loan_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dFDEEF8cDUr1"
   },
   "source": [
    "# Importing all necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uARrd1o71SkG",
    "outputId": "123b26cc-cb22-41af-b8ed-1794e0333000"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting keras-tuner\n",
      "  Downloading keras_tuner-1.3.5-py3-none-any.whl (176 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m176.1/176.1 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.9/dist-packages (from keras-tuner) (23.1)\n",
      "Collecting kt-legacy\n",
      "  Downloading kt_legacy-1.0.5-py3-none-any.whl (9.6 kB)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from keras-tuner) (2.27.1)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->keras-tuner) (3.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->keras-tuner) (1.26.15)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->keras-tuner) (2.0.12)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->keras-tuner) (2022.12.7)\n",
      "Installing collected packages: kt-legacy, keras-tuner\n",
      "Successfully installed keras-tuner-1.3.5 kt-legacy-1.0.5\n"
     ]
    }
   ],
   "source": [
    "pip install keras-tuner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "d12H50deDMWu"
   },
   "outputs": [],
   "source": [
    "# For reading our dataset\n",
    "import pandas as pd\n",
    "\n",
    "# For visualization\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "\n",
    "# For warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')\n",
    "\n",
    "# Importing necessary packages for building and tuning our model\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from collections import Counter\n",
    "from imblearn.under_sampling import NearMiss               # For Undersampling\n",
    "from imblearn.over_sampling import RandomOverSampler       # For Oversampling\n",
    "from imblearn.combine import SMOTETomek                    # For Both Undersampling & Oversampling\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n",
    "from keras_tuner.tuners import RandomSearch\n",
    "\n",
    "# For checking acuracy\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VRb4OENTDz57",
    "outputId": "3fb5b66f-0a40-41e0-faa7-43b8877cdba2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')\n",
    "\n",
    "# Accessing the dataset\n",
    "dataset_path = \"/content/drive/MyDrive/dataset/loan.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 404
    },
    "id": "iGV_56_3EUIk",
    "outputId": "14dfd55f-a06a-469f-9a15-ab44c7545226"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the dataset: (887379, 74)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-c326dcbc-4156-41fe-94e6-8fab58bc9498\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1077501</td>\n",
       "      <td>1296599</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>4975.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.65</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1077175</td>\n",
       "      <td>1313524</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>15.96</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1076863</td>\n",
       "      <td>1277178</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1075358</td>\n",
       "      <td>1311748</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.69</td>\n",
       "      <td>67.79</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c326dcbc-4156-41fe-94e6-8fab58bc9498')\"\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 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\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",
       "    [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-c326dcbc-4156-41fe-94e6-8fab58bc9498 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-c326dcbc-4156-41fe-94e6-8fab58bc9498');\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",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "        id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "0  1077501    1296599     5000.0       5000.0           4975.0   36 months   \n",
       "1  1077430    1314167     2500.0       2500.0           2500.0   60 months   \n",
       "2  1077175    1313524     2400.0       2400.0           2400.0   36 months   \n",
       "3  1076863    1277178    10000.0      10000.0          10000.0   36 months   \n",
       "4  1075358    1311748     3000.0       3000.0           3000.0   60 months   \n",
       "\n",
       "   int_rate  installment grade sub_grade  ... total_bal_il il_util  \\\n",
       "0     10.65       162.87     B        B2  ...          NaN     NaN   \n",
       "1     15.27        59.83     C        C4  ...          NaN     NaN   \n",
       "2     15.96        84.33     C        C5  ...          NaN     NaN   \n",
       "3     13.49       339.31     C        C1  ...          NaN     NaN   \n",
       "4     12.69        67.79     B        B5  ...          NaN     NaN   \n",
       "\n",
       "  open_rv_12m  open_rv_24m max_bal_bc all_util total_rev_hi_lim inq_fi  \\\n",
       "0         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "1         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "2         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "3         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "4         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "\n",
       "  total_cu_tl inq_last_12m  \n",
       "0         NaN          NaN  \n",
       "1         NaN          NaN  \n",
       "2         NaN          NaN  \n",
       "3         NaN          NaN  \n",
       "4         NaN          NaN  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the dataset\n",
    "df_read=pd.read_csv(dataset_path,low_memory=False)    # Setting low_memory=False to avoid DtypeWarning for columns with mixed data types\n",
    "print(\"Shape of the dataset:\",df_read.shape)\n",
    "df_read.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ChraZavDIC-L"
   },
   "source": [
    "### NOTE:- As we can see there are too many features, 74 to be exact in which many of them are useless so we have to minimize the features available. So let's start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Q7ewQcTbKy4-",
    "outputId": "a1480637-f555-456d-9baa-f78eb385fb94"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 887379 entries, 0 to 887378\n",
      "Data columns (total 74 columns):\n",
      " #   Column                       Non-Null Count   Dtype  \n",
      "---  ------                       --------------   -----  \n",
      " 0   id                           887379 non-null  int64  \n",
      " 1   member_id                    887379 non-null  int64  \n",
      " 2   loan_amnt                    887379 non-null  float64\n",
      " 3   funded_amnt                  887379 non-null  float64\n",
      " 4   funded_amnt_inv              887379 non-null  float64\n",
      " 5   term                         887379 non-null  object \n",
      " 6   int_rate                     887379 non-null  float64\n",
      " 7   installment                  887379 non-null  float64\n",
      " 8   grade                        887379 non-null  object \n",
      " 9   sub_grade                    887379 non-null  object \n",
      " 10  emp_title                    835917 non-null  object \n",
      " 11  emp_length                   842554 non-null  object \n",
      " 12  home_ownership               887379 non-null  object \n",
      " 13  annual_inc                   887375 non-null  float64\n",
      " 14  verification_status          887379 non-null  object \n",
      " 15  issue_d                      887379 non-null  object \n",
      " 16  loan_status                  887379 non-null  object \n",
      " 17  pymnt_plan                   887379 non-null  object \n",
      " 18  url                          887379 non-null  object \n",
      " 19  desc                         126028 non-null  object \n",
      " 20  purpose                      887379 non-null  object \n",
      " 21  title                        887227 non-null  object \n",
      " 22  zip_code                     887379 non-null  object \n",
      " 23  addr_state                   887379 non-null  object \n",
      " 24  dti                          887379 non-null  float64\n",
      " 25  delinq_2yrs                  887350 non-null  float64\n",
      " 26  earliest_cr_line             887350 non-null  object \n",
      " 27  inq_last_6mths               887350 non-null  float64\n",
      " 28  mths_since_last_delinq       433067 non-null  float64\n",
      " 29  mths_since_last_record       137053 non-null  float64\n",
      " 30  open_acc                     887350 non-null  float64\n",
      " 31  pub_rec                      887350 non-null  float64\n",
      " 32  revol_bal                    887379 non-null  float64\n",
      " 33  revol_util                   886877 non-null  float64\n",
      " 34  total_acc                    887350 non-null  float64\n",
      " 35  initial_list_status          887379 non-null  object \n",
      " 36  out_prncp                    887379 non-null  float64\n",
      " 37  out_prncp_inv                887379 non-null  float64\n",
      " 38  total_pymnt                  887379 non-null  float64\n",
      " 39  total_pymnt_inv              887379 non-null  float64\n",
      " 40  total_rec_prncp              887379 non-null  float64\n",
      " 41  total_rec_int                887379 non-null  float64\n",
      " 42  total_rec_late_fee           887379 non-null  float64\n",
      " 43  recoveries                   887379 non-null  float64\n",
      " 44  collection_recovery_fee      887379 non-null  float64\n",
      " 45  last_pymnt_d                 869720 non-null  object \n",
      " 46  last_pymnt_amnt              887379 non-null  float64\n",
      " 47  next_pymnt_d                 634408 non-null  object \n",
      " 48  last_credit_pull_d           887326 non-null  object \n",
      " 49  collections_12_mths_ex_med   887234 non-null  float64\n",
      " 50  mths_since_last_major_derog  221703 non-null  float64\n",
      " 51  policy_code                  887379 non-null  float64\n",
      " 52  application_type             887379 non-null  object \n",
      " 53  annual_inc_joint             511 non-null     float64\n",
      " 54  dti_joint                    509 non-null     float64\n",
      " 55  verification_status_joint    511 non-null     object \n",
      " 56  acc_now_delinq               887350 non-null  float64\n",
      " 57  tot_coll_amt                 817103 non-null  float64\n",
      " 58  tot_cur_bal                  817103 non-null  float64\n",
      " 59  open_acc_6m                  21372 non-null   float64\n",
      " 60  open_il_6m                   21372 non-null   float64\n",
      " 61  open_il_12m                  21372 non-null   float64\n",
      " 62  open_il_24m                  21372 non-null   float64\n",
      " 63  mths_since_rcnt_il           20810 non-null   float64\n",
      " 64  total_bal_il                 21372 non-null   float64\n",
      " 65  il_util                      18617 non-null   float64\n",
      " 66  open_rv_12m                  21372 non-null   float64\n",
      " 67  open_rv_24m                  21372 non-null   float64\n",
      " 68  max_bal_bc                   21372 non-null   float64\n",
      " 69  all_util                     21372 non-null   float64\n",
      " 70  total_rev_hi_lim             817103 non-null  float64\n",
      " 71  inq_fi                       21372 non-null   float64\n",
      " 72  total_cu_tl                  21372 non-null   float64\n",
      " 73  inq_last_12m                 21372 non-null   float64\n",
      "dtypes: float64(49), int64(2), object(23)\n",
      "memory usage: 501.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df_read.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u82gBC5nKFRg"
   },
   "source": [
    "### There seems to be many columns with null values. So let's first write a simple lines of code to identify the percentage of null values in each column in order to drop certain columns that don't meet a percentage threshold."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gUGaiuzsKSKv"
   },
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145
    },
    "id": "VImI_fcVlRE9",
    "outputId": "4f310478-66fb-48da-8b59-61f06028ef5a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-d94c3041-f4d8-4c9e-8d38-b8d78a16ca22\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>pymnt_plan</th>\n",
       "      <th>url</th>\n",
       "      <th>desc</th>\n",
       "      <th>purpose</th>\n",
       "      <th>title</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <th>mths_since_last_record</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>next_pymnt_d</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>mths_since_last_major_derog</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>application_type</th>\n",
       "      <th>annual_inc_joint</th>\n",
       "      <th>dti_joint</th>\n",
       "      <th>verification_status_joint</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <th>open_acc_6m</th>\n",
       "      <th>open_il_6m</th>\n",
       "      <th>open_il_12m</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Percentage</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.799326</td>\n",
       "      <td>5.051393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000451</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85.797726</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017129</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003268</td>\n",
       "      <td>0.003268</td>\n",
       "      <td>0.003268</td>\n",
       "      <td>51.197065</td>\n",
       "      <td>84.555303</td>\n",
       "      <td>0.003268</td>\n",
       "      <td>0.003268</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.056571</td>\n",
       "      <td>0.003268</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.990018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.507661</td>\n",
       "      <td>0.005973</td>\n",
       "      <td>0.01634</td>\n",
       "      <td>75.015974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.942415</td>\n",
       "      <td>99.94264</td>\n",
       "      <td>99.942415</td>\n",
       "      <td>0.003268</td>\n",
       "      <td>7.919502</td>\n",
       "      <td>7.919502</td>\n",
       "      <td>97.591559</td>\n",
       "      <td>97.591559</td>\n",
       "      <td>97.591559</td>\n",
       "      <td>97.591559</td>\n",
       "      <td>97.654892</td>\n",
       "      <td>97.591559</td>\n",
       "      <td>97.902024</td>\n",
       "      <td>97.591559</td>\n",
       "      <td>97.591559</td>\n",
       "      <td>97.591559</td>\n",
       "      <td>97.591559</td>\n",
       "      <td>7.919502</td>\n",
       "      <td>97.591559</td>\n",
       "      <td>97.591559</td>\n",
       "      <td>97.591559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d94c3041-f4d8-4c9e-8d38-b8d78a16ca22')\"\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 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\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",
       "    [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-d94c3041-f4d8-4c9e-8d38-b8d78a16ca22 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-d94c3041-f4d8-4c9e-8d38-b8d78a16ca22');\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",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "             id  member_id  loan_amnt  funded_amnt  funded_amnt_inv  term  \\\n",
       "Percentage  0.0        0.0        0.0          0.0              0.0   0.0   \n",
       "\n",
       "            int_rate  installment  grade  sub_grade  emp_title  emp_length  \\\n",
       "Percentage       0.0          0.0    0.0        0.0   5.799326    5.051393   \n",
       "\n",
       "            home_ownership  annual_inc  verification_status  issue_d  \\\n",
       "Percentage             0.0    0.000451                  0.0      0.0   \n",
       "\n",
       "            loan_status  pymnt_plan  url       desc  purpose     title  \\\n",
       "Percentage          0.0         0.0  0.0  85.797726      0.0  0.017129   \n",
       "\n",
       "            zip_code  addr_state  dti  delinq_2yrs  earliest_cr_line  \\\n",
       "Percentage       0.0         0.0  0.0     0.003268          0.003268   \n",
       "\n",
       "            inq_last_6mths  mths_since_last_delinq  mths_since_last_record  \\\n",
       "Percentage        0.003268               51.197065               84.555303   \n",
       "\n",
       "            open_acc   pub_rec  revol_bal  revol_util  total_acc  \\\n",
       "Percentage  0.003268  0.003268        0.0    0.056571   0.003268   \n",
       "\n",
       "            initial_list_status  out_prncp  out_prncp_inv  total_pymnt  \\\n",
       "Percentage                  0.0        0.0            0.0          0.0   \n",
       "\n",
       "            total_pymnt_inv  total_rec_prncp  total_rec_int  \\\n",
       "Percentage              0.0              0.0            0.0   \n",
       "\n",
       "            total_rec_late_fee  recoveries  collection_recovery_fee  \\\n",
       "Percentage                 0.0         0.0                      0.0   \n",
       "\n",
       "            last_pymnt_d  last_pymnt_amnt  next_pymnt_d  last_credit_pull_d  \\\n",
       "Percentage      1.990018              0.0     28.507661            0.005973   \n",
       "\n",
       "            collections_12_mths_ex_med  mths_since_last_major_derog  \\\n",
       "Percentage                     0.01634                    75.015974   \n",
       "\n",
       "            policy_code  application_type  annual_inc_joint  dti_joint  \\\n",
       "Percentage          0.0               0.0         99.942415   99.94264   \n",
       "\n",
       "            verification_status_joint  acc_now_delinq  tot_coll_amt  \\\n",
       "Percentage                  99.942415        0.003268      7.919502   \n",
       "\n",
       "            tot_cur_bal  open_acc_6m  open_il_6m  open_il_12m  open_il_24m  \\\n",
       "Percentage     7.919502    97.591559   97.591559    97.591559    97.591559   \n",
       "\n",
       "            mths_since_rcnt_il  total_bal_il    il_util  open_rv_12m  \\\n",
       "Percentage           97.654892     97.591559  97.902024    97.591559   \n",
       "\n",
       "            open_rv_24m  max_bal_bc   all_util  total_rev_hi_lim     inq_fi  \\\n",
       "Percentage    97.591559   97.591559  97.591559          7.919502  97.591559   \n",
       "\n",
       "            total_cu_tl  inq_last_12m  \n",
       "Percentage    97.591559     97.591559  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentage of missing values in df_read\n",
    "percentage_missing = df_read.isnull().sum() / len(df_read) * 100\n",
    "\n",
    "# Create a new DataFrame with columns from df and index set to None\n",
    "new_df = pd.DataFrame(columns=df_read.columns, index=None)\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "# Creating new Percentage index\n",
    "new_df.loc['Percentage'] = percentage_missing.values\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4AasNkLsMvyr",
    "outputId": "bed8c2ac-cc1c-4677-d60a-1dc5e1fea542"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total features before: 74\n",
      "Total features now: 52\n"
     ]
    }
   ],
   "source": [
    "# Keeping only those features with less than 20% of missing values\n",
    "features_to_keep = df_read.columns[((df_read.isnull().sum()/len(df_read))*100 < 20)].to_list()\n",
    "print(\"Total features before:\",len(df_read.columns))\n",
    "print(\"Total features now:\",len(features_to_keep))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A69HLXQTqZ74",
    "outputId": "740f6a01-8c61-4ab3-b26f-b5eee47cd619"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(887379, 52)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=df_read[features_to_keep]\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b3EyRUH7b5nS"
   },
   "source": [
    "### Previously our dataset had more features than a toolbox, but we've managed to narrow it down from 74 to 52. However, that still wasn't enough, so we called upon the mystical powers of a random expert in this field. They gave us their blessing and bestowed upon us the 20 most fortunate features, which we will now use for further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xZX8YAo1npFY",
    "outputId": "2258efb1-39bf-49e6-9c23-afc6f0843652"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n"
     ]
    }
   ],
   "source": [
    "lucky_features=['loan_amnt','term', 'int_rate', 'installment', 'grade', 'sub_grade','emp_length','home_ownership',\n",
    "                'annual_inc','verification_status','purpose','dti','delinq_2yrs','inq_last_6mths','open_acc',\n",
    "                'pub_rec','revol_bal','revol_util','total_acc','last_pymnt_amnt','loan_status']\n",
    "print(len(lucky_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DvkU7ZkVb1ll"
   },
   "source": [
    "#####  So now that we have chosen our features let's proceed to describe each one of them\n",
    "### **Loan Dataset Columns**\n",
    "#### **Loan Information**\n",
    "- **loan_amnt**: the amount of the loan\n",
    "- **term**: the term of the loan (36 or 60 months)\n",
    "- **int_rate**: the interest rate of the loan\n",
    "- **installment**: the monthly payment of the loan\n",
    "\n",
    "#### **Borrower Information**\n",
    "- **grade**: the grade assigned by LendingClub based on the creditworthiness of the borrower\n",
    "- **sub_grade**: a more detailed grade based on the borrower's creditworthiness\n",
    "- **emp_length**: the length of employment of the borrower\n",
    "- **home_ownership**: the type of home ownership of the borrower\n",
    "- **annual_inc**: the annual income of the borrower\n",
    "- **verification_status**: whether the income was verified by LendingClub\n",
    "\n",
    "#### **Loan Details**\n",
    "- **purpose**: the purpose of the loan\n",
    "- **dti**: the debt-to-income ratio of the borrower\n",
    "- **delinq_2yrs**: the number of delinquencies in the past 2 years\n",
    "- **inq_last_6mths**: the number of inquiries in the last 6 months\n",
    "- **open_acc**: the number of open credit lines in the borrower's credit file\n",
    "- **pub_rec**: the number of derogatory public records\n",
    "- **revol_bal**: the total credit revolving balance\n",
    "- **revol_util**: the amount of credit the borrower is using relative to their total credit limit\n",
    "- **total_acc**: the total number of credit lines in the borrower's credit file\n",
    "- **last_pymnt_amnt**: the last payment amount received"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 375
    },
    "id": "1BJdMUAens56",
    "outputId": "72f89a4c-1556-4d76-c193-71d4099abda2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the dataset: (887379, 21)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-4e328336-5d4a-487d-899c-d2705ee4d692\">\n",
       "    <div 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>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>purpose</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.65</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>27.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13648.0</td>\n",
       "      <td>83.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>171.62</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>car</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1687.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>119.66</td>\n",
       "      <td>Charged Off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2400.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>15.96</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>12252.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>small_business</td>\n",
       "      <td>8.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2956.0</td>\n",
       "      <td>98.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>649.91</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>other</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5598.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>357.48</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.69</td>\n",
       "      <td>67.79</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>other</td>\n",
       "      <td>17.94</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27783.0</td>\n",
       "      <td>53.9</td>\n",
       "      <td>38.0</td>\n",
       "      <td>67.79</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4e328336-5d4a-487d-899c-d2705ee4d692')\"\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 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\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",
       "    [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-4e328336-5d4a-487d-899c-d2705ee4d692 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-4e328336-5d4a-487d-899c-d2705ee4d692');\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",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   loan_amnt        term  int_rate  installment grade sub_grade emp_length  \\\n",
       "0     5000.0   36 months     10.65       162.87     B        B2  10+ years   \n",
       "1     2500.0   60 months     15.27        59.83     C        C4   < 1 year   \n",
       "2     2400.0   36 months     15.96        84.33     C        C5  10+ years   \n",
       "3    10000.0   36 months     13.49       339.31     C        C1  10+ years   \n",
       "4     3000.0   60 months     12.69        67.79     B        B5     1 year   \n",
       "\n",
       "  home_ownership  annual_inc verification_status         purpose    dti  \\\n",
       "0           RENT     24000.0            Verified     credit_card  27.65   \n",
       "1           RENT     30000.0     Source Verified             car   1.00   \n",
       "2           RENT     12252.0        Not Verified  small_business   8.72   \n",
       "3           RENT     49200.0     Source Verified           other  20.00   \n",
       "4           RENT     80000.0     Source Verified           other  17.94   \n",
       "\n",
       "   delinq_2yrs  inq_last_6mths  open_acc  pub_rec  revol_bal  revol_util  \\\n",
