffVEpKiurXr69r166pTp06cnd31+uvv65evXplRowAAADAA7P3IGFHHpCc7saByWTSW2+9pf79++vw4cO6evWqQkND5e3tnRnxAQAAALCTB14Ezc3NTaGhoRkZCwAAAAADpbtx8MQTT/zjIIvVq1c/VEAAAABARjKZ7LtqsQOPR05/46BChQpWj2/duqVdu3bp999/V0REREbFBQAAAMDO0t04GD16dJrlQ4YM0dWrVx86IAAAACAjuZhMcrHj1/n2PFdGS/dUpvfTsWNHffnllxl1OAAAAAB29sADkv8uKipKHh4eGXU4AAAAIEO4KAO/EbfxfI4q3Y2Dp59+2uqx2WzWuXPntG3bNr399tsZFhgAAAAA+0p348DPz8/qsYuLi0qUKKFhw4apYcOGGRYYAAAAAPtKV+MgOTlZXbt2VdmyZZUzZ87MigkAAADIMExlart0dYlydXVVw4YNFRcXl0nhAAAAADBKursVlSlTRkePHlVISEhmxAMAAABkKBfZeSpTOW7qIN2DqYcPH67XX39dCxcu1Llz55SQkGC1AQAAAHBMNmcOhg0bptdee02NGzeWJDVv3lymv7TAzGazTCaTkpOTMz5KAAAA4AEx5sB2NjcOhg4dqpdffllr1qzJzHgAAAAAGMTmxoHZbJYk1a1bN9OCAQAAAGCcdA1INjlyjgQAAAD/SS6mO5s9z+eo0tU4KF68+L82EC5duvRQAQEAAAAwRroaB0OHDk21QjIAAACQlZlMsutUpo7c2SZdjYN27dopICAgs2IBAAAAYCCbGweMNwAAAIAjYipT29m8CNq92YoAAAAAOCebMwcpKSmZGQcAAAAAg6VrzAEAAADgaJjK1HY2dysCAAAA4NzIHAAAAMCpme7+s+f5HBWZAwAAAACSyBwAAADAyTHmwHZkDgAAAABIonEAAAAA4C66FSFNLo6cD8vCfD34kcsMx77sZHQITqnAM+OMDsFpnZvX2+gQnJJHdlejQ0AWRbci25E5AAAAACCJzAEAAACcnMlkkslkx6lM7XiujEbmAAAAAIAkMgcAAABwcow5sB2ZAwAAAACSaBwAAAAAuItuRQAAAHBqJtOdzZ7nc1RkDgAAAABIInMAAAAAJ+diMsnFjl/n2/NcGY3MAQAAAABJNA4AAAAA3EW3IgAAADg11jmwHZkDAAAAAJLIHAAAAMDZ2XkqU5E5AAAAAODoyBwAAADAqbnIJBc7fp1vz3NlNDIHAAAAACTROAAAAABwF92KAAAA4NRMdh6Q7MALJJM5AAAAAIw0ceJElStXTr6+vvL19VVYWJiWLFli2X/jxg1FRkYqd+7c8vb2VuvWrRUTE2N1jJMnT6pJkybKkSOHAgIC1L9/f92+fTvdsdA4AAAAgFO7twiaPbf0KFCggN5//31t375d27ZtU7169dSiRQvt3btXktS3b18tWLBAc+fO1bp163T27Fk9/fTTlucnJyerSZMmSkpK0saNGzVjxgxNnz5dgwcPTve1olsRAAAAYKBmzZpZPX7vvfc0ceJEbdq0SQUKFNDUqVM1a9Ys1atXT5I0bdo0lSpVSps2bVL16tW1fPly7du3TytXrlRgYKAqVKigd999VwMGDNCQIUPk5uZmcyxkDgAAAODUXEwmu28PKjk5WbNnz1ZiYqLCwsK0fft23bp1Sw0aNLDUKVmypIKDgxUVFSVJioqKUtmyZRUYGGipEx4eroSEBEv2wVZkDgAAAIBMkJCQYPXY3d1d7u7uadbds2ePwsLCdOPGDXl7e2vevHkKDQ3Vrl275ObmJn9/f6v6gYGBio6OliRFR0dbNQzu7b+3Lz3IHAAAAACZoGDBgvLz87NsI0eOvG/dEiVKaNeuXdq8ebN69OihiIgI7du3z47R3kHmAAAAAE7NqKlMT506JV9fX0v5/bIGkuTm5qZixYpJkipVqqStW7fq008/Vdu2bZWUlKS4uDir7EFMTIyCgoIkSUFBQdqyZYvV8e7NZnSvjq3IHAAAAACZ4N7UpPe2f2oc/F1KSopu3rypSpUqKXv27Fq1apVl34EDB3Ty5EmFhYVJksLCwrRnzx7FxsZa6qxYsUK+vr4KDQ1NV8xkDgAAAODUXPRwg4Qf5HzpMXDgQDVq1EjBwcG6cuWKZs2apbVr12rZsmXy8/NTt27d1K9fP+XKlUu+vr7q1auXwsLCVL16dUlSw4YNFRoaqk6dOmnUqFGKjo7WoEGDFBkZma4GiUTjAAAAADBUbGysOnfurHPnzsnPz0/lypXTsmXL9OSTT0qSRo8eLRcXF7Vu3Vo3b95UeHi4JkyYYHm+q6urFi5cqB49eigsLExeXl6KiIjQsGHD0h0LjQMAAAA4NaPGHNhq6tSp/7jfw8ND48eP1/jx4+9bp1ChQlq8eHH6TpwGxhwAAAAAkETjAAAAAMBdNA4y2YcfjFTN6lWUN6ePgvMHqE3rljp44IDRYTm8XzasV+uWzRQSnF+e2U36ef5PRofkcD7+8H3VrVlN+fP6qUhwkNq3aaVDB63vzWlTJ6txw3p6JMBfvp6uiouLMyZYB3Pu7BlFvthFoSH5FBLkpydqPKZdO7dLkm7duqXh7/xPT9R4TEXy51SFkoXV66XnFX3urMFRZy1/zHhe15f2TbWNjnxCkuSe3VWjI5/Q6e9e1vl5kfp2UFMF+OewPL9sSB7NeLORDn31gi7N76WdkzsrskVFo15OlvbJh++rXq3qKhjgr0cL5VOHZ59O9bvg2NEj6ti2tYoFByk4MKe6dmyn2LvTJMI2/N0ylosBm6Ny5Ngdwob16/Ryj0it+2WTFi5Zodu3bqlp44ZKTEw0OjSHlpiYqLLlymvM2Pv3vcM/+2XDOr34cg+tWrdR8xcu063bt9Sy6VNW9+a1a9fV4MlwvdZ/oIGROpa4uMtqHv6EsmfLrm++/1nrNu3SO8M/sMxNff3aNe3ZvVN9+/9Py9dt0tSv5ujI4YOKaN/a2MCzmFq9v1Xh9p9btsYDf5Ak/bjhkCRp1Et11aRaEXV4b5Ea9p+rfLm9NPvtZpbnV3w0UOfjrqvrqCV67KWZ+mD2Fg3rWlMvNytvyOvJyjZuWK8XXuqh5Wt/1Y8LlurWrVt6ulkjy++CxMREPd2skUwmk+YvXqElq9YrKSlJ7Z9poZSUFIOjdxz83YKjMJnNZrPRQTyohIQE+fn5KeZivNUCE1nZ+fPnFZw/QCtWr1Ot2nWMDscpeGY3ac7389S8RUujQ/lXt25n3T+kF86fV5HgIC1ZsUY1a1nfmxvWr1WT8Po6ee5iquXbs4LEm7eNDsHivSFvacvmKM1fstrm5+zasU2N6tXU1j2HVKBgcCZGlz4hbbPOh5gPX6qrRtWKqMzz0+Sbw02n5rysLh8s0bxf7jQWihfIqd1fdFHdPt9qyx/RaR5jdOQTKlkwlxq9+YM9Q0/TuXm9jQ7hvi6cP69HC+XTwuWrVbNWHa1euVxtWjbVsbMXLH9r4+PjFZI/j35csESP12tgcMR/8sjuanQINnGUv1sJCQkKzO2n+HjH+Zz1d/c+K05cs1ee3j52O+/1q1fU44nSDnntyBzYWUJ8vCQpZ85cBkcCWItP4N7MCMuWLFT5Co+pe0R7lSlWQE/WrqqvZ/zzLBQJCfEymUzy8/O3T5AOJns2F7WrV0ozlv0u6U5WwC27q1bvPGmpc/D0ZZ2MSVC1Uvnuexw/L3ddvnIz0+N1dAl/+11w8+ZNmUwmq7nSPTw85OLiok0bfzUkRgCZh8aBHaWkpKj/a30UVqOmSpcpY3Q4gEVKSore7N9X1cNqKrQ09+bDOHn8mGZ+OVkhRYvp2x8WqnO3F/X2gH76btZXada/ceOGhr/zllo+01Y+Dvbtkr00Dysmf293fb1inyQpKGcO3Uy6rfhE6w/6sXHXFJjTK81jVC+VT8/UKa6pS37L9HgdWUpKigb276dqYTUsvwuqVK2uHF5eGjJooK5du6bExES9PfANJScnKzo67SwNkNWYDNgcFY0DO+rTK1J79/6umd/MNjoUwMprfXpq/969mjZzltGhOLyUlBSVLV9R/xv8rsqWr6BOXV5Qh87Pa+a0Kanq3rp1Sy91eU5ms1kffDzOgGgdQ8RTpbVs63Gdu/RgY7VCC+XWd+8013vfbNKqHSf//Qn/Ya/36aX9+/Zq6ow/fxfkyZtX07+eraWLF6pAXj8VCsql+Pg4la/wmFxc+BgBOJss81P9/vvvy2QyqU+fPkaHkin69O6pxYsXatmKNSpQoIDR4QAWr/XppaWLF2nhslV6hHvzoQUE5lPxEqWsyh4tUVJnTp+yKrt165Ze7PKcTp86qTk/LSZrcB/BAT6qVyFY05fusZRFX74md7ds8vNyt6ob4J9DMZetGxAlg3Np8fut9eWSPfrg2y12idlR9e/bW8uWLNKCpStT/S6o16Chdu49qEMnzunIqRh9PnWGzp09o8IhIQZFCyCzZInGwdatW/X555+rXLlyRoeS4cxms/r07qmf58/T0uWr+UWKLMNsNuu1Pr208OeftGDpShUuzL2ZEapWD9Phwwetyo4cth5ofK9hcOzoYc2Zv0S5cuW2d5gOo1PD0oqNv64lW45ZynYeilHSrWQ9UaGgpezRAjkVHOirzfvPWcpKFcqtpR88o29W7teQGRvtGrcjMZvN6t+3txb9/JN+XrJChf7hd0HuPHnk5++v9WtX6/z5WDVq0uy+dYGsxMVksvvmqLIZHcDVq1fVoUMHTZkyRcOHDzc6nAzXp1ek5syepbk/zpe3j4+lf6afn588PT0Njs5xXb16VUcOH7Y8Pn7smHbv2qWcuXIpODjrzPaSlfXr01Pfz/lW386dJx9vH8XcvTd9/3JvxkRHKyYmWkeP3LnW+37fI28fHxUoGKxcuRi4nJYXX+mtZg3r6tOPP1DzVq21c/s2fT1jqj4cM0HSnYZB987ttOe3XZo5e55SkpMVG3Pn2vvnzCU3Nzcjw89STCap85Ol9c2KfUpO+XNivYRrSZq+7Hd98GJdXbpyQ1euJemTV57Qpn1nLTMVhRbKrSUfPKOV209o7I/bFZjzzhoIySlmXYi/bsjryape79NL33/3rWZ996O87/O74JuZ01W8ZEnlyZNXWzZv0sD+ffVKr1f1aPESRobuUPi7BUdh+FSmERERypUrl0aPHq3HH39cFSpU0JgxY9Kse/PmTd28+ecAtISEBBUsWDBLT2XqmT3tluPkL6apU0QX+wbjRNavW6vwBk+kKu/YKUJTvpxu/4BslJWmMvX1THvKv4mTp6pDpy6SpBHDh+r994b9Y52sICtNZSpJK5Yu0ohhb+vYkcMqWKiwXop8VR0jukmSTp04rqrl0/5A9cOC5apRu649Q/1HRk9lWv+xYC0c0Vplu03T4TNxVvvcs7vq/Rfr6NnHS8o9u6tWbj+uVz9brZjL1yRJb3WsrkEdw1Id80RMvEpGfGmP8P9RVprKNGeOtL8nHP/5VD3XKUKSNOTtgfr2q5m6fPmSggsVVtcXXtQrvfrIlMW+Hc3KU5k64t8tZ5rKdPLafcphx6lMr129ohcfD3XIa2do42D27Nl67733tHXrVnl4ePxr42DIkCEaOnRoqvKs3DgA/iorNQ6cSVZrHDgLoxsHziwrNQ6cSVZuHDgiGgcPzpEbB4aNOTh16pReffVVffPNN/Lw8LDpOQMHDlR8fLxlO3Xq1L8/CQAAAP9pJpP9N0dl2JiD7du3KzY2Vo899pilLDk5WevXr9dnn32mmzdvytXV+hsAd3d3q0VYAAAAAGQcwxoH9evX1549e6zKunbtqpIlS2rAgAGpGgYAAAAAMpdhjQMfHx+V+dsqwV5eXsqdO3eqcgAAAOBBmUwmuw6gz2qD9dMjS6xzAAAAAMB4hq9z8Fdr1641OgQAAAA4GRfZ9xtxR/723ZFjBwAAAJCBaBwAAAAAkJTFuhUBAAAAGY0BybYjcwAAAABAEpkDAAAAODnT3c2e53NUZA4AAAAASCJzAAAAACfHmAPbkTkAAAAAIInGAQAAAIC76FYEAAAAp8YKybZz5NgBAAAAZCAyBwAAAHBqDEi2HZkDAAAAAJLIHAAAAMDJsQia7cgcAAAAAJBE4wAAAADAXXQrAgAAgFMzme5s9jyfoyJzAAAAAEASmQMAAAA4OReZ5GLHYcL2PFdGI3MAAAAAQBKZAwAAADg5xhzYjswBAAAAAEk0DgAAAADcRbciAAAAODXT3X/2PJ+jInMAAAAAQBKZAwAAADg5BiTbjswBAAAAAElkDgAAAODkTHZeBI0xBwAAAAAcHo0DAAAAAJLoVgQAAAAnx4Bk25E5AAAAACCJzAEAAACcHJkD25E5AAAAACCJzAEAAACcnOnuP3uez1GROQAAAAAgicYBAAAAgLvoVgTYUfZstMczg59rdqNDcErR83obHYLTCqrxqtEhOKXLWz8zOgRkUS6mO5s9z+eo+KQCAAAAQBKZAwAAADg5BiTbjswBAAAAAEk0DgAAAADcRbciAAAAODVWSLYdmQMAAAAAksgcAAAAwMmZZN9Bwg6cOCBzAAAAAOAOMgcAAABwaiyCZjsyBwAAAAAk0TgAAAAADDVy5EhVqVJFPj4+CggIUMuWLXXgwAGrOjdu3FBkZKRy584tb29vtW7dWjExMVZ1Tp48qSZNmihHjhwKCAhQ//79dfv27XTFQuMAAAAATs1kwL/0WLdunSIjI7Vp0yatWLFCt27dUsOGDZWYmGip07dvXy1YsEBz587VunXrdPbsWT399NOW/cnJyWrSpImSkpK0ceNGzZgxQ9OnT9fgwYPTFQtjDgAAAAADLV261Orx9OnTFRAQoO3bt6tOnTqKj4/X1KlTNWvWLNWrV0+SNG3aNJUqVUqbNm1S9erVtXz5cu3bt08rV65UYGCgKlSooHfffVcDBgzQkCFD5ObmZlMsZA4AAADg1O4tgmbP7WHEx8dLknLlyiVJ2r59u27duqUGDRpY6pQsWVLBwcGKioqSJEVFRals2bIKDAy01AkPD1dCQoL27t1r87nJHAAAAACZICEhweqxu7u73N3d//E5KSkp6tOnj2rWrKkyZcpIkqKjo+Xm5iZ/f3+ruoGBgYqOjrbU+WvD4N7+e/tsReYAAAAATs1kwCZJBQsWlJ+fn2UbOXLkv8YaGRmp33//XbNnz374F/4AyBwAAAAAmeDUqVPy9fW1PP63rEHPnj21cOFCrV+/XgUKFLCUBwUFKSkpSXFxcVbZg5iYGAUFBVnqbNmyxep492YzulfHFmQOAAAAgEzg6+trtd2vcWA2m9WzZ0/NmzdPq1evVkhIiNX+SpUqKXv27Fq1apWl7MCBAzp58qTCwsIkSWFhYdqzZ49iY2MtdVasWCFfX1+FhobaHDOZAwAAADg1F5nk8rCjhNN5vvSIjIzUrFmzNH/+fPn4+FjGCPj5+cnT01N+fn7q1q2b+vXrp1y5csnX11e9evVSWFiYqlevLklq2LChQkND1alTJ40aNUrR0dEaNGiQIiMj/zVj8Vc0DgAAAAADTZw4UZL0+OOPW5VPmzZNXbp0kSSNHj1aLi4uat26tW7evKnw8HBNmDDBUtfV1VULFy5Ujx49FBYWJi8vL0VERGjYsGHpioXGAQAAAJzaXwcJ2+t86WE2m/+1joeHh8aPH6/x48fft06hQoW0ePHidJ7dGmMOAAAAAEgicwAAAABnl9VTB1kImQMAAAAAkmgcAAAAALiLbkUAAABwaqa7/+x5PkdF5gAAAACAJDIHAAAAcHYmyY5roDEgGQAAAIDjI3MAAAAAp8ZMprYjcwAAAABAEo0DAAAAAHfROLCTSRPGq0SxwvL39lDtGtW0dcsWo0NyClzXjPXhByNVs3oV5c3po+D8AWrTuqUOHjhgdFgOb/iwIcrh5mK1VShTyuiwHM4nH76vJ2pVV4EAfxUrlE/PPfu0Dh388/48ceK4/HNkS3P76cfvDYw86/hj0VBd3/lZqm30m88qOF+uNPdd3/mZnm5QUZJUtvgjmjGyiw4teVeXoj7Rzh8GKbL948a+KAfD3y2DmAzYHBRjDuxg7ndzNKB/P40bP0lVqlbTZ2PHqHmTcO3ee0ABAQFGh+ewuK4Zb8P6dXq5R6QqVa6i27dv6523/6emjRtq52/75OXlZXR4Di00tLQWLl1heZwtG79+0+vXDev1wks99Filyrp9+7befWeQWjVrpM079sjLy0sFChTUgaOnrZ4z/cspGjfmYzVo+JRBUWcttTp+KFeXPz+1hBbLr8WTeunHFTt1OuayCjcYaFX/+dY11bdzAy37da8kqWKpgjp/6Yq6Dpqh09GXVb18EY0f1F7JKSmaNGe9XV+LI+LvFhyByWw2m40O4kElJCTIz89PMRfj5evra3Q491W7RjVVqlxFY8Z+JklKSUlRsZCC6hHZS/3feNPg6BwX1zXznT9/XsH5A7Ri9TrVql3H6HDuK6v/Ghs+bIgW/Dxfm7ftNDqUdEm6nWJ0CP/owvnzKlYonxYtX62atdK+P2tXr6zyFSrqs0lT7BzdPwuq8arRIUiSPny9tRrVLqMyLYamuT/q2wHa9ccp9Rg6677HGP3msyoZEqhGL43LrDBtdnnrZ0aH8I8c7e9WQkKCAnP7KT4+a3/O+if3Piuu2X1K3j72ew1XryToifIFHfLa0a0okyUlJWnnju2qV7+BpczFxUX16jXQlk1RBkbm2Liu9pEQHy9Jypkzl8GROL4jhw+pSKFHFFqiqLp27qhTJ08aHZLDS0j45/tz147t2vPbLnXq0tWeYTmM7Nlc1a5xFc2Yn/bvzIqlCqpCyYKa8dM//0718/bQ5YRrmRGiU+HvFhwFjYNMduHCBSUnJysgINCqPCAwUNHR0QZF5fi4rpkvJSVF/V/ro7AaNVW6TBmjw3FoVapW0+Qvpmn+giX6dNwEHT9+TA3q1dGVK1eMDs1hpaSkaGD/fqoeVkOhpdO+P7+aMU0lSpZSteo17BydY2j+RDn5+3jq6wWb09wf0TJM+4+e06bdx+57jOrlQ/RMw0qa+sOvmRWm0+DvlrFMJvtvjsrQxsGQIUNkMpmstpIlSxoZEoC7+vSK1N69v2vmN7ONDsXhhT/VSE8/00Zly5XTkw3DNe/nRYqPi9MP339ndGgO6/U+vbRv315NnZF2d5fr169r7nffqmMEWYP7iWhZQ8t+3adz5+NT7fNwz662jSr/Y9YgtGg+fTf6Rb03ebFWbfojM0MFYEeGj4grXbq0Vq5caXnsbIP08uTJI1dXV8XGxliVx8bEKCgoyKCoHB/XNXP16d1Tixcv1MrV61WgQAGjw3E6/v7+KvZocR09fNjoUBxS/769tWzJIi1asUaP3Of+nD/vB12/dk3tn+tk5+gcQ3C+nKpXrYTavZ72WIxWDSooh4ebvlmY9kw6JYsEafHnvfTlDxv1wRfLMjNUp8HfLTgKw7sVZcuWTUFBQZYtT548RoeUodzc3FTxsUpas3qVpSwlJUVr1qxS1ephBkbm2LiumcNsNqtP7576ef48LV2+WoVDQowOySldvXpVx44eUVC+fEaH4lDMZrP69+2thT//pJ+XrFDhwve/P7+a8aUaNWmmPHnz2jFCx9GpeZhiL13Rkg1709zfpWUNLVq3RxcuX021r1SRIC2d3FvfLNisIeMXZHaoToO/W8ZiJlPbGf41/aFDh5Q/f355eHgoLCxMI0eOVHBwsNFhZajeffqp+/MRqlSpsipXqarPxo7RtcREdSbd/VC4rhmvT69IzZk9S3N/nC9vHx9LP1g/Pz95enoaHJ3jGjjgdTVu0kzBwYV07txZDR82RK6urmrTtr3RoTmU1/v00tzvvtWs736Ut7ePYu7en75/uz+PHjmsjb9s0Nx5fHBNi8lkUucW1fXNws1KTk49I1WRgnlU67GiatlrYqp9oUXzacnk3lq5cb/Gfr1agbl9JEnJKeY0GxKwxt8tOAJDGwfVqlXT9OnTVaJECZ07d05Dhw5V7dq19fvvv8vHxydV/Zs3b+rmzZuWxwkJCfYM94G1ebatLpw/r2FDBysmOlrlylfQ/IVLFRgY+O9Pxn1xXTPe5M/vfBhoWP9x6/IvpqlTRBf7B+Qkzpw+o4hOz+nSxYvKkzevatSopbUbopSXb7XTZeqUSZKkpuH1rcrHfz5VHTpFWB5/PWOaHnmkgOo1aGjX+BxFvWolFJwvl2b8tCnN/REtwnQmJk4ro1KPI2jVoKICcvnouaZV9VzTqpbyE2cvqmSTdzItZmfB3y0D2fvrfAdOHWSpdQ7i4uJUqFAhffLJJ+rWrVuq/UOGDNHQoannYs7q6xwAyFxZ6NeYU8nq6xw4sqyyzoGzyerrHDgaZ1rnYN0e+69zULcs6xw8NH9/fxUvXlyH7zNIb+DAgYqPj7dsp06dsnOEAAAAgPPKUo2Dq1ev6siRI8p3n0F67u7u8vX1tdoAAACAf2Iy4J+jMrRx8Prrr2vdunU6fvy4Nm7cqFatWsnV1VXt2zNIDwAAALA3Qwcknz59Wu3bt9fFixeVN29e1apVS5s2bWKQHgAAADKMvVctduQVkg1tHMyezcqrAAAAQFZh+DoHAAAAQGZiJlPbZakByQAAAACMQ+MAAAAAgCS6FQEAAMDZ0a/IZmQOAAAAAEgicwAAAAAnZ++FyVgEDQAAAIDDI3MAAAAAp8YiaLYjcwAAAABAEo0DAAAAAHfRrQgAAABOjZlMbUfmAAAAAIAkMgcAAABwdqQObEbmAAAAAIAkMgcAAABwciyCZjsyBwAAAAAk0TgAAAAAcBfdigAAAODUWCHZdmQOAAAAAEgicwAAAAAnx0ymtiNzAAAAAEASmQMAAAA4O1IHNiNzAAAAAEASjQMAAAAAd9GtCAAAAE6NFZJtR+YAAAAAgCQyBwAAAHByLIJmOzIHAAAAACSROQAAAICTYyZT25E5AAAAACCJxgEAAACAu+hWBAAAAOdGvyKbkTkAAAAAIInMAQAAAJwci6DZjswBAAAAAEk0DgAAAADcRbciAAAAODc7r5DswL2KaBwAcHwmR16nPgtzz+5qdAhO6/LWz4wOwSnlDOtndAhOxZx80+gQYAAaBwAAAHBqzGRqO8YcAAAAAJBE4wAAAADOzmTAlg7r169Xs2bNlD9/fplMJv30009W+81mswYPHqx8+fLJ09NTDRo00KFDh6zqXLp0SR06dJCvr6/8/f3VrVs3Xb16NX2BiMYBAAAAYKjExESVL19e48ePT3P/qFGjNHbsWE2aNEmbN2+Wl5eXwsPDdePGDUudDh06aO/evVqxYoUWLlyo9evX68UXX0x3LIw5AAAAAAzUqFEjNWrUKM19ZrNZY8aM0aBBg9SiRQtJ0syZMxUYGKiffvpJ7dq10/79+7V06VJt3bpVlStXliSNGzdOjRs31kcffaT8+fPbHAuZAwAAADg1kwH/MsqxY8cUHR2tBg0aWMr8/PxUrVo1RUVFSZKioqLk7+9vaRhIUoMGDeTi4qLNmzen63xkDgAAAIBMkJCQYPXY3d1d7u7u6TpGdHS0JCkwMNCqPDAw0LIvOjpaAQEBVvuzZcumXLlyWerYiswBAAAAnJrJZP9NkgoWLCg/Pz/LNnLkSGMvhA3IHAAAAACZ4NSpU/L19bU8Tm/WQJKCgoIkSTExMcqXL5+lPCYmRhUqVLDUiY2NtXre7du3denSJcvzbUXmAAAAAE7NqJlMfX19rbYHaRyEhIQoKChIq1atspQlJCRo8+bNCgsLkySFhYUpLi5O27dvt9RZvXq1UlJSVK1atXSdj8wBAAAAYKCrV6/q8OHDlsfHjh3Trl27lCtXLgUHB6tPnz4aPny4Hn30UYWEhOjtt99W/vz51bJlS0lSqVKl9NRTT6l79+6aNGmSbt26pZ49e6pdu3bpmqlIonEAAAAAGGrbtm164oknLI/79esnSYqIiND06dP1xhtvKDExUS+++KLi4uJUq1YtLV26VB4eHpbnfPPNN+rZs6fq168vFxcXtW7dWmPHjk13LCaz2Wx++JdkjISEBPn5+SnmYrxVfy4AAPDfkzOsn9EhOBVz8k3d3DlB8fGO+znr3mfF347FyMfHfq/hypUElQsJdMhrx5gDAAAAAJLoVgQAAAAnl9ELk9lyPkdF5gAAAACAJDIHAAAAcHIm/bkwmb3O56jIHAAAAACQROMAAAAAwF10KwIAAIBT++uqxfY6n6MicwAAAABAEpkDAAAAODmTyc4Dkh04dUDmAAAAAIAkMgcAAABweow6sBWZAwAAAACSaBwAAAAAuItuRQAAAHBqDEi2HZkDAAAAAJLIHAAAAMDJMRzZdmQOAAAAAEgicwAAAAAnx5gD25E5AAAAACCJxkGm+2XDerVu2Uwhwfnlmd2kn+f/ZHRITmXShPEqUayw/L09VLtGNW3dssXokJwC1zVzcF0zFr9fMwfXNf3+mD9I17d+kmob/cbTkqRlk15JtW/sm89YHaNSaEEtnvCyzq1+T2dXDdfPY19U2UfzG/Fy8B9H4yCTJSYmqmy58hozdrzRoTidud/N0YD+/fTWoHcUtWWHypUrr+ZNwhUbG2t0aA6N65o5uK4Zj9+vmYPrmn61Ikar8FPvWLbGkRMlST+u3G2pM3VelFWdt8YtsOzz8nTT/E9f1KnoONXpOkb1u4/T1Ws39fO4F5XNlY9qGcFkwD9HxR2XycKfaqQhw4arRctWRofidMaO+URdu3VX5y5dVSo0VOMmTJJnjhyaMf1Lo0NzaFzXzMF1zXj8fs0cXNf0uxCXqJiLVyxb41qldeTUBW3YccRS5/qNW1Z1riTetOwrUThAuf299O7nS3XoxHntPxqj96YsV1BuXwXny2XES8J/GI0DOKSkpCTt3LFd9eo3sJS5uLioXr0G2rIpysDIHBvXNXNwXYH/juzZXNWu0WOa8fNmq/K2Tz2mUyuGadvs/hoW2USe7tkt+w6eOK8LcVcV0byasmdzlYd7dnVpUU37j0brxLlL9n4JzslkwOagmK0IDunChQtKTk5WQECgVXlAYKAOHPjDoKgcH9c1c3Bdgf+O5o+Xkb+3p75euNVSNmfZDp08d1nnzieo7KP5NLxnUxUvlFft3pguSbp67abCX56g7z58XgO7PSlJOnzqvJr3mqzk5BQjXgb+wwzPHJw5c0YdO3ZU7ty55enpqbJly2rbtm1GhwUAAJBuEc2raVnUHzp3IcFS9uW8TVq56YD2Hjmn2Ut3qNuQWWrxRDmFPJJbkuThnl2TBrVV1O5jqvv8p6r3wjjtOxKtH8e8II+/ZBjw4Egc2M7QxsHly5dVs2ZNZc+eXUuWLNG+ffv08ccfK2fOnEaGBQeQJ08eubq6KjY2xqo8NiZGQUFBBkXl+LiumYPrCvw3BAflVL2qxTX9p03/WG/r7yclSUUL5pEktQ1/TMH5cunFYbO1fd8pbfn9hCIGfa3C+XOpWZ3SmR438FeGNg4++OADFSxYUNOmTVPVqlUVEhKihg0bqmjRokaGBQfg5uamio9V0prVqyxlKSkpWrNmlapWDzMwMsfGdc0cXFfgv6FTs6qKvXxVS37d/4/1yhe/M0Vp9N3sQg6P7Eoxm2U2my117jy+Mz4JsCdDxxz8/PPPCg8PV5s2bbRu3To98sgjeuWVV9S9e3cjw8pQV69e1ZHDhy2Pjx87pt27dilnrlwKDg42MDLH17tPP3V/PkKVKlVW5SpV9dnYMbqWmKjOEV2NDs2hcV0zB9c14/H7NXNwXR+MyWRS52ZV9M2irVbjBEIeya22Tz2mZb/u18X4RJV9NL9G9W2hDTuO6PfD5yRJqzYf1IjezTRmQGtNnLNBLi4mvR5RX7eTU7Ru2yGjXpJTYYVk2xnaODh69KgmTpyofv366X//+5+2bt2q3r17y83NTREREanq37x5Uzdv/jn1V0JCQqo6Wc2O7dsU3uAJy+MB/ftJkjp2itCUL6cbFJVzaPNsW104f17Dhg5WTHS0ypWvoPkLlyowMPDfn4z74rpmDq5rxuP3a+bguj6YelUfVXC+XJrxs/XihrduJ6te1eLq2a6OvDzddDomTj+t/k3vf7nCUufgiVi17jdVb3VvqLVfvqqUFLN2HzytFr0nK/riFXu/FPzHmcx/zWHZmZubmypXrqyNGzdaynr37q2tW7cqKir19H5DhgzR0KFDU5XHXIyXr69vpsYKAACytpxh/YwOwamYk2/q5s4Jio933M9ZCQkJ8vPz05HTF+Vjx9dwJSFBRQvkdshrZ2hHtnz58ik0NNSqrFSpUjp58mSa9QcOHKj4+HjLdurUKXuECQAAAPwnGNqtqGbNmjpw4IBV2cGDB1WoUKE067u7u8vd3d0eoQEAAAD/OYZmDvr27atNmzZpxIgROnz4sGbNmqXJkycrMjLSyLAAAADgTFjowGaGNg6qVKmiefPm6dtvv1WZMmX07rvvasyYMerQoYORYQEAAAD/SYZ2K5Kkpk2bqmnTpkaHAQAAACdl7y/zHThxYGzmAAAAAEDWYXjmAAAAAMhMLIJmOzIHAAAAACTROAAAAABwF92KAAAA4ORMMjEk2SZkDgAAAABIInMAAAAAJ8eAZNuROQAAAAAgicYBAAAAgLtoHAAAAACQROMAAAAAwF0MSAYAAIBTY0Cy7cgcAMD/27v7qCiu8w/g31mQBWTFKAZBEImgAgWNYDx4UokpKaRJo3JSfEFBRNpIORqJKLa8GWNoTGIVT40mVjHWCGoaa42ag1aNKZpEEFNEViC+YMUYQpS3Iwu79/eHML9uiBaQZRz6/Xg4sjOXmYd79izzzHPvXCIiIgLAygERERER9XFSLy+C1rsLrvUsVg6IiIiIiAgAKwdERERE1MdxzkHnsXJAREREREQAmBwQEREREVEbDisiIiIioj5NavvqzfOpFSsHREREREQEgJUDIiIiIurrWDroNFYOiIiIiIgIACsHRERERNTHcRG0zmPlgIiIiIiIADA5ICIiIiKiNhxWRERERER9GldI7jxWDoiIiIiICAArB0RERETUx/FJpp3HygEREREREQFg5YCIiIiI+jqWDjqNlQMiIiIiIgLA5ICIiIiIiNowOSAiIiKiPk1S4F93/OlPf8KIESNga2uLiRMn4osvvujhnvjvmBwQERERESksLy8PSUlJyMjIQFFREcaOHYuwsDDcvHmzV+NgckBEREREfVr7Imi9+dVVa9euRXx8PGJjY+Hr64tNmzbB3t4eW7du7fkOuQ8mB0RERERECjIYDCgsLERoaKi8TaPRIDQ0FKdOnerVWFT9KFMhBACgvq5O4UiIiIhIacLYrHQIfYowGu7+33a9pWZ1vXyt2H6+H55Xq9VCq9V2aF9TUwOj0QhnZ2ez7c7OzigrK7NcoD9C1clBfX09AMDL013hSIiIiIj6pvr6ejg6OiodRrfY2Nhg6NCh8FbgWtHBwQHu7ubnzcjIQGZmZq/H0hWqTg5cXV1RVVUFnU4HqTuDu3pRXV0d3N3dUVVVhQEDBigdTp/BfrUM9qtlsF8tg/1qGexXy1BTvwohUF9fD1dXV6VD6TZbW1tcunQJBoOh188thOhwffpjVQMAcHJygpWVFb755huz7d988w2GDh1qsRh/jKqTA41GAzc3N6XD6JIBAwY89B8GasR+tQz2q2WwXy2D/WoZ7FfLUEu/qrVi8J9sbW1ha2urdBj3ZWNjg8DAQBw9ehTTpk0DAJhMJhw9ehSJiYm9GouqkwMiIiIior4gKSkJMTExCAoKwhNPPIF169ahsbERsbGxvRoHkwMiIiIiIoXNmDED3377LdLT03Hjxg2MGzcOhw8f7jBJ2dKYHPQSrVaLjIyMe441o+5hv1oG+9Uy2K+WwX61DParZbBf6X4SExN7fRjRD0miLzyfioiIiIiIHhgXQSMiIiIiIgBMDoiIiIiIqA2TAyIiIiIiAsDkgIiIiIiI2jA5sDCTyQSj0ah0GERdwucUkBpUV1ejtLRU6TD6nPa/Wfwc6FlNTU2KrNJL1FVMDiyotLQU0dHRCAsLw8KFC1FQUKB0SH0Cky3LaGxsRH19Perq6jos907dV1tbi7KyMpSXl/PCoAf9+9//hr+/P1JTU3HmzBmlw+kziouLMW3aNDQ1NfFzoAeVlJQgMjISp0+fRnNzs9LhEN0XkwML0ev1mDRpEoxGIyZMmIBTp05h8eLFyM7OVjo0Vbt48SLWrVuH6upqpUPpU0pLSxEREYGQkBD4+Phg586dAHjn8EGVlJQgNDQUkZGR8Pf3x5o1a5jc9pDy8nLcvn0bt2/fxoYNG1BUVCTv4/u2e86dO4dJkybBz88P9vb28nb254M5f/48fvrTn8LNzQ2enp5c34AeelznwAKEEEhNTUVFRQXy8vIAAPX19cjOzsbevXsxa9YsLFu2TOEo1aeiogITJ07E999/j5SUFCQlJcHJyUnpsFSvtLQUkydPRnR0NIKCglBYWIgNGzbgiy++wLhx45QOT7Xa+zU2NhaxsbE4dOgQkpOTceXKFbi7uysdnurV1tYiNjYWzz33HDZv3gwfHx+sWLECfn5+MJlM0Gh476srvvrqK0yaNAkJCQlYs2aNvN1gMMDGxkbByNStsbERERERGDlyJDZu3AgAKCsrw507dzBo0CAMHz5c4QiJOuIKyRYgSRKuX7+OGzduyNt0Oh0WLVoEW1tb5ObmYtiwYYiKilIwSnVpbGxEVlYWXnjhBUyYMAGJiYlobW3FsmXLmCA8gNraWixZsgRRUVFYu3YtAGD27NkoKirC1q1bkZ2dDSEEhxd0UU1NDRYuXIg5c+bgzTffBAD4+PjgyJEjuHbtGr777jsMHjyYSUI3GY1GGI1GlJWVYePGjRgyZAiysrKwfv16nD9/Hi4uLti7d6/SYarGjRs3EBYWhieffFKubi1duhTl5eWorKzEb37zG4SHh2PMmDFKh6o61tbWaGpqQnx8PIxGI5577jl5qKGfnx8WLFiAuLg4pcMkMsPkoIe1X0iNHz8e5eXl0Ov1GD16NIC7CcL8+fOh1+uxceNGTJ8+3ax0S/em0WgQGBiIwYMHY8aMGXBycsLMmTMBgAnCA2hpacGtW7fw4osvAoB8x9XT0xO1tbUAwMSgGyRJQnh4uNyvAPDaa6/hk08+wY0bN1BTUwM/Pz+kpqbiySefVDBSddJoNBgyZAgmTJiAkpISTJ8+HVqtFjExMWhubkZ8fLzSIapOcHAwqqqq8Le//Q2bNm1CS0sLxo0bhxEjRiA7OxslJSVIT0/nne4uunXrFvR6PWpqapCcnAwA2LJlC65fv45//OMfSE1NhaOjo9lnBZHiBFlERUWFcHJyEvPnzxf19fVCCCFMJpMQQoirV68KSZLEoUOHlAxRdRoaGsxe5+bmCkmSxNKlS0VNTY0QQgij0Si+/vprJcJTrYsXL8rfGwwGIYQQqampYu7cuWbt2t/H1Dl1dXXy97t27RKSJIm8vDzx3XffiRMnTogJEyaIzMxMBSNUv+joaJGSkiKEECIuLk488sgjwtfXV8yfP198/vnnCkenLtevXxfR0dHCzs5OPPPMM/JnqhBC7Ny5UwwcOFAcPHhQwQjVyWQyiZkzZ4