US_Company_Bankruptcy_Detection_Project.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# US Company Bankruptcy Detection\n",
        "Dataset: https://www.kaggle.com/datasets/utkarshx27/american-companies-bankruptcy-prediction-dataset"
      ],
      "metadata": {
        "id": "VelrRyK2WJbk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Group 1 members:\n",
        "\n",
        "\n",
        "1. Chotiros Srisiam #101411914\n",
        "2. Kajhonprom Trongkitroongruang #101446812\n",
        "3. Pat Boonprasertsri #101410612\n",
        "4. Pek Chansatit #101439953\n",
        "5. Vitchaya Siripoppohn #101481464\n"
      ],
      "metadata": {
        "id": "FiOMBl2JNzRv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import libraries"
      ],
      "metadata": {
        "id": "w2Pd8zg6ObSs"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uky7yL93UJcq"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_curve, auc, precision_recall_curve, mean_squared_error, r2_score\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "from sklearn.exceptions import ConvergenceWarning\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore', category=ConvergenceWarning)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown 1TyN3LmRarxNtpcyOJNIqjskMplwDEEgU"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IhmB287yqiW2",
        "outputId": "77dbbfd9-38f6-4438-ef84-a4b0c3e9dea2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1TyN3LmRarxNtpcyOJNIqjskMplwDEEgU\n",
            "To: /content/american_bankruptcy.csv\n",
            "100% 11.3M/11.3M [00:00<00:00, 68.6MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"/content/american_bankruptcy.csv\"\n",
        "df = pd.read_csv(path)"
      ],
      "metadata": {
        "id": "r13P7xxLPenQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inspect the Dataset"
      ],
      "metadata": {
        "id": "s7n7Fq_7OmBM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Variable Name Description\n",
        "\n",
        "* X1 Current assets - All the assets of a company that are expected to be sold or used as a result of standard business operations over the next year\n",
        "* X2 Cost of goods sold - The total amount a company paid as a cost directly related to the sale of products\n",
        "* X3 Depreciation and amortization - Depreciation refers to the loss of value of a tangible fixed asset over\n",
        "time (such as property, machinery, buildings, and plant). Amortization refers to the loss of value of intangible assets over time.\n",
        "* X4 EBITDA - Earnings before interest, taxes, depreciation, and amortization. It is a measure of a company's overall financial performance, serving as an alternative to net income.\n",
        "* X5 Inventory - The accounting of items and raw materials that a company either uses in production or sells.\n",
        "* X6 Net Income - The overall profitability of a company after all expenses and costs have been deducted from total revenue.\n",
        "* X7 Total Receivables - The balance of money due to a firm for goods or services delivered or used but not yet paid for by customers.\n",
        "* X8 Market value - The price of an asset in a marketplace. In this dataset, it refers to the market capitalization since companies are publicly traded in the stock market.\n",
        "* X9 Net sales - The sum of a company's gross sales minus its returns, allowances, and discounts.\n",
        "* X10 Total assets - All the assets, or items of value, a business owns.\n",
        "* X11 Total Long-term debt - A company's loans and other liabilities that will not become due within one year of the balance sheet date.\n",
        "* X12 EBIT - Earnings before interest and taxes.\n",
        "* X13 Gross Profit - The profit a business makes after subtracting all the costs that are related to manufacturing and selling its products or services.\n",
        "* X14 Total Current Liabilities - The sum of accounts payable, accrued liabilities, and taxes such as Bonds payable at the end of the year, salaries, and commissions remaining.\n",
        "* X15 Retained Earnings - The amount of profit a company has left over after paying all its direct costs, indirect costs, income taxes, and its dividends to shareholders.\n",
        "* X16 Total Revenue - The amount of income that a business has made from all sales before subtracting expenses. It may include interest and dividends from investments.\n",
        "* X17 Total Liabilities - The combined debts and obligations that the company owes to outside parties.\n",
        "* X18 Total Operating Expenses - The expenses a business incurs through its normal business operations."
      ],
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        "df.tail()"
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      "execution_count": null,
      "outputs": [
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              "      company_name status_label  year       X1      X2      X3       X4  \\\n",
              "78677       C_8971        alive  2014  233.211  43.338  14.094   45.615   \n",
              "78678       C_8971        alive  2015  105.559  59.184  42.592  202.133   \n",
              "78679       C_8971        alive  2016   63.971  69.074  65.057   79.051   \n",
              "78680       C_8971        alive  2017  135.207  66.527  65.330   69.171   \n",
              "78681       C_8971        alive  2018   82.589  68.817  65.201   67.262   \n",
              "\n",
              "          X5       X6      X7  ...       X9       X10      X11      X12  \\\n",
              "78677  3.376   25.261  22.846  ...  104.223  1099.101  184.666   31.521   \n",
              "78678  2.288  129.688  54.611  ...  291.153  1865.926  770.103  159.541   \n",
              "78679  2.581   -1.442  42.467  ...  169.858  1746.235  683.985   13.994   \n",
              "78680  2.013  -20.401  27.217  ...  161.884  1736.110  694.035    3.841   \n",
              "78681  2.112  -50.946  45.839  ...  160.513  1625.370  632.122    2.061   \n",
              "\n",
              "           X13     X14      X15      X16      X17     X18  \n",
              "78677   60.885  28.197   28.095  104.223  225.887  58.608  \n",
              "78678  231.969  88.128  157.783  291.153  880.327  89.020  \n",
              "78679  100.784  85.765  156.341  169.858  770.233  90.807  \n",
              "78680   95.357  82.010  135.941  161.884  776.697  92.713  \n",
              "78681   91.696  79.365   84.995  160.513  712.687  93.251  \n",
              "\n",
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              "      <th>78677</th>\n",
              "      <td>C_8971</td>\n",
              "      <td>alive</td>\n",
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              "      <td>14.094</td>\n",
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              "      <td>25.261</td>\n",
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              "      <td>28.197</td>\n",
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              "      <td>59.184</td>\n",
              "      <td>42.592</td>\n",
              "      <td>202.133</td>\n",
              "      <td>2.288</td>\n",
              "      <td>129.688</td>\n",
              "      <td>54.611</td>\n",
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              "      <td>291.153</td>\n",
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              "      <td>231.969</td>\n",
              "      <td>88.128</td>\n",
              "      <td>157.783</td>\n",
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              "      <td>C_8971</td>\n",
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              "      <td>2016</td>\n",
              "      <td>63.971</td>\n",
              "      <td>69.074</td>\n",
              "      <td>65.057</td>\n",
              "      <td>79.051</td>\n",
              "      <td>2.581</td>\n",
              "      <td>-1.442</td>\n",
              "      <td>42.467</td>\n",
              "      <td>...</td>\n",
              "      <td>169.858</td>\n",
              "      <td>1746.235</td>\n",
              "      <td>683.985</td>\n",
              "      <td>13.994</td>\n",
              "      <td>100.784</td>\n",
              "      <td>85.765</td>\n",
              "      <td>156.341</td>\n",
              "      <td>169.858</td>\n",
              "      <td>770.233</td>\n",
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              "      <th>78680</th>\n",
              "      <td>C_8971</td>\n",
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              "      <td>135.207</td>\n",
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              "      <td>65.330</td>\n",
              "      <td>69.171</td>\n",
              "      <td>2.013</td>\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\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-4567cc83-b5d3-410b-871b-8b2f5bad7edc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "id": "jMC2olY2Peh4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "35e6d7ba-5ed9-4bed-cf1f-294d0937ebcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               year             X1             X2            X3            X4  \\\n",
              "count  78682.000000   78682.000000   78682.000000  78682.000000  78682.000000   \n",
              "mean    2007.506317     880.362485    1594.529029    121.234256    376.759424   \n",
              "std        5.742768    3928.564794    8930.484664    652.376804   2012.023142   \n",
              "min     1999.000000      -7.760000    -366.645000      0.000000 -21913.000000   \n",
              "25%     2002.000000      18.924000      17.038250      1.192000     -0.811000   \n",
              "50%     2007.000000     100.449500     103.661000      7.929500     15.034500   \n",
              "75%     2012.000000     431.526750     634.548000     47.971750    139.655250   \n",
              "max     2018.000000  169662.000000  374623.000000  28430.000000  81730.000000   \n",
              "\n",
              "                 X5             X6            X7            X8             X9  \\\n",
              "count  78682.000000   78682.000000  78682.000000  7.868200e+04   78682.000000   \n",
              "mean     201.605717     129.382453    286.832743  3.414355e+03    2364.019706   \n",
              "std     1060.766096    1265.532022   1335.978571  1.841410e+04   11950.068842   \n",
              "min        0.000000  -98696.000000     -0.006000  1.000000e-04   -1964.999000   \n",
              "25%        0.000000      -7.415750      3.281250  3.498000e+01      27.548500   \n",
              "50%        7.023000       1.616000     22.820000  2.275118e+02     186.598500   \n",
              "75%       74.747250      40.144250    131.580500  1.244890e+03    1046.402500   \n",
              "max    62567.000000  104821.000000  65812.000000  1.073391e+06  511729.000000   \n",
              "\n",
              "                 X10            X11           X12            X13  \\\n",
              "count   78682.000000   78682.000000  78682.000000   78682.000000   \n",
              "mean     2867.110620     722.483710    255.525035     769.490783   \n",
              "std     12917.944421    3242.170946   1494.643534    3774.703114   \n",
              "min         0.001000      -0.023000 -25913.000000  -21536.000000   \n",
              "25%        37.363500       0.000000     -2.787000       8.521250   \n",
              "50%       213.203500       7.593500      6.518000      63.581500   \n",
              "75%      1171.364750     248.760750     87.599000     344.074250   \n",
              "max    531864.000000  166250.000000  71230.000000  137106.000000   \n",
              "\n",
              "                 X14            X15            X16            X17  \\\n",
              "count   78682.000000   78682.000000   78682.000000   78682.000000   \n",
              "mean      610.072255     532.467069    2364.019706    1773.563963   \n",
              "std      2938.387443    6369.159440   11950.068842    8053.684902   \n",
              "min         0.001000 -102362.000000   -1964.999000       0.001000   \n",
              "25%         8.889250     -68.282750      27.548500      13.486000   \n",
              "50%        43.333000      -1.131000     186.598500      81.988000   \n",
              "75%       222.817000     146.070000    1046.402500     629.975000   \n",
              "max    116866.000000  402089.000000  511729.000000  337980.000000   \n",
              "\n",
              "                 X18  \n",
              "count   78682.000000  \n",
              "mean     1987.260307  \n",
              "std     10419.629038  \n",
              "min      -317.197000  \n",
              "25%        32.872500  \n",
              "50%       168.912000  \n",
              "75%       875.522250  \n",
              "max    481580.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7501d4a3-11c7-44c5-96ae-0cd0c3c65d69\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: top;\n",
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              "\n",
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              "        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>year</th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "      <th>X3</th>\n",
              "      <th>X4</th>\n",
              "      <th>X5</th>\n",
              "      <th>X6</th>\n",
              "      <th>X7</th>\n",
              "      <th>X8</th>\n",
              "      <th>X9</th>\n",
              "      <th>X10</th>\n",
              "      <th>X11</th>\n",
              "      <th>X12</th>\n",
              "      <th>X13</th>\n",
              "      <th>X14</th>\n",
              "      <th>X15</th>\n",
              "      <th>X16</th>\n",
              "      <th>X17</th>\n",
              "      <th>X18</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>78682.000000</td>\n",
              "      <td>78682.000000</td>\n",
              "      <td>78682.000000</td>\n",
              "      <td>78682.000000</td>\n",
              "      <td>78682.000000</td>\n",
              "      <td>78682.000000</td>\n",
              "      <td>78682.000000</td>\n",
              "      <td>78682.000000</td>\n",
              "      <td>7.868200e+04</td>\n",
              "      <td>78682.000000</td>\n",
              "      <td>78682.000000</td>\n",
              "      <td>78682.000000</td>\n",
              "      <td>78682.000000</td>\n",
              "      <td>78682.000000</td>\n",
              "      <td>78682.000000</td>\n",
              "      <td>78682.000000</td>\n",
              "      <td>78682.000000</td>\n",
              "      <td>78682.000000</td>\n",
              "      <td>78682.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2007.506317</td>\n",
              "      <td>880.362485</td>\n",
              "      <td>1594.529029</td>\n",
              "      <td>121.234256</td>\n",
              "      <td>376.759424</td>\n",
              "      <td>201.605717</td>\n",
              "      <td>129.382453</td>\n",
              "      <td>286.832743</td>\n",
              "      <td>3.414355e+03</td>\n",
              "      <td>2364.019706</td>\n",
              "      <td>2867.110620</td>\n",
              "      <td>722.483710</td>\n",
              "      <td>255.525035</td>\n",
              "      <td>769.490783</td>\n",
              "      <td>610.072255</td>\n",
              "      <td>532.467069</td>\n",
              "      <td>2364.019706</td>\n",
              "      <td>1773.563963</td>\n",
              "      <td>1987.260307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.742768</td>\n",
              "      <td>3928.564794</td>\n",
              "      <td>8930.484664</td>\n",
              "      <td>652.376804</td>\n",
              "      <td>2012.023142</td>\n",
              "      <td>1060.766096</td>\n",
              "      <td>1265.532022</td>\n",
              "      <td>1335.978571</td>\n",
              "      <td>1.841410e+04</td>\n",
              "      <td>11950.068842</td>\n",
              "      <td>12917.944421</td>\n",
              "      <td>3242.170946</td>\n",
              "      <td>1494.643534</td>\n",
              "      <td>3774.703114</td>\n",
              "      <td>2938.387443</td>\n",
              "      <td>6369.159440</td>\n",
              "      <td>11950.068842</td>\n",
              "      <td>8053.684902</td>\n",
              "      <td>10419.629038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1999.000000</td>\n",
              "      <td>-7.760000</td>\n",
              "      <td>-366.645000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-21913.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-98696.000000</td>\n",
              "      <td>-0.006000</td>\n",
              "      <td>1.000000e-04</td>\n",
              "      <td>-1964.999000</td>\n",
              "      <td>0.001000</td>\n",
              "      <td>-0.023000</td>\n",
              "      <td>-25913.000000</td>\n",
              "      <td>-21536.000000</td>\n",
              "      <td>0.001000</td>\n",
              "      <td>-102362.000000</td>\n",
              "      <td>-1964.999000</td>\n",
              "      <td>0.001000</td>\n",
              "      <td>-317.197000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2002.000000</td>\n",
              "      <td>18.924000</td>\n",
              "      <td>17.038250</td>\n",
              "      <td>1.192000</td>\n",
              "      <td>-0.811000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-7.415750</td>\n",
              "      <td>3.281250</td>\n",
              "      <td>3.498000e+01</td>\n",
              "      <td>27.548500</td>\n",
              "      <td>37.363500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-2.787000</td>\n",
              "      <td>8.521250</td>\n",
              "      <td>8.889250</td>\n",
              "      <td>-68.282750</td>\n",
              "      <td>27.548500</td>\n",
              "      <td>13.486000</td>\n",
              "      <td>32.872500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2007.000000</td>\n",
              "      <td>100.449500</td>\n",
              "      <td>103.661000</td>\n",
              "      <td>7.929500</td>\n",
              "      <td>15.034500</td>\n",
              "      <td>7.023000</td>\n",
              "      <td>1.616000</td>\n",
              "      <td>22.820000</td>\n",
              "      <td>2.275118e+02</td>\n",
              "      <td>186.598500</td>\n",
              "      <td>213.203500</td>\n",
              "      <td>7.593500</td>\n",
              "      <td>6.518000</td>\n",
              "      <td>63.581500</td>\n",
              "      <td>43.333000</td>\n",
              "      <td>-1.131000</td>\n",
              "      <td>186.598500</td>\n",
              "      <td>81.988000</td>\n",
              "      <td>168.912000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2012.000000</td>\n",
              "      <td>431.526750</td>\n",
              "      <td>634.548000</td>\n",
              "      <td>47.971750</td>\n",
              "      <td>139.655250</td>\n",
              "      <td>74.747250</td>\n",
              "      <td>40.144250</td>\n",
              "      <td>131.580500</td>\n",
              "      <td>1.244890e+03</td>\n",
              "      <td>1046.402500</td>\n",
              "      <td>1171.364750</td>\n",
              "      <td>248.760750</td>\n",
              "      <td>87.599000</td>\n",
              "      <td>344.074250</td>\n",
              "      <td>222.817000</td>\n",
              "      <td>146.070000</td>\n",
              "      <td>1046.402500</td>\n",
              "      <td>629.975000</td>\n",
              "      <td>875.522250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2018.000000</td>\n",
              "      <td>169662.000000</td>\n",
              "      <td>374623.000000</td>\n",
              "      <td>28430.000000</td>\n",
              "      <td>81730.000000</td>\n",
              "      <td>62567.000000</td>\n",
              "      <td>104821.000000</td>\n",
              "      <td>65812.000000</td>\n",
              "      <td>1.073391e+06</td>\n",
              "      <td>511729.000000</td>\n",
              "      <td>531864.000000</td>\n",
              "      <td>166250.000000</td>\n",
              "      <td>71230.000000</td>\n",
              "      <td>137106.000000</td>\n",
              "      <td>116866.000000</td>\n",
              "      <td>402089.000000</td>\n",
              "      <td>511729.000000</td>\n",
              "      <td>337980.000000</td>\n",
              "      <td>481580.000000</td>\n",
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              "    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-79a1a838-daf4-486a-8dd7-d72018bc2a62 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the ratio of our labels\n",
        "\n",
        "alive_count = df['status_label'].value_counts()['alive']\n",
        "failed_count = df['status_label'].value_counts()['failed']\n",
        "total_count = alive_count + failed_count\n",
        "alive_ratio = alive_count / total_count\n",
        "failed_ratio = failed_count / total_count\n",
        "\n",
        "print(\"Alive Ratio:\", alive_ratio)\n",
        "print(\"Failed Ratio:\", failed_ratio)"
      ],
      "metadata": {
        "id": "EYAC-hswWlHM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "341ecadd-a0b4-4ef2-df85-d7ddbe2dd658"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Alive Ratio: 0.9336569990595054\n",
            "Failed Ratio: 0.06634300094049465\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The \"Alive Ratio\" is approximately 0.934, which means that around 93.4% of the samples in the dataset belong to the \"alive\" class (e.g., companies that have not failed or gone bankrupt).\n",
        "\n",
        "* The \"Failed Ratio\" is approximately 0.066, which means that only about 6.6% of the samples in the dataset belong to the \"failed\" class (e.g., companies that have experienced bankruptcy or failure).\n",
        "\n",
        "This significant class imbalance can have implications for building predictive models. When the dataset is highly imbalanced, models may be biased towards the majority class (in this case, \"alive\") and may not perform well in identifying the minority class (in this case, \"failed\").\n",
        "\n",
        "**To address this issue, we consider employing techniques such as:**\n",
        "\n",
        "* Class Balancing Techniques: Using methods like oversampling the minority class (e.g., Synthetic Minority Over-sampling Technique - SMOTE) or undersampling the majority class to balance the class distribution.\n",
        "\n",
        "* Algorithm Selection: Choose algorithms that are less sensitive to class imbalance, such as ensemble methods (e.g., Random Forest, Gradient Boosting) or anomaly detection methods.\n",
        "\n",
        "* Cost-sensitive Learning: Assigning different misclassification costs for each class during model training to reflect the importance of correctly predicting the minority class."
      ],
      "metadata": {
        "id": "7N_5_k00XHyN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Finding Outlier"
      ],