       "0          0.0             1.0       3.0      0.0    13648.0        83.7   \n",
       "1          0.0             5.0       3.0      0.0     1687.0         9.4   \n",
       "2          0.0             2.0       2.0      0.0     2956.0        98.5   \n",
       "3          0.0             1.0      10.0      0.0     5598.0        21.0   \n",
       "4          0.0             0.0      15.0      0.0    27783.0        53.9   \n",
       "\n",
       "   total_acc  last_pymnt_amnt  loan_status  \n",
       "0        9.0           171.62   Fully Paid  \n",
       "1        4.0           119.66  Charged Off  \n",
       "2       10.0           649.91   Fully Paid  \n",
       "3       37.0           357.48   Fully Paid  \n",
       "4       38.0            67.79      Current  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df1[lucky_features]\n",
    "print(\"Shape of the dataset:\",df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 364
    },
    "id": "uoSlwjw5OsJB",
    "outputId": "9541b3d8-3fc3-40ae-c2fe-7e5c34fad7a8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-5c38071d-24cb-4ac7-8b0c-e13cd29a3975\">\n",
       "    <div 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>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>887379.000000</td>\n",
       "      <td>887379.000000</td>\n",
       "      <td>887379.000000</td>\n",
       "      <td>8.873750e+05</td>\n",
       "      <td>887379.000000</td>\n",
       "      <td>887350.000000</td>\n",
       "      <td>887350.000000</td>\n",
       "      <td>887350.000000</td>\n",
       "      <td>887350.000000</td>\n",
       "      <td>8.873790e+05</td>\n",
       "      <td>886877.000000</td>\n",
       "      <td>887350.000000</td>\n",
       "      <td>887379.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14755.264605</td>\n",
       "      <td>13.246740</td>\n",
       "      <td>436.717127</td>\n",
       "      <td>7.502759e+04</td>\n",
       "      <td>18.157039</td>\n",
       "      <td>0.314442</td>\n",
       "      <td>0.694623</td>\n",
       "      <td>11.548469</td>\n",
       "      <td>0.195307</td>\n",
       "      <td>1.692079e+04</td>\n",
       "      <td>55.067693</td>\n",
       "      <td>25.268026</td>\n",
       "      <td>2164.145585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8435.455601</td>\n",
       "      <td>4.381867</td>\n",
       "      <td>244.186593</td>\n",
       "      <td>6.469830e+04</td>\n",
       "      <td>17.190626</td>\n",
       "      <td>0.862244</td>\n",
       "      <td>0.998448</td>\n",
       "      <td>5.317313</td>\n",
       "      <td>0.582091</td>\n",
       "      <td>2.242679e+04</td>\n",
       "      <td>23.834344</td>\n",
       "      <td>11.840561</td>\n",
       "      <td>4794.783233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>5.320000</td>\n",
       "      <td>15.670000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8000.000000</td>\n",
       "      <td>9.990000</td>\n",
       "      <td>260.705000</td>\n",
       "      <td>4.500000e+04</td>\n",
       "      <td>11.910000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.443000e+03</td>\n",
       "      <td>37.700000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>280.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13000.000000</td>\n",
       "      <td>12.990000</td>\n",
       "      <td>382.550000</td>\n",
       "      <td>6.500000e+04</td>\n",
       "      <td>17.650000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.187500e+04</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>462.780000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20000.000000</td>\n",
       "      <td>16.200000</td>\n",
       "      <td>572.600000</td>\n",
       "      <td>9.000000e+04</td>\n",
       "      <td>23.950000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.082900e+04</td>\n",
       "      <td>73.600000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>831.245000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>35000.000000</td>\n",
       "      <td>28.990000</td>\n",
       "      <td>1445.460000</td>\n",
       "      <td>9.500000e+06</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>2.904836e+06</td>\n",
       "      <td>892.300000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>36475.590000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5c38071d-24cb-4ac7-8b0c-e13cd29a3975')\"\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 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\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",
       "    [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-5c38071d-24cb-4ac7-8b0c-e13cd29a3975 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-5c38071d-24cb-4ac7-8b0c-e13cd29a3975');\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",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "           loan_amnt       int_rate    installment    annual_inc  \\\n",
       "count  887379.000000  887379.000000  887379.000000  8.873750e+05   \n",
       "mean    14755.264605      13.246740     436.717127  7.502759e+04   \n",
       "std      8435.455601       4.381867     244.186593  6.469830e+04   \n",
       "min       500.000000       5.320000      15.670000  0.000000e+00   \n",
       "25%      8000.000000       9.990000     260.705000  4.500000e+04   \n",
       "50%     13000.000000      12.990000     382.550000  6.500000e+04   \n",
       "75%     20000.000000      16.200000     572.600000  9.000000e+04   \n",
       "max     35000.000000      28.990000    1445.460000  9.500000e+06   \n",
       "\n",
       "                 dti    delinq_2yrs  inq_last_6mths       open_acc  \\\n",
       "count  887379.000000  887350.000000   887350.000000  887350.000000   \n",
       "mean       18.157039       0.314442        0.694623      11.548469   \n",
       "std        17.190626       0.862244        0.998448       5.317313   \n",
       "min         0.000000       0.000000        0.000000       0.000000   \n",
       "25%        11.910000       0.000000        0.000000       8.000000   \n",
       "50%        17.650000       0.000000        0.000000      11.000000   \n",
       "75%        23.950000       0.000000        1.000000      14.000000   \n",
       "max      9999.000000      39.000000       33.000000      90.000000   \n",
       "\n",
       "             pub_rec     revol_bal     revol_util      total_acc  \\\n",
       "count  887350.000000  8.873790e+05  886877.000000  887350.000000   \n",
       "mean        0.195307  1.692079e+04      55.067693      25.268026   \n",
       "std         0.582091  2.242679e+04      23.834344      11.840561   \n",
       "min         0.000000  0.000000e+00       0.000000       1.000000   \n",
       "25%         0.000000  6.443000e+03      37.700000      17.000000   \n",
       "50%         0.000000  1.187500e+04      56.000000      24.000000   \n",
       "75%         0.000000  2.082900e+04      73.600000      32.000000   \n",
       "max        86.000000  2.904836e+06     892.300000     169.000000   \n",
       "\n",
       "       last_pymnt_amnt  \n",
       "count    887379.000000  \n",
       "mean       2164.145585  \n",
       "std        4794.783233  \n",
       "min           0.000000  \n",
       "25%         280.170000  \n",
       "50%         462.780000  \n",
       "75%         831.245000  \n",
       "max       36475.590000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AahrNPe8H31m"
   },
   "source": [
    "Since 'loan_status' is the target variavle therefore it is important to explore this feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "v8LHGB_VHqu_",
    "outputId": "eeee5521-b0ce-4c44-d058-3f3dc5b7efe3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Fully Paid', 'Charged Off', 'Current', 'Default',\n",
       "       'Late (31-120 days)', 'In Grace Period', 'Late (16-30 days)',\n",
       "       'Does not meet the credit policy. Status:Fully Paid',\n",
       "       'Does not meet the credit policy. Status:Charged Off', 'Issued'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_read['loan_status'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tF6-mKGIV6X2"
   },
   "source": [
    "### As we can see there are so many labels in loan_status but i want my model to predict whether the loan was full paid or charged off. So <u>in my case</u> I am selecting only those samples that have loan status either full paid or charged off."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "smakSiAGRSR4",
    "outputId": "e1ec265b-916a-4608-eccc-295c39684222"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(252971, 21)\n"
     ]
    }
   ],
   "source": [
    "target_loan= [\"Fully Paid\",\"Charged Off\"]\n",
    "df=df[df[\"loan_status\"].isin(target_loan)]\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j4W0sy-fXdLu"
   },
   "source": [
    "### You can choose any combination of labels for your model.\n",
    "\n",
    "### For example :- If you want your model to predict whether the person that has taken the loan is a defaulter or not. For this case make the 'Default' label as 1 and rest as 0 to create a binary classification problem.\n",
    "\n",
    "#### --> 'loan_status' column's labels information:\n",
    "\n",
    "- Fully Paid: The loan has been fully paid off by the borrower.\n",
    "- Charged Off: The loan has not been fully repaid and Lending Club has charged off the remaining balance as a loss.\n",
    "- Current: The loan is currently being repaid on schedule.\n",
    "- Default: The borrower has failed to make payments on the loan and the loan is in default.\n",
    "- Late (31-120 days): The borrower has missed payments and is between 31 and 120 days late on their payment schedule.\n",
    "- In Grace Period: The borrower is in a grace period and has missed a payment.\n",
    "- Late (16-30 days): The borrower has missed a payment and is between 16 and 30 days late on their payment schedule.\n",
    "- Does not meet the credit policy. Status:Fully Paid: The loan has been fully paid off, but did not meet Lending Club's credit underwriting policy.\n",
    "- Does not meet the credit policy. Status:Charged Off: The loan did not meet Lending Club's credit underwriting policy and has been charged off as a loss.\n",
    "- Issued: The loan has been issued but has not yet been funded by investors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jkHAX0lt0zWX"
   },
   "source": [
    "## Explorarotry Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BmsUJ6-zRSY4",
    "outputId": "f2c271e3-e235-415e-a2f1-280b55e05af0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                 0\n",
       "term                      0\n",
       "int_rate                  0\n",
       "installment               0\n",
       "grade                     0\n",
       "sub_grade                 0\n",
       "emp_length             9897\n",
       "home_ownership            0\n",
       "annual_inc                0\n",
       "verification_status       0\n",
       "purpose                   0\n",
       "dti                       0\n",
       "delinq_2yrs               0\n",
       "inq_last_6mths            0\n",
       "open_acc                  0\n",
       "pub_rec                   0\n",
       "revol_bal                 0\n",
       "revol_util              199\n",
       "total_acc                 0\n",
       "last_pymnt_amnt           0\n",
       "loan_status               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YkELHl63lsAk"
   },
   "source": [
    "### Handling null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "WGBT_ex1RSfe"
   },
   "outputs": [],
   "source": [
    "df['emp_length']=df['emp_length'].fillna(df['emp_length'].mode()[0])\n",
    "df['revol_util']=df['revol_util'].fillna(df['revol_util'].median())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K53K2ZLQ1X6V"
   },
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 436
    },
    "id": "XVflbYd0xkfD",
    "outputId": "00a2bc3d-34f2-4b27-9a58-470962caef5b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Loan status count')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax =plt.subplots(figsize=(10,4))\n",
    "sns.countplot(data=df,x=\"loan_status\",hue=\"term\",palette='dark')\n",
    "ax.set(xlabel='Status', ylabel='')\n",
    "ax.set_title('Loan status count', size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 436
    },
    "id": "7YHp8WbnxktW",
    "outputId": "87c05cc5-a3b0-4eb2-c72a-514775c862c5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Loan status count')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax =plt.subplots(figsize=(10,4))\n",
    "sns.countplot(data=df,x=\"loan_status\",hue=\"verification_status\",palette='coolwarm')\n",
    "ax.set(xlabel='Status', ylabel='')\n",
    "ax.set_title('Loan status count', size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 476
    },
    "id": "TlogqUlHxkvW",
    "outputId": "081d9aa6-6d06-4bc8-a996-e0dc83978122"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax =plt.subplots(figsize=(10,4))\n",
    "sns.countplot(data=df,x=\"emp_length\",palette='spring')\n",
    "ax.set(xlabel='Length of Employmnet', ylabel='')\n",
    "ax.set_title('Loan status count', size=20)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 418
    },
    "id": "8KUmoaHWCb-o",
    "outputId": "2e47aae2-7f35-4cb6-ecb1-f0486d67397d"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax =plt.subplots(figsize=(10,4))\n",
    "sns.countplot(data=df,y=\"grade\",palette='rocket')\n",
    "ax.set_title('Loan Grades count', size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 543
    },
    "id": "8UouE2Xa6J6-",
    "outputId": "37fca066-b8a0-4137-bf48-56fb180510d9"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax =plt.subplots(figsize=(10,4))\n",
    "sns.barplot(data=df,x=\"purpose\",y='loan_amnt',palette='spring')\n",
    "ax.set(xlabel='Purpose', ylabel='Amount')\n",
    "ax.set_title('Purpose vs Loan_ Amount', size=20)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 418
    },
    "id": "sIRPPaj27deT",
    "outputId": "1b183184-993d-475b-bfcf-ed4140509007"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax =plt.subplots(figsize=(10,4))\n",
    "sns.barplot(data=df,x=\"home_ownership\",y='annual_inc',palette='viridis')\n",
    "ax.set(xlabel='Home Ownership', ylabel='Annual Income')\n",
    "ax.set_title('Home Ownership vs Annual Income', size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 942
    },
    "id": "Zt_0J8qd0XTu",
    "outputId": "601cbe5c-885e-4d61-93e2-449a018778dc"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a heatmap\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(df.corr(),annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "C1Tjk8bunlXW"
   },
   "outputs": [],
   "source": [
    "# Dividing our features into categorical and numerical\n",
    "categorical=[feature for feature in df.columns if df[feature].dtype=='object']\n",
    "numerical=[feature for feature in df.columns if feature not in categorical]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EDehw0NOnlaG",
    "outputId": "5e790330-fc7b-4edc-d450-ea170f2e23b6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical columns: ['term', 'grade', 'sub_grade', 'emp_length', 'home_ownership', 'verification_status', 'purpose', 'loan_status']\n",
      "Numerical columns: ['loan_amnt', 'int_rate', 'installment', 'annual_inc', 'dti', 'delinq_2yrs', 'inq_last_6mths', 'open_acc', 'pub_rec', 'revol_bal', 'revol_util', 'total_acc', 'last_pymnt_amnt']\n"
     ]
    }
   ],
   "source": [
    "print(\"Categorical columns:\",categorical)\n",
    "print(\"Numerical columns:\",numerical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 537
    },
    "id": "OyBZVadkFPFA",
    "outputId": "1668d54d-1fc9-4c61-ba94-6e386d6f2506"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABdIAAAJcCAYAAAD961aBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3xT9frA8U/Ske4JHUBbypC9Zx0IilQFr1zRq4iAgnrhFq+ACj+8qDjuxYWoFxAnqICI14EM2RvKKmWVTQsFSlqgbdKZtsn5/RETGjopadPxvF+vvGhzvuec56TJl5PnfM/zVSmKoiCEEEIIIYQQQgghhBBCiFKpHR2AEEIIIYQQQgghhBBCCFGbSSJdCCGEEEIIIYQQQgghhCiHJNKFEEIIIYQQQgghhBBCiHJIIl0IIYQQQgghhBBCCCGEKIck0oUQQgghhBBCCCGEEEKIckgiXQghhBBCCCGEEEIIIYQohyTShRBCCCGEEEIIIYQQQohySCJdCCGEEEIIIYQQQgghhCiHJNKFEEIIIYQQQgghhBBCiHJIIl0IIYQQogpmzJiBSqVCpVI5OpRb1r9/f1QqFf3793d0KDclOTmZv//977Rs2RI3Nzfr3+O3335zdGj12pYtW6yv9ZYtWxwdTq1T3Z+nc+fOWV//hQsXVnk7CxcutG7n3LlzdotPCCGEEKK+kkS6EEIIIeq94om/GTNmVGqdp59+WpJMtVhycjI9evTgiy++IDExEYPB4OiQhBBCCCGEEPWYJNKFEEIIIWqR+jja114jaIt75513uHr1Ks7Ozrz33nvExsZy5MgRjhw5wr333muXfQghhBBCCCGEhbOjAxBCCCGEqItmzJhR6dHtwv42bNgAwNChQ5kyZYqDoxHiuvpyAUwIIYQQQtiSEelCCCGEEKLOuXTpEgC33XabgyMRQgghhBBCNASSSBdCCCGEEHVOQUEBAC4uLg6ORAghhBBCCNEQSCJdCCGEEKIKZsyYYa37XZZNmzYxfPhwIiMjcXd3x8PDg4iICPr27cvLL7/Mpk2brG0tdcQHDBhgfW7AgAHWfZRWY/zGGDIzM3njjTfo0KEDXl5eBAQEMGDAAH744Qe7HPOOHTsYOXIkzZs3x83NDT8/P7p168b06dO5cuVKqeuoVCoiIyOtvz/zzDMljqmyJXIWLlxY4jV/8803bbb19NNPl1jvypUrTJ8+nW7duuHn54ebmxvNmzdn5MiR7Nixo9x9Nm/e3Ga7cXFxPP3000RGRqLRaMr9+1dk8+bNjB49mhYtWuDh4YGPjw+dOnXilVdeISUlpdx1jx49yjvvvEN0dDTNmjVDo9Hg5eVF69atGT16NLt37650HDt37uTZZ5+lTZs2+Pj44OrqSrNmzRgyZAhz584lMzOzwm0sW7aMe++9l8aNG+Pu7k6bNm2YMmUK6enplY6juG3btln/pl9++WWF7WfOnGltf+zYMZtliYmJzJo1i4ceeojmzZvj7u6Ou7s7ERERPP7446xZs6bcbRd/3507dw6DwcDHH39M3759adSoUYn3cP/+/VGpVPTv37/U7WVkZLBgwQKeeuop2rdvj5eXF66uroSEhBAdHc0XX3xhvVBUWT/99BMDBw4kKCgId3d32rZty7Rp0yr1t6uI0Wjk22+/ZciQITRp0gSNRkNgYCB33nknH330EXl5ebe8DyGEEEKIOkERQgghhKjnNm/erAAKoLzxxhuVWmf06NHWdZKSkkosf+ONN6zLSzNx4kTr8rIegYGB1vZJSUkVtgeUBQsWlBpDYmKi0rJlyzLX+9vf/qYUFhaWGuvdd9+tAMrdd99d6nKj0ajExMSUG5evr6+ybt26EutW5pgq+zdZsGBBhdsaPXq0zTpr165VfHx8yl0nJiZGMRqNpe4zIiLCut3PPvtMcXZ2LrH+zcrLy1OeeOKJcmPy9PRUfv/991LXL/5+Lu/xf//3f+XGkZubqwwfPvym/z7F979x40blqaeeKnPdVq1aKZcvX77p18hkMinh4eEKoPTv37/C9h07dlQApWvXrjbPJyYmVuq1euqpp8r8fBR/3+3bt0/p2rVrua9RRZ8ny3uqvEe3bt3KfN2K9xULFixQxowZU+Z2mjRpohw/frzC4yqtj1MURTl//rzSpUuXcmNt1aqVcvLkyVLXF0IIIYSoT2SyUSGEEEIIO1u5ciUff/wxAJ07d2b8+PG0a9cOX19fMjMzSUhIYMOGDezdu9e6TtOmTTly5Aj79u1jzJgxAHzzzTf06tXLZtvNmjUrdZ+PP/44SUlJjBs3jkcffRRfX18OHz7Me++9x6lTp1i2bBlNmjRh9uzZN308//d//8fcuXMBiIyMZOrUqXTv3p2cnBx+//135syZg06nY8iQIezdu5cuXbpY1z1y5AgpKSlER0cD8M477/Dwww/bbD8oKKhScQwdOpSePXsC0KlTJwDGjx/PP/7xD2sbf39/688HDx7koYceoqCgABcXFyZMmMBf/vIXPD09iY+P59133yUpKYm5c+fi6enJe++9V+a+9+3bx6JFiwgLC+Pll1+mZ8+eFBUVsX379krFbqEoCo8++iirVq0C4KGHHuJvf/sbLVq0QK1Ws3fvXmbNmkVycjKPPvooO3futB6zRVFREZ6engwePJh77rmHtm3b4uPjQ1paGgkJCXz66aecP3+ed999l9tuu41nnnmmRBwmk4mHH36Y9evXA9C6dWv+8Y9/0LNnTzw8PLh8+TK7du1i2bJl5R7Pa6+9xq5duxg6dCijRo0iIiKC1NRU5s6dy6pVqzhz5gyTJk266bsiVCoVw4cP57333mPbtm1cunSJpk2bltr28OHDHD16FIARI0bYLDMajbi6uhIdHc19991H+/btCQgIID09nVOnTjF37lwSEhJYtGgRLVq04M033yw3rrFjx3LkyBFGjRrF448/TkhICMnJyWg0mkofm9FopE+fPgwZMoRu3boRHBxMQUEBSUlJLFq0iDVr1hAfH88TTzxR4cSl8+bNY9++ffTu3ZtJkybRunVr0tLSWLhwIcuWLbN+9o4ePYq3t3elYwS4du0ad955JxcuXECj0fDcc89x991307x5c7Kzs1m3bh2ffPIJZ86c4YEHHuDAgQP4+vre1D6EEEIIIeoUR2fyhRBCCCGqW/ERtOPHj1eOHDlS4ePhhx8ud7RmeSPSR44cqQBKRESEkpWVVWZc165dKzfWzZs3l3tcxWMAlCVLlpRoo9frrSNK1Wq1cuTIkRJtyhtBe/jwYUWtViuA0rFjRyUjI6NEmz/++MPapnfv3iWW3ziC1h4s2ytvNHuvXr0UQHFyclLWrl1bYnl6errSvn1762tz9OjREm2Kjx7u1KlTqcd/M7744gsFUFxcXJQ//vij1Dbp6elKhw4dFEC54447Siy/cuVKuXEYDAblvvvus74Hi4qKSrT55JNPrMf117/+VcnPzy91W0ajUbl48aLNczeOiH/nnXdKrGcymZRBgwYpgOLs7KykpaWVGW9ZDh8+bN3HBx98UGa7qVOnWv+GN8aanZ2tpKSklLmuyWRSnn76aetdAJmZmSXa3HgnxFdffVVu3BWNSD916lS563/zzTfWfW3YsKHE8hvvXnnwwQdLHU3/1ltvWdu88sor5R5XaX3ck08+aX0PJSYmlhrrgQMHFE9PTwVQXn311XKPSwghhBCirpMa6UIIIYRoUD777DM6depU4WP58uVV3odWqwWge/fueHl5ldkuICCgyvu40ZAhQxg+fHiJ5729vfniiy8A8yjk+fPn39R2P/vsM0wmEwBfffUVfn5+Jdrcf//91lH0e/fuZd++fTcZvf0Vj+O5555j0KBBJdr4+/vbvDbz5s0rd5tz584t9fgrS1EU66j3f/7zn9x///2ltvP39+eDDz4AzPXLT58+bbO8UaNG5cbh6upqXf/8+fMcPHjQZrnJZLIub9asGd99912ZI6rVanWZI8EBevTowauvvlrieZVKxeTJkwHzCPrY2Ngyt1EWy2cRYPHixaW2URTFOtr97rvvLhGrp6cnoaGhZe5DpVIxa9YsnJycyMnJYcOGDeXGdM899zB27NibOYwSWrduXe7yZ555hq5duwLw22+/ldtWo9Hw5Zdf4uxc8kbjf/3rX3Ts2BGAr7/++qbqrp87d44ff/wRgDlz5tjMc1Bct27diImJAbCZv0EIIYQQoj6SRLoQQgghhJ1ZEnfbtm3j7NmzNbLP0sp3WPTu3ZsOHToAVJgovJGlfYcOHejTp0+Z7Z577rkS6zhS8RjKS3zecccdtGvXrsQ6NwoLC+Ouu+66pZiOHTtmfT88+uij5bbt16+f9eeKktAGg4Hk5GSOHTvG0aNHOXr0KIqiWJcfOnTIpv3Bgwe5ePEiYP67lXexpyJPPvlkmROu9ujRw/pzYmJilbZvKdVy8OBBjh8/XmL5jh07SE5OtmlbnsLCQi5evMjx48etr1VKSgqBgYFAydeqrHjsRVEUtFotp06dssZz9OhR6wWBiuIZNGgQTZo0KXWZWq1m9OjRAKSnp3PgwIFKx7Vq1SqMRiMeHh488MAD5ba1vFdTUlKsfwshhBBCiPpIEulCCCGEaFDeeOMNFEWp8GFJQFXFqFGjAHON4Y4dO/LEE0+wYMECzpw5Y6/DKOHGWuo36t27NwCnTp2q9MhUg8FgHQ1dXhIdzCNTXVxcAKz1qh3JEoOrq6t1dG9ZLMd2+vTpMl+bzp0733JM+/fvt/4cFRWFSqUq81E8uW25w6G4nJwcZs6cSZcuXfD09CQiIoIOHTpYR3F369bN2vbq1as268bHx1t/vtWLA23bti1zWfE7LrKysqq0/eHDh1sT9aWNSl+yZAlgHpk9bNiwUrdRWFjI3Llz6du3L15eXoSFhdG+fXubO1DS0tKAkq/VjezxPgBzonrIkCH4+voSGhpKmzZtbOKx1NCvKJ7Kfu7BPF9BZVneq7m5uTg7O5f7Xh0yZIh1vdLeq0IIIYQQ9YUk0oUQQggh7Ozee+9lzpw5uLu7k5+fz48//siYMWNo3bo1zZo1Y9y4cRWONL1ZFU3YGRwcDJhHwGZkZFRqm8XbVbR9FxcX66je9PT0Sm2/OlliCAgIKLXsRXEhISFA+a9N8UlMq8qSrL1Zubm5Nr+fO3eOTp068eqrr3L48GGMRmO56+fl5dn8Xjw5W17Zk8rw8PAoc5laff2rRkUxliU8PNya7LckzS0KCwv56aefABg8eHCp5W7S09OJiopiwoQJ7Nmzp8KLSDe+Vje61feBoig8++yzDBkyhFWrVlV4gaGieCr7uYeb+1za670qhBBCCFGflP+tQgghhBBCVElMTAyPPfYYS5YsYf369ezcuROdTselS5f4/PPP+eKLL3j11Vd555137LK/sspr2Et1b7+62CtuJyenW95G8WTyihUraN68eaXWuzFZOnLkSJKSklCpVDzzzDM88cQTtGvXjsaNG+Pq6opKpcJkMlljLl7mpS4aMWIE27ZtIykpidjYWKKiogBYu3Yt165ds7YpzYsvvkhcXBwAQ4cOZcyYMXTu3JmgoCDc3Nys74/w8HAuXLhQ4Wt1q++Db775hq+//hqArl27MnHiRPr06UPTpk3x8PCwbn/UqFF8//33FcZTXZ9Ly3u1UaNGbN68udLrlVVLXQghhBCiPpBEuhBCCCFENQkKCmLixIlMnDgRk8nEwYMH+fXXX5kzZw6ZmZn8+9//plevXjz88MO3vK/U1FTCwsLKXQ7mxFtlR9UWb2dZvyxFRUXWpKY9J1GtKksM165do6ioqNxR6ZZyFDfz2lSFZcQ+gJ+fn3UiyJtx4sQJduzYAVDuhZjyRh83atTI+vPly5fLLc9SGzz22GO88MILFBQUsHjxYmsi3TJC3dfXl8GDB5dYT6/XWyfMHDFiBIsWLSpzH5W9S+NWffnllwC0atWKXbt24e7uXmq7yo4er+hzWXz5zXwuLe/VrKws2rVrZ5cLSUIIIYQQdZ2UdhFCCCGEqAFqtZru3bvz9ttvs3HjRuvzy5Yts2lX1RGm+/btq9Ty1q1b4+rqWqltajQaWrduDcCePXvKbRsfH09hYSFAiQSxI0azW2IoKCjg4MGD5bbdu3cvcHOvTVUUr1u+c+fOKm0jISHB+vPjjz9eZrvi9dhv1L17d+vP27Ztq1IcNcnf39864eWyZcsoKioiJyeH5cuXA+aJWzUaTYn1Tp8+bX1PlvdanThxguzs7GqIvCTL3+8vf/lLmUl0RVEqPTFoZT/3UPJzWR7Le9VgMJT7XhJCCCGEaEgkkS6EEEIIUcO6d+9uHfl842SCbm5u1p8NBkOlt/ntt9+WuWzfvn3WyTcHDhx4M6Fa2yckJFgTzqX56quvSqxjUdVjuhXFY/jmm2/KbBcbG8uxY8dKrFMdunfvTrNmzQD44osvyM/Pv+ltFBUVWX/Oyckps938+fPLXNalSxfr3QtfffVVjSWRb4WldMuVK1dYv349v/32m7Ued1llXezxWtmbJaby4lm+fDmXL1+u1PbWrVtXZluTyWTtF/z9/W0uoFTkoYcesl4A+/jjjyu9nhBCCCFEfSaJdCGEEEIIO/vxxx/LnSRw//791lISN9YULj7549mzZyu9z99//73E6HaA7Oxs/v73vwPmUfGWnytr/Pjx1kkjn3/+efR6fYk269ats9Z97t27N7169bJZHhgYaB3pfTPHdCt69+5Nz549AXM5jeJ3AVjodDqb12b8+PHVGpNarebVV18FIDExkVGjRpV7YUGv1zNnzhyb5yx3CAAsXLiw1PU+++wz62jtsuJ45ZVXALh48SKjRo0qcxJOk8lESkpKmduqKQ899BA+Pj4ALF682FrWpWnTptx9992lrtOqVStrMvjbb78ttd74ihUrSrzG1cny91uxYkWp5VvOnj1LTExMpbdnMBj4+9//Xupkru+++y5HjhwBYMyYMaWO2i9LmzZteOyxxwBYunQpH330Ubntk5KS+OGHHyq9fSGEEEKIukhqpAshhBBC2NnUqVMZN24cDz/8MP369eO2227D09OTa9eusWPHDv773/8C5okLn332WZt1w8PDadasGRcvXuTDDz+kWbNmtGnTxlqjODg4GG9v7xL77NmzJ08++SRbt27l0UcfxcfHh8OHD/Pee+9x8uRJwDwBaufOnW/qWDp16sRLL73EBx98wKFDh+jevTtTp06lW7du5OTksGLFCj799FOMRiOurq58/vnnJbbh7OxMr1692LlzJ9988w3dunWja9euuLi4AObazdVRV/3LL7+kT58+FBQU8OCDD/LCCy/w0EMP4enpSXx8PO+++y6JiYkAvPzyy1WqWX6zxo0bx/r16/n111/56aefOHDgAH//+9/p3bs3vr6+6PV6Tpw4wZYtW/j9999xc3NjwoQJ1vW7detGx44dOXr0KJ9//jkZGRmMHDmS0NBQLl68yKJFi/jf//7HHXfcUW75mJiYGFasWGGNpVOnTvzjH/+gZ8+eeHh4oNVq2b17Nz/88ANPPvkkM2bMqPbXpjxubm488sgjLFy4kN9++82a+B8+fLj1Qs+NAgMDefDBB1m1ahVr1qxh0KBBjB8/noiICNLS0vj5559ZuHAhLVq0IDMzkytXrlT7cYwaNYpXXnmFlJQUoqKimDp1Kh07diQ/P59Nmzbx8ccfYzAY6N69e6XKu/Ts2ZMVK1Zwxx13MGnSJFq3bk1aWhrffvstS5cuBaBZs2a89tprNx3rZ599xv79+0lMTOSll15i+fLljBo1ig4dOqDRaLh27RqHDh1izZo1bNq0ib/+9a8MHz78pvcjhBBCCFFnKEIIIYQQ9dzmzZsVQAGUN954o1LrjB492rpOUlJSieVvvPGGdfmNIiIirMvKemg0GmXBggWl7nvevHllrld8neIxJCYmKpGRkWWuN2zYMKWwsLDU/d19990KoNx9992lLjcajco//vGPco/H19dXWbt2bZmv58qVKxWVSlXqupX9mxRX2XXXrl2r+Pj4lBt7TEyMYjQaS13f8rccPXr0TcdYloKCAmX8+PFlvh7FH5GRkSXWj4+PV/z9/ctcp1OnTkpKSkqFr1FOTo7y6KOPVhjDjesX/zxt3ry53GO9lb/xjdavX18itvj4+HLXSU5OVsLDw8s8tvDwcCUhIaHcv/OCBQvK7QtuVN7nqaCgQBk0aFCZ8bi7uyvLli2z9j8REREltpGUlGTTHzz99NNlbi80NFRJSEgoNc7KHNfly5eVu+66q8L3CKA888wzFb42QgghhBB1mZR2EUIIIYSws82bN/PJJ58wbNgwOnXqROPGjXF2dsbHx4du3brx8ssvc+zYMZ5++ulS1x8/fjw///wzgwYNIigoCGfnim8ijIyMJC4ujldffZV27drh4eGBr68v/fr1s45Srsx2SqNWq5k7dy7btm1jxIgRhIeHo9Fo8PHxoWvXrrz66qucPn2aQYMGlbmNwYMHs3HjRh5++GGaNGliHY1e3QYNGsSZM2d49dVX6dq1Kz4+Pmg0GsLDwxkxYgTbt29nzpw5ZY5qrg4uLi7MmzePQ4cO8cILL9CpUyd8fX1xcnLC19eXrl27MnbsWP73v/9x/PjxEut37dqVgwcPMm7cOCIiInBxcSEgIIDevXvz4YcfsnfvXpsSQWXx8PDgp59+YtOmTYwcOZLIyEjc3d1xdXUlLCyMhx56iM8//5yXXnqpOl6Gm3bPPffYHFf79u3p2rVrueuEhYVx4MABXnnlFW677TY0Gg2+vr506dKFN954g4MHD9K+fftqjvw6FxcXVq1axaeffmod/e/u7k6rVq0YN24cBw4csJZUqawFCxawZMkS+vfvT2BgIBqNhttuu40pU6aQkJBwS8cXEhLCtm3bWLlyJSNGjKBFixZ4eHjg4uJC48aNuf3223nppZfYunVruXMRCCGEEELUBypFKaVYoBBCCCGEqPVmzJjBm2++CVBq/WchhBBCCCGEEPYhI9KFEEIIIYQQQgghhBBCiHJIIl0IIYQQQgghhBBCCCGEKIck0oUQQgghhBBCCCGEEEKIckgiXQghhBBCCCGEEEIIIYQohyTShRBCCCGEEEIIIYQQQohyqBRFURwdhBBCCCGEEEIIIYQQQghRW8mIdCGEEEIIIYQQQgghhBCiHJJIF0IIIYQQQgghhBBCCCHKIYl0IYQQQgghhBBCCCGEEKIckkgXQgghhBBCCCGEEEIIIcohiXQhhBBCCCGEEEIIIYQQohySSBdCCCGEEEIIIYQQQgghyiGJdCGEEEIIIYQQQgghhBCiHJJIF0IIIYQQQgghhBBCCCHKIYl0IYQQQgghhBBCCCGEEKIckkgXQgghhBBCCCGEEEIIIcohiXQhhBBCCCGEEEIIIYQQohySSBdCCCGEEEIIIYQQQgghyiGJdCGEEEIIIYQQQgghhBCiHJJIF0IIIYQQQgghhBBCCCHKIYl0IYQQQgghhBBCCCGEEKIckkgXQgghhBBCCCGEEEIIIcohiXQhhBBCCCGEEEIIIYQQohySSBdCCCGEEEIIIYQQQgghyiGJdCGEEEIIIYQQQgghhBCiHJJIF0IIIYQQQgghhBBCCCHKIYl0IYQQQgghhBBCCCGEEKIckkgXQgghhBBCCCGEEEIIIcohiXQhhBBCCCGEEEIIIYQQohySSBdCCCGEEEIIIYQQQgghyiGJdCGEEEIIIYQQQgghhBCiHJJIF0IIIYQQQgghhBBCCCHKIYl0IYQQQgghhBBCCCGEEKIckkgXQgghhBBCCCGEEEIIIcohiXQhhBBCCCGEEEIIIYQQohySSBdCCCGEEEIIIYQQQgghyuHsyJ3PnDmTX375hRMnTuDu7s7tt9/Oe++9R5s2baxt8vPzeemll1i6dCkGg4Ho6GjmzZtHcHCwtU1ycjLjx49n8+bNeHl5MXr0aGbOnImz8/XD27JlC5MnTyYhIYGwsDCmT5/O008/bRPP3Llz+eCDD9BqtXTp0oX//ve/9O7du1LHYjKZSElJwdvbG5VKdWsvjBB1lKIoZGVl0aRJE9RquU5Xm0gfJYT0UbWZ9FFCSB9Vm0kfJYT0UbWZ9FFC1GAfpThQdHS0smDBAuXo0aPKwYMHlQcffFAJDw9XsrOzrW3GjRunhIWFKRs3blT279+v9O3bV7n99tuty4uKipSOHTsqAwcOVOLj45XVq1crjRo1UqZNm2Ztk5iYqHh4eCiTJ09Wjh07pvz3v/9VnJyclDVr1ljbLF26VHF1dVW++eYbJSEhQXnuuecUPz8/JTU1tVLHcuHCBQWQhzzkAcqFCxfs0EMIe5I+Sh7yuP6QPqr2kT5KHvK4/pA+qvaRPkoe8rj+kD6q9pE+Sh7yuP6o7j5KpSiKQi1x5coVgoKC2Lp1K/369UOn09G4cWOWLFnCo48+CsCJEydo164dsbGx9O3blz/++IMhQ4aQkpJiHaU+f/58pk6dypUrV3B1dWXq1KmsWrWKo0ePWvf1xBNPkJmZyZo1awDo06cPvXr1Ys6cOYD5il5YWBgvvPAC//d//1ciVoPBgMFgsP6u0+kIDw/nwoUL+Pj4VNtrJERtptfrCQsLIzMzE19fX0eHI4rR6XT4+flJHyUaNOmjai/po4SQPqo2kz5KCOmjajPpo4SouT7KoaVdbqTT6QAICAgAIC4ujsLCQgYOHGht07ZtW8LDw62J9NjYWDp16mRT6iU6Oprx48eTkJBAt27diI2NtdmGpc3EiRMBKCgoIC4ujmnTplmXq9VqBg4cSGxsbKmxzpw5