rExETx/PPPi8OHD8v7qqqqxJw5c8RLL70kWltb5WsEIqWxcmAhI0eOxO7du/Hss8/Czs4OmZmZ8t3tfv36ISAgAI6OjgpHqS79+/cHcHdIgUajwYwZMyCEwOzZsyFJEl5++WW89dZbuHLlCnbs2MGqTCd5e3sDuFs16NevH4C7FbCbN2/KbbKysqDVarFo0SJYW/NjozN0Op38fXBwMM6cOYPx48cDACZPnoxHH30UhYWFSoWnaqKtQvv000/j0qVLSEhIwMGDB1FYWIji4mIkJyfDxsYGAQEBsLW1VTpcVXBxcUFWVhaGDRuG0NBQDB48WO7n2bNnIyMjA8eOHcOzzz6rdKiqIkkSXnnlFTz11FNoamrCr3/9a3mfm5sbnJ2d8eWXX0Kj0bBKSw8N/pW3oClTpmDPnj341a9+herqakRGRiIgIADvv/8+bt68yfHG3WRlZQUhBEwmE2bOnAlJkjB37lzs378flZWV+PLLL5kYdINGozGbX9A+oTM9PR2vvfYazp49y8Sgmzw8PODh4QHgbhJmMBjg4OCAgIAAhSNTp/b3qKenJ2JjY+Hs7IwDBw7A09MTnp6ekCQJY8eOZWLQRa6urkhJSZH7TZIkCCFQW1uLIUOG8AEF3RQUFIRDhw4hJCQE7777Lh577DH4+fkBuDu0c9SoUWhtbZVvzhApjU8r6gVFRUVISkrC5cuXYW1tDSsrK+Tm5uLxxx9XOjRVa3/rSpKEn/3sZyguLsbx48fh7++vcGTq1T7nIDMzE9XV1fD29kZqaioKCgrku9704NLT07F9+3YcOXJErtxQ17W0tGDHjh0ICgpCQEAAJ89bSEZGBnbt2oX8/Hw5yaWu+/TTTzFr1iy4ubnB398fBoMB+/fvx2effYaf/OQnSodHJGNy0Evq6upQW1uL+vp6uLi4cAJtDzEajUhOTsa6detQXFzMO7E9ZPXq1UhLS8OAAQNw5MgRBAUFKR1Sn7Bnzx6cOHECubm5yM/P5w2CHsDHllpObm4ujh07hj179uDo0aN8v/YAvV6Pv/zlLzh9+jS8vb2RkJDAxIAeOkwOSNWMRiNycnIQGBjIkncPOnPmDJ544gmUlJTA19dX6XD6jPPnz+PVV19FZmYmfHx8lA6H6L6++uor/O53v8Mbb7whD4OhnmEymQCAiS09lJgckOpxKIFlNDY2ypPAqee0tLRwbDGpBhdBI/rfw+SAiIiIiIgAAKxnERERERERACYHRERERETUhskBEREREREBYHJARERERERtmBwQEREREREAJgdERERERNSGyQER0Q/MmzcP06ZNk18/9dRTePnll3s9juPHj0OSJNy6deuebSRJwr59+zp9zMzMzAdeMPDy5cuQJAnFxcUPdBwiInr4MDkgIlWYN28eJEmCJEmwsbGBl5cXXn31VbS2tlr83H/961+xatWqTrXtzAU9ERHRw8pa6QCIiDorPDwc27ZtQ3NzMw4ePIjf/va36NevH1asWNGhbU+u7Dpo0KAeOQ4REdHDjpUDIlINrVaLoUOHwsPDAwsXLkRoaCj2798P4P+HAq1evRqurq4YPXo0AKCqqgqRkZEYOHAgBg0ahKlTp+Ly5cvyMY1GI5KSkjBw4EAMHjwYy5Ytww8Xjv/hsKLm5mYsX74c7u7u0Gq18PLywp///GdcvnwZU6ZMAQA88sgjkCQJ8+bNAwCYTCZkZWXB09MTdnZ2GDt2LPbu3Wt2noMHD2LUqFGws7PDlClTzOLsrOXLl2PUqFGwt7fHY489hrS0NLS0tHRot3nzZri7u8Pe3h6RkZG4ffu22f4tW7bAx8cHtra2GDNmDDZu3NjlWIiISH2YHBCRatnZ2cFgMMivjx49Cr1ej/z8fBw4cAAtLS0ICwuDTqfDyZMn8c9//hMODg4IDw+Xf+7tt99GTk4Otm7dis8++wy1tbX46KOP7nve6Oho7Nq1C9nZ2bhw4QI2b94MBwcHuLu748MPPwQA6PV6VFdXY/369QCArKwsvP/++9i0aRPOnz+PJUuWYM6cOThx4gSAu0lMREQEfvnLX6K4uBgLFixASkpKl/tEp9MhJycHpaWlWL9+Pd577z388Y9/NGtTUVGB3bt34+9//zsOHz6Ms2fPIiEhQd6/c+dOpKenY/Xq1bhw4QJef/11pKWlYfv27V2Oh4iIVEYQEalATEyMmDp1qhBCCJPJJPLz84VWqxVLly6V9zs7O4vm5mb5Z3bs2CFGjx4tTCaTvK25uVnY2dmJTz75RAghhIuLi1izZo28v6WlRbi5ucnnEkKIkJAQsXjxYiGEEHq9XgAQ+fn5PxrnsWPHBADx/fffy9vu3Lkj7O3tRUFBgVnbuLg4MWvWLCGEECtWrBC+vr5m+5cvX97hWD8EQHz00Uf33P/mm2+KwMBA+XVGRoawsrIS165dk7cdOnRIaDQaUV1dLYQQYuTIkeKDDz4wO86qVatEcHCwEEKIS5cuCQDi7Nmz9zwvERGpE+ccEJFqHDhwAA4ODmhpaYHJZMLs2bORmZkp7/f39zebZ3Du3DlUVFRAp9OZHefOnTuorKzE7du3UV1djYkTJ8r7rK2tERQU1GFoUbvi4mJYWVkhJCSk03FXVFSgqakJzzzzjNl2g8GAxx9/HABw4cIFszgAIDg4uNPnaJeXl4fs7GxUVlaioaEBra2tGDBggFmb4cOHY9iwYWbnMZlM0Ov10Ol0qKysRFxcHOLj4+U2ra2tcHR07HI8RESkLkwOiEg1pkyZgnfeeQc2NjZwdXWFtbX5R1j//v3NXjc0NCAwMBA7d+7scKwhQ4Z0KwY7O7su/0xDQwMA4OOPPza7KAfuzqPoKadOnUJUVBRWrlyJsLAwODo6Ijc3F2+//XaXY33vvfc6JCtWVlY9FisRET2cmBwQkWr0798fXl5enW4/fvx45OXl4dFHH+1w97ydi4sLPv/8c0yePBnA3TvkhYWFGD9+/I+29/f3h8lkwokTJxAaGtphf3vlwmg0ytt8fX2h1Wpx9erVe1YcfHx85MnV7U6fPv3ff8n/UFBQAA8PD/z+97+Xt125cqVDu6tXr+L69etwdXWVz6PRaDB69Gg4OzvD1dUVX3/9NaKiorp0fiIiUj9OSCaiPisqKgpOTk6YOnUqTp48iUuXLuH48eNYtGgRrl27BgBYvHgx/vCHP2Dfvn0oKytDQkLCfdcoGDFiBGJiYjB//nzs27dPPubu3bsBAB4eHpAkCQcOHMC3336LhoYG6HQ6LF26FEuWLMH27dtRWVmJoqIibNiwQZ7k+9JLL6G8vBzJycnQ6/X44IMPkJOT06Xf19vbG1evXkVubi4qKyuRnZ39o5OrbW1tERMTg3PnzuHkyZNYtGgRIiMjMXToUADAypUrkZWVhezsbFy8eBH/+te/sG3bNqxdu7ZL8RARkfowOSCiPsve3h6ffvophg8fjoiICPj4+CAuLg537tyRKwmvvPIK5s6di5iYGAQHB0On02H69On3Pe4777yDF198EQkJCRgzZgzi4+PR2NgIABg2bBhWrlyJlJQUODs7IzExEQCwatUqpKWlISsrCz4+PggPD8fHH38MT09PAHfnAXz44YfYt28fxo4di02bNuH111/v0u/7wgsvYMmSJUhMTMS4ceNQUFCAtLS0Du28vLwQERGBX/ziF/j5z3+OgIAAs0eVLliwAFu2bMG2bdvg7++PkJAQ5OTkyLESEVHfJYl7zbojIiIiIqL/KawcEBERERERACYHRERERETUhskBEREREREBYHJARERERERtmBwQEREREREAJgdERERERNSGyQEREREREQFgckBERERERG2YHBAREREREQAmB0RERERE1IbJARERERERAWByQEREREREbf4Pq6nm07Uw8WIAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2nd Model - XGboost"
      ],
      "metadata": {
        "id": "K6KNAf4aOs1S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Xgboost\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "best_params = {\n",
        "    'grow_policy': 'depthwise',\n",
        "    'n_estimators': 1318,\n",
        "    'learning_rate': 0.06854096966928382,\n",
        "    'gamma': 0.6829197356513476,\n",
        "    'subsample': 0.8942042552212566,\n",
        "    'colsample_bytree': 0.32036152443164206,\n",
        "    'max_depth': 18,\n",
        "    'min_child_weight': 10,\n",
        "    'reg_lambda': 4.33935436979607e-05,\n",
        "    'reg_alpha': 0.00014155585622193723\n",
        "}\n",
        "best_params['booster'] = 'gbtree'\n",
        "best_params['objective'] = 'multi:softmax'\n",
        "best_params[\"verbosity\"] = 0\n",
        "\n",
        "xgb = XGBClassifier(**best_params)\n",
        "xgb.fit(X_train, y_train)\n",
        "\n",
        "y_pred = xgb.predict(X_test)\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "# Plot confusion matrix\n",
        "#plt.figure(figsize=(10, 7))\n",
        "#ax = plt.subplot(1, 1, 1)\n",
        "#_ = utilities.plot_confusion_matrix_heatmap(ax, y_test, y_pred, cmap='Reds')\n",
        "plt.figure(figsize=(10, 7))\n",
        "plot_confusion_matrix(cm, classes=xgb.classes_)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 967
        },
        "id": "eljTjMFauZtl",
        "outputId": "919b5198-959f-467b-d320-0d8175f3befa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.95      0.94       809\n",
            "           1       0.87      0.88      0.87       841\n",
            "           2       0.83      0.81      0.82       829\n",
            "           3       0.81      0.85      0.83       801\n",
            "           4       0.90      0.87      0.88       818\n",
            "           5       0.96      0.97      0.96       806\n",