      "metadata": {
        "id": "dDNNS4CHi7cI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Outlier\n",
        "Q1 = df[['X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8', 'X9', 'X10', 'X11', 'X12', 'X13', 'X14', 'X15', 'X16', 'X17', 'X18']].quantile(0.25)\n",
        "Q3 = df[['X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8', 'X9', 'X10', 'X11', 'X12', 'X13', 'X14', 'X15', 'X16', 'X17', 'X18']].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "outliers = ((df[['X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8', 'X9', 'X10', 'X11', 'X12', 'X13', 'X14', 'X15', 'X16', 'X17', 'X18']] < (Q1 - 1.5 * IQR)) | (df[['X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8', 'X9', 'X10', 'X11', 'X12', 'X13', 'X14', 'X15', 'X16', 'X17', 'X18']] > (Q3 + 1.5 * IQR)))\n",
        "\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.boxplot(data=df[['X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8', 'X9', 'X10', 'X11', 'X12', 'X13', 'X14', 'X15', 'X16', 'X17', 'X18']])\n",
        "plt.xticks(rotation=90)\n",
        "plt.title(\"Box Plot of Numerical Features with Outliers\")\n",
        "plt.xlabel(\"Features\")\n",
        "plt.ylabel(\"Values\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "2ByQgXHsWlE5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "outputId": "a05a1733-b69d-4b82-8f3e-cf15621aab52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cut off Outlier"
      ],
      "metadata": {
        "id": "KztUzBrRwuD9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the rows to be removed by finding the value each features from the outliers from above graph\n",
        "rows_to_remove = df[((df['company_name'] == 'C_102') | (df['company_name'] == 'C_1452')) & ((df['year'] == 2018) | (df['year'] == 2017)) # X8 and X11 Outlier Positive\n",
        "                   |((df['company_name'] == 'C_644') & (df['year'] == 2009))| # X6 Outlier Positive\n",
        "                    ((df['company_name'] == 'C_3532') & (df['year'] == 2002))].index # X6 Outlier Negative\n",
        "\n",
        "# Remove the specified rows\n",
        "cleaned_df = df.drop(rows_to_remove)\n",
        "\n",
        "# Plot boxplot without the specified rows\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.boxplot(data=cleaned_df[['X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8', 'X9', 'X10', 'X11', 'X12', 'X13', 'X14', 'X15', 'X16', 'X17', 'X18']])\n",
        "plt.xticks(rotation=90)\n",
        "plt.title(\"Box Plot of Numerical Features without Specific Outliers\")\n",
        "plt.xlabel(\"Features\")\n",
        "plt.ylabel(\"Values\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 647
        },
        "id": "2hT88boswry5",
        "outputId": "fd75a10a-1700-49dc-e849-b384368648bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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hqq+PiNwftxUQEV1FRkYGtm/fju3bt2PdunW45557MHv2bLz//vtSn4KCAqhUKsydO1f2bxcuXAhBEGTVDQwGA/r27Ytp06bhb3/7G4YPH273765l5syZ6NChAzp16oSYmBicP38ea9euleVFqO/kyZMoKirC9OnT0a5dO+n47bffjvvvvx8FBQUNfu76vvnmG5w+fRp/+9vfZPt1Y2Ji0Lt3b7vlzTciIiICDz/8MFauXImTJ0/+4e8nmj17tvS1SqXCHXfcAUEQMGvWLOl4UFAQevXqJasEsWHDBvTp0we9e/fGb7/9Jv0nbu347LPPZM8zfPhwREZGXnMsNpsNH374IcaOHevwdyguG/bz85OS2lmtVpSVlaFNmzbo1asXvvvuu0b+BP5nyZIl0vtb/C80NBRFRUU4cuQI4uLiUFZWJr3W8+fPY8SIETAajbDZbAAAf39/6fvV1dWhrKwMf/rTnxAUFPSHxnYtc+bMka202b59OyoqKjB16lTZ70alUmHw4MHS78bf3x++vr74/PPPUV5e3qjnHDZsGL788ksAl1dc7Nu3D48++ijat28vHf/yyy8RFBT0hypqREZGYtiwYdLjDh062L0XCwoKMGjQINx1113SsTZt2uDRRx/FTz/9hAMHDtzw8zeEQqHAtm3bsHTpUqjVarz77rt47LHH0K1bN0yePNlhzoHHHntM9vjxxx+XXgsAvP/++7DZbIiNjZX9DkNDQ3HrrbdKv8O9e/fCYrFg/vz5dvkDrrXMvqEa+pl05OzZswCAtm3bXrWP2FZVVfUHR9o4Df2M1JeUlCR7LK4M2LZtG6qrq5tl3ETU/LitgIjoKgYNGiQ7QZw6dSr69++P5ORkjBkzBr6+vvj555/RqVMnuxNCccnqzz//LB3z9fXF22+/jTvvvBMtW7bE6tWrG3VCu2TJEgwbNgwqlQrt27dHnz59rrmkV3zuXr162bX16dMH27Ztu6FEU9f6vr1795Yttf4jUlJSkJOTgxdeeEG2n/iPuOWWW2SPAwMD0bJlS2krRf3jZWVl0uMjR47ghx9+uOpWCzHxoig8PPy6Yzlz5gyqqqquezFps9nw2muv4Y033oDFYpHlp7jWlpLr0Wg0Dpc2HzlyBACkfBGOVFZWQq1W48KFC3j++eexevVqlJSUyEq1NdU+5Ct/tuJ4xUDNlQICAgBcDrK8+OKLWLhwIUJCQjBkyBCMGTMGjzzyCEJDQ6/5nMOGDUNWVhZ+/PFHHD16FAqFAlqtVgoazJkzB19++SWio6Nl1Qka68r3JwCo1WpZMOPnn3/G4MGD7frVn3OauuSnn58fFi9ejMWLF+PkyZP44osv8NprryE/Px8+Pj5Yt26drP+tt94qe9yjRw8olUppT/yRI0cgCIJdP5GPjw+A/22laqrX19DPpCPi34CzZ89eNQFrQwIITaGhnxFRixYtZPk8gMufuyeeeAKvvPIKcnNzMWzYMIwbN07K4UJEnoHBASKiBlIqlbjnnnvw2muv4ciRI9Ie4MbYtm0bAODixYs4cuRIgy4iRVe7mPNUERERSEhIwMqVK+3KkgFXv4t3reSOjnI7XC3fQ/0LXZvNBo1GI+WLuFLXrl1lj+vfUf+jli1bBr1ej5kzZyI9PV3KCzB//nzpDv7NJH7Pl156CVFRUQ77iHvSH3/8caxevRrz58+HVqtFYGAgFAoFpkyZ8ofHdrXf45U/W/F5cnJyHF7k1w+gzZ8/H2PHjsWHH36Ibdu2Qa/X4/nnn8enn36K/v37X3Us4l16o9GIY8eOYcCAAWjdujWGDRuGFStW4Ny5c9i7dy+ee+65Rr/O+hryXmyoG/l83IiwsDBMmTIFEyZMQN++fZGfn481a9ZcM3B55dhsNhsUCgU+/vhjhz+Da+VAcBV9+vTBhx9+iO+//17KVXIlMamluKqouX5HjfmMAPLVSvW9/PLLmD59Ov7973/jv//9L+bOnSvlkbgymEBE7onBASKiRrh06RIA4Ny5cwAuJ+f65JNPcPbsWdndoIMHD0rtou+//x7PPvssZsyYgaKiIsyePRtms7nJ7rqIz33o0CG7toMHD6J9+/bSqoHGrGCo/32vvBN16NAh2Wv+o1JSUrBu3Tq8+OKLdm1qtRoA7JYx11+tcbP06NED+/btw4gRI27K8mXg8pLxgIAA7N+//5r9Nm7ciHvuuQerVq2SHa+oqLBb8XAziEkjAwICrhuM2rhxI6ZNmyar7nDx4kW738m1fmZqtdquf21tbYO3k4jj7dixY4OCZz169MDChQuxcOFCHDlyBFFRUXj55Zft7nbXd8stt+CWW27Bl19+iWPHjklL/3U6HZ544gls2LABVqv1qheFopvx3unWrdtVP9NiO9C4z8fNGJePjw9uv/12HDlyRNoSILoyEPrjjz/CZrNJSfB69OgBQRAQHh6Onj17XvU5xN/1/v37myRQ2tDPpCNjxozB888/j3feecfh+8BqtSIvLw9qtVqq9NBcv6PGfkauRaPRQKPRICUlBbt27UJ0dDSysrKwdOnSP/R9icg1MOcAEVED1dXV4b///S98fX2lJbyjR4+G1WrFv/71L1nf5cuXQ6FQYNSoUdK/nT59Ojp16oTXXnsNa9aswalTp7BgwYImG29YWBiioqKwdu1a2cnn/v378d///hejR4+WjolBgobUKL/jjjvQsWNHZGVlycr4ffzxx/jhhx8QExNz015Djx49kJCQgDfffBOlpaWytoCAALRv3x5Go1F2/I033rhpzy+KjY1FSUkJ3nrrLbu2CxcuSFnzG0OpVGL8+PHYsmULvvnmG7t28W6xSqWyu3O8YcMGWdnIm2ngwIHo0aMH/vnPf0pBsPrOnDkjfe1obK+//rrdnc9rvb969Ohh9ztcuXJlg++ejhw5EgEBAVi2bBnq6uquOt7q6mq78nk9evRA27Zt7cpROjJs2DB8+umnKCwslIIDUVFRaNu2LV544QX4+/tj4MCB1/wejfmcXc3o0aNRWFgIk8kkHTt//jxWrlyJ7t27S3elxQvC+j9bq9WKlStXOhxXQ7eBHDlyBMePH7c7XlFRAZPJBLVabbf9RizDJ3r99dcBQJofH3roIahUKqSlpdm9nwRBkLb4DBgwAOHh4Xj11VftfoY3srriSg39TDoydOhQ3HfffVi9ejW2bt1q17548WIcPnwYTz/9tLT6pVu3blCpVA2aw/7Ie6ehn5FrqaqqkoLjIo1GA6VS2aDPDxG5B64cICK6io8//li6G3f69Gnk5eXhyJEj+Pvf/y7t0Rw7dizuueceLF68GD/99BP69euH//73v/j3v/+N+fPnSyfoS5cuRVFREXbs2IG2bdvi9ttvx5IlS5CSkoKJEyfKLtRvppdeegmjRo2CVqvFrFmzpFKGgYGBstre4kXN4sWLMWXKFPj4+GDs2LEO8xH4+PjgxRdfxIwZMzB8+HBMnTpVKmXYvXv3mx7wWLx4MXJycnDo0CG7rRyzZ8/GCy+8gNmzZ+OOO+6A0WjE4cOHb+rzA8DDDz+M/Px8JCYm4rPPPkN0dDSsVisOHjyI/Px8bNu27aqJIa9l2bJl+O9//4vhw4dLJRJPnjyJDRs24KuvvkJQUBDGjBkjrTgZOnQozGYzcnNzERERcdNfJ3D5Aik7OxujRo1C3759MWPGDHTu3BklJSX47LPPEBAQIJVtGzNmDHJychAYGIjIyEiYTCZ88skndrkQoqKioFKp8OKLL6KyshJ+fn6499570bFjR8yePRuJiYmYMGEC7r//fuzbtw/btm1r8KqIgIAAZGZm4uGHH8aAAQMwZcoUdOjQAcePH8dHH32E6Oho/Otf/8Lhw4cxYsQIxMbGIjIyEi1atMAHH3yAU6dOYcqUKdd9nmHDhiE3NxcKhULaZqBSqTB06FBs27YNd99991XL3DXk59BQf//73/Huu+9i1KhRmDt3Ltq1a4e1a9fCYrFg06ZN0nLwvn37YsiQIXjmmWfw+++/o127dnjvvffsLvCAy5//9evX44knnsCdd96JNm3aYOzYsQ6ff9++fYiLi8OoUaMwbNgwtGvXDiUlJVi7di1OnDiBV1991W5rgMViwbhx4/CXv/wFJpMJ69atQ1xcHPr16wfgciBj6dKleOaZZ/DTTz9h/PjxaNu2LSwWCz744AM8+uijePLJJ6FUKpGZmYmxY8ciKioKM2bMQFhYGA4ePIji4mJp29Yf0ZDP5NW88847GDFiBB544AHExcVh2LBhqKmpwfvvv4/PP/8ckydPxlNPPSX1DwwMxKRJk/D6669DoVCgR48e2Lp1q10OE+B/c/TcuXMxcuRIqFSqBr1vgYZ/Rq7l008/RXJyMiZNmoSePXvi0qVLyMnJgUqlwoQJExo0DiJyA84okUBE5MoclTJs2bKlEBUVJWRmZtqVszp79qywYMECoVOnToKPj49w6623Ci+99JLU79tvvxVatGghPP7447J/d+nSJeHOO+8UOnXqdM3yV2K5q+uVkLpaKbtPPvlEiI6OFvz9/YWAgABh7NixwoEDB+z+fXp6utC5c2dBqVQ2qKzh+vXrhf79+wt+fn5Cu3bthPj4eOHXX3+V9bnRUoZXEstrXVmKrLq6Wpg1a5YQGBgotG3bVoiNjRVOnz591VKGZ86csfu+rVu3tns+R2XPamtrhRdffFHo27ev4OfnJ6jVamHgwIFCWlqaUFlZKfUDIDz22GMOX+OV4xIEQfj555+FRx55ROjQoYPg5+cnRERECI899phQU1MjCMLlUoYLFy4UwsLCBH9/fyE6OlowmUzC8OHDheHDh0vfp7GlDK/3ftq7d6/w0EMPCcHBwYKfn5/QrVs3ITY2VtixY4fUp7y8XJgxY4bQvn17oU2bNsLIkSOFgwcP2pXwEwRBeOutt4SIiAhBpVLJyrdZrVbh//7v/4T27dsLrVq1EkaOHCn8+OOPVy1leLX30meffSaMHDlSCAwMFFq2bCn06NFDmD59uvDNN98IgiAIv/32m/DYY48JvXv3Flq3bi0EBgYKgwcPFvLz86/5cxAVFxcLAIQ+ffrIji9dulQAIOj1ert/05ifQ7du3YSYmBi773Hl71kQBOHo0aPCxIkThaCgIKFly5bCoEGDhK1bt9r926NHjwr33Xef4OfnJ4SEhAiLFi0Stm/fblc+79y5c0JcXJwQFBQkALhmWcNTp04JL7zwgjB8+HAhLCxMaNGihaBWq4V7771X2Lhxo6yv+Lk7cOCAMHHiRKFt27aCWq0WkpOTZWVQRZs2bRLuuusuoXXr1kLr1q2F3r17C4899phw6NAhWb+vvvpKuP/++4W2bdsKrVu3Fm6//Xbh9ddft3veK3+ODSllKAjX/0xey9mzZwWDwSD07dtX8Pf3F9q2bStER0cLa9ascVgK8cyZM8KECROEVq1aCWq1WvjrX/8q7N+/3+6zfOnSJeHxxx8XOnToICgUCtnru/I1XKuM5bU+I4Jw9Tnx2LFjwsyZM4UePXoILVu2FNq1ayfcc889wieffHLdnwkRuQ+FINyEdVhERERERPUYDAakpaXhzJkzTZIfg4iIbi7mHCAiIiIiIiLycgwOEBEREREREXk5BgeIiIiIiIiIvBxzDhARERERERF5Oa4cICIiIiIiIvJyLZw9AG9is9lw4sQJtG3bFgqFwtnDISIiIiIiIg8nCALOnj2LTp06Qam8+voABgea0YkTJ9C1a1dnD4OIiIiIiIi8zC+//IIuXbpctZ3BgWbUtm1bAJd/KQEBAU4eDREREREREXm6qqoqdO3aVboevRoGB5qRuJUgICCAwQEiIiIiIiJqNtfb2s6EhERERERERERejsEBIiIiIiIiIi/H4AARERERERGRl2NwgIiIiIiIiMjLMThARERERERE5OUYHCAiIiIiIiLycgwOEBEREREREXk5BgeIiIiIiIiIvByDA0RERERERERejsEBIiIiIiIiIi/H4AARERERERGRl2NwgIiIiIiIiMjLMThARERERERE5OUYHCAiIiIiIiLycgwOEBEREREREXk5BgeIiIiIiIiIvByDA0RERERERERejsEBIiIiIiIiIi/H4AARERERERGRl2NwgIiIiIiIiMjLtXD2AIiIiLyF1WqF2WxGWVkZgoODodFooFKpnD0sIiIiIgYHiIiImoPRaERmZiZKS0ulY6GhoUhKSoJOp3PiyIiIiIi4rYCIiKjJGY1GGAwGREREICMjAwUFBcjIyEBERAQMBgOMRqOzh0hEREReTiEIguDsQXiLqqoqBAYGorKyEgEBAc4eDhERNQOr1YqEhAREREQgPT0dSuX/4vI2mw16vR4WiwU5OTncYkBEREQ3XUOvQ7lygIiIqAmZzWaUlpYiPj5eFhgAAKVSibi4OJw8eRJms9lJIyQiIiJicICIiKhJlZWVAQDCw8MdtovHxX5EREREzsDgABERURMKDg4GAFgsFoft4nGxHxEREZEzMDhARETUhDQaDUJDQ5GbmwubzSZrs9lsyMvLQ1hYGDQajZNGSERERMTgABERUZNSqVRISkqCyWSCXq9HcXExqqurUVxcDL1eD5PJhMTERCYjJCIiIqditYJmxGoFRETey2g0IjMzE6WlpdKxsLAwJCYmQqfTOXFkRERE5Mkaeh3K4EAzYnCAiMi7Wa1WmM1mlJWVITg4GBqNhisGiIiIqEk19Dq0RTOOiYiIyKupVCpERUU5exhEREREdphzgIiIiIiIiMjLceUAERFRM+G2AiIiInJVDA4QERE1A0cJCUNDQ5GUlMSEhEREROR03FZARETUxIxGIwwGAyIiIpCRkYGCggJkZGQgIiICBoMBRqPR2UMkIiIiL8dqBc2I1QqIiLyP1WpFQkICIiIikJ6eDqXyf3F5m80GvV4Pi8WCnJwcbjEgIiKim66h16FcOUBERNSEzGYzSktLER8fLwsMAIBSqURcXBxOnjwJs9nspBESERERMecAERFRkyorKwMAhIeHO0xIGB4eLutHRERE5AwMDhARETWh4OBgAMAHH3yALVu22CUkHDNmjKwfERERkTMwOEBERNSENBoNgoKC8NZbb0Gr1UKv1yM8PBwWiwXr1q1DdnY2goKCoNFonD1UIiIi8mLMOUBERNSMBEGQ/hMpFAonjoiIiIiIKweIiIialNlsRkVFBWbPno0tW7YgOTlZagsNDcXs2bORnZ0Ns9mMqKgo5w2UiIiIvBpXDhARETUhMdFgSEiIwxUCHTt2lPUjIiIicgauHCAiImpCYqLBZcuW2eUcyM3NxfPPPy/rR0REROQMXDlARETUhCIjI6FSqRAUFIS0tDRERkbC398fkZGRSEtLQ1BQEFQqFSIjI509VCIiIvJiDA4QERE1oQMHDsBqtaK8vBypqakoLi5GdXU1iouLkZqaivLyclitVhw4cMDZQyUiIiIvxm0FRERETUjMJbBo0SK8/fbbsoSEYWFhWLRoEZYtW8acA0RERORUDA4QERE1ITGXQOfOnbFu3TqYzWaUlZUhODgYGo0GBw8elPUjIiIicgZuKyAiImpCGo0GoaGhyM3NhUKhQFRUFEaMGIGoqCgoFArk5eUhLCwMGo3G2UMlIiIiL8bgABERURNSqVRISkqCyWSCXq+X5RzQ6/UwmUxITEyESqVy9lCJiIjIiykEQRCcPQhvUVVVhcDAQFRWViIgIMDZwyEiomZkNBqRmZmJ0tJS6VhYWBgSExOh0+mcODIiIiLyZA29DmVwoBkxOEBE5N2sVqtdzgGuGCAiIqKm1NDrUCYkJCIiaiYqlQpRUVHOHgYRERGRHeYcICIiIiIiIvJyDA4QEREREREReTkGB4iIiIiIiIi8HIMDRERERERERF6OwQEiIiIiIiIiL8fgABEREREREZGXY3CAiIiIiIiIyMsxOEBERERERETk5RgcICIiIiIiIvJyDA4QEREREREReTkGB4iIiIiIiIi8HIMDRERERERERF6OwQEiIiIiIiIiL8fgABEREREREZGXY3CAiIiIiIiIyMsxOEBERERERETk5RgcICIiIiIiIvJyDA4QEREREREReTkGB4iIiIiIiIi8HIMDRERERERERF6OwQEiIiIiIiIiL8fgABEREREREZGXY3CAiIiIiIiIyMsxOEBERERERETk5RgcICIiIiIiIvJyDA4QEREREREReTkGB4iIiIiIiIi8HIMDRERERERERF6OwQEiIiIiIiIiL8fgABEREREREZGXY3CAiIiIiIiIyMsxOEBERERERETk5RgcICIiIiIiIvJyDA4QEREREREReTkGB4iIiIiIiIi8HIMDRERERERERF6OwQEiIiIiIiIiL8fgABEREREREZGXY3CAiIiIiIiIyMsxOEBERERERETk5RgcICIiIiIiIvJyDA4QEREREREReTkGB4iIiIiIiIi8HIMDRERERERERF6OwQEiIiIiIiIiL8fgABEREREREZGXY3CAiIiIiIiIyMsxOEBERERERETk5RgcICIiIiIiIvJyDA4QEREREREReTkGB4iIiIiIiIi8HIMDRERERERERF6uhbMHQERE5C2sVivMZjPKysoQHBwMjUYDlUrl7GERERERMThARETUHIxGIzIzM1FaWiodCw0NRVJSEnQ6nRNHRkRERMRtBURERE3OaDTCYDAgIiICGRkZKCgoQEZGBiIiImAwGGA0Gp09RCIiIvJyCkEQBGcPwltUVVUhMDAQlZWVCAgIcPZwiIioGVitViQkJCAiIgLp6elQKv8Xl7fZbNDr9bBYLMjJyeEWAyIiIrrpGnodypUDRERETchsNqO0tBTx8fGywAAAKJVKxMXF4eTJkzCbzU4aIRERERGDA0RERE2qrKwMABAeHu6wXTwu9iMiIiJyBgYHiIiImlBwcDAAwGKxOGwXj4v9iIiIiJyBwQEiIqImpNFoEBoaitzcXNTV1aGoqAg7duxAUVER6urqkJeXh7CwMGg0GmcPlYiIiLwYSxkSERE1IZVKhaSkJKSmpmLs2LGoqamR2vz8/FBTU4O0tDQmIyQiIiKn4soBIiKiZqBQKBp1nIiIiKg5ceUAERFRE7JarcjMzIRWq4XBYEBxcTHKysoQHByMvn37wmAwICsrC9HR0Vw9QERERE7DlQNERERNqH4pQx8fH0RFRWHEiBGIioqCj48PSxkSERGRS2BwgIiIqAmxlCERERG5AwYHiIiImhBLGRIREZE7YHCAiIioCdUvZWiz2WRtNpuNpQyJiIjIJTAhIRERURMSSxkaDAakpKTgzjvvlEoYfv3119i9ezcMBgOTERIREZFTMThARETUxHQ6HWJjY7Fx40aYTCbpuEqlQmxsLHQ6nRNHR0RERMTgABERUZMzGo3Iz8/HkCFDMGjQIGnlQGFhIfLz8xEZGckAARERETmV03MOlJSUICEhAcHBwfD394dGo8E333wjtQuCgCVLliAsLAz+/v647777cOTIEdn3+P333xEfH4+AgAAEBQVh1qxZOHfunKzP999/j2HDhqFly5bo2rUr/vGPf9iNZcOGDejduzdatmwJjUaDgoICWXtDxkJERFSf1WpFZmYmtFotli5divHjx2PUqFEYP348li5dCq1Wi6ysLFitVmcPlYiIiLyYU4MD5eXliI6Oho+PDz7++GMcOHAAL7/8MtRqtdTnH//4B1asWIGsrCzs2bMHrVu3xsiRI3Hx4kWpT3x8PIqLi7F9+3Zs3boVRqMRjz76qNReVVWFP//5z+jWrRu+/fZbvPTSSzAYDFi5cqXUZ9euXZg6dSpmzZqFvXv3Yvz48Rg/fjz279/fqLEQERHVZzabUVpaivj4eCiV8j+7SqUScXFxOHnyJMxms5NGSERERAQoBEEQnPXkf//737Fz5058+eWXDtsFQUCnTp2wcOFCPPnkkwCAyspKhISEYM2aNZgyZQp++OEHREZG4uuvv8Ydd9wBAPjPf/6D0aNH49dff0WnTp2QmZmJxYsXo7S0FL6+vtJzf/jhhzh48CAAYPLkyTh//jy2bt0qPf+QIUMQFRWFrKysBo3leqqqqhAYGIjKykoEBATc+A+OiIjcxo4dO7B06VIUFBTA39/frr26uhoxMTFISUnBiBEjnDBCIiIi8mQNvQ516sqBzZs344477sCkSZPQsWNH9O/fH2+99ZbUbrFYUFpaivvuu086FhgYiMGDB0sJnUwmE4KCgqTAAADcd999UCqV2LNnj9RHp9NJgQEAGDlyJA4dOoTy8nKpT/3nEfuIz9OQsVyppqYGVVVVsv+IiMi7BAcHA7j8d8QR8bjYj4iIiMgZnBocOHbsGDIzM3Hrrbdi27ZtSEpKwty5c7F27VoAQGlpKQAgJCRE9u9CQkKkttLSUnTs2FHW3qJFC7Rr107Wx9H3qP8cV+tTv/16Y7nS888/j8DAQOm/rl27Xu9HQkREHkaj0SA0NBS5ubmw2WyyNpvNhry8PISFhUGj0ThphEREREROrlZgs9lwxx13YNmyZQCA/v37Y//+/cjKysK0adOcObSb4plnnsETTzwhPa6qqmKAgIjIy6hUKiQlJcFgMCAlJQWDBg2Cr68vamtrUVhYiN27d8NgMEClUjl7qEREROTFnBocCAsLQ2RkpOxYnz59sGnTJgBAaGgoAODUqVMICwuT+pw6dQpRUVFSn9OnT8u+x6VLl/D7779L/z40NBSnTp2S9REfX69P/fbrjeVKfn5+8PPzu8ZPgIiIvIFOp0NsbCw2btwo24qmUqkQGxvLMoZERETkdE7dVhAdHY1Dhw7Jjh0+fBjdunUDAISHhyM0NBQ7duyQ2quqqrBnzx5otVoAgFarRUVFBb799lupz6effgqbzYbBgwdLfYxGI+rq6qQ+27dvR69evaTKCFqtVvY8Yh/xeRoyFiIiIkeMRiPy8/MxaNAgzJs3D08//TTmzZuHQYMGIT8/H0aj0dlDJCIiIm8nOFFhYaHQokUL4bnnnhOOHDki5ObmCq1atRLWrVsn9XnhhReEoKAg4d///rfw/fffCw888IAQHh4uXLhwQerzl7/8Rejfv7+wZ88e4auvvhJuvfVWYerUqVJ7RUWFEBISIjz88MPC/v37hffee09o1aqV8Oabb0p9du7cKbRo0UL45z//Kfzwww9Camqq4OPjI5jN5kaN5VoqKysFAEJlZeUf+bEREZEbuXTpkjBlyhRh0aJFgtVqlbVZrVZh0aJFwtSpU4VLly45aYRERETkyRp6HerU4IAgCMKWLVuE2267TfDz8xN69+4trFy5UtZus9kEvV4vhISECH5+fsKIESOEQ4cOyfqUlZUJU6dOFdq0aSMEBAQIM2bMEM6ePSvrs2/fPuGuu+4S/Pz8hM6dOwsvvPCC3Vjy8/OFnj17Cr6+vkLfvn2Fjz76qNFjuRYGB4iIvM/evXuFu+++WyguLnbYvn//fuHuu+8W9u7d27wDIyIiIq/Q0OtQhSAIgnPXLniPhtaXJCIiz7Fjxw4sXboUBQUF8Pf3t2uvrq5GTEwMUlJSMGLECCeMkIiIiDxZQ69DnZpzgIiIyNMFBwcDACwWi8N28bjYj4iIiMgZGBwgIiJqQhqNBqGhocjNzYXNZpO12Ww25OXlISwsDBqNxkkjJCIiImJwgIiIqEmpVCokJSXBZDJBr9ejuLgY1dXVKC4uhl6vh8lkQmJiIlQqlbOHSkRERF6MOQeaEXMOEBF5L6PRiMzMTJSWlkrHwsLCkJiYCJ1O58SRERERkSdr6HUogwPNiMEBIiLvZrVaYTabUVZWhuDgYGg0Gq4YICIioibV0OvQFs04JiIiIq+mUqkQFRXl7GEQERER2WHOASIiIiIiIiIvx+AAERERERERkZdjcICIiIiIiIjIyzE4QEREREREROTlmJCQiIiombBaAREREbkqBgeIiIiagdFoRGZmJkpLS6VjoaGhSEpKgk6nc+LIiIiIiLitgIiIqMkZjUYYDAZEREQgIyMDBQUFyMjIQEREBAwGA4xGo7OHSERERF5OIQiC4OxBeIuqqioEBgaisrISAQEBzh4OERE1A6vVioSEBERERCA9PR1K5f/i8jabDXq9HhaLBTk5OdxiQERERDddQ69DuXKAiIioCZnNZpSWliI+Pl4WGAAApVKJuLg4nDx5Emaz2UkjJCIiImJwgIiIqEmVlZUBAMLDwx22i8fFfkRERETOwOAAERFREwoODgYAWCwWh+3icbEfERERkTMwOEBERNSENBoNQkNDkZubC5vNJmuz2WzIy8tDWFgYNBqNk0ZIRERExOAAERFRk1KpVEhKSoLJZIJer0dxcTGqq6tRXFwMvV4Pk8mExMREJiMkIiIip2K1gmbEagVERN7LaDQiMzMTpaWl0rGwsDAkJiZCp9M5cWRERETkyRp6HcrgQDNicICIyLtZrVaYzWaUlZUhODgYGo2GKwaIiIioSTX0OrRFM46JiIjIq6lUKkRFRTl7GERERER2mHOAiIiIiIiIyMsxOEBERERERETk5RgcICIiIiIiIvJyDA4QEREREREReTkGB4iIiIiIiIi8HKsVEBERNROWMiQiIiJXxeAAERFRMzAajcjMzERpaal0LDQ0FElJSdDpdE4cGRERERG3FRARETU5o9EIg8GAiIgIZGRkoKCgABkZGYiIiIDBYIDRaHT2EImIiMjLKQRBEJw9CG9RVVWFwMBAVFZWIiAgwNnDISKiZmC1WpGQkICIiAikp6dDqfxfXN5ms0Gv18NisSAnJ4dbDIiIiOima+h1KFcOEBERNSGz2YzS0lLEx8fLAgMAoFQqERcXh5MnT8JsNjtphEREREQMDhARETWpsrIyAEB4eLjDdvG42I+IiIjIGZiQkIiIqAkFBwcDACwWC3r16mVXrcBiscj6ERERETkDgwNERERNSKPRIDQ0FCtWrEBFRQVOnToltYWEhCAoKAhhYWHQaDROHCURERF5O24rICIiakIqlQrDhw/HoUOHUFtbi9jYWMybNw+xsbGora3FoUOHoNPpmIyQiIiInIrVCpoRqxUQEXkfsVqBUqnEqVOnYLVapTaVSoWQkBAIgsBqBURERNQkGnodym0FRERETUisVqBQKDBkyBAMGjQIfn5+qKmpQWFhIXbv3g1BEGA2mxEVFeXs4RIREZGXYnCAiIioCZ05cwYAMGjQICxdulRWznDcuHF45plnUFhYKPUjIiIicgbmHCAiImpClZWVAIBhw4bJAgMAoFQqcdddd8n6ERERETkDgwNERERNKDAwEADw5ZdfwmazydpsNht27twp60dERETkDAwOEBERNaEOHToAAPbs2QO9Xo/i4mJUV1ejuLgYer0ee/bskfUjIiIicgbmHCAiImpCGo0GoaGhCAwMxNGjR5GcnCy1hYaGolevXqiqqoJGo3HiKImIiMjbMThARETUhFQqFZKSkmAwGDBkyBBMmTIFvr6+qK2tlaoVGAwGljEkIiIip1IIgiA4exDeoqH1JYmIyPMYjUZkZmaitLRUOhYWFobExETodDonjoyIiIg8WUOvQ7lygIiIqBnodDoMGTIEmzdvRklJCTp37oxx48bB19fX2UO7YVarFWazGWVlZQgODoZGo+EKCCIiF8J5mhqDwQEiIqJm4GjlwKZNm5CUlOSWKwccvZ7Q0FC3fT1ERJ6G8zQ1FqsVEBERNTGj0QiDwYCIiAhkZGSgoKAAGRkZiIiIgMFggNFodPYQG8XTXg8RkafhPE03gjkHmhFzDhAReR+r1YqEhAREREQgPT0dSuX/4vI2mw16vR4WiwU5OTlusdTT014PEZGn4TxNV2rodShXDhARETUhs9mM0tJSxMfHy07QAECpVCIuLg4nT56E2Wx20ggbx9NeDxGRp+E8TTeKOQeIiIiaUFlZGQAgPDzcYWKo8PBwWT9XV//1OOJur4eIyNNwnqYbxeAAERFREwoODgYAvP/++9i6datdYqgxY8bI+rk6cZwWiwWRkZF27RaLRdaPiIiaF+dpulHcVkBERNSENBoNgoKCkJ2djfDwcFliqPDwcGRnZ0OtVkOj0Th7qA2i0WgQGhqK3Nxc2Gw2WZvNZkNeXh7CwsLc5vUQEXkaztN0oxgcICIiakaCIEj/1T/mLlQqFZKSkmAymaDX61FcXIzq6moUFxdDr9fDZDIhMTGRSa6IiJyE8zTdKFYraEasVkBE5H2KioqwYMECzJkzB1u2bJFtKwgLC0NMTAyys7OxfPlyREVFOW+gjeSofnZYWBgSExNZP5uIyAVwniZRQ69DmXOAiIioCYkJnx588EFMnjzZLiFhTU0NsrOz3S4xlE6nw5AhQ7B582aUlJSgc+fOGDduHHx9fZ09NCIiAudpajwGB4iIiJrQlYmhrlwd4K6JoRzdkdq0aROSkpJ4R4qIyAVwnqbGYs4BIiKiJuSJiaGMRiMMBgMiIiJkCRYjIiJgMBhgNBqdPUQiIq/GeZpuBHMONCPmHCAi8k7iSZpWq0VcXBzCw8NhsViQl5cHk8kEg8HgNndxrFYrEhISEBERgfT0dCiV/7vPYLPZoNfrYbFYkJOTw2RXREROwHmartTQ61CuHCAiImpiOp0OBoMBx44dQ3JyMmJiYpCcnAyLxeJWgQEAMJvNKC0tRXx8vOyEEwCUSiXi4uJw8uRJmM1mJ42QiMi7cZ6mG8WcA0RERM1Ap9MhOjraLiGhu921ERMnhoeHO2wXj7tbgkUiIk/BeZpuFIMDREREzUSlUrlVuUJHrkyweCV3TbBIROQpOE/TjeK2AiIiImowT0ywSETkSThP041icICIiIgaTKVSISkpCSaTCXq9HsXFxaiurkZxcTH0ej1MJhMSExPdbrsEEZGn4DxNN4rVCpoRqxUQEZGncFQ/OzQ0lPWziYhcBOdpEjX0OpQ5B4iIiOiGOFquSkREroPztOuyWq0ul6SYKweaEVcOEBGRJzAajUhNTYWfnx9qamqk4+LjtLQ03pUiInIiztOurblXdTT0OpQ5B4iIiKjBrFYrli9ffs0+y5cvh9VqbaYRERFRfZynXZvRaITBYEBERAQyMjJQUFCAjIwMREREwGAwwGg0Om1sDA4QERFRg+3btw8VFRUAgAEDBshObAYMGAAAqKiowL59+5w4SiIi78V52nVZrVZkZmZCq9UiPT0dkZGR8Pf3R2RkJNLT06HVapGVleW0wA2DA0RERNRg3333HQAgMjISS5culZ3YLF26FH369JH1IyKi5sV52nWZzWaUlpYiPj4eSqX8UlypVCIuLg4nT56E2Wx2yvgYHCAiIqIGO336NADgvvvugyAIKCoqwo4dO1BUVARBEDBixAhZPyIial6cp11XWVkZACA8PNxhu3hc7NfcWK2AiIiIGqxjx44AgPfffx/r16/HqVOnpLaQkBD4+PjI+hERUfPiPO26goODAQAWiwWRkZF27RaLRdavuXHlABERETWYuF/1119/RU1NDRYuXIiNGzdi4cKFqKmpwa+//irrR0REzYvztOvSaDQIDQ1Fbm6uwzKTeXl5CAsLg0ajccr4WMqwGbGUIRERubva2lqMGjUKNpsNvr6+qK2tldrEEllKpRIff/wxfH19nThSIiLvxHnatYnVCrRaLeLi4hAeHg6LxYK8vDyYTCYYDIabXs6wodeh3FZAREREDXbgwAHpbodCoXDYx2az4cCBA4iKimrGkREREcB52tXpdDoYDAZkZmYiOTlZOh4aGtokgYHG4LYCIiKiZmK1WmWJodyxxrSYJGnx4sUICgqStQUFBWHRokWyfkRE1Lw4T7uHKxfwu8KCfq4cICIiagZGoxGZmZkoLS2VjoWGhiIpKcmpdwkaS0ySVD/BVX1i9mtnJVMiIvJ2nKddW/1tBUuWLJG2FeTm5sJgMDh19QBzDjQj5hwgIvJO9U8E4uPjZScCTbW/sKlYrVZMnDgRFRUV0Gq1SEhIkF7PunXrYDKZoFarsWHDBqhUKmcPl4jI63Cedl1WqxUJCQmIiIhAeno6lMr/LeS32WzQ6/WwWCzIycm5qb+bhl6HclsBERFRE7JarcjMzIRWq0V6ejoiIyPh7++PyMhIpKenQ6vVIisryy23GACXl0GK/9U/RkREroHztOswm80oLS1FfHy8LDAAAEqlEnFxcTh58iTMZrNTxsfgABERUROqfyIgCIIs54AgCE4/EWgss9mMiooKzJkzBxaLBcnJyYiJiUFycjJ++uknzJ49GxUVFW7zeoiIPA3nadcl5nkIDw932C4ed1Y+COYcICIiakLiH/iSkhKkp6fb5RyYOXOmrJ+rE8f54IMPYvLkyTCbzSgrK0NwcDA0Gg1qamqQnZ3tNq+HiMjTcJ52XWKeB4vFgsjISLt2i8Ui69fcGBwgIiJqQuIf+GXLlmHo0KHQ6/WynAPLli2T9XN1V57YXFkGy9knNkRE3o7ztOvSaDQIDQ1Fbm6uw5wDeXl5CAsLg0ajccr4uK2AiIioCUVGRkKlUkGtViMtLU2WcyAtLQ1qtRoqlcrhHQRXVP/ERqyjLXKFExsiIm/Hedp1qVQqJCUlwWQyQa/Xo7i4GNXV1SguLoZer4fJZEJiYqLTEkUyOEBERNSEDhw4AKvVioqKCqSmpspOBFJTU1FRUQGr1YoDBw44e6gN4uonNkRE3o7ztGvT6XQwGAw4duyYLB+ExWJxevUiljJsRixlSETkfXbs2IGlS5di8eLFWLVqlSznQFhYGGbMmIFly5YhJSUFI0aMcOJIG8doNCIzM9Pu9SQmJrpNWUYiIk/Gedq11dbWYvPmzSgpKUHnzp0xbtw4+Pr6NslzNfQ6lDkHiIiImpC4p7NTp05Yt26dXWKogwcPyvq5C51Oh+joaLvXwztRRESugfO063IUuNm0aROSkpK4csBbcOUAEZH3sVqtSEhIQEREhMPkQ3q9HhaLBTk5OTxhIyIi8nBGoxEGgwFDhgzBoEGD4Ofnh5qaGhQWFmL37t1NsrWgodehDA40IwYHiIi8k3gioNVqERcXJ1UryMvLg8lkcvoeQyIiImp64g2DwMBAlJeX4/Tp01Jbx44doVarUVVVddNvGHBbARERkYsQkw9lZmYiOTlZOh4WFsbAABERkZcwm80oLS1FaWkp/Pz8ZG2VlZVSsMBsNtuVoGwODA4QERE1A0/c+2m1Wj3q9RAReRrO067lzJkz0tcDBgxAQkKCtJpw3bp1MJlMdv2aE4MDREREzUSlUjnlTkBTcJRMKTQ01OnJlIiI6DLO066nvLwcANCjRw8sXbpUykMUGRmJpUuXYs6cOTh27JjUr7kpr9+FiIiI6H/EHAoRERHIyMhAQUEBMjIyEBERAYPBAKPR6OwhEhF5Nc7TrqmyshIA0LJlS4ft4nGxX3NjQsJmxISERETk7lh9gYjItXGedl1vv/02cnJyoFAopGoFvr6+qK2tlaoVCIKAhx9+GDNnzrxpz9vQ61CuHCAiImomtbW12LhxI1577TVs3LgRtbW1zh5So4nJlOLj42UnnACgVCoRFxeHkydPwmw2O2mERETejfO06+rXrx8AIDg4GHv27MFrr72Gl156Ca+99hoKCwsRHBws69fcmHOAiIioGWRlZWHjxo2wWq2yYxMnTkRiYqITR9Y4ZWVlAIDw8HCH7eJxsR8RETUvztOuKyoqCq1bt8Zvv/2GwMBAjBw5Ep06dcKJEyewbds2/Pbbb2jdurXT8hMxOEBERNTEsrKysH79eqjVasyaNQtarRYmkwmrVq3C+vXrAcBtAgTiXQ2LxYLIyEi7dovFIutHRETNi/O0a/Px8QEAXLx4Efn5+dJxsbSh2O4M3FZARETUhMStBGq1Gvn5+YiJiUG7du0QExOD/Px8qNVqt9pioNFoEBoaitzcXNTV1aGoqAg7duxAUVER6urqkJeXh7CwMGg0GmcPlYjIK3Gedl1msxkVFRWYM2cO1Gq1rE2tVmP27NmoqKhw2pYPrhwgIiJqQps3b4bVasWsWbPQooX8z26LFi0wY8YMvPLKK9i8eTMmTpzopFE2nEqlQlJSEgwGA8aOHYuamhqpzc/PD7W1tTAYDExyRUTkJJynXZe4lePBBx/ExIkTsXnzZpSUlKBz584YN24cLl26hOzsbKdt+WBwgIiIqAmVlJQAALRarcN28bjYz11crdgRiyAREbkGztOuR9zK8f7772Pr1q0oLS2V2jZt2oQxY8bI+jU3BgeIiIiaUOfOnQEAJpMJMTExdu0mk0nWz9VZrVZkZmZi6NChMBgMKC4uRllZGYKDg9G3b18YDAZkZWUhOjqad6WIiJyA87Tr0mg0CAoKQnZ2NrRaLfR6PcLDw2GxWLBu3TpkZ2dDrVY7bcsHcw4QERE1oXHjxkGlUmHVqlWoqamR7f2sqanB6tWroVKpMG7cOGcPtUHql8jy8fFBVFQURowYgaioKPj4+LBEFhGRk3Gedh+CIEj/1T/mLFw5QERE1IR8fX0xceJErF+/HqNHj4bNZpPalEolbDYbJk+eDF9fXyeOsuFYIouIyLVxnnZd9RMSbtmyBcnJyVJbWFgYZs+ejezsbJjNZqeUM2RwgIiIqImJpaTqBwbqP3ZUaspV1S+R1atXL5jNZmm5qkajYYksIiIn4zztuuonJJw8ebLd76ampoYJCYmIiDxV/b2fer0eW7dulTITjxkzBunp6W6191MskbVixQpUVFTg1KlTUltISAiCgoJYIouIyIk4T7uu+oGbyMhIu9UBzg7cMOcAERFRE7py7+ef/vQn3HbbbfjTn/7klns/VSoVhg8fjkOHDqG2thYLFy7Exo0bsXDhQtTW1uLQoUPQ6XRuEeggIvJEnKddlxi4yc3NdbiaMC8vz6mBG64cICIiakLi0sATJ04gPT1dVrYoNDQUM2fOlPVzdVarFV988QV69eqF8vJyvPzyy1JbSEgIevXqBaPRiDlz5vDEk4jICThPuy6VSoWkpCQYDAbo9XrExcVJ1Qry8vJgMplgMBic9nvhygEiIqImJC4NXLZsGSIiIpCRkYGCggJkZGQgIiICzz//vKyfqxNXQnTt2tUuoPHbb7+hS5cubrUSgojI03Cedm06nQ4GgwHHjh1DcnIyYmJikJycDIvFAoPBAJ1O57SxceUAERFRE4qMjIRKpUJAQADS0tLQokUL6XhaWhpiY2NRVVXlNkkJxRPNTz75BGq1GrNmzYJWq4XJZMKqVauwY8cOWT8iImpenKddn06nQ3R0tF1CQmev5GBwgIiIqAkdOHAAVqsV5eXlSE1NtVtCWF5eLvVzRtmixgoMDAQAtG3bFvn5+VKwIyYmBiNHjsRDDz2Es2fPSv2IiKh5cZ52DyqVyuX+7nNbARERURMS78wsWrTI4RLCRYsWyfq5uqNHjwIAOnbsCKVSfhqhVCrRoUMHWT8iImpenKfpRnHlABERURMScwl07twZ69ats1tCePDgQVk/VycmVDx69KjDZErHjh2T9SMioubFedo9WK1WbisgIiLyJvXLFqWnp8uWELpC2aLG6ty5MwBg3LhxKCwsRHJystQWFhaGsWPHYsuWLVI/IiJqXpynXZ/RaERmZqZdBaOkpCSnJiRUCIIgOO3ZvUxVVRUCAwNRWVmJgIAAZw+HiIiaidFohMFggFarvWrZImeeDDRGbW0tRo8ejYCAALz77rv44YcfpLseffr0wdSpU1FVVYWCggL4+vo6e7hERF6H87Rrq39OEB8fL50T5ObmNtk5QUOvQ5lzgIiIqIm5ctmixvL19cXEiRNRXl6OqVOn4pdffkG/fv3wyy+/YOrUqSgvL8fEiRN5wklE5CScp12X1WpFZmYmtFot0tPTERkZCX9/f0RGRiI9PR1arRZZWVmwWq1OGR9XDjQjrhwgIvJurri/8EZlZWVhw4YNsNls0jGVSoWJEyciMTHRiSMjIiKA87QrKioqwoIFC5CRkeGwhHFxcTGSk5OxfPnym1rJoKHXocw5QERE1ExcsWzRjYqMjESHDh1w6tQp6Vj79u0dnuwQEVHz4zztesTKROHh4Q7bxePOqmDEbQVERETUKOJ+yR49eiAjIwMFBQXIyMhAjx49YDAYYDQanT3EG2K1WlFUVIQdO3agqKjIacs6iYj+KE+dp92dWJnIYrE4bBePO6uCEbcVNCNuKyAiIndntVqRkJCAiIgIGAwGFBcXS9sk+vbtC4PBAIvFgpycHLfaMuGqmaOJqHl5wvYvT52nPUH93016ejqUyv/dq7fZbNDr9U3yu+G2AiIiIhfjCSedZrMZpaWlGDNmDB555BG7i+kxY8Zg165dMJvNbrOFQrzDdmVyrvLychgMBrdLGklEN8ZTgoSeOE97CpVKhaSkJBgMBuj1+qtWMHLWuQGDA0RERM3AaDTijTfekO39DAkJwd/+9je3OukU90FmZ2dj6NCh0Ov1sjJM2dnZsn6uzmq1Yvny5RAEAQMGDEBCQoL0etatWweTyYRXX30V0dHRbhfIIaKGE4OEQ4YMweTJk+Hn54eamhoUFha6XZDQ0+ZpTyNWMMrMzERycrJ0PCwszOnvM5fJOfDCCy9AoVBg/vz50rGLFy/iscceQ3BwMNq0aYMJEybITqoA4Pjx44iJiUGrVq3QsWNHPPXUU7h06ZKsz+eff44BAwbAz88Pf/rTn7BmzRq758/IyED37t3RsmVLDB48GIWFhbL2hoyFiIjIEaPRiNTUVFRUVMiOV1RUIDU11a32fgYFBQEANBqNwzJMGo1G1s/VFRUVoaKiAhqNBkuXLpW9nqVLl0Kj0aC8vBxFRUXOHioRNRGxvFzPnj1x7NgxvPbaa/jHP/6B1157DceOHUPPnj2dWl6usTxtnvZEOp0O69atw/Lly5GSkoLly5cjJyfH6QEolwgOfP3113jzzTdx++23y44vWLAAW7ZswYYNG/DFF1/gxIkTeOihh6R2q9WKmJgY1NbWYteuXVi7di3WrFmDJUuWSH0sFgtiYmJwzz33oKioCPPnz8fs2bOxbds2qc/69evxxBNPIDU1Fd999x369euHkSNH4vTp0w0eCxERkSPinWkAuDLNj/h4+fLlbnPSqVAortkuvqbr9XMV+/btAwBMnz5dtvcTAJRKJaZNmybrR0SeR1yGf+jQIYcJ/A4dOoSTJ0/CbDY7e6gN4mnztKcSKxiNGDECUVFRLrE6zenBgXPnziE+Ph5vvfUW1Gq1dLyyshKrVq3CK6+8gnvvvRcDBw7E6tWrsWvXLuzevRsA8N///hcHDhzAunXrEBUVhVGjRiE9PR0ZGRmora0FcLm+Z3h4OF5++WX06dMHycnJmDhxonSiBgCvvPIK5syZgxkzZiAyMhJZWVlo1aoV3n777QaPxZGamhpUVVXJ/iMiIu+yb98+acXAwIEDZSedAwcOBHB5BYG7XHyWl5cDAPbv3w+9Xo/i4mJUV1ejuLhYely/n6tr6Eky8zcTea4zZ84AAAYPHuzwTvvgwYNl/Vydp83T1HycHhx47LHHEBMTg/vuu092/Ntvv0VdXZ3seO/evXHLLbfAZDIBAEwmEzQaDUJCQqQ+I0eORFVVlfSmN5lMdt975MiR0veora3Ft99+K+ujVCpx3333SX0aMhZHnn/+eQQGBkr/de3atVE/GyIicn/fffcdgMv1ptPS0qTVbrW1tUhLS5PqTYv9XJ1YXmn27Nk4duwYkpOTERMTg+TkZFgsFsyaNUvWz9X1798fALB69WrYbDZZm81mk7Yiiv2IyPNUVlYCAIYNG+ZwBVF0dLSsn6vztHmamo9TExK+9957+O677/D111/btZWWlsLX19duL0xISIiUcbO0tFQWGBDbxbZr9amqqsKFCxdQXl4Oq9XqsM/BgwcbPBZHnnnmGTzxxBPS46qqKgYIiIi8jLhFrXv37g6zRvfv3x8HDhyQbWVzZRqNBqGhoSguLsY777zjsERWWFiYtKfV1fXr1w9BQUEwm81ISUlBfHy8LHHX/v37ERQUhH79+jl7qETURAIDAwEAX375JUaNGmVXXu6rr76S9XN1njZPU/NxWnDgl19+wbx587B9+3a0bNnSWcNoUn5+fvDz83P2MIiIyIk6duwIACgoKLDLgr1nzx58/PHHsn6urn4ZptTUVAwaNAi+vr746aefkJ+fj927dzu1DFNjqVQqLFiwQMo7VH9FoPg3fMGCBW7zeoio8Tp06AAAKCwsREpKijSv1dbWorCwULqRKfZzdZ42T1PzcVpw4Ntvv8Xp06cxYMAA6ZjVaoXRaMS//vUvbNu2DbW1taioqJDdsT916hRCQ0MBXL7jcmVVAbGCQP0+V1YVOHXqFAICAuDv7w+VSgWVSuWwT/3vcb2xEBERORIVFYXc3FwAwDfffCPLVdOiRQtZP3eh0+kQGxuLjRs3yi6mVSoVYmNjnZ5tubF0Oh3S0tLsSk2q1Wq3q29ORI0n3mlXKpXYs2ePbF5TKpUICwuDIAhudafd0+ZpT2S1WmE2m6VVHRqNxukBG6cFB0aMGGGX8XPGjBno3bs3/u///g9du3aFj48PduzYgQkTJgAADh06hOPHj0Or1QIAtFotnnvuOZw+fVq647J9+3YEBARIezi1Wi0KCgpkz7N9+3bpe/j6+mLgwIHYsWMHxo8fD+Dy8qEdO3ZIdScHDhx43bEQERE5Uj/R3ZWldus/dqes0UajEfn5+RgyZAgGDRokqween5+PyMhItzvx1Ol0iI6OdrkTNSJqeiqVCsOHD8f69evt5mJBEHDixAlMnjzZreYDT5ynPYnRaERmZqbdVkNnB6QVggul37377rsRFRWFV199FQCQlJSEgoICrFmzBgEBAXj88ccBALt27QJwOdoSFRWFTp064R//+AdKS0vx8MMPY/bs2Vi2bBmAy6UMb7vtNjz22GOYOXMmPv30U8ydOxcfffQRRo4cCeByKcNp06bhzTffxKBBg/Dqq68iPz8fBw8elHIRXG8sDVFVVYXAwEBUVlYiICDgpvzMiIjItW3fvl36m6RUKmVJ7+o/XrRoEe6//36njLExrFYrEhISEBERgfT0dLu9uXq9HhaLBTk5OW51Ik1E3stqtWLixImoqKiQLqJF4uOgoCBs3LjRLeY1ztOuzWg0wmAwQKvV2uW5MZlMMBgMNz1A0NDrUKdXK7iW5cuXY8yYMZgwYQJ0Oh1CQ0Px/vvvS+0qlQpbt26FSqWCVqtFQkICHnnkETz77LNSn/DwcHz00UfYvn07+vXrh5dffhnZ2dlSYAAAJk+ejH/+859YsmQJoqKiUFRUhP/85z+yJIXXGwsREZEjYqmokJAQtG/fXtbWoUMH6W+Nu5SUEuuBx8fHO8zqHRcX51b1wImIxJKzt9xyi10C8qCgIHTt2tWtSs5ynnZdVqsVmZmZ0Gq1DstmarVaZGVlwWq1OmV8Tq1WcKXPP/9c9rhly5bIyMhARkbGVf9Nt27d7LYNXOnuu+/G3r17r9knOTlZ2kbgSEPGQkREdCWx9FX79u2xfPlyu6zR8+fPx6lTp9ymRFZZWRmAy8F3R8TjYj8iIlcnXif88ssv0Gq1WLJkid3dXLFf/XxprorztOsSAzd6vf6qgZvk5GSYzWan5CJy6ZUDRERE7k5csnngwAEYDAb4+PhAq9XCx8cHBoMBP/zwg6yfqxPrYlssFoft4nHWzyYidyHusu7Tp4/Du7l9+vSR9XN1nKddl6sHbhgcICIiakL9+vUDAHTt2hXHjh1DcnIyYmJikJycDIvFgq5du8r6uToxq3dubq4sfwJweS9rXl4e62cTkVtp27YtAMhyDdR38eJFWT9Xx3nadbl64IbBASIioiYUFRWFoKAgHD9+HN27d8fcuXPx1FNPYe7cuejWrRuOHz8OtVrtNqUMxfrZJpMJer0excXFqK6uRnFxMfR6PUwmExITE91mJQQRUbt27QAAR48eRUpKimxeS0lJwbFjx2T9XB3naddVP3BTV1eHoqIi7NixA0VFRairq3N64MalqhV4OlYrICLyTmJmYh8fH9TW1krHfX19UVdX1ySZiZua0WjEG2+8gVOnTknHXKEMExFRYxUVFWHBggUAcNVqBcDlBOXuEsgFOE+7KvGcwNfX1+69Vltby2oFRI5YrVZZNM1ZWTuJiP4onU6H2NhY1NXVyY7X1dUhNjbWbU/SrqwHTkTkjsS7uZ06dZIFcIHL83SnTp3cdhk+52nXJAiC3TaWmpoap+e14MqBZsSVAw1nNBqRmZmJ0tJS6RgjnUTkroxGI1JTU6/anpaW5lZzmzNqNBMRNaWsrCysX78earUa999/P8LCwnDy5Els374d5eXlmDx5MhITE509zAbjPO2arFYrJk6ciIqKCgwZMgSDBw+WVqfs2bMHu3fvhlqtxoYNG27qto+GXocyONCMGBxoGE5mRORJrFYrYmJiUFNTgxYtWiA2NhajR49GQUEB8vPzcenSJfj5+eGjjz5yi/2fVqsVCQkJiIiIQHp6uqwUk81mg16vh8ViQU5Ojlu8HiIicV4Tz9Pr35wKCwtDQEAAqqqq3GZe4zztur799ls8+eST0Gg0ePXVV+1+N/Pnz4fZbMY///lPDBw48KY9L7cVkFuyWq3IzMyEVqt1WEpGq9UiKyuLWwyIyG188803qKmpgUqlwkcffYQ5c+agc+fOmDNnjhQQqKmpwTfffOPsoTaIWKM5Pj7+qjWaT548CbPZ7KQREhE1jjivzZ07F+vWrcPy5cuRkpKC5cuXIycnB48//rhbzWucp13Xvn37AADTp093+LuZNm2arF9zY3CAXAonMyLyNBs2bAAAPPTQQ/D19ZW1+fr6Yvz48bJ+rs7VazQTETVW/XlNpVIhKioKI0aMQFRUFFQqldvNa5ynXZe4aP96uSCctbifwQFyKZzMiMjTnD17FgDQt29fh+2RkZGyfq7O1Ws0ExE1lqfNa572ejxJ//79AQCrV6+GzWaTtdlsNqxZs0bWr7kxOEAuhZMZEXma3r17AwDeeusthycCq1atkvVzda5eo5mIqLHqz2uO5ml3m9c4T7uufv36ISgoCGazGSkpKSguLkZ1dTWKi4uRkpKC/fv3IygoCP369XPK+JiQsBkxIeH1MYEKEXmaCxcuYPTo0QCAQYMGQavVwtfXF7W1tTCZTCgsLAQAFBQUwN/f35lDbTBn1GgmImpK9RNix8XFSQmx8/Ly3DIhNudp1yVWMBKrFIjEx01Rwaih16EtbuqzEv1BKpUKSUlJMBgMSElJwaBBg6ST6MLCQuzevRsGg4GBASJyG/7+/oiOjsbOnTtRWFgoBQPqi46OdpvAgOhq9xZ4z4GI3JFOp4PBYEBmZiaSk5Ol42FhYW57Ic152jXpdDpMnjzZLtdQXV0dJk+e7NT3GoMD5HJ0Oh1iY2OxceNGmEwm6bhKpUJsbKxbTs5E5N3+/Oc/Y+fOnddsdxdiVZmhQ4fCYDCguLgYZWVlCA4ORt++fWEwGJCVlYXo6GgGconIreh0OkRHR8NsNkvzmkajcbu5jPO0azMajcjPz8eQIUMwaNAgacVAYWEh8vPzERkZ6bTrHQYHyOW48geGiKix6p+kLVq0CNnZ2fj111/RpUsXzJ49G8uWLXOrkzSxqoxer79qVZnk5GSYzWZERUU5Z5BERDdIrFbgzjhPu64ry7bX//2MGzcOer3eqecEDA6QS3H1DwwRUWPVP0lr2bIlhg8fLt3BadmypdudpInVYkpKSpCeno7S0lKpLTQ0FDNnzpT1IyKi5sV52nW5euCGwQFyKfU/MIIgoKioSLasy9kfGCKixhJPvk6cOOERJ2litZhly5Zh6NCh0Ov1UuKu3NxcLFu2TNaPiIiaF+dp1+XqZdsZHCCXwkgnEXma+idpgwYNQs+ePXH27Fm0bdsWNTU1eP7552X9XF1kZCRUKhUCAgKQlpaGFi1aSMfT0tIQGxuLqqoqREZGOnmkRETeifO066pftr1Xr152+S2cXbadwQFyKYx0EpGnEU/SlEol9uzZY9fu4+MDm83mNidpBw4cgNVqRUVFBVJTU+1KflVUVEAQBBw4cIArvIjI7VitVrdPSMh52nVpNBqEhoZixYoVqKystLsRGhgYiLCwMGg0GqeMj8EBcin1I52LFy9GQUEBtm/fjs6dO2Px4sVISEhgpJOI3Ip4kma1WqFQKHDfffchNjYW+fn5+OSTT1BXVyf1c4eTNHHl1qJFi7Bq1Sq7kl/PPPMMli1bxhVeROR2jEYjMjMz7S7YkpKS3CoZNudp16VSqTB8+HCsX78earUaCxcuhFarhclkwttvv41Dhw5h8uTJTgtINTo48Msvv0ChUKBLly4AgMLCQuTl5SEyMhKPPvroTR8geRfxJLq8vBwxMTGytoyMDFk/dziJJiI6efIkgMuJhtq3b4/t27dj+/btAICQkBCcOXMGNpsNJ0+edIt5TVy51alTJ6xbt87uDtvBgwdl/dyJJ9wxJKIbYzQaYTAYoNVq7VauGgwGGAwGtwkQePI87e6sViu++OIL9OrVC+Xl5Xj55ZeltpCQEPTq1QtGoxFz5sxxyt8f5fW7yMXFxeGzzz4DAJSWluL+++9HYWEhFi9ejGefffamD5C8S/0IpqMMno76ERG5sp07dwIA7rvvPuTl5WH58uVISUnB8uXLkZubi3vvvVfWz9WJSyJzc3OhUCgQFRWFESNGICoqCgqFAnl5eU5dEnmjjEYj4uPjsWDBAixduhQLFixAfHw8jEajs4dGRE3sympZkZGR8Pf3R2RkJNLT06HVapGVlQWr1ersoTaIp87TnkBMvq7T6eyudRQKBYYNG4aTJ0/CbDY7ZXyNDg7s378fgwYNAgDk5+fjtttuw65du5Cbm4s1a9bc7PGRlwkMDAQA+Pv720Uzg4OD4e/vL+tHROTqLl68CAA4c+aMw5M0Mdgp9nN1KpUKSUlJMJlM0Ov1KC4uRnV1NYqLi6HX62EymZCYmOhWd9yNRiNSU1NRXl4uO15eXo7U1FQGCIg8nHjBFh8ff9Xycs68YGssT5ynPYX4Nz87Oxvh4eGYN28enn76acybNw/h4eFYtWqVrF9za/S2grq6Ovj5+QEAPvnkE4wbNw4A0Lt3b2npJNGNOnr0KADgwoULdtHZyspK1NbWSv3uuOOOZh8fEVFjde3aFd9++y327t0LvV5vlxhq7969Uj93odPpYDAY8MYbb8j2soaGhrrV0lvg8h3D5cuXA4D0N0YkPl6+fDmio6N5Ik3koVy9vNyN8KR52pOo1WoAl//mHzt2DCaTSWoLCQlB165dcfz4calfc2t0cKBv377IyspCTEwMtm/fjvT0dACX6zdz3wr9UfUDTK1bt8b48eMRFhaGkydPYvv27dKJGgNRROQuHn30UXz44YdQqVT48ccfZSdpISEhUKlUsFqtbpm3R6FQOHsIf9i+fftQUVEB4PJJ26xZs6TkUKtWrUJ5eTkqKiqwb98+DBgwwLmDJaImUb+8nKOk184uL/dHeMI87UkEQQAAHD9+HFqtFkuWLJFuGKxbt04KFoj9mlujgwMvvvgiHnzwQbz00kuYNm0a+vXrBwDYvHmztN2A6EbZbDYAlwMDVVVVyM/Pl9pUKhVat26N8+fPS/2IiFydv78/oqOjsXPnTpw+fVrWdurUKQBAdHS0tG3KHYiJu4YMGYLJkyfDz88PNTU1KCwsdLvEXd988w0AoE2bNsjPz5fqgcfExGDkyJF48MEHce7cOXzzzTcMDhB5qPp79NPT02VbC2w2m1vu0fekedqT/P7777LHgiBI/12rX3NpdHDg7rvvxm+//YaqqirZcodHH30UrVq1uqmDI+/Tpk0bAMD58+cxePBgDBkyRJrMdu/eLdUIF/sREVHzEhN39ezZ0+GSyJ49eyIrK8ttluEfPnwYADBq1CgpMCBq0aIFRo4ciU2bNkn9iMjziHv0DQaDw+1fJpMJBoPBLeY0wPPmaU9SWVkJABg3bhwKCwvtykyOHTsWW7Zskfo1t0YHB4DLEY5vv/0WR48eRVxcHNq2bQtfX18GB+gPqx+pPXz4MLp16yZtK6h/YnZlshgiIld14cIF7Ny5Ez4+PtiwYQPWrFmDX3/9FV26dMH06dMxadIk7Ny5ExcuXHCL1QNi4q7S0lIMHTpUtiQyNzcXu3btkvq5Q2nGli1bArg8XpvNZnfHUExAJvYjIs8k7tHPzMy0u2Bzt7vsnjZPexIxqfqpU6fwzjvvoLi4WCoz2bdvX6SkpMj6NbdGBwd+/vln/OUvf8Hx48dRU1OD+++/H23btsWLL76ImpoaZGVlNcU4yUsEBAQAAFq1aoXy8nLZtgLxeHV1tdSPiMjVrVy5EgAwadIkBAYGYt68ebL2CRMm4L333sPKlSvt2lzRmTNnAACDBw+WLb8VS34tWrQIe/bskfq5uttvvx07d+7EwYMHkZKSgvj4eNlJtBiYvv322508UiJqajqdDtHR0TCbzdIFm0ajcbu76542T3uSDh06AIC0vSMuLg5arRYWiwUGgwFff/21rF9za3RwYN68ebjjjjuwb98+WVKOBx98EHPmzLmpgyPvI25Vqa6uRmBgIPr374+WLVvi4sWL2Lt3r7TExlkZPImIGuvXX38FAIwePdph++jRo/Hee+9J/VydOA8PGzbMYcmv6Oho7Nmzx2lLIhtr/PjxePPNN2Gz2fDNN9/Ilt/6+voCuPy6xo8f76QRElFzUqlUbn833dPmaU8i5rcIDAzEsWPH7Fap9OzZE1VVVU7Lb9HotdlffvklUlJSpD+You7du6OkpOSmDYy8U7t27aSvL168iM8//xz/+c9/8Pnnn8tqgNfvR0Tkyrp06QIAKCgocNguHhf7uTpxqeOXX36Juro6FBUVYceOHSgqKkJdXR2++uorWT9X5+vri0mTJgG4XK65PrFCzqRJk+zOe4iIXJWnzdOeRMxvcfjwYYSHh2Pu3Ll46qmnMHfuXHTv3h2HDx9GYmKi01arNHrlgM1ms6s/D1y+M9K2bdubMijyXmK5lVtuuQUXL16UZfYOCgqCn58fjh8/zrIsROQ2xFKGGzZsQEJCAg4dOiQtV+3Vqxc2bdok9XMH4lLHPXv2YOzYsaipqZHaxASy9fu5A0elyxrTTkTkSjxxnvYkYn6LN954Q7ZaLTQ01On5LRodHPjzn/+MV199VdpDqVAocO7cOaSmpl51ySRRQ5WXlwMAfvnlFwwZMgRTp06Fr68vamtrUVhYiN27d8v6ERG5uvqlDK/2d9KdShlqNBoEBQWhoqLiqn3UarXblPwSs3r36tULv//+u2wPbocOHdCuXTtm9SYit+Jp87SncsWbnY0ODrz88ssYOXIkIiMjcfHiRcTFxeHIkSNo37493n333aYYI3kRMY/F7NmzsWXLFlk0LSwsDLNmzUJ2drYs3wURkav785//jJ07d16z3R31798fgwYNktXP3r17t129ZldWP6v3lVsHKisrpWABs3oTkTvyhHna0xiNRhgMBmi1Wuj1elkSXIPB4NTVA40ODnTp0gX79u3De++9h++//x7nzp3DrFmzEB8f7zZ3Pch1iUk6iouLsXr1amzduhUlJSXo3LkzxowZg/T0dISFhTHSSURuQ7wzPXToUPzf//0f/vGPf+DEiRPo1KkTnn76abz44otudWfabDajoqICc+bMwZYtW6QVXcDlJZGzZ89Gdna221xM118pcOVdnPqPmdWbiNyFp83TnkQ8J9BqtQ4rSej1eqeeEzQ6OAAALVq0QEJCws0eC5GUpMNgMOCBBx6QkkEBwFtvvYW6ujoYDAa3OIEmIgL+d2d6zJgx+Otf/4rS0lIAgMViwV//+leMGTMGu3btcpuTtLKyMgCXl9xfeedJEAR07NhR1s/V/f7779LXAwYMQEJCgnQXZ926ddIKtvr9iIhcmafN055EPCfQ6/UOK0nExcUhOTnZaecEjQ4OvPPOO9dsf+SRR254MEQiQRBkgQEAdo+JiNyBePKVnZ2NoUOH2i0hzM7OlvVzdeK2rmXLlmHo0KFYsmSJ7PUsW7ZM1s/ViaW82rRpg2effRYtWlw+NYqMjMSzzz6LBx98EOfOnWPJLyJyG542T3sS8W99eHi4w3bxuLPOCRodHJg3b57scV1dHaqrq+Hr64tWrVoxOEB/iNVqxfLlywEAQ4YMweDBg6U9Unv27MHu3buxfPlyt1l+S0QUFBQE4PK2KYPBgOLiYuzatQvBwcEwGAxYuHAhzGaz1M/VRUZGQqVSISAgAGlpabKL6bS0NMTGxqKqqsptMvyL2wXE5MpxcXHSSXReXh7OnTsn60dE5Oo8bZ72JGJAxmKxOPz5WywWWb/m1ujggKMs8UeOHEFSUhKeeuqpmzIo8l779u1DRUUFNBoNnnvuOdlym3HjxmHevHnYv38/9u3bhwEDBjhxpEREDSPuW6+srMTDDz+MU6dOSW0hISHw8/OT9XN1Bw4cgNVqRUVFhcOL6YqKCgiCgAMHDrjFNomQkBAAl3MqHT16FMnJyVJbaGgoOnfujJKSEqkfEXk2q9UKs9kslZzVaDRud0PK0+ZpTyLmVxOTDxYXF0vvtb59+yIvL8+p+dVuKOfAlW699Va88MILSEhIwMGDB2/GtyQvtXfvXgDAjBkzHO7DmT59Op588kns3buXwQEicgtiUP348eN289qZM2dgs9lk/VyduNRx0aJFWLVqlexiOiwsDM888wyWLVvmNtsk+vfvj9zcXPz666/w8fGRtZWVlaGurk7qR0SezWg0IjMzU8oNA1wOEiYlJTm19nxjedo87UnE/GqpqakYO3YsampqpDZxtXRaWprTAlI3JTgAXE5SeOLEiZv17chLiXfOrldexV3usBERqdVq6WsfHx/ZiUD9x/X7uTJxqWOnTp2wbt06uzts4k0Cd9nLGhUVhdatW+P8+fO4dOmSrE183Lp1a95dI/JwrlxerrE8bZ72RFe7lnH2NU6jgwObN2+WPRYEASdPnsS//vUvREdH37SBkXfq168fcnJysGbNGmg0GrulNmvXrpX6ERG5A6vVCgBo27Yt3nvvPRQUFEglWkePHo0pU6bg7NmzUj9XV39JZHp6uuyi2WazOX1J5I0QVwz4+PjIkt/6+vqipqbGbkUBEXkWVy8v11ieOE97ivrvNUfbCgwGg3uVMhw/frzssUKhQIcOHXDvvffi5ZdfvlnjIi8VFRWFoKAgmM1mjBkzxu4krba2Fmq1mndwiMhtmM1mAMDZs2fx0EMPyVYOZGdnS4/NZjPuvPNOp4yxMeqXnNXr9XZ7WU0mk1uVnL2yHnj95cRqtRpjxoxhPXAiD+fq5eUay9PmaU9S/73m4+Nj935y9nut0cEBcW8kUVNQqVQYOXIk1q9ff9XlnX/+8585mRGR26i/TepaW6aut53Kleh0OhgMBmRmZtrtZXWnpbfA//bmPvjgg5g8ebLd8tuamhpkZ2dzby6RB3P18nI3wpPmaU/i6u+1m5ZzgOhmsFqt+OKLL9CrVy9UVFTIsnp37NgRgYGBMBqNmDNnDgMEROQW+vXrh3Xr1sHf3x9t27bF6dOnpbbAwEBUVVXh4sWLbrddSqfTYciQIdi8ebO0TWLcuHHw9fV19tAa5cqyUlfeqXF2WSkianquXl7uRnnKPO1J6r/XevXqZReQdvZ7rUHBgSeeeKLB3/CVV1654cEQ1V9q4+gDc/DgQbda1kVEJCYXunDhAlq2bIlJkyahU6dOOHHiBD755BNcvHhR1s9dOMrqvWnTJrfL6n3l3tz6S4q5N5fIO3jqPOAp87QnEd9rK1assLsRGhISgqCgINcvZSiWl7sedzuxIddTf6mNSqWyCwA4e6kNEVFj/f7779LX5eXl2LBhw3X7uTpPyurNvblE5InzgCfN055EpVJh+PDhWL9+PdRqNRYuXAitVguTyYS3334bhw4dwuTJk532XlMI7rTJ0c1VVVUhMDAQlZWVCAgIcPZwXFJRUREWLFiAjIwMh8u6iouLkZycjOXLl3PlABG5hY0bNyIjI+O6/R577DFMnDixGUb0x1itViQkJCAiIsLhHTa9Xg+LxYKcnBy3O5G+8g5bWFgYEhMTeQJN5CU8ZR7w1HnaE4i/G/Ga8Mr3WkBAAKqqqm7676ah16HMOUAupf6yLkflPdx1WRcRea+2bdtKXw8ePBhDhgyBn58fampqsHv3buzZs8eunyvztKzeIp1Oh+joaLvtbDxxJvIenjIPeOo87QlcfQv1DQUHvvnmG+Tn5+P48eOyUnMA8P7779+UgZF3Epd1paamYuzYsbKSX+LJdFpamttN0kTkvSorK6WvlUolbr31Vml5Z2FhocN+rszVMy3/EY62sxGRd/GEecCT52l35+pbqJXX7yL33nvvYejQofjhhx/wwQcfoK6uDsXFxfj0008RGBjYFGMkL3S1/BXMa0FE7qaqqgoA0KVLF1gsFiQnJyMmJgbJycn46aef0LlzZ1k/V1c/07Ijzs60TETk7ThPuy5X/900OjiwbNkyLF++HFu2bIGvry9ee+01HDx4ELGxsbjllluaYozkRaxWKzIzM6HVarFlyxYsX74cKSkp0ntOq9UiKysLVqvV2UMlImoQcUnnr7/+ivDwcMydOxdPPfUU5s6di+7du6OkpETWz9XV3/5ls9lkbe6c1Ru4/DeoqKgIO3bsQFFREf/WEJFb8uR52t25+u+m0WciR48eRUxMDADA19cX58+fh0KhwIIFC7By5cqbPkDyLuI+nPj4ePj4+CAqKgojRoxAVFQUfHx8EBcXh5MnT8JsNjt7qEREDdKvXz8AwC233IJjx45hxYoVeOmll7BixQpYLBYpsC72c3Xi9i+TyQS9Xo/i4mJUV1ejuLgYer0eJpMJiYmJbrf9y2g0IiEhAQsWLMDSpUuxYMECJCQkwGg0OntoRESN4qnztCdw9d9No3MOqNVqnD17FgDQuXNn7N+/HxqNBhUVFaiurr7pAyTvUn8fTm1tLTZv3oySkhJ07twZ48aNc/o+HCKixoqKikJQUBCOHz8OHx8fWVtZWRnq6uqgVqvdao+rTqeDwWBAZmYmkpOTpeNhYWFuWR6LJb+IyNN42jztSVz5d9Pg4MD+/ftx2223QafTYfv27dBoNJg0aRLmzZuHTz/9FNu3b8eIESOacqzkBcT9NS+//DI+++wz2XKbzMxM3HPPPbJ+RESuTqVSYeTIkVi/fj0uXbokaxMf//nPf3a7OziektW7/na2+iW/IiMjkZ6eDr1ej6ysLERHR7vdayMi7+Yp87QnctXfjUIQBKEhHZVKJe68806MHz8eCQkJ6Nq1K2w2G/7xj39g165duPXWW5GSkgK1Wt3UY3ZbDa0v6c2sViseeOABabtK/ben+Lh169b497//7fQPDxFRQ4g1jZVKJUpLS2VBT5VKhZCQEAiCwHrTTlJUVIQFCxYgIyMDkZGRdu3FxcVITk7G8uXL3Wp1BxERkaih16ENXjnwxRdfYPXq1Xj++efx3HPPYcKECZg9ezb+/ve/35QBEwGXT6LF7SlBQUGYOXMmtFotTCYT3n77bZSXl6O6uhpWq5Un0UTkFsRcKgqFAkOGDMGgQYOk0qyFhYXYvXs3BEFgvWknYckvIqrParW63N1coubS4ODAsGHDMGzYMLz++uvIz8/HmjVrMHz4cPzpT3/CrFmzMG3aNISGhjblWMkLfPjhhxAEASEhIVAoFHj55ZeltrCwMISEhODUqVP48MMPERsb68SREhE1zJkzZwAAgwYNQlpaGoqLi1FWVoawsDDExMQgJSUFhYWFUj9qXvXLSvXq1cvuosDZZaWIqPkYjUZkZmaitLRUOhYaGoqkpCTu0Sev0OiEhK1bt8aMGTMwY8YM/Pjjj1i9ejUyMjKg1+vxl7/8BZs3b26KcZKX+P777wEA8+fPx5133ml3krZ7926kpKTg+++/Z3CAiNxCZWUlACAkJASPPPKI3UnnnXfeKetHzUssK7VixQpUVlba/X4CAwNZ8ovICzAxKTU3V1yl0ujgQH1/+tOfsGjRInTr1g3PPPMMPvroo5s1LvJSrVq1AgCcPHkSKpXKbomteNIm9iMicnWBgYEAgM2bN8PX11fW9vvvv2PLli2yftS8VCoVhg8fjvXr10OtViM2NhZhYWE4efIktm/fjkOHDmHy5MlOP2EjoqbDxKTU3Fx1lYryRv+h0WjE9OnTERoaiqeeegoPPfQQdu7ceTPHRl7o/vvvBwCsXr3aYVbvtWvXyvoREbm6du3aSV/X1dXJ2uo/rt+Pmo/VasUXX3yBTp06obKyEvn5+XjttdeQn5+PyspKdOrUCUajEVar1dlDJaImIuaGiY+PlwIDIqVSibi4OJw8eRJms9lJIyRPIq5SCQ8Px7x58/D0009j3rx5CA8Ph8FggNFodNrYGrVy4MSJE1izZg3WrFmDH3/8EUOHDsWKFSsQGxuL1q1bN9UYyYsMGDAArVu3xtmzZzFp0iS7hIRnz55F69atMWDAAGcPlYioQepXJ7iyQFD9x/X7UfMRLwoAQKvV2iWMNJlMUj8mjCTyTExMSs1FXKXSs2dPHD16VPobAwAdO3ZEz549nbpKpcHBgVGjRuGTTz5B+/bt8cgjj2DmzJno1atXU46NvJBKpcLTTz+N1NRUVFRU4JVXXrHr8/TTT3NJFxG5jX379klfK5VKWRCg/uN9+/ZJ+QfchSvul2wsMRHk4MGDsXTpUtldw3HjxmHRokXYs2cPE0YSebD6iUkdlTR158SknjBPexIxIF1aWgo/Pz9ZW2VlJU6fPi31c0ZAusHBAR8fH2zcuBFjxozhG4qalE6nQ1paGt544w2cOnVKOu4K+3CIiBqr/jzm4+ODmpoah4/r93MHrrpfsrHERJDDhg1zuJw4Ojoae/bsYcJIIg8mJibNzc2V5RwALq/qysvLc8vEpJ4yT3uS+oHmAQMGICEhQUp+uW7dOmklgbMC0g0ODrAKATUnnU6H6OhoRjqJyO2JWwdatWqFDRs2oKCgACUlJejcuTNGjx6NiRMn4sKFC3ZbDlyZJ2X1FhNBfvnllxg1apTdRcFXX30l60dEnkelUiEpKQkGgwF6vR5xcXHSvJaXlweTyQSDweBW56GeNE97kvLycgBAjx49ZKvVIiMjsXTpUsyZMwfHjh2T+jW3P1StgIiIiK5NoVAAAKqrq/HQQw/JVg5kZ2dLj8V+rs7Tsnp36NABAFBYWOjwouDrr7+W9SMiz6TT6WAwGJCZmYnk5GTpeFhYmNtdSHvaPO1JxFVoLVu2dNguHnfWajUGB8glcRkUEXmKkJAQ6etrVSuo38+Vifsl9Xo9BEFAUVGRbIVXXFwckpOT3SaBn7icODAwEMeOHbO7KOjZsyeqqqrcbjkxETWep6xc9bR5WlRbW4vNmzdLq+/GjRtnVyLY1YnvpQMHDjgMSP/www+yfs2NwQFyOVwGRUSepF+/fsjNzQVgX5Gg/uN+/fo167hulJitu6SkBOnp6XZB3JkzZ8r6ubr6y4kHDx6MoUOHora2Fr6+vjhx4gT27NnjdsuJiejGqVQqt7pgdsTT5mkAyMrKwsaNG2VlZbOysjBx4kQkJiY6cWSN069fP+Tk5KBr164OA9Jdu3bF8ePHnXZOwOAAuRQugyIiT9PQucpd5jQxW/eyZcswdOhQuyDusmXLZP3cgU6nQ2xsLDZs2CAL2KhUKsTGxjIgTURuxdPm6aysLKxfvx5qtRqzZs2SypyvWrUK69evBwC3CRBERUUhKCgIx48fx5AhQxAbGysrn7t7926o1WqnBaiU1+9C1HzEZVDx8fEOs0bHxcXh5MmTMJvNThohEVHjNPTOjLvcwYmMjIRKpYJarcaSJUtQW1uLXbt2oba2FkuWLIFarYZKpXJYDsxVGY1G5Ofno0UL+T0TlUqF/Px8GI1GJ42MiKjxPGmerq2txcaNG6FWq5Gfn4+YmBi0a9cOMTExyM/Ph1qtxsaNG1FbW+vsoTaISqXCggULoFAosHfvXqxYsQIvvfQSVqxYgb1790KhUGD+/PncVkAE/O/kODw83GG7eNxdTqKJiBqaVMhdSuUdOHAAVqsV5eXleOCBB2QJFsW7H2I/d1iaa7VasXz58qtWixAEAa+++ipXrBGR2/CkeXrz5s2wWq2YNWuWXQC3RYsWmDFjBl555RVs3rwZEydOdNIoG6d+8sv6Wz7UarXT86sxOEAuRVzeZLFY0KtXL7uEMBaLRdaPiMjV+fv7S1+3bdsWo0aNQqdOnXDixAl8/PHHOHv2rF0/V+ZpKyGKiopQUVEB4HLN6cGDB0snz3v27MHu3btRXl6OoqIiDBw40LmDJaImZ7Va3T4hoSfN0yUlJQAArVbrsF08LvZzF66a/JLBAXIpYtboFStWoLy8HKdPn5baOnbsCLVajbCwMGaNJiK3sXPnTunryMhIhIaGwsfHB6GhoYiMjMSePXukfjExMc4aZoMFBQUBuDxfv/zyyyguLpZObPr27YuFCxfCbDZL/Vzd3r17AQBdunSBxWLB7t27pbaQkBB06dIFv/76K/bu3cvgAJGH85RqWZ40T3fu3BkAYDKZHP6NNJlMsn7uxBWTXzI4QC5FpVJh+PDhWL9+vV3Ogd9++w2nT5/G5MmTnR5VIyJqqF9++QUA0KZNG3z99ddSMAC4POe1adMG586dk/q5OoVCIX195YmNzWaTlufX7+fKxCD0r7/+atd26tQpu35E5Jk8qVqWJ83T48aNQ1ZWFlatWoWRI0fKthZcunQJq1evhkqlwrhx45w4yhvjiqtUmJCQXIrVasW2bdsAAD4+PrI28fG2bdtkZUyIiFyZeCJz7tw5u1KGVqsV586dk/VzdeXl5QCA/fv3Q6/Xo7i4GNXV1SguLpYe1+/n6hq6TY3b2Yg815XVsiIjI+Hv7y9Vy9JqtcjKynKb809Pmqd9fX0xceJElJeXIzY2Flu2bMFvv/2GLVu2IDY2FuXl5Zg4cSJ8fX2dPdRGMRqNiIuLw4IFC7B06VIsWLAAcXFxTk+A6x5nIuQ19u3bh4qKiqsug3riiSewf/9+7Nu3DwMGDHD2cImIrmvEiBFYtWpVg/q5A/Eiefbs2diyZYtdjeZZs2YhOzvbbS6mAwICpK9btGiB8PBw+Pr6ora2FhaLBZcuXbLrR0SeRayWpdfrr1otKzk5GWaz2eWWgTviafO0WKZw48aNeOWVV6TjKpUKkydPdpsyhiKj0YjU1FS746dPn0ZqairS0tKctkqFwQFyKeLezxkzZsDHx8duAp4+fTqefPJJ7N27l8EBInILt956q+xxr169MHjwYOzZsweHDh26aj9XJeaGKS4uxurVq7F161aUlJSgc+fOGDNmDNLT090qN8yRI0ekry9duiR7fLV+RORZPK1alqfN08DlAMHMmTOxefNm6bWMGzfO7VYMWK1WvPjiiwAuVyeYNWsWtFotTCYTVq1ahfLycrz44otOq5DD4AC5FHHv09VKSl3Zj4jI1e3bt0/2+NChQ7KgQP1+gwcPbq5h3TCVSoWkpCQYDAaMHz9eViIrOzsbtbW1MBgMTt832VBiFZyb1Y+I3E/9almRkZF27e5WLcvT5mmRuMXAne3duxfV1dVo27Yt8vPzpS2FMTExGDlyJB566CGcPXsWe/fuxR133NHs42POAXIp/fr1AwCsWbPGbm+uzWbD2rVrZf2IiFzd4cOHAQA9e/Z02C4eF/u5i6sFca8X3HU19UtIOlpO7KgfEXkW8U57bm6uw/PPvLw8t7vTDnjOPO1JxNxqM2bMsMs11KJFC0ybNk3Wr7lx5QC5lKioKAQFBcFsNiMlJQXx8fGybLFmsxlqtdot9nsREQGAn58fAOD8+fPo2LGjXYlWMSGh2M/ViYm7hg4dCoPBYJcbxmAwICsry2lLIhur/knywIED4e/vj6qqKgQEBODChQv4+uuv7foRkWepf6ddr9cjLi5OOv/My8uDyWRyqzvtnjZPe5KLFy8CuFwi0xHxuNivuTE4QC5FpVJhwYIFMBgM+Pbbb6XapcDlpUQKhQLz58/nREZEbqNfv37YtWsXSkpK7LZEnTlzRrrodJcVUfUTdznKDeNuibvqZx8XAwHX60dEnken08FgMCAzM9MugZ87lTEEPG+e9iS33XYbvvrqK6xatQqDBw+WrVCz2Wx4++23pX7OwOAAuRydTofY2Fhs3LhRdtxqtSI2NtatJmciorFjxyIzMxOA/d3n+o/Hjh3brOO6UfUTdzmq0exuibvatm17U/sRkfvS6XQYMmSI2ye987R52pM8+OCDWLlyJY4ePYrFixcjISFBWqWybt06HDt2DEqlEg8++KBTxsfgALkco9GI/Px8DBkyBIMGDYKfnx9qampQWFiI/Px8REZGMkBARG7jwIEDDe43cODAJh7NHycm5Prggw+wZcsWlJaWSm2hoaEYM2aMrJ+rmzBhAr777rsG9SMiz2Y0GpGZmSmb1zZt2oSkpCS3Ovf0tHnak/j6+mLSpElYv349CgsLsXv3bqlNXEUwadIkpwWkmJCQXIq4R0qr1SItLQ3du3eHr68vunfvjrS0NGi1WmRlZXF5JxG5DbFE683q52wajQZBQUF466238Pvvv8vafv/9d2RnZyMoKMhtEncdP35c9rh169YIDAxE69atr9mPiDyL0WiEwWBAREQEMjIyUFBQgIyMDERERMBgMMBoNDp7iA3mafO0p0lMTMTkyZPtthoqFApMnjwZiYmJThoZVw6QixH3SI0ZMwYPP/wwTp06JbWFhIRg7Nix2LVrF/dIEZHbEO/YBAQE4Ny5c7JM2EqlEm3atEFVVZXszo6rq6urAwC0atUKjz/+uKxGc21trdTuDr7//nvZ4/Pnz1+135QpU5pjSETUzOrfnEpPT5fu4EZGRiI9PR16vd7tEvh50jztiRITEzFz5kyX28LC4AC5FHHvU3Z2tl3m7oqKCmRnZ8v6ERG5OvHOQFVVlV2bzWaTjl95B8FVFRUV4fz587jllltQW1uLl19+WWoLCwtDQEAAjh8/jqKiIrfYJtHQvyf8u0Pkueon8HNU0tTdEvh52jztqXx9fTFx4kRnD0OGwQFyKUFBQdLXAwYMsEvSIVYvqN+PiMiVNXRPp7vs/dy3bx8AYN68eejXr59doquioiI8+eST2Ldvn1ucdKrVaunrO++8E0OHDpVy3ezatUuqYFC/HxF5lvoJ/BxxtwR+njZPU/NhcIBcipi5u23btnj22WfRosXlt2hkZCSeffZZPPTQQzh79izrTROR2zh37txN7eds4vyrUCigUqmuehfNXebps2fPSl//+OOPGDZsGO68806YTCb8+OOPDvsRkWcRg7MWiwWRkZF27RaLRdbP1XnaPE3NhwkJyaWIkc5z584hNTUVxcXFqK6uRnFxMVJTU6WTZ7EfEZGr++mnn25qP2fr378/AGD16tWoq6tDUVERduzYgaKiItTV1WHNmjWyfq6u/r7b8vJyvPLKK5g0aRJeeeUVlJeXO+xHRJ5Fo9EgNDQUubm5srwwwOXtX3l5eQgLC3ObBH6eNk9T8+HKAXIp4p7bRx55BNu2bUNycrLUFhYWhkceeQRr1651m725RET1E9z5+vqitrbW4eOrJcJzNf369UNQUBDMZjPGjh2LmpoaqU1cjh8UFIR+/fo5cZQNFxgYKH2tUChkd9LqP67fj4g8i0qlQlJSEgwGA/R6PeLi4qRtrXl5eTCZTDAYDG6TjNDT5mlqPgwOkEvp168fcnJy8N133+Gdd95BcXGxtEeqb9++WLhwodSPiMgdqNVq/PTTT1CpVHZ3ny9dugSVSgWr1eo2e9pVKhVGjhyJ9evX270e8fHIkSPd5iR64sSJ+OabbwDYL7Gt/9jVkkYR0c2l0+lgMBiQmZlpd3PKYDBAp9M5cXSN42nzNDUfBgfIpURFRUmRztTUVMTHx0Or1cJisSA1NRVmsxlqtdotMsUSEQFA7969sXfvXlitVrRo0QIajQbBwcEoKyuD2WzGpUuXpH7uwGq14osvvkCvXr1QXl6O06dPS20dOnRAUFAQjEYj5syZ4xYnnnfccYcUoLkalUqFO+64oxlHRUTOoNPpEB0dbZfAzx3msvo8bZ6m5sPgALkUlUqFBQsWwGAw4LvvvpOqEwCXl0EpFArMnz+fExkRuY3+/fvj3XffBXB5pcDevXuv2s8d1C/51atXL7uT6IMHD7pVyS+r1XrNwED9PvzbQ+T5rpXAz1142jztqWpra7F582aUlJSgc+fOGDduHHx9fZ06JgYHyOXUX9ZVWloqHVer1UhKSnKrZV1ERFfWzP6j/ZytfskvRyfR7lbya9OmTdLX18oJsWnTJkydOrXZx0dE1FieNk97oqysLGzcuFEWnM7KysLEiRORmJjotHExOEAuyVOWdRER/f777ze1n7PVL/kVHh6OlStX4tdff0WXLl3w6KOPul3Jr507dwK4vNRWqVTi1KlTUptarYbVasVvv/2GnTt3MjhARG7B0+ZpT5OVlYX169fb3RQQBAHr168HAKcFCBgcICIiakL1L/qvdWfaXYIDYsmvlJQUWam/b775Bh9++CHUarVblfwSq0ScOXMGQ4cOxZIlS6Qs5bm5udi1a5esHxGRq/O0edqT1NbWYsOGDQCAQYMG4eGHH5b+5uTk5GD37t3YsGEDZs6c6ZQtBgwOkEsyGo122wpCQ0O5rYCI3E5VVRUAoGXLlggICJAlhgoKCkJVVRUuXrwo9XN1KpUKQUFBOHjwIACgZ8+e6Ny5M0pKSnD48GGUl5ejd+/ebrPSKyIiAj/99BOUSiUWLlyIV155BSdOnECnTp2wcOFC7N69GzabDREREc4eKhFRg3jaPO1JPvjgA9hsNvTo0QPPPfectHogMjISzz33HObMmYNjx47hgw8+wOTJk5t9fO6xwZG8itFohMFgQEREBDIyMlBQUICMjAxERETAYDDAaDQ6e4hERA0mBgMuXrwoCwyIbRcvXpT1c3UXLlzAwYMHoVAoAACHDx/GZ599hsOHDwMAFAoFDh48iAsXLjhzmA126623AgBsNhsmTJiAnTt3wmKxYOfOnZgwYQJsNpusHxGRq/O0edqT7N+/HwAwa9Ysu20FSqUSM2fOlPVrbgwOkEuxWq3IzMyEVqtFeno6IiMj4e/vj8jISKSnp0Or1SIrK+u6maWJiFxFhw4dbmo/Z1u5ciWAy3sj1Wo17r77bvzlL3/B3XffDbVaDUEQZP1cXUP33HJvLpF3sFqtKCoqwo4dO1BUVOSW55yeNk97kpYtWwIASktLHb7XxFXTYr/mxm0F5FLql15xFE2Li4tj6RUicitt2rSRPe7duzcGDx6MPXv2SEs+HfVzVcePHwcAtGrVCr6+vvj888+ltpCQELRq1QrV1dVSP1fX0J+7u/x+iOjGecq2Vk+bpz3JyJEj8cknn+Ctt97C+vXrZUlwQ0JCUFlZKfVzBq4cIJdSv/SKIyy9QkTuRlzGCVxOQHjw4EGsXbsWBw8ehJ+fn8N+rkxMoFhdXY0ePXrItn/16NED1dXVsn6ubvPmzdLXVyZ/qv+4fj8i8jyetK3V0+ZpkSes6ujfvz98fX1x4cIF/P7775g6dSpycnIwdepU/P7777h48SJ8fX3Rv39/p4yPwQFyKfVLrziaAFh6hYjcjdlslr4Wl3KKxP3sV/ZzZd26dZO+XrRoEWpra7Fr1y7U1tZi0aJFDvu5sp9++kn6+soT5fqP6/cjIs/iadtaPW2eBi4HbxISErBgwQIsXboUCxYsQEJCglsFbUStWrUCANTV1eHdd9/Fww8/jHfffRd1dXUAgNatWzttbNxWQC5FLL2yYsUKVFRU2C21CQoKYukVInIrYkCgTZs20t0akdVqRZs2bXDu3Dm7wIGr8vHxkb4eM2ZMg/q5soZm62ZWbyLP5WnbWj1tnhZXdWi1Wuj/v707j4+quv/H/5qZ7HsIkIQ9EVkiAwlrFo0bigpSKgoKFavip6CxorjVEhyk6rf2g1pbTKxR0I+gRKwISFshIFEIQTCJQyKLEgRCErasBLJMzu8Pfvd2JhsTMsxd8no+Hnk4d+5bOIeZnLnzvue8T2qqw3azFosFFotFM8s+rFYrKisrMXfuXKxfv96hGHF4eDjuvPNOZGRkKPZeY3KAVMVkMuH666/HmjVrEBoaioULFyIhIQE5OTl4//33ceDAAcycOZMXaUSkGcOGDUNOTg5qa2sRHByMnj17oqGhAV5eXjh9+rS8vnDYsGEKt9Q5zrZTK/2ZMGEC/vnPfwIARo8ejeuuuw7e3t6or6/HN998g++//16OIyJ90tuyVj2N0/azOiwWCwoLC7Fz506EhYXJiYH09HQkJSVp4vuB9B666667cO+998JqteLMmTMICwuD2WxGfX09MjIyFHuvMTlAqmKz2bB9+3YMHToUlZWVWLZsmXwuIiICQ4cORXZ2Nh555BFNDABERM899xymTZsGAKiqqpKTAW3FaUHPnj3lxwaDwWHGg/2xfZyanThxQn6cl5cH4OKa0Ly8PPm4ZRwR6Yv9staYmJhW57W2rFVP47Q0q+POO+/E/fff32pW8Z133omdO3dqZlZHy/dayzYr/V5jzQFSFWkAuO6661qdE0LguuuuQ2lpqWbW5hIRSR/0ropTmrRvNtC6hkLLC1AtOHv2rPxYCIHvv/8e7733Hr7//nuH/tjHEZG+SMtaV61a5VALBrhYG2b16tWaWtaqp3FauoP+7rvvorKy0uFcZWUlMjIyHOLUTu3vNc4cIFWRfrEzMjKQmJiIxYsXO6wr0toAQERUWlrqdJwW7nrYj7/jx4+Hj48PampqEBgYiAsXLmD37t2t4tQsMDDQpXFEpD0mkwnz58+HxWJBamoqZs2aJV9/rl69Gjk5ObBYLJqZtaqncTo0NFR+XF9f73DO/tg+Ts3s32uLFi3C+PHj4eXlhYaGBuzevRu7du1S9L3G5ACpSkhICICLWbWlS5fKRWGkarELFiyA1WqV44iI1O7bb78FcLHwk1SJ2J70/Lfffovbb7/d3c3rNGlZxNixY7F3716H6t0mkwljx47Fnj172l0+oTbTp0/H3r17nYojIv1KTk6GxWJBWloaUlJS5OcjIyM1VfAO0Nc4bd/2kJAQzJ07V65HlpGRIc8m0MpOEsDF99qMGTOwdu1a5OTkyM+bTCbMmDFD0fcakwOkKvbTm2w2G3744Qe5SMc111wjT4XSwjQoIiIAOH36NAC0mRiwf16KU7vg4GAAwJ49ezq8IyXFaYlUFHfo0KE4cOAAtm/frqkLTiLqmuTkZCQlJbUqEqeVGQMSPY3T9rVfhg0bhkGDBsHHxweDBg3CsGHDsGvXLjlu3LhxSjWzU7Kzs5GZmYn4+HiMHz9eLoK7e/duZGZmIiYmRrEEAZMDpCoVFRUALtYeuPPOOx2mC0m/OPZxRERqJ+1nLAkJCUHPnj1x+vRph/WTLePUyr5IknSBeak4Nfvss8/kxzabDVu3bsXWrVvbjEtISHBn04hIASaTSRNLvDqip3H60KFDAICEhAQUFxe3mtUxYcIE5ObmynFqZ7/7gv0saQCYOnUqUlNTFd19gQUJSVWcHaS0MJgREQGAh4djHr6yshI//fRTq8JKLePUqmVxq67GKa2mpgYAkJiY2Ob+5vHx8Q5xRERqp6dx2tvbG8DFG4MrVqzAY489hmnTpuGxxx7D+++/L3+WSnFqJxVfnz17dpufObNmzVK0+Lo2rkSo24iJiYHJZEJQUBA+/vhj/Pjjj/K0ruHDh+O+++5DdXV1m9vMEBGp0Y8//ujSOKU5u/xBK8skhg0bhoMHD2Lnzp1yUSiJh4eHPGVVC/uBExEB+hqnR44ciR07dmD//v2YOnWqwxK9f/zjH/LxyJEjlWpip0hFIKOiotDQ0ID169ejpKQEffv2xdSpUxEVFeUQ526cOUCqUlRUBJvNhoqKCrz00kvw9PREQkICPD098dJLL6GiogI2mw1FRUVKN5WIyCnOrlnXytp2+/HX09PT4Zz9sVbG6UceeUR+7O3tjVGjRmHkyJEYNWqUw50o+zgiIjXT0zg9bdo0udZYy9o90rHBYMC0adPc3bTLIs1+XrZsGe644w4sX74c69atw/Lly3HHHXfg9ddfd4hzN84cIFWRsmQvvPAC3n///Vbril544QW88sormth6hYgIcH65gFaWFUh3mry9vREcHIyTJ0/K50JDQ1FVVYX6+npN3JECgAMHDsiPa2pqUFBQ0G7cmDFj3NUsIqLLpqdx2mQywcvLq9U2hva8vLw0UzTSbDbDz88PWVlZCAkJwa233oo+ffrgxIkT+Oqrr5CVlQV/f3+YzWZF2qeNKxHqNqQsWd++ffHRRx+1qha7f/9+hzgiIrVrampyaZzSpAu0+vp6NDY2YsaMGYiMjERpaSk2b97scF4L2ksGtBXH5AARaYGexumCgoJLtrO+vh4FBQUYPXq0m1p1+Ww2Gy5cuAAAqKurQ2ZmpnzOy8sLAHD+/HnYbDZFEh5