kzfffLPE8z4+PtJxiQZPbierfSx/E+mjhJA+qjaSPkqI66SPqn2kjxLiOumjah/po4S4rrr7qFpT2MpkMjFx4kTuuOMOOnbsCIBWq8XV1RU/Pz+btsHBwWi1Wmub4kl0y3LLsvLa6PV68vLyuHr1KkajsdQ2lm3caNq0aeh0OuvjwoULVTtwIYQQQgghhBBCCCGEELVarRmRHhMTw9GjR9mxY4ejQ6kUjUaDRqNxdBg1ymQykZaWBkBQUJBMMCKEqJUURSEjIwMAf39/GTUjhKgRiqKg0+nw9fWVfkcIIYQQooqUPyeNlMlTRW1UKzKhEyZMYOXKlWzevJlmzZpZnw8JCaGgoIDMzEyb9qmpqYSEhFjbpKamllhuWVZeGx8fH9zd3WnUqBFOTk6ltrFsQ0BaWhqzlu9l1vK91oS6EELUNhkZGcxacYBZKw5YE+pCCFHddDod//5xm7VUoRBCCCGEuHlZWVn856edZGVlOToUIUpwaCJdURQmTJjAr7/+yqZNm4iMjLRZ3qNHD1xcXNi4caP1uZMnT5KcnExUVBQAUVFRHDlyxCaxu379enx8fGjfvr21TfFtWNpYtuHq6kqPHj1s2phMJjZu3GhtI8y8/ALx8gt0dBhCCFEudy8f3L1urj6goiikp6dTi+bgFkLUEZbR6G4eno4ORQghhBCiztO4255TKYqCXq+X72rC4RyaSI+JiWHRokUsWbIEb29vtFotWq2WvLw8AHx9fRk7diyTJ09m8+bNxMXF8cwzzxAVFUXfvn0BGDRoEO3bt2fkyJEcOnSItWvXMn36dGJiYqylV8aNG0diYiJTpkzhxIkTzJs3j2XLljFp0iRrLJMnT+bLL7/k22+/5fjx44wfP56cnByeeeaZmn9hhBBC1LiMjAze+WGzjGIXQtw0nU7Hh7/sxGAocHQoQgghhBB1WqHRRE6BiYIiI3q93vqQUeqiNnBojfTPPvsMgP79+9s8v2DBAp5++mkAZs+ejVqtZtiwYRgMBqKjo5k3b561rZOTEytXrmT8+PFERUXh6enJ6NGjeeutt6xtIiMjWbVqFZMmTeKTTz6hWbNmfPXVV0RHR1vbPP7441y5coXXX38drVZL165dWbNmTYkJSIUQQtRfbp4yy70Qomrc3L0cHYIQQgghRJ2Vps/nvdUJrDqaSn6RwvJTsQR5qon0c2H6/a1sRqlLHXXhKA4v7VLaw5JEB3Bzc2Pu3Lmkp6eTk5PDL7/8UqJueUREBKtXryY3N5crV67w4Ycf4uxse42gf//+xMfHYzAYOHv2rM0+LCZMmMD58+cxGAzs2bOHPn36VMdhCyGEEELUiEuXLvHUU08RGBiIu7s7nTp1Yv/+/dbliqLw+uuvExoairu7OwMHDuT06dM220hPT2fEiBH4+Pjg5+fH2LFjyc7Otmlz+PBh7rrrLtzc3AgLC+P999+vkeMTQgghhBB1l6U83rpD5xk0eys/H9SSX2Qu31JoUriUZWTHhXwe+jqBDUm5zN16jjVHL3M8OY1/L9shI9RFjXPoiHQhhBBCCFE9MjIyuOOOOxgwYAB//PEHjRs35vTp0/j7+1vbvP/++3z66ad8++23REZG8tprrxEdHc2xY8dwc3MDYMSIEVy+fJn169dTWFjIM888w/PPP8+SJUsA0Ov1DBo0iIEDBzJ//nyOHDnCmDFj8PPz4/nnn3fIsQshhBBCiNovKyuLp+dvJj7DGVDhr4FOfkU08XNnSI8WfLztEmeu5pGZb+RSlpHPd16AnRcACHRX83BKFrf7yF3FouY4dES6EEIIIYSoHu+99x5hYWEsWLCA3r17ExkZyaBBg2jZsiVgHgH08ccfM336dB5++GE6d+7Md999R0pKCr/99hsAx48fZ82aNXz11Vf06dOHO++8k//+978sXbqUlJQUABYvXkxBQQHffPMNHTp04IknnuCf//wnH330kaMOXQghhBDCrt59911UKhUTJ060Ppefn09MTAyBgYF4eXkxbNgwUlNTbdZLTk5m8ODBeHh4EBQUxCuvvEJRUZFNmy1bttC9e3c0Gg2tWrVi4cKFNXBEtUPC5SwOZbgAKu5r6cXAptDIzTwiPdClkB7NvHiscyPmPdSU3k1cGdwuAF8XEyoUruWZGPXdIZYfvOTYgxANiiTShRBCCCHqod9//52ePXvy2GOPERQURLdu3fjyyy+ty5OSktBqtQwcOND6nK+vL3369CE2NhaA2NhY/Pz86Nmzp7XNwIEDUavV7Nmzx9qmX79+uLq6WttER0dz8uTJMifvNRgMNpNH6fV6ux67EEIIIYS97Nu3j88//5zOnTvbPD9p0iRWrFjBTz/9xNatW0lJSeGRRx6xLjcajQwePJiCggJ27drFt99+y8KFC3n99detbZKSkhg8eDADBgzg4MGDTJw4kWeffZa1a9fW2PE5SrahiKm/ncAENPEw4aJPwWgsBKAgP5c5fxzEUFBAQX4uq/Ycp4W3woTefjwQ4cQDzYpo5qWiyKQw6ceD/Bp/0bEHIxoMSaQLIYQQQtRDiYmJfPbZZ7Ru3Zq1a9cyfvx4/vnPf/Ltt98CoNVqAUpMrB4cHGxdptVqCQoKslnu7OxMQECATZvStlF8HzeaOXMmvr6+1kdYWNgtHq0QorbZtm0bDz30EE2aNEGlUlnvdLGoyTkafvrpJ9q2bYubmxudOnVi9erVdj9eIUT9lJ2dzYgRI/jyyy9tyuPpdDq+/vprPvroI+655x569OjBggUL2LVrF7t37wZg3bp1HDt2jEWLFtG1a1ceeOAB3n77bebOnUtBQQEA8+fPJzIyklmzZtGuXTsmTJjAo48+yuzZsx1yvDXp9d+OkpyRj4czdA8w4ubhbrNc4+5u87MluV5QWICbE/QKKKC5pwmTAi8tO8RvkkwXNUAS6UIIIYQQ9ZDJZKJ79+785z//oVu3bjz//PM899xzzJ8/39GhMW3aNHQ6nfVx4cIFR4ckhLCznJwcunTpwty5c0tdbpmjYf78+ezZswdPT0+io6PJz8+3thkxYgQJCQmsX7+elStXsm3bNpu5FyxzNERERBAXF8cHH3zAjBkz+OKLL6xtdu3axfDhwxk7dizx8fEMHTqUoUOHcvTo0eo7eCFEvRETE8PgwYNt7uADiIuLo7Cw0Ob5tm3bEh4ebnNnX6dOnWwGHERHR6PX60lISLC2uXHb0dHR1m2Upj7c2fdr/EV+ib8EKPQMLMLVqXLrFU+uq1TQLdBIuFsBJgUmLzvEikMp1ROwEH+SyUaFEEIIIeqh0NBQ2rdvb/Ncu3bt+PnnnwEICQkBIDU1ldDQUGub1NRUunbtam2TlpZms42ioiLS09Ot64eEhJSoB2r53dLmRhqNBo1GU8UjE0LUBQ888AAPPPBAqctunKMB4LvvviM4OJjffvuNJ554wjpHw759+6zlpf773//y4IMP8uGHH9KkSRObORpcXV3p0KEDBw8e5KOPPrIm3D/55BPuv/9+XnnlFQDefvtt1q9fz5w5c8q8sGgwGDAYDNbf62KSSghx65YuXcqBAwfYt29fiWVarRZXV1f8/Pxsnr/xzr6K7torq41erycvLw93d9tR2mC+s+/NN9+s8nE5kqIoHEtO41+/mi9mdgxQ08itqIK1yqZSQY8gFaprJs7nqJm4NB4XJxX3dwyteGUhqkBGpAshhBBC1EN33HEHJ0+etHnu1KlTREREABAZGUlISAgbN260Ltfr9ezZs4eoqCgAoqKiyMzMJC4uztpm06ZNmEwm+vTpY22zbds2CgsLrW3Wr19PmzZtbG6BFkIIi5qco6Eqoz2l/JQQ4sKFC7z44ossXrwYNzc3R4djoy7f2ZeeqWPUwgPkFhjp0sST9nY4VVSpoHugkeY+aowKTFgSz7oELXq9HkVRbn0HQhQjiXQhhBDiBoqikJ6eLideok6bNGkSu3fv5j//+Q9nzpxhyZIlfPHFF8TExACgUqmYOHEi77zzDr///jtHjhxh1KhRNGnShKFDhwLmEez3338/zz33HHv37mXnzp1MmDCBJ554giZNmgDw5JNP4urqytixY0lISODHH3/kk08+YfLkyY46dCFELVeTczSU1aasORygbiephBD2ERcXR1paGt27d8fZ2RlnZ2e2bt3Kp59+irOzM8HBwRQUFJCZmWmzXmpq6k3dtVdWGx8fn1JHo4P5zj4fHx+bR22nKAp6vZ6Fuy9yLc+Eq5OKyVGBqFX22b5KBb1D1Axs7UeRSeEfiw8Qs2AHWVlZ9tmBEH+SRLoQQghxg4yMDN75YbN1NJsQdVGvXr349ddf+eGHH+jYsSNvv/02H3/8MSNGjLC2mTJlCi+88ALPP/88vXr1Ijs7mzVr1tiMvFq8eDFt27bl3nvv5cEHH+TOO++0qT/s6+vLunXrSEpKokePHrz00ku8/vrrNnWMhRCiLqlqkmr2+lPVHJkQoqbce++9HDlyhIMHD1ofPXv2ZMSIEdafXVxcbO7sO3nyJMnJyTZ39h05csSmTN769evx8fGxlt+Lioqy2YaljWUb9UVWVhZTF23ns+3nAejdRMOy7UcpKCyw2z6KDHkEqnOI8HWmyKSwU6uw/Uy63bYvBEiNdCGEEKJUbp61f2SHEBUZMmQIQ4YMKXO5SqXirbfe4q233iqzTUBAAEuWLCl3P507d2b79u1VjlMI0bDU5BwNZbUpaw4HIYQA8Pb2pmPHjjbPeXp6EhgYaH1+7NixTJ48mYCAAHx8fHjhhReIioqib9++AAwaNIj27dszcuRI3n//fbRaLdOnTycmJsY6V8y4ceOYM2cOU6ZMYcyYMWzatIlly5axatWqmj3gaqYoCvvToMgETX1caOHnTLa+9BH3t8Ld05t+4QVsPJVBisGFV347QYiPhu4tQ1Cp7DT8XTRoMiJdCCGEEEIIIUSNqck5GhrKaE8hRM2bPXs2Q4YMYdiwYfTr14+QkBB++eUX63InJydWrlyJk5MTUVFRPPXUU4waNcpmAENkZCSrVq1i/fr1dOnShVmzZvHVV18RHR3tiEOqNgcu6LmSD2qVQlS4R7UmtdUqFb2DIchDRW6BkTHfHeBqhq7a9icaFkmkCyFEFTVv3hyVSlXiYak/3L9//xLLxo0bZ7ON5ORkBg8ejIeHB0FBQbzyyisUFdnOWr5lyxa6d++ORqOhVatWLFy4sKYOUQghRCWYFIUik6OjEKJ2yc7OtpZDAPMEowcPHiQ5OblG52h48cUXWbNmDbNmzeLEiRPMmDGD/fv3M2HChJp+SYQQddyWLVv4+OOPrb+7ubkxd+5c0tPTycnJ4Zdffilxt0tERASrV68mNzeXK1eu8OGHH+LsbFscon///sTHx2MwGDh79ixPP/10DRxNzVEUhW92nQMg3FPBy9Wp2vepVkHPgAJcVSZ0BfDZljMy/5WwCyntIoQQVbRv3z6MRqP196NHj3Lffffx2GOPWZ977rnnbEYceHh4WH82Go0MHjyYkJAQdu3axeXLlxk1ahQuLi785z//AcxfOgcPHsy4ceNYvHgxGzdu5NlnnyU0NLTejVIQQoi66PhlPc8s2I9W70THAPmCJoTF/v37GTBggPV3S3J79OjRLFy4kClTppCTk8Pzzz9PZmYmd955Z6lzNEyYMIF7770XtVrNsGHD+PTTT63LLXM0xMTE0KNHDxo1alRijobbb7+dJUuWMH36dF599VVat27Nb7/9VqJkgxBCiOpxLDmN7Yk6QEUrb2OF7e3FzQm6BprYe1XNt/u0PNnnKq2aNq6x/Yv6SRLpQghRRY0b2/4n/O6779KyZUvuvvtu63MeHh5l1uBct24dx44dY8OGDQQHB9O1a1fefvttpk6dyowZM3B1dWX+/PlERkYya9YswDw6a8eOHcyePbvcRLrBYMBgMFh/1+v1t3Ko9YaiKNYJRP39/aVOnhDilphMCtN+OYJWb54oKyFd4ezVXHr4+Tk2MCFqgf79+5c7+q8m52h47LHHbAY6CCGEqDmL96UAKoLdTPi41uy+m3ooBGhMpBvU/HfreT55UhLp4tZIaRchhLCDgoICFi1axJgxY2ySs4sXL6ZRo0Z07NiRadOmkZuba10WGxtLp06dCA4Otj4XHR2NXq8nISHB2mbgwIE2+4qOjiY2NrbceGbOnImvr6/1ERYWZo/DrDRFUUhPT691t89lZGQwa8UBZq04YE2oCyFEVW05mcbBC5m4OavwdVFQsHxZFEIIIYQQutxCfj2kBaC1T83XwVOpoLO/eb+/H04lIUVqpYtbI4l0IYSwg99++43MzEybenZPPvkkixYtYvPmzUybNo3vv/+ep556yrpcq9XaJNEB6+9arbbcNnq9nry8vDLjmTZtGjqdzvq4cOHCrR7iTcnIyOCdHzbXymS1u5cP7l4+jg5DCFEP/Lj3HADNPEy09zHfBbT5dDpFRimYLoQQQgjxw75k8gpN+LpCYzfHDLIK0CiEe6tRgA/XnnRIDKL+kNIuQghhB19//TUPPPCAdfIrwKY+Z6dOnQgNDeXee+/l7NmztGzZslrj0Wg0aDSaat1HRdw8JVkthKi/9PmFbD51DYDbGrujzs3DVQ0ZuYXsTUrn9laNHByhEEIIIYTjFBpNLNx5DoA2fubR4Y7SubETKTkKm09eYU/iNfq0CHRcMKJOkxHpolqYTCa0Wi1arRaTSUZlifrt/PnzbNiwgWeffbbcdn369AHgzJkzAISEhJCammrTxvK7pa56WW18fHxwd3e3S/xCCCFu3tqjWgqMCn5uTgS4qVCroImnedmWU1ccG5wQQgghhIOtPnIZrT4fjVoh1K3QobF4ucDgdv4AvL/2ZK0rQSrqDkmki2qRlpbGrOV7mbV8L2lpaY4OR4hqtWDBAoKCghg8eHC57Q4ePAhAaGgoAFFRURw5csTmM7J+/Xp8fHxo3769tc3GjRtttrN+/XqioqLseAQ1q7bWTxdCNAyKopCZmXnLfdDyg+Za6K0auVnnxgjxMP+7/fTVWwtSCCGEEKIOM5lMzNl4CoDWfmqcHDgaHaAgP5ds7XmcVApx5zPYcDy14pWEKIUk0kW18fILxMtPbpcR9ZvJZGLBggWMHj0aZ+fr1bLOnj3L22+/TVxcHOfOneP3339n1KhR9OvXj86dOwMwaNAg2rdvz8iRIzl06BBr165l+vTpxMTEWMuyjBs3jsTERKZMmcKJEyeYN28ey5YtY9KkSQ45XnuozfXThRD1n06n498/bkOnq/pkU2n6fHadNSfLWwW6WZ8P+vNGoeOX9VzJMtxSnEIIIYQQddWq+POcvpKLu7OK23xrxwAqXy83WnqbKybMXHVM5rQRVSKJdCGEuAUbNmwgOTmZMWPG2Dzv6urKhg0bGDRoEG3btuWll15i2LBhrFixwtrGycmJlStX4uTkRFRUFE899RSjRo3irbfesraJjIxk1apVrF+/ni5dujBr1iy++uoroqOja+wYq4Mj6qcrivJn8r76TuRktL0QdYObh9dNtbeMYreMZF95+DImBfxdinCl6Pp2nVW0CTLXd7Ek2oUQQgghGhJFUfhq1wUAQl3zweTYsi7F3eZrQuMEidfyWLI32dHhiDpIJhsVQohbMGjQoFKTpmFhYWzdurXC9SMiIli9enW5bfr37098fHyVYxRmGRkZzPp5OwFNW+Dm5lbxClXcxzs/bGb68AEEBARUyz6EEDVPp9Mxa6W5H548uCu/xl8CoLl3yTEpfSP9OJmWw/bTV3m4a9MajVMIIYQQwtH2JKVz6FIWahW08a9d43dd1dCpkRP7U43MWneSAS28aRYUYC3TJ0RFatc7WgghhKhGGg/vat+HI0bbCyGqj6Io6HQ63D29cPf0Zvc5HUcu6XB1UtHMs+QtwX2b+wGw4/RVuTtFCCGEEA3OnE1nAGjhDe61cPhuC18Vzf1c0OUV8fx3cWRlZTk6JFGHSCJdCCGEEEI0aOVNQKrT6fjwl53k5xsoMinM3pQEQFNNPipjQYnttPRRcHVWo9Xnc/ZKdo3EL4QQQghRGxy6kMmOM1dRodDSu/aUdCmuyJBHMJkAnLhWSOLVXMcGJOoUSaQLIYSoM6QGuRCiOlQ0AambuxcmRWHLWR2nruTi7+FMx8CSQ6wMeTnMWRVHt2bmu1+2nZI66UIIIYRoOOZsNo9Gj/BW4VkLR6NbNPV1JczXBQX4YEOio8MRdYgk0oUQQtQZlhrk5klDhRDCfsqbgNRogi3ncklMN6BC4V/3NMXNqazteF4v73JGEulCCCGEaBhOaPWsP5YKKLSupaPRi+vV1AO1CnYmZrDqQJIM1hKVIol0IYQQdYrUIBdC2IOlnEtZJV2Kt9t3TcVFfRFOKrgzVE2vsPLnW4j6M5G+O/EahiKjPcMWQgghhKiV5m0+C0CYpwpvFwcHUwk+bk60CzQH+n+/JJCeWfqdiUIUJ4l0IYQQQgjR4Oj1ematjGfWynj0en2Z7ZYfTuNCjhoVMCBCQ6inqtztKopCsFsRwT4acguMbD15xc6RCyGEEELULpcz81h1OAWA9gEODuYmdA7WoFErZBWp+DHusqPDEXVAlRLpLVq04Nq1ayWez8zMpEWLFrcclBBCVCfpw4QQtZn0UTXH3dMbd8+yR5frcgv5ZMs5ALqFuhHqVUY9l2IMeTnM/n0/97UJBOD3Qyl2iVWImiD9jxCirpN+zDG+23kGowIBLkV4qmt/WRcLVycV7f3Mdw9+tiOZzNyCCtYQDV2VEunnzp3DaCx5m6rBYODSpUu3HJQQQlQn6cOEELWZ9FE1S1GUPycZLVneZdb6k2TkFeHtotC+sWult+nm4ckD7RsBsP5YKrrcuvOFUjRs0v8IIeo66cdqXpHRxM8HtQC09HVwMFXQ3EvBT6MiK7+IT9cfl1rpolw3NYfu77//bv157dq1+Ppe/4QYjUY2btxI8+bN7RacaHhMJhNpaWkABAUFoVZL9SFhP9KHCSFqM+mjHCMtQ8/M3y/TNNj2PuRjKXoW7T4PQEcfA2qVqpRUe+kURaGpu5HbGntw6koui3ae4smeTfDz80OlKr80jBCOIP2PEKKuk37McTYcTyMtqwCNEzTxqHtJaJUKOjVSs/2SkW93X2R0n2ZEhAQ6OixRS91UIn3o0KEAqFQqRo8ebbPMxcWF5s2bM2vWLLsFJxqetLQ0Zi3fC8BLD/cmJCTEwRGJ+kT6MCFEbSZ9VM0qMilsOqPjzLV8wB9vg8JfLmXRz8+PIqOJ15YfxaRAMw8TjTWmm9q2IS+HeRtP4O9iHhDw2ZaznDh2nLdHD8TPz8/+ByPELZL+RwhR10k/5jhL9yUD0MIbnOroeIFGTvn4OqvRFTnx7a4kXvtrgAx+EKW6qeG+JpMJk8lEeHg4aWlp1t9NJhMGg4GTJ08yZMiQ6opVNBBefoF4+cnVP2F/0ocJe1AUhfT0dLnlT9hddfdR7777LiqViokTJ1qfy8/PJyYmhsDAQLy8vBg2bBipqak26yUnJzN48GA8PDwICgrilVdeoaioyKbNli1b6N69OxqNhlatWrFw4cIqx1lTvt2f9mcSHdQoZBXCmMVH+Gj9KSYtO0Tc+QzcXdR0CbBNopdXCqY4Nw8v2jXxw91FTbbRCb3KHZ1OR2ZmpvQfotaRcyQhRF0n/ZhjXM3KZ/sp88TqkT4ODuYWqFTQxs98frZov5bzyRflfE2Uqkp1M5KSkmjUqJG9YxFCiBohfZi4FRkZGbzzw2YyMjIcHYqop6qjj9q3bx+ff/45nTt3tnl+0qRJrFixgp9++omtW7eSkpLCI488Yl1uNBoZPHgwBQUF7Nq1i2+//ZaFCxfy+uuv28Q7ePBgBgwYwMGDB5k4cSLPPvssa9eutesx2FNuocLiePOXvjuauRIdkkczT/Mo9U83nmbFnxOEtvfIwslkO+mUIS+HuavjMRRUPBmVq5Oa+1qbby0/nV7E3A3HmbUy/s9EvBC1j5wjCSHqOunHatYv+89hVMBfA26quj0nTBMPBY1aocCkYtr/4snKynJ0SKIWuqnSLsVt3LiRjRs3Wq/0FffNN9/ccmBCCFGdpA8Tt8LN08c6Mh3A399fbv0TdmXPPio7O5sRI0bw5Zdf8s4771if1+l0fP311yxZsoR77rkHgAULFtCuXTt2795N3759WbduHceOHWPDhg0EBwfTtWtX3n77baZOncqMGTNwdXVl/vz5REZGWm+XbteuHTt27GD27NlER0ff4itRPU6mF2E0QYi3Cy38nNGlQ99gaB3RlA1JuahQMapXCFv3HcFQYCixvsbDo1L7URSFeyNc+f0YXM53QnHxwN2zcusK4ShyjiSEqOukH6sZiqKw8vCfk4wGaIC6nUhXqyDM08SZLCcu5lc5XSrquSqNSH/zzTcZNGgQGzdu5OrVq2RkZNg8hKjNTCYTWq0WrVZb4j9V0TBIHybsITMzk1krDjBrxQF53wi7sncfFRMTw+DBgxk4cKDN83FxcRQWFto837ZtW8LDw4mNjQUgNjaWTp06ERwcbG0THR2NXq8nISHB2ubGbUdHR1u3URqDwYBer7d51ARFUUjPyORMprk0TaeQ60ntgvxcEk6e4csn2rNsXBR3tvS/5f3l52azOjYBfxcjCirOpFc8il0IR5JzJCFEXSf9WM05efEKhy/nANDcr34knsM8zTmilGwFQ5Hki0RJVXqnz58/n4ULFzJy5Eh7xyNEtZMJTYX0YXWboijWk2BHjwR396rDhQBFrWXPPmrp0qUcOHCAffv2lVim1WpxdXUtMfllcHAwWq3W2qZ4Et2y3LKsvDZ6vZ68vDzc3d1L7HvmzJm8+eabVT6uqtLpdExftof8Ii+8XNWE+2koyLs+esrNw9Pu+9R4eBCuFJGhc+JcZiG9Iuy+CyHsRs6RhBB1nfRjNWfNMXOZvBAvZzxd1NSHQih+ruDupJBnVLH3XCaDA/wcHZKoZao0Ir2goIDbb7/d3rEIUWNkQtOGTfqwyrnZSTUt7at7Is78nCzmbTguI8FFvWWvPurChQu8+OKLLF68GDc3NztEZj/Tpk1Dp9NZHxcuXKixfV81mkehRzX3xkld8kKcoihkZmZWakLRygpxMwIKGfkm9PnGCttbYpCJSUVNk3MkIURdJ/1YzVmdYE6ktwhwdXAk9qNSQYi7eST6ltPXHByNqI2qlEh/9tlnWbJkib1jEUKIGiF9WOXc7KSaGRkZ1lInmZmZ1Rqbu5ePjAYX9Za9+qi4uDjS0tLo3r07zs7OODs7s3XrVj799FOcnZ0JDg6moKCgxOc1NTXVerdWSEgIqampJZZblpXXxsfHp9TR6AAajQYfHx+bR03R5pmT53c2L32fer2eWSvjmbPmcKUmFK0MjRM0cjV/KTuXkV9he51Ox6yV8TIxqahxco4khKjrpB+rGadSsziVloMaiPCrP4l0gBB38yCGnYkZMqBBlFCl0i75+fl88cUXbNiwgc6dO+Pi4mKz/KOPPrJLcEIIUR2kD6s8N8+bS25JcluIW2evPuree+/lyJEjNs8988wztG3blqlTpxIWFoaLiwsbN25k2LBhAJw8eZLk5GSioqIAiIqK4t///jdpaWkEBQUBsH79enx8fGjfvr21zerVq232s379eus2apOM3EL0heZEetcmnlzSlZ4od/f0BsBgqDjpXVkhbkauFjiRlF5y8tLyYhCiJsk5khCirpN+rPopisKPuxMBCPUEjbMapajiO+7qisZuCmogRWfg/LVcmjeyf+k/UXdVKZF++PBhunbtCsDRo0dtljmyVq0QQlSGvfqwGTNmlKjx26ZNG06cOAGYT+Jeeuklli5disFgIDo6mnnz5tnUEk5OTmb8+PFs3rwZLy8vRo8ezcyZM3F2vt49b9myhcmTJ5OQkEBYWBjTp0/n6aefvsmjFkLUFfbqo7y9venYsaPNc56engQGBlqfHzt2LJMnTyYgIAAfHx9eeOEFoqKi6Nu3LwCDBg2iffv2jBw5kvfffx+tVsv06dOJiYlBo9EAMG7cOObMmcOUKVMYM2YMmzZtYtmyZaxataqqL0G1OXDBPKmpjyuoCnKwV+mWygh1N3JUD6nZhVzLKeCG0vRWiqIUKysj59WiZsn3PCFEXSf9WPXL1OlZsu8iAM3r4XV/ZzU08lCRlquw7fQVSaQLG1VKpG/evNnecQghRI2xZx/WoUMHNmzYYP29eAJ80qRJrFq1ip9++glfX18mTJjAI488ws6dOwEwGo0MHjyYkJAQdu3axeXLlxk1ahQuLi785z//ASApKYnBgwczbtw4Fi9ezMaNG3n22WcJDQ0lOjrabsdR29SmCUWFqGk1eZ41e/Zs1Go1w4YNs7ngZ+Hk5MTKlSsZP348UVFReHp6Mnr0aN566y1rm8jISFatWsWkSZP45JNPaNasGV999VWt7KP2J5vLpAQ4FzJ3dTzBES1rbN/uTgqB7k5cyzOy+XQ6LZsGldpOp9Px4S878Q8Jr3W17UX9J9/zhBB1nfRj1W/f+UzyisBZpdDItcjR4VSLEE81ablGtp26yqio5o4OR9QiVUqkCyGEMHN2drbWCS5Op9Px9ddfs2TJEu655x4AFixYQLt27di9ezd9+/Zl3bp1HDt2jA0bNhAcHEzXrl15++23mTp1KjNmzMDV1ZX58+cTGRnJrFmzAGjXrh07duxg9uzZ5SapDAYDBsP18gF6vd7OR169LPXWAV56qDsBAQEOjkiI+mHLli02v7u5uTF37lzmzp1b5joRERElSrfcqH///sTHx9sjxGoV9+eI9EYaExp3jxrff7ivM9fyjGw5nc6z/ctu5+buVWMxCSGEEELcjN+PpAEQ5mnCqZ6Odwr1VHH4CsSevUqh0YSLU5WmmBT1UJUS6QMGDCh3dOCmTZsqtZ1t27bxwQcfEBcXx+XLl/n1118ZOnSodbmiKLzxxht8+eWXZGZmcscdd/DZZ5/RunVra5v09HReeOEFVqxYYR1R9cknn+Dldf0LyOHDh4mJiWHfvn00btyYF154gSlTptjE8tNPP/Haa69x7tw5WrduzXvvvceDDz5YyVfEcUwmE2lp5k4sKCgItVo+3EJUxF59GMDp06dp0qQJbm5uREVFMXPmTMLDw4mLi6OwsJCBAwda27Zt25bw8HBiY2Pp27cvsbGxdOrUyabUS3R0NOPHjychIYFu3boRGxtrsw1Lm4kTJ5Yb18yZM0uUnalraqLeevGR7zKRjKgt7NlHiet0uYWcTssBIMDVMXU8w3xdiNca2HMuk2xDEV6a66filpIu0hcJR5L+RwhR10k/Vr2yDUVsOHkVgHCv+nvO4ucKfm5OZOYbiU/OpHekDOwSZlXKunbt2pUuXbpYH+3bt6egoIADBw7QqVOnSm8nJyeHLl26lDkK6v333+fTTz9l/vz57NmzB09PT6Kjo8nPvz7x04gRI0hISGD9+vWsXLmSbdu28fzzz1uX6/V6Bg0aREREBHFxcXzwwQfMmDGDL774wtpm165dDB8+nLFjxxIfH8/QoUMZOnRoiXpatVFaWhqzlu9l1vK91oS6EKJ89urD+vTpw8KFC1mzZg2fffYZSUlJ3HXXXWRlZaHVanF1dcXvhiK4wcHBaLVaALRarU0S3bLcsqy8Nnq9nry8vDJjmzZtGjqdzvq4cOFCpY+rIbGMfJ+14gCZmZlV2oYlGS/JL2Ev9uqjhK2959JRAG8XBTen0tvY1ie3P1+NGh+NE4VGhW2nrtgs0+l0/PvHbVzLyORaPmQXmKolBiHKI/2PEKKuk36sev0Wf4n8QhPeLhDgWn+//xQa8vBSFwKw/fSVClqLhqRKI9Jnz55d6vMzZswgOzu70tt54IEHeOCBB0pdpigKH3/8MdOnT+fhhx8G4LvvviM4OJjffvuNJ554guPHj7NmzRr27dtHz549Afjvf//Lgw8+yIcffkiTJk1YvHgxBQUFfPPNN7i6utKhQwcOHjzIRx99ZE24f/LJJ9x///288sorALz99tusX7+eOXPmMH/+/Eofj6N4+QU6OgQh6hR79mEWnTt3pk+fPkRERLBs2TLc3d1vOc5bodForBMBivIVH/muzTHx4s8nuLOFX6UT43nZemb9dpZ3nvGvrhBFA2OvPkrY2p14DYDGmrI/24a8nGqtna5SqWgeoOHw5VzWH0vlwU6hNsuLnD0Ys+wsyZlOqLRZREVUSxhClEn6HyFEXSf9WPVRFIVFu88D0MoX6vs0Vk19XLiYa2Tb6au8NKiNo8MRtYRd64A89dRTfPPNN3bZVlJSElqt1qakga+vL3369CE2NhaA2NhY/Pz8rEl0gIEDB6JWq9mzZ4+1Tb9+/XB1dbW2iY6O5uTJk9bb+csqnWDZT2kMBgN6vd7mIYSo2261D/Pz8+O2227jzJkzhISEUFBQUGKUc2pqqrWmekhICKmpqSWWW5aV18bHx8fhyfq6SJunZu3ZHLYm6rmWU2Cz7Oy1fLZeLGL72Qxmrk/idGblR4O6e9XD6epFrWPP86yGKPbsn4n0Cubv1HhUb+305v7mi5wbj6dSaLzezxQUmdicXEBypnl+CwXYdT6LIylZ1RqPEJUh/Y8Qoq6TfuzWxZ3P4IQ2CyeVQlO3QkeHU+1CPM0p08MXM8nMLaigtWgo7JpIj42Nxc2tgm8nlWQpa1BaSYPiJQ+CgoJsljs7OxMQEGCX0gmW5aWZOXMmvr6+1kdYWNjNHmK1MplMaLVaTCa5LViIyrrVPiw7O5uzZ88SGhpKjx49cHFxYePGjdblJ0+eJDk5maioKACioqI4cuSITVmm9evX4+PjQ/v27a1tim/D0sayDVF5R1Ky2HPNhSu5Jk5fM/DKb6esSSxFUZgbm4qx2EDVY9eMNkkue1EUhfT0dCkFI26aPc+zGprM3AKOa82DHoLcHPvZC/JyIdDTBX1+ERuPX79Q+vnOC2QaFFxVJu4LzqOFvwsAM9eelvM54XDS/wgh6jrpx27d97Hm0ejhXipcyyiTV594uKho1dgDRYEtJ6W8izCrUmmXRx55xOZ3RVG4fPky+/fv57XXXrNLYLXdtGnTmDx5svV3vV5fq5LpaWlpvPntGt4Yfb91ZGtpZLJS0RDZqw97+eWXeeihh4iIiCAlJYU33ngDJycnhg8fjq+vL2PHjmXy5MkEBATg4+PDCy+8QFRUFH379gVg0KBBtG/fnpEjR/L++++j1WqZPn06MTEx1rIs48aNY86cOUyZMoUxY8awadMmli1bxqpVq+z3gtRDRUYFQ7EkeKHRxNtrzmJChYezCoNJ4eClLJbFm5NYl/SFHLqch1oFv4ztytgfEriWU0hsko6hjRvZNbaMjAze+WEz04cPICBAJq0RJcl5lv3tTkxHUaBFoDtuztkYHDioSK1SMbRzMF/HXmTBznPc3zGUA8kZLNh9EYDOfoW4Oyn0CHXjXGYhx1Lz2HH8Ev061J7zTFF/Sf8jhKjrpB+rHlezDaw+ehmA5p5FDo6mZiiKwl3NvTlzJZdf4y8xtFtTR4ckaoEqZU2Lj8T29fUlICCA/v37s3r1at544w27BGZJ/pZW0qB4yYMbJ9gsKioiPT3dLqUTyktAazQafHx8bB61jadvxfV6ZbJS0RDZqw+7ePEiw4cPp02bNvztb38jMDCQ3bt307hxY8Bcn2/IkCEMGzaMfv36ERISwi+//GJd38nJiZUrV+Lk5ERUVBRPPfUUo0aN4q233rK2iYyMZNWqVaxfv54uXbowa9YsvvrqK6Kjo+33gtQjiqKwIj6ZHw5d44dD6XywMYkio4nvYs9z5moeLmqFwa09iQr3AuCLnRfIKTCy/2IOAK391IQHuHNPa3OCe2dSRrXE6eZZ+/7PELVHTZxnNTSW+ug9I3wdHInZY91CcFar2JOUzqu/HuEfiw5gUiDc00QTdyMA7i5qIn3Nw72WHrjsyHBFAyL9jxCirrNXPzZz5kx69eqFt7c3QUFBDB06lJMnT9q0yc/PJyYmhsDAQLy8vBg2bFiJ3FJycjKDBw/Gw8ODoKAgXnnlFYqKbBPRW7ZsoXv37mg0Glq1asXChQurfPzVZdm+CxQaFQI04F/OfDP1SUF+LpcuXQLME45qdfkOjkjUBlUakb5gwQJ7x1FCZGQkISEhbNy4ka5duwLmUd979uxh/PjxgLnkQWZmJnFxcfTo0QOATZs2YTKZ6NOnj7XNv/71LwoLC3FxMd8iu379etq0aYO/v7+1zcaNG5k4caJ1/w2pdIJMVioaGnv1YUuXLi13uZubG3PnzmXu3LlltomIiGD16tXlbqd///7Ex8dXKcaGJll7hXc2XKBQMV8n/iFOS9zF7SReMSfKO/gUoXFWcVsjDVfzFE6k5vDDoXTAnLTqEGhOWt3Rwp+fDqayMzETRVFQ1feZdEStUhPnWQ2JoijsOG0eLNAzzId96ZccHo+7ks+EAa34eONpluxJBiAy0J0untmYin23bhPozNlMIxtPXkOfX4iPm4uDohYNRU32PzNmzODNN9+0ea5NmzacOHECMCeoXnrpJZYuXYrBYCA6Opp58+bZlORMTk5m/PjxbN68GS8vL0aPHs3MmTNxdr7+NXfLli1MnjyZhIQEwsLCmD59Ok8//XSNHKMQoubZqx/bunUrMTEx9OrVi6KiIl599VUGDRrEsWPH8PT0BGDSpEmsWrWKn376CV9fXyZMmMAjjzzCzp07ATAajQwePJiQkBB27drF5cuXGTVqFC4uLvznP/8BzPMDDh48mHHjxrF48WI2btzIs88+S2hoaK0ZPKUoCj/sNZd1ae7VMEajWwR4u9O1iSsHU3JYsCuJaQ+0c3RIwsGqlEi3iIuL4/jx4wB06NCBbt263dT62dnZnDlzxvp7UlISBw8eJCAggPDwcCZOnMg777xD69atiYyM5LXXXqNJkyYMHToUgHbt2nH//ffz3HPPMX/+fAoLC5kwYQJPPPEETZo0AeDJJ5/kzTffZOzYsUydOpWjR4/yySef2Mzk/OKLL3L33Xcza9YsBg8ezNKlS9m/fz9ffPHFrbw8Qoha7lb7MFH7LI3TUqio8XZV0bOZF9vPZXMqNRuArk29aY65tp1apeI/Q1rz1HeHyS00l4CZEBXEpTRzUr1XuA9qFaToDJy9kkOrIC/HHJBo0KSPunmKoqDT6QDziDSVSkXS5aucuZILQBt/NfscGSCQn5vNrOXnef2pe8krNLL6yGVaN3ZnfJ9GLNt+DEOxtgFuanxcQV9gYtXhywzvHe6wuEXDUlP9T4cOHdiwYYP19+IJ8IaSoBJCVI9b7cfWrFlj8/vChQsJCgoiLi6Ofv36odPp+Prrr1myZAn33HMPYE7it2vXjt27d9O3b1/WrVvHsWPH2LBhA8HBwXTt2pW3336bqVOnMmPGDFxdXZk/fz6RkZHMmjULMOe5duzYwezZs2tNP7U3KZ0LGfk4q6CZR8MYjW5RkJ+LW2Ee4Mzi3ck8f1cLAr00jg5LOFCVEulpaWk88cQTbNmyBT8/PwAyMzMZMGAAS5cutZY1qMj+/fsZMGCA9XdLzfHRo0ezcOFCpkyZQk5ODs8//zyZmZnceeedrFmzxmaCiMWLFzNhwgTuvfde1Go1w4YN49NPP7Uu9/X1Zd26dcTExNCjRw8aNWrE66+/zvPPP29tc/vtt7NkyRKmT5/Oq6++SuvWrfntt9/o2LFjVV4eIUQtZ68+TNQuiqKwOsGcKO8UpKFloBtj72zOL0fSCfJxY0TXQD76+fokMc0D3fnxmS68vfoU/u5O3N/Gj6//TKS7uzrR2F1Faq7CrrNXaySRrigKGRkZ+Pv7ywj4Bk76qKrT6XTMWmm+g+elId3w8/MjNtFcoinA3Rk/91saQ2I3bh6eOKlg/O2hjIsK4aNVB1m24wSGAtvi7SqViubeKg5fU/g57qIk0kW1q+n+x9nZudRymg0pQSWEsK/q6scsF+otcxzFxcVRWFjIwIEDrW3atm1LeHg4sbGx9O3bl9jYWDp16mRzJ010dDTjx48nISGBbt26ERsba7MNS5viFRNuZDAYMBiuX3rX6/VVOqbK+mF3EgBNPUw4N8Bp9ULdFXxdFXSGIj5Ye5J3h3V2dEjCgar0EXjhhRfIysoiISGB9PR00tPTOXr0KHq9nn/+85+V3k7//v1RFKXEw1IPSqVS8dZbb6HVasnPz2fDhg3cdtttNtsICAhgyZIlZGVlodPp+Oabb/Dysk14dO7cme3bt5Ofn8/FixeZOnVqiVgee+wxTp48icFg4OjRozz44IM3/8IIIeoEe/VhonY5fjmL8xn5qFEI8zEny9qHePHR4135vwfa4qkpObV8Uz83Ood6EOZXclRBiKf5v8gdp69Wb+B/skxCmpFRPXXZRd0hfdStcff0xt3T2/r7hpPm+ugR/rVn9JCiKFy4cIF//7gNvV6Pu6c3bp6lX7AL91JQq2D/+QzOXc2p4UhFQ1PT/c/p06dp0qQJLVq0YMSIESQnm0sdVZSgAspMUOn1ehISEqxtSktQWbZRFoPBgF6vt3lUhklpWCM1haiNqqMfM5lMTJw4kTvuuMM64FKr1eLq6mpN1lsEBwej1WqtbYr3UZbllmXltdHr9eTl5ZUaz8yZM23qwIeFVd+E5DmGItYeNw9GivAyVdt+ajOVCnoGm79f/rjvAjuOX0SR/r7BqlIifc2aNcybN4927a7XBmrfvj1z587ljz/+sFtwQghRHaQPq59WHUkBINjNhIvTrY/oDvYwb2N34jWMppo5UZJJSAVIH2VPOYYidiVlAtAioPYk0g15OcxdHY/KqeKa5+rCXAJdzROQ/hLv2Pruov6ryf6nT58+LFy4kDVr1vDZZ5+RlJTEXXfdRVZWlkMTVFC1JNX/4i4yf+tZdp6pmQvwQojSVUc/FhMTw9GjRyucI6umTJs2DZ1OZ31cuHCh2va15qiWvEITns4Q2EAmGS2Nrzqf5j5qFGDCDwdJz9Q5OiThIFVKpJtMJuvEncW5uLhgMjXMK1RCiLpD+rCqUxTFOrKjNlyFt8RjMpnrBwM08zDaZdsBbiq8NE7o84s4eqnkiVJtOH5RP0kfZT+/H0rBUGTC182JAI/aUdbFQuPhUem2LXzNd9T8cuAiphq6sCcapprsfx544AEee+wxOnfuTHR0NKtXryYzM5Nly5bZdT9VUZUk1cs/HaLQqDBmoaNnYhCiYbN3PzZhwgRWrlzJ5s2badasmfX5kJAQCgoKyMzMtGmfmppqLVkVEhJCampqieWWZeW18fHxwd3dvdSYNBoNPj4+No/q8r+4iwBE+phHZjdkvSN8cHWCzAIV38RedHQ4wkGqlEi/5557ePHFF0lJSbE+d+nSJSZNmsS9995rt+CEELWDyWRCq9XWmwSO9GFVl5GRwawVB5i14kCtKEGSmZnJOz9sJvbERc5dy0XjrCLEzT7vU7VKRc8w80npjhtGlx25amTod6f535F0ruTWj8+FqD2kj7IPRVH4bmciAK38Xer03AONnHJxVilczMhjT1K6o8MR9Zgj+x8/Pz9uu+02zpw549AEFdxakqqm7mITQpTOXv2YoihMmDCBX3/9lU2bNhEZGWmzvEePHri4uLBx40brcydPniQ5OZmoqCgAoqKiOHLkCGlpadY269evx8fHh/bt21vbFN+GpY1lG450IT2X2MRrgEIz90JHh+NwHi5q+jQxz9n4+Y5kdp9KkcFVDVCVEulz5sxBr9fTvHlzWrZsScuWLYmMjESv1/Pf//7X3jEKIRwsLS2NN79dY3MCUJdJH3Zr3L18cPeqPSVI3Dx9+O2w+b3Zr2WAXSfA6d3cF4BdZ65aR+GvOJrGkatGsgtMZOYb2XyhiBw5rxR2JH2Ufew5r+N4ag5qFFoGVFxCxREURflz8rLyv4Q5qSDc23whYNGe8zUQmWioHNn/ZGdnc/bsWUJDQ+t0gqpIEulCOJS9+rGYmBgWLVrEkiVL8Pb2RqvVotVqrWWhfH19GTt2LJMnT2bz5s3ExcXxzDPPEBUVRd++fQEYNGgQ7du3Z+TIkRw6dIi1a9cyffp0YmJi0GjMJefGjRtHYmIiU6ZM4cSJE8ybN49ly5YxadIk+784N+mXA+aScsHuKmrZjX0OE+nnTDMvFUUmhb8viudahpR4aWiq9FEICwvjwIEDbNiwgRMnTgDmGdBvnMhFCFF/ePr6OzoEu5E+rG4zKnDsSgE5xkI6BLtjMCr8kWCeAOfRrsGs2VlxDWFFUcjIyChRe9Xy/J+/EdXcvHx34jX+9f1mnr6/NzPXmWetH9LWj70XsknLKSL+Wt0d6SpqH+mjbp2iKMzbbp60sIW3gps9r7DZkaVWenBEywrbtvJVkahXWHNUy6XMPJr6lT2aVoiqqsn+5+WXX+ahhx4iIiKClJQU3njjDZycnBg+fLhNgiogIAAfHx9eeOGFMhNU77//PlqtttQE1Zw5c5gyZQpjxoxh06ZNLFu2jFWrVtn9eIQQtYO9+rHPPvsMgP79+9s8v2DBAp5++mkAZs+ejVqtZtiwYRgMBqKjo5k3b561rZOTEytXrmT8+PFERUXh6enJ6NGjeeutt6xtIiMjWbVqFZMmTeKTTz6hWbNmfPXVV0RHR1fh6O3HZFL4X5y5rFVk7RlD5XAqlYoufgWk5TihK1Dz6dZzvPXXro4OS9Sgm0qkb9q0iQkTJrB79258fHy47777uO+++wDQ6XR06NCB+fPnc9ddd1VLsEIIcSukD6u9iiew/f3Lv2gTl+7CpTwDAJNWJlufvy3Yi57hPqzZWfH+8rL1zPrtLC8N7WPzfH5OFvM2HEcpKkDl7EJEgDu9mwew91w6p3I9mPLbKfKLTIR6qnjxzmC+2KPif0cySM1TcfRyFh1DvW/yyIW4Tvoo+7moK+DwpSw0zmra+tpn3oTqUtla6b6u0L2pJwcu5fDpumO8+1j3Ol2uRtQujuh/Ll68yPDhw7l27RqNGzfmzjvvZPfu3TRu3Bio3wkqIYT92bsfq0zJDjc3N+bOncvcuXPLbBMREcHq1avL3U7//v2Jj4+vVFw1Ze+5dC5k5OGsUghyLXJ0OLWKxgl6NDIRe0XNd3sucWcLf+7rHC7nZQ3ETQ3P+fjjj3nuuedKrRHn6+vL3//+dz766CO7BSeEEPYkfVjtVdna63EX9FzKM0+6F+LlguVUxc1FzezHu97UyYu7V+lJb3cvH9w8va3J/WGdGwFwVmfifEY+wd6uRIU6o1ap8HVzprmP+b/SBbsrHgl/syyTqUrtvYZB+ij7UBSFuEvZgPkuFfd6ciuyIS8Hp0xzvdef4rUknE+tYA0hKs8R/c/SpUtJSUnBYDBw8eJFli5dSsuW1+/OsCSo0tPTycnJ4ZdffrHWPrewJKhyc3O5cuUKH374Ic7Oth96S4LKYDBw9uxZ60hSIUT9IudR9mWZZDTcW2XX0pn1RaiHQis/8wvz4rKjXEiVOWwaipv6OBw6dIj777+/zOWDBg0iLi7uloMSQojqIH1Y7VaZ2us/xF0GzBMHDmnnx+ePNKdvqBP/G9OVDk18S7S3JKKrkozOz81m3objHDp1jsc6BQAQ7u/GR39tg5vz9YR9+0AnQGHz6QwSr+aWu01Lcl5RTGRkZFQYV0ZGBu/8sLlWTOwqqp/0UfaRml1IWnYRahSGta9f9yKH+roQ6uWMSYF/rz0rF9mE3Uj/I4So66Qfs58cQxGrj5i/d0V6yyjrsnRtrKaZjwt5Rnhz9Wk5L2sgbiqRnpqaiotL2ZM1OTs7c+XKlVsOSojiTCaTdWIPk8nk6HBEHSZ9WPUrMJrYmqjnqaVnSc2x7+dVn1/IjrPmhHKbQPPfsWWgGy18nWjiqyl1nczMTOtI98zMzJvep7uXDx7evozrG8TQli78b2xX2oV42bTx1aho8mdlhm/3ppS7vYyMDGb9vB1dxjXmbThe4Qh8ME+mKhoG6aPs4/Bl8wWtCB8VgZ61c5LRW9GnmRtOKohNyuT3Q+X3OUJUlvQ/Qoi6Tvox+1AUhV/2JpJbYKSZryuN3CQ5XBZTQR7NnTJQAxtPXWPhdkmmNwQ3lUhv2rQpR48eLXP54cOHCQ0NveWghCguLS2NWcv3Mmv5XtLS0hwdjqjDpA+rfgcu5XL6moHLWYXsTS2i0Gi/ZPrao1oKjArezib83Cr/31dlRrpXhoeLCmd16SMy2viaT5jWHL9KXlH5J08aD2+7xiXqD+mjbt359DzOZZjnUGjjZ/t5VRQFnU4H1O0vOD4aJ7o19QRgxu8JXEgv/04YISpD+h8hRF0n/Zh9ZGVl8ckG8ySt/k6FFBYVOjii2i3IR0PnxubSo+/8cZrD56T0Xn13U4n0Bx98kNdee438/PwSy/Ly8njjjTcYMmSI3YITwsLLLxAvv8AK28nodVEe6cOqV6HRxJlr11/brALYeNJ+teIsIy+beRhr3UQuAW7QuYkXhUaF0xm1e2JDUXtJH3Xrlv5Z/qmptxpvF2wS5/m52cxdHY+hoMCBEdpHl1BP2oV4kpFbyJgFe8jOr9yXXEVRyMzMlNFSogTpf4QQdZ30Y/ZxMTOfqwZzYvi2oMpNiN7QtfFX0SXEDaMCry4/SUamTs616rGbSqRPnz6d9PR0brvtNt5//32WL1/O8uXLee+992jTpg3p6en861//qq5YhYPp84vQ5RtrdYcgo9dFeaQPq167kjLJL1LwcFHzt87mmuLbztonkX4128Cus9cAaOZROy+SjejZBIDTmSYMRbUzRlG72buPmjlzJr169cLb25ugoCCGDh3KyZMnbdrk5+cTExNDYGAgXl5eDBs2jNRU25E0ycnJDB48GA8PD4KCgnjllVcoKiqyabNlyxa6d++ORqOhVatWLFy4sMqvQ1UVGk2sOWa+ZbtNgAuGvJwSiXONR/34QqhWwct9fNCoTZy+ksvfv9tLfmHFF/F0Oh3//nHbnxcYhLhOzpGEEHWd9GP2seKI+Tww1NsZL1eZZbQyCg15NCq8gosaErTZPP/NTrKyshwdlqgmzhU3uS44OJhdu3Yxfvx4pk2bZk2oqlQqoqOjmTt3LsHBwdUSqHCszSfSGPf9YQxGhWY+zjx7V+1Npldm5LpomKQPq15xyXoAwv1ciQr3YtnhdGKTdBhNCk5llESprN/iL2E0KXQI8cTL2XaUiWUCT8vPjjLgtgBCfFzR6gv449gVxgQ1sonP39/fYbGJusHefdTWrVuJiYmhV69eFBUV8eqrrzJo0CCOHTuGp6e5NMikSZNYtWoVP/30E76+vkyYMIFHHnmEnTt3AmA0Ghk8eDAhISHs2rWLy5cvM2rUKFxcXPjPf/4DQFJSEoMHD2bcuHEsXryYjRs38uyzzxIaGkp0dLSdX6Wy7Th9lYy8Ityd1YR6qcnKqD+J8xvl52azbGsyvQK8iL3mxs7ETEZ9s5cvR/XE193FWsbG19e3xB08bh5eZWxVNGRyjiSEqOukH7t1JpPC70fMAxJbBZY+B5Uona+XGx3VauKvmDiajgysqsduKpEOEBERwerVq8nIyODMmTMoikLr1q0lQVCPpWXl84/FBzAYzf8RXdQX8eXuFN4cJvXFRN0jfVj1OXjJfNU9xNuFDsHuuKhBl1/EoYuZdA+v/OtrKT1gLsegQlEUluxNBuDhzsEcP3bNpn1+ThbzNhzHzc2NMXdG2ulobp6zWsUT3UP5eMt5Fu+/zDN3t0WlUpGRkcE7P2xm+vABDotN1B327KPWrFlj8/vChQsJCgoiLi6Ofv36odPp+Prrr1myZAn33HMPAAsWLKBdu3bs3r2bvn37sm7dOo4dO8aGDRsIDg6ma9euvP3220ydOpUZM2bg6urK/PnziYyMZNasWQC0a9eOHTt2MHv27DIT6QaDAYPBYP1dr9ff9PHd6LeDlwBoEahBXcvKP1UHjYcHGkz0a6Ji3xU1e5PSGThrM2881IEOvoXMW3uYfz3eDz8/P6D+1IgX1UfOkYQQdZ30Y7dmT9I1LmXm46yCCD9XMBVVvJKwinA3cFztRF6Rml8PaXl+gJ+jQxLVoMr3afj7+9OrVy969+4tnVI9t2DnOfIKjbQP9qBfhHlk14/xaaTn1P0ao9XNUrNd6rXXPtKH2VdegZETqTkABHu54KRWEexhTmTtSby58i6ZmZnM+nk7+fnmJNua41dJvJKDp6sTD7RrVOo6pU3caZuQrxl/7RyEswrOXs1jx5mr1ufdPK/Hlpqv5niWGyeuFlBkkoSWKF119FGWch4BAebSS3FxcRQWFjJw4EBrm7Zt2xIeHk5sbCwAsbGxdOrUyWYEV3R0NHq9noSEBGub4tuwtLFsozQzZ87E19fX+ggLC6vycSmKQkraNdYlmG9Fbt3IrcrbqosaucHb9zTCx1XFlexCJvxwkAe/OMKRDCe2nk7n+GU9uxOvsTT2DP9cvJerWYaKNyoaNDlHqpoGcP1OiDpD+rGq+WF3EgDNPE043+IdxQ2RWgVt/Mzf777bcwmTfNerl6TgUQNSlYk4s/ILWbT7PABP9w6lhb8Lge5O5BeZ+C72XDVGWz+kpaXx5rdrpF57PVWZ+sP9+/dHpVLZPMaNG2fTpq7UHy7PoYuZFJnM9dEttfSCPMz/7km6Vt6qpSpw8SHhioEd57J4a81ZAP5+d0s8NU6V3kZetp65q+OsCfma4O3mTAs/83F/syPJZpmiKHywMYldV11JytOw/7KB5ccyuJotFyVF9TOZTEycOJE77riDjh07AqDVanF1dbWOWLYIDg5Gq9Va29x4G7Tl94ra6PV68vLySo1n2rRp6HQ66+PChQtVPjadTsek73eRV2jEw8mIt1PDmvDXkJfDmt0JPNTGm25NPPF0VZNnUnMiE178+TgPfLKdJ77YzbTfT3FI78EfyQpf7LxQq+e8EaIuKp50ks+XEKKu0ecVsO64eSBQhKcMBKyqCE8TLmrzpK1bTkkeqD6SRHoDUpWJOH/Ym0xWfhGtgry4s4W5zmbHIHOtrKV7L1BklA62Ip6+cgW8vrLUH969ezfr16+nsLCQQYMGkZOTY9Puueee4/Lly9bH+++/b11mqT9cUFDArl27+Pbbb1m4cCGvv/66tY2l/vCAAQM4ePAgEydO5Nlnn2Xt2rU1dqwV2X/OPOo82MvFWo836M8R6fvPZdxUX7HlrJ5Nqa7Eaws4cSUfQ5FC78gAxvdvedNxuXnWfC3gNv5OqIDNJ69w/PL1chUL96TwQ5wWUAjRFODmrCIjz8j4ZccqfYePoiikp6fLF3Rx02JiYjh69ChLly51dCgAaDQafHx8bB63QmtwASDcS1WiJnhDoPHwwEmtomczTxYODaVPIyPNvSHSX0OAhwsRAW4EuhTh72K+yDBvezLfbj8lfYkQdlR8PhipjSuEqGuW7Ukiv8iEt4tCgEbOD6rKWQ0tfM2p1m93nXdwNKI6SCK9DqrKyHILL7/ASk/GaSgy8vWfIyqf79fCWm80ws8FP3dntPp8Np+8cnPBC1GPrFmzhqeffpoOHTrQpUsXFi5cSHJyMnFxcTbtPDw8CAkJsT6KJ4ws9YcXLVpE165deeCBB3j77beZO3cuBQXm5Grx+sPt2rVjwoQJPProo8yePbvM2AwGA3q93uZRnfafN0/2Gex9feoNP40Kb40T2YYiDl/SVWo7OQVGPtquxYSKxh5OdA5xZ+ZDrfnhub64ONWN/7K8XVXc19bcz76z6hgmRSFJZ+S/28x13jv7FtHdN4/oFh54uKg5ezWPp77agy6v0GY71ydRvX4ia6m3bplcVYjKmDBhAitXrmTz5s00a9bM+nxISAgFBQV/lkC6LjU1lZCQEGub1NTUEssty8pr4+Pjg7u7u70Pp4SCIhMp2eYEcVOPhp28ys/N5qt1hwh2NdDZO5dejYoY3MKF7x5vxT1NVdzV2EAbb/P/Le+sOUNiipzHCWEvzurr5yn5hQ3rzhghRN2mKAr/i78MQKSXSUpV3aLWfw6s2nrqColXsh0djrCzupGVEDaKjyy/evVqxStUgclk4vutx0nVGwj20TC0a1PrMie1isHtzUmiJXvkChvc2sUNUX/cWH/YYvHixTRq1IiOHTsybdo0cnNzrcvqQv3hiphMCnF/JtJDvFysz6tVKvo09wVgyw0X3RRFKVEfXFEUdpzLJqfQhL+riftauNM7zIvodo1sRnlVRWn10quzhvoL/cJxdVKz88w1nlhwiN2XzV+oR/YKpaW3+WdvjZoH2/gS6OnCsct6hn19iN2Xi/hsezL/i7tI8uUrzPp5u82EjGBbb12I8iiKwoQJE/j111/ZtGkTkZG2k/H26NEDFxcXNm7caH3u5MmTJCcnExUVBUBUVBRHjhyxuZNt/fr1+Pj40L59e2ub4tuwtLFso7rtS9ZRaAKNyogHUipJ4+Fh/dnNwws3Dy+bSUZv8yoi0N2JIhPM35ooo9KFsJPin6X8Qvk+IISoO45c0nEiNQc1ECZlXW6Zt6uKu1qZcwLf75acWX0jifQ6qqyR5bq8QpYfvMTCvZc5q1dx9mrptUkrcumyltmbzwHwty6NcHW2fas83NE84d+WU1e4kJ574+oNTlXK5oj6pbT6wwBPPvkkixYtYvPmzUybNo3vv/+ep556yrq8LtQfrsjJ1