            "           6       1.00      1.00      1.00       761\n",
            "\n",
            "    accuracy                           0.90      5665\n",
            "   macro avg       0.90      0.90      0.90      5665\n",
            "weighted avg       0.90      0.90      0.90      5665\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3rd Model - LGBM\n"
      ],
      "metadata": {
        "id": "4xlIoPqSO4Y3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import lightgbm as lgb\n",
        "def optim_lgb(trial):\n",
        "    lgbParams = {\n",
        "        'num_class': 7,\n",
        "        'random_state': 42,\n",
        "        'metric': 'multi_logloss',\n",
        "        \"boosting_type\": \"gbdt\",\n",
        "        'objective': 'multiclass',\n",
        "\n",
        "        'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.05),\n",
        "        'n_estimators': trial.suggest_int('n_estimators', 400, 600),\n",
        "        'reg_alpha': trial.suggest_loguniform('reg_alpha', 1e-3, 10.0),\n",
        "        'reg_lambda': trial.suggest_loguniform('reg_lambda', 1e-1, 10.0),\n",
        "        'max_depth': trial.suggest_int('max_depth', 6, 20),\n",
        "        'colsample_bytree': trial.suggest_float('colsample_bytree', 0.3, 0.9),\n",
        "        'subsample': trial.suggest_float('subsample', 0.8, 1.0),\n",
        "        'min_child_samples': trial.suggest_int('min_child_samples', 10, 50),\n",
        "    }\n",
        "\n",
        "    lgb_model=lgb.LGBMClassifier(**lgbParams)\n",
        "\n",
        "    lgb_model.fit(X_train,y_train)\n",
        "\n",
        "    y_pred = lgb_model.predict(X_test)\n",
        "\n",
        "    acc = accuracy_score(y_test, y_pred)\n",
        "\n",
        "    return acc"
      ],
      "metadata": {
        "id": "rdWjSisz-j2S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if ran_optuna:\n",
        "    print('Number of finished trials: {}'.format(len(study.trials)))\n",
        "\n",
        "    print(f'Best trial: {study.best_trial.params}')\n",
        "\n",
        "    optuna.visualization.plot_optimization_history(study)\n",
        "\n",
        "    study.trials_dataframe().sort_values('value', ascending=False)\n",
        "\n",
        "    optuna.visualization.plot_slice(study)\n",
        "\n",
        "else:\n",
        "    # Moaz HyperParams\n",
        "    lgbParams = {\n",
        "        \"objective\": \"multiclass\",          # Objective function for the model\n",
        "        \"metric\": \"multi_logloss\",          # Evaluation metric\n",
        "        \"verbosity\": -1,                    # Verbosity level (-1 for silent)\n",
        "        \"boosting_type\": \"gbdt\",            # Gradient boosting type\n",
        "        \"random_state\": 42,       # Random state for reproducibility\n",
        "        \"num_class\": 7,                     # Number of classes in the dataset\n",
        "        'learning_rate': 0.030962211546832760,  # Learning rate for gradient boosting\n",
        "        'n_estimators': 500,                # Number of boosting iterations\n",
        "        'lambda_l1': 0.009667446568254372,  # L1 regularization term\n",
        "        'lambda_l2': 0.04018641437301800,   # L2 regularization term\n",
        "        'max_depth': 10,                    # Maximum depth of the trees\n",
        "        'colsample_bytree': 0.40977129346872643,  # Fraction of features to consider for each tree\n",
        "        'subsample': 0.9535797422450176,    # Fraction of samples to consider for each boosting iteration\n",
        "        'min_child_samples': 26,            # Minimum number of data needed in a leaf\n",
        "        'boosting_type': 'gbdt',\n",
        "        'num_class': 7,\n",
        "        'random_state': 42,\n",
        "        'metric': 'multi_logloss',\n",
        "    }\n",
        "\n",
        "\n",
        "lgb_model_final = lgb.LGBMClassifier(**lgbParams)\n",
        "lgb_model_final.fit(X_train, y_train)\n",
        "\n",
        "y_pred = lgb_model_final.predict(X_test)\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "# Plot confusion matrix\n",
        "# plt.figure(figsize=(10, 7))\n",
        "# ax = plt.subplot(1, 1, 1)\n",
        "# utilities.plot_confusion_matrix_heatmap(ax, y_test, y_pred, cmap='Greens')\n",
        "plt.figure(figsize=(10, 7))\n",
        "plot_confusion_matrix(cm, classes=lgb_model_final.classes_)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 967
        },
        "id": "hgzqe34u-lmv",
        "outputId": "3208603b-18b3-4c9b-adcc-d15b5ff33996"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.94      0.94       809\n",
            "           1       0.87      0.87      0.87       841\n",
            "           2       0.83      0.82      0.82       829\n",
            "           3       0.82      0.85      0.84       801\n",
            "           4       0.90      0.87      0.89       818\n",
            "           5       0.96      0.97      0.96       806\n",
            "           6       1.00      1.00      1.00       761\n",
            "\n",
            "    accuracy                           0.90      5665\n",
            "   macro avg       0.90      0.90      0.90      5665\n",
            "weighted avg       0.90      0.90      0.90      5665\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Round 2 Models"
      ],
      "metadata": {
        "id": "OdDbrhykPGGL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set Prameters for Reproduciblity\n",
        "pd.set_option(\"display.max_rows\",100)\n",
        "FILE_PATH = \"/content/Dataset/\"\n",
        "TARGET = \"NObeyesdad\"\n",
        "n_splits = 9\n",
        "RANDOM_SEED = 73"
      ],
      "metadata": {