MDpCqmM1mREREYNWqVbBYLA7nmpubsXr1akRGRiqWTSMi6izpIsBVcUobPHgw9u7dC4PBgKqqKocLG6PRCIPBACEEBg8erGArnWdfYyAuLg5lZWWora1FQEAAIiIi5G207OOIiNRMT+P03r17AQD9+/dHQ0MDysvL5XPh4eHw9PTE8ePHsXfvXk0kB9avX4/m5mYArT9XpOPm5masX78ed999t9vbx+QAqYrJZML8+fNhsVgwZcoUh18aLy8vNDY2wmKxaGbqEBGR3vTo0QPAxSrXLStdSxc89nFq98svvwC4uGbVfj/tmpoalJaWtoojIlI7PY3Tp06dAgAcO3YMiYmJWLx4MaKiolBcXIxVq1Zh586dDnFqd/z4cZfGuRoLEpIqCSHazKZpYcsVIiJ7UiElV8UpLTg42KVxSjt79iyAS2/pJcUREamdnsbpnj17AgACAgKwZMkSxMTEwNfXFzExMViyZAkCAgIc4tTOPjnj6emJWbNm4aOPPsKsWbMcikXax7kTkwOkKjabDX/+858BtL5Qlo7//Oc/a6aqNxGRs9MctTAdEoC8pzTw3/WRbR3bx6mZn5+fS+OIiJSmp3FaSmDU1tZi8eLFKCwsRF1dHQoLC7F48WLU1tY6xKldVVWV/HjdunV45JFH0LdvXzzyyCNYt25dm3HupGhy4NVXX8W4ceMQGBiI3r17Y9q0aQ5Vg4GLazAfe+wxhIWFISAgANOnT3dYawIAR48exeTJk+Hn54fevXvjmWeeaVXY6euvv8bo0aPh7e2NwYMHY+XKla3as3z5cgwaNAg+Pj6YMGECdu/e3em2UNfk5eWhrq4OADBhwgQsX74cmzZtwvLlyzFhwgQAF4t32E/9JCJSs2uuucalcUqrqakBcHH9Z1BQkMO54OBg9OvXzyFO7ey3K3RFHBGR0vQ0Ttsvfdi7dy9SUlIwefJkpKSkyPUIWsapmf133d/85jfYsGEDTp8+jQ0bNuA3v/lNm3HupGhyYPv27Xjsscewa9cubN68GY2Njbj11ltx7tw5OebJJ5/Ehg0b8Omnn2L79u04ceIE7rrrLvm8zWbD5MmT0dDQgJ07d+KDDz7AypUrsXjxYjmmuLgYkydPxo033oj8/HwsWLAAc+fOxX/+8x85Zs2aNXjqqafw4osv4vvvv8eoUaMwadIkh60/LtUW6rp///vfAICoqCi89NJL8uva0NCAl156CYMGDXKIIyJSu23btrk0TmnSLK5jx4612gbr1KlT8jpJrSyTKCkpcWkcEZHS9DRO9+rVS37c3qzilnFqJm1bHBgYiKqqKrz++uu455578Prrr6OqqgqBgYEOcW5vnyJ/6/+v5Re8lStXonfv3ti7dy+Sk5NRVVWF9957D6tXr8ZNN90EAFixYgWGDx+OXbt2IT4+Hl999RWKioqwZcsWhIeHIzY2FkuXLsVzzz0Hi8UCLy8vpKenIyoqCsuWLQMADB8+HN9++y3eeOMNTJo0CQDw+uuv45FHHsGDDz4IAEhPT8eXX36J999/H88//7xTbaGuk2ZiXH311bj//vtbVSQdOXIkjhw5whkbRKQZJ06ccGmc0uLi4vDRRx8BgFzxWmJ/HBcXp0j7OsvZCzClLtSIiDpLT+O0tJNZcHAwKisrHb4DhISEICQkBNXV1ZrZyWz06NEoKSlBTU0Nxo8fj/79++PChQvw8fHBsWPH5JnrSi01VFXNAWlthTQtZO/evWhsbMTEiRPlmGHDhmHAgAHIyckBAOTk5MBsNiM8PFyOmTRpEqqrq1FYWCjH2P8ZUoz0ZzQ0NGDv3r0OMUajERMnTpRjnGlLS/X19aiurnb4oY5Jr+NXX33Vah1UZWUlNm/e7BBHRKR2zhZS1UrB1eHDh8uPW7bZ/tg+Ts30tuyDiEhP47S0k9nBgwcRFRWF6dOnY/LkyZg+fTqioqJw8OBBzJs3TzM7mc2fP19+/N133+Gzzz7Dl19+ic8++wzfffddm3HupJrkQHNzMxYsWICkpCSMGDECAFBWVgYvLy+EhIQ4xIaHh6OsrEyOaflFUTq+VEx1dTXOnz+P06dPw2aztRlj/2dcqi0tvfrqqwgODpZ/+vfv7+S/Rvd1yy23yI/b2/uzZRwRkZrZVx92RZzSvvzyS5fGKc3f39+lcUSkbTabDfn5+cjKykJ+fr4mi2DrbZxOTk7GjBkz2vwyPWPGDCQnJyvdRKf5+voiKSkJQPuJm6SkJPj6+rq9bYDCywrsPfbYY9i3bx++/fZbpZviMn/4wx/w1FNPycfV1dVMEFyCfdavo0ynVrKDRESNjY0ujVPasWPHAFycZt+y+K/981Kc2h08eNClcUSkXdnZ2UhLS3O48RcREYH58+dr6guo3sbp7OxsZGZmIj4+HuPHj4e3tzfq6+uxe/duZGZmIiYmRlOvz6233oodO3Z0eF4pqpg5kJKSgo0bN2Lbtm1y9Uzg4i9jQ0NDq+nl5eXliIiIkGNarj+Xji8VExQUBF9fX/Ts2RMmk6nNGPs/41Jtacnb2xtBQUEOP9Sx/Px8l8YRESnN2b2KldrTuLPOnDkDAGhqaoLR6HgZYTQa5QtRKU7t9FYTgoguT3Z2NiwWC6Kjox12y4qOjobFYkF2drbSTXSansZpm82GtLQ0JCQk4E9/+hOmTZuG22+/HdOmTcOf/vQnJCQkID09XTMzPKT+JCYm4osvvkBSUhKioqKQlJSEL774AomJiYr2R9HkgBACKSkp+Pzzz7F161ZERUU5nB8zZgw8PT2RlZUlP3fgwAEcPXoUCQkJAICEhARYrVaHXQU2b96MoKAgxMTEyDH2f4YUI/0ZXl5eGDNmjENMc3MzsrKy5Bhn2kJdJyVogoKC2hzMpAQLCxISESnDfnldW+N0W3FqduHCBflxy6KD9sf2cUSkL/ZfQJcuXYqYmBj4+voiJiYGS5cu1dwXUD2N01arFWVlZZg9e3abfZk1axZKS0thtVoVamHnSP255ppr8Lvf/Q47duxAcXExduzYgd/97neIiYlRtD+KLit47LHHsHr1anzxxRcIDAyUp/AEBwfD19cXwcHBePjhh/HUU0+hR48eCAoKwuOPP46EhAR5d4Bbb70VMTExuP/++/Haa6+hrKwMixYtwmOPPSbvSTxv3jz8/e9/x7PPPouHHnoIW7duRWZmpsM6m6eeegoPPPAAxo4di/Hjx+PNN9/EuXPn5N0LnGkLdZ20dKC6uhpeXl4OdQY8PDzkoo5aKdxFROTp6enUkgGt1ByoqKiQH7ecrmp/bB+nZqGhofJnS0f9CQ0NdWu7iMh9pC9sqamp7X4BTUlJgdVqRWxsrDKN7AQ9jdPS7IaoqCjYbDZYrVacOXMGYWFhMJvN8s1lLcyCAP7bzoyMDMTHx2PmzJkOyyTee+89hzh3UzQ5kJaWBgC44YYbHJ5fsWIFfvvb3wIA3njjDRiNRkyfPh319fWYNGkS3n77bTnWZDJh48aNmD9/PhISEuDv748HHngAL730khwTFRWFL7/8Ek8++ST++te/ol+/fsjIyJC3MQSAmTNn4tSpU1i8eDHKysoQGxuLf//73w5FCi/VFuq63r17y487KkhoH0dEpGYBAQFOXYAFBAS4oTVdp7dlErfccgsyMjKciiMifbL/AtoWrX0B1dM4HRYWBgD4/PPPsWHDhlb1IKZMmeIQp3ZSorl///44fPiww6534eHh6N+/P44ePapYQlrR5IAzd399fHywfPlyLF++vN2YgQMHYtOmTR3+OTfccAPy8vI6jElJSUFKSkqX2kJdY1+XwWg0Ogxa9ses30BEWlFTU+PSOKW1TNx2NU5p7X0ZuNw4ItIe6YtlcXGxvCzZXnFxsUOc2ulpnDabzQgJCcG7774rzwqXVFRUICMjAyEhITCbzQq1sHOk779Hjx6Fl5eXw7mKigr5NVFqlrQqChISSaSpnUDrKbb2x/ZxRERqpqc7OIDz0+u1Mg1/7dq1Lo0jIu0xm82IiIjAqlWrWo3Fzc3NWL16NSIjIzXzBVRv47S0NM/X1xcLFy7E2rVrsXDhQnm7P63s9gMAZ8+elR/7+fnhhhtuwG233YYbbrgBfn5+bca5k2q2MiQCgFOnTsmP2xqc24ojIlIzZ7P/Wqml0rNnT/mxyWRCVFSUvF6yuLhYLthlH6dm0ueJj49Pm0UHpef5uUOkXyaTCfPnz4fFYsGiRYswfvx4ufbV7t27sWvXLlgsFs1spa2ncTo/Px/nzp3DgAEDcOHCBSxbtkw+Fx4ejgEDBuDo0aPIz8/HmDFjFGypc6Qv/X5+fqisrMTXX3/tcN7Pzw91dXVMDhABkGs8BAYGtppi29jYiICAANTW1jrUgiAiUjODweDUF3+DweCG1nRdXV2d/Nhms+Gnn366ZJyaSXeeLly40GpPcA8PDzlhIMURkT4lJydjxowZWLt2rcM6cJPJhBkzZiA5OVnB1nWOnsbpgoICAMBNN93Uahm5EAI33HADPvzwQxQUFGgiOSB9v6mrq2tzCbX0mii11JDJAVKVuLg4rFq1CjU1NfDw8EBycjKGDRuG/fv3Izs7G7W1tXIcEZEW6G1ZgbN3M5S669FZcXFxOHToEICOq3rzc4dI37Kzs5GZmYnx48fD29sbNTU1CAwMRH19PTIzMxETE6OZBIGexmkpub5y5cpW506ePIkPP/zQIU7t7D/rg4OD8dBDDyEhIQE5OTl4//335QLGSl0TMDlAqmJfBMZkMmHr1q3YunUrAMDb21u+UGurWAwRkRqZTCan9sbWynRVHx8fl8YprUePHi6NIyLtsdlsSEtLQ2RkJPbs2eMwZptMJkRGRiI9PR1JSUmaGKv1NE6PGjUKH330kVNxWiDd6PT09ISnp2erZRLS9sdSnLuxICGpypdffik/bpkBtD+2jyMiUjMPD+fy8M7GKW3gwIHy45ZLIeyP7ePUrLKy0qVxRKQ9VqsVZWVlOHHiBIKCgvD000/js88+w9NPP42goCCcOHECpaWlsFqtSjfVKXoap+2LDXp4eOC+++7D//3f/+G+++5z+NzUSlFCabZGY2Njq22Oz549K/dDqVkdTA6QqpSUlAAAnnnmmVZ3aXr06IGnn37aIY6ISO30VpAwPz9ffmwymRAXF4eJEyciLi7O4Y6afZyaHThwwKVxRKQ95eXlAICQkBBkZmZi8uTJ6NGjByZPnozMzEyEhIQ4xKmdnsZp+51ijEYjPv74Y9x///34+OOPYTQa24xTM/sdCVomNOyP7ePcSRu3Kajb6Nu3L4CLF8kfffQRrFYrzpw5g7CwMJjNZrkQiRRHRKR2eqs5YF8kqampCXl5eZeMU7OWd266GkdE2rN//34AwB133IHm5masXbsWJSUl6Nu3L6ZOnYrbbrsNn3zyCfbv349JkyYp3NpL09M4ffToUQDA2LFjcfToUZw8eVI+Fxoair59++L777+X49Tu5ptvxubNm52KUwKTAzpjs9lafaHWwtooydSpU5Geno733nsPEydOdDjX1NSEFStWwGQyYerUqQq1kIioc+zrpVwqTgt69eolF/C7VJwWOHt3Rqm7OER05Ukzt7KysvDJJ584JGvT0tLk8UwrM7z0NE4HBgbi9OnTKCoqarW7Qnl5uZzgCAwMVKJ5usPkgI5kZ2cjLS0NZWVl8nMRERGYP3++Zqqrenl54e6778aaNWvk7K1E2u5j5syZ8PLyUrCVRETOs5/26Io4pSUmJmLnzp0AgICAAFx11VUQQsBgMODnn3+WiyglJiYq2Uyn9e/fH0VFRU7FEZE+9evXD0Dbywaam5vl56U4tdPTOD1x4kS8++677W67KD3f8qaiWjkza0CKmzBhwhVuTWvauBKhS8rOzobFYkF0dDSWL1+OTZs2Yfny5YiOjobFYkF2drbSTXSatBNByym20jF3KiAiLWloaHBpnNLsKyjX1taioKAAP/zwAwoKClqd0wJn7q51Jo6ItOe2226TH7csDuvp6dlmnJrpaZx2drawVmYV//zzz/Ljljc77Y/t49yJyQEdkLZfSUhIwNKlSxETEwNfX1/ExMRg6dKlSEhIQHp6ulNbaSnNZrPhtdde6zDmtdde00RfiIgA5ysoa6XSsrNrVLWwlhUATp065dI4ItIeqaYVcHF6+owZM/DEE09gxowZCAgIaDNOzfQ0Tq9fv96lcUqTlqZ4eHjIhS4lISEhcnJKqSUsTA7ogLT9yuzZs1tNSzUajZg1a5Zmtl/5/vvvce7cOQCtM7fS8blz5/D999+7vW1ERKS/AovOJpuZlCbSrx9++AEAcN1116G6uhqZmZn461//iszMTFRXV+Paa691iFM7PY3TnZmGrwVhYWEALtZSsy+uCAAnT56UaxRJce7G5IAOnDlzBgAQFRXV5nnpeSlOzb766iv5ccsCXvbH9nFERGqmt5oDVVVVLo1Tmt62miSizpMKjsbFxWHDhg2YNm0axo4di2nTpmHDhg2IjY11iFM7PY3T9tf/48aNQ3JyMuLi4pCcnIxx48a1GadmgwcPdmmcq7EgoQ5ImaXi4mIMHTq01W4FxcXFDnFqZl9M0WAwOFyM2R/bxxERqZnevnwWFha6NE5pFy5ccGkcEWnPLbfcgs2bN+Mf//gHMjMz5evMPXv2YNeuXaisrJTjtEBP47T9LgRHjhxxWOJlv9uCVnYraLmUoKtxrsbkgA6YzWZERETgrbfeQmVlpUOl1fDwcISEhCAyMhJms1nBVjrHfilBywtl++OWSw6IiMg9pKVfropTmt6SN0TUeaNHj4aXlxcuXLiAM2fO4N5778Udd9yBTZs24bPPPkNjYyO8vb0xevRopZvqFD2N0/ZfklvWfrE/VurLdGcdPnzYpXGupo05jNQhk8mE66+/HgcOHEBDQwMWLlyItWvXYuHChWhoaMCBAweQnJwMk8mkdFMvyWAwuDSOiEhpevvyGRwc7NI4IiI1kJYMNDY24pNPPsGcOXPwySefyMVitbKkANDXOO3sv7tWXp/z58+7NM7VmBzQAZvNhu3bt2Po0KHw9vbGsmXLcPfdd2PZsmXw8fHB0KFDkZ2drYliSpzeSUSkbs7u862V/cB9fX1dGkdE2mO1WlFZWYm5c+ciPDzc4Vx4eDjmzp2LiooKTRT3BvQ1TkdHR7s0TmnObsmu1NbtnJutA9JuBampqW3WHNi/fz9SUlJgtVrlgipqpbctv4iI9EZPha6Ai1NRnblDo5Upq0TUeVLR7rvuugv33HMP1q9fj5KSEvTt2xdTp05FU1MTMjIyNFHcG9DXOK32afidpfbZhEwO6ID9bgUmk6lVAkBLuxV4e3u7NI6ISGlGo9Gp7aK0sltBRUWFS+OU5mwNG9a6IdIvqWj3P//5T2zcuNGh8PVnn32GKVOmOMSpnZ7GabVPw++sXbt2OR03e/bsK9ya1rRxJUIdst+toC1a2q0gMjLSpXFEREpzdkmXFpZ+Afq7UGtoaHBpHBFpj9lsRkhICDIyMhAVFYXly5dj06ZNWL58OaKiopCRkYHQ0FBNFPcG9DVO2yfXW9Ycsz92JgmvBmq/JmByQAek3QpWrVrV6hejubkZq1ev1sxuBTfddJNL44iIlKb2KYTdnbOJcy0k2InINYQQ8o/9c6SsMWPGYPr06ZgyZQqmT5+OMWPGKN0k3WFyQAdMJhPmz5+PnJwcpKamorCwEHV1dSgsLERqaipycnIwb948TexWoLd1RUREeqO3ZMfAgQNdGkdE2iMVJHzkkUdQXFyMlJQUTJ48GSkpKThy5Ajmzp2LyspKzRQk1NM4ffbsWfnxnj178Nlnn2Hjxo347LPPsGfPnjbj1Mx+FprBYMAtt9yCf/zjH7jlllscZkIoNVuNC+h0Ijk5GRaLBWlpaUhJSZGfj4yMhMViQXJysoKtc57a1+EQEXWW3moONDU1uTROafZri10RR0TaI9Xl+vWvf42ZM2e2Ku5dX1+vqYKEehqne/bsiYMHDzoVpwX2O64JIbB582Zs3ry5wzh3YnJAR5KTk5GUlNRqQNPCjAGJs+uFtLKuiIjI09MT9fX1TsVpgZ+fn1NFrLSy53R5eblL44hIe+zrd8XExLQq7q2l+l2AvsbpESNGYOfOnQAuFoYdNGgQvL29UV9fjyNHjsgJjhEjRijZTKc5+71Mqe9vTA7oTFu7FWiJnqZBEREB+hvX4uLiUFJS4lScFjiTuOlMHBFpj339rqVLlzrM5NJa/S5AX+P0VVddJT9uamrCTz/9dMk4NQsMDHRpnKtpYw4jdRuNjY0ujSMiUprequE7O3VTK1M8uYUuEempfhegr3Ha2ToPWqkHkZCQ4NI4V2NyQGdsNhvy8/ORlZWF/Px8zWyNJWm5RUlX44iIyLW+/vprl8YREamBVL/r8OHDDgUJi4uLNVW/C9DXOK232XdqX0LNZQU6kp2djbS0NIeiSREREZg/f75mBjRn1z5pYY0UEZEe6anQFaC/mR1EdPn0UL8L0Nc4HRAQ4NI4pWVlZTkdd//991/h1rTG5IBOZGdnw2KxICEhAampqYiKikJxcTFWrVoFi8WimYxnVVWVw3G/fv0QGBiImpoaHD9+vN04IiJyj379+jmMxx3FacG5c+dcGkfkDJvNpvkvoHql9fpdgL7G6erqapfGKe3UqVMujXM1Jgd0wGazIS0tDQkJCbBYLCgsLMTOnTsRFhYmJwbS09ORlJSk+g8ef39/h+P2BraWcURE5B4DBgxwatvZAQMGuKE1Xefp6Ynz5887FUfkCnqY6alnekjc6Gmctt8pxmAwOCwfsD/Wyo4y9u2Pi4tDWVkZamtrERAQgIiICOTl5bWKcycmB3TAarWirKwMd955J+bMmdPqw2bKlCnYuXMnrFar6jOh0dHRKCwsdCqOiIjcz9l9vrWyH3hYWJhTd5y0soUZqZs00zM+Ph4zZ86Ut2TbvXu3pmZ66pVeEjd6GqcrKysBXNzGsEePHjh58qR8rlevXjh79iyamprkOLXz9vaWE9JSIgAAampqUFpa6hCnBCYHdED6xc7IyGhzWcF7773nEKdmw4cPx4YNG5yKIyIi9zt9+rRL45R24cIFl8YRtUea6TlkyBAcPnwYOTk58rnw8HAMGTJEMzM99UhK3LScJXT27FnNJW70NE5L9V6ampocEgMAHI61UhcmODjYqURGcHDwlW9MG7hbgQ6EhoYCAEaMGIGlS5ciJiYGvr6+iImJwdKlS3HNNdc4xKmZ3tYVERHpjf2dDVfEKc2ZJQWdiSNqjzTT88CBA4iOjsYTTzyBZ599Fk888QSio6Nx4MABlJaWamZLNj2x2Wx44403IIRotSOWNHX9zTff1MwuYHoapyMiIlwap7SBAwe6NM7VOHNABy61JkUa5LSwxUdNTY1L44iIyLX0VsCvvr7epXFE7ZEKjF199dVtzhy4+uqrcejQIcUKkXVn+fn58t3cuLg4TJgwQV7ykZubi127dqGiogL5+fkYM2aMso11gp7G6ZtuuglbtmwBcLH2S2Njo3zO/vimm25SpH2d1XL2Q1fjXI3JAR2QBjOr1YrU1FTMmjVLXlawevVqOQOthbU4vEgjIlI3Hx8fpy4ofXx83NCarlP7ntOkH9JOS4cOHUJ8fDyuvfZa1NfXw9vbGyUlJXIBOe7I5H7S2u9+/fqhuLjYoZhfeHi4XP0/Ly9PE8kBPY3Tx44dkx+33HrR/vjYsWNISEhwW7sul31/XBHnakwO6IBUJGnu3LnYuHEjUlJS5HORkZGYO3cuMjIyNFFMKTc31+m4xx577Aq3hoiIWvLz83Oqho2fn58bWtN1zq5T1cp6VlKvwMBAABcLjeXm5raqui7dqZbiyH2kSvfHjx9HQkIC7r33XodikdIsD61UxNfTOG2/9KHlLGj7Yy0skQDU/5nD5IAOmM1mREREoKioCBkZGcjIyMDx48fRr18/zJ07F6+88goiIyNhNpuVbuol2VeHdUUcERG5ltovbDrL2SV3WliaR+omLYmsr6+Hp6cnrrvuOgwbNgz79+/HN998I8+K5NJJ9+vduzeAi3fS21ryISUKpDi109M43bdvXwDA1KlTkZub65CgCQ8Px/jx47FhwwY5Tu0CAwNx9uxZp+KUwOSADphMJsyfPx8vvvgipkyZIj+/Z88erFu3DgCwZMkSTVS+tV9H5Io4IiJyrdraWpfGKc1kMjlVZEwLn6Gkbv7+/gAAo9EIm82GrVu3YuvWrfJzRqMRzc3Nchy5T1BQEICLu5L4+vrinnvuQZ8+fXDixAls2bJFTtxIcWqnp3F66tSpSE9PxzfffIP09HQ899xzOHPmDMLCwvDnP/8Z8+bNg8lkwtSpU5VuqlOGDBnisGylozglMDmgE0VFRQAgf7BIpOOioiLNbL9CRETqpac7UsB/v6g5E0fUFQcPHgRwsX5Fy4r4Qgh5dsrBgwdx++23u7193VlISIj8uKKiAp9++ukl49RMT+O0l5cX7r77bqxZswYzZ86Un6+pqZGPZ86cCS8vL6Wa2CmJiYlOJQcSExPd0JrW+EmnAw0NDVi7di1CQ0OxadMmvPHGG1i0aBHeeOMNbNq0CaGhoVi7dq0mBgBfX1+XxhERkWvpbYZXyy9pXY0jao/9zZuWX2Tsj1n80v3sl3K0/F23TwxqZcmH3sbpb7/9tkvn1eTQoUMujXM1zhzQgfXr18Nms+Hhhx+Gt7c3YmNjHc4/+OCDeP3117F+/XrcfffdyjTSSRERESguLnYqjoiIqKs8PDycSp57eGjrkslms8FqtcrTb81mM5dGKMy+bkVsbCz69esn71Zw/PhxuSgz61u4n7S+28/PD4GBgQ7r2nv37o3q6mrU1dWxWKQCqqqqUFJS0mFMSUkJqqqqEBwc7KZWXb6ffvrJpXGupq1POmqT9AvT3vYd0vOX+sUiIiLqbi5cuODSODXIzs5GWlqaQ/HeiIgIzJ8/n0sMFRQQEADg4t7s3333ncMOTUajUd6zXYoj95FmBNTV1WHkyJHtbjOplZkDevKHP/zB6bi33377Crem644fP+7SOFdjckAHpOqcOTk5uO2221rdKZAqrmqhiqcz2650Jo6IiKgjzk7h1spU7+zsbFgsFiQkJCA1NRVRUVEoLi7GqlWrYLFYYLFYmCBQiDRzo7GxER4eHrjhhhvk3Qqys7PlKd6c4eF+0h3nyMhI7N69u1X9rsjISJSWlmrizrTeODOjuDNxSlP7Zw6TAzogVfFMS0vDypUrcfr0aflcz549cf78ec1U8WxqanJpHBERUXdhs9mQlpYmJwY2btyIzZs3o2/fvkhNTcXSpUuRnp6OpKQkfgFVwMiRIwFcnDnQ3NzssFuByWSSZw5IceQ+vXr1AgCUlpa2Otfc3Cw/L8WR+6j9y3Rnqf27DpMDOuDl5YX4+Hjs2LED586dczgnJQqSkpI0UcUzICAAdXV1TsUREZH7GQwGp9ZEs4Cf+1mtVpSVleGaa67BlClTHHZhSE9Pxw033IDS0lJYrdZW9YnoyrOfOTB+/Hj4+PigpqYGgYGBuHDhAnbv3u0QR+4TExNzybHNYDAgJibGja26fHoap728vJyqC6OF7zkAnNodpzNxrsbkgA7YbDbk5eV1GJOXlwebzab6D5zw8HCcPHnSqTgiInI/PV106o205C4rKwuhoaG45ZZb5L3aN2/ejKysLIc4cq+Kigr5sZQIuFQcuUdBQYE8rnl7e6O+vl4+Jx0LIVBQUIBx48Yp1Uyn6WmcdrZAp1YKeTq7La5S2+cyOaADeXl5cgXVTz75BJs2bUJJSQn69u2LO+64A/feey9qamqQl5eHsWPHKt3cDrU1nasrcURE5Fomk8mp6ZtqT0brUVBQEICL2/16enoiMzNTPte7d2/4+vri/Pnzchy5V1hYmPy45d1Q+2P7OHKPzZs3Ox2nheSAnsZpZ7di18KW7QDg7+/vVFv9/f3d0JrWlElJkEv95z//AXBxy0Jvb28MHjwYI0aMwODBg+Ht7Y0HHnjAIU7N9DYAEBHpTc+ePV0aR64jFeQ6f/48oqOj8cQTT+DZZ5/FE088gejoaJw/f94hjtwrJiYGJpMJ/v7+rQrbBQcHw9/fHyaTSTNT1/VEWpbbt2/fNl8bqah3y+W7aqWncdrZBIYWEh2A89viKrV9LmcO6IC0vdLp06fxm9/8ptXWRTfeeKNDnJp5e3u7NI6IiFzL2YtjrVxE68mJEyfkx99//728/RrguB7XPo7cp6ioCDabDefOnYOnpydmzJghL/v46quv5N+ZoqIi1oRwM+lLcklJSau165WVlfJNKS18mQb0NU7rbVmB2vvD5IAOjBgxAt9++y1Wr14NT09Ph3NnzpzBxx9/LMepXc+ePXHq1Cmn4oiIyP1qa2tdGkeuY79+uOUMO/tjLawz1iPp+iYyMhLl5eUOyz5MJpO8XZ4z10HkWsOGDcP69esBAH5+fnj88ceRkJCAnJwcvPfee/Lvz7Bhw5RsptP0NE6rvYBfZ7X8rtbVOFdjckAHpK0MgdbbXtgfa2ErQ71NHSIi0hu9bSulJ0OGDJEfG43GVnu1S8f2ceQ+VVVVAC7WTWpZc8BkMsn1lKQ4cp/q6mr5cVVVFZYtWyYf2yfT7OPUTE/jtJ76AsCpXdk6E+dqrDmgA0VFRfLjllNQ7I/t49QqMTHRpXFERETdRU1Njfy45YWy/bF9HLlPYGCg/Lij6zX7OHIP6XfCx8enzdfGx8fHIY7octnvhOGKOFdjckAHpG0M2/swkZ6/1HaHajBo0CCXxhEREXUXlZWVLo0j17L/dw8ICMDChQuxdu1aLFy4EAEBAW3GkXtIswPaq88lPc8lOe6n9jX6ndXY2OjSOFfjsgIdKC8vB3AxmxkfH4++ffuivr4e3t7eKCkpkQsSSXFq1pmtZBISEq5wa4iIiLTj5MmT8uOOtsqzjyP3kaake3t7w9vb22HqekREBLy9vVFfX6+Zqet6Yl+Xy2AwOHzRtD/WQv0uvdFbckDtNRSYHNCB3r17A7iYhV66dKnD1hdNTU349a9/jdraWjlOzZyd3aCFWRBERETuVFFR0e45+zueHcXRlSMlZerr6xEVFYUZM2bICYHvvvtO3m2KyRv3+/nnn+XHY8eOha+vL6qrqxEUFITz58/ju+++k+MmTJigVDOJrjgmB3QgKCgIwMWKo4sXL8bs2bMRFRWF4uJirFq1Sq5EKsWpmZ62XiEiInIn+21+4+LiEB8fL88Y2LVrF3Jzc1vFkfuEh4cDAPr164eff/4ZOTk5Duf69euH48ePy3FaYbPZYLVacebMGYSFhcFsNmuucLT9ayElAtqLmzVrljuaRDolJQSdiVMCkwM60KNHD/nx3r17HQY4+71a7ePUSm9Th4iIiNylV69e8uO8vDw5GQA4Xg/Yx5H7xMXFYdWqVTh+/Hirc/ZLP+Pi4tzZrC7Jzs5GWlqaPOsBuLhEYv78+UhOTlawZaQXJpPJqSn2WklIOVu3Qqn6FixIqAMdfcjbv7G0cDHQcivGrsYRERF1FzExMfJj+3oDLY/t48h9YmNjL3k30NvbG7Gxse5pUBdlZ2fDYrG0WqZSUVEBi8WC7OxshVrWefHx8fLj4OBgxMbGYuTIkYiNjUVwcHCbceQe9sulXRGntPaKXl5unKtp41+ROmQ2mxEREYHg4GBUVlY6ZJ9DQ0MRHByM6upqmM1mBVtJREREV5Kz09G1Nm1dL2w2W6ukTUsNDQ2w2Wyqvwtqs9nwxhtvQAiBuLg4TJgwQZ4unZubi127duHNN99EUlKS6vsCOG71WVVVhfz8/EvGkXuovYCf3jA5oAMmkwnz58+HxWLB+PHjMXToUNTU1CAwMBD19fXYvXs3LBaLJgZnIiIiujwxMTEwmUzw8PBAQ0NDq4rrXl5eaGpq4swBhXz++ecQQsjJGfubORERERBCoLy8HJ9//jlmzpypVDOdkp+fj8rKSgwYMADFxcXyzljAxeTTgAEDcPToUeTn52PMmDEKttQ5u3fvdjru/vvvv8KtIXucVexeTA7oRHJyMhITE7Fjx45W55KSkrjui4iISOeKiopgs9lgs9labWXo6ekpF8EqKirSzNR1Pdm3bx8A4IknnsCYMWOwfv16lJSUoG/fvpg6dSq+++47LFq0CPv27VN9cqCgoAAAcPToUSQmJmLx4sUOxbB37twpx2khOeDsjADOHKCuarlVZkdxSmByQCfS09OxY8eONvdm3bFjB9LT0zFv3jwFW0hERERX0pkzZ+THLS8s7Y/t48h9fHx8AADffvst3nzzTYctCz/99FP5S7QUp2bSl+SYmBgsXboURqPR4fjxxx9HUVGRZr5MBwYGujSOqD1MDtAV19DQgE8//RTAxWIcjY2N8jnp+NNPP8VDDz3kUK1YjTw8PJyaFqSVoiNERETuEhISAuBiLaJly5ahsLBQ3l7ummuuwcKFC2G1WuU4cq9JkyZhy5Yt2LRpU6tzJ0+exL/+9S85Tu2k7bHr6+ths9nwww8/OLzXpGJqWthGGwBqampcGkfUHrXPUuE3LB1Yt26d/AZq705Bc3Mz1q1bhxkzZri9fURERHTl2V8DmEwmh6UDzc3N8t0qpe5IdXcjR450OB44cCASExOxc+dO/PLLL+3GqVFoaCgA4Oeff8add97psG+7/T7uUpza1dbWujSOSKuYHNABad0X0PHWRQUFBapPDjgzzaYzcXTl2Gw2WK1W+U6B2Wxm0UsiIgVJW8rt27cPixYtwrhx4+Qvat999x0KCwsd4si99u7d63D8yy+/OCQF7OMSEhLc1azLYr89dkfXnlrYRhtg0TsiCZMDOmC/D2ZbNQekY6X2y+wMbleiDdnZ2UhLS0NZWZn8XEREBObPn8/il0RECgkLCwMA3Hzzzdi2bRtycnLkcyaTCTfddBOysrLkOHKvlStXOh2n9uSAtDOGwWBo9YVZCAEPDw8IITSzM4b9zAdXxBFpFZMDOmC/nmvMmDEYOHAg6uvr4e3tjV9++QV79uxpFUd0ubKzs2GxWBAfH4+ZM2fKd6WkLTMtFgsTBERECjCbzQgJCcGWLVswYcIE9OvXT74eOH78OLKyshASEgKz2ax0U7ul6upq+XFHN3Ps49RK2hmjPVLCQCs7YwQEBDhVqDMgIMANrSFSDpMDOmD/IbJnzx45GdBRHNHlsNlsSEtLw5AhQ1BcXOxwVyoiIgJDhgxBeno6kpKSuMSAiEhBeXl5yM3NlY+lgsSsN6CcHj16yDPuWi6PtD/u0aOHW9t1OcrLy10ap7SIiIg2l3i0FUekZ0alG0Bd13KtV1fjiNpjtVpRVlaGgwcPIjo6GsuXL8emTZuwfPlyREdH4+DBgygtLYXValW6qURE3Y7VakVlZWWHMRUVFRyjFdKzZ0+XxilJql/hqjilOXsDjTfaSO+YHNABPX3YOHu3mXellXHq1CkAwPjx47F06VLExMTA19dX3td43LhxDnFEROQ+0tgbERHhsK0xADQ2Nsp3PTlGK+P06dMujVOS/XvIw8MDs2bNwkcffYRZs2Y5bDetlfeanl4bvXF2thNnRbkGkwM6EBgY6NI4Jal978/urqqqCgBw3XXXwWh0HD6MRiOuvfZahzgiInIfaewtKytDSEgIZsyYgQULFmDGjBkICQmRp7RzjFaGnore2X/pHz16NHr27ImCggL07NkTo0ePbjNOzVom07oaR65jn2xyRRx1jP+KOqCnDxtuZahuwcHBAIBvvvkGt99+u0OCoLm5GTt27HCIIyIi9/H39wdwcXadh4cHMjMz5XO9evWCyWSCzWaT47RED9vn1tTUuDROSbW1tfLjPXv2YPfu3fKx/bWBfZyatbzh0dU4ch3eOHQvJgd0QPpC5qo4ovZI+xXn5ubihRdegJeXF2praxEQEICGhga5+JVW9jUmItKTgwcPArj4Rbrl9OfTp0/LifWDBw/i9ttvd3v7Lpdets+tq6tzaZySPD095cctv5TZH9vHqVnL7Ri7Gkeuw23O3YvJAR04d+6cS+OI2mM2mxEREYGamhqHKtgSf39/BAUFcZssIiIF2H8p66gavpbusEnb5yYkJCA1NRVRUVEoLi7GqlWrNLd9rp6SAyNHjsTx48editMCPc3CJeoKzo3RARbqIHcxmUwICQlpN9F07tw5BAcHa26qJ6mbzWZDfn4+srKykJ+fz7sDRN2EtH1uQkICLBYLGhoasHPnTjQ0NMgJg/T0dM2MCXqaHp2YmOjSOKWx5gDRRZw5oANeXl5OZTKlPY7VzNPT06mBVyvT1PTm/Pnz2L9/f4cx+/fvx/nz5+Hr6+umVpGe6WU6MZE7ODvuamV8lrbPvfPOOzFnzpxW48CUKVOwc+dOWK1WxMbGKtfQbujrr792Oi4pKenKNsYFTCaTU0kZ3vwgvWNyQAd8fHycSg74+Pi4oTVd4+vr61RyQCsXNnqTnp4uP46Pj8eECRPg7e2N+vp65ObmYteuXXLck08+qVQzO00Pha70SJpOHB8fj5kzZ8rvtd27d2tuOjGRO5w5c8alcUqT2pmRkdHmsoL33nvPIY7cp7S01KVxSouIiMCxY8eciiPSMyYHSFWcrWqrleq3evPjjz8CAIYMGYKXX37ZoWrv1KlTMW/ePBw6dEiO0wLemVYnaTrxkCFDcPjwYeTk5MjnwsPDMWTIEKSnpyMpKYmJHKL/n952/AkNDQUAjBgxAkuXLpU/c2JiYrB06VI88cQT2LdvnxyndmFhYU4lMsLCwtzQmq5xdjaqFmatAkBQUJBL44i0ijUHdEBPNQf0tB5Pj6T3UL9+/SCEcFgHLoRA3759HeLUTrozHR0djeXLl2PTpk1Yvnw5oqOjYbFYkJ2drXQTuy1pOvGBAwdw1VVXObw+V111FQ4cOIDS0lJYrValm0qkGvZf+lsuv7M/1kpywL6dbdUekT5rtNKf6Ohol8YpSW/V/YuLi10aR6RVnDmgA3qqfkvqNnr0aBw8eBDbtm3DDz/84LBVVs+ePeU7IqNHj1aqiU6zL3TV1h2p1NRU3plW0KlTpwAAEyZMaPP1eeGFF5CbmyvHEZHjHuwtl+jZH2tlr/bKykoAF5OFd955p8MSSmmZkX2c2pWXl7s0Tkl6+zLNgoREF2nj04E6pLfsLanX2LFjAVy8S9PRHtpSnJpJd6Znz57d6kLZaDRi1qxZvDOtoKqqKgDAdddd1+YsFanAlRRHRECvXr1cGqc0++n1LWcM2h9rYRo+AFRUVLg0TkkXLlxwaZzS9FbMk+hyceaADnAqPrmL2Wx2aZySpFkOUVFRbZ6XnmehK2UEBwcDAL744gv83//9n8OdtPDwcHndpxRHREBgYKBL45QWExMDk8kEDw8PNDQ0OJxramqCt7c3mpqaEBMTo1ALO0dP12seHh5O3XTy8NDGVw1nZwhyJiHpHWcOEJHTCgoKXBqnJOlOU3tTHqXntXJHyl5ba3O1RrqzeejQITQ0NGDhwoVYu3YtFi5ciIaGBhw6dMghjoiA6upql8YpraioCDabDfX19a3qCgghUF9fD5vNhqKiIoVa2DnOLufQwrKPfv36uTROaS2TT12NI9IqbaTziEgVvvrqK/mxwWBwuFizP/7qq68wbtw4t7evM8xmMyIiIrBq1SqHNe3Axbs2q1evRmRkpCZmQdjTy+4L0h1DHx8feHt7Y9myZfK5iIgI+Pv748KFC5q5Y0jkDgcPHnRpnNL0tEYfgNOJWi0kdJ2t86CVehB6em2IukL9qUkiUg1pv2JfX1988cUXmDZtGsaOHYtp06bhiy++gI+Pj0OcmplMJsyfPx85OTlITU1FYWEh6urqUFhYiNTUVOTk5GDevHmamkKop90XpDuGdXV1iIqKwu9//3s888wz+P3vf4+oqCjU1dVp6o4hkTvYFx5ueffZfizTSoHiwsJC+XHLLfHsj+3j1My+oKIr4pTkbL0XrdSF0ds2oESXizMHiMhp0nQ6IQSmTp0qP79nzx6sW7dOTg5oZdpdcnIyLBYL0tLSkJKSIj8fGRkJi8WiqTvtett9Qar18MILL+C9995DTk6OfC4yMhJ/+MMf8Morr7AmBJEd+907Wq5bt7/jqZVdPux/v0eNGgVfX1/U1NQgMDAQ58+fx3fffdcqTs30dHdab8Ww9VQPgqgrmBwgIqf16tULhw4darf6sPS8ltaBJycnIz4+HuvXr0dJSQn69u2LqVOntrpLpXbS7gupqant7r6QkpICq9WK2NhYZRrZCVKthz59+uCjjz6C1WrFmTNnEBYWBrPZjP379zvEEZH+vuDYf9ZIiYBLxZF7mEwmp774ayEZDXArQyIJkwNE5LRrrrkGO3fudCpOK9pao//ZZ59pbo2+/e4LNput1Zdpre2+0LImhH1CQ8s1IYiupNDQUKe2wQsNDXVDa7rO2XZqpT96Is3icCaOiLSDyQEicprephFKa/QNBoPD8ydPnoTFYtHU0gLpDvrnn3+OL774AidPnpTP9e7dW14GopU77VJNCIvFgtTUVMyaNQtRUVEoLi7G6tWrkZOTA4vFopm7UkTuoLcv0/bjVXBwMCZNmoTIyEiUlpbiP//5j7yeXSvjmp6cO3fOpXFEpA5MDhCR07KyspyOmzNnzhVuTdfYbDa88cYbEEK0KjAkTbl98803NbNG32w2IyQkBO+++26rcydPnkRGRgZCQkI0daddTzUhiNzhxx9/dGmc0kpKSuTHVVVVyMzMvGQcuYfelrAQ0UVMDhCR05ydkq6Fqev5+fmX3GKpoqIC+fn5GDNmjHsa1UVSBXJPT08kJydjyJAhOHjwILKzs9HY2Ijz588r3MLOS05ORlJSUqtlElpI2BC5m56q4QOOtQS8vLwcit3aH7PmABGRazA5QEROazn9vqtxStq1a5fD8bBhwxAfH49du3bJxe6kOC0kB/Ly8tDQ0CBfMGdlZTnM9PDy8kJ9fT3y8vIwduxYBVvaeSaTSRNFFImUZjQanap037JoqVr1798fe/fudSqO3MtgMDi1rZ8WrgeI6L+08elARKrgbAV/LVT6t//i3KtXL+zfvx8rV67E/v37HXZbcHYphdL+85//AGh/G0npeSlOS2w2G/Lz85GVlYX8/HxNbPNFpAS9VVz/n//5HwAXv2C2HNsaGhrkL55SHLmPs7O3OMuLSFs4c4CIuiWpkBUAXHXVVZg1axa8vb1RX1+P3NxceR9w+zg1c3bJgNaWFrS1m0RERITmdpMgos7z9fVF3759260pIIRA3759WRFfAXorUExEFzE5QERO01N1Yvupjt99953DMgP7Ox1amRKptyrlwH93k5gwYQKSkpJQX18Pb29vlJSUaG43CSLqvIaGBpSWlnYYU1paKi+pIiKirmFygIi6pT59+uDYsWMA0Gqauv1xnz593Nquy1VbW+vSOKXZbDakpaUhMjISu3fvdqh4bTQaERkZifT0dM3sJkFEnff555+jubm5VTFCifT8559/jpkzZyrQQiIifWHNASJymrNFrLRQ7Gr69OkujVPakSNHXBqnNKvVirKyMpw4cQKBgYEYNWoURo4ciVGjRiEwMBAnTpxAaWkprFar0k0loitk3759AC5dS0WKIyKiruHMASJymo+Pj1Nr1n18fNzQmq5xdusrrWyR5UzV6M7EKa28vBwA4OHhgaqqKhQUFDic9/DwQFNTkxxHRPrj4eHcZaqzcURE1DH1394jItWorKx0aZySDhw44NI4pemt5oC0nWR7xayk5+23nSQifbEvROqKOCIi6hiTA0TkND3dnT58+LD8uOWadftj+zg1u/rqq10apzRWwiYiZ2cGcQYREZFrcB4WkZvYbDZYrVacOXMGYWFhMJvNLKSmoOrqavmxh4eHQxFC+2P7ODXT21aGRUVFLo0jIu2pr693aRwREXWMyQEiN+Be7erj5+eHiooKAK3vPtsf+/n5ubVdl8vZGQ5amQmht/4QUef5+fmhrq7OqTgiIuo6LisgusKkvdqjo6OxfPlybNq0CcuXL0d0dDQsFguys7OVbmK3NHDgQPlxR1sZ2sepmd5mDhARERGRezE5QHQFSXu1JyQk4IUXXsDmzZuxePFibN68GS+88AISEhKQnp7e6sspXXmJiYkujVNa//79XRpHRKS02tpal8YREVHHuKyA6AqS9mrv3bs3pkyZIj+/Z88erFu3DmazWd6rPTY2VrmGdkM1NTUujVMa1+YSkd7obctZIiK148wBoivozJkzAIAffvihzfNWq9Uhjtzn559/dmmc0o4ePerSOCIiIiLqXpgcILqCAgICXBpHruNMkavOxCnt9OnTLo0jIiIiou6FyQGiK2j79u0ujSPXEUK4NE5pjY2NLo0jIiIiou6FyQGiK+ibb75xaRy5jt6SA0REREREXcHkANEVxErL6sWt/4iIiIiI/ovJASLqloqLi10aR0RERESkZUwOEFG3VF1d7dI4IiIiIiItY3KAiIiIiIiIqJtjcoCIiIiIiIiom2NygIiIiIiIiKibY3KAiIiIiIiIqJtjcoCIiIiIiIiom2NygIiIiIiIiKibY3KAiIiIiIiIqJvzULoBWrN8+XL85S9/QVlZGUaNGoW//e1vGD9+vNLNIiIihV24cAFHjx7t0p9x8ODBNp8fMGAAfHx8uvRnd2eueG2Atl8fvjZERKQXTA50wpo1a/DUU08hPT0dEyZMwJtvvolJkybhwIED6N27t9LN0xReRBORlpSXl6OqqqrDmKNHj+Lll1/u0t/zu9/9rs3n//jHP2LAgAEd/r/BwcEIDw/v0t9v70qN00qM0UePHm3337Yz2voz3nnnHQwZMqTLfzYRUWfoLempt+8GWv0MNQghxBX703VmwoQJGDduHP7+978DAJqbm9G/f388/vjjeP7551vF19fXo76+Xj6urq5G//79UVVVhaCgIPn5n376CUeOHOnw766rq8PPP//c5rn169c73YepU6e2eu6qq66Cn59fh//foEGDMHjwYKf+Dmf6U1paivfff9+pP6+zHnroIURGRnYY42x/nOkL0P7r447XBnBtf/T2XrtSrw3g2v7wveZITe+18vJy3D/nfjQ2NDrdJiV4enni/z78v0smCJx9r12pcdqZMRronu81gOOaPS2Oa42NjTh9+nSb5zrz+/TQQw+1eq5nz57w9PTs8P/je61j7b0+7nhtANf2RyvX0oBz/Tl27Bg+/PBDZ5vYKXPmzEH//v07jHH1e+1K9ceZvgCt+1NdXY3g4OBW30NbYnLASQ0NDfDz88PatWsxbdo0+fkHHngAlZWV+OKLL1r9PxaLBUuWLGn1fMsXZcGCBSgoKLgi7XaVUaNG4c0333QqVk/90UJfAH31h+81ddNTf5ztS3l5OWbNnoVmW/OVb1QXGE1GrF61+pLJAS28NkD3fK8B+uqPFvoC6Ks/fK+pm576w/eaurXsD5MDLnbixAn07dsXO3fuREJCgvz8s88+i+3btyM3N7fV/8OZA+3TSrazO9710Nt7TW93Pfhea80d77X9+/fj+PHjHcZ09Y5hW3ejJM7clerXrx+GDRt2yb+nO84c6Oi1Abr2+vBubvu647imlbufQPd8r6ntbm579HQtDXRtnO7qrA7AteO0VmepMDngYpeTHGjJ2Rels2688UanY7dt2+ayv7crOlqH48y60Hfeeafdc2qqOaDF16YjeuqPnvoCsD9a0FGf1NiH9sbp3bt347333rvk///www+3WbBXTWO0Pa29Pm3R2++NFvvT1esbSVvXOWr63dHiawO0//po8bXp6L2mt3Hamfebmt5nl6JEf5z9HsqChE7q2bMnTCYTysvLHZ4vLy9HRESEQq26aNu2bZr7pfHx8Wm3gNOl+qOmflyKFl+b7oKvjbrp8fVpr09q7UN743RdXZ1T//+IESM0VahPa69PW/T4e6M1HV3fdIbaf3e0+l5zxeujltemo77obZzW03cDQN39MSr2N2uMl5cXxowZg6ysLPm55uZmZGVlOcwkUMql3kRa/KXpzPNqprfXRk/42qibHl+fbdu2tfrRGrPZjIiICCQmJrZ5PjExEZGRkTCbzW5uWdfp4fXR4++NXoSEhLg0Tml8r6mXHsdpPX03ANTbH84c6ISnnnoKDzzwAMaOHYvx48fjzTffxLlz5/Dggw8q3TQA+rjrYU+r7W6LXl6bmJgYFBUVORWnFXp5bfSKr4/6mEwmzJ8/HxaLBYmJiZg1axaioqJQXFyM1atXIycnBxaLBSaTSemmdlv8vVGnUaNGYfv27U7FaQXfa+qk13Fab+8rNfaHyYFOmDlzJk6dOoXFixejrKwMsbGx+Pe//+3SfaW7So1vMrpID69NZGSkU8kBZ4qNqYkeXhs94+ujPsnJybBYLEhLS0NKSor8fGRkJCwWC5KTkxVsHQH6+L3x8fHBhQsXnIrTgqamJpfGqYUe3mt6xHGaLgeTA52UkpLi8AtG1J04W19D6Toc3ZG/vz/OnTvnVByRKyQnJyMpKQlWqxVnzpxBWFgYzGaz5u5EkXoNHTrUqS3Dhg4d6obWdN3IkSOxY8cOGI1GNDe33hpVen7kyJEKtI70iOM0dRZrDhCR0+Li4lwaR65js9lcGkfkDJPJhNjYWNx8882IjY3lBSe5VFtV1LsSp7Rp06bJCYAxY8YgIiICgYGBiIiIwJgxY9Dc3Ayj0Yhp06Yp3VTSEY7T1BlMDhCR02JjY+VCSV5eXg7npOPQ0FDExsa6uWXU8vXoahwRkdJ69erl0jileXl54Z577gEA5OXloaysDDU1NSgrK0NeXh4A4J577uE4TUSKYXKAiJxmMpnw5JNPwmAwtHneYDBgwYIFzEorIDg42KVxRERK01tyAADmzZuHpKSkVssKmpubkZSUhHnz5inUMiIi1hwgok5KTk7GjBkz8Omnnzo8b7PZMGPGDBa4UYizBbm0UriLiEjaji04OBhnz57FqVOn5HO9evVCjx49UF1drant2LKzs7Fz507Ex8ejb9++qK+vh7e3N0pKSrBz505kZ2fzc5SIFMPkABF1SnZ2NjIzMxEfH4/x48fD29sb9fX12L17NzIzMxETE8MLGwUEBga6NI6ISGn227FNmDABycnJuHDhAnx8fFBSUoLc3FxNbcdms9mQlpaGhIQELF26FEbjfyfwNjc3IzU1Fenp6UhKStJMn4hIX5gcICKndXRhM3XqVF7YKCguLg7ff/+9U3FERFohzVZbu3Ytdu3aJT9vMpk0N1vNarWirKwMqampDp+fwMWdCmbNmoWUlBRYrVbW7iEiRTA5QERO44WNejmzF3hn4oiI1EBPs9XOnDkDAIiKimrzvPS8FEdE5G5MDhCR03hho17tFYm83DgiIqXpbbZaWFgYAKC4uBgxMTGtzhcXFzvEERG5G3crICKn2V/YtIUXNsoZNWoUAMDT07NVAsBoNMLT09MhjohI7aTZarNnz253tlppaSmsVqtCLewcqcDiqlWr2tytYPXq1YiMjNRUgUUi0hcmB4jIabywUS8pIdDY2Ijg4GDMmDEDCxYswIwZMxAUFITGxkaHOCIitdPbbDWpwGJOTg5SU1NRWFiIuro6FBYWIjU1FTk5OZg3b54mZkHojb+/v0vjiLSKywqIyGn2laNTU1Mxa9YsREVFobi4GKtXr0ZOTo6mKkfrydmzZ+XHdXV1yMzMlI+9vLzajCMiUjM9TsNPTk6