Cyy8otwd1ET4OFss+z2SHNpo60nzZ8LRVH4atdFfjxVSP9P9rJwZ5L1i2diuoELugJc1Cq6+xda74KxRzkTS710Q8H1JFt+bna11VBv6ufGu8M6AXDmah4K8EC7RrzYP8KmnZ+7M5/9rT0RgR6k5xaSqDPxZewlXv7pEIM/P0AavnaPTTQcMTExLFq0iCVLluDt7W294GvpN3x9fRk7diyTJ09m8+bNxMXF8cwzzxAVFUXfvn0BGDRoEO3bt2fkyJEcOnSItWvXMn36dGJiYtBozKXexo0bR2JiIlOmTOHEiRPMmzePZcuWMWnSpBo5zi2nzaWlQtxlBFVpzH1dvLX/U6mgW6j5b/fz4askyah0Ieyi0FQ8kS4j0oUQdYdlXrwwL7iJKalEGRRF4S/tzN/j/rf/IjmGogrWEHWJc8VNRF2gKAqL47R8vecguQWWEzcnRiw6RpdmlxgZ1ZxewbbJ8IycAvYm6zmfWYCvxskmSbXsYBo5RSrcnVX8tVPjEvsL93fjrtaN2H76KnM3n+HdYZ0B2Hg8lXf/OMGlzFzCfZzp1bT6b+muLSpbMkfUT5b6wzt27LB5/vnnn7f+3KlTJ0JDQ7n33ns5e/YsLVvefM3vytJoNNYkV3WLPWueTLRrM29r8tvijhZ+ABy+pOPslWzWHNUyb4c5qZ9baGLGimNk5hVyf2tvYpPNt72N6BaILvWidRuZmZnMXXOQ6cMH3FKcpdVLr84a6o90b0ZYgAfrD1/g+Hkt7wxpVWrt5laNPVg/6W5WxiWyYNtpIoN8OHatiDNp2ew3uFKkqtqoEEtpGH9//wZZM1rAZ599BpgnPS5uwYIFPP300wDMnj0btVrNsGHDMBgMREdHM2/ePGtbJycnVq5cyfjx44mKisLT05PRo0fz1ltvWdtERkayatUqJk2axCeffEKzZs346quviI6OrvZjNJkUayI91E0SV2UpPkodINTLmUB3NdfyTPx4QMu0pkEOikyI+qP4fDD5RdIfCSHqhpTMPH45cAmA5p6S8LWHgvxc9p+6iLeriixDEUv3JjP2rhaODkvYiSTSazGTyWQd3RwUFIRaXfoNBCZFYfv5XBIzzGUlWgV50aaRhv2JV0gvdOLQRR2HfjqEr5sTjT3UqFCx7fsEzl7Lt9lOfNox/tJVj1qtYt4Oc0faLdQND9fSL0lOHHgb209fZdn+C3QN8+N0Wra1pjrAiasFFBoV/lHGKFLL8ZV3bELUBZb6w9u2bbOpP1yaPn36AHDmzBlatmxJSEgIe/futWlT2+oPV2R3ojmR3qGRK+bSAdeTto29XBnYLpgNx1O5d9ZW6/NdGqnpHhHAgrirfLzhNB9vMD/v7+7EE10C+XzdRYqrDeVMrtcsh8qWg+nVPICWPvBhelq5yWxXZzX9WwewfV8uhqsZLBkdzSdbzrN4/2UOXVMRf1HPvTeUDKqIpZ769OEDSpQbEg1DZe7icHNzY+7cucydO7fMNhEREaxevbrc7fTv35/4+PibjvFWJaTouZJdgLNKIVAjtyJXlkqlokMjZ7ZdKODHA5eZGN0B9zLO94QQFTOZFIpXrJPSLkKIuuLL7YkUmRSC3CHQTcq92YubpzcdG+cQe8nAnM1neLx3OF4aScHWB5K9rMUqWy5k36V8EjMKcVar+PdfO7J+Uj9eG9Scu0JMLB/biVei29DUzx1dvpEz6YWcTi+wJtHD/TU08nBCrYJz6fl8uukMH284jVGBlv4u3BboWuZ+e0T480i3ppgU+L9fjliT6E/f3pzp90WgAs5mFLI9sfRJBtPS0njz2zU2x5aWVcD+S3nsOJ/L3uTqnRxRiFtVUf3h0hw8eBCA0NBQoG7UHy5PkdHEniTzaNBjJ0+VWiYlZkBLm3ILj3QJooVbLvr0K/QIdsL5z/rn3ho1g1r74uJUMuFsSWLfOPlmTcrPyWLehuPMXXOoRN1ye3L38sZZrWLygAjCPIwoqPjXitNk5t587efacAFCiOq09ZS57wx2h1K6DlGOMB8nPJ0hM6+I/x24WPEKQogyFd4wR1JegYxIF0LUflezDfywNxmA9v4ODqYeahXggpcLZOQW8t4fJxwdjrATuRxSy1VULmTVqSyOXTEndGbc35wRfWzr7wZ4uBAzIIxxd7dk+Z5TfL/3IooCT/Rswn1dW1CYncHnW89SYFRoFerP/hQDOQVF9GnqjjZdV2E5gHeHdcbZScUfR7U09XPnleg23NsuGK1Wy/JDWo6kGfgiNoVHo9qgLmWyQE/f6711QoqOkYuPocs3n3j+85fTnMtS8bcO3pV6rYSoaTExMSxZsoTly5db6w+Due6wu7s7Z8+eZcmSJTz44IMEBgZy+PBhJk2aRL9+/ejc2VwOqXj94ffffx+tVltq/eE5c+YwZcoUxowZw6ZNm1i2bBmrVq1y2LFb7DhzFV1eIX7uzjT2Ln10fLdwf74e3ZNvd52nT4sA/tbJn3//kIy7lw/dGrnx9l/DuKQzsPNUqjWpfiNLElspKsBQUIBndR5UOdy9fDAVGjAY8itufItUKhVd/ArJKFCjzSrgX78e5e0Hmlf7foWoS7aeMtf3DnGXEVQ3S61S0doPDl6Fr7cn8mTv8Fue2FmIhqrIaNsHSWkXIURdMG/zGfILTfi7mPB3ln7L3tQqFV39i9iR5sz3u8/TI8Kfod2aOjoscYskkV6HpWYXsfaMuaZwj1A3Bt5W9q37TmoVt0f6ciT5KgD9W/kT6KVBa14dVycVD7QL5JkB5lISWq2Wz7dWPCLcWQ2T7wxm8p3BJUq0dArWcOKqgTNX89h0Io2B7YPL3I4ut5C/fx+HLt+Iv5saf3cnEjMK+Wj9KfycKh7l25BJiRzHqaj+sKurKxs2bODjjz8mJyeHsLAwhg0bxvTp061ta3v9YQtDkZG48xno9VkUGhXrqHFLPb372zXCkJZd5vr3tA3mnrbmPiA9Pd1mWYiPhhAfDXvOlD9RryWJnV8DSezawkUNvYMUtl5WserIZQa0lAuLQljo8go5kJwJQLC7AlJJ4aY1cc4lQeXOuWu5rD+m5f6OoY4OSYg66cZEukEmGxVC1HJJV3P4LtY8yWh7f5mwvboEuSu09VdzIsPESz8d4poumzF33yZzWNVhkkivo/IKTWw5l4NRgUg/FzoF18ykgjeylJ8BeOnh3taazgAaZzVtGmk4mmbgqx2J5SbSP1h3gosZeTTz1dAvXIPGWc1drdz4dp+W9zad58HWXni5SpK4NJYSOW+Mvt/m9RfVr6L6w2FhYWzdurXcNlC76w8DJFzO5uX5B0jVm+9+cXFS0TrQDZ99Kaw8nALAkA6N+TntnEPiq+/8NTCqT1MW7L7Eu+uT6N+k9HYyuahoaHaeuYrRpNAi0B1P52wMN1/9qMFzVkMrPxXHM+C9NSfp3yYINxfzBPQ6nQ5fX1/pT4SohBKlXSSRLoSo5d794zhFJoVQjz8HJIhq087bQG6BE8k5at5ec4YLmfm8/pfOpVZtELWfZCbrIKNJYeu5XHILFZr5OHNHuMctf8mxjGrWarWYTDc3pMvLL7DMEjTtG2twUsPuxHSOXiq9VvoxbQ6L95jrck0bGIHG2fy2fC6qCd3C/cgpMLH7Ym6lJk1rqIqXyBHCnvKKFMYvO0aq3oC/hwvB3q4UGhWOpeXx0ebzmBT4a7emtA/1cnSo9UZp9eCfv70ZLRp5cjWnkPi0kl/OFUUhMTGRd37YVGxCVCHqL0VRWH/EXNe7R1MPHDV3Qn3Qxk9FI08Xkq7m8O9Vx61J9H//uA2drvRzNyGELaPJtg/KMUgiXQhRe8WdT2dtQioqFDr4Fjo6nHpPpYIegUZu8zTfWb1w90We+3YPuYYiB0cmqkIS6XWMyaTw/qZkLmcX4ayG/7uzUakT81VuW+bkuaIo5Ogz+Hzj8VInNjWZTGi1Wmvbm+Hpqube1uYk76frjpVI0isKvL8pGeXPZFyPsOtlC5zVKt4b1hlntYoLuiLOZ9qvg8/MM3IkNZ+1J9IxmhTrMVblQoIQ9dmBVCPZBiOdmvqyfeo9rBrXnftv8yXCz5Vgb1f+0qkxk/s1lQtddpSfm319UtOCAhRFITdLx6v3NQfgrM7EnvO2ya2MjAxm/bwdlbNj7k4SoqZlZmay9pi5Pnri2UQMBTIcvapc1DD5rlBUKvh+93n+8f1ezl3Lxc1DLpAKUVmFRtvvD9mSHBFC1FKKovDBH8cBiPRR4ePq4IAaCJUKOjZyomdgEWoUNp68xmPzd5KmbzhlS+sLSaTXIUVGE//67SjLj15FBdwZ7kG4n4tNm8okvS0J9KNHj/Lhj5vIzc0FzKOaSxtZfvXqVWYt38v8NfHWtjfjgUhzz7z+5DX2n0y2WZaYpeJEWi7ebs5Me7BtiXVvC/ZmVC9zuZLdF/PQ59/6SemGU+ksP5nF/pR83liTxMiv93AhRcus5XtLvZAgREN1KTOf81nmL4YzH+mEl8YZtUpFM19X7mvty5InWuBdpGf+ukNkZmY6NthaRlEU0tPTSU9Pr9JFBncvH9w8zRcWLYn1zQfP8Jf2fgC8s+YsOTd8Sdd4SP100XCcvZpLbpGCkwpCfFwqXkGUyZCXw+7DJ3nxzlBUwB/HrjL0y3h+P5PPJ1vOsf9ceonRthVRFIXMzEy5yCoajBtrpGfb4TuLEEJUh23HLrL7XCYqFFp7y2j0mhbupXBnsBFXJ3MJ1b/M2cGxFLkDsC6RRHodcS49n2Gf7eKHvcmoVXBXhAeR/iUvHVpqls9fE09eXl6p27KMPp+/Jh61S+VGL3r5BeJRxfIhLQNcCfNxRkHF3B2XrF+qLukMHMkwvwVfHtSGIG+3Utd/ulcIvho1eUUKc3ZcqlIMFsnXcvn3enM5Cn83Ne4uanadvcbHWy+UW6KmpskIeVEb/O9gKgB9m/vSsalvqW3cvXxw9/KpybDqhMzMTGatOMCHv8dx7tw5FMVUolzLzbC8zs/2aoyHs7n/nLBoL1euXpVElWiQdiZmAhDsqaaKN+aJYjTuHtwT5sxf2vsT5ueKkwp0BoUFuy/x6PxYev17Ay8s2kfcufSKNwZSGkY0OEUmGZEuhKi9FEVBr9ej1+v5bLt5cGOElwlPmTXRIRq5KdwX7kxTHxe0egOPzo9l8wkZ0FlXSCK9llMUhaOpeYxafIxDF3X4uDnzzoMtaBlQ9v03lUl6e/r6VzkxXhXdm7ijAraezeT73efR6vKZ8vsZihQVnZt48lTfiDLXdXVWc0e4BwC/H73K7sRrVYrBaFJ4+adD5BWaCPZy4i9tvZk5uAUqFfx65CppObXnhNdyQURGyAtHMZkUVv9ZNuHRrmVPFFwZltHZDS3ha7nAMHd1HLqMazblWqrK09WJnv4GVChsPp3Bk/O2Sk100SDtSjS/75t6Ozk4kvrBkJfD3NXx+LqYuP82P74f1pQ+jYw0dSvCy1VNek4BK46mMWx+LA//dxvbT1+x6dNLG4EupWFEQ1J444h0SaQLIWqRrKwsZq08yGs/xf05Gh3a+sqAPUfSmPJo73qNIA8VuQVGnv12H6vizzW478x1kSTSa7HUrALWnMlhX4qBAqNCsLvCohHtuKd13ZtYMsDdiW6h5hHnry9PoO/MjZy9lo+bk8Jb97fAqYLZioO9nGkTaL548OovR8gvvPkJfL7ekcjec+l4uKi5K9wDtUpF3+a+PNq9GQB7L+bVqk6rNo2QFw3PoYuZXMkuxFkNd7Qw9zmWSTArO6La0r6hT4Lp5mlOJhUv13IrAjUKfZqa+9PTuW58vvMCpkr0XcXLzZhMpgZ5cUPUD9n5hRy4oAegiZck0u1F42EetJCfm823Gw4R7GqgR0AB94fD3SEmwtyLUKFw6FIWI7/ey6PzdvBH/DmKjCZ0Oh3vLN1KcnIy19Iz5G460eBIaRchRG3n5uHF2Uxz3xTsbsJDRqM7nLenO7c3KqCJphCjAi/+lMCJC1ccHZaogHx0aglL3XKAoKAgdiem848lx8nMK8JJpdClsROdm3gT5H3zM0FYtl2VyULtqXOwhm7h/ny3X0uhUaF9sAdhznpCKjm7Rc+m7lzLV0i8msNbK4/x76EdUakqdz/3sRQ9H649BcCLd4ehvXb9VuNXotuw8nAKV3KNJGZIjTAhANYmmMu6NPFUo3E2X3O1TGgZ0LQFGo3mz7roClD65zA/J4t5G46jFBXIJJh21irAlez8Qo5eM/H5zoscuKAnoAi8yikVnZGRwawVBwAYc2ckc9ccZPrwAQQEBFgvevj7+1e6XxXCUTYeuUChScHLVY23qwp9jqMjqn8sSXUAD09vwt0K8VVfpZ0PnMl2ISnHmbgLeuJ+TMDz1xMEeLpwRe/Eb/OPUqSAt8aJJp6gyyvEz89xxyFETSmU0i5CiFpOURQSM835jggvueBdW6hV0DsYdl1zJi2niH8tP843I13x9fWV72W1lCTSawlLKQ/FZKJJSBBz9mZgUswjuXsFFuDr4XrTHyKlWAL9u13nyNVn4NUotJqOoGIqlYrnopow+cHO6PIKUeVl8uGvu20uIJTH1UnFtIERvPz7GZbsScZQaGLyoNtwVqvYm5TOjtNX2ZN4hczcAkK9nOlxQc8DQcGcv5bD89/vp8Bo4t62QfylQyBfbLueSA/ycWN0rxDm70phf0oeeVUY7S5EfaIoCmsTtACEedv2O5YJLfOy9cxdnUSTFm1xcyt9fgMwj8I2FRowGGQ2cnvr3NgZjSqfBJ0z+5L1qNHQyhfSsgwEBJS+TvF69m6e13/OyMjgnR82WxPrQtRmO5MsZV2c5QtGDXNzgo6+hbT0KiK5yJekzEJyCozkFBgpflE1y2DkpAEe/yaepX+/neaNpMyLqN9uHJGeJYl0IUQtczW3iNxCBSeVQoi73JVam6hVcGdzT5Yf03HgUjb/+GYb85/tj4+PzEVWG0kivRbx8gskPjmd/+0xf0Hs39yDMF8X8vSVm9jpRnnZej7feBylMB+vRqF2q4lefPR8VW7d9dQ446lxRpuvIzdLx+cbj+PhcY6XHu5d4bp3RPry76GdmP7bEX4+cJGfD1wstV1mfgETfj6N+++J5BcZURRo5qvhg0c7UZCdWaL98O7BLNqvJbvAxMK9Wt4Ma3rTx2VxPj2fPdpLuLk4EekpJ9Gi7jmdlk3S1RxcnFQ08Sy7ApilZIk9WersVmVCTke6XvYGbib24iPBq6K5N0wf2pl31iYSd0HPKR08OP8AfSIDaRvkRrLeREZuYZmJ9eKKJ9ZlhLqorUwmE9tOm+dKaeItp7GO4u6k0CfEnTtb+HJXC1+MTm78sPkQXr6+OGMk6YKW4/neaLMKeWTeThY/15d2oaVPWi1EfVBkvGFEer7c5SqEqF3Op5sHNgW7KzJRey3ko3GibaALCVcLOaF3lhKctZjUSK9FjqTmc/Ca+U/SobGGSVEBOFdQO7witzqpqMlk4urVqzYf4hx9Bp9vPM6Hv+7mxIkTt/wB9/T1v6la4E/2CWfxs33pFOoJmK/etQ3x5tk7I/nwLy25J9KD1gGu+Lg5kVdoTqI3dlPo6pNbahIdQOOspvefNYeXHEjl3NWbv09cbzCy9kw2j3+XwItLD/L37+N4+OsjnNSppBMUdcrao+bR6H0ifHGp4bMs80j3OOuEnHUlsZ6fm22dTDQ/P7/SMedl65n1255bqh8fEeDOF0+0p29gAY00CiYFYhOvsWD3JXakFPHAZ3G8tOwQZ67kVnqblhHqGRkZFU4W21AnkxWOceDsZbRZhahRCJVEusMV5OXw87bDNEaHv0bBR+OEu7OKpr4u3NkoHx9nE+m5RTz++W4OXsh0dLhCVJtCk0w2KoSo3c5nmBPpoe5S1qW26hjkilqlkJ6vEPfnfECi9pFEei3xv0Np7E8xd2ztAtT0aupW4SjA0pLc9pajz+Dz1XvJzbVNwHj6+oNabV1mGaWu1WprZIKpqJaBfPl4W/4aUcSWmG6smdiP6UPac2cLPyL8XLkzwoPVz3Vhw+S7+X1sJ4a086dxo/KT9eG+LjTxdqbQqPDyT4coMpowmUyVOqb9F/T8fiKLlKwinNTQM8KfFo08ySs0cSTDif9sOF/nk0y6Avj18BUuZeY5OhRRzdYeMyfS77mtaiU+bjX5XXyk+42J9dqitGO0TCaan5vN3NVx5OcbKrUtd69bn4BUpVIR6m7i7iYKy5/rxr//2pG/dg7C11VFgVHh5wMXeXzBITae0XM+w0BypoEio4mMjIwyk+CWEerFk+qlqWi5EPa07Yz5fdZYY7rlwQbCPlQqmLs6vkQ/7eYEtzf6f/buO77Jav8D+CdJM7oXdEFbCsjeqxZlCZeCyE8UvaioqAhXb1GGguJVQLhaLwLiqKAi1IUIKqiAQClLthQqlFFWsay2QOlukzY5vz9KHpouOtKsft6vV16QPCdPvqdpvj3PN+c5TxF8NUBOUQnGfrEf645chl5vQFZWlt2Pi4jKMs5I19y6rgwvNkpEtiQtR4vrBaV5icu62C6NkxyhrqV/T745eNnK0VBVOJXHiozF5/1XS7Bw+0UAQHsvAzo3UdboVPr8nJv4LOEEQjv0gKura4PF6exW9am4xm2ls9SvVFii5Xaxv/ZrvNeEQgaonCr/PshJIUNrPzekGfJqtC+ZTIa+wc7YeDYfh/6+if9uOIl/9fbB3K83Y/a4YQgICKj0edtOpWPaurMoNgB+rgp8+mh79GoXCiEElsQl4f1tf+O34zfQcusZTP1Hmzr31VqKivWY9XsK4q44Ie5KKny8PTGmd4i1w6IGIITA8QtXkXQ5B3IZMKC1D85eqHz5pOoY10/XuLrBmJnqs4RJTZeQseTsdWOxPKhlu0q3N8SyNzUV7K1B11ZBGH6XO9RFmbinXTOsO5WL+NOZSLmpRcpNLeLOls5w+PX8STR1UWBiRGC1RS1jUd34Pnp5ed36WZc+VnZZGKKGtOtc6XJ3/hpez8SWlL04aVkqOdA/SIYreg8c+DsbU35IxLsbTsDJUIRBbf3QITQAXZp7opmrgLeXF5eSIrtVfGuNdI1SgaISA/J1eugNAgp+4UdENmDHrWXx/FwUUCu49JQta+1hQEqeAjtO38CF6/lo0aThan1UN5yRbkUZGRmYtCwer6z5CwJAuyYqdPCqXQHIWMg2lLmwqLVm+FS2REtlM9qNs7ytGWtV3NUKvPmPFgCA2L0X8Mnuy3D2qLrw90viZUz8OgE6vUCIpxOGtXZDcy81gNLC/MNdmqKHb+k3ih/Gn8G6I/b3reJb65KwJTkTgECvYHd4uaisHRI1kJs3b2LmqoMAgN4tfODtoqzzvsoXkosK8uq9hMmdWHr2ujWL5TWlLcjDjmMXEKDIx+cPt0D7php4OyvgrCz981+kl+FirgFvbbmM9eeL8cPhNBToSmer6A0Cp9Lz8VtSBv66VoIV+y9j27G/MXfldqSkpGDhb4ex8LfDUkH9TrgEDNVXZr4ORy/nAmAh3Z44yYB3I5vj+T5+UMoFMvJ0uFIgx3dHruM/65Iw8pM9uPv9PXjjxyM4k55r7XCJ6qTk1tmrGqVCeixfx1npRGQbdpwpnYgQ7KG4Q0uyNnclEOQqgwCwfE+KtcOhSnBGuhUdvZKHI/me0AtgaFtvBDkbkJddumxG2ZncNWGcEW68sGhdlb2QqLmKHZUV+7/eewEFOTdRVFSE2s5jLLsfg14vxevn52eWeO+7yxuzR3bA27+dwHcJ6fBRK/DIjUKUnZBuMAh8uTsF72w8CQAY2tYHgc56yCuZSRXmLtAhzB/fHErHjJ+OIqyJK7oGe5kl1ob2U8IlrEm4BLkM6NvUgAWj21Q5M5/snxACqUUqAAIjuwbV6nk1mQle2RImxuLqrXs1fs2q8AKoFTm7eUCj0aCVrwb3tHCHEALDWyiwfM8F6J19caVAICVTi1ydAf/bmoLFO/6Gp8YJ1/KK8cPpo9J+ju9KBQCo5BpgTxpkchf4ulQcRpS98GrZi5Ual4B58/FB8KnJ1U+Jytl+KgMGAXgqBZwV9vl5bIy0hfn4bPspiJJiPBAqR6+WTbH6cBqytQYUQYnrhQI6gwzfJ1zF9wlX0auFN8aGh2B4p0CToiSRLSu5NSNd6SSDSiGHTm9AXlEJPDR1n5RARGQuUwa1gFZXjBBXARRZOxq6k5auxbiS74TVhy7ilX+0hWc9JriR+bGQbiWn0nLwyi9noRcyRLTwwFtDW2D57tvfNpVdtqWmXD29YSiuX1asb0G+ukJ8+X27eHpLM9XLP89YeDE+LoSAEKJCIb6goACfxRdXWFKmvp69Jww+riq88fNRZGoNeOKbE+jbKh392zSFQQhsTkrDX5eyAQDP9G2Bib198MWu81Xu78V7muFqvsDWkxmY+M0h/BJ1LwI8NdXGYBACekP1X2ikXM/H6oNXceSGHN8eSsNj93iguXflp1fX1tmMPLy5LgkAMD48EDfTLpplv2S7TqblI1sroFLIMLJLEPRFNZsZWNlSLpUpW2A1FqUL83Lw7vd/AUCVy6RYQnXF8pr2rz6vXf7n0pAK83Lw6e+l/WnqqkCwrwbRw4Ix+/dzyNAqcTGrCBl5pbP63dVytPRRI7+gCM193XD4cgFytHqsO5EFAGji4gRXlRyFJbfjvnnzJhb+dhgA8MrIHiZFcy4BQ/WxLrH0rK5mLiyi2xuNixuEvhjZmdexK+EkQl1cAGfAq2kT6Et0OHf1Js7nynCtWIVDF27i0IWbmP3LcYzu0RwP9WiGzs08q132RQiB7OxseHpW346ooRTfWiNdLpPBTeOEzHwdLzhKRDajnb8bejUvvZZTLgvpNq+pRsBLLUOW1oAvd5/HtKFtrR0SlcFCuhVcuJ6HJ7/Yj1ytHj4qA6b2ccfNGxUvGlp+bfKGmC1emfoU5O9UiK9q3+Wf5+zsLBXNF/ywDQGtOsBQXFRpId7V01tqD8Ck6A5AulBoTYr1ZQ++HuzWDC1c9Xjx+79wtUCOveduYO+5G9J2jVKO14e1w7i+LZCenl7tz0Uuk+GDMd3w0Kd7cTYjD08s24+vnu2DYB/ToreuxICtJ9Px9e6z+DM1B3oB/Hr6L/QJu4S7W/ri7pa+cNc4Ye+5G1hz6CIOp2YZXwGf7L6MJXuv4Nm+LTDlH23gpq77x7tQp0fUd4dRWKzHPa198UyfQHzwKwvpju6rg1cAAIPb+MLD2Qnnr96EsbB7u9hbee6pyUzwooI8fLr1JESJDlqdTipK13UWuTlnit+pWN6Qy7iU/7lYQvn+OCvlaOutwGcjuyFPaHAp4wa+jfsTLmo1oM9CkboIzsKAwU11uFashJOrJ3al5OJ6QQk+3FOa/44tT0S/tv7o4q+GwtkNKkXp8jHG353qfn+I7uRqdiF2n70OAAh1E/xVsmPl11OXy2Tw0xjgpwEK9UW4WuKC81kG5BSVYMXeC1ix9wIC3FUY2MYHg9v4YmDHYCidTGeqZ2dn450fduE/Y/rDy8urwmuWL7TnFhXj0s0C3LiZA4UcCPLUwEPjBC+u0051VGIoTUoKmQz6W//PLeI6xEREVHsyGdDJV4HdV0qwdNd5jOwahLv8K57dTdbBQrqFXckqxJNf7Mf1/GK4KUrQXnYVX+0sqNEMcHMt39LQ6lqIL/u8sn2VK9V33Hf5n42x6O6sOY/723pACFWFNsYLtGZkZGDh6u3wb9leKsgbi++ywixENNXjycFdsPeyDn9duA5AoE1TZwxv3wTtw5qZHHCVLdaXX2rGXaPEimd647HP9+P8tXzc/9EfmNCvJcLDfJBVoMOmv1Kx42wWbhaazl7JKdJj68kMbD2ZUaHfchnQJ8QDWVlZcHH3wOFLeVi2OwUbjl3F7JEdEdnRv9YHhEIIzPolCcnpuWjipsbiMd2hz2+4da3JNpy/loetyaVfFI0LDyqdVfzTH3D2LL3ugfE+nFT1mpXt7OYBQ7EWRdr6T4Uw90xxa655bs6fC3DnLz6qIpfJ0MLXFR5yLZwVgIv77biMMfpri/Dvwc2w4s9rOHujCDcKS3DmuhZnrxfi7PULAAAZgKauTrhWdBbZhSX462IWCooNUCkA3e+n8OoD3RHg6WyWvlLjsPbIZQgB9GjuATenm9Ba5jsnsjBnhUCPAA90DSidpZ6SK8P1YhXScnVYlZCGVQlpcFWdRLcQbwR7u0CpkCNfV4IbOQU4me6EpOWH4ePhAne1Em4aJ7ipnSAzFCOroBj7z6TBzdUF6XklyCmqOFNYLRcY0KYJ+rUNQHhLX9zlV/o3gTPdqSZKys5IVzshu7AYp9Jy0TOUS5kREVHtNXeXoW+YF/amZGHc8oP4YEw39Anz4XjEBrCQbkHJabkYt/wg0nK0cFPJMcBfhvxrolaFZ3Ms32IvjH0te6HSmrQvf/+zjQelJXLKtilb9HZ2K11uoGyxXabUQBQXQcidoCjKRj9fICX5GgpybiIlS4PYyxfxyoNK+Pn5STPajc+vaqmZYB8X/DAxHBO/OogT6QVYFHe6QpsmrkqM6OCL7Nx8uKnkGNQhEOdygN1nbyAx9Sa0JQa0auqGB7sF4aHuzWAoyMKCdQfw6qi2OJktx6xfknAxsxAvfJuA+9r54dWhbdEuwB2AMCnyy+UVrzVcVKzH278dx5qES5DJgA8f64am7mqk5dfoLSA7lpZdhAAPNeQGHdr4lZal1S7uJsuOqF3cLDZjuqbs4YKf5lKbGfh1+eLD+F7fqQhvjEOjlKFTgAse7+aLmO3nEN4hDH+lFWLnqTRczilGRn4Jfjl2rcwzZdAagB+P3cTGUzsw5/864dFezTkYpBp5vHcI3NRO8HQyYHcCv9x1dGVnqetFIa5pFbgh3HAppxj5OgP2nL0B4EaF52UUFAIZhVXtFSi8PU5UygxwkgNCpkBRiYDWIMOWUzew5VTpfn1dVeje3B3Xr13Df0b1QM/WQZDLma+ocsW31kiXy4EQHxdczirEH6evY2x4qJUjIyIie/X6oCC8nK3FhcxCjPl8P/zc1WjdxBnNvTTwc1ejeRMPNHFTw9tVBV9XFQK9NFA78foyDY2F9HJiYmLw/vvvIy0tDV27dsXHH3+MPn3qv/b2sUvZGLtsP3KKStDCR4PegSqIgptgfbLhlV8ix6hs0bzsRU+NxXa5UgNDcRGuX72Mz+JPmiwpI1dqKl1+xvj8skvNlL8gqpMuFx2cc6F0LcCVYmcUGhRo4qaCWiFDsIcTosJ9oJDL8PMJOWQyGToGuGJwtwA8f2+YyWx5uVyOJm4qZJT5nmFQWz/ETR2AT7adxWe7zmHbqQxsO5UBD40C/m4qZOUVQCkHOof4wsfDDS4qBZyVcuiKCpFbokDcyQxcz9MCAN59qDPuad2kAd4RqquGyk8A0Ld1E6yb0A3/++WwyePWWHaEKlfbGfhqF3doddoa778oP1d6r7Myr1f5OkUFeYjZmICglu2g0ZRe60GtAHr5y9HL3xVZZy+jd9swZOrk6NTcCx4aJxy+cAPy4gJk3MzGhSIXZGoNmPHTUWw9mY53H+6MJm7qSl6J7E1D5ihvVxWejmiBrKws7E4wyy7JTihkQIBGj3ZNXaAv0eFSRiau5emhEzKoXNygEHroi/LholFBBqBIq4PS1QPFJXrkFxRBoXFB7xAPJF+5CU8Pd7goDCi8dgmurqVLzHg1DYROp8PFjJu4rpVD4+qOpIwi3MjX3TpTS45HlyfCx/UEBrRpikHt/HB3Sx/4uVd/rRuyPQ2Zo0oMt2ekh/i4YN/5G9hz7jp0JQaonCpOXiEiKq8hcxTZH11RAT5bvx93yQG9RoPLWiUycrXIyK36+E6jlCM81Auje4VgWKdA/v1pICykl/HDDz9g2rRpWLp0KcLDw7F48WJERkYiOTm5wjIdtRXaxAVBXs64S+2Ed4eHYNWBv5Fbs4nW1IBqOuu9sjMBqlp+pvw24wVRnTXn8fQ9LQEAbl4+uEsGtL1VkB/gX4xdGSrkZWdi2faMCmvFG2fPf7s/FQU5NyFTauCsUUv7MxbrjUX2Jzq7o2+zdli8PQUJVwqRU6RHTlEhShdcAK6cygSQWWlf/d2UmHFfMCKaO+HKldI1s69fr7iGP1lWQ+YnI6VCDo1Txdl25l52hOqu/Ax8c1+o1Phea+7wXpePo2wRHk5quKnk8HVXY3Q7V3h5eeF6Vh4KcosgV5cg1MsAX78AxCZcx5YT6dh77gZGdQ9CjxBvhPq6QqOUQ+0kh9pJAblcBrkMkKH0X8hKixQy3PpXBshu/Wt8XKNUQMFZoxZniRxFJJfJ4KkU8PQuPTD0aupx6yKmRQD0pY2cFeUeL8CNi9fRzMUFXh4+EPpiyF1N12lXKmTwVRvgqzZAW3AZwQEuKFC4ISO/BOkFApk6OTLzdVh75DLWHim96K23ixNaN3FBSBN3NHXXoKm7Gk3d1fByVpZOVFAp4KJygrNSASeFDEq5HAqFDC5KBWe2W0FD5yhpRrpMBj8PNZyVCuQWlWDmz8cwoX8YXJROUDnJpZuTvPRvV3kyVP67UdXJW/xNInNzUrDwZg0cR1Fl1M7OUAPo7Qp0N5Tgep4OhTI1DCo3FGj1yMovgM4gh84gg84gQ1GxATvPZmLn2Uz4up3AP3sFI6KlL1r7lU6itNfjpLKlKFv4coCF9DIWLVqECRMm4NlnnwUALF26FBs2bMDy5cvx+uuvm7TVarXQam9/E5SdnQ0AyMnJqXL/nz7aHvk5mbhxJRU3rl5EQU4Wcm6kQ6nWQBRrIVOqTf6tbltN2tT3+ZZ4DWs/35IxanNv4r3vz0OUFMPNx09qk1msxftxZ9H8ro4mz7uZfhmZxVq8d7QAoqQYWTeu3W6jN5jsT1tUiPcuXYAoKYbMSSn9qygpxkBvP+QWleBKWho8A1qguKQE2uISlBgMMMicoDcYUKIX0CjlcBMF8MkrwY9rj+CnMvsx/pub2xEu5S4SVpbx959Fd/OrTX4C6pajcnJykH09DampqQCA3MwMFGl1kMudIEp0yLlZerp72fvVbbvTfUs919Fep+z97Js3EL0qA9AXo0irg8bFtdL3T6NR49KlQmRdy0JhXq7JfozbcjMzUFyir1WMZZ+r8fCt0HbOkf14fmi3cq97DbmZGbjbXYPLenecu1GAr3fm4OsqfzNr5/One6Jvq8rPpmGOajiWyFEAcOnSJeTczIBOWwKZXA5RUoLcLNOlXip7vDZtbWUfjNm8bXVFhTXeh66oEB5N5HBTl6Bp4U0YVMD1ohJk6jXIlrkjr0SGG1oZbtzMwYEzaaiNjZPvRYhP5ecVMUc1nIbOUfm5uTBoC6DXKqAryEe/UBdsOp6ONftOY82+iks5EtkiuQw4Oieyyu3MUQ2noXNU6XFeOrSFBcjLzapxXKJEX+P2tWnLfdetvSsAV+TC3V1AKPTI099uKwSQWyJHhlaJi1olrt2QI2ZzFmJq/Oq2r4WvC9a/3K/K7RbLUYKEEEJotVqhUCjE2rVrTR5/+umnxf/93/9VaD979myB0ul/vPHGW7nbxYsXLfTJbRxqm5+EYI7ijbfqbsxR5sUcxRtv5r0xR5kXcxRvvJn3xhxlXsxRvPFm3ltD5yjOSL/l+vXr0Ov18Pf3N3nc398fp06dqtB+5syZmDZtmnTfYDAgMzMTvr6+JhdOy8nJQXBwMC5evAgPD48K+7E3jtQf9sX8hBDIzc1FUFCQ1WJwRLXNT0DNc5StspXfaVvHn1PNGX9WJ06cYI4ys4bOUY3197yx9htovH3nOKphWGIc5Ui/s+yL7bGVfjBHNQzmqIbDfjeuflsqR7GQXkdqtRpqtem62F5eXlW29/DwcKhfYEfqD/tiXp6enlZ9fSpV2xxlq2zhd9oe8OdUc82aNYNcbv219Rq7uuSoxvp73lj7DTTOvnMcZRvqOo5ypN9Z9sX22EI/mKNsA3NU7bDfjYclchSPJG9p0qQJFAoF0tPTTR5PT09HQECAlaIiImJ+IiLbxhxFRLaMOYqIbBlzFJF9YSH9FpVKhZ49eyI+Pl56zGAwID4+HhEREVaMjIgaO+YnIrJlzFFEZMuYo4jIljFHEdkXLu1SxrRp0zBu3Dj06tULffr0weLFi5Gfny9dObku1Go1Zs+eXeG0G3vlSP1hX8ieNER+smX8na4Z/pxqjj+rhtWQOaqxvneNtd9A4+47NYyGHkc50u8s+2J7HKUfVDXmqIbBfjeufluKTAghrB2ELfnkk0/w/vvvIy0tDd26dcNHH32E8PBwa4dFRMT8REQ2jTmKiGwZcxQR2TLmKCL7wEI6EREREREREREREVE1uEY6EREREREREREREVE1WEgnIiIiIiIiIiIiIqoGC+lERERERERERERERNVgIZ2IiIiIiIiIiIiIqBospDewmJgYtGjRAhqNBuHh4Th48KBFX3/Xrl0YOXIkgoKCIJPJsG7dOpPtQgjMmjULgYGBcHZ2xpAhQ3DmzBmTNpmZmRg7diw8PDzg5eWF8ePHIy8vz6TN0aNH0a9fP2g0GgQHB2P+/PkVYlmzZg3atWsHjUaDzp07Y+PGjbXqS3R0NHr37g13d3f4+flh1KhRSE5ONmlTVFSEqKgo+Pr6ws3NDaNHj0Z6erpJm9TUVIwYMQIuLi7w8/PD9OnTUVJSYtJmx44d6NGjB9RqNVq3bo3Y2NgK8dTnvV2yZAm6dOkCDw8PeHh4ICIiAr///rvd9YOoPsyRnxoDc+W+xsAcuZVsi6P9jZozZw5kMpnJrV27dtJ2c/39twW2NAYlMofa5qP6Hvs0hJqMKcqLjY2tkLc0Go2FIq7anfJpZWzxPQGAFi1aVOiLTCZDVFRUpe1t9T0h2+Vo46myeKxU6r333oNMJsOUKVOkxxpDv61CUINZtWqVUKlUYvny5eL48eNiwoQJwsvLS6Snp1ssho0bN4r//Oc/4ueffxYAxNq1a022v/fee8LT01OsW7dO/PXXX+L//u//RFhYmCgsLJTaDBs2THTt2lXs379f/PHHH6J169bi8ccfl7ZnZ2cLf39/MXbsWJGUlCS+//574ezsLD777DOpzZ49e4RCoRDz588XJ06cEG+++aZQKpXi2LFjNe5LZGSkWLFihUhKShKJiYni/vvvFyEhISIvL09q88ILL4jg4GARHx8vDh06JO6++27Rt29faXtJSYno1KmTGDJkiDhy5IjYuHGjaNKkiZg5c6bU5vz588LFxUVMmzZNnDhxQnz88cdCoVCITZs2SW3q+97++uuvYsOGDeL06dMiOTlZvPHGG0KpVIqkpCS76gdRfZgjPzUG5sh9jUV9cyvZFkf8GzV79mzRsWNHcfXqVel27do1abs5/v7bClsZgxKZQ23zkTmOfRpCTcYU5a1YsUJ4eHiY5K20tDQLRl25O+XT8mz1PRFCiIyMDJN+xMXFCQBi+/btlba31feEbJMjjqfK4rGSEAcPHhQtWrQQXbp0EZMnT5Yed/R+WwsL6Q2oT58+IioqSrqv1+tFUFCQiI6Otko85Q9iDAaDCAgIEO+//770WFZWllCr1eL7778XQghx4sQJAUD8+eefUpvff/9dyGQycfnyZSGEEJ9++qnw9vYWWq1WavPaa6+Jtm3bSvf/+c9/ihEjRpjEEx4eLv71r3/VuT8ZGRkCgNi5c6cUu1KpFGvWrJHanDx5UgAQ+/btE0KUHtTJ5XKTgcaSJUuEh4eHFP+MGTNEx44dTV5rzJgxIjIyUrrfEO+tt7e3WLZsmd33g6gu6pKfGqu65L7GrDa5lWyLI/6Nmj17tujatWul28z1998WWXMMSmQOtc1HDXHs0xDKjykqs2LFCuHp6Wm5oGqounxaGXt5T4QQYvLkyaJVq1bCYDBUut1W3xOyTY44nqpOYztWys3NFXfddZeIi4sTAwYMkArpjt5va+LSLg1Ep9MhISEBQ4YMkR6Ty+UYMmQI9u3bZ8XIbktJSUFaWppJjJ6enggPD5di3LdvH7y8vNCrVy+pzZAhQyCXy3HgwAGpTf/+/aFSqaQ2kZGRSE5Oxs2bN6U2ZV/H2KY+P4vs7GwAgI+PDwAgISEBxcXFJq/Trl07hISEmPSnc+fO8Pf3N4kjJycHx48fr1Gs5n5v9Xo9Vq1ahfz8fERERNhtP4jMqSb5qbGqS+5rjOqSW8l2OPLfqDNnziAoKAgtW7bE2LFjkZqaCsB84xh7YMkxKFF91SUfNcSxT0MoP6aoSl5eHkJDQxEcHIwHH3zQZvJNVfm0Mvbynuh0Onz77bd47rnnIJPJqmxnq+8J2RZHHk9VpbEdK0VFRWHEiBEV8puj99uaWEhvINevX4derzc50AEAf39/pKWlWSkqU8Y4qosxLS0Nfn5+JtudnJzg4+Nj0qayfZR9jara1PVnYTAYMGXKFNxzzz3o1KmT9BoqlQpeXl7V9qeusebk5KCwsNBs7+2xY8fg5uYGtVqNF154AWvXrkWHDh3srh9EDaEm+akxqmvua0zqk1vJdjjq36jw8HDExsZi06ZNWLJkCVJSUtCvXz/k5uaa7e+/PbDkGJSovuqSj8x97NMQKhtTVKZt27ZYvnw5fvnlF3z77bcwGAzo27cvLl26ZMFoK6oun1bGHt4TAFi3bh2ysrLwzDPPVNnGVt8Tsj2OOp6qSmM7Vlq1ahUOHz6M6OjoCtscud/W5mTtAIjqIioqCklJSdi9e7e1Q6mztm3bIjExEdnZ2fjxxx8xbtw47Ny509phEZENc4Tc19CYW8mWDR8+XPp/ly5dEB4ejtDQUKxevRrOzs5WjIyIGpuajikiIiIQEREh3e/bty/at2+Pzz77DPPmzWvoMKtUXT4dP3681eKqry+//BLDhw9HUFBQlW1s9T0hsrbGdKx08eJFTJ48GXFxcbzYsIVxRnoDadKkCRQKRYUr4qanpyMgIMBKUZkyxlFdjAEBAcjIyDDZXlJSgszMTJM2le2j7GtU1aYuP4tJkyZh/fr12L59O5o3b27SH51Oh6ysrGr7U9dYPTw84OzsbLb3VqVSoXXr1ujZsyeio6PRtWtXfPjhh3bXD6KGUJP81NjUJ/c1JvXJrWQ7GsvfKC8vL7Rp0wZnz541299/e2DJMShRfdUlH5nz2KchVDWmqAmlUonu3bvj7NmzDRRd3ZTNp5Wx9fcEAP7++29s3boVzz//fK2eZ6vvCVlfYxlPAY3vWCkhIQEZGRno0aMHnJyc4OTkhJ07d+Kjjz6Ck5MT/P39HbLftoCF9AaiUqnQs2dPxMfHS48ZDAbEx8ebfHtsTWFhYQgICDCJMScnBwcOHJBijIiIQFZWFhISEqQ227Ztg8FgQHh4uNRm165dKC4ultrExcWhbdu28Pb2ltqUfR1jm9r8LIQQmDRpEtauXYtt27YhLCzMZHvPnj2hVCpNXic5ORmpqakm/Tl27JjJgVlcXBw8PDzQoUOHGsXaUO+twWCAVqu1+34QmUNN8lNjYY7c15jVJreS7Wgsf6Py8vJw7tw5BAYGmu3vvz2w5BiUqL7qko/McezTEO40pqgJvV6PY8eOITAwsAEirLuy+bQytvqelLVixQr4+flhxIgRtXqerb4nZH2NYTzVWI+VBg8ejGPHjiExMVG69erVC2PHjpX+74j9tgnWvdapY1u1apVQq9UiNjZWnDhxQkycOFF4eXmJtLQ0i8WQm5srjhw5Io4cOSIAiEWLFokjR46Iv//+WwghxHvvvSe8vLzEL7/8Io4ePSoefPBBERYWJgoLC6V9DBs2THTv3l0cOHBA7N69W9x1113i8ccfl7ZnZWUJf39/8dRTT4mkpCSxatUq4eLiIj777DOpzZ49e4STk5NYsGCBOHnypJg9e7ZQKpXi2LFjNe7Liy++KDw9PcWOHTvE1atXpVtBQYHU5oUXXhAhISFi27Zt4tChQyIiIkJERERI20tKSkSnTp3E0KFDRWJioti0aZNo2rSpmDlzptTm/PnzwsXFRUyfPl2cPHlSxMTECIVCITZt2iS1qe97+/rrr4udO3eKlJQUcfToUfH6668LmUwmtmzZYlf9IKoPc+SnxsAcua+xqG9uJdviiH+jXnnlFbFjxw6RkpIi9uzZI4YMGSKaNGkiMjIyhBDm+ftvK2xlDEpkDnfKR0899ZR4/fXXpfbmOPZpCDUZU5Tvy9tvvy02b94szp07JxISEsRjjz0mNBqNOH78uDW6ILlTPrWX98RIr9eLkJAQ8dprr1XYZi/vCdkmRxxPlcVjpdsGDBggJk+eLN1vLP22NBbSG9jHH38sQkJChEqlEn369BH79++36Otv375dAKhwGzdunBBCCIPBIN566y3h7+8v1Gq1GDx4sEhOTjbZx40bN8Tjjz8u3NzchIeHh3j22WdFbm6uSZu//vpL3HvvvUKtVotmzZqJ9957r0Isq1evFm3atBEqlUp07NhRbNiwoVZ9qawfAMSKFSukNoWFheLf//638Pb2Fi4uLuKhhx4SV69eNdnPhQsXxPDhw4Wzs7No0qSJeOWVV0RxcXGFn1u3bt2ESqUSLVu2NHkNo/q8t88995wIDQ0VKpVKNG3aVAwePFgq9NhTP4jqwxz5qTEwV+5rDMyRW8m2ONrfqDFjxojAwEChUqlEs2bNxJgxY8TZs2el7eb6+28LbGkMSmQO1eWjAQMGSL/bRvU99mkINRlTlO/LlClTpH77+/uL+++/Xxw+fNjywZdzp3xqL++J0ebNmwWASse69vKekO1ytPFUWTxWuq18Ib2x9NvSZEII0UCT3YmIiIiIiIiIiIiI7B7XSCciIiIiIiIiIiIiqgYL6URERERERERERERE1WAhnYiIiIiIiIiIiIioGiykExERERERERERERFVg4V0IiIiIiIiIiIiIqJqsJBORERERERERERERFQNFtKJiIiIiIiIiIiIiKrBQjrV28CBAzFlyhRrh0FEVC/MZURkKbaQb1q0aIHFixdL92UyGdatW2e1eIiI6mrHjh2QyWTIysq6Y9vY2Fh4eXk1eEzWsGvXLowcORJBQUF1zulCCCxYsABt2rSBWq1Gs2bN8M4775g/WCIHcacxnSPnnMaKhXQiM7lw4QJkMhkSExOtHQoR1cHPP/+MefPm1aitJT/vtTk4JCL7UJt8cyeOVgDnAScRNaQxY8bg9OnT1g6jQeTn56Nr166IiYmp8z4mT56MZcuWYcGCBTh16hR+/fVX9OnTx4xREjmu8pMUAMfOOY2Vk7UDICIisgU+Pj4WfT2dTgeVSmXR1yQi22DpfENERKWcnZ3h7Oxs7TAaxPDhwzF8+PAqt2u1WvznP//B999/j6ysLHTq1An/+9//MHDgQADAyZMnsWTJEiQlJaFt27YAgLCwMEuETuSwHDnnNFackU5mdfPmTTz99NPw9vaGi4sLhg8fjjNnzkjbb9y4gccffxzNmjWDi4sLOnfujO+//95kHwMHDsTLL7+MGTNmwMfHBwEBAZgzZ06NY1i0aBE6d+4MV1dXBAcH49///jfy8vKk7caZTuvXr0fbtm3h4uKCRx55BAUFBfjqq6/QokULeHt74+WXX4Zer5ee16JFC7z77rt47rnn4O7ujpCQEHz++efSduMgo3v37pDJZNKAhIjsQ9nT8hri8/7MM89g1KhReOeddxAUFCQdoHzzzTfo1asX3N3dERAQgCeeeAIZGRkASme+Dxo0CADg7e0NmUyGZ555BgBgMBgQHR2NsLAwODs7o2vXrvjxxx/N9NMgooZUm3yj0+kwadIkBAYGQqPRIDQ0FNHR0dJzAeChhx6CTCaT7p87dw4PPvgg/P394ebmht69e2Pr1q01js941s3q1avRr18/ODs7o3fv3jh9+jT+/PNP9OrVC25ubhg+fDiuXbtm8txly5ahffv20Gg0aNeuHT799NMK+/35558xaNAguLi4oGvXrti3bx+A0jNwnn32WWRnZ0Mmk0Emk9VqDEhE5rVp0ybce++98PLygq+vLx544AGcO3cOwJ0/z8Dt467Nmzejffv2cHNzw7Bhw3D16lWpTWXLIowaNUoa7wDVj5Vqq/xZL3PmzEG3bt3wzTffoEWLFvD09MRjjz2G3NxcqY3BYMD8+fPRunVrqNVqhISE2OVyJ5MmTcK+ffuwatUqHD16FI8++iiGDRsmHa//9ttvaNmyJdavX4+wsDC0aNECzz//PDIzM60cOZFtyM/Px9NPPw03NzcEBgZi4cKF0raBAwfi77//xtSpU6UxDMAz7RwRC+lkVs888wwOHTqEX3/9Ffv27YMQAvfffz+Ki4sBAEVFRejZsyc2bNiApKQkTJw4EU899RQOHjxosp+vvvoKrq6uOHDgAObPn4+5c+ciLi6uRjHI5XJ89NFHOH78OL766its27YNM2bMMGlTUFCAjz76CKtWrcKmTZuwY8cOPPTQQ9i4cSM2btyIb775Bp999lmFotTChQvRq1cvHDlyBP/+97/x4osvIjk5GQCkPmzduhVXr17Fzz//XKefIRHZhob4vMfHxyM5ORlxcXFYv349AKC4uBjz5s3DX3/9hXXr1uHChQvSwWNwcDB++uknAEBycjKuXr2KDz/8EAAQHR2Nr7/+GkuXLsXx48cxdepUPPnkk9i5c6c5fwxEZAHV5ZuPPvoIv/76K1avXo3k5GR89913UsH8zz//BACsWLECV69ele7n5eXh/vvvR3x8PI4cOYJhw4Zh5MiRSE1NrVVcs2fPxptvvonDhw/DyckJTzzxBGbMmIEPP/wQf/zxB86ePYtZs2ZJ7b/77jvMmjUL77zzDk6ePIl3330Xb731Fr766iuT/f7nP//Bq6++isTERLRp0waPP/44SkpK0LdvXyxevBgeHh64evUqrl69ildffbWuP1Yiqqf8/HxMmzYNhw4dQnx8PORyOR566CEYDAapTVWfZ6OCggIsWLAA33zzDXbt2oXU1NRaf66rGyuZw7lz57Bu3TqsX78e69evx86dO/Hee+9J22fOnIn33nsPb731Fk6cOIGVK1fC39/fbK9vCampqVixYgXWrFmDfv36oVWrVnj11Vdx7733YsWKFQCA8+fP4++//8aaNWvw9ddfIzY2FgkJCXjkkUesHD2RbZg+fTp27tyJX375BVu2bMGOHTtw+PBhAKXL9jVv3hxz586VxjDkoARRPQ0YMEBMnjxZnD59WgAQe/bskbZdv35dODs7i9WrV1f5/BEjRohXXnnFZH/33nuvSZvevXuL1157rU7xrVmzRvj6+kr3V6xYIQCIs2fPSo/961//Ei4uLiI3N1d6LDIyUvzrX/+S7oeGhoonn3xSum8wGISfn59YsmSJEEKIlJQUAUAcOXKkTnESkXUZc5kQDfN5HzdunPD39xdarbbadn/++acAIOWj7du3CwDi5s2bUpuioiLh4uIi9u7da/Lc8ePHi8cff7zGMRGRddQm37z00kvivvvuEwaDodJ9ARBr166942t27NhRfPzxx9L90NBQ8cEHH1S6H2OOW7ZsmbT9+++/FwBEfHy89Fh0dLRo27atdL9Vq1Zi5cqVJq87b948ERERUeV+jx8/LgCIkydPCiFKx2menp537A8RWd61a9cEAHHs2LEaf57LH3fFxMQIf39/6X7ZfGj04IMPinHjxlUZR03GSlUpn2Nmz54tXFxcRE5OjvTY9OnTRXh4uBBCiJycHKFWq8UXX3xxx33bkvJ/G9avXy8ACFdXV5Obk5OT+Oc//ymEEGLChAkCgEhOTpael5CQIACIU6dOWboLRDYlNzdXqFQqk9rWjRs3hLOzs8mYruzYSgiOaxwR10gnszl58iScnJwQHh4uPebr64u2bdvi5MmTAAC9Xo93330Xq1evxuXLl6HT6aDVauHi4mKyry5dupjcDwwMrPHpe1u3bkV0dDROnTqFnJwclJSUoKioCAUFBdLruLi4oFWrVtJz/P390aJFC7i5uZk8Vv41y8Ylk8kQEBBQ59MKici2NcTnvXPnzhXWRU9ISMCcOXPw119/4ebNm9Isr9TUVHTo0KHS/Zw9exYFBQX4xz/+YfK4TqdD9+7d6xUjEVledfnmmWeewT/+8Q+0bdsWw4YNwwMPPIChQ4dWu7+8vDzMmTMHGzZswNWrV1FSUoLCwsJaz0gvG5dx9mXnzp1NHjPGmZ+fj3PnzmH8+PGYMGGC1KakpASenp5V7jcwMBAAkJGRgXbt2tUqPiJqWGfOnMGsWbNw4MABXL9+vdIxyp0+z+WPu2pzXGdUl7FSbbRo0QLu7u6Vxnjy5ElotVoMHjy43q9jTXl5eVAoFEhISIBCoTDZZjwGDgwMhJOTE9q0aSNta9++PYDSn7VxWUKixujcuXPQ6XQm9S4fHx9+LhohFtLJot5//318+OGHWLx4sbSO+ZQpU6DT6UzaKZVKk/symczkFMKqXLhwAQ888ABefPFFvPPOO/Dx8cHu3bsxfvx46HQ6qZBe2f5r8pp1jYuI7E9DfN5dXV1N7ufn5yMyMhKRkZH47rvv0LRpU6SmpiIyMrJCXizLeN2HDRs2oFmzZibb1Gp1vWIkIsurLt/06NEDKSkp+P3337F161b885//xJAhQ6q9JsKrr76KuLg4LFiwAK1bt4azszMeeeSRavPKneIyrvVZ/jFjnMa89MUXX5gcZAKoULSpbL8cTxHZnpEjRyI0NBRffPEFgoKCYDAY0KlTJ5NccqfPc2X5TQgh3ZfL5Sb3AUjLggJ1HyvVRnU52FEuEti9e3fo9XpkZGSgX79+lba55557UFJSgnPnzklffpw+fRoAEBoaarFYiYhsGQvpZDbt27dHSUkJDhw4gL59+wIovbhocnKyNFNgz549ePDBB/Hkk08CKB1knT592iwzCYDS2QoGgwELFy6EXF56CYDVq1ebZd93YpxlWvYCpUTkmMz1eT916hRu3LiB9957D8HBwQCAQ4cO3fG1OnToALVajdTUVAwYMKBeMRCR7fPw8MCYMWMwZswYPPLIIxg2bBgyMzPh4+MDpVJZIRft2bMHzzzzDB566CEApUXuCxcuNGiM/v7+CAoKwvnz5zF27Ng670elUnEsRWQDjMdxX3zxhVR43b17t9lfp2nTpiZrCev1eiQlJUkXW6/JWKkh3XXXXXB2dkZ8fDyef/55i71uXeTl5eHs2bPS/ZSUFCQmJsLHxwdt2rTB2LFj8fTTT2PhwoXo3r07rl27hvj4eHTp0gUjRozAkCFD0KNHDzz33HNYvHgxDAYDoqKi8I9//MNkljpRY9SqVSsolUocOHAAISEhAICbN2/i9OnT0vEYxzCNAy82SmZz11134cEHH8SECROwe/du/PXXX3jyySfRrFkzPPjgg1KbuLg47N27FydPnsS//vUvpKenmy2G1q1bo7i4GB9//DHOnz+Pb775BkuXLjXb/qvj5+cHZ2dnbNq0Cenp6cjOzrbI6xKR5Znr8x4SEgKVSiXlrF9//RXz5s0zaRMaGgqZTIb169fj2rVryMvLg7u7O1599VVMnToVX331Fc6dO4fDhw/j448/rnBRPyKyb4sWLcL333+PU6dO4fTp01izZg0CAgLg5eUFoHRJgvj4eKSlpeHmzZsASsdbP//8MxITE/HXX3/hiSeesMiM77fffhvR0dH46KOPcPr0aRw7dgwrVqzAokWLaryPFi1aIC8vD/Hx8bh+/ToKCgoaMGIiqoq3tzd8fX3x+eef4+zZs9i2bRumTZtm9te57777sGHDBmzYsAGnTp3Ciy++iKysLGl7TcZKDUmj0eC1117DjBkz8PXXX+PcuXPYv38/vvzyS4vFUFOHDh1C9+7dpWX+pk2bhu7du0sXhV6xYgWefvppvPLKK2jbti1GjRqFP//8UyoKyuVy/Pbbb2jSpAn69++PESNGoH379li1apXV+kRkK9zc3DB+/HhMnz4d27ZtQ1JSEp555hlpAidQOobZtWsXLl++jOvXr1sxWmpILKSTWa1YsQI9e/bEAw88gIiICAghsHHjRul0uTfffBM9evRAZGQkBg4ciICAAIwaNcpsr9+1a1csWrQI//vf/9CpUyd89913iI6ONtv+q+Pk5ISPPvoIn332GYKCgqQvD4jI8Zjr8960aVPExsZizZo16NChA9577z0sWLDApE2zZs3w9ttv4/XXX4e/vz8mTZoEAJg3bx7eeustREdHo3379hg2bBg2bNiAsLCwevePiGyHu7s75s+fj169eqF37964cOECNm7cKB24LVy4EHFxcQgODpaKJ4sWLYK3tzf69u2LkSNHIjIyEj169GjwWJ9//nksW7YMK1asQOfOnTFgwADExsbWKi/17dsXL7zwAsaMGYOmTZti/vz5DRgxEVVFLpdj1apVSEhIQKdOnTB16lS8//77Zn+d5557DuPGjcPTTz+NAQMGoGXLltJsdKBmY6WG9tZbb+GVV17BrFmz0L59e4wZM8Ymr5M1cOBACCEq3GJjYwGULmHz9ttvIyUlBTqdDleuXMHPP/9scu2LoKAg/PTTT8jNzUVaWhpWrFgBHx8fK/WIyLa8//776NevH0aOHIkhQ4bg3nvvRc+ePaXtc+fOxYULF9CqVSs0bdrUipFSQ5KJ8guSERERERERERERERGRhDPSiYiIiIiIiIiIiIiqwUI62ZXvvvsObm5uld46duxo7fCIqBGrKje5ubnhjz/+sHZ4RERERA5v+PDhVY7H3n33XWuHR0REdo5Lu5Bdyc3NrfLipEqlEqGhoRaOiIio1NmzZ6vc1qxZMzg7O1swGiIiIqLG5/LlyygsLKx0m4+PD9f7JiKiemEhnYiIiIiIiIiIiIioGlzahYiIiIiIiIiIiIioGiykExERERERERERERFVg4V0IiIiIiIiIiIiIqJqsJBORERERERERERERFQNFtKJiIiIiIiIiIiIiKrBQjoRERERERERERERUTVYSCciIiIiIiIiIiIiqgYL6URERERERERERERE1WAhnYiIiIiIiIiIiIioGiykExERERERERERERFVg4V0IiIiIiIiIiIiIqJqsJBORERERERERERERFQNFtKJiIiIiIiIiIiIiKrBQjoRERERERERERERUTVYSG8Au3btwsiRIxEUFASZTIZ169bVeh9CCCxYsABt2rSBWq1Gs2bN8M4775g/WCIiIiIiIrojHucRERE1bk7WDsAR5efno2vXrnjuuefw8MMP12kfkydPxpYtW7BgwQJ07twZmZmZyMzMNHOkREREREREVBM8ziMiImrcZEIIYe0gHJlMJsPatWsxatQo6TGtVov//Oc/+P7775GVlYVOnTrhf//7HwYOHAgAOHnyJLp06YKkpCS0bdvWOoETERERERFRpXicR0RE1PhwaRcrmDRpEvbt24dVq1bh6NGjePTRRzFs2DCcOXMGAPDbb7+hZcuWWL9+PcLCwtCiRQs8//zznKlARERERERko3icR0RE5NhYSLew1NRUrFixAmvWrEG/fv3QqlUrvPrqq7j33nuxYsUKAMD58+fx999/Y82aNfj6668RGxuLhIQEPPLII1aOnoiIiIiIiMrjcR4REZHj4xrpFnbs2DHo9Xq0adPG5HGtVgtfX18AgMFggFarxddffy21+/LLL9GzZ08kJyfzNEAiIiIiIiIbwuM8IiIix8dCuoXl5eVBoVAgISEBCoXCZJubmxsAIDAwEE5OTiaDsPbt2wMonenAARYREREREZHt4HEeERGR42Mh3cK6d+8OvV6PjIwM9OvXr9I299xzD0pKSnDu3Dm0atUKAHD69GkAQGhoqMViJSIiIiIiojvjcR4REZHjkwkhhLWDcDR5eXk4e/YsgNIB1aJFizBo0CD4+PggJCQETz75JPbs2YOFCxeie/fuuHbtGuLj49GlSxeMGDECBoMBvXv3hpubGxYvXgyDwYCoqCh4eHhgy5YtVu4dERERERFR48PjPCIiosaNhfQGsGPHDgwaNKjC4+PGjUNsbCyKi4vx3//+F19//TUuX76MJk2a4O6778bbb7+Nzp07AwCuXLmCl156CVu2bIGrqyuGDx+OhQsXwsfHx9LdISIiIiIiavR4nEdERNS4sZBORERERERERERERFQNubUDICIiIiIiIiIisiWXL1/Gk08+CV9fXzg7O6Nz5844dOiQtF0IgVmzZiEwMBDOzs4YMmQIzpw5Y7KPzMxMjB07Fh4eHvDy8sL48eORl5dn0ubo0aPo168fNBoNgoODMX/+fIv0j4hqj4V0IiIiIiIickjR0dHo3bs33N3d4efnh1GjRiE5OdnaYRGRjbt58ybuueceKJVK/P777zhx4gQWLlwIb29vqc38+fPx0UcfYenSpThw4ABcXV0RGRmJoqIiqc3YsWNx/PhxxMXFYf369di1axcmTpwobc/JycHQoUMRGhqKhIQEvP/++5gzZw4+//xzi/aXiGqGS7uYicFgwJUrV+Du7g6ZTGbtcIisQgiB3NxcBAUFQS7n93S2hDmKiDnKljFHETFHNZRhw4bhscceQ+/evVFSUoI33ngDSUlJOHHiBFxdXWu0D+YoosaXo15//XXs2bMHf/zxR6XbhRAICgrCK6+8gldffRUAkJ2dDX9/f8TGxuKxxx7DyZMn0aFDB/z555/o1asXAGDTpk24//77cenSJQQFBWHJkiX4z3/+g7S0NKhUKum1161bh1OnTtUoVuYoIsvlKBbSzeTSpUsIDg62dhhENuHixYto3ry5tcOgMpijiG5jjrI9zFFEtzFHNaxr167Bz88PO3fuRP/+/Stto9VqodVqpfuXL19Ghw4dLBUikU1rLDmqQ4cOiIyMxKVLl7Bz5040a9YM//73vzFhwgQAwPnz59GqVSscOXIE3bp1k543YMAAdOvWDR9++CGWL1+OV155BTdv3pS2l5SUQKPRYM2aNXjooYfw9NNPIycnB+vWrZPabN++Hffddx8yMzNNZsAbMUcRVa2hc5RTg+25kXF3dwdQ+oZ5eHhYORoi68jJyUFwcLD0eSDbwRxFxBxly5ijiJijLCU7OxsA4OPjU2Wb6OhovP322xUeZ46ixqyx5ajz589jyZIlmDZtGt544w38+eefePnll6FSqTBu3DikpaUBAPz9/U2e5+/vL21LS0uDn5+fyXYnJyf4+PiYtAkLC6uwD+O2ygrpzFFEFVkqR7GQbibG02c8PDyYuKjR4+lktoc5iug25ijbwxxFdBtzVMMxGAyYMmUK7rnnHnTq1KnKdjNnzsS0adOk+8aDc+YoosaTowwGA3r16oV3330XANC9e3ckJSVh6dKlGDdunFVjY44iqlpD5ygW0i1EbxC4fLMQBiEQ6uvSaP74EBERERER2YKoqCgkJSVh9+7d1bZTq9VQq9U13m92QTGyCnVwVTuhiVvNn0dEtiswMLDCcint27fHTz/9BAAICAgAAKSnpyMwMFBqk56eLi31EhAQgIyMDJN9lJSUIDMzU3p+QEAA0tPTTdoY7xvblFfbHEVE5uP4V4iwETmFxej//nYMXLADXJWeiGyREAKFhYXgpTOIqDEQQkCr1TLnETUSkyZNwvr167F9+3azr526Ym8KBry/A4u3njbrfonIeu655x4kJyebPHb69GmEhoYCAMLCwhAQEID4+Hhpe05ODg4cOICIiAgAQEREBLKyspCQkCC12bZtGwwGA8LDw6U2u3btQnFxsdQmLi4Obdu2rXRZFyKyLhbSLUReZga6gQdsRGSDioqK8N66QygqKrJ2KEREDU6n02H+r0eg0+msHQoRNSAhBCZNmoS1a9di27ZtFdYiNgfjsZ7eYPZdE5GVTJ06Ffv378e7776Ls2fPYuXKlfj8888RFRUFoHT5iClTpuC///0vfv31Vxw7dgxPP/00goKCMGrUKAClM9iHDRuGCRMm4ODBg9izZw8mTZqExx57DEFBQQCAJ554AiqVCuPHj8fx48fxww8/4MMPPzRZusUcDAYDDAYmKaL6YiH9lujoaPTu3Rvu7u7w8/PDqFGjKnz7WB+yMj9pPQvpRGSjlCqeIkhEjYeTUmXtEIiogUVFReHbb7/FypUr4e7ujrS0NKSlpaGwsNBsr6GQlxbSeYYLkePo3bs31q5di++//x6dOnXCvHnzsHjxYowdO1ZqM2PGDLz00kuYOHEievfujby8PGzatAkajUZq891336Fdu3YYPHgw7r//ftx77734/PPPpe2enp7YsmULUlJS0LNnT7zyyiuYNWsWJk6caNb+sJBOZB5cI/2WnTt3IioqCr1790ZJSQneeOMNDB06FCdOnICrq2u9968oMyOd4ysiIiIiIqKGt2TJEgDAwIEDTR5fsWIFnnnmGbO8hvFQT2/ggR6RI3nggQfwwAMPVLldJpNh7ty5mDt3bpVtfHx8sHLlympfp0uXLvjjjz/qHGdN8Is+IvNgIf2WTZs2mdyPjY2Fn58fEhIS0L9//3rvn0u7EBERERERWZYlikfGYz3W0YnIVrGQTmQeLKRXITs7G0Dpt4eV0Wq10Gq10v2cnJxq91emjs6ZCkRERERWIoSATqfjASURmY3x7GPmFSIiIsfGNdIrYTAYMGXKFNxzzz3o1KlTpW2io6Ph6ekp3YKDg6vdp+mMdLOGS0REREQ1xIuMEpG5SUu7sJBORDZKCMEv+4jMgIX0SkRFRSEpKQmrVq2qss3MmTORnZ0t3S5evFjtPo0XoAE4U4GIiIjImspfZLT8mYZERLVhPNbjhCkislUspBOZB5d2KWfSpElYv349du3ahebNm1fZTq1WQ61W13i/ZeroHGARERERERE5CGmNdB7oEZGNYhGdyDxYSL9FCIGXXnoJa9euxY4dOxAWFmbW/cvKLO3CNdKJiIiIiIgcg1yakc7jPCKyTSykE5kHC+m3REVFYeXKlfjll1/g7u6OtLQ0AICnpyecnZ3N8hoKuQx6A0+nISIiIiIichTGs485YYqIiMixcY30W5YsWYLs7GwMHDgQgYGB0u2HH34w22sYB1gcXxERERERETkGaWkXHucRERE5NM5Iv8USs8RLl3cRPOWPiIiIiIjIQShuFdJ55jER2aqU63k4kJKFp+5tDaWCc2qJ6oqFdAviKX9ERERERESOxXg5LD0L6URko0Z8+icAwCB3wvP9Wlo5GiL7xa+hLOj2TAUrB0JERERERERmoZBzaRcisg8HUzKtHQKRXeOMdAu6vXYeR1hERERElqTVamvcRq1WN3Q4RORApOM8VtKJyMYxSxHVD2ekWxBP+SMiIiJziI6ORu/eveHu7g4/Pz+MGjUKycnJJm2KiooQFRUFX19fuLm5YfTo0UhPTzdpk5qaihEjRsDFxQV+fn6YPn06SkpKTNrs2LEDPXr0gFqtRuvWrREbG1shnpiYGLRo0QIajQbh4eE4ePCg2ftMRGSrjMd5nDBFRLaOaYqoflhItyDjKX+8CA0RERHVx86dOxEVFYX9+/cjLi4OxcXFGDp0KPLz86U2U6dOxW+//YY1a9Zg586duHLlCh5++GFpu16vx4gRI6DT6bB371589dVXiI2NxaxZs6Q2KSkpGDFiBAYNGoTExERMmTIFzz//PDZv3iy1+eGHHzBt2jTMnj0bhw8fRteuXREZGYmMjAzL/DCIiKzMeJzHa2ERkS0yrUExTxHVBwvpFnR7aRcrB0JE2LVrF0aOHImgoCDIZDKsW7fOZLsQArNmzUJgYCCcnZ0xZMgQnDlzxqRNZmYmxo4dCw8PD3h5eWH8+PHIy8szaXP06FH069cPGo0GwcHBmD9/foVY1qxZg3bt2kGj0aBz587YuHFjrWMhosZl06ZNeOaZZ9CxY0d07doVsbGxSE1NRUJCAgAgOzsbX375JRYtWoT77rsPPXv2xIoVK7B3717s378fALBlyxacOHEC3377Lbp164bhw4dj3rx5iImJgU6nAwAsXboUYWFhWLhwIdq3b49JkybhkUcewQcffCDFsmjRIkyYMAHPPvssOnTogKVLl8LFxQXLly+3/A+GiMgK5LwWFhHZCeYpovphId2CZFwjnchm5Ofno2vXroiJial0+/z58/HRRx9h6dKlOHDgAFxdXREZGYmioiKpzdixY3H8+HHExcVh/fr12LVrFyZOnChtz8nJwdChQxEaGoqEhAS8//77mDNnDj7//HOpzd69e/H4449j/PjxOHLkCEaNGoVRo0YhKSmpVrEQUeOWnZ0NAPDx8QEAJCQkoLi4GEOGDJHatGvXDiEhIdi3bx8AYN++fejcuTP8/f2lNpGRkcjJycHx48elNmX3YWxj3IdOp0NCQoJJG7lcjiFDhkhtKqPVapGTk2NyIyKyV7wWFhHZC2YpovphId2C5MY10jklncjqhg8fjv/+97946KGHKmwTQmDx4sV488038eCDD6JLly74+uuvceXKFWnm+smTJ7Fp0yYsW7YM4eHhuPfee/Hxxx9j1apVuHLlCgDgu+++g06nw/Lly9GxY0c89thjePnll7Fo0SLptT788EMMGzYM06dPR/v27TFv3jz06NEDn3zySY1jqQyLVESNh8FgwJQpU3DPPfegU6dOAIC0tDSoVCp4eXmZtPX390daWprUpmwR3bjduK26Njk5OSgsLMT169eh1+srbWPcR2Wio6Ph6ekp3YKDg2vfcSIiGyHntbCIyE5wqWGi+mEh3YJur5Fu5UCIqFopKSlIS0szmWHp6emJ8PBwk5mcXl5e6NWrl9RmyJAhkMvlOHDggNSmf//+UKlUUpvIyEgkJyfj5s2bUpvqZnvWJJbKsEhF1HhERUUhKSkJq1atsnYoNTZz5kxkZ2dLt4sXL1o7JCKiOjMe53G+FBHZOtajiOqHhXQL4il/RPbBOIuyuhmWaWlp8PPzM9nu5OQEHx8fs8z2LLv9TrFUhkUqosZh0qRJWL9+PbZv347mzZtLjwcEBECn0yErK8ukfXp6OgICAqQ26enpFbYbt1XXxsPDA87OzmjSpAkUCkWlbYz7qIxarYaHh4fJjYjIXknHeaykE5GNY5Yiqh8W0i1IxqVdiMhCWKQicmxCCEyaNAlr167Ftm3bEBYWZrK9Z8+eUCqViI+Plx5LTk5GamoqIiIiAAARERE4duwYMjIypDZxcXHw8PBAhw4dpDZl92FsY9yHSqVCz549TdoYDAbEx8dLbYiIHJ3xOI8TpojI1nFpF6L6YSHdgm7PSLdyIERULeMsyupmWAYEBJgUnwCgpKQEmZmZZpntWXb7nWIhosYnKioK3377LVauXAl3d3ekpaUhLS0NhYWFAEqXgBo/fjymTZuG7du3IyEhAc8++ywiIiJw9913AwCGDh2KDh064KmnnsJff/2FzZs3480330RUVBTUajUA4IUXXsD58+cxY8YMnDp1Cp9++ilWr16NqVOnSrFMmzYNX3zxBb766iucPHkSL774IvLz8/Hss89a/gdDRGQFxqVdOGGKiGwdsxRR/bCQbkG310hn6iKyZWFhYQgICDCZYZmTk4MDBw6YzOTMyspCQkKC1Gbbtm0wGAwIDw+X2uzatQvFxcVSm7i4OLRt2xbe3t5Sm+pme9YkFiJqfJYsWYLs7GwMHDgQgYGB0u2HH36Q2nzwwQd44IEHMHr0aPTv3x8BAQH4+eefpe0KhQLr16+HQqFAREQEnnzySTz99NOYO3eu1CYsLAwbNmxAXFwcunbtioULF2LZsmWIjIyU2owZMwYLFizArFmz0K1bNyQmJmLTpk0VlqSyJ0IIaLVajtmIqEaME6aYMojI1vHMGaL6cbJ2AI3J7VP+rBsHEQF5eXk4e/asdD8lJQWJiYnw8fFBSEgIpkyZgv/+97+46667EBYWhrfeegtBQUEYNWoUAKB9+/YYNmwYJkyYgKVLl6K4uBiTJk3CY489hqCgIADAE088gbfffhvjx4/Ha6+9hqSkJHz44Yf44IMPpNedPHkyBgwYgIULF2LEiBFYtWoVDh06hM8//xwAIJPJ7hgLETU+NSnwajQaxMTEICYmpso2oaGh2LhxY7X7GThwII4cOVJtm0mTJmHSpEl3jMle6HQ6zP/1CGb8X3dpdj4RUVV4LSwishdMU0T1w0K6BRkHWDzlj8j6Dh06hEGDBkn3p02bBgAYN24cYmNjMWPGDOTn52PixInIysrCvffei02bNkGj0UjP+e677zBp0iQMHjwYcrkco0ePxkcffSRt9/T0xJYtWxAVFYWePXuiSZMmmDVrFiZOnCi16du3L1auXIk333wTb7zxBu666y6sW7cOnTp1ktrUJBYiIjIvJ6XK2iEQkZ2QG6+FxQoVEdk4nm1HVD8spFuQQsalXYhsxcCBA6v9LMpkMsydO9dkiYPyfHx8sHLlympfp0uXLvjjjz+qbfPoo4/i0UcfrVcsRERERGQdcjmXdiEiImoMWEi3IC7tQkRERGRZWq3W2iEQkYPjmcdEZC+YpYjqhxcbtSCunUdERERERORY5NKEKR7nETmq9957T7p+lVFRURGioqLg6+sLNzc3jB49Gunp6SbPS01NxYgRI+Di4gI/Pz9Mnz4dJSUlJm127NiBHj16QK1Wo3Xr1oiNjTVr7GXPxOYKCUT1w0K6Bclv/bS5dh4REREREZFjUNyqpBs4I53IIf3555/47LPP0KVLF5PHp06dit9++w1r1qzBzp07ceXKFTz88MPSdr1ejxEjRkCn02Hv3r346quvEBsbi1mzZkltUlJSMGLECAwaNAiJiYmYMmUKnn/+eWzevNls8Zc9W4Zpiqh+WEi3IK6RTkRERERE5Fhun3ls5UCIyOzy8vIwduxYfPHFF/D29pYez87OxpdffolFixbhvvvuQ8+ePbFixQrs3bsX+/fvBwBs2bIFJ06cwLfffotu3bph+PDhmDdvHmJiYqDT6QAAS5cuRVhYGBYuXIj27dtj0qRJeOSRR/DBBx+YrQ8my04xTxHVCwvpFiQzDrAMVg6EiIiIiIiIzIJLeBI5rqioKIwYMQJDhgwxeTwhIQHFxcUmj7dr1w4hISHYt28fAGDfvn3o3Lkz/P39pTaRkZHIycnB8ePHpTbl9x0ZGSntozJarRY5OTkmt+qUlCmkC1bSierF7gvpLVu2xI0bNyo8npWVhZYtW1ohoqoZ187j0i5EdWdPn3kiovKYw4iIasae8qVxCU8W0omsz5y5Y9WqVTh8+DCio6MrbEtLS4NKpYKXl5fJ4/7+/khLS5PalC2iG7cbt1XXJicnB4WFhZXGFR0dDU9PT+kWHBxcbT/KzkhnmiKqH7svpF+4cAF6vb7C41qtFpcvX7ZCRFUzrp3HpV2I6s6ePvNEROUxhxER1Yw95Usu7UJkO8yVOy5evIjJkyfju+++g0ajMWeI9TZz5kxkZ2dLt4sXL1bb3nRGOhHVh5O1A6irX3/9Vfr/5s2b4enpKd3X6/WIj49HixYtrBBZ1WQcYBHVmT1+5omIjJjDiIhqxh7zpbGQrueBHpHVmDt3JCQkICMjAz169DDZz65du/DJJ59g8+bN0Ol0yMrKMpmVnp6ejoCAAABAQEAADh48aLLf9PR0aZvxX+NjZdt4eHjA2dm50tjUajXUanWN+1I2NzFPEdWP3RbSR40aBaC0OD1u3DiTbUqlEi1atMDChQutEFnVjEu78JQ/otqzx888EZERcxgRUc3YY77kcR6R9Zk7dwwePBjHjh0zeezZZ59Fu3bt8NprryE4OBhKpRLx8fEYPXo0ACA5ORmpqamIiIgAAEREROCdd95BRkYG/Pz8AABxcXHw8PBAhw4dpDYbN240eZ24uDhpH+ZQtnheoudF+4jqw24L6YZbV+wMCwvDn3/+iSZNmlg5ojvjTAWiurPHzzwRkRFzmH0SQkCr1UKlUklnFhJRw7LHfGlcwtPA4zwiqzF37nB3d0enTp1MHnN1dYWvr6/0+Pjx4zFt2jT4+PjAw8MDL730EiIiInD33XcDAIYOHYoOHTrgqaeewvz585GWloY333wTUVFR0ozyF154AZ988glmzJiB5557Dtu2bcPq1auxYcOGesVfVtmlXYqZp4jqxW4L6UYpKSnWDqHGbq+RbuVAiOyYPX3miYjKYw6zLzqdDh9uPoEZ/9e9VqdQE1H92VO+5BrpRLbDkrnjgw8+gFwux+jRo6HVahEZGYlPP/1U2q5QKLB+/Xq8+OKLiIiIgKurK8aNG4e5c+dKbcLCwrBhwwZMnToVH374IZo3b45ly5YhMjLSbHHqDYYy/2eiIqoPuy+kA0B8fDzi4+ORkZEhfQtptHz5citFVdHtNdKZuIjqw14+80RElWEOsy9OSpW1QyBqtOwlX8q4tAuRTWmo3LFjxw6T+xqNBjExMYiJianyOaGhoRWWbilv4MCBOHLkSJ3jupMSLu1CZDZ2X0h/++23MXfuXPTq1QuBgYE2fdqtce08fgNIVHf29JknIiqPOYyIqGbsKV9KS7uwkE5kdfaUOyylbA2qWM88RVQfdl9IX7p0KWJjY/HUU09ZO5Q7Mp7yx/EVUd3Z02eeiKg85jAiopqxp3zJpV2IbIc95Q5LMZmRbuCMdKL6kFs7gPrS6XTo27evtcOoETmXdiGqN3v6zBMRlcccRkRUM/aUL43HeTzzmMj67Cl3WIrBZGkX5imi+rD7Qvrzzz+PlStXWjuMGpFLa+dZNw4ie2ZPn3kChBAoLCyE4BeIRACYw4iIasqe8qW8zMoRHPMQWZc95Q5LMZ2RzhxFVB92v7RLUVERPv/8c2zduhVdunSBUqk02b5o0SIrRVaRNFOBgyuiOrOnzzyVvl/vrTuE10f1grOzs7XDIbI65jAiopqxp3ypKFNJ1xsEnBRck5nIWuwpd1gK10gnMh+7L6QfPXoU3bp1AwAkJSWZbLO1i0oYB1icpUBUd/b0madSSpXa2iEQ2QzmMCKimrGnfFk2Hk72JLIue8odlsI10onMx+4L6du3b7d2CDVmzNkGjq6I6syePvNEROUxhxER1Yw95cuyS7vwelhE1mVPucNS9GWK51wjnah+7H6NdHtye2kXKwdCRERERETUSOzatQsjR45EUFAQZDIZ1q1bZ9b9l13ahYV0IrI1ZYvnArwwMlF92P2M9EGDBlV7es62bdssGE31jOMrLu1CVHf29JknIiqPOcx+CSGg0+mgUqka7anhRJZkznyZn5+Prl274rnnnsPDDz9sjvBMyLm0C5HN4FirovKF8xKDAQq5wkrRENk3uy+kG9e+MiouLkZiYiKSkpIwbtw46wRVBfmtSjpnKRDVnT195omIymMOs186nQ7zfz2CGf/XHWo1r/1A1NDMmS+HDx+O4cOHmzE6U2UL6ZzpSWRdHGtVpC+3LnqJXkBt99VAIuuw+4/OBx98UOnjc+bMQV5enoWjqZ5xgMWxFVHd2dNnnoioPOYw++akVFk7BKJGw5r5UqvVQqvVSvdzcnKqbV92jXSefUxkXRxrVVRSfkY61xsmqjOHXSP9ySefxPLly60dhgnjAIuzFIjMzxY/80RENcUcZl7li2BE5DgskS+jo6Ph6ekp3YKDg6ttzxnpRLavMY+1yuel4nIz1Imo5hy2kL5v3z5oNJoat2/oC9AAty9Cw1kKROZX2888EZEtYQ4jIqoZS+TLmTNnIjs7W7pdvHix2vZyOddIJ7J1jXmsxRnpROZj90u7lL9YjBACV69exaFDh/DWW2/VeD8NfQEaANIFLzi4Iqo7c33miYisgTmMiKhmrJkv1Wp1ra+FIJeVHudx0hSRdXGsVVGFGel6zkgnqiu7L6R7enqa3JfL5Wjbti3mzp2LoUOH1ng/tb0ATW3XzQNuL+3Ci40S1Z25PvNERNbAHEZEVDP2li8VchkMegE9j/WIrMrecocllJS/2ChndxLVmd0X0lesWGGV142Ojsbbb79dq+dIFxtl0iKqM2t95omIzIE5jIioZsyZL/Py8nD27FnpfkpKChITE+Hj44OQkBCzvEbp2ceCZx8TWRnHWhXp9eWXduGMdKK6svtCulFCQgJOnjwJAOjYsSO6d+/eoK83c+ZMTJs2Tbqfk5NT44vQcHBFVH+W/swTEZkTcxgRUc2YI18eOnQIgwYNku4bj+PGjRuH2NhYs8Sp4KQpIpvCsdZtFZd2YZ4iqiu7L6RnZGTgsccew44dO+Dl5QUAyMrKwqBBg7Bq1So0bdq0QV63buvmGQvpTFpEdWWtzzwRkTkwhxER1Yw58+XAgQMbfO1yLuNJZBs41qqowsVGDZyRTlRXcmsHUF8vvfQScnNzcfz4cWRmZiIzMxNJSUnIycnByy+/bO3wTBgHV1w3j6ju7OkzT0RUHnMYEVHN2Fu+NE6aKj/zk4gsy95yhyVwRjqR+dh9IX3Tpk349NNP0b59e+mxDh06ICYmBr///rsVI6tIfquSzjo6Ud3Z02eeiKg8c+awXbt2YeTIkQgKCoJMJsO6detMtgshMGvWLAQGBsLZ2RlDhgzBmTNnTNpkZmZi7Nix8PDwgJeXF8aPH4+8vDyTNkePHkW/fv2g0WgQHByM+fPnV4hlzZo1aNeuHTQaDTp37oyNGzfWqi/2RAgBrVbb4LNbiRo7exvzGY/1WEcnsi57yx2WUH4yp66EM9KJ6sruC+kGgwFKpbLC40qlEoZanK6Sl5eHxMREJCYmArh9AZrU1FRzhcqLjRKZgbk+8zUxZ84cyGQyk1u7du2k7UVFRYiKioKvry/c3NwwevRopKenm+wjNTUVI0aMgIuLC/z8/DB9+nSUlJSYtNmxYwd69OgBtVqN1q1bV7pWZ0xMDFq0aAGNRoPw8HAcPHjQrH0lIsswZw7Lz89H165dERMTU+n2+fPn46OPPsLSpUtx4MABuLq6IjIyEkVFRVKbsWPH4vjx44iLi8P69euxa9cuTJw4Udqek5ODoUOHIjQ0FAkJCXj//fcxZ84cfP7551KbvXv34vHHH8f48eNx5MgRjBo1CqNGjUJSUlKt+mMvdDod5v96BDqdztqhEDk0S475zMF49jG/ZCOyLnvLHZZQfkZ6UYneSpEQ2T+7L6Tfd999mDx5Mq5cuSI9dvnyZUydOhWDBw+u8X4OHTqE7t27SxegmDZtGrp3745Zs2aZLdbb6+aZbZdEjY65PvM11bFjR1y9elW67d69W9o2depU/Pbbb1izZg127tyJK1eu4OGHH5a26/V6jBgxAjqdDnv37sVXX32F2NhYk7ySkpKCESNGYNCgQUhMTMSUKVPw/PPPY/PmzVKbH374AdOmTcPs2bNx+PBhdO3aFZGRkcjIyDB7f4moYZkzhw0fPhz//e9/8dBDD1XYJoTA4sWL8eabb+LBBx9Ely5d8PXXX+PKlSvSzPWTJ09i06ZNWLZsGcLDw3Hvvffi448/xqpVq6T4vvvuO+h0OixfvhwdO3bEY489hpdffhmLFi2SXuvDDz/EsGHDMH36dLRv3x7z5s1Djx498Mknn1QZu1arRU5OjsnNnjgpVdYOgcjhWXrMV1+KWwd7XMaTyLrsLXdYQkm5pVy0xSykE9WV3RfSP/nkE+Tk5KBFixZo1aoVWrVqhbCwMOTk5ODjjz+u8X6MF6ApfzPXVdwBXmyUyBzM9ZmvKScnJwQEBEi3Jk2aAACys7Px5ZdfYtGiRbjvvvvQs2dPrFixAnv37sX+/fsBAFu2bMGJEyfw7bffolu3bhg+fDjmzZuHmJgYaSbj0qVLERYWhoULF6J9+/aYNGkSHnnkEXzwwQdSDIsWLcKECRPw7LPPokOHDli6dClcXFywfPnyKuO29yIVkaOyVA5LSUlBWloahgwZIj3m6emJ8PBw7Nu3DwCwb98+eHl5oVevXlKbIUOGQC6X48CBA1Kb/v37Q6W6XTiOjIxEcnIybt68KbUp+zrGNsbXqUx0dDQ8PT2lW3BwcP07TUQOxdJjvvqSSWcfWzkQokbO3nKHJVSYkV7MREVUV07WDqC+goODcfjwYWzduhWnTp0CALRv377CAZ0tuL1uHgvpRHVl6c/8mTNnEBQUBI1Gg4iICERHRyMkJAQJCQkoLi42ed127dohJCQE+/btw9133419+/ahc+fO8Pf3l9pERkbixRdfxPHjx9G9e/cqC1BTpkwBULqEQEJCAmbOnCltl8vlGDJkyB2LVG+//baZfgpEZC6WymFpaWkAYJJ/jPeN29LS0uDn52ey3cnJCT4+PiZtwsLCKuzDuM3b2xtpaWnVvk5lZs6ciWnTpkn3c3JyWEwnIhP2dJwHlD37mMd6RNZkb7nDEkoqFNI5I52orux2Rvq2bdvQoUMH5OTkQCaT4R//+AdeeuklvPTSS+jduzc6duyIP/74w9phmuDgiqjurPGZDw8PR2xsLDZt2oQlS5YgJSUF/fr1Q25uLtLS0qBSqeDl5WXynPJFqsqKS8Zt1bXJyclBYWEhrl+/Dr1eX6ciVXZ2tnS7ePFinX4GRGQe9jhuaUhqtRoeHh4mNyIiwH7zpYJnHxNZlb3mDkvQlztVhoV0orqz20L64sWLMWHChEoPvDw9PfGvf/3LZP1OW2Bc2kXPs2iIas0an/nhw4fj0UcfRZcuXRAZGYmNGzciKysLq1evNuvrNAQWqYhsi6VzWEBAAABUuAByenq6tC0gIKDCtRZKSkqQmZlp0qayfZR9jaraGLcTEdWGPR7nAbeXdim/hAIRWYa95g5LKLdEOopKWJQiqiu7LaT/9ddfGDZsWJXbhw4dioSEBAtGdGe8kjtR3dnCZ97Lywtt2rTB2bNnERAQAJ1Oh6ysLJM25YtUdS1AeXh4wNnZGU2aNIFCoWCRisjOWTqHhYWFISAgAPHx8dJjOTk5OHDgACIiIgAAERERyMrKMnndbdu2wWAwIDw8XGqza9cuFBcXS23i4uLQtm1beHt7S23Kvo6xjfF1iIhqwxbGfHWhkJbxtHIgRI2UuXNHdHQ0evfuDXd3d/j5+WHUqFFITk42aVNUVISoqCj4+vrCzc0No0ePrnDclpqaihEjRsDFxQV+fn6YPn06SkpKTNrs2LEDPXr0gFqtRuvWrc16rT6AM9KJzMluC+np6elQKpVVbndycsK1a9csGNGdcY10orqzhc98Xl4ezp07h8DAQPTs2RNKpdKkeJScnIzU1FSTItWxY8dMZnzGxcXBw8MDHTp0kNpUV4BSqVTo2bOnSRuDwYD4+HgWqYjsSEPksLy8PCQmJiIxMRFA6QVGExMTkZqaCplMhilTpuC///0vfv31Vxw7dgxPP/00goKCMGrUKACl64UOGzYMEyZMwMGDB7Fnzx5MmjQJjz32GIKCggAATzzxBFQqFcaPH4/jx4/jhx9+wIcffmiyvvnkyZOxadMmLFy4EKdOncKcOXNw6NAhTJo0qXY/JCIi2MaYry44aYrIusydO3bu3ImoqCjs378fcXFxKC4uxtChQ5Gfny+1mTp1Kn777TesWbMGO3fuxJUrV/Dwww9L2/V6PUaMGAGdToe9e/fiq6++QmxsLGbNmiW1SUlJwYgRIzBo0CAkJiZiypQpeP7557F58+Za/gSqVnGNdM5IJ6oru73YaLNmzZCUlITWrVtXuv3o0aMIDAy0cFTVk8s4S4GorqzxmX/11VcxcuRIhIaG4sqVK5g9ezYUCgUef/xxeHp6Yvz48Zg2bRp8fHzg4eGBl156CREREbj77rsBlM566NChA5566inMnz8faWlpePPNNxEVFQW1Wg0AeOGFF/DJJ59gxowZeO6557Bt2zasXr0aGzZskOKYNm0axo0bh169eqFPnz5YvHgx8vPz8eyzz5q1v0TUcBoihx06dAiDBg2S7huL2+PGjUNsbCxmzJiB/Px8TJw4EVlZWbj33nuxadMmaDQa6TnfffcdJk2ahMGDB0Mul2P06NH46KOPpO2enp7YsmULoqKi0LNnTzRp0gSzZs3CxIkTpTZ9+/bFypUr8eabb+KNN97AXXfdhXXr1qFTp0616o890mq1ACDldCKqP3s8zgPKLuPJgz0iazB37ti0aZPJ/djYWPj5+SEhIQH9+/dHdnY2vvzyS6xcuRL33XcfAGDFihVo37499u/fj7vvvhtbtmzBiRMnsHXrVvj7+6Nbt26YN28eXnvtNcyZMwcqlQpLly5FWFgYFi5cCKB0osPu3bvxwQcfIDIystLYtFqtNAYBSs86rI5ez4uNEpmL3c5Iv//++/HWW2+hqKiowrbCwkLMnj0bDzzwgBUiq5p0sVEOrohqzRqf+UuXLuHxxx9H27Zt8c9//hO+vr7Yv38/mjZtCgD44IMP8MADD2D06NHo378/AgIC8PPPP0vPVygUWL9+PRQKBSIiIvDkk0/i6aefxty5c6U2YWFh2LBhA+Li4tC1a1csXLgQy5YtMxk0jRkzBgsWLMCsWbPQrVs3JCYmYtOmTRUuQEpEtqshctjAgQMhhKhwM54OLJPJMHfuXKSlpaGoqAhbt25FmzZtTPbh4+ODlStXIjc3F9nZ2Vi+fDnc3NxM2nTp0gV//PEHioqKcOnSJbz22msVYnn00UeRnJwMrVaLpKQk3H///bXqCxGRkT0e5wFlzz62ciBEjVRD547s7GwApWMnAEhISEBxcTGGDBkitWnXrh1CQkKwb98+AMC+ffvQuXNnk+O2yMhI5OTk4Pjx41KbsvswtjHuozLR0dHw9PSUbsHBwdXGbpyRbiwAaktYSCeqK7udkf7mm2/i559/Rps2bTBp0iS0bdsWAHDq1CnExMRAr9fjP//5j5WjNCXnldyJ6swan/lVq1ZVu12j0SAmJgYxMTFVtgkNDcXGjRur3c/AgQNx5MiRattMmjSJyyQQ2TF7HLcQEVmDveZLadIUj/WIrKIhc4fBYMCUKVNwzz33SGfcpaWlQaVSwcvLy6Stv78/0tLSpDblJz8Z79+pTU5ODgoLC+Hs7FwhnpkzZ5oss5eTk1NtMd14poxCLmAwyFCoYyGdqK7stpDu7++PvXv34sUXX8TMmTOltehkMhkiIyMRExNjc7M1ubQLUd3Z42eeiMiIOYyIqGbsNV9y0hSRdTVk7oiKikJSUhJ2795tzpDrTK1W12pZOf2tn4WTDCgGUFTCNdKJ6spuC+nA7ZmeN2/exNmzZyGEwF133QVvb29rh1YpzlIgqh97+8wTEZXFHEZEVDP2mC+5RjqR9TVE7pg0aRLWr1+PXbt2oXnz5tLjAQEB0Ol0yMrKMpmVnp6ejoCAAKnNwYMHTfaXnp4ubTP+a3ysbBsPD49KZ6PXhbeLEi5KGdQygUI910gnqg+7LqQbeXt7o3fv3tYO445ur5vHwRVRfdjLZ56IqDLMYQ2n7IW3iMj+2VO+VNw61uOhHpH1mSN3CCHw0ksvYe3atdixYwfCwsJMtvfs2RNKpRLx8fEYPXo0ACA5ORmpqamIiIgAAEREROCdd95BRkYG/Pz8AABxcXHw8PBAhw4dpDbllwKNi4uT9mEO/7m/PXIzr+NMRh5u3gC0LKQT1ZlDFNLthXS6H8+iISIiIiIichg8+5jIsURFRWHlypX45Zdf4O7uLq1p7unpCWdnZ3h6emL8+PGYNm0afHx84OHhgZdeegkRERG4++67AQBDhw5Fhw4d8NRTT2H+/PlIS0vDm2++iaioKGlplhdeeAGffPIJZsyYgeeeew7btm3D6tWrsWHDBrP3SXErT3FGOlHdye/chMyF6+YRERERERE5HhmXdiFyKEuWLEF2djYGDhyIwMBA6fbDDz9IbT744AM88MADGD16NPr374+AgAD8/PPP0naFQoH169dDoVAgIiICTz75JJ5++mnMnTtXahMWFoYNGzYgLi4OXbt2xcKFC7Fs2TJERkaavU9yqZDO2Z1EdcUZ6RbEWQpEROZTWFgIAGZbO5CIqK6MS8rU5sJfRORYFNIynlYOhIjMQtSgbqPRaBATE4OYmJgq2xjXba/OwIEDceTIkVrHWFvSjPQSzkgnqivOSLeg2zPSrRwIERERERERmQ0nTRGRreOMdKL6YyHdgnixUSIiIiIiIsfDZTyJyNZxjXSi+mMh3YJuz1KwbhxERERERERkPnKukU5ENk5xqwKo5Yx0ojpjId2CpFkKHFwRERERWYWuxIB8XelMrBsFxUjNLLByRETkCOS3jqw5IZ2IbJW8zBrpNVkDnogq4sVGLYhLuxARERFZ17yNyVhzOAOHLufjVHo+fj52Hb1beOP/ugTgkR7N6rVvIQR0Oh1UKhVktyZQEFHjwKVdiMjWKcqsklCsF1A5caxCVFuckW5BvAANERERkfWU6A1YnXAZAsCp9HwApQeTB1Ju4j+/nESxvn6nOut0Osz/9Qh0Op0ZoiUie8KlXYjI1snL1M2LSrhOOlFdcEa6Bd1e2sXKgRARERE1IkIIJF7KRoGu+oPG+VvO4Ny1fMwd2b7Or+WkVNX5uURkvxS3KlScM0VEtqrsTNoinR4eGqXVYiGyVyykWxBnpBMRERFZ3paTGXj5h2MVHg/2VONitla6//X+iwCAZXv+hofCYuERkQMwHuvpeaxHRDZKJgOUcqDYAOQUlcDPw9oREdkfFtItiOvmEREREVne7rOZJvc7+LtgVLdApGXm4Xy2Hs5KBbafvoai4tLTBk9czUGfZi7WCJWI7BSP9YjIHqgUMhQbBLIKuAwdUV2wkG5BtwdXVg6EiIiIqJHYmJSO1QmXAQBPhQfj4e6B+P1IKh7rGYSYbWfx2djOAIBTablYuPUsdp25gb8u5SA5LQ/DuwTV+XW12tKZ7mq1uv6dICKbd3sZTx7sEZHtUimA/GIgM5+FdKK64MVGLUh+66fNWQpEREREDS/h70xMXXN7SZcX+4ehQ6AHZDJZhbbtAtzx+dhu0v2iEgNe+fkkLxxIRDVy+1jPunEQEVVHdStX3eSMdKI6YSHdgni6HxEREZHl/PDnRen/wd7O8HWr/kKgMpkMkwaGSRcNTLlRiCOX83Alq6hB4yQi+8djPSKyB8ZCemZ+sXUDIbJTLKRb0O3T/awcCBFRJXacvo5L2ZyZQET2RQgBrVYLUa54VVSsx/qjVwEAfVp44b8Ptq/R/l4a1ArHZ92HDn6la6QnXM7Dc98crrB/IqKyjMd6PIuFiGyZUl6aozgjnahuWEi3IM5SICJb9VPCJbz4/VHsvpCLwmK9tcMhIqoxnU6H+b8egU5nekB44MJNFOj0CPRU4+tneuLuMJ8a71MmkyHMVyPdT7legKQruWaLmYgcj/FMFh7qEZEtc5KVJqnMfK2VIyGyTyykW9CtsVWFQvqhC5n4et8FznQiIqsZ3jkAgR5q5OkM+HLP39YOx2qEECgsLGQ+JrIzTkrTJVsMBoFVf14CAPS/q0mla6LfSZCHCsM7NJXux+w8jxv5xbWeEKHVaqULjxKR4zKmGT3HEERkw6Q10rm0C1GdsJBuQXK5cUa66eOPLN2HWb8cx64z160QFRER4KJywitDWgEAfvkrzcrRWE9RURHeW3cIRUVcD5nInk37MQnbkkvHVQPualKnfchlMvxvVDtEtvUGAGxPvo4fj13Hyj+vmC1OInIcPPuYiOzB7TXSubQLUV2wkG5Bdxpcnb+WZ8lwiIhMDGzTBDIAl7KKcDmr0NrhWI1SpbZ2CERUD/naEsSdzAAAhPi4oG/Lmi/pUplQLzVmj2gn3f/5rzQU62tfKKtqLXcicgxOtyZN6WuQH06l5eDQhcyGDomIqAKlovRfrpFOVDcspFuQcWmX02fOQuPsUnpz85C2vzLtFWicXdCqdRsrRUhEjZmr2glNXJ0AAAfO37ByNEREdfPn31koMQg089Jg5/SBcFYp6rU/mUyGJ/o0x5+vD4BcBpy9VoDlf6bhQErtimBVreVORI7BXVM6hpr73oLbx3qV3Fq2boNhi//AI0v34UojnrhARNZxe2kXjkeI6sLJ2gE0Jsb1OX0DQ/DKr4kAgDxtCb7cnQIAeGDia+ge8h5m/l83K0VIRI1doLsS1/JLsP/8DTzco7lZ9imEQInBYJZ9ERHdye6zpV8E3tPKt05ro1fFw1mJUG8NUjJLl3569afjWD2hN3xclFAra1asL7+WOxE5Di+X0s93rwfGYuiMV6ps98bou+F/6/9nMvIQ5OVsgeiIiEoZC+nZRSUoKtZDU8MxDBGV4ox0C7p9Jffbp/sVFeul/2tLWGgiIusKdFcCQK1nWlYlITULPxzNxKglB3EtlxfbI6KGlZGrxU9HStcwH9DG1+z779/SE3NH3AUXpRwZuVoMXLQbj37xJ3S1GMNxiRcix+TlUjqG0hZXnw/kzrfPSM7M59iIiCxLKS+9AUBqZoF1gyGyQyykW5BxaZeyh02FOhbSich2+LsrIZcBf98owNXs+p1unK8tQdT3R5GnM+Dc9QJM+eGImaIkIqpIV2LA62uPo0CnR9fmHrivTVOzv4bGSY5RXQMwvJ0PVE6lw+jk9DzM33LGZExXbZxc4oXIIXnfmpFeWFx9LihbSL98k0u7EJFlyWSAq7K0OJVyPd/K0RDZHxbSLch4sdGyE5DKzkgvusOgi4iooakUcnQIdAcAHDhfv1npPyZcQnZRiXR/z9kbOHElp177JCKqyjcHLmLPuUw4K+V4+4H2kMvNt6xLeU1clfhxYh9EdvCTXvveBbuw6Xh6jZ5vXOKFs9OJHEddZqRfYiGdiKzAxal03HH+Wp6VIyGyPyykW5Bx5lKx/vbgqpBLuxCRjekT6g0A2F+PC44KIfDVvgsAgL6hbojsUDozdPWhi/WOj4gIMC1CCyGkJV1ei2yD9re+EGxIbf3dsGB0J4y7OxhN3VXI0+oxefUxfL7/KuJOZtRoH5ydTuQ4vJxrPyOdhXQisgaXW8uiX7jBpV2IaouFdAtqdutCMtoSA7S3BlhFZWYscEY6EdmC3i28ANSvkH7kYhbOX8uHs1KO1r5qPNI9CADw8+FLZsl1RcV6bDmZge2nr8Ng4ExOosaobBH6YrYW567lQ+0kx8jOARaLQeUkxxvD22LH1HsRHlb6JaQA8PIPR/HUikNYsjMF+jvkKCelijPTiRyAt+utGekl+mo/y3KXsoV0FrGIyPJupStcuM4Z6US1xUK6BbmqnaDPvwkAyCosBlBuRnq50wALdCXYc/Y6D6qIyKJ6hnhBLiudoZCWXVSnffyYcAkAMKR9U6gUckS09EEzL2fkFJVgU1JaveLLzNdhVMweTF6dhH9/fxTjVhzkF5FEjZSTUoUjF7OxJbl0fDWikz/cNE6Wj0Mhx6ePd8X8hzuiuacaBgEcvJCFxdvO4R8f7sGCuDNIz6k6n3JmOpH9M66RbhCATl/1mcZyZ0/p/5ezCk3OViYisgS3W4X041dyeBxFVEsspJcTExODFi1aQKPRIDw8HAcPHjTr/vXZpWtnZt8qpJuskV5imsBm/HgUY5cdwE+HL5s1BiKyTw2dn4zcNU7oGFR6kLfr9LVaP/96nhY/3SqkG2eiK+QyPNqrOQBg+Z6UOn9BWKw34F/fHMKptFzIZaUXcf7jzHW8sfaYWb50FEIgT6vH1Tp+gUDUmFkqR5WVr9PjlZ9PQi+AgW2aYNaIdg3+mlVxUzvhwa6BGNHeB6sn9MY/ezYDAFzOKsIXu/9G/4W78XVCOt7feh7nbxRWuMCXcd10rVYLrVZr8fiJHF1D5yiNUgFRUvplWFE166SXXdqlWC+QnJZr1jiIyD5ZchzloQScnYA8rR6/H7vaYK9D5IhYSC/jhx9+wLRp0zB79mwcPnwYXbt2RWRkJDIyarbOZU3os0pnYhpnpBfoTGekGwtB13K1WH+0NKF9vO1Mhf1wljpR42KJ/FTWsE6lSyN8tuvcHZclKO+j+DPQlhjQtbkneod6SY8/eXcoXFQKHL2ULeW32hBCYNYvSfjzwk24qZ3wy4vhWPZkNyjkMvx8+DKW7jxf632WtffcdYxdcRirjmbivsV7MfLj3XX6IqEqhYWFKCzkWqjkmCydowDg4IWb2HAyE9fzi+Hj7IRFj3SCs0rRYK9XG12be2Le/7XHhqi78c6D7dE9uPTLycJiA745eBlxZ7Iw7ON9GLJ4D9afuIGPd1xA4pU87E/JxN83CpCnLZEK6sZlXwwGA5d/IaojS+UoQ2FpUby6GZ6yMoV0ADiSetOsMRCR/bH0OEomA4JdS///0bazuJrNYxSimrL8ua82bNGiRZgwYQKeffZZAMDSpUuxYcMGLF++HK+//rpZXqMku7SQnpZdhJTr+Sbr4umFwIUbBVAGtcP7m09Jj6fnFGH3metwUsiQXViMlQdSse/cDTwRHoLht4pdeiFw8mou8opK0DbADT6uaun513K1uFmgQ5CXBq6q22+5tsSArMJiuGuc4KJUQCaTmaWP5NhCfV3g76GxdhiNjiXyU1lPR4Ti813nce5aPl74NgEPd28Gd40SCrkMQggYBGAQAgYhIG79v1gv8MeZa/juQCoAYOo/2pjklSZuaoy/NwwfbzuL6T/+hZTr+Wgb4A4PjRLVpZ/CYj2u52rx619X8MeZ65DLgEX/7IrWTV3Ruqkr3hzRHm//dgL/23QKJ6/mYEgHf7irnaByksNJLoO+TIzGuIUQ0BtK/5+WXYQNx67iYEqm9JoyAMcuZ+Pp5QfRt5UvhrT3R4iPCzRKBeSy0gYyyCCXla6HbKjiNfK1etzI0+JGvg7pWQXI0ZbA21UDX1cVvF1V8HVVwcdVJV2M2lgfEyhTKBMm/wAA9AaBEoMBxXqBYr0BhUVayBVKKJ3kUDvJobr1r6IR5vV2AR7wdFFaO4xGp6Fz1PU8Lc5l5KFYL5CvK8HWE+lYc+vMFw+NE4a28Yar2vaGta393NDazw0PdwvC2Wv5WLT5JNQqJQ6nZuNaQTEu3rrQ4Bd7Sy/EfCD2MIDSA9wmrio4qxTwdVUhLSsfHYI8celGHsJb+8FFrYLaSQ6NUgGFvDRnGQwGKJ0UkMtkkN9KTgaDHkonBYTBACcnJ8jlMshlMsgAyOWA/FaOkMtuPX7rTB+ZsY1MBrm8NN+VbjP9t7TVndU0FdU0Y9nzmLVLc09olLbxhU9jYqlxlKEoFwp3X1y6WQhdiUH63S/7WVGHdAYAFF+7AGXTFpi++BtEPf0rIG7/9W/apAlWrvwOAJByPR9Ld55DTmExnrsnDL1a+Ej7KvtRMP5XpzfgdHoelAoZWjd1k8YsZdvLYN+fI2o4fcJ87tyIzM7Sx3oAEOwmkJonQ8r1fPR9bxt6hHjhLj83+Hto4KZ2govaCSqFHEqFHE4KGZzkMjjJjf+XQ36HabnVjRGqSz/VZSbmrcZNo5SjS3Mva4fBQrqRTqdDQkICZs6cKT0ml8sxZMgQ7Nu3r0L78qfdZmdnAwBycnKqfZ2SG5dg0Bbg3OUCnLtcOtOxubcz0nO0KNYbsO7gWXj/30ys2nNaek6BFnji0+0V9rV8+wks336idh0lqqc3H2iPx3qHVLrN+PvP2XLmVdv8BNQtRxUWFiIvKxPp6elwdnbG9AGBmPVbMjYfScHmIym1ivn5e0LQ3ksgPT3dZJ//7OSBg8ka7DufiQXrE2u1TwBQKuR4Y1hrdG0iQ3p66VJZw1s542qfplj6x99Yd/As1h08W+v9AoBSLsPIzk1h0Objuf5tsPJwOn48fBW7T1zE7hMX67RPsrzPn+6Jvq2aVLqNOaphWCJHbf7rMmb+nFTh8bt8VXhnZCv8lngZ165VPINErVZXukxKXvZNXL9+HXnZNyt9XlVtavq8yrZ5y4EglRbj+wXhyz/y8GCvVricVYjv9/8NHw9nJF3OgU7IkF1UgkKtAelFpZMtLtx6/uXrpT+bk5c5e9WebZrcD819XCrdxhzVMCw1jgKAktzrUHj4Ydfx1GrbiZJi9O3aDgdSMqEKagdVkOmyVCUA/vnxtgrPe78OYyeimpLLgKNzIqvczhzVMCyRo/R6PbQFedAV5EMI4xfmAl3dgBNZMmRrZfjzdAH+PF3p04lsQgtfF6x/uV+V2y2WowQJIYS4fPmyACD27t1r8vj06dNFnz59KrSfPXu2QOkEQd54463c7eLFi5b66DYKtc1PQjBH8cZbdTfmKPNijuKNN/PemKPMizmKN97Me2OOMi/mKN54M++toXMUZ6TX0cyZMzFt2jTpvsFgQGZmJnx9fas83SQnJwfBwcG4ePEiPDw8Km3jaNhnx+9z2f66u7sjNzcXQUFB1g6r0WOOMsW+2Sdz+yn9UgAAk0JJREFU900IwRxlI5ijqsZ+Opba9JM5ynYwR1WtsfQTYF/LY46yHbXNUY70u+wofXGUfgC20xdL5SgW0m9p0qQJFAqFtFSAUXp6OgICAiq0V6vVUKvVJo95eXnV6LU8PDzs/oNSW+yz4zP219PT09qhOJza5ieAOaoq7Jt9MmffmKPMjzmqYbCfjqWm/WSOMj/mqIbRWPoJsK9lMUeZnyVzlCP9LjtKXxylH4Bt9MUSOeoOlwdoPFQqFXr27In4+HjpMYPBgPj4eERERFgxMiJq7JifiMiWMUcRkS1jjiIiW8YcRWRfOCO9jGnTpmHcuHHo1asX+vTpg8WLFyM/P1+6cjIRkbUwPxGRLWOOIiJbxhxFRLaMOYrIfrCQXsaYMWNw7do1zJo1C2lpaejWrRs2bdoEf39/s+xfrVZj9uzZFU7BcWTss+NrbP21lobOT4Bjv5fsm31y5L45GuYo82E/HUtj6aetY44yn8bST4B9JcthLarmHKUvjtIPwLH6UhMyIYSwdhBERERERERERERERLaKa6QTEREREREREREREVWDhXQiIiIiIiIiIiIiomqwkE5EREREREREREREVA0W0omIiIiIiIiIiIiIqsFCOhERERERERERERFRNVhIt5CYmBi0aNECGo0G4eHhOHjwoLVDMqtdu3Zh5MiRCAoKgkwmw7p160y2CyEwa9YsBAYGwtnZGUOGDMGZM2esE6wZREdHo3fv3nB3d4efnx9GjRqF5ORkkzZFRUWIioqCr68v3NzcMHr0aKSnp1sp4vpbsmQJunTpAg8PD3h4eCAiIgK///67tN3R+uuoapuL1qxZg3bt2kGj0aBz587YuHGjhSKtuZp8HsuLjY2FTCYzuWk0GgtFXHNz5sypEGe7du2qfY49vGcA0KJFiwp9k8lkiIqKqrS9vbxn1DAcbRxVk7w1cODACr/zL7zwgpUirps75TBHGTvcKZ85wntJ1XO0HFWZuoxJ7EVjOpa9U1+feeaZCu/zsGHDrBMs1YojHefVpi+2eoxwp89aZXbs2IEePXpArVajdevWiI2NbfA476S2/dixY0elY6K0tDTLBGwBLKRbwA8//IBp06Zh9uzZOHz4MLp27YrIyEhkZGRYOzSzyc/PR9euXRETE1Pp9vnz5+Ojjz7C0qVLceDAAbi6uiIyMhJFRUUWjtQ8du7ciaioKOzfvx9xcXEoLi7G0KFDkZ+fL7WZOnUqfvvtN6xZswY7d+7ElStX8PDDD1sx6vpp3rw53nvvPSQkJODQoUO477778OCDD+L48eMAHK+/jqi2uWjv3r14/PHHMX78eBw5cgSjRo3CqFGjkJSUZOHIq1eTz2NlPDw8cPXqVen2999/Wyji2unYsaNJnLt3766yrb28ZwDw559/mvQrLi4OAPDoo49W+Rx7ec/IvBxxHFXTvDVhwgST3/n58+dbKeK6qy6HOcrYoSb5zBHeS6qcI+aoqtRmTGJPGtOx7J36CgDDhg0zeZ+///57C0ZIdeFIx3l1yam2eIxQk89aWSkpKRgxYgQGDRqExMRETJkyBc8//zw2b97cwJFWr7b9MEpOTjZ5T/z8/BooQisQ1OD69OkjoqKipPt6vV4EBQWJ6OhoK0bVcACItWvXSvcNBoMICAgQ77//vvRYVlaWUKvV4vvvv7dChOaXkZEhAIidO3cKIUr7p1QqxZo1a6Q2J0+eFADEvn37rBWm2Xl7e4tly5Y1mv7au9rmon/+859ixIgRJo+Fh4eLf/3rXw0aZ32V/zxWZsWKFcLT09NyQdXR7NmzRdeuXWvc3l7fMyGEmDx5smjVqpUwGAyVbreX94zMrzGMoyrLWwMGDBCTJ0+2XlBmUF0Oc+SxQ/l85gjvJVWtMeQoIWo/JrFXjelYtnxfhRBi3Lhx4sEHH7RKPFR3jnScV9u+2MMxQmWftfJmzJghOnbsaPLYmDFjRGRkZANGVjs16cf27dsFAHHz5k2LxGQNnJHewHQ6HRISEjBkyBDpMblcjiFDhmDfvn1WjMxyUlJSkJaWZvIz8PT0RHh4uMP8DLKzswEAPj4+AICEhAQUFxeb9Lldu3YICQlxiD7r9XqsWrUK+fn5iIiIcPj+OoK65KJ9+/aZtAeAyMhIm39Py38eq5KXl4fQ0FAEBwebnF1ha86cOYOgoCC0bNkSY8eORWpqapVt7fU90+l0+Pbbb/Hcc89BJpNV2c5e3jMyn8Yyjqoqb3333Xdo0qQJOnXqhJkzZ6KgoMAa4dVLVTnMUccOVeUzR3gvqaLGkqOMajMmcRSN4Vi2vB07dsDPzw9t27bFiy++iBs3blg7JKqGIx3n1TWnOsIxgq2+J3XVrVs3BAYG4h//+Af27Nlj7XDMioX0Bnb9+nXo9Xr4+/ubPO7v7+9QawRVx9hPR/0ZGAwGTJkyBffccw86deoEoLTPKpUKXl5eJm3tvc/Hjh2Dm5sb1Go1XnjhBaxduxYdOnRw2P46krrkorS0NLv73Fb2eaxM27ZtsXz5cvzyyy/49ttvYTAY0LdvX1y6dMmC0d5ZeHg4YmNjsWnTJixZsgQpKSno168fcnNzK21vj+8ZAKxbtw5ZWVl45plnqmxjL+8ZmVdjGEdVlbeeeOIJfPvtt9i+fTtmzpyJb775Bk8++aQVI6296nKYo44dKstnjvBeUuUaQ44yqu2YxFE4+rFsecOGDcPXX3+N+Ph4/O9//8POnTsxfPhw6PV6a4dGVXCk47y69MVRjhGqek9ycnJQWFhopahqLzAwEEuXLsVPP/2En376CcHBwRg4cCAOHz5s7dDMxsnaARDZu6ioKCQlJTnMGoHVadu2LRITE5GdnY0ff/wR48aNw86dO60dFpGkpp/HiIgIRERESPf79u2L9u3b47PPPsO8efMaOswaGz58uPT/Ll26IDw8HKGhoVi9ejXGjx9vxcjM68svv8Tw4cMRFBRUZRt7ec+IaquqvDVx4kTp/507d0ZgYCAGDx6Mc+fOoVWrVpYOs06qy2HOzs5WjKzhVJbPHOG9JGosY5LG7rHHHpP+37lzZ3Tp0gWtWrXCjh07MHjwYCtGRlQ5HiPYlrZt26Jt27bS/b59++LcuXP44IMP8M0331gxMvPhjPQG1qRJEygUCqSnp5s8np6ejoCAACtFZVnGfjriz2DSpElYv349tm/fjubNm0uPBwQEQKfTISsry6S9vfdZpVKhdevW6NmzJ6Kjo9G1a1d8+OGHDttfR1KXXBQQEGBXn9uqPo81oVQq0b17d5w9e7aBojMPLy8vtGnTpso47e09A4C///4bW7duxfPPP1+r59nLe0b14+jjqNrkrfDwcACw69/5sjnMEccONc1njvBeUilHz1HVudOYxFE48rFsTbRs2RJNmjRx+PfZnjnScZ45cqq9HiNU9Z54eHjY/eSDPn362N37UR0W0huYSqVCz549ER8fLz1mMBgQHx9v8q2ZIwsLC0NAQIDJzyAnJwcHDhyw25+BEAKTJk3C2rVrsW3bNoSFhZls79mzJ5RKpUmfk5OTkZqaard9rozBYIBWq200/bVndclFERERJu0BIC4uzube0zt9HmtCr9fj2LFjCAwMbIAIzScvLw/nzp2rMk57ec/KWrFiBfz8/DBixIhaPc9e3jOqH0cdR9UlbyUmJgKAXf/Ol81hjjh2qGk+c4T3kko5ao6qiTuNSRyFIx7L1salS5dw48YNh3+f7ZkjHeeZI6fa6zGCrb4n5pCYmGh370e1rHyx00Zh1apVQq1Wi9jYWHHixAkxceJE4eXlJdLS0qwdmtnk5uaKI0eOiCNHjggAYtGiReLIkSPi77//FkII8d577wkvLy/xyy+/iKNHj4oHH3xQhIWFicLCQitHXjcvvvii8PT0FDt27BBXr16VbgUFBVKbF154QYSEhIht27aJQ4cOiYiICBEREWHFqOvn9ddfFzt37hQpKSni6NGj4vXXXxcymUxs2bJFCOF4/XVEd8pFTz31lHj99del9nv27BFOTk5iwYIF4uTJk2L27NlCqVSKY8eOWasLlarJ57F8395++22xefNmce7cOZGQkCAee+wxodFoxPHjx63RhSq98sorYseOHSIlJUXs2bNHDBkyRDRp0kRkZGQIIez3PTPS6/UiJCREvPbaaxW22et7RubniOOoO+Wts2fPirlz54pDhw6JlJQU8csvv4iWLVuK/v37Wzny2rlTDnOksUNV+cxR3kuqmiPmqMrc6fNszxrTsWx1fc3NzRWvvvqq2Ldvn0hJSRFbt24VPXr0EHfddZcoKiqyduhUDUc6zqttX2z1GOFOeeX1118XTz31lNT+/PnzwsXFRUyfPl2cPHlSxMTECIVCITZt2mStLgghat+PDz74QKxbt06cOXNGHDt2TEyePFnI5XKxdetWa3XB7FhIt5CPP/5YhISECJVKJfr06SP2799v7ZDMavv27QJAhdu4ceOEEEIYDAbx1ltvCX9/f6FWq8XgwYNFcnKydYOuh8r6CkCsWLFCalNYWCj+/e9/C29vb+Hi4iIeeughcfXqVesFXU/PPfecCA0NFSqVSjRt2lQMHjxYKqIL4Xj9dVTV5aIBAwZIn1mj1atXizZt2giVSiU6duwoNmzYYOGI76wmn8fyfZsyZYr0c/D39xf333+/OHz4sOWDv4MxY8aIwMBAoVKpRLNmzcSYMWPE2bNnpe32+p4Zbd68WQCo9O+Bvb5n1DAcbRx1p7yVmpoq+vfvL3x8fIRarRatW7cW06dPF9nZ2dYNvJbulMMcaexQVT5zlPeSqudoOaoyd/o827PGdCxbXV8LCgrE0KFDRdOmTYVSqRShoaFiwoQJDvelkKNypOO82vTFVo8R7pRXxo0bJwYMGFDhOd26dRMqlUq0bNnS5HjWWmrbj//973+iVatWQqPRCB8fHzFw4ECxbds26wTfQGRCCGHuWe5ERERERERERERERI6Ca6QTEREREREREREREVWDhXQiIiIiIiIiIiIiomqwkE5EREREREREREREVA0W0omIiIiIiIiIiIiIqsFCOhERERERERERERFRNVhIJyIiIiIiIiIiIiKqBgvpRERERERERERERETVYCGdzGbgwIGYMmVKjdrGxsbCy8tLuj9nzhx069atQeIiIvtWm9xSXzt27IBMJkNWVpZFXs/aGlt/iYiIyLZcuHABMpkMiYmJ1g6FiKjOZDIZ1q1bd8d25sp5zzzzDEaNGlWvfVjarl27MHLkSAQFBdX451WeEAILFixAmzZtoFar0axZM7zzzjvmD7YaLKSTTXj11VcRHx9vsdfLzMzESy+9hLZt28LZ2RkhISF4+eWXkZ2dbbEYiKhmfv75Z8ybN8/aYdRJXQc4GzZsQHh4OJydneHt7W2WQZIlv5AgIiIiIiIiMsrPz0fXrl0RExNT531MnjwZy5Ytw4IFC3Dq1Cn8+uuv6NOnjxmjvDMni74aURXc3Nzg5uZmsde7cuUKrly5ggULFqBDhw74+++/8cILL+DKlSv48ccf67zf4uJiKJVKM0ZKRD4+PtYOwaJ++uknTJgwAe+++y7uu+8+lJSU/H979x0WxbX+Afy7hV3qgtKxAJao2LBERWNJ9IpKvJp4c40xdk00kGtJ1JjYzY0l1iREUxTMjZHEXzRFDBYUk9hFsUsUCzaKIFXYhd3z+wOZuNJhqX4/z7MP7MyZmXN22ZfZd8++g/Pnz1d3t4iIiIhqPCEE9Ho9lEqmOogI0Ol0UKlU1d0NAjBw4EAMHDiwyPVarRYffPABtm7dipSUFLRp0wbLly9Hnz59AACXLl3C+vXrcf78ebRo0QIA4OnpWRVdN8IZ6VQumZmZGD16NKytreHq6opVq1YZrddqtXj33XfRoEEDWFlZoWvXroiIiChyf0+Wdsmfxbly5Uq4urrC3t4e/v7+yMnJkdokJCRg8ODBsLCwgKenJ7Zs2QIPDw+sXbu2xP63adMGP/74IwYPHoymTZvihRdewH//+1/8+uuvyM3NhRACzZo1w8qVK422i4qKgkwmw9WrVwHkfX1n/fr1+Oc//wkrKyv897//xYMHDzBy5Eg4OjrCwsICzZs3R1BQUMkPKhEV6vGZ1B4eHvjoo48wfvx42NjYoHHjxvjyyy+N2h8/fhwdOnSAubk5OnfujB07dpT763NJSUkYMWIEGjRoAEtLS7Rt2xZbt241avN///d/aNu2LSwsLGBvb49+/fohMzMTCxcuxObNm/Hzzz9DJpNBJpMVGwcBIDc3F1OnTsXHH3+MyZMn45lnnoGXlxf+/e9/S23yy7Hs3r0bHTp0gIWFBV544QUkJCTgt99+Q6tWraDRaPDaa6/h4cOHAPJi6sGDB7Fu3TqpLzdu3JD2GRkZic6dO8PS0hLdu3dHdHS0tO7MmTN4/vnnYWNjA41Gg06dOuHkyZNlfiyJqHS0Wi3+85//wMnJCebm5njuuedw4sQJAH+//kNDQ9GuXTuYm5ujW7duBT5s+/PPP9GzZ09YWFigUaNG+M9//oPMzExpfWliaXFmz56NZ555BpaWlmjSpAnmzZtndI4GAL/++iueffZZmJubw8HBAS+99JLRGGfPno1GjRpBrVajWbNm2LhxY3keLiKqZn369EFAQAACAgJga2sLBwcHzJs3D0IIAIWXO7Czs0NwcLDRssuXL6N79+4wNzdHmzZtcPDgwVIdPz8u/vbbb+jUqRPUajX+/PNPGAwGLF26FJ6enrCwsED79u0LTJi6cOECXnzxRWg0GtjY2KBnz56IiYkp92NBRNUvPyZNmzYNDg4O8PX1xfnz5zFw4EBYW1vD2dkZo0aNwv379wEAX375Jdzc3GAwGIz2M2TIEIwfP166v379ejRt2hQqlQotWrTA//73vwr1s7iYp9frMWHCBCl+tWjRAuvWravQ8WqDgIAAHDlyBCEhITh79ixeeeUVDBgwAFeuXAGQd27ZpEkT7Ny5E56envDw8MDEiRORnJxcpf1kIp3KZebMmTh48CB+/vln7NmzBxERETh16pS0vqQXQGkcOHAAMTExOHDgADZv3ozg4GCjE66xY8fi1q1bOHDgAP7v//4Pn3/+ORISEso9ptTUVGg0GiiVSshkMowfP75AAjwoKAi9evVCs2bNpGULFy7ESy+9hHPnzmH8+PGYN28eLl68iN9++036xMzBwaHc/SIiY6tWrULnzp1x+vRpvPXWW5gyZYqU+M3IyMCLL74ILy8vREZGYuHChXj33XfLfazs7Gx06tQJoaGhOH/+PN544w2MGjUKx48fBwDcu3cPI0aMwPjx43Hp0iVERETg5ZdfhhAC7777Lv79739jwIABuHfvHu7du4fu3bsXe7xTp07hzp07kMvl6NChA1xdXTFw4MBCZ6QvXLgQn332GQ4fPoxbt27h3//+N9auXYvvvvsOoaGh2LNnDz799FMAwLp16+Dj44NJkyZJfWnUqJG0rw8++ACrVq3CyZMnoVQqjU4aR44ciYYNG+LEiROIjIzEe++9x2/eEFWiWbNm4ccff8TmzZtx6tQpNGvWDL6+vkZvEmbOnIlVq1bhxIkTcHR0xODBg6VEdkxMDAYMGIBhw4bh7Nmz+P777/Hnn38iICDA6DjFxdKS2NjYIDg4GBcvXsS6devw1VdfYc2aNdL60NBQvPTSSxg0aBBOnz6N8PBwo6/djh49Glu3bsUnn3yCS5cu4YsvvqjSbyYSkWlt3rwZSqUSx48fx7p167B69Wp8/fXXZdrHzJkz8c477+D06dPw8fHB4MGDkZSUVOrt33vvPSxbtgyXLl1Cu3btsHTpUnzzzTfYsGEDLly4gOnTp+P111+XklV37txBr169oFarsX//fkRGRmL8+PHIzc0tU7+JqObZvHkzVCoVDh06hGXLluGFF15Ahw4dcPLkSYSFhSE+Pl6aqPTKK68gKSkJBw4ckLZPTk5GWFgYRo4cCQDYsWMHpk6dinfeeQfnz5/Hm2++iXHjxhltU1bFxTyDwYCGDRti27ZtuHjxIubPn4/3338fP/zwQwUelZotNjYWQUFB2LZtG3r27ImmTZvi3XffxXPPPSfl5a5du4abN29i27Zt+OabbxAcHIzIyEj861//qtrOCqIySk9PFyqVSvzwww/SsqSkJGFhYSGmTp0qbt68KRQKhbhz547Rdn379hVz5swRQggRFBQkbG1tpXULFiwQ7du3l+6PGTNGuLu7i9zcXGnZK6+8IoYPHy6EECI6OloAEMePH5fWX7p0SQAQa9asKfOYEhMTRePGjcX7778vLbtz545QKBTi2LFjQgghdDqdcHBwEMHBwVIbAGLatGlG+xo8eLAYN25cmftARIXr3bu3mDp1qhBCCHd3d/H6669L6wwGg3BychLr168XQgjxxRdfCHt7e5GVlSW1Wb9+vQAgTp8+XeKxDhw4IACIBw8eFNnGz89PvPPOO0IIISIjIwUAcePGjULbjhkzRgwZMqTE4+bbunWrACAaN24s/u///k+cPHlSjBgxQtjb24ukpCSjPu7bt0/abunSpQKAiImJkZa9+eabwtfXV7r/+OP45Hgf31doaKgAID2GNjY2RnGPiCpPRkaGMDMzE1u2bJGW6XQ64ebmJlasWCG9ZkNCQqT1+edg33//vRBCiAkTJog33njDaL9//PGHkMvl0uu6pFhaVh9//LHo1KmTdN/Hx0eMHDmy0Lb553B79+4t17GIqGbp3bu3aNWqlTAYDNKy2bNni1atWgkh8t4v7dixw2gbW1tbERQUJIQQ4vr16wKAWLZsmbQ+JydHNGzYUCxfvrzE4+fHxZ9++klalp2dLSwtLcXhw4eN2k6YMEGMGDFCCCHEnDlzhKenp9DpdGUaLxHVbL179xYdOnSQ7i9ZskT079/fqM2tW7cEABEdHS2EEGLIkCFi/Pjx0vovvvhCuLm5Cb1eL4QQonv37mLSpElG+3jllVfEoEGDpPuFxbrClDfm+fv7i2HDhkn3y/o+s6Z58vHauXOnACCsrKyMbkqlUvz73/8WQggxadIko+dNiL/fj1++fLnK+s4Z6VRmMTEx0Ol06Nq1q7Ssfv36Uo2ic+fOQa/X45lnnpFqn1tbW+PgwYNl+qpc69atoVAopPuurq7SjPNLly5BqVSiU6dO0vqWLVvCzs6uzONJS0uDn58fvLy8sHDhQmm5m5sb/Pz8sGnTJgB5XyPRarV45ZVXjLbv3Lmz0f0pU6YgJCQE3t7emDVrFg4fPlzmPhFR0dq1ayf9LpPJ4OLiYhQb8ssd5PPx8Sn3sfR6PZYsWYK2bduifv36sLa2xu7duxEbGwsAaN++Pfr27Yu2bdvilVdewVdffYUHDx6U+3j5Xyn84IMPMGzYMHTq1AlBQUGQyWTYtm2bUdvHHwdnZ2epxMLjy0r7LZ3H9+Xq6goA0rYzZszAxIkT0a9fPyxbtoxfeSaqRDExMcjJyUGPHj2kZWZmZujSpQsuXbokLXs8ruWfg+WvP3PmDIKDg43OwXx9fWEwGHD9+nVpu+JiaUm+//579OjRAy4uLrC2tsbcuXOluAjklcLr27dvodtGRUVBoVCgd+/epToWEdV83bp1g0wmk+77+PjgypUr0Ov1pd7H43FNqVSic+fORnGvJI+/J7t69SoePnyIf/zjH0ax8JtvvpHOY6KiotCzZ09+y46oDno8T3TmzBkcOHDAKBa0bNkSAKR4MHLkSPz444/QarUAgC1btuDVV1+FXJ6XMr106ZLRuRkA9OjRo0wx6kklxbzAwEB06tQJjo6OsLa2xpdffml0rlXXZGRkQKFQIDIyElFRUdLt0qVLUlkbV1dXKJVKPPPMM9J2rVq1AoAqfWx4BQ4yucdfAI8nwgGU6Wu7T57UyGSyAnWrKio9PR0DBgyAjY0NduzYUeCYEydOxKhRo7BmzRoEBQVh+PDhsLS0NGpjZWVldH/gwIG4efMmdu3ahb1796Jv377w9/cvUG+diMqnKmJDvo8//hjr1q3D2rVr0bZtW1hZWWHatGnQ6XQAAIVCgb179+Lw4cNSKZUPPvgAx44dK9eFT/KT2F5eXtIytVqNJk2aFDg5ePxxkMlkFXpcntwX8HdSf+HChXjttdcQGhqK3377DQsWLEBISIhRvWMiqjkyMjLw5ptv4j//+U+BdY0bN5Z+L2/MOHLkCEaOHIlFixbB19cXtra2CAkJMbpejoWFRZHbF7eOiOoemUwm1UvP9+Q1FUzh8fdkGRkZAPLKTDVo0MConVqtBsBYRFSXPRkPBg8ejOXLlxdol//ea/DgwRBCIDQ0FM8++yz++OMPo5J1VS0kJATvvvsuVq1aBR8fH9jY2ODjjz/GsWPHqq1Pla1Dhw7Q6/VISEhAz549C23To0cP5ObmIiYmBk2bNgUA/PXXXwAAd3f3KusrZ6RTmTVt2hRmZmZGL+IHDx5If8CPvwCaNWtmdHNxcTFJH1q2bInc3FxERkZKy6Kjo5GSklLqfaSlpaF///5QqVT45ZdfjGaw5hs0aBCsrKywfv16hIWFGdUNLo6joyPGjBmDb7/9FmvXri3TBbyIqPxatWqFs2fPIjs7W1p29OjRcu/v0KFDGDJkCF5//XW0b98eTZo0kWJdPplMhh49emDRokU4ffo0VCoVduzYAQBQqVRlmo2Vf5Gsx+sU5+Tk4MaNGxU+OShrXx73zDPPYPr06dizZw9efvllXkCZqJLkX8Tq0KFD0rKcnBycOHHC6AO2x+Na/jlY/oycjh074uLFiwXOwZo1awaVSlXhPh4+fBju7u744IMP0LlzZzRv3hw3b940atOuXTuEh4cXun3btm1hMBhKfSFBIqr5nkzuHD16FM2bN4dCoYCjoyPu3bsnrbty5Yp0MfQnt8mX/z4vP66VlZeXF9RqNWJjYwvEwfxrxLRr1w5//PFHpST1iajm6NixIy5cuAAPD48C8SA/4W5ubo6XX34ZW7ZswdatW9GiRQt07NhR2kerVq2Mzs2AvPeJj5+blVVxMe/QoUPo3r073nrrLXTo0AHNmjWrE98KzsjIkGaaA8D169cRFRWF2NhYPPPMMxg5ciRGjx6N7du34/r16zh+/DiWLl2K0NBQAEC/fv3QsWNHjB8/HqdPn0ZkZCTefPNN/OMf/zCapV7ZmEinMrO2tsaECRMwc+ZM7N+/H+fPn8fYsWOlr72U5gVQUS1atMCAAQPw5ptv4tixY4iMjMTEiRNLPbMgP4memZmJjRs3Ii0tDXFxcYiLizNKNCkUCowdOxZz5sxB8+bNS1UiYv78+fj5559x9epVXLhwATt37iz3SSARlc1rr70GmUyGSZMm4eLFi9i1a1eFvg3SvHlzacb5pUuX8OabbyI+Pl5af+zYMXz00Uc4efIkYmNjsX37diQmJkqveQ8PD5w9exbR0dG4f/9+iW/WNBoNJk+ejAULFmDPnj2Ijo7GlClTAKBAWamy8vDwwLFjx3Djxg3cv3+/VDNPs7KyEBAQgIiICNy8eROHDh3CiRMnGNOIKomVlRWmTJmCmTNnIiwsDBcvXsSkSZPw8OFDTJgwQWq3ePFihIeHS+dgDg4OGDp0KABg9uzZOHz4MAICAhAVFYUrV67g559/LnCx0fJq3rw5YmNjERISgpiYGHzyySfSh4f5FixYgK1bt2LBggW4dOkSzp07J80E8/DwwJgxYzB+/Hj89NNPuH79OiIiIur0BbSI6rrY2FjMmDED0dHR2Lp1Kz799FNMnToVAPDCCy/gs88+w+nTp3Hy5ElMnjy50HIqgYGB2LFjBy5fvgx/f388ePCg1JOYnmRjY4N3330X06dPx+bNmxETE4NTp07h008/xebNmwEAAQEBSEtLw6uvvoqTJ0/iypUr+N///lfqiy4TUe3g7++P5ORkjBgxAidOnEBMTAx2796NcePGGeV+Ro4cidDQUGzatEm6yGi+mTNnIjg4GOvXr8eVK1ewevVqbN++He+++265+1VczGvevDlOnjyJ3bt346+//sK8efNw4sSJch+rpjh58iQ6dOiADh06AMgrIdqhQwfMnz8fABAUFITRo0fjnXfeQYsWLTB06FCcOHFC+kalXC7Hr7/+CgcHB/Tq1Qt+fn5o1aoVQkJCqnQcLO1C5fLxxx9LX5GxsbHBO++8g9TUVGl9UFAQPvzwQ7zzzju4c+cOHBwc0K1bN7z44osm60NQUBAmTpyI3r17w9nZGR9++CHmzZtXqm1PnTolzZxo1qyZ0brr16/Dw8NDuj9hwgR89NFHGDduXKn2rVKpMGfOHNy4cQMWFhbo2bNnlb+wiZ5W1tbW+PXXXzF58mR06NABXl5eWL58OYYNG1au/c2dOxfXrl2Dr68vLC0t8cYbb2Do0KFSvNNoNPj999+xdu1apKWlwd3dHatWrcLAgQMBAJMmTUJERAQ6d+6MjIwMHDhwAH369Cn2mB9//DGUSiVGjRqFrKwsdO3aFfv370e9evXKNYZ87777LsaMGQMvLy9kZWUZ1UouikKhQFJSEkaPHo34+Hg4ODjg5ZdfxqJFiyrUFyIq2rJly2AwGDBq1Cikp6ejc+fO2L17t1EMWLZsGaZOnYorV67A29sbv/76qzTbvF27djh48CA++OAD9OzZE0IING3aFMOHDzdJ//75z39i+vTpCAgIgFarhZ+fH+bNm2d0nZk+ffpg27ZtWLJkCZYtWwaNRoNevXpJ69evX4/3338fb731FpKSktC4cWO8//77JukfEVW90aNHIysrC126dIFCocDUqVPxxhtvAABWrVqFcePGoWfPnnBzc8O6deuMvlWcb9myZVi2bBmioqLQrFkz/PLLL3BwcCh3n5YsWQJHR0csXboU165dg52dHTp27CjFGnt7e+zfvx8zZ85E7969oVAo4O3tXaAOMhHVbm5ubjh06BBmz56N/v37Q6vVwt3dHQMGDJAmgwJ5H/rVr18f0dHReO2114z2MXToUKxbtw4rV67E1KlT4enpiaCgoBLf1xWnuJj35ptv4vTp0xg+fDhkMhlGjBiBt956C7/99lu5j1cT9OnTp0Cpr8eZmZlh0aJFxb7XdHNzw48//lgZ3Ss1mShuFES1jIeHB6ZNm4Zp06aZbJ9//PEH+vbti1u3bsHZ2dlk+yWiqnHjxg14enri9OnT8Pb2ru7uEBGVW0REBJ5//nk8ePCgXBdYJyIytT59+sDb2xtr166t7q4QERFVOs5IJyqCVqtFYmIiFi5ciFdeeYVJdCIiIiIiIiIioqcUa6RTnbRlyxZYW1sXemvdunWp9rF161a4u7sjJSUFK1asqOQeE1FVmDx5cpGxYfLkyZV+/I8++qjI4+eXgyEiqk6MU0RUk1T3uRsRUVnwPKruY2kXqpPS09ONLgj4ODMzM7i7u1dxj4ioJkhISEBaWlqh6zQaDZycnCr1+MnJyUhOTi50nYWFBRo0aFCpxyciKgnjFBHVJNV97kZEVBY8j6r7mEgnIiIiIiIiIiIiIioGS7sQERERERERERERERWDiXQiIiIiIiIiIiIiomIwkU5EREREREREREREVAwm0omIiIiIiIiIiIiIisFEOhERERERERERERFRMZhIJyIiIiIiIiIiIiIqBhPpRERERERERERERETFYCKdiIiIiIiIiIiIiKgYTKQTERERERERERERERWDiXQiIiIiIiIiIiIiomIwkU5EREREREREREREVAwm0omIiIiIiIiIiIiIisFEOhERERERERERERFRMZhIJyIiIiIiIiIiIiIqBhPpRERERERERERERETFYCKdiIiIiIiIiIiIiKgYTKQTERERERERERERERWDiXQiIiIiIiIiIiIiomIwkU5EREREREREREREVAwm0omIiIiIiIiIiIiIisFEOhERERERERERERFRMZhIJyIiIiIiIiIiIiIqBhPpRERERERERERERETFYCKdiIiIiIiIiIiIiKgYTKQTERERERFRtfv9998xePBguLm5QSaT4aeffipxm4iICHTs2BFqtRrNmjVDcHBwgTaBgYHw8PCAubk5unbtiuPHj5u+80RU5zFGERET6URERERERFTtMjMz0b59ewQGBpaq/fXr1+Hn54fnn38eUVFRmDZtGiZOnIjdu3dLbb7//nvMmDEDCxYswKlTp9C+fXv4+voiISGhsoZBRHUUYxQRyYQQoro7URcYDAbcvXsXNjY2kMlk1d0domohhEB6ejrc3Nwgl/NzupqEMYqobseoZcuWYc6cOZg6dSrWrl0LAMjOzsY777yDkJAQaLVa+Pr64vPPP4ezs7O0XWxsLKZMmYIDBw7A2toaY8aMwdKlS6FUKqU2ERERmDFjBi5cuIBGjRph7ty5GDt2rNHxAwMD8fHHHyMuLg7t27fHp59+ii5dupS6/4xRRHU7RpWHTCbDjh07MHTo0CLbzJ49G6GhoTh//ry07NVXX0VKSgrCwsIAAF27dsWzzz6Lzz77DEBevGnUqBHefvttvPfee4XuV6vVQqvVSvcNBgOSk5Nhb2/PGEVPLcYoY4xRRDVLlcUoQSZx69YtAYA33ngDxK1bt6r7JUlPYIzijbe/b3UtRh0/flx4eHiIdu3aialTp0rLJ0+eLBo1aiTCw8PFyZMnRbdu3UT37t2l9bm5uaJNmzaiX79+4vTp02LXrl3CwcFBzJkzR2pz7do1YWlpKWbMmCEuXrwoPv30U6FQKERYWJjUJiQkRKhUKrFp0yZx4cIFMWnSJGFnZyfi4+NLPQbGKN54+/tW12JUeQEQO3bsKLZNz549jeKeEEJs2rRJaDQaIYQQWq1WKBSKAvsZPXq0+Oc//1nkfhcsWFDtfwe88VZTb4xReQDGKN54q4m3yo5Rf083ogqxsbEBANy6dQsajaaae0NUPdLS0tCoUSPp9UA1B2MUUd2MURkZGRg5ciS++uorfPjhh9Ly1NRUbNy4Ed999x1eeOEFAEBQUBBatWqFo0ePolu3btizZw8uXryIffv2wdnZGd7e3liyZAlmz56NhQsXQqVSYcOGDfD09MSqVasAAK1atcKff/6JNWvWwNfXFwCwevVqTJo0CePGjQMAbNiwAaGhodi0aVORM6mexBhFVDdjVGWLi4sz+pYNADg7OyMtLQ1ZWVl48OAB9Hp9oW0uX75c5H7nzJmDGTNmSPdTU1PRuHFjxih6qjFGlR1jFFHVqaoYxUS6ieR/fUaj0TBw0VOPXyereRijiP5Wl2KUv78//Pz80K9fP6NEemRkJHJyctCvXz9pWcuWLdG4cWMcOXIE3bp1w5EjR9C2bVujN2++vr6YMmUKLly4gA4dOuDIkSNG+8hvM23aNACATqdDZGQk5syZI62Xy+Xo168fjhw5UmS/n/xKcnp6OgDGKCKgbsWo2kqtVkOtVhdYzhhFxBhVEzBGERWtsmMUE+lEREREtVBISAhOnTqFEydOFFgXFxcHlUoFOzs7o+XOzs6Ii4uT2hQ2Ayp/XXFtKjqTaunSpVi0aFHpBkpEVAQXFxfEx8cbLYuPj4dGo4GFhQUUCgUUCkWhbVxcXKqyq0T0FGKMIqp7eIUIIiIiolrm1q1bmDp1KrZs2QJzc/Pq7k6ZzZkzB6mpqdLt1q1b1d0lIqqFfHx8EB4ebrRs79698PHxAQCoVCp06tTJqI3BYEB4eLjUhoiosjBGEdU9TKQTEVGlEUIgKysLWVlZEEJUd3eI6ozIyEgkJCSgY8eOUCqVUCqVOHjwID755BMolUo4OztDp9MhJSXFaLvHZzgVNUsqf11xbfJnUjk4OJRrJpVarZa+flzaryELIZCUlMRYQlSHZWRkICoqClFRUQCA69evIyoqCrGxsQDyPoQbPXq01H7y5Mm4du0aZs2ahcuXL+Pzzz/HDz/8gOnTp0ttZsyYga+++gqbN2/GpUuXMGXKFGRmZkrXdSAiKi3GKCJiIr2G0uv10Ov11d0NIqIK0Wq1+O//HcFHPx5DamoqE2BEJtK3b1+cO3dOejMXFRWFzp07Y+TIkdLvZmZmRjOcoqOjERsbK81w8vHxwblz55CQkCC12bt3LzQaDby8vKQ2NWUmVXJyMpZs2Y/k5GST7peIao6TJ0+iQ4cO6NChA4C8BFOHDh0wf/58AMC9e/ekhBUAeHp6IjQ0FHv37kX79u2xatUqfP3119LFkAFg+PDhWLlyJebPnw9vb29ERUUhLCysQEkqIqKSMEYRkUwwq2ESaWlpsLW1RWpqqkku7pCfRFcoFBXeF1FVMfXrgEynup6b7OxsLP85Erm5uZABeH9Y11pZhoLqhroeo/r06QNvb2+sXbsWADBlyhTs2rULwcHB0Gg0ePvttwEAhw8fBpB3ruHt7Q03NzesWLECcXFxGDVqFCZOnIiPPvoIQN5MqzZt2sDf3x/jx4/H/v378Z///AehoaHSm8Dvv/8eY8aMwRdffIEuXbpg7dq1+OGHH3D58uVSvwkszXOTlJSElb+cxLv/7Ax7e/uKPFRENVJdj1G1GZ8bIr4OajI+N0RV9zrgxUaJiKhSZGdnIzs7W7qvVBW8sjwRVZ41a9ZALpdj2LBh0Gq18PX1xeeffy6tVygU2LlzJ6ZMmQIfHx9YWVlhzJgxWLx4sdQmfybV9OnTsW7dOjRs2LDQmVSJiYmYP38+4uLi4O3tzZlURERERERU5zCRXoMJIaDX6yGXyyGTyaq7O0REpSKEgFarLbSMS35inbPSiUwvIiLC6L65uTkCAwMRGBhY5Dbu7u7YtWtXsfvt06cPTp8+XWybgIAABAQElLqvREREREREtQ1rpNdg2dnZWLHzLAwGQ3V3hYioVIQQSE1NxfKfT0Kr1VZ3d4iIiIiIiIiITIKJ9BpKr9fju+O3EXI6AZfupVV3d4iISkWr1WLlr6cgkxf9hacnS74QEREREREREdV0TKTXUPsvJ+KjPTFIzc7FJ+FXCi2RQERUE5mxFjoRERERERER1TFMpNdAp2MfICDkjHQ//HIiUh+yRAIRERERERERERFRdWAivQbacfoOcg0CDWyUsFHJoRfA71fuV3e3iIiKJYR4VLKF36AhIiIiIiIiorqlViXSly1bBplMhmnTpknLsrOz4e/vD3t7e1hbW2PYsGGIj4832i42NhZ+fn6wtLSEk5MTZs6cidzcXKM2ERER6NixI9RqNZo1a4bg4OAqGFHhjl5LAgA0s1fD1TqvzvC526nV1h8iotLIr4+em6svsW1+0p1lq4iIiIiIiIioNqg1ifQTJ07giy++QLt27YyWT58+Hb/++iu2bduGgwcP4u7du3j55Zel9Xq9Hn5+ftDpdDh8+DA2b96M4OBgzJ8/X2pz/fp1+Pn54fnnn0dUVBSmTZuGiRMnYvfu3VU2vnxJGVr8FZ8BAHCylKO+Zd5TdOleepX3hYiorEpbH12r1WL5z5HQalm2ioiIiIiIiIhqvlqRSM/IyMDIkSPx1VdfoV69etLy1NRUbNy4EatXr8YLL7yATp06ISgoCIcPH8bRo0cBAHv27MHFixfx7bffwtvbGwMHDsSSJUsQGBgInU4HANiwYQM8PT2xatUqtGrVCgEBAfjXv/6FNWvWFNknrVaLtLQ0o5spHL+WCACwVcthYSZHfQsFAOBiXBpnbhJRncKLkhIRERERERFRbVErEun+/v7w8/NDv379jJZHRkYiJyfHaHnLli3RuHFjHDlyBABw5MgRtG3bFs7OzlIbX19fpKWl4cKFC1KbJ/ft6+sr7aMwS5cuha2trXRr1KhRhccJ/D3z3N4i76mpZ66ADEByZg7i0zhzk4jqDpZ3IaKyEkIgOTkZSUlJjB1ERERERFSlanwiPSQkBKdOncLSpUsLrIuLi4NKpYKdnZ3RcmdnZ8TFxUltHk+i56/PX1dcm7S0NGRlZRXarzlz5iA1NVW63bp1q1zje9JfCXllXeqZy2Ew6CGXAbbmebPSz956YJJjEBHVBLk5OqwOPcPyLkRUatmZ6VgffhGrfo1EcnJydXeHiIiIiIieIsrq7kBxbt26halTp2Lv3r0wNzev7u4YUavVUKtNX5bgclxeIt3O/O/POOpbKJCSrceFu6no38bV5MckIqqo7OxsZGdnl3k7Jcu7EFEZWVrbwtzCorq7QURERERET5kaPSM9MjISCQkJ6NixI5RKJZRKJQ4ePIhPPvkESqUSzs7O0Ol0SElJMdouPj4eLi4uAAAXFxfEx8cXWJ+/rrg2Go0GFlX4Ri1Lp8fNpIcA8mak58uvk37+bmqV9YWIiIiIiIiIiIiI8tToRHrfvn1x7tw5REVFSbfOnTtj5MiR0u9mZmYIDw+XtomOjkZsbCx8fHwAAD4+Pjh37hwSEhKkNnv37oVGo4GXl5fU5vF95LfJ30dVuZqQAQHAXCmDhdnfT439o0T6pXsZVdofIiqeh4cHZDJZgZu/vz8AoE+fPgXWTZ482WgfsbGx8PPzg6WlJZycnDBz5kzk5uYatYmIiEDHjh2hVqvRrFkzBAcHV9UQKx3rpBMRERERERFRbVCjS7vY2NigTZs2RsusrKxgb28vLZ8wYQJmzJiB+vXrQ6PR4O2334aPjw+6desGAOjfvz+8vLwwatQorFixAnFxcZg7dy78/f2l0iyTJ0/GZ599hlmzZmH8+PHYv38/fvjhB4SGhlbpeK/dz0uU59dEz5c/I/1uajZSs3Jga2FWpf0iosKdOHECer1eun/+/Hn84x//wCuvvCItmzRpEhYvXizdt7S0lH7X6/Xw8/ODi4sLDh8+jHv37mH06NEwMzPDRx99BAC4fv06/Pz8MHnyZGzZsgXh4eGYOHEiXF1d4evrWwWjrFz5ddLfH9a1xpXwIiIiIiIiIiLKV6MT6aWxZs0ayOVyDBs2DFqtFr6+vvj888+l9QqFAjt37sSUKVPg4+MDKysrjBkzxiix5enpidDQUEyfPh3r1q1Dw4YN8fXXX1d5kirm0YVGNWrjRLpSLmChlCErV+BmUibaNbSr0n4RUeEcHR2N7i9btgxNmzZF7969pWWWlpZSGakn7dmzBxcvXsS+ffvg7OwMb29vLFmyBLNnz8bChQuhUqmwYcMGeHp6YtWqVQCAVq1a4c8//8SaNWvqRCIdYJ10IiIiIiIiIqr5anRpl8JERERg7dq10n1zc3MEBgYiOTkZmZmZ2L59e4Gklbu7O3bt2oWHDx8iMTERK1euhFJp/BlCnz59cPr0aWi1WsTExGDs2LFVMBpjMYnpAAAbFWB4osyBtSrvqYpNfljl/SKikul0Onz77bcYP348ZDKZtHzLli1wcHBAmzZtMGfOHDx8+Pdr+MiRI2jbti2cnZ2lZb6+vkhLS8OFCxekNv369TM6lq+vL44cOVJsf7RaLdLS0oxulaW8FxolIiIielJgYCA8PDxgbm6Orl274vjx40W2LayMnkwmg5+fn9Rm7NixBdYPGDCgKoZCRHUQYxTR063Wz0ivS64lZgIANKqCn29Yq2RIfAjcTMqs6m4RUSn89NNPSElJMfoQ7rXXXoO7uzvc3Nxw9uxZzJ49G9HR0di+fTsAIC4uziiJDkC6HxcXV2ybtLQ0ZGVlFXlB5KVLl2LRokWmGh4RERFRpfv+++8xY8YMbNiwAV27dsXatWvh6+uL6OhoODk5FWi/fft26HQ66X5SUhLat29vVGYPAAYMGICgoCDpfn6JTyKismCMIiIm0msIIQRuJmUBADTqwhPpAHAziTPSiWqijRs3YuDAgXBzc5OWvfHGG9Lvbdu2haurK/r27YuYmBg0bdq0UvszZ84czJgxQ7qflpaGRo0aVeoxKyJ/RjvrpBMRET29Vq9ejUmTJmHcuHEAgA0bNiA0NBSbNm3Ce++9V6B9/fr1je6HhITA0tKyQJJKrVYXWWqvMFqtFlqtVrpfmd/sI6LagzGKiGpdaZe6KiEtGw9z9JDh7zIuj7NhaReiGuvmzZvYt28fJk6cWGy7rl27AgCuXr0KAHBxcUF8fLxRm/z7+SdSRbXRaDRFzkYH8k7GNBqN0c3UhBDIzs6GeKIUFREREVFZ6XQ6REZGGpW0k8vl6NevX4kl7fJt3LgRr776KqysrIyWR0REwMnJCS1atMCUKVOQlJRU7H6WLl0KW1tb6VaTJyMQUdVgjCIigIn0GuPWowS5pZkMCrmswPr8Gem3mEgnqnGCgoLg5ORkVOuuMFFRUQAAV1dXAICPjw/OnTuHhIQEqc3evXuh0Wjg5eUltQkPDzfaz969e+Hj42PCEZSPVqvF8p8jjWZDEBEREZXH/fv3odfrCy1pl1/yrjjHjx/H+fPnC0xsGDBgAL755huEh4dj+fLlOHjwIAYOHAi9Xl/kvubMmYPU1FTpduvWrfINiojqDMYoIgJY2qXGuJWcV/vcqpDZ6ABgbZaXSL+Xmg29QRSabCeiqmcwGBAUFIQxY8YYXcQ4JiYG3333HQYNGgR7e3ucPXsW06dPR69evdCuXTsAQP/+/eHl5YVRo0ZhxYoViIuLw9y5c+Hv7y/VxZs8eTI+++wzzJo1C+PHj8f+/fvxww8/IDQ0tFrG+yQzlenq97G8CxEREZXXxo0b0bZtW3Tp0sVo+auvvir93rZtW7Rr1w5NmzZFREQE+vbtW+i+1Go1axQTkUkxRhHVDZyRXkPcS81LIFmZFf6UqJUyyAAYBJCUwdmfRDXFvn37EBsbi/HjxxstV6lU2LdvH/r374+WLVvinXfewbBhw/Drr79KbRQKBXbu3AmFQgEfHx+8/vrrGD16NBYvXiy18fT0RGhoKPbu3Yv27dtj1apV+Prrr+Hr61tlYyQiIiKqbA4ODlAoFIWWtCupdnBmZiZCQkIwYcKEEo/TpEkTODg4SKX2iIhKgzGKiADOSK8x7qTkJdILq48OAHKZDBZmcjzMMSA+TQsnDWdsEtUE/fv3L7RGeKNGjXDw4MESt3d3d8euXbuKbdOnTx+cPn263H0kIiIiqulUKhU6deqE8PBwDB06FEDeN//Cw8MREBBQ7Lbbtm2DVqvF66+/XuJxbt++jaSkJKnUHhFRaTBGERHAGek1xt2ULACAlaroki0Wyrx1cWlZVdInIiIiIiKiqjJjxgx89dVX2Lx5My5duoQpU6YgMzMT48aNAwCMHj0ac+bMKbDdxo0bMXToUNjb2xstz8jIwMyZM3H06FHcuHED4eHhGDJkCJo1a8Zv9xFRmTFGERFnpNcQd0so7QIAFmYyIAuIS2UinYjqJiEEsrOzoVarIZPxWhBERERPk+HDhyMxMRHz589HXFwcvL29ERYWJl3cLzY2FnK58ful6Oho/Pnnn9izZ0+B/SkUCpw9exabN29GSkoK3Nzc0L9/fyxZsoT1hYmozBijiIiJ9Boif0Z6UaVdAMDi0bOVkMYa6URUN2m1WqzbfQGzh3TiRUeJiIieQgEBAUWWSYiIiCiwrEWLFoWW2QMACwsL7N6925TdI6KnHGMU0dONpV1qgLTsHKRr9QBKmJGuzFuXkM5EOhHVXWYqzr4gIiIiIiIiopqFifQaIH82ulohg5mimBrpZnnrmEgnouqWX4IFKHx2BRERERERERFRXcJEehXS6/XQ6/UFlt958OhCo2YyGIr4yg8AWD662Gh8WnbldJCIqJS0Wi1W/noKubkFYxoRERERERERUV1TaYn0Jk2aICkpqcDylJQUNGnSpLIOWyvlz0i3LKY+OsAZ6USmwvhkGpVVgiV/tntRtQSJajvGICIiIiIiotqn0hLpN27cKHT2tVarxZ07dyrrsLXS7fwLjRZTHx0ALB8l0pMydcjRGyq9X0R1FeNTzZabo8Pq0DPQavmhIdVNjEFERERERES1j9LUO/zll1+k33fv3g1bW1vpvl6vR3h4ODw8PEx92FpNKu2iKro+OpBXQ10GQAjgfoYWrrYWVdA7orqD8an2UPKCo1QHMQYRERERERHVXiZPpA8dOhQAIJPJMGbMGKN1ZmZm8PDwwKpVq0x92FrtzoOHAP6ecV4UmUwGCzMZHuYIJKQxkU5UVoxPRFSdGIOIiIiIiIhqL5Mn0g2GvJIjnp6eOHHiBBwcHEx9iDrnTkrexUOtSkikA4ClmRwPc/S84ChROTA+1R75ddLVajVkspJjI1FtwBhERERERERUe1VajfTr16/zDWIp6HINSMzIqwNcmkS6hTLvKWMinaj8GJ9qPtZJp7qMMYiIiIiIiKj2MfmM9MeFh4cjPDwcCQkJ0iysfJs2barMQ9cacanZEAKQywBzZSkS6WZ/b0dE5cf4VH75s8UBUanHYZ10qssYg4iIiIiIiGqXSkukL1q0CIsXL0bnzp3h6urKr+YX4faj+uhWZrJSPUaWj2akJ6QzkU5UXoxPFaPVarHy11OQyRUwU1Xq57FEdRJjEBERERERUe1TaRmQDRs2IDg4GKNGjaqsQ9QJUiJdVboqOxaPyr/Ep7HcAVF5MT5VnJlKjdzc3OruBlGtxBhERERERERU+1RajXSdTofu3btX1u7rjLspWQAA61LURwf+Lv+SlKmrtD4R1XWMT0RUnRiDiIiIiIiIap9KS6RPnDgR3333XWXtvs648yiRbmVWuqfC/FFpl2Qm0onKjfGp9sjOzn5Uj52o7mAMIiIiIiIiqn0qrbRLdnY2vvzyS+zbtw/t2rWDmZmZ0frVq1dX1qFrlbspeQkiK1XpZqSrH81IT87UQQjBuqpE5cD4RETViTGIiIiIiIio9qm0Gelnz56Ft7c35HI5zp8/j9OnT0u3qKioyjpsrZN/0dD8ki0lMVfktdPmGvBQp6+0fhHVZaaKTwsXLoRMJjO6tWzZUlqfnZ0Nf39/2Nvbw9raGsOGDUN8fLzRPmJjY+Hn5wdLS0s4OTlh5syZBWqPR0REoGPHjlCr1WjWrBmCg4MrMnwiqmY8RyIiIiIiIqp9Km1G+oEDBypr13VKYkZeiRaLUibSlXJAIQP0AkjK0MJKXWlPIVGdZcr41Lp1a+zbt0+6r1T+/ZqcPn06QkNDsW3bNtja2iIgIAAvv/wyDh06BADQ6/Xw8/ODi4sLDh8+jHv37mH06NEwMzPDRx99BAC4fv06/Pz8MHnyZGzZsgXh4eGYOHEiXF1d4evra7JxlBZLrRBVHM+RiIiKFhgYiI8//hhxcXFo3749Pv30U3Tp0qXQtsHBwRg3bpzRMrVabXSuIoTAggUL8NVXXyElJQU9evTA+vXr0bx580odBxHVTYxRRE+3SpuRTiXT5RqQ8jAHQOlnpMtkMqm8y/10JrOIqptSqYSLi4t0c3BwAACkpqZi48aNWL16NV544QV06tQJQUFBOHz4MI4ePQoA2LNnDy5evIhvv/0W3t7eGDhwIJYsWYLAwEDodHkfsm3YsAGenp5YtWoVWrVqhYCAAPzrX//CmjVriu2XVqtFWlqa0a22YvKeiIjo6fD9999jxowZWLBgAU6dOoX27dvD19cXCQkJRW6j0Whw79496Xbz5k2j9StWrMAnn3yCDRs24NixY7CysoKvry/PLYiozBijiKjSEunPP/88XnjhhSJvBCRlagEAMgBqRem3M3+sTjoRlZ0p49OVK1fg5uaGJk2aYOTIkYiNjQUAREZGIicnB/369ZPatmzZEo0bN8aRI0cAAEeOHEHbtm3h7OwstfH19UVaWhouXLggtXl8H/lt8vdRlKVLl8LW1la6NWrUqEzjIqLKY4oYtHTpUjz77LOwsbGBk5MThg4diujoaKM2VVleKjAwEB4eHjA3N0fXrl1x/Pjxsj0oRETIu0bEpEmTMG7cOHh5eWHDhg2wtLTEpk2bitxGJpMZTWp4/LxKCIG1a9di7ty5GDJkCNq1a4dvvvkGd+/exU8//VQFIyKiuoQxiogqLZHu7e2N9u3bSzcvLy/odDqcOnUKbdu2razD1iqJ6XmJdAszWZkuGppfJz3pIRPpROVhqvjUtWtXBAcHIywsDOvXr8f169fRs2dPpKenIy4uDiqVCnZ2dkbbODs7Iy4uDgAQFxdndCKVvz5/XXFt0tLSkJWVVWTf5syZg9TUVOl269atUo+LiCqXKWLQwYMH4e/vj6NHj2Lv3r3IyclB//79kZmZKbWZPn06fv31V2zbtg0HDx7E3bt38fLLL0vr88tL6XQ6HD58GJs3b0ZwcDDmz58vtckvL/X8888jKioK06ZNw8SJE7F7926pTXlmZxERPUmn0yEyMtJoAoFcLke/fv2KnUCQkZEBd3d3NGrUCEOGDJEmIwB5MSwuLs5on7a2tujatWux+6xL3+wjItNgjCIioBJrpBdVdmDhwoXIyMiorMPWKglpjxLpyrJ9nqFWygHokfQoEU9EZWOq+DRw4EDp93bt2qFr165wd3fHDz/8AAsLiwr3syLUajXUanW19oGICmeKGBQWFmZ0Pzg4GE5OToiMjESvXr2k8lLfffedNMs9KCgIrVq1wtGjR9GtWzepvNS+ffvg7OwMb29vLFmyBLNnz8bChQuhUqmMyksBQKtWrfDnn39izZo10nUaHp+dBeSVpAoNDcWmTZvw3nvvFdp/rVYLrfbv8xi+ASSi+/fvQ6/XFzqB4PLly4Vu06JFC2zatAnt2rVDamoqVq5cie7du+PChQto2LChNDGhsH3mryvM0qVLsWjRogqOiIjqEsYoIgKqoUb666+/XuzXXp4miRn5ifTSz0YHWNqFqLJUND7Z2dnhmWeewdWrV+Hi4gKdToeUlBSjNvHx8XBxcQEAuLi4FCizkH+/pDYajabak/WlEXP/ISLvZuHEXS1iU3QQQpRrP0IIZGdnl3t7otqgIjEoNTUVAFC/fn0AVVdeqryzs1h+iohMwcfHB6NHj4a3tzd69+6N7du3w9HREV988UWF9stv9hGRKTBGEdU9VZ5IP3LkCMzNzav6sDXS46VdyiL/YqMPHl2olIhMo6LxKSMjAzExMXB1dUWnTp1gZmaG8PBwaX10dDRiY2Ph4+MDIO/E6ty5c0blD/bu3QuNRgMvLy+pzeP7yG+Tv4+aSgiBoKO3MXxTFM7Ga3E5KQf7YtIRsO0S0rNzS97BE7RaLZb/HGk0g5WorilvDDIYDJg2bRp69OiBNm3aAECVlZcqbnZWcTOp+AaQiJ7k4OAAhUJR6ASC/AkGJTEzM0OHDh1w9epVAH9PTCjrPtVqNTQajdGNiJ5ujFFEBFRiaZfHa3ACeUmVe/fu4eTJk5g3b15lHbZWSUjPuwqzeRlLu+Q3f5ijN3WXiJ4KpopP7777LgYPHgx3d3fcvXsXCxYsgEKhwIgRI2Bra4sJEyZgxowZqF+/PjQaDd5++234+PigW7duAID+/fvDy8sLo0aNwooVKxAXF4e5c+fC399fKssyefJkfPbZZ5g1axbGjx+P/fv344cffkBoaKjpHhATy8ox4NCNNNxLTwYAuNkoYWUGXHuQi0PXHmDy9xfw1Yg2Zd6vmYqlaqhuMPU5kr+/P86fP48///zTVF2sdCw/RURPUqlU6NSpE8LDwzF06FAAeR8UhoeHIyAgoFT70Ov1OHfuHAYNGgQA8PT0hIuLC8LDw+Ht7Q0gr5TUsWPHMGXKlMoYBhHVUYxRRARUYiLd1tbW6L5cLkeLFi2wePFi9O/fv7IOW6vcS8m7UGBZS7soH12YVJdrMHmfiJ4GpopPt2/fxogRI5CUlARHR0c899xzOHr0KBwdHQHk1UGWy+UYNmwYtFotfH198fnnn0vbKxQK7Ny5E1OmTIGPjw+srKwwZswYLF68WGrj6emJ0NBQTJ8+HevWrUPDhg3x9ddfS7WJa5qsHAP2XMtCmk7A3EyOWf08EXsvb8ZqK2cFfr+RiQv3MrD4t6tw5ZeT6CllynOkgIAA7Ny5E7///jsaNmwoLX+8vNTjs9KfLC91/Phxo/2VtbyUQqGo8OwsIqJ8M2bMwJgxY9C5c2d06dIFa9euRWZmpnQNhtGjR6NBgwZYunQpAGDx4sXo1q0bmjVrhpSUFHz88ce4efMmJk6cCACQyWSYNm0aPvzwQzRv3hyenp6YN28e3NzcpEQYEVFpMUYRUaUl0oOCgiq8j6VLl2L79u24fPkyLCws0L17dyxfvhwtWrSQ2mRnZ+Odd95BSEiIUaLq8a8Yx8bGYsqUKThw4ACsra0xZswYLF26FErl38OPiIjAjBkzcOHCBTRq1Ahz587F2LFjKzyG4txNyZuRbqUqWyJd8WhGejZnpBOViyniEwCEhIQUu97c3ByBgYEIDAwsso27uzt27dpV7H769OmD06dPl6uPVUkIgT9jHyJNJ2ClkuPbMe3haW+JdXFJAAAHSyXWDmuFCVvOIezSffR2t0RjTem/kZNfJ12tVkMmK1vcJKpJTBGDhBB4++23sWPHDkRERMDT09No/ePlpYYNGwag8PJS//3vf5GQkAAnJycAhZeXejJGPV5eyhSzs4iI8g0fPhyJiYmYP38+4uLi4O3tjbCwMOm9XWxsLOTyv88dHjx4gEmTJiEuLg716tVDp06dcPjwYSmGAcCsWbOQmZmJN954AykpKXjuuecQFhbGcqNEVGaMUURUaYn0fJGRkbh06RIAoHXr1ujQoUOptz148CD8/f3x7LPPIjc3F++//z769++PixcvwsrKCgAwffp0hIaGYtu2bbC1tUVAQABefvllHDp0CEDeV2f8/Pzg4uKCw4cP4969exg9ejTMzMzw0UcfAQCuX78OPz8/TJ48GVu2bEF4eDgmTpwIV1fXSp31eS/1USLdrGylXRSPEkhMpBNVTEXi09MoOzsb2dnZRa7fczkJt9NyIZcB/ZvZwNPeskAb74YaTOrRCBv+vIUjtx/Cvrkl7FSlO35ujg6rQ8/g/WFdeWJJdUJFYpC/vz++++47/Pzzz7CxsZHqkdva2sLCwqJKy0uVNDuLiKgsAgICivwgLiIiwuj+mjVrsGbNmmL3J5PJsHjxYqNv/BERlRdjFNHTrdIS6QkJCXj11VcREREhfaU4JSUFzz//PEJCQqTSB8UJCwszuh8cHAwnJydERkaiV69eSE1NxcaNG/Hdd9/hhRdeAJA3y6tVq1Y4evQounXrhj179uDixYvYt28fnJ2d4e3tjSVLlmD27NlYuHAhVCoVNmzYAE9PT6xatQoA0KpVK/z5559Ys2ZNpSXSM7W5SMnKu1iolUoOoPRlWvJnpGtZ2oWoXEwRn8hYjt6Az36/CQBo46hCPYui/71M8GmIP2Me4Py9DBy/o8U/mpc+Ka5knXSqA0wRg9avXw8g7xsrjwsKCpK+UVdV5aVKmp1FRERERERUF5RtKnQZvP3220hPT8eFCxeQnJyM5ORknD9/HmlpafjPf/5Trn2mpqYCAOrXrw8gbyZXTk4O+vXrJ7Vp2bIlGjdujCNHjgAAjhw5grZt2xq9mfP19UVaWhouXLggtXl8H/lt8vdRGK1Wi7S0NKNbWdx9VB/dTA6oFGUs7fJoRjoT6UTlUxnx6Wn389kE3HqQDXOlDF4OZsW2NVPIsXBQc8hlwO10PW6k6Kqol0Q1gylikBCi0NvjZenyy0slJycjMzMT27dvL1C3PL+81MOHD5GYmIiVK1calb4D/i4vpdVqERMTU2jpu4CAANy8eRNarRbHjh1D165dy/y4lIZeAHfTc6E3iErZPxERERERUVEqbUZ6WFgY9u3bh1atWknLvLy8EBgYWK6LjRoMBkybNg09evRAmzZtAABxcXFQqVRGF9ECAGdnZ+krznFxcQVmROXfL6lNWloasrKyYGFhUaA/S5cuxaJFi8o8jny3HzwEAFiryv5ZhjQjPYeJdKLyMHV8etpl5+jxxaFbAID2LuYwK8WHg80cLdHWWY0zcVocjc1EWnYuNOYl/0tinXSqCxiDyu9CqhmuZWrh9sCASb14HkRERERERFWn0makGwwGmJkVnJVoZmYGg6Hsb3z8/f1x/vz5Ei/uV1XmzJmD1NRU6Xbr1q0ybZ+fSLcqTyKdM9KJKsTU8elpt/NCIhIzdHDVqNHCvpQFzwG0czaHRi1DVq7A2gM3SrVNfp10rVZbzt4SVT/GoPLJytHjembeB25303Kw83xiNfeIiIiIiIieJpWWSH/hhRcwdepU3L17V1p2584dTJ8+HX379i3TvgICArBz504cOHAADRs2lJa7uLhAp9MhJSXFqH18fLz01WUXFxfEx8cXWJ+/rrg2Go2m0NnoAKBWq6HRaIxuZXE3pXwXGgX+npGencuLjRKVhynj09NOCIH/Hb8DABj5rBsU8tLPElfKZejWIK8++vYz8TgZm1q67VgnnWo5xqDyOXDlAQT+jjEnShkziIiIiIiITKHSEumfffYZ0tLS4OHhgaZNm6Jp06bw9PREWloaPv3001LtQwiBgIAA7NixA/v374enp6fR+k6dOsHMzAzh4eHSsujoaMTGxsLHxwcA4OPjg3PnziEhIUFqs3fvXmg0Gnh5eUltHt9Hfpv8fVSGm0mZAAAbdXkS6XlvInWckU5ULqaIT0+T/HIqQhSsSXwrNQc3k7NhrVbgpXZOZd63s5UCLRzyEuNLwq7ymzb0VGAMKp9zd9MBAPYWeedOp26lFxqXiIiIiIiIKkOl1Uhv1KgRTp06hX379uHy5csAgFatWhW4qGdx/P398d133+Hnn3+GjY2NVNPc1tYWFhYWsLW1xYQJEzBjxgzUr18fGo0Gb7/9Nnx8fNCtWzcAQP/+/eHl5YVRo0ZhxYoViIuLw9y5c+Hv7w+1Oi95M3nyZHz22WeYNWsWxo8fj/379+OHH35AaGioiR+Vv8Um511s1KZcpV3yfmbnGCCEYJ1gojIyRXx6mmi1Wiz/ORJTfVsXWHc+Pi+WvdLBBVbq8v1L6dzAEsnZBtxMzsbGw2Urk0VUGzEGlc/VxLyyeM3qKZGSrUPSwxzEJGaimZN1NfeMiIiIiIieBiafkb5//354eXkhLS0NMpkM//jHP/D222/j7bffxrPPPovWrVvjjz/+KNW+1q9fj9TUVPTp0weurq7S7fvvv5farFmzBi+++CKGDRuGXr16wcXFBdu3b5fWKxQK7Ny5EwqFAj4+Pnj99dcxevRoLF68WGrj6emJ0NBQ7N27F+3bt8eqVavw9ddfw9fX13QPzBNikx/VSDcrexJc+WhGugCQo+dMLKLSMmV8etqYFVJOJV1nQFxGLmQAXu3oWu59q5VyzOrXBADwzfG7eMgLKVMdxRhUfkIIXL2fd+5kbyGHvVXeB3cX76VVZ7eIiIiIiOgpYvIZ6WvXrsWkSZMKrRlua2uLN998E6tXr0bPnj1L3Fdpvq5rbm6OwMBABAYGFtnG3d0du3btKnY/ffr0wenTp0s8nimkPsxBWnYuAMC6AjPSAUCbq4dKWWkVeojqFFPGJwJupOTFsWfdbeGsqVjd8n4t7NGugQ3O3knHmbhsPOta+ouWEtUWjEHll5CuRVq2HjII2KrlsDNXICEjFzEJGdXdNSIiIiIiekqYPAN75swZDBgwoMj1/fv3R2RkpKkPW6vkz0Y3V8pgpij7jPTHr+WXzZmbRKXG+GRaN1PzEukDvBwqvC+ZTIa3e7kDAP5K0iFdV3xsy87ORnZ2doWPS1SVGIPK73JcXn10K6WAQi6DnbkCABCTyEQ6ERERERFVDZMn0uPj42FmZlbkeqVSicTERFMftla5mfzoQqPlmI0O5CWc8vPv2ly9qbpFVOcxPplOhs6AB9kGyAA839zeJPt81t0W3TzsYBDA2Xhdie2ZTKfahjGo/G49moSgMcv7kM3WIi+Rfi0xs9r6RERERERETxeTJ9IbNGiA8+fPF7n+7NmzcHUtfy3duuD6ozd9NuryP/yKR9PSOSOdqPQYn0znVmoOAMDJWol6lkUnBssqoHdjAMD1lFw8yMo12X6JagLGoPJ7vZs7Dv6nM9ra5sUeO/O86oTX7mfAYOD1YoiIiIiIqPKZPJE+aNAgzJs3r9BZgllZWViwYAFefPFFUx+2Vrn6qJ6nrbrsZV3ycUY6UdkxPplO7KNEemNb09Yyb+Nqg8a2ZhAATt/NMum+iaobY1DFWKuVsFDkJc2tVTIo5XkTCu6l8ZspRERERERU+Ux+sdG5c+di+/bteOaZZxAQEIAWLVoAAC5fvozAwEDo9Xp88MEHpj5srXIlIa/Op6bCM9IFZ6QTlQHjk2lkaHMRl5E3W7yxnekvCtrR1RyxqTm4kaLDxbgMeLlYF9lWCIHs7Gyo1WrIZOX/cJKoKjAGmY42Mx1qmR65UCAmIQMN7Cyqu0tERERERFTHmTyR7uzsjMOHD2PKlCmYM2cOhMibOSSTyeDr64vAwEA4Ozub+rC1ht4gEPOotItthRLpeT85I52o9BifTOPw9RQYBKBRyWD76IJ/plTPQoEmdkpcS8nFpwdvYv3w1kW21Wq1WLf7AmYP6QRzc3OT94XIlBiDTEujkiEzC7iWmIFezzhWd3eIiIiIiKiOM3kiHQDc3d2xa9cuPHjwAFevXoUQAs2bN0e9evUq43C1yp2ULGhzDZDL8r6WXF7KRzMvtZyRTlQmjE9lkz/jG/i7BvHvV5MBAA01lfIvBADQzlmFG6m5OHI9BSdupuBZd7si25qp1JXWDyJTYwwyHRsz4F4WpAkKRERERERElcnkNdIfV69ePTz77LPo0qUL3yA+Eh33d1kXeQXKEMhZI52oQkwRn5YuXYpnn30WNjY2cHJywtChQxEdHW3Upk+fPpDJZEa3yZMnG7WJjY2Fn58fLC0t4eTkhJkzZyI31/hCmxEREejYsSPUajWaNWuG4ODgcvW5rLRaLVb+egq5j2KNQQgcuZ4CAGhgY/rZ6PlsVHK0cMhLkH9y8KY0c7cw+cn+4toQ1TQ8R6o4G7O81/y1+xnV3BMiIiIiInoaVGoinQqKupUCALC3qNhDn1/ahTXSiarPwYMH4e/vj6NHj2Lv3r3IyclB//79kZlpPDty0qRJuHfvnnRbsWKFtE6v18PPzw86nQ6HDx/G5s2bERwcjPnz50ttrl+/Dj8/Pzz//POIiorCtGnTMHHiROzevbtKxvn4jO/o+EwkZeZAKQccLSsvkQ4A7V0tYW4mx7m7GYi4klxku9wcHVaHnoFWq63U/hBRzZKfSI9J4Ix0orokMDAQHh4eMDc3R9euXXH8+PEi23711Vfo2bMn6tWrh3r16qFfv34F2o8dO7bApIYBAwZU9jCIqI5ijCJ6ujGRXsXyE+kVTUAp8ku7cEY6UbUJCwvD2LFj0bp1a7Rv3x7BwcGIjY1FZGSkUTtLS0u4uLhIN41GI63bs2cPLl68iG+//Rbe3t4YOHAglixZgsDAQOh0OgDAhg0b4OnpiVWrVqFVq1YICAjAv/71L6xZs6ZKxwsAh649AAC42igfXfS48liayTGysxsA4NPfb8JQzIxzJcu7ED11rB9Vl4pLy0aGNrf4xkRUK3z//feYMWMGFixYgFOnTqF9+/bw9fVFQkJCoe0jIiIwYsQIHDhwAEeOHEGjRo3Qv39/3Llzx6jdgAEDjCY1bN26tSqGQ0R1DGMUETGRXoX0BoGzd9IAAPUrOCNdyRnpRDVOamoqAKB+/fpGy7ds2QIHBwe0adMGc+bMwcOHD6V1R44cQdu2bY0uMOjr64u0tDRcuHBBatOvXz+jffr6+uLIkSNF9kWr1SItLc3oZgqHr6UAABramJlkfyUZ07UBNOZKXLufhWvJOUW2Y3kXoqePSgHUt8yLRddZJ52oTli9ejUmTZqEcePGwcvLCxs2bIClpSU2bdpUaPstW7bgrbfegre3N1q2bImvv/4aBoMB4eHhRu3UarXRpAaW1CKi8mCMIiIm0qvQlYQMPNTpYSYHbNUVm8nJGelENYvBYMC0adPQo0cPtGnTRlr+2muv4dtvv8WBAwcwZ84c/O9//8Prr78urY+LizNKogOQ7sfFxRXbJi0tDVlZWYX2Z+nSpbC1tZVujRo1qvAY07NzcebRh4ENKvFCo4/TmCsxrlsDAMDpuGzoDYUnylnehejp5F7fHADrpBPVBTqdDpGRkUaTB+RyOfr161fs5IHHPXz4EDk5OQUmNURERMDJyQktWrTAlClTkJSUVOx+KmtCAhHVXoxRRAQwkV6lTsXmlURwsFRW6EKjAGukE9U0/v7+OH/+PEJCQoyWv/HGG/D19UXbtm0xcuRIfPPNN9ixYwdiYmIqtT9z5sxBamqqdLt161aF93n8Zir0AvCobwEbdeXWR3/cq51c4WhthgydAVeKmZXO8i5ETx+P+hYAgBjOSCeq9e7fvw+9Xl/o5IH8yQUlmT17Ntzc3IwSXQMGDMA333yD8PBwLF++HAcPHsTAgQOh1xc9IakyJiQQUe3GGEVEAFA1UwoJABAVmwIAcLSqeAIqvzZxdg5npBNVt4CAAOzcuRO///47GjZsWGzbrl27AgCuXr2Kpk2bwsXFpcAFZ+Lj4wEALi4u0s/8ZY+30Wg0sLCwKPQ4arUaarVpE8t/PqqP3r2JHaAvfCZ8ZbAwU+CNHo3w393XcDYhBy2ci/4AMTs7GwBgbm5eVd0jomqUPyM9JpEz0omedsuWLUNISAgiIiKMzgNeffVV6fe2bduiXbt2aNq0KSIiItC3b99C9zVnzhzMmDFDup+WlsZEFRFVCGMUUd3AGelV6NSjRLp9BeujA4Di0YR2zkgnqj5CCAQEBGDHjh3Yv38/PD09S9wmKioKAODq6goA8PHxwblz54wuULN3715oNBp4eXlJbZ6so7d37174+PiYaCQlE0Lg8KNEeo8mVV+zb2g7Z2jUcmj1Amfjsqv8+ERUM7k/mpF+jTPSiWo9BwcHKBSKQicP5E8uKMrKlSuxbNky7NmzB+3atSu2bZMmTeDg4ICrV68W2UatVkOj0RjdiOjpxhhFRAAT6VUm5aEO15PyLjDoaFnxGenKRzPSszgjnaja+Pv749tvv8V3330HGxsbxMXFIS4uTqpbHhMTgyVLliAyMhI3btzAL7/8gtGjR6NXr17SCVT//v3h5eWFUaNG4cyZM9i9ezfmzp0Lf39/aUb55MmTce3aNcyaNQuXL1/G559/jh9++AHTp0+v1PHlX8ATEEjJ1iM+XQe1Uo5Ojar+RM1MIUdnt7yZGxfisxCfxlroRAR4Son0DOTqObmAqDZTqVTo1KmT0eSB/IvyFTd5YMWKFViyZAnCwsLQuXPnEo9z+/ZtJCUlSZMaiIhKgzGKiAAm0qtMVo4eL3u7oqFGCbWy4g+7UqqRzkQ6UXVZv349UlNT0adPH7i6ukq377//HkDeyda+ffvQv39/tGzZEu+88w6GDRuGX3/9VdqHQqHAzp07oVAo4OPjg9dffx2jR4/G4sWLpTaenp4IDQ3F3r170b59e6xatQpff/01fH19K3V8Wq0WK389hdxcPW6n5dUm79xYA3OzqquP/rjGtmZwspRDL4DAP2KLbJednS2VeCGiuq2BnRqWKgW0uQbcSOKsdKLabsaMGfjqq6+wefNmXLp0CVOmTEFmZibGjRsHABg9ejTmzJkjtV++fDnmzZuHTZs2wcPDQ5rUkJGRV+4pIyMDM2fOxNGjR3Hjxg2Eh4djyJAhaNasWaWfRxFR3cMYRUSskV5FXG0t8N8hrbD85yzoDRWfMZVf2iVLl1vhfRFR+Qghil3fqFEjHDx4sMT9uLu7Y9euXcW26dOnD06fPl2m/pmCmUqN3Nxc3EnNS6R396z6si75ZDIZOrqqERaThV/PJeC1Tq5o6WJdaNv82fRqtRqyCl7cmYhqLrlMhpYuNjgVm4KL99LRzMmmurtERBUwfPhwJCYmYv78+YiLi4O3tzfCwsKki/vFxsZCLv97UtL69euh0+nwr3/9y2g/CxYswMKFC6FQKHD27Fls3rwZKSkpcHNzQ//+/bFkyRKTX0uGiOo+xigiYiK9llLI8hJ4LO1CRJUtRy8Ql5GXSO/RxK5a++JoqYBnPRWuP9Dh/Z1/4bsx7Qttp9VqsW73Bcwe0okXHiWq41q5anAqNgWX7qXhn+3dqrs7RFRBAQEBCAgIKHRdRESE0f0bN24Uuy8LCwvs3r3bRD0jImKMInraMZFeSyke1UhnaRciqmzxmXoYBOBmq5Yu7FedujWyQrpO4Nr9LKzafwOFz0nPm01PRHVfK9e86zZcvJtWzT0hIiIiIqK6jDXSa6m/S7vwwlpEVLnupud9YNejSb0aUSbFwkyOxS82BwBsOx2Hmym6Qtvll3cpqQQPEdVubRrYAgDO3E6BwcDXOxERERERVQ4m0msp5aMZ6SztQkSV7W5G3rUYqrusy+O6e9bD6C55JRwOxWYhs5APFXNzdFi1MwqpqalMphPVYa3dNLBUKZDyMAd/JaRXd3eIiIiIiKiOYiK9llI+euZY2oWIKlNqth7pOgG5DHi2sW11d8fI273d4eViDa1e4I9b2TAUliyXybA69Ay0Wm3Vd5CIKpUQAsnJyUhLeYCOjfMuhHw0Jqmae0VERERERHUVE+m11N810lnahYhMLzs7G9nZ2biTnjcb3dlaCSt1zbqshplCjo+HtoBKASQ+NOD47YeFtlOyVjpRnZSdmY714Rex6tdItHPJu37DISbSiYiIiIiokjCRXktJNdI5I52IKtGdtBwAQEONqpp7UrgGduZ4rrElAOBiQja2nLhbaLv8DwaIqG6xtLaFpY0teja1AwAcjE5E6sOc6u0UERERERHVSUyk11Is7UJElS1Lp8e9RzPSG9iaVXNviuZup4K3c16if2X4dfx2MbGae0REVe0ZJyu0dLGBTm/Ar2cL/0CNiIiIiIioIphIr6XyS7vkGgRy9CzvQkSmd+jaA+gFYG0mQz1zRXV3p1htHM3QylENAWDur39h96X7BdpkZ2cjKysL2dnZvPgoUR30r04NAQCfH7iKLB0nGhARERERkWkxkV5LKWV//87yLkRUGfZfSQYANLZVQiaTldC6eslkMnRtZIXBbZygF8D7v0TjxgNdgXZarRbLf47kxUeJ6qCRXd3RwM4Cd1OzMe3700jPLn2JFyEEkpKS+CEbEREREREVqWZdOY5KTS4DZAAE8sq7aMxrbtkFIqp9dLkG/H41L5HeSFM7/lXIZTIsHNQMQgjsvJCIiBsP0bOxOZo5Gl9sVGmmQnZ2NtRqdY3/gICISiaEQHJyMurVq4f3/+GOadujsftCPA7+tQ9N6lvASq2AUi6DeNRWCEi/Gx7lzXU5OUhOzUD/9o3h1dABXm4aPONsA5WSc06IiIiIiChP7ciOUAEymQxKuQw5BoFsHUu7EJHpZGdn488r8cjQ6mGhlMHRsvYkkhRyGRb5NYcAEHohEX/EZiPLIIcQQkqa5+bosDr0DN4f1hXm5ual3rcQAlqtlgl4ohomKyMNq3b8hfF9HuDQxQfo6SZDdIocd9L1uBifWYY9KRF87C5wLK/GuplChuaOlmjlbIVnmzqjtZstmjtbw9ysZpe6IiIiIiKiysFEei2meJRIZ2kXIjK1/dFJAIBGtma1LmmskMuw2K85/rqXiivJOpy4/RAzf4rGokHNYKXO+7cnAGRlZQFAqRPj+WVhZg/pVKYEPBFVPplMhvW7TqJBMy+4WOlgSLiKDs1aIi1Lh5TUVFjZ1gcMemSmp8LGzh7CkIuHaamwqWcPoc9FRloKsg1y6BQWSNcJPNACOXoFLsZl4mJcJn48kwAg7xuBjeuZo5mjJdo0tIeHgxUa21vCw94K9SxrX7wkIiIiIqLSYyK9Fsv/tjET6URkSrpcA/Zczkuke9jVzrJRCrkMPRpboJ45cOKuDvuikxCdkIkPX2wutdFqtVi3+wKmDWgDW1vbUiXAzFTqEtsQUfUwt7KRfrewtoGduRwapRxW2TrY1zODIVfggU4HezslDLkGPNDqYG+b/3tePXV7VycYcnVIToiH2r4B7mfqcDc5AxlQI1Ung84gw43kbNxIzsa+6GSj41upFGhop0YTJw08HKzQqJ4lXG3N4WJrDheNOWwtlHjw4AHq16/PhDsRERERUS3ERHotppTnvQnLZiKdiEzowF/3kZadCycbFVxtlDDoa2eMkclkaGGvgpONOU7czcatB9kY9+05tHFSo7WDUmqzamcU3nnRu9TJdCKq+2QywEYth5VCDo02C/auDtDnaBGXkAiZxgXJD3MQn5yGHKUVMnMFsvRyZOr0iE54iOiEh4XuU62QQS3To0tTB3g1qI+mTtZo6mgNTwcr6dsyRERERERUc/GsvRZTPEqkc0Y6EZnSz2fuAQD8WjsC2kzU9qswOFmbYduEVlix9xp2XkjE2Xgt/krSoZ5dHLJyDDCTycpVM52Ini4ymQzmcgF7GyVcLAxwNehg7+oMQ64O9xPioarfAGlZOtyNT0SWUEKvskZWLvAwRyAHCmj1AlrIsS86ucBsdidrFVw0KthbmaG+pRkcrMzg7lwPzhpzuNpawNXWHHYsHUNEREREVK2YSK/FpBnpOibSicg0kjK0+P1KXlmXwW2c8Gvk9WrukWlozJX4cPAzeP4ZeywIjUaGTuDzQ3cgA9BAo0ALR0ukZ+bNIlWr1cjOzgYAmJubM3FFRCVSyPBYKRkAyIW9qw0MuTo8SIyHnXNDpGfrcC/xAXRKS6TpBFIe5iJbpobWIENChg4JGbon9nrH6J5aKUc9CyXqWZrBzkIJO0sl3OrZoL61GvWtVKhnqYKDtQqudhZwtlFDqag9F4omIiIiIqoNmEivxVgjnYhMSQiBkGM3kGsQaO1qjSYOltXdJZPr28IeZ69rcOV+NlJyFLgY/xC30/S4nZaO/oEn4dfaES+2ccQvx6KhkCuMSr7Ep2Ujkx9cElE5KOQyaFQy6FU5sHfVSAl2e1d7ZGm1uBOfDJWtIx7qcvAgLRNagwy5cpXxjPZcA+LSdYhLfzzhnlTk8Vw05nC1M4eDhRweTraws1TBxlwJa7USGnOzvN/NlbAxN4O1Om95/rcdiYiIiIioICbSnxAYGIiPP/4YcXFxaN++PT799FN06dKlurtVKLNHb3bupWZXc0+IqCpUdnxKz8xCYEQMAOAVbxeT7bemkctkaFrPDP59W2BZ2F/46342rqfk4sHDHHx74i6+PXEXChmgUctx8POjaN/YHteSHuLCvQwAQFruBXz8b2+YmymqeSRENUttOoeqSdQKGeqZ5f594dPcLACAvau90Yz2zGwdEu8nQV3PCdnaXDxIS4dQmiM714CH2ToYzCyQnSuQpZdBbwDupGThTkrevnCp8IT7kyzN5LBSK2CtUsBKrYCVSgFrtQJWKqW03KmeDWwtVLCzNEN9KxXsLFWob6WCrYUZE/FkEmWNJdu2bcO8efNw48YNNG/eHMuXL8egQYOk9UIILFiwAF999RVSUlLQo0cPrF+/Hs2bNy9yn0RERWGMInq6MZH+mO+//x4zZszAhg0b0LVrV6xduxa+vr6Ijo6Gk5NTdXevABdrJW6k6LDvUjz8n29W3d0hokpUFfFpwx838DBHwMlGhUGtHU2yz5qunoUCnVzV6NLYBh2bOOOXcwn4I+YBtLkGPMg24EE2EHs+wWibX8/FITX7JD59rSNsLcyqqedENUttO4eqbRRyGaxVMuSY6WFvo4TBwoAHOdmwd3V8lGxPk35PTohHWuZDqOq5IFNnQFp2LnIV5tDpDcjK1gEqc+ToAV2uHnqZErkGwIC8BPjDHAMe5hiQiJwy91EGwEqtgEohh0ohg0qZ99NMIYeZQgalXAaFXAYzuQyKR/eVchmUChmUMhmsLC1gppADeh3sbKxgbqZ4dJPDXPnY749+qpUKKBUyKGQyyOUyyGX5v+c9XgqZDDKZTPo9f7lclndTyvO2o5qlrLHk8OHDGDFiBJYuXYoXX3wR3333HYYOHYpTp06hTZs2AIAVK1bgk08+webNm+Hp6Yl58+bB19cXFy9e5LVRiKhMGKOIiIn0x6xevRqTJk3CuHHjAAAbNmxAaGgoNm3ahPfee6+ae1dQA5u82i6nY1Mw+//OwMXWHHKZHHIZIJfLIJMBMuT9rGxCVP4xqGbo2dwBbRrYVnc3njqVHZ8Ox9zH+oN59dCnP+8BlfLpqq0rl8nQq1l99GpWH3qDwEc7zyEpMwfZBgU6e9rDwUqF55rWw8dhF7EvJhO/X7mPLv/dB3d7S5grFTBT5NUvtlCbwVypgFophxwGyB4lbMzMFFJSx6DXQyYD5DJAqVRCnhesAeT90OfqoVQqAJksfzH0+rxlj8d0KbQ/WiB77G7+lk+2le4/9o/h7zbG29RWfm1d0ah+3StLVNPVtnOoukwmA2ytLWHvYPtYCZmCCfe85Y2ki6VqnBpCq9MhKSkJlvWcocvJQUpqKnKFDCprO+hy9MjIfAihVEGnF3iozYVBoYJWD+QKGQSADK0eQO0qgaWQ/Z1gV8hlUMghJecVsrz7QN65bv7pbt55b969/GUyAEq5HEpF/k8ZLM3kj8rmKOCgsYKNhRkmPOcJB2t11Q6yFilrLFm3bh0GDBiAmTNnAgCWLFmCvXv34rPPPsOGDRsghMDatWsxd+5cDBkyBADwzTffwNnZGT/99BNeffXVqhscEdV6jFFExET6IzqdDpGRkZgzZ460TC6Xo1+/fjhy5EiB9lqtFlqtVrqfmpoKAEhLSyvyGFlZWcjOTIfeoIdB6CGTySBD3tl5ee/by3VIzDJg66G/KvgIEJWO4sVWaGxTeKYt/+9f8JMVkyprfALKHqMshBbuNgIiV4fOjkBiYiIAICM1GYbcXCjN1NKysi43xT4qY3liYmKRbeTZqbAXuVCq1BjSvAEAQGSlQmNIx3MOOpxMEEjPFLicmVHo40nVx0PTCbZKh0LXMUZVjqqIUQCQnp6O1PsJyMnNhSE3B2nJiZArldLvAIzul/X3im5fk/pS1u0zHyTCzCzvd1laItTmMpjl5kCfmbcvO2s9DLIcpOkSYWfnlre9NhF29nm/pyQlwsrBDdqcXKSnPIBlfUfoc/VIT02FATJY2daHXp+LzIx0WGjqQa/XIysjA2obW+j1BmQ/zIRCqYJeGJCTq4dMqYbeIKDVaaFUWSJXCOhycmGADDKlCnoB6PUCkOeV2DIYBGRyGYQADAKATPYowV3yJ4MGoBzz78tvcEs7qAyFf9j3tMeo8sSSI0eOYMaMGUbLfH198dNPPwEArl+/jri4OPTr109ab2tri65du+LIkSNFJqnKE6OI6jrGKMYoopqsqmIUE+mP3L9/H3q9Hs7OzkbLnZ2dcfny5QLtly5dikWLFhVY3qhRo0rrI1FN8OZa4M0S2qSnp8PWlrPWTaWs8QmoWIx6ZmHhy1cU0b4sy02xD1MtX13BfVLNNHBtyW0Yo0yrqmMUUW3Wem3JbZ7WGFWeWBIXF1do+7i4OGl9/rKi2hSGMYqoaElJSYxRj2GMIqpZKjtGMZFeTnPmzDH6ZNFgMCA5ORn29vZGX5l/XFpaGho1aoRbt25Bo9FUVVdNqi6MAagb46iJYxBCID09HW5ubtXdladeaWNUTfw7qkwcb91VmrEyRtUcT+t5VG3Gx7/yMUbVHE/GqJSUFLi7uyM2NrZWJhDrwuu3to+htvcfyJv13LhxY9SvX7+6u/LUY4yqeWr7GGp7/4Gqi1FMpD/i4OAAhUKB+Ph4o+Xx8fFwcXEp0F6tVkOtNq5vaGdnV6pjaTSaWvuHma8ujAGoG+OoaWOojf+4a7qyxieg7DGqpv0dVTaOt+4qaayMUaZXFTHqcU/T33NNxMe/cj3NMao8scTFxaXY9vk/4+Pj4erqatTG29u7yL4UFqOAvOenNv/914XXb20fQ23vP5BXzuRpxBhV+erC66O2j6G29x+o/Bj1dEbAQqhUKnTq1Anh4eHSMoPBgPDwcPj4+FRjz4joacf4REQ1GWMUEZlCeWKJj4+PUXsA2Lt3r9Te09MTLi4uRm3S0tJw7NgxxiciKhPGKCICOCPdyIwZMzBmzBh07twZXbp0wdq1a5GZmSldkZmIqLowPhFRTcYYRUSmUFIsGT16NBo0aIClS5cCAKZOnYrevXtj1apV8PPzQ0hICE6ePIkvv/wSACCTyTBt2jR8+OGHaN68OTw9PTFv3jy4ublh6NCh1TVMIqqlGKOIiIn0xwwfPhyJiYmYP38+4uLi4O3tjbCwsAIXfigvtVqNBQsWFPoVnNqiLowBqBvjqAtjoNKrrPj0tP0dcbx119M01pqoss+hAD7H1Y2PP1WFkmJJbGys0Ve2u3fvju+++w5z587F+++/j+bNm+Onn35CmzZtpDazZs1CZmYm3njjDaSkpOC5555DWFgYzM3NS92v2v73X9v7D9T+MdT2/gN1YwwVxRhVOWp7/4HaP4ba3n+g6sYgE0KISj0CEREREREREREREVEtxhrpRERERERERERERETFYCKdiIiIiIiIiIiIiKgYTKQTERERERERERERERWDiXQiIiIiIiIiIiIiomIwkV5FAgMD4eHhAXNzc3Tt2hXHjx+v7i5Jli5dimeffRY2NjZwcnLC0KFDER0dbdSmT58+kMlkRrfJkycbtYmNjYWfnx8sLS3h5OSEmTNnIjc3t8rGsXDhwgJ9bNmypbQ+Ozsb/v7+sLe3h7W1NYYNG4b4+PgaNQYPD48CY5DJZPD39wdQO54Hql1qcmwqL1PEgprs999/x+DBg+Hm5gaZTIaffvrJaL0QAvPnz4erqyssLCzQr18/XLlyxahNcnIyRo4cCY1GAzs7O0yYMAEZGRlVOIrSK2m8Y8eOLfB8DxgwwKhNbRovFa4uxqqqUJpzPFOdH0VERKBjx45Qq9Vo1qwZgoODC/SHzyPVFGX9W9y2bRtatmwJc3NztG3bFrt27TJaX5r/vaZWljF89dVX6NmzJ+rVq4d69eqhX79+BdqX5v9pdfU/ODi4QN/Mzc2N2tT056Cw93EymQx+fn5Sm6p8Dko6vyoM43zVYYxijKrqMTBGlYGgShcSEiJUKpXYtGmTuHDhgpg0aZKws7MT8fHx1d01IYQQvr6+IigoSJw/f15ERUWJQYMGicaNG4uMjAypTe/evcWkSZPEvXv3pFtqaqq0Pjc3V7Rp00b069dPnD59WuzatUs4ODiIOXPmVNk4FixYIFq3bm3Ux8TERGn95MmTRaNGjUR4eLg4efKk6Natm+jevXuNGkNCQoJR//fu3SsAiAMHDgghasfzQLVHTY9N5VXRWFDT7dq1S3zwwQdi+/btAoDYsWOH0fply5YJW1tb8dNPP4kzZ86If/7zn8LT01NkZWVJbQYMGCDat28vjh49Kv744w/RrFkzMWLEiCoeSemUNN4xY8aIAQMGGD3fycnJRm1q03ipoLoaq6pCac7xTHF+dO3aNWFpaSlmzJghLl68KD799FOhUChEWFiY1IbPI9UUZf1bPHTokFAoFGLFihXi4sWLYu7cucLMzEycO3dOalOa/73VOYbXXntNBAYGitOnT4tLly6JsWPHCltbW3H79m2pTWn+n1ZX/4OCgoRGozHqW1xcnFGbmv4cJCUlGfX//PnzQqFQiKCgIKlNVT4HJZ1fPYlxvuowRjFGVccYGKNKj4n0KtClSxfh7+8v3dfr9cLNzU0sXbq0GntVtISEBAFAHDx4UFrWu3dvMXXq1CK32bVrl5DL5UbBYv369UKj0QitVluZ3ZUsWLBAtG/fvtB1KSkpwszMTGzbtk1adunSJQFAHDlyRAhRM8bwpKlTp4qmTZsKg8EghKgdzwPVHrUtNpVWRWNBbfLkSYXBYBAuLi7i448/lpalpKQItVottm7dKoQQ4uLFiwKAOHHihNTmt99+EzKZTNy5c6fK+l4eRSXShwwZUuQ2tXm8lKeuxqrq8OQ5nqnOj2bNmiVat25tdKzhw4cLX19f6T6fR6opyvq3+O9//1v4+fkZLevatat48803hRCl+99rahV9PeXm5gobGxuxefNmaVlJ/09Nqaz9DwoKEra2tkXurzY+B2vWrBE2NjZGH2xW5XPwuNIkqRjnqw5jFGOUKTBGVV6MYmmXSqbT6RAZGYl+/fpJy+RyOfr164cjR45UY8+KlpqaCgCoX7++0fItW7bAwcEBbdq0wZw5c/Dw4UNp3ZEjR9C2bVs4OztLy3x9fZGWloYLFy5UTccBXLlyBW5ubmjSpAlGjhyJ2NhYAEBkZCRycnKMnoeWLVuicePG0vNQU8aQT6fT4dtvv8X48eMhk8mk5bXheaCarzbGprKoSCyoza5fv464uDij8dna2qJr165Gsc7Ozg6dO3eW2vTr1w9yuRzHjh2r8j6bQkREBJycnNCiRQtMmTIFSUlJ0rq6ON6nSV2PVVXtyXM8U50fHTlyxGgf+W3y98HnkWqK8vwtlvT3XZr/vdU9hic9fPgQOTk5Bd7vFff/1FTK2/+MjAy4u7ujUaNGGDJkiNF7m9r4HGzcuBGvvvoqrKysjJZXxXNQHozzVYMxKg9jVPWM4XGMUUVjIr2S3b9/H3q93ujNBwA4OzsjLi6umnpVNIPBgGnTpqFHjx5o06aNtPy1117Dt99+iwMHDmDOnDn43//+h9dff11aHxcXV+gY89dVha5duyI4OBhhYWFYv349rl+/jp49eyI9PR1xcXFQqVSws7Mr0Mf8/tWEMTzup59+QkpKCsaOHSstqw3PA9UOtS02lUVFY0Ftlj+G4p7XuLg4ODk5Ga1XKpWoX79+rXwMBgwYgG+++Qbh4eFYvnw5Dh48iIEDB0Kv1wOoe+N92tTlWFXVCjvHM9X5UVFt0tLSkJWVxeeRaozy/C0W9ff9+N9//rLS7rMiTPF6mj17Ntzc3IwSCiX9P63O/rdo0QKbNm3Czz//jG+//RYGgwHdu3fH7du3AdS+5+D48eM4f/48Jk6caLS8qp6D8mCcrxqMUXkYo6p+DI9jjCqessK9pTrF398f58+fx59//mm0/I033pB+b9u2LVxdXdG3b1/ExMSgadOmVd3NQg0cOFD6vV27dujatSvc3d3xww8/wMLCohp7Vj4bN27EwIED4ebmJi2rDc8DUXWra7GAivfqq69Kv7dt2xbt2rVD06ZNERERgb59+1Zjz4hqlqLO8Yjo6bJs2TKEhIQgIiLC6GJ4Nfn/qY+PD3x8fKT73bt3R6tWrfDFF19gyZIl1diz8tm4cSPatm2LLl26GC2vyc8BUVVhjKp+jFHF44z0Subg4ACFQoH4+Hij5fHx8XBxcammXhUuICAAO3fuxIEDB9CwYcNi23bt2hUAcPXqVQCAi4tLoWPMX1cd7Ozs8Mwzz+Dq1atwcXGBTqdDSkqKUZvHn4eaNIabN29i3759BT4BfFJteB6oZqpNsamiyhoLarP8MRT3vLq4uCAhIcFofW5uLpKTk+vEY9CkSRM4ODgYxcW6PN667mmKVZWpqHM8U50fFdVGo9HAwsKCzyPVGOX5Wyzq7/vxv//8ZaXdZ0VU5PW0cuVKLFu2DHv27EG7du2Kbfvk/1NTMUU8MDMzQ4cOHYz+1+fvo7z7LIuKjCEzMxMhISGYMGFCiceprOegPBjnqwZjFGOUKTBG5amsGMVEeiVTqVTo1KkTwsPDpWUGgwHh4eFGn1hVJyEEAgICsGPHDuzfvx+enp4lbhMVFQUAcHV1BZD3Cdy5c+eMkhV79+6FRqOBl5dXpfS7JBkZGYiJiYGrqys6deoEMzMzo+chOjoasbGx0vNQk8YQFBQEJycn+Pn5FduuNjwPVDPVhthkKmWNBbWZp6cnXFxcjMaXlpaGY8eOGcW6lJQUREZGSm32798Pg8EgfThXm92+fRtJSUlGcbEuj7eue5piVWUo6RzPVOdHPj4+RvvIb5O/Dz6PVFOU52+xpL/v0vzvre4xAMCKFSuwZMkShIWFGV03pChP/j81FVPEA71ej3Pnzkl9qy3PAQBs27YNWq3WqDxnUSrrOSgPxvmqwRjFGGUKjFF5Ki1GlenSpFQuISEhQq1Wi+DgYHHx4kXxxhtvCDs7OxEXF1fdXRNCCDFlyhRha2srIiIixL1796Tbw4cPhRBCXL16VSxevFicPHlSXL9+Xfz888+iSZMmolevXtI+cnNzRZs2bUT//v1FVFSUCAsLE46OjmLOnDlVNo533nlHREREiOvXr4tDhw6Jfv36CQcHB5GQkCCEEGLy5MmicePGYv/+/eLkyZPCx8dH+Pj41KgxCJF35eDGjRuL2bNnGy2vLc8D1R41PTaVV0VjQU2Xnp4uTp8+LU6fPi0AiNWrV4vTp0+LmzdvCiGEWLZsmbCzsxM///yzOHv2rBgyZIjw9PQUWVlZ0j4GDBggOnToII4dOyb+/PNP0bx5czFixIjqGlKxihtvenq6ePfdd8WRI0fE9evXxb59+0THjh1F8+bNRXZ2trSP2jReKqiuxqqqUNI5nhCmOT+6du2asLS0FDNnzhSXLl0SgYGBQqFQiLCwMKkNn0eqKUr6Wxw1apR47733pPaHDh0SSqVSrFy5Uly6dEksWLBAmJmZiXPnzkltSvO/tzrHsGzZMqFSqcT//d//GcWC9PR0IYQo9f/T6ur/okWLxO7du0VMTIyIjIwUr776qjA3NxcXLlwwGmNNfg7yPffcc2L48OEFllf1c1DS+eR7770nRo0aJbVnnK86jFGMUdUxhnyMUSVjIr2KfPrpp6Jx48ZCpVKJLl26iKNHj1Z3lyQACr0FBQUJIYSIjY0VvXr1EvXr1xdqtVo0a9ZMzJw5U6Smphrt58aNG2LgwIHCwsJCODg4iHfeeUfk5ORU2TiGDx8uXF1dhUqlEg0aNBDDhw8XV69eldZnZWWJt956S9SrV09YWlqKl156Sdy7d69GjUEIIXbv3i0AiOjoaKPlteV5oNqlJsem8jJFLKjJDhw4UGjMHjNmjBBCCIPBIObNmyecnZ2FWq0Wffv2LRBPkpKSxIgRI4S1tbXQaDRi3Lhx0olqTVPceB8+fCj69+8vHB0dhZmZmXB3dxeTJk0qcDJUm8ZLhauLsaoqlHSOJ4Tpzo8OHDggvL29hUqlEk2aNDE6Rj4+j1RTFPe32Lt3b+l/ar4ffvhBPPPMM0KlUonWrVuL0NBQo/Wl+d9bnWNwd3cvNBYsWLBACCFK/f+0uvo/bdo0qa2zs7MYNGiQOHXqlNH+avpzIIQQly9fFgDEnj17Cuyrqp+Dks4nx4wZI3r37l1gG8b5qsEYxRhV1WMQgjGqtGRCCFG2OexERERERERERERERE8P1kgnIiIiIiIiIiIiIioGE+lERERERERERERERMVgIp2IiIiIiIiIiIiIqBhMpBMRERERERERERERFYOJdCIiIiIiIiIiIiKiYjCRTkRERERERERERERUDCbSiYiIiIiIiIiIiIiKwUQ6EREREREREREREVExmEinOkUmk+Gnn36q1GN4eHhg7dq1VXpMIqq9xo4di6FDh1Z3N4ionPr06YNp06ZVdzeIiIiIiKiaMZFOVITg4GDY2dkVWH7ixAm88cYbVd8hIjKZ8iTGmEwjooq4ceMGZDIZoqKiqrsrJhEREQGZTIaUlJTq7kqpPDkRgoiIiIiorJTV3QF6Ouh0OqhUquruhkk4OjpWdxeIiIiIiIiIiIioCnFGOlWKPn36ICAgANOmTYODgwN8fX1x/vx5DBw4ENbW1nB2dsaoUaNw//59AMCXX34JNzc3GAwGo/0MGTIE48ePl+6vX78eTZs2hUqlQosWLfC///2vXP0rbBZVVFQUZDIZbty4gYiICIwbNw6pqamQyWSQyWRYuHAhAM5oIqrtxo4di4MHD2LdunXS6/vGjRs4ePAgunTpArVaDVdXV7z33nvIzc0tdhu9Xo8JEybA09MTFhYWaNGiBdatW1fuvoWFheG5556DnZ0d7O3t8eKLLyImJsaoze3btzFixAjUr18fVlZW6Ny5M44dOyat//XXX/Hss8/C3NwcDg4OeOmll8rdHyIy9r///Q+dO3eGjY0NXFxc8NprryEhIUFa/+DBA4wcORKOjo6wsLBA8+bNERQUBADw9PQEAHTo0AEymQx9+vQp8Xj5paEWLVoER0dHaDQaTJ48GTqdDgDwzTffwN7eHlqt1mi7oUOHYtSoUQCAhQsXwtvbG5s2bULjxo1hbW2Nt956C3q9HitWrICLiwucnJzw3//+12gfMpkMX3/9NV566SVYWlqiefPm+OWXXwDkza5//vnnAQD16tWDTCbD2LFjSxxPSTEuf9b+Dz/8gJ49e8LCwgLPPvss/vrrL5w4cQKdO3eGtbU1Bg4ciMTExAKP08qVK+Hq6gp7e3v4+/sjJycHQN556c2bNzF9+nQphhMRERERlRUT6VRpNm/eDJVKhUOHDmHZsmV44YUX0KFDB5w8eRJhYWGIj4/Hv//9bwDAK6+8gqSkJBw4cEDaPjk5GWFhYRg5ciQAYMeOHZg6dSreeecdnD9/Hm+++SbGjRtntI2pdO/eHWvXroVGo8G9e/dw7949vPvuuyY/DhFVvXXr1sHHxweTJk2SXt9mZmYYNGgQnn32WZw5cwbr16/Hxo0b8eGHHxa5TaNGjWAwGNCwYUNs27YNFy9exPz58/H+++/jhx9+KFffMjMzMWPGDJw8eRLh4eGQy+V46aWXpA8ZMzIy0Lt3b9y5cwe//PILzpw5g1mzZknrQ0ND8dJLL2HQoEE4ffo0wsPD0aVLF9M8cESEnJwcLFmyBGfOnMFPP/2EGzduGCWQ582bh4sXL+K3337DpUuXsH79ejg4OAAAjh8/DgDYt28f7t27h+3bt5fqmOHh4bh06RIiIiKwdetWbN++HYsWLQKQd/6k1+ulBDcAJCQkIDQ01GgiQkxMDH777TeEhYVh69at2LhxI/z8/HD79m0cPHgQy5cvx9y5c40+lAOARYsW4d///jfOnj2LQYMGYeTIkUhOTkajRo3w448/AgCio6Nx7969Un2IWFKMy7dgwQLMnTsXp06dglKpxGuvvYZZs2Zh3bp1+OOPP3D16lXMnz/faJsDBw4gJiYGBw4cwObNmxEcHIzg4GAAwPbt29GwYUMsXrxYiuFERERERGUmiCpB7969RYcOHaT7S5YsEf379zdqc+vWLQFAREdHCyGEGDJkiBg/fry0/osvvhBubm5Cr9cLIYTo3r27mDRpktE+XnnlFTFo0CDpPgCxY8eOEvt34MABAUA8ePBAWnb69GkBQFy/fl0IIURQUJCwtbUtsK27u7tYs2ZNmY9JRDVH7969xdSpU6X777//vmjRooUwGAzSssDAQGFtbS3FoCe3KYq/v78YNmyYdH/MmDFiyJAh5epnYmKiACDOnTsnhMiLizY2NiIpKanQ9j4+PmLkyJHlOhYRFa641/6JEycEAJGeni6EEGLw4MFi3Lhxhba9fv26ACBOnz5d6mOPGTNG1K9fX2RmZkrL1q9fbxSbpkyZIgYOHCitX7VqlWjSpIkUzxYsWCAsLS1FWlqa1MbX11d4eHhI+xBCiBYtWoilS5dK9wGIuXPnSvczMjIEAPHbb78JIQo/lyqrJ2Nc/mP09ddfS222bt0qAIjw8HBp2dKlS0WLFi2k+2PGjBHu7u4iNzdXWvbKK6+I4cOHS/efPH8jIiIiIiorzkinStOpUyfp9zNnzuDAgQOwtraWbi1btgQA6Su9I0eOxI8//ih9PXnLli149dVXIZfn/ZleunQJPXr0MDpGjx49cOnSpaoYDhHVYZcuXYKPj4/R1/179OiBjIwM3L59u9htAwMD0alTJzg6OsLa2hpffvklYmNjy9WPK1euYMSIEWjSpAk0Gg08PDwAQNpfVFQUOnTogPr16xe6fVRUFPr27VuuYxNRySIjIzF48GA0btwYNjY26N27N4C/X6NTpkxBSEgIvL29MWvWLBw+fLjCx2zfvj0sLS2l+z4+PsjIyMCtW7cAAJMmTcKePXtw584dAHkXSx87dqxRPPPw8ICNjY1039nZGV5eXtI5Vv6yx8vUAEC7du2k362srKDRaAq0KYuSYlxhx3V2dgYAtG3btti+tm7dGgqFQrrv6upaob4SERERET2JiXSqNFZWVtLvGRkZGDx4MKKiooxuV65cQa9evQAAgwcPhhACoaGhuHXrFv744w+prIup5b9xFEJIy/LraBIRlVZISAjeffddTJgwAXv27EFUVBTGjRsn1S8uq8GDByM5ORlfffUVjh07JpVZyN+fhYVFsduXtJ6Iyi8zMxO+vr7QaDTYsmULTpw4gR07dgD4+zU6cOBAqRb33bt30bdv30ovDdehQwe0b98e33zzDSIjI3HhwoUC9crNzMyM7stkskKXPVlipTRtyqKkGFfYcfM/EHhyWWX3lYiIiIjoScrq7gA9HTp27Igff/wRHh4eUCoL/7MzNzfHyy+/jC1btuDq1ato0aIFOnbsKK1v1aoVDh06hDFjxkjLDh06BC8vrzL3x9HREQBw79491KtXD0DeTM7HqVQq6PX6Mu+biGq+J1/frVq1wo8//gghhJS0OXToEGxsbNCwYcNCt8lv0717d7z11lvSsicvDlpaSUlJiI6OxldffYWePXsCAP7880+jNu3atcPXX3+N5OTkQmelt2vXDuHh4Rg3bly5+kBERbt8+TKSkpKwbNkyNGrUCABw8uTJAu0cHR0xZswYjBkzBj179sTMmTOxcuVKqFQqACjzucWZM2eQlZUlfVB29OhRWFtbS30AgIkTJ2Lt2rW4c+cO+vXrZ7SuspR1PKWJcZWJ53VEREREVFGckU5Vwt/fH8nJyRgxYgROnDiBmJgY7N69G+PGjTN6UzNy5EiEhoZi06ZNBWajz5w5E8HBwVi/fj2uXLmC1atXY/v27eWa6dWsWTM0atQICxcuxJUrVxAaGopVq1YZtfHw8EBGRgbCw8Nx//59PHz4sHyDJ6Iax8PDA8eOHcONGzdw//59vPXWW7h16xbefvttXL58GT///DMWLFiAGTNmSN9geXIbg8GA5s2b4+TJk9i9ezf++usvzJs3DydOnChXn+rVqwd7e3t8+eWXuHr1Kvbv348ZM2YYtRkxYgRcXFwwdOhQHDp0CNeuXcOPP/6II0eOAMi7QN/WrVuxYMECXLp0CefOncPy5csr9mAREQCgcePGUKlU+PTTT3Ht2jX88ssvWLJkiVGb+fPn4+eff8bVq1dx4cIF7Ny5E61atQIAODk5wcLCQrrgempqaqmOq9PpMGHCBFy8eBG7du3CggULEBAQYFSW5bXXXsPt27fx1VdfGV1ktDK5u7tDJpNh586dSExMREZGRrHtSxPjKpOHhwd+//133LlzB/fv36+y4xIRERFR3cFEOlUJNzc3HDp0CHq9Hv3790fbtm0xbdo02NnZGb0RfOGFF1C/fn1ER0fjtddeM9rH0KFDsW7dOqxcuRKtW7fGF198gaCgIPTp06fM/TEzM8PWrVtx+fJltGvXDsuXL8eHH35o1KZ79+6YPHkyhg8fDkdHR6xYsaJcYyeimufdd9+FQqGAl5cXHB0dkZOTg127duH48eNo3749Jk+ejAkTJmDu3LlFbhMbG4s333wTL7/8MoYPH46uXbsiKSnJaHZ6WcjlcoSEhCAyMhJt2rTB9OnT8fHHHxu1UalU2LNnD5ycnDBo0CC0bdsWy5Ytk+oC9+nTB9u2bcMvv/wCb29vvPDCCzh+/Hj5Hygikjg6OiI4OBjbtm2Dl5cXli1bhpUrVxq1UalUmDNnDtq1a4devXpBoVAgJCQEAKBUKvHJJ5/giy++gJubG4YMGVKq4/bt2xfNmzdHr169MHz4cPzzn//EwoULjdrY2tpi2LBhsLa2xtChQ00x3BI1aNAAixYtwnvvvQdnZ2cEBAQU2740Ma4yLV68GDdu3EDTpk2lbyYSEREREZWFTDxeJJqIiIiIiGqEsWPHIiUlBT/99FOJbfv27YvWrVvjk08+qfyOERERERE9hVgjnYiIiIiolnrw4AEiIiIQERGBzz//vLq7Q0RERERUZzGRTnXSRx99hI8++qjQdT179sRvv/1WxT0ioqddbGxssRdHvnjxIho3blyFPSKi6mZtbV3kutKeq3To0AEPHjzA8uXL0aJFC1N1rcwY44iIiIiormNpF6qTkpOTkZycXOg6CwsLNGjQoIp7RERPu9zcXNy4caPI9R4eHlAq+fk20dPk6tWrRa5r0KABLCwsqrA3FcMYR0RERER1HRPpRERERERERERERETFkFd3B4iIiIiIiIiIiIiIajIm0omIiIiIiIiIiIiIisFEOhERERERERERERFRMZhIJyIiIiIiIiIiIiIqBhPpRERERERERERERETFYCKdiIiIiIiIiIiIiKgYTKQTERERERERERERERXj/wHZvATaXTtJZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1500x600 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histplot for each variable in numerical list\n",
    "def histplot_visual(data,column):\n",
    "    fig, ax = plt.subplots(3,5,figsize=(15,6))\n",
    "    fig.suptitle('Histplot for each variable',y=1, size=20)\n",
    "    ax=ax.flatten()\n",
    "    for i,feature in enumerate(column):\n",
    "        sns.histplot(data=data[feature],ax=ax[i], kde=True)\n",
    "histplot_visual(data=df,column=numerical)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 540
    },
    "id": "CtAn8kLrDbdH",
    "outputId": "55a09553-b098-4004-ff2e-bd8ef1f49390"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x600 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot for each variable in numerical list\n",
    "def boxplots_visual(data,column):\n",
    "    fig, ax = plt.subplots(3,5,figsize=(15,6))\n",
    "    fig.suptitle('Boxplot for each variable',y=1, size=20)\n",
    "    ax=ax.flatten()\n",
    "    for i,feature in enumerate(column):\n",
    "        sns.boxplot(data=data[feature],ax=ax[i], orient='h')\n",
    "        ax[i].set_title(feature+ ', skewness is: '+str(round(data[feature].skew(axis = 0, skipna = True),2)),fontsize=10)\n",
    "        ax[i].set_xlim([min(data[feature]), max(data[feature])])\n",
    "boxplots_visual(data=df,column=numerical)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tXV1cliVnm1b"
   },
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UxJ3lQt9nlMd",
    "outputId": "564bb02e-894c-40c0-a270-f61ae633d873"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before: [' 36 months' ' 60 months']\n",
      "After: ['36' '60']\n"
     ]
    }
   ],
   "source": [
    "# Term column\n",
    "print(\"Before:\",df['term'].unique())\n",
    "df['term'] = df['term'].map(lambda x: x.lstrip(' ').rstrip(' months'))\n",
    "print(\"After:\",df['term'].unique())\n",
    "df['term']=df['term'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hQgdpRCinlId",
    "outputId": "0a7e209a-1dbb-4567-f858-8f7de8f10531"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before: ['10+ years' '< 1 year' '3 years' '9 years' '4 years' '5 years' '1 year'\n",
      " '6 years' '2 years' '7 years' '8 years']\n",
      "After: ['10+' '<1' '3' '9' '4' '5' '1' '6' '2' '7' '8']\n"
     ]
    }
   ],
   "source": [
    "# emp_length column\n",
    "print(\"Before:\",df['emp_length'].unique())\n",
    "df['emp_length']= df['emp_length'].str.replace(r'[a-zA-Z]','')\n",
    "df['emp_length']= df['emp_length'].str.replace(' ','')\n",
    "print(\"After:\",df['emp_length'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "HS45MaEuUJ4a"
   },
   "outputs": [],
   "source": [
    "# Creating a pickle file for the TfidfVectorizer\n",
    "with open('dataset.pkl', 'wb') as f:\n",
    "    pickle.dump(df, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "61UyCdb5xkxQ"
   },
   "outputs": [],
   "source": [
    "# Label Encoding\n",
    "df['grade']= df['grade'].map({'A':0,'B':1, 'C':2, 'D':3, 'E':4, 'F':5, 'G':6})\n",
    "\n",
    "df['sub_grade']= df['sub_grade'].map({'A1': 0, 'A2': 1, 'A3': 2, 'A4': 3, 'A5': 4, \n",
    "                                      'B1': 5, 'B2': 6, 'B3': 7, 'B4': 8, 'B5': 9, \n",
    "                                      'C1': 10, 'C2': 11, 'C3': 12, 'C4': 13, 'C5': 14, \n",
    "                                      'D1': 15, 'D2': 16, 'D3': 17, 'D4': 18, 'D5': 19, \n",
    "                                      'E1': 20, 'E2': 21, 'E3': 22, 'E4': 23, 'E5': 24, \n",
    "                                      'F1': 25, 'F2': 26, 'F3': 27, 'F4': 28, 'F5': 29, \n",
    "                                      'G1': 30, 'G2': 31, 'G3': 32, 'G4': 33, 'G5': 34})\n",
    "\n",
    "df['emp_length']= df['emp_length'].map({'<1': 0,'1': 1,'2': 2,'3': 3,\n",
    "                                        '4': 4, '5': 5,'6': 6,'7': 7,\n",
    "                                        '8': 8,'9': 9, '10+': 10})\n",
    "\n",
    "df['loan_status']= df['loan_status'].map({'Fully Paid':0, 'Charged Off':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "id": "RCNLYmNVOvsd",
    "outputId": "b0053e39-e8e7-4d35-a653-ed4e6c3e9376"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-96d9d66e-9ccf-4f58-8c3a-48158493ca3c\">\n",
       "    <div 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>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>home_ownership_MORTGAGE</th>\n",
       "      <th>home_ownership_NONE</th>\n",
       "      <th>home_ownership_OTHER</th>\n",
       "      <th>home_ownership_OWN</th>\n",
       "      <th>home_ownership_RENT</th>\n",
       "      <th>verification_status_Source Verified</th>\n",
       "      <th>verification_status_Verified</th>\n",
       "      <th>purpose_credit_card</th>\n",
       "      <th>purpose_debt_consolidation</th>\n",
       "      <th>purpose_educational</th>\n",
       "      <th>purpose_home_improvement</th>\n",
       "      <th>purpose_house</th>\n",
       "      <th>purpose_major_purchase</th>\n",
       "      <th>purpose_medical</th>\n",
       "      <th>purpose_moving</th>\n",
       "      <th>purpose_other</th>\n",
       "      <th>purpose_renewable_energy</th>\n",
       "      <th>purpose_small_business</th>\n",
       "      <th>purpose_vacation</th>\n",
       "      <th>purpose_wedding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>10.65</td>\n",
       "      <td>162.87</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>27.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13648.0</td>\n",
       "      <td>83.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>171.62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>60</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1687.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>119.66</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2400.0</td>\n",
       "      <td>36</td>\n",
       "      <td>15.96</td>\n",
       "      <td>84.33</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>12252.0</td>\n",