        "id": "w84XnBR5Cbx3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = pd.read_csv(os.path.join(FILE_PATH, \"train.csv\"))"
      ],
      "metadata": {
        "id": "RAk0ADItMkx9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# In age_rounder, height_rounder func we multiply values\n",
        "# by some value this sometimes improve model CV score\n",
        "# In Extract features we combine features to get new features\n",
        "\n",
        "def age_rounder(x):\n",
        "    x_copy = x.copy()\n",
        "    x_copy['Age'] = (x_copy['Age']*100).astype(np.uint16)\n",
        "    return x_copy\n",
        "\n",
        "def height_rounder(x):\n",
        "    x_copy = x.copy()\n",
        "    x_copy['Height'] = (x_copy['Height']*100).astype(np.uint16)\n",
        "    return x_copy\n",
        "\n",
        "def extract_features(x):\n",
        "    x_copy = x.copy()\n",
        "    x_copy['BMI'] = (x_copy['Weight']/x_copy['Height']**2)\n",
        "#     x_copy['PseudoTarget'] = pd.cut(x_copy['BMI'],bins = [0,18.4,24.9,29,34.9,39.9,100],labels = [0,1,2,3,4,5],)\n",
        "    return x_copy\n",
        "\n",
        "def col_rounder(x):\n",
        "    x_copy = x.copy()\n",
        "    cols_to_round = ['FCVC',\"NCP\",\"CH2O\",\"FAF\",\"TUE\"]\n",
        "    for col in cols_to_round:\n",
        "        x_copy[col] = round(x_copy[col])\n",
        "        x_copy[col] = x_copy[col].astype('int')\n",
        "    return x_copy\n",
        "\n",
        "AgeRounder = FunctionTransformer(age_rounder)\n",
        "HeightRounder = FunctionTransformer(height_rounder)\n",
        "ExtractFeatures = FunctionTransformer(extract_features)\n",
        "ColumnRounder = FunctionTransformer(col_rounder)"
      ],
      "metadata": {
        "id": "hHamz1-0NlME"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# In cross_val_model we cross vaidate models using\n",
        "# Stratified K-Fold.\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.cluster import KMeans\n",
        "from statistics import mean, stdev\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn import linear_model\n",
        "from sklearn import datasets\n",
        "from sklearn.base import clone\n",
        "\n",
        "# Encoding target values with int\n",
        "target_mapping = {\n",
        "                  'Insufficient_Weight':0,\n",
        "                  'Normal_Weight':1,\n",
        "                  'Overweight_Level_I':2,\n",
        "                  'Overweight_Level_II':3,\n",
        "                  'Obesity_Type_I':4,\n",
        "                  'Obesity_Type_II':5 ,\n",
        "                  'Obesity_Type_III':6\n",
        "                  }\n",
        "\n",
        "# Define a method for Cross validation here we are using StartifiedKFold\n",
        "skf = StratifiedKFold(n_splits=n_splits)\n",
        "\n",
        "def cross_val_model(estimators,cv = skf, verbose = True):\n",
        "    '''\n",
        "        estimators : pipeline consists preprocessing, encoder & model\n",
        "        cv : Method for cross validation (default: StratifiedKfold)\n",
        "        verbose : print train/valid score (yes/no)\n",
        "    '''\n",
        "\n",
        "    # X = df_train.drop(['NObeyesdad', 'id'], axis=1)\n",
        "    # y = df_train['NObeyesdad']\n",
        "    X = df_train\n",
        "    # y= X.drop(['NObeyesdad', 'id'], axis=1)\n",
        "    y = X.pop(TARGET)\n",
        "    y = y.map(target_mapping)\n",
        "    y\n",
        "    test_predictions = np.zeros((len(df_test),7))\n",
        "    valid_predictions = np.zeros((len(X),7))\n",
        "\n",
        "    val_scores, train_scores = [],[]\n",
        "    for fold, (train_ind, valid_ind) in enumerate(skf.split(X,y)):\n",
        "        model = clone(estimators)\n",
        "        #define train set\n",
        "        X_train = X.iloc[train_ind]\n",
        "        y_train = y.iloc[train_ind]\n",
        "        #define valid set\n",
        "        X_valid = X.iloc[valid_ind]\n",
        "        y_valid = y.iloc[valid_ind]\n",
        "\n",
        "        model.fit(X_train, y_train)\n",
        "        if verbose:\n",
        "            print(\"-\" * 100)\n",
        "            print(f\"Fold: {fold}\")\n",
        "            print(f\"Train Accuracy Score:-{accuracy_score(y_true=y_train,y_pred=model.predict(X_train))}\")\n",
        "            print(f\"Valid Accuracy Score:-{accuracy_score(y_true=y_valid,y_pred=model.predict(X_valid))}\")\n",
        "            print(\"-\" * 100)\n",
        "\n",
        "\n",
        "        test_predictions += model.predict_proba(df_test)/cv.get_n_splits()\n",
        "        valid_predictions[valid_ind] = model.predict_proba(X_valid)\n",
        "        val_scores.append(accuracy_score(y_true=y_valid,y_pred=model.predict(X_valid)))\n",
        "    if verbose:\n",
        "        print(f\"Average Mean Accuracy Score:- {np.array(val_scores).mean()}\")\n",
        "    return val_scores, valid_predictions, test_predictions"
      ],
      "metadata": {
        "id": "GWP_-mDxBD-T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "t_WbIkdQJkLn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove NaN values from 'y' and corresponding rows from 'X'\n",
        "na_indices = y.isna()\n",
        "X = X[~na_indices]\n",
        "y = y.dropna()\n"
      ],
      "metadata": {
        "id": "u4_vKsf2KRAa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y.isnull().sum())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6sWg7p83EZb1",
        "outputId": "e896b8fc-ab2e-4a84-e12b-592af4704d1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "twGfmvx9GEuW",
        "outputId": "253785f0-d820-4573-944d-839940a35755"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        3\n",