GxWJBWloaUlJS5OcjIyNhsVg0Uz9Bb6QEuqviiLSKyQEi6hRe2KhTVVUVgIvrcHNzc1FeXi6f69GjB8aNG4cNGzbIcUREamc/W62trf+0OlstOTkZSUlJsFqtOHPmDMLCwmA2m5lYV1BTU5NL44i0iskBIuo0XtioT3BwMACgvLwcK1euxMaNG1FSUoK+fftiypQpsFgsDnFERGqn59lqJpOJu/qoSMulkl2NI9IqJgeI6LLwwkZdevXqBQDIzc3FtGnTUF9fL5/LyMiQj6U4IiIt4Gw1cgcPDw+nZgV4ePCrE+kb3+FERDpgNpsREhKCyspKCCHajAkJCdHc9FsiIs5WoystLCzMYTleR3FEesbkABGRzowePRoTJkyAl5cXGhoakJubi127dnGnAiLSLM5WoyuJuxUQXcTkABGRDlitVlRWVmLu3LnYuHEjdu3aJZ+LjIzE3LlzkZGRAavVygtsIiIiOw0NDS6NI9IqJgeIiHRA2uf7rrvuwr333ttq+m19fT0yMjI0sx84ERGRu/Tu3RvHjx93Ko5Iz5gcICLSgZb7gbecHaDF/cCJiIjcITY2Ft9//71TcUR6Zrx0CBERqZ39fuAtt1rS8n7gREREV9rPP//s0jgirWJygIhIB6T9wHNycpCamorCwkLU1dWhsLAQqampyMnJwbx581jdm4iIqIWCggKXxhFpFZcVEBHpBPcDJyIi6rza2lr5ca9evXDq1Cn5uHfv3jh58mSrOCI9YnKAiEhHuB84ERFR59hv9RsdHY1Zs2bJ2wHv2rVLTg5wS2DSOyYHiIh0hvuBExEROa9///44fPgwACA3Nxe5ubntxhHpGWsOEBHpjM1mQ35+PrKyspCfnw+bzaZ0k4iIiFRr7ty5Lo0j0irOHCAi0pHs7GykpaWhrKxMfi4iIgLz589nzQEiIqI2jB8/Hh4eHmhqamo3xsPDA+PHj3djq4jcjzMHiIh0Ijs7GxaLBdHR0Vi+fDk2bdqE5cuXIzo6GhaLBdnZ2Uo3kYiISHVMJhNSU1M7jElNTWX9HtI9JgeIiHTAZrMhLS0NCQkJWLp0KWJiYuDr64uYmBgsXboUCQkJSE9P5xIDIiKiDnh5eXV4TKRnTA4QEemA1WpFWVkZZs+eDaPRcWg3Go2YNWsWSktLYbVaFWohERGROkkJ9sTERGzcuBFvvPEGFi1ahDfeeAMbN25EYmIiE+zULTA5QESkA2fOnAEAREVFtXleel6KIyIioovsE+yenp6IjY3FzTffjNjYWHh6ejLBTt0GkwNERDoQFhYGACguLm7zvPS8FEdEREQX2SfY29rxhwl26i64WwERkQ6YzWZERERg1apVWLp0qcPSgubmZqxevRqRkZEwm80KtpKIiEh9pMT5559/js8++wxnz56Vz/Xo0QN33XWXQxyRXnHmABGRDphMJsyfPx85OTlITU1FYWEh6urqUFhYiNTUVOTk5GDevHmstExERNSC2WxGSEgI3n33XYfEAACcPXsWGRkZCAkJYYKddM8ghBBKN6K7qK6uRnBwMKqqqhAUFKR0c4hIh7Kzs5GWloaysjL5ucjISMybNw/JyckKtoyIiEidbDYbJk6ceMm4LVu2MMlOmuTs91AuKyAi0pHk5GQkJSXBarXizJkzCAsLg9ls5sUMERFRO3JycpyOu/baa69wa4iUw5kDbsSZA0RERERE6jJ16lTU1NQAAAwGA8aMGYO4uDjk5eVh7969kL4uBQYGYv369Uo2leiyOPs9lDUHiIiIiIio25ISAwCwYcMGzJ49G+Hh4Zg9ezY2bNjQZhyRHnFZAREREREREYCHH34Y5eXl8nF4eLiCrSFyL84cICIiIiKibsu+Lk9tbS0WLlyItWvXYuHChaitrW0zjkiPOHOAiIiIiIi6rbi4OOzZswcAcO7cOSxbtqzdOCI948wBIiIiIiLqtqZPn+7SOCKtYnKAiIiIiIi6rXPnzrk0jkirmBwgIiIiIqJuS9razWAwtHleep5bkZPeMTlARERERETdVnFxMQBACIGxY8ciOjoaPXv2RHR0NMaOHQshhEMckV6xICEREREREXVbJ06ckB//8MMPaGhoAACcPn0ax48fbzOOSI84c4CIiIiIiLot++UELZcWdHSOSG+YHCAiIiIiom5ryJAhAACTyYTAwECHc4GBgTCZTA5xRHrFZQVERERERNRtSbsQ2Gw2nD592uGc/TF3KyC948wBIiIiIiLqtlrOFuhqHJFWMTlARERERETdVlVVlUvjiLSKyQEiIiIiIuq2mBwguojJASIiIiIi6raam5tdGkekVUwOEBERERFRt/XLL7+4NI5Iq5gcICIiIiKibqvlDgVdjSPSKiYHiIiIiIio2zIYDC6NI9IqJgeIiIiIiKjb6tOnj/zYy8vL4Zy3t3ebcUR65KF0A4iIiIiIiJRiMpnkxzabDTfeeCOGDRuG/fv3Izs7u804Ij1icoCIiIiIiLot++UCNpsN27Ztw7Zt2zqMI9IjLisgIiIiIqJuKzw8XH7cclmB/bF9HJEeMTlARERERETdVlxcnEvjiLSKyQEiIiIiIuq2YmNjERISAqD10gHpODQ0FLGxsW5uGZF7MTlARERERETdlslkwpNPPgmDwQAhhMM5IQQMBgMWLFjAgoSke0wOEBERERFRt5acnAyLxYIePXo4PN+jRw9YLBYkJycr1DIi9zGIlukxumKqq6sRHByMqqoqBAUFKd0cIiIiIiKyY7PZYLVacebMGYSFhcFsNnPGAGmes99DuZUhERERERERLi4xYG0B6q64rICIiIiIiIiom2NygIiIiIiIiKibY3KAiIiIiIiIqJtjcoCIiIiIiIiom2NygIiIiIiIiKibY3KAiIiIiIiIqJtjcoCIiIiIiIiom2NygIiIiIiIiKibY3KAiIiIiIiIqJtjcoCIiIiIiIiom2NygIiIiIiIiKibY3KAiIiIiIiIqJtjcoCIiIiIiIiom2NygIiIiIiIiKibY3KAiIiIiIiIqJtjcoCIiIiIiIiom2NygIiIiIiIiKibY3KAiIiIiIiIqJtjcoCIiIiIiIiom2NygIiIiIiIiKib81C6Ad2JEAIAUF1drXBLiIiIiIiIqDuQvn9K30fbw+SAG9XU1AAA+vfvr3BLiIiIiIiIqDupqalBcHBwu+cN4lLpA3KZ5uZmnDhxAoGBgTAYDFfs76murkb//v1x7NgxBAUFXbG/xx301BeA/VEzPfUFYH/UTE99AdgfNdNTXwD2R8301BeA/VEzPfUFcF9/hBCoqalBnz59YDS2X1mAMwfcyGg0ol+/fm77+4KCgnTxSwPoqy8A+6NmeuoLwP6omZ76ArA/aqanvgDsj5rpqS8A+6NmeuoL4J7+dDRjQMKChERERERERETdHJMDRERERERERN0ckwM65O3tjRdffBHe3t5KN6XL9NQXgP1RMz31BWB/1ExPfQHYHzXTU18A9kfN9NQXgP1RMz31BVBff1iQkIiIiIiIiKib48wBIiIiIiIiom6OyQEiIiIiIiKibo7JASIiIiIiIqJujskBIiIiIiIiom6OyQEiIiIiIiKibo7JASIiIiIiIqJujskBHWtqasLRo0eVbgbZKS8v19VrsmTJEpw+fVrpZrhMY2Oj0k3osqamJmzevBnvvfcetmzZApvNpnSTOkVP7yeJzWbD4cOH0dzcDACor69HZmYmPvnkE5SXlyvcus47d+4csrOzsWbNGnz66afYu3cvtL4rclVVFQ4cOIADBw6gqqpK6eZQNyGE0NwY3ZGVK1fy90eFDh06hKysLPz0009KN4WAVr/zu3fvxq5du1BfX69Qi1oQpFv5+fnCaDQq3YxOWb58ubj55pvFPffcI7Zs2eJw7tSpUyIqKkqhlnVOdXW1mD17thgwYICYM2eOqK+vF48++qgwGAzCaDSK5ORkUVVVpXQznVZVVdXqp7KyUnh6eorc3Fz5Oa1Ys2aNqK+vl4//9re/iQEDBgij0SjCwsLEkiVLFGxd56SkpIgNGzYIIYQ4duyYGDZsmDCZTCI8PFyYTCZhNpvF8ePHFW6l84xGo7jpppvEqlWrxIULF5RuTpcVFBSIyMhIYTQaxYgRI8TRo0fFiBEjhL+/vwgICBChoaFi9+7dSjfTKTabTTzzzDPCz89PGI1GYTQahcFgEAaDQQwcOFCsX79e6SZ22rvvviuGDx8u90f6GT58uMjIyFC6eS6ltWuCL7/8Ujz88MPimWeeET/++KPDubNnz4obb7xRoZZ1XmNjo/jjH/8okpOTxeLFi4UQQrz22mvCz89PeHl5ydcJWufp6SmKioqUbkan5ObmiqamJvl4w4YNIjk5WfTp00eMGTNGfPDBBwq2rvNeeeUV+fr57Nmz4uabb5bHaaPRKG677TZRUVGhbCM7ISAgQDz00ENix44dSjely44cOSLGjBkjTCaTuO2220RVVZWYOHGi/PpER0eLAwcOKN1MwZkDpBpvvfUWnnnmGQwbNgze3t6444478Oqrr8rnbTYbfvnlFwVb6LwXXngBe/fuxdNPP42jR49ixowZyM7OxjfffINt27bh9OnT+POf/6x0M50WGhra6qdHjx5oampCQkICQkJCEBoaqnQznXbfffehsrISALBixQo888wz+O1vf4sNGzbgySefxGuvvYaMjAxlG+mkTz/9FIMGDQIALFy4EP369UNZWRnKyspw8uRJDBw4EAsWLFC0jZ0hhICXlxcefPBBREZG4vHHH0d+fr7Szbpszz77LJKSklBQUICbb74ZkyZNwvDhw1FRUYGKigpMnjwZL7zwgtLNdMoLL7yAjRs3Ys2aNfjPf/6Da6+9Fv/v//0/FBUVYc6cObjnnnvw1VdfKd1Mp/3lL3/BE088gV/96lfIysrCvn37sG/fPmRlZWHatGl44okn8L//+79KN9OlhEZmeKxevRpTp05FWVkZcnJyEBcXh1WrVsnnGxoasH37dgVb2DlLlixBRkYGxo4di7Vr12L+/Pn429/+hn/84x949913kZWVhTfffFPpZjqtR48ebf5I1wTSsRYkJCTgzJkzAIANGzbgV7/6FQYNGoQ//vGPiIuLw8MPP4zPP/9c4VY67+2335b/7Z999lmcPXsWe/fuRV1dHb7//ntUVlbi6aefVriVzjt37hxyc3Nx7bXXYvjw4Vi2bBlOnTqldLMuy8KFCxEQEIB169YhKCgId9xxB5qamnDs2DGUlJTg6quvxnPPPad0MzlzQMvi4uI6/Bk2bJim7hLExMSIVatWycc7duwQvXr1EqmpqUIIIcrKyjTTn/79+4utW7cKIYQoKSkRBoNBvrsrhBAbN24UQ4cOVap5nda3b18xefJksXXrVvH111+Lr7/+Wmzbtk2YTCaxYsUK+TmtMBgMory8XAghxPjx48Vrr73mcP7tt98WcXFxSjSt03x8fMThw4eFEEL069dP5ObmOpy3Wq2iZ8+eSjTtskivzalTp8T//u//ipiYGGE0GsXo0aPF22+/rakZKkIIERoaKt9Jq6urEyaTyeE12rdvnwgLC1OqeZ0SGRkpsrOz5ePjx4+LgIAAeYbHSy+9JBISEpRqXqcNGDBArFmzpt3zn3zyiejfv78bW9Q1v/71rzv8uemmmzTzGRobGyv++te/ysdr1qwR/v7+8mwOLV0PCCFEdHS0fA1w6NAhYTQaxSeffCKfX7NmjRgxYoRSzeu0gIAAMXnyZLFy5Ur5Z8WKFcJkMomXX35Zfk4L7K8Hrr32WvH88887nH/55ZdFfHy8Ek27LN7e3uLIkSNCCCEGDRoktm/f7nB+z549IjIyUommXRbp9cnPzxcpKSmiR48ewsvLS9x1111i06ZNorm5WekmOq1Xr14iLy9PCCFEZWWlMBgM4ptvvpHP7927V4SHhyvUuv/izAENKyoqwsiRI/GrX/2qzZ/rr79e6SZ2SnFxMRITE+XjxMREbN26Ff/4xz/whz/8QcGWdd7JkycxePBgAECfPn3g6+uLIUOGyOdHjBiBY8eOKdW8Tvvhhx/g6emJpUuXYvDgwbj++utxww03wGAwYPz48bj++us1934zGAwAgMOHD+PWW291OHfrrbdqZm3ekCFDsHv3bgBAYGAgqqurHc7X1NTIa921pGfPnli4cCEKCwvx7bffIjY2Fs899xwiIyMxZ84cpZvnNCEEPDw8AKDVfwHAZDJp5vWpra1F37595ePIyEhcuHABFRUVAIDp06ejoKBAqeZ12smTJ2E2m9s9bzabNVUDY8OGDbhw4QKCg4Pb/AkICFC6iU47dOgQ7rzzTvl4xowZ2LBhAxYsWID09HQFW3Z5Tpw4gVGjRgEABg8eDC8vL/kYAMaNG6eZmZEAkJeXh5MnT2Lr1q2YPn06HnjgAfz2t7+FwWDAtGnT8MADD+CBBx5QupmddvDgQdx9990Oz02fPh379+9XqEWdN3DgQOzbtw/Axesc+88b4OJnzrlz55RoWpeMGjUKf/vb33DixAm5tsWUKVMwYMAALF68WOnmOUUan4GL12smkwmBgYHy+aCgINTV1SnVvP9SOjtBl2/MmDHi7bffbvd8Xl6epjLr/fv3d7grJSksLBTh4eFizpw5mulPnz59xN69e+Xj++67T85MC3HxbmFoaKgSTeuSt99+W/Tp00esXr1aCCGEh4eHKCwsVLhVnWcwGMSHH34ovvjiC9GvXz+xc+dOh/P79u0TQUFBCrWuc1asWCH69esntm3bJj788EMxfPhwsWXLFlFSUiK2bt0qzGazmDt3rtLNdJrRaHT4XbFXW1srMjIyRGJioptbdfluvvlm8fDDD4vjx4+LJUuWiMGDB4sHH3xQPv/oo4+K6667TsEWOi8xMVH86U9/ko8//vhjERISIh9brVZNjWvXXXedmDNnjmhsbGx1rqmpScyZM0ckJycr0LLLYzabO6yToKVrgsjISJGTk9Pq+a+//loEBASIP/7xj5rpixBChIeHix9++EE+TkxMdKgF8+OPP2rmM0fS2Ngonn32WXHVVVeJb7/9VgihzWsCg8Egtm3bJgoKCsTAgQNb1YDZv3+/CAgIUKh1nfeXv/xFDB8+XBw6dEgsW7ZMJCQkiJ9++kkIIcThw4fFDTfcIO6++26FW+m8jq4JiouLxaJFizQzwys+Pl4sWrRICCHE+++/L8LDwx1mqrz00ktizJgxSjVPxuSAhv3+978XTzzxRLvnf/rpJ3HDDTe4r0FddN9994kFCxa0eW7fvn2iV69emrkYuO2220R6enq751esWKGpLzj2CgsLxahRo8R9992nyQsBIYRc/EX6sf/CI4QQGRkZmllWIIQQy5YtE35+fsLX11d4eXk5FFabNm2aqK6uVrqJTrOf4qkHu3fvFmFhYcJoNIpevXqJffv2iQkTJoiIiAjRp08f4evr26r4qlpt2bJFeHt7i/Hjx4vk5GTh4eEh3njjDfn8X/7yF3HTTTcp18BOKigoEBERESIsLEz8+te/FvPmzRPz5s0Tv/71r0VYWJiIjIwUVqtV6WY67be//a149NFH2z1fVFQkBg0a5MYWXb5f/epXcuG+lrZt2yb8/f01cz0ghBA33nhjh9PsMzMzVfGl4HJkZWWJAQMGiD/84Q/C09NTc9cEUqE+6XrAfkwT4mISNCYmRpnGXabHH39ceHp6imHDhgkfHx9hNBrla4OxY8eK0tJSpZvoNGeuCbSytODf//638PHxEV5eXsLHx0ds375dDBkyRIwfP17Ex8cLk8nU4VI3dzEIoZHqNKR7P/zwA/bu3YsHH3ywzfP79u3D2rVrYbFY3Nuwy3D27FkYjUaEhIS0ef5f//oX/Pz8NDcVX9LQ0IDnn38e27Ztwz//+U9ERUUp3SSX2rhxIzw9PTFp0iSlm+K0yspKbN68Wd4yLzIyEklJSbj66quVblqnfPDBB7j33nvh7e2tdFNc5ty5c9i/fz+GDh2KgIAAXLhwAatWrcL58+dxyy23YOjQoUo30WkFBQXIzMxEfX09Jk2ahFtuuUXpJnVJTU0NPvroI+zatQtlZWUAgIiICCQkJGDWrFkICgpSuIXOq6+vh81mg5+fn9JN6bLt27dj586d7S4p3LZtGz788EOsWLHCzS27PAcPHoSnp2e7n5WrV6+Gh4cHZsyY4eaWucaZM2fwyCOPYNu2bdi1a5emxrSWyzkCAgIQFhYmH3/44YcAoKnlbADw448/YuPGja2uCSZOnCgvq9SCJUuW4JlnntHFuAYAR44cwd69ezFmzBgMGjQI5eXlWL58Oerq6jB58mTceOONSjeRywq0bNGiRQ7br7T0yy+/iIkTJ7qxRV2zaNGiNqd3SrTUn0v15ciRI5rpixD6em2EuPTvjpZeHz2+Nnrrj17Gab29NkREROSIBQk17IMPPsDYsWPlwiP23nnnHYwYMaJVIRI1++CDDzB+/Hhd9OeDDz7AuHHj2u3LyJEjNdMX4NL90dJrA1z6d0dLr48eXxu9jAOAvsZpvb02l9LY2IijR48q3QyXaWpq0k1/9NQXgP1RMz31BeC4pmaq6YvS2Qm6fFVVVeL+++8X3t7e4pVXXhE2m0388ssv4uabbxZBQUHinXfeUbqJnaKn/uipL0KwP2qmp74Iwf6omZ764oz8/HxNrWu/FD31R099EYL9UTM99UUI9kfN1NIXJgd0YN26dSI8PFyMGjVKBAUFiYkTJ8p7nGqRnvqjp74Iwf6omZ76IgT7o2Z66ktH1HKh5ip66o+e+iIE+6NmeuqLEOyPmqmlL/qZ/9eNxcfHw2w2IysrC/7+/li0aBEGDhyodLMum576o6e+AOyPmumpLwD7o2Z66cvo0aM7PH/+/Hk3tcQ19NQfPfUFYH/UTE99AdgfNdNKX5gc0LiPP/4YKSkpiI2NxY8//oj33nsPt956Kx599FG8+uqr8PHxUbqJnaKn/uipLwD7o2Z66gvA/qiZnvpSVFSEe++9t90K8qWlpTh48KCbW3X59NQfPfUFYH/UTE99AdgfNdNMX5SeukCX76677hL+/v7irbfecnh+x44dYsiQIWLIkCFi586dCrWu8/TUHz31RQj2R8301Bch2B8101NfhBBizJgx4u233273fF5eniqmeDpLT/3RU1+EYH/UTE99EYL9UTOt9IUzBzSsrKwMeXl5rfYxT0xMRH5+Pp5//nlcf/31aGhoUKiFnaOn/uipLwD7o2Z66gvA/qiZnvoCAElJSThw4EC75wMDA5GcnOzGFnWNnvqjp74A7I+a6akvAPujZlrpi0EIIZRuBF2e5uZmGI0d70aZnZ2tijeaM/TUHz31BWB/1ExPfQHYHzXTU1+IiIiotY4/5UnVLnWRBkBTF2l66o+e+gKwP2qmp74A7I+a6akvAJCamgqbzdbu+aNHj+KWW25xY4u6Rk/90VNfAPZHzfTUF4D9UTOt9IXJASIiIup2PvjgA4wdOxb79u1rde6dd97BiBEj4OGhndWXeuqPnvoCsD9qpqe+AOyPmmmmL0oXPSAiIiJyt6qqKnH//fcLb29v8corrwibzSZ++eUXcfPNN4ugoCDxzjvvKN3ETtFTf/TUFyHYHzXTU1+EYH/UTCt9YXKAiIiIuq1169aJ8PBwMWrUKBEUFCQmTpwojhw5onSzLpue+qOnvgjB/qiZnvoiBPujZmrvC5cVEBERUbcVHx8Ps9mMH374Ac3NzVi0aBEGDhyodLMum576o6e+AOyPmumpLwD7o2Zq7wuTA0RERNQtffzxx4iJiUFzczN+/PFHzJ8/H7feeiuefPJJXLhwQenmdZqe+qOnvgDsj5rpqS8A+6NmmuiL0lMXiIiIiNztrrvuEv7+/uKtt95yeH7Hjh1iyJAhYsiQIWLnzp0Kta7z9NQfPfVFCPZHzfTUFyHYHzXTSl+YHCAiIqJuJzExURw8eLDNc3V1deL3v/+98PT0dHOrLp+e+qOnvgjB/qiZnvoiBPujZlrpi0EIIZSevUBERETkTs3NzTAaO15dmZ2djeTkZDe1qGv01B899QVgf9RMT30B2B8100pfmBwgIiIiIiIi6uZYkJCIiIiIiIiom2NygIiIiIiIiKibY3KAiIiIiIiIqJtjcoCIiIiIiIiom2NygIiIiIiIiKibY3KAiIiIOu23v/0tDAZDq5+ffvqpy3/2ypUrERIS0vVGEhERkdM8lG4AERERadNtt92GFStWODzXq1cvhVrTtsbGRnh6eirdDCIiItXjzAEiIiK6LN7e3oiIiHD4MZlM+OKLLzB69Gj4+PggOjoaS5YsQVNTk/z/vf766zCbzfD390f//v3x6KOPora2FgDw9ddf48EHH0RVVZU8G8FisQAADAYD1q1b59CGkJAQrFy5EgBw5MgRGAwGrFmzBtdffz18fHywatUqAEBGRgaGDx8OHx8fDBs2DG+//bb8ZzQ0NCAlJQWRkZHw8fHBwIED8eqrr165fzgiIiIV4swBIiIicplvvvkGc+bMwVtvvYXrrrsOP//8M/7nf/4HAPDiiy8CAIxGI9566y1ERUXh8OHDePTRR/Hss8/i7bffRmJiIt58800sXrwYBw4cAAAEBAR0qg3PP/88li1bhri4ODlBsHjxYvz9739HXFwc8vLy8Mgjj8Df3x8PPPAA3nrrLaxfvx6ZmZkYMGAAjh07hmPHjrn2H4aIiEjlmBwgIiKiy7Jx40aHL+633347Kioq8Pzzz+OBBx4AAERHR2Pp0qV49tln5eTAggUL5P9n0KBB+NOf/oR58+bh7bffhpeXF4KDg2EwGBAREXFZ7VqwYAHuuusu+fjFF1/EsmXL5OeioqJQVFSEd955Bw888ACOHj2Kq6++Gtdeey0MBgMGDhx4WX8vERGRljE5QERERJflxhtvRFpamnzs7++PkSNHYseOHXj55Zfl5202Gy5cuIC6ujr4+flhy5YtePXVV7F//35UV1ejqanJ4XxXjR07Vn587tw5/Pzzz3j44YfxyCOPyM83NTUhODgYwMXiirfccguGDh2K2267DVOmTMGtt97a5XYQERFpCZMDREREdFn8/f0xePBgh+dqa2uxZMkShzv3Eh8fHxw5cgRTpkzB/Pnz8fLLL6NHjx749ttv8fDDD6OhoaHD5IDBYIAQwuG5xsbGNttl3x4AePfddzFhwgSHOJPJBAAYPXo0iouL8a9//QtbtmzBjBkzMHHiRKxdu/YS/wJERET6weQAERERuczo0aNx4MCBVkkDyd69e9Hc3Ixly5bBaLxYFzkzM9MhxsvLCzabrdX/26tXL5SWlsrHhw4dQl1dXYftCQ8PR58+fXD48GHMnj273bigoCDMnDkTM2fOxN13343bbrsNZ8+eRY8ePTr884mIiPSCyQEiIiJymcWLF2PKlCkYMGAA7r77bhiNRhQUFGDfvn3405/+hMGDB6OxsRF/+9vfcOedd2LHjh1IT093+DMGDRqE2tpaZGVlYdSoUfDz84Ofnx9uuukm/P3vf0dCQgJsNhuee+45p7YpXLJkCX7/+98jODgYt912G+rr67Fnzx5UVFTgqaeewuuvv47IyEjExcXBaDTi008/RUREBEJCQq7QvxIREZH6cCtDIiIicplJkyZh48aN+OqrrzBu3DjEx8fjjTfekIv8jRo1Cq+//jr+/Oc/Y8SIEVi1alWrbQMTExMxb948zJw5E7169cJrr70GAFi2bBn69++P6667DrNmzcLTTz/tVI2CuXPnIiMjAytWrIDZbMb111+PlStXIioqCgAQGBiI1157DWPHjsW4ceNw5MgRbNq0SZ7ZQERE1B0YRMvFe0RERERERETUrTAlTkRERERERNTNMTlARERERERE1M0xOUBERERERETUzTE5QERERERERNTNMTlARERERERE1M0xOUBERERERETUzTE5QERERERERNTNMTlARERERERE1M0xOUBERERERETUzTE5QERERERERNTNMTlARERERERE1M39f9e6VcVzo12hAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dealing with outliers is crucial to ensure that they do not adversely affect the performance of the predictive models. Outliers can skew the distribution of data and impact the model's ability to generalize to unseen data.\n",
        "\n",
        "**Here are the strategy to handle outliers in the dataset:**\n",
        "* Remove Outliers: One straightforward approach is to remove the outliers from the dataset. However, this should be done with caution, as outliers may contain valuable information or represent rare but significant events. Removing too many outliers can lead to loss of important data."
      ],
      "metadata": {
        "id": "izMMHNc6XksL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Analysis"
      ],
      "metadata": {
        "id": "uKy2OJqHi3sC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation Matrix\n",
        "variables = ['X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8', 'X9', 'X10', 'X11', 'X12', 'X13', 'X14', 'X15', 'X16', 'X17', 'X18']\n",
        "data = df[variables]\n",
        "correlation_matrix = data.corr()\n",
        "mask = np.triu(np.ones_like(correlation_matrix, dtype=bool))\n",
        "\n",
        "plt.figure(figsize=(12, 10))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=0.5, mask=mask)\n",
        "plt.title('Correlation Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "-kQ5rRcJXg_m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        },
        "outputId": "7ad1478b-afd1-4f74-f3f8-421bc7cab6ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pre-Processing\n",
        "df['status_label'] = df['status_label'].map({'alive': 0, 'failed': 1})\n",
        "X = df.drop(columns=['company_name', 'status_label'])\n",
        "y = df['status_label']"
      ],
      "metadata": {
        "id": "tOLEXSRoX1KD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Convert the categorical target variable 'status_label' to numerical labels (0 for 'alive' and 1 for 'failed').\n",
        "* Create the feature matrix X, excluding the 'company_name' and 'status_label' columns, to be used for training the machine learning model.\n",
        "* Set the target variable y as the numerical 'status_label', representing the labels for the training dataset.\n",
        "\n",
        "After these preprocessing steps, you can proceed with model training using X as the feature matrix and y as the target variable."
      ],
      "metadata": {
        "id": "yyxsS9dhYMYF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train Model"
      ],
      "metadata": {
        "id": "KC9ekIxzYjMo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select all features\n",
        "selected_features_rf = ['year','X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8', 'X9', 'X10', 'X11', 'X12', 'X13', 'X14', 'X15', 'X16', 'X17', 'X18']\n",
        "df_rf_selected = df[['status_label'] + selected_features_rf]\n",
        "\n",
        "print(\"\\nNew dataset with selected features from random forests:\")\n",
        "print(df_rf_selected.head())"
      ],
      "metadata": {
        "id": "t-tryLMLYdwa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "385eeb6b-87cb-4771-d9f2-5908adb3ef04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "New dataset with selected features from random forests:\n",
            "   status_label  year       X1       X2      X3      X4       X5      X6  \\\n",
            "0             0  1999  511.267  833.107  18.373  89.031  336.018  35.163   \n",
            "1             0  2000  485.856  713.811  18.577  64.367  320.590  18.531   \n",
            "2             0  2001  436.656  526.477  22.496  27.207  286.588 -58.939   \n",
            "3             0  2002  396.412  496.747  27.172  30.745  259.954 -12.410   \n",
            "4             0  2003  432.204  523.302  26.680  47.491  247.245   3.504   \n",
            "\n",
            "        X7        X8        X9      X10      X11     X12      X13      X14  \\\n",
            "0  128.348  372.7519  1024.333  740.998  180.447  70.658  191.226  163.816   \n",
            "1  115.187  377.1180   874.255  701.854  179.987  45.790  160.444  125.392   \n",
            "2   77.528  364.5928   638.721  710.199  217.699   4.711  112.244  150.464   \n",
            "3   66.322  143.3295   606.337  686.621  164.658   3.573  109.590  203.575   \n",
            "4  104.661  308.9071   651.958  709.292  248.666  20.811  128.656  131.261   \n",
            "\n",
            "       X15       X16      X17      X18  \n",
            "0  201.026  1024.333  401.483  935.302  \n",
            "1  204.065   874.255  361.642  809.888  \n",
            "2  139.603   638.721  399.964  611.514  \n",
            "3  124.106   606.337  391.633  575.592  \n",
            "4  131.884   651.958  407.608  604.467  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_rf_selected.shape"
      ],
      "metadata": {
        "id": "36lsH5CqYdu4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "345f08d9-9d5a-43e0-b5c4-33cb16a78fbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(78682, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train Test Split: We split the dataset by concerning the time series event"
      ],
      "metadata": {
        "id": "Iy0_2uDdP9cr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Training set (-2011) 75%\n",
        "X_rf_train = df.loc[df[\"year\"] <= 2011, df_rf_selected.columns.drop([\"status_label\"])]\n",
        "y_rf_train = df.loc[df[\"year\"] <= 2011, \"status_label\"]\n",
        "\n",
        "# Validation set I(2012-2014) 13%\n",
        "X_rf_test = df.loc[df[\"year\"].between(2012, 2014), df_rf_selected.columns.drop([\"status_label\"])]\n",
        "y_rf_test = df.loc[df[\"year\"].between(2012, 2014), \"status_label\"]\n",
        "\n",
        "# Test set (2015+) 12%\n",
        "X_val = df.loc[df[\"year\"] >= 2015, df_rf_selected.columns.drop([\"status_label\"])]\n",
        "y_val = df.loc[df[\"year\"] >= 2015, \"status_label\"]\n",
        "\n",
        "rf_model = RandomForestClassifier(random_state=42)"
      ],
      "metadata": {
        "id": "60nerkgJs4sd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Forest Model\n"
      ],
      "metadata": {
        "id": "7IsDYahBQMU4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Random forest\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rf = RandomForestClassifier(n_estimators=200, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1,\n",
        "                            max_features='auto', bootstrap=True, n_jobs=4, random_state=42)\n",
        "\n",
        "rf.fit(X_rf_train, y_rf_train)"
      ],
      "metadata": {
        "id": "ZqDtsvysxfLC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "3aca9f26-8693-4784-eaba-c465ed62b826"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py:424: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
            "  warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_features='auto', n_estimators=200, n_jobs=4,\n",
              "                       random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_features=&#x27;auto&#x27;, n_estimators=200, n_jobs=4,\n",
              "                       random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_features=&#x27;auto&#x27;, n_estimators=200, n_jobs=4,\n",
              "                       random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = rf.predict(X_val)\n",
        "print(classification_report(y_val, pred))\n",
        "print(confusion_matrix(y_val, pred))"
      ],
      "metadata": {
        "id": "kLidRYZVxfI1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b6604d1-88cf-4ad0-faab-e5e4208e1c58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99     11995\n",
            "           1       0.47      0.05      0.09       287\n",
            "\n",
            "    accuracy                           0.98     12282\n",
            "   macro avg       0.72      0.53      0.54     12282\n",
            "weighted avg       0.97      0.98      0.97     12282\n",
            "\n",
            "[[11978    17]\n",
            " [  272    15]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate ROC curve and AUC\n",
        "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, roc_auc_score\n",
        "probabilities = rf.predict_proba(X_val)[:, 1]  # Assuming it's a binary classification task\n",
        "fpr, tpr, thresholds = roc_curve(y_val, probabilities)\n",
        "roc_auc = roc_auc_score(y_val, probabilities)\n",
        "\n",
        "# Plot ROC curve\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr, tpr, label=f'AUC = {roc_auc:.2f}')\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Random')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "kuHpXdwmztr4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "510cd366-b232-46f1-8239-aa13b3ebb7c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## XGBoost Model"
      ],
      "metadata": {
        "id": "WX83C5SXQISZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# XGBoost\n",
        "from xgboost import XGBClassifier\n",
        "xgb = XGBClassifier(objective=\"reg:logistic\", n_estimators = 1000, learning_rate=0.02, subsample=0.8, n_jobs=4, eval_metric=\"aucpr\", early_stopping_rounds=12)\n",
        "\n",
        "xgb.fit(X_rf_train, y_rf_train, eval_set=[(X_rf_test, y_rf_test)])"
      ],
      "metadata": {
        "id": "NsKDEGYzw1Ml",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "fc650e81-3d87-4d34-de3d-0cd9e8161e46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tvalidation_0-aucpr:0.14879\n",
            "[1]\tvalidation_0-aucpr:0.17706\n",
            "[2]\tvalidation_0-aucpr:0.18739\n",
            "[3]\tvalidation_0-aucpr:0.19024\n",
            "[4]\tvalidation_0-aucpr:0.19434\n",
            "[5]\tvalidation_0-aucpr:0.19375\n",
            "[6]\tvalidation_0-aucpr:0.19242\n",
            "[7]\tvalidation_0-aucpr:0.19371\n",
            "[8]\tvalidation_0-aucpr:0.18929\n",
            "[9]\tvalidation_0-aucpr:0.19132\n",
            "[10]\tvalidation_0-aucpr:0.19487\n",
            "[11]\tvalidation_0-aucpr:0.19647\n",
            "[12]\tvalidation_0-aucpr:0.19761\n",
            "[13]\tvalidation_0-aucpr:0.20214\n",
            "[14]\tvalidation_0-aucpr:0.20387\n",
            "[15]\tvalidation_0-aucpr:0.20565\n",
            "[16]\tvalidation_0-aucpr:0.20942\n",
            "[17]\tvalidation_0-aucpr:0.21301\n",
            "[18]\tvalidation_0-aucpr:0.21703\n",
            "[19]\tvalidation_0-aucpr:0.21536\n",
            "[20]\tvalidation_0-aucpr:0.21531\n",
            "[21]\tvalidation_0-aucpr:0.21632\n",
            "[22]\tvalidation_0-aucpr:0.21904\n",
            "[23]\tvalidation_0-aucpr:0.22196\n",
            "[24]\tvalidation_0-aucpr:0.22286\n",
            "[25]\tvalidation_0-aucpr:0.21865\n",
            "[26]\tvalidation_0-aucpr:0.22246\n",
            "[27]\tvalidation_0-aucpr:0.22634\n",
            "[28]\tvalidation_0-aucpr:0.22498\n",
            "[29]\tvalidation_0-aucpr:0.22426\n",
            "[30]\tvalidation_0-aucpr:0.22896\n",
            "[31]\tvalidation_0-aucpr:0.22932\n",
            "[32]\tvalidation_0-aucpr:0.23057\n",
            "[33]\tvalidation_0-aucpr:0.23381\n",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=12,\n",
              "              enable_categorical=False, eval_metric='aucpr', feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.02, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=1000, n_jobs=4,\n",
              "              num_parallel_tree=None, objective='reg:logistic', ...)"