       "      <td>8.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2956.0</td>\n",
       "      <td>98.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>649.91</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5598.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>357.48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>7.90</td>\n",
       "      <td>156.46</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>11.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7963.0</td>\n",
       "      <td>28.3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>161.03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96d9d66e-9ccf-4f58-8c3a-48158493ca3c')\"\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 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\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",
       "    [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-96d9d66e-9ccf-4f58-8c3a-48158493ca3c 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-96d9d66e-9ccf-4f58-8c3a-48158493ca3c');\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",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   loan_amnt  term  int_rate  installment  grade  sub_grade  emp_length  \\\n",
       "0     5000.0    36     10.65       162.87      1          6          10   \n",
       "1     2500.0    60     15.27        59.83      2         13           0   \n",
       "2     2400.0    36     15.96        84.33      2         14          10   \n",
       "3    10000.0    36     13.49       339.31      2         10          10   \n",
       "5     5000.0    36      7.90       156.46      0          3           3   \n",
       "\n",
       "   annual_inc    dti  delinq_2yrs  inq_last_6mths  open_acc  pub_rec  \\\n",
       "0     24000.0  27.65          0.0             1.0       3.0      0.0   \n",
       "1     30000.0   1.00          0.0             5.0       3.0      0.0   \n",
       "2     12252.0   8.72          0.0             2.0       2.0      0.0   \n",
       "3     49200.0  20.00          0.0             1.0      10.0      0.0   \n",
       "5     36000.0  11.20          0.0             3.0       9.0      0.0   \n",
       "\n",
       "   revol_bal  revol_util  total_acc  last_pymnt_amnt  loan_status  \\\n",
       "0    13648.0        83.7        9.0           171.62            0   \n",
       "1     1687.0         9.4        4.0           119.66            1   \n",
       "2     2956.0        98.5       10.0           649.91            0   \n",
       "3     5598.0        21.0       37.0           357.48            0   \n",
       "5     7963.0        28.3       12.0           161.03            0   \n",
       "\n",
       "   home_ownership_MORTGAGE  home_ownership_NONE  home_ownership_OTHER  \\\n",
       "0                        0                    0                     0   \n",
       "1                        0                    0                     0   \n",
       "2                        0                    0                     0   \n",
       "3                        0                    0                     0   \n",
       "5                        0                    0                     0   \n",
       "\n",
       "   home_ownership_OWN  home_ownership_RENT  \\\n",
       "0                   0                    1   \n",
       "1                   0                    1   \n",
       "2                   0                    1   \n",
       "3                   0                    1   \n",
       "5                   0                    1   \n",
       "\n",
       "   verification_status_Source Verified  verification_status_Verified  \\\n",
       "0                                    0                             1   \n",
       "1                                    1                             0   \n",
       "2                                    0                             0   \n",
       "3                                    1                             0   \n",
       "5                                    1                             0   \n",
       "\n",
       "   purpose_credit_card  purpose_debt_consolidation  purpose_educational  \\\n",
       "0                    1                           0                    0   \n",
       "1                    0                           0                    0   \n",
       "2                    0                           0                    0   \n",
       "3                    0                           0                    0   \n",
       "5                    0                           0                    0   \n",
       "\n",
       "   purpose_home_improvement  purpose_house  purpose_major_purchase  \\\n",
       "0                         0              0                       0   \n",
       "1                         0              0                       0   \n",
       "2                         0              0                       0   \n",
       "3                         0              0                       0   \n",
       "5                         0              0                       0   \n",
       "\n",
       "   purpose_medical  purpose_moving  purpose_other  purpose_renewable_energy  \\\n",
       "0                0               0              0                         0   \n",
       "1                0               0              0                         0   \n",
       "2                0               0              0                         0   \n",
       "3                0               0              1                         0   \n",
       "5                0               0              0                         0   \n",
       "\n",
       "   purpose_small_business  purpose_vacation  purpose_wedding  \n",
       "0                       0                 0                0  \n",
       "1                       0                 0                0  \n",
       "2                       1                 0                0  \n",
       "3                       0                 0                0  \n",
       "5                       0                 0                1  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One hot encoding\n",
    "df=pd.get_dummies(data=df,columns=['home_ownership','verification_status','purpose'],drop_first=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nk6IpZpVXDr5"
   },
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "CVfN0qnWRzZs"
   },
   "outputs": [],
   "source": [
    "# Dividing our dataset into independent and dependent features\n",
    "X=df.drop('loan_status',axis=1)\n",
    "y=df['loan_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "jZpBl6NMRzgQ"
   },
   "outputs": [],
   "source": [
    "# Splitting our dataset between training and testing set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "g72d_D65VEMj"
   },
   "outputs": [],
   "source": [
    "# MinMax Scaler\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "L3OfJKDtTsfG",
    "outputId": "b062be2b-858b-4889-9156-760686b46c84"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    207723\n",
       "1     45248\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['loan_status'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UhKnuYTN9rzU"
   },
   "source": [
    "Let's look at the percentage of each category in our target column before model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gro709eF91t4",
    "outputId": "36176bfc-0a64-42a6-dbc8-b7bd50c10a4f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fully Paid: 82.11336477303722\n",
      "Charged Off: 17.886635226962774\n"
     ]
    }
   ],
   "source": [
    "print(\"Fully Paid:\",df['loan_status'].value_counts()[0]/len(df['loan_status'])*100)\n",
    "print(\"Charged Off:\",df['loan_status'].value_counts()[1]/len(df['loan_status'])*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5i6xUa5w-s_l"
   },
   "source": [
    "It's time to apply some resampling techniques !!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uczivVRUAQuI"
   },
   "source": [
    "# Handling Imbalanced Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8t1bQyRD_D2p"
   },
   "source": [
    "## UnderSampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HuJoCzpS-YsR",
    "outputId": "f235070b-cf1b-416f-8588-71c1c6e685ee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of classes before fit Counter({0: 155708, 1: 34020})\n",
      "The number of classes after fit Counter({0: 34020, 1: 34020})\n"
     ]
    }
   ],
   "source": [
    "ns=NearMiss()\n",
    "X_train_ns,y_train_ns=ns.fit_resample(X_train,y_train)\n",
    "print(\"The number of classes before fit {}\".format(Counter(y_train)))\n",
    "print(\"The number of classes after fit {}\".format(Counter(y_train_ns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "id": "Obpk2Eon_JWv",
    "outputId": "e40510dd-9488-4248-9782-809fe258150d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1=RandomForestClassifier()\n",
    "model1.fit(X_train_ns,y_train_ns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dfc3HXc4_yW8",
    "outputId": "4be8a8f2-c979-4d85-c332-812b8ca91bd0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix: \n",
      " [[35896 16119]\n",
      " [ 1389  9839]]\n",
      "\n",
      "\n",
      "Accuracy Score: \n",
      " 0.7231630378065557\n",
      "\n",
      "\n",
      "Classification Report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.69      0.80     52015\n",
      "           1       0.38      0.88      0.53     11228\n",
      "\n",
      "    accuracy                           0.72     63243\n",
      "   macro avg       0.67      0.78      0.67     63243\n",
      "weighted avg       0.86      0.72      0.76     63243\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred=model1.predict(X_test)\n",
    "print(\"Confusion Matrix: \\n\",confusion_matrix(y_test,y_pred))\n",
    "print('\\n')\n",
    "print(\"Accuracy Score: \\n\",accuracy_score(y_test,y_pred))\n",
    "print('\\n')\n",
    "print(\"Classification Report: \\n\",classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NUJr875V_0rp"
   },
   "source": [
    "## OverSampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lZgkIOl4_5GX",
    "outputId": "7dc6e541-50b9-4d2c-8cf4-2c6be791bd4b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of classes before fit Counter({0: 155708, 1: 34020})\n",
      "The number of classes after fit Counter({0: 155708, 1: 155708})\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "os=RandomOverSampler()\n",
    "X_train_os,y_train_os=os.fit_resample(X_train,y_train)\n",
    "print(\"The number of classes before fit {}\".format(Counter(y_train)))\n",
    "print(\"The number of classes after fit {}\".format(Counter(y_train_os)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "id": "RRjax1qw_440",
    "outputId": "909567c2-6c99-4305-b6b0-5a12410e3870"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2=RandomForestClassifier()\n",
    "model2.fit(X_train_os,y_train_os)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AqTzNtJx_4MY",
    "outputId": "361ede50-eed0-4b6a-c865-fae7a045227d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix: \n",
      " [[48777  3238]\n",
      " [ 2397  8831]]\n",
      "\n",
      "\n",
      "Accuracy Score: \n",
      " 0.9108992299543033\n",
      "\n",
      "\n",
      "Classification Report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.94      0.95     52015\n",
      "           1       0.73      0.79      0.76     11228\n",
      "\n",
      "    accuracy                           0.91     63243\n",
      "   macro avg       0.84      0.86      0.85     63243\n",
      "weighted avg       0.91      0.91      0.91     63243\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred=model2.predict(X_test)\n",
    "print(\"Confusion Matrix: \\n\",confusion_matrix(y_test,y_pred))\n",
    "print('\\n')\n",
    "print(\"Accuracy Score: \\n\",accuracy_score(y_test,y_pred))\n",
    "print('\\n')\n",
    "print(\"Classification Report: \\n\",classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qOXyY6qrDxgn"
   },
   "source": [
    "## SMOTETomek(Do both undersampling and oversampling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OpuVM4uPDzef",
    "outputId": "b8c68192-9e37-4c7e-dc37-c237b1ad86f9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of classes before fit Counter({0: 155708, 1: 34020})\n",
      "The number of classes after fit Counter({0: 155138, 1: 155138})\n"
     ]
    }
   ],
   "source": [
    "smt=SMOTETomek()\n",
    "X_train_smt,y_train_smt=smt.fit_resample(X_train,y_train)\n",
    "print(\"The number of classes before fit {}\".format(Counter(y_train)))\n",
    "print(\"The number of classes after fit {}\".format(Counter(y_train_smt)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "id": "BEhpVBFcDzXT",
    "outputId": "20e3f42b-09cb-4a66-c838-112adb825569"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3=RandomForestClassifier()\n",
    "model3.fit(X_train_smt,y_train_smt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "T3_sg3gZQ40U",
    "outputId": "eaa45f7d-5e2b-4945-8106-06e4a3e5ebae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix: \n",
      " [[48084  3931]\n",
      " [ 2098  9130]]\n",
      "\n",
      "\n",
      "Accuracy Score: \n",
      " 0.9046692914630868\n",
      "\n",
      "\n",
      "Classification Report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.92      0.94     52015\n",
      "           1       0.70      0.81      0.75     11228\n",
      "\n",
      "    accuracy                           0.90     63243\n",
      "   macro avg       0.83      0.87      0.85     63243\n",
      "weighted avg       0.91      0.90      0.91     63243\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred=model3.predict(X_test)\n",
    "print(\"Confusion Matrix: \\n\",confusion_matrix(y_test,y_pred))\n",
    "print('\\n')\n",
    "print(\"Accuracy Score: \\n\",accuracy_score(y_test,y_pred))\n",
    "print('\\n')\n",
    "print(\"Classification Report: \\n\",classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vdnXXW6_HfIg"
   },
   "source": [
    "Overall oversampling has the best result so we will go with oversampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SxxnsDPQ7Vvy"
   },
   "source": [
    "#### Hyperparameter Tuning with Keras Tuner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "Ot5qbY43Rzlq"
   },
   "outputs": [],
   "source": [
    "# Define the build_model function for Keras Tuner\n",
    "def build_model(hp):\n",
    "    model = keras.Sequential()\n",
    "    for i in range(hp.Int('num_layers', 2, 20)):\n",
    "        model.add(layers.Dense(units=hp.Int('units_' + str(i), min_value=32, max_value=512, step=32),\n",
    "                               activation='relu'))\n",
    "    model.add(layers.Dense(1, activation='sigmoid'))\n",
    "    model.compile(\n",
    "        optimizer=keras.optimizers.Adam(hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])),\n",
    "        loss='binary_crossentropy',\n",
    "        metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-BBJb24t7J6p"
   },
   "source": [
    "### Explanation of my 'build_model' function for the Keras Tuner\n",
    "- This function takes in the hyperparameters defined by the tuner and returns a compiled Keras model.\n",
    "- The function defines a Keras Sequential model and adds a variable number of Dense layers (between 2 and 20) with a variable number of units (between 32 and 512).\n",
    "- The output layer has one unit with a sigmoid activation function(Because it's a classification problem).\n",
    "- The function compiles the model using binary crossentropy as the loss function and the Adam optimizer with a learning rate that is randomly chosen from [1e-2, 1e-3, 1e-4]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1NNmUXTMRzzg",
    "outputId": "2eb7738e-6b09-4c01-dcd2-65b1857277af"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 3 Complete [00h 20m 48s]\n",
      "val_accuracy: 0.8632022440433502\n",
      "\n",
      "Best val_accuracy So Far: 0.8714244961738586\n",
      "Total elapsed time: 01h 04m 27s\n"
     ]
    }
   ],
   "source": [
    "tuner = RandomSearch(                   # The tuner uses the build_model function defined earlier as its model-building function,\n",
    "    build_model,                        # and searches for the best hyperparameters based on validation accuracy.\n",
    "    objective='val_accuracy',\n",
    "    max_trials=3,\n",
    "    executions_per_trial=2)\n",
    "tuner.search(X_train_os, y_train_os, epochs=10, validation_data=(X_test, y_test))\n",
    "\n",
    "best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]                # Storing my best hyperparameters in the best_hps variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5jezwdcq7sn2"
   },
   "source": [
    "### Now's the hightime to actually start building our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "G4vK7kDe7EEr"
   },
   "outputs": [],
   "source": [
    "# Define the create_model function to build a Keras model with the optimal hyperparameters\n",
    "def create_model(num_layers, num_nodes, activation):\n",
    "    model = keras.Sequential()\n",
    "    for i in range(num_layers):\n",
    "        if i==0:                                                           # checks whether the current layer is the first layer of the model.\n",
    "            model.add(layers.Dense(num_nodes[i], input_dim=X_train.shape[1]))\n",
    "            model.add(layers.Activation(activation))\n",
    "            model.add(layers.Dropout(0.3))\n",
    "        else:\n",
    "            model.add(layers.Dense(num_nodes[i]))\n",
    "            model.add(layers.Activation(activation))\n",
    "            model.add(layers.Dropout(0.3))       \n",
    "    model.add(layers.Dense(units=1, kernel_initializer='glorot_uniform', activation='sigmoid'))\n",
    "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "# Generate a list of integers for the number of nodes in each layer\n",
    "num_nodes = [best_hps.get('units_' + str(i)) for i in range(best_hps.get('num_layers'))]\n",
    "\n",
    "model = KerasClassifier(\n",
    "    build_fn=create_model,\n",
    "    num_layers=best_hps.get('num_layers'),\n",
    "    num_nodes=num_nodes,\n",
    "    activation='relu'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QhtvHJYo704r"
   },
   "source": [
    "### Explanation of my 'create_model' function\n",
    "- This function uses the best hyperparameters obtained from the tuner.\n",
    "- This function takes in three arguments- num_layers, num_nodes and activation which are used to build the model with the given number of layers and activation function for each layer.\n",
    "- The model object is created using the keras.Sequential() method.\n",
    "- A loop is run over the layers to add the dense layers to the model.\n",
    "- In the first iteration, a check is performed to see if the current layer is the first layer of the model. If yes, input_dim is added as the input shape for the layer\n",
    "- The num_nodes variable is a list of integers representing the number of nodes in each layer of the model, extracted from the best hyperparameters dictionary.\n",
    "- An activation function and a dropout layer is added after each dense layer. The activation argument is used to specify the activation function for each layer.\n",
    "- The output layer is added with a sigmoid activation function and binary crossentropy loss function. The model is then compiled using the adam optimizer.\n",
    "\n",
    "### Finally, we use the KerasClassifier class from scikit-learn to wrap the create_model function and create a scikit-learn compatible estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KWR7iqRx7EJh",
    "outputId": "a3b9cd26-bd3b-4852-9be6-8e1f5b9aba73"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "9732/9732 [==============================] - 69s 7ms/step - loss: 0.3104 - accuracy: 0.8740 - val_loss: 0.2510 - val_accuracy: 0.8499\n",
      "Epoch 2/20\n",
      "9732/9732 [==============================] - 67s 7ms/step - loss: 0.2856 - accuracy: 0.8875 - val_loss: 0.2680 - val_accuracy: 0.8541\n",
      "Epoch 3/20\n",
      "9732/9732 [==============================] - 66s 7ms/step - loss: 0.2837 - accuracy: 0.8890 - val_loss: 0.2554 - val_accuracy: 0.8639\n",
      "Epoch 4/20\n",
      "9732/9732 [==============================] - 65s 7ms/step - loss: 0.2893 - accuracy: 0.8879 - val_loss: 0.2492 - val_accuracy: 0.8507\n",
      "Epoch 5/20\n",
      "9732/9732 [==============================] - 65s 7ms/step - loss: 0.2855 - accuracy: 0.8879 - val_loss: 0.2396 - val_accuracy: 0.8738\n",
      "Epoch 6/20\n",
      "9732/9732 [==============================] - 66s 7ms/step - loss: 0.2826 - accuracy: 0.8887 - val_loss: 0.2382 - val_accuracy: 0.8468\n",
      "Epoch 7/20\n",
      "9732/9732 [==============================] - 67s 7ms/step - loss: 0.2828 - accuracy: 0.8882 - val_loss: 0.2587 - val_accuracy: 0.8417\n",
      "Epoch 8/20\n",
      "9732/9732 [==============================] - 67s 7ms/step - loss: 0.3008 - accuracy: 0.8874 - val_loss: 0.2751 - val_accuracy: 0.8383\n",
      "Epoch 9/20\n",
      "9732/9732 [==============================] - 66s 7ms/step - loss: 0.2846 - accuracy: 0.8883 - val_loss: 0.2384 - val_accuracy: 0.8584\n",
      "Epoch 10/20\n",
      "9732/9732 [==============================] - 66s 7ms/step - loss: 0.2844 - accuracy: 0.8882 - val_loss: 0.2619 - val_accuracy: 0.8523\n",
      "Epoch 11/20\n",
      "9732/9732 [==============================] - 65s 7ms/step - loss: 0.3098 - accuracy: 0.8853 - val_loss: 0.2599 - val_accuracy: 0.8750\n",
      "Epoch 12/20\n",
      "9732/9732 [==============================] - 70s 7ms/step - loss: 0.3187 - accuracy: 0.8853 - val_loss: 0.2308 - val_accuracy: 0.8756\n",
      "Epoch 13/20\n",
      "9732/9732 [==============================] - 67s 7ms/step - loss: 0.2961 - accuracy: 0.8861 - val_loss: 0.2399 - val_accuracy: 0.8714\n",
      "Epoch 14/20\n",
      "9732/9732 [==============================] - 65s 7ms/step - loss: 0.2946 - accuracy: 0.8844 - val_loss: 0.2581 - val_accuracy: 0.8379\n",
      "Epoch 15/20\n",
      "9732/9732 [==============================] - 70s 7ms/step - loss: 0.2876 - accuracy: 0.8854 - val_loss: 0.2851 - val_accuracy: 0.8367\n",
      "Epoch 16/20\n",
      "9732/9732 [==============================] - 66s 7ms/step - loss: 0.3106 - accuracy: 0.8853 - val_loss: 0.2605 - val_accuracy: 0.8578\n",
      "Epoch 17/20\n",
      "9732/9732 [==============================] - 67s 7ms/step - loss: 0.2878 - accuracy: 0.8844 - val_loss: 0.2578 - val_accuracy: 0.8573\n",
      "Epoch 18/20\n",
      "9732/9732 [==============================] - 70s 7ms/step - loss: 0.3509 - accuracy: 0.8859 - val_loss: 0.2612 - val_accuracy: 0.8349\n",
      "Epoch 19/20\n",
      "9732/9732 [==============================] - 66s 7ms/step - loss: 0.3311 - accuracy: 0.8860 - val_loss: 0.2623 - val_accuracy: 0.8462\n",
      "Epoch 20/20\n",
      "9732/9732 [==============================] - 71s 7ms/step - loss: 0.2878 - accuracy: 0.8845 - val_loss: 0.2416 - val_accuracy: 0.8734\n"
     ]
    }
   ],
   "source": [
    "# Fit the Keras Classifier model with the optimal hyperparameters\n",
    "mymodel= model.fit(X_train_os, y_train_os, epochs=20, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2GrNiV6VL7Wa",
    "outputId": "582f0f14-8ad5-401f-8956-4914762f95c2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1977/1977 [==============================] - 4s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred=model.predict(X_test)\n",
    "y_pred = (y_pred > 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ld5T0JeD7EPy",
    "outputId": "aa9c270a-b394-45ff-d08d-08e3dca849c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix: \n",
      " [[45170  6845]\n",
      " [ 1160 10068]]\n",
      "\n",
      "\n",
      "Accuracy Score: \n",
      " 0.87342472684724\n",
      "\n",
      "\n",
      "Classification Report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.87      0.92     52015\n",
      "           1       0.60      0.90      0.72     11228\n",
      "\n",
      "    accuracy                           0.87     63243\n",
      "   macro avg       0.79      0.88      0.82     63243\n",
      "weighted avg       0.91      0.87      0.88     63243\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Confusion Matrix: \\n\",confusion_matrix(y_test,y_pred))\n",
    "print('\\n')\n",
    "print(\"Accuracy Score: \\n\",accuracy_score(y_test,y_pred))\n",
    "print('\\n')\n",
    "print(\"Classification Report: \\n\",classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "-g2H490Z7ESY"
   },
   "outputs": [],
   "source": [
    "# Save the model\n",
    "# with open('my_model.pkl', 'wb') as file:\n",
    "#     pickle.dump(model, file)\n",
    "model.model.save('my_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kkK_FZLzXiGq"
   },
   "source": [
    "### So that's it while the overall accuracy of the model is good at 87%, the precision and f1-score for class 1 (lending loans that are not paid back) are relatively low compared to class 0 (lending loans that are paid back). This means that the model is better at predicting loans that will be paid back than loans that will not be paid back.\n",
    "### This can be due to the imbalancy b/w the classes(0 & 1) in the dataset which may have contributed to this issue, and that further analysis or adjustments to the model may be necessary to improve its performance on predicting class 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DKiR-KIH7EVb",
    "outputId": "f910713b-4669-4696-846a-9b191b5e2e8c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1977/1977 [==============================] - 3s 2ms/step\n",
      "[[0.01268252]\n",
      " [0.79058653]\n",
      " [0.38559973]\n",
      " ...\n",
      " [0.01270323]\n",
      " [0.73523915]\n",
      " [0.01270323]]\n"
     ]
    }
   ],
   "source": [
    "# Checkingif the model is working fine or not\n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "# Load the saved model\n",
    "model = load_model('my_model.h5')\n",
    "\n",
    "# Use the model to make predictions on X_test\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Print the predicted values\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uhY6qe2nj81Q"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}