              "1        1\n",
              "2        0\n",
              "3        6\n",
              "4        3\n",
              "        ..\n",
              "28317    5\n",
              "28318    5\n",
              "28319    5\n",
              "28320    5\n",
              "28321    5\n",
              "Name: NObeyesdad, Length: 28322, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_scores,val_predictions,test_predictions = cross_val_model(xgb)\n",
        "\n",
        "for k,v in target_mapping .items():\n",
        "    oof_list[f\"xgb_{k}\"] = val_predictions[:,v]\n",
        "\n",
        "for k,v in target_mapping.items():\n",
        "    predict_list[f\"xgb_{k}\"] = test_predictions[:,v]\n",
        "\n",
        "# 0.90634942296329\n",
        "#0.9117093455898445 with rounder\n",
        "#0.9163506382522121"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "as6630NA96ax",
        "outputId": "700a005d-a7cf-44d8-9e0d-2fde30e1164f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "DataFrame.dtypes for data must be int, float, bool or category. When categorical type is supplied, The experimental DMatrix parameter`enable_categorical` must be set to `True`.  Invalid columns:Gender: object, family_history_with_overweight: object, FAVC: object, CAEC: object, SMOKE: object, SCC: object, CALC: object, MTRANS: object",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-239-7aa462f50d82>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mval_scores\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mval_predictions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxgb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtarget_mapping\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0moof_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf\"xgb_{k}\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mval_predictions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-238-776c0c91c99e>\u001b[0m in \u001b[0;36mcross_val_model\u001b[0;34m(estimators, cv, verbose)\u001b[0m\n\u001b[1;32m     51\u001b[0m         \u001b[0my_valid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvalid_ind\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     54\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-\"\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    728\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    729\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 730\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    731\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    732\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, base_margin, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set, base_margin_eval_set, feature_weights, callbacks)\u001b[0m\n\u001b[1;32m   1498\u001b[0m                 \u001b[0mxgb_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meval_metric\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mearly_stopping_rounds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1499\u001b[0m             )\n\u001b[0;32m-> 1500\u001b[0;31m             train_dmatrix, evals = _wrap_evaluation_matrices(\n\u001b[0m\u001b[1;32m   1501\u001b[0m                 \u001b[0mmissing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1502\u001b[0m                 \u001b[0mX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py\u001b[0m in \u001b[0;36m_wrap_evaluation_matrices\u001b[0;34m(missing, X, y, group, qid, sample_weight, base_margin, feature_weights, eval_set, sample_weight_eval_set, base_margin_eval_set, eval_group, eval_qid, create_dmatrix, enable_categorical, feature_types)\u001b[0m\n\u001b[1;32m    519\u001b[0m     \"\"\"Convert array_like evaluation matrices into DMatrix.  Perform validation on the\n\u001b[1;32m    520\u001b[0m     way.\"\"\"\n\u001b[0;32m--> 521\u001b[0;31m     train_dmatrix = create_dmatrix(\n\u001b[0m\u001b[1;32m    522\u001b[0m         \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m         \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py\u001b[0m in \u001b[0;36m_create_dmatrix\u001b[0;34m(self, ref, **kwargs)\u001b[0m\n\u001b[1;32m    956\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_can_use_qdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_method\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbooster\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"gblinear\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    957\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 958\u001b[0;31m                 return QuantileDMatrix(\n\u001b[0m\u001b[1;32m    959\u001b[0m                     \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mref\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnthread\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_bin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_bin\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    960\u001b[0m                 )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    728\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    729\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 730\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    731\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    732\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, label, weight, base_margin, missing, silent, feature_names, feature_types, nthread, max_bin, ref, group, qid, label_lower_bound, label_upper_bound, feature_weights, enable_categorical, data_split_mode)\u001b[0m\n\u001b[1;32m   1527\u001b[0m                 )\n\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1529\u001b[0;31m         self._init(\n\u001b[0m\u001b[1;32m   1530\u001b[0m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1531\u001b[0m             \u001b[0mref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mref\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36m_init\u001b[0;34m(self, data, ref, enable_categorical, **meta)\u001b[0m\n\u001b[1;32m   1586\u001b[0m             \u001b[0mctypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbyref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1587\u001b[0m         )\n\u001b[0;32m-> 1588\u001b[0;31m         \u001b[0mit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1589\u001b[0m         \u001b[0;31m# delay check_call to throw intermediate exception first\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1590\u001b[0m         \u001b[0m_check_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    574\u001b[0m             \u001b[0mexc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    575\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 576\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m  \u001b[0;31m# pylint: disable=raising-bad-type\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    577\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    578\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__del__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36m_handle_exception\u001b[0;34m(self, fn, dft_ret)\u001b[0m\n\u001b[1;32m    555\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 557\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    558\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m             \u001b[0;31m# Defer the exception in order to return 0 and stop the iteration.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    639\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m         \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 641\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    642\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    643\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mabstractmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/data.py\u001b[0m in \u001b[0;36mnext\u001b[0;34m(self, input_data)\u001b[0m\n\u001b[1;32m   1278\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1279\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mit\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1280\u001b[0;31m         \u001b[0minput_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1281\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1282\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    728\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    729\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 730\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    731\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    732\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36minput_data\u001b[0;34m(data, feature_names, feature_types, **kwargs)\u001b[0m\n\u001b[1;32m    622\u001b[0m                 \u001b[0mnew\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcat_codes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_types\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_temporary_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 624\u001b[0;31m                 new, cat_codes, feature_names, feature_types = _proxy_transform(\n\u001b[0m\u001b[1;32m    625\u001b[0m                     \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m                     \u001b[0mfeature_names\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/data.py\u001b[0m in \u001b[0;36m_proxy_transform\u001b[0;34m(data, feature_names, feature_types, enable_categorical)\u001b[0m\n\u001b[1;32m   1313\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1314\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_is_pandas_df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1315\u001b[0;31m         arr, feature_names, feature_types = _transform_pandas_df(\n\u001b[0m\u001b[1;32m   1316\u001b[0m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menable_categorical\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_types\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1317\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/data.py\u001b[0m in \u001b[0;36m_transform_pandas_df\u001b[0;34m(data, enable_categorical, feature_names, feature_types, meta, meta_type)\u001b[0m\n\u001b[1;32m    488\u001b[0m             \u001b[0;32mor\u001b[0m \u001b[0mis_pa_ext_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m         ):\n\u001b[0;32m--> 490\u001b[0;31m             \u001b[0m_invalid_dataframe_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    491\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_pa_ext_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m             \u001b[0mpyarrow_extension\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/data.py\u001b[0m in \u001b[0;36m_invalid_dataframe_dtype\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    306\u001b[0m     \u001b[0mtype_err\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"DataFrame.dtypes for data must be int, float, bool or category.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m     \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"\"\"{type_err} {_ENABLE_CAT_ERR} {err}\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 308\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    309\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: DataFrame.dtypes for data must be int, float, bool or category. When categorical type is supplied, The experimental DMatrix parameter`enable_categorical` must be set to `True`.  Invalid columns:Gender: object, family_history_with_overweight: object, FAVC: object, CAEC: object, SMOKE: object, SCC: object, CALC: object, MTRANS: object"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y.isnull().sum())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KhMjwPxVEVez",
        "outputId": "70f5b8d7-b1e7-4f32-d269-41fcbb4dbdeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    }
  ]
}