            ],
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              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=12,\n",
              "              enable_categorical=False, eval_metric=&#x27;aucpr&#x27;, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.02, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=1000, n_jobs=4,\n",
              "              num_parallel_tree=None, objective=&#x27;reg:logistic&#x27;, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=12,\n",
              "              enable_categorical=False, eval_metric=&#x27;aucpr&#x27;, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.02, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=1000, n_jobs=4,\n",
              "              num_parallel_tree=None, objective=&#x27;reg:logistic&#x27;, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = xgb.predict(X_val)\n",
        "print(classification_report(y_val, pred))\n",
        "print(confusion_matrix(y_val, pred))"
      ],
      "metadata": {
        "id": "LUidXge2w1KN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d18db82-3e52-4e01-b824-91719c687f8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99     11995\n",
            "           1       0.64      0.12      0.20       287\n",
            "\n",
            "    accuracy                           0.98     12282\n",
            "   macro avg       0.81      0.56      0.60     12282\n",
            "weighted avg       0.97      0.98      0.97     12282\n",
            "\n",
            "[[11975    20]\n",
            " [  252    35]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate ROC curve and AUC\n",
        "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, roc_auc_score\n",
        "probabilities = xgb.predict_proba(X_val)[:, 1]  # Assuming it's a binary classification task\n",
        "fpr, tpr, thresholds = roc_curve(y_val, probabilities)\n",
        "roc_auc = roc_auc_score(y_val, probabilities)\n",
        "\n",
        "# Plot ROC curve\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr, tpr, label=f'AUC = {roc_auc:.2f}')\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Random')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "abWm9AXfzfX9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "d9678bff-edce-45d6-e390-112910e75a18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Weighted XGBoost for Class Imbalance"
      ],
      "metadata": {
        "id": "MhcXakhuFaFu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# XGBoost with scale_pos_weight (Weighted XGBoost)\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "xgb = XGBClassifier(objective=\"reg:logistic\", n_estimators=1000, learning_rate=0.02, subsample=0.8, n_jobs=4, eval_metric=\"aucpr\", early_stopping_rounds=12, scale_pos_weight=len(y_rf_train[y_rf_train==0])/len(y_rf_train[y_rf_train==1]))\n",
        "\n",
        "xgb.fit(X_rf_train, y_rf_train, eval_set=[(X_rf_test, y_rf_test)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TH0LX2yqT8ZK",
        "outputId": "19395550-53dd-4517-ac43-d038f1c8bb25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tvalidation_0-aucpr:0.14924\n",
            "[1]\tvalidation_0-aucpr:0.16956\n",
            "[2]\tvalidation_0-aucpr:0.18177\n",
            "[3]\tvalidation_0-aucpr:0.18399\n",
            "[4]\tvalidation_0-aucpr:0.18854\n",
            "[5]\tvalidation_0-aucpr:0.18955\n",
            "[6]\tvalidation_0-aucpr:0.18900\n",
            "[7]\tvalidation_0-aucpr:0.19584\n",
            "[8]\tvalidation_0-aucpr:0.19764\n",
            "[9]\tvalidation_0-aucpr:0.19656\n",
            "[10]\tvalidation_0-aucpr:0.19343\n",
            "[11]\tvalidation_0-aucpr:0.19724\n",
            "[12]\tvalidation_0-aucpr:0.19677\n",
            "[13]\tvalidation_0-aucpr:0.19711\n",
            "[14]\tvalidation_0-aucpr:0.19801\n",
            "[15]\tvalidation_0-aucpr:0.19922\n",
            "[16]\tvalidation_0-aucpr:0.20070\n",
            "[17]\tvalidation_0-aucpr:0.20870\n",
            "[18]\tvalidation_0-aucpr:0.20996\n",
            "[19]\tvalidation_0-aucpr:0.20910\n",
            "[20]\tvalidation_0-aucpr:0.21125\n",
            "[21]\tvalidation_0-aucpr:0.21173\n",
            "[22]\tvalidation_0-aucpr:0.21242\n",
            "[23]\tvalidation_0-aucpr:0.21353\n",
            "[24]\tvalidation_0-aucpr:0.21559\n",
            "[25]\tvalidation_0-aucpr:0.21465\n",
            "[26]\tvalidation_0-aucpr:0.21920\n",
            "[27]\tvalidation_0-aucpr:0.21892\n",
            "[28]\tvalidation_0-aucpr:0.21770\n",
            "[29]\tvalidation_0-aucpr:0.21809\n",
            "[30]\tvalidation_0-aucpr:0.21793\n",
            "[31]\tvalidation_0-aucpr:0.21884\n",
            "[32]\tvalidation_0-aucpr:0.21940\n",
            "[33]\tvalidation_0-aucpr:0.22046\n",
            "[34]\tvalidation_0-aucpr:0.22101\n",
            "[35]\tvalidation_0-aucpr:0.22189\n",
            "[36]\tvalidation_0-aucpr:0.22116\n",
            "[37]\tvalidation_0-aucpr:0.22000\n",
            "[38]\tvalidation_0-aucpr:0.22140\n",
            "[39]\tvalidation_0-aucpr:0.22193\n",
            "[40]\tvalidation_0-aucpr:0.22159\n",
            "[41]\tvalidation_0-aucpr:0.22233\n",
            "[42]\tvalidation_0-aucpr:0.22283\n",
            "[43]\tvalidation_0-aucpr:0.22409\n",
            "[44]\tvalidation_0-aucpr:0.22494\n",
            "[45]\tvalidation_0-aucpr:0.22896\n",
            "[46]\tvalidation_0-aucpr:0.22945\n",
            "[47]\tvalidation_0-aucpr:0.22850\n",
            "[48]\tvalidation_0-aucpr:0.22977\n",
            "[49]\tvalidation_0-aucpr:0.22895\n",
            "[50]\tvalidation_0-aucpr:0.23478\n",
            "[51]\tvalidation_0-aucpr:0.23560\n",
            "[52]\tvalidation_0-aucpr:0.23331\n",
            "[53]\tvalidation_0-aucpr:0.23201\n",
            "[54]\tvalidation_0-aucpr:0.23367\n",
            "[55]\tvalidation_0-aucpr:0.23236\n",
            "[56]\tvalidation_0-aucpr:0.23480\n",
            "[57]\tvalidation_0-aucpr:0.23515\n",
            "[58]\tvalidation_0-aucpr:0.23481\n",
            "[59]\tvalidation_0-aucpr:0.23769\n",
            "[60]\tvalidation_0-aucpr:0.23832\n",
            "[61]\tvalidation_0-aucpr:0.23812\n",
            "[62]\tvalidation_0-aucpr:0.23985\n",
            "[63]\tvalidation_0-aucpr:0.23955\n",
            "[64]\tvalidation_0-aucpr:0.24280\n",
            "[65]\tvalidation_0-aucpr:0.24547\n",
            "[66]\tvalidation_0-aucpr:0.24907\n",
            "[67]\tvalidation_0-aucpr:0.25140\n",
            "[68]\tvalidation_0-aucpr:0.25080\n",
            "[69]\tvalidation_0-aucpr:0.24939\n",
            "[70]\tvalidation_0-aucpr:0.24987\n",
            "[71]\tvalidation_0-aucpr:0.25288\n",
            "[72]\tvalidation_0-aucpr:0.25317\n",
            "[73]\tvalidation_0-aucpr:0.25411\n",
            "[74]\tvalidation_0-aucpr:0.25449\n",
            "[75]\tvalidation_0-aucpr:0.25797\n",
            "[76]\tvalidation_0-aucpr:0.25792\n",
            "[77]\tvalidation_0-aucpr:0.25981\n",
            "[78]\tvalidation_0-aucpr:0.26057\n",
            "[79]\tvalidation_0-aucpr:0.25985\n",
            "[80]\tvalidation_0-aucpr:0.25899\n",
            "[81]\tvalidation_0-aucpr:0.26008\n",
            "[82]\tvalidation_0-aucpr:0.26171\n",
            "[83]\tvalidation_0-aucpr:0.26224\n",
            "[84]\tvalidation_0-aucpr:0.26167\n",
            "[85]\tvalidation_0-aucpr:0.26313\n",
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          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=12,\n",
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              "              interaction_constraints=None, learning_rate=0.02, max_bin=None,\n",
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              "              multi_strategy=None, n_estimators=1000, n_jobs=4,\n",
              "              num_parallel_tree=None, objective='reg:logistic', ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=12,\n",
              "              enable_categorical=False, eval_metric=&#x27;aucpr&#x27;, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.02, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=1000, n_jobs=4,\n",
              "              num_parallel_tree=None, objective=&#x27;reg:logistic&#x27;, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=12,\n",
              "              enable_categorical=False, eval_metric=&#x27;aucpr&#x27;, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.02, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=1000, n_jobs=4,\n",
              "              num_parallel_tree=None, objective=&#x27;reg:logistic&#x27;, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = xgb.predict(X_val)\n",
        "print(classification_report(y_val, pred))\n",
        "print(confusion_matrix(y_val, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QwrwzhnDV8s3",
        "outputId": "9ed6b717-dc6f-4754-9d97-4df6e1c4b4e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.84      0.91     11995\n",
            "           1       0.10      0.70      0.17       287\n",
            "\n",
            "    accuracy                           0.84     12282\n",
            "   macro avg       0.54      0.77      0.54     12282\n",
            "weighted avg       0.97      0.84      0.89     12282\n",
            "\n",
            "[[10099  1896]\n",
            " [   86   201]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate ROC curve and AUC\n",
        "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, roc_auc_score\n",
        "probabilities = xgb.predict_proba(X_val)[:, 1]  # Assuming it's a binary classification task\n",
        "fpr, tpr, thresholds = roc_curve(y_val, probabilities)\n",
        "roc_auc = roc_auc_score(y_val, probabilities)\n",
        "\n",
        "# Plot ROC curve\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr, tpr, label=f'AUC = {roc_auc:.2f}')\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Random')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "OAaauAo7WFGe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "4c090fee-9e34-40eb-8ad7-9dbf82c478e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"year\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QFEH13n440uJ",
        "outputId": "2084ea77-efc8-4327-ecbe-f7c4b8f47a1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        1999\n",
              "1        2000\n",
              "2        2001\n",
              "3        2002\n",
              "4        2003\n",
              "         ... \n",
              "78677    2014\n",
              "78678    2015\n",
              "78679    2016\n",
              "78680    2017\n",
              "78681    2018\n",
              "Name: year, Length: 78682, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#save the result\n",
        "results_df = pd.DataFrame({'company_name': df.loc[df[\"year\"] >= 2019]['company_name'], 'year': df.loc[df[\"year\"] >= 2019]['year'], 'True_Label': y_val, 'Predicted_Label': pred})\n",
        "results_df.to_csv('predictions_with_id_xgb_with_weight.csv', index=False)"
      ],
      "metadata": {
        "id": "kSpiifUZ0bhB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Resampling XGBoost for Class Imbalance"
      ],
      "metadata": {
        "id": "sU1mC8_KeZt-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# XGBoost with scale_pos_weight (Weighted XGBoost)\n",
        "from xgboost import XGBClassifier\n",
        "xgb = XGBClassifier(objective=\"reg:logistic\", n_estimators = 1000, learning_rate=0.02, subsample=0.8, n_jobs=4, eval_metric=\"aucpr\", early_stopping_rounds=12)\n",
        "\n",
        "xgb.fit(X_rf_train, y_rf_train, eval_set=[(X_rf_test, y_rf_test)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "X-SV6eBTeBO0",
        "outputId": "47715464-9d53-4cf0-fca1-1fac224d61d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tvalidation_0-aucpr:0.14879\n",
            "[1]\tvalidation_0-aucpr:0.17706\n",
            "[2]\tvalidation_0-aucpr:0.18739\n",
            "[3]\tvalidation_0-aucpr:0.19024\n",
            "[4]\tvalidation_0-aucpr:0.19434\n",
            "[5]\tvalidation_0-aucpr:0.19375\n",
            "[6]\tvalidation_0-aucpr:0.19242\n",
            "[7]\tvalidation_0-aucpr:0.19371\n",
            "[8]\tvalidation_0-aucpr:0.18929\n",
            "[9]\tvalidation_0-aucpr:0.19132\n",
            "[10]\tvalidation_0-aucpr:0.19487\n",
            "[11]\tvalidation_0-aucpr:0.19647\n",
            "[12]\tvalidation_0-aucpr:0.19761\n",
            "[13]\tvalidation_0-aucpr:0.20214\n",
            "[14]\tvalidation_0-aucpr:0.20387\n",
            "[15]\tvalidation_0-aucpr:0.20565\n",
            "[16]\tvalidation_0-aucpr:0.20942\n",
            "[17]\tvalidation_0-aucpr:0.21301\n",
            "[18]\tvalidation_0-aucpr:0.21703\n",
            "[19]\tvalidation_0-aucpr:0.21536\n",
            "[20]\tvalidation_0-aucpr:0.21531\n",
            "[21]\tvalidation_0-aucpr:0.21632\n",
            "[22]\tvalidation_0-aucpr:0.21904\n",
            "[23]\tvalidation_0-aucpr:0.22196\n",
            "[24]\tvalidation_0-aucpr:0.22286\n",
            "[25]\tvalidation_0-aucpr:0.21865\n",
            "[26]\tvalidation_0-aucpr:0.22246\n",
            "[27]\tvalidation_0-aucpr:0.22634\n",
            "[28]\tvalidation_0-aucpr:0.22498\n",
            "[29]\tvalidation_0-aucpr:0.22426\n",
            "[30]\tvalidation_0-aucpr:0.22896\n",
            "[31]\tvalidation_0-aucpr:0.22932\n",
            "[32]\tvalidation_0-aucpr:0.23057\n",
            "[33]\tvalidation_0-aucpr:0.23381\n",
            "[34]\tvalidation_0-aucpr:0.23424\n",
            "[35]\tvalidation_0-aucpr:0.23463\n",
            "[36]\tvalidation_0-aucpr:0.23471\n",
            "[37]\tvalidation_0-aucpr:0.23453\n",
            "[38]\tvalidation_0-aucpr:0.23542\n",
            "[39]\tvalidation_0-aucpr:0.23417\n",
            "[40]\tvalidation_0-aucpr:0.23544\n",
            "[41]\tvalidation_0-aucpr:0.23850\n",
            "[42]\tvalidation_0-aucpr:0.23946\n",
            "[43]\tvalidation_0-aucpr:0.23981\n",
            "[44]\tvalidation_0-aucpr:0.23867\n",
            "[45]\tvalidation_0-aucpr:0.24097\n",
            "[46]\tvalidation_0-aucpr:0.24141\n",
            "[47]\tvalidation_0-aucpr:0.23854\n",
            "[48]\tvalidation_0-aucpr:0.23887\n",
            "[49]\tvalidation_0-aucpr:0.24230\n",
            "[50]\tvalidation_0-aucpr:0.24412\n",
            "[51]\tvalidation_0-aucpr:0.24601\n",
            "[52]\tvalidation_0-aucpr:0.24500\n",
            "[53]\tvalidation_0-aucpr:0.24635\n",
            "[54]\tvalidation_0-aucpr:0.24625\n",
            "[55]\tvalidation_0-aucpr:0.24672\n",
            "[56]\tvalidation_0-aucpr:0.24753\n",
            "[57]\tvalidation_0-aucpr:0.24747\n",
            "[58]\tvalidation_0-aucpr:0.24683\n",
            "[59]\tvalidation_0-aucpr:0.24820\n",
            "[60]\tvalidation_0-aucpr:0.24947\n",
            "[61]\tvalidation_0-aucpr:0.24990\n",
            "[62]\tvalidation_0-aucpr:0.24970\n",
            "[63]\tvalidation_0-aucpr:0.24992\n",
            "[64]\tvalidation_0-aucpr:0.25092\n",
            "[65]\tvalidation_0-aucpr:0.25180\n",
            "[66]\tvalidation_0-aucpr:0.25289\n",
            "[67]\tvalidation_0-aucpr:0.25329\n",
            "[68]\tvalidation_0-aucpr:0.25482\n",
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        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=12,\n",
              "              enable_categorical=False, eval_metric='aucpr', feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.02, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=1000, n_jobs=4,\n",
              "              num_parallel_tree=None, objective='reg:logistic', ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=12,\n",
              "              enable_categorical=False, eval_metric=&#x27;aucpr&#x27;, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.02, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=1000, n_jobs=4,\n",
              "              num_parallel_tree=None, objective=&#x27;reg:logistic&#x27;, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=12,\n",
              "              enable_categorical=False, eval_metric=&#x27;aucpr&#x27;, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.02, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=1000, n_jobs=4,\n",
              "              num_parallel_tree=None, objective=&#x27;reg:logistic&#x27;, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Resampling\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "\n",
        "# Example of using RandomOverSampler\n",
        "ros = RandomOverSampler(random_state=42)\n",
        "X_rf_train, y_rf_train = ros.fit_resample(X_rf_train, y_rf_train)"
      ],
      "metadata": {
        "id": "AdQwf1spWqMF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred = xgb.predict(X_val)\n",
        "print(classification_report(y_val, pred))\n",
        "print(confusion_matrix(y_val, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "273ZimlleHHk",
        "outputId": "b952c618-cf10-43cc-c310-1213a13b6034"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99     11995\n",
            "           1       0.64      0.12      0.20       287\n",
            "\n",
            "    accuracy                           0.98     12282\n",
            "   macro avg       0.81      0.56      0.60     12282\n",
            "weighted avg       0.97      0.98      0.97     12282\n",
            "\n",
            "[[11975    20]\n",
            " [  252    35]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate ROC curve and AUC\n",
        "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, roc_auc_score\n",
        "probabilities = xgb.predict_proba(X_val)[:, 1]  # Assuming it's a binary classification task\n",
        "fpr, tpr, thresholds = roc_curve(y_val, probabilities)\n",
        "roc_auc = roc_auc_score(y_val, probabilities)\n",
        "\n",
        "# Plot ROC curve\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr, tpr, label=f'AUC = {roc_auc:.2f}')\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Random')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Jf81Na9ceMfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "67f88dd7-11ae-4603-caa5-f531d03c0706"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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UKfe2ytO6dWsEBgZi9erVGD9+PID7XQzc3d21H8xpaWm4e/cufvjhB/zwww+Vuo/69etXWHOx4l9U2dnZOl95llRe4G3cuHGpfZs0aYI1a9YA0O95rqjuvLw8zJs3D8uWLcPNmzd1pgp7MLTp68HgUhxI7ty5AwDaOUMbNWqks5+FhUW5X3+X5OjoCOC/57A66iq+zQMHDmD27NmIj49Hbm6uzv6ZmZlwcnLSXi7v9VCZ27h06RIAoEWLFno9hmL6vj8q+9r95JNPMGbMGPj5+aF9+/YYOHAgRo8ejQYNGuhdY/EfL1V9jADKncIuICAAixcvhkajwaVLlzB37lykpaWV+sPAwcHhoQHzwfeio6OjtnZ9a31YSK/qa7eyyjrPFhYWiIiIwMqVK1FQUAArKyvExsaisLBQJ8w+ymcumS6GWTIpnTp1QocOHQDcbz3s1q0bRo4cifPnz8Pe3l47v+O0adPKbK0CSoeXimg0GrRs2RLz588v83o/P78Kjx8xYgTmzp2L9PR0ODg4IC4uDk899ZS2JbC43meeeaZU39pirVq10rlcmVZZ4H6f0vXr1+Off/5Bjx49ytznn3/+AYBKtZaVVJXnuay6p0yZgmXLluHVV19F586d4eTkBJlMhieffLLcuTorq7xpmcoLJvoKDAwEAJw8eRJt2rSp9HEPq+vSpUvo27cvAgMDMX/+fPj5+UGpVGLz5s344osvSj0vZT2v+t5GVen7/qjsa3f48OHo3r071q1bhz///BOffvopPv74Y8TGxmLAgAGPXHdlubm5AfjvD6AH2dnZ6fQ179q1K9q1a4eZM2fiyy+/1G5v1qwZEhIScO3atVJ/zBR78L0YGBiI48eP4/r16w/9nCnpzp07Zf4xWpK+r93ywrFarS5ze3nn+cknn8T333+PP/74A+Hh4VizZg0CAwPRunVr7T6P+plLpolhlkyWQqHAvHnz0Lt3b3z99deYPn26tuXG0tJS55dMWRo2bIhTp049dJ8TJ06gb9++lfra9UEjRozAu+++i5iYGHh6eiIrK0s70AEA6tSpAwcHB6jV6ofWq6/Bgwdj3rx5WLFiRZlhVq1WY+XKlXBxcUHXrl11rvv3339L7X/hwgVti6U+z3NFoqOjMWbMGHz++efabfn5+bh7967OflV57h+meAL8ixcvonfv3trtRUVFSExMLPVHxIMGDBgAhUKBX375pVoH0mzYsAEFBQWIi4vTCT76fL1a2dto2LAhAODUqVMV/pFX3vP/qO+Pinh7e+Oll17CSy+9hNTUVLRr1w5z587VhtnK3l/xa/Vh7/WyFIe+K1euVGr/Vq1a4ZlnnsH333+PadOmaZ/7wYMH47fffsOKFSvw9ttvlzouKysLv//+OwIDA7XnISwsDL/99ht++eUXzJgxo1L3X1RUhOvXr2PIkCEV7qfva9fFxaXUexKA3iui9ejRA97e3li9ejW6deuGnTt34q233tLZx5CvKTJe7DNLJq1Xr17o1KkTFixYgPz8fHh4eKBXr174/vvvkZSUVGr/tLQ07f8jIiJw4sQJrFu3rtR+xa1kw4cPx82bN7F48eJS++Tl5WlH5ZenWbNmaNmyJVavXo3Vq1fD29tbJ1gqFApEREQgJiamzF+2JevVV5cuXRASEoJly5aVucLQW2+9hQsXLuCNN94o1ZKyfv16nT6vhw4dwsGDB7VBQp/nuSIKhaJUS+lXX31VqsXHzs4OAMr8hVpVHTp0gJubGxYvXoyioiLt9l9//bXclriS/Pz8MGHCBPz555/46quvSl2v0Wjw+eef48aNG3rVVdxy+2CXi2XLllX7bTz++ONwcHDAvHnzkJ+fr3NdyWPt7OzK7PbxqO+PsqjV6lL35eHhgbp166KgoOChNT2oTp066NGjB5YuXYpr167pXPewVnofHx/4+fnptRrWG2+8gcLCQp2WxcjISAQFBeGjjz4qdVsajQYvvvgi7ty5g9mzZ+sc07JlS8ydOxfx8fGl7ic7O7tUEDxz5gzy8/PRpUuXCmvU97XbsGFDZGZmaluPASApKanMz86KyOVyREZGYsOGDfj5559RVFSk08UAMMxriowfW2bJ5L3++uuIiorC8uXLMXHiRHzzzTfo1q0bWrZsiQkTJqBBgwZISUlBfHw8bty4oV3u8fXXX9euLPXss8+iffv2yMjIQFxcHBYtWoTWrVtj1KhRWLNmDSZOnIhdu3aha9euUKvVOHfuHNasWYOtW7dquz2UZ8SIEZg1axasra0xfvx4yOW6f2N+9NFH2LVrF4KDgzFhwgQEBQUhIyMDx44dw/bt25GRkVHl52bFihXo27cvhg4dipEjR6J79+4oKChAbGwsdu/ejREjRuD1118vdVyjRo3QrVs3vPjiiygoKMCCBQvg5uaGN954Q7tPZZ/nigwePBg///wznJycEBQUhPj4eGzfvl379W6xNm3aQKFQ4OOPP0ZmZiasrKzQp08feHh4VPm5USqVmDNnDqZMmYI+ffpg+PDhSExMxPLly9GwYcNKtQp9/vnnuHTpEl5++WXExsZi8ODBcHFxwbVr17B27VqcO3dOpyW+Mh5//HEolUqEhYXhhRdewL1797B48WJ4eHiU+YfDo9yGo6MjvvjiCzz33HPo2LEjRo4cCRcXF5w4cQK5ubn46aefAADt27fH6tWrMXXqVHTs2BH29vYICwurlvfHg7Kzs+Hr64vIyEjtEq7bt2/H4cOHdVrwy6upLF9++SW6deuGdu3a4fnnn0f9+vWRmJiITZs2ISEhocJ6hg4dinXr1lWqLypwv5vAwIED8eOPP+Kdd96Bm5sblEoloqOj0bdvX3Tr1k1nBbCVK1fi2LFj+N///qfzWrG0tERsbCxCQkLQo0cPDB8+HF27doWlpSVOnz6t/Val5NRi27Ztg62tLfr16/fQOvV57T755JN48803MWzYMLz88svIzc3Fd999hyZNmug9UHPEiBH46quvMHv2bLRs2bLUFHuGeE2RCaj5CRSIql95iyYIcX+FmYYNG4qGDRtqp366dOmSGD16tPDy8hKWlpbCx8dHDB48WERHR+sce/v2bTF58mTh4+OjnZx7zJgxOtNkqVQq8fHHH4vmzZsLKysr4eLiItq3by/effddkZmZqd3vwam5iv3777/aid33799f5uNLSUkRkyZNEn5+fsLS0lJ4eXmJvn37ih9++EG7T/GUU2vXrtXrucvOzhZz5swRzZs3FzY2NsLBwUF07dpVLF++vNTURCUXTfj888+Fn5+fsLKyEt27dxcnTpwodduVeZ4rOnd37twR48aNE+7u7sLe3l6EhoaKc+fOlflcLl68WDRo0EAoFIpKLZrw4PNU3mT6X375pfD39xdWVlaiU6dO4sCBA6J9+/aif//+lXh276+W9OOPP4ru3bsLJycnYWlpKfz9/cW4ceN0pj4qbwWw4uen5EIRcXFxolWrVsLa2loEBASIjz/+WCxdurTUfsWLJpSlsrdRvG+XLl2EjY2NcHR0FJ06dRK//fab9vp79+6JkSNHCmdn51KLJlT2/YH/n0y/LCgxNVdBQYF4/fXXRevWrYWDg4Ows7MTrVu3LrXgQ3k1lXeeT506JYYNGyacnZ2FtbW1aNq0qXjnnXfKrKekY8eOCQClpooqb9EEIYTYvXt3qenGhBAiNTVVTJ06VTRq1EhYWVkJZ2dnERISop2Oqyx37twRs2bNEi1bthS2trbC2tpatGjRQsyYMUMkJSXp7BscHCyeeeaZhz6mYpV97QohxJ9//ilatGghlEqlaNq0qfjll18qXDShPBqNRvj5+QkA4oMPPihzn8q+psh8yISoptEORGTyEhMTUb9+fXz66aeYNm2a1OVIQqPRoE6dOnjiiSfK/KqTzE/fvn1Rt25d/Pzzz1KXUq6EhAS0a9cOx44d02tAIpExYJ9ZIqJy5Ofnl+o3uWLFCmRkZKBXr17SFEW1zocffojVq1frPeCpJn300UeIjIxkkCWTxD6zRETl+Pvvv/Haa68hKioKbm5uOHbsGJYsWYIWLVogKipK6vKolggODoZKpZK6jAqtWrVK6hKIDIZhloioHAEBAfDz88OXX36JjIwMuLq6YvTo0fjoo490Vg8jIiLpsM8sERERERkt9pklIiIiIqPFMEtERERERsvs+sxqNBrcunULDg4OXAqPiIiIqBYSQiA7Oxt169YttZjQg8wuzN66dQt+fn5Sl0FERERED3H9+nX4+vpWuI/ZhVkHBwcA958cR0dHiashIiIiogdlZWXBz89Pm9sqYnZhtrhrgaOjI8MsERERUS1WmS6hHABGREREREaLYZaIiIiIjBbDLBEREREZLbPrM1sZQggUFRVBrVZLXQpJQKFQwMLCglO3ERERGQGG2QeoVCokJSUhNzdX6lJIQra2tvD29oZSqZS6FCIiIqoAw2wJGo0GV65cgUKhQN26daFUKtk6Z2aEEFCpVEhLS8OVK1fQuHHjh07WTERERNJhmC1BpVJBo9HAz88Ptra2UpdDErGxsYGlpSWuXr0KlUoFa2trqUsiIiKicrDJqQxsiSO+BoiIiIwDf2MTERERkdFimCUiIiIio8UwS0RERERGi2HWxMTHx0OhUGDQoEGlrtu9ezdkMhnu3r1b6rqAgAAsWLBAZ9uuXbswcOBAuLm5wdbWFkFBQfjf//6HmzdvGqh6ID8/H5MmTYKbmxvs7e0RERGBlJSUCo+5d+8eJk+eDF9fX9jY2CAoKAiLFi0qtV98fDz69OkDOzs7ODo6okePHsjLyzPUQyEiIqIawDBrYpYsWYIpU6Zg7969uHXrVpVv5/vvv0dISAi8vLwQExODM2fOYNGiRcjMzMTnn39ejRXreu2117BhwwasXbsWe/bswa1bt/DEE09UeMzUqVOxZcsW/PLLLzh79ixeffVVTJ48GXFxcdp94uPj0b9/fzz++OM4dOgQDh8+jMmTJ3OgFxERkZHj1FwPIYRAXqE0K4HZWCr0muf23r17WL16NY4cOYLk5GQsX74cM2fO1Pt+b9y4gZdffhkvv/wyvvjiC+32gIAA9OjRo8yW3eqQmZmJJUuWYOXKlejTpw8AYNmyZWjWrBn+/vtvPPbYY2Ue99dff2HMmDHo1asXAOD555/H999/j0OHDmHIkCEA7ofkl19+GdOnT9ce17RpU4M8DiIiIqo5kobZvXv34tNPP8XRo0eRlJSEdevWITw8vMJjdu/ejalTp+L06dPw8/PD22+/jbFjxxqsxrxCNYJmbTXY7VfkzHuhsFVW/hStWbMGgYGBaNq0KZ555hm8+uqrmDFjht4LP6xduxYqlQpvvPFGmdc7OzuXe+yAAQOwb9++cq/39/fH6dOny7zu6NGjKCwsREhIiHZbYGAg6tWrh/j4+HLDbJcuXRAXF4dnn30WdevWxe7du3HhwgVtEE9NTcXBgwfx9NNPo0uXLrh06RICAwMxd+5cdOvWrdxaiYiIqPaTNMzm5OSgdevWePbZZx/6VTIAXLlyBYMGDcLEiRPx66+/YseOHXjuuefg7e2N0NDQGqi4dluyZAmeeeYZAED//v2RmZmJPXv2aFssK+vff/+Fo6MjvL299a7hxx9/rLAfqqWlZbnXJScnQ6lUlgrLnp6eSE5OLve4r776Cs8//zx8fX1hYWEBuVyOxYsXo0ePHgCAy5cvAwDmzJmDzz77DG3atMGKFSvQt29fnDp1Co0bN9bjERIREVFtImmYHTBgAAYMGFDp/RctWoT69etr+2w2a9YM+/fvxxdffGGwMGtjqcCZ96QJyjaWikrve/78eRw6dAjr1q0DAFhYWGDEiBFYsmSJ3mFWCFHlZXx9fHyqdNyj+Oqrr/D3338jLi4O/v7+2Lt3LyZNmoS6desiJCQEGo0GAPDCCy9g3LhxAIC2bdtix44dWLp0KebNm1fjNRMREZVFyu6NFdFoNJDL5Xp3gawJRtVnNj4+XucraAAIDQ3Fq6++Wu4xBQUFKCgo0F7OysrS6z5lMpleX/VLZcmSJSgqKkLdunW124QQsLKywtdffw0nJyc4OjoCuN839cHWz7t378LJyQkA0KRJE2RmZiIpKUnv1tlH6Wbg5eUFlUqFu3fv6tSXkpICLy+vMo/Jy8vDzJkzsW7dOu0MDq1atUJCQgI+++wzhISEaB9DUFCQzrHNmjXDtWvX9Hl4REREBqPRCAz+aj/OJOmXVQxLoLEiHc0tUrC5IBAJ7w2qdbnIqIZyJycnw9PTU2ebp6cnsrKyyv1qe968eXByctL++Pn51USpNaqoqAgrVqzA559/joSEBO3PiRMnULduXfz2228AgMaNG0Mul+Po0aM6x1++fBmZmZlo0qQJACAyMhJKpRKffPJJmfdX0QCwH3/8UaeGB382b95c7rHt27eHpaUlduzYod12/vx5XLt2DZ07dy7zmMLCQhQWFpaalUChUGhbZAMCAlC3bl2cP39eZ58LFy7A39+/3HqIiIhqihC1L8haQI0ellfQTXkVLvJ8NLVIk7qkMtWuaG0AM2bMwNSpU7WXs7KyTC7Qbty4EXfu3MH48eO1ravFIiIisGTJEkycOBEODg547rnn8L///Q8WFhZo2bIlrl+/jjfffBOPPfYYunTpAgDw8/PDF198gcmTJyMrKwujR49GQEAAbty4gRUrVsDe3r7c6bkepZuBk5MTxo8fj6lTp8LV1RWOjo6YMmUKOnfurDP4KzAwEPPmzcOwYcPg6OiInj174vXXX4eNjQ38/f2xZ88erFixAvPnzwdwv3X99ddfx+zZs9G6dWu0adMGP/30E86dO4fo6Ogq10tERPQoSnYpyFWptUG2vrsdNk7pBim/zU9JScaGdetw504GZDIZuvfshWmPddarC2RNMaow6+XlVWoC/ZSUFDg6OsLGxqbMY6ysrGBlZVUT5UlmyZIlCAkJKRVkgfth9pNPPsE///yDVq1aYeHChfjoo4/w5ptv4urVq/Dy8kK/fv0wd+5cnT4wL730Epo0aYLPPvsMw4YNQ15eHgICAjB48GCdPw6q2xdffAG5XI6IiAgUFBQgNDQU3377rc4+58+fR2ZmpvbyqlWrMGPGDDz99NPIyMiAv78/5s6di4kTJ2r3efXVV5Gfn4/XXnsNGRkZaN26NbZt24aGDRsa7LEQEZHpqO6+rEIAUYviy2yJ3TilG+yspIloQggcOXIEW7duhVqthqOjIyIjI2t1Q6BMCCGkLgK433r2sKm53nzzTWzevBknT57Ubhs5ciQyMjKwZcuWSt1PVlYWnJyckJmZqe1DWiw/Px9XrlxB/fr1YW1tXaXHQaaBrwUiIiomhEDkongcvXrH4PfVwd8Fayd2lmyQ1e3bt/Htt99Co9GgSZMmGDp0KGxtbWu8jory2oMkbZm9d+8eLl68qL185coVJCQkwNXVFfXq1cOMGTNw8+ZNrFixAgAwceJEfP3113jjjTfw7LPPYufOnVizZg02bdok1UMgIiIiE5dXqDZYkA3ydvz/8Hr/stSzBbi5uSE0NBRqtRqPPfZYrZu5oCyShtkjR46gd+/e2svFX1+PGTMGy5cvR1JSks5o8/r162PTpk147bXXsHDhQvj6+uLHH3/kHLNERERkEEII5Kr+615w5O0Q2Cqrr9+o1OFVCIFDhw7B399fO3NQp06dJKunKiQNs7169UJFvRyWL19e5jHHjx83YFVEREREZXcvsFUqat3UVFWVl5eHuLg4nDt3Dq6urnjhhRegVCqlLktvpnE2iIiIiKpJ8WCvXJVu94IO/i61cjR/Vdy4cQPR0dHIzMyEQqFAcHBwhat01mYMs0RERGT2igNsebMMHHk7BG52SqPoQ1oRIQTi4+OxY8cOaDQauLi4IDIyUmfRJWPDMEtEREQmqzJTalU0TRZwv0XWFIKsSqVCTEwMLly4AABo3rw5wsLCjH4KU4ZZIiIiMkmPMqVWyVkGpB6kVV0sLS1RVFQEhUKB/v37o3379ibxuBhmiYiIyCjou3DBg31eH8YUA6wQAmq1GhYWFpDJZBg2bBju3bunnbnAFDDMEhERUY14lFW0HtYV4GEqM6WWqQTYYjk5OVi3bh2cnJwQFhYGALC3t4e9vb3ElVUvhll6ZJVZvY2IiMxbTa6i9SBT6fOqj8TERMTExODevXuwsLBAt27d4OLiInVZBsEwayLGjh2Ln376CQBgYWEBX19fREVF4b333uNyrEREVG2q2rqq71f+5XlwxazKMLUW14poNBrs27cPe/bsgRAC7u7uiIqKMtkgCzDMmpT+/ftj2bJlKCwsxNGjRzFmzBjIZDJ8/PHHUpdGRERGoqKw+qhf9Rd7lFW0zCmY6uvevXuIjY3FlStXAABt2rTBgAEDjHIhBH0wzFaSSqUq9zq5XA4LC4tK7SuTyXQmJS5v36q88KysrLQduv38/BASEoJt27bh448/xu3btzF58mTs3bsXd+7cQcOGDTFz5kw89dRT2uN79eqFVq1awdraGj/++COUSiUmTpyIOXPmaPf5999/MX78eBw6dAgNGjTAwoULS9Vx8uRJvPLKK4iPj4etrS0iIiIwf/58bR+dsWPH4u7du+jUqRMWLlyIgoICTJ06FTNnzsSMGTOwZMkS2Nra4v3338e4ceP0fh6IiKhqaqIrgDl+5V8ThBBYsWIF0tLSYGlpiUGDBqF169ZSl1UjGGYrad68eeVe17hxY4wcOVJ7+bPPPkNhYWGZ+/r7+2Ps2LHaywsXLkRubm6p/WbPnl31YgGcOnUKf/31F/z9/QEA+fn5aN++Pd588004Ojpi06ZNGDVqFBo2bKizBvNPP/2EqVOn4uDBg4iPj8fYsWPRtWtX9OvXDxqNBk888QQ8PT1x8OBBZGZm4tVXX9W535ycHISGhqJz5844fPgwUlNT8dxzz2Hy5Mk6yxPv3LkTvr6+2Lt3Lw4cOIDx48fjr7/+Qo8ePXDw4EGsXr0aL7zwAvr16wdfX99Hei6IiKhy8gor1xWgKl/1F2PLqmHIZDKEhIRg586diIyMhLu7u9Ql1RiGWROyceNG2Nvbo6ioCAUFBZDL5fj6668BAD4+Ppg2bZp23ylTpmDr1q1Ys2aNTpht1aqVNkg3btwYX3/9NXbs2IF+/fph+/btOHfuHLZu3apdKeTDDz/EgAEDtMevXLkS+fn5WLFiBezs7AAAX3/9NcLCwvDxxx/D09MTAODq6oovv/wScrkcTZs2xSeffILc3FzMnDkTADBjxgx89NFH2L9/P5588kkDPmtERDXnUUbz14Rc1X+1VdQVgIG0dsjOzkZGRoa24apJkyZo1KgR5HK5xJXVLIbZSpoxY0a51z34oikZGh/04Jv/lVdeebTCSujduze+++475OTk4IsvvoCFhQUiIiIAAGq1Gh9++CHWrFmDmzdvQqVSoaCgALa2tjq30apVK53L3t7eSE1NBQCcPXsWfn5+Okvede7cWWf/s2fPonXr1togCwBdu3aFRqPB+fPntWG2efPmOs+bp6cnWrRoob2sUCjg5uamvW8iImMn5Wj+qrBVKmCrZEyorS5evIh169ZBo9HghRdegLOzM4DSmcQc8FVaSfr0YTXUvg9jZ2eHRo0aAQCWLl2K1q1bY8mSJRg/fjw+/fRTLFy4EAsWLEDLli1hZ2eHV199tVSf3ZL9eYH74Vuj0VRbjRXdT03dNxFRRQzVelpdo/lrQgd/F9hYVm2AFhmWRqPBzp07ceDAAQCAl5eX2f+uZJg1UXK5HDNnzsTUqVMxcuRIHDhwAEOHDsUzzzwD4P6b4cKFCwgKCqr0bTZr1gzXr19HUlISvL29AQB///13qX2WL1+OnJwcbevsgQMHtN0JiIhqo+IAW12j9R/mUUbz1wR2I6idMjMzERMTg+vXrwMAOnTogNDQUJ1B6ObIvB+9iYuKisLrr7+Ob775Bo0bN0Z0dDT++usvuLi4YP78+UhJSdErzIaEhKBJkyYYM2YMPv30U2RlZeGtt97S2efpp5/G7NmzMWbMGMyZMwdpaWmYMmUKRo0ape1iQERUm9T01/8czU9VceHCBaxfvx55eXmwsrJCWFgYmjdvLnVZtQLDrAmzsLDA5MmT8cknn+D48eO4fPkyQkNDYWtri+effx7h4eHIzMys9O3J5XKsW7cO48ePR6dOnRAQEIAvv/wS/fv31+5ja2uLrVu34pVXXkHHjh11puYiIqotSnYlKOvr/0cZrf8wbPWkqvj333+Rl5eHunXrIjIy0qQXQdCXTAghpC6iJmVlZcHJyQmZmZlwdHTUuS4/Px9XrlxB/fr1uWqWmeNrgcg0lNX/taKuBMVf/zNwUm1TVFSEgwcPIjg42Cy6FVSU1x5k+s8GERGZJX27D/Drf6pNzp07h3/++QeRkZHaxZm6du0qdVm1EsMsERGZpIctAPBgVwK2xlJtUFRUhG3btuHQoUMAgOPHj6N9+/YSV1W7McwSEZHRqcz0WQ9bAIDhlWqbjIwMREdHIykpCcD9udzbtGkjbVFGgGGWiIiqRU2tblWV6bO4AADVdqdPn8aGDRtQUFAAGxsbhIeHo0mTJlKXZRT4zi6DmY2JozLwNUCkn9q8uhUXAKDabt++fdi5cycAwM/PDxEREXBycpK4KuPBMFtC8QpUubm5sLGxkbgaklJubi6A0iuVEVHZHtY/1RAqO30WuxNQbdekSRPs27cPwcHB6N27t1kuSfsoGGZLUCgUcHZ2RmpqKoD7c6byA9C8CCGQm5uL1NRUODs7Q6Fgaw5RWR7sUvCw/qmGwJBKxuz27dtwc3MDAHh6emLKlClwcHCQuCrjxDD7AC8vLwDQBloyT87OztrXAhHpeliXAvZPJSpfYWEhtmzZgoSEBIwbNw6+vr4AwCD7CPhp8wCZTAZvb294eHigsLBQ6nJIApaWlmyRJZNVHYO0yloxqxj7pxKVLy0tDdHR0doGs5s3b2rDLFUdw2w5FAoFAw0RGbUHg2tVZgF4mAe7FPCrf6KyJSQkYPPmzSgsLISdnR2eeOIJNGjQQOqyTALDLBGRCaqJ2QW4YhbRw6lUKmzevBknTpwAANSvXx9PPPEE7O3tJa7MdDDMEhEZSE3Nu1qWiroCVHYWgIdhKyzRw506dQonTpyATCZDr1690K1bN85WUM0YZomIDKA2zbvKrgBE0mnbti1u3ryJli1bIiAgQOpyTBL/NCAiMgAp5l0tS3FXAFulhfaHQZbIcAoKCrBt2zYUFBQAuD+wPCwsjEHWgNgyS0RUDWrDvKtlYSssUc1JTk5GdHQ0bt++jZycHISHh0tdkllgmCUiegRCCOSq1BXOEsB5V4lMmxACR48exZYtW6BWq+Ho6Ih27dpJXZbZ4KcrEVEVVaZfLOddJTJt+fn52LhxI06fPg3g/tK0Q4cOha2trcSVmQ+GWSKiKnqwX2xZswTwa34i05WamopVq1bhzp07kMvlCAkJwWOPPcb3fA1jmCUiqiIh/vv/kbdDOOcqkZmxtbWFSqWCk5MTIiMjuZqXRBhmiYiqQAiBqEXx2su2SrbAEpmDwsJCWFpaAgDs7e3x9NNPw9nZGTY2NhJXZr44NRcR0UPcH+RVpPNzO0elHfAV5O3IfrFEZuDGjRv45ptvcOrUKe02b29vBlmJsWWWiKgCGo3A4K/2lztTAYD/7yfLVlkiUyWEwN9//43t27dDo9HgwIEDaN68Od/3tQTDLBGZrYctNysEMPir/biSnlPuPh38XSSbQ5aIDC83Nxe///47Lly4AAAICgpCWFgYg2wtwjBLRGZJ3+Vm67vbYeOUbnjw9xdnKyAyXdevX0d0dDSysrKgUCjQv39/tG/fnu/5WoZhloiMwsNaUfWVq6r8crNB3o7YOKUb5HL+AiMyF3fu3MHy5cuh0Wjg6uqKqKgoeHl5SV0WlYFhlohqtcqssPWoHrbcLFtficyPi4sLgoODce/ePQwaNAhWVlZSl0TlYJglolpL364AVdHB34XzwxIRACAxMREuLi5wcnICAISEhEAmk/HzoZZjmCWiWuvBrgBlrbD1qNjqSkQajQb79u3Dnj174OPjg7Fjx0KhUEAu5wymxoBhlohqneKuBYO/2q/dxhW2iMgQ7t27h9jYWFy5cgUA4ObmBo1GA4WCs5QYC4ZZIqpVyupaEOTtyCBLRNXuypUriImJQU5ODiwtLTFw4EC0adNG6rJITwyzRCSpB2cpKKtrwf0psRhkiah6aDQa7NmzB3v37gUAeHh4IDIyEnXq1JG4MqoKhlkiqhZVmTpLCFQ4SwG7FhCRIWg0Gpw/fx4A0LZtWwwYMACWlpYSV0VVxTBLRI/MELMOcJYBIjIUCwsLREZGIikpCS1btpS6HHpEDLNE9EiEELido3qkIFvWLAWcZYCIqotGo8HOnTuhVCrRo0cPAIC7uzvc3d0lroyqA8MsEemlZHeCsroJPGwBgrIwuBKRoWRmZiImJgbXr1+HTCZD8+bN4ebmJnVZVI0YZomo0h7WnYBdA4ioNrlw4QLWr1+PvLw8WFlZISwsjEHWBDHMElGlVNSdoLibgK2SLaxEJD21Wo0dO3YgPj4eAODt7Y3IyEi4urpKXBkZAsMsET1UWS2yJbsTsJsAEdUWQgj88ssvSExMBAB06tQJ/fr1g4UFI4+p4pkloofKK9Sd+5XdCYiotiruF5ucnIwhQ4agWbNmUpdEBsYwS0QPJcR//+fcr0RU2xQVFSErK0vbjaB9+/YIDAyEvb29xJVRTWCYJSIA5S96IAQw+Kv92svsF0tEtcmdO3ewdu1a5Obm4oUXXoCNjQ1kMhmDrBlhmCWiSi96EOTtCBtL/abdIiIylDNnziAuLg4FBQWwsbHB7du34evrK3VZVMMYZonMTFktsLkqdaWC7MYp3dgqS0SSKyoqwtatW3HkyBEAgJ+fHyIiIuDk5CRxZSQFhlkiE/ewRQ4eVN6iB5yxgIhqg9u3byM6OhrJyckAgK5du6J3795QKPitkblimCUyQcUBtjLhtSTOUkBEtd3u3buRnJwMW1tbDBs2DI0aNZK6JJIYwyyRialM/9fiRQ4ezKxsfSWi2m7AgAEAgH79+sHR0VHiaqg2YJglMiHlrdL1YHhlaCUiY5GWloZTp06hV69ekMlksLW1RUREhNRlUS3CMEtkIipapYvhlYiM0YkTJ7Bp0yYUFhbC1dUVrVu3lrokqoUYZolMBFfpIiJToVKp8McffyAhIQEAUL9+fTRs2FDaoqjWYpglMnLFg71yVf9Nt8VVuojIWKWmpmLt2rVIT0+HTCZDz5490b17d8jlcqlLo1qKYZbIiDw4R2x5sxVwlS4iMkYnT55EXFwcioqKYG9vj4iICAQEBEhdFtVyDLNERqKyq3R18HfhKl1EZJTs7OxQVFSEhg0bYtiwYbCzs5O6JDICDLNEtUhZq3MVq2iVrpKzFXCwFxEZE5VKBaVSCQBo0KABxo4di3r16vFzjCqNYZaolqhsyytQepUuBlgiMjZCCBw9ehS7du3C+PHj4erqCgDw9/eXuDIyNuxNTVRLPDgbQXmKZymwVVpofxhkiciYFBQUICYmBps2bUJubi6OHDkidUlkxCRvmf3mm2/w6aefIjk5Ga1bt8ZXX32FTp06lbv/ggUL8N133+HatWtwd3dHZGQk5s2bB2tr6xqsmujRlNWd4MHZCEq2vJbEVlgiMma3bt1CdHQ07ty5A7lcjr59+6Jz585Sl0VGTNIwu3r1akydOhWLFi1CcHAwFixYgNDQUJw/fx4eHh6l9l+5ciWmT5+OpUuXokuXLrhw4QLGjh0LmUyG+fPnS/AIiPQjhECuSl3mDAQl2SoVsFVK/rcmEVG1EULg0KFD2LZtG9RqNZycnBAZGQlfX1+pSyMjJ+lvy/nz52PChAkYN24cAGDRokXYtGkTli5diunTp5fa/6+//kLXrl0xcuRIAEBAQACeeuopHDx4sEbrJqoKzkZAROYsISEBW7ZsAQAEBgZiyJAhsLGxkbgqMgWShVmVSoWjR49ixowZ2m1yuRwhISGIj48v85guXbrgl19+waFDh9CpUydcvnwZmzdvxqhRo8q9n4KCAhQUFGgvZ2WV3xpGZAglFzUoGWRLzkBQErsREJEpatWqFRISEhAUFIROnTrxc46qjWRhNj09HWq1Gp6enjrbPT09ce7cuTKPGTlyJNLT09GtWzcIIVBUVISJEydi5syZ5d7PvHnz8O6771Zr7UQPKm9KrfIWNeAKXURk6oQQOHnyJJo3bw6FQgGFQqHtGkhUnYyqU97u3bvx4Ycf4ttvv0VwcDAuXryIV155Be+//z7eeeedMo+ZMWMGpk6dqr2clZUFPz+/miqZzIA+U2oB/81GwA90IjJVeXl5WL9+PS5cuIDU1FSEhIQAAD/3yCAkC7Pu7u5QKBRISUnR2Z6SkgIvL68yj3nnnXcwatQoPPfccwCAli1bIicnB88//zzeeuutMtdttrKygpWVVfU/AKL/V5kptbioARGZi+vXryM6OhpZWVlQKBRwcnKSuiQycZKFWaVSifbt22PHjh0IDw8HAGg0GuzYsQOTJ08u85jc3NxSgVWhuD9QRghh0HqJylI8O0Gx8qbUYoAlIlMnhMCBAwewc+dOCCHg6uqKqKiochuoiKqLpN0Mpk6dijFjxqBDhw7o1KkTFixYgJycHO3sBqNHj4aPjw/mzZsHAAgLC8P8+fPRtm1bbTeDd955B2FhYdpQS1RTNBqBwV/t1+kPyym1iMgc5eTkYP369bh48SIAoEWLFhg8eDC/GaUaIelv3REjRiAtLQ2zZs1CcnIy2rRpgy1btmgHhV27dk2nJfbtt9+GTCbD22+/jZs3b6JOnToICwvD3LlzpXoIZKaEKB1kOaUWEZmrvLw8XL16FRYWFhgwYADatm3Lb6OoxsiEmX0/n5WVBScnJ2RmZsLR0VHqcshI5RQUofnsrQCA+u522DilG2yV7EpARObr3LlzcHFxKTVLEVFV6JPXSo+YIqIKFXcvKLZxSjfYWVkwyBKR2bh37x5++eUXXL16VbstMDCQQZYkwTBLpIfi7gVX0nMA3J+loKwBX0REpury5ctYtGgRLl26hLi4OGg0GqlLIjPHkSpElSSEwO0clbafbHH3ArbIEpE50Gg02LNnD/bu3QsAqFOnDqKiosqcFpOoJjHMElWgeGWvslby2jilG+RyBlkiMn3Z2dmIjY1FYmIiAKBt27YYMGAALC0tpS2MCAyzROWqaGWvDv4u7F5ARGYhMzMTP/zwA3Jzc2FpaYnBgwejVatWUpdFpMUwS2atuOW1LLmq0it7Fa/kxZkLiMhcODo6on79+khPT0dUVBTc3NykLolIB8Msma2yFj0oT/HKXlzJi4jMQVZWFpRKJaytrSGTyRAWFga5XM5uBVQrsdc2maWyFj0oTwd/F7jZKWGr5PRbRGT6Lly4gEWLFiEuLk67VLyVlRWDLNVabJkls1P+rARl78/WWCIyB2q1Gjt27EB8fDwA4O7duygoKIC1tbXElRFVjGGWzEpZXQuKFz0gIjJXd+/eRUxMDG7cuAEA6NSpE/r16wcLC342Uu3HVymZjbK6FnBWAiIyd+fOncPvv/+O/Px8WFlZYejQoWjWrJnUZRFVGsMsmY1clbpU1wLOSkBE5qywsBB//PEH8vPz4ePjg4iICLi4uEhdFpFeGGbJLAghELUoXnuZXQuIiABLS0tERETg3Llz6Nu3LxQKflNFxoe/zcks5BX+1yob5O3IrgVEZLbOnDmDoqIi7cIH9erVQ7169SSuiqjqGGbJZJVcECFX9d/CCGsndmbXAiIyO0VFRdi6dSuOHDkCCwsL+Pj4cAEEMgkMs2SSKloQgTmWiMzN7du3ER0djeTkZABAcHAwnJ2dpS2KqJowzJJJEUIgV6XG4K/240p6TqnrO/i7wMaSXQyIyHycOnUKGzZsgEqlgq2tLcLDw9G4cWOpyyKqNgyzZDKEEIhcFI+jV+9otz24IAIXQCAicyGEwKZNm3D06FEA9/vGRkREwNHRUeLKiKoXwyyZjLxCtU6QDfJ2xMYp3SCXM7wSkfmRyWSwtbUFAHTv3h29evWCXM5V7Mn0MMyS0Sse6FVykNeRt0PgZqdkKywRmR2VSgWlUgkA6NWrFxo3bgw/Pz+JqyIyHIZZMmpldS0AwMUQiMjsqFQq/PHHH0hJScGzzz4LCwsLyOVyBlkyeQyzVGuVnFqrPLkqdakgy0FeRGRuUlNTER0djbS0NMhkMiQmJqJRo0ZSl0VUIxhmqVYpDrBCAFGL4sucWqs8R94Oga1SwUFeRGQ2hBBISEjA5s2bUVRUBHt7e0RERCAgIEDq0ohqDMMs1RrldRmojA7+LuwjS0RmpaCgAJs2bcLJkycBAA0bNsSwYcNgZ2cncWVENYthlmoFIQRu56hKBdkgb8f/X7Gr4uPZGktE5mbjxo04deoUZDIZevfujW7duvFzkMwSwyxJqniRgwe7FLDLABFRxfr06YOUlBQMHjwY9erVk7ocIskwzJJkyutWwC4DRESlFRQU4OLFi2jevDkAwMXFBS+++CI/K8nsMcySZMpa5GDtxM6cVouI6AFJSUlYu3Yt7ty5AysrK+1MBfysJGKYpRpS1jRbXOSAiKhiQggcPnwYf/75J9RqNZycnGBtbS11WUS1CsMsGVxlZilgaywRka78/HzExcXh7NmzAICmTZti6NChsLGxkbgyotqFYZYMqrxZCkriIgdERLpu3ryJ6Oho3L17F3K5HP369UNwcDD/6CcqA8MsGcTDZikoiTMWEBHpSk9Px927d+Hs7IzIyEj4+PhIXRJRrcUwS9WOsxQQEelPCKH9fGzdujVUKhVatmzJPrJED8EwS9UuV8VZCoiI9HH9+nX8+eefeOqpp2BrawsA6Nixo8RVERkHhlmqVkIIRC2K117mLAVEROUTQuCvv/7Cjh07IITAzp07MXjwYKnLIjIqDLNUrfIK1do+skHejgyyRETlyMnJwfr163Hx4kUAQIsWLdCvXz+JqyIyPgyzZDBrJ3ZmkCUiKsPVq1cRExOD7OxsWFhYoH///mjXrh0/M4mqgGGWDIafyUREpZ07dw5r1qyBEAJubm6IioqCp6en1GURGS2GWapWQkhdARFR7RYQEABnZ2f4+flh0KBBUCqVUpdEZNQYZqnaPDj4i4iI7ktJSYGHhwdkMhmsra3x3HPPwcbGht0KiKqBXOoCyDQUr/RVcvAXV/UiInOn0Wiwe/duLFq0CEeOHNFut7W1ZZAlqiZsmaVHptEIDP5qv85KXxz8RUTmLjs7G7GxsUhMTAQApKamSlsQkYlimKVHIkTpINvB36XUkrVERObk0qVLWLduHXJycmBpaYnBgwejVatWUpdFZJIYZumR5Kr+m1e2vrsdNk7pxpW+iMhsFXcr2LdvHwDA09MTkZGRcHd3l7gyItPFMEtV9uCAr41TusHOii8pIjJfKSkp2L9/PwCgffv2CA0NhaWlpcRVEZk2Jg+qsgdX+2LXAiIyd97e3ujXrx8cHBzQokULqcshMgsMs1RlJeeU5YAvIjJHarUau3fvRqtWrVCnTh0AQOfOnSWuisi8cGouqpIHuxgwxxKRucnMzMTy5cuxf/9+REdHQ61WS10SkVliyyxVyYNdDDinLBGZk/Pnz2P9+vXIz8+HlZUVevbsCYWCn4NEUmCYpUfGLgZEZC7UajW2bduGgwcPAgDq1q2LyMhIuLi4SFwZkflimKUqKdlfljmWiMxBTk4OVq5ciVu3bgEAHnvsMYSEhLBFlkhiDLOktwf7yxIRmQMbGxtYWFjA2toa4eHhaNq0qdQlEREYZqkKSi6UwP6yRGTKioqKIJPJoFAoIJfLERERAY1GA2dnZ6lLI6L/x9kMSC8azf3la4uxvywRmaqMjAwsWbIE27Zt025zdHRkkCWqZdgyS5UmxP0geyU9BwAXSiAi03Xq1Cls2LABKpUKWVlZ6NGjB2xtbaUui4jKwDBLlVaye0F9dztsnNKNrbJEZFIKCwuxZcsWHDt2DABQr149REREMMgS1WIMs1QpDw762jilG+RyBlkiMh3p6elYu3YtUlNTAQDdu3dHr169IJezRx5RbcYwSw8lhMDtHJXOoC92LyAiU1JUVIQVK1YgOzsbdnZ2GDZsGBo2bCh1WURUCY8UZvPz82FtbV1dtVAtJIRA5KJ4HL16R7uNg76IyNRYWFggNDQUR44cwRNPPAEHBwepSyKiStL7uxONRoP3338fPj4+sLe3x+XLlwEA77zzDpYsWVLtBZK08grVOkG2g78LW2WJyCSkpqbi6tWr2svNmzfH6NGjGWSJjIzeYfaDDz7A8uXL8cknn0CpVGq3t2jRAj/++GO1FkfSK7nS15G3Q9gqS0RGTwiB48ePY/HixVizZg2ys7O11/Hzjcj46B1mV6xYgR9++AFPP/20zhJ+rVu3xrlz56q1OJLWg4O+bJUKftATkVFTqVRYv3494uLiUFRUBC8vLw7wIjJyeveZvXnzJho1alRqu0ajQWFhYbUURbVDXiFX+iIi05GSkoK1a9fi9u3bkMlk6N27N7p14xSDRMZO7zAbFBSEffv2wd/fX2d7dHQ02rZtW22FkfRKdjFg9wIiMlZCCBw7dgxbtmxBUVERHBwcEBERUer3GBEZJ73D7KxZszBmzBjcvHkTGo0GsbGxOH/+PFasWIGNGzcaokaSwINdDJhjichYyWQyXL9+HUVFRWjUqBGGDRvGRRCITIjeYXbo0KHYsGED3nvvPdjZ2WHWrFlo164dNmzYgH79+hmiRqohQgjkFaoB6K72xS4GRGSMhBDab5QGDhwIX19ftG/fnt8yEZkYmRAlv0w2fVlZWXByckJmZiYcHR2lLqdWEEIgV6VG1KJ4bYAt6fS7obCz4voaRGQchBA4fPgwEhMTERUVxfBKZIT0yWt6J5QGDRrg8OHDcHNz09l+9+5dtGvXTjvvLBmHshZFKInzyhKRMcnPz8eGDRtw5swZAMDZs2cRFBQkcVVEZEh6h9nExESo1epS2wsKCnDz5s1qKYpqTq5Kd1GEIG/H/x/sdf+yjSWn4yIi43Dz5k1ER0fj7t27kMvl6NevH5o1ayZ1WURkYJUOs3Fxcdr/b926FU5OTtrLarUaO3bsQEBAQLUWR4al0QgM/mq/9vKRt0PgZqdkeCUioyKEwMGDB7Ft2zZoNBo4OzsjMjISPj4+UpdGRDWg0mE2PDwcwP1RoWPGjNG5ztLSEgEBAfj888+rtTgyHCHuB9kr6TkA7rfIMsgSkTH6448/cPjwYQBAs2bNMGTIEFhbW0tcFRHVlEqHWY1GAwCoX78+Dh8+DHd3d4MVRYZXckGE+u522DiFE4cTkXFq3bo1Tpw4gb59+6Jjx478LCMyM3r3mb1y5Yoh6qAaVnIOi41TukEu54c/ERkHIQRSUlLg5eUFAPDx8cGrr74KGxsbiSsjIilUaUHqnJwcbN68GYsWLcKXX36p86Ovb775BgEBAbC2tkZwcDAOHTpU4f53797FpEmT4O3tDSsrKzRp0gSbN2+uysMwW1wQgYiMVW5uLn777Tf8+OOPSE5O1m5nkCUyX3q3zB4/fhwDBw5Ebm4ucnJy4OrqivT0dNja2sLDwwMvv/xypW9r9erVmDp1KhYtWoTg4GAsWLAAoaGhOH/+PDw8PErtr1Kp0K9fP3h4eCA6Oho+Pj64evUqnJ2d9X0YZq1kFwMuiEBExuLq1auIiYlBdnY2FAoF0tPTta2zRGS+9A6zr732GsLCwrBo0SI4OTnh77//hqWlJZ555hm88soret3W/PnzMWHCBIwbNw4AsGjRImzatAlLly7F9OnTS+2/dOlSZGRk4K+//oKlpSUAcAaFR3R/Gi42zRJR7SWEwP79+7Fr1y4IIeDm5oaoqCh4enpKXRoR1QJ6dzNISEjA//73P8jlcigUChQUFMDPzw+ffPIJZs6cWenbUalUOHr0KEJCQv4rRi5HSEgI4uPjyzwmLi4OnTt3xqRJk+Dp6YkWLVrgww8/LHPe22IFBQXIysrS+aH/MMcSUW2Wk5ODX3/9FTt37oQQAq1atcLzzz/PIEtEWnqHWUtLS8jl9w/z8PDAtWvXAABOTk64fv16pW8nPT0darW61AeSp6enTj+oki5fvozo6Gio1Wps3rwZ77zzDj7//HN88MEH5d7PvHnz4OTkpP3x8/OrdI1ERCStf/75B5cuXYKFhQWGDBmC8PBwKJVKqcsiolpE724Gbdu2xeHDh9G4cWP07NkTs2bNQnp6On7++We0aNHCEDVqaTQaeHh44IcffoBCoUD79u1x8+ZNfPrpp5g9e3aZx8yYMQNTp07VXs7KyjL7QFtyJgMiotrsscceQ0ZGBjp27FjmWAoiIr1bZj/88EN4e3sDAObOnQsXFxe8+OKLSEtLw/fff1/p23F3d4dCoUBKSorO9pLTrTzI29sbTZo0gULx34ClZs2aITk5GSqVqsxjrKys4OjoqPNjzh6cyYCIqDbJzs7Gxo0bUVhYCOD+Qj2DBg1ikCWicundMtuhQwft/z08PLBly5Yq3bFSqUT79u2xY8cO7epiGo0GO3bswOTJk8s8pmvXrli5ciU0Go22q8OFCxfg7e3Nr50qiTMZEFFtdenSJaxbtw45OTmQy+UYOHCg1CURkRGo0jyzZTl27BgGDx6s1zFTp07F4sWL8dNPP+Hs2bN48cUXkZOTo53dYPTo0ZgxY4Z2/xdffBEZGRl45ZVXcOHCBWzatAkffvghJk2aVF0Pw6xwJgMiqg00Gg127tyJX375BTk5OfDw8ECnTp2kLouIjIReLbNbt27Ftm3boFQq8dxzz6FBgwY4d+4cpk+fjg0bNiA0NFSvOx8xYgTS0tIwa9YsJCcno02bNtiyZYt2UNi1a9e0LbAA4Ofnh61bt+K1115Dq1at4OPjg1deeQVvvvmmXvdrroQQyFX9N/MDcywRSS0rKwsxMTHawcTt2rVD//79tdMvEhE9jEyIyg0HWrJkCSZMmABXV1fcuXMHbm5umD9/PqZMmYIRI0bglVdeQbNmzQxd7yPLysqCk5MTMjMzzar/rBACkYvicfTqHe22M++Fwlapd08TIqJqce3aNaxevRq5ublQKpUICwsz+EBiIjIO+uS1SieZhQsX4uOPP8brr7+OmJgYREVF4dtvv8XJkyfh6+v7yEWT4QghcDtHpRNkO/i7sL8sEUnKyckJQgh4eXkhMjISbm5uUpdEREao0i2zdnZ2OH36NAICAiCEgJWVFXbt2oWuXbsausZqZW4ts2W1yB55OwRudkr2lyWiGpefnw9ra2vt5eTkZLi7u8PCgt8SEdF/9MlrlR4AlpeXB1tbWwD3p0qxsrLSTtFFtVdeobpUiyyDLBFJ4fz58/jyyy9x/vx57TYvLy8GWSJ6JHp9gvz444+wt7cHABQVFWH58uVwd3fX2efll1+uvurokZVsd2eLLBFJQa1WY/v27fj7778BAIcPH0bTpk0lroqITEWluxkEBAQ8NATJZDJcvny5WgozFHPqZqDRCPSdvwdX0nMAcMAXEdW8O3fuICYmBjdv3gQABAcHo1+/fjqL3xARPcggA8ASExMftS6qQUIIDP5qvzbIcoEEIqppZ8+exe+//46CggJYW1tj6NChCAwMlLosIjIxbKYzUbmq/1b6qu9uh41TurF7ARHVmKSkJKxZswYA4Ovri4iICDg7O0tbFBGZJIZZEySEQNSieO3ljVO6QS5nkCWimuPt7Y0OHTpAqVSiT58+7FZARAbDMGuC8gr/a5UN8naErZK/RIjI8M6cOYN69eppBwoPHDiQ3wgRkcFVemouMh4lh/StndiZv0yIyKAKCwuxceNGrF27FrGxsdBoNADAzx4iqhFsmTUxD3Yx4O8SIjKk9PR0REdHIyUlBQDg4+MjcUVEZG6qFGYvXbqEZcuW4dKlS1i4cCE8PDzwxx9/oF69emjevHl110h6eLCLAWcwICJD+eeff7Bx40YUFhbC1tYWTzzxBBo2bCh1WURkZvTuZrBnzx60bNkSBw8eRGxsLO7duwcAOHHiBGbPnl3tBZJ+2MWAiAytsLAQcXFxWLduHQoLCxEQEICJEycyyBKRJPQOs9OnT8cHH3yAbdu2QalUarf36dNHu7oLSYNdDIioJgghcP36dQBAz549MWrUKDg4OEhcFRGZK727GZw8eRIrV64std3DwwPp6enVUhRVDbsYEJEhCSEgk8mgVCoRGRmJnJwcNGjQQOqyiMjM6d0y6+zsjKSkpFLbjx8/zo7/tQi7GBBRdVGpVFi/fr3Ot2+enp4MskRUK+gdZp988km8+eabSE5Ohkwmg0ajwYEDBzBt2jSMHj3aEDVSJZXsL8scS0TVISUlBYsXL8aJEyewc+dO7TgJIqLaQu9uBh9++CEmTZoEPz8/qNVqBAUFQa1WY+TIkXj77bcNUSNVwoP9ZYmIHoUQAseOHcOWLVtQVFQEBwcHREREaBdEICKqLfQOs0qlEosXL8Y777yDU6dO4d69e2jbti0aN25siPqokthfloiqS0FBATZu3IhTp04BABo1aoTw8HDY2dlJXBkRUWl6h9n9+/ejW7duqFevHurVq2eImugRsb8sEVWVWq3GkiVLkJaWBplMhr59+6JLly78TCGiWkvvPrN9+vRB/fr1MXPmTJw5c8YQNVEVsL8sEVUHhUKBtm3bwtHREePGjUPXrl0ZZImoVtM7zN66dQv/+9//sGfPHrRo0QJt2rTBp59+ihs3bhiiPqoE9pclokeRn5+P27dvay8/9thjePHFF+Hn5ydhVURElaN3mHV3d8fkyZNx4MABXLp0CVFRUfjpp58QEBCAPn36GKJGeohcFfvLElHV3Lp1C99//z1+++03FBQUAABkMhmsra0lroyIqHL07jNbUv369TF9+nS0bt0a77zzDvbs2VNddVElPdgqy/6yRFQZQggcPHgQ27Ztg0ajgbOzM7Kzs2FlZSV1aUREeqlymD1w4AB+/fVXREdHIz8/H0OHDsW8efOqszaqhAdnMbBVslWWiCqWl5eHuLg4nDt3DgAQGBiIoUOHsjWWiIyS3mF2xowZWLVqFW7duoV+/fph4cKFGDp0KGxtbQ1RH+mBrbJE9DA3btxAdHQ0MjMzoVAo8Pjjj6Njx4787CAio6V3mN27dy9ef/11DB8+HO7u7oaoifTAWQyISB979uxBZmYmXFxcEBkZibp160pdEhHRI9E7zB44cMAQdVAVcBYDItLX0KFDsXv3bvTr14/9Y4nIJFQqzMbFxWHAgAGwtLREXFxchfsOGTKkWgqjh+OqX0T0MNeuXcOlS5fQu3dvAIC9vT0GDx4scVVERNWnUmE2PDwcycnJ8PDwQHh4eLn7yWQyqNXq6qqN9MD+skRUkhAC+/fvx65duyCEgLe3NwIDA6Uui4io2lUqzGo0mjL/T7UHcywRFcvJycG6detw6dIlAECrVq3QoEEDiasiIjIMvRdNWLFihXZi7ZJUKhVWrFhRLUVR5ZQc/EVEBACJiYlYtGgRLl26BAsLCwwZMgTh4eFQKpVSl0ZEZBB6h9lx48YhMzOz1Pbs7GyMGzeuWoqih9NoBAZ/tV/qMoioFomPj8eKFStw7949uLu7Y8KECWjbti27IBGRSdN7NgMhRJkfjDdu3ICTk1O1FEUVE+J+kL2SngOAg7+I6D5XV1cIIdCmTRsMGDCArbFEZBYqHWaL/7qXyWTo27cvLCz+O1StVuPKlSvo37+/QYokXSVnMajvboeNU7qx5YXITOXn52tX7mratCkmTJjAuWOJyKxUOswWz2KQkJCA0NBQ2Nvba69TKpUICAhAREREtRdIFds4pRvkcgZZInOj0Wiwe/duHD16FM8//7z2mzEGWSIyN5UOs7NnzwYABAQEYMSIEVzDu5ZggyyR+cnKykJsbCyuXr0KADhz5gw6d+4scVVERNLQu8/smDFjDFEHERFVwsWLF7Fu3Trk5uZCqVQiLCwMLVq0kLosIiLJVCrMurq64sKFC3B3d4eLi0uF/TMzMjKqrTgiIrpPrVZj165d2iXFvby8EBkZCTc3N4krIyKSVqXC7BdffAEHBwft/znYiIioZh08eFAbZDt27IjHH39cZyAuEZG5qtQnYcmuBWPHjjVULUREVI6OHTvi/PnzCA4ORlBQkNTlEBHVGnovmnDs2DGcPHlSe/n3339HeHg4Zs6cCZVKVa3FUWlCCOSq1FKXQUQGplarceTIEe0S4paWlhg7diyDLBHRA/QOsy+88AIuXLgAALh8+TJGjBgBW1tbrF27Fm+88Ua1F0j/EUIgclE8OnywXepSiMiA7t69i2XLlmHTpk3Yt2+fdju7eBERlaZ3mL1w4QLatGkDAFi7di169uyJlStXYvny5YiJianu+qiEvEI1jl69o73cwd+FK38RmZizZ8/i+++/x82bN2FtbQ1PT0+pSyIiqtWqtJxt8dde27dvx+DBgwEAfn5+SE9Pr97qSIcQ//3/yNshcLNTsqWGyEQUFRVh27ZtOHToEADA19cXERERcHZ2lrYwIqJaTu8w26FDB3zwwQcICQnBnj178N133wEArly5whYEAxJCIGpRvPayrVLBIEtkIjIyMhAdHY2kpCQAQOfOndG3b18oFPzmhYjoYfQOswsWLMDTTz+N9evX46233kKjRo0AANHR0ejSpUu1F0j35RWqcSYpCwAQ5O3I7gVEJkSlUiE1NRU2NjYIDw9HkyZNpC6JiMhoyIQo+eV11eXn50OhUMDS0rI6bs5gsrKy4OTkhMzMTDg6OkpdTqXlqooQNGsrAOD0u6Gws+L8kkTGTAih8+3KuXPn4O3tDScnJwmrIiKqHfTJa1VOREePHsXZs2cBAEFBQWjXrl1Vb4oqoeSfHOxdQGTcbt++jdjYWAwcOBA+Pj4AgMDAQImrIiIyTnqH2dTUVIwYMQJ79uzRDky4e/cuevfujVWrVqFOnTrVXaPZe7C/LBEZr5MnT2Ljxo1QqVT4448/MH78ePZ/JyJ6BHpPzTVlyhTcu3cPp0+fRkZGBjIyMnDq1ClkZWXh5ZdfNkSNZo/9ZYmMX2FhIeLi4hAbGwuVSoWAgACMGDGCQZaI6BHp3TK7ZcsWbN++Hc2aNdNuCwoKwjfffIPHH3+8Wouj0tZO7MxffkRGJi0tDdHR0UhNTQUA9OzZEz169IBcrnd7AhERPUDvMKvRaMoc5GVpaamdf5YMhzmWyLikpqbixx9/RGFhIezs7BAREYH69etLXRYRkcnQu1mgT58+eOWVV3Dr1i3ttps3b+K1115D3759q7U4IiJjV6dOHdSvXx/169fHxIkTGWSJiKqZ3i2zX3/9NYYMGYKAgAD4+fkBAK5fv44WLVrgl19+qfYCiYiMTWpqKpydnaFU3l+lLyIiAhYWFuxWQERkAHqHWT8/Pxw7dgw7duzQTs3VrFkzhISEVHtxdF/1zARMRIYmhMDx48fxxx9/ICgoCOHh4ZDJZFAqlVKXRkRksvQKs6tXr0ZcXBxUKhX69u2LKVOmGKou+n+clovIOBQUFGDTpk04efIkACA3NxdqtRoWFlzghIjIkCr9Kfvdd99h0qRJaNy4MWxsbBAbG4tLly7h008/NWR9Zo/TchHVfsnJyVi7di0yMjIgk8nQt29fdOnShTOPEBHVgEp34Pr6668xe/ZsnD9/HgkJCfjpp5/w7bffGrI2gm4XA07LRVS7CCFw+PBh/Pjjj8jIyICjoyPGjRuHrl278r1KRFRDKh1mL1++jDFjxmgvjxw5EkVFRUhKSjJIYVS6iwF/NxLVLvn5+dizZw/UajWaNGmCF154QTswloiIakaluxkUFBTAzs5Oe1kul0OpVCIvL88ghRG7GBDVdjY2NnjiiSeQkpKCxx57jK2xREQS0GtkwjvvvANbW1vtZZVKhblz58LJyUm7bf78+dVXHWmxiwGR9IQQOHToEBwcHBAUFAQAaNCgARo0aCBxZURE5qvSYbZHjx44f/68zrYuXbrg8uXL2ssMW4bDp5ZIWnl5eYiLi8O5c+egVCrh6+sLR0dHqcsiIjJ7lQ6zu3fvNmAZRES1140bNxAdHY3MzEwoFAr07dsXDg4OUpdFRESowqIJVHO4WAKRtIQQiI+Px44dO6DRaODi4oLIyEjUrVtX6tKIiOj/MczWUlwsgUhaGo0Gq1evxoULFwAAzZs3R1hYGKysrCSujIiISmKYraU4kwGRtORyOVxdXaFQKNC/f3+0b9+e4wKIiGohhlkjwJkMiGqGEAIFBQWwtrYGAISEhKBdu3aoU6eOxJUREVF5Kr1oAkmHOZbI8HJycrBy5UqsXLkSarUaAKBQKBhkiYhquSqF2X379uGZZ55B586dcfPmTQDAzz//jP3791drceaMg7+Iak5iYiK+//57XLx4EUlJSUhOTpa6JCIiqiS9w2xMTAxCQ0NhY2OD48ePo6CgAACQmZmJDz/8sNoLNEcc/EVUMzQaDfbs2YMVK1YgOzsb7u7umDBhAnx8fKQujYiIKknvMPvBBx9g0aJFWLx4MSwtLbXbu3btimPHjlVrceaKg7+IDO/evXv45ZdfsHv3bggh0KZNG0yYMAEeHh5Sl0ZERHrQewDY+fPn0aNHj1LbnZyccPfu3eqoiUrg4C8iw1i3bh2uXLkCS0tLDBo0CK1bt5a6JCIiqgK9W2a9vLxw8eLFUtv3799f5fXJv/nmGwQEBMDa2hrBwcE4dOhQpY5btWoVZDIZwsPDq3S/xoA5lsgwBgwYAF9fXzz//PMMskRERkzvMDthwgS88sorOHjwIGQyGW7duoVff/0V06ZNw4svvqh3AatXr8bUqVMxe/ZsHDt2DK1bt0ZoaChSU1MrPC4xMRHTpk1D9+7d9b7P2o6Dv4iqX3Z2Nk6ePKm97O7ujmeffRbu7u4SVkVERI9K724G06dPh0ajQd++fZGbm4sePXrAysoK06ZNw5QpU/QuYP78+ZgwYQLGjRsHAFi0aBE2bdqEpUuXYvr06WUeo1ar8fTTT+Pdd9/Fvn37TKp7Awd/EVW/ixcvYt26dcjLy4OjoyP8/f0BgF14iIhMgN5hViaT4a233sLrr7+Oixcv4t69ewgKCoK9vb3ed65SqXD06FHMmDFDu00ulyMkJATx8eUHuvfeew8eHh4YP3489u3bV+F9FBQUaGdcAICsrCy966xJHPxFVH00Gg127tyJAwcOALjfTaoqn1VERFR7VXkFMKVSiaCgoEe68/T0dKjVanh6eups9/T0xLlz58o8Zv/+/ViyZAkSEhIqdR/z5s3Du++++0h1SoWDv4iqLjMzEzExMbh+/ToAoEOHDggNDYWFBRc+JCIyJXp/qvfu3bvCgLVz585HKqgi2dnZGDVqFBYvXlzpfm4zZszA1KlTtZezsrLg5+dnqBKrFXMsUdVcuHAB69evR15eHqysrBAWFobmzZtLXRYRERmA3mG2TZs2OpcLCwuRkJCAU6dOYcyYMXrdlru7OxQKBVJSUnS2p6SkwMvLq9T+ly5dQmJiIsLCwrTbNBoNAMDCwgLnz59Hw4YNdY6xsrKClZWVXnURkXHLzMxEXl4evL29ERkZCVdXV6lLIiIiA9E7zH7xxRdlbp8zZw7u3bun120plUq0b98eO3bs0E6vpdFosGPHDkyePLnU/oGBgTqjkQHg7bffRnZ2NhYuXGg0La4V4UwGRFUjhNB+a9ShQwdYWlqiRYsW7FZARGTiqu1T/plnnkGnTp3w2Wef6XXc1KlTMWbMGHTo0AGdOnXCggULkJOTo53dYPTo0fDx8cG8efNgbW2NFi1a6Bzv7OwMAKW2GyPOZEBUNefOncPevXsxevRoWFtbQyaTlfoWiYiITFO1hdn4+HhYW1vrfdyIESOQlpaGWbNmITk5GW3atMGWLVu0g8KuXbsGuVzv6XCNEmcyINJPUVERtm/fjoMHDwIA/vrrL/Tp00fiqoiIqCbJhNDvi+0nnnhC57IQAklJSThy5AjeeecdzJ49u1oLrG5ZWVlwcnJCZmYmHB0dpS5HR05BEZrP3goAOP1uKOys+PUoUXkyMjIQHR2NpKQkAEDnzp3Rt29fKBT8I5CIyNjpk9f0TktOTk46l+VyOZo2bYr33nsPjz/+uL43R//vwS4GnMmAqHynT5/Ghg0bUFBQABsbG4SHh6NJkyZSl0VERBLQK8yq1WqMGzcOLVu2hIuLi6FqMkvsYkBUOUePHsXGjRsBAH5+foiMjKx137IQEVHN0aszqkKhwOOPP25Sy8fWRlwsgah8zZo1g6OjI7p164axY8cyyBIRmTm9R1a1aNECly9fNkQt9P+YY4l0Fa/iBQC2trZ46aWX0LdvX7MZHEpEROXT+zfBBx98gGnTpmHjxo1ISkpCVlaWzg9VDeeXJSqtsLAQcXFxWLp0qc4S1lwIhYiIilW6z+x7772H//3vfxg4cCAAYMiQITpfhRdPWK5Wq6u/ShPH+WWJSktLS0N0dDRSU1MB3F/OmoiI6EGVDrPvvvsuJk6ciF27dhmyHrPEwV9Euk6cOIFNmzahsLAQdnZ2eOKJJ9CgQQOpyyIiolqo0mG2eDranj17GqwY4uAvMm8qlQp//PGHtktBgwYNMGzYMNjb20tbGBER1Vp6Tc3FkGV4fIrJnN26dQsJCQmQyWTo1asXunXrxkFeRERUIb3CbJMmTR4aaDMyMh6pIHPEwV9E9wUEBODxxx+Ht7c3AgICpC6HiIiMgF5h9t133y21Ahg9Gg7+InNWUFCAP//8E127doWrqyuA+8vSEhERVZZeYfbJJ5+Eh4eHoWoxSxz8ReYqOTkZ0dHRuH37NlJTU/Hss8+yKxMREemt0mGWv2QMj4O/yBwIIXD06FFs2bIFarUajo6O6NevH1/7RERUJXrPZkCGw9/lZOry8/OxceNGnD59GsD9fvhDhw6Fra2txJUREZGxqnSY1Wg0hqyDiEzcnTt38PPPP+POnTuQy+UICQnBY489xhZZIiJ6JHr1mSUiqipHR0fY2NhAo9EgMjISvr6+UpdEREQmgGGWiAwmPz8fSqUScrkcCoUCw4cPh1KphI2NjdSlERGRieBs5ERkEDdv3sT333+vswS2k5MTgywREVUrhlmJcVwdmRohBOLj47F06VLcvXsXZ86cgUqlkrosIiIyUexmICEumECmJi8vD+vXr8eFCxcAAEFBQQgLC4NSqZS4MiIiMlUMsxLigglkSq5fv47o6GhkZWVBoVCgf//+aN++PWcrICIig2KYrSW4YAIZs/z8fPz6668oKCiAq6sroqKi4OXlJXVZRERkBhhmawnmWDJm1tbW6N+/Py5fvoxBgwbByspK6pKIiMhMMMwSUZVcvXoVcrkcfn5+AIA2bdqgdevW/IaBiIhqFMOshDiTARkjjUaD/fv3Y/fu3bC3t8fEiRO1y9EyyBIRUU1jmJUIZzIgY3Tv3j2sW7cOly9fBgA0aNAAFhb8GCEiIunwt5BEOJMBGZsrV64gJiYGOTk5sLS0xMCBA9GmTRupyyIiIjPHMFsLcCYDqs2EENi9ezf27t0LAPDw8EBkZCTq1KkjcWVEREQMs7UCcyzVdunp6QCAtm3bYsCAAbC0tJS4IiIiovsYZomoTEIIyGQyyGQyhIWFoXnz5ggKCpK6LCIiIh1yqQsgotpFo9Fg+/btiI6Ohvj/KTesra0ZZImIqFZiyywRaWVmZiImJgbXr18HcH8u2YCAAGmLIiIiqgDDLBEBAC5cuID169cjLy8PVlZWCAsLY5AlIqJaj2FWIlwwgWoLtVqNHTt2ID7+/rzH3t7eiIyMhKurq8SVERERPRzDrAS4YALVJjExMTh79iwAoFOnTujXrx8XQiAiIqPB31gS4IIJVJsEBwfj6tWrCAsLQ2BgoNTlEBER6YVhVmJcMIFqWlFREZKTk+Hr6wsA8Pf3xyuvvAKlUilxZURERPrj1FwSY46lmnTnzh0sXboUK1asQFpamnY7gywRERkrtswSmYkzZ84gLi4OBQUFsLGxwb1797gkLRERGT2GWSITV1RUhK1bt+LIkSMAAD8/P0RERMDJyUniyoiIiB4dw6wEOC0X1ZTbt28jOjoaycnJAICuXbuid+/eUCg46JCIiEwDw2wN47RcVJP++ecfJCcnw9bWFsOGDUOjRo2kLomIiKhaMczWME7LRTWpZ8+eUKlU6Ny5MxwdHaUuh4iIqNpxNgMJcVouqm7p6elYv349ioqKAAByuRyhoaEMskREZLLYMish5liqTidOnMCmTZtQWFgIR0dH9OnTR+qSiIiIDI5hlsjIqVQq/PHHH0hISAAA1K9fH506dZK2KCIiohrCMEtkxFJTUxEdHY20tDTIZDL07NkT3bt3h1zOHkRERGQeGGaJjNS5c+cQExODoqIi2NvbIyIiAgEBAVKXRUREVKMYZomMlIeHBxQKBfz9/TFs2DDY2dlJXRIREVGNY5glMiI5OTna0Orq6orx48fD3d2ds2IQEZHZYse6GsbVv6gqhBA4cuQIFixYgEuXLmm316lTh0GWiIjMGltmaxBX/6KqyM/Px8aNG3H69GkAwKlTp9CwYUOJqyIiIqodGGZrEFf/In3dunUL0dHRuHPnDuRyOfr27YvOnTtLXRYREVGtwTArEa7+RRURQuDQoUPYtm0b1Go1nJycEBkZCV9fX6lLIyIiqlUYZiXCHEsVuXLlCrZs2QIACAwMxJAhQ2BjYyNxVURERLUPwyxRLdSgQQO0a9cOHh4e6NSpE1vxiYiIysEwS1QLFM9W0Lx5c9ja2gIAwsLCJK6KiIio9uPUXEQSy83NxapVq7B582asX78egvO3ERERVRpbZokkdP36dURHRyMrKwsKhQKNGzeWuiQiIiKjwjBLJAEhBA4cOICdO3dCCAFXV1dERUXBy8tL6tKIiIiMCsMsUQ3Lzc3FunXrcPHiRQBAixYtMHjwYFhZWUlcGRERkfFhmCWqYXK5HOnp6bCwsMCAAQPQtm1bzlZARERURQyzRDWgeFCXTCaDtbU1hg8fDrlcDk9PT4krIyIiMm6czYDIwO7du4dffvkFR44c0W7z9vZmkCUiIqoGbJklMqArV64gJiYGOTk5SEpKQqtWrdg3loiIqBoxzBIZgEajwZ49e7B3714AQJ06dRAVFcUgS0REVM0YZomqWXZ2NmJjY5GYmAgAaNu2LQYMGABLS0tpCyMiIjJBDLNE1UilUuGHH37AvXv3YGlpicGDB6NVq1ZSl0VERGSyGGaJqpFSqUTHjh1x5swZREVFwc3NTeqSiIiITBrDbA36/9mZyMRkZWWhsLBQG1y7deuGLl26wMKCby8iIiJD49RcNUQIgahF8VKXQdXswoULWLRoEdasWYPCwkIA9xdFYJAlIiKqGfyNW0PyCtU4k5QFAAjydoSNpULiiuhRqNVq7NixA/Hx9/9AcXZ2Rl5eHgd5ERER1TCGWQmsndiZy5casbt37yImJgY3btwAAHTq1An9+vVjaywREZEEakU3g2+++QYBAQGwtrZGcHAwDh06VO6+ixcvRvfu3eHi4gIXFxeEhIRUuH9txBxrvM6dO4fvv/8eN27cgJWVFYYPH44BAwYwyBIREUlE8jC7evVqTJ06FbNnz8axY8fQunVrhIaGIjU1tcz9d+/ejaeeegq7du1CfHw8/Pz88Pjjj+PmzZs1XDmZGyEE4uPjkZ+fj7p16+KFF15As2bNpC6LiIjIrMmEkHaMfXBwMDp27Iivv/4awP2Vk/z8/DBlyhRMnz79ocer1Wq4uLjg66+/xujRox+6f1ZWFpycnJCZmQlHR8dHrr+yclVFCJq1FQBw5r1Q2CrZkmeMMjMzceTIEfTq1QsKBfs9ExERGYI+eU3SllmVSoWjR48iJCREu00ulyMkJEQ7sOZhcnNzUVhYCFdX1zKvLygoQFZWls4PUWWdOXMGu3bt0l52cnJC3759GWSJiIhqCUnDbHp6OtRqNTw9PXW2e3p6Ijk5uVK38eabb6Ju3bo6gbikefPmwcnJSfvj5+f3yHWT6SsqKsKmTZuwdu1a7N27F1euXJG6JCIiIiqD5H1mH8VHH32EVatWYd26dbC2ti5znxkzZiAzM1P7c/369RqukozN7du3sWTJEhw5cgQA0LVrV9SrV0/iqoiIiKgsknbcdHd3h0KhQEpKis72lJQUeHl5VXjsZ599ho8++gjbt29Hq1atyt3PysoKVlZW1VIvmb6TJ09i48aNUKlUsLW1xbBhw9CoUSOpyyIiIqJySNoyq1Qq0b59e+zYsUO7TaPRYMeOHejcuXO5x33yySd4//33sWXLFnTo0KEmSn1kXMq29tu6dStiY2OhUqng7++PF154gUGWiIiolpN8SP3UqVMxZswYdOjQAZ06dcKCBQuQk5ODcePGAQBGjx4NHx8fzJs3DwDw8ccfY9asWVi5ciUCAgK0fWvt7e1hb28v2eOoCJeyNQ6+vr4AgO7du6NXr16Qy426Fw4REZFZkDzMjhgxAmlpaZg1axaSk5PRpk0bbNmyRTso7Nq1azqh4rvvvoNKpUJkZKTO7cyePRtz5sypydIrjUvZ1l737t3T/hHUvHlzeHp6wt3dXeKqiIiIqLIkn2e2pkkxz2zJOWZPvxsKOyvJ/4YweyqVCn/88Qf+/fdfTJw4sda26hMREZkjffIaU1UN41K20ktNTUV0dDTS0tIgk8lw+fLlCgcREhERUe3FMEtmQwiBhIQEbN68GUVFRbC3t0dERAQCAgKkLo2IiIiqiGGWzIJKpcLGjRtx8uRJAEDDhg0xbNgw2NnZSVwZERERPQqGWTILe/fuxcmTJyGTydC7d29069YNMvb5ICIiMnoMs2QWevTogaSkJPTs2ZOreREREZkQTqRJJqmgoAB//fUXiifrUCqVGDVqFIMsERGRiWHLLJmcpKQkREdHIyMjAwDQpUsXiSsiIiIiQ2GYJZMhhMDhw4fx559/Qq1Ww8nJiS2xREREJo5hlkxCfn4+4uLicPbsWQBA06ZNMXToUNjY2EhcGRERERkSwywZvVu3bmHt2rW4e/cu5HI5+vXrh+DgYM5WQEREZAYYZsnoCSGQlZUFZ2dnREZGwsfHR+qSiIiIqIYwzJJR0mg0kMvvT8bh4+ODESNGoF69erC2tpa4MiIiIqpJnJqLjM7169fx7bffIjk5WbutSZMmDLJERERmiGG2Bvz/VKf0iIQQOHDgAJYtW4bbt29j586dUpdEREREEmM3AwMTQiBqUbzUZRi9nJwcrF+/HhcvXgQAtGjRAoMHD5a4KiIiIpIaw6yB5RWqcSYpCwAQ5O0IG0uFxBUZn6tXryImJgbZ2dmwsLBA//790a5dO85WQERERAyzNWntxM4MYHq6du0afvrpJwgh4ObmhqioKHh6ekpdFhEREdUSDLM1iDlWf76+vggICICDgwMGDRoEpVIpdUlERERUizDMUq1z7do1eHt7w9LSEnK5HE899RQsLS2lLouIiIhqIc5mQLWGRqPB7t27sWzZMmzdulW7nUGWiIiIysOWWaoVsrOzERsbi8TERACAWq3WWRiBiIiIqCwMsyS5S5cuITY2Frm5ubC0tMTgwYPRqlUrqcsiIiIiI8AwS5LRaDTYtWsX9u/fDwDw9PREZGQk3N3dJa6MiIiIjAXDLEkmJycHR48eBQC0b98eoaGh7B9LREREemGYJck4ODggPDwcKpUKLVq0kLocIiIiMkIMs1Rj1Go1du7ciXr16qFp06YAgCZNmkhcFRERERkzDhWnGpGZmYnly5fjr7/+wu+//478/HypSyIiIiITwJZZMrjz589j/fr1yM/Ph5WVFcLCwmBtbS11WURERGQCGGbJYNRqNbZt24aDBw8CAOrWrYvIyEi4uLhIXBkRERGZCoZZMojCwkIsX74ct27dAgA89thjCAkJgUKhkLgyIiIiMiUMs2QQlpaW8PLyQkZGBsLDw7UDvoiIiIiqE8MsVZuioiIUFhbCxsYGANC/f3/06NEDTk5OEldGREREpoqzGVC1yMjIwJIlS7B27VpoNBoA91tnGWSJiIjIkNgyS4/s1KlT2LBhA1QqFWxsbHDnzh24ublJXRYRERGZAYZZqrLCwkJs2bIFx44dAwDUq1cPERERcHR0lLgyIiIiMhcMs1Ql6enpiI6ORkpKCgCge/fu6NWrF+Ry9lwhIiKimsMwS3oTQiA2NhYpKSmwtbXFE088gYYNG0pdFhEREZkhhlnSm0wmw5AhQ7Bjxw4MGTIEDg4OUpdEREREZorfCVOlpKam4p9//tFe9vLywtNPP80gS0RERJJiyyxVSAiBhIQEbN68GRqNBm5ubvDx8ZG6LCIiIiIADLNUAZVKhU2bNmlbZBs0aABnZ2dpiyIiIiIqgWGWypSSkoK1a9fi9u3bkMlk6N27N7p16waZTCZ1aURERERaDLNUyrFjx7B582ao1Wo4ODggIiIC/v7+UpdFREREVArDLJWSn58PtVqNRo0aYdiwYbC1tZW6JCIiIqIyMcwSAECj0WgXPOjcuTOcnJwQFBTEbgVERERUq3FqLjMnhMChQ4fwww8/QKVSAbg/j2zz5s0ZZImIiKjWY8usGcvPz0dcXBzOnj0L4H5f2ccee0ziqoiIiIgqj2HWTN28eRPR0dG4e/cu5HI5+vXrh+DgYKnLIiIiItILw6yZEULg4MGD2LZtGzQaDZydnREZGcmFEIiIiMgoMcyamb1792L37t0AgGbNmmHIkCGwtraWtigiIiKiKmKYNTPt27fH8ePH0aVLF3Ts2JGDvIiIiMioMcyaOCEELl++jIYNGwIA7O3tMXnyZFhY8NQTERGR8ePUXCYsNzcXv/32G3755RecPn1au51BloiIiEwFU42Junr1KmJiYpCdnQ2FQoHCwkKpSyIiIiKqdgyzJkYIgf3792PXrl0QQsDNzQ1RUVHw9PSUujQiIiKiascwa0JycnIQGxuLy5cvAwBatWqFQYMGQalUSlwZERERkWEwzJqQmzdv4vLly7CwsMDAgQPRpk0bzlZAREREJo1h1oQ0adIEjz/+OBo2bAgPDw+pyyEiIiIyOM5mYMSys7OxZs0aZGZmard17tyZQZaIiIjMBltmjdSlS5ewbt065OTkQKVS4ZlnnpG6JCIiIqIaxzBrZDQaDXbv3o19+/YBADw8PNC/f3+JqyIiIiKSBsOsEcnKykJMTAyuXbsGAGjXrh369+8PS0tLiSsjIiIikgbDrJFITk7GihUrkJeXB6VSibCwMLRo0ULqsoiIiIgkxTBrJNzc3ODg4AAnJydERkbCzc1N6pKIiIiIJMcwW4tlZ2fD3t4eMpkMlpaWGDlyJOzs7GBhwdNGREREBHBqrlrr/Pnz+Pbbb7UDvQDAycmJQZaIiIioBIbZWkatVmPr1q1YtWoV8vPz8e+//0Kj0UhdFhEREVGtxGa+WuTOnTuIiYnBzZs3AQDBwcHo168f5HL+zUFERERUFobZWuLs2bP4/fffUVBQAGtrawwdOhSBgYFSl0VERERUqzHMGpgQD98nOzsbMTExUKvV8PX1RUREBJydnQ1eGxEREZGxY5g1ICEEohbFP3Q/BwcH9O/fHxkZGejbty8UCkUNVEdERERk/BhmDSivUI0zSVkAgCBvR9hY/hdST58+DWdnZ/j4+AAAOnToIEmNRERERMaMYbaGrJ3YGTKZDIWFhdi6dSuOHj0KZ2dnvPDCC7C2tpa6PCIiIiKjVCuGyX/zzTcICAiAtbU1goODcejQoQr3X7t2LQIDA2FtbY2WLVti8+bNNVRp1clkQHp6OpYsWYKjR48CAFq0aAGlUilxZURERETGS/Iwu3r1akydOhWzZ8/GsWPH0Lp1a4SGhiI1NbXM/f/66y889dRTGD9+PI4fP47w8HCEh4fj1KlTNVy5fk6fOokffvgBKSkpsLW1xTPPPIO+ffty2i0iIiKiRyATojLj7Q0nODgYHTt2xNdffw0A0Gg08PPzw5QpUzB9+vRS+48YMQI5OTnYuHGjdttjjz2GNm3aYNGiRQ+9v6ysLDg5OSEzMxOOjo7V90DKkKsqQotZf6Cz5TU0sUgHAAQEBOCJJ56Ag4ODQe+biIiIyFjpk9ckbRZUqVQ4evQoQkJCtNvkcjlCQkIQH1/2LADx8fE6+wNAaGhoufsXFBQgKytL56cmaSCDjawQANCzZ0+MGjWKQZaIiIiomkgaZtPT06FWq+Hp6amz3dPTE8nJyWUek5ycrNf+8+bNg5OTk/bHz8+veoqvNBn2qQIwYuQz6NWrF7sVEBEREVUjk5/NYMaMGZg6dar2clZWVo0FWhtLBc68F6r9PxERERFVL0nDrLu7OxQKBVJSUnS2p6SkwMvLq8xjvLy89NrfysoKVlZW1VOwnmQyGWyVJv/3AhEREZFkJP3OW6lUon379tixY4d2m0ajwY4dO9C5c+cyj+ncubPO/gCwbdu2cvcnIiIiItMlebPh1KlTMWbMGHTo0AGdOnXCggULkJOTg3HjxgEARo8eDR8fH8ybNw8A8Morr6Bnz574/PPPMWjQIKxatQpHjhzBDz/8IOXDICIiIiIJSB5mR4wYgbS0NMyaNQvJyclo06YNtmzZoh3kde3aNZ1BU126dMHKlSvx9ttvY+bMmWjcuDHWr1+PFi1aSPUQiIiIiEgiks8zW9Nqcp5ZIiIiItKf0cwzS0RERET0KBhmiYiIiMhoMcwSERERkdFimCUiIiIio8UwS0RERERGi2GWiIiIiIwWwywRERERGS2GWSIiIiIyWgyzRERERGS0GGaJiIiIyGgxzBIRERGR0WKYJSIiIiKjxTBLREREREbLQuoCapoQAgCQlZUlcSVEREREVJbinFac2ypidmE2OzsbAODn5ydxJURERERUkezsbDg5OVW4j0xUJvKaEI1Gg1u3bsHBwQEymczg95eVlQU/Pz9cv34djo6OBr8/qn48h8aP59D48RwaN54/41fT51AIgezsbNStWxdyecW9Ys2uZVYul8PX17fG79fR0ZFvYCPHc2j8eA6NH8+hceP5M341eQ4f1iJbjAPAiIiIiMhoMcwSERERkdFimDUwKysrzJ49G1ZWVlKXQlXEc2j8eA6NH8+hceP5M361+Rya3QAwIiIiIjIdbJklIiIiIqPFMEtERERERothloiIiIiMFsMsERERERkthtlq8M033yAgIADW1tYIDg7GoUOHKtx/7dq1CAwMhLW1NVq2bInNmzfXUKVUHn3O4eLFi9G9e3e4uLjAxcUFISEhDz3nZHj6vg+LrVq1CjKZDOHh4YYtkB5K33N49+5dTJo0Cd7e3rCyskKTJk34eSohfc/fggUL0LRpU9jY2MDPzw+vvfYa8vPza6haetDevXsRFhaGunXrQiaTYf369Q89Zvfu3WjXrh2srKzQqFEjLF++3OB1lknQI1m1apVQKpVi6dKl4vTp02LChAnC2dlZpKSklLn/gQMHhEKhEJ988ok4c+aMePvtt4WlpaU4efJkDVdOxfQ9hyNHjhTffPONOH78uDh79qwYO3ascHJyEjdu3KjhyqmYvuew2JUrV4SPj4/o3r27GDp0aM0US2XS9xwWFBSIDh06iIEDB4r9+/eLK1euiN27d4uEhIQarpyE0P/8/frrr8LKykr8+uuv4sqVK2Lr1q3C29tbvPbaazVcORXbvHmzeOutt0RsbKwAINatW1fh/pcvXxa2trZi6tSp4syZM+Krr74SCoVCbNmypWYKLoFh9hF16tRJTJo0SXtZrVaLunXrinnz5pW5//Dhw8WgQYN0tgUHB4sXXnjBoHVS+fQ9hw8qKioSDg4O4qeffjJUifQQVTmHRUVFokuXLuLHH38UY8aMYZiVmL7n8LvvvhMNGjQQKpWqpkqkCuh7/iZNmiT69Omjs23q1Kmia9euBq2TKqcyYfaNN94QzZs319k2YsQIERoaasDKysZuBo9ApVLh6NGjCAkJ0W6Ty+UICQlBfHx8mcfEx8fr7A8AoaGh5e5PhlWVc/ig3NxcFBYWwtXV1VBlUgWqeg7fe+89eHh4YPz48TVRJlWgKucwLi4OnTt3xqRJk+Dp6YkWLVrgww8/hFqtrqmy6f9V5fx16dIFR48e1XZFuHz5MjZv3oyBAwfWSM306GpTnrGo8Xs0Ienp6VCr1fD09NTZ7unpiXPnzpV5THJycpn7JycnG6xOKl9VzuGD3nzzTdStW7fUm5pqRlXO4f79+7FkyRIkJCTUQIX0MFU5h5cvX8bOnTvx9NNPY/Pmzbh48SJeeuklFBYWYvbs2TVRNv2/qpy/kSNHIj09Hd26dYMQAkVFRZg4cSJmzpxZEyVTNSgvz2RlZSEvLw82NjY1VgtbZokewUcffYRVq1Zh3bp1sLa2lrocqoTs7GyMGjUKixcvhru7u9TlUBVpNBp4eHjghx9+QPv27TFixAi89dZbWLRokdSlUSXs3r0bH374Ib799lscO3YMsbGx2LRpE95//32pSyMjxJbZR+Du7g6FQoGUlBSd7SkpKfDy8irzGC8vL732J8Oqyjks9tlnn+Gjjz7C9u3b0apVK0OWSRXQ9xxeunQJiYmJCAsL027TaDQAAAsLC5w/fx4NGzY0bNGkoyrvQ29vb1haWkKhUGi3NWvWDMnJyVCpVFAqlQatmf5TlfP3zjvvYNSoUXjuuecAAC1btkROTg6ef/55vPXWW5DL2dZW25WXZxwdHWu0VRZgy+wjUSqVaN++PXbs2KHdptFosGPHDnTu3LnMYzp37qyzPwBs27at3P3JsKpyDgHgk08+wfvvv48tW7agQ4cONVEqlUPfcxgYGIiTJ08iISFB+zNkyBD07t0bCQkJ8PPzq8nyCVV7H3bt2hUXL17U/iECABcuXIC3tzeDbA2ryvnLzc0tFViL/zARQhiuWKo2tSrP1PiQMxOzatUqYWVlJZYvXy7OnDkjnn/+eeHs7CySk5OFEEKMGjVKTJ8+Xbv/gQMHhIWFhfjss8/E2bNnxezZszk1l8T0PYcfffSRUCqVIjo6WiQlJWl/srOzpXoIZk/fc/ggzmYgPX3P4bVr14SDg4OYPHmyOH/+vNi4caPw8PAQH3zwgVQPwazpe/5mz54tHBwcxG+//SYuX74s/vzzT9GwYUMxfPhwqR6C2cvOzhbHjx8Xx48fFwDE/PnzxfHjx8XVq1eFEEJMnz5djBo1Srt/8dRcr7/+ujh79qz45ptvODWXMfvqq69EvXr1hFKpFJ06dRJ///239rqePXuKMWPG6Oy/Zs0a0aRJE6FUKkXz5s3Fpk2barhiepA+59Df318AKPUze/bsmi+ctPR9H5bEMFs76HsO//rrLxEcHCysrKxEgwYNxNy5c0VRUVENV03F9Dl/hYWFYs6cOaJhw4bC2tpa+Pn5iZdeekncuXOn5gsnIYQQu3btKvN3W/F5GzNmjOjZs2epY9q0aSOUSqVo0KCBWLZsWY3XLYQQMiHYnk9ERERExol9ZomIiIjIaDHMEhEREZHRYpglIiIiIqPFMEtERERERothloiIiIiMFsMsERERERkthlkiIiIiMloMs0RERERktBhmiYgALF++HM7OzlKXUWUymQzr16+vcJ+xY8ciPDy8RuohIqopDLNEZDLGjh0LmUxW6ufixYtSl4bly5dr65HL5fD19cW4ceOQmppaLbeflJSEAQMGAAASExMhk8mQkJCgs8/ChQuxfPnyarm/8syZM0f7OBUKBfz8/PD8888jIyNDr9th8CaiyrKQugAiourUv39/LFu2TGdbnTp1JKpGl6OjI86fPw+NRoMTJ05g3LhxuHXrFrZu3frIt+3l5fXQfZycnB75fiqjefPm2L59O9RqNc6ePYtnn30WmZmZWL16dY3cPxGZF7bMEpFJsbKygpeXl86PQqHA/Pnz0bJlS9jZ2cHPzw8vvfQS7t27V+7tnDhxAr1794aDgwMcHR3Rvn17HDlyRHv9/v370b17d9jY2MDPzw8vv/wycnJyKqxNJpPBy8sLdevWxYABA/Dyyy9j+/btyMvLg0ajwXvvvQdfX19YWVmhTZs22LJli/ZYlUqFyZMnw9vbG9bW1vD398e8efN0bru4m0H9+vUBAG3btoVMJkOvXr0A6LZ2/vDDD6hbty40Go1OjUOHDsWzzz6rvfz777+jXbt2sLa2RoMGDfDuu++iqKiowsdpYWEBLy8v+Pj4ICQkBFFRUdi2bZv2erVajfHjx6N+/fqwsbFB06ZNsXDhQu31c+bMwU8//YTff/9d28q7e/duAMD169cxfPhwODs7w9XVFUOHDkViYmKF9RCRaWOYJSKzIJfL8eWXX+L06dP46aefsHPnTrzxxhvl7v/000/D19cXhw8fxtGjRzF9+nRYWloCAC5duoT+/fsjIiIC//zzD1avXo39+/dj8uTJetVkY2MDjUaDoqIiLFy4EJ9//jk+++wz/PPPPwgNDcWQIUPw77//AgC+/PJLxMXFYc2aNTh//jx+/fVXBAQElHm7hw4dAgBs374dSUlJiI2NLbVPVFQUbt++jV27dmm3ZWRkYMuWLXj66acBAPv27cPo0aPxyiuv4MyZM/j++++xfPlyzJ07t9KPMTExEVu3boVSqdRu02g08PX1xdq1a3HmzBnMmjULM2fOxJo1awAA06ZNw/Dhw9G/f38kJSUhKSkJXbp0QWFhIUJDQ+Hg4IB9+/bhwIEDsLe3R//+/aFSqSpdExGZGEFEZCLGjBkjFAqFsLOz0/5ERkaWue/atWuFm5ub9vKyZcuEk5OT9rKDg4NYvnx5mceOHz9ePP/88zrb9u3bJ+RyucjLyyvzmAdv/8KFC6JJkyaiQ4cOQggh6tatK+bOnatzTMeOHcVLL730f+3dbUjTWxwH8O9dYZtrvhgluRcWpA6hrJarzCJaT0bGcIUrBwWZiKWGVtQL00ZkWTih6EEQg9ZIKYikpUYvLFsQ9jCFyi1r9kAQZOCQXNp27otw3OVD2IV773a/n3f///+c8/+dsze//XaOCiGEKCoqEjqdTgQCgTHHByBu3LghhBDC4/EIAOLZs2chbXbu3Cn0en3wWq/Xi127dgWva2trhUqlEn6/XwghxJo1a0RlZWXIGFarVcTFxY0ZgxBCVFRUCIlEIuRyuZBKpQKAACAsFsu4fYQQYu/evWLLli3jxjrybrVaHbIG3759EzKZTLS2tk44PhFFLu6ZJaKIsnr1aly4cCF4LZfLAfyoUp44cQLd3d3wer34/v07fD4fvn79iujo6FHjlJaWYvfu3bBarcGfyufOnQvgxxaErq4u2Gy2YHshBAKBADweD5KTk8eMrb+/H9OnT0cgEIDP58OKFStQV1cHr9eLjx8/Ij09PaR9eno6Ojs7AfzYIrBu3Tqo1WpkZGQgMzMT69ev/1trZTKZkJeXh/Pnz2PatGmw2WzYtm0bJBJJcJ4OhyOkEuv3+ydcNwBQq9VoamqCz+fDlStX4HQ6UVRUFNLm3LlzqK+vx7t37zA4OIihoSEsXLhwwng7OzvR09MDhUIRct/n8+H169e/sQJEFAmYzBJRRJHL5UhISAi519vbi8zMTBQUFOD48eNQKpV48OABcnNzMTQ0NGZSdvToUeTk5MBut6O5uRkVFRVoaGhAVlYWBgYGkJ+fj+Li4lH94uPjx41NoVDg6dOnkEgkiIuLg0wmAwB4vd5fzkuj0cDj8aC5uRl3795FdnY21q5di+vXr/+y73g2b94MIQTsdju0Wi3a29tRU1MTfD4wMACz2QyDwTCqr1QqHXfcqKio4Gdw8uRJbNq0CWazGceOHQMANDQ04MCBA6iurkZaWhoUCgVOnz6NR48eTRjvwMAAFi9eHPIlYsR/5ZAfEf3zmMwSUcR78uQJAoEAqqurg1XHkf2ZE0lKSkJSUhJKSkqwfft2XLp0CVlZWdBoNHjx4sWopPlXJBLJmH1iYmKgUqngcDiwatWq4H2Hw4ElS5aEtDMajTAajdi6dSsyMjLw5csXKJXKkPFG9qf6/f4J45FKpTAYDLDZbOjp6YFarYZGowk+12g0cLlck57nz8rKyqDT6VBQUBCc5/Lly7Fnz55gm58rq1FRUaPi12g0aGxsRGxsLGJiYv5WTEQUOXgAjIgiXkJCAoaHh3H27Fm8efMGVqsVFy9eHLf94OAgCgsL0dbWhrdv38LhcKCjoyO4feDQoUN4+PAhCgsL4XQ68erVK9y8eXPSB8D+6uDBg6iqqkJjYyNcLhcOHz4Mp9OJffv2AQAsFguuXr2K7u5uuN1uXLt2DbNmzRrzHz3ExsZCJpOhpaUFnz59Qn9//7jvNZlMsNvtqK+vDx78GlFeXo7Lly/DbDbj+fPnePnyJRoaGlBWVjapuaWlpSElJQWVlZUAgMTERDx+/Bitra1wu904cuQIOjo6QvrMmTMHXV1dcLlc+Pz5M4aHh2EymTBjxgzo9Xq0t7fD4/Ggra0NxcXF+PDhw6RiIqLIwWSWiCLeggULYLFYUFVVhXnz5sFms4X8WaufTZkyBX19fdixYweSkpKQnZ2NjRs3wmw2AwBSUlJw7949uN1urFy5EosWLUJ5eTlUKtVvx1hcXIzS0lLs378f8+fPR0tLC5qampCYmAjgxxaFU6dOITU1FVqtFr29vbh9+3aw0vxXU6dOxZkzZ1BbWwuVSgW9Xj/ue3U6HZRKJVwuF3JyckKebdiwAbdu3cKdO3eg1WqxbNky1NTUYPbs2ZOeX0lJCerq6vD+/Xvk5+fDYDDAaDRi6dKl6OvrC6nSAkBeXh7UajVSU1Mxc+ZMOBwOREdH4/79+4iPj4fBYEBycjJyc3Ph8/lYqSX6H/tDCCH+7SCIiIiIiH4HK7NEREREFLaYzBIRERFR2GIyS0RERERhi8ksEREREYUtJrNEREREFLaYzBIRERFR2GIyS0RERERhi8ksEREREYUtJrNEREREFLaYzBIRERFR2GIyS0RERERh60+l7HPpkBANAQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Our Chosen Model\n",
        "Weighted XGBoost"
      ],
      "metadata": {
        "id": "4-gjadVZlFzq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Showcase\n",
        "Our Plan\n",
        "\n",
        "\n",
        "1. Pick bankruptcy firms between 2023-2024\n",
        "2. Collect historical data (year 2023-)\n",
        "3. Use our final model to detect abnormal event\n",
        "4. Repeat with another different firms"
      ],
      "metadata": {
        "id": "Noaya52Pfm62"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Showcase Plan\n",
        "# 1) Pick bankruptcy firms between 2023-2024\n",
        "# 2) Collect historical data (year 2023-)\n",
        "# 3) Use our final model to detect abnormal event\n",
        "# 4) Repeat with another different firms"
      ],
      "metadata": {
        "id": "jLsu_UBxfewb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"/content/american_bankruptcy_with_showcase.csv\"\n",
        "df = pd.read_csv(path)"
      ],
      "metadata": {
        "id": "F1ZRhENf2QH5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "xFSTe2JBXBlO",
        "outputId": "88e9a997-76c6-4022-c71f-13b45433e470"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          company_name status_label  year         X1         X2        X3  \\\n",
              "78685           WeWork        alive  2021  1472000.0  3085000.0  709000.0   \n",
              "78686           WeWork        alive  2022  1329228.0  2914000.0  641000.0   \n",
              "78687  SmileDirectClub        alive  2020   580776.0   206852.0   56390.0   \n",
              "78688  SmileDirectClub        alive  2021   467740.0   177597.0   70113.0   \n",
              "78689  SmileDirectClub        alive  2022   297419.0   142890.0   74395.0   \n",
              "\n",
              "              X4       X5         X6        X7  ...         X9         X10  \\\n",
              "78685 -3675000.0      0.0 -4439000.0  285000.0  ...  2570000.0  21756000.0   \n",
              "78686 -1391000.0      0.0 -2034000.0  173000.0  ...  3245000.0  17863000.0   \n",
              "78687  -173977.0  29247.0   -78366.0  221973.0  ...   656780.0    884789.0   \n",
              "78688  -241115.0  40803.0  -102442.0  184558.0  ...   637611.0    794558.0   \n",
              "78689  -186139.0  44387.0   -86404.0  143082.0  ...   470743.0    597063.0   \n",
              "\n",
              "             X11        X12       X13        X14         X15        X16  \\\n",
              "78685  2866000.0 -4384000.0 -515000.0  2133000.0 -14143000.0  2570000.0   \n",
              "78686  3208000.0 -2032000.0  331000.0  2230000.0 -16177000.0  3245000.0   \n",
              "78687   381942.0  -230367.0  449928.0   186541.0   -192879.0   656780.0   \n",
              "78688   729973.0  -311228.0  460014.0   178240.0   -295321.0   637611.0   \n",
              "78689   849379.0  -260534.0  327853.0   116800.0   -381725.0   470743.0   \n",
              "\n",
              "              X17        X18  \n",
              "78685  23169000.0  1879000.0  \n",
              "78686  21318000.0  1497000.0  \n",
              "78687    650651.0   634901.0  \n",
              "78688    928912.0   714019.0  \n",
              "78689    982261.0   569009.0  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fbeb2788-2c56-441b-8549-d742aa2bb17c\" 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>company_name</th>\n",
              "      <th>status_label</th>\n",
              "      <th>year</th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "      <th>X3</th>\n",
              "      <th>X4</th>\n",
              "      <th>X5</th>\n",
              "      <th>X6</th>\n",
              "      <th>X7</th>\n",
              "      <th>...</th>\n",
              "      <th>X9</th>\n",
              "      <th>X10</th>\n",
              "      <th>X11</th>\n",
              "      <th>X12</th>\n",
              "      <th>X13</th>\n",
              "      <th>X14</th>\n",
              "      <th>X15</th>\n",
              "      <th>X16</th>\n",
              "      <th>X17</th>\n",
              "      <th>X18</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>78685</th>\n",
              "      <td>WeWork</td>\n",
              "      <td>alive</td>\n",
              "      <td>2021</td>\n",
              "      <td>1472000.0</td>\n",
              "      <td>3085000.0</td>\n",
              "      <td>709000.0</td>\n",
              "      <td>-3675000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-4439000.0</td>\n",
              "      <td>285000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2570000.0</td>\n",
              "      <td>21756000.0</td>\n",
              "      <td>2866000.0</td>\n",
              "      <td>-4384000.0</td>\n",
              "      <td>-515000.0</td>\n",
              "      <td>2133000.0</td>\n",
              "      <td>-14143000.0</td>\n",
              "      <td>2570000.0</td>\n",
              "      <td>23169000.0</td>\n",
              "      <td>1879000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78686</th>\n",
              "      <td>WeWork</td>\n",
              "      <td>alive</td>\n",
              "      <td>2022</td>\n",
              "      <td>1329228.0</td>\n",
              "      <td>2914000.0</td>\n",
              "      <td>641000.0</td>\n",
              "      <td>-1391000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2034000.0</td>\n",
              "      <td>173000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>3245000.0</td>\n",
              "      <td>17863000.0</td>\n",
              "      <td>3208000.0</td>\n",
              "      <td>-2032000.0</td>\n",
              "      <td>331000.0</td>\n",
              "      <td>2230000.0</td>\n",
              "      <td>-16177000.0</td>\n",
              "      <td>3245000.0</td>\n",
              "      <td>21318000.0</td>\n",
              "      <td>1497000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78687</th>\n",
              "      <td>SmileDirectClub</td>\n",
              "      <td>alive</td>\n",
              "      <td>2020</td>\n",
              "      <td>580776.0</td>\n",
              "      <td>206852.0</td>\n",
              "      <td>56390.0</td>\n",
              "      <td>-173977.0</td>\n",
              "      <td>29247.0</td>\n",
              "      <td>-78366.0</td>\n",
              "      <td>221973.0</td>\n",
              "      <td>...</td>\n",
              "      <td>656780.0</td>\n",
              "      <td>884789.0</td>\n",
              "      <td>381942.0</td>\n",
              "      <td>-230367.0</td>\n",
              "      <td>449928.0</td>\n",
              "      <td>186541.0</td>\n",
              "      <td>-192879.0</td>\n",
              "      <td>656780.0</td>\n",
              "      <td>650651.0</td>\n",
              "      <td>634901.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78688</th>\n",
              "      <td>SmileDirectClub</td>\n",
              "      <td>alive</td>\n",
              "      <td>2021</td>\n",
              "      <td>467740.0</td>\n",
              "      <td>177597.0</td>\n",
              "      <td>70113.0</td>\n",
              "      <td>-241115.0</td>\n",
              "      <td>40803.0</td>\n",
              "      <td>-102442.0</td>\n",
              "      <td>184558.0</td>\n",
              "      <td>...</td>\n",
              "      <td>637611.0</td>\n",
              "      <td>794558.0</td>\n",
              "      <td>729973.0</td>\n",
              "      <td>-311228.0</td>\n",
              "      <td>460014.0</td>\n",
              "      <td>178240.0</td>\n",
              "      <td>-295321.0</td>\n",
              "      <td>637611.0</td>\n",
              "      <td>928912.0</td>\n",
              "      <td>714019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78689</th>\n",
              "      <td>SmileDirectClub</td>\n",
              "      <td>alive</td>\n",
              "      <td>2022</td>\n",
              "      <td>297419.0</td>\n",
              "      <td>142890.0</td>\n",
              "      <td>74395.0</td>\n",
              "      <td>-186139.0</td>\n",
              "      <td>44387.0</td>\n",
              "      <td>-86404.0</td>\n",
              "      <td>143082.0</td>\n",
              "      <td>...</td>\n",
              "      <td>470743.0</td>\n",
              "      <td>597063.0</td>\n",
              "      <td>849379.0</td>\n",
              "      <td>-260534.0</td>\n",
              "      <td>327853.0</td>\n",
              "      <td>116800.0</td>\n",
              "      <td>-381725.0</td>\n",
              "      <td>470743.0</td>\n",
              "      <td>982261.0</td>\n",
              "      <td>569009.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fbeb2788-2c56-441b-8549-d742aa2bb17c')\"\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-fbeb2788-2c56-441b-8549-d742aa2bb17c 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-fbeb2788-2c56-441b-8549-d742aa2bb17c');\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-4ab72b85-e9a4-4ea5-b0aa-bd1bc16f9378\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4ab72b85-e9a4-4ea5-b0aa-bd1bc16f9378')\"\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-4ab72b85-e9a4-4ea5-b0aa-bd1bc16f9378 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pre-Processing"
      ],
      "metadata": {
        "id": "nPnEKCYjj5Xp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Pre-Processing\n",
        "df['status_label'] = df['status_label'].map({'alive': 0, 'failed': 1})\n",
        "X = df.drop(columns=['company_name', 'status_label'])\n",
        "y = df['status_label']"
      ],
      "metadata": {
        "id": "uY_o3LLZXCNE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Select all features\n",
        "selected_features_rf = ['year','X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8', 'X9', 'X10', 'X11', 'X12', 'X13', 'X14', 'X15', 'X16', 'X17', 'X18']\n",
        "df_rf_selected = df[['status_label'] + selected_features_rf]\n",
        "\n",
        "print(\"\\nNew dataset with selected features from random forests:\")\n",
        "print(df_rf_selected.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RJ1CF0jaXWRK",
        "outputId": "54060046-ab08-4386-9cb1-69d6d063049b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "New dataset with selected features from random forests:\n",
            "   status_label  year       X1       X2      X3      X4       X5      X6  \\\n",
            "0             0  1999  511.267  833.107  18.373  89.031  336.018  35.163   \n",
            "1             0  2000  485.856  713.811  18.577  64.367  320.590  18.531   \n",
            "2             0  2001  436.656  526.477  22.496  27.207  286.588 -58.939   \n",
            "3             0  2002  396.412  496.747  27.172  30.745  259.954 -12.410   \n",
            "4             0  2003  432.204  523.302  26.680  47.491  247.245   3.504   \n",
            "\n",
            "        X7        X8        X9      X10      X11     X12      X13      X14  \\\n",
            "0  128.348  372.7519  1024.333  740.998  180.447  70.658  191.226  163.816   \n",
            "1  115.187  377.1180   874.255  701.854  179.987  45.790  160.444  125.392   \n",
            "2   77.528  364.5928   638.721  710.199  217.699   4.711  112.244  150.464   \n",
            "3   66.322  143.3295   606.337  686.621  164.658   3.573  109.590  203.575   \n",
            "4  104.661  308.9071   651.958  709.292  248.666  20.811  128.656  131.261   \n",
            "\n",
            "       X15       X16      X17      X18  \n",
            "0  201.026  1024.333  401.483  935.302  \n",
            "1  204.065   874.255  361.642  809.888  \n",
            "2  139.603   638.721  399.964  611.514  \n",
            "3  124.106   606.337  391.633  575.592  \n",
            "4  131.884   651.958  407.608  604.467  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train-Test Split"
      ],
      "metadata": {
        "id": "ASQXM1hbj_E5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Training set (-2011) 75%\n",
        "X_rf_train = df.loc[df[\"year\"] <= 2011, df_rf_selected.columns.drop([\"status_label\"])]\n",
        "y_rf_train = df.loc[df[\"year\"] <= 2011, \"status_label\"]\n",
        "\n",
        "# Validation set I(2012-2014) 13%\n",
        "X_rf_test = df.loc[df[\"year\"].between(2012, 2014), df_rf_selected.columns.drop([\"status_label\"])]\n",
        "y_rf_test = df.loc[df[\"year\"].between(2012, 2014), \"status_label\"]\n",
        "\n",
        "# Test set (2015+) 12%\n",
        "X_val = df.loc[df[\"year\"] >= 2019, df_rf_selected.columns.drop([\"status_label\"])]\n",
        "y_val = df.loc[df[\"year\"] >= 2019, \"status_label\"]\n",
        "\n",
        "rf_model = RandomForestClassifier(random_state=42)"
      ],
      "metadata": {
        "id": "zoW-PYw2XfIW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## XGBoost"
      ],
      "metadata": {
        "id": "2bOEmaxvkGpj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# XGBoost\n",
        "from xgboost import XGBClassifier\n",
        "xgb = XGBClassifier(objective=\"reg:logistic\", n_estimators = 1000, learning_rate=0.02, subsample=0.8, n_jobs=4, eval_metric=\"aucpr\", early_stopping_rounds=12)\n",
        "\n",
        "xgb.fit(X_rf_train, y_rf_train, eval_set=[(X_rf_test, y_rf_test)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UNMoUzLEXnnC",
        "outputId": "7ae0272e-d093-4815-b6f9-4787cac2d38d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tvalidation_0-aucpr:0.14879\n",
            "[1]\tvalidation_0-aucpr:0.17706\n",
            "[2]\tvalidation_0-aucpr:0.18739\n",
            "[3]\tvalidation_0-aucpr:0.19024\n",
            "[4]\tvalidation_0-aucpr:0.19434\n",
            "[5]\tvalidation_0-aucpr:0.19375\n",
            "[6]\tvalidation_0-aucpr:0.19242\n",
            "[7]\tvalidation_0-aucpr:0.19371\n",
            "[8]\tvalidation_0-aucpr:0.18929\n",
            "[9]\tvalidation_0-aucpr:0.19132\n",
            "[10]\tvalidation_0-aucpr:0.19487\n",
            "[11]\tvalidation_0-aucpr:0.19647\n",
            "[12]\tvalidation_0-aucpr:0.19761\n",
            "[13]\tvalidation_0-aucpr:0.20214\n",
            "[14]\tvalidation_0-aucpr:0.20387\n",
            "[15]\tvalidation_0-aucpr:0.20565\n",
            "[16]\tvalidation_0-aucpr:0.20942\n",
            "[17]\tvalidation_0-aucpr:0.21301\n",
            "[18]\tvalidation_0-aucpr:0.21703\n",
            "[19]\tvalidation_0-aucpr:0.21536\n",
            "[20]\tvalidation_0-aucpr:0.21531\n",
            "[21]\tvalidation_0-aucpr:0.21632\n",
            "[22]\tvalidation_0-aucpr:0.21904\n",
            "[23]\tvalidation_0-aucpr:0.22196\n",
            "[24]\tvalidation_0-aucpr:0.22286\n",
            "[25]\tvalidation_0-aucpr:0.21865\n",
            "[26]\tvalidation_0-aucpr:0.22246\n",
            "[27]\tvalidation_0-aucpr:0.22634\n",
            "[28]\tvalidation_0-aucpr:0.22498\n",
            "[29]\tvalidation_0-aucpr:0.22426\n",
            "[30]\tvalidation_0-aucpr:0.22896\n",
            "[31]\tvalidation_0-aucpr:0.22932\n",
            "[32]\tvalidation_0-aucpr:0.23057\n",
            "[33]\tvalidation_0-aucpr:0.23381\n",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=12,\n",
              "              enable_categorical=False, eval_metric='aucpr', feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.02, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=1000, n_jobs=4,\n",
              "              num_parallel_tree=None, objective='reg:logistic', ...)"
            ],
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              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=12,\n",
              "              enable_categorical=False, eval_metric=&#x27;aucpr&#x27;, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.02, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=1000, n_jobs=4,\n",
              "              num_parallel_tree=None, objective=&#x27;reg:logistic&#x27;, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=12,\n",
              "              enable_categorical=False, eval_metric=&#x27;aucpr&#x27;, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.02, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=1000, n_jobs=4,\n",
              "              num_parallel_tree=None, objective=&#x27;reg:logistic&#x27;, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = xgb.predict(X_val)\n",
        "print(classification_report(y_val, pred))\n",
        "print(confusion_matrix(y_val, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cd8rzdAzXqCx",
        "outputId": "3df1f7c9-e065-418f-8f1d-7ec185ecfdc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.50      0.67         8\n",
            "           1       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.50         8\n",
            "   macro avg       0.50      0.25      0.33         8\n",
            "weighted avg       1.00      0.50      0.67         8\n",
            "\n",
            "[[4 4]\n",
            " [0 0]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Weighted XGBoost"
      ],
      "metadata": {
        "id": "gRNwSUMtkKQc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# XGBoost with scale_pos_weight (Weighted XGBoost)\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "xgb = XGBClassifier(objective=\"reg:logistic\", n_estimators=1000, learning_rate=0.02, subsample=0.8, n_jobs=4, eval_metric=\"aucpr\", early_stopping_rounds=12, scale_pos_weight=len(y_rf_train[y_rf_train==0])/len(y_rf_train[y_rf_train==1]))\n",
        "\n",
        "xgb.fit(X_rf_train, y_rf_train, eval_set=[(X_rf_test, y_rf_test)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iIxLsHKlXzGu",
        "outputId": "5b535c44-d9bc-49d7-fee0-933140db1540"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tvalidation_0-aucpr:0.14924\n",
            "[1]\tvalidation_0-aucpr:0.16956\n",
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            "[386]\tvalidation_0-aucpr:0.35672\n",
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            "[389]\tvalidation_0-aucpr:0.35683\n",
            "[390]\tvalidation_0-aucpr:0.35628\n",
            "[391]\tvalidation_0-aucpr:0.35668\n",
            "[392]\tvalidation_0-aucpr:0.35604\n",
            "[393]\tvalidation_0-aucpr:0.35606\n",
            "[394]\tvalidation_0-aucpr:0.35587\n",
            "[395]\tvalidation_0-aucpr:0.35589\n",
            "[396]\tvalidation_0-aucpr:0.35588\n",
            "[397]\tvalidation_0-aucpr:0.35652\n",
            "[398]\tvalidation_0-aucpr:0.35647\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=12,\n",
              "              enable_categorical=False, eval_metric='aucpr', feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.02, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=1000, n_jobs=4,\n",
              "              num_parallel_tree=None, objective='reg:logistic', ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=12,\n",
              "              enable_categorical=False, eval_metric=&#x27;aucpr&#x27;, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.02, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=1000, n_jobs=4,\n",
              "              num_parallel_tree=None, objective=&#x27;reg:logistic&#x27;, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=12,\n",
              "              enable_categorical=False, eval_metric=&#x27;aucpr&#x27;, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.02, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=1000, n_jobs=4,\n",
              "              num_parallel_tree=None, objective=&#x27;reg:logistic&#x27;, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Our Prediction"
      ],
      "metadata": {
        "id": "QFMqAsAMkPmn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred = xgb.predict(X_val)\n",
        "print(classification_report(y_val, pred))\n",
        "print(confusion_matrix(y_val, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AXDQQ0P2X7Yr",
        "outputId": "f028c4a0-289c-4f22-845e-65db23b91259"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.62      0.77         8\n",
            "           1       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.62         8\n",
            "   macro avg       0.50      0.31      0.38         8\n",
            "weighted avg       1.00      0.62      0.77         8\n",
            "\n",
            "[[5 3]\n",
            " [0 0]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Save Result"
      ],
      "metadata": {
        "id": "6eT1wMF9kSrM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# save the result\n",
        "results_df = pd.DataFrame({'company_name': df.loc[df[\"year\"] >= 2019]['company_name'], 'year': df.loc[df[\"year\"] >= 2019]['year'], 'True_Label': y_val, 'Predicted_Label': pred})\n",
        "results_df.to_csv('predictions_with_id_xgb_with_weight_test.csv', index=False)"
      ],
      "metadata": {
        "id": "_-Nr42NRYDZp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# XGBoost with scale_pos_weight (Weighted XGBoost)\n",
        "from xgboost import XGBClassifier\n",
        "xgb = XGBClassifier(objective=\"reg:logistic\", n_estimators = 1000, learning_rate=0.02, subsample=0.8, n_jobs=4, eval_metric=\"aucpr\", early_stopping_rounds=12)\n",
        "\n",
        "xgb.fit(X_rf_train, y_rf_train, eval_set=[(X_rf_test, y_rf_test)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "e3pcyW2iYPJv",
        "outputId": "15db5ff2-d638-4490-db1a-56ddcbe1b413"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tvalidation_0-aucpr:0.14879\n",
            "[1]\tvalidation_0-aucpr:0.17706\n",
            "[2]\tvalidation_0-aucpr:0.18739\n",
            "[3]\tvalidation_0-aucpr:0.19024\n",
            "[4]\tvalidation_0-aucpr:0.19434\n",
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            "[11]\tvalidation_0-aucpr:0.19647\n",
            "[12]\tvalidation_0-aucpr:0.19761\n",
            "[13]\tvalidation_0-aucpr:0.20214\n",
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            "[15]\tvalidation_0-aucpr:0.20565\n",
            "[16]\tvalidation_0-aucpr:0.20942\n",
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          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=12,\n",
              "              enable_categorical=False, eval_metric='aucpr', feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.02, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=1000, n_jobs=4,\n",
              "              num_parallel_tree=None, objective='reg:logistic', ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=12,\n",
              "              enable_categorical=False, eval_metric=&#x27;aucpr&#x27;, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.02, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=1000, n_jobs=4,\n",
              "              num_parallel_tree=None, objective=&#x27;reg:logistic&#x27;, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=12,\n",
              "              enable_categorical=False, eval_metric=&#x27;aucpr&#x27;, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.02, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=1000, n_jobs=4,\n",
              "              num_parallel_tree=None, objective=&#x27;reg:logistic&#x27;, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = xgb.predict(X_val)\n",
        "print(classification_report(y_val, pred))\n",
        "print(confusion_matrix(y_val, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8-IFcw9QYSgC",
        "outputId": "70d74290-b292-46e0-dad0-010c260bfcea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.50      0.67         8\n",
            "           1       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.50         8\n",
            "   macro avg       0.50      0.25      0.33         8\n",
            "weighted avg       1.00      0.50      0.67         8\n",
            "\n",
            "[[4 4]\n",
            " [0 0]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    }